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-ID,TITLE,ABSTRACT,Computer Science,Physics,Mathematics,Statistics,Quantitative Biology,Quantitative Finance
-16778,Probing Primordial-Black-Hole Dark Matter with Gravitational Waves,"  Primordial black holes (PBHs) have long been suggested as a candidate for
-making up some or all of the dark matter in the Universe. Most of the
-theoretically possible mass range for PBH dark matter has been ruled out with
-various null observations of expected signatures of their interaction with
-standard astrophysical objects. However, current constraints are significantly
-less robust in the 20 M_sun < M_PBH < 100 M_sun mass window, which has received
-much attention recently, following the detection of merging black holes with
-estimated masses of ~30 M_sun by LIGO and the suggestion that these could be
-black holes formed in the early Universe. We consider the potential of advanced
-LIGO (aLIGO) operating at design sensitivity to probe this mass range by
-looking for peaks in the mass spectrum of detected events. To quantify the
-background, which is due to black holes that are formed from dying stars, we
-model the shape of the stellar-black-hole mass function and calibrate its
-amplitude to match the O1 results. Adopting very conservative assumptions about
-the PBH and stellar-black-hole merger rates, we show that ~5 years of aLIGO
-data can be used to detect a contribution of >20 M_sun PBHs to dark matter down
-to f_PBH<0.5 at >99.9% confidence level. Combined with other probes that
-already suggest tension with f_PBH=1, the obtainable independent limits from
-aLIGO will thus enable a firm test of the scenario that PBHs make up all of
-dark matter.
-",0,1,0,0,0,0
-16779,Fundamental limits of low-rank matrix estimation: the non-symmetric case,"  We consider the high-dimensional inference problem where the signal is a
-low-rank matrix which is corrupted by an additive Gaussian noise. Given a
-probabilistic model for the low-rank matrix, we compute the limit in the large
-dimension setting for the mutual information between the signal and the
-observations, as well as the matrix minimum mean square error, while the rank
-of the signal remains constant. This allows to locate the information-theoretic
-threshold for this estimation problem, i.e. the critical value of the signal
-intensity below which it is impossible to recover the low-rank matrix.
-",0,0,1,0,0,0
-16780,Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees,"  Gaussian processes (GPs) offer a flexible class of priors for nonparametric
-Bayesian regression, but popular GP posterior inference methods are typically
-prohibitively slow or lack desirable finite-data guarantees on quality. We
-develop an approach to scalable approximate GP regression with finite-data
-guarantees on the accuracy of pointwise posterior mean and variance estimates.
-Our main contribution is a novel objective for approximate inference in the
-nonparametric setting: the preconditioned Fisher (pF) divergence. We show that
-unlike the Kullback--Leibler divergence (used in variational inference), the pF
-divergence bounds the 2-Wasserstein distance, which in turn provides tight
-bounds the pointwise difference of the mean and variance functions. We
-demonstrate that, for sparse GP likelihood approximations, we can minimize the
-pF divergence efficiently. Our experiments show that optimizing the pF
-divergence has the same computational requirements as variational sparse GPs
-while providing comparable empirical performance--in addition to our novel
-finite-data quality guarantees.
-",0,0,0,1,0,0
-16781,Integral representations and asymptotic behaviours of Mittag-Leffler type functions of two variables,"  The paper explores various special functions which generalize the
-two-parametric Mittag-Leffler type function of two variables. Integral
-representations for these functions in different domains of variation of
-arguments for certain values of the parameters are obtained. The asymptotic
-expansions formulas and asymptotic properties of such functions are also
-established for large values of the variables. This provides statements of
-theorems for these formulas and their corresponding properties.
-",0,0,1,0,0,0
-16782,Efficient Mendler-Style Lambda-Encodings in Cedille,"  It is common to model inductive datatypes as least fixed points of functors.
-We show that within the Cedille type theory we can relax functoriality
-constraints and generically derive an induction principle for Mendler-style
-lambda-encoded inductive datatypes, which arise as least fixed points of
-covariant schemes where the morphism lifting is defined only on identities.
-Additionally, we implement a destructor for these lambda-encodings that runs in
-constant-time. As a result, we can define lambda-encoded natural numbers with
-an induction principle and a constant-time predecessor function so that the
-normal form of a numeral requires only linear space. The paper also includes
-several more advanced examples.
-",1,0,0,0,0,0
-16783,Super Jack-Laurent Polynomials,"  Let $\mathcal{D}_{n,m}$ be the algebra of the quantum integrals of the
-deformed Calogero-Moser-Sutherland problem corresponding to the root system of
-the Lie superalgebra $\frak{gl}(n,m)$. The algebra $\mathcal{D}_{n,m}$ acts
-naturally on the quasi-invariant Laurent polynomials and we investigate the
-corresponding spectral decomposition. Even for general value of the parameter
-$k$ the spectral decomposition is not simple and we prove that the image of the
-algebra $\mathcal{D}_{n,m}$ in the algebra of endomorphisms of the generalised
-eigen-space is $k[\varepsilon]^{\otimes r}$ where $k[\varepsilon]$ is the
-algebra of the dual numbers the corresponding representation is the regular
-representation of the algebra $k[\varepsilon]^{\otimes r}$.
-",0,0,1,0,0,0
-16784,A New Classification of Technologies,"  This study here suggests a classification of technologies based on taxonomic
-characteristics of interaction between technologies in complex systems that is
-not a studied research field in economics of technical change. The proposed
-taxonomy here categorizes technologies in four typologies, in a broad analogy
-with the ecology: 1) technological parasitism is a relationship between two
-technologies T1 and T2 in a complex system S where one technology T1 benefits
-from the interaction with T2, whereas T2 has a negative side from interaction
-with T1; 2) technological commensalism is a relationship between two
-technologies in S where one technology benefits from the other without
-affecting it; 3) technological mutualism is a relationship in which each
-technology benefits from the activity of the other within complex systems; 4)
-technological symbiosis is a long-term interaction between two (or more)
-technologies that evolve together in complex systems. This taxonomy
-systematizes the typologies of interactive technologies within complex systems
-and predicts their evolutionary pathways that generate stepwise coevolutionary
-processes of complex systems of technology. This study here begins the process
-of generalizing, as far as possible, critical typologies of interactive
-technologies that explain the long-run evolution of technology. The theoretical
-framework developed here opens the black box of the interaction between
-technologies that affects, with different types of technologies, the
-evolutionary pathways of complex systems of technology over time and space.
-Overall, then, this new theoretical framework may be useful for bringing a new
-perspective to categorize the gradient of benefit to technologies from
-interaction with other technologies that can be a ground work for development
-of more sophisticated concepts to clarify technological and economic change in
-human society.
-",1,0,0,0,0,0
-16785,Potential functions on Grassmannians of planes and cluster transformations,"  With a triangulation of a planar polygon with $n$ sides, one can associate an
-integrable system on the Grassmannian of 2-planes in an $n$-space. In this
-paper, we show that the potential functions of Lagrangian torus fibers of the
-integrable systems associated with different triangulations glue together by
-cluster transformations. We also prove that the cluster transformations
-coincide with the wall-crossing formula in Lagrangian intersection Floer
-theory.
-",0,0,1,0,0,0
-16786,Physical properties of the first spectroscopically confirmed red supergiant stars in the Sculptor Group galaxy NGC 55,"  We present K-band Multi-Object Spectrograph (KMOS) observations of 18 Red
-Supergiant (RSG) stars in the Sculptor Group galaxy NGC 55. Radial velocities
-are calculated and are shown to be in good agreement with previous estimates,
-confirming the supergiant nature of the targets and providing the first
-spectroscopically confirmed RSGs in NGC 55. Stellar parameters are estimated
-for 14 targets using the $J$-band analysis technique, making use of
-state-of-the-art stellar model atmospheres. The metallicities estimated confirm
-the low-metallicity nature of NGC 55, in good agreement with previous studies.
-This study provides an independent estimate of the metallicity gradient of NGC
-55, in excellent agreement with recent results published using hot massive
-stars. In addition, we calculate luminosities of our targets and compare their
-distribution of effective temperatures and luminosities to other RSGs, in
-different environments, estimated using the same technique.
-",0,1,0,0,0,0
-16787,"Gas near a wall: a shortened mean free path, reduced viscosity, and the manifestation of a turbulent Knudsen layer in the Navier-Stokes solution of a shear flow","  For the gas near a solid planar wall, we propose a scaling formula for the
-mean free path of a molecule as a function of the distance from the wall, under
-the assumption of a uniform distribution of the incident directions of the
-molecular free flight. We subsequently impose the same scaling onto the
-viscosity of the gas near the wall, and compute the Navier-Stokes solution of
-the velocity of a shear flow parallel to the wall. This solution exhibits the
-Knudsen velocity boundary layer in agreement with the corresponding Direct
-Simulation Monte Carlo computations for argon and nitrogen. We also find that
-the proposed mean free path and viscosity scaling sets the second derivative of
-the velocity to infinity at the wall boundary of the flow domain, which
-suggests that the gas flow is formally turbulent within the Knudsen boundary
-layer near the wall.
-",0,1,0,0,0,0
-16788,Greater data science at baccalaureate institutions,"  Donoho's JCGS (in press) paper is a spirited call to action for
-statisticians, who he points out are losing ground in the field of data science
-by refusing to accept that data science is its own domain. (Or, at least, a
-domain that is becoming distinctly defined.) He calls on writings by John
-Tukey, Bill Cleveland, and Leo Breiman, among others, to remind us that
-statisticians have been dealing with data science for years, and encourages
-acceptance of the direction of the field while also ensuring that statistics is
-tightly integrated.
-As faculty at baccalaureate institutions (where the growth of undergraduate
-statistics programs has been dramatic), we are keen to ensure statistics has a
-place in data science and data science education. In his paper, Donoho is
-primarily focused on graduate education. At our undergraduate institutions, we
-are considering many of the same questions.
-",0,0,0,1,0,0
-16789,Direct evidence of hierarchical assembly at low masses from isolated dwarf galaxy groups,"  The demographics of dwarf galaxy populations have long been in tension with
-predictions from the Cold Dark Matter (CDM) paradigm. If primordial density
-fluctuations were scale-free as predicted, dwarf galaxies should themselves
-host dark matter subhaloes, the most massive of which may have undergone star
-formation resulting in dwarf galaxy groups. Ensembles of dwarf galaxies are
-observed as satellites of more massive galaxies, and there is observational and
-theoretical evidence to suggest that these satellites at z=0 were captured by
-the massive host halo as a group. However, the evolution of dwarf galaxies is
-highly susceptible to environment making these satellite groups imperfect
-probes of CDM in the low mass regime. We have identified one of the clearest
-examples to date of hierarchical structure formation at low masses: seven
-isolated, spectroscopically confirmed groups with only dwarf galaxies as
-members. Each group hosts 3-5 known members, has a baryonic mass of ~4.4 x 10^9
-to 2 x 10^10 Msun, and requires a mass-to-light ratio of <100 to be
-gravitationally bound. Such groups are predicted to be rare theoretically and
-found to be rare observationally at the current epoch and thus provide a unique
-window into the possible formation mechanism of more massive, isolated
-galaxies.
-",0,1,0,0,0,0
-16790,Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data,"  Risk prediction is central to both clinical medicine and public health. While
-many machine learning models have been developed to predict mortality, they are
-rarely applied in the clinical literature, where classification tasks typically
-rely on logistic regression. One reason for this is that existing machine
-learning models often seek to optimize predictions by incorporating features
-that are not present in the databases readily available to providers and policy
-makers, limiting generalizability and implementation. Here we tested a number
-of machine learning classifiers for prediction of six-month mortality in a
-population of elderly Medicare beneficiaries, using an administrative claims
-database of the kind available to the majority of health care payers and
-providers. We show that machine learning classifiers substantially outperform
-current widely-used methods of risk prediction but only when used with an
-improved feature set incorporating insights from clinical medicine, developed
-for this study. Our work has applications to supporting patient and provider
-decision making at the end of life, as well as population health-oriented
-efforts to identify patients at high risk of poor outcomes.
-",1,0,0,1,0,0
-16791,R-C3D: Region Convolutional 3D Network for Temporal Activity Detection,"  We address the problem of activity detection in continuous, untrimmed video
-streams. This is a difficult task that requires extracting meaningful
-spatio-temporal features to capture activities, accurately localizing the start
-and end times of each activity. We introduce a new model, Region Convolutional
-3D Network (R-C3D), which encodes the video streams using a three-dimensional
-fully convolutional network, then generates candidate temporal regions
-containing activities, and finally classifies selected regions into specific
-activities. Computation is saved due to the sharing of convolutional features
-between the proposal and the classification pipelines. The entire model is
-trained end-to-end with jointly optimized localization and classification
-losses. R-C3D is faster than existing methods (569 frames per second on a
-single Titan X Maxwell GPU) and achieves state-of-the-art results on THUMOS'14.
-We further demonstrate that our model is a general activity detection framework
-that does not rely on assumptions about particular dataset properties by
-evaluating our approach on ActivityNet and Charades. Our code is available at
-this http URL.
-",1,0,0,0,0,0
-16792,Regularization for Deep Learning: A Taxonomy,"  Regularization is one of the crucial ingredients of deep learning, yet the
-term regularization has various definitions, and regularization methods are
-often studied separately from each other. In our work we present a systematic,
-unifying taxonomy to categorize existing methods. We distinguish methods that
-affect data, network architectures, error terms, regularization terms, and
-optimization procedures. We do not provide all details about the listed
-methods; instead, we present an overview of how the methods can be sorted into
-meaningful categories and sub-categories. This helps revealing links and
-fundamental similarities between them. Finally, we include practical
-recommendations both for users and for developers of new regularization
-methods.
-",1,0,0,1,0,0
-16793,Re-Evaluating the Netflix Prize - Human Uncertainty and its Impact on Reliability,"  In this paper, we examine the statistical soundness of comparative
-assessments within the field of recommender systems in terms of reliability and
-human uncertainty. From a controlled experiment, we get the insight that users
-provide different ratings on same items when repeatedly asked. This volatility
-of user ratings justifies the assumption of using probability densities instead
-of single rating scores. As a consequence, the well-known accuracy metrics
-(e.g. MAE, MSE, RMSE) yield a density themselves that emerges from convolution
-of all rating densities. When two different systems produce different RMSE
-distributions with significant intersection, then there exists a probability of
-error for each possible ranking. As an application, we examine possible ranking
-errors of the Netflix Prize. We are able to show that all top rankings are more
-or less subject to high probabilities of error and that some rankings may be
-deemed to be caused by mere chance rather than system quality.
-",1,0,0,0,0,0
-16794,Infinite monochromatic sumsets for colourings of the reals,"  N. Hindman, I. Leader and D. Strauss proved that it is consistent that there
-is a finite colouring of $\mathbb R$ so that no infinite sumset
-$X+X=\{x+y:x,y\in X\}$ is monochromatic. Our aim in this paper is to prove a
-consistency result in the opposite direction: we show that, under certain
-set-theoretic assumptions, for any $c:\mathbb R\to r$ with $r$ finite there is
-an infinite $X\subseteq \mathbb R$ so that $c$ is constant on $X+X$.
-",0,0,1,0,0,0
-16795,Mean-Field Games with Differing Beliefs for Algorithmic Trading,"  Even when confronted with the same data, agents often disagree on a model of
-the real-world. Here, we address the question of how interacting heterogenous
-agents, who disagree on what model the real-world follows, optimize their
-trading actions. The market has latent factors that drive prices, and agents
-account for the permanent impact they have on prices. This leads to a large
-stochastic game, where each agents' performance criteria is computed under a
-different probability measure. We analyse the mean-field game (MFG) limit of
-the stochastic game and show that the Nash equilibria is given by the solution
-to a non-standard vector-valued forward-backward stochastic differential
-equation. Under some mild assumptions, we construct the solution in terms of
-expectations of the filtered states. We prove the MFG strategy forms an
-\epsilon-Nash equilibrium for the finite player game. Lastly, we present a
-least-squares Monte Carlo based algorithm for computing the optimal control and
-illustrate the results through simulation in market where agents disagree on
-the model.
-",0,0,0,0,0,1
-16796,"Energy efficiency of finite difference algorithms on multicore CPUs, GPUs, and Intel Xeon Phi processors","  In addition to hardware wall-time restrictions commonly seen in
-high-performance computing systems, it is likely that future systems will also
-be constrained by energy budgets. In the present work, finite difference
-algorithms of varying computational and memory intensity are evaluated with
-respect to both energy efficiency and runtime on an Intel Ivy Bridge CPU node,
-an Intel Xeon Phi Knights Landing processor, and an NVIDIA Tesla K40c GPU. The
-conventional way of storing the discretised derivatives to global arrays for
-solution advancement is found to be inefficient in terms of energy consumption
-and runtime. In contrast, a class of algorithms in which the discretised
-derivatives are evaluated on-the-fly or stored as thread-/process-local
-variables (yielding high compute intensity) is optimal both with respect to
-energy consumption and runtime. On all three hardware architectures considered,
-a speed-up of ~2 and an energy saving of ~2 are observed for the high compute
-intensive algorithms compared to the memory intensive algorithm. The energy
-consumption is found to be proportional to runtime, irrespective of the power
-consumed and the GPU has an energy saving of ~5 compared to the same algorithm
-on a CPU node.
-",1,1,0,0,0,0
-16797,A Plane of High Velocity Galaxies Across the Local Group,"  We recently showed that several Local Group (LG) galaxies have much higher
-radial velocities (RVs) than predicted by a 3D dynamical model of the standard
-cosmological paradigm. Here, we show that 6 of these 7 galaxies define a thin
-plane with root mean square thickness of only 101 kpc despite a widest extent
-of nearly 3 Mpc, much larger than the conventional virial radius of the Milky
-Way (MW) or M31. This plane passes within ${\sim 70}$ kpc of the MW-M31
-barycentre and is oriented so the MW-M31 line is inclined by $16^\circ$ to it.
-We develop a toy model to constrain the scenario whereby a past MW-M31 flyby
-in Modified Newtonian Dynamics (MOND) forms tidal dwarf galaxies that settle
-into the recently discovered planes of satellites around the MW and M31. The
-scenario is viable only for a particular MW-M31 orbital plane. This roughly
-coincides with the plane of LG dwarfs with anomalously high RVs.
-Using a restricted $N$-body simulation of the LG in MOND, we show how the
-once fast-moving MW and M31 gravitationally slingshot test particles outwards
-at high speeds. The most distant such particles preferentially lie within the
-MW-M31 orbital plane, probably because the particles ending up with the highest
-RVs are those flung out almost parallel to the motion of the perturber. This
-suggests a dynamical reason for our finding of a similar trend in the real LG,
-something not easily explained as a chance alignment of galaxies with an
-isotropic or mildly flattened distribution (probability $= {0.0015}$).
-",0,1,0,0,0,0
-16798,Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps,"  We present a method for scalable and fully 3D magnetic field simultaneous
-localisation and mapping (SLAM) using local anomalies in the magnetic field as
-a source of position information. These anomalies are due to the presence of
-ferromagnetic material in the structure of buildings and in objects such as
-furniture. We represent the magnetic field map using a Gaussian process model
-and take well-known physical properties of the magnetic field into account. We
-build local maps using three-dimensional hexagonal block tiling. To make our
-approach computationally tractable we use reduced-rank Gaussian process
-regression in combination with a Rao-Blackwellised particle filter. We show
-that it is possible to obtain accurate position and orientation estimates using
-measurements from a smartphone, and that our approach provides a scalable
-magnetic field SLAM algorithm in terms of both computational complexity and map
-storage.
-",1,0,0,1,0,0
-16799,K-means Algorithm over Compressed Binary Data,"  We consider a network of binary-valued sensors with a fusion center. The
-fusion center has to perform K-means clustering on the binary data transmitted
-by the sensors. In order to reduce the amount of data transmitted within the
-network, the sensors compress their data with a source coding scheme based on
-binary sparse matrices. We propose to apply the K-means algorithm directly over
-the compressed data without reconstructing the original sensors measurements,
-in order to avoid potentially complex decoding operations. We provide
-approximated expressions of the error probabilities of the K-means steps in the
-compressed domain. From these expressions, we show that applying the K-means
-algorithm in the compressed domain enables to recover the clusters of the
-original domain. Monte Carlo simulations illustrate the accuracy of the
-obtained approximated error probabilities, and show that the coding rate needed
-to perform K-means clustering in the compressed domain is lower than the rate
-needed to reconstruct all the measurements.
-",1,0,1,0,0,0
-16800,Variational Inference for Gaussian Process Models with Linear Complexity,"  Large-scale Gaussian process inference has long faced practical challenges
-due to time and space complexity that is superlinear in dataset size. While
-sparse variational Gaussian process models are capable of learning from
-large-scale data, standard strategies for sparsifying the model can prevent the
-approximation of complex functions. In this work, we propose a novel
-variational Gaussian process model that decouples the representation of mean
-and covariance functions in reproducing kernel Hilbert space. We show that this
-new parametrization generalizes previous models. Furthermore, it yields a
-variational inference problem that can be solved by stochastic gradient ascent
-with time and space complexity that is only linear in the number of mean
-function parameters, regardless of the choice of kernels, likelihoods, and
-inducing points. This strategy makes the adoption of large-scale expressive
-Gaussian process models possible. We run several experiments on regression
-tasks and show that this decoupled approach greatly outperforms previous sparse
-variational Gaussian process inference procedures.
-",1,0,0,1,0,0
-16801,Incorporation of prior knowledge of the signal behavior into the reconstruction to accelerate the acquisition of MR diffusion data,"  Diffusion MRI measurements using hyperpolarized gases are generally acquired
-during patient breath hold, which yields a compromise between achievable image
-resolution, lung coverage and number of b-values. In this work, we propose a
-novel method that accelerates the acquisition of MR diffusion data by
-undersampling in both spatial and b-value dimensions, thanks to incorporating
-knowledge about the signal decay into the reconstruction (SIDER). SIDER is
-compared to total variation (TV) reconstruction by assessing their effect on
-both the recovery of ventilation images and estimated mean alveolar dimensions
-(MAD). Both methods are assessed by retrospectively undersampling diffusion
-datasets of normal volunteers and COPD patients (n=8) for acceleration factors
-between x2 and x10. TV led to large errors and artefacts for acceleration
-factors equal or larger than x5. SIDER improved TV, presenting lower errors and
-histograms of MAD closer to those obtained from fully sampled data for
-accelerations factors up to x10. SIDER preserved image quality at all
-acceleration factors but images were slightly smoothed and some details were
-lost at x10. In conclusion, we have developed and validated a novel compressed
-sensing method for lung MRI imaging and achieved high acceleration factors,
-which can be used to increase the amount of data acquired during a breath-hold.
-This methodology is expected to improve the accuracy of estimated lung
-microstructure dimensions and widen the possibilities of studying lung diseases
-with MRI.
-",1,1,0,0,0,0
-16802,Rabi noise spectroscopy of individual two-level tunneling defects,"  Understanding the nature of two-level tunneling defects is important for
-minimizing their disruptive effects in various nano-devices. By exploiting the
-resonant coupling of these defects to a superconducting qubit, one can probe
-and coherently manipulate them individually. In this work we utilize a phase
-qubit to induce Rabi oscillations of single tunneling defects and measure their
-dephasing rates as a function of the defect's asymmetry energy, which is tuned
-by an applied strain. The dephasing rates scale quadratically with the external
-strain and are inversely proportional to the Rabi frequency. These results are
-analyzed and explained within a model of interacting standard defects, in which
-pure dephasing of coherent high-frequency (GHz) defects is caused by
-interaction with incoherent low-frequency thermally excited defects.
-",0,1,0,0,0,0
-16803,Learning rate adaptation for federated and differentially private learning,"  We propose an algorithm for the adaptation of the learning rate for
-stochastic gradient descent (SGD) that avoids the need for validation set use.
-The idea for the adaptiveness comes from the technique of extrapolation: to get
-an estimate for the error against the gradient flow which underlies SGD, we
-compare the result obtained by one full step and two half-steps. The algorithm
-is applied in two separate frameworks: federated and differentially private
-learning. Using examples of deep neural networks we empirically show that the
-adaptive algorithm is competitive with manually tuned commonly used
-optimisation methods for differentially privately training. We also show that
-it works robustly in the case of federated learning unlike commonly used
-optimisation methods.
-",0,0,0,1,0,0
-16804,Holomorphic Hermite polynomials in two variables,"  Generalizations of the Hermite polynomials to many variables and/or to the
-complex domain have been located in mathematical and physical literature for
-some decades. Polynomials traditionally called complex Hermite ones are mostly
-understood as polynomials in $z$ and $\bar{z}$ which in fact makes them
-polynomials in two real variables with complex coefficients. The present paper
-proposes to investigate for the first time holomorphic Hermite polynomials in
-two variables. Their algebraic and analytic properties are developed here.
-While the algebraic properties do not differ too much for those considered so
-far, their analytic features are based on a kind of non-rotational
-orthogonality invented by van Eijndhoven and Meyers. Inspired by their
-invention we merely follow the idea of Bargmann's seminal paper (1961) giving
-explicit construction of reproducing kernel Hilbert spaces based on those
-polynomials. ""Homotopic"" behavior of our new formation culminates in comparing
-it to the very classical Bargmann space of two variables on one edge and the
-aforementioned Hermite polynomials in $z$ and $\bar{z}$ on the other. Unlike in
-the case of Bargmann's basis our Hermite polynomials are not product ones but
-factorize to it when bonded together with the first case of limit properties
-leading both to the Bargmann basis and suitable form of the reproducing kernel.
-Also in the second limit we recover standard results obeyed by Hermite
-polynomials in $z$ and $\bar{z}$.
-",0,0,1,0,0,0
-16805,"Equilibria, information and frustration in heterogeneous network games with conflicting preferences","  Interactions between people are the basis on which the structure of our
-society arises as a complex system and, at the same time, are the starting
-point of any physical description of it. In the last few years, much
-theoretical research has addressed this issue by combining the physics of
-complex networks with a description of interactions in terms of evolutionary
-game theory. We here take this research a step further by introducing a most
-salient societal factor such as the individuals' preferences, a characteristic
-that is key to understand much of the social phenomenology these days. We
-consider a heterogeneous, agent-based model in which agents interact
-strategically with their neighbors but their preferences and payoffs for the
-possible actions differ. We study how such a heterogeneous network behaves
-under evolutionary dynamics and different strategic interactions, namely
-coordination games and best shot games. With this model we study the emergence
-of the equilibria predicted analytically in random graphs under best response
-dynamics, and we extend this test to unexplored contexts like proportional
-imitation and scale free networks. We show that some theoretically predicted
-equilibria do not arise in simulations with incomplete Information, and we
-demonstrate the importance of the graph topology and the payoff function
-parameters for some games. Finally, we discuss our results with available
-experimental evidence on coordination games, showing that our model agrees
-better with the experiment that standard economic theories, and draw hints as
-to how to maximize social efficiency in situations of conflicting preferences.
-",1,1,0,0,0,0
-16806,Scalable Generalized Dynamic Topic Models,"  Dynamic topic models (DTMs) model the evolution of prevalent themes in
-literature, online media, and other forms of text over time. DTMs assume that
-word co-occurrence statistics change continuously and therefore impose
-continuous stochastic process priors on their model parameters. These dynamical
-priors make inference much harder than in regular topic models, and also limit
-scalability. In this paper, we present several new results around DTMs. First,
-we extend the class of tractable priors from Wiener processes to the generic
-class of Gaussian processes (GPs). This allows us to explore topics that
-develop smoothly over time, that have a long-term memory or are temporally
-concentrated (for event detection). Second, we show how to perform scalable
-approximate inference in these models based on ideas around stochastic
-variational inference and sparse Gaussian processes. This way we can train a
-rich family of DTMs to massive data. Our experiments on several large-scale
-datasets show that our generalized model allows us to find interesting patterns
-that were not accessible by previous approaches.
-",0,0,0,1,0,0
-16807,Session Types for Orchestrated Interactions,"  In the setting of the pi-calculus with binary sessions, we aim at relaxing
-the notion of duality of session types by the concept of retractable compliance
-developed in contract theory. This leads to extending session types with a new
-type operator of ""speculative selection"" including choices not necessarily
-offered by a compliant partner. We address the problem of selecting successful
-communicating branches by means of an operational semantics based on
-orchestrators, which has been shown to be equivalent to the retractable
-semantics of contracts, but clearly more feasible. A type system, sound with
-respect to such a semantics, is hence provided.
-",1,0,0,0,0,0
-16808,An Agent-Based Approach for Optimizing Modular Vehicle Fleet Operation,"  Modularity in military vehicle designs enables on-base assembly, disassembly,
-and reconfiguration of vehicles, which can be beneficial in promoting fleet
-adaptability and life cycle cost savings. To properly manage the fleet
-operation and to control the resupply, demand prediction, and scheduling
-process, this paper illustrates an agent-based approach customized for highly
-modularized military vehicle fleets and studies the feasibility and flexibility
-of modularity for various mission scenarios. Given deterministic field demands
-with operation stochasticity, we compare the performance of a modular fleet to
-a conventional fleet in equivalent operation strategies and also compare fleet
-performance driven by heuristic rules and optimization. Several indicators are
-selected to quantify the fleet performance, including operation costs, total
-resupplied resources, and fleet readiness.
-When the model is implemented for military Joint Tactical Transport System
-(JTTS) mission, our results indicate that fleet modularity can reduce total
-resource supplies without significant losses in fleet readiness. The benefits
-of fleet modularity can also be amplified through a real-time optimized
-operation strategy. To highlight the feasibility of fleet modularity, a
-parametric study is performed to show the impacts from working capacity on
-modular fleet performance. Finally, we provide practical suggestions of modular
-vehicle designs based on the analysis and other possible usage.
-",1,0,0,0,0,0
-16809,Delta-epsilon functions and uniform continuity on metric spaces,"  Under certain general conditions, an explicit formula to compute the greatest
-delta-epsilon function of a continuous function is given. From this formula, a
-new way to analyze the uniform continuity of a continuous function is given.
-Several examples illustrating the theory are discussed.
-",0,0,1,0,0,0
-16810,Deterministic Dispersion of Mobile Robots in Dynamic Rings,"  In this work, we study the problem of dispersion of mobile robots on dynamic
-rings. The problem of dispersion of $n$ robots on an $n$ node graph, introduced
-by Augustine and Moses Jr. [1], requires robots to coordinate with each other
-and reach a configuration where exactly one robot is present on each node. This
-problem has real world applications and applies whenever we want to minimize
-the total cost of $n$ agents sharing $n$ resources, located at various places,
-subject to the constraint that the cost of an agent moving to a different
-resource is comparatively much smaller than the cost of multiple agents sharing
-a resource (e.g. smart electric cars sharing recharge stations). The study of
-this problem also provides indirect benefits to the study of scattering on
-graphs, the study of exploration by mobile robots, and the study of load
-balancing on graphs.
-We solve the problem of dispersion in the presence of two types of dynamism
-in the underlying graph: (i) vertex permutation and (ii) 1-interval
-connectivity. We introduce the notion of vertex permutation dynamism and have
-it mean that for a given set of nodes, in every round, the adversary ensures a
-ring structure is maintained, but the connections between the nodes may change.
-We use the idea of 1-interval connectivity from Di Luna et al. [10], where for
-a given ring, in each round, the adversary chooses at most one edge to remove.
-We assume robots have full visibility and present asymptotically time optimal
-algorithms to achieve dispersion in the presence of both types of dynamism when
-robots have chirality. When robots do not have chirality, we present
-asymptotically time optimal algorithms to achieve dispersion subject to certain
-constraints. Finally, we provide impossibility results for dispersion when
-robots have no visibility.
-",1,0,0,0,0,0
-16811,A brain signature highly predictive of future progression to Alzheimer's dementia,"  Early prognosis of Alzheimer's dementia is hard. Mild cognitive impairment
-(MCI) typically precedes Alzheimer's dementia, yet only a fraction of MCI
-individuals will progress to dementia, even when screened using biomarkers. We
-propose here to identify a subset of individuals who share a common brain
-signature highly predictive of oncoming dementia. This signature was composed
-of brain atrophy and functional dysconnectivity and discovered using a machine
-learning model in patients suffering from dementia. The model recognized the
-same brain signature in MCI individuals, 90% of which progressed to dementia
-within three years. This result is a marked improvement on the state-of-the-art
-in prognostic precision, while the brain signature still identified 47% of all
-MCI progressors. We thus discovered a sizable MCI subpopulation which
-represents an excellent recruitment target for clinical trials at the prodromal
-stage of Alzheimer's disease.
-",0,0,0,1,0,0
-16812,Deep scattering transform applied to note onset detection and instrument recognition,"  Automatic Music Transcription (AMT) is one of the oldest and most
-well-studied problems in the field of music information retrieval. Within this
-challenging research field, onset detection and instrument recognition take
-important places in transcription systems, as they respectively help to
-determine exact onset times of notes and to recognize the corresponding
-instrument sources. The aim of this study is to explore the usefulness of
-multiscale scattering operators for these two tasks on plucked string
-instrument and piano music. After resuming the theoretical background and
-illustrating the key features of this sound representation method, we evaluate
-its performances comparatively to other classical sound representations. Using
-both MIDI-driven datasets with real instrument samples and real musical pieces,
-scattering is proved to outperform other sound representations for these AMT
-subtasks, putting forward its richer sound representation and invariance
-properties.
-",1,0,0,1,0,0
-16813,Gaschütz Lemma for Compact Groups,"  We prove the Gaschütz Lemma holds for all metrisable compact groups.
-",0,0,1,0,0,0
-16814,Driven flow with exclusion and spin-dependent transport in graphenelike structures,"  We present a simplified description for spin-dependent electronic transport
-in honeycomb-lattice structures with spin-orbit interactions, using
-generalizations of the stochastic non-equilibrium model known as the totally
-asymmetric simple exclusion process. Mean field theory and numerical
-simulations are used to study currents, density profiles and current
-polarization in quasi- one dimensional systems with open boundaries, and
-externally-imposed particle injection ($\alpha$) and ejection ($\beta$) rates.
-We investigate the influence of allowing for double site occupancy, according
-to Pauli's exclusion principle, on the behavior of the quantities of interest.
-We find that double occupancy shows strong signatures for specific combinations
-of rates, namely high $\alpha$ and low $\beta$, but otherwise its effects are
-quantitatively suppressed. Comments are made on the possible relevance of the
-present results to experiments on suitably doped graphenelike structures.
-",0,1,0,0,0,0
-16815,MOG: Mapper on Graphs for Relationship Preserving Clustering,"  The interconnected nature of graphs often results in difficult to interpret
-clutter. Typically techniques focus on either decluttering by clustering nodes
-with similar properties or grouping edges with similar relationship. We propose
-using mapper, a powerful topological data analysis tool, to summarize the
-structure of a graph in a way that both clusters data with similar properties
-and preserves relationships. Typically, mapper operates on a given data by
-utilizing a scalar function defined on every point in the data and a cover for
-scalar function codomain. The output of mapper is a graph that summarize the
-shape of the space. In this paper, we outline how to use this mapper
-construction on an input graphs, outline three filter functions that capture
-important structures of the input graph, and provide an interface for
-interactively modifying the cover. To validate our approach, we conduct several
-case studies on synthetic and real world data sets and demonstrate how our
-method can give meaningful summaries for graphs with various complexities
-",0,0,0,1,0,0
-16816,"Variation Evolving for Optimal Control Computation, A Compact Way","  A compact version of the Variation Evolving Method (VEM) is developed for the
-optimal control computation. It follows the idea that originates from the
-continuous-time dynamics stability theory in the control field. The optimal
-solution is analogized to the equilibrium point of a dynamic system and is
-anticipated to be obtained in an asymptotically evolving way. With the
-introduction of a virtual dimension, the variation time, the Evolution Partial
-Differential Equation (EPDE), which describes the variation motion towards the
-optimal solution, is deduced from the Optimal Control Problem (OCP), and the
-equivalent optimality conditions with no employment of costates are
-established. In particular, it is found that theoretically the analytic
-feedback optimal control law does not exist for general OCPs because the
-optimal control is related to the future state. Since the derived EPDE is
-suitable to be solved with the semi-discrete method in the field of PDE
-numerical calculation, the resulting Initial-value Problems (IVPs) may be
-solved with mature Ordinary Differential Equation (ODE) numerical integration
-methods.
-",1,0,0,0,0,0
-16817,Transforming Sensor Data to the Image Domain for Deep Learning - an Application to Footstep Detection,"  Convolutional Neural Networks (CNNs) have become the state-of-the-art in
-various computer vision tasks, but they are still premature for most sensor
-data, especially in pervasive and wearable computing. A major reason for this
-is the limited amount of annotated training data. In this paper, we propose the
-idea of leveraging the discriminative power of pre-trained deep CNNs on
-2-dimensional sensor data by transforming the sensor modality to the visual
-domain. By three proposed strategies, 2D sensor output is converted into
-pressure distribution imageries. Then we utilize a pre-trained CNN for transfer
-learning on the converted imagery data. We evaluate our method on a gait
-dataset of floor surface pressure mapping. We obtain a classification accuracy
-of 87.66%, which outperforms the conventional machine learning methods by over
-10%.
-",1,0,0,0,0,0
-16818,The Price of Differential Privacy For Online Learning,"  We design differentially private algorithms for the problem of online linear
-optimization in the full information and bandit settings with optimal
-$\tilde{O}(\sqrt{T})$ regret bounds. In the full-information setting, our
-results demonstrate that $\epsilon$-differential privacy may be ensured for
-free -- in particular, the regret bounds scale as
-$O(\sqrt{T})+\tilde{O}\left(\frac{1}{\epsilon}\right)$. For bandit linear
-optimization, and as a special case, for non-stochastic multi-armed bandits,
-the proposed algorithm achieves a regret of
-$\tilde{O}\left(\frac{1}{\epsilon}\sqrt{T}\right)$, while the previously known
-best regret bound was
-$\tilde{O}\left(\frac{1}{\epsilon}T^{\frac{2}{3}}\right)$.
-",1,0,0,1,0,0
-16819,Simulation chain and signal classification for acoustic neutrino detection in seawater,"  Acoustic neutrino detection is a promising approach to extend the energy
-range of neutrino telescopes to energies beyond $10^{18}$\,eV. Currently
-operational and planned water-Cherenkov neutrino telescopes, most notably
-KM3NeT, include acoustic sensors in addition to the optical ones. These
-acoustic sensors could be used as instruments for acoustic detection, while
-their main purpose is the position calibration of the detection units. In this
-article, a Monte Carlo simulation chain for acoustic detectors will be
-presented, covering the initial interaction of the neutrino up to the signal
-classification of recorded events. The ambient and transient background in the
-simulation was implemented according to data recorded by the acoustic set-up
-AMADEUS inside the ANTARES detector. The effects of refraction on the neutrino
-signature in the detector are studied, and a classification of the recorded
-events is implemented. As bipolar waveforms similar to those of the expected
-neutrino signals are also emitted from other sound sources, additional features
-like the geometrical shape of the propagation have to be considered for the
-signal classification. This leads to a large improvement of the background
-suppression by almost two orders of magnitude, since a flat cylindrical
-""pancake"" propagation pattern is a distinctive feature of neutrino signals. An
-overview of the simulation chain and the signal classification will be
-presented and preliminary studies of the performance of the classification will
-be discussed.
-",0,1,0,0,0,0
-16820,Parameter Space Noise for Exploration,"  Deep reinforcement learning (RL) methods generally engage in exploratory
-behavior through noise injection in the action space. An alternative is to add
-noise directly to the agent's parameters, which can lead to more consistent
-exploration and a richer set of behaviors. Methods such as evolutionary
-strategies use parameter perturbations, but discard all temporal structure in
-the process and require significantly more samples. Combining parameter noise
-with traditional RL methods allows to combine the best of both worlds. We
-demonstrate that both off- and on-policy methods benefit from this approach
-through experimental comparison of DQN, DDPG, and TRPO on high-dimensional
-discrete action environments as well as continuous control tasks. Our results
-show that RL with parameter noise learns more efficiently than traditional RL
-with action space noise and evolutionary strategies individually.
-",1,0,0,1,0,0
-16821,Deep Illumination: Approximating Dynamic Global Illumination with Generative Adversarial Network,"  We present Deep Illumination, a novel machine learning technique for
-approximating global illumination (GI) in real-time applications using a
-Conditional Generative Adversarial Network. Our primary focus is on generating
-indirect illumination and soft shadows with offline rendering quality at
-interactive rates. Inspired from recent advancement in image-to-image
-translation problems using deep generative convolutional networks, we introduce
-a variant of this network that learns a mapping from Gbuffers (depth map,
-normal map, and diffuse map) and direct illumination to any global illumination
-solution. Our primary contribution is showing that a generative model can be
-used to learn a density estimation from screen space buffers to an advanced
-illumination model for a 3D environment. Once trained, our network can
-approximate global illumination for scene configurations it has never
-encountered before within the environment it was trained on. We evaluate Deep
-Illumination through a comparison with both a state of the art real-time GI
-technique (VXGI) and an offline rendering GI technique (path tracing). We show
-that our method produces effective GI approximations and is also
-computationally cheaper than existing GI techniques. Our technique has the
-potential to replace existing precomputed and screen-space techniques for
-producing global illumination effects in dynamic scenes with physically-based
-rendering quality.
-",1,0,0,0,0,0
-16822,Fraternal Dropout,"  Recurrent neural networks (RNNs) are important class of architectures among
-neural networks useful for language modeling and sequential prediction.
-However, optimizing RNNs is known to be harder compared to feed-forward neural
-networks. A number of techniques have been proposed in literature to address
-this problem. In this paper we propose a simple technique called fraternal
-dropout that takes advantage of dropout to achieve this goal. Specifically, we
-propose to train two identical copies of an RNN (that share parameters) with
-different dropout masks while minimizing the difference between their
-(pre-softmax) predictions. In this way our regularization encourages the
-representations of RNNs to be invariant to dropout mask, thus being robust. We
-show that our regularization term is upper bounded by the expectation-linear
-dropout objective which has been shown to address the gap due to the difference
-between the train and inference phases of dropout. We evaluate our model and
-achieve state-of-the-art results in sequence modeling tasks on two benchmark
-datasets - Penn Treebank and Wikitext-2. We also show that our approach leads
-to performance improvement by a significant margin in image captioning
-(Microsoft COCO) and semi-supervised (CIFAR-10) tasks.
-",1,0,0,1,0,0
-16823,Finite-sample bounds for the multivariate Behrens-Fisher distribution with proportional covariances,"  The Behrens-Fisher problem is a well-known hypothesis testing problem in
-statistics concerning two-sample mean comparison. In this article, we confirm
-one conjecture in Eaton and Olshen (1972), which provides stochastic bounds for
-the multivariate Behrens-Fisher test statistic under the null hypothesis. We
-also extend their results on the stochastic ordering of random quotients to the
-arbitrary finite dimensional case. This work can also be seen as a
-generalization of Hsu (1938) that provided the bounds for the univariate
-Behrens-Fisher problem. The results obtained in this article can be used to
-derive a testing procedure for the multivariate Behrens-Fisher problem that
-strongly controls the Type I error.
-",0,0,1,1,0,0
-16824,Evidence for mixed rationalities in preference formation,"  Understanding the mechanisms underlying the formation of cultural traits,
-such as preferences, opinions and beliefs is an open challenge. Trait formation
-is intimately connected to cultural dynamics, which has been the focus of a
-variety of quantitative models. Recently, some studies have emphasized the
-importance of connecting those models to snapshots of cultural dynamics that
-are empirically accessible. By analyzing data obtained from different sources,
-it has been suggested that culture has properties that are universally present,
-and that empirical cultural states differ systematically from randomized
-counterparts. Hence, a question about the mechanism responsible for the
-observed patterns naturally arises. This study proposes a stochastic structural
-model for generating cultural states that retain those robust, empirical
-properties. One ingredient of the model, already used in previous work, assumes
-that every individual's set of traits is partly dictated by one of several,
-universal ""rationalities"", informally postulated by several social science
-theories. The second, new ingredient taken from the same theories assumes that,
-apart from a dominant rationality, each individual also has a certain exposure
-to the other rationalities. It is shown that both ingredients are required for
-reproducing the empirical regularities. This key result suggests that the
-effects of cultural dynamics in the real world can be described as an interplay
-of multiple, mixing rationalities, and thus provides indirect evidence for the
-class of social science theories postulating such mixing. The model should be
-seen as a static, effective description of culture, while a dynamical, more
-fundamental description is left for future research.
-",1,1,0,0,0,0
-16825,A Variance Maximization Criterion for Active Learning,"  Active learning aims to train a classifier as fast as possible with as few
-labels as possible. The core element in virtually any active learning strategy
-is the criterion that measures the usefulness of the unlabeled data based on
-which new points to be labeled are picked. We propose a novel approach which we
-refer to as maximizing variance for active learning or MVAL for short. MVAL
-measures the value of unlabeled instances by evaluating the rate of change of
-output variables caused by changes in the next sample to be queried and its
-potential labelling. In a sense, this criterion measures how unstable the
-classifier's output is for the unlabeled data points under perturbations of the
-training data. MVAL maintains, what we refer to as, retraining information
-matrices to keep track of these output scores and exploits two kinds of
-variance to measure the informativeness and representativeness, respectively.
-By fusing these variances, MVAL is able to select the instances which are both
-informative and representative. We employ our technique both in combination
-with logistic regression and support vector machines and demonstrate that MVAL
-achieves state-of-the-art performance in experiments on a large number of
-standard benchmark datasets.
-",1,0,0,1,0,0
-16826,"Polarization, plasmon, and Debye screening in doped 3D ani-Weyl semimetal","  We compute the polarization function in a doped three-dimensional
-anisotropic-Weyl semimetal, in which the fermion energy dispersion is linear in
-two components of the momenta and quadratic in the third. Through detailed
-calculations, we find that the long wavelength plasmon mode depends on the
-fermion density $n_e$ in the form $\Omega_{p}^{\bot}\propto n_{e}^{3/10}$
-within the basal plane and behaves as $\Omega_{p}^{z}\propto n_{e}^{1/2}$ along
-the third direction. This unique characteristic of the plasmon mode can be
-probed by various experimental techniques, such as electron energy-loss
-spectroscopy. The Debye screening at finite chemical potential and finite
-temperature is also analyzed based on the polarization function.
-",0,1,0,0,0,0
-16827,Identifying Product Order with Restricted Boltzmann Machines,"  Unsupervised machine learning via a restricted Boltzmann machine is an useful
-tool in distinguishing an ordered phase from a disordered phase. Here we study
-its application on the two-dimensional Ashkin-Teller model, which features a
-partially ordered product phase. We train the neural network with spin
-configuration data generated by Monte Carlo simulations and show that distinct
-features of the product phase can be learned from non-ergodic samples resulting
-from symmetry breaking. Careful analysis of the weight matrices inspires us to
-define a nontrivial machine-learning motivated quantity of the product form,
-which resembles the conventional product order parameter.
-",0,1,0,0,0,0
-16828,A finite temperature study of ideal quantum gases in the presence of one dimensional quasi-periodic potential,"  We study the thermodynamics of ideal Bose gas as well as the transport
-properties of non interacting bosons and fermions in a one dimensional
-quasi-periodic potential, namely Aubry-André (AA) model at finite
-temperature. For bosons in finite size systems, the effect of quasi-periodic
-potential on the crossover phenomena corresponding to Bose-Einstein
-condensation (BEC), superfluidity and localization phenomena at finite
-temperatures are investigated. From the ground state number fluctuation we
-calculate the crossover temperature of BEC which exhibits a non monotonic
-behavior with the strength of AA potential and vanishes at the self-dual
-critical point following power law. Appropriate rescaling of the crossover
-temperatures reveals universal behavior which is studied for different
-quasi-periodicity of the AA model. Finally, we study the temperature and flux
-dependence of the persistent current of fermions in presence of a
-quasi-periodic potential to identify the localization at the Fermi energy from
-the decay of the current.
-",0,1,0,0,0,0
-16829,High-Frequency Analysis of Effective Interactions and Bandwidth for Transient States after Monocycle Pulse Excitation of Extended Hubbard Model,"  Using a high-frequency expansion in periodically driven extended Hubbard
-models, where the strengths and ranges of density-density interactions are
-arbitrary, we obtain the effective interactions and bandwidth, which depend
-sensitively on the polarization of the driving field. Then, we numerically
-calculate modulations of correlation functions in a quarter-filled extended
-Hubbard model with nearest-neighbor interactions on a triangular lattice with
-trimers after monocycle pulse excitation. We discuss how the resultant
-modulations are compatible with the effective interactions and bandwidth
-derived above on the basis of their dependence on the polarization of
-photoexcitation, which is easily accessible by experiments. Some correlation
-functions after monocycle pulse excitation are consistent with the effective
-interactions, which are weaker or stronger than the original ones. However, the
-photoinduced enhancement of anisotropic charge correlations previously
-discussed for the three-quarter-filled organic conductor
-$\alpha$-(bis[ethylenedithio]-tetrathiafulvalene)$_2$I$_3$
-[$\alpha$-(BEDT-TTF)$_2$I$_3$] in the metallic phase is not fully explained by
-the effective interactions or bandwidth, which are derived independently of the
-filling.
-",0,1,0,0,0,0
-16830,"Fast binary embeddings, and quantized compressed sensing with structured matrices","  This paper deals with two related problems, namely distance-preserving binary
-embeddings and quantization for compressed sensing . First, we propose fast
-methods to replace points from a subset $\mathcal{X} \subset \mathbb{R}^n$,
-associated with the Euclidean metric, with points in the cube $\{\pm 1\}^m$ and
-we associate the cube with a pseudo-metric that approximates Euclidean distance
-among points in $\mathcal{X}$. Our methods rely on quantizing fast
-Johnson-Lindenstrauss embeddings based on bounded orthonormal systems and
-partial circulant ensembles, both of which admit fast transforms. Our
-quantization methods utilize noise-shaping, and include Sigma-Delta schemes and
-distributed noise-shaping schemes. The resulting approximation errors decay
-polynomially and exponentially fast in $m$, depending on the embedding method.
-This dramatically outperforms the current decay rates associated with binary
-embeddings and Hamming distances. Additionally, it is the first such binary
-embedding result that applies to fast Johnson-Lindenstrauss maps while
-preserving $\ell_2$ norms.
-Second, we again consider noise-shaping schemes, albeit this time to quantize
-compressed sensing measurements arising from bounded orthonormal ensembles and
-partial circulant matrices. We show that these methods yield a reconstruction
-error that again decays with the number of measurements (and bits), when using
-convex optimization for reconstruction. Specifically, for Sigma-Delta schemes,
-the error decays polynomially in the number of measurements, and it decays
-exponentially for distributed noise-shaping schemes based on beta encoding.
-These results are near optimal and the first of their kind dealing with bounded
-orthonormal systems.
-",0,0,0,1,0,0
-16831,The Many Faces of Link Fraud,"  Most past work on social network link fraud detection tries to separate
-genuine users from fraudsters, implicitly assuming that there is only one type
-of fraudulent behavior. But is this assumption true? And, in either case, what
-are the characteristics of such fraudulent behaviors? In this work, we set up
-honeypots (""dummy"" social network accounts), and buy fake followers (after
-careful IRB approval). We report the signs of such behaviors including oddities
-in local network connectivity, account attributes, and similarities and
-differences across fraud providers. Most valuably, we discover and characterize
-several types of fraud behaviors. We discuss how to leverage our insights in
-practice by engineering strongly performing entropy-based features and
-demonstrating high classification accuracy. Our contributions are (a)
-instrumentation: we detail our experimental setup and carefully engineered data
-collection process to scrape Twitter data while respecting API rate-limits, (b)
-observations on fraud multimodality: we analyze our honeypot fraudster
-ecosystem and give surprising insights into the multifaceted behaviors of these
-fraudster types, and (c) features: we propose novel features that give strong
-(>0.95 precision/recall) discriminative power on ground-truth Twitter data.
-",1,0,0,0,0,0
-16832,Diophantine approximation by special primes,"  We show that whenever $\delta>0$, $\eta$ is real and constants $\lambda_i$
-satisfy some necessary conditions, there are infinitely many prime triples
-$p_1,\, p_2,\, p_3$ satisfying the inequality $|\lambda_1p_1 + \lambda_2p_2 +
-\lambda_3p_3+\eta|<(\max p_j)^{-1/12+\delta}$ and such that, for each
-$i\in\{1,2,3\}$, $p_i+2$ has at most $28$ prime factors.
-",0,0,1,0,0,0
-16833,A Compositional Treatment of Iterated Open Games,"  Compositional Game Theory is a new, recently introduced model of economic
-games based upon the computer science idea of compositionality. In it, complex
-and irregular games can be built up from smaller and simpler games, and the
-equilibria of these complex games can be defined recursively from the
-equilibria of their simpler subgames. This paper extends the model by providing
-a final coalgebra semantics for infinite games. In the course of this, we
-introduce a new operator on games to model the economic concept of subgame
-perfection.
-",1,0,0,0,0,0
-16834,Bayesian inference for spectral projectors of covariance matrix,"  Let $X_1, \ldots, X_n$ be i.i.d. sample in $\mathbb{R}^p$ with zero mean and
-the covariance matrix $\mathbf{\Sigma^*}$. The classic principal component
-analysis estimates the projector $\mathbf{P^*_{\mathcal{J}}}$ onto the direct
-sum of some eigenspaces of $\mathbf{\Sigma^*}$ by its empirical counterpart
-$\mathbf{\widehat{P}_{\mathcal{J}}}$. Recent papers [Koltchinskii, Lounici
-(2017)], [Naumov et al. (2017)] investigate the asymptotic distribution of the
-Frobenius distance between the projectors $\|
-\mathbf{\widehat{P}_{\mathcal{J}}} - \mathbf{P^*_{\mathcal{J}}} \|_2$. The
-problem arises when one tries to build a confidence set for the true projector
-effectively. We consider the problem from Bayesian perspective and derive an
-approximation for the posterior distribution of the Frobenius distance between
-projectors. The derived theorems hold true for non-Gaussian data: the only
-assumption that we impose is the concentration of the sample covariance
-$\mathbf{\widehat{\Sigma}}$ in a vicinity of $\mathbf{\Sigma^*}$. The obtained
-results are applied to construction of sharp confidence sets for the true
-projector. Numerical simulations illustrate good performance of the proposed
-procedure even on non-Gaussian data in quite challenging regime.
-",0,0,1,1,0,0
-16835,Handling Incomplete Heterogeneous Data using VAEs,"  Variational autoencoders (VAEs), as well as other generative models, have
-been shown to be efficient and accurate to capture the latent structure of vast
-amounts of complex high-dimensional data. However, existing VAEs can still not
-directly handle data that are heterogenous (mixed continuous and discrete) or
-incomplete (with missing data at random), which is indeed common in real-world
-applications.
-In this paper, we propose a general framework to design VAEs, suitable for
-fitting incomplete heterogenous data. The proposed HI-VAE includes likelihood
-models for real-valued, positive real valued, interval, categorical, ordinal
-and count data, and allows to estimate (and potentially impute) missing data
-accurately. Furthermore, HI-VAE presents competitive predictive performance in
-supervised tasks, outperforming super- vised models when trained on incomplete
-data
-",0,0,0,1,0,0
-16836,Geometric mean of probability measures and geodesics of Fisher information metric,"  The space of all probability measures having positive density function on a
-connected compact smooth manifold $M$, denoted by $\mathcal{P}(M)$, carries the
-Fisher information metric $G$. We define the geometric mean of probability
-measures by the aid of which we investigate information geometry of
-$\mathcal{P}(M)$, equipped with $G$. We show that a geodesic segment joining
-arbitrary probability measures $\mu_1$ and $\mu_2$ is expressed by using the
-normalized geometric mean of its endpoints. As an application, we show that any
-two points of $\mathcal{P}(M)$ can be joined by a geodesic. Moreover, we prove
-that the function $\ell$ defined by $\ell(\mu_1, \mu_2):=2\arccos\int_M
-\sqrt{p_1\,p_2}\,d\lambda$, $\mu_i=p_i\,\lambda$, $i=1,2$ gives the distance
-function on $\mathcal{P}(M)$. It is shown that geodesics are all minimal.
-",0,0,1,0,0,0
-16837,On automorphism groups of Toeplitz subshifts,"  In this article we study automorphisms of Toeplitz subshifts. Such groups are
-abelian and any finitely generated torsion subgroup is finite and cyclic. When
-the complexity is non superlinear, we prove that the automorphism group is,
-modulo a finite cyclic group, generated by a unique root of the shift. In the
-subquadratic complexity case, we show that the automorphism group modulo the
-torsion is generated by the roots of the shift map and that the result of the
-non superlinear case is optimal. Namely, for any $\varepsilon > 0$ we construct
-examples of minimal Toeplitz subshifts with complexity bounded by $C
-n^{1+\epsilon}$ whose automorphism groups are not finitely generated. Finally,
-we observe the coalescence and the automorphism group give no restriction on
-the complexity since we provide a family of coalescent Toeplitz subshifts with
-positive entropy such that their automorphism groups are arbitrary finitely
-generated infinite abelian groups with cyclic torsion subgroup (eventually
-restricted to powers of the shift).
-",0,0,1,0,0,0
-16838,How to Generate Pseudorandom Permutations Over Other Groups,"  Recent results by Alagic and Russell have given some evidence that the
-Even-Mansour cipher may be secure against quantum adversaries with quantum
-queries, if considered over other groups than $(\mathbb{Z}/2)^n$. This prompts
-the question as to whether or not other classical schemes may be generalized to
-arbitrary groups and whether classical results still apply to those generalized
-schemes. In this thesis, we generalize the Even-Mansour cipher and the Feistel
-cipher. We show that Even and Mansour's original notions of secrecy are
-obtained on a one-key, group variant of the Even-Mansour cipher. We generalize
-the result by Kilian and Rogaway, that the Even-Mansour cipher is pseudorandom,
-to super pseudorandomness, also in the one-key, group case. Using a Slide
-Attack we match the bound found above. After generalizing the Feistel cipher to
-arbitrary groups we resolve an open problem of Patel, Ramzan, and Sundaram by
-showing that the 3-round Feistel cipher over an arbitrary group is not super
-pseudorandom. We generalize a result by Gentry and Ramzan showing that the
-Even-Mansour cipher can be implemented using the Feistel cipher as the public
-permutation. In this result, we also consider the one-key case over a group and
-generalize their bound. Finally, we consider Zhandry's result on quantum
-pseudorandom permutations, showing that his result may be generalized to hold
-for arbitrary groups. In this regard, we consider whether certain card shuffles
-may be generalized as well.
-",1,0,1,0,0,0
-16839,Measures of Tractography Convergence,"  In the present work, we use information theory to understand the empirical
-convergence rate of tractography, a widely-used approach to reconstruct
-anatomical fiber pathways in the living brain. Based on diffusion MRI data,
-tractography is the starting point for many methods to study brain
-connectivity. Of the available methods to perform tractography, most
-reconstruct a finite set of streamlines, or 3D curves, representing probable
-connections between anatomical regions, yet relatively little is known about
-how the sampling of this set of streamlines affects downstream results, and how
-exhaustive the sampling should be. Here we provide a method to measure the
-information theoretic surprise (self-cross entropy) for tract sampling schema.
-We then empirically assess four streamline methods. We demonstrate that the
-relative information gain is very low after a moderate number of streamlines
-have been generated for each tested method. The results give rise to several
-guidelines for optimal sampling in brain connectivity analyses.
-",0,0,0,1,1,0
-16840,Network Flow Based Post Processing for Sales Diversity,"  Collaborative filtering is a broad and powerful framework for building
-recommendation systems that has seen widespread adoption. Over the past decade,
-the propensity of such systems for favoring popular products and thus creating
-echo chambers have been observed. This has given rise to an active area of
-research that seeks to diversify recommendations generated by such algorithms.
-We address the problem of increasing diversity in recommendation systems that
-are based on collaborative filtering that use past ratings to predicting a
-rating quality for potential recommendations. Following our earlier work, we
-formulate recommendation system design as a subgraph selection problem from a
-candidate super-graph of potential recommendations where both diversity and
-rating quality are explicitly optimized: (1) On the modeling side, we define a
-new flexible notion of diversity that allows a system designer to prescribe the
-number of recommendations each item should receive, and smoothly penalizes
-deviations from this distribution. (2) On the algorithmic side, we show that
-minimum-cost network flow methods yield fast algorithms in theory and practice
-for designing recommendation subgraphs that optimize this notion of diversity.
-(3) On the empirical side, we show the effectiveness of our new model and
-method to increase diversity while maintaining high rating quality in standard
-rating data sets from Netflix and MovieLens.
-",1,0,0,0,0,0
-16841,Lattice Model for Production of Gas,"  We define a lattice model for rock, absorbers, and gas that makes it possible
-to examine the flow of gas to a complicated absorbing boundary over long
-periods of time. The motivation is to deduce the geometry of the boundary from
-the time history of gas absorption. We find a solution to this model using
-Green's function techniques, and apply the solution to three absorbing networks
-of increasing complexity.
-",0,1,0,0,0,0
-16842,Adaptive Representation Selection in Contextual Bandit,"  We consider an extension of the contextual bandit setting, motivated by
-several practical applications, where an unlabeled history of contexts can
-become available for pre-training before the online decision-making begins. We
-propose an approach for improving the performance of contextual bandit in such
-setting, via adaptive, dynamic representation learning, which combines offline
-pre-training on unlabeled history of contexts with online selection and
-modification of embedding functions. Our experiments on a variety of datasets
-and in different nonstationary environments demonstrate clear advantages of our
-approach over the standard contextual bandit.
-",0,0,0,1,0,0
-16843,Algebraic surfaces with zero-dimensional cohomology support locus,"  Using the theory of cohomology support locus, we give a necessary condition
-for the Albanese map of a smooth projective surface being a submersion. More
-precisely, assuming the cohomology support locus of any finite abelian cover of
-a smooth projective surface consists of finitely many points, we prove that the
-surface has trivial first Betti number, or is a ruled surface of genus one, or
-is an abelian surface.
-",0,0,1,0,0,0
-16844,Insight into the temperature dependent properties of the ferromagnetic Kondo lattice YbNiSn,"  Analyzing temperature dependent photoemission (PE) data of the ferromagnetic
-Kondo-lattice (KL) system YbNiSn in the light of the Periodic Anderson model
-(PAM) we show that the KL behavior is not limited to temperatures below a
-temperature T_K, defined empirically from resistivity and specificic heat
-measurements. As characteristic for weakly hybridized Ce and Yb systems, the PE
-spectra reveal a 4f-derived Fermi level peak, which reflects contributions from
-the Kondo resonance and its crystal electric field (CEF) satellites. In YbNiSn
-this peak has an unusual temperature dependence: With decreasing temperature a
-steady linear increase of intensity is observed which extends over a large
-interval ranging from 100 K down to 1 K without showing any peculiarities in
-the region of T_K ~ TC= 5.6 K. In the light of the single-impurity Anderson
-model (SIAM) this intensity variation reflects a linear increase of 4f
-occupancy with decreasing temperature, indicating an onset of Kondo screening
-at temperatures above 100 K. Within the PAM this phenomenon could be described
-by a non-Fermi liquid like T- linear damping of the self-energy which accounts
-phenomenologically for the feedback from the closely spaced CEF-states.
-",0,1,0,0,0,0
-16845,Some Ageing Properties of Dynamic Additive Mean Residual Life Model,"  Although proportional hazard rate model is a very popular model to analyze
-failure time data, sometimes it becomes important to study the additive hazard
-rate model. Again, sometimes the concept of the hazard rate function is
-abstract, in comparison to the concept of mean residual life function. A new
-model called `dynamic additive mean residual life model' where the covariates
-are time-dependent has been defined in the literature. Here we study the
-closure properties of the model for different positive and negative ageing
-classes under certain condition(s). Quite a few examples are presented to
-illustrate different properties of the model.
-",0,0,1,1,0,0
-16846,From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine Training Through Stopping Sets,"  We propose a Las Vegas transformation of Markov Chain Monte Carlo (MCMC)
-estimators of Restricted Boltzmann Machines (RBMs). We denote our approach
-Markov Chain Las Vegas (MCLV). MCLV gives statistical guarantees in exchange
-for random running times. MCLV uses a stopping set built from the training data
-and has maximum number of Markov chain steps K (referred as MCLV-K). We present
-a MCLV-K gradient estimator (LVS-K) for RBMs and explore the correspondence and
-differences between LVS-K and Contrastive Divergence (CD-K), with LVS-K
-significantly outperforming CD-K training RBMs over the MNIST dataset,
-indicating MCLV to be a promising direction in learning generative models.
-",1,0,0,1,0,0
-16847,CD meets CAT,"  We show that if a noncollapsed $CD(K,n)$ space $X$ with $n\ge 2$ has
-curvature bounded above by $\kappa$ in the sense of Alexandrov then $K\le
-(n-1)\kappa$ and $X$ is an Alexandrov space of curvature bounded below by
-$K-\kappa (n-2)$. We also show that if a $CD(K,n)$ space $Y$ with finite $n$
-has curvature bounded above then it is infinitesimally Hilbertian.
-",0,0,1,0,0,0
-16848,Cost Models for Selecting Materialized Views in Public Clouds,"  Data warehouse performance is usually achieved through physical data
-structures such as indexes or materialized views. In this context, cost models
-can help select a relevant set ofsuch performance optimization structures.
-Nevertheless, selection becomes more complex in the cloud. The criterion to
-optimize is indeed at least two-dimensional, with monetary cost balancing
-overall query response time. This paper introduces new cost models that fit
-into the pay-as-you-go paradigm of cloud computing. Based on these cost models,
-an optimization problem is defined to discover, among candidate views, those to
-be materialized to minimize both the overall cost of using and maintaining the
-database in a public cloud and the total response time ofa given query
-workload. We experimentally show that maintaining materialized views is always
-advantageous, both in terms of performance and cost.
-",1,0,0,0,0,0
-16849,Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes,"  Bayesian optimization (BO) methods are useful for optimizing functions that
-are expensive to evaluate, lack an analytical expression and whose evaluations
-can be contaminated by noise. These methods rely on a probabilistic model of
-the objective function, typically a Gaussian process (GP), upon which an
-acquisition function is built. This function guides the optimization process
-and measures the expected utility of performing an evaluation of the objective
-at a new point. GPs assume continous input variables. When this is not the
-case, such as when some of the input variables take integer values, one has to
-introduce extra approximations. A common approach is to round the suggested
-variable value to the closest integer before doing the evaluation of the
-objective. We show that this can lead to problems in the optimization process
-and describe a more principled approach to account for input variables that are
-integer-valued. We illustrate in both synthetic and a real experiments the
-utility of our approach, which significantly improves the results of standard
-BO methods on problems involving integer-valued variables.
-",0,0,0,1,0,0
-16850,Second-order constrained variational problems on Lie algebroids: applications to optimal control,"  The aim of this work is to study, from an intrinsic and geometric point of
-view, second-order constrained variational problems on Lie algebroids, that is,
-optimization problems defined by a cost functional which depends on
-higher-order derivatives of admissible curves on a Lie algebroid. Extending the
-classical Skinner and Rusk formalism for the mechanics in the context of Lie
-algebroids, for second-order constrained mechanical systems, we derive the
-corresponding dynamical equations. We find a symplectic Lie subalgebroid where,
-under some mild regularity conditions, the second-order constrained variational
-problem, seen as a presymplectic Hamiltonian system, has a unique solution. We
-study the relationship of this formalism with the second-order constrained
-Euler-Poincaré and Lagrange-Poincaré equations, among others. Our study is
-applied to the optimal control of mechanical systems.
-",0,0,1,0,0,0
-16851,"The Galactic Cosmic Ray Electron Spectrum from 3 to 70 MeV Measured by Voyager 1 Beyond the Heliopause, What This Tells Us About the Propagation of Electrons and Nuclei In and Out of the Galaxy at Low Energies","  The cosmic ray electrons measured by Voyager 1 between 3-70 MeV beyond the
-heliopause have intensities several hundred times those measured at the Earth
-by PAMELA at nearly the same energies. This paper compares this new V1 data
-with data from the earth-orbiting PAMELA experiment up to energies greater than
-10 GeV where solar modulation effects are negligible. In this energy regime we
-assume the main parameters governing electron propagation are diffusion and
-energy loss and we use a Monte Carlo program to describe this propagation in
-the galaxy. To reproduce the new Voyager electron spectrum, which is E-1.3,
-together with that measured by PAMELA which is E-3.20 above 10 GeV, we require
-a diffusion coefficient which is P 0.45 at energies above 0.5 GeV changing to a
-P-1.00 dependence at lower rigidities. The entire electron spectrum observed at
-both V1 and PAMELA from 3 MeV to 30 GeV can then be described by a simple
-source spectrum, dj/dP P-2.25, with a spectral exponent that is independent of
-rigidity. The change in exponent of the measured electron spectrum from -1.3 at
-low energies to 3.2 at the highest energies can be explained by galactic
-propagation effects related to the changing dependence of the diffusion
-coefficient below 0.5 GeV, and the increasing importance above 0.5 GV of energy
-loss from synchrotron and inverse Compton radiation, which are both E2, and
-which are responsible for most of the changing spectral exponent above 1.0 GV.
-As a result of the P-1.00 dependence of the diffusion coefficient below 0.5
-GV that is required to fit the V1 electron spectrum, there is a rapid flow of
-these low energy electrons out of the galaxy. These electrons in local IG space
-are unobservable to us at any wave length and therefore form a dark energy
-component which is 100 times the electrons rest energy.
-",0,1,0,0,0,0
-16852,Online Scheduling of Spark Workloads with Mesos using Different Fair Allocation Algorithms,"  In the following, we present example illustrative and experimental results
-comparing fair schedulers allocating resources from multiple servers to
-distributed application frameworks. Resources are allocated so that at least
-one resource is exhausted in every server. Schedulers considered include DRF
-(DRFH) and Best-Fit DRF (BF-DRF), TSF, and PS-DSF. We also consider server
-selection under Randomized Round Robin (RRR) and based on their residual
-(unreserved) resources. In the following, we consider cases with frameworks of
-equal priority and without server-preference constraints. We first give typical
-results of a illustrative numerical study and then give typical results of a
-study involving Spark workloads on Mesos which we have modified and
-open-sourced to prototype different schedulers.
-",1,0,0,0,0,0
-16853,On the representation dimension and finitistic dimension of special multiserial algebras,"  For monomial special multiserial algebras, which in general are of wild
-representation type, we construct radical embeddings into algebras of finite
-representation type. As a consequence, we show that the representation
-dimension of monomial and self-injective special multiserial algebras is less
-or equal to three. This implies that the finitistic dimension conjecture holds
-for all special multiserial algebras.
-",0,0,1,0,0,0
-16854,Would You Like to Motivate Software Testers? Ask Them How,"  Context. Considering the importance of software testing to the development of
-high quality and reliable software systems, this paper aims to investigate how
-can work-related factors influence the motivation of software testers. Method.
-We applied a questionnaire that was developed using a previous theory of
-motivation and satisfaction of software engineers to conduct a survey-based
-study to explore and understand how professional software testers perceive and
-value work-related factors that could influence their motivation at work.
-Results. With a sample of 80 software testers we observed that software testers
-are strongly motivated by variety of work, creative tasks, recognition for
-their work, and activities that allow them to acquire new knowledge, but in
-general the social impact of this activity has low influence on their
-motivation. Conclusion. This study discusses the difference of opinions among
-software testers, regarding work-related factors that could impact their
-motivation, which can be relevant for managers and leaders in software
-engineering practice.
-",1,0,0,0,0,0
-16855,POMDP Structural Results for Controlled Sensing,"  This article provides a short review of some structural results in controlled
-sensing when the problem is formulated as a partially observed Markov decision
-process. In particular, monotone value functions, Blackwell dominance and
-quickest detection are described.
-",1,0,0,0,0,0
-16856,Low Rank Matrix Recovery with Simultaneous Presence of Outliers and Sparse Corruption,"  We study a data model in which the data matrix D can be expressed as D = L +
-S + C, where L is a low rank matrix, S an element-wise sparse matrix and C a
-matrix whose non-zero columns are outlying data points. To date, robust PCA
-algorithms have solely considered models with either S or C, but not both. As
-such, existing algorithms cannot account for simultaneous element-wise and
-column-wise corruptions. In this paper, a new robust PCA algorithm that is
-robust to simultaneous types of corruption is proposed. Our approach hinges on
-the sparse approximation of a sparsely corrupted column so that the sparse
-expansion of a column with respect to the other data points is used to
-distinguish a sparsely corrupted inlier column from an outlying data point. We
-also develop a randomized design which provides a scalable implementation of
-the proposed approach. The core idea of sparse approximation is analyzed
-analytically where we show that the underlying ell_1-norm minimization can
-obtain the representation of an inlier in presence of sparse corruptions.
-",1,0,0,1,0,0
-16857,Power-Sum Denominators,"  The power sum $1^n + 2^n + \cdots + x^n$ has been of interest to
-mathematicians since classical times. Johann Faulhaber, Jacob Bernoulli, and
-others who followed expressed power sums as polynomials in $x$ of degree $n+1$
-with rational coefficients. Here we consider the denominators of these
-polynomials, and prove some of their properties. A remarkable one is that such
-a denominator equals $n+1$ times the squarefree product of certain primes $p$
-obeying the condition that the sum of the base-$p$ digits of $n+1$ is at least
-$p$. As an application, we derive a squarefree product formula for the
-denominators of the Bernoulli polynomials.
-",0,0,1,0,0,0
-16858,A resource-frugal probabilistic dictionary and applications in bioinformatics,"  Indexing massive data sets is extremely expensive for large scale problems.
-In many fields, huge amounts of data are currently generated, however
-extracting meaningful information from voluminous data sets, such as computing
-similarity between elements, is far from being trivial. It remains nonetheless
-a fundamental need. This work proposes a probabilistic data structure based on
-a minimal perfect hash function for indexing large sets of keys. Our structure
-out-compete the hash table for construction, query times and for memory usage,
-in the case of the indexation of a static set. To illustrate the impact of
-algorithms performances, we provide two applications based on similarity
-computation between collections of sequences, and for which this calculation is
-an expensive but required operation. In particular, we show a practical case in
-which other bioinformatics tools fail to scale up the tested data set or
-provide lower recall quality results.
-",1,0,0,0,0,0
-16859,Fast learning rate of deep learning via a kernel perspective,"  We develop a new theoretical framework to analyze the generalization error of
-deep learning, and derive a new fast learning rate for two representative
-algorithms: empirical risk minimization and Bayesian deep learning. The series
-of theoretical analyses of deep learning has revealed its high expressive power
-and universal approximation capability. Although these analyses are highly
-nonparametric, existing generalization error analyses have been developed
-mainly in a fixed dimensional parametric model. To compensate this gap, we
-develop an infinite dimensional model that is based on an integral form as
-performed in the analysis of the universal approximation capability. This
-allows us to define a reproducing kernel Hilbert space corresponding to each
-layer. Our point of view is to deal with the ordinary finite dimensional deep
-neural network as a finite approximation of the infinite dimensional one. The
-approximation error is evaluated by the degree of freedom of the reproducing
-kernel Hilbert space in each layer. To estimate a good finite dimensional
-model, we consider both of empirical risk minimization and Bayesian deep
-learning. We derive its generalization error bound and it is shown that there
-appears bias-variance trade-off in terms of the number of parameters of the
-finite dimensional approximation. We show that the optimal width of the
-internal layers can be determined through the degree of freedom and the
-convergence rate can be faster than $O(1/\sqrt{n})$ rate which has been shown
-in the existing studies.
-",1,0,1,1,0,0
-16860,Closed-loop field development optimization with multipoint geostatistics and statistical assessment,"  Closed-loop field development (CLFD) optimization is a comprehensive
-framework for optimal development of subsurface resources. CLFD involves three
-major steps: 1) optimization of full development plan based on current set of
-models, 2) drilling new wells and collecting new spatial and temporal
-(production) data, 3) model calibration based on all data. This process is
-repeated until the optimal number of wells is drilled. This work introduces an
-efficient CLFD implementation for complex systems described by multipoint
-geostatistics (MPS). Model calibration is accomplished in two steps:
-conditioning to spatial data by a geostatistical simulation method, and
-conditioning to production data by optimization-based PCA. A statistical
-procedure is presented to assess the performance of CLFD. Methodology is
-applied to an oil reservoir example for 25 different true-model cases.
-Application of a single-step of CLFD, improved the true NPV in 64%--80% of
-cases. The full CLFD procedure (with three steps) improved the true NPV in 96%
-of cases, with an average improvement of 37%.
-",1,0,0,1,0,0
-16861,Reduction and regular $t$-balanced Cayley maps on split metacyclic 2-groups,"  A regular $t$-balanced Cayley map (RBCM$_t$ for short) on a group $\Gamma$ is
-an embedding of a Cayley graph on $\Gamma$ into a surface with some special
-symmetric properties. We propose a reduction method to study RBCM$_t$'s, and as
-a first practice, we completely classify RBCM$_t$'s for a class of split
-metacyclic 2-groups.
-",0,0,1,0,0,0
-16862,Perovskite Substrates Boost the Thermopower of Cobaltate Thin Films at High Temperatures,"  Transition metal oxides are promising candidates for thermoelectric
-applications, because they are stable at high temperature and because strong
-electronic correlations can generate large Seebeck coefficients, but their
-thermoelectric power factors are limited by the low electrical conductivity. We
-report transport measurements on Ca3Co4O9 films on various perovskite
-substrates and show that reversible incorporation of oxygen into SrTiO3 and
-LaAlO3 substrates activates a parallel conduction channel for p-type carriers,
-greatly enhancing the thermoelectric performance of the film-substrate system
-at temperatures above 450 °C. Thin-film structures that take advantage of
-both electronic correlations and the high oxygen mobility of transition metal
-oxides thus open up new perspectives for thermopower generation at high
-temperature.
-",0,1,0,0,0,0
-16863,Motion of a rod pushed at one point in a weightless environment in space,"  We analyze the motion of a rod floating in a weightless environment in space
-when a force is applied at some point on the rod in a direction perpendicular
-to its length. If the force applied is at the centre of mass, then the rod gets
-a linear motion perpendicular to its length. However, if the same force is
-applied at a point other than the centre of mass, say, near one end of the rod,
-thereby giving rise to a torque, then there will also be a rotation of the rod
-about its centre of mass, in addition to the motion of the centre of mass
-itself. If the force applied is for a very short duration, but imparting
-nevertheless a finite impulse, like in a sudden (quick) hit at one end of the
-rod, then the centre of mass will move with a constant linear speed and
-superimposed on it will be a rotation of the rod with constant angular speed
-about the centre of mass. However, if force is applied continuously, say by
-strapping a tiny rocket at one end of the rod, then the rod will spin faster
-and faster about the centre of mass, with angular speed increasing linearly
-with time. As the direction of the applied force, as seen by an external
-(inertial) observer, will be changing continuously with the rotation of the
-rod, the acceleration of the centre of mass would also be not in one fixed
-direction. However, it turns out that the locus of the velocity vector of the
-centre of mass will describe a Cornu spiral, with the velocity vector reaching
-a final constant value with time. The mean motion of the centre of mass will be
-in a straight line, with superposed initial oscillations that soon die down.
-",0,1,0,0,0,0
-16864,Dimension theory and components of algebraic stacks,"  We prove some basic results on the dimension theory of algebraic stacks, and
-on the multiplicities of their irreducible components, for which we do not know
-a reference.
-",0,0,1,0,0,0
-16865,When is a polynomial ideal binomial after an ambient automorphism?,"  Can an ideal I in a polynomial ring k[x] over a field be moved by a change of
-coordinates into a position where it is generated by binomials $x^a - cx^b$
-with c in k, or by unital binomials (i.e., with c = 0 or 1)? Can a variety be
-moved into a position where it is toric? By fibering the G-translates of I over
-an algebraic group G acting on affine space, these problems are special cases
-of questions about a family F of ideals over an arbitrary base B. The main
-results in this general setting are algorithms to find the locus of points in B
-over which the fiber of F
-- is contained in the fiber of a second family F' of ideals over B;
-- defines a variety of dimension at least d;
-- is generated by binomials; or
-- is generated by unital binomials.
-A faster containment algorithm is also presented when the fibers of F are
-prime. The big-fiber algorithm is probabilistic but likely faster than known
-deterministic ones. Applications include the setting where a second group T
-acts on affine space, in addition to G, in which case algorithms compute the
-set of G-translates of I
-- whose stabilizer subgroups in T have maximal dimension; or
-- that admit a faithful multigrading by $Z^r$ of maximal rank r.
-Even with no ambient group action given, the final application is an
-algorithm to
-- decide whether a normal projective variety is abstractly toric.
-All of these loci in B and subsets of G are constructible; in some cases they
-are closed.
-",1,0,1,0,0,0
-16866,Annihilators in $\mathbb{N}^k$-graded and $\mathbb{Z}^k$-graded rings,"  It has been shown by McCoy that a right ideal of a polynomial ring with
-several indeterminates has a non-trivial homogeneous right annihilator of
-degree 0 provided its right annihilator is non-trivial to begin with. In this
-note, it is documented that any $\mathbb{N}$-graded ring $R$ has a slightly
-weaker property: the right annihilator of a right ideal contains a homogeneous
-non-zero element, if it is non-trivial to begin with. If $R$ is a subring of a
-$\mathbb{Z}^k$ -graded ring $S$ satisfying a certain non-annihilation property
-(which is the case if $S$ is strongly graded, for example), then it is possible
-to find annihilators of degree 0.
-",0,0,1,0,0,0
-16867,q-Neurons: Neuron Activations based on Stochastic Jackson's Derivative Operators,"  We propose a new generic type of stochastic neurons, called $q$-neurons, that
-considers activation functions based on Jackson's $q$-derivatives with
-stochastic parameters $q$. Our generalization of neural network architectures
-with $q$-neurons is shown to be both scalable and very easy to implement. We
-demonstrate experimentally consistently improved performances over
-state-of-the-art standard activation functions, both on training and testing
-loss functions.
-",0,0,0,1,0,0
-16868,Liveness-Driven Random Program Generation,"  Randomly generated programs are popular for testing compilers and program
-analysis tools, with hundreds of bugs in real-world C compilers found by random
-testing. However, existing random program generators may generate large amounts
-of dead code (computations whose result is never used). This leaves relatively
-little code to exercise a target compiler's more complex optimizations.
-To address this shortcoming, we introduce liveness-driven random program
-generation. In this approach the random program is constructed bottom-up,
-guided by a simultaneous structural data-flow analysis to ensure that the
-generator never generates dead code.
-The algorithm is implemented as a plugin for the Frama-C framework. We
-evaluate it in comparison to Csmith, the standard random C program generator.
-Our tool generates programs that compile to more machine code with a more
-complex instruction mix.
-",1,0,0,0,0,0
-16869,Modeling and optimal control of HIV/AIDS prevention through PrEP,"  Pre-exposure prophylaxis (PrEP) consists in the use of an antiretroviral
-medication to prevent the acquisition of HIV infection by uninfected
-individuals and has recently demonstrated to be highly efficacious for HIV
-prevention. We propose a new epidemiological model for HIV/AIDS transmission
-including PrEP. Existence, uniqueness and global stability of the disease free
-and endemic equilibriums are proved. The model with no PrEP is calibrated with
-the cumulative cases of infection by HIV and AIDS reported in Cape Verde from
-1987 to 2014, showing that it predicts well such reality. An optimal control
-problem with a mixed state control constraint is then proposed and analyzed,
-where the control function represents the PrEP strategy and the mixed
-constraint models the fact that, due to PrEP costs, epidemic context and
-program coverage, the number of individuals under PrEP is limited at each
-instant of time. The objective is to determine the PrEP strategy that satisfies
-the mixed state control constraint and minimizes the number of individuals with
-pre-AIDS HIV-infection as well as the costs associated with PrEP. The optimal
-control problem is studied analytically. Through numerical simulations, we
-demonstrate that PrEP reduces HIV transmission significantly.
-",0,0,1,0,0,0
-16870,STARIMA-based Traffic Prediction with Time-varying Lags,"  Based on the observation that the correlation between observed traffic at two
-measurement points or traffic stations may be time-varying, attributable to the
-time-varying speed which subsequently causes variations in the time required to
-travel between the two points, in this paper, we develop a modified Space-Time
-Autoregressive Integrated Moving Average (STARIMA) model with time-varying lags
-for short-term traffic flow prediction. Particularly, the temporal lags in the
-modified STARIMA change with the time-varying speed at different time of the
-day or equivalently change with the (time-varying) time required to travel
-between two measurement points. Firstly, a technique is developed to evaluate
-the temporal lag in the STARIMA model, where the temporal lag is formulated as
-a function of the spatial lag (spatial distance) and the average speed.
-Secondly, an unsupervised classification algorithm based on ISODATA algorithm
-is designed to classify different time periods of the day according to the
-variation of the speed. The classification helps to determine the appropriate
-time lag to use in the STARIMA model. Finally, a STARIMA-based model with
-time-varying lags is developed for short-term traffic prediction. Experimental
-results using real traffic data show that the developed STARIMA-based model
-with time-varying lags has superior accuracy compared with its counterpart
-developed using the traditional cross-correlation function and without
-employing time-varying lags.
-",1,0,1,0,0,0
-16871,Electric Vehicle Charging Station Placement Method for Urban Areas,"  For accommodating more electric vehicles (EVs) to battle against fossil fuel
-emission, the problem of charging station placement is inevitable and could be
-costly if done improperly. Some researches consider a general setup, using
-conditions such as driving ranges for planning. However, most of the EV growths
-in the next decades will happen in the urban area, where driving ranges is not
-the biggest concern. For such a need, we consider several practical aspects of
-urban systems, such as voltage regulation cost and protection device upgrade
-resulting from the large integration of EVs. Notably, our diversified objective
-can reveal the trade-off between different factors in different cities
-worldwide. To understand the global optimum of large-scale analysis, we add
-constraint one-by-one to see how to preserve the problem convexity. Our
-sensitivity analysis before and after convexification shows that our approach
-is not only universally applicable but also has a small approximation error for
-prioritizing the most urgent constraint in a specific setup. Finally, numerical
-results demonstrate the trade-off, the relationship between different factors
-and the global objective, and the small approximation error. A unique
-observation in this study shows the importance of incorporating the protection
-device upgrade in urban system planning on charging stations.
-",1,0,0,0,0,0
-16872,The Steinberg linkage class for a reductive algebraic group,"  Let G be a reductive algebraic group over a field of positive characteristic
-and denote by C(G) the category of rational G-modules. In this note we
-investigate the subcategory of C(G) consisting of those modules whose
-composition factors all have highest weights linked to the Steinberg weight.
-This subcategory is denoted ST and called the Steinberg component. We give an
-explicit equivalence between ST and C(G) and we derive some consequences. In
-particular, our result allows us to relate the Frobenius contracting functor to
-the projection functor from C(G) onto ST .
-",0,0,1,0,0,0
-16873,Detection and Resolution of Rumours in Social Media: A Survey,"  Despite the increasing use of social media platforms for information and news
-gathering, its unmoderated nature often leads to the emergence and spread of
-rumours, i.e. pieces of information that are unverified at the time of posting.
-At the same time, the openness of social media platforms provides opportunities
-to study how users share and discuss rumours, and to explore how natural
-language processing and data mining techniques may be used to find ways of
-determining their veracity. In this survey we introduce and discuss two types
-of rumours that circulate on social media; long-standing rumours that circulate
-for long periods of time, and newly-emerging rumours spawned during fast-paced
-events such as breaking news, where reports are released piecemeal and often
-with an unverified status in their early stages. We provide an overview of
-research into social media rumours with the ultimate goal of developing a
-rumour classification system that consists of four components: rumour
-detection, rumour tracking, rumour stance classification and rumour veracity
-classification. We delve into the approaches presented in the scientific
-literature for the development of each of these four components. We summarise
-the efforts and achievements so far towards the development of rumour
-classification systems and conclude with suggestions for avenues for future
-research in social media mining for detection and resolution of rumours.
-",1,0,0,0,0,0
-16874,On harmonic analysis of spherical convolutions on semisimple Lie groups,"  This paper contains a non-trivial generalization of the Harish-Chandra
-transforms on a connected semisimple Lie group $G,$ with finite center, into
-what we term spherical convolutions. Among other results we show that its
-integral over the collection of bounded spherical functions at the identity
-element $e \in G$ is a weighted Fourier transforms of the Abel transform at
-$0.$ Being a function on $G,$ the restriction of this integral of its spherical
-Fourier transforms to the positive-definite spherical functions is then shown
-to be (the non-zero constant multiple of) a positive-definite distribution on
-$G,$ which is tempered and invariant on $G=SL(2,\mathbb{R}).$ These results
-suggest the consideration of a calculus on the Schwartz algebras of spherical
-functions. The Plancherel measure of the spherical convolutions is also
-explicitly computed.
-",0,0,1,0,0,0
-16875,Relaxation-based viscosity mapping for magnetic particle imaging,"  Magnetic Particle Imaging (MPI) has been shown to provide remarkable contrast
-for imaging applications such as angiography, stem cell tracking, and cancer
-imaging. Recently, there is growing interest in the functional imaging
-capabilities of MPI, where color MPI techniques have explored separating
-different nanoparticles, which could potentially be used to distinguish
-nanoparticles in different states or environments. Viscosity mapping is a
-promising functional imaging application for MPI, as increased viscosity levels
-in vivo have been associated with numerous diseases such as hypertension,
-atherosclerosis, and cancer. In this work, we propose a viscosity mapping
-technique for MPI through the estimation of the relaxation time constant of the
-nanoparticles. Importantly, the proposed time constant estimation scheme does
-not require any prior information regarding the nanoparticles. We validate this
-method with extensive experiments in an in-house magnetic particle spectroscopy
-(MPS) setup at four different frequencies (between 250 Hz and 10.8 kHz) and at
-three different field strengths (between 5 mT and 15 mT) for viscosities
-ranging between 0.89 mPa.s to 15.33 mPa.s. Our results demonstrate the
-viscosity mapping ability of MPI in the biologically relevant viscosity range.
-",0,1,0,0,0,0
-16876,Detecting Statistically Significant Communities,"  Community detection is a key data analysis problem across different fields.
-During the past decades, numerous algorithms have been proposed to address this
-issue. However, most work on community detection does not address the issue of
-statistical significance. Although some research efforts have been made towards
-mining statistically significant communities, deriving an analytical solution
-of p-value for one community under the configuration model is still a
-challenging mission that remains unsolved. To partially fulfill this void, we
-present a tight upper bound on the p-value of a single community under the
-configuration model, which can be used for quantifying the statistical
-significance of each community analytically. Meanwhile, we present a local
-search method to detect statistically significant communities in an iterative
-manner. Experimental results demonstrate that our method is comparable with the
-competing methods on detecting statistically significant communities.
-",1,0,0,0,0,0
-16877,On the effectivity of spectra representing motivic cohomology theories,"  Let k be an infinite perfect field. We provide a general criterion for a
-spectrum in the stable homotopy category over k to be effective, i.e. to be in
-the localizing subcategory generated by the suspension spectra of smooth
-schemes. As a consequence, we show that two recent versions of generalized
-motivic cohomology theories coincide.
-",0,0,1,0,0,0
-16878,Aggregated Momentum: Stability Through Passive Damping,"  Momentum is a simple and widely used trick which allows gradient-based
-optimizers to pick up speed along low curvature directions. Its performance
-depends crucially on a damping coefficient $\beta$. Large $\beta$ values can
-potentially deliver much larger speedups, but are prone to oscillations and
-instability; hence one typically resorts to small values such as 0.5 or 0.9. We
-propose Aggregated Momentum (AggMo), a variant of momentum which combines
-multiple velocity vectors with different $\beta$ parameters. AggMo is trivial
-to implement, but significantly dampens oscillations, enabling it to remain
-stable even for aggressive $\beta$ values such as 0.999. We reinterpret
-Nesterov's accelerated gradient descent as a special case of AggMo and analyze
-rates of convergence for quadratic objectives. Empirically, we find that AggMo
-is a suitable drop-in replacement for other momentum methods, and frequently
-delivers faster convergence.
-",0,0,0,1,0,0
-16879,On the Number of Bins in Equilibria for Signaling Games,"  We investigate the equilibrium behavior for the decentralized quadratic cheap
-talk problem in which an encoder and a decoder, viewed as two decision makers,
-have misaligned objective functions. In prior work, we have shown that the
-number of bins under any equilibrium has to be at most countable, generalizing
-a classical result due to Crawford and Sobel who considered sources with
-density supported on $[0,1]$. In this paper, we refine this result in the
-context of exponential and Gaussian sources. For exponential sources, a
-relation between the upper bound on the number of bins and the misalignment in
-the objective functions is derived, the equilibrium costs are compared, and it
-is shown that there also exist equilibria with infinitely many bins under
-certain parametric assumptions. For Gaussian sources, it is shown that there
-exist equilibria with infinitely many bins.
-",1,0,0,0,0,0
-16880,The Dantzig selector for a linear model of diffusion processes,"  In this paper, a linear model of diffusion processes with unknown drift and
-diagonal diffusion matrices is discussed. We will consider the estimation
-problems for unknown parameters based on the discrete time observation in
-high-dimensional and sparse settings. To estimate drift matrices, the Dantzig
-selector which was proposed by Candés and Tao in 2007 will be applied. Then,
-we will prove two types of consistency of the estimator of drift matrix; one is
-the consistency in the sense of $l_q$ norm for every $q \in [1,\infty]$ and the
-other is the variable selection consistency. Moreover, we will construct an
-asymptotically normal estimator of the drift matrix by using the variable
-selection consistency of the Dantzig selector.
-",0,0,1,1,0,0
-16881,A Spatio-Temporal Multivariate Shared Component Model with an Application in Iran Cancer Data,"  Among the proposals for joint disease mapping, the shared component model has
-become more popular. Another recent advance to strengthen inference of disease
-data has been the extension of purely spatial models to include time and
-space-time interaction. Such analyses have additional benefits over purely
-spatial models. However, only a few proposed spatio-temporal models could
-address analysing multiple diseases jointly.
-In the proposed model, each component is shared by different subsets of
-diseases, spatial and temporal trends are considered for each component, and
-the relative weight of these trends for each component for each relevant
-disease can be estimated. We present an application of the proposed method on
-incidence rates of seven prevalent cancers in Iran. The effect of the shared
-components on the individual cancer types can be identified. Regional and
-temporal variation in relative risks is shown. We present a model which
-combines the benefits of shared-components with spatio-temporal techniques for
-multivariate data. We show, how the model allows to analyse geographical and
-temporal variation among diseases beyond previous approaches.
-",0,0,0,1,0,0
-16882,The dynamo effect in decaying helical turbulence,"  We show that in decaying hydromagnetic turbulence with initial kinetic
-helicity, a weak magnetic field eventually becomes fully helical. The sign of
-magnetic helicity is opposite to that of the kinetic helicity - regardless of
-whether or not the initial magnetic field was helical. The magnetic field
-undergoes inverse cascading with the magnetic energy decaying approximately
-like t^{-1/2}. This is even slower than in the fully helical case, where it
-decays like t^{-2/3}. In this parameter range, the product of magnetic energy
-and correlation length raised to a certain power slightly larger than unity, is
-approximately constant. This scaling of magnetic energy persists over long time
-scales. At very late times and for domain sizes large enough to accommodate the
-growing spatial scales, we expect a cross-over to the t^{-2/3} decay law that
-is commonly observed for fully helical magnetic fields. Regardless of the
-presence or absence of initial kinetic helicity, the magnetic field experiences
-exponential growth during the first few turnover times, which is suggestive of
-small-scale dynamo action. Our results have applications to a wide range of
-experimental dynamos and astrophysical time-dependent plasmas, including
-primordial turbulence in the early universe.
-",0,1,0,0,0,0
-16883,Geometrically finite amalgamations of hyperbolic 3-manifold groups are not LERF,"  We prove that, for any two finite volume hyperbolic $3$-manifolds, the
-amalgamation of their fundamental groups along any nontrivial geometrically
-finite subgroup is not LERF. This generalizes the author's previous work on
-nonLERFness of amalgamations of hyperbolic $3$-manifold groups along abelian
-subgroups. A consequence of this result is that closed arithmetic hyperbolic
-$4$-manifolds have nonLERF fundamental groups. Along with the author's previous
-work, we get that, for any arithmetic hyperbolic manifold with dimension at
-least $4$, with possible exceptions in $7$-dimensional manifolds defined by the
-octonion, its fundamental group is not LERF.
-",0,0,1,0,0,0
-16884,"Dining Philosophers, Leader Election and Ring Size problems, in the quantum setting","  We provide the first quantum (exact) protocol for the Dining Philosophers
-problem (DP), a central problem in distributed algorithms. It is well known
-that the problem cannot be solved exactly in the classical setting. We then use
-our DP protocol to provide a new quantum protocol for the tightly related
-problem of exact leader election (LE) on a ring, improving significantly in
-both time and memory complexity over the known LE protocol by Tani et. al. To
-do this, we show that in some sense the exact DP and exact LE problems are
-equivalent; interestingly, in the classical non-exact setting they are not.
-Hopefully, the results will lead to exact quantum protocols for other important
-distributed algorithmic questions; in particular, we discuss interesting
-connections to the ring size problem, as well as to a physically motivated
-question of breaking symmetry in 1D translationally invariant systems.
-",1,0,0,0,0,0
-16885,An Online Secretary Framework for Fog Network Formation with Minimal Latency,"  Fog computing is seen as a promising approach to perform distributed,
-low-latency computation for supporting Internet of Things applications.
-However, due to the unpredictable arrival of available neighboring fog nodes,
-the dynamic formation of a fog network can be challenging. In essence, a given
-fog node must smartly select the set of neighboring fog nodes that can provide
-low-latency computations. In this paper, this problem of fog network formation
-and task distribution is studied considering a hybrid cloud-fog architecture.
-The goal of the proposed framework is to minimize the maximum computational
-latency by enabling a given fog node to form a suitable fog network, under
-uncertainty on the arrival process of neighboring fog nodes. To solve this
-problem, a novel approach based on the online secretary framework is proposed.
-To find the desired set of neighboring fog nodes, an online algorithm is
-developed to enable a task initiating fog node to decide on which other nodes
-can be used as part of its fog network, to offload computational tasks, without
-knowing any prior information on the future arrivals of those other nodes.
-Simulation results show that the proposed online algorithm can successfully
-select an optimal set of neighboring fog nodes while achieving a latency that
-is as small as the one resulting from an ideal, offline scheme that has
-complete knowledge of the system. The results also show how, using the proposed
-approach, the computational tasks can be properly distributed between the fog
-network and a remote cloud server.
-",1,0,0,0,0,0
-16886,Computer-assisted proof of heteroclinic connections in the one-dimensional Ohta-Kawasaki model,"  We present a computer-assisted proof of heteroclinic connections in the
-one-dimensional Ohta-Kawasaki model of diblock copolymers. The model is a
-fourth-order parabolic partial differential equation subject to homogeneous
-Neumann boundary conditions, which contains as a special case the celebrated
-Cahn-Hilliard equation. While the attractor structure of the latter model is
-completely understood for one-dimensional domains, the diblock copolymer
-extension exhibits considerably richer long-term dynamical behavior, which
-includes a high level of multistability. In this paper, we establish the
-existence of certain heteroclinic connections between the homogeneous
-equilibrium state, which represents a perfect copolymer mixture, and all local
-and global energy minimizers. In this way, we show that not every solution
-originating near the homogeneous state will converge to the global energy
-minimizer, but rather is trapped by a stable state with higher energy. This
-phenomenon can not be observed in the one-dimensional Cahn-Hillard equation,
-where generic solutions are attracted by a global minimizer.
-",1,0,1,0,0,0
-16887,Dust and Gas in Star Forming Galaxies at z~3 - Extending Galaxy Uniformity to 11.5 Billion Years,"  We present millimetre dust emission measurements of two Lyman Break Galaxies
-at z~3 and construct for the first time fully sampled infrared spectral energy
-distributions (SEDs), from mid-IR to the Rayleigh-Jeans tail, of individually
-detected, unlensed, UV-selected, main sequence (MS) galaxies at $z=3$. The SED
-modelling of the two sources confirms previous findings, based on stacked
-ensembles, of an increasing mean radiation field <U> with redshift, consistent
-with a rapidly decreasing gas metallicity in z > 2 galaxies. Complementing our
-study with CO[3-2] emission line observations, we measure the molecular gas
-mass (M_H2) reservoir of the systems using three independent approaches: 1) CO
-line observations, 2) the dust to gas mass ratio vs metallicity relation and 3)
-a single band, dust emission flux on the Rayleigh-Jeans side of the SED. All
-techniques return consistent M_H2 estimates within a factor of ~2 or less,
-yielding gas depletion time-scales (tau_dep ~ 0.35 Gyrs) and gas-to-stellar
-mass ratios (M_H2/M* ~ 0.5-1) for our z~3 massive MS galaxies. The overall
-properties of our galaxies are consistent with trends and relations established
-at lower redshifts, extending the apparent uniformity of star-forming galaxies
-over the last 11.5 billion years.
-",0,1,0,0,0,0
-16888,Flow speed has little impact on propulsive characteristics of oscillating foils,"  Experiments are reported on the performance of a pitching and heaving
-two-dimensional foil in a water channel in either continuous or intermittent
-motion. We find that the thrust and power are independent of the mean
-freestream velocity for two-fold changes in the mean velocity (four-fold in the
-dynamic pressure), and for oscillations in the velocity up to 38\% of the mean,
-where the oscillations are intended to mimic those of freely swimming motions
-where the thrust varies during the flapping cycle. We demonstrate that the
-correct velocity scale is not the flow velocity but the mean velocity of the
-trailing edge. We also find little or no impact of streamwise velocity change
-on the wake characteristics such as vortex organization, vortex strength, and
-time-averaged velocity profile development---the wake is both qualitatively and
-quantitatively unchanged. Our results suggest that constant velocity studies
-can be used to make robust conclusions about swimming performance without a
-need to explore the free-swimming condition.
-",0,1,0,0,0,0
-16889,"Switching and Data Injection Attacks on Stochastic Cyber-Physical Systems: Modeling, Resilient Estimation and Attack Mitigation","  In this paper, we consider the problem of attack-resilient state estimation,
-that is to reliably estimate the true system states despite two classes of
-attacks: (i) attacks on the switching mechanisms and (ii) false data injection
-attacks on actuator and sensor signals, in the presence of unbounded stochastic
-process and measurement noise signals. We model the systems under attack as
-hidden mode stochastic switched linear systems with unknown inputs and propose
-the use of a multiple-model inference algorithm to tackle these security
-issues. Moreover, we characterize fundamental limitations to resilient
-estimation (e.g., upper bound on the number of tolerable signal attacks) and
-discuss the topics of attack detection, identification and mitigation under
-this framework. Simulation examples of switching and false data injection
-attacks on a benchmark system and an IEEE 68-bus test system show the efficacy
-of our approach to recover resilient (i.e., asymptotically unbiased) state
-estimates as well as to identify and mitigate the attacks.
-",1,0,1,0,0,0
-16890,Generating Sentence Planning Variations for Story Telling,"  There has been a recent explosion in applications for dialogue interaction
-ranging from direction-giving and tourist information to interactive story
-systems. Yet the natural language generation (NLG) component for many of these
-systems remains largely handcrafted. This limitation greatly restricts the
-range of applications; it also means that it is impossible to take advantage of
-recent work in expressive and statistical language generation that can
-dynamically and automatically produce a large number of variations of given
-content. We propose that a solution to this problem lies in new methods for
-developing language generation resources. We describe the ES-Translator, a
-computational language generator that has previously been applied only to
-fables, and quantitatively evaluate the domain independence of the EST by
-applying it to personal narratives from weblogs. We then take advantage of
-recent work on language generation to create a parameterized sentence planner
-for story generation that provides aggregation operations, variations in
-discourse and in point of view. Finally, we present a user evaluation of
-different personal narrative retellings.
-",1,0,0,0,0,0
-16891,"Detection of planet candidates around K giants, HD 40956, HD 111591, and HD 113996","  Aims. The purpose of this paper is to detect and investigate the nature of
-long-term radial velocity (RV) variations of K-type giants and to confirm
-planetary companions around the stars.
-Methods. We have conducted two planet search programs by precise RV
-measurement using the 1.8 m telescope at Bohyunsan Optical Astronomy
-Observatory (BOAO) and the 1.88 m telescope at Okayama Astrophysical
-Observatory (OAO). The BOAO program searches for planets around 55 early K
-giants. The OAO program is looking for 190 G-K type giants.
-Results. In this paper, we report the detection of long-period RV variations
-of three K giant stars, HD 40956, HD 111591, and HD 113996. We investigated the
-cause of the observed RV variations and conclude the substellar companions are
-most likely the cause of the RV variations. The orbital analyses yield P =
-578.6 $\pm$ 3.3 d, $m$ sin $i$ = 2.7 $\pm$ 0.6 $M_{\rm{J}}$, $a$ = 1.4 $\pm$
-0.1 AU for HD 40956; P = 1056.4 $\pm$ 14.3 d, $m$ sin $i$ = 4.4 $\pm$ 0.4
-$M_{\rm{J}}$, $a$ = 2.5 $\pm$ 0.1 AU for HD 111591; P = 610.2 $\pm$ 3.8 d, $m$
-sin $i$ = 6.3 $\pm$ 1.0 $M_{\rm{J}}$, $a$ = 1.6 $\pm$ 0.1 AU for HD 113996.
-",0,1,0,0,0,0
-16892,Perfect Half Space Games,"  We introduce perfect half space games, in which the goal of Player 2 is to
-make the sums of encountered multi-dimensional weights diverge in a direction
-which is consistent with a chosen sequence of perfect half spaces (chosen
-dynamically by Player 2). We establish that the bounding games of Jurdziński
-et al. (ICALP 2015) can be reduced to perfect half space games, which in turn
-can be translated to the lexicographic energy games of Colcombet and
-Niwiński, and are positionally determined in a strong sense (Player 2 can
-play without knowing the current perfect half space). We finally show how
-perfect half space games and bounding games can be employed to solve
-multi-dimensional energy parity games in pseudo-polynomial time when both the
-numbers of energy dimensions and of priorities are fixed, regardless of whether
-the initial credit is given as part of the input or existentially quantified.
-This also yields an optimal 2-EXPTIME complexity with given initial credit,
-where the best known upper bound was non-elementary.
-",1,0,0,0,0,0
-16893,"Energy-efficient Analog Sensing for Large-scale, High-density Persistent Wireless Monitoring","  The research challenge of current Wireless Sensor Networks~(WSNs) is to
-design energy-efficient, low-cost, high-accuracy, self-healing, and scalable
-systems for applications such as environmental monitoring. Traditional WSNs
-consist of low density, power-hungry digital motes that are expensive and
-cannot remain functional for long periods on a single charge. In order to
-address these challenges, a \textit{dumb-sensing and smart-processing}
-architecture that splits sensing and computation capabilities among tiers is
-proposed. Tier-1 consists of dumb sensors that only sense and transmit, while
-the nodes in Tier-2 do all the smart processing on Tier-1 sensor data. A
-low-power and low-cost solution for Tier-1 sensors has been proposed using
-Analog Joint Source Channel Coding~(AJSCC). An analog circuit that realizes the
-rectangular type of AJSCC has been proposed and realized on a Printed Circuit
-Board for feasibility analysis. A prototype consisting of three Tier-1 sensors
-(sensing temperature and humidity) communicating to a Tier-2 Cluster Head has
-been demonstrated to verify the proposed approach. Results show that our
-framework is indeed feasible to support large scale high density and persistent
-WSN deployment.
-",1,0,0,0,0,0
-16894,Computation of ground-state properties in molecular systems: back-propagation with auxiliary-field quantum Monte Carlo,"  We address the computation of ground-state properties of chemical systems and
-realistic materials within the auxiliary-field quantum Monte Carlo method. The
-phase constraint to control the fermion phase problem requires the random walks
-in Slater determinant space to be open-ended with branching. This in turn makes
-it necessary to use back-propagation (BP) to compute averages and correlation
-functions of operators that do not commute with the Hamiltonian. Several BP
-schemes are investigated and their optimization with respect to the phaseless
-constraint is considered. We propose a modified BP method for the computation
-of observables in electronic systems, discuss its numerical stability and
-computational complexity, and assess its performance by computing ground-state
-properties for several substances, including constituents of the primordial
-terrestrial atmosphere and small organic molecules.
-",0,1,0,0,0,0
-16895,Demonstration of the Relationship between Sensitivity and Identifiability for Inverse Uncertainty Quantification,"  Inverse Uncertainty Quantification (UQ), or Bayesian calibration, is the
-process to quantify the uncertainties of random input parameters based on
-experimental data. The introduction of model discrepancy term is significant
-because ""over-fitting"" can theoretically be avoided. But it also poses
-challenges in the practical applications. One of the mostly concerned and
-unresolved problem is the ""lack of identifiability"" issue. With the presence of
-model discrepancy, inverse UQ becomes ""non-identifiable"" in the sense that it
-is difficult to precisely distinguish between the parameter uncertainties and
-model discrepancy when estimating the calibration parameters. Previous research
-to alleviate the non-identifiability issue focused on using informative priors
-for the calibration parameters and the model discrepancy, which is usually not
-a viable solution because one rarely has such accurate and informative prior
-knowledge. In this work, we show that identifiability is largely related to the
-sensitivity of the calibration parameters with regards to the chosen responses.
-We adopted an improved modular Bayesian approach for inverse UQ that does not
-require priors for the model discrepancy term. The relationship between
-sensitivity and identifiability was demonstrated with a practical example in
-nuclear engineering. It was shown that, in order for a certain calibration
-parameter to be statistically identifiable, it should be significant to at
-least one of the responses whose data are used for inverse UQ. Good
-identifiability cannot be achieved for a certain calibration parameter if it is
-not significant to any of the responses. It is also demonstrated that ""fake
-identifiability"" is possible if model responses are not appropriately chosen,
-or inaccurate but informative priors are specified.
-",0,0,0,1,0,0
-16896,The GENIUS Approach to Robust Mendelian Randomization Inference,"  Mendelian randomization (MR) is a popular instrumental variable (IV)
-approach. A key IV identification condition known as the exclusion restriction
-requires no direct effect of an IV on the outcome not through the exposure
-which is unrealistic in most MR analyses. As a result, possible violation of
-the exclusion restriction can seldom be ruled out in such studies. To address
-this concern, we introduce a new class of IV estimators which are robust to
-violation of the exclusion restriction under a large collection of data
-generating mechanisms consistent with parametric models commonly assumed in the
-MR literature. Our approach named ""MR G-Estimation under No Interaction with
-Unmeasured Selection"" (MR GENIUS) may be viewed as a modification to Robins'
-G-estimation approach that is robust to both additive unmeasured confounding
-and violation of the exclusion restriction assumption. We also establish that
-estimation with MR GENIUS may also be viewed as a robust generalization of the
-well-known Lewbel estimator for a triangular system of structural equations
-with endogeneity. Specifically, we show that unlike Lewbel estimation, MR
-GENIUS is under fairly weak conditions also robust to unmeasured confounding of
-the effects of the genetic IVs, another possible violation of a key IV
-Identification condition. Furthermore, while Lewbel estimation involves
-specification of linear models both for the outcome and the exposure, MR GENIUS
-generally does not require specification of a structural model for the direct
-effect of invalid IVs on the outcome, therefore allowing the latter model to be
-unrestricted. Finally, unlike Lewbel estimation, MR GENIUS is shown to equally
-apply for binary, discrete or continuous exposure and outcome variables and can
-be used under prospective sampling, or retrospective sampling such as in a
-case-control study.
-",0,0,0,1,0,0
-16897,Evaluation of Trace Alignment Quality and its Application in Medical Process Mining,"  Trace alignment algorithms have been used in process mining for discovering
-the consensus treatment procedures and process deviations. Different alignment
-algorithms, however, may produce very different results. No widely-adopted
-method exists for evaluating the results of trace alignment. Existing
-reference-free evaluation methods cannot adequately and comprehensively assess
-the alignment quality. We analyzed and compared the existing evaluation
-methods, identifying their limitations, and introduced improvements in two
-reference-free evaluation methods. Our approach assesses the alignment result
-globally instead of locally, and therefore helps the algorithm to optimize
-overall alignment quality. We also introduced a novel metric to measure the
-alignment complexity, which can be used as a constraint on alignment algorithm
-optimization. We tested our evaluation methods on a trauma resuscitation
-dataset and provided the medical explanation of the activities and patterns
-identified as deviations using our proposed evaluation methods.
-",1,0,0,0,0,0
-16898,Size Constraints on Majorana Beamsplitter Interferometer: Majorana Coupling and Surface-Bulk Scattering,"  Topological insulator surfaces in proximity to superconductors have been
-proposed as a way to produce Majorana fermions in condensed matter physics. One
-of the simplest proposed experiments with such a system is Majorana
-interferometry. Here, we consider two possibly conflicting constraints on the
-size of such an interferometer. Coupling of a Majorana mode from the edge (the
-arms) of the interferometer to vortices in the centre of the device sets a
-lower bound on the size of the device. On the other hand, scattering to the
-usually imperfectly insulating bulk sets an upper bound. From estimates of
-experimental parameters, we find that typical samples may have no size window
-in which the Majorana interferometer can operate, implying that a new
-generation of more highly insulating samples must be explored.
-",0,1,0,0,0,0
-16899,Counting Arithmetical Structures on Paths and Cycles,"  Let $G$ be a finite, simple, connected graph. An arithmetical structure on
-$G$ is a pair of positive integer vectors $\mathbf{d},\mathbf{r}$ such that
-$(\mathrm{diag}(\mathbf{d})-A)\mathbf{r}=0$, where $A$ is the adjacency matrix
-of $G$. We investigate the combinatorics of arithmetical structures on path and
-cycle graphs, as well as the associated critical groups (the cokernels of the
-matrices $(\mathrm{diag}(\mathbf{d})-A)$). For paths, we prove that
-arithmetical structures are enumerated by the Catalan numbers, and we obtain
-refined enumeration results related to ballot sequences. For cycles, we prove
-that arithmetical structures are enumerated by the binomial coefficients
-$\binom{2n-1}{n-1}$, and we obtain refined enumeration results related to
-multisets. In addition, we determine the critical groups for all arithmetical
-structures on paths and cycles.
-",0,0,1,0,0,0
-16900,From synaptic interactions to collective dynamics in random neuronal networks models: critical role of eigenvectors and transient behavior,"  The study of neuronal interactions is currently at the center of several
-neuroscience big collaborative projects (including the Human Connectome, the
-Blue Brain, the Brainome, etc.) which attempt to obtain a detailed map of the
-entire brain matrix. Under certain constraints, mathematical theory can advance
-predictions of the expected neural dynamics based solely on the statistical
-properties of such synaptic interaction matrix. This work explores the
-application of free random variables (FRV) to the study of large synaptic
-interaction matrices. Besides recovering in a straightforward way known results
-on eigenspectra of neural networks, we extend them to heavy-tailed
-distributions of interactions. More importantly, we derive analytically the
-behavior of eigenvector overlaps, which determine stability of the spectra. We
-observe that upon imposing the neuronal excitation/inhibition balance, although
-the eigenvalues remain unchanged, their stability dramatically decreases due to
-strong non-orthogonality of associated eigenvectors. It leads us to the
-conclusion that the understanding of the temporal evolution of asymmetric
-neural networks requires considering the entangled dynamics of both
-eigenvectors and eigenvalues, which might bear consequences for learning and
-memory processes in these models. Considering the success of FRV analysis in a
-wide variety of branches disciplines, we hope that the results presented here
-foster additional application of these ideas in the area of brain sciences.
-",0,0,0,0,1,0
-16901,Exploring cosmic origins with CORE: mitigation of systematic effects,"  We present an analysis of the main systematic effects that could impact the
-measurement of CMB polarization with the proposed CORE space mission. We employ
-timeline-to-map simulations to verify that the CORE instrumental set-up and
-scanning strategy allow us to measure sky polarization to a level of accuracy
-adequate to the mission science goals. We also show how the CORE observations
-can be processed to mitigate the level of contamination by potentially worrying
-systematics, including intensity-to-polarization leakage due to bandpass
-mismatch, asymmetric main beams, pointing errors and correlated noise. We use
-analysis techniques that are well validated on data from current missions such
-as Planck to demonstrate how the residual contamination of the measurements by
-these effects can be brought to a level low enough not to hamper the scientific
-capability of the mission, nor significantly increase the overall error budget.
-We also present a prototype of the CORE photometric calibration pipeline, based
-on that used for Planck, and discuss its robustness to systematics, showing how
-CORE can achieve its calibration requirements. While a fine-grained assessment
-of the impact of systematics requires a level of knowledge of the system that
-can only be achieved in a future study phase, the analysis presented here
-strongly suggests that the main areas of concern for the CORE mission can be
-addressed using existing knowledge, techniques and algorithms.
-",0,1,0,0,0,0
-16902,A non-ordinary peridynamics implementation for anisotropic materials,"  Peridynamics (PD) represents a new approach for modelling fracture mechanics,
-where a continuum domain is modelled through particles connected via physical
-bonds. This formulation allows us to model crack initiation, propagation,
-branching and coalescence without special assumptions. Up to date, anisotropic
-materials were modelled in the PD framework as different isotropic materials
-(for instance, fibre and matrix of a composite laminate), where the stiffness
-of the bond depends on its orientation. A non-ordinary state-based formulation
-will enable the modelling of generally anisotropic materials, where the
-material properties are directly embedded in the formulation. Other material
-models include rocks, concrete and biomaterials such as bones. In this paper,
-we implemented this model and validated it for anisotropic composite materials.
-A composite damage criterion has been employed to model the crack propagation
-behaviour. Several numerical examples have been used to validate the approach,
-and compared to other benchmark solution from the finite element method (FEM)
-and experimental results when available.
-",1,1,0,0,0,0
-16903,Discrete-attractor-like Tracking in Continuous Attractor Neural Networks,"  Continuous attractor neural networks generate a set of smoothly connected
-attractor states. In memory systems of the brain, these attractor states may
-represent continuous pieces of information such as spatial locations and head
-directions of animals. However, during the replay of previous experiences,
-hippocampal neurons show a discontinuous sequence in which discrete transitions
-of neural state are phase-locked with the slow-gamma (30-40 Hz) oscillation.
-Here, we explored the underlying mechanisms of the discontinuous sequence
-generation. We found that a continuous attractor neural network has several
-phases depending on the interactions between external input and local
-inhibitory feedback. The discrete-attractor-like behavior naturally emerges in
-one of these phases without any discreteness assumption. We propose that the
-dynamics of continuous attractor neural networks is the key to generate
-discontinuous state changes phase-locked to the brain rhythm.
-",0,0,0,0,1,0
-16904,Framework for an Innovative Perceptive Mobile Network Using Joint Communication and Sensing,"  In this paper, we develop a framework for an innovative perceptive mobile
-(i.e. cellular) network that integrates sensing with communication, and
-supports new applications widely in transportation, surveillance and
-environmental sensing. Three types of sensing methods implemented in the
-base-stations are proposed, using either uplink or downlink multiuser
-communication signals. The required changes to system hardware and major
-technical challenges are briefly discussed. We also demonstrate the feasibility
-of estimating sensing parameters via developing a compressive sensing based
-scheme and providing simulation results to validate its effectiveness.
-",1,0,0,0,0,0
-16905,On the smallest non-abelian quotient of $\mathrm{Aut}(F_n)$,"  We show that the smallest non-abelian quotient of $\mathrm{Aut}(F_n)$ is
-$\mathrm{PSL}_n(\mathbb{Z}/2\mathbb{Z}) = \mathrm{L}_n(2)$, thus confirming a
-conjecture of Mecchia--Zimmermann. In the course of the proof we give an
-exponential (in $n$) lower bound for the cardinality of a set on which
-$\mathrm{SAut}(F_n)$, the unique index $2$ subgroup of $\mathrm{Aut}(F_n)$, can
-act non-trivially. We also offer new results on the representation theory of
-$\mathrm{SAut(F_n)}$ in small dimensions over small, positive characteristics,
-and on rigidity of maps from $\mathrm{SAut}(F_n)$ to finite groups of Lie type
-and algebraic groups in characteristic $2$.
-",0,0,1,0,0,0
-16906,Property Testing in High Dimensional Ising models,"  This paper explores the information-theoretic limitations of graph property
-testing in zero-field Ising models. Instead of learning the entire graph
-structure, sometimes testing a basic graph property such as connectivity, cycle
-presence or maximum clique size is a more relevant and attainable objective.
-Since property testing is more fundamental than graph recovery, any necessary
-conditions for property testing imply corresponding conditions for graph
-recovery, while custom property tests can be statistically and/or
-computationally more efficient than graph recovery based algorithms.
-Understanding the statistical complexity of property testing requires the
-distinction of ferromagnetic (i.e., positive interactions only) and general
-Ising models. Using combinatorial constructs such as graph packing and strong
-monotonicity, we characterize how target properties affect the corresponding
-minimax upper and lower bounds within the realm of ferromagnets. On the other
-hand, by studying the detection of an antiferromagnetic (i.e., negative
-interactions only) Curie-Weiss model buried in Rademacher noise, we show that
-property testing is strictly more challenging over general Ising models. In
-terms of methodological development, we propose two types of correlation based
-tests: computationally efficient screening for ferromagnets, and score type
-tests for general models, including a fast cycle presence test. Our correlation
-screening tests match the information-theoretic bounds for property testing in
-ferromagnets.
-",0,0,1,1,0,0
-16907,Stratification and duality for homotopical groups,"  In this paper, we show that the category of module spectra over
-$C^*(B\mathcal{G},\mathbb{F}_p)$ is stratified for any $p$-local compact group
-$\mathcal{G}$, thereby giving a support-theoretic classification of all
-localizing subcategories of this category. To this end, we generalize Quillen's
-$F$-isomorphism theorem, Quillen's stratification theorem, Chouinard's theorem,
-and the finite generation of cohomology rings from finite groups to homotopical
-groups. Moreover, we show that $p$-compact groups admit a homotopical form of
-Gorenstein duality.
-",0,0,1,0,0,0
-16908,"Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE","  We present a family of Python modules for the numerical integration of
-ordinary, delay, or stochastic differential equations. The key features are
-that the user enters the derivative symbolically and it is
-just-in-time-compiled, allowing the user to efficiently integrate differential
-equations from a higher-level interpreted language. The presented modules are
-particularly suited for large systems of differential equations such as used to
-describe dynamics on complex networks. Through the selected method of input,
-the presented modules also allow to almost completely automatize the process of
-estimating regular as well as transversal Lyapunov exponents for ordinary and
-delay differential equations. We conceptually discuss the modules' design,
-analyze their performance, and demonstrate their capabilities by application to
-timely problems.
-",1,1,0,0,0,0
-16909,Adaptive Diffusions for Scalable Learning over Graphs,"  Diffusion-based classifiers such as those relying on the Personalized
-PageRank and the Heat kernel, enjoy remarkable classification accuracy at
-modest computational requirements. Their performance however is affected by the
-extent to which the chosen diffusion captures a typically unknown label
-propagation mechanism, that can be specific to the underlying graph, and
-potentially different for each class. The present work introduces a
-disciplined, data-efficient approach to learning class-specific diffusion
-functions adapted to the underlying network topology. The novel learning
-approach leverages the notion of ""landing probabilities"" of class-specific
-random walks, which can be computed efficiently, thereby ensuring scalability
-to large graphs. This is supported by rigorous analysis of the properties of
-the model as well as the proposed algorithms. Furthermore, a robust version of
-the classifier facilitates learning even in noisy environments.
-Classification tests on real networks demonstrate that adapting the diffusion
-function to the given graph and observed labels, significantly improves the
-performance over fixed diffusions; reaching -- and many times surpassing -- the
-classification accuracy of computationally heavier state-of-the-art competing
-methods, that rely on node embeddings and deep neural networks.
-",0,0,0,1,0,0
-16910,On the Discrimination Power and Effective Utilization of Active Learning Measures in Version Space Search,"  Active Learning (AL) methods have proven cost-saving against passive
-supervised methods in many application domains. An active learner, aiming to
-find some target hypothesis, formulates sequential queries to some oracle. The
-set of hypotheses consistent with the already answered queries is called
-version space. Several query selection measures (QSMs) for determining the best
-query to ask next have been proposed. Assuming binaryoutcome queries, we
-analyze various QSMs wrt. to the discrimination power of their selected queries
-within the current version space. As a result, we derive superiority and
-equivalence relations between these QSMs and introduce improved versions of
-existing QSMs to overcome identified issues. The obtained picture gives a hint
-about which QSMs should preferably be used in pool-based AL scenarios.
-Moreover, we deduce properties optimal queries wrt. QSMs must satisfy. Based on
-these, we demonstrate how efficient heuristic search methods for optimal
-queries in query synthesis AL scenarios can be devised.
-",1,0,0,0,0,0
-16911,Torchbearer: A Model Fitting Library for PyTorch,"  We introduce torchbearer, a model fitting library for pytorch aimed at
-researchers working on deep learning or differentiable programming. The
-torchbearer library provides a high level metric and callback API that can be
-used for a wide range of applications. We also include a series of built in
-callbacks that can be used for: model persistence, learning rate decay,
-logging, data visualization and more. The extensive documentation includes an
-example library for deep learning and dynamic programming problems and can be
-found at this http URL. The code is licensed under the MIT
-License and available at this https URL.
-",0,0,0,1,0,0
-16912,On estimation of contamination from hydrogen cyanide in carbon monoxide line intensity mapping,"  Line-intensity mapping surveys probe large-scale structure through spatial
-variations in molecular line emission from a population of unresolved
-cosmological sources. Future such surveys of carbon monoxide line emission,
-specifically the CO(1-0) line, face potential contamination from a disjoint
-population of sources emitting in a hydrogen cyanide emission line, HCN(1-0).
-This paper explores the potential range of the strength of HCN emission and its
-effect on the CO auto power spectrum, using simulations with an empirical model
-of the CO/HCN--halo connection. We find that effects on the observed CO power
-spectrum depend on modeling assumptions but are very small for our fiducial
-model based on our understanding of the galaxy--halo connection, with the bias
-in overall CO detection significance due to HCN expected to be less than 1%.
-",0,1,0,0,0,0
-16913,Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints,"  Classifiers can be trained with data-dependent constraints to satisfy
-fairness goals, reduce churn, achieve a targeted false positive rate, or other
-policy goals. We study the generalization performance for such constrained
-optimization problems, in terms of how well the constraints are satisfied at
-evaluation time, given that they are satisfied at training time. To improve
-generalization performance, we frame the problem as a two-player game where one
-player optimizes the model parameters on a training dataset, and the other
-player enforces the constraints on an independent validation dataset. We build
-on recent work in two-player constrained optimization to show that if one uses
-this two-dataset approach, then constraint generalization can be significantly
-improved. As we illustrate experimentally, this approach works not only in
-theory, but also in practice.
-",0,0,0,1,0,0
-16914,Automated Website Fingerprinting through Deep Learning,"  Several studies have shown that the network traffic that is generated by a
-visit to a website over Tor reveals information specific to the website through
-the timing and sizes of network packets. By capturing traffic traces between
-users and their Tor entry guard, a network eavesdropper can leverage this
-meta-data to reveal which website Tor users are visiting. The success of such
-attacks heavily depends on the particular set of traffic features that are used
-to construct the fingerprint. Typically, these features are manually engineered
-and, as such, any change introduced to the Tor network can render these
-carefully constructed features ineffective. In this paper, we show that an
-adversary can automate the feature engineering process, and thus automatically
-deanonymize Tor traffic by applying our novel method based on deep learning. We
-collect a dataset comprised of more than three million network traces, which is
-the largest dataset of web traffic ever used for website fingerprinting, and
-find that the performance achieved by our deep learning approaches is
-comparable to known methods which include various research efforts spanning
-over multiple years. The obtained success rate exceeds 96% for a closed world
-of 100 websites and 94% for our biggest closed world of 900 classes. In our
-open world evaluation, the most performant deep learning model is 2% more
-accurate than the state-of-the-art attack. Furthermore, we show that the
-implicit features automatically learned by our approach are far more resilient
-to dynamic changes of web content over time. We conclude that the ability to
-automatically construct the most relevant traffic features and perform accurate
-traffic recognition makes our deep learning based approach an efficient,
-flexible and robust technique for website fingerprinting.
-",1,0,0,0,0,0
-16915,"DataCite as a novel bibliometric source: Coverage, strengths and limitations","  This paper explores the characteristics of DataCite to determine its
-possibilities and potential as a new bibliometric data source to analyze the
-scholarly production of open data. Open science and the increasing data sharing
-requirements from governments, funding bodies, institutions and scientific
-journals has led to a pressing demand for the development of data metrics. As a
-very first step towards reliable data metrics, we need to better comprehend the
-limitations and caveats of the information provided by sources of open data. In
-this paper, we critically examine records downloaded from the DataCite's OAI
-API and elaborate a series of recommendations regarding the use of this source
-for bibliometric analyses of open data. We highlight issues related to metadata
-incompleteness, lack of standardization, and ambiguous definitions of several
-fields. Despite these limitations, we emphasize DataCite's value and potential
-to become one of the main sources for data metrics development.
-",1,0,0,0,0,0
-16916,Parameter Estimation of Complex Fractional Ornstein-Uhlenbeck Processes with Fractional Noise,"  We obtain strong consistency and asymptotic normality of a least squares
-estimator of the drift coefficient for complex-valued Ornstein-Uhlenbeck
-processes disturbed by fractional noise, extending the result of Y. Hu and D.
-Nualart, [Statist. Probab. Lett., 80 (2010), 1030-1038] to a special
-2-dimensions. The strategy is to exploit the Garsia-Rodemich-Rumsey inequality
-and complex fourth moment theorems. The main ingredients of this paper are the
-sample path regularity of a multiple Wiener-Ito integral and two equivalent
-conditions of complex fourth moment theorems in terms of the contractions of
-integral kernels and complex Malliavin derivatives.
-",0,0,1,1,0,0
-16917,E-learning Information Technology Based on an Ontology Driven Learning Engine,"  In the article, proposed is a new e-learning information technology based on
-an ontology driven learning engine, which is matched with modern pedagogical
-technologies. With the help of proposed engine and developed question database
-we have conducted an experiment, where students were tested. The developed
-ontology driven system of e-learning facilitates the creation of favorable
-conditions for the development of personal qualities and creation of a holistic
-understanding of the subject area among students throughout the educational
-process.
-",1,0,0,0,0,0
-16918,Global regularity for 1D Eulerian dynamics with singular interaction forces,"  The Euler-Poisson-Alignment (EPA) system appears in mathematical biology and
-is used to model, in a hydrodynamic limit, a set agents interacting through
-mutual attraction/repulsion as well as alignment forces. We consider
-one-dimensional EPA system with a class of singular alignment terms as well as
-natural attraction/repulsion terms. The singularity of the alignment kernel
-produces an interesting effect regularizing the solutions of the equation and
-leading to global regularity for wide range of initial data. This was recently
-observed in the paper by Do, Kiselev, Ryzhik and Tan. Our goal in this paper is
-to generalize the result and to incorporate the attractive/repulsive potential.
-We prove that global regularity persists for these more general models.
-",0,0,1,0,0,0
-16919,A $q$-generalization of the para-Racah polynomials,"  New bispectral orthogonal polynomials are obtained from an unconventional
-truncation of the Askey-Wilson polynomials. In the limit $q \to 1$, they reduce
-to the para-Racah polynomials which are orthogonal with respect to a quadratic
-bi-lattice. The three term recurrence relation and q-difference equation are
-obtained through limits of those of the Askey-Wilson polynomials. An explicit
-expression in terms of hypergeometric series and the orthogonality relation are
-provided. A $q$-generalization of the para-Krawtchouk polynomials is obtained
-as a special case. Connections with the $q$-Racah and dual-Hahn polynomials are
-also presented.
-",0,0,1,0,0,0
-16920,Data Poisoning Attack against Unsupervised Node Embedding Methods,"  Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have
-attracted growing interests given their simplicity and effectiveness. However,
-although these methods have been proved effective in a variety of applications,
-none of the existing work has analyzed the robustness of them. This could be
-very risky if these methods are attacked by an adversarial party. In this
-paper, we take the task of link prediction as an example, which is one of the
-most fundamental problems for graph analysis, and introduce a data positioning
-attack to node embedding methods. We give a complete characterization of
-attacker's utilities and present efficient solutions to adversarial attacks for
-two popular node embedding methods: DeepWalk and LINE. We evaluate our proposed
-attack model on multiple real-world graphs. Experimental results show that our
-proposed model can significantly affect the results of link prediction by
-slightly changing the graph structures (e.g., adding or removing a few edges).
-We also show that our proposed model is very general and can be transferable
-across different embedding methods. Finally, we conduct a case study on a
-coauthor network to better understand our attack method.
-",1,0,0,0,0,0
-16921,Entanglement properties of the two-dimensional SU(3) AKLT state,"  Two-dimensional (spin-$2$) Affleck-Kennedy-Lieb-Tasaki (AKLT) type valence
-bond solids on the square lattice are known to be symmetry protected
-topological (SPT) gapped spin liquids [Shintaro Takayoshi, Pierre Pujol, and
-Akihiro Tanaka Phys. Rev. B ${\bf 94}$, 235159 (2016)]. Using the projected
-entangled pair state (PEPS) framework, we extend the construction of the AKLT
-state to the case of $SU(3)$, relevant for cold atom systems. The entanglement
-spectrum is shown to be described by an alternating $SU(3)$ chain of ""quarks""
-and ""antiquarks"", subject to exponentially decaying (with distance) Heisenberg
-interactions, in close similarity with its $SU(2)$ analog. We discuss the SPT
-feature of the state.
-",0,1,0,0,0,0
-16922,Heart Rate Variability during Periods of Low Blood Pressure as a Predictor of Short-Term Outcome in Preterms,"  Efficient management of low blood pressure (BP) in preterm neonates remains
-challenging with a considerable variability in clinical practice. The ability
-to assess preterm wellbeing during episodes of low BP will help to decide when
-and whether hypotension treatment should be initiated. This work aims to
-investigate the relationship between heart rate variability (HRV), BP and the
-short-term neurological outcome in preterm infants less than 32 weeks
-gestational age (GA). The predictive power of common HRV features with respect
-to the outcome is assessed and shown to improve when HRV is observed during
-episodes of low mean arterial pressure (MAP) - with a single best feature
-leading to an AUC of 0.87. Combining multiple features with a boosted decision
-tree classifier achieves an AUC of 0.97. The work presents a promising step
-towards the use of multimodal data in building an objective decision support
-tool for clinical prediction of short-term outcome in preterms who suffer
-episodes of low BP.
-",0,0,0,1,0,0
-16923,Understanding News Outlets' Audience-Targeting Patterns,"  The power of the press to shape the informational landscape of a population
-is unparalleled, even now in the era of democratic access to all information
-outlets. However, it is known that news outlets (particularly more traditional
-ones) tend to discriminate who they want to reach, and who to leave aside. In
-this work, we attempt to shed some light on the audience targeting patterns of
-newspapers, using the Chilean media ecosystem. First, we use the gravity model
-to analyze geography as a factor in explaining audience reachability. This
-shows that some newspapers are indeed driven by geographical factors (mostly
-local news outlets) but some others are not (national-distribution outlets).
-For those which are not, we use a regression model to study the influence of
-socioeconomic and political characteristics in news outlets adoption. We
-conclude that indeed larger, national-distribution news outlets target
-populations based on these factors, rather than on geography or immediacy.
-",1,0,0,0,0,0
-16924,Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings,"  Selecting a representative vector for a set of vectors is a very common
-requirement in many algorithmic tasks. Traditionally, the mean or median vector
-is selected. Ontology classes are sets of homogeneous instance objects that can
-be converted to a vector space by word vector embeddings. This study proposes a
-methodology to derive a representative vector for ontology classes whose
-instances were converted to the vector space. We start by deriving five
-candidate vectors which are then used to train a machine learning model that
-would calculate a representative vector for the class. We show that our
-methodology out-performs the traditional mean and median vector
-representations.
-",1,0,0,0,0,0
-16925,The ESA Gaia Archive: Data Release 1,"  ESA Gaia mission is producing the more accurate source catalogue in astronomy
-up to now. That represents a challenge on the archiving area to make accessible
-this information to the astronomers in an efficient way. Also, new astronomical
-missions have reinforced the change on the development of archives. Archives,
-as simple applications to access the data are being evolving into complex data
-center structures where computing power services are available for users and
-data mining tools are integrated into the server side. In the case of astronomy
-science that involves the use of big catalogues, as in Gaia (or Euclid to
-come), the common ways to work on the data need to be changed to a new paradigm
-""move code close to the data"", what implies that data mining functionalities
-are becoming a must to allow the science exploitation. To enable these
-capabilities, a TAP+ interface, crossmatch capabilities, full catalogue
-histograms, serialisation of intermediate results in cloud resources like
-VOSpace, etc have been implemented for the Gaia DR1, to enable the exploitation
-of these science resources by the community without the bottlenecks on the
-connection bandwidth. We present the architecture, infrastructure and tools
-already available in the Gaia Archive Data Release 1
-(this http URL) and we describe capabilities and
-infrastructure.
-",0,1,0,0,0,0
-16926,Efficient algorithm for large spectral partitions,"  We present an amelioration of current known algorithms for optimal spectral
-partitioning problems. The idea is to use the advantage of a representation
-using density functions while decreasing the computational time. This is done
-by restricting the computation to neighbourhoods of regions where the
-associated densities are above a certain threshold. The algorithm extends and
-improves known methods in the plane and on surfaces in dimension 3. It also
-makes possible to make some of the first computations of volumic 3D spectral
-partitions on sufficiently large discretizations.
-",0,0,1,0,0,0
-16927,A Martian Origin for the Mars Trojan Asteroids,"  Seven of the nine known Mars Trojan asteroids belong to an orbital cluster
-named after its largest member 5261 Eureka. Eureka is likely the progenitor of
-the whole cluster, which formed at least 1 Gyr ago. It was suggested that the
-thermal YORP effect spun-up Eureka resulting with fragments being ejected by
-the rotational-fission mechanism. Eureka's spectrum exhibits a broad and deep
-absorption band around 1 {\mu}m, indicating an olivine-rich composition. Here
-we show evidence that the Trojan Eureka cluster progenitor could have
-originated as impact debris excavated from the Martian mantle. We present new
-near-infrared observations of two Trojans (311999 2007 NS2 and 385250 2001
-DH47) and find that both exhibit an olivine-rich reflectance spectrum similar
-to Eureka's. These measurements confirm that the progenitor of the cluster has
-an achondritic composition. Olivine-rich reflectance spectra are rare amongst
-asteroids but are seen around the largest basins on Mars. They are also
-consistent with some Martian meteorites (e.g. Chassigny), and with the material
-comprising much of the Martian mantle. Using numerical simulations, we show
-that the Mars Trojans are more likely to be impact ejecta from Mars than
-captured olivine-rich asteroids transported from the main belt. This result
-links directly specific asteroids to debris from the forming planets.
-",0,1,0,0,0,0
-16928,Far-infrared metallicity diagnostics: Application to local ultraluminous infrared galaxies,"  The abundance of metals in galaxies is a key parameter which permits to
-distinguish between different galaxy formation and evolution models. Most of
-the metallicity determinations are based on optical line ratios. However, the
-optical spectral range is subject to dust extinction and, for high-z objects (z
-> 3), some of the lines used in optical metallicity diagnostics are shifted to
-wavelengths not accessible to ground based observatories. For this reason, we
-explore metallicity diagnostics using far-infrared (IR) line ratios which can
-provide a suitable alternative in such situations. To investigate these far-IR
-line ratios, we modeled the emission of a starburst with the photoionization
-code CLOUDY. The most sensitive far-IR ratios to measure metallicities are the
-[OIII]52$\mu$m and 88$\mu$m to [NIII]57$\mu$m ratios. We show that this ratio
-produces robust metallicities in the presence of an AGN and is insensitive to
-changes in the age of the ionizing stellar. Another metallicity sensitive ratio
-is the [OIII]88$\mu$m/[NII]122$\mu$m ratio, although it depends on the
-ionization parameter. We propose various mid- and far-IR line ratios to break
-this dependency. Finally, we apply these far-IR diagnostics to a sample of 19
-local ultraluminous IR galaxies (ULIRGs) observed with Herschel and Spitzer. We
-find that the gas-phase metallicity in these local ULIRGs is in the range 0.7 <
-Z_gas/Z_sun < 1.5, which corresponds to 8.5 < 12 + log (O/H) < 8.9. The
-inferred metallicities agree well with previous estimates for local ULIRGs and
-this confirms that they lie below the local mass-metallicity relation.
-",0,1,0,0,0,0
-16929,Quantum communication by means of collapse of the wave function,"  We show that quantum communication by means of collapse of the wave function
-is possible. In this study, quantum communication does not mean quantum
-teleportation or quantum cryptography, but transmission of information itself.
-Because of consistency with special relativity, the possibility of the quantum
-communication leads to another conclusion that the collapse of the wave
-function must propagate at the speed of light or slower.
-We show this requirement is consistent with nonlocality in quantum mechanics.
-We also demonstrate that the Einstein-Podolsky-Rosen experiment does not
-disprove our conclusion.
-",0,1,0,0,0,0
-16930,DeepTerramechanics: Terrain Classification and Slip Estimation for Ground Robots via Deep Learning,"  Terramechanics plays a critical role in the areas of ground vehicles and
-ground mobile robots since understanding and estimating the variables
-influencing the vehicle-terrain interaction may mean the success or the failure
-of an entire mission. This research applies state-of-the-art algorithms in deep
-learning to two key problems: estimating wheel slip and classifying the terrain
-being traversed by a ground robot. Three data sets collected by ground robotic
-platforms (MIT single-wheel testbed, MSL Curiosity rover, and tracked robot
-Fitorobot) are employed in order to compare the performance of traditional
-machine learning methods (i.e. Support Vector Machine (SVM) and Multi-layer
-Perceptron (MLP)) against Deep Neural Networks (DNNs) and Convolutional Neural
-Networks (CNNs). This work also shows the impact that certain tuning parameters
-and the network architecture (MLP, DNN and CNN) play on the performance of
-those methods. This paper also contributes a deep discussion with the lessons
-learned in the implementation of DNNs and CNNs and how these methods can be
-extended to solve other problems.
-",1,0,0,0,0,0
-16931,"Characterizations of multinormality and corresponding tests of fit, including for Garch models","  We provide novel characterizations of multivariate normality that incorporate
-both the characteristic function and the moment generating function, and we
-employ these results to construct a class of affine invariant, consistent and
-easy-to-use goodness-of-fit tests for normality. The test statistics are
-suitably weighted $L^2$-statistics, and we provide their asymptotic behavior
-both for i.i.d. observations as well as in the context of testing that the
-innovation distribution of a multivariate GARCH model is Gaussian. We also
-study the finite-sample behavior of the new tests and compare the new criteria
-with alternative existing tests.
-",0,0,1,1,0,0
-16932,Grouped Gaussian Processes for Solar Power Prediction,"  We consider multi-task regression models where the observations are assumed
-to be a linear combination of several latent node functions and weight
-functions, which are both drawn from Gaussian process priors. Driven by the
-problem of developing scalable methods for forecasting distributed solar and
-other renewable power generation, we propose coupled priors over groups of
-(node or weight) processes to exploit spatial dependence between functions. We
-estimate forecast models for solar power at multiple distributed sites and
-ground wind speed at multiple proximate weather stations. Our results show that
-our approach maintains or improves point-prediction accuracy relative to
-competing solar benchmarks and improves over wind forecast benchmark models on
-all measures. Our approach consistently dominates the equivalent model without
-coupled priors, achieving faster gains in forecast accuracy. At the same time
-our approach provides better quantification of predictive uncertainties.
-",0,0,0,1,0,0
-16933,Modeling epidemics on d-cliqued graphs,"  Since social interactions have been shown to lead to symmetric clusters, we
-propose here that symmetries play a key role in epidemic modeling. Mathematical
-models on d-ary tree graphs were recently shown to be particularly effective
-for modeling epidemics in simple networks [Seibold & Callender, 2016]. To
-account for symmetric relations, we generalize this to a new type of networks
-modeled on d-cliqued tree graphs, which are obtained by adding edges to regular
-d-trees to form d-cliques. This setting gives a more realistic model for
-epidemic outbreaks originating, for example, within a family or classroom and
-which could reach a population by transmission via children in schools.
-Specifically, we quantify how an infection starting in a clique (e.g. family)
-can reach other cliques through the body of the graph (e.g. public places).
-Moreover, we propose and study the notion of a safe zone, a subset that has a
-negligible probability of infection.
-",1,0,0,0,1,0
-16934,On the K-theory stable bases of the Springer resolution,"  Cohomological and K-theoretic stable bases originated from the study of
-quantum cohomology and quantum K-theory. Restriction formula for cohomological
-stable bases played an important role in computing the quantum connection of
-cotangent bundle of partial flag varieties. In this paper we study the
-K-theoretic stable bases of cotangent bundles of flag varieties. We describe
-these bases in terms of the action of the affine Hecke algebra and the twisted
-group algebra of Kostant-Kumar. Using this algebraic description and the method
-of root polynomials, we give a restriction formula of the stable bases. We
-apply it to obtain the restriction formula for partial flag varieties. We also
-build a relation between the stable basis and the Casselman basis in the
-principal series representations of the Langlands dual group. As an
-application, we give a closed formula for the transition matrix between
-Casselman basis and the characteristic functions.
-",0,0,1,0,0,0
-16935,Recency Bias in the Era of Big Data: The Need to Strengthen the Status of History of Mathematics in Nigerian Schools,"  The amount of information available to the mathematics teacher is so enormous
-that the selection of desirable content is gradually becoming a huge task in
-itself. With respect to the inclusion of elements of history of mathematics in
-mathematics instruction, the era of Big Data introduces a high likelihood of
-Recency Bias, a hitherto unconnected challenge for stakeholders in mathematics
-education. This tendency to choose recent information at the expense of
-relevant older, composite, historical facts stands to defeat the aims and
-objectives of the epistemological and cultural approach to mathematics
-instructional delivery. This study is a didactic discourse with focus on this
-threat to the history and pedagogy of mathematics, particularly as it affects
-mathematics education in Nigeria. The implications for mathematics curriculum
-developers, teacher-training programmes, teacher lesson preparation, and
-publication of mathematics instructional materials were also deeply considered.
-",1,0,1,0,0,0
-16936,Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings,"  This paper presents a convergence analysis of kernel-based quadrature rules
-in misspecified settings, focusing on deterministic quadrature in Sobolev
-spaces. In particular, we deal with misspecified settings where a test
-integrand is less smooth than a Sobolev RKHS based on which a quadrature rule
-is constructed. We provide convergence guarantees based on two different
-assumptions on a quadrature rule: one on quadrature weights, and the other on
-design points. More precisely, we show that convergence rates can be derived
-(i) if the sum of absolute weights remains constant (or does not increase
-quickly), or (ii) if the minimum distance between design points does not
-decrease very quickly. As a consequence of the latter result, we derive a rate
-of convergence for Bayesian quadrature in misspecified settings. We reveal a
-condition on design points to make Bayesian quadrature robust to
-misspecification, and show that, under this condition, it may adaptively
-achieve the optimal rate of convergence in the Sobolev space of a lesser order
-(i.e., of the unknown smoothness of a test integrand), under a slightly
-stronger regularity condition on the integrand.
-",1,0,0,1,0,0
-16937,The toric Frobenius morphism and a conjecture of Orlov,"  We combine the Bondal-Uehara method for producing exceptional collections on
-toric varieties with a result of the first author and Favero to expand the set
-of varieties satisfying Orlov's Conjecture on derived dimension.
-",0,0,1,0,0,0
-16938,Friendship Maintenance and Prediction in Multiple Social Networks,"  Due to the proliferation of online social networks (OSNs), users find
-themselves participating in multiple OSNs. These users leave their activity
-traces as they maintain friendships and interact with other users in these
-OSNs. In this work, we analyze how users maintain friendship in multiple OSNs
-by studying users who have accounts in both Twitter and Instagram.
-Specifically, we study the similarity of a user's friendship and the evenness
-of friendship distribution in multiple OSNs. Our study shows that most users in
-Twitter and Instagram prefer to maintain different friendships in the two OSNs,
-keeping only a small clique of common friends in across the OSNs. Based upon
-our empirical study, we conduct link prediction experiments to predict missing
-friendship links in multiple OSNs using the neighborhood features, neighborhood
-friendship maintenance features and cross-link features. Our link prediction
-experiments shows that un- supervised methods can yield good accuracy in
-predicting links in one OSN using another OSN data and the link prediction
-accuracy can be further improved using supervised method with friendship
-maintenance and others measures as features.
-",1,1,0,0,0,0
-16939,Learning to Generate Music with BachProp,"  As deep learning advances, algorithms of music composition increase in
-performance. However, most of the successful models are designed for specific
-musical structures. Here, we present BachProp, an algorithmic composer that can
-generate music scores in many styles given sufficient training data. To adapt
-BachProp to a broad range of musical styles, we propose a novel representation
-of music and train a deep network to predict the note transition probabilities
-of a given music corpus. In this paper, new music scores generated by BachProp
-are compared with the original corpora as well as with different network
-architectures and other related models. We show that BachProp captures
-important features of the original datasets better than other models and invite
-the reader to a qualitative comparison on a large collection of generated
-songs.
-",1,0,0,0,0,0
-16940,How to Quantize $n$ Outputs of a Binary Symmetric Channel to $n-1$ Bits?,"  Suppose that $Y^n$ is obtained by observing a uniform Bernoulli random vector
-$X^n$ through a binary symmetric channel with crossover probability $\alpha$.
-The ""most informative Boolean function"" conjecture postulates that the maximal
-mutual information between $Y^n$ and any Boolean function $\mathrm{b}(X^n)$ is
-attained by a dictator function. In this paper, we consider the ""complementary""
-case in which the Boolean function is replaced by
-$f:\left\{0,1\right\}^n\to\left\{0,1\right\}^{n-1}$, namely, an $n-1$ bit
-quantizer, and show that $I(f(X^n);Y^n)\leq (n-1)\cdot\left(1-h(\alpha)\right)$
-for any such $f$. Thus, in this case, the optimal function is of the form
-$f(x^n)=(x_1,\ldots,x_{n-1})$.
-",1,0,1,0,0,0
-16941,Semi-Supervised Deep Learning for Monocular Depth Map Prediction,"  Supervised deep learning often suffers from the lack of sufficient training
-data. Specifically in the context of monocular depth map prediction, it is
-barely possible to determine dense ground truth depth images in realistic
-dynamic outdoor environments. When using LiDAR sensors, for instance, noise is
-present in the distance measurements, the calibration between sensors cannot be
-perfect, and the measurements are typically much sparser than the camera
-images. In this paper, we propose a novel approach to depth map prediction from
-monocular images that learns in a semi-supervised way. While we use sparse
-ground-truth depth for supervised learning, we also enforce our deep network to
-produce photoconsistent dense depth maps in a stereo setup using a direct image
-alignment loss. In experiments we demonstrate superior performance in depth map
-prediction from single images compared to the state-of-the-art methods.
-",1,0,0,0,0,0
-16942,Approximation Schemes for Clustering with Outliers,"  Clustering problems are well-studied in a variety of fields such as data
-science, operations research, and computer science. Such problems include
-variants of centre location problems, $k$-median, and $k$-means to name a few.
-In some cases, not all data points need to be clustered; some may be discarded
-for various reasons.
-We study clustering problems with outliers. More specifically, we look at
-Uncapacitated Facility Location (UFL), $k$-Median, and $k$-Means. In UFL with
-outliers, we have to open some centres, discard up to $z$ points of $\cal X$
-and assign every other point to the nearest open centre, minimizing the total
-assignment cost plus centre opening costs. In $k$-Median and $k$-Means, we have
-to open up to $k$ centres but there are no opening costs. In $k$-Means, the
-cost of assigning $j$ to $i$ is $\delta^2(j,i)$. We present several results.
-Our main focus is on cases where $\delta$ is a doubling metric or is the
-shortest path metrics of graphs from a minor-closed family of graphs. For
-uniform-cost UFL with outliers on such metrics we show that a multiswap simple
-local search heuristic yields a PTAS. With a bit more work, we extend this to
-bicriteria approximations for the $k$-Median and $k$-Means problems in the same
-metrics where, for any constant $\epsilon > 0$, we can find a solution using
-$(1+\epsilon)k$ centres whose cost is at most a $(1+\epsilon)$-factor of the
-optimum and uses at most $z$ outliers. We also show that natural local search
-heuristics that do not violate the number of clusters and outliers for
-$k$-Median (or $k$-Means) will have unbounded gap even in Euclidean metrics.
-Furthermore, we show how our analysis can be extended to general metrics for
-$k$-Means with outliers to obtain a $(25+\epsilon,1+\epsilon)$ bicriteria.
-",1,0,0,0,0,0
-16943,Order preserving pattern matching on trees and DAGs,"  The order preserving pattern matching (OPPM) problem is, given a pattern
-string $p$ and a text string $t$, find all substrings of $t$ which have the
-same relative orders as $p$. In this paper, we consider two variants of the
-OPPM problem where a set of text strings is given as a tree or a DAG. We show
-that the OPPM problem for a single pattern $p$ of length $m$ and a text tree
-$T$ of size $N$ can be solved in $O(m+N)$ time if the characters of $p$ are
-drawn from an integer alphabet of polynomial size. The time complexity becomes
-$O(m \log m + N)$ if the pattern $p$ is over a general ordered alphabet. We
-then show that the OPPM problem for a single pattern and a text DAG is
-NP-complete.
-",1,0,0,0,0,0
-16944,Categorical Probabilistic Theories,"  We present a simple categorical framework for the treatment of probabilistic
-theories, with the aim of reconciling the fields of Categorical Quantum
-Mechanics (CQM) and Operational Probabilistic Theories (OPTs). In recent years,
-both CQM and OPTs have found successful application to a number of areas in
-quantum foundations and information theory: they present many similarities,
-both in spirit and in formalism, but they remain separated by a number of
-subtle yet important differences. We attempt to bridge this gap, by adopting a
-minimal number of operationally motivated axioms which provide clean
-categorical foundations, in the style of CQM, for the treatment of the problems
-that OPTs are concerned with.
-",0,0,1,0,0,0
-16945,Maximal polynomial modulations of singular integrals,"  Let $K$ be a standard Hölder continuous Calderón--Zygmund kernel on
-$\mathbb{R}^{\mathbf{d}}$ whose truncations define $L^2$ bounded operators. We
-show that the maximal operator obtained by modulating $K$ by polynomial phases
-of a fixed degree is bounded on $L^p(\mathbb{R}^{\mathbf{d}})$ for $1 < p <
-\infty$. This extends Sjölin's multidimensional Carleson theorem and Lie's
-polynomial Carleson theorem.
-",0,0,1,0,0,0
-16946,Effects of Hubbard term correction on the structural parameters and electronic properties of wurtzite Zn,"  The effects of including the Hubbard on-site Coulombic correction to the
-structural parameters and valence energy states of wurtzite ZnO was explored.
-Due to the changes in the structural parameters caused by correction of
-hybridization between Zn d states and O p states, suitable parameters of
-Hubbard terms have to be determined for an accurate prediction of ZnO
-properties. Using the LDA+${U}$ method by applying Hubbard corrections $U_p$ to
-Zn 3d states and $U_p$ to O 2p states, the lattice constants were
-underestimated for all tested Hubbard parameters. The combination of both $U_d$
-and $U_p$ correction terms managed to widen the band gap of wurtzite ZnO to the
-experimental value. Pairs of $U_p$ and $U_p$ parameters with the correct
-positioning of d-band and accurate bandwidths were selected, in addition to
-predicting an accurate band gap value. Inspection of vibrational properties,
-however, revealed mismatches between the estimated gamma phonon frequencies and
-experimental values. The selection of Hubbard terms based on electronic band
-properties alone cannot ensure an accurate vibrational description in LDA+${U}$
-calculation.
-",0,1,0,0,0,0
-16947,A multi-scale Gaussian beam parametrix for the wave equation: the Dirichlet boundary value problem,"  We present a construction of a multi-scale Gaussian beam parametrix for the
-Dirichlet boundary value problem associated with the wave equation, and study
-its convergence rate to the true solution in the highly oscillatory regime. The
-construction elaborates on the wave-atom parametrix of Bao, Qian, Ying, and
-Zhang and extends to a multi-scale setting the technique of Gaussian beam
-propagation from a boundary of Katchalov, Kurylev and Lassas.
-",0,0,1,0,0,0
-16948,Uncertainty and sensitivity analysis of functional risk curves based on Gaussian processes,"  A functional risk curve gives the probability of an undesirable event as a
-function of the value of a critical parameter of a considered physical system.
-In several applicative situations, this curve is built using phenomenological
-numerical models which simulate complex physical phenomena. To avoid cpu-time
-expensive numerical models, we propose to use Gaussian process regression to
-build functional risk curves. An algorithm is given to provide confidence
-bounds due to this approximation. Two methods of global sensitivity analysis of
-the models' random input parameters on the functional risk curve are also
-studied. In particular, the PLI sensitivity indices allow to understand the
-effect of misjudgment on the input parameters' probability density functions.
-",0,0,1,1,0,0
-16949,Global optimization for low-dimensional switching linear regression and bounded-error estimation,"  The paper provides global optimization algorithms for two particularly
-difficult nonconvex problems raised by hybrid system identification: switching
-linear regression and bounded-error estimation. While most works focus on local
-optimization heuristics without global optimality guarantees or with guarantees
-valid only under restrictive conditions, the proposed approach always yields a
-solution with a certificate of global optimality. This approach relies on a
-branch-and-bound strategy for which we devise lower bounds that can be
-efficiently computed. In order to obtain scalable algorithms with respect to
-the number of data, we directly optimize the model parameters in a continuous
-optimization setting without involving integer variables. Numerical experiments
-show that the proposed algorithms offer a higher accuracy than convex
-relaxations with a reasonable computational burden for hybrid system
-identification. In addition, we discuss how bounded-error estimation is related
-to robust estimation in the presence of outliers and exact recovery under
-sparse noise, for which we also obtain promising numerical results.
-",1,0,0,1,0,0
-16950,An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction,"  During visuomotor tasks, robots must compensate for temporal delays inherent
-in their sensorimotor processing systems. Delay compensation becomes crucial in
-a dynamic environment where the visual input is constantly changing, e.g.,
-during the interacting with a human demonstrator. For this purpose, the robot
-must be equipped with a prediction mechanism for using the acquired perceptual
-experience to estimate possible future motor commands. In this paper, we
-present a novel neural network architecture that learns prototypical visuomotor
-representations and provides reliable predictions on the basis of the visual
-input. These predictions are used to compensate for the delayed motor behavior
-in an online manner. We investigate the performance of our method with a set of
-experiments comprising a humanoid robot that has to learn and generate visually
-perceived arm motion trajectories. We evaluate the accuracy in terms of mean
-prediction error and analyze the response of the network to novel movement
-demonstrations. Additionally, we report experiments with incomplete data
-sequences, showing the robustness of the proposed architecture in the case of a
-noisy and faulty visual sensor.
-",1,0,0,0,0,0
-16951,A CutFEM method for two-phase flow problems,"  In this article, we present a cut finite element method for two-phase
-Navier-Stokes flows. The main feature of the method is the formulation of a
-unified continuous interior penalty stabilisation approach for, on the one
-hand, stabilising advection and the pressure-velocity coupling and, on the
-other hand, stabilising the cut region. The accuracy of the algorithm is
-enhanced by the development of extended fictitious domains to guarantee a well
-defined velocity from previous time steps in the current geometry. Finally, the
-robustness of the moving-interface algorithm is further improved by the
-introduction of a curvature smoothing technique that reduces spurious
-velocities. The algorithm is shown to perform remarkably well for low capillary
-number flows, and is a first step towards flexible and robust CutFEM algorithms
-for the simulation of microfluidic devices.
-",1,0,0,0,0,0
-16952,Learning under selective labels in the presence of expert consistency,"  We explore the problem of learning under selective labels in the context of
-algorithm-assisted decision making. Selective labels is a pervasive selection
-bias problem that arises when historical decision making blinds us to the true
-outcome for certain instances. Examples of this are common in many
-applications, ranging from predicting recidivism using pre-trial release data
-to diagnosing patients. In this paper we discuss why selective labels often
-cannot be effectively tackled by standard methods for adjusting for sample
-selection bias, even if there are no unobservables. We propose a data
-augmentation approach that can be used to either leverage expert consistency to
-mitigate the partial blindness that results from selective labels, or to
-empirically validate whether learning under such framework may lead to
-unreliable models prone to systemic discrimination.
-",0,0,0,1,0,0
-16953,Opacity limit for supermassive protostars,"  We present a model for the evolution of supermassive protostars from their
-formation at $M_\star \simeq 0.1\,\text{M}_\odot$ until their growth to
-$M_\star \simeq 10^5\,\text{M}_\odot$. To calculate the initial properties of
-the object in the optically thick regime we follow two approaches: based on
-idealized thermodynamic considerations, and on a more detailed one-zone model.
-Both methods derive a similar value of $n_{\rm F} \simeq 2 \times 10^{17}
-\,\text{cm}^{-3}$ for the density of the object when opacity becomes important,
-i.e. the opacity limit. The subsequent evolution of the growing protostar is
-determined by the accretion of gas onto the object and can be described by a
-mass-radius relation of the form $R_\star \propto M_\star^{1/3}$ during the
-early stages, and of the form $R_\star \propto M_\star^{1/2}$ when internal
-luminosity becomes important. For the case of a supermassive protostar, this
-implies that the radius of the star grows from $R_\star \simeq 0.65 \,{\rm AU}$
-to $R_\star \simeq 250 \,{\rm AU}$ during its evolution. Finally, we use this
-model to construct a sub-grid recipe for accreting sink particles in numerical
-simulations. A prime ingredient thereof is a physically motivated prescription
-for the accretion radius and the effective temperature of the growing protostar
-embedded inside it. From the latter, we can conclude that photo-ionization
-feedback can be neglected until very late in the assembly process of the
-supermassive object.
-",0,1,0,0,0,0
-16954,Learning to Imagine Manipulation Goals for Robot Task Planning,"  Prospection is an important part of how humans come up with new task plans,
-but has not been explored in depth in robotics. Predicting multiple task-level
-is a challenging problem that involves capturing both task semantics and
-continuous variability over the state of the world. Ideally, we would combine
-the ability of machine learning to leverage big data for learning the semantics
-of a task, while using techniques from task planning to reliably generalize to
-new environment. In this work, we propose a method for learning a model
-encoding just such a representation for task planning. We learn a neural net
-that encodes the $k$ most likely outcomes from high level actions from a given
-world. Our approach creates comprehensible task plans that allow us to predict
-changes to the environment many time steps into the future. We demonstrate this
-approach via application to a stacking task in a cluttered environment, where
-the robot must select between different colored blocks while avoiding
-obstacles, in order to perform a task. We also show results on a simple
-navigation task. Our algorithm generates realistic image and pose predictions
-at multiple points in a given task.
-",1,0,0,0,0,0
-16955,On the Combinatorial Power of the Weisfeiler-Lehman Algorithm,"  The classical Weisfeiler-Lehman method WL[2] uses edge colors to produce a
-powerful graph invariant. It is at least as powerful in its ability to
-distinguish non-isomorphic graphs as the most prominent algebraic graph
-invariants. It determines not only the spectrum of a graph, and the angles
-between standard basis vectors and the eigenspaces, but even the angles between
-projections of standard basis vectors into the eigenspaces. Here, we
-investigate the combinatorial power of WL[2]. For sufficiently large k, WL[k]
-determines all combinatorial properties of a graph. Many traditionally used
-combinatorial invariants are determined by WL[k] for small k. We focus on two
-fundamental invariants, the num- ber of cycles Cp of length p, and the number
-of cliques Kp of size p. We show that WL[2] determines the number of cycles of
-lengths up to 6, but not those of length 8. Also, WL[2] does not determine the
-number of 4-cliques.
-",1,0,0,0,0,0
-16956,Learning to Generate Reviews and Discovering Sentiment,"  We explore the properties of byte-level recurrent language models. When given
-sufficient amounts of capacity, training data, and compute time, the
-representations learned by these models include disentangled features
-corresponding to high-level concepts. Specifically, we find a single unit which
-performs sentiment analysis. These representations, learned in an unsupervised
-manner, achieve state of the art on the binary subset of the Stanford Sentiment
-Treebank. They are also very data efficient. When using only a handful of
-labeled examples, our approach matches the performance of strong baselines
-trained on full datasets. We also demonstrate the sentiment unit has a direct
-influence on the generative process of the model. Simply fixing its value to be
-positive or negative generates samples with the corresponding positive or
-negative sentiment.
-",1,0,0,0,0,0
-16957,Designing magnetism in Fe-based Heusler alloys: a machine learning approach,"  Combining material informatics and high-throughput electronic structure
-calculations offers the possibility of a rapid characterization of complex
-magnetic materials. Here we demonstrate that datasets of electronic properties
-calculated at the ab initio level can be effectively used to identify and
-understand physical trends in magnetic materials, thus opening new avenues for
-accelerated materials discovery. Following a data-centric approach, we utilize
-a database of Heusler alloys calculated at the density functional theory level
-to identify the ideal ions neighbouring Fe in the $X_2$Fe$Z$ Heusler prototype.
-The hybridization of Fe with the nearest neighbour $X$ ion is found to cause
-redistribution of the on-site Fe charge and a net increase of its magnetic
-moment proportional to the valence of $X$. Thus, late transition metals are
-ideal Fe neighbours for producing high-moment Fe-based Heusler magnets. At the
-same time a thermodynamic stability analysis is found to restrict $Z$ to main
-group elements. Machine learning regressors, trained to predict magnetic moment
-and volume of Heusler alloys, are used to determine the magnetization for all
-materials belonging to the proposed prototype. We find that Co$_2$Fe$Z$ alloys,
-and in particular Co$_2$FeSi, maximize the magnetization, which reaches values
-up to 1.2T. This is in good agreement with both ab initio and experimental
-data. Furthermore, we identify the Cu$_2$Fe$Z$ family to be a cost-effective
-materials class, offering a magnetization of approximately 0.65T.
-",0,1,0,0,0,0
-16958,On a diffuse interface model for tumour growth with non-local interactions and degenerate mobilities,"  We study a non-local variant of a diffuse interface model proposed by
-Hawkins--Darrud et al. (2012) for tumour growth in the presence of a chemical
-species acting as nutrient. The system consists of a Cahn--Hilliard equation
-coupled to a reaction-diffusion equation. For non-degenerate mobilities and
-smooth potentials, we derive well-posedness results, which are the non-local
-analogue of those obtained in Frigeri et al. (European J. Appl. Math. 2015).
-Furthermore, we establish existence of weak solutions for the case of
-degenerate mobilities and singular potentials, which serves to confine the
-order parameter to its physically relevant interval. Due to the non-local
-nature of the equations, under additional assumptions continuous dependence on
-initial data can also be shown.
-",0,0,1,0,0,0
-16959,Gradient Descent Can Take Exponential Time to Escape Saddle Points,"  Although gradient descent (GD) almost always escapes saddle points
-asymptotically [Lee et al., 2016], this paper shows that even with fairly
-natural random initialization schemes and non-pathological functions, GD can be
-significantly slowed down by saddle points, taking exponential time to escape.
-On the other hand, gradient descent with perturbations [Ge et al., 2015, Jin et
-al., 2017] is not slowed down by saddle points - it can find an approximate
-local minimizer in polynomial time. This result implies that GD is inherently
-slower than perturbed GD, and justifies the importance of adding perturbations
-for efficient non-convex optimization. While our focus is theoretical, we also
-present experiments that illustrate our theoretical findings.
-",1,0,1,1,0,0
-16960,Spectral parameter power series for arbitrary order linear differential equations,"  Let $L$ be the $n$-th order linear differential operator $Ly = \phi_0y^{(n)}
-+ \phi_1y^{(n-1)} + \cdots + \phi_ny$ with variable coefficients. A
-representation is given for $n$ linearly independent solutions of $Ly=\lambda r
-y$ as power series in $\lambda$, generalizing the SPPS (spectral parameter
-power series) solution which has been previously developed for $n=2$. The
-coefficient functions in these series are obtained by recursively iterating a
-simple integration process, begining with a solution system for $\lambda=0$. It
-is shown how to obtain such an initializing system working upwards from
-equations of lower order. The values of the successive derivatives of the power
-series solutions at the basepoint of integration are given, which provides a
-technique for numerical solution of $n$-th order initial value problems and
-spectral problems.
-",0,0,1,0,0,0
-16961,Antropologia de la Informatica Social: Teoria de la Convergencia Tecno-Social,"  The traditional humanism of the twentieth century, inspired by the culture of
-the book, systematically distanced itself from the new society of digital
-information; the Internet and tools of information processing revolutionized
-the world, society during this period developed certain adaptive
-characteristics based on coexistence (Human - Machine), this transformation
-sets based on the impact of three technology segments: devices, applications
-and infrastructure of social communication, which are involved in various
-physical, behavioural and cognitive changes of the human being; and the
-emergence of new models of influence and social control through the new
-ubiquitous communication; however in this new process of conviviality new
-models like the ""collaborative thinking"" and ""InfoSharing"" develop; managing
-social information under three Human ontological dimensions (h) - Information
-(i) - Machine (m), which is the basis of a new physical-cyber ecosystem, where
-they coexist and develop new social units called ""virtual communities "". This
-new communication infrastructure and social management of information given
-discovered areas of vulnerability ""social perspective of risk"", impacting all
-social units through massive impact vector (i); The virtual environment ""H + i
-+ M""; and its components, as well as the life cycle management of social
-information allows us to understand the path of integration ""Techno - Social""
-and setting a new contribution to cybernetics, within the convergence of
-technology with society and the new challenges of coexistence, aimed at a new
-holistic and not pragmatic vision, as the human component (h) in the virtual
-environment is the precursor of the future and needs to be studied not as an
-application, but as the hub of a new society.
-",1,0,0,0,0,0
-16962,A Deterministic Approach to Avoid Saddle Points,"  Loss functions with a large number of saddle points are one of the main
-obstacles to training many modern machine learning models. Gradient descent
-(GD) is a fundamental algorithm for machine learning and converges to a saddle
-point for certain initial data. We call the region formed by these initial
-values the ""attraction region."" For quadratic functions, GD converges to a
-saddle point if the initial data is in a subspace of up to n-1 dimensions. In
-this paper, we prove that a small modification of the recently proposed
-Laplacian smoothing gradient descent (LSGD) [Osher, et al., arXiv:1806.06317]
-contributes to avoiding saddle points without sacrificing the convergence rate
-of GD. In particular, we show that the dimension of the LSGD's attraction
-region is at most floor((n-1)/2) for a class of quadratic functions which is
-significantly smaller than GD's (n-1)-dimensional attraction region.
-",1,0,0,1,0,0
-16963,Automatic Generation of Typographic Font from a Small Font Subset,"  This paper addresses the automatic generation of a typographic font from a
-subset of characters. Specifically, we use a subset of a typographic font to
-extrapolate additional characters. Consequently, we obtain a complete font
-containing a number of characters sufficient for daily use. The automated
-generation of Japanese fonts is in high demand because a Japanese font requires
-over 1,000 characters. Unfortunately, professional typographers create most
-fonts, resulting in significant financial and time investments for font
-generation. The proposed method can be a great aid for font creation because
-designers do not need to create the majority of the characters for a new font.
-The proposed method uses strokes from given samples for font generation. The
-strokes, from which we construct characters, are extracted by exploiting a
-character skeleton dataset. This study makes three main contributions: a novel
-method of extracting strokes from characters, which is applicable to both
-standard fonts and their variations; a fully automated approach for
-constructing characters; and a selection method for sample characters. We
-demonstrate our proposed method by generating 2,965 characters in 47 fonts.
-Objective and subjective evaluations verify that the generated characters are
-similar to handmade characters.
-",1,0,0,0,0,0
-16964,The Second Postulate of Euclid and the Hyperbolic Geometry,"  The article deals with the connection between the second postulate of Euclid
-and non-Euclidean geometry. It is shown that the violation of the second
-postulate of Euclid inevitably leads to hyperbolic geometry. This eliminates
-misunderstandings about the sums of some divergent series. The connection
-between hyperbolic geometry and relativistic computations is noted.
-",0,0,1,0,0,0
-16965,Transkernel: An Executor for Commodity Kernels on Peripheral Cores,"  Modern mobile and embedded platforms see a large number of ephemeral tasks
-driven by background activities. In order to execute such a task, the OS kernel
-wakes up the platform beforehand and puts it back to sleep afterwards. In doing
-so, the kernel operates various IO devices and orchestrates their power state
-transitions. Such kernel execution phases are lengthy, having high energy cost,
-and yet difficult to optimize. We advocate for relieving the CPU from these
-kernel phases by executing them on a low-power, microcontroller-like core,
-dubbed peripheral core, hence leaving the CPU off. Yet, for a peripheral core
-to execute phases in a complex commodity kernel (e.g. Linux), existing
-approaches either incur high engineering effort or high runtime overhead. We
-take a radical approach with a new executor model called transkernel. Running
-on a peripheral core, a transkernel executes the binary of the commodity kernel
-through cross-ISA, dynamic binary translation (DBT). The transkernel translates
-stateful kernel code while emulating a small set of stateless kernel services;
-it sets a narrow, stable binary interface for emulated services; it specializes
-for kernel's beaten paths; it exploits ISA similarities for low DBT cost. With
-a concrete implementation on a heterogeneous ARM SoC, we demonstrate the
-feasibility and benefit of transkernel. Our result contributes a new OS
-structure that combines cross-ISA DBT and emulation for harnessing a
-heterogeneous SoC. Our result demonstrates that while cross-ISA DBT is
-typically used under the assumption of efficiency loss, it can be used for
-efficiency gain, even atop off-the-shelf hardware.
-",1,0,0,0,0,0
-16966,No iterated identities satisfied by all finite groups,"  We show that there is no iterated identity satisfied by all finite groups.
-For $w$ being a non-trivial word of length $l$, we show that there exists a
-finite group $G$ of cardinality at most $\exp(l^C)$ which does not satisfy the
-iterated identity $w$. The proof uses the approach of Borisov and Sapir, who
-used dynamics of polynomial mappings for the proof of non residual finiteness
-of some groups.
-",0,0,1,0,0,0
-16967,Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning,"  The goal of this tutorial is to introduce key models, algorithms, and open
-questions related to the use of optimization methods for solving problems
-arising in machine learning. It is written with an INFORMS audience in mind,
-specifically those readers who are familiar with the basics of optimization
-algorithms, but less familiar with machine learning. We begin by deriving a
-formulation of a supervised learning problem and show how it leads to various
-optimization problems, depending on the context and underlying assumptions. We
-then discuss some of the distinctive features of these optimization problems,
-focusing on the examples of logistic regression and the training of deep neural
-networks. The latter half of the tutorial focuses on optimization algorithms,
-first for convex logistic regression, for which we discuss the use of
-first-order methods, the stochastic gradient method, variance reducing
-stochastic methods, and second-order methods. Finally, we discuss how these
-approaches can be employed to the training of deep neural networks, emphasizing
-the difficulties that arise from the complex, nonconvex structure of these
-models.
-",1,0,0,1,0,0
-16968,A Unified Parallel Algorithm for Regularized Group PLS Scalable to Big Data,"  Partial Least Squares (PLS) methods have been heavily exploited to analyse
-the association between two blocs of data. These powerful approaches can be
-applied to data sets where the number of variables is greater than the number
-of observations and in presence of high collinearity between variables.
-Different sparse versions of PLS have been developed to integrate multiple data
-sets while simultaneously selecting the contributing variables. Sparse
-modelling is a key factor in obtaining better estimators and identifying
-associations between multiple data sets. The cornerstone of the sparsity
-version of PLS methods is the link between the SVD of a matrix (constructed
-from deflated versions of the original matrices of data) and least squares
-minimisation in linear regression. We present here an accurate description of
-the most popular PLS methods, alongside their mathematical proofs. A unified
-algorithm is proposed to perform all four types of PLS including their
-regularised versions. Various approaches to decrease the computation time are
-offered, and we show how the whole procedure can be scalable to big data sets.
-",0,0,0,1,0,0
-16969,Asymptotic behaviour of the fifth Painlevé transcendents in the space of initial values,"  We study the asymptotic behaviour of the solutions of the fifth Painlevé
-equation as the independent variable approaches zero and infinity in the space
-of initial values. We show that the limit set of each solution is compact and
-connected and, moreover, that any solution with the essential singularity at
-zero has an infinite number of poles and zeroes, and any solution with the
-essential singularity at infinity has infinite number of poles and takes value
-$1$ infinitely many times.
-",0,1,1,0,0,0
-16970,Hidden Treasures - Recycling Large-Scale Internet Measurements to Study the Internet's Control Plane,"  Internet-wide scans are a common active measurement approach to study the
-Internet, e.g., studying security properties or protocol adoption. They involve
-probing large address ranges (IPv4 or parts of IPv6) for specific ports or
-protocols. Besides their primary use for probing (e.g., studying protocol
-adoption), we show that - at the same time - they provide valuable insights
-into the Internet control plane informed by ICMP responses to these probes - a
-currently unexplored secondary use. We collect one week of ICMP responses
-(637.50M messages) to several Internet-wide ZMap scans covering multiple TCP
-and UDP ports as well as DNS-based scans covering > 50% of the domain name
-space. This perspective enables us to study the Internet's control plane as a
-by-product of Internet measurements. We receive ICMP messages from ~171M
-different IPs in roughly 53K different autonomous systems. Additionally, we
-uncover multiple control plane problems, e.g., we detect a plethora of outdated
-and misconfigured routers and uncover the presence of large-scale persistent
-routing loops in IPv4.
-",1,0,0,0,0,0
-16971,Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms,"  We present a method for metric optimization in the Large Deformation
-Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced
-Riemannian metric on the space of diffeomorphisms as a kernel in a machine
-learning context. For simplicity, we choose the kernel Fischer Linear
-Discriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters
-in an Expectation-Maximization framework, we define model fidelity via the
-hinge loss of the decision function. The resulting algorithm optimizes the
-parameters of the LDDMM norm-inducing differential operator as a solution to a
-group-wise registration and classification problem. In practice, this may lead
-to a biology-aware registration, focusing its attention on the predictive task
-at hand such as identifying the effects of disease. We first tested our
-algorithm on a synthetic dataset, showing that our parameter selection improves
-registration quality and classification accuracy. We then tested the algorithm
-on 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our
-Schizpohrenia-Control predictive model showed significant improvement in ROC
-AUC compared to baseline parameters.
-",0,0,0,1,0,0
-16972,On a direct algorithm for constructing recursion operators and Lax pairs for integrable models,"  We suggested an algorithm for searching the recursion operators for nonlinear
-integrable equations. It was observed that the recursion operator $R$ can be
-represented as a ratio of the form $R=L_1^{-1}L_2$ where the linear
-differential operators $L_1$ and $L_2$ are chosen in such a way that the
-ordinary differential equation $(L_2-\lambda L_1)U=0$ is consistent with the
-linearization of the given nonlinear integrable equation for any value of the
-parameter $\lambda\in \textbf{C}$. For constructing the operator $L_1$ we use
-the concept of the invariant manifold which is a generalization of the
-symmetry. Then for searching $L_2$ we take an auxiliary linear equation
-connected with the linearized equation by the Darboux transformation.
-Connection of the invariant manifold with the Lax pairs and the
-Dubrovin-Weierstrass equations is discussed.
-",0,1,0,0,0,0
-16973,Network Classification and Categorization,"  To the best of our knowledge, this paper presents the first large-scale study
-that tests whether network categories (e.g., social networks vs. web graphs)
-are distinguishable from one another (using both categories of real-world
-networks and synthetic graphs). A classification accuracy of $94.2\%$ was
-achieved using a random forest classifier with both real and synthetic
-networks. This work makes two important findings. First, real-world networks
-from various domains have distinct structural properties that allow us to
-predict with high accuracy the category of an arbitrary network. Second,
-classifying synthetic networks is trivial as our models can easily distinguish
-between synthetic graphs and the real-world networks they are supposed to
-model.
-",1,0,0,1,0,0
-16974,A Polynomial-Time Algorithm for Solving the Minimal Observability Problem in Conjunctive Boolean Networks,"  Many complex systems in biology, physics, and engineering include a large
-number of state-variables, and measuring the full state of the system is often
-impossible. Typically, a set of sensors is used to measure part of the
-state-variables. A system is called observable if these measurements allow to
-reconstruct the entire state of the system. When the system is not observable,
-an important and practical problem is how to add a \emph{minimal} number of
-sensors so that the system becomes observable. This minimal observability
-problem is practically useful and theoretically interesting, as it pinpoints
-the most informative nodes in the system. We consider the minimal observability
-problem for an important special class of Boolean networks, called conjunctive
-Boolean networks (CBNs). Using a graph-theoretic approach, we provide a
-necessary and sufficient condition for observability of a CBN with $n$
-state-variables, and an efficient~$O(n^2)$-time algorithm for solving the
-minimal observability problem. We demonstrate the usefulness of these results
-by studying the properties of a class of random CBNs.
-",1,0,1,0,0,0
-16975,The Description and Scaling Behavior for the Inner Region of the Boundary Layer for 2-D Wall-bounded Flows,"  A second derivative-based moment method is proposed for describing the
-thickness and shape of the region where viscous forces are dominant in
-turbulent boundary layer flows. Rather than the fixed location sublayer model
-presently employed, the new method defines thickness and shape parameters that
-are experimentally accessible without differentiation. It is shown
-theoretically that one of the new length parameters used as a scaling parameter
-is also a similarity parameter for the velocity profile. In fact, we show that
-this new length scale parameter removes one of the theoretical inconsistencies
-present in the traditional Prandtl Plus scalings. Furthermore, the new length
-parameter and the Prandtl Plus scaling parameters perform identically when
-operating on experimental datasets. This means that many of the past successes
-ascribed to the Prandtl Plus scaling also apply to the new parameter set but
-without one of the theoretical inconsistencies. Examples are offered to show
-how the new description method is useful in exploring the actual physics of the
-boundary layer.
-",0,1,0,0,0,0
-16976,Completely Sidon sets in $C^*$-algebras (New title),"  A sequence in a $C^*$-algebra $A$ is called completely Sidon if its span in
-$A$ is completely isomorphic to the operator space version of the space
-$\ell_1$ (i.e. $\ell_1$ equipped with its maximal operator space structure).
-The latter can also be described as the span of the free unitary generators in
-the (full) $C^*$-algebra of the free group $\F_\infty$ with countably
-infinitely many generators. Our main result is a generalization to this context
-of Drury's classical theorem stating that Sidon sets are stable under finite
-unions. In the particular case when $A=C^*(G)$ the (maximal) $C^*$-algebra of a
-discrete group $G$, we recover the non-commutative (operator space) version of
-Drury's theorem that we recently proved. We also give several non-commutative
-generalizations of our recent work on uniformly bounded orthonormal systems to
-the case of von Neumann algebras equipped with normal faithful tracial states.
-",0,0,1,0,0,0
-16977,Conflict-Free Coloring of Planar Graphs,"  A conflict-free k-coloring of a graph assigns one of k different colors to
-some of the vertices such that, for every vertex v, there is a color that is
-assigned to exactly one vertex among v and v's neighbors. Such colorings have
-applications in wireless networking, robotics, and geometry, and are
-well-studied in graph theory. Here we study the natural problem of the
-conflict-free chromatic number chi_CF(G) (the smallest k for which
-conflict-free k-colorings exist). We provide results both for closed
-neighborhoods N[v], for which a vertex v is a member of its neighborhood, and
-for open neighborhoods N(v), for which vertex v is not a member of its
-neighborhood.
-For closed neighborhoods, we prove the conflict-free variant of the famous
-Hadwiger Conjecture: If an arbitrary graph G does not contain K_{k+1} as a
-minor, then chi_CF(G) <= k. For planar graphs, we obtain a tight worst-case
-bound: three colors are sometimes necessary and always sufficient. We also give
-a complete characterization of the computational complexity of conflict-free
-coloring. Deciding whether chi_CF(G)<= 1 is NP-complete for planar graphs G,
-but polynomial for outerplanar graphs. Furthermore, deciding whether
-chi_CF(G)<= 2 is NP-complete for planar graphs G, but always true for
-outerplanar graphs. For the bicriteria problem of minimizing the number of
-colored vertices subject to a given bound k on the number of colors, we give a
-full algorithmic characterization in terms of complexity and approximation for
-outerplanar and planar graphs.
-For open neighborhoods, we show that every planar bipartite graph has a
-conflict-free coloring with at most four colors; on the other hand, we prove
-that for k in {1,2,3}, it is NP-complete to decide whether a planar bipartite
-graph has a conflict-free k-coloring. Moreover, we establish that any general}
-planar graph has a conflict-free coloring with at most eight colors.
-",1,0,1,0,0,0
-16978,Explicit solutions to utility maximization problems in a regime-switching market model via Laplace transforms,"  We study the problem of utility maximization from terminal wealth in which an
-agent optimally builds her portfolio by investing in a bond and a risky asset.
-The asset price dynamics follow a diffusion process with regime-switching
-coefficients modeled by a continuous-time finite-state Markov chain. We
-consider an investor with a Constant Relative Risk Aversion (CRRA) utility
-function. We deduce the associated Hamilton-Jacobi-Bellman equation to
-construct the solution and the optimal trading strategy and verify optimality
-by showing that the value function is the unique constrained viscosity solution
-of the HJB equation. By means of a Laplace transform method, we show how to
-explicitly compute the value function and illustrate the method with the two-
-and three-states cases. This method is interesting in its own right and can be
-adapted in other applications involving hybrid systems and using other types of
-transforms with basic properties similar to the Laplace transform.
-",0,0,0,0,0,1
-16979,Spectroscopic study of the elusive globular cluster ESO452-SC11 and its surroundings,"  Globular clusters (GCs) are amongst the oldest objects in the Galaxy and play
-a pivotal role in deciphering its early history. We present the first
-spectroscopic study of the GC ESO452-SC11 using the AAOmega spectrograph at
-medium resolution. Given the sparsity of this object and high degree of
-foreground contamination due to its location toward the bulge, few details are
-known for this cluster: there is no consensus of its age, metallicity, or its
-association with the disk or bulge. We identify 5 members based on radial
-velocity, metallicity, and position within the GC. Using spectral synthesis,
-accurate abundances of Fe and several $\alpha$-, Fe-peak, neutron-capture
-elements (Si,Ca,Ti,Cr,Co,Ni,Sr,Eu) were measured. Two of the 5 cluster
-candidates are likely non-members, as they have deviant Fe abundances and
-[$\alpha$/Fe] ratios. The mean radial velocity is 19$\pm$2 km s$^{-1}$ with a
-low dispersion of 2.8$\pm$3.4 km s$^{-1}$, in line with its low mass. The mean
-Fe-abundance from spectral fitting is $-0.88\pm0.03$, with a spread driven by
-observational errors. The $\alpha$-elements of the GC candidates are marginally
-lower than expected for the bulge at similar metallicities. As spectra of
-hundreds of stars were collected in a 2 degree field around ESO452-SC11,
-detailed abundances in the surrounding field were measured. Most non-members
-have higher [$\alpha$/Fe] ratios, typical of the nearby bulge population. Stars
-with measured Fe-peak abundances show a large scatter around Solar values,
-though with large uncertainties. Our study provides the first systematic
-measurement of Sr in a Galactic bulge GC. The Eu and Sr abundances of the GC
-candidates are consistent with a disk or bulge association. Our calculations
-place ESO452 on an elliptical orbit in the central 3 kpc of the bulge. We find
-no evidence of extratidal stars in our data. (Abridged)
-",0,1,0,0,0,0
-16980,The Fundamental Infinity-Groupoid of a Parametrized Family,"  Given an infinity-category C, one can naturally construct an
-infinity-category Fam(C) of families of objects in C indexed by
-infinity-groupoids. An ordinary categorical version of this construction was
-used by Borceux and Janelidze in the study of generalized covering maps in
-categorical Galois theory. In this paper, we develop the homotopy theory of
-such ""parametrized families"" as generalization of the classical homotopy theory
-of spaces. In particular, we study homotopy-theoretical constructions that
-arise from the fundamental infinity-groupoids of families in an
-infinity-category. In the same spirit, we show that Fam(C) admits a
-Grothendieck topology which generalizes the canonical/epimorphism topology on
-the infinity-topos of infinity-groupoids in the sense of Carchedi.
-",0,0,1,0,0,0
-16981,Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation,"  Training 3D object detectors for autonomous driving has been limited to small
-datasets due to the effort required to generate annotations. Reducing both task
-complexity and the amount of task switching done by annotators is key to
-reducing the effort and time required to generate 3D bounding box annotations.
-This paper introduces a novel ground truth generation method that combines
-human supervision with pretrained neural networks to generate per-instance 3D
-point cloud segmentation, 3D bounding boxes, and class annotations. The
-annotators provide object anchor clicks which behave as a seed to generate
-instance segmentation results in 3D. The points belonging to each instance are
-then used to regress object centroids, bounding box dimensions, and object
-orientation. Our proposed annotation scheme requires 30x lower human annotation
-time. We use the KITTI 3D object detection dataset to evaluate the efficiency
-and the quality of our annotation scheme. We also test the the proposed scheme
-on previously unseen data from the Autonomoose self-driving vehicle to
-demonstrate generalization capabilities of the network.
-",0,0,0,1,0,0
-16982,Epidemic spreading in multiplex networks influenced by opinion exchanges on vaccination,"  We study the changes of opinions about vaccination together with the
-evolution of a disease. In our model we consider a multiplex network consisting
-of two layers. One of the layers corresponds to a social network where people
-share their opinions and influence others opinions. The social model that rules
-the dynamic is the M-model, which takes into account two different processes
-that occurs in a society: persuasion and compromise. This two processes are
-related through a parameter $r$, $r<1$ describes a moderate and committed
-society, for $r>1$ the society tends to have extremist opinions, while $r=1$
-represents a neutral society. This social network may be of real or virtual
-contacts. On the other hand, the second layer corresponds to a network of
-physical contacts where the disease spreading is described by the SIR-Model. In
-this model the individuals may be in one of the following four states:
-Susceptible ($S$), Infected($I$), Recovered ($R$) or Vaccinated ($V$). A
-Susceptible individual can: i) get vaccinated, if his opinion in the other
-layer is totally in favor of the vaccine, ii) get infected, with probability
-$\beta$ if he is in contact with an infected neighbor. Those $I$ individuals
-recover after a certain period $t_r=6$. Vaccinated individuals have an
-extremist positive opinion that does not change. We consider that the vaccine
-has a certain effectiveness $\omega$ and as a consequence vaccinated nodes can
-be infected with probability $\beta (1 - \omega)$ if they are in contact with
-an infected neighbor. In this case, if the infection process is successful, the
-new infected individual changes his opinion from extremist positive to totally
-against the vaccine. We find that depending on the trend in the opinion of the
-society, which depends on $r$, different behaviors in the spread of the
-epidemic occurs. An epidemic threshold was found.
-",0,1,0,0,0,0
-16983,CASP Solutions for Planning in Hybrid Domains,"  CASP is an extension of ASP that allows for numerical constraints to be added
-in the rules. PDDL+ is an extension of the PDDL standard language of automated
-planning for modeling mixed discrete-continuous dynamics.
-In this paper, we present CASP solutions for dealing with PDDL+ problems,
-i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP
-CASP solver in order to solve CASP programs arising from PDDL+ domains. An
-experimental analysis, performed on well-known linear and non-linear variants
-of PDDL+ domains, involving various configurations of the EZCSP solver, other
-CASP solvers, and PDDL+ planners, shows the viability of our solution.
-",1,0,0,0,0,0
-16984,Primordial black holes from inflaton and spectator field perturbations in a matter-dominated era,"  We study production of primordial black holes (PBHs) during an early
-matter-dominated phase. As a source of perturbations, we consider either the
-inflaton field with a running spectral index or a spectator field that has a
-blue spectrum and thus provides a significant contribution to the PBH
-production at small scales. First, we identify the region of the parameter
-space where a significant fraction of the observed dark matter can be produced,
-taking into account all current PBH constraints. Then, we present constraints
-on the amplitude and spectral index of the spectator field as a function of the
-reheating temperature. We also derive constraints on the running of the
-inflaton spectral index, ${\rm d}n/{\rm d}{\rm ln}k \lesssim -0.002$, which are
-comparable to those from the Planck satellite for a scenario where the
-spectator field is absent.
-",0,1,0,0,0,0
-16985,State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning,"  Typical reinforcement learning (RL) agents learn to complete tasks specified
-by reward functions tailored to their domain. As such, the policies they learn
-do not generalize even to similar domains. To address this issue, we develop a
-framework through which a deep RL agent learns to generalize policies from
-smaller, simpler domains to more complex ones using a recurrent attention
-mechanism. The task is presented to the agent as an image and an instruction
-specifying the goal. This meta-controller guides the agent towards its goal by
-designing a sequence of smaller subtasks on the part of the state space within
-the attention, effectively decomposing it. As a baseline, we consider a setup
-without attention as well. Our experiments show that the meta-controller learns
-to create subgoals within the attention.
-",1,0,0,1,0,0
-16986,Robust Imitation of Diverse Behaviors,"  Deep generative models have recently shown great promise in imitation
-learning for motor control. Given enough data, even supervised approaches can
-do one-shot imitation learning; however, they are vulnerable to cascading
-failures when the agent trajectory diverges from the demonstrations. Compared
-to purely supervised methods, Generative Adversarial Imitation Learning (GAIL)
-can learn more robust controllers from fewer demonstrations, but is inherently
-mode-seeking and more difficult to train. In this paper, we show how to combine
-the favourable aspects of these two approaches. The base of our model is a new
-type of variational autoencoder on demonstration trajectories that learns
-semantic policy embeddings. We show that these embeddings can be learned on a 9
-DoF Jaco robot arm in reaching tasks, and then smoothly interpolated with a
-resulting smooth interpolation of reaching behavior. Leveraging these policy
-representations, we develop a new version of GAIL that (1) is much more robust
-than the purely-supervised controller, especially with few demonstrations, and
-(2) avoids mode collapse, capturing many diverse behaviors when GAIL on its own
-does not. We demonstrate our approach on learning diverse gaits from
-demonstration on a 2D biped and a 62 DoF 3D humanoid in the MuJoCo physics
-environment.
-",1,0,0,0,0,0
-16987,Efficient Measurement of the Vibrational Rogue Waves by Compressive Sampling Based Wavelet Analysis,"  In this paper we discuss the possible usage of the compressive sampling based
-wavelet analysis for the efficient measurement and for the early detection of
-one dimensional (1D) vibrational rogue waves. We study the construction of the
-triangular (V-shaped) wavelet spectra using compressive samples of rogue waves
-that can be modeled as Peregrine and Akhmediev-Peregrine solitons. We show that
-triangular wavelet spectra can be sensed by compressive measurements at the
-early stages of the development of vibrational rogue waves. Our results may
-lead to development of the efficient vibrational rogue wave measurement and
-early sensing systems with reduced memory requirements which use the
-compressive sampling algorithms. In typical solid mechanics applications,
-compressed measurements can be acquired by randomly positioning single sensor
-and multisensors.
-",0,0,1,0,0,0
-16988,SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes,"  This paper presents SceneCut, a novel approach to jointly discover previously
-unseen objects and non-object surfaces using a single RGB-D image. SceneCut's
-joint reasoning over scene semantics and geometry allows a robot to detect and
-segment object instances in complex scenes where modern deep learning-based
-methods either fail to separate object instances, or fail to detect objects
-that were not seen during training. SceneCut automatically decomposes a scene
-into meaningful regions which either represent objects or scene surfaces. The
-decomposition is qualified by an unified energy function over objectness and
-geometric fitting. We show how this energy function can be optimized
-efficiently by utilizing hierarchical segmentation trees. Moreover, we leverage
-a pre-trained convolutional oriented boundary network to predict accurate
-boundaries from images, which are used to construct high-quality region
-hierarchies. We evaluate SceneCut on several different indoor environments, and
-the results show that SceneCut significantly outperforms all the existing
-methods.
-",1,0,0,0,0,0
-16989,An Invariant Model of the Significance of Different Body Parts in Recognizing Different Actions,"  In this paper, we show that different body parts do not play equally
-important roles in recognizing a human action in video data. We investigate to
-what extent a body part plays a role in recognition of different actions and
-hence propose a generic method of assigning weights to different body points.
-The approach is inspired by the strong evidence in the applied perception
-community that humans perform recognition in a foveated manner, that is they
-recognize events or objects by only focusing on visually significant aspects.
-An important contribution of our method is that the computation of the weights
-assigned to body parts is invariant to viewing directions and camera parameters
-in the input data. We have performed extensive experiments to validate the
-proposed approach and demonstrate its significance. In particular, results show
-that considerable improvement in performance is gained by taking into account
-the relative importance of different body parts as defined by our approach.
-",1,0,0,0,0,0
-16990,Forecasting Crime with Deep Learning,"  The objective of this work is to take advantage of deep neural networks in
-order to make next day crime count predictions in a fine-grain city partition.
-We make predictions using Chicago and Portland crime data, which is augmented
-with additional datasets covering weather, census data, and public
-transportation. The crime counts are broken into 10 bins and our model predicts
-the most likely bin for a each spatial region at a daily level. We train this
-data using increasingly complex neural network structures, including variations
-that are suited to the spatial and temporal aspects of the crime prediction
-problem. With our best model we are able to predict the correct bin for overall
-crime count with 75.6% and 65.3% accuracy for Chicago and Portland,
-respectively. The results show the efficacy of neural networks for the
-prediction problem and the value of using external datasets in addition to
-standard crime data.
-",0,0,0,1,0,0
-16991,A family of compact semitoric systems with two focus-focus singularities,"  About 6 years ago, semitoric systems were classified by Pelayo & Vu Ngoc by
-means of five invariants. Standard examples are the coupled spin oscillator on
-$\mathbb{S}^2 \times \mathbb{R}^2$ and coupled angular momenta on $\mathbb{S}^2
-\times \mathbb{S}^2$, both having exactly one focus-focus singularity. But so
-far there were no explicit examples of systems with more than one focus-focus
-singularity which are semitoric in the sense of that classification. This paper
-introduces a 6-parameter family of integrable systems on $\mathbb{S}^2 \times
-\mathbb{S}^2$ and proves that, for certain ranges of the parameters, it is a
-compact semitoric system with precisely two focus-focus singularities. Since
-the twisting index (one of the semitoric invariants) is related to the
-relationship between different focus-focus points, this paper provides systems
-for the future study of the twisting index.
-",0,0,1,0,0,0
-16992,Mixed Threefolds Isogenous to a Product,"  In this paper we study \emph{threefolds isogenous to a product of mixed type}
-i.e. quotients of a product of three compact Riemann surfaces $C_i$ of genus at
-least two by the action of a finite group $G$, which is free, but not diagonal.
-In particular, we are interested in the systematic construction and
-classification of these varieties. Our main result is the full classification
-of threefolds isogenous to a product of mixed type with $\chi(\mathcal O_X)=-1$
-assuming that any automorphism in $G$, which restricts to the trivial element
-in $Aut(C_i)$ for some $C_i$, is the identity on the product. Since the
-holomorphic Euler-Poincaré-characteristic of a smooth threefold of general
-type with ample canonical class is always negative, these examples lie on the
-boundary, in the sense of threefold geography. To achieve our result we use
-techniques from computational group theory. Indeed, we develop a MAGMA
-algorithm to classify these threefolds for any given value of $\chi(\mathcal
-O_X)$.
-",0,0,1,0,0,0
-16993,Discriminatory Transfer,"  We observe standard transfer learning can improve prediction accuracies of
-target tasks at the cost of lowering their prediction fairness -- a phenomenon
-we named discriminatory transfer. We examine prediction fairness of a standard
-hypothesis transfer algorithm and a standard multi-task learning algorithm, and
-show they both suffer discriminatory transfer on the real-world Communities and
-Crime data set. The presented case study introduces an interaction between
-fairness and transfer learning, as an extension of existing fairness studies
-that focus on single task learning.
-",1,0,0,1,0,0
-16994,Ultrafast relaxation of hot phonons in Graphene-hBN Heterostructures,"  Fast carrier cooling is important for high power graphene based devices.
-Strongly Coupled Optical Phonons (SCOPs) play a major role in the relaxation of
-photoexcited carriers in graphene. Heterostructures of graphene and hexagonal
-boron nitride (hBN) have shown exceptional mobility and high saturation
-current, which makes them ideal for applications, but the effect of the hBN
-substrate on carrier cooling mechanisms is not understood. We track the cooling
-of hot photo-excited carriers in graphene-hBN heterostructures using ultrafast
-pump-probe spectroscopy. We find that the carriers cool down four times faster
-in the case of graphene on hBN than on a silicon oxide substrate thus
-overcoming the hot phonon (HP) bottleneck that plagues cooling in graphene
-devices.
-",0,1,0,0,0,0
-16995,Non-linear Associative-Commutative Many-to-One Pattern Matching with Sequence Variables,"  Pattern matching is a powerful tool which is part of many functional
-programming languages as well as computer algebra systems such as Mathematica.
-Among the existing systems, Mathematica offers the most expressive pattern
-matching. Unfortunately, no open source alternative has comparable pattern
-matching capabilities. Notably, these features include support for associative
-and/or commutative function symbols and sequence variables. While those
-features have individually been subject of previous research, their
-comprehensive combination has not yet been investigated. Furthermore, in many
-applications, a fixed set of patterns is matched repeatedly against different
-subjects. This many-to-one matching can be sped up by exploiting similarities
-between patterns. Discrimination nets are the state-of-the-art solution for
-many-to-one matching. In this thesis, a generalized discrimination net which
-supports the full feature set is presented. All algorithms have been
-implemented as an open-source library for Python. In experiments on real world
-examples, significant speedups of many-to-one over one-to-one matching have
-been observed.
-",1,0,0,0,0,0
-16996,Pair Background Envelopes in the SiD Detector,"  The beams at the ILC produce electron positron pairs due to beam-beam
-interactions. This note presents for the first time a study of these processes
-in a detailed simulation, which shows that these pair background particles
-appear at angles that extend to the inner layers of the detector. The full data
-set of pairs produced in one bunch crossing was used to calculate the helix
-tracks, which the particles form in the solenoid field of the SiD detector. The
-results suggest to further study the reduction of the beam pipe radius and
-therefore to either add another SiD vertex detector layer, or reduce the radius
-of the existing vertex detector layers, without increasing the detector
-occupancy significantly. This has to go along with additional studies whether
-the improvement in physics reconstruction methods, like c-tagging, is worth the
-increased background level at smaller radii.
-",0,1,0,0,0,0
-16997,Expansion of percolation critical points for Hamming graphs,"  The Hamming graph $H(d,n)$ is the Cartesian product of $d$ complete graphs on
-$n$ vertices. Let $m=d(n-1)$ be the degree and $V = n^d$ be the number of
-vertices of $H(d,n)$. Let $p_c^{(d)}$ be the critical point for bond
-percolation on $H(d,n)$. We show that, for $d \in \mathbb N$ fixed and $n \to
-\infty$,
-\begin{equation*}
-p_c^{(d)}= \dfrac{1}{m} + \dfrac{2d^2-1}{2(d-1)^2}\dfrac{1}{m^2}
-+ O(m^{-3}) + O(m^{-1}V^{-1/3}),
-\end{equation*} which extends the asymptotics found in
-\cite{BorChaHofSlaSpe05b} by one order. The term $O(m^{-1}V^{-1/3})$ is the
-width of the critical window. For $d=4,5,6$ we have $m^{-3} =
-O(m^{-1}V^{-1/3})$, and so the above formula represents the full asymptotic
-expansion of $p_c^{(d)}$. In \cite{FedHofHolHul16a} \st{we show that} this
-formula is a crucial ingredient in the study of critical bond percolation on
-$H(d,n)$ for $d=2,3,4$. The proof uses a lace expansion for the upper bound and
-a novel comparison with a branching random walk for the lower bound. The proof
-of the lower bound also yields a refined asymptotics for the susceptibility of
-a subcritical Erdős-Rényi random graph.
-",0,0,1,0,0,0
-16998,Efficiently Manifesting Asynchronous Programming Errors in Android Apps,"  Android, the #1 mobile app framework, enforces the single-GUI-thread model,
-in which a single UI thread manages GUI rendering and event dispatching. Due to
-this model, it is vital to avoid blocking the UI thread for responsiveness. One
-common practice is to offload long-running tasks into async threads. To achieve
-this, Android provides various async programming constructs, and leaves
-developers themselves to obey the rules implied by the model. However, as our
-study reveals, more than 25% apps violate these rules and introduce
-hard-to-detect, fail-stop errors, which we term as aysnc programming errors
-(APEs). To this end, this paper introduces APEChecker, a technique to
-automatically and efficiently manifest APEs. The key idea is to characterize
-APEs as specific fault patterns, and synergistically combine static analysis
-and dynamic UI exploration to detect and verify such errors. Among the 40
-real-world Android apps, APEChecker unveils and processes 61 APEs, of which 51
-are confirmed (83.6% hit rate). Specifically, APEChecker detects 3X more APEs
-than the state-of-art testing tools (Monkey, Sapienz and Stoat), and reduces
-testing time from half an hour to a few minutes. On a specific type of APEs,
-APEChecker confirms 5X more errors than the data race detection tool,
-EventRacer, with very few false alarms.
-",1,0,0,0,0,0
-16999,AI Challenges in Human-Robot Cognitive Teaming,"  Among the many anticipated roles for robots in the future is that of being a
-human teammate. Aside from all the technological hurdles that have to be
-overcome with respect to hardware and control to make robots fit to work with
-humans, the added complication here is that humans have many conscious and
-subconscious expectations of their teammates - indeed, we argue that teaming is
-mostly a cognitive rather than physical coordination activity. This introduces
-new challenges for the AI and robotics community and requires fundamental
-changes to the traditional approach to the design of autonomy. With this in
-mind, we propose an update to the classical view of the intelligent agent
-architecture, highlighting the requirements for mental modeling of the human in
-the deliberative process of the autonomous agent. In this article, we outline
-briefly the recent efforts of ours, and others in the community, towards
-developing cognitive teammates along these guidelines.
-",1,0,0,0,0,0
-17000,"Generalizing the MVW involution, and the contragredient","  For certain quasi-split reductive groups $G$ over a general field $F$, we
-construct an automorphism $\iota_G$ of $G$ over $F$, well-defined as an element
-of ${\rm Aut}(G)(F)/jG(F)$ where $j:G(F) \rightarrow {\rm Aut}(G)(F)$ is the
-inner-conjugation action of $G(F)$ on $G$. The automorphism $\iota_G$
-generalizes (although only for quasi-split groups) an involution due to
-Moeglin-Vigneras-Waldspurger in [MVW] for classical groups which takes any
-irreducible admissible representation $\pi$ of $G(F)$ for $G$ a classical group
-and $F$ a local field, to its contragredient $\pi^\vee$. The paper also
-formulates a conjecture on the contragredient of an irreducible admissible
-representation of $G(F)$ for $G$ a reductive algebraic group over a local field
-$F$ in terms of the (enhanced) Langlands parameter of the representation.
-",0,0,1,0,0,0
-17001,Miraculous cancellations for quantum $SL_2$,"  In earlier work, Helen Wong and the author discovered certain ""miraculous
-cancellations"" for the quantum trace map connecting the Kauffman bracket skein
-algebra of a surface to its quantum Teichmueller space, occurring when the
-quantum parameter $q$ is a root of unity. The current paper is devoted to
-giving a more representation theoretic interpretation of this phenomenon, in
-terms of the quantum group $U_q(sl_2)$ and its dual Hopf algebra $SL_2^q$.
-",0,0,1,0,0,0
-17002,Energy and time measurements with high-granular silicon devices,"  This note is a short summary of the workshop on ""Energy and time measurements
-with high-granular silicon devices"" that took place on the 13/6/16 and the
-14/6/16 at DESY/Hamburg in the frame of the first AIDA-2020 Annual Meeting.
-This note tries to put forward trends that could be spotted and to emphasise in
-particular open issues that were addressed by the speakers.
-",0,1,0,0,0,0
-17003,Action Tubelet Detector for Spatio-Temporal Action Localization,"  Current state-of-the-art approaches for spatio-temporal action localization
-rely on detections at the frame level that are then linked or tracked across
-time. In this paper, we leverage the temporal continuity of videos instead of
-operating at the frame level. We propose the ACtion Tubelet detector
-(ACT-detector) that takes as input a sequence of frames and outputs tubelets,
-i.e., sequences of bounding boxes with associated scores. The same way
-state-of-the-art object detectors rely on anchor boxes, our ACT-detector is
-based on anchor cuboids. We build upon the SSD framework. Convolutional
-features are extracted for each frame, while scores and regressions are based
-on the temporal stacking of these features, thus exploiting information from a
-sequence. Our experimental results show that leveraging sequences of frames
-significantly improves detection performance over using individual frames. The
-gain of our tubelet detector can be explained by both more accurate scores and
-more precise localization. Our ACT-detector outperforms the state-of-the-art
-methods for frame-mAP and video-mAP on the J-HMDB and UCF-101 datasets, in
-particular at high overlap thresholds.
-",1,0,0,0,0,0
-17004,Significance of Side Information in the Graph Matching Problem,"  Percolation based graph matching algorithms rely on the availability of seed
-vertex pairs as side information to efficiently match users across networks.
-Although such algorithms work well in practice, there are other types of side
-information available which are potentially useful to an attacker. In this
-paper, we consider the problem of matching two correlated graphs when an
-attacker has access to side information, either in the form of community labels
-or an imperfect initial matching. In the former case, we propose a naive graph
-matching algorithm by introducing the community degree vectors which harness
-the information from community labels in an efficient manner. Furthermore, we
-analyze a variant of the basic percolation algorithm proposed in literature for
-graphs with community structure. In the latter case, we propose a novel
-percolation algorithm with two thresholds which uses an imperfect matching as
-input to match correlated graphs.
-We evaluate the proposed algorithms on synthetic as well as real world
-datasets using various experiments. The experimental results demonstrate the
-importance of communities as side information especially when the number of
-seeds is small and the networks are weakly correlated.
-",1,1,0,0,0,0
-17005,Extended Gray-Wyner System with Complementary Causal Side Information,"  We establish the rate region of an extended Gray-Wyner system for 2-DMS
-$(X,Y)$ with two additional decoders having complementary causal side
-information. This extension is interesting because in addition to the
-operationally significant extreme points of the Gray-Wyner rate region, which
-include Wyner's common information, G{á}cs-K{ö}rner common information and
-information bottleneck, the rate region for the extended system also includes
-the K{ö}rner graph entropy, the privacy funnel and excess functional
-information, as well as three new quantities of potential interest, as extreme
-points. To simplify the investigation of the 5-dimensional rate region of the
-extended Gray-Wyner system, we establish an equivalence of this region to a
-3-dimensional mutual information region that consists of the set of all triples
-of the form $(I(X;U),\,I(Y;U),\,I(X,Y;U))$ for some $p_{U|X,Y}$. We further
-show that projections of this mutual information region yield the rate regions
-for many settings involving a 2-DMS, including lossless source coding with
-causal side information, distributed channel synthesis, and lossless source
-coding with a helper.
-",1,0,1,0,0,0
-17006,Learning Powers of Poisson Binomial Distributions,"  We introduce the problem of simultaneously learning all powers of a Poisson
-Binomial Distribution (PBD). A PBD of order $n$ is the distribution of a sum of
-$n$ mutually independent Bernoulli random variables $X_i$, where
-$\mathbb{E}[X_i] = p_i$. The $k$'th power of this distribution, for $k$ in a
-range $[m]$, is the distribution of $P_k = \sum_{i=1}^n X_i^{(k)}$, where each
-Bernoulli random variable $X_i^{(k)}$ has $\mathbb{E}[X_i^{(k)}] = (p_i)^k$.
-The learning algorithm can query any power $P_k$ several times and succeeds in
-learning all powers in the range, if with probability at least $1- \delta$:
-given any $k \in [m]$, it returns a probability distribution $Q_k$ with total
-variation distance from $P_k$ at most $\epsilon$. We provide almost matching
-lower and upper bounds on query complexity for this problem. We first show a
-lower bound on the query complexity on PBD powers instances with many distinct
-parameters $p_i$ which are separated, and we almost match this lower bound by
-examining the query complexity of simultaneously learning all the powers of a
-special class of PBD's resembling the PBD's of our lower bound. We study the
-fundamental setting of a Binomial distribution, and provide an optimal
-algorithm which uses $O(1/\epsilon^2)$ samples. Diakonikolas, Kane and Stewart
-[COLT'16] showed a lower bound of $\Omega(2^{1/\epsilon})$ samples to learn the
-$p_i$'s within error $\epsilon$. The question whether sampling from powers of
-PBDs can reduce this sampling complexity, has a negative answer since we show
-that the exponential number of samples is inevitable. Having sampling access to
-the powers of a PBD we then give a nearly optimal algorithm that learns its
-$p_i$'s. To prove our two last lower bounds we extend the classical minimax
-risk definition from statistics to estimating functions of sequences of
-distributions.
-",1,0,1,1,0,0
-17007,Geometry of simplices in Minkowski spaces,"  There are many problems and configurations in Euclidean geometry that were
-never extended to the framework of (normed or) finite dimensional real Banach
-spaces, although their original versions are inspiring for this type of
-generalization, and the analogous definitions for normed spaces represent a
-promising topic. An example is the geometry of simplices in non-Euclidean
-normed spaces. We present new generalizations of well known properties of
-Euclidean simplices. These results refer to analogues of circumcenters, Euler
-lines, and Feuerbach spheres of simplices in normed spaces. Using duality, we
-also get natural theorems on angular bisectors as well as in- and exspheres of
-(dual) simplices.
-",0,0,1,0,0,0
-17008,DLR : Toward a deep learned rhythmic representation for music content analysis,"  In the use of deep neural networks, it is crucial to provide appropriate
-input representations for the network to learn from. In this paper, we propose
-an approach to learn a representation that focus on rhythmic representation
-which is named as DLR (Deep Learning Rhythmic representation). The proposed
-approach aims to learn DLR from the raw audio signal and use it for other music
-informatics tasks. A 1-dimensional convolutional network is utilised in the
-learning of DLR. In the experiment, we present the results from the source task
-and the target task as well as visualisations of DLRs. The results reveals that
-DLR provides compact rhythmic information which can be used on multi-tagging
-task.
-",1,0,0,0,0,0
-17009,Phylogeny-based tumor subclone identification using a Bayesian feature allocation model,"  Tumor cells acquire different genetic alterations during the course of
-evolution in cancer patients. As a result of competition and selection, only a
-few subgroups of cells with distinct genotypes survive. These subgroups of
-cells are often referred to as subclones. In recent years, many statistical and
-computational methods have been developed to identify tumor subclones, leading
-to biologically significant discoveries and shedding light on tumor
-progression, metastasis, drug resistance and other processes. However, most
-existing methods are either not able to infer the phylogenetic structure among
-subclones, or not able to incorporate copy number variations (CNV). In this
-article, we propose SIFA (tumor Subclone Identification by Feature Allocation),
-a Bayesian model which takes into account both CNV and tumor phylogeny
-structure to infer tumor subclones. We compare the performance of SIFA with two
-other commonly used methods using simulation studies with varying sequencing
-depth, evolutionary tree size, and tree complexity. SIFA consistently yields
-better results in terms of Rand Index and cellularity estimation accuracy. The
-usefulness of SIFA is also demonstrated through its application to whole genome
-sequencing (WGS) samples from four patients in a breast cancer study.
-",0,0,0,1,1,0
-17010,Confidence-based Graph Convolutional Networks for Semi-Supervised Learning,"  Predicting properties of nodes in a graph is an important problem with
-applications in a variety of domains. Graph-based Semi-Supervised Learning
-(SSL) methods aim to address this problem by labeling a small subset of the
-nodes as seeds and then utilizing the graph structure to predict label scores
-for the rest of the nodes in the graph. Recently, Graph Convolutional Networks
-(GCNs) have achieved impressive performance on the graph-based SSL task. In
-addition to label scores, it is also desirable to have confidence scores
-associated with them. Unfortunately, confidence estimation in the context of
-GCN has not been previously explored. We fill this important gap in this paper
-and propose ConfGCN, which estimates labels scores along with their confidences
-jointly in GCN-based setting. ConfGCN uses these estimated confidences to
-determine the influence of one node on another during neighborhood aggregation,
-thereby acquiring anisotropic capabilities. Through extensive analysis and
-experiments on standard benchmarks, we find that ConfGCN is able to outperform
-state-of-the-art baselines. We have made ConfGCN's source code available to
-encourage reproducible research.
-",1,0,0,1,0,0
-17011,Long-range fluctuations and multifractality in connectivity density time series of a wind speed monitoring network,"  This paper studies the daily connectivity time series of a wind
-speed-monitoring network using multifractal detrended fluctuation analysis. It
-investigates the long-range fluctuation and multifractality in the residuals of
-the connectivity time series. Our findings reveal that the daily connectivity
-of the correlation-based network is persistent for any correlation threshold.
-Further, the multifractality degree is higher for larger absolute values of the
-correlation threshold
-",0,0,0,1,0,0
-17012,The Dynamics of Norm Change in the Cultural Evolution of Language,"  What happens when a new social convention replaces an old one? While the
-possible forces favoring norm change - such as institutions or committed
-activists - have been identified since a long time, little is known about how a
-population adopts a new convention, due to the difficulties of finding
-representative data. Here we address this issue by looking at changes occurred
-to 2,541 orthographic and lexical norms in English and Spanish through the
-analysis of a large corpora of books published between the years 1800 and 2008.
-We detect three markedly distinct patterns in the data, depending on whether
-the behavioral change results from the action of a formal institution, an
-informal authority or a spontaneous process of unregulated evolution. We
-propose a simple evolutionary model able to capture all the observed behaviors
-and we show that it reproduces quantitatively the empirical data. This work
-identifies general mechanisms of norm change and we anticipate that it will be
-of interest to researchers investigating the cultural evolution of language
-and, more broadly, human collective behavior.
-",0,0,0,0,1,0
-17013,Bayesian Joint Spike-and-Slab Graphical Lasso,"  In this article, we propose a new class of priors for Bayesian inference with
-multiple Gaussian graphical models. We introduce fully Bayesian treatments of
-two popular procedures, the group graphical lasso and the fused graphical
-lasso, and extend them to a continuous spike-and-slab framework to allow
-self-adaptive shrinkage and model selection simultaneously. We develop an EM
-algorithm that performs fast and dynamic explorations of posterior modes. Our
-approach selects sparse models efficiently with substantially smaller bias than
-would be induced by alternative regularization procedures. The performance of
-the proposed methods are demonstrated through simulation and two real data
-examples.
-",0,0,0,1,0,0
-17014,Variations on the theme of the uniform boundary condition,"  The uniform boundary condition in a normed chain complex asks for a uniform
-linear bound on fillings of null-homologous cycles. For the $\ell^1$-norm on
-the singular chain complex, Matsumoto and Morita established a characterisation
-of the uniform boundary condition in terms of bounded cohomology. In
-particular, spaces with amenable fundamental group satisfy the uniform boundary
-condition in every degree. We will give an alternative proof of statements of
-this type, using geometric F{\o}lner arguments on the chain level instead of
-passing to the dual cochain complex. These geometric methods have the advantage
-that they also lead to integral refinements. In particular, we obtain
-applications in the context of integral foliated simplicial volume.
-",0,0,1,0,0,0
-17015,revisit: a Workflow Tool for Data Science,"  In recent years there has been widespread concern in the scientific community
-over a reproducibility crisis. Among the major causes that have been identified
-is statistical: In many scientific research the statistical analysis (including
-data preparation) suffers from a lack of transparency and methodological
-problems, major obstructions to reproducibility. The revisit package aims
-toward remedying this problem, by generating a ""software paper trail"" of the
-statistical operations applied to a dataset. This record can be ""replayed"" for
-verification purposes, as well as be modified to enable alternative analyses.
-The software also issues warnings of certain kinds of potential errors in
-statistical methodology, again related to the reproducibility issue.
-",1,0,0,1,0,0
-17016,Programmatically Interpretable Reinforcement Learning,"  We present a reinforcement learning framework, called Programmatically
-Interpretable Reinforcement Learning (PIRL), that is designed to generate
-interpretable and verifiable agent policies. Unlike the popular Deep
-Reinforcement Learning (DRL) paradigm, which represents policies by neural
-networks, PIRL represents policies using a high-level, domain-specific
-programming language. Such programmatic policies have the benefits of being
-more easily interpreted than neural networks, and being amenable to
-verification by symbolic methods. We propose a new method, called Neurally
-Directed Program Search (NDPS), for solving the challenging nonsmooth
-optimization problem of finding a programmatic policy with maximal reward. NDPS
-works by first learning a neural policy network using DRL, and then performing
-a local search over programmatic policies that seeks to minimize a distance
-from this neural ""oracle"". We evaluate NDPS on the task of learning to drive a
-simulated car in the TORCS car-racing environment. We demonstrate that NDPS is
-able to discover human-readable policies that pass some significant performance
-bars. We also show that PIRL policies can have smoother trajectories, and can
-be more easily transferred to environments not encountered during training,
-than corresponding policies discovered by DRL.
-",1,0,0,1,0,0
-17017,Kinetic Simulation of Collisional Magnetized Plasmas with Semi-Implicit Time Integration,"  Plasmas with varying collisionalities occur in many applications, such as
-tokamak edge regions, where the flows are characterized by significant
-variations in density and temperature. While a kinetic model is necessary for
-weakly-collisional high-temperature plasmas, high collisionality in colder
-regions render the equations numerically stiff due to disparate time scales. In
-this paper, we propose an implicit-explicit algorithm for such cases, where the
-collisional term is integrated implicitly in time, while the advective term is
-integrated explicitly in time, thus allowing time step sizes that are
-comparable to the advective time scales. This partitioning results in a more
-efficient algorithm than those using explicit time integrators, where the time
-step sizes are constrained by the stiff collisional time scales. We implement
-semi-implicit additive Runge-Kutta methods in COGENT, a finite-volume
-gyrokinetic code for mapped, multiblock grids and test the accuracy,
-convergence, and computational cost of these semi-implicit methods for test
-cases with highly-collisional plasmas.
-",1,1,0,0,0,0
-17018,VC-dimension of short Presburger formulas,"  We study VC-dimension of short formulas in Presburger Arithmetic, defined to
-have a bounded number of variables, quantifiers and atoms. We give both lower
-and upper bounds, which are tight up to a polynomial factor in the bit length
-of the formula.
-",1,0,1,0,0,0
-17019,Real-time Traffic Accident Risk Prediction based on Frequent Pattern Tree,"  Traffic accident data are usually noisy, contain missing values, and
-heterogeneous. How to select the most important variables to improve real-time
-traffic accident risk prediction has become a concern of many recent studies.
-This paper proposes a novel variable selection method based on the Frequent
-Pattern tree (FP tree) algorithm. First, all the frequent patterns in the
-traffic accident dataset are discovered. Then for each frequent pattern, a new
-criterion, called the Relative Object Purity Ratio (ROPR) which we proposed, is
-calculated. This ROPR is added to the importance score of the variables that
-differentiate one frequent pattern from the others. To test the proposed
-method, a dataset was compiled from the traffic accidents records detected by
-only one detector on interstate highway I-64 in Virginia in 2005. This dataset
-was then linked to other variables such as real-time traffic information and
-weather conditions. Both the proposed method based on the FP tree algorithm, as
-well as the widely utilized, random forest method, were then used to identify
-the important variables or the Virginia dataset. The results indicate that
-there are some differences between the variables deemed important by the FP
-tree and those selected by the random forest method. Following this, two
-baseline models (i.e. a nearest neighbor (k-NN) method and a Bayesian network)
-were developed to predict accident risk based on the variables identified by
-both the FP tree method and the random forest method. The results show that the
-models based on the variable selection using the FP tree performed better than
-those based on the random forest method for several versions of the k-NN and
-Bayesian network models.The best results were derived from a Bayesian network
-model using variables from FP tree. That model could predict 61.11% of
-accidents accurately while having a false alarm rate of 38.16%.
-",1,0,0,1,0,0
-17020,Do Developers Update Their Library Dependencies? An Empirical Study on the Impact of Security Advisories on Library Migration,"  Third-party library reuse has become common practice in contemporary software
-development, as it includes several benefits for developers. Library
-dependencies are constantly evolving, with newly added features and patches
-that fix bugs in older versions. To take full advantage of third-party reuse,
-developers should always keep up to date with the latest versions of their
-library dependencies. In this paper, we investigate the extent of which
-developers update their library dependencies. Specifically, we conducted an
-empirical study on library migration that covers over 4,600 GitHub software
-projects and 2,700 library dependencies. Results show that although many of
-these systems rely heavily on dependencies, 81.5% of the studied systems still
-keep their outdated dependencies. In the case of updating a vulnerable
-dependency, the study reveals that affected developers are not likely to
-respond to a security advisory. Surveying these developers, we find that 69% of
-the interviewees claim that they were unaware of their vulnerable dependencies.
-Furthermore, developers are not likely to prioritize library updates, citing it
-as extra effort and added responsibility. This study concludes that even though
-third-party reuse is commonplace, the practice of updating a dependency is not
-as common for many developers.
-",1,0,0,0,0,0
-17021,Is Smaller Better: A Proposal To Consider Bacteria For Biologically Inspired Modeling,"  Bacteria are easily characterizable model organisms with an impressively
-complicated set of capabilities. Among their capabilities is quorum sensing, a
-detailed cell-cell signaling system that may have a common origin with
-eukaryotic cell-cell signaling. Not only are the two phenomena similar, but
-quorum sensing, as is the case with any bacterial phenomenon when compared to
-eukaryotes, is also easier to study in depth than eukaryotic cell-cell
-signaling. This ease of study is a contrast to the only partially understood
-cellular dynamics of neurons. Here we review the literature on the strikingly
-neuron-like qualities of bacterial colonies and biofilms, including ion-based
-and hormonal signaling, and action potential-like behavior. This allows them to
-feasibly act as an analog for neurons that could produce more detailed and more
-accurate biologically-based computational models. Using bacteria as the basis
-for biologically feasible computational models may allow models to better
-harness the tremendous ability of biological organisms to make decisions and
-process information. Additionally, principles gleaned from bacterial function
-have the potential to influence computational efforts divorced from biology,
-just as neuronal function has in the abstract influenced countless machine
-learning efforts.
-",1,0,0,0,0,0
-17022,A Bayesian Data Augmentation Approach for Learning Deep Models,"  Data augmentation is an essential part of the training process applied to
-deep learning models. The motivation is that a robust training process for deep
-learning models depends on large annotated datasets, which are expensive to be
-acquired, stored and processed. Therefore a reasonable alternative is to be
-able to automatically generate new annotated training samples using a process
-known as data augmentation. The dominant data augmentation approach in the
-field assumes that new training samples can be obtained via random geometric or
-appearance transformations applied to annotated training samples, but this is a
-strong assumption because it is unclear if this is a reliable generative model
-for producing new training samples. In this paper, we provide a novel Bayesian
-formulation to data augmentation, where new annotated training points are
-treated as missing variables and generated based on the distribution learned
-from the training set. For learning, we introduce a theoretically sound
-algorithm --- generalised Monte Carlo expectation maximisation, and demonstrate
-one possible implementation via an extension of the Generative Adversarial
-Network (GAN). Classification results on MNIST, CIFAR-10 and CIFAR-100 show the
-better performance of our proposed method compared to the current dominant data
-augmentation approach mentioned above --- the results also show that our
-approach produces better classification results than similar GAN models.
-",1,0,0,0,0,0
-17023,Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach,"  Knowledge bases are employed in a variety of applications from natural
-language processing to semantic web search; alas, in practice their usefulness
-is hurt by their incompleteness. Embedding models attain state-of-the-art
-accuracy in knowledge base completion, but their predictions are notoriously
-hard to interpret. In this paper, we adapt ""pedagogical approaches"" (from the
-literature on neural networks) so as to interpret embedding models by
-extracting weighted Horn rules from them. We show how pedagogical approaches
-have to be adapted to take upon the large-scale relational aspects of knowledge
-bases and show experimentally their strengths and weaknesses.
-",0,0,0,1,0,0
-17024,Parameterized complexity of machine scheduling: 15 open problems,"  Machine scheduling problems are a long-time key domain of algorithms and
-complexity research. A novel approach to machine scheduling problems are
-fixed-parameter algorithms. To stimulate this thriving research direction, we
-propose 15 open questions in this area whose resolution we expect to lead to
-the discovery of new approaches and techniques both in scheduling and
-parameterized complexity theory.
-",1,0,0,0,0,0
-17025,"Potential Conditional Mutual Information: Estimators, Properties and Applications","  The conditional mutual information I(X;Y|Z) measures the average information
-that X and Y contain about each other given Z. This is an important primitive
-in many learning problems including conditional independence testing, graphical
-model inference, causal strength estimation and time-series problems. In
-several applications, it is desirable to have a functional purely of the
-conditional distribution p_{Y|X,Z} rather than of the joint distribution
-p_{X,Y,Z}. We define the potential conditional mutual information as the
-conditional mutual information calculated with a modified joint distribution
-p_{Y|X,Z} q_{X,Z}, where q_{X,Z} is a potential distribution, fixed airport. We
-develop K nearest neighbor based estimators for this functional, employing
-importance sampling, and a coupling trick, and prove the finite k consistency
-of such an estimator. We demonstrate that the estimator has excellent practical
-performance and show an application in dynamical system inference.
-",1,0,0,1,0,0
-17026,"A new approach to divergences in quantum electrodynamics, concrete examples","  An interesting attempt for solving infrared divergence problems via the
-theory of generalized wave operators was made by P. Kulish and L. Faddeev. Our
-method of using the ideas from the theory of generalized wave operators is
-essentially different. We assume that the unperturbed operator $A_0$ is known
-and that the scattering operator $S$ and the unperturbed operator $A_0$ are
-permutable. (In the Kulish-Faddeev theory this basic property is not
-fulfilled.) The permutability of $S$ and $A_0$ gives us an important
-information about the structure of the scattering operator. We show that the
-divergences appeared because the deviations of the initial and final waves from
-the free waves were not taken into account. The approach is demonstrated on
-important examples.
-",0,0,1,0,0,0
-17027,Indefinite boundary value problems on graphs,"  We consider the spectral structure of indefinite second order boundary-value
-problems on graphs. A variational formulation for such boundary-value problems
-on graphs is given and we obtain both full and half-range completeness results.
-This leads to a max-min principle and as a consequence we can formulate an
-analogue of Dirichlet-Neumann bracketing and this in turn gives rise to
-asymptotic approximations for the eigenvalues.
-",0,0,1,0,0,0
-17028,Integral curvatures of Finsler manifolds and applications,"  In this paper, we study the integral curvatures of Finsler manifolds. Some
-Bishop-Gromov relative volume comparisons and several Myers type theorems are
-obtained. We also establish a Gromov type precompactness theorem and a
-Yamaguchi type finiteness theorem. Furthermore, the isoperimetric and Sobolev
-constants of a closed Finsler manifold are estimated by integral curvature
-bounds.
-",0,0,1,0,0,0
-17029,L-functions and sharp resonances of infinite index congruence subgroups of $SL_2(\mathbb{Z})$,"  For convex co-compact subgroups of SL2(Z) we consider the ""congruence
-subgroups"" for p prime. We prove a factorization formula for the Selberg zeta
-function in term of L-functions related to irreducible representations of the
-Galois group SL2(Fp) of the covering, together with a priori bounds and
-analytic continuation. We use this factorization property combined with an
-averaging technique over representations to prove a new existence result of
-non-trivial resonances in an effective low frequency strip.
-",0,0,1,0,0,0
-17030,An Enhanced Lumped Element Electrical Model of a Double Barrier Memristive Device,"  The massive parallel approach of neuromorphic circuits leads to effective
-methods for solving complex problems. It has turned out that resistive
-switching devices with a continuous resistance range are potential candidates
-for such applications. These devices are memristive systems - nonlinear
-resistors with memory. They are fabricated in nanotechnology and hence
-parameter spread during fabrication may aggravate reproducible analyses. This
-issue makes simulation models of memristive devices worthwhile.
-Kinetic Monte-Carlo simulations based on a distributed model of the device
-can be used to understand the underlying physical and chemical phenomena.
-However, such simulations are very time-consuming and neither convenient for
-investigations of whole circuits nor for real-time applications, e.g. emulation
-purposes. Instead, a concentrated model of the device can be used for both fast
-simulations and real-time applications, respectively. We introduce an enhanced
-electrical model of a valence change mechanism (VCM) based double barrier
-memristive device (DBMD) with a continuous resistance range. This device
-consists of an ultra-thin memristive layer sandwiched between a tunnel barrier
-and a Schottky-contact. The introduced model leads to very fast simulations by
-using usual circuit simulation tools while maintaining physically meaningful
-parameters.
-Kinetic Monte-Carlo simulations based on a distributed model and experimental
-data have been utilized as references to verify the concentrated model.
-",1,1,0,0,0,0
-17031,Non-perturbative positive Lyapunov exponent of Schrödinger equations and its applications to skew-shift,"  We first study the discrete Schrödinger equations with analytic potentials
-given by a class of transformations. It is shown that if the coupling number is
-large, then its logarithm equals approximately to the Lyapunov exponents. When
-the transformation becomes the skew-shift, we prove that the Lyapunov exponent
-is week Hölder continuous, and the spectrum satisfies Anderson Localization
-and contains large intervals. Moreover, all of these conclusions are
-non-perturbative.
-",0,0,1,0,0,0
-17032,Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees,"  Greedy optimization methods such as Matching Pursuit (MP) and Frank-Wolfe
-(FW) algorithms regained popularity in recent years due to their simplicity,
-effectiveness and theoretical guarantees. MP and FW address optimization over
-the linear span and the convex hull of a set of atoms, respectively. In this
-paper, we consider the intermediate case of optimization over the convex cone,
-parametrized as the conic hull of a generic atom set, leading to the first
-principled definitions of non-negative MP algorithms for which we give explicit
-convergence rates and demonstrate excellent empirical performance. In
-particular, we derive sublinear ($\mathcal{O}(1/t)$) convergence on general
-smooth and convex objectives, and linear convergence ($\mathcal{O}(e^{-t})$) on
-strongly convex objectives, in both cases for general sets of atoms.
-Furthermore, we establish a clear correspondence of our algorithms to known
-algorithms from the MP and FW literature. Our novel algorithms and analyses
-target general atom sets and general objective functions, and hence are
-directly applicable to a large variety of learning settings.
-",1,0,0,1,0,0
-17033,Probing the accretion disc structure by the twin kHz QPOs and spins of neutron stars in LMXBs,"  We analyze the relation between the emission radii of twin kilohertz
-quasi-periodic oscillations (kHz QPOs) and the co-rotation radii of the 12
-neutron star low mass X-ray binaries (NS-LMXBs) which are simultaneously
-detected with the twin kHz QPOs and NS spins. We find that the average
-co-rotation radius of these sources is r_co about 32 km, and all the emission
-positions of twin kHz QPOs lie inside the corotation radii, indicating that the
-twin kHz QPOs are formed in the spin-up process. It is noticed that the upper
-frequency of twin kHz QPOs is higher than NS spin frequency by > 10%, which may
-account for a critical velocity difference between the Keplerian motion of
-accretion matter and NS spin that is corresponding to the production of twin
-kHz QPOs. In addition, we also find that about 83% of twin kHz QPOs cluster
-around the radius range of 15-20 km, which may be affected by the hard surface
-or the local strong magnetic field of NS. As a special case, SAX J1808.4-3658
-shows the larger emission radii of twin kHz QPOs of r about 21-24 km, which may
-be due to its low accretion rate or small measured NS mass (< 1.4 solar mass).
-",0,1,0,0,0,0
-17034,Can scientists and their institutions become their own open access publishers?,"  This article offers a personal perspective on the current state of academic
-publishing, and posits that the scientific community is beset with journals
-that contribute little valuable knowledge, overload the community's capacity
-for high-quality peer review, and waste resources. Open access publishing can
-offer solutions that benefit researchers and other information users, as well
-as institutions and funders, but commercial journal publishers have influenced
-open access policies and practices in ways that favor their economic interests
-over those of other stakeholders in knowledge creation and sharing. One way to
-free research from constraints on access is the diamond route of open access
-publishing, in which institutions and funders that produce new knowledge
-reclaim responsibility for publication via institutional journals or other open
-platforms. I argue that research journals (especially those published for
-profit) may no longer be fit for purpose, and hope that readers will consider
-whether the time has come to put responsibility for publishing back into the
-hands of researchers and their institutions. The potential advantages and
-challenges involved in a shift away from for-profit journals in favor of
-institutional open access publishing are explored.
-",1,0,0,0,0,0
-17035,Character sums for elliptic curve densities,"  If $E$ is an elliptic curve over $\mathbb{Q}$, then it follows from work of
-Serre and Hooley that, under the assumption of the Generalized Riemann
-Hypothesis, the density of primes $p$ such that the group of
-$\mathbb{F}_p$-rational points of the reduced curve $\tilde{E}(\mathbb{F}_p)$
-is cyclic can be written as an infinite product $\prod \delta_\ell$ of local
-factors $\delta_\ell$ reflecting the degree of the $\ell$-torsion fields,
-multiplied by a factor that corrects for the entanglements between the various
-torsion fields. We show that this correction factor can be interpreted as a
-character sum, and the resulting description allows us to easily determine
-non-vanishing criteria for it. We apply this method in a variety of other
-settings. Among these, we consider the aforementioned problem with the
-additional condition that the primes $p$ lie in a given arithmetic progression.
-We also study the conjectural constants appearing in Koblitz's conjecture, a
-conjecture which relates to the density of primes $p$ for which the cardinality
-of the group of $\mathbb{F}_p$-points of $E$ is prime.
-",0,0,1,0,0,0
-17036,A monolithic fluid-structure interaction formulation for solid and liquid membranes including free-surface contact,"  A unified fluid-structure interaction (FSI) formulation is presented for
-solid, liquid and mixed membranes. Nonlinear finite elements (FE) and the
-generalized-alpha scheme are used for the spatial and temporal discretization.
-The membrane discretization is based on curvilinear surface elements that can
-describe large deformations and rotations, and also provide a straightforward
-description for contact. The fluid is described by the incompressible
-Navier-Stokes equations, and its discretization is based on stabilized
-Petrov-Galerkin FE. The coupling between fluid and structure uses a conforming
-sharp interface discretization, and the resulting non-linear FE equations are
-solved monolithically within the Newton-Raphson scheme. An arbitrary
-Lagrangian-Eulerian formulation is used for the fluid in order to account for
-the mesh motion around the structure. The formulation is very general and
-admits diverse applications that include contact at free surfaces. This is
-demonstrated by two analytical and three numerical examples exhibiting strong
-coupling between fluid and structure. The examples include balloon inflation,
-droplet rolling and flapping flags. They span a Reynolds-number range from
-0.001 to 2000. One of the examples considers the extension to rotation-free
-shells using isogeometric FE.
-",1,1,0,0,0,0
-17037,Different Non-extensive Models for heavy-ion collisions,"  The transverse momentum ($p_T$) spectra from heavy-ion collisions at
-intermediate momenta are described by non-extensive statistical models.
-Assuming a fixed relative variance of the temperature fluctuating event by
-event or alternatively a fixed mean multiplicity in a negative binomial
-distribution (NBD), two different linear relations emerge between the
-temperature, $T$, and the Tsallis parameter $q-1$. Our results qualitatively
-agree with that of G.~Wilk. Furthermore we revisit the ""Soft+Hard"" model,
-proposed recently by G.~G.~Barnaföldi \textit{et.al.}, by a $T$-independent
-average $p_T^2$ assumption. Finally we compare results with those predicted by
-another deformed distribution, using Kaniadakis' $\kappa$ parametrization.
-",0,1,0,0,0,0
-17038,Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees,"  Toxicity prediction of chemical compounds is a grand challenge. Lately, it
-achieved significant progress in accuracy but using a huge set of features,
-implementing a complex blackbox technique such as a deep neural network, and
-exploiting enormous computational resources. In this paper, we strongly argue
-for the models and methods that are simple in machine learning characteristics,
-efficient in computing resource usage, and powerful to achieve very high
-accuracy levels. To demonstrate this, we develop a single task-based chemical
-toxicity prediction framework using only 2D features that are less compute
-intensive. We effectively use a decision tree to obtain an optimum number of
-features from a collection of thousands of them. We use a shallow neural
-network and jointly optimize it with decision tree taking both network
-parameters and input features into account. Our model needs only a minute on a
-single CPU for its training while existing methods using deep neural networks
-need about 10 min on NVidia Tesla K40 GPU. However, we obtain similar or better
-performance on several toxicity benchmark tasks. We also develop a cumulative
-feature ranking method which enables us to identify features that can help
-chemists perform prescreening of toxic compounds effectively.
-",1,0,0,1,0,0
-17039,Minmax Hierarchies and Minimal Surfaces in Manifolds,"  We introduce a general scheme that permits to generate successive min-max
-problems for producing critical points of higher and higher indices to
-Palais-Smale Functionals in Banach manifolds equipped with Finsler structures.
-We call the resulting tree of minmax problems a minmax hierarchy. Using the
-viscosity approach to the minmax theory of minimal surfaces introduced by the
-author in a series of recent works, we explain how this scheme can be deformed
-for producing smooth minimal surfaces of strictly increasing area in arbitrary
-codimension. We implement this scheme to the case of the $3-$dimensional
-sphere. In particular we are giving a min-max characterization of the Clifford
-Torus and conjecture what are the next minimal surfaces to come in the $S^3$
-hierarchy. Among other results we prove here the lower semi continuity of the
-Morse Index in the viscosity method below an area level.
-",0,0,1,0,0,0
-17040,Nonseparable Multinomial Choice Models in Cross-Section and Panel Data,"  Multinomial choice models are fundamental for empirical modeling of economic
-choices among discrete alternatives. We analyze identification of binary and
-multinomial choice models when the choice utilities are nonseparable in
-observed attributes and multidimensional unobserved heterogeneity with
-cross-section and panel data. We show that derivatives of choice probabilities
-with respect to continuous attributes are weighted averages of utility
-derivatives in cross-section models with exogenous heterogeneity. In the
-special case of random coefficient models with an independent additive effect,
-we further characterize that the probability derivative at zero is proportional
-to the population mean of the coefficients. We extend the identification
-results to models with endogenous heterogeneity using either a control function
-or panel data. In time stationary panel models with two periods, we find that
-differences over time of derivatives of choice probabilities identify utility
-derivatives ""on the diagonal,"" i.e. when the observed attributes take the same
-values in the two periods. We also show that time stationarity does not
-identify structural derivatives ""off the diagonal"" both in continuous and
-multinomial choice panel models.
-",0,0,0,1,0,0
-17041,Corona limits of tilings : Periodic case,"  We study the limit shape of successive coronas of a tiling, which models the
-growth of crystals. We define basic terminologies and discuss the existence and
-uniqueness of corona limits, and then prove that corona limits are completely
-characterized by directional speeds. As an application, we give another proof
-that the corona limit of a periodic tiling is a centrally symmetric convex
-polyhedron (see [Zhuravlev 2001], [Maleev-Shutov 2011]).
-",0,0,1,0,0,0
-17042,The Spatial Range of Conformity,"  Properties of galaxies like their absolute magnitude and their stellar mass
-content are correlated. These correlations are tighter for close pairs of
-galaxies, which is called galactic conformity. In hierarchical structure
-formation scenarios, galaxies form within dark matter halos. To explain the
-amplitude and the spatial range of galactic conformity two--halo terms or
-assembly bias become important. With the scale dependent correlation
-coefficients the amplitude and the spatial range of conformity are determined
-from galaxy and halo samples. The scale dependent correlation coefficients are
-introduced as a new descriptive statistic to quantify the correlations between
-properties of galaxies or halos, depending on the distances to other galaxies
-or halos. These scale dependent correlation coefficients can be applied to the
-galaxy distribution directly. Neither a splitting of the sample into
-subsamples, nor an a priori clustering is needed. This new descriptive
-statistic is applied to galaxy catalogues derived from the Sloan Digital Sky
-Survey III and to halo catalogues from the MultiDark simulations. In the galaxy
-sample the correlations between absolute Magnitude, velocity dispersion,
-ellipticity, and stellar mass content are investigated. The correlations of
-mass, spin, and ellipticity are explored in the halo samples. Both for galaxies
-and halos a scale dependent conformity is confirmed. Moreover the scale
-dependent correlation coefficients reveal a signal of conformity out to 40Mpc
-and beyond. The halo and galaxy samples show a differing amplitude and range of
-conformity.
-",0,1,0,0,0,0
-17043,Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis,"  Stochastic Gradient Langevin Dynamics (SGLD) is a popular variant of
-Stochastic Gradient Descent, where properly scaled isotropic Gaussian noise is
-added to an unbiased estimate of the gradient at each iteration. This modest
-change allows SGLD to escape local minima and suffices to guarantee asymptotic
-convergence to global minimizers for sufficiently regular non-convex objectives
-(Gelfand and Mitter, 1991). The present work provides a nonasymptotic analysis
-in the context of non-convex learning problems, giving finite-time guarantees
-for SGLD to find approximate minimizers of both empirical and population risks.
-As in the asymptotic setting, our analysis relates the discrete-time SGLD
-Markov chain to a continuous-time diffusion process. A new tool that drives the
-results is the use of weighted transportation cost inequalities to quantify the
-rate of convergence of SGLD to a stationary distribution in the Euclidean
-$2$-Wasserstein distance.
-",1,0,1,1,0,0
-17044,Multilevel preconditioner of Polynomial Chaos Method for quantifying uncertainties in a blood pump,"  More than 23 million people are suffered by Heart failure worldwide. Despite
-the modern transplant operation is well established, the lack of heart
-donations becomes a big restriction on transplantation frequency. With respect
-to this matter, ventricular assist devices (VADs) can play an important role in
-supporting patients during waiting period and after the surgery. Moreover, it
-has been shown that VADs by means of blood pump have advantages for working
-under different conditions. While a lot of work has been done on modeling the
-functionality of the blood pump, but quantifying uncertainties in a numerical
-model is a challenging task. We consider the Polynomial Chaos (PC) method,
-which is introduced by Wiener for modeling stochastic process with Gaussian
-distribution. The Galerkin projection, the intrusive version of the generalized
-Polynomial Chaos (gPC), has been densely studied and applied for various
-problems. The intrusive Galerkin approach could represent stochastic process
-directly at once with Polynomial Chaos series expansions, it would therefore
-optimize the total computing effort comparing with classical non-intrusive
-methods. We compared different preconditioning techniques for a steady state
-simulation of a blood pump configuration in our previous work, the comparison
-shows that an inexact multilevel preconditioner has a promising performance. In
-this work, we show an instationary blood flow through a FDA blood pump
-configuration with Galerkin Projection method, which is implemented in our open
-source Finite Element library Hiflow3. Three uncertainty sources are
-considered: inflow boundary condition, rotor angular speed and dynamic
-viscosity, the numerical results are demonstrated with more than 30 Million
-degrees of freedom by using supercomputer.
-",0,1,0,0,0,0
-17045,Superradiant Mott Transition,"  The combination of strong correlation and emergent lattice can be achieved
-when quantum gases are confined in a superradiant Fabry-Perot cavity. In
-addition to the discoveries of exotic phases, such as density wave ordered Mott
-insulator and superfluid, a surprising kink structure is found in the slope of
-the cavity strength as a function of the pumping strength. In this Letter, we
-show that the appearance of such a kink is a manifestation of a liquid-gas like
-transition between two superfluids with different densities. The slopes in the
-immediate neighborhood of the kink become divergent at the liquid-gas critical
-points and display a critical scaling law with a critical exponent 1 in the
-quantum critical region. Our predictions could be tested in current
-experimental set-up.
-",0,1,0,0,0,0
-17046,Communication via FRET in Nanonetworks of Mobile Proteins,"  A practical, biologically motivated case of protein complexes (immunoglobulin
-G and FcRII receptors) moving on the surface of mastcells, that are common
-parts of an immunological system, is investigated. Proteins are considered as
-nanomachines creating a nanonetwork. Accurate molecular models of the proteins
-and the fluorophores which act as their nanoantennas are used to simulate the
-communication between the nanomachines when they are close to each other. The
-theory of diffusion-based Brownian motion is applied to model movements of the
-proteins. It is assumed that fluorophore molecules send and receive signals
-using the Forster Resonance Energy Transfer. The probability of the efficient
-signal transfer and the respective bit error rate are calculated and discussed.
-",0,0,0,0,1,0
-17047,"Multivariate generalized Pareto distributions: parametrizations, representations, and properties","  Multivariate generalized Pareto distributions arise as the limit
-distributions of exceedances over multivariate thresholds of random vectors in
-the domain of attraction of a max-stable distribution. These distributions can
-be parametrized and represented in a number of different ways. Moreover,
-generalized Pareto distributions enjoy a number of interesting stability
-properties. An overview of the main features of such distributions are given,
-expressed compactly in several parametrizations, giving the potential user of
-these distributions a convenient catalogue of ways to handle and work with
-generalized Pareto distributions.
-",0,0,1,1,0,0
-17048,Invertibility of spectral x-ray data with pileup--two dimension-two spectrum case,"  In the Alvarez-Macovski method, the line integrals of the x-ray basis set
-coefficients are computed from measurements with multiple spectra. An important
-question is whether the transformation from measurements to line integrals is
-invertible. This paper presents a proof that for a system with two spectra and
-a photon counting detector, pileup does not affect the invertibility of the
-system. If the system is invertible with no pileup, it will remain invertible
-with pileup although the reduced Jacobian may lead to increased noise.
-",0,1,0,0,0,0
-17049,Steinberg representations and harmonic cochains for split adjoint quasi-simple groups,"  Let $G$ be an adjoint quasi-simple group defined and split over a
-non-archimedean local field $K$. We prove that the dual of the Steinberg
-representation of $G$ is isomorphic to a certain space of harmonic cochains on
-the Bruhat-Tits building of $G$. The Steinberg representation is considered
-with coefficients in any commutative ring.
-",0,0,1,0,0,0
-17050,Lorentzian surfaces and the curvature of the Schmidt metric,"  The b-boundary is a mathematical tool used to attach a topological boundary
-to incomplete Lorentzian manifolds using a Riemaniann metric called the Schmidt
-metric on the frame bundle. In this paper, we give the general form of the
-Schmidt metric in the case of Lorentzian surfaces. Furthermore, we write the
-Ricci scalar of the Schmidt metric in terms of the Ricci scalar of the
-Lorentzian manifold and give some examples. Finally, we discuss some
-applications to general relativity.
-",0,0,1,0,0,0
-17051,Mixed Precision Solver Scalable to 16000 MPI Processes for Lattice Quantum Chromodynamics Simulations on the Oakforest-PACS System,"  Lattice Quantum Chromodynamics (Lattice QCD) is a quantum field theory on a
-finite discretized space-time box so as to numerically compute the dynamics of
-quarks and gluons to explore the nature of subatomic world. Solving the
-equation of motion of quarks (quark solver) is the most compute-intensive part
-of the lattice QCD simulations and is one of the legacy HPC applications. We
-have developed a mixed-precision quark solver for a large Intel Xeon Phi (KNL)
-system named ""Oakforest-PACS"", employing the $O(a)$-improved Wilson quarks as
-the discretized equation of motion. The nested-BiCGSTab algorithm for the
-solver was implemented and optimized using mixed-precision,
-communication-computation overlapping with MPI-offloading, SIMD vectorization,
-and thread stealing techniques. The solver achieved 2.6 PFLOPS in the
-single-precision part on a $400^3\times 800$ lattice using 16000 MPI processes
-on 8000 nodes on the system.
-",0,1,0,0,0,0
-17052,A spectral/hp element MHD solver,"  A new MHD solver, based on the Nektar++ spectral/hp element framework, is
-presented in this paper. The velocity and electric potential quasi-static MHD
-model is used. The Hartmann flow in plane channel and its stability, the
-Hartmann flow in rectangular duct, and the stability of Hunt's flow are
-explored as examples. Exponential convergence is achieved and the resulting
-numerical values were found to have an accuracy up to $10^{-12}$ for the state
-flows compared to an exact solution, and $10^{-5}$ for the stability
-eigenvalues compared to independent numerical results.
-",0,1,0,0,0,0
-17053,"Journalists' information needs, seeking behavior, and its determinants on social media","  We describe the results of a qualitative study on journalists' information
-seeking behavior on social media. Based on interviews with eleven journalists
-along with a study of a set of university level journalism modules, we
-determined the categories of information need types that lead journalists to
-social media. We also determined the ways that social media is exploited as a
-tool to satisfy information needs and to define influential factors, which
-impacted on journalists' information seeking behavior. We find that not only is
-social media used as an information source, but it can also be a supplier of
-stories found serendipitously. We find seven information need types that expand
-the types found in previous work. We also find five categories of influential
-factors that affect the way journalists seek information.
-",1,0,0,0,0,0
-17054,Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models,"  The problem of low rank matrix completion is considered in this paper. To
-exploit the underlying low-rank structure of the data matrix, we propose a
-hierarchical Gaussian prior model, where columns of the low-rank matrix are
-assumed to follow a Gaussian distribution with zero mean and a common precision
-matrix, and a Wishart distribution is specified as a hyperprior over the
-precision matrix. We show that such a hierarchical Gaussian prior has the
-potential to encourage a low-rank solution. Based on the proposed hierarchical
-prior model, a variational Bayesian method is developed for matrix completion,
-where the generalized approximate massage passing (GAMP) technique is embedded
-into the variational Bayesian inference in order to circumvent cumbersome
-matrix inverse operations. Simulation results show that our proposed method
-demonstrates superiority over existing state-of-the-art matrix completion
-methods.
-",1,0,0,1,0,0
-17055,Emergence of superconductivity in the canonical heavy-electron metal YbRh2Si2,"  We report magnetic and calorimetric measurements down to T = 1 mK on the
-canonical heavy-electron metal YbRh2Si2. The data reveal the development of
-nuclear antiferromagnetic order slightly above 2 mK. The latter weakens the
-primary electronic antiferromagnetism, thereby paving the way for
-heavy-electron superconductivity below Tc = 2 mK. Our results demonstrate that
-superconductivity driven by quantum criticality is a general phenomenon.
-",0,1,0,0,0,0
-17056,Obtaining a Proportional Allocation by Deleting Items,"  We consider the following control problem on fair allocation of indivisible
-goods. Given a set $I$ of items and a set of agents, each having strict linear
-preference over the items, we ask for a minimum subset of the items whose
-deletion guarantees the existence of a proportional allocation in the remaining
-instance; we call this problem Proportionality by Item Deletion (PID). Our main
-result is a polynomial-time algorithm that solves PID for three agents. By
-contrast, we prove that PID is computationally intractable when the number of
-agents is unbounded, even if the number $k$ of item deletions allowed is small,
-since the problem turns out to be W[3]-hard with respect to the parameter $k$.
-Additionally, we provide some tight lower and upper bounds on the complexity of
-PID when regarded as a function of $|I|$ and $k$.
-",1,0,0,0,0,0
-17057,DeepFace: Face Generation using Deep Learning,"  We use CNNs to build a system that both classifies images of faces based on a
-variety of different facial attributes and generates new faces given a set of
-desired facial characteristics. After introducing the problem and providing
-context in the first section, we discuss recent work related to image
-generation in Section 2. In Section 3, we describe the methods used to
-fine-tune our CNN and generate new images using a novel approach inspired by a
-Gaussian mixture model. In Section 4, we discuss our working dataset and
-describe our preprocessing steps and handling of facial attributes. Finally, in
-Sections 5, 6 and 7, we explain our experiments and results and conclude in the
-following section. Our classification system has 82\% test accuracy.
-Furthermore, our generation pipeline successfully creates well-formed faces.
-",1,0,0,0,0,0
-17058,High quality mesh generation using cross and asterisk fields: Application on coastal domains,"  This paper presents a method to generate high quality triangular or
-quadrilateral meshes that uses direction fields and a frontal point insertion
-strategy. Two types of direction fields are considered: asterisk fields and
-cross fields. With asterisk fields we generate high quality triangulations,
-while with cross fields we generate right-angled triangulations that are
-optimal for transformation to quadrilateral meshes. The input of our algorithm
-is an initial triangular mesh and a direction field calculated on it. The goal
-is to compute the vertices of the final mesh by an advancing front strategy
-along the direction field. We present an algorithm that enables to efficiently
-generate the points using solely information from the base mesh. A
-multi-threaded implementation of our algorithm is presented, allowing us to
-achieve significant speedup of the point generation. Regarding the
-quadrangulation process, we develop a quality criterion for right-angled
-triangles with respect to the local cross field and an optimization process
-based on it. Thus we are able to further improve the quality of the output
-quadrilaterals. The algorithm is demonstrated on the sphere and examples of
-high quality triangular and quadrilateral meshes of coastal domains are
-presented.
-",1,0,0,0,0,0
-17059,ELFI: Engine for Likelihood-Free Inference,"  Engine for Likelihood-Free Inference (ELFI) is a Python software library for
-performing likelihood-free inference (LFI). ELFI provides a convenient syntax
-for arranging components in LFI, such as priors, simulators, summaries or
-distances, to a network called ELFI graph. The components can be implemented in
-a wide variety of languages. The stand-alone ELFI graph can be used with any of
-the available inference methods without modifications. A central method
-implemented in ELFI is Bayesian Optimization for Likelihood-Free Inference
-(BOLFI), which has recently been shown to accelerate likelihood-free inference
-up to several orders of magnitude by surrogate-modelling the distance. ELFI
-also has an inbuilt support for output data storing for reuse and analysis, and
-supports parallelization of computation from multiple cores up to a cluster
-environment. ELFI is designed to be extensible and provides interfaces for
-widening its functionality. This makes the adding of new inference methods to
-ELFI straightforward and automatically compatible with the inbuilt features.
-",1,0,0,1,0,0
-17060,Boosting Adversarial Attacks with Momentum,"  Deep neural networks are vulnerable to adversarial examples, which poses
-security concerns on these algorithms due to the potentially severe
-consequences. Adversarial attacks serve as an important surrogate to evaluate
-the robustness of deep learning models before they are deployed. However, most
-of existing adversarial attacks can only fool a black-box model with a low
-success rate. To address this issue, we propose a broad class of momentum-based
-iterative algorithms to boost adversarial attacks. By integrating the momentum
-term into the iterative process for attacks, our methods can stabilize update
-directions and escape from poor local maxima during the iterations, resulting
-in more transferable adversarial examples. To further improve the success rates
-for black-box attacks, we apply momentum iterative algorithms to an ensemble of
-models, and show that the adversarially trained models with a strong defense
-ability are also vulnerable to our black-box attacks. We hope that the proposed
-methods will serve as a benchmark for evaluating the robustness of various deep
-models and defense methods. With this method, we won the first places in NIPS
-2017 Non-targeted Adversarial Attack and Targeted Adversarial Attack
-competitions.
-",1,0,0,1,0,0
-17061,Information spreading during emergencies and anomalous events,"  The most critical time for information to spread is in the aftermath of a
-serious emergency, crisis, or disaster. Individuals affected by such situations
-can now turn to an array of communication channels, from mobile phone calls and
-text messages to social media posts, when alerting social ties. These channels
-drastically improve the speed of information in a time-sensitive event, and
-provide extant records of human dynamics during and afterward the event.
-Retrospective analysis of such anomalous events provides researchers with a
-class of ""found experiments"" that may be used to better understand social
-spreading. In this chapter, we study information spreading due to a number of
-emergency events, including the Boston Marathon Bombing and a plane crash at a
-western European airport. We also contrast the different information which may
-be gleaned by social media data compared with mobile phone data and we estimate
-the rate of anomalous events in a mobile phone dataset using a proposed anomaly
-detection method.
-",1,1,0,0,0,0
-17062,Large-Scale Plant Classification with Deep Neural Networks,"  This paper discusses the potential of applying deep learning techniques for
-plant classification and its usage for citizen science in large-scale
-biodiversity monitoring. We show that plant classification using near
-state-of-the-art convolutional network architectures like ResNet50 achieves
-significant improvements in accuracy compared to the most widespread plant
-classification application in test sets composed of thousands of different
-species labels. We find that the predictions can be confidently used as a
-baseline classification in citizen science communities like iNaturalist (or its
-Spanish fork, Natusfera) which in turn can share their data with biodiversity
-portals like GBIF.
-",1,0,0,1,0,0
-17063,Deep Reinforcement Learning for General Video Game AI,"  The General Video Game AI (GVGAI) competition and its associated software
-framework provides a way of benchmarking AI algorithms on a large number of
-games written in a domain-specific description language. While the competition
-has seen plenty of interest, it has so far focused on online planning,
-providing a forward model that allows the use of algorithms such as Monte Carlo
-Tree Search.
-In this paper, we describe how we interface GVGAI to the OpenAI Gym
-environment, a widely used way of connecting agents to reinforcement learning
-problems. Using this interface, we characterize how widely used implementations
-of several deep reinforcement learning algorithms fare on a number of GVGAI
-games. We further analyze the results to provide a first indication of the
-relative difficulty of these games relative to each other, and relative to
-those in the Arcade Learning Environment under similar conditions.
-",0,0,0,1,0,0
-17064,Purely infinite labeled graph $C^*$-algebras,"  In this paper, we consider pure infiniteness of generalized Cuntz-Krieger
-algebras associated to labeled spaces $(E,\mathcal{L},\mathcal{E})$. It is
-shown that a $C^*$-algebra $C^*(E,\mathcal{L},\mathcal{E})$ is purely infinite
-in the sense that every nonzero hereditary subalgebra contains an infinite
-projection (we call this property (IH)) if $(E, \mathcal{L},\mathcal{E})$ is
-disagreeable and every vertex connects to a loop. We also prove that under the
-condition analogous to (K) for usual graphs,
-$C^*(E,\mathcal{L},\mathcal{E})=C^*(p_A, s_a)$ is purely infinite in the sense
-of Kirchberg and R{\o}rdam if and only if every generating projection $p_A$,
-$A\in \mathcal{E}$, is properly infinite, and also if and only if every
-quotient of $C^*(E,\mathcal{L},\mathcal{E})$ has the property (IH).
-",0,0,1,0,0,0
-17065,From safe screening rules to working sets for faster Lasso-type solvers,"  Convex sparsity-promoting regularizations are ubiquitous in modern
-statistical learning. By construction, they yield solutions with few non-zero
-coefficients, which correspond to saturated constraints in the dual
-optimization formulation. Working set (WS) strategies are generic optimization
-techniques that consist in solving simpler problems that only consider a subset
-of constraints, whose indices form the WS. Working set methods therefore
-involve two nested iterations: the outer loop corresponds to the definition of
-the WS and the inner loop calls a solver for the subproblems. For the Lasso
-estimator a WS is a set of features, while for a Group Lasso it refers to a set
-of groups. In practice, WS are generally small in this context so the
-associated feature Gram matrix can fit in memory. Here we show that the
-Gauss-Southwell rule (a greedy strategy for block coordinate descent
-techniques) leads to fast solvers in this case. Combined with a working set
-strategy based on an aggressive use of so-called Gap Safe screening rules, we
-propose a solver achieving state-of-the-art performance on sparse learning
-problems. Results are presented on Lasso and multi-task Lasso estimators.
-",1,0,1,1,0,0
-17066,Exoplanet Radius Gap Dependence on Host Star Type,"  Exoplanets smaller than Neptune are numerous, but the nature of the planet
-populations in the 1-4 Earth radii range remains a mystery. The complete Kepler
-sample of Q1-Q17 exoplanet candidates shows a radius gap at ~ 2 Earth radii, as
-reported by us in January 2017 in LPSC conference abstract #1576 (Zeng et al.
-2017). A careful analysis of Kepler host stars spectroscopy by the CKS survey
-allowed Fulton et al. (2017) in March 2017 to unambiguously show this radius
-gap. The cause of this gap is still under discussion (Ginzburg et al. 2017;
-Lehmer & Catling 2017; Owen & Wu 2017). Here we add to our original analysis
-the dependence of the radius gap on host star type.
-",0,1,0,0,0,0
-17067,Mapping the Invocation Structure of Online Political Interaction,"  The surge in political information, discourse, and interaction has been one
-of the most important developments in social media over the past several years.
-There is rich structure in the interaction among different viewpoints on the
-ideological spectrum. However, we still have only a limited analytical
-vocabulary for expressing the ways in which these viewpoints interact.
-In this paper, we develop network-based methods that operate on the ways in
-which users share content; we construct \emph{invocation graphs} on Web domains
-showing the extent to which pages from one domain are invoked by users to reply
-to posts containing pages from other domains. When we locate the domains on a
-political spectrum induced from the data, we obtain an embedded graph showing
-how these interaction links span different distances on the spectrum. The
-structure of this embedded network, and its evolution over time, helps us
-derive macro-level insights about how political interaction unfolded through
-2016, leading up to the US Presidential election. In particular, we find that
-the domains invoked in replies spanned increasing distances on the spectrum
-over the months approaching the election, and that there was clear asymmetry
-between the left-to-right and right-to-left patterns of linkage.
-",1,0,0,0,0,0
-17068,Collective decision for open set recognition,"  In open set recognition (OSR), almost all existing methods are designed
-specially for recognizing individual instances, even these instances are
-collectively coming in batch. Recognizers in decision either reject or
-categorize them to some known class using empirically-set threshold. Thus the
-threshold plays a key role, however, the selection for it usually depends on
-the knowledge of known classes, inevitably incurring risks due to lacking
-available information from unknown classes. On the other hand, a more realistic
-OSR system should NOT just rest on a reject decision but should go further,
-especially for discovering the hidden unknown classes among the reject
-instances, whereas existing OSR methods do not pay special attention. In this
-paper, we introduce a novel collective/batch decision strategy with an aim to
-extend existing OSR for new class discovery while considering correlations
-among the testing instances. Specifically, a collective decision-based OSR
-framework (CD-OSR) is proposed by slightly modifying the Hierarchical Dirichlet
-process (HDP). Thanks to the HDP, our CD-OSR does not need to define the
-specific threshold and can automatically reserve space for unknown classes in
-testing, naturally resulting in a new class discovery function. Finally,
-extensive experiments on benchmark datasets indicate the validity of CD-OSR.
-",0,0,0,1,0,0
-17069,HARPS-N high spectral resolution observations of Cepheids I. The Baade-Wesselink projection factor of δ Cep revisited,"  The projection factor p is the key quantity used in the Baade-Wesselink (BW)
-method for distance determination; it converts radial velocities into pulsation
-velocities. Several methods are used to determine p, such as geometrical and
-hydrodynamical models or the inverse BW approach when the distance is known. We
-analyze new HARPS-N spectra of delta Cep to measure its cycle-averaged
-atmospheric velocity gradient in order to better constrain the projection
-factor. We first apply the inverse BW method to derive p directly from
-observations. The projection factor can be divided into three subconcepts: (1)
-a geometrical effect (p0); (2) the velocity gradient within the atmosphere
-(fgrad); and (3) the relative motion of the optical pulsating photosphere with
-respect to the corresponding mass elements (fo-g). We then measure the fgrad
-value of delta Cep for the first time. When the HARPS-N mean cross-correlated
-line-profiles are fitted with a Gaussian profile, the projection factor is
-pcc-g = 1.239 +/- 0.034(stat) +/- 0.023(syst). When we consider the different
-amplitudes of the radial velocity curves that are associated with 17 selected
-spectral lines, we measure projection factors ranging from 1.273 to 1.329. We
-find a relation between fgrad and the line depth measured when the Cepheid is
-at minimum radius. This relation is consistent with that obtained from our best
-hydrodynamical model of delta Cep and with our projection factor decomposition.
-Using the observational values of p and fgrad found for the 17 spectral lines,
-we derive a semi-theoretical value of fo-g. We alternatively obtain fo-g =
-0.975+/-0.002 or 1.006+/-0.002 assuming models using radiative transfer in
-plane-parallel or spherically symmetric geometries, respectively. The new
-HARPS-N observations of delta Cep are consistent with our decomposition of the
-projection factor.
-",0,1,0,0,0,0
-17070,Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US,"  The United States spends more than $1B each year on initiatives such as the
-American Community Survey (ACS), a labor-intensive door-to-door study that
-measures statistics relating to race, gender, education, occupation,
-unemployment, and other demographic factors. Although a comprehensive source of
-data, the lag between demographic changes and their appearance in the ACS can
-exceed half a decade. As digital imagery becomes ubiquitous and machine vision
-techniques improve, automated data analysis may provide a cheaper and faster
-alternative. Here, we present a method that determines socioeconomic trends
-from 50 million images of street scenes, gathered in 200 American cities by
-Google Street View cars. Using deep learning-based computer vision techniques,
-we determined the make, model, and year of all motor vehicles encountered in
-particular neighborhoods. Data from this census of motor vehicles, which
-enumerated 22M automobiles in total (8% of all automobiles in the US), was used
-to accurately estimate income, race, education, and voting patterns, with
-single-precinct resolution. (The average US precinct contains approximately
-1000 people.) The resulting associations are surprisingly simple and powerful.
-For instance, if the number of sedans encountered during a 15-minute drive
-through a city is higher than the number of pickup trucks, the city is likely
-to vote for a Democrat during the next Presidential election (88% chance);
-otherwise, it is likely to vote Republican (82%). Our results suggest that
-automated systems for monitoring demographic trends may effectively complement
-labor-intensive approaches, with the potential to detect trends with fine
-spatial resolution, in close to real time.
-",1,0,0,0,0,0
-17071,Gaussian Process Neurons Learn Stochastic Activation Functions,"  We propose stochastic, non-parametric activation functions that are fully
-learnable and individual to each neuron. Complexity and the risk of overfitting
-are controlled by placing a Gaussian process prior over these functions. The
-result is the Gaussian process neuron, a probabilistic unit that can be used as
-the basic building block for probabilistic graphical models that resemble the
-structure of neural networks. The proposed model can intrinsically handle
-uncertainties in its inputs and self-estimate the confidence of its
-predictions. Using variational Bayesian inference and the central limit
-theorem, a fully deterministic loss function is derived, allowing it to be
-trained as efficiently as a conventional neural network using mini-batch
-gradient descent. The posterior distribution of activation functions is
-inferred from the training data alongside the weights of the network.
-The proposed model favorably compares to deep Gaussian processes, both in
-model complexity and efficiency of inference. It can be directly applied to
-recurrent or convolutional network structures, allowing its use in audio and
-image processing tasks.
-As an preliminary empirical evaluation we present experiments on regression
-and classification tasks, in which our model achieves performance comparable to
-or better than a Dropout regularized neural network with a fixed activation
-function. Experiments are ongoing and results will be added as they become
-available.
-",1,0,0,1,0,0
-17072,The short-term price impact of trades is universal,"  We analyze a proprietary dataset of trades by a single asset manager,
-comparing their price impact with that of the trades of the rest of the market.
-In the context of a linear propagator model we find no significant difference
-between the two, suggesting that both the magnitude and time dependence of
-impact are universal in anonymous, electronic markets. This result is important
-as optimal execution policies often rely on propagators calibrated on anonymous
-data. We also find evidence that in the wake of a trade the order flow of other
-market participants first adds further copy-cat trades enhancing price impact
-on very short time scales. The induced order flow then quickly inverts, thereby
-contributing to impact decay.
-",0,1,0,0,0,0
-17073,Questions on mod p representations of reductive p-adic groups,"  This is a list of questions raised by our joint work arXiv:1412.0737 and its
-sequels.
-",0,0,1,0,0,0
-17074,Filamentary superconductivity in semiconducting policrystalline ZrSe2 compound with Zr vacancies,"  ZrSe2 is a band semiconductor studied long time ago. It has interesting
-electronic properties, and because its layers structure can be intercalated
-with different atoms to change some of the physical properties. In this
-investigation we found that Zr deficiencies alter the semiconducting behavior
-and the compound can be turned into a superconductor. In this paper we report
-our studies related to this discovery. The decreasing of the number of Zr atoms
-in small proportion according to the formula ZrxSe2, where x is varied from
-about 8.1 to 8.6 K, changing the semiconducting behavior to a superconductor
-with transition temperatures ranging between 7.8 to 8.5 K, it depending of the
-deficiencies. Outside of those ranges the compound behaves as semiconducting
-with the properties already known. In our experiments we found that this new
-superconductor has only a very small fraction of superconducting material
-determined by magnetic measurements with applied magnetic field of 10 Oe. Our
-conclusions is that superconductivity is filamentary. However, in one studied
-sample the fraction was about 10.2 %, whereas in others is only about 1 % or
-less. We determined the superconducting characteristics; the critical fields
-that indicate a type two superonductor with Ginzburg-Landau ? parameter of the
-order about 2.7. The synthesis procedure is quite normal fol- lowing the
-conventional solid state reaction. In this paper are included, the electronic
-characteristics, transition temperature, and evolution with temperature of the
-critical fields.
-",0,1,0,0,0,0
-17075,Stochastic Block Model Reveals the Map of Citation Patterns and Their Evolution in Time,"  In this study we map out the large-scale structure of citation networks of
-science journals and follow their evolution in time by using stochastic block
-models (SBMs). The SBM fitting procedures are principled methods that can be
-used to find hierarchical grouping of journals into blocks that show similar
-incoming and outgoing citations patterns. These methods work directly on the
-citation network without the need to construct auxiliary networks based on
-similarity of nodes. We fit the SBMs to the networks of journals we have
-constructed from the data set of around 630 million citations and find a
-variety of different types of blocks, such as clusters, bridges, sources, and
-sinks. In addition we use a recent generalization of SBMs to determine how much
-a manually curated classification of journals into subfields of science is
-related to the block structure of the journal network and how this relationship
-changes in time. The SBM method tries to find a network of blocks that is the
-best high-level representation of the network of journals, and we illustrate
-how these block networks (at various levels of resolution) can be used as maps
-of science.
-",1,1,0,0,0,0
-17076,Limits on the anomalous speed of gravitational waves from binary pulsars,"  A large class of modified theories of gravity used as models for dark energy
-predict a propagation speed for gravitational waves which can differ from the
-speed of light. This difference of propagations speeds for photons and
-gravitons has an impact in the emission of gravitational waves by binary
-systems. Thus, we revisit the usual quadrupolar emission of binary system for
-an arbitrary propagation speed of gravitational waves and obtain the
-corresponding period decay formula. We then use timing data from the
-Hulse-Taylor binary pulsar and obtain that the speed of gravitational waves can
-only differ from the speed of light at the percentage level. This bound places
-tight constraints on dark energy models featuring an anomalous propagations
-speed for the gravitational waves.
-",0,1,0,0,0,0
-17077,Central limit theorems for entropy-regularized optimal transport on finite spaces and statistical applications,"  The notion of entropy-regularized optimal transport, also known as Sinkhorn
-divergence, has recently gained popularity in machine learning and statistics,
-as it makes feasible the use of smoothed optimal transportation distances for
-data analysis. The Sinkhorn divergence allows the fast computation of an
-entropically regularized Wasserstein distance between two probability
-distributions supported on a finite metric space of (possibly) high-dimension.
-For data sampled from one or two unknown probability distributions, we derive
-the distributional limits of the empirical Sinkhorn divergence and its centered
-version (Sinkhorn loss). We also propose a bootstrap procedure which allows to
-obtain new test statistics for measuring the discrepancies between multivariate
-probability distributions. Our work is inspired by the results of Sommerfeld
-and Munk (2016) on the asymptotic distribution of empirical Wasserstein
-distance on finite space using unregularized transportation costs. Incidentally
-we also analyze the asymptotic distribution of entropy-regularized Wasserstein
-distances when the regularization parameter tends to zero. Simulated and real
-datasets are used to illustrate our approach.
-",0,0,1,1,0,0
-17078,Inference for Stochastically Contaminated Variable Length Markov Chains,"  In this paper, we present a methodology to estimate the parameters of
-stochastically contaminated models under two contamination regimes. In both
-regimes, we assume that the original process is a variable length Markov chain
-that is contaminated by a random noise. In the first regime we consider that
-the random noise is added to the original source and in the second regime, the
-random noise is multiplied by the original source. Given a contaminated sample
-of these models, the original process is hidden. Then we propose a two steps
-estimator for the parameters of these models, that is, the probability
-transitions and the noise parameter, and prove its consistency. The first step
-is an adaptation of the Baum-Welch algorithm for Hidden Markov Models. This
-step provides an estimate of a complete order $k$ Markov chain, where $k$ is
-bigger than the order of the variable length Markov chain if it has finite
-order and is a constant depending on the sample size if the hidden process has
-infinite order. In the second estimation step, we propose a bootstrap Bayesian
-Information Criterion, given a sample of the Markov chain estimated in the
-first step, to obtain the variable length time dependence structure associated
-with the hidden process. We present a simulation study showing that our
-methodology is able to accurately recover the parameters of the models for a
-reasonable interval of random noises.
-",0,0,0,1,0,0
-17079,Variable-Length Resolvability for General Sources and Channels,"  We introduce the problem of variable-length source resolvability, where a
-given target probability distribution is approximated by encoding a
-variable-length uniform random number, and the asymptotically minimum average
-length rate of the uniform random numbers, called the (variable-length)
-resolvability, is investigated. We first analyze the variable-length
-resolvability with the variational distance as an approximation measure. Next,
-we investigate the case under the divergence as an approximation measure. When
-the asymptotically exact approximation is required, it is shown that the
-resolvability under the two kinds of approximation measures coincides. We then
-extend the analysis to the case of channel resolvability, where the target
-distribution is the output distribution via a general channel due to the fixed
-general source as an input. The obtained characterization of the channel
-resolvability is fully general in the sense that when the channel is just the
-identity mapping, the characterization reduces to the general formula for the
-source resolvability. We also analyze the second-order variable-length
-resolvability.
-",1,0,0,0,0,0
-17080,Diattenuation of Brain Tissue and its Impact on 3D Polarized Light Imaging,"  3D-Polarized Light Imaging (3D-PLI) reconstructs nerve fibers in histological
-brain sections by measuring their birefringence. This study investigates
-another effect caused by the optical anisotropy of brain tissue -
-diattenuation. Based on numerical and experimental studies and a complete
-analytical description of the optical system, the diattenuation was determined
-to be below 4 % in rat brain tissue. It was demonstrated that the diattenuation
-effect has negligible impact on the fiber orientations derived by 3D-PLI. The
-diattenuation signal, however, was found to highlight different anatomical
-structures that cannot be distinguished with current imaging techniques, which
-makes Diattenuation Imaging a promising extension to 3D-PLI.
-",0,1,0,0,0,0
-17081,Higgs Modes in the Pair Density Wave Superconducting State,"  The pair density wave (PDW) superconducting state has been proposed to
-explain the layer- decoupling effect observed in the compound
-La$_{2-x}$Ba$_x$CuO$_4$ at $x=1/8$ (Phys. Rev. Lett. 99, 127003). In this state
-the superconducting order parameter is spatially modulated, in contrast with
-the usual superconducting (SC) state where the order parameter is uniform. In
-this work, we study the properties of the amplitude (Higgs) modes in a
-unidirectional PDW state. To this end we consider a phenomenological model of
-PDW type states coupled to a Fermi surface of fermionic quasiparticles. In
-contrast to conventional superconductors that have a single Higgs mode,
-unidirectional PDW superconductors have two Higgs modes. While in the PDW state
-the Fermi surface largely remains gapless, we find that the damping of the PDW
-Higgs modes into fermionic quasiparticles requires exceeding an energy
-threshold. We show that this suppression of damping in the PDW state is due to
-kinematics. As a result, only one of the two Higgs modes is significantly
-damped. In addition, motivated by the experimental phase diagram, we discuss
-the mixing of Higgs modes in the coexistence regime of the PDW and uniform SC
-states. These results should be observable directly in a Raman spectroscopy, in
-momentum resolved electron energy loss spectroscopy, and in resonant inelastic
-X-ray scattering, thus providing evidence of the PDW states.
-",0,1,0,0,0,0
-17082,A Serverless Tool for Platform Agnostic Computational Experiment Management,"  Neuroscience has been carried into the domain of big data and high
-performance computing (HPC) on the backs of initiatives in data collection and
-an increasingly compute-intensive tools. While managing HPC experiments
-requires considerable technical acumen, platforms and standards have been
-developed to ease this burden on scientists. While web-portals make resources
-widely accessible, data organizations such as the Brain Imaging Data Structure
-and tool description languages such as Boutiques provide researchers with a
-foothold to tackle these problems using their own datasets, pipelines, and
-environments. While these standards lower the barrier to adoption of HPC and
-cloud systems for neuroscience applications, they still require the
-consolidation of disparate domain-specific knowledge. We present Clowdr, a
-lightweight tool to launch experiments on HPC systems and clouds, record rich
-execution records, and enable the accessible sharing of experimental summaries
-and results. Clowdr uniquely sits between web platforms and bare-metal
-applications for experiment management by preserving the flexibility of
-do-it-yourself solutions while providing a low barrier for developing,
-deploying and disseminating neuroscientific analysis.
-",1,0,0,0,0,0
-17083,Traveling-wave parametric amplifier based on three-wave mixing in a Josephson metamaterial,"  We have developed a recently proposed Josephson traveling-wave parametric
-amplifier with three-wave mixing [A. B. Zorin, Phys. Rev. Applied 6, 034006,
-2016]. The amplifier consists of a microwave transmission line formed by a
-serial array of nonhysteretic one-junction SQUIDs. These SQUIDs are flux-biased
-in a way that the phase drops across the Josephson junctions are equal to 90
-degrees and the persistent currents in the SQUID loops are equal to the
-Josephson critical current values. Such a one-dimensional metamaterial
-possesses a maximal quadratic nonlinearity and zero cubic (Kerr) nonlinearity.
-This property allows phase matching and exponential power gain of traveling
-microwaves to take place over a wide frequency range. We report the
-proof-of-principle experiment performed at a temperature of T = 4.2 K on Nb
-trilayer samples, which has demonstrated that our concept of a practical
-broadband Josephson parametric amplifier is valid and very promising for
-achieving quantum-limited operation.
-",0,1,0,0,0,0
-17084,Measuring LDA Topic Stability from Clusters of Replicated Runs,"  Background: Unstructured and textual data is increasing rapidly and Latent
-Dirichlet Allocation (LDA) topic modeling is a popular data analysis methods
-for it. Past work suggests that instability of LDA topics may lead to
-systematic errors. Aim: We propose a method that relies on replicated LDA runs,
-clustering, and providing a stability metric for the topics. Method: We
-generate k LDA topics and replicate this process n times resulting in n*k
-topics. Then we use K-medioids to cluster the n*k topics to k clusters. The k
-clusters now represent the original LDA topics and we present them like normal
-LDA topics showing the ten most probable words. For the clusters, we try
-multiple stability metrics, out of which we recommend Rank-Biased Overlap,
-showing the stability of the topics inside the clusters. Results: We provide an
-initial validation where our method is used for 270,000 Mozilla Firefox commit
-messages with k=20 and n=20. We show how our topic stability metrics are
-related to the contents of the topics. Conclusions: Advances in text mining
-enable us to analyze large masses of text in software engineering but
-non-deterministic algorithms, such as LDA, may lead to unreplicable
-conclusions. Our approach makes LDA stability transparent and is also
-complementary rather than alternative to many prior works that focus on LDA
-parameter tuning.
-",1,0,0,0,0,0
-17085,Continuum Foreground Polarization and Na~I Absorption in Type Ia SNe,"  We present a study of the continuum polarization over the 400--600 nm range
-of 19 Type Ia SNe obtained with FORS at the VLT. We separate them in those that
-show Na I D lines at the velocity of their hosts and those that do not.
-Continuum polarization of the sodium sample near maximum light displays a broad
-range of values, from extremely polarized cases like SN 2006X to almost
-unpolarized ones like SN 2011ae. The non--sodium sample shows, typically,
-smaller polarization values. The continuum polarization of the sodium sample in
-the 400--600 nm range is linear with wavelength and can be characterized by the
-mean polarization (P$_{\rm{mean}}$). Its values span a wide range and show a
-linear correlation with color, color excess, and extinction in the visual band.
-Larger dispersion correlations were found with the equivalent width of the Na I
-D and Ca II H & K lines, and also a noisy relation between P$_{\rm{mean}}$ and
-$R_{V}$, the ratio of total to selective extinction. Redder SNe show stronger
-continuum polarization, with larger color excesses and extinctions. We also
-confirm that high continuum polarization is associated with small values of
-$R_{V}$.
-The correlation between extinction and polarization -- and polarization
-angles -- suggest that the dominant fraction of dust polarization is imprinted
-in interstellar regions of the host galaxies.
-We show that Na I D lines from foreground matter in the SN host are usually
-associated with non-galactic ISM, challenging the typical assumptions in
-foreground interstellar polarization models.
-",0,1,0,0,0,0
-17086,Toward Faultless Content-Based Playlists Generation for Instrumentals,"  This study deals with content-based musical playlists generation focused on
-Songs and Instrumentals. Automatic playlist generation relies on collaborative
-filtering and autotagging algorithms. Autotagging can solve the cold start
-issue and popularity bias that are critical in music recommender systems.
-However, autotagging remains to be improved and cannot generate satisfying
-music playlists. In this paper, we suggest improvements toward better
-autotagging-generated playlists compared to state-of-the-art. To assess our
-method, we focus on the Song and Instrumental tags. Song and Instrumental are
-two objective and opposite tags that are under-studied compared to genres or
-moods, which are subjective and multi-modal tags. In this paper, we consider an
-industrial real-world musical database that is unevenly distributed between
-Songs and Instrumentals and bigger than databases used in previous studies. We
-set up three incremental experiments to enhance automatic playlist generation.
-Our suggested approach generates an Instrumental playlist with up to three
-times less false positives than cutting edge methods. Moreover, we provide a
-design of experiment framework to foster research on Songs and Instrumentals.
-We give insight on how to improve further the quality of generated playlists
-and to extend our methods to other musical tags. Furthermore, we provide the
-source code to guarantee reproducible research.
-",1,0,0,0,0,0
-17087,Direct observation of the band gap transition in atomically thin ReS$_2$,"  ReS$_2$ is considered as a promising candidate for novel electronic and
-sensor applications. The low crystal symmetry of the van der Waals compound
-ReS$_2$ leads to a highly anisotropic optical, vibrational, and transport
-behavior. However, the details of the electronic band structure of this
-fascinating material are still largely unexplored. We present a
-momentum-resolved study of the electronic structure of monolayer, bilayer, and
-bulk ReS$_2$ using k-space photoemission microscopy in combination with
-first-principles calculations. We demonstrate that the valence electrons in
-bulk ReS$_2$ are - contrary to assumptions in recent literature - significantly
-delocalized across the van der Waals gap. Furthermore, we directly observe the
-evolution of the valence band dispersion as a function of the number of layers,
-revealing a significantly increased effective electron mass in single-layer
-crystals. We also find that only bilayer ReS$_2$ has a direct band gap. Our
-results establish bilayer ReS$_2$ as a advantageous building block for
-two-dimensional devices and van der Waals heterostructures.
-",0,1,0,0,0,0
-17088,Lattice embeddings between types of fuzzy sets. Closed-valued fuzzy sets,"  In this paper we deal with the problem of extending Zadeh's operators on
-fuzzy sets (FSs) to interval-valued (IVFSs), set-valued (SVFSs) and type-2
-(T2FSs) fuzzy sets. Namely, it is known that seeing FSs as SVFSs, or T2FSs,
-whose membership degrees are singletons is not order-preserving. We then
-describe a family of lattice embeddings from FSs to SVFSs. Alternatively, if
-the former singleton viewpoint is required, we reformulate the intersection on
-hesitant fuzzy sets and introduce what we have called closed-valued fuzzy sets.
-This new type of fuzzy sets extends standard union and intersection on FSs. In
-addition, it allows handling together membership degrees of different nature
-as, for instance, closed intervals and finite sets. Finally, all these
-constructions are viewed as T2FSs forming a chain of lattices.
-",1,0,0,0,0,0
-17089,Coupling of Magneto-Thermal and Mechanical Superconducting Magnet Models by Means of Mesh-Based Interpolation,"  In this paper we present an algorithm for the coupling of magneto-thermal and
-mechanical finite element models representing superconducting accelerator
-magnets. The mechanical models are used during the design of the mechanical
-structure as well as the optimization of the magnetic field quality under
-nominal conditions. The magneto-thermal models allow for the analysis of
-transient phenomena occurring during quench initiation, propagation, and
-protection. Mechanical analysis of quenching magnets is of high importance
-considering the design of new protection systems and the study of new
-superconductor types. We use field/circuit coupling to determine temperature
-and electromagnetic force evolution during the magnet discharge. These
-quantities are provided as a load to existing mechanical models. The models are
-discretized with different meshes and, therefore, we employ a mesh-based
-interpolation method to exchange coupled quantities. The coupling algorithm is
-illustrated with a simulation of a mechanical response of a standalone
-high-field dipole magnet protected with CLIQ (Coupling-Loss Induced Quench)
-technology.
-",1,1,0,0,0,0
-17090,Converging expansions for Lipschitz self-similar perforations of a plane sector,"  In contrast with the well-known methods of matching asymptotics and
-multiscale (or compound) asymptotics, the "" functional analytic approach "" of
-Lanza de Cristoforis (Analysis 28, 2008) allows to prove convergence of
-expansions around interior small holes of size $\epsilon$ for solutions of
-elliptic boundary value problems. Using the method of layer potentials, the
-asymptotic behavior of the solution as $\epsilon$ tends to zero is described
-not only by asymptotic series in powers of $\epsilon$, but by convergent power
-series. Here we use this method to investigate the Dirichlet problem for the
-Laplace operator where holes are collapsing at a polygonal corner of opening
-$\omega$. Then in addition to the scale $\epsilon$ there appears the scale
-$\eta = \epsilon^{\pi/\omega}$. We prove that when $\pi/\omega$ is irrational,
-the solution of the Dirichlet problem is given by convergent series in powers
-of these two small parameters. Due to interference of the two scales, this
-convergence is obtained, in full generality, by grouping together integer
-powers of the two scales that are very close to each other. Nevertheless, there
-exists a dense subset of openings $\omega$ (characterized by Diophantine
-approximation properties), for which real analyticity in the two variables
-$\epsilon$ and $\eta$ holds and the power series converge unconditionally. When
-$\pi/\omega$ is rational, the series are unconditionally convergent, but
-contain terms in log $\epsilon$.
-",0,0,1,0,0,0
-17091,A Viral Timeline Branching Process to study a Social Network,"  Bio-inspired paradigms are proving to be useful in analyzing propagation and
-dissemination of information in networks. In this paper we explore the use of
-multi-type branching processes to analyse viral properties of content in a
-social network, with and without competition from other sources. We derive and
-compute various virality measures, e.g., probability of virality, expected
-number of shares, or the rate of growth of expected number of shares etc. They
-allow one to predict the emergence of global macro properties (e.g., viral
-spread of a post in the entire network) from the laws and parameters that
-determine local interactions. The local interactions, greatly depend upon the
-structure of the timelines holding the content and the number of friends (i.e.,
-connections) of users of the network. We then formulate a non-cooperative game
-problem and study the Nash equilibria as a function of the parameters. The
-branching processes modelling the social network under competition turn out to
-be decomposable, multi-type and continuous time variants. For such processes
-types belonging to different sub-classes evolve at different rates and have
-different probabilities of extinction etc. We compute content provider wise
-extinction probability, rate of growth etc. We also conjecture the
-content-provider wise growth rate of expected shares.
-",1,0,0,0,0,0
-17092,Algorithmic Bio-surveillance For Precise Spatio-temporal Prediction of Zoonotic Emergence,"  Viral zoonoses have emerged as the key drivers of recent pandemics. Human
-infection by zoonotic viruses are either spillover events -- isolated
-infections that fail to cause a widespread contagion -- or species jumps, where
-successful adaptation to the new host leads to a pandemic. Despite expensive
-bio-surveillance efforts, historically emergence response has been reactive,
-and post-hoc. Here we use machine inference to demonstrate a high accuracy
-predictive bio-surveillance capability, designed to pro-actively localize an
-impending species jump via automated interrogation of massive sequence
-databases of viral proteins. Our results suggest that a jump might not purely
-be the result of an isolated unfortunate cross-infection localized in space and
-time; there are subtle yet detectable patterns of genotypic changes
-accumulating in the global viral population leading up to emergence. Using tens
-of thousands of protein sequences simultaneously, we train models that track
-maximum achievable accuracy for disambiguating host tropism from the primary
-structure of surface proteins, and show that the inverse classification
-accuracy is a quantitative indicator of jump risk. We validate our claim in the
-context of the 2009 swine flu outbreak, and the 2004 emergence of H5N1
-subspecies of Influenza A from avian reservoirs; illustrating that
-interrogation of the global viral population can unambiguously track a near
-monotonic risk elevation over several preceding years leading to eventual
-emergence.
-",0,0,0,1,1,0
-17093,Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward,"  Operationalizing machine learning based security detections is extremely
-challenging, especially in a continuously evolving cloud environment.
-Conventional anomaly detection does not produce satisfactory results for
-analysts that are investigating security incidents in the cloud. Model
-evaluation alone presents its own set of problems due to a lack of benchmark
-datasets. When deploying these detections, we must deal with model compliance,
-localization, and data silo issues, among many others. We pose the problem of
-""attack disruption"" as a way forward in the security data science space. In
-this paper, we describe the framework, challenges, and open questions
-surrounding the successful operationalization of machine learning based
-security detections in a cloud environment and provide some insights on how we
-have addressed them.
-",1,0,0,0,0,0
-17094,SOI RF Switch for Wireless Sensor Network,"  The objective of this research was to design a 0-5 GHz RF SOI switch, with
-0.18um power Jazz SOI technology by using Cadence software, for health care
-applications. This paper introduces the design of a RF switch implemented in
-shunt-series topology. An insertion loss of 0.906 dB and an isolation of 30.95
-dB were obtained at 5 GHz. The switch also achieved a third order distortion of
-53.05 dBm and 1 dB compression point reached 50.06dBm. The RF switch
-performance meets the desired specification requirements.
-",1,0,0,0,0,0
-17095,The Pentagonal Inequality,"  Given a positive linear combination of five (respectively seven) cosines,
-where the angles are positive and sum to pi, the aim of this article is to
-express the sharp bound of the combination as a Positive Real Fraction in the
-coefficients (hence cosine-free). The method uses algebraic and arithmetic
-manipulations with judicious transformations.
-",0,0,1,0,0,0
-17096,The Landscape of Deep Learning Algorithms,"  This paper studies the landscape of empirical risk of deep neural networks by
-theoretically analyzing its convergence behavior to the population risk as well
-as its stationary points and properties. For an $l$-layer linear neural
-network, we prove its empirical risk uniformly converges to its population risk
-at the rate of $\mathcal{O}(r^{2l}\sqrt{d\log(l)}/\sqrt{n})$ with training
-sample size of $n$, the total weight dimension of $d$ and the magnitude bound
-$r$ of weight of each layer. We then derive the stability and generalization
-bounds for the empirical risk based on this result. Besides, we establish the
-uniform convergence of gradient of the empirical risk to its population
-counterpart. We prove the one-to-one correspondence of the non-degenerate
-stationary points between the empirical and population risks with convergence
-guarantees, which describes the landscape of deep neural networks. In addition,
-we analyze these properties for deep nonlinear neural networks with sigmoid
-activation functions. We prove similar results for convergence behavior of
-their empirical risks as well as the gradients and analyze properties of their
-non-degenerate stationary points.
-To our best knowledge, this work is the first one theoretically
-characterizing landscapes of deep learning algorithms. Besides, our results
-provide the sample complexity of training a good deep neural network. We also
-provide theoretical understanding on how the neural network depth $l$, the
-layer width, the network size $d$ and parameter magnitude determine the neural
-network landscapes.
-",1,0,1,1,0,0
-17097,"The effect of the environment on the structure, morphology and star-formation history of intermediate-redshift galaxies","  With the aim of understanding the effect of the environment on the star
-formation history and morphological transformation of galaxies, we present a
-detailed analysis of the colour, morphology and internal structure of cluster
-and field galaxies at $0.4 \le z \le 0.8$. We use {\em HST} data for over 500
-galaxies from the ESO Distant Cluster Survey (EDisCS) to quantify how the
-galaxies' light distribution deviate from symmetric smooth profiles. We
-visually inspect the galaxies' images to identify the likely causes for such
-deviations. We find that the residual flux fraction ($RFF$), which measures the
-fractional contribution to the galaxy light of the residuals left after
-subtracting a symmetric and smooth model, is very sensitive to the degree of
-structural disturbance but not the causes of such disturbance. On the other
-hand, the asymmetry of these residuals ($A_{\rm res}$) is more sensitive to the
-causes of the disturbance, with merging galaxies having the highest values of
-$A_{\rm res}$. Using these quantitative parameters we find that, at a fixed
-morphology, cluster and field galaxies show statistically similar degrees of
-disturbance. However, there is a higher fraction of symmetric and passive
-spirals in the cluster than in the field. These galaxies have smoother light
-distributions than their star-forming counterparts. We also find that while
-almost all field and cluster S0s appear undisturbed, there is a relatively
-small population of star-forming S0s in clusters but not in the field. These
-findings are consistent with relatively gentle environmental processes acting
-on galaxies infalling onto clusters.
-",0,1,0,0,0,0
-17098,Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning,"  Recommender systems play a crucial role in mitigating the problem of
-information overload by suggesting users' personalized items or services. The
-vast majority of traditional recommender systems consider the recommendation
-procedure as a static process and make recommendations following a fixed
-strategy. In this paper, we propose a novel recommender system with the
-capability of continuously improving its strategies during the interactions
-with users. We model the sequential interactions between users and a
-recommender system as a Markov Decision Process (MDP) and leverage
-Reinforcement Learning (RL) to automatically learn the optimal strategies via
-recommending trial-and-error items and receiving reinforcements of these items
-from users' feedback. Users' feedback can be positive and negative and both
-types of feedback have great potentials to boost recommendations. However, the
-number of negative feedback is much larger than that of positive one; thus
-incorporating them simultaneously is challenging since positive feedback could
-be buried by negative one. In this paper, we develop a novel approach to
-incorporate them into the proposed deep recommender system (DEERS) framework.
-The experimental results based on real-world e-commerce data demonstrate the
-effectiveness of the proposed framework. Further experiments have been
-conducted to understand the importance of both positive and negative feedback
-in recommendations.
-",0,0,0,1,0,0
-17099,Accumulated Gradient Normalization,"  This work addresses the instability in asynchronous data parallel
-optimization. It does so by introducing a novel distributed optimizer which is
-able to efficiently optimize a centralized model under communication
-constraints. The optimizer achieves this by pushing a normalized sequence of
-first-order gradients to a parameter server. This implies that the magnitude of
-a worker delta is smaller compared to an accumulated gradient, and provides a
-better direction towards a minimum compared to first-order gradients, which in
-turn also forces possible implicit momentum fluctuations to be more aligned
-since we make the assumption that all workers contribute towards a single
-minima. As a result, our approach mitigates the parameter staleness problem
-more effectively since staleness in asynchrony induces (implicit) momentum, and
-achieves a better convergence rate compared to other optimizers such as
-asynchronous EASGD and DynSGD, which we show empirically.
-",1,0,0,1,0,0
-17100,An Optimal Algorithm for Changing from Latitudinal to Longitudinal Formation of Autonomous Aircraft Squadrons,"  This work presents an algorithm for changing from latitudinal to longitudinal
-formation of autonomous aircraft squadrons. The maneuvers are defined
-dynamically by using a predefined set of 3D basic maneuvers. This formation
-changing is necessary when the squadron has to perform tasks which demand both
-formations, such as lift off, georeferencing, obstacle avoidance and landing.
-Simulations show that the formation changing is made without collision. The
-time complexity analysis of the transformation algorithm reveals that its
-efficiency is optimal, and the proof of correction ensures its longitudinal
-formation features.
-",1,0,0,0,0,0
-17101,Ideal Cluster Points in Topological Spaces,"  Given an ideal $\mathcal{I}$ on $\omega$, we show that a sequence in a
-topological space $X$ is $\mathcal{I}$-convergent if and only if there exists a
-""big"" $\mathcal{I}$-convergent subsequence. In addition, we study several
-properties of $\mathcal{I}$-cluster points. As a consequence, the underlying
-topology $\tau$ coincides with the topology generated by the pair
-$(\tau,\mathcal{I})$. Then, we obtain two characterizations of the set of
-$\mathcal{I}$-cluster points as classical cluster points of a filters on $X$
-and as the smallest closed set containing ""almost all"" the sequence.
-",0,0,1,0,0,0
-17102,Spin Hall effect of gravitational waves,"  Gravitons possess a Berry curvature due to their helicity. We derive the
-semiclassical equations of motion for gravitons taking into account the Berry
-curvature. We show that this quantum correction leads to the splitting of the
-trajectories of right- and left-handed gravitational waves in curved space, and
-that this correction can be understood as a topological phenomenon. This is the
-spin Hall effect (SHE) of gravitational waves. We find that the SHE of
-gravitational waves is twice as large as that of light. Possible future
-observations of the SHE of gravitational waves can potentially test the quantum
-nature of gravitons beyond the classical general relativity.
-",0,1,0,0,0,0
-17103,Many-Goals Reinforcement Learning,"  All-goals updating exploits the off-policy nature of Q-learning to update all
-possible goals an agent could have from each transition in the world, and was
-introduced into Reinforcement Learning (RL) by Kaelbling (1993). In prior work
-this was mostly explored in small-state RL problems that allowed tabular
-representations and where all possible goals could be explicitly enumerated and
-learned separately. In this paper we empirically explore 3 different extensions
-of the idea of updating many (instead of all) goals in the context of RL with
-deep neural networks (or DeepRL for short). First, in a direct adaptation of
-Kaelbling's approach we explore if many-goals updating can be used to achieve
-mastery in non-tabular visual-observation domains. Second, we explore whether
-many-goals updating can be used to pre-train a network to subsequently learn
-faster and better on a single main task of interest. Third, we explore whether
-many-goals updating can be used to provide auxiliary task updates in training a
-network to learn faster and better on a single main task of interest. We
-provide comparisons to baselines for each of the 3 extensions.
-",0,0,0,1,0,0
-17104,Localized Structured Prediction,"  Key to structured prediction is exploiting the problem structure to simplify
-the learning process. A major challenge arises when data exhibit a local
-structure (e.g., are made by ""parts"") that can be leveraged to better
-approximate the relation between (parts of) the input and (parts of) the
-output. Recent literature on signal processing, and in particular computer
-vision, has shown that capturing these aspects is indeed essential to achieve
-state-of-the-art performance. While such algorithms are typically derived on a
-case-by-case basis, in this work we propose the first theoretical framework to
-deal with part-based data from a general perspective. We derive a novel
-approach to deal with these problems and study its generalization properties
-within the setting of statistical learning theory. Our analysis is novel in
-that it explicitly quantifies the benefits of leveraging the part-based
-structure of the problem with respect to the learning rates of the proposed
-estimator.
-",0,0,0,1,0,0
-17105,Routing in FRET-based Nanonetworks,"  Nanocommunications, understood as communications between nanoscale devices,
-is commonly regarded as a technology essential for cooperation of large groups
-of nanomachines and thus crucial for development of the whole area of
-nanotechnology. While solutions for point-to-point nanocommunications have been
-already proposed, larger networks cannot function properly without routing. In
-this article we focus on the nanocommunications via Forster Resonance Energy
-Transfer (FRET), which was found to be a technique with a very high signal
-propagation speed, and discuss how to route signals through nanonetworks. We
-introduce five new routing mechanisms, based on biological properties of
-specific molecules. We experimentally validate one of these mechanisms.
-Finally, we analyze open issues showing the technical challenges for signal
-transmission and routing in FRET-based nanocommunications.
-",0,0,0,0,1,0
-17106,"FFT Convolutions are Faster than Winograd on Modern CPUs, Here is Why","  Winograd-based convolution has quickly gained traction as a preferred
-approach to implement convolutional neural networks (ConvNet) on various
-hardware platforms because it requires fewer floating point operations than
-FFT-based or direct convolutions.
-This paper compares three highly optimized implementations (regular FFT--,
-Gauss--FFT--, and Winograd--based convolutions) on modern multi-- and
-many--core CPUs. Although all three implementations employed the same
-optimizations for modern CPUs, our experimental results with two popular
-ConvNets (VGG and AlexNet) show that the FFT--based implementations generally
-outperform the Winograd--based approach, contrary to the popular belief.
-To understand the results, we use a Roofline performance model to analyze the
-three implementations in detail, by looking at each of their computation phases
-and by considering not only the number of floating point operations, but also
-the memory bandwidth and the cache sizes. The performance analysis explains
-why, and under what conditions, the FFT--based implementations outperform the
-Winograd--based one, on modern CPUs.
-",1,0,0,0,0,0
-17107,The application of selection principles in the study of the properties of function spaces,"  In this paper we investigate the properties of function spaces using the
-selection principles.
-",0,0,1,0,0,0
-17108,Progressive Neural Architecture Search,"  We propose a new method for learning the structure of convolutional neural
-networks (CNNs) that is more efficient than recent state-of-the-art methods
-based on reinforcement learning and evolutionary algorithms. Our approach uses
-a sequential model-based optimization (SMBO) strategy, in which we search for
-structures in order of increasing complexity, while simultaneously learning a
-surrogate model to guide the search through structure space. Direct comparison
-under the same search space shows that our method is up to 5 times more
-efficient than the RL method of Zoph et al. (2018) in terms of number of models
-evaluated, and 8 times faster in terms of total compute. The structures we
-discover in this way achieve state of the art classification accuracies on
-CIFAR-10 and ImageNet.
-",1,0,0,1,0,0
-17109,Lower Bounds for Maximum Gap in (Inverse) Cyclotomic Polynomials,"  The maximum gap $g(f)$ of a polynomial $f$ is the maximum of the differences
-(gaps) between two consecutive exponents that appear in $f$. Let $\Phi_{n}$ and
-$\Psi_{n}$ denote the $n$-th cyclotomic and $n$-th inverse cyclotomic
-polynomial, respectively. In this paper, we give several lower bounds for
-$g(\Phi_{n})$ and $g(\Psi_{n})$, where $n$ is the product of odd primes. We
-observe that they are very often exact. We also give an exact expression for
-$g(\Psi_{n})$ under a certain condition. Finally we conjecture an exact
-expression for $g(\Phi_{n})$ under a certain condition.
-",0,0,1,0,0,0
-17110,"Dynamical Analysis of Cylindrically Symmetric Anisotropic Sources in $f(R,T)$ Gravity","  In this paper, we have analyzed the stability of cylindrically symmetric
-collapsing object filled with locally anisotropic fluid in $f(R,T)$ theory,
-where $R$ is the scalar curvature and $T$ is the trace of stress-energy tensor
-of matter. Modified field equations and dynamical equations are constructed in
-$f(R,T)$ gravity. Evolution or collapse equation is derived from dynamical
-equations by performing linear perturbation on them. Instability range is
-explored in both Newtonian and post-Newtonian regimes with the help of
-adiabetic index, which defines the impact of physical parameters on the
-instability range. Some conditions are imposed on physical quantities to secure
-the stability of the gravitating sources.
-",0,1,0,0,0,0
-17111,Trace and Kunneth formulas for singularity categories and applications,"  We present an $\ell$-adic trace formula for saturated and admissible
-dg-categories over a base monoidal dg-category. Moreover, we prove Künneth
-formulas for dg-category of singularities, and for inertia-invariant vanishing
-cycles. As an application, we prove a version of Bloch's Conductor Conjecture
-(stated by Spencer Bloch in 1985), under the additional hypothesis that the
-monodromy action of the inertia group is unipotent.
-",0,0,1,0,0,0
-17112,A Redshift Survey of the Nearby Galaxy Cluster Abell 2199: Comparison of the Spatial and Kinematic Distributions of Galaxies with the Intracluster Medium,"  We present the results from an extensive spectroscopic survey of the central
-region of the nearby galaxy cluster Abell 2199 at $z=0.03$. By combining 775
-new redshifts from the MMT/Hectospec observations with the data in the
-literature, we construct a large sample of 1624 galaxies with measured
-redshifts at $R<30^\prime$, which results in high spectroscopic completeness at
-$r_{\rm petro,0}<20.5$ (77%). We use these data to study the kinematics and
-clustering of galaxies focusing on the comparison with those of the
-intracluster medium (ICM) from Suzaku X-ray observations. We identify 406
-member galaxies of A2199 at $R<30^\prime$ using the caustic technique. The
-velocity dispersion profile of cluster members appears smoothly connected to
-the stellar velocity dispersion profile of the cD galaxy. The luminosity
-function is well fitted with a Schechter function at $M_r<-15$. The radial
-velocities of cluster galaxies generally agree well with those of the ICM, but
-there are some regions where the velocity difference between the two is about a
-few hundred kilometer per second. The cluster galaxies show a hint of global
-rotation at $R<5^\prime$ with $v_{\rm rot}=300{-}600\,\textrm{km s}^{-1}$, but
-the ICM in the same region do not show such rotation. We apply a
-friends-of-friends algorithm to the cluster galaxy sample at $R<60^\prime$ and
-identify 32 group candidates, and examine the spatial correlation between the
-galaxy groups and X-ray emission. This extensive survey in the central region
-of A2199 provides an important basis for future studies of interplay among the
-galaxies, the ICM and the dark matter in the cluster.
-",0,1,0,0,0,0
-17113,A trapped field of 13.4 T in a stack of HTS tapes with 30 μm substrate,"  Superconducting bulk (RE)Ba$_2$Cu$_3$O$_{7-x}$ materials (RE-rare earth
-elements) have been successfully used to generate magnetic flux densities in
-excess of 17 T. This work investigates an alternative approach by trapping flux
-in stacks of second generation high temperature superconducting tape from
-several manufacturers using field cooling and pulsed field magnetisation
-techniques. Flux densities of up to 13.4 T were trapped by field cooling at ~5
-K between two 12 mm square stacks, an improvement of 70% over previous value
-achieved in an HTS tape stack. The trapped flux approaches the record values in
-(RE)BCO bulks and reflects the rapid developments still being made in the HTS
-tape performance.
-",0,1,0,0,0,0
-17114,Exact Simulation of the Extrema of Stable Processes,"  We exhibit an exact simulation algorithm for the supremum of a stable process
-over a finite time interval using dominated coupling from the past (DCFTP). We
-establish a novel perpetuity equation for the supremum (via the representation
-of the concave majorants of Lévy processes) and apply it to construct a
-Markov chain in the DCFTP algorithm. We prove that the number of steps taken
-backwards in time before the coalescence is detected is finite.
-",0,0,0,1,0,0
-17115,Nonparametric Bayesian estimation of a Hölder continuous diffusion coefficient,"  We consider a nonparametric Bayesian approach to estimate the diffusion
-coefficient of a stochastic differential equation given discrete time
-observations over a fixed time interval. As a prior on the diffusion
-coefficient, we employ a histogram-type prior with piecewise constant
-realisations on bins forming a partition of the time interval. Specifically,
-these constants are realizations of independent inverse Gamma distributed
-randoma variables. We justify our approach by deriving the rate at which the
-corresponding posterior distribution asymptotically concentrates around the
-data-generating diffusion coefficient. This posterior contraction rate turns
-out to be optimal for estimation of a Hölder-continuous diffusion coefficient
-with smoothness parameter $0<\lambda\leq 1.$ Our approach is straightforward to
-implement, as the posterior distributions turn out to be inverse Gamma again,
-and leads to good practical results in a wide range of simulation examples.
-Finally, we apply our method on exchange rate data sets.
-",0,0,1,1,0,0
-17116,Dirac State in a Centrosymmetric Superconductor alpha-PdBi2,"  Topological superconductor (TSC) hosting Majorana fermions has been
-established as a milestone that may shift our scientific trajectory from
-research to applications in topological quantum computing. Recently,
-superconducting Pd-Bi binaries have attracted great attention as a possible
-medium for the TSC phase as a result of their large spin-orbit coupling
-strength. Here, we report a systematic high-resolution angle-resolved
-photoemission spectroscopy (ARPES) study on the normal state electronic
-structure of superconducting alpha-PdBi2 (Tc = 1.7 K). Our results show the
-presence of Dirac states at higher-binding energy with the location of the
-Dirac point at 1.26 eV below the chemical potential at the zone center.
-Furthermore, the ARPES data indicate multiple band crossings at the chemical
-potential, consistent with the metallic behavior of alpha-PdBi2. Our detailed
-experimental studies are complemented by first-principles calculations, which
-reveal the presence of surface Rashba states residing in the vicinity of the
-chemical potential. The obtained results provide an opportunity to investigate
-the relationship between superconductivity and topology, as well as explore
-pathways to possible future platforms for topological quantum computing.
-",0,1,0,0,0,0
-17117,Hölder and Lipschitz continuity of functions definable over Henselian rank one valued fields,"  Consider a Henselian rank one valued field $K$ of equicharacteristic zero
-with the three-sorted language $\mathcal{L}$ of Denef--Pas. Let $f: A \to K$ be
-a continuous $\mathcal{L}$-definable (with parameters) function on a closed
-bounded subset $A \subset K^{n}$. The main purpose is to prove that then $f$ is
-Hölder continuous with some exponent $s\geq 0$ and constant $c \geq 0$, a
-fortiori, $f$ is uniformly continuous. Further, if $f$ is locally Lipschitz
-continuous with a constant $c$, then $f$ is (globally) Lipschitz continuous
-with possibly some larger constant $d$. Also stated are some problems
-concerning continuous and Lipschitz continuous functions definable over
-Henselian valued fields.
-",0,0,1,0,0,0
-17118,Bistability of Cavity Magnon Polaritons,"  We report the first observation of the magnon-polariton bistability in a
-cavity magnonics system consisting of cavity photons strongly interacting with
-the magnons in a small yttrium iron garnet (YIG) sphere. The bistable behaviors
-are emerged as sharp frequency switchings of the cavity magnon-polaritons
-(CMPs) and related to the transition between states with large and small number
-of polaritons. In our experiment, we align, respectively, the [100] and [110]
-crystallographic axes of the YIG sphere parallel to the static magnetic field
-and find very different bistable behaviors (e.g., clockwise and
-counter-clockwise hysteresis loops) in these two cases. The experimental
-results are well fitted and explained as being due to the Kerr nonlinearity
-with either positive or negative coefficient. Moreover, when the magnetic field
-is tuned away from the anticrossing point of CMPs, we observe simultaneous
-bistability of both magnons and cavity photons by applying a drive field on the
-lower branch.
-",0,1,0,0,0,0
-17119,Errors and secret data in the Italian research assessment exercise. A comment to a reply,"  Italy adopted a performance-based system for funding universities that is
-centered on the results of a national research assessment exercise, realized by
-a governmental agency (ANVUR). ANVUR evaluated papers by using 'a dual system
-of evaluation', that is by informed peer review or by bibliometrics. In view of
-validating that system, ANVUR performed an experiment for estimating the
-agreement between informed review and bibliometrics. Ancaiani et al. (2015)
-presents the main results of the experiment. Baccini and De Nicolao (2017)
-documented in a letter, among other critical issues, that the statistical
-analysis was not realized on a random sample of articles. A reply to the letter
-has been published by Research Evaluation (Benedetto et al. 2017). This note
-highlights that in the reply there are (1) errors in data, (2) problems with
-'representativeness' of the sample, (3) unverifiable claims about weights used
-for calculating kappas, (4) undisclosed averaging procedures; (5) a statement
-about 'same protocol in all areas' contradicted by official reports. Last but
-not least: the data used by the authors continue to be undisclosed. A general
-warning concludes: many recently published papers use data originating from
-Italian research assessment exercise. These data are not accessible to the
-scientific community and consequently these papers are not reproducible. They
-can be hardly considered as containing sound evidence at least until authors or
-ANVUR disclose the data necessary for replication.
-",1,0,0,0,0,0
-17120,Exploring Features for Predicting Policy Citations,"  In this study we performed an initial investigation and evaluation of
-altmetrics and their relationship with public policy citation of research
-papers. We examined methods for using altmetrics and other data to predict
-whether a research paper is cited in public policy and applied receiver
-operating characteristic curve on various feature groups in order to evaluate
-their potential usefulness. From the methods we tested, classifying based on
-tweet count provided the best results, achieving an area under the ROC curve of
-0.91.
-",1,0,0,0,0,0
-17121,Recovery guarantees for compressed sensing with unknown errors,"  From a numerical analysis perspective, assessing the robustness of
-l1-minimization is a fundamental issue in compressed sensing and sparse
-regularization. Yet, the recovery guarantees available in the literature
-usually depend on a priori estimates of the noise, which can be very hard to
-obtain in practice, especially when the noise term also includes unknown
-discrepancies between the finite model and data. In this work, we study the
-performance of l1-minimization when these estimates are not available,
-providing robust recovery guarantees for quadratically constrained basis
-pursuit and random sampling in bounded orthonormal systems. Several
-applications of this work are approximation of high-dimensional functions,
-infinite-dimensional sparse regularization for inverse problems, and fast
-algorithms for non-Cartesian Magnetic Resonance Imaging.
-",0,0,1,0,0,0
-17122,Synthetic Homology in Homotopy Type Theory,"  This paper defines homology in homotopy type theory, in the process stable
-homotopy groups are also defined. Previous research in synthetic homotopy
-theory is relied on, in particular the definition of cohomology. This work lays
-the foundation for a computer checked construction of homology.
-",1,0,1,0,0,0
-17123,Spatial Models of Vector-Host Epidemics with Directed Movement of Vectors Over Long Distances,"  We investigate a time-dependent spatial vector-host epidemic model with
-non-coincident domains for the vector and host populations. The host population
-resides in small non-overlapping sub-regions, while the vector population
-resides throughout a much larger region. The dynamics of the populations are
-modeled by a reaction-diffusion-advection compartmental system of partial
-differential equations. The disease is transmitted through vector and host
-populations in criss-cross fashion. We establish global well-posedness and
-uniform a prior bounds as well as the long-term behavior. The model is applied
-to simulate the outbreak of bluetongue disease in sheep transmitted by midges
-infected with bluetongue virus. We show that the long-range directed movement
-of the midge population, due to wind-aided movement, enhances the transmission
-of the disease to sheep in distant sites.
-",0,0,0,0,1,0
-17124,Complex and Quaternionic Principal Component Pursuit and Its Application to Audio Separation,"  Recently, the principal component pursuit has received increasing attention
-in signal processing research ranging from source separation to video
-surveillance. So far, all existing formulations are real-valued and lack the
-concept of phase, which is inherent in inputs such as complex spectrograms or
-color images. Thus, in this letter, we extend principal component pursuit to
-the complex and quaternionic cases to account for the missing phase
-information. Specifically, we present both complex and quaternionic proximity
-operators for the $\ell_1$- and trace-norm regularizers. These operators can be
-used in conjunction with proximal minimization methods such as the inexact
-augmented Lagrange multiplier algorithm. The new algorithms are then applied to
-the singing voice separation problem, which aims to separate the singing voice
-from the instrumental accompaniment. Results on the iKala and MSD100 datasets
-confirmed the usefulness of phase information in principal component pursuit.
-",0,0,0,1,0,0
-17125,Light axion-like dark matter must be present during inflation,"  Axion-like particles (ALPs) might constitute the totality of the cold dark
-matter (CDM) observed. The parameter space of ALPs depends on the mass of the
-particle $m$ and on the energy scale of inflation $H_I$ , the latter being
-bound by the non-detection of primordial gravitational waves. We show that the
-bound on HI implies the existence of a mass scale $m_\chi = 10 {\rm \,neV}
-÷ 0.5 {\rm \,peV}$, depending on the ALP susceptibility $\chi$, such that
-the energy density of ALPs of mass smaller than $m_\chi$ is too low to explain
-the present CDM budget, if the ALP field has originated after the end of
-inflation. This bound affects Ultra-Light Axions (ULAs), which have recently
-regained popularity as CDM candidates. Light ($m < m_\chi$) ALPs can then be
-CDM candidates only if the ALP field has already originated during the
-inflationary period, in which case the parameter space is constrained by the
-non-detection of axion isocurvature fluctuations. We comment on the effects on
-these bounds from additional physics beyond the Standard Model, besides ALPs.
-",0,1,0,0,0,0
-17126,Boolean function analysis meets stochastic optimization: An approximation scheme for stochastic knapsack,"  The stochastic knapsack problem is the stochastic variant of the classical
-knapsack problem in which the algorithm designer is given a a knapsack with a
-given capacity and a collection of items where each item is associated with a
-profit and a probability distribution on its size. The goal is to select a
-subset of items with maximum profit and violate the capacity constraint with
-probability at most $p$ (referred to as the overflow probability). While
-several approximation algorithms have been developed for this problem, most of
-these algorithms relax the capacity constraint of the knapsack. In this paper,
-we design efficient approximation schemes for this problem without relaxing the
-capacity constraint.
-(i) Our first result is in the case when item sizes are Bernoulli random
-variables. In this case, we design a (nearly) fully polynomial time
-approximation scheme (FPTAS) which only relaxes the overflow probability. (ii)
-Our second result generalizes the first result to the case when all the item
-sizes are supported on a (common) set of constant size. (iii) Our third result
-is in the case when item sizes are so-called ""hypercontractive"" random
-variables i.e., random variables whose second and fourth moments are within
-constant factors of each other. In other words, the kurtosis of the random
-variable is upper bounded by a constant.
-Crucially, all of our algorithms meet the capacity constraint exactly, a
-result which was previously known only when the item sizes were Poisson or
-Gaussian random variables. Our results rely on new connections between Boolean
-function analysis and stochastic optimization. We believe that these ideas and
-techniques may prove to be useful in other stochastic optimization problems as
-well.
-",1,0,0,0,0,0
-17127,Nonparanormal Information Estimation,"  We study the problem of using i.i.d. samples from an unknown multivariate
-probability distribution $p$ to estimate the mutual information of $p$. This
-problem has recently received attention in two settings: (1) where $p$ is
-assumed to be Gaussian and (2) where $p$ is assumed only to lie in a large
-nonparametric smoothness class. Estimators proposed for the Gaussian case
-converge in high dimensions when the Gaussian assumption holds, but are
-brittle, failing dramatically when $p$ is not Gaussian. Estimators proposed for
-the nonparametric case fail to converge with realistic sample sizes except in
-very low dimensions. As a result, there is a lack of robust mutual information
-estimators for many realistic data. To address this, we propose estimators for
-mutual information when $p$ is assumed to be a nonparanormal (a.k.a., Gaussian
-copula) model, a semiparametric compromise between Gaussian and nonparametric
-extremes. Using theoretical bounds and experiments, we show these estimators
-strike a practical balance between robustness and scaling with dimensionality.
-",1,0,1,1,0,0
-17128,Controlling a population,"  We introduce a new setting where a population of agents, each modelled by a
-finite-state system, are controlled uniformly: the controller applies the same
-action to every agent. The framework is largely inspired by the control of a
-biological system, namely a population of yeasts, where the controller may only
-change the environment common to all cells. We study a synchronisation problem
-for such populations: no matter how individual agents react to the actions of
-the controller, the controller aims at driving all agents synchronously to a
-target state. The agents are naturally represented by a non-deterministic
-finite state automaton (NFA), the same for every agent, and the whole system is
-encoded as a 2-player game. The first player (Controller) chooses actions, and
-the second player (Agents) resolves non-determinism for each agent. The game
-with m agents is called the m -population game. This gives rise to a
-parameterized control problem (where control refers to 2 player games), namely
-the population control problem: can Controller control the m-population game
-for all m in N whatever Agents does?
-",1,0,0,0,0,0
-17129,Finite scale local Lyapunov exponents distribution in fully developed homogeneous isotropic turbulence,"  The present work analyzes the distribution function of the finite scale local
-Lyapunov exponent of a pair fluid particles trajectories in fully developed
-incompressible homogeneous isotropic turbulence. According to the hypothesis of
-fully developed chaos, this PDF is reasonably estimated by maximizing the
-entropy associated to such distribution, resulting to be an uniform
-distribution function in a proper interval of variation of the local Lyapunov
-exponents. From this PDF, we determine the relationship between the average and
-maximum Lyapunov exponents and the longitudinal velocity correlation function.
-This link, which leads to the closure of von Kàrmàn--Howarth and Corrsin
-equations, agrees with the relation obtained in the previous work, supporting
-the proposed PDF calculation, at least for the purposes of the energy cascade
-effect estimation. Furthermore, through the property that the Lyapunov vectors
-tend to align to the direction of the maximum growth rate of trajectories
-distance, we obtain the link between maximum and average Lyapunov exponents in
-line with the previous result.
-",0,1,0,0,0,0
-17130,Improved Power Decoding of One-Point Hermitian Codes,"  We propose a new partial decoding algorithm for one-point Hermitian codes
-that can decode up to the same number of errors as the Guruswami--Sudan
-decoder. Simulations suggest that it has a similar failure probability as the
-latter one. The algorithm is based on a recent generalization of the power
-decoding algorithm for Reed--Solomon codes and does not require an expensive
-root-finding step. In addition, it promises improvements for decoding
-interleaved Hermitian codes.
-",1,0,0,0,0,0
-17131,Finite numbers of initial ideals in non-Noetherian polynomial rings,"  In this article, we generalize the well-known result that ideals of
-Noetherian polynomial rings have only finitely many initial ideals to the
-situation of ascending ideal chains in non-Noetherian polynomial rings. More
-precisely, we study ideal chains in the polynomial ring $R=K[x_{i,j}\,|\,1\leq
-i\leq c,j\in N]$ that are invariant under the action of the monoid $Inc(N)$ of
-strictly increasing functions on $N$, which acts on $R$ by shifting the second
-variable index. We show that for every such ideal chain, the number of initial
-ideal chains with respect to term orders on $R$ that are compatible with the
-action of $Inc(N)$ is finite. As a consequence of this, we will see that
-$Inc(N)$-invariant ideals of $R$ have only finitely many initial ideals with
-respect to $Inc(N)$-compatible term orders. The article also addresses the
-question of how many such term orders exist. We give a complete list of the
-$Inc(N)$-compatible term orders for the case $c=1$ and show that there are
-infinitely many for $c >1$. This answers a question by Hillar, Kroner, Leykin.
-",0,0,1,0,0,0
-17132,The Diederich-Fornaess Index and Good Vector Fields,"  We consider the relationship between two sufficient conditions for regularity
-of the Bergman Projection on smooth, bounded, pseudoconvex domains. We show
-that if the set of infinite type points is reasonably well-behaved, then the
-existence of a family of good vector fields in the sense of Boas and Straube
-implies that the Diederich-Fornaess Index of the domain is equal to one.
-",0,0,1,0,0,0
-17133,Complete Minors of Self-Complementary Graphs,"  We show that any self-complementary graph with $n$ vertices contains a
-$K_{\lfloor \frac{n+1}{2}\rfloor}$ minor. We derive topological properties of
-self-complementary graphs.
-",0,0,1,0,0,0
-17134,Statistical estimation of the Oscillating Brownian Motion,"  We study the asymptotic behavior of estimators of a two-valued, discontinuous
-diffusion coefficient in a Stochastic Differential Equation, called an
-Oscillating Brownian Motion. Using the relation of the latter process with the
-Skew Brownian Motion, we propose two natural consistent estimators, which are
-variants of the integrated volatility estimator and take the occupation times
-into account. We show the stable convergence of the renormalized errors'
-estimations toward some Gaussian mixture, possibly corrected by a term that
-depends on the local time. These limits stem from the lack of ergodicity as
-well as the behavior of the local time at zero of the process. We test both
-estimators on simulated processes, finding a complete agreement with the
-theoretical predictions.
-",0,0,1,1,0,0
-17135,Competing Ferromagnetic and Anti-Ferromagnetic interactions in Iron Nitride $ζ$-Fe$_2$N,"  The paper discusses the magnetic state of zeta phase of iron nitride viz.
-$\zeta$-Fe$_2$N on the basis of spin polarized first principles electronic
-structure calculations together with a review of already published data.
-Results of our first principles study suggest that the ground state of
-$\zeta$-Fe$_2$N is ferromagnetic (FM) with a magnetic moment of 1.528
-$\mu_\text{B}$ on the Fe site. The FM ground state is lower than the
-anti-ferromagnetic (AFM) state by 8.44 meV and non-magnetic(NM) state by 191
-meV per formula unit. These results are important in view of reports which
-claim that $\zeta$-Fe$_2$N undergoes an AFM transition below 10K and others
-which do not observe any magnetic transition up to 4.2K. We argue that the
-experimental results of AFM transition below 10K are inconclusive and we
-propose the presence of competing FM and AFM superexchange interactions between
-Fe sites mediated by nitrogen atoms, which are consistent with
-Goodenough-Kanamori-Anderson rules. We find that the anti-ferromagnetically
-coupled Fe sites are outnumbered by ferromagnetically coupled Fe sites leading
-to a stable FM ground state. A Stoner analysis of the results also supports our
-claim of a FM ground state.
-",0,1,0,0,0,0
-17136,A cautionary tale: limitations of a brightness-based spectroscopic approach to chromatic exoplanet radii,"  Determining wavelength-dependent exoplanet radii measurements is an excellent
-way to probe the composition of exoplanet atmospheres. In light of this, Borsa
-et al. (2016) sought to develop a technique to obtain such measurements by
-comparing ground-based transmission spectra to the expected brightness
-variations during an exoplanet transit. However, we demonstrate herein that
-this is not possible due to the transit light curve normalisation necessary to
-remove the effects of the Earth's atmosphere on the ground-based observations.
-This is because the recoverable exoplanet radius is set by the planet-to-star
-radius ratio within the transit light curve; we demonstrate this both
-analytically and with simulated planet transits, as well as through a
-reanalysis of the HD 189733b data.
-",0,1,0,0,0,0
-17137,Generating Memorable Mnemonic Encodings of Numbers,"  The major system is a mnemonic system that can be used to memorize sequences
-of numbers. In this work, we present a method to automatically generate
-sentences that encode a given number. We propose several encoding models and
-compare the most promising ones in a password memorability study. The results
-of the study show that a model combining part-of-speech sentence templates with
-an $n$-gram language model produces the most memorable password
-representations.
-",1,0,0,0,0,0
-17138,De-excitation spectroscopy of strongly interacting Rydberg gases,"  We present experimental results on the controlled de-excitation of Rydberg
-states in a cold gas of Rb atoms. The effect of the van der Waals interactions
-between the Rydberg atoms is clearly seen in the de-excitation spectrum and
-dynamics. Our observations are confirmed by numerical simulations. In
-particular, for off-resonant (facilitated) excitation we find that the
-de-excitation spectrum reflects the spatial arrangement of the atoms in the
-quasi one-dimensional geometry of our experiment. We discuss future
-applications of this technique and implications for detection and controlled
-dissipation schemes.
-",0,1,0,0,0,0
-17139,Hyperplane Clustering Via Dual Principal Component Pursuit,"  We extend the theoretical analysis of a recently proposed single subspace
-learning algorithm, called Dual Principal Component Pursuit (DPCP), to the case
-where the data are drawn from of a union of hyperplanes. To gain insight into
-the properties of the $\ell_1$ non-convex problem associated with DPCP, we
-develop a geometric analysis of a closely related continuous optimization
-problem. Then transferring this analysis to the discrete problem, our results
-state that as long as the hyperplanes are sufficiently separated, the dominant
-hyperplane is sufficiently dominant and the points are uniformly distributed
-inside the associated hyperplanes, then the non-convex DPCP problem has a
-unique global solution, equal to the normal vector of the dominant hyperplane.
-This suggests the correctness of a sequential hyperplane learning algorithm
-based on DPCP. A thorough experimental evaluation reveals that hyperplane
-learning schemes based on DPCP dramatically improve over the state-of-the-art
-methods for the case of synthetic data, while are competitive to the
-state-of-the-art in the case of 3D plane clustering for Kinect data.
-",1,0,0,1,0,0
-17140,Utilizing Domain Knowledge in End-to-End Audio Processing,"  End-to-end neural network based approaches to audio modelling are generally
-outperformed by models trained on high-level data representations. In this
-paper we present preliminary work that shows the feasibility of training the
-first layers of a deep convolutional neural network (CNN) model to learn the
-commonly-used log-scaled mel-spectrogram transformation. Secondly, we
-demonstrate that upon initializing the first layers of an end-to-end CNN
-classifier with the learned transformation, convergence and performance on the
-ESC-50 environmental sound classification dataset are similar to a CNN-based
-model trained on the highly pre-processed log-scaled mel-spectrogram features.
-",1,0,0,1,0,0
-17141,Testing Microfluidic Fully Programmable Valve Arrays (FPVAs),"  Fully Programmable Valve Array (FPVA) has emerged as a new architecture for
-the next-generation flow-based microfluidic biochips. This 2D-array consists of
-regularly-arranged valves, which can be dynamically configured by users to
-realize microfluidic devices of different shapes and sizes as well as
-interconnections. Additionally, the regularity of the underlying structure
-renders FPVAs easier to integrate on a tiny chip. However, these arrays may
-suffer from various manufacturing defects such as blockage and leakage in
-control and flow channels. Unfortunately, no efficient method is yet known for
-testing such a general-purpose architecture. In this paper, we present a novel
-formulation using the concept of flow paths and cut-sets, and describe an
-ILP-based hierarchical strategy for generating compact test sets that can
-detect multiple faults in FPVAs. Simulation results demonstrate the efficacy of
-the proposed method in detecting manufacturing faults with only a small number
-of test vectors.
-",1,0,0,0,0,0
-17142,"Fundamental groups, slalom curves and extremal length","  We define the extremal length of elements of the fundamental group of the
-twice punctured complex plane and give upper and lower bounds for this
-invariant. The bounds differ by a multiplicative constant. The main motivation
-comes from $3$-braid invariants and their application.
-",0,0,1,0,0,0
-17143,Topology and stability of the Kondo phase in quark matter,"  We investigate properties of the ground state of a light quark matter with
-heavy quark impurities. This system exhibits the ""QCD Kondo effect"" where the
-interaction strength between a light quark near the Fermi surface and a heavy
-quark increases with decreasing energy of the light quark towards the Fermi
-energy, and diverges at some scale near the Fermi energy, called the Kondo
-scale. Around and below the Kondo scale, we must treat the dynamics
-nonperturbatively. As a typical nonperturbative method to treat the strong
-coupling regime, we adopt a mean-field approach where we introduce a
-condensate, the Kondo condensate, representing a mixing between a light quark
-and a heavy quark, and determine the ground state in the presence of the Kondo
-condensate. We show that the ground state is a topologically non-trivial state
-and the heavy quark spin forms the hedgehog configuration in the momentum
-space. We can define the Berry phase for the ground-state wavefunction in the
-momentum space which is associated with a monopole at the position of a heavy
-quark. We also investigate fluctuations around the mean field in the
-random-phase approximation, and show the existence of (exciton-like) collective
-excitations made of a hole $h$ of a light quark and a heavy quark $Q$.
-",0,1,0,0,0,0
-17144,Second order nonlinear gyrokinetic theory : From the particle to the gyrocenter,"  A gyrokinetic reduction is based on a specific ordering of the different
-small parameters characterizing the background magnetic field and the
-fluctuating electromagnetic fields. In this tutorial, we consider the following
-ordering of the small parameters: $\epsilon\_B=\epsilon\_\delta^2$ where
-$\epsilon\_B$ is the small parameter associated with spatial inhomogeneities of
-the background magnetic field and $\epsilon\_\delta$ characterizes the small
-amplitude of the fluctuating fields. In particular, we do not make any
-assumption on the amplitude of the background magnetic field. Given this choice
-of ordering, we describe a self-contained and systematic derivation which is
-particularly well suited for the gyrokinetic reduction, following a two-step
-procedure. We follow the approach developed in [Sugama, Physics of Plasmas 7,
-466 (2000)]:In a first step, using a translation in velocity, we embed the
-transformation performed on the symplectic part of the gyrocentre reduction in
-the guiding-centre one. In a second step, using a canonical Lie transform, we
-eliminate the gyroangle dependence from the Hamiltonian. As a consequence, we
-explicitly derive the fully electromagnetic gyrokinetic equations at the second
-order in $\epsilon\_\delta$.
-",0,1,0,0,0,0
-17145,On the Classification and Algorithmic Analysis of Carmichael Numbers,"  In this paper, we study the properties of Carmichael numbers, false positives
-to several primality tests. We provide a classification for Carmichael numbers
-with a proportion of Fermat witnesses of less than 50%, based on if the
-smallest prime factor is greater than a determined lower bound. In addition, we
-conduct a Monte Carlo simulation as part of a probabilistic algorithm to detect
-if a given composite number is Carmichael. We modify this highly accurate
-algorithm with a deterministic primality test to create a novel, more efficient
-algorithm that differentiates between Carmichael numbers and prime numbers.
-",0,0,1,0,0,0
-17146,Phase reduction and synchronization of a network of coupled dynamical elements exhibiting collective oscillations,"  A general phase reduction method for a network of coupled dynamical elements
-exhibiting collective oscillations, which is applicable to arbitrary networks
-of heterogeneous dynamical elements, is developed. A set of coupled adjoint
-equations for phase sensitivity functions, which characterize phase response of
-the collective oscillation to small perturbations applied to individual
-elements, is derived. Using the phase sensitivity functions, collective
-oscillation of the network under weak perturbation can be described
-approximately by a one-dimensional phase equation. As an example, mutual
-synchronization between a pair of collectively oscillating networks of
-excitable and oscillatory FitzHugh-Nagumo elements with random coupling is
-studied.
-",0,1,0,0,0,0
-17147,Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs,"  Knowledge graphs are large, useful, but incomplete knowledge repositories.
-They encode knowledge through entities and relations which define each other
-through the connective structure of the graph. This has inspired methods for
-the joint embedding of entities and relations in continuous low-dimensional
-vector spaces, that can be used to induce new edges in the graph, i.e., link
-prediction in knowledge graphs. Learning these representations relies on
-contrasting positive instances with negative ones. Knowledge graphs include
-only positive relation instances, leaving the door open for a variety of
-methods for selecting negative examples. In this paper we present an empirical
-study on the impact of negative sampling on the learned embeddings, assessed
-through the task of link prediction. We use state-of-the-art knowledge graph
-embeddings -- \rescal , TransE, DistMult and ComplEX -- and evaluate on
-benchmark datasets -- FB15k and WN18. We compare well known methods for
-negative sampling and additionally propose embedding based sampling methods. We
-note a marked difference in the impact of these sampling methods on the two
-datasets, with the ""traditional"" corrupting positives method leading to best
-results on WN18, while embedding based methods benefiting the task on FB15k.
-",1,0,0,0,0,0
-17148,Reverse Curriculum Generation for Reinforcement Learning,"  Many relevant tasks require an agent to reach a certain state, or to
-manipulate objects into a desired configuration. For example, we might want a
-robot to align and assemble a gear onto an axle or insert and turn a key in a
-lock. These goal-oriented tasks present a considerable challenge for
-reinforcement learning, since their natural reward function is sparse and
-prohibitive amounts of exploration are required to reach the goal and receive
-some learning signal. Past approaches tackle these problems by exploiting
-expert demonstrations or by manually designing a task-specific reward shaping
-function to guide the learning agent. Instead, we propose a method to learn
-these tasks without requiring any prior knowledge other than obtaining a single
-state in which the task is achieved. The robot is trained in reverse, gradually
-learning to reach the goal from a set of start states increasingly far from the
-goal. Our method automatically generates a curriculum of start states that
-adapts to the agent's performance, leading to efficient training on
-goal-oriented tasks. We demonstrate our approach on difficult simulated
-navigation and fine-grained manipulation problems, not solvable by
-state-of-the-art reinforcement learning methods.
-",1,0,0,0,0,0
-17149,Mutually touching infinite cylinders in the 3D world of lines,"  Recently we gave arguments that only two unique topologically different
-configurations of 7 equal all mutually touching round cylinders (the
-configurations being mirror reflections of each other) are possible in 3D,
-although a whole world of configurations is possible already for round
-cylinders of arbitrary radii. It was found that as many as 9 round cylinders
-(all mutually touching) are possible in 3D while the upper bound for arbitrary
-cylinders was estimated to be not more than 14 under plausible arguments. Now
-by using the chirality and Ring matrices that we introduced earlier for the
-topological classification of line configurations, we have given arguments that
-the maximal number of mutually touching straight infinite cylinders of
-arbitrary cross-section (provided that its boundary is a smooth curve) in 3D
-cannot exceed 10. We generated numerically several configurations of 10
-cylinders, restricting ourselves with elliptic cylinders. Configurations of 8
-and 9 equal elliptic cylinders (all in mutually touching) are generated
-numerically as well. A possibility and restriction of continuous
-transformations from elliptic into round cylinder configurations are discussed.
-Some curious results concerning the properties of the chirality matrix (which
-coincides with Seidel's adjacency matrix important for the Graph theory) are
-presented.
-",0,0,1,0,0,0
-17150,Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields,"  This paper is a tutorial on Formal Concept Analysis (FCA) and its
-applications. FCA is an applied branch of Lattice Theory, a mathematical
-discipline which enables formalisation of concepts as basic units of human
-thinking and analysing data in the object-attribute form. Originated in early
-80s, during the last three decades, it became a popular human-centred tool for
-knowledge representation and data analysis with numerous applications. Since
-the tutorial was specially prepared for RuSSIR 2014, the covered FCA topics
-include Information Retrieval with a focus on visualisation aspects, Machine
-Learning, Data Mining and Knowledge Discovery, Text Mining and several others.
-",1,0,0,1,0,0
-17151,Fully stripped? The dynamics of dark and luminous matter in the massive cluster collision MACSJ0553.4$-$3342,"  We present the results of a multiwavelength investigation of the very X-ray
-luminous galaxy cluster MACSJ0553.4-3342 ($z = 0.4270$; hereafter MACSJ0553).
-Combining high-resolution data obtained with the Hubble Space Telescope and the
-Chandra X-ray Observatory with ground-based galaxy spectroscopy, our analysis
-establishes the system unambiguously as a binary, post-collision merger of
-massive clusters. Key characteristics include perfect alignment of luminous and
-dark matter for one component, a separation of almost 650 kpc (in projection)
-between the dark-matter peak of the other subcluster and the second X-ray peak,
-extremely hot gas (k$T > 15$ keV) at either end of the merger axis, a potential
-cold front in the east, an unusually low gas mass fraction of approximately
-0.075 for the western component, a velocity dispersion of $1490_{-130}^{+104}$
-km s$^{-1}$, and no indication of significant substructure along the line of
-sight. We propose that the MACSJ0553 merger proceeds not in the plane of the
-sky, but at a large inclination angle, is observed very close to turnaround,
-and that the eastern X-ray peak is the cool core of the slightly less massive
-western component that was fully stripped and captured by the eastern
-subcluster during the collision. If correct, this hypothesis would make
-MACSJ0553 a superb target for a competitive study of ram-pressure stripping and
-the collisional behaviour of luminous and dark matter during cluster formation.
-",0,1,0,0,0,0
-17152,When intuition fails in assessing conditional risks: the example of the frog riddle,"  Recently, the educational initiative TED-Ed has published a popular brain
-teaser coined the 'frog riddle', which illustrates non-intuitive implications
-of conditional probabilities. In its intended form, the frog riddle is a
-reformulation of the classic boy-girl paradox. However, the authors alter the
-narrative of the riddle in a form, that subtly changes the way information is
-conveyed. The presented solution, unfortunately, does not take this point into
-full account, and as a consequence, lacks consistency in the sense that
-different parts of the problem are treated on unequal footing. We here review,
-how the mechanism of receiving information matters, and why this is exactly the
-reason that such kind of problems challenge intuitive thinking. Subsequently,
-we present a generalized solution, that accounts for the above difficulties,
-and preserves full logical consistency. Eventually, the relation to the
-boy-girl paradox is discussed.
-",0,1,0,0,0,0
-17153,Tuning parameter selection rules for nuclear norm regularized multivariate linear regression,"  We consider the tuning parameter selection rules for nuclear norm regularized
-multivariate linear regression (NMLR) in high-dimensional setting.
-High-dimensional multivariate linear regression is widely used in statistics
-and machine learning, and regularization technique is commonly applied to deal
-with the special structures in high-dimensional data. As we know, how to select
-the tuning parameter is an essential issue for regularization approach and it
-directly affects the model estimation performance. To the best of our
-knowledge, there are no rules about the tuning parameter selection for NMLR
-from the point of view of optimization. In order to establish such rules, we
-study the duality theory of NMLR. Then, we claim the choice of tuning parameter
-for NMLR is based on the sample data and the solution of NMLR dual problem,
-which is a projection on a nonempty, closed and convex set. Moreover, based on
-the (firm) nonexpansiveness and the idempotence of the projection operator, we
-build four tuning parameter selection rules PSR, PSRi, PSRfn and PSR+.
-Furthermore, we give a sequence of tuning parameters and the corresponding
-intervals for every rule, which states that the rank of the estimation
-coefficient matrix is no more than a fixed number for the tuning parameter in
-the given interval. The relationships between these rules are also discussed
-and PSR+ is the most efficient one to select the tuning parameter. Finally, the
-numerical results are reported on simulation and real data, which show that
-these four tuning parameter selection rules are valuable.
-",0,0,1,1,0,0
-17154,Understanding the evolution of multimedia content in the Internet through BitTorrent glasses,"  Today's Internet traffic is mostly dominated by multimedia content and the
-prediction is that this trend will intensify in the future. Therefore, main
-Internet players, such as ISPs, content delivery platforms (e.g. Youtube,
-Bitorrent, Netflix, etc) or CDN operators, need to understand the evolution of
-multimedia content availability and popularity in order to adapt their
-infrastructures and resources to satisfy clients requirements while they
-minimize their costs. This paper presents a thorough analysis on the evolution
-of multimedia content available in BitTorrent. Specifically, we analyze the
-evolution of four relevant metrics across different content categories: content
-availability, content popularity, content size and user's feedback. To this end
-we leverage a large-scale dataset formed by 4 snapshots collected from the most
-popular BitTorrent portal, namely The Pirate Bay, between Nov. 2009 and Feb.
-2012. Overall our dataset is formed by more than 160k content that attracted
-more than 185M of download sessions.
-",1,0,0,0,0,0
-17155,The weak rate of convergence for the Euler-Maruyama approximation of one-dimensional stochastic differential equations involving the local times of the unknown process,"  In this paper, we consider the weak convergence of the Euler-Maruyama
-approximation for one dimensional stochastic differential equations involving
-the local times of the unknown process. We use a transformation in order to
-remove the local time from the stochastic differential equations and we provide
-the approximation of Euler-maruyama for the stochastic differential equations
-without local time. After that, we conclude the approximation of Euler-maruyama
-for one dimensional stochastic differential equations involving the local times
-of the unknown process , and we provide the rate of weak convergence for any
-function G in a certain class.
-",0,0,1,0,0,0
-17156,Study of deteriorating semiopaque turquoise lead-potassium glass beads at different stages of corrosion using micro-FTIR spectroscopy,"  Nowadays, a problem of historical beadworks conservation in museum
-collections is actual more than ever because of fatal corrosion of the 19th
-century glass beads. Vibrational spectroscopy is a powerful method for
-investigation of glass, namely, of correlation of the structure-chemical
-composition. Therefore, Fourier-transform infrared spectroscopy was used for
-examination of degradation processes in cloudy turquoise glass beads, which in
-contrast to other color ones deteriorate especially strongly. Micro-X-ray
-fluorescence spectrometry of samples has shown that lead-potassium glass
-PbO-K$_2$O-SiO$_2$ with small amount of Cu and Sb was used for manufacture of
-cloudy turquoise beads. Fourier-transform infrared spectroscopy study of the
-beads at different stages of glass corrosion was carried out in the range from
-200 to 4000 cm$^{-1}$ in the attenuated total reflection mode. In all the
-spectra, we have observed shifts of two major absorption bands to low-frequency
-range (~1000 and ~775 cm$^{-1}$) compared to ones typical for amorphous SiO2
-(~1100 and 800 cm$^{-1}$, respectively). Such an effect is connected with
-Pb$^{2+}$ and K$^+$ appending to the glass network. The presence of a weak band
-at ~1630 cm$^{-1}$ in all the spectra is attributed to the adsorption of
-H$_2$O. After annealing of the beads, the band disappeared completely in less
-deteriorated samples and became significantly weaker in more destroyed ones.
-Based on that we conclude that there is adsorbed molecular water on the beads.
-However, products of corrosion (e.g., alkali in the form of white crystals or
-droplets of liquid alkali) were not observed on the glass surface. We have also
-observed glass depolymerisation in the strongly degraded beads, which is
-exhibited in domination of the band peaked at ~1000 cm$^{-1}$.
-",0,1,0,0,0,0
-17157,Stratified surgery and K-theory invariants of the signature operator,"  In work of Higson-Roe the fundamental role of the signature as a homotopy and
-bordism invariant for oriented manifolds is made manifest in how it and related
-secondary invariants define a natural transformation between the
-(Browder-Novikov-Sullivan-Wall) surgery exact sequence and a long exact
-sequence of C*-algebra K-theory groups.
-In recent years the (higher) signature invariants have been extended from
-closed oriented manifolds to a class of stratified spaces known as L-spaces or
-Cheeger spaces. In this paper we show that secondary invariants, such as the
-rho-class, also extend from closed manifolds to Cheeger spaces. We revisit a
-surgery exact sequence for stratified spaces originally introduced by
-Browder-Quinn and obtain a natural transformation analogous to that of
-Higson-Roe. We also discuss geometric applications.
-",0,0,1,0,0,0
-17158,Weighted Tensor Decomposition for Learning Latent Variables with Partial Data,"  Tensor decomposition methods are popular tools for learning latent variables
-given only lower-order moments of the data. However, the standard assumption is
-that we have sufficient data to estimate these moments to high accuracy. In
-this work, we consider the case in which certain dimensions of the data are not
-always observed---common in applied settings, where not all measurements may be
-taken for all observations---resulting in moment estimates of varying quality.
-We derive a weighted tensor decomposition approach that is computationally as
-efficient as the non-weighted approach, and demonstrate that it outperforms
-methods that do not appropriately leverage these less-observed dimensions.
-",0,0,0,1,0,0
-17159,Sparse Bounds for Discrete Quadratic Phase Hilbert Transform,"  Consider the discrete quadratic phase Hilbert Transform acting on $\ell^{2}$
-finitely supported functions $$ H^{\alpha} f(n) : = \sum_{m \neq 0} \frac{e^{2
-\pi i\alpha m^2} f(n - m)}{m}. $$ We prove that, uniformly in $\alpha \in
-\mathbb{T}$, there is a sparse bound for the bilinear form $\langle H^{\alpha}
-f , g \rangle$. The sparse bound implies several mapping properties such as
-weighted inequalities in an intersection of Muckenhoupt and reverse Hölder
-classes.
-",0,0,1,0,0,0
-17160,Fast Distributed Approximation for TAP and 2-Edge-Connectivity,"  The tree augmentation problem (TAP) is a fundamental network design problem,
-in which the input is a graph $G$ and a spanning tree $T$ for it, and the goal
-is to augment $T$ with a minimum set of edges $Aug$ from $G$, such that $T \cup
-Aug$ is 2-edge-connected.
-TAP has been widely studied in the sequential setting. The best known
-approximation ratio of 2 for the weighted case dates back to the work of
-Frederickson and JáJá, SICOMP 1981. Recently, a 3/2-approximation was
-given for the unweighted case by Kortsarz and Nutov, TALG 2016, and recent
-breakthroughs by Adjiashvili, SODA 2017, and by Fiorini et al., 2017, give
-approximations better than 2 for bounded weights.
-In this paper, we provide the first fast distributed approximations for TAP.
-We present a distributed $2$-approximation for weighted TAP which completes in
-$O(h)$ rounds, where $h$ is the height of $T$. When $h$ is large, we show a
-much faster 4-approximation algorithm for the unweighted case, completing in
-$O(D+\sqrt{n}\log^*{n})$ rounds, where $n$ is the number of vertices and $D$ is
-the diameter of $G$.
-Immediate consequences of our results are an $O(D)$-round 2-approximation
-algorithm for the minimum size 2-edge-connected spanning subgraph, which
-significantly improves upon the running time of previous approximation
-algorithms, and an $O(h_{MST}+\sqrt{n}\log^{*}{n})$-round 3-approximation
-algorithm for the weighted case, where $h_{MST}$ is the height of the MST of
-the graph. Additional applications are algorithms for verifying
-2-edge-connectivity and for augmenting the connectivity of any connected
-spanning subgraph to 2.
-Finally, we complement our study with proving lower bounds for distributed
-approximations of TAP.
-",1,0,0,0,0,0
-17161,Generative Adversarial Source Separation,"  Generative source separation methods such as non-negative matrix
-factorization (NMF) or auto-encoders, rely on the assumption of an output
-probability density. Generative Adversarial Networks (GANs) can learn data
-distributions without needing a parametric assumption on the output density. We
-show on a speech source separation experiment that, a multi-layer perceptron
-trained with a Wasserstein-GAN formulation outperforms NMF, auto-encoders
-trained with maximum likelihood, and variational auto-encoders in terms of
-source to distortion ratio.
-",1,0,0,1,0,0
-17162,Bootstrapping Generalization Error Bounds for Time Series,"  We consider the problem of finding confidence intervals for the risk of
-forecasting the future of a stationary, ergodic stochastic process, using a
-model estimated from the past of the process. We show that a bootstrap
-procedure provides valid confidence intervals for the risk, when the data
-source is sufficiently mixing, and the loss function and the estimator are
-suitably smooth. Autoregressive (AR(d)) models estimated by least squares obey
-the necessary regularity conditions, even when mis-specified, and simulations
-show that the finite- sample coverage of our bounds quickly converges to the
-theoretical, asymptotic level. As an intermediate step, we derive sufficient
-conditions for asymptotic independence between empirical distribution functions
-formed by splitting a realization of a stochastic process, of independent
-interest.
-",0,0,1,1,0,0
-17163,Sign reversal of magnetoresistance and p to n transition in Ni doped ZnO thin film,"  We report the magnetoresistance and nonlinear Hall effect studies over a wide
-temperature range in pulsed laser deposited Ni0.07Zn0.93O thin film. Negative
-and positive contributions to magnetoresistance at high and low temperatures
-have been successfully modeled by the localized magnetic moment and two band
-conduction process involving heavy and light hole subbands, respectively.
-Nonlinearity in the Hall resistance also agrees well with the two channel
-conduction model. A negative Hall voltage has been found for T $\gte 50 K$,
-implying a dominant conduction mainly by electrons whereas positive Hall
-voltage for T less than 50 K shows hole dominated conduction in this material.
-Crossover in the sign of magnetoresistance from negative to positive reveals
-the spin polarization of the charge carriers and hence the applicability of Ni
-doped ZnO thin film for spintronic applications.
-",0,1,0,0,0,0
-17164,Proceedings of the Third Workshop on Formal Integrated Development Environment,"  This volume contains the proceedings of F-IDE 2016, the third international
-workshop on Formal Integrated Development Environment, which was held as an FM
-2016 satellite event, on November 8, 2016, in Limassol (Cyprus). High levels of
-safety, security and also privacy standards require the use of formal methods
-to specify and develop compliant software (sub)systems. Any standard comes with
-an assessment process, which requires a complete documentation of the
-application in order to ease the justification of design choices and the review
-of code and proofs. Thus tools are needed for handling specifications, program
-constructs and verification artifacts. The aim of the F-IDE workshop is to
-provide a forum for presenting and discussing research efforts as well as
-experience returns on design, development and usage of formal IDE aiming at
-making formal methods ""easier"" for both specialists and non-specialists.
-",1,0,0,0,0,0
-17165,A Useful Motif for Flexible Task Learning in an Embodied Two-Dimensional Visual Environment,"  Animals (especially humans) have an amazing ability to learn new tasks
-quickly, and switch between them flexibly. How brains support this ability is
-largely unknown, both neuroscientifically and algorithmically. One reasonable
-supposition is that modules drawing on an underlying general-purpose sensory
-representation are dynamically allocated on a per-task basis. Recent results
-from neuroscience and artificial intelligence suggest the role of the general
-purpose visual representation may be played by a deep convolutional neural
-network, and give some clues how task modules based on such a representation
-might be discovered and constructed. In this work, we investigate module
-architectures in an embodied two-dimensional touchscreen environment, in which
-an agent's learning must occur via interactions with an environment that emits
-images and rewards, and accepts touches as input. This environment is designed
-to capture the physical structure of the task environments that are commonly
-deployed in visual neuroscience and psychophysics. We show that in this
-context, very simple changes in the nonlinear activations used by such a module
-can significantly influence how fast it is at learning visual tasks and how
-suitable it is for switching to new tasks.
-",1,0,0,1,0,0
-17166,3D Move to See: Multi-perspective visual servoing for improving object views with semantic segmentation,"  In this paper, we present a new approach to visual servoing for robotics,
-referred to as 3D Move to See (3DMTS), based on the principle of finding the
-next best view using a 3D camera array and a robotic manipulator to obtain
-multiple samples of the scene from different perspectives. The method uses
-semantic vision and an objective function applied to each perspective to sample
-a gradient representing the direction of the next best view. The method is
-demonstrated within simulation and on a real robotic platform containing a
-custom 3D camera array for the challenging scenario of robotic harvesting in a
-highly occluded and unstructured environment. It was shown on a real robotic
-platform that by moving the end effector using the gradient of an objective
-function leads to a locally optimal view of the object of interest, even
-amongst occlusions. The overall performance of the 3DMTS method obtained a mean
-increase in target size by 29.3% compared to a baseline method using a single
-RGB-D camera, which obtained 9.17%. The results demonstrate qualitatively and
-quantitatively that the 3DMTS method performed better in most scenarios, and
-yielded three times the target size compared to the baseline method. The
-increased target size in the final view will improve the detection of key
-features of the object of interest for further manipulation, such as grasping
-and harvesting.
-",1,0,0,0,0,0
-17167,Modeling influenza-like illnesses through composite compartmental models,"  Epidemiological models for the spread of pathogens in a population are
-usually only able to describe a single pathogen. This makes their application
-unrealistic in cases where multiple pathogens with similar symptoms are
-spreading concurrently within the same population. Here we describe a method
-which makes possible the application of multiple single-strain models under
-minimal conditions. As such, our method provides a bridge between theoretical
-models of epidemiology and data-driven approaches for modeling of influenza and
-other similar viruses.
-Our model extends the Susceptible-Infected-Recovered model to higher
-dimensions, allowing the modeling of a population infected by multiple viruses.
-We further provide a method, based on an overcomplete dictionary of feasible
-realizations of SIR solutions, to blindly partition the time series
-representing the number of infected people in a population into individual
-components, each representing the effect of a single pathogen.
-We demonstrate the applicability of our proposed method on five years of
-seasonal influenza-like illness (ILI) rates, estimated from Twitter data. We
-demonstrate that our method describes, on average, 44\% of the variance in the
-ILI time series. The individual infectious components derived from our model
-are matched to known viral profiles in the populations, which we demonstrate
-matches that of independently collected epidemiological data. We further show
-that the basic reproductive numbers ($R0$) of the matched components are in
-range known for these pathogens.
-Our results suggest that the proposed method can be applied to other
-pathogens and geographies, providing a simple method for estimating the
-parameters of epidemics in a population.
-",1,1,0,0,0,0
-17168,Notes on the Polish Algorithm,"  We study, with the help of a computer program, the Polish Algorithm for
-finite terms satisfying various algebraic laws, e.g., left distributivity a(bc)
-= (ab)(ac). While the termination of the algorithm for left distributivity
-remains open in general, we can establish some partial results, which might be
-useful towards a positive solution. In contrast, we show the divergence of the
-algorithm for the laws a(bc) = (ab)(cc) and a(bc) = (ab)(a(ac)).
-",0,0,1,0,0,0
-17169,Penalized Maximum Tangent Likelihood Estimation and Robust Variable Selection,"  We introduce a new class of mean regression estimators -- penalized maximum
-tangent likelihood estimation -- for high-dimensional regression estimation and
-variable selection. We first explain the motivations for the key ingredient,
-maximum tangent likelihood estimation (MTE), and establish its asymptotic
-properties. We further propose a penalized MTE for variable selection and show
-that it is $\sqrt{n}$-consistent, enjoys the oracle property. The proposed
-class of estimators consists penalized $\ell_2$ distance, penalized exponential
-squared loss, penalized least trimmed square and penalized least square as
-special cases and can be regarded as a mixture of minimum Kullback-Leibler
-distance estimation and minimum $\ell_2$ distance estimation. Furthermore, we
-consider the proposed class of estimators under the high-dimensional setting
-when the number of variables $d$ can grow exponentially with the sample size
-$n$, and show that the entire class of estimators (including the aforementioned
-special cases) can achieve the optimal rate of convergence in the order of
-$\sqrt{\ln(d)/n}$. Finally, simulation studies and real data analysis
-demonstrate the advantages of the penalized MTE.
-",0,0,0,1,0,0
-17170,A minimally-dissipative low-Mach number solver for complex reacting flows in OpenFOAM,"  Large eddy simulation (LES) has become the de-facto computational tool for
-modeling complex reacting flows, especially in gas turbine applications.
-However, readily usable general-purpose LES codes for complex geometries are
-typically academic or proprietary/commercial in nature. The objective of this
-work is to develop and disseminate an open source LES tool for low-Mach number
-turbulent combustion using the OpenFOAM framework. In particular, a
-collocated-mesh approach suited for unstructured grid formulation is provided.
-Unlike other fluid dynamics models, LES accuracy is intricately linked to
-so-called primary and secondary conservation properties of the numerical
-discretization schemes. This implies that although the solver only evolves
-equations for mass, momentum, and energy, the implied discrete equation for
-kinetic energy (square of velocity) should be minimally-dissipative. Here, a
-specific spatial and temporal discretization is imposed such that this kinetic
-energy dissipation is minimized. The method is demonstrated using manufactured
-solutions approach on regular and skewed meshes, a canonical flow problem, and
-a turbulent sooting flame in a complex domain relevant to gas turbines
-applications.
-",0,1,0,0,0,0
-17171,Surface energy of strained amorphous solids,"  Surface stress and surface energy are fundamental quantities which
-characterize the interface between two materials. Although these quantities are
-identical for interfaces involving only fluids, the Shuttleworth effect
-demonstrates that this is not the case for most interfaces involving solids,
-since their surface energies change with strain. Crystalline materials are
-known to have strain dependent surface energies, but in amorphous materials,
-such as polymeric glasses and elastomers, the strain dependence is debated due
-to a dearth of direct measurements. Here, we utilize contact angle measurements
-on strained glassy and elastomeric solids to address this matter. We show
-conclusively that interfaces involving polymeric glasses exhibit strain
-dependent surface energies, and give strong evidence for the absence of such a
-dependence for incompressible elastomers. The results provide fundamental
-insight into our understanding of the interfaces of amorphous solids and their
-interaction with contacting liquids.
-",0,1,0,0,0,0
-17172,Biaxial magnetic field setup for angular magnetic measurements of thin films and spintronic nanodevices,"  The biaxial magnetic-field setup for angular magnetic measurements of thin
-film and spintronic devices is designed and presented. The setup allows for
-application of the in-plane magnetic field using a quadrupole electromagnet,
-controlled by power supply units and integrated with an electromagnet biaxial
-magnetic field sensor. In addition, the probe station is equipped with a
-microwave circuitry, which enables angle-resolved spin torque oscillation
-measurements. The angular dependencies of magnetoresistance and spin diode
-effect in a giant magnetoresistance strip are shown as an operational
-verification of the experimental setup. We adapted an analytical macrospin
-model to reproduce both the resistance and spin-diode angular dependency
-measurements.
-",0,1,0,0,0,0
-17173,Active matter invasion of a viscous fluid: unstable sheets and a no-flow theorem,"  We investigate the dynamics of a dilute suspension of hydrodynamically
-interacting motile or immotile stress-generating swimmers or particles as they
-invade a surrounding viscous fluid. Colonies of aligned pusher particles are
-shown to elongate in the direction of particle orientation and undergo a
-cascade of transverse concentration instabilities, governed at small times by
-an equation which also describes the Saffman-Taylor instability in a Hele-Shaw
-cell, or Rayleigh-Taylor instability in two-dimensional flow through a porous
-medium. Thin sheets of aligned pusher particles are always unstable, while
-sheets of aligned puller particles can either be stable (immotile particles),
-or unstable (motile particles) with a growth rate which is non-monotonic in the
-force dipole strength. We also prove a surprising ""no-flow theorem"": a
-distribution initially isotropic in orientation loses isotropy immediately but
-in such a way that results in no fluid flow everywhere and for all time.
-",0,0,0,0,1,0
-17174,Interplay of synergy and redundancy in diamond motif,"  The formalism of partial information decomposition provides independent or
-non-overlapping components constituting total information content provided by a
-set of source variables about the target variable. These components are
-recognised as unique information, synergistic information and, redundant
-information. The metric of net synergy, conceived as the difference between
-synergistic and redundant information, is capable of detecting synergy,
-redundancy and, information independence among stochastic variables. And it can
-be quantified, as it is done here, using appropriate combinations of different
-Shannon mutual information terms. Utilisation of such a metric in network
-motifs with the nodes representing different biochemical species, involved in
-information sharing, uncovers rich store for interesting results. In the
-current study, we make use of this formalism to obtain a comprehensive
-understanding of the relative information processing mechanism in a diamond
-motif and two of its sub-motifs namely bifurcation and integration motif
-embedded within the diamond motif. The emerging patterns of synergy and
-redundancy and their effective contribution towards ensuring high fidelity
-information transmission are duly compared in the sub-motifs and independent
-motifs (bifurcation and integration). In this context, the crucial roles played
-by various time scales and activation coefficients in the network topologies
-are especially emphasised. We show that the origin of synergy and redundancy in
-information transmission can be physically justified by decomposing diamond
-motif into bifurcation and integration motif.
-",0,1,0,0,0,0
-17175,Hardy-Sobolev equations with asymptotically vanishing singularity: Blow-up analysis for the minimal energy,"  We study the asymptotic behavior of a sequence of positive solutions
-$(u_{\epsilon})_{\epsilon >0}$ as $\epsilon \to 0$ to the family of equations
-\begin{equation*} \left\{\begin{array}{ll} \Delta
-u_{\epsilon}+a(x)u_{\epsilon}=
-\frac{u_{\epsilon}^{2^*(s_{\epsilon})-1}}{|x|^{s_{\epsilon}}}& \hbox{ in
-}\Omega\\ u_{\epsilon}=0 & \hbox{ on }\partial\Omega. \end{array}\right.
-\end{equation*} where $(s_{\epsilon})_{\epsilon >0}$ is a sequence of positive
-real numbers such that $\lim \limits_{\epsilon \rightarrow 0} s_{\epsilon}=0$,
-$2^{*}(s_{\epsilon}):= \frac{2(n-s_{\epsilon})}{n-2}$ and $\Omega \subset
-\mathbb{R}^{n}$ is a bounded smooth domain such that $0 \in \partial \Omega$.
-When the sequence $(u_{\epsilon})_{\epsilon >0}$ is uniformly bounded in
-$L^{\infty}$, then upto a subsequence it converges strongly to a minimizing
-solution of the stationary Schrödinger equation with critical growth. In
-case the sequence blows up, we obtain strong pointwise control on the blow up
-sequence, and then using the Pohozaev identity localize the point of
-singularity, which in this case can at most be one, and derive precise blow up
-rates. In particular when $n=3$ or $a\equiv 0$ then blow up can occur only at
-an interior point of $\Omega$ or the point $0 \in \partial \Omega$.
-",0,0,1,0,0,0
-17176,"Some results on Ricatti Equations, Floquet Theory and Applications","  In this paper, we present two new results to the classical Floquet theory,
-which provides the Floquet multipliers for two classes of the planar periodic
-system. One these results provides the Floquet multipliers independently of the
-solution of system. To demonstrate the application of these analytical results,
-we consider a cholera epidemic model with phage dynamics and seasonality
-incorporated.
-",0,0,1,0,0,0
-17177,"Size, Shape, and Phase Control in Ultrathin CdSe Nanosheets","  Ultrathin two-dimensional nanosheets raise a rapidly increasing interest due
-to their unique dimensionality-dependent properties. Most of the
-two-dimensional materials are obtained by exfoliation of layered bulk materials
-or are grown on substrates by vapor deposition methods. To produce
-free-standing nanosheets, solution-based colloidal methods are emerging as
-promising routes. In this work, we demonstrate ultrathin CdSe nanosheets with
-controllable size, shape and phase. The key of our approach is the use of
-halogenated alkanes as additives in a hot-injection synthesis. Increasing
-concentrations of bromoalkanes can tune the shape from sexangular to
-quadrangular to triangular and the phase from zinc blende to wurtzite. Geometry
-and crystal structure evolution of the nanosheets take place in the presence of
-halide ions, acting as cadmium complexing agents and as surface X-type ligands,
-according to mass spectrometry and X-ray photoelectron spectroscopies. Our
-experimental findings show that the degree of these changes depends on the
-molecular structure of the halogen alkanes and the type of halogen atom.
-",0,1,0,0,0,0
-17178,Transfer of magnetic order and anisotropy through epitaxial integration of 3$d$ and 4$f$ spin systems,"  Resonant x-ray scattering at the Dy $M_5$ and Ni $L_3$ absorption edges was
-used to probe the temperature and magnetic field dependence of magnetic order
-in epitaxial LaNiO$_3$-DyScO$_3$ superlattices. For superlattices with 2 unit
-cell thick LaNiO$_3$ layers, a commensurate spiral state develops in the Ni
-spin system below 100 K. Upon cooling below $T_{ind} = 18$ K, Dy-Ni exchange
-interactions across the LaNiO$_3$-DyScO$_3$ interfaces induce collinear
-magnetic order of interfacial Dy moments as well as a reorientation of the Ni
-spins to a direction dictated by the strong magneto-crystalline anisotropy of
-Dy. This transition is reversible by an external magnetic field of 3 T.
-Tailored exchange interactions between rare-earth and transition-metal ions
-thus open up new perspectives for the manipulation of spin structures in
-metal-oxide heterostructures and devices.
-",0,1,0,0,0,0
-17179,Exotic limit sets of Teichmüller geodesics in the HHS boundary,"  We answer a question of Durham, Hagen, and Sisto, proving that a
-Teichmüller geodesic ray does not necessarily converge to a unique point in
-the hierarchically hyperbolic space boundary of Teichmüller space. In fact,
-we prove that the limit set can be almost anything allowed by the topology.
-",0,0,1,0,0,0
-17180,"Decoupling ""when to update"" from ""how to update""","  Deep learning requires data. A useful approach to obtain data is to be
-creative and mine data from various sources, that were created for different
-purposes. Unfortunately, this approach often leads to noisy labels. In this
-paper, we propose a meta algorithm for tackling the noisy labels problem. The
-key idea is to decouple ""when to update"" from ""how to update"". We demonstrate
-the effectiveness of our algorithm by mining data for gender classification by
-combining the Labeled Faces in the Wild (LFW) face recognition dataset with a
-textual genderizing service, which leads to a noisy dataset. While our approach
-is very simple to implement, it leads to state-of-the-art results. We analyze
-some convergence properties of the proposed algorithm.
-",1,0,0,0,0,0
-17181,Advection of potential temperature in the atmosphere of irradiated exoplanets: a robust mechanism to explain radius inflation,"  The anomalously large radii of strongly irradiated exoplanets have remained a
-major puzzle in astronomy. Based on a 2D steady state atmospheric circulation
-model, the validity of which is assessed by comparison to 3D calculations, we
-reveal a new mechanism, namely the advection of the potential temperature due
-to mass and longitudinal momentum conservation, a process occuring in the
-Earth's atmosphere or oceans. At depth, the vanishing heating flux forces the
-atmospheric structure to converge to a hotter adiabat than the one obtained
-with 1D calculations, implying a larger radius for the planet. Not only do the
-calculations reproduce the observed radius of HD209458b, but also the observed
-correlation between radius inflation and irradiation for transiting planets.
-Vertical advection of potential temperature induced by non uniform atmospheric
-heating thus provides a robust mechanism explaining the inflated radii of
-irradiated hot Jupiters.
-",0,1,0,0,0,0
-17182,Band depths based on multiple time instances,"  Bands of vector-valued functions $f:T\mapsto\mathbb{R}^d$ are defined by
-considering convex hulls generated by their values concatenated at $m$
-different values of the argument. The obtained $m$-bands are families of
-functions, ranging from the conventional band in case the time points are
-individually considered (for $m=1$) to the convex hull in the functional space
-if the number $m$ of simultaneously considered time points becomes large enough
-to fill the whole time domain. These bands give rise to a depth concept that is
-new both for real-valued and vector-valued functions.
-",0,0,1,1,0,0
-17183,On Biased Correlation Estimation,"  In general, underestimation of risk is something which should be avoided as
-far as possible. Especially in financial asset management, equity risk is
-typically characterized by the measure of portfolio variance, or indirectly by
-quantities which are derived from it. Since there is a linear dependency of the
-variance and the empirical correlation between asset classes, one is compelled
-to control or to avoid the possibility of underestimating correlation
-coefficients. In the present approach, we formalize common practice and
-classify these approaches by computing their probability of underestimation. In
-addition, we introduce a new estimator which is characterized by having the
-advantage of a constant and controllable probability of underestimation. We
-prove that the new estimator is statistically consistent.
-",0,0,1,1,0,0
-17184,Atomic Swaptions: Cryptocurrency Derivatives,"  The atomic swap protocol allows for the exchange of cryptocurrencies on
-different blockchains without the need to trust a third-party. However, market
-participants who desire to hold derivative assets such as options or futures
-would also benefit from trustless exchange. In this paper I propose the atomic
-swaption, which extends the atomic swap to allow for such exchanges. Crucially,
-atomic swaptions do not require the use of oracles. I also introduce the margin
-contract, which provides the ability to create leveraged and short positions.
-Lastly, I discuss how atomic swaptions may be routed on the Lightning Network.
-",0,0,0,0,0,1
-17185,An arbitrary order scheme on generic meshes for miscible displacements in porous media,"  We design, analyse and implement an arbitrary order scheme applicable to
-generic meshes for a coupled elliptic-parabolic PDE system describing miscible
-displacement in porous media. The discretisation is based on several
-adaptations of the Hybrid-High-Order (HHO) method due to Di Pietro et al.
-[Computational Methods in Applied Mathematics, 14(4), (2014)]. The equation
-governing the pressure is discretised using an adaptation of the HHO method for
-variable diffusion, while the discrete concentration equation is based on the
-HHO method for advection-diffusion-reaction problems combined with numerically
-stable flux reconstructions for the advective velocity that we have derived
-using the results of Cockburn et al. [ESAIM: Mathematical Modelling and
-Numerical Analysis, 50(3), (2016)]. We perform some rigorous analysis of the
-method to demonstrate its $L^2$ stability under the irregular data often
-presented by reservoir engineering problems and present several numerical tests
-to demonstrate the quality of the results that are produced by the proposed
-scheme.
-",1,0,0,0,0,0
-17186,"Parametricity, automorphisms of the universe, and excluded middle","  It is known that one can construct non-parametric functions by assuming
-classical axioms. Our work is a converse to that: we prove classical axioms in
-dependent type theory assuming specific instances of non-parametricity. We also
-address the interaction between classical axioms and the existence of
-automorphisms of a type universe. We work over intensional Martin-Löf
-dependent type theory, and in some results assume further principles including
-function extensionality, propositional extensionality, propositional
-truncation, and the univalence axiom.
-",1,0,1,0,0,0
-17187,Convergence Rates for Deterministic and Stochastic Subgradient Methods Without Lipschitz Continuity,"  We extend the classic convergence rate theory for subgradient methods to
-apply to non-Lipschitz functions. For the deterministic projected subgradient
-method, we present a global $O(1/\sqrt{T})$ convergence rate for any convex
-function which is locally Lipschitz around its minimizers. This approach is
-based on Shor's classic subgradient analysis and implies generalizations of the
-standard convergence rates for gradient descent on functions with Lipschitz or
-Hölder continuous gradients. Further, we show a $O(1/\sqrt{T})$ convergence
-rate for the stochastic projected subgradient method on convex functions with
-at most quadratic growth, which improves to $O(1/T)$ under either strong
-convexity or a weaker quadratic lower bound condition.
-",1,0,0,0,0,0
-17188,A quantum dynamics method for excited electrons in molecular aggregate system using a group diabatic Fock matrix,"  We introduce a practical calculation scheme for the description of excited
-electron dynamics in molecular aggregated systems within a locally group
-diabatic Fock representation. This scheme makes it easy to analyze the
-interacting time-dependent excitations of local sites in complex systems. In
-addition, light-electron couplings are considered. The present scheme is
-intended for investigations on the migration dynamics of excited electrons in
-light-energy conversion systems. The scheme was applied to two systems: a
-naphthalene(NPTL)-tetracyanoethylene(TCNE) dimer and a 20-mer circle of
-ethylene molecules. Through local group analyses of the dynamical electrons, we
-obtained an intuitive understanding of the electron transfers between the
-monomers.
-",0,1,0,0,0,0
-17189,Products of topological groups in which all closed subgroups are separable,"  We prove that if $H$ is a topological group such that all closed subgroups of
-$H$ are separable, then the product $G\times H$ has the same property for every
-separable compact group $G$.
-Let $c$ be the cardinality of the continuum. Assuming $2^{\omega_1} = c$, we
-show that there exist:
-(1) pseudocompact topological abelian groups $G$ and $H$ such that all closed
-subgroups of $G$ and $H$ are separable, but the product $G\times H$ contains a
-closed non-separable $\sigma$-compact subgroup;
-(2) pseudocomplete locally convex vector spaces $K$ and $L$ such that all
-closed vector subspaces of $K$ and $L$ are separable, but the product $K\times
-L$ contains a closed non-separable $\sigma$-compact vector subspace.
-",0,0,1,0,0,0
-17190,Low rank solutions to differentiable systems over matrices and applications,"  Differentiable systems in this paper means systems of equations that are
-described by differentiable real functions in real matrix variables. This paper
-proposes algorithms for finding minimal rank solutions to such systems over
-(arbitrary and/or several structured) matrices by using the Levenberg-Marquardt
-method (LM-method) for solving least squares problems. We then apply these
-algorithms to solve several engineering problems such as the low-rank matrix
-completion problem and the low-dimensional Euclidean embedding one. Some
-numerical experiments illustrate the validity of the approach.
-On the other hand, we provide some further properties of low rank solutions
-to systems linear matrix equations. This is useful when the differentiable
-function is linear or quadratic.
-",0,0,1,0,0,0
-17191,A Simple and Realistic Pedestrian Model for Crowd Simulation and Application,"  The simulation of pedestrian crowd that reflects reality is a major challenge
-for researches. Several crowd simulation models have been proposed such as
-cellular automata model, agent-based model, fluid dynamic model, etc. It is
-important to note that agent-based model is able, over others approaches, to
-provide a natural description of the system and then to capture complex human
-behaviors. In this paper, we propose a multi-agent simulation model in which
-pedestrian positions are updated at discrete time intervals. It takes into
-account the major normal conditions of a simple pedestrian situated in a crowd
-such as preferences, realistic perception of environment, etc. Our objective is
-to simulate the pedestrian crowd realistically towards a simulation of
-believable pedestrian behaviors. Typical pedestrian phenomena, including the
-unidirectional and bidirectional movement in a corridor as well as the flow
-through bottleneck, are simulated. The conducted simulations show that our
-model is able to produce realistic pedestrian behaviors. The obtained
-fundamental diagram and flow rate at bottleneck agree very well with classic
-conclusions and empirical study results. It is hoped that the idea of this
-study may be helpful in promoting the modeling and simulation of pedestrian
-crowd in a simple way.
-",1,1,0,0,0,0
-17192,Local connectivity modulates multi-scale relaxation dynamics in a metallic glass-forming system,"  The structural description for the intriguing link between the fast
-vibrational dynamics and slow diffusive dynamics in glass-forming systems is
-one of the most challenging issues in physical science. Here, in a model of
-metallic supercooled liquid, we find that local connectivity as an atomic-level
-structural order parameter tunes the short-time vibrational excitations of the
-icosahedrally coordinated particles and meanwhile modulates their long-time
-relaxation dynamics changing from stretched to compressed exponentials,
-denoting a dynamic transition from subdiffusive to hyperdiffusive motions of
-such particles. Our result indicates that long-time dynamics has an
-atomic-level structural origin which is related to the short-time dynamics,
-thus suggests a structural bridge to link the fast vibrational dynamics and the
-slow structural relaxation in glassy materials.
-",0,1,0,0,0,0
-17193,SATR-DL: Improving Surgical Skill Assessment and Task Recognition in Robot-assisted Surgery with Deep Neural Networks,"  Purpose: This paper focuses on an automated analysis of surgical motion
-profiles for objective skill assessment and task recognition in robot-assisted
-surgery. Existing techniques heavily rely on conventional statistic measures or
-shallow modelings based on hand-engineered features and gesture segmentation.
-Such developments require significant expert knowledge, are prone to errors,
-and are less efficient in online adaptive training systems. Methods: In this
-work, we present an efficient analytic framework with a parallel deep learning
-architecture, SATR-DL, to assess trainee expertise and recognize surgical
-training activity. Through an end-to-end learning technique, abstract
-information of spatial representations and temporal dynamics is jointly
-obtained directly from raw motion sequences. Results: By leveraging a shared
-high-level representation learning, the resulting model is successful in the
-recognition of trainee skills and surgical tasks, suturing, needle-passing, and
-knot-tying. Meanwhile, we explore the use of ensemble in classification at the
-trial level, where the SATR-DL outperforms state-of-the-art performance by
-achieving accuracies of 0.960 and 1.000 in skill assessment and task
-recognition, respectively. Conclusion: This study highlights the potential of
-SATR-DL to provide improvements for an efficient data-driven assessment in
-intelligent robotic surgery.
-",1,0,0,0,0,0
-17194,Equivalence of weak and strong modes of measures on topological vector spaces,"  A strong mode of a probability measure on a normed space $X$ can be defined
-as a point $u$ such that the mass of the ball centred at $u$ uniformly
-dominates the mass of all other balls in the small-radius limit. Helin and
-Burger weakened this definition by considering only pairwise comparisons with
-balls whose centres differ by vectors in a dense, proper linear subspace $E$ of
-$X$, and posed the question of when these two types of modes coincide. We show
-that, in a more general setting of metrisable vector spaces equipped with
-measures that are finite on bounded sets, the density of $E$ and a uniformity
-condition suffice for the equivalence of these two types of modes. We
-accomplish this by introducing a new, intermediate type of mode. We also show
-that these modes can be inequivalent if the uniformity condition fails. Our
-results shed light on the relationships between among various notions of
-maximum a posteriori estimator in non-parametric Bayesian inference.
-",0,0,1,1,0,0
-17195,Algebras of Quasi-Plücker Coordinates are Koszul,"  Motivated by the theory of quasi-determinants, we study non-commutative
-algebras of quasi-Plücker coordinates. We prove that these algebras provide
-new examples of non-homogeneous quadratic Koszul algebras by showing that their
-quadratic duals have quadratic Gröbner bases.
-",0,0,1,0,0,0
-17196,Doubly-Attentive Decoder for Multi-modal Neural Machine Translation,"  We introduce a Multi-modal Neural Machine Translation model in which a
-doubly-attentive decoder naturally incorporates spatial visual features
-obtained using pre-trained convolutional neural networks, bridging the gap
-between image description and translation. Our decoder learns to attend to
-source-language words and parts of an image independently by means of two
-separate attention mechanisms as it generates words in the target language. We
-find that our model can efficiently exploit not just back-translated in-domain
-multi-modal data but also large general-domain text-only MT corpora. We also
-report state-of-the-art results on the Multi30k data set.
-",1,0,0,0,0,0
-17197,prDeep: Robust Phase Retrieval with a Flexible Deep Network,"  Phase retrieval algorithms have become an important component in many modern
-computational imaging systems. For instance, in the context of ptychography and
-speckle correlation imaging, they enable imaging past the diffraction limit and
-through scattering media, respectively. Unfortunately, traditional phase
-retrieval algorithms struggle in the presence of noise. Progress has been made
-recently on more robust algorithms using signal priors, but at the expense of
-limiting the range of supported measurement models (e.g., to Gaussian or coded
-diffraction patterns). In this work we leverage the regularization-by-denoising
-framework and a convolutional neural network denoiser to create prDeep, a new
-phase retrieval algorithm that is both robust and broadly applicable. We test
-and validate prDeep in simulation to demonstrate that it is robust to noise and
-can handle a variety of system models.
-A MatConvNet implementation of prDeep is available at
-this https URL.
-",0,0,0,1,0,0
-17198,Novel Exotic Magnetic Spin-order in Co5Ge3 Nano-size Materials,"  The Cobalt-germanium (Co-Ge) is a fascinating complex alloy system that has
-unique structure and exhibit range of interesting magnetic properties which
-would change when reduce to nanoscale dimension. At this experimental work, the
-high-aspect-ratio Co5Ge3 nanoparticle with average size of 8nm was synthesized
-by gas aggregation-type cluster-deposition technology. The nanostructure
-morphology of the as-made binary Co5Ge3 nanoparticles demonstrate excellent
-single-crystalline hexagonal structure with mostly preferable growth along
-(110) and (102) directions. In contrast the bulk possess Pauli paramagnetic
-spin-order at all range of temperature, here we discover size-driven new
-magnetic ordering of as-synthesized Co5Ge3 nanoparticles exhibiting
-ferromagnetism at room temperature with saturation magnetization of Ms = 32.2
-emu/cm3. This is first report of observing such new magnetic spin ordering in
-this kind of material at nano-size which the magnetization has lower
-sensitivity to thermal energy fluctuation and exhibit high Curie temperature
-close to 850 K. This ferromagnetic behavior along with higher Curie temperature
-at Co5Ge3 nanoparticles are attributes to low-dimension and quantum-confinement
-effect which imposes strong spin coupling and provides a new set of size-driven
-spin structures in Co5Ge3 nanoparticle which no such magnetic behavior being
-present in the bulk of same material. This fundamental scientific study
-provides important insights into the formation, structural, and the magnetic
-property of sub 10nm Co5Ge3 nanostructure which shall lead to promising
-practical versatile applications for magneto- germanide based nano-devices.
-",0,1,0,0,0,0
-17199,Toward Multimodal Image-to-Image Translation,"  Many image-to-image translation problems are ambiguous, as a single input
-image may correspond to multiple possible outputs. In this work, we aim to
-model a \emph{distribution} of possible outputs in a conditional generative
-modeling setting. The ambiguity of the mapping is distilled in a
-low-dimensional latent vector, which can be randomly sampled at test time. A
-generator learns to map the given input, combined with this latent code, to the
-output. We explicitly encourage the connection between output and the latent
-code to be invertible. This helps prevent a many-to-one mapping from the latent
-code to the output during training, also known as the problem of mode collapse,
-and produces more diverse results. We explore several variants of this approach
-by employing different training objectives, network architectures, and methods
-of injecting the latent code. Our proposed method encourages bijective
-consistency between the latent encoding and output modes. We present a
-systematic comparison of our method and other variants on both perceptual
-realism and diversity.
-",1,0,0,1,0,0
-17200,On the Sample Complexity of the Linear Quadratic Regulator,"  This paper addresses the optimal control problem known as the Linear
-Quadratic Regulator in the case when the dynamics are unknown. We propose a
-multi-stage procedure, called Coarse-ID control, that estimates a model from a
-few experimental trials, estimates the error in that model with respect to the
-truth, and then designs a controller using both the model and uncertainty
-estimate. Our technique uses contemporary tools from random matrix theory to
-bound the error in the estimation procedure. We also employ a recently
-developed approach to control synthesis called System Level Synthesis that
-enables robust control design by solving a convex optimization problem. We
-provide end-to-end bounds on the relative error in control cost that are nearly
-optimal in the number of parameters and that highlight salient properties of
-the system to be controlled such as closed-loop sensitivity and optimal control
-magnitude. We show experimentally that the Coarse-ID approach enables efficient
-computation of a stabilizing controller in regimes where simple control schemes
-that do not take the model uncertainty into account fail to stabilize the true
-system.
-",1,0,0,1,0,0
-17201,Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification,"  Deep neural networks (DNNs) have transformed several artificial intelligence
-research areas including computer vision, speech recognition, and natural
-language processing. However, recent studies demonstrated that DNNs are
-vulnerable to adversarial manipulations at testing time. Specifically, suppose
-we have a testing example, whose label can be correctly predicted by a DNN
-classifier. An attacker can add a small carefully crafted noise to the testing
-example such that the DNN classifier predicts an incorrect label, where the
-crafted testing example is called adversarial example. Such attacks are called
-evasion attacks. Evasion attacks are one of the biggest challenges for
-deploying DNNs in safety and security critical applications such as
-self-driving cars. In this work, we develop new methods to defend against
-evasion attacks. Our key observation is that adversarial examples are close to
-the classification boundary. Therefore, we propose region-based classification
-to be robust to adversarial examples. For a benign/adversarial testing example,
-we ensemble information in a hypercube centered at the example to predict its
-label. In contrast, traditional classifiers are point-based classification,
-i.e., given a testing example, the classifier predicts its label based on the
-testing example alone. Our evaluation results on MNIST and CIFAR-10 datasets
-demonstrate that our region-based classification can significantly mitigate
-evasion attacks without sacrificing classification accuracy on benign examples.
-Specifically, our region-based classification achieves the same classification
-accuracy on testing benign examples as point-based classification, but our
-region-based classification is significantly more robust than point-based
-classification to various evasion attacks.
-",1,0,0,1,0,0
-17202,Partition-free families of sets,"  Let $m(n)$ denote the maximum size of a family of subsets which does not
-contain two disjoint sets along with their union. In 1968 Kleitman proved that
-$m(n) = {n\choose m+1}+\ldots +{n\choose 2m+1}$ if $n=3m+1$. Confirming the
-conjecture of Kleitman, we establish the same equality for the cases $n=3m$ and
-$n=3m+2$, and also determine all extremal families. Unlike the case $n=3m+1$,
-the extremal families are not unique. This is a plausible reason behind the
-relative difficulty of our proofs. We completely settle the case of several
-families as well.
-",1,0,0,0,0,0
-17203,Measuring the Galactic Cosmic Ray Flux with the LISA Pathfinder Radiation Monitor,"  Test mass charging caused by cosmic rays will be a significant source of
-acceleration noise for space-based gravitational wave detectors like LISA.
-Operating between December 2015 and July 2017, the technology demonstration
-mission LISA Pathfinder included a bespoke monitor to help characterise the
-relationship between test mass charging and the local radiation environment.
-The radiation monitor made in situ measurements of the cosmic ray flux while
-also providing information about its energy spectrum. We describe the monitor
-and present measurements which show a gradual 40% increase in count rate
-coinciding with the declining phase of the solar cycle. Modulations of up to
-10% were also observed with periods of 13 and 26 days that are associated with
-co-rotating interaction regions and heliospheric current sheet crossings. These
-variations in the flux above the monitor detection threshold (approximately 70
-MeV) are shown to be coherent with measurements made by the IREM monitor
-on-board the Earth orbiting INTEGRAL spacecraft. Finally we use the measured
-deposited energy spectra, in combination with a GEANT4 model, to estimate the
-galactic cosmic ray differential energy spectrum over the course of the
-mission.
-",0,1,0,0,0,0
-17204,Thread-Modular Static Analysis for Relaxed Memory Models,"  We propose a memory-model-aware static program analysis method for accurately
-analyzing the behavior of concurrent software running on processors with weak
-consistency models such as x86-TSO, SPARC-PSO, and SPARC-RMO. At the center of
-our method is a unified framework for deciding the feasibility of inter-thread
-interferences to avoid propagating spurious data flows during static analysis
-and thus boost the performance of the static analyzer. We formulate the
-checking of interference feasibility as a set of Datalog rules which are both
-efficiently solvable and general enough to capture a range of hardware-level
-memory models. Compared to existing techniques, our method can significantly
-reduce the number of bogus alarms as well as unsound proofs. We implemented the
-method and evaluated it on a large set of multithreaded C programs. Our
-experiments showthe method significantly outperforms state-of-the-art
-techniques in terms of accuracy with only moderate run-time overhead.
-",1,0,0,0,0,0
-17205,Bidirectional Evaluation with Direct Manipulation,"  We present an evaluation update (or simply, update) algorithm for a
-full-featured functional programming language, which synthesizes program
-changes based on output changes. Intuitively, the update algorithm retraces the
-steps of the original evaluation, rewriting the program as needed to reconcile
-differences between the original and updated output values. Our approach,
-furthermore, allows expert users to define custom lenses that augment the
-update algorithm with more advanced or domain-specific program updates.
-To demonstrate the utility of evaluation update, we implement the algorithm
-in Sketch-n-Sketch, a novel direct manipulation programming system for
-generating HTML documents. In Sketch-n-Sketch, the user writes an ML-style
-functional program to generate HTML output. When the user directly manipulates
-the output using a graphical user interface, the update algorithm reconciles
-the changes. We evaluate bidirectional evaluation in Sketch-n-Sketch by
-authoring ten examples comprising approximately 1400 lines of code in total.
-These examples demonstrate how a variety of HTML documents and applications can
-be developed and edited interactively in Sketch-n-Sketch, mitigating the
-tedious edit-run-view cycle in traditional programming environments.
-",1,0,0,0,0,0
-17206,Extreme Event Statistics in a Drifting Markov Chain,"  We analyse extreme event statistics of experimentally realized Markov chains
-with various drifts. Our Markov chains are individual trajectories of a single
-atom diffusing in a one dimensional periodic potential. Based on more than 500
-individual atomic traces we verify the applicability of the Sparre Andersen
-theorem to our system despite the presence of a drift. We present detailed
-analysis of four different rare event statistics for our system: the
-distributions of extreme values, of record values, of extreme value occurrence
-in the chain, and of the number of records in the chain. We observe that for
-our data the shape of the extreme event distributions is dominated by the
-underlying exponential distance distribution extracted from the atomic traces.
-Furthermore, we find that even small drifts influence the statistics of extreme
-events and record values, which is supported by numerical simulations, and we
-identify cases in which the drift can be determined without information about
-the underlying random variable distributions. Our results facilitate the use of
-extreme event statistics as a signal for small drifts in correlated
-trajectories.
-",0,1,0,0,0,0
-17207,On a Possibility of Self Acceleration of Electrons in a Plasma,"  The self-consistent nonlinear interaction of a monoenergetic bunch with cold
-plasma is considered. It is shown that under certain conditions a
-self-acceleration of the bunch tail electrons up to high energies is possible.
-",0,1,0,0,0,0
-17208,An Adaptive Strategy for Active Learning with Smooth Decision Boundary,"  We present the first adaptive strategy for active learning in the setting of
-classification with smooth decision boundary. The problem of adaptivity (to
-unknown distributional parameters) has remained opened since the seminal work
-of Castro and Nowak (2007), which first established (active learning) rates for
-this setting. While some recent advances on this problem establish adaptive
-rates in the case of univariate data, adaptivity in the more practical setting
-of multivariate data has so far remained elusive. Combining insights from
-various recent works, we show that, for the multivariate case, a careful
-reduction to univariate-adaptive strategies yield near-optimal rates without
-prior knowledge of distributional parameters.
-",1,0,0,1,0,0
-17209,Towards Adversarial Retinal Image Synthesis,"  Synthesizing images of the eye fundus is a challenging task that has been
-previously approached by formulating complex models of the anatomy of the eye.
-New images can then be generated by sampling a suitable parameter space. In
-this work, we propose a method that learns to synthesize eye fundus images
-directly from data. For that, we pair true eye fundus images with their
-respective vessel trees, by means of a vessel segmentation technique. These
-pairs are then used to learn a mapping from a binary vessel tree to a new
-retinal image. For this purpose, we use a recent image-to-image translation
-technique, based on the idea of adversarial learning. Experimental results show
-that the original and the generated images are visually different in terms of
-their global appearance, in spite of sharing the same vessel tree.
-Additionally, a quantitative quality analysis of the synthetic retinal images
-confirms that the produced images retain a high proportion of the true image
-set quality.
-",1,0,0,1,0,0
-17210,Tailoring the SiC surface - a morphology study on the epitaxial growth of graphene and its buffer layer,"  We investigate the growth of the graphene buffer layer and the involved step
-bunching behavior of the silicon carbide substrate surface using atomic force
-microscopy. The formation of local buffer layer domains are identified to be
-the origin of undesirably high step edges in excellent agreement with the
-predictions of a general model of step dynamics. The applied polymer-assisted
-sublimation growth method demonstrates that the key principle to suppress this
-behavior is the uniform nucleation of the buffer layer. In this way, the
-silicon carbide surface is stabilized such that ultra-flat surfaces can be
-conserved during graphene growth on a large variety of silicon carbide
-substrate surfaces. The analysis of the experimental results describes
-different growth modes which extend the current understanding of epitaxial
-graphene growth by emphasizing the importance of buffer layer nucleation and
-critical mass transport processes.
-",0,1,0,0,0,0
-17211,Polarization of the Vaccination Debate on Facebook,"  Vaccine hesitancy has been recognized as a major global health threat. Having
-access to any type of information in social media has been suggested as a
-potential powerful influence factor to hesitancy. Recent studies in other
-fields than vaccination show that access to a wide amount of content through
-the Internet without intermediaries resolved into major segregation of the
-users in polarized groups. Users select the information adhering to theirs
-system of beliefs and tend to ignore dissenting information. In this paper we
-assess whether there is polarization in Social Media use in the field of
-vaccination. We perform a thorough quantitative analysis on Facebook analyzing
-2.6M users interacting with 298.018 posts over a time span of seven years and 5
-months. We used community detection algorithms to automatically detect the
-emergent communities from the users activity and to quantify the cohesiveness
-over time of the communities. Our findings show that content consumption about
-vaccines is dominated by the echo-chamber effect and that polarization
-increased over years. Communities emerge from the users consumption habits,
-i.e. the majority of users only consumes information in favor or against
-vaccines, not both. The existence of echo-chambers may explain why social-media
-campaigns providing accurate information may have limited reach, may be
-effective only in sub-groups and might even foment further polarization of
-opinions. The introduction of dissenting information into a sub-group is
-disregarded and can have a backfire effect, further reinforcing the existing
-opinions within the sub-group.
-",1,0,0,0,0,0
-17212,"Predicting Demographics, Moral Foundations, and Human Values from Digital Behaviors","  Personal electronic devices including smartphones give access to behavioural
-signals that can be used to learn about the characteristics and preferences of
-individuals. In this study, we explore the connection between demographic and
-psychological attributes and the digital behavioural records, for a cohort of
-7,633 people, closely representative of the US population with respect to
-gender, age, geographical distribution, education, and income. Along with the
-demographic data, we collected self-reported assessments on validated
-psychometric questionnaires for moral traits and basic human values and
-combined this information with passively collected multi-modal digital data
-from web browsing behaviour and smartphone usage. A machine learning framework
-was then designed to infer both the demographic and psychological attributes
-from the behavioural data. In a cross-validated setting, our models predicted
-demographic attributes with good accuracy as measured by the weighted AUROC
-score (Area Under the Receiver Operating Characteristic), but were less
-performant for the moral traits and human values. These results call for
-further investigation since they are still far from unveiling individuals'
-psychological fabric. This connection, along with the most predictive features
-that we provide for each attribute, might prove useful for designing
-personalised services, communication strategies, and interventions, and can be
-used to sketch a portrait of people with a similar worldview.
-",1,0,0,0,0,0
-17213,Isotonic regression in general dimensions,"  We study the least squares regression function estimator over the class of
-real-valued functions on $[0,1]^d$ that are increasing in each coordinate. For
-uniformly bounded signals and with a fixed, cubic lattice design, we establish
-that the estimator achieves the minimax rate of order
-$n^{-\min\{2/(d+2),1/d\}}$ in the empirical $L_2$ loss, up to poly-logarithmic
-factors. Further, we prove a sharp oracle inequality, which reveals in
-particular that when the true regression function is piecewise constant on $k$
-hyperrectangles, the least squares estimator enjoys a faster, adaptive rate of
-convergence of $(k/n)^{\min(1,2/d)}$, again up to poly-logarithmic factors.
-Previous results are confined to the case $d \leq 2$. Finally, we establish
-corresponding bounds (which are new even in the case $d=2$) in the more
-challenging random design setting. There are two surprising features of these
-results: first, they demonstrate that it is possible for a global empirical
-risk minimisation procedure to be rate optimal up to poly-logarithmic factors
-even when the corresponding entropy integral for the function class diverges
-rapidly; second, they indicate that the adaptation rate for shape-constrained
-estimators can be strictly worse than the parametric rate.
-",0,0,1,1,0,0
-17214,Extraction of Schottky barrier height insensitive to temperature via forward currentvoltage- temperature measurements,"  The thermal stability of most electronic and photo-electronic devices
-strongly depends on the relationship between Schottky Barrier Height (SBH) and
-temperature. In this paper, the possible of thermionic current depicted via
-correct and reliability relationship between forward current and voltage is
-consequently discussed, the intrinsic SBH insensitive to temperature can be
-calculated by modification on Richardson- Dushman`s formula suggested in this
-paper. The results of application on four hetero-junctions prove that the
-method proposed is credible in this paper, this suggests that the I/V/T method
-is a feasible alternative to characterize these heterojunctions.
-",0,1,0,0,0,0
-17215,Emergent Open-Endedness from Contagion of the Fittest,"  In this paper, we study emergent irreducible information in populations of
-randomly generated computable systems that are networked and follow a
-""Susceptible-Infected-Susceptible"" contagion model of imitation of the fittest
-neighbor. We show that there is a lower bound for the stationary prevalence (or
-average density of ""infected"" nodes) that triggers an unlimited increase of the
-expected local emergent algorithmic complexity (or information) of a node as
-the population size grows. We call this phenomenon expected (local) emergent
-open-endedness. In addition, we show that static networks with a power-law
-degree distribution following the Barabási-Albert model satisfy this lower
-bound and, thus, display expected (local) emergent open-endedness.
-",1,0,0,0,0,0
-17216,Incompressible Limit of isentropic Navier-Stokes equations with Navier-slip boundary,"  This paper concerns the low Mach number limit of weak solutions to the
-compressible Navier-Stokes equations for isentropic fluids in a bounded domain
-with a Navier-slip boundary condition. In \cite{DGLM99}, it has been proved
-that if the velocity is imposed the homogeneous Dirichlet boundary condition,
-as the Mach number goes to 0, the velocity of the compressible flow converges
-strongly in $L^2$ under the geometrical assumption (H) on the domain. We
-justify the same strong convergence when the slip length in the Navier
-condition is the reciprocal of the square root of the Mach number.
-",0,0,1,0,0,0
-17217,On Some Exponential Sums Related to the Coulter's Polynomial,"  In this paper, the formulas of some exponential sums over finite field,
-related to the Coulter's polynomial, are settled based on the Coulter's
-theorems on Weil sums, which may have potential application in the construction
-of linear codes with few weights.
-",1,0,0,0,0,0
-17218,Distribution-Preserving k-Anonymity,"  Preserving the privacy of individuals by protecting their sensitive
-attributes is an important consideration during microdata release. However, it
-is equally important to preserve the quality or utility of the data for at
-least some targeted workloads. We propose a novel framework for privacy
-preservation based on the k-anonymity model that is ideally suited for
-workloads that require preserving the probability distribution of the
-quasi-identifier variables in the data. Our framework combines the principles
-of distribution-preserving quantization and k-member clustering, and we
-specialize it to two variants that respectively use intra-cluster and Gaussian
-dithering of cluster centers to achieve distribution preservation. We perform
-theoretical analysis of the proposed schemes in terms of distribution
-preservation, and describe their utility in workloads such as covariate shift
-and transfer learning where such a property is necessary. Using extensive
-experiments on real-world Medical Expenditure Panel Survey data, we demonstrate
-the merits of our algorithms over standard k-anonymization for a hallmark
-health care application where an insurance company wishes to understand the
-risk in entering a new market. Furthermore, by empirically quantifying the
-reidentification risk, we also show that the proposed approaches indeed
-maintain k-anonymity.
-",1,0,0,1,0,0
-17219,Using controlled disorder to probe the interplay between charge order and superconductivity in NbSe2,"  The interplay between superconductivity and charge density waves (CDW) in
-$H$-NbSe2 is not fully understood despite decades of study. Artificially
-introduced disorder can tip the delicate balance between two competing forms of
-long-range order, and reveal the underlying interactions that give rise to
-them. Here we introduce disorders by electron irradiation and measure in-plane
-resistivity, Hall resistivity, X-ray scattering, and London penetration depth.
-With increasing disorder, $T_{\textrm{c}}$ varies nonmonotonically, whereas
-$T_{\textrm{CDW}}$ monotonically decreases and becomes unresolvable above a
-critical irradiation dose where $T_{\textrm{c}}$ drops sharply. Our results
-imply that CDW order initially competes with superconductivity, but eventually
-assists it. We argue that at the transition where the long-range CDW order
-disappears, the cooperation with superconductivity is dramatically suppressed.
-X-ray scattering and Hall resistivity measurements reveal that the short-range
-CDW survives above the transition. Superconductivity persists to much higher
-dose levels, consistent with fully gapped superconductivity and moderate
-interband pairing.
-",0,1,0,0,0,0
-17220,A training process for improving the quality of software projects developed by a practitioner,"  Background: The quality of a software product depends on the quality of the
-software process followed in developing the product. Therefore, many higher
-education institutions (HEI) and software organizations have implemented
-software process improvement (SPI) training courses to improve the software
-quality. Objective: Because the duration of a course is a concern for HEI and
-software organizations, we investigate whether the quality of software projects
-will be improved by reorganizing the activities of the ten assignments of the
-original personal software process (PSP) course into a modified PSP having
-fewer assignments (i.e., seven assignments). Method: The assignments were
-developed by following a modified PSP with fewer assignments but including the
-phases, forms, standards, and logs suggested in the original PSP. The
-measurement of the quality of the software assignments was based on defect
-density. Results: When the activities in the original PSP were reordered into
-fewer assignments, as practitioners progress through the PSP training, the
-defect density improved with statistical significance. Conclusions: Our
-modified PSP could be applied in academy and industrial environments which are
-concerned in the sense of reducing the PSP training time
-",1,0,0,0,0,0
-17221,Gaia Data Release 1. Cross-match with external catalogues - Algorithm and results,"  Although the Gaia catalogue on its own will be a very powerful tool, it is
-the combination of this highly accurate archive with other archives that will
-truly open up amazing possibilities for astronomical research. The advanced
-interoperation of archives is based on cross-matching, leaving the user with
-the feeling of working with one single data archive. The data retrieval should
-work not only across data archives, but also across wavelength domains. The
-first step for seamless data access is the computation of the cross-match
-between Gaia and external surveys. The matching of astronomical catalogues is a
-complex and challenging problem both scientifically and technologically
-(especially when matching large surveys like Gaia). We describe the cross-match
-algorithm used to pre-compute the match of Gaia Data Release 1 (DR1) with a
-selected list of large publicly available optical and IR surveys. The overall
-principles of the adopted cross-match algorithm are outlined. Details are given
-on the developed algorithm, including the methods used to account for position
-errors, proper motions, and environment; to define the neighbours; and to
-define the figure of merit used to select the most probable counterpart.
-Statistics on the results are also given. The results of the cross-match are
-part of the official Gaia DR1 catalogue.
-",0,1,0,0,0,0
-17222,"Masses of Kepler-46b, c from Transit Timing Variations","  We use 16 quarters of the \textit{Kepler} mission data to analyze the transit
-timing variations (TTVs) of the extrasolar planet Kepler-46b (KOI-872). Our
-dynamical fits confirm that the TTVs of this planet (period
-$P=33.648^{+0.004}_{-0.005}$ days) are produced by a non-transiting planet
-Kepler-46c ($P=57.325^{+0.116}_{-0.098}$ days). The Bayesian inference tool
-\texttt{MultiNest} is used to infer the dynamical parameters of Kepler-46b and
-Kepler-46c. We find that the two planets have nearly coplanar and circular
-orbits, with eccentricities $\simeq 0.03$ somewhat higher than previously
-estimated. The masses of the two planets are found to be
-$M_{b}=0.885^{+0.374}_{-0.343}$ and $M_{c}=0.362^{+0.016}_{-0.016}$ Jupiter
-masses, with $M_{b}$ being determined here from TTVs for the first time. Due to
-the precession of its orbital plane, Kepler-46c should start transiting its
-host star in a few decades from now.
-",0,1,0,0,0,0
-17223,Recovering water wave elevation from pressure measurements,"  The reconstruction of water wave elevation from bottom pressure measurements
-is an important issue for coastal applications, but corresponds to a difficult
-mathematical problem. In this paper we present the derivation of a method which
-allows the elevation reconstruction of water waves in intermediate and shallow
-waters. From comparisons with numerical Euler solutions and wave-tank
-experiments we show that our nonlinear method provides much better results of
-the surface elevation reconstruction compared to the linear transfer function
-approach commonly used in coastal applications. More specifically, our
-methodaccurately reproduces the peaked and skewed shape of nonlinear wave
-fields. Therefore, it is particularly relevant for applications on extreme
-waves and wave-induced sediment transport.
-",0,1,0,0,0,0
-17224,Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization,"  The Schatten quasi-norm was introduced to bridge the gap between the trace
-norm and rank function. However, existing algorithms are too slow or even
-impractical for large-scale problems. Motivated by the equivalence relation
-between the trace norm and its bilinear spectral penalty, we define two
-tractable Schatten norms, i.e.\ the bi-trace and tri-trace norms, and prove
-that they are in essence the Schatten-$1/2$ and $1/3$ quasi-norms,
-respectively. By applying the two defined Schatten quasi-norms to various rank
-minimization problems such as MC and RPCA, we only need to solve much smaller
-factor matrices. We design two efficient linearized alternating minimization
-algorithms to solve our problems and establish that each bounded sequence
-generated by our algorithms converges to a critical point. We also provide the
-restricted strong convexity (RSC) based and MC error bounds for our algorithms.
-Our experimental results verified both the efficiency and effectiveness of our
-algorithms compared with the state-of-the-art methods.
-",0,0,0,1,0,0
-17225,Spectral Radii of Truncated Circular Unitary Matrices,"  Consider a truncated circular unitary matrix which is a $p_n$ by $p_n$
-submatrix of an $n$ by $n$ circular unitary matrix by deleting the last $n-p_n$
-columns and rows. Jiang and Qi (2017) proved that the maximum absolute value of
-the eigenvalues (known as spectral radius) of the truncated matrix, after
-properly normalized, converges in distribution to the Gumbel distribution if
-$p_n/n$ is bounded away from $0$ and $1$. In this paper we investigate the
-limiting distribution of the spectral radius under one of the following four
-conditions: (1). $p_n\to\infty$ and $p_n/n\to 0$ as $n\to\infty$; (2).
-$(n-p_n)/n\to 0$ and $(n-p_n)/(\log n)^3\to\infty$ as $n\to\infty$; (3).
-$n-p_n\to\infty$ and $(n-p_n)/\log n\to 0$ as $n\to\infty$ and (4). $n-p_n=k\ge
-1$ is a fixed integer. We prove that the spectral radius converges in
-distribution to the Gumbel distribution under the first three conditions and to
-a reversed Weibull distribution under the fourth condition.
-",0,0,1,1,0,0
-17226,Information Assisted Dictionary Learning for fMRI data analysis,"  In this paper, the task-related fMRI problem is treated in its matrix
-factorization formulation. The focus of the reported work is on the dictionary
-learning (DL) matrix factorization approach. A major novelty of the paper lies
-in the incorporation of well-established assumptions associated with the GLM
-technique, which is currently in use by the neuroscientists. These assumptions
-are embedded as constraints in the DL formulation. In this way, our approach
-provides a framework of combining well-established and understood techniques
-with a more ``modern'' and powerful tool. Furthermore, this paper offers a way
-to relax a major drawback associated with DL techniques; that is, the proper
-tuning of the DL regularization parameter. This parameter plays a critical role
-in DL-based fMRI analysis since it essentially determines the shape and
-structures of the estimated functional brain networks. However, in actual fMRI
-data analysis, the lack of ground truth renders the a priori choice of the
-regularization parameter a truly challenging task. Indeed, the values of the DL
-regularization parameter, associated with the $\ell_1$ sparsity promoting norm,
-do not convey any tangible physical meaning. So it is practically difficult to
-guess its proper value. In this paper, the DL problem is reformulated around a
-sparsity-promoting constraint that can directly be related to the minimum
-amount of voxels that the spatial maps of the functional brain networks occupy.
-Such information is documented and it is readily available to neuroscientists
-and experts in the field.
-The proposed method is tested against a number of other popular techniques
-and the obtained performance gains are reported using a number of synthetic
-fMRI data. Results with real data have also been obtained in the context of a
-number of experiments and will be soon reported in a different publication.
-",0,0,0,1,0,0
-17227,"Efficient, Certifiably Optimal Clustering with Applications to Latent Variable Graphical Models","  Motivated by the task of clustering either $d$ variables or $d$ points into
-$K$ groups, we investigate efficient algorithms to solve the Peng-Wei (P-W)
-$K$-means semi-definite programming (SDP) relaxation. The P-W SDP has been
-shown in the literature to have good statistical properties in a variety of
-settings, but remains intractable to solve in practice. To this end we propose
-FORCE, a new algorithm to solve this SDP relaxation. Compared to the naive
-interior point method, our method reduces the computational complexity of
-solving the SDP from $\tilde{O}(d^7\log\epsilon^{-1})$ to
-$\tilde{O}(d^{6}K^{-2}\epsilon^{-1})$ arithmetic operations for an
-$\epsilon$-optimal solution. Our method combines a primal first-order method
-with a dual optimality certificate search, which when successful, allows for
-early termination of the primal method. We show for certain variable clustering
-problems that, with high probability, FORCE is guaranteed to find the optimal
-solution to the SDP relaxation and provide a certificate of exact optimality.
-As verified by our numerical experiments, this allows FORCE to solve the P-W
-SDP with dimensions in the hundreds in only tens of seconds. For a variation of
-the P-W SDP where $K$ is not known a priori a slight modification of FORCE
-reduces the computational complexity of solving this problem as well: from
-$\tilde{O}(d^7\log\epsilon^{-1})$ using a standard SDP solver to
-$\tilde{O}(d^{4}\epsilon^{-1})$.
-",0,0,0,1,0,0
-17228,Two- and three-dimensional wide-field weak lensing mass maps from the Hyper Suprime-Cam Subaru Strategic Program S16A data,"  We present wide-field (167 deg$^2$) weak lensing mass maps from the Hyper
-Supreme-Cam Subaru Strategic Program (HSC-SSP). We compare these weak lensing
-based dark matter maps with maps of the distribution of the stellar mass
-associated with luminous red galaxies. We find a strong correlation between
-these two maps with a correlation coefficient of $\rho=0.54\pm0.03$ (for a
-smoothing size of $8'$). This correlation is detected even with a smaller
-smoothing scale of $2'$ ($\rho=0.34\pm 0.01$). This detection is made uniquely
-possible because of the high source density of the HSC-SSP weak lensing survey
-($\bar{n}\sim 25$ arcmin$^{-2}$). We also present a variety of tests to
-demonstrate that our maps are not significantly affected by systematic effects.
-By using the photometric redshift information associated with source galaxies,
-we reconstruct a three-dimensional mass map. This three-dimensional mass map is
-also found to correlate with the three-dimensional galaxy mass map.
-Cross-correlation tests presented in this paper demonstrate that the HSC-SSP
-weak lensing mass maps are ready for further science analyses.
-",0,1,0,0,0,0
-17229,A Time-spectral Approach to Numerical Weather Prediction,"  Finite difference methods are traditionally used for modelling the time
-domain in numerical weather prediction (NWP). Time-spectral solution is an
-attractive alternative for reasons of accuracy and efficiency and because time
-step limitations associated with causal, CFL-like critera are avoided. In this
-work, the Lorenz 1984 chaotic equations are solved using the time-spectral
-algorithm GWRM. Comparisons of accuracy and efficiency are carried out for both
-explicit and implicit time-stepping algorithms. It is found that the efficiency
-of the GWRM compares well with these methods, in particular at high accuracy.
-For perturbative scenarios, the GWRM was found to be as much as four times
-faster than the finite difference methods. A primary reason is that the GWRM
-time intervals typically are two orders of magnitude larger than those of the
-finite difference methods. The GWRM has the additional advantage to produce
-analytical solutions in the form of Chebyshev series expansions. The results
-are encouraging for pursuing further studies, including spatial dependence, of
-the relevance of time-spectral methods for NWP modelling.
-",0,1,0,0,0,0
-17230,Trivial Constraints on Orbital-free Kinetic Energy Density Functionals,"  Kinetic energy density functionals (KEDFs) are central to orbital-free
-density functional theory. Limitations on the spatial derivative dependencies
-of KEDFs have been claimed from differential virial theorems. We point out a
-central defect in the argument: the relationships are not true for an arbitrary
-density but hold only for the minimizing density and corresponding chemical
-potential. Contrary to the claims therefore, the relationships are not
-constraints and provide no independent information about the spatial derivative
-dependencies of approximate KEDFs. A simple argument also shows that validity
-for arbitrary $v$-representable densities is not restored by appeal to the
-density-potential bijection.
-",0,1,0,0,0,0
-17231,The Multi-layer Information Bottleneck Problem,"  The muti-layer information bottleneck (IB) problem, where information is
-propagated (or successively refined) from layer to layer, is considered. Based
-on information forwarded by the preceding layer, each stage of the network is
-required to preserve a certain level of relevance with regards to a specific
-hidden variable, quantified by the mutual information. The hidden variables and
-the source can be arbitrarily correlated. The optimal trade-off between rates
-of relevance and compression (or complexity) is obtained through a
-single-letter characterization, referred to as the rate-relevance region.
-Conditions of successive refinabilty are given. Binary source with BSC hidden
-variables and binary source with BSC/BEC mixed hidden variables are both proved
-to be successively refinable. We further extend our result to Guassian models.
-A counterexample of successive refinability is also provided.
-",1,0,0,1,0,0
-17232,A geometric approach to non-linear correlations with intrinsic scatter,"  We propose a new mathematical model for $n-k$-dimensional non-linear
-correlations with intrinsic scatter in $n$-dimensional data. The model is based
-on Riemannian geometry, and is naturally symmetric with respect to the measured
-variables and invariant under coordinate transformations. We combine the model
-with a Bayesian approach for estimating the parameters of the correlation
-relation and the intrinsic scatter. A side benefit of the approach is that
-censored and truncated datasets and independent, arbitrary measurement errors
-can be incorporated. We also derive analytic likelihoods for the typical
-astrophysical use case of linear relations in $n$-dimensional Euclidean space.
-We pay particular attention to the case of linear regression in two dimensions,
-and compare our results to existing methods. Finally, we apply our methodology
-to the well-known $M_\text{BH}$-$\sigma$ correlation between the mass of a
-supermassive black hole in the centre of a galactic bulge and the corresponding
-bulge velocity dispersion. The main result of our analysis is that the most
-likely slope of this correlation is $\sim 6$ for the datasets used, rather than
-the values in the range $\sim 4$-$5$ typically quoted in the literature for
-these data.
-",0,1,1,1,0,0
-17233,Computing simplicial representatives of homotopy group elements,"  A central problem of algebraic topology is to understand the homotopy groups
-$\pi_d(X)$ of a topological space $X$. For the computational version of the
-problem, it is well known that there is no algorithm to decide whether the
-fundamental group $\pi_1(X)$ of a given finite simplicial complex $X$ is
-trivial. On the other hand, there are several algorithms that, given a finite
-simplicial complex $X$ that is simply connected (i.e., with $\pi_1(X)$
-trivial), compute the higher homotopy group $\pi_d(X)$ for any given $d\geq 2$.
-%The first such algorithm was given by Brown, and more recently, Čadek et
-al.
-However, these algorithms come with a caveat: They compute the isomorphism
-type of $\pi_d(X)$, $d\geq 2$ as an \emph{abstract} finitely generated abelian
-group given by generators and relations, but they work with very implicit
-representations of the elements of $\pi_d(X)$. Converting elements of this
-abstract group into explicit geometric maps from the $d$-dimensional sphere
-$S^d$ to $X$ has been one of the main unsolved problems in the emerging field
-of computational homotopy theory.
-Here we present an algorithm that, given a~simply connected space $X$,
-computes $\pi_d(X)$ and represents its elements as simplicial maps from a
-suitable triangulation of the $d$-sphere $S^d$ to $X$. For fixed $d$, the
-algorithm runs in time exponential in $size(X)$, the number of simplices of
-$X$. Moreover, we prove that this is optimal: For every fixed $d\geq 2$, we
-construct a family of simply connected spaces $X$ such that for any simplicial
-map representing a generator of $\pi_d(X)$, the size of the triangulation of
-$S^d$ on which the map is defined, is exponential in $size(X)$.
-",1,0,1,0,0,0
-17234,Multiobjective Optimization of Solar Powered Irrigation System with Fuzzy Type-2 Noise Modelling,"  Optimization is becoming a crucial element in industrial applications
-involving sustainable alternative energy systems. During the design of such
-systems, the engineer/decision maker would often encounter noise factors (e.g.
-solar insolation and ambient temperature fluctuations) when their system
-interacts with the environment. In this chapter, the sizing and design
-optimization of the solar powered irrigation system was considered. This
-problem is multivariate, noisy, nonlinear and multiobjective. This design
-problem was tackled by first using the Fuzzy Type II approach to model the
-noise factors. Consequently, the Bacterial Foraging Algorithm (BFA) (in the
-context of a weighted sum framework) was employed to solve this multiobjective
-fuzzy design problem. This method was then used to construct the approximate
-Pareto frontier as well as to identify the best solution option in a fuzzy
-setting. Comprehensive analyses and discussions were performed on the generated
-numerical results with respect to the implemented solution methods.
-",1,0,0,0,0,0
-17235,Convergence Analysis of Gradient EM for Multi-component Gaussian Mixture,"  In this paper, we study convergence properties of the gradient
-Expectation-Maximization algorithm \cite{lange1995gradient} for Gaussian
-Mixture Models for general number of clusters and mixing coefficients. We
-derive the convergence rate depending on the mixing coefficients, minimum and
-maximum pairwise distances between the true centers and dimensionality and
-number of components; and obtain a near-optimal local contraction radius. While
-there have been some recent notable works that derive local convergence rates
-for EM in the two equal mixture symmetric GMM, in the more general case, the
-derivations need structurally different and non-trivial arguments. We use
-recent tools from learning theory and empirical processes to achieve our
-theoretical results.
-",1,0,1,1,0,0
-17236,The Gravitational-Wave Physics,"  The direct detection of gravitational wave by Laser Interferometer
-Gravitational-Wave Observatory indicates the coming of the era of
-gravitational-wave astronomy and gravitational-wave cosmology. It is expected
-that more and more gravitational-wave events will be detected by currently
-existing and planned gravitational-wave detectors. The gravitational waves open
-a new window to explore the Universe and various mysteries will be disclosed
-through the gravitational-wave detection, combined with other cosmological
-probes. The gravitational-wave physics is not only related to gravitation
-theory, but also is closely tied to fundamental physics, cosmology and
-astrophysics. In this review article, three kinds of sources of gravitational
-waves and relevant physics will be discussed, namely gravitational waves
-produced during the inflation and preheating phases of the Universe, the
-gravitational waves produced during the first-order phase transition as the
-Universe cools down and the gravitational waves from the three phases:
-inspiral, merger and ringdown of a compact binary system, respectively. We will
-also discuss the gravitational waves as a standard siren to explore the
-evolution of the Universe.
-",0,1,0,0,0,0
-17237,Multivariant Assertion-based Guidance in Abstract Interpretation,"  Approximations during program analysis are a necessary evil, as they ensure
-essential properties, such as soundness and termination of the analysis, but
-they also imply not always producing useful results. Automatic techniques have
-been studied to prevent precision loss, typically at the expense of larger
-resource consumption. In both cases (i.e., when analysis produces inaccurate
-results and when resource consumption is too high), it is necessary to have
-some means for users to provide information to guide analysis and thus improve
-precision and/or performance. We present techniques for supporting within an
-abstract interpretation framework a rich set of assertions that can deal with
-multivariance/context-sensitivity, and can handle different run-time semantics
-for those assertions that cannot be discharged at compile time. We show how the
-proposed approach can be applied to both improving precision and accelerating
-analysis. We also provide some formal results on the effects of such assertions
-on the analysis results.
-",1,0,0,0,0,0
-17238,An Experimental Comparison of Uncertainty Sets for Robust Shortest Path Problems,"  Through the development of efficient algorithms, data structures and
-preprocessing techniques, real-world shortest path problems in street networks
-are now very fast to solve. But in reality, the exact travel times along each
-arc in the network may not be known. This lead to the development of robust
-shortest path problems, where all possible arc travel times are contained in a
-so-called uncertainty set of possible outcomes.
-Research in robust shortest path problems typically assumes this set to be
-given, and provides complexity results as well as algorithms depending on its
-shape. However, what can actually be observed in real-world problems are only
-discrete raw data points. The shape of the uncertainty is already a modelling
-assumption. In this paper we test several of the most widely used assumptions
-on the uncertainty set using real-world traffic measurements provided by the
-City of Chicago. We calculate the resulting different robust solutions, and
-evaluate which uncertainty approach is actually reasonable for our data. This
-anchors theoretical research in a real-world application and allows us to point
-out which robust models should be the future focus of algorithmic development.
-",1,0,1,0,0,0
-17239,An experimental comparison of velocities underneath focussed breaking waves,"  Nonlinear wave interactions affect the evolution of steep wave groups, their
-breaking and the associated kinematic field. Laboratory experiments are
-performed to investigate the effect of the underlying focussing mechanism on
-the shape of the breaking wave and its velocity field. In this regard, it is
-found that the shape of the wave spectrum plays a substantial role. Broader
-underlying wave spectra leads to energetic plungers at a relatively low
-amplitude. For narrower spectra waves break at a higher amplitudes but with a
-less energetic spiller. Comparison with standard engineering methods commonly
-used to predict the velocity underneath extreme waves shows that, under certain
-conditions, the measured velocity profile strongly deviates from engineering
-predictions.
-",0,1,0,0,0,0
-17240,Full Momentum and Energy Resolved Spectral Function of a 2D Electronic System,"  The single-particle spectral function measures the density of electronic
-states (DOS) in a material as a function of both momentum and energy, providing
-central insights into phenomena such as superconductivity and Mott insulators.
-While scanning tunneling microscopy (STM) and other tunneling methods have
-provided partial spectral information, until now only angle-resolved
-photoemission spectroscopy (ARPES) has permitted a comprehensive determination
-of the spectral function of materials in both momentum and energy. However,
-ARPES operates only on electronic systems at the material surface and cannot
-work in the presence of applied magnetic fields. Here, we demonstrate a new
-method for determining the full momentum and energy resolved electronic
-spectral function of a two-dimensional (2D) electronic system embedded in a
-semiconductor. In contrast with ARPES, the technique remains operational in the
-presence of large externally applied magnetic fields and functions for
-electronic systems with zero electrical conductivity or with zero electron
-density. It provides a direct high-resolution and high-fidelity probe of the
-dispersion and dynamics of the interacting 2D electron system. By ensuring the
-system of interest remains under equilibrium conditions, we uncover delicate
-signatures of many-body effects involving electron-phonon interactions,
-plasmons, polarons, and a novel phonon analog of the vacuum Rabi splitting in
-atomic systems.
-",0,1,0,0,0,0
-17241,Complete parallel mean curvature surfaces in two-dimensional complex space-forms,"  The purpose of this article is to determine explicitly the complete surfaces
-with parallel mean curvature vector, both in the complex projective plane and
-the complex hyperbolic plane. The main results are as follows: When the
-curvature of the ambient space is positive, there exists a unique such surface
-up to rigid motions of the target space. On the other hand, when the curvature
-of the ambient space is negative, there are `non-trivial' complete parallel
-mean curvature surfaces generated by Jacobi elliptic functions and they exhaust
-such surfaces.
-",0,0,1,0,0,0
-17242,Parallelized Linear Classification with Volumetric Chemical Perceptrons,"  In this work, we introduce a new type of linear classifier that is
-implemented in a chemical form. We propose a novel encoding technique which
-simultaneously represents multiple datasets in an array of microliter-scale
-chemical mixtures. Parallel computations on these datasets are performed as
-robotic liquid handling sequences, whose outputs are analyzed by
-high-performance liquid chromatography. As a proof of concept, we chemically
-encode several MNIST images of handwritten digits and demonstrate successful
-chemical-domain classification of the digits using volumetric perceptrons. We
-additionally quantify the performance of our method with a larger dataset of
-binary vectors and compare the experimental measurements against predicted
-results. Paired with appropriate chemical analysis tools, our approach can work
-on increasingly parallel datasets. We anticipate that related approaches will
-be scalable to multilayer neural networks and other more complex algorithms.
-Much like recent demonstrations of archival data storage in DNA, this work
-blurs the line between chemical and electrical information systems, and offers
-early insight into the computational efficiency and massive parallelism which
-may come with computing in chemical domains.
-",0,0,0,0,1,0
-17243,Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification,"  Multi-label image classification is a fundamental but challenging task in
-computer vision. Great progress has been achieved by exploiting semantic
-relations between labels in recent years. However, conventional approaches are
-unable to model the underlying spatial relations between labels in multi-label
-images, because spatial annotations of the labels are generally not provided.
-In this paper, we propose a unified deep neural network that exploits both
-semantic and spatial relations between labels with only image-level
-supervisions. Given a multi-label image, our proposed Spatial Regularization
-Network (SRN) generates attention maps for all labels and captures the
-underlying relations between them via learnable convolutions. By aggregating
-the regularized classification results with original results by a ResNet-101
-network, the classification performance can be consistently improved. The whole
-deep neural network is trained end-to-end with only image-level annotations,
-thus requires no additional efforts on image annotations. Extensive evaluations
-on 3 public datasets with different types of labels show that our approach
-significantly outperforms state-of-the-arts and has strong generalization
-capability. Analysis of the learned SRN model demonstrates that it can
-effectively capture both semantic and spatial relations of labels for improving
-classification performance.
-",1,0,0,0,0,0
-17244,The critical binary star separation for a planetary system origin of white dwarf pollution,"  The atmospheres of between one quarter and one half of observed single white
-dwarfs in the Milky Way contain heavy element pollution from planetary debris.
-The pollution observed in white dwarfs in binary star systems is, however, less
-clear, because companion star winds can generate a stream of matter which is
-accreted by the white dwarf. Here we (i) discuss the necessity or lack thereof
-of a major planet in order to pollute a white dwarf with orbiting minor planets
-in both single and binary systems, and (ii) determine the critical binary
-separation beyond which the accretion source is from a planetary system. We
-hence obtain user-friendly functions relating this distance to the masses and
-radii of both stars, the companion wind, and the accretion rate onto the white
-dwarf, for a wide variety of published accretion prescriptions. We find that
-for the majority of white dwarfs in known binaries, if pollution is detected,
-then that pollution should originate from planetary material.
-",0,1,0,0,0,0
-17245,A quantum Mirković-Vybornov isomorphism,"  We present a quantization of an isomorphism of Mirković and Vybornov which
-relates the intersection of a Slodowy slice and a nilpotent orbit closure in
-$\mathfrak{gl}_N$ , to a slice between spherical Schubert varieties in the
-affine Grassmannian of $PGL_n$ (with weights encoded by the Jordan types of the
-nilpotent orbits). A quantization of the former variety is provided by a
-parabolic W-algebra and of the latter by a truncated shifted Yangian. Building
-on earlier work of Brundan and Kleshchev, we define an explicit isomorphism
-between these non-commutative algebras, and show that its classical limit is a
-variation of the original isomorphism of Mirković and Vybornov. As a
-corollary, we deduce that the W-algebra is free as a left (or right) module
-over its Gelfand-Tsetlin subalgebra, as conjectured by Futorny, Molev, and
-Ovsienko.
-",0,0,1,0,0,0
-17246,Portfolio diversification and model uncertainty: a robust dynamic mean-variance approach,"  This paper is concerned with a multi-asset mean-variance portfolio selection
-problem under model uncertainty. We develop a continuous time framework for
-taking into account ambiguity aversion about both expected return rates and
-correlation matrix of the assets, and for studying the effects on portfolio
-diversification. We prove a separation principle for the associated robust
-control problem, which allows to reduce the determination of the optimal
-dynamic strategy to the parametric computation of the minimal risk premium
-function. Our results provide a justification for under-diversification, as
-documented in empirical studies. We explicitly quantify the degree of
-under-diversification in terms of correlation and Sharpe ratio ambiguity. In
-particular, we show that an investor with a poor confidence in the expected
-return estimation does not hold any risky asset, and on the other hand, trades
-only one risky asset when the level of ambiguity on correlation matrix is
-large. This extends to the continuous-time setting the results obtained by
-Garlappi, Uppal and Wang [13], and Liu and Zeng [24] in a one-period model. JEL
-Classification: G11, C61 MSC Classification: 91G10, 91G80, 60H30
-",0,0,0,0,0,1
-17247,TensorLayer: A Versatile Library for Efficient Deep Learning Development,"  Deep learning has enabled major advances in the fields of computer vision,
-natural language processing, and multimedia among many others. Developing a
-deep learning system is arduous and complex, as it involves constructing neural
-network architectures, managing training/trained models, tuning optimization
-process, preprocessing and organizing data, etc. TensorLayer is a versatile
-Python library that aims at helping researchers and engineers efficiently
-develop deep learning systems. It offers rich abstractions for neural networks,
-model and data management, and parallel workflow mechanism. While boosting
-efficiency, TensorLayer maintains both performance and scalability. TensorLayer
-was released in September 2016 on GitHub, and has helped people from academia
-and industry develop real-world applications of deep learning.
-",1,0,0,1,0,0
-17248,Effects of excess carriers on native defects in wide bandgap semiconductors: illumination as a method to enhance p-type doping,"  Undesired unintentional doping and doping limits in semiconductors are
-typically caused by compensating defects with low formation energies. Since the
-formation energy of a charged defect depends linearly on the Fermi level,
-doping limits can be especially pronounced in wide bandgap semiconductors where
-the Fermi level can vary substantially. Introduction of non-equilibrium carrier
-concentrations during growth or processing alters the chemical potentials of
-band carriers and thus provides the possibility of modifying populations of
-charged defects in ways impossible at thermal equilibrium. Herein we
-demonstrate that, for an ergodic system with excess carriers, the rates of
-carrier capture and emission involving a defect charge transition level
-rigorously determine the admixture of electron and hole quasi-Fermi levels
-determining the formation energy of non-zero charge states of that defect type.
-To catalog the range of possible responses to excess carriers, we investigate
-the behavior of a single donor-like defect as functions of extrinsic doping and
-energy of the charge transition level. The technologically most important
-finding is that excess carriers will increase the formation energy of
-compensating defects for most values of the charge transition level in the
-bandgap. Thus, it may be possible to overcome limitations on doping imposed by
-native defects. Cases also exist in wide bandgap semiconductors in which the
-concentration of defects with the same charge polarity as the majority dopant
-is either left unchanged or actually increases. The causes of these various
-behaviors are rationalized in terms of the capture and emission rates and
-guidelines for carrying out experimental tests of this model are given.
-",0,1,0,0,0,0
-17249,LATTE: Application Oriented Social Network Embedding,"  In recent years, many research works propose to embed the network structured
-data into a low-dimensional feature space, where each node is represented as a
-feature vector. However, due to the detachment of embedding process with
-external tasks, the learned embedding results by most existing embedding models
-can be ineffective for application tasks with specific objectives, e.g.,
-community detection or information diffusion. In this paper, we propose study
-the application oriented heterogeneous social network embedding problem.
-Significantly different from the existing works, besides the network structure
-preservation, the problem should also incorporate the objectives of external
-applications in the objective function. To resolve the problem, in this paper,
-we propose a novel network embedding framework, namely the ""appLicAtion
-orienTed neTwork Embedding"" (Latte) model. In Latte, the heterogeneous network
-structure can be applied to compute the node ""diffusive proximity"" scores,
-which capture both local and global network structures. Based on these computed
-scores, Latte learns the network representation feature vectors by extending
-the autoencoder model model to the heterogeneous network scenario, which can
-also effectively unite the objectives of network embedding and external
-application tasks. Extensive experiments have been done on real-world
-heterogeneous social network datasets, and the experimental results have
-demonstrated the outstanding performance of Latte in learning the
-representation vectors for specific application tasks.
-",1,0,0,0,0,0
-17250,Giant paramagnetism induced valley polarization of electrons in charge-tunable monolayer MoSe2,"  For applications exploiting the valley pseudospin degree of freedom in
-transition metal dichalcogenide monolayers, efficient preparation of electrons
-or holes in a single valley is essential. Here, we show that a magnetic field
-of 7 Tesla leads to a near-complete valley polarization of electrons in MoSe2
-monolayer with a density 1.6x10^{12} cm^{-2}; in the absence of exchange
-interactions favoring single-valley occupancy, a similar degree of valley
-polarization would have required a pseudospin g-factor exceeding 40. To
-investigate the magnetic response, we use polarization resolved
-photoluminescence as well as resonant reflection measurements. In the latter,
-we observe gate voltage dependent transfer of oscillator strength from the
-exciton to the attractive-Fermi-polaron: stark differences in the spectrum of
-the two light helicities provide a confirmation of valley polarization. Our
-findings suggest an interaction induced giant paramagnetic response of MoSe2,
-which paves the way for valleytronics applications.
-",0,1,0,0,0,0
-17251,Highrisk Prediction from Electronic Medical Records via Deep Attention Networks,"  Predicting highrisk vascular diseases is a significant issue in the medical
-domain. Most predicting methods predict the prognosis of patients from
-pathological and radiological measurements, which are expensive and require
-much time to be analyzed. Here we propose deep attention models that predict
-the onset of the high risky vascular disease from symbolic medical histories
-sequence of hypertension patients such as ICD-10 and pharmacy codes only,
-Medical History-based Prediction using Attention Network (MeHPAN). We
-demonstrate two types of attention models based on 1) bidirectional gated
-recurrent unit (R-MeHPAN) and 2) 1D convolutional multilayer model (C-MeHPAN).
-Two MeHPAN models are evaluated on approximately 50,000 hypertension patients
-with respect to precision, recall, f1-measure and area under the curve (AUC).
-Experimental results show that our MeHPAN methods outperform standard
-classification models. Comparing two MeHPANs, R-MeHPAN provides more better
-discriminative capability with respect to all metrics while C-MeHPAN presents
-much shorter training time with competitive accuracy.
-",1,0,0,1,0,0
-17252,Agent based simulation of the evolution of society as an alternate maximization problem,"  Understanding the evolution of human society, as a complex adaptive system,
-is a task that has been looked upon from various angles. In this paper, we
-simulate an agent-based model with a high enough population tractably. To do
-this, we characterize an entity called \textit{society}, which helps us reduce
-the complexity of each step from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$. We
-propose a very realistic setting, where we design a joint alternate
-maximization step algorithm to maximize a certain \textit{fitness} function,
-which we believe simulates the way societies develop. Our key contributions
-include (i) proposing a novel protocol for simulating the evolution of a
-society with cheap, non-optimal joint alternate maximization steps (ii)
-providing a framework for carrying out experiments that adhere to this
-joint-optimization simulation framework (iii) carrying out experiments to show
-that it makes sense empirically (iv) providing an alternate justification for
-the use of \textit{society} in the simulations.
-",1,0,0,1,0,0
-17253,Can a heart rate variability biomarker identify the presence of autism spectrum disorder in eight year old children?,"  Autonomic nervous system (ANS) activity is altered in autism spectrum
-disorder (ASD). Heart rate variability (HRV) derived from electrocardiogram
-(ECG) has been a powerful tool to identify alterations in ANS due to a plethora
-of pathophysiological conditions, including psychological ones such as
-depression. ECG-derived HRV thus carries a yet to be explored potential to be
-used as a diagnostic and follow-up biomarker of ASD. However, few studies have
-explored this potential. In a cohort of boys (ages 8 - 11 years) with (n=18)
-and without ASD (n=18), we tested a set of linear and nonlinear HRV measures,
-including phase rectified signal averaging (PRSA), applied to a segment of ECG
-collected under resting conditions for their predictive properties of ASD. We
-identified HRV measures derived from time, frequency and geometric
-signal-analytical domains which are changed in ASD children relative to peers
-without ASD and correlate to psychometric scores (p<0.05 for each). Receiver
-operating curves area ranged between 0.71 - 0.74 for each HRV measure. Despite
-being a small cohort lacking external validation, these promising preliminary
-results warrant larger prospective validation studies.
-",0,0,0,0,1,0
-17254,Semantic Entity Retrieval Toolkit,"  Unsupervised learning of low-dimensional, semantic representations of words
-and entities has recently gained attention. In this paper we describe the
-Semantic Entity Retrieval Toolkit (SERT) that provides implementations of our
-previously published entity representation models. The toolkit provides a
-unified interface to different representation learning algorithms, fine-grained
-parsing configuration and can be used transparently with GPUs. In addition,
-users can easily modify existing models or implement their own models in the
-framework. After model training, SERT can be used to rank entities according to
-a textual query and extract the learned entity/word representation for use in
-downstream algorithms, such as clustering or recommendation.
-",1,0,0,0,0,0
-17255,Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection,"  In the Best-$k$-Arm problem, we are given $n$ stochastic bandit arms, each
-associated with an unknown reward distribution. We are required to identify the
-$k$ arms with the largest means by taking as few samples as possible. In this
-paper, we make progress towards a complete characterization of the
-instance-wise sample complexity bounds for the Best-$k$-Arm problem. On the
-lower bound side, we obtain a novel complexity term to measure the sample
-complexity that every Best-$k$-Arm instance requires. This is derived by an
-interesting and nontrivial reduction from the Best-$1$-Arm problem. We also
-provide an elimination-based algorithm that matches the instance-wise lower
-bound within doubly-logarithmic factors. The sample complexity of our algorithm
-strictly dominates the state-of-the-art for Best-$k$-Arm (module constant
-factors).
-",1,0,0,1,0,0
-17256,Dimensions of equilibrium measures on a class of planar self-affine sets,"  We study equilibrium measures (Käenmäki measures) supported on
-self-affine sets generated by a finite collection of diagonal and anti-diagonal
-matrices acting on the plane and satisfying the strong separation property. Our
-main result is that such measures are exact dimensional and the dimension
-satisfies the Ledrappier-Young formula, which gives an explicit expression for
-the dimension in terms of the entropy and Lyapunov exponents as well as the
-dimension of the important coordinate projection of the measure. In particular,
-we do this by showing that the Käenmäki measure is equal to the sum of (the
-pushforwards) of two Gibbs measures on an associated subshift of finite type.
-",0,0,1,0,0,0
-17257,Hubble PanCET: An isothermal day-side atmosphere for the bloated gas-giant HAT-P-32Ab,"  We present a thermal emission spectrum of the bloated hot Jupiter HAT-P-32Ab
-from a single eclipse observation made in spatial scan mode with the Wide Field
-Camera 3 (WFC3) aboard the Hubble Space Telescope (HST). The spectrum covers
-the wavelength regime from 1.123 to 1.644 microns which is binned into 14
-eclipse depths measured to an averaged precision of 104 parts-per million. The
-spectrum is unaffected by a dilution from the close M-dwarf companion
-HAT-P-32B, which was fully resolved. We complemented our spectrum with
-literature results and performed a comparative forward and retrieval analysis
-with the 1D radiative-convective ATMO model. Assuming solar abundance of the
-planet atmosphere, we find that the measured spectrum can best be explained by
-the spectrum of a blackbody isothermal atmosphere with Tp = 1995 +/- 17K, but
-can equally-well be described by a spectrum with modest thermal inversion. The
-retrieved spectrum suggests emission from VO at the WFC3 wavelengths and no
-evidence of the 1.4 micron water feature. The emission models with temperature
-profiles decreasing with height are rejected at a high confidence. An
-isothermal or inverted spectrum can imply a clear atmosphere with an absorber,
-a dusty cloud deck or a combination of both. We find that the planet can have
-continuum of values for the albedo and recirculation, ranging from high albedo
-and poor recirculation to low albedo and efficient recirculation. Optical
-spectroscopy of the planet's day-side or thermal emission phase curves can
-potentially resolve the current albedo with recirculation degeneracy.
-",0,1,0,0,0,0
-17258,One Model To Learn Them All,"  Deep learning yields great results across many fields, from speech
-recognition, image classification, to translation. But for each problem,
-getting a deep model to work well involves research into the architecture and a
-long period of tuning. We present a single model that yields good results on a
-number of problems spanning multiple domains. In particular, this single model
-is trained concurrently on ImageNet, multiple translation tasks, image
-captioning (COCO dataset), a speech recognition corpus, and an English parsing
-task. Our model architecture incorporates building blocks from multiple
-domains. It contains convolutional layers, an attention mechanism, and
-sparsely-gated layers. Each of these computational blocks is crucial for a
-subset of the tasks we train on. Interestingly, even if a block is not crucial
-for a task, we observe that adding it never hurts performance and in most cases
-improves it on all tasks. We also show that tasks with less data benefit
-largely from joint training with other tasks, while performance on large tasks
-degrades only slightly if at all.
-",1,0,0,1,0,0
-17259,Porcupine Neural Networks: (Almost) All Local Optima are Global,"  Neural networks have been used prominently in several machine learning and
-statistics applications. In general, the underlying optimization of neural
-networks is non-convex which makes their performance analysis challenging. In
-this paper, we take a novel approach to this problem by asking whether one can
-constrain neural network weights to make its optimization landscape have good
-theoretical properties while at the same time, be a good approximation for the
-unconstrained one. For two-layer neural networks, we provide affirmative
-answers to these questions by introducing Porcupine Neural Networks (PNNs)
-whose weight vectors are constrained to lie over a finite set of lines. We show
-that most local optima of PNN optimizations are global while we have a
-characterization of regions where bad local optimizers may exist. Moreover, our
-theoretical and empirical results suggest that an unconstrained neural network
-can be approximated using a polynomially-large PNN.
-",1,0,0,1,0,0
-17260,Configuration Path Integral Monte Carlo Approach to the Static Density Response of the Warm Dense Electron Gas,"  Precise knowledge of the static density response function (SDRF) of the
-uniform electron gas (UEG) serves as key input for numerous applications, most
-importantly for density functional theory beyond generalized gradient
-approximations. Here we extend the configuration path integral Monte Carlo
-(CPIMC) formalism that was previously applied to the spatially uniform electron
-gas to the case of an inhomogeneous electron gas by adding a spatially periodic
-external potential. This procedure has recently been successfully used in
-permutation blocking path integral Monte Carlo simulations (PB-PIMC) of the
-warm dense electron gas [Dornheim \textit{et al.}, Phys. Rev. E in press,
-arXiv:1706.00315], but this method is restricted to low and moderate densities.
-Implementing this procedure into CPIMC allows us to obtain exact finite
-temperature results for the SDRF of the electron gas at \textit{high to
-moderate densities} closing the gap left open by the PB-PIMC data. In this
-paper we demonstrate how the CPIMC formalism can be efficiently extended to the
-spatially inhomogeneous electron gas and present the first data points.
-Finally, we discuss finite size errors involved in the quantum Monte Carlo
-results for the SDRF in detail and present a solution how to remove them that
-is based on a generalization of ground state techniques.
-",0,1,0,0,0,0
-17261,Superzone gap formation and low lying crystal electric field levels in PrPd$_2$Ge$_2$ single crystal,"  The magnetocrystalline anisotropy exhibited in PrPd$_2$Ge$_2$ single crystal
-has been investigated by measuring the magnetization, magnetic susceptibility,
-electrical resistivity and heat capacity. PrPd$_2$Ge$_2$ crystallizes in the
-well known ThCr$_2$Si$_2$\--type tetragonal structure. The antiferromagnetic
-ordering is confirmed as 5.1~K with the [001]-axis as the easy axis of
-magnetization. A superzone gap formation is observed from the electrical
-resistivity measurement when the current is passed along the [001] direction.
-The crystal electric field (CEF) analysis on the magnetic susceptibility,
-magnetization and the heat capacity measurements confirms a doublet ground
-state with a relatively low over all CEF level splitting. The CEF level
-spacings and the Zeeman splitting at high fields become comparable and lead to
-metamagnetic transition at 34~T due to the CEF level crossing.
-",0,1,0,0,0,0
-17262,Adaptive Real-Time Software Defined MIMO Visible Light Communications using Spatial Multiplexing and Spatial Diversity,"  In this paper, we experimentally demonstrate a real-time software defined
-multiple input multiple output (MIMO) visible light communication (VLC) system
-employing link adaptation of spatial multiplexing and spatial diversity.
-Real-time MIMO signal processing is implemented by using the Field Programmable
-Gate Array (FPGA) based Universal Software Radio Peripheral (USRP) devices.
-Software defined implantation of MIMO VLC can assist in enabling an adaptive
-and reconfigurable communication system without hardware changes. We measured
-the error vector magnitude (EVM), bit error rate (BER) and spectral efficiency
-performance for single carrier M-QAM MIMO VLC using spatial diversity and
-spatial multiplexing. Results show that spatial diversity MIMO VLC improves
-error performance at the cost of spectral efficiency that spatial multiplexing
-should enhance. We propose the adaptive MIMO solution that both modulation
-schema and MIMO schema are dynamically adapted to the changing channel
-conditions for enhancing the error performance and spectral efficiency. The
-average error-free spectral efficiency of adaptive 2x2 MIMO VLC achieved 12
-b/s/Hz over 2 meters indoor dynamic transmission.
-",1,0,1,0,0,0
-17263,Maximum Principle Based Algorithms for Deep Learning,"  The continuous dynamical system approach to deep learning is explored in
-order to devise alternative frameworks for training algorithms. Training is
-recast as a control problem and this allows us to formulate necessary
-optimality conditions in continuous time using the Pontryagin's maximum
-principle (PMP). A modification of the method of successive approximations is
-then used to solve the PMP, giving rise to an alternative training algorithm
-for deep learning. This approach has the advantage that rigorous error
-estimates and convergence results can be established. We also show that it may
-avoid some pitfalls of gradient-based methods, such as slow convergence on flat
-landscapes near saddle points. Furthermore, we demonstrate that it obtains
-favorable initial convergence rate per-iteration, provided Hamiltonian
-maximization can be efficiently carried out - a step which is still in need of
-improvement. Overall, the approach opens up new avenues to attack problems
-associated with deep learning, such as trapping in slow manifolds and
-inapplicability of gradient-based methods for discrete trainable variables.
-",1,0,0,1,0,0
-17264,Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations,"  With the popularity of Linked Open Data (LOD) and the associated rise in
-freely accessible knowledge that can be accessed via LOD, exploiting LOD for
-recommender systems has been widely studied based on various approaches such as
-graph-based or using different machine learning models with LOD-enabled
-features. Many of the previous approaches require construction of an additional
-graph to run graph-based algorithms or to extract path-based features by
-combining user- item interactions (e.g., likes, dislikes) and background
-knowledge from LOD. In this paper, we investigate Factorization Machines (FMs)
-based on particularly lightweight LOD-enabled features which can be directly
-obtained via a public SPARQL Endpoint without any additional effort to
-construct a graph. Firstly, we aim to study whether using FM with these
-lightweight LOD-enabled features can provide competitive performance compared
-to a learning-to-rank approach leveraging LOD as well as other well-established
-approaches such as kNN-item and BPRMF. Secondly, we are interested in finding
-out to what extent each set of LOD-enabled features contributes to the
-recommendation performance. Experimental evaluation on a standard dataset shows
-that our proposed approach using FM with lightweight LOD-enabled features
-provides the best performance compared to other approaches in terms of five
-evaluation metrics. In addition, the study of the recommendation performance
-based on different sets of LOD-enabled features indicate that property-object
-lists and PageRank scores of items are useful for improving the performance,
-and can provide the best performance through using them together for FM. We
-observe that subject-property lists of items does not contribute to the
-recommendation performance but rather decreases the performance.
-",1,0,0,0,0,0
-17265,Wave propagation and homogenization in 2D and 3D lattices: a semi-analytical approach,"  Wave motion in two- and three-dimensional periodic lattices of beam members
-supporting longitudinal and flexural waves is considered. An analytic method
-for solving the Bloch wave spectrum is developed, characterized by a
-generalized eigenvalue equation obtained by enforcing the Floquet condition.
-The dynamic stiffness matrix is shown to be explicitly Hermitian and to admit
-positive eigenvalues. Lattices with hexagonal, rectangular, tetrahedral and
-cubic unit cells are analyzed. The semi-analytical method can be asymptotically
-expanded for low frequency yielding explicit forms for the Christoffel matrix
-describing wave motion in the quasistatic limit.
-",0,1,0,0,0,0
-17266,Waring's problem for unipotent algebraic groups,"  In this paper, we formulate an analogue of Waring's problem for an algebraic
-group $G$. At the field level we consider a morphism of varieties $f\colon
-\mathbb{A}^1\to G$ and ask whether every element of $G(K)$ is the product of a
-bounded number of elements $f(\mathbb{A}^1(K)) = f(K)$. We give an affirmative
-answer when $G$ is unipotent and $K$ is a characteristic zero field which is
-not formally real.
-The idea is the same at the integral level, except one must work with
-schemes, and the question is whether every element in a finite index subgroup
-of $G(\mathcal{O})$ can be written as a product of a bounded number of elements
-of $f(\mathcal{O})$. We prove this is the case when $G$ is unipotent and
-$\mathcal{O}$ is the ring of integers of a totally imaginary number field.
-",0,0,1,0,0,0
-17267,Spreading of localized attacks in spatial multiplex networks,"  Many real-world multilayer systems such as critical infrastructure are
-interdependent and embedded in space with links of a characteristic length.
-They are also vulnerable to localized attacks or failures, such as terrorist
-attacks or natural catastrophes, which affect all nodes within a given radius.
-Here we study the effects of localized attacks on spatial multiplex networks of
-two layers. We find a metastable region where a localized attack larger than a
-critical size induces a nucleation transition as a cascade of failures spreads
-throughout the system, leading to its collapse. We develop a theory to predict
-the critical attack size and find that it exhibits novel scaling behavior. We
-further find that localized attacks in these multiplex systems can induce a
-previously unobserved combination of random and spatial cascades. Our results
-demonstrate important vulnerabilities in real-world interdependent networks and
-show new theoretical features of spatial networks.
-",1,1,0,0,0,0
-17268,Greedy Sparse Signal Reconstruction Using Matching Pursuit Based on Hope-tree,"  The reconstruction of sparse signals requires the solution of an
-$\ell_0$-norm minimization problem in Compressed Sensing. Previous research has
-focused on the investigation of a single candidate to identify the support
-(index of nonzero elements) of a sparse signal. To ensure that the optimal
-candidate can be obtained in each iteration, we propose here an iterative
-greedy reconstruction algorithm (GSRA). First, the intersection of the support
-sets estimated by the Orthogonal Matching Pursuit (OMP) and Subspace Pursuit
-(SP) is set as the initial support set. Then, a hope-tree is built to expand
-the set. Finally, a developed decreasing subspace pursuit method is used to
-rectify the candidate set. Detailed simulation results demonstrate that GSRA is
-more accurate than other typical methods in recovering Gaussian signals, 0--1
-sparse signals, and synthetic signals.
-",1,0,1,0,0,0
-17269,Attack-Aware Multi-Sensor Integration Algorithm for Autonomous Vehicle Navigation Systems,"  In this paper, we propose a fault detection and isolation based attack-aware
-multi-sensor integration algorithm for the detection of cyberattacks in
-autonomous vehicle navigation systems. The proposed algorithm uses an extended
-Kalman filter to construct robust residuals in the presence of noise, and then
-uses a parametric statistical tool to identify cyberattacks. The parametric
-statistical tool is based on the residuals constructed by the measurement
-history rather than one measurement at a time in the properties of
-discrete-time signals and dynamic systems. This approach allows the proposed
-multi-sensor integration algorithm to provide quick detection and low false
-alarm rates for applications in dynamic systems. An example of INS/GNSS
-integration of autonomous navigation systems is presented to validate the
-proposed algorithm by using a software-in-the-loop simulation.
-",1,0,0,0,0,0
-17270,"Turbulence, cascade and singularity in a generalization of the Constantin-Lax-Majda equation","  We study numerically a Constantin-Lax-Majda-De Gregorio model generalized by
-Okamoto, Sakajo and Wunsch, which is a model of fluid turbulence in one
-dimension with an inviscid conservation law. In the presence of the viscosity
-and two types of the large-scale forcings, we show that turbulent cascade of
-the inviscid invariant, which is not limited to quadratic quantity, occurs and
-that properties of this model's turbulent state are related to singularity of
-the inviscid case by adopting standard tools of analyzing fluid turbulence.
-",0,1,0,0,0,0
-17271,Fitting phase--type scale mixtures to heavy--tailed data and distributions,"  We consider the fitting of heavy tailed data and distribution with a special
-attention to distributions with a non--standard shape in the ""body"" of the
-distribution. To this end we consider a dense class of heavy tailed
-distributions introduced recently, employing an EM algorithm for the the
-maximum likelihood estimates of its parameters. We present methods for fitting
-to observed data, histograms, censored data, as well as to theoretical
-distributions. Numerical examples are provided with simulated data and a
-benchmark reinsurance dataset. We empirically demonstrate that our model can
-provide excellent fits to heavy--tailed data/distributions with minimal
-assumptions
-",0,0,1,1,0,0
-17272,Deep Incremental Boosting,"  This paper introduces Deep Incremental Boosting, a new technique derived from
-AdaBoost, specifically adapted to work with Deep Learning methods, that reduces
-the required training time and improves generalisation. We draw inspiration
-from Transfer of Learning approaches to reduce the start-up time to training
-each incremental Ensemble member. We show a set of experiments that outlines
-some preliminary results on some common Deep Learning datasets and discuss the
-potential improvements Deep Incremental Boosting brings to traditional Ensemble
-methods in Deep Learning.
-",1,0,0,1,0,0
-17273,Empirical Likelihood for Linear Structural Equation Models with Dependent Errors,"  We consider linear structural equation models that are associated with mixed
-graphs. The structural equations in these models only involve observed
-variables, but their idiosyncratic error terms are allowed to be correlated and
-non-Gaussian. We propose empirical likelihood (EL) procedures for inference,
-and suggest several modifications, including a profile likelihood, in order to
-improve tractability and performance of the resulting methods. Through
-simulations, we show that when the error distributions are non-Gaussian, the
-use of EL and the proposed modifications may increase statistical efficiency
-and improve assessment of significance.
-",0,0,0,1,0,0
-17274,Grassmannian flows and applications to nonlinear partial differential equations,"  We show how solutions to a large class of partial differential equations with
-nonlocal Riccati-type nonlinearities can be generated from the corresponding
-linearized equations, from arbitrary initial data. It is well known that
-evolutionary matrix Riccati equations can be generated by projecting linear
-evolutionary flows on a Stiefel manifold onto a coordinate chart of the
-underlying Grassmann manifold. Our method relies on extending this idea to the
-infinite dimensional case. The key is an integral equation analogous to the
-Marchenko equation in integrable systems, that represents the coodinate chart
-map. We show explicitly how to generate such solutions to scalar partial
-differential equations of arbitrary order with nonlocal quadratic
-nonlinearities using our approach. We provide numerical simulations that
-demonstrate the generation of solutions to
-Fisher--Kolmogorov--Petrovskii--Piskunov equations with nonlocal
-nonlinearities. We also indicate how the method might extend to more general
-classes of nonlinear partial differential systems.
-",0,1,1,0,0,0
-17275,The Reinhardt Conjecture as an Optimal Control Problem,"  In 1934, Reinhardt conjectured that the shape of the centrally symmetric
-convex body in the plane whose densest lattice packing has the smallest density
-is a smoothed octagon. This conjecture is still open. We formulate the
-Reinhardt Conjecture as a problem in optimal control theory. The smoothed
-octagon is a Pontryagin extremal trajectory with bang-bang control. More
-generally, the smoothed regular $6k+2$-gon is a Pontryagin extremal with
-bang-bang control. The smoothed octagon is a strict (micro) local minimum to
-the optimal control problem. The optimal solution to the Reinhardt problem is a
-trajectory without singular arcs. The extremal trajectories that do not meet
-the singular locus have bang-bang controls with finitely many switching times.
-Finally, we reduce the Reinhardt problem to an optimization problem on a
-five-dimensional manifold. (Each point on the manifold is an initial condition
-for a potential Pontryagin extremal lifted trajectory.) We suggest that the
-Reinhardt conjecture might eventually be fully resolved through optimal control
-theory. Some proofs are computer-assisted using a computer algebra system.
-",0,0,1,0,0,0
-17276,Deep submillimeter and radio observations in the SSA22 field. I. Powering sources and Lyα escape fraction of Lyα blobs,"  We study the heating mechanisms and Ly{\alpha} escape fractions of 35
-Ly{\alpha} blobs (LABs) at z = 3.1 in the SSA22 field. Dust continuum sources
-have been identified in 11 of the 35 LABs, all with star formation rates (SFRs)
-above 100 Msun/yr. Likely radio counterparts are detected in 9 out of 29
-investigated LABs. The detection of submm dust emission is more linked to the
-physical size of the Ly{\alpha} emission than to the Ly{\alpha} luminosities of
-the LABs. A radio excess in the submm/radio detected LABs is common, hinting at
-the presence of active galactic nuclei. Most radio sources without X-ray
-counterparts are located at the centers of the LABs. However, all X-ray
-counterparts avoid the central regions. This may be explained by absorption due
-to exceptionally large column densities along the line-of-sight or by LAB
-morphologies, which are highly orientation dependent. The median Ly{\alpha}
-escape fraction is about 3\% among the submm-detected LABs, which is lower than
-a lower limit of 11\% for the submm-undetected LABs. We suspect that the large
-difference is due to the high dust attenuation supported by the large SFRs, the
-dense large-scale environment as well as large uncertainties in the extinction
-corrections required to apply when interpreting optical data.
-",0,1,0,0,0,0
-17277,Modeling temporal constraints for a system of interactive scores,"  In this chapter we explain briefly the fundamentals of the interactive scores
-formalism. Then we develop a solution for implementing the ECO machine by
-mixing petri nets and constraints propagation. We also present another solution
-for implementing the ECO machine using concurrent constraint programming.
-Finally, we present an extension of interactive score with conditional
-branching.
-",1,0,0,0,0,0
-17278,Electronic structure of ThRu2Si2 studied by angle-resolved photoelectron spectroscopy: Elucidating the contribution of U 5f states in URu2Si2,"  The electronic structure of ThRu2Si2 was studied by angle-resolved
-photoelectron spectroscopy (ARPES) with incident photon energies of hn=655-745
-eV. Detailed band structure and the three-dimensional shapes of Fermi surfaces
-were derived experimentally, and their characteristic features were mostly
-explained by means of band structure calculations based on the density
-functional theory. Comparison of the experimental ARPES spectra of ThRu2Si2
-with those of URu2Si2 shows that they have considerably different spectral
-profiles particularly in the energy range of 1 eV from the Fermi level,
-suggesting that U 5f states are substantially hybridized in these bands. The
-relationship between the ARPES spectra of URu2Si2 and ThRu2Si2 is very
-different from the one between the ARPES spectra of CeRu2Si2 and LaRu2Si2,
-where the intrinsic difference in their spectra is limited only in the very
-vicinity of the Fermi energy. The present result suggests that the U 5f
-electrons in URu2Si2 have strong hybridization with ligand states and have an
-essentially itinerant character.
-",0,1,0,0,0,0
-17279,Non-zero constant curvature factorable surfaces in pseudo-Galilean space,"  Factorable surfaces, i.e. graphs associated with the product of two functions
-of one variable, constitute a wide class of surfaces. Such surfaces in the
-pseudo-Galilean space with zero Gaussian and mean curvature were obtained in
-[1]. In this study, we provide new classification results relating to the
-factorable surfaces with non-zero Gaussian and mean curvature.
-",0,0,1,0,0,0
-17280,Darboux and Binary Darboux Transformations for Discrete Integrable Systems. II. Discrete Potential mKdV Equation,"  The paper presents two results. First it is shown how the discrete potential
-modified KdV equation and its Lax pairs in matrix form arise from the
-Hirota-Miwa equation by a 2-periodic reduction. Then Darboux transformations
-and binary Darboux transformations are derived for the discrete potential
-modified KdV equation and it is shown how these may be used to construct exact
-solutions.
-",0,1,0,0,0,0
-17281,Nearly second-order asymptotic optimality of sequential change-point detection with one-sample updates,"  Sequential change-point detection when the distribution parameters are
-unknown is a fundamental problem in statistics and machine learning. When the
-post-change parameters are unknown, we consider a set of detection procedures
-based on sequential likelihood ratios with non-anticipating estimators
-constructed using online convex optimization algorithms such as online mirror
-descent, which provides a more versatile approach to tackle complex situations
-where recursive maximum likelihood estimators cannot be found. When the
-underlying distributions belong to a exponential family and the estimators
-satisfy the logarithm regret property, we show that this approach is nearly
-second-order asymptotically optimal. This means that the upper bound for the
-false alarm rate of the algorithm (measured by the average-run-length) meets
-the lower bound asymptotically up to a log-log factor when the threshold tends
-to infinity. Our proof is achieved by making a connection between sequential
-change-point and online convex optimization and leveraging the logarithmic
-regret bound property of online mirror descent algorithm. Numerical and real
-data examples validate our theory.
-",1,0,1,1,0,0
-17282,Algorithms in the classical Néron Desingularization,"  We give algorithms to construct the Néron Desingularization and the easy
-case from \cite{KK} of the General Néron Desingularization.
-",0,0,1,0,0,0
-17283,Recent Advances in Neural Program Synthesis,"  In recent years, deep learning has made tremendous progress in a number of
-fields that were previously out of reach for artificial intelligence. The
-successes in these problems has led researchers to consider the possibilities
-for intelligent systems to tackle a problem that humans have only recently
-themselves considered: program synthesis. This challenge is unlike others such
-as object recognition and speech translation, since its abstract nature and
-demand for rigor make it difficult even for human minds to attempt. While it is
-still far from being solved or even competitive with most existing methods,
-neural program synthesis is a rapidly growing discipline which holds great
-promise if completely realized. In this paper, we start with exploring the
-problem statement and challenges of program synthesis. Then, we examine the
-fascinating evolution of program induction models, along with how they have
-succeeded, failed and been reimagined since. Finally, we conclude with a
-contrastive look at program synthesis and future research recommendations for
-the field.
-",1,0,0,0,0,0
-17284,Generator Reversal,"  We consider the problem of training generative models with deep neural
-networks as generators, i.e. to map latent codes to data points. Whereas the
-dominant paradigm combines simple priors over codes with complex deterministic
-models, we propose instead to use more flexible code distributions. These
-distributions are estimated non-parametrically by reversing the generator map
-during training. The benefits include: more powerful generative models, better
-modeling of latent structure and explicit control of the degree of
-generalization.
-",1,0,0,1,0,0
-17285,Finite model reasoning over existential rules,"  Ontology-based query answering (OBQA) asks whether a Boolean conjunctive
-query is satisfied by all models of a logical theory consisting of a relational
-database paired with an ontology. The introduction of existential rules (i.e.,
-Datalog rules extended with existential quantifiers in rule-heads) as a means
-to specify the ontology gave birth to Datalog+/-, a framework that has received
-increasing attention in the last decade, with focus also on decidability and
-finite controllability to support effective reasoning. Five basic decidable
-fragments have been singled out: linear, weakly-acyclic, guarded, sticky, and
-shy. Moreover, for all these fragments, except shy, the important property of
-finite controllability has been proved, ensuring that a query is satisfied by
-all models of the theory iff it is satisfied by all its finite models. In this
-paper we complete the picture by demonstrating that finite controllability of
-OBQA holds also for shy ontologies, and it therefore applies to all basic
-decidable Datalog+/- classes. To make the demonstration, we devise a general
-technique to facilitate the process of (dis)proving finite controllability of
-an arbitrary ontological fragment. This paper is under consideration for
-acceptance in TPLP.
-",1,0,0,0,0,0
-17286,On the convergence properties of a $K$-step averaging stochastic gradient descent algorithm for nonconvex optimization,"  Despite their popularity, the practical performance of asynchronous
-stochastic gradient descent methods (ASGD) for solving large scale machine
-learning problems are not as good as theoretical results indicate. We adopt and
-analyze a synchronous K-step averaging stochastic gradient descent algorithm
-which we call K-AVG. We establish the convergence results of K-AVG for
-nonconvex objectives and explain why the K-step delay is necessary and leads to
-better performance than traditional parallel stochastic gradient descent which
-is a special case of K-AVG with $K=1$. We also show that K-AVG scales better
-than ASGD. Another advantage of K-AVG over ASGD is that it allows larger
-stepsizes. On a cluster of $128$ GPUs, K-AVG is faster than ASGD
-implementations and achieves better accuracies and faster convergence for
-\cifar dataset.
-",1,0,0,1,0,0
-17287,Adversarial Neural Machine Translation,"  In this paper, we study a new learning paradigm for Neural Machine
-Translation (NMT). Instead of maximizing the likelihood of the human
-translation as in previous works, we minimize the distinction between human
-translation and the translation given by an NMT model. To achieve this goal,
-inspired by the recent success of generative adversarial networks (GANs), we
-employ an adversarial training architecture and name it as Adversarial-NMT. In
-Adversarial-NMT, the training of the NMT model is assisted by an adversary,
-which is an elaborately designed Convolutional Neural Network (CNN). The goal
-of the adversary is to differentiate the translation result generated by the
-NMT model from that by human. The goal of the NMT model is to produce high
-quality translations so as to cheat the adversary. A policy gradient method is
-leveraged to co-train the NMT model and the adversary. Experimental results on
-English$\rightarrow$French and German$\rightarrow$English translation tasks
-show that Adversarial-NMT can achieve significantly better translation quality
-than several strong baselines.
-",1,0,0,1,0,0
-17288,Surface group amalgams that (don't) act on 3-manifolds,"  We determine which amalgamated products of surface groups identified over
-multiples of simple closed curves are not fundamental groups of 3-manifolds. We
-prove each surface amalgam considered is virtually the fundamental group of a
-3-manifold. We prove that each such surface group amalgam is abstractly
-commensurable to a right-angled Coxeter group from a related family. In an
-appendix, we determine the quasi-isometry classes among these surface amalgams
-and their related right-angled Coxeter groups.
-",0,0,1,0,0,0
-17289,Shading Annotations in the Wild,"  Understanding shading effects in images is critical for a variety of vision
-and graphics problems, including intrinsic image decomposition, shadow removal,
-image relighting, and inverse rendering. As is the case with other vision
-tasks, machine learning is a promising approach to understanding shading - but
-there is little ground truth shading data available for real-world images. We
-introduce Shading Annotations in the Wild (SAW), a new large-scale, public
-dataset of shading annotations in indoor scenes, comprised of multiple forms of
-shading judgments obtained via crowdsourcing, along with shading annotations
-automatically generated from RGB-D imagery. We use this data to train a
-convolutional neural network to predict per-pixel shading information in an
-image. We demonstrate the value of our data and network in an application to
-intrinsic images, where we can reduce decomposition artifacts produced by
-existing algorithms. Our database is available at
-this http URL.
-",1,0,0,0,0,0
-17290,Koszul cycles and Golod rings,"  Let $S$ be the power series ring or the polynomial ring over a field $K$ in
-the variables $x_1,\ldots,x_n$, and let $R=S/I$, where $I$ is proper ideal
-which we assume to be graded if $S$ is the polynomial ring. We give an explicit
-description of the cycles of the Koszul complex whose homology classes generate
-the Koszul homology of $R=S/I$ with respect to $x_1,\ldots,x_n$. The
-description is given in terms of the data of the free $S$-resolution of $R$.
-The result is used to determine classes of Golod ideals, among them proper
-ordinary powers and proper symbolic powers of monomial ideals. Our theory is
-also applied to stretched local rings.
-",0,0,1,0,0,0
-17291,PacGAN: The power of two samples in generative adversarial networks,"  Generative adversarial networks (GANs) are innovative techniques for learning
-generative models of complex data distributions from samples. Despite
-remarkable recent improvements in generating realistic images, one of their
-major shortcomings is the fact that in practice, they tend to produce samples
-with little diversity, even when trained on diverse datasets. This phenomenon,
-known as mode collapse, has been the main focus of several recent advances in
-GANs. Yet there is little understanding of why mode collapse happens and why
-existing approaches are able to mitigate mode collapse. We propose a principled
-approach to handling mode collapse, which we call packing. The main idea is to
-modify the discriminator to make decisions based on multiple samples from the
-same class, either real or artificially generated. We borrow analysis tools
-from binary hypothesis testing---in particular the seminal result of Blackwell
-[Bla53]---to prove a fundamental connection between packing and mode collapse.
-We show that packing naturally penalizes generators with mode collapse, thereby
-favoring generator distributions with less mode collapse during the training
-process. Numerical experiments on benchmark datasets suggests that packing
-provides significant improvements in practice as well.
-",1,0,0,1,0,0
-17292,Stein-like Estimators for Causal Mediation Analysis in Randomized Trials,"  Causal mediation analysis aims to estimate the natural direct and indirect
-effects under clearly specified assumptions. Traditional mediation analysis
-based on Ordinary Least Squares (OLS) relies on the absence of unmeasured
-causes of the putative mediator and outcome. When this assumption cannot be
-justified, Instrumental Variables (IV) estimators can be used in order to
-produce an asymptotically unbiased estimator of the mediator-outcome link.
-However, provided that valid instruments exist, bias removal comes at the cost
-of variance inflation for standard IV procedures such as Two-Stage Least
-Squares (TSLS). A Semi-Parametric Stein-Like (SPSL) estimator has been proposed
-in the literature that strikes a natural trade-off between the unbiasedness of
-the TSLS procedure and the relatively small variance of the OLS estimator.
-Moreover, the SPSL has the advantage that its shrinkage parameter can be
-directly estimated from the data. In this paper, we demonstrate how this
-Stein-like estimator can be implemented in the context of the estimation of
-natural direct and natural indirect effects of treatments in randomized
-controlled trials. The performance of the competing methods is studied in a
-simulation study, in which both the strength of hidden confounding and the
-strength of the instruments are independently varied. These considerations are
-motivated by a trial in mental health evaluating the impact of a primary
-care-based intervention to reduce depression in the elderly.
-",0,0,0,1,0,0
-17293,Structure-Based Subspace Method for Multi-Channel Blind System Identification,"  In this work, a novel subspace-based method for blind identification of
-multichannel finite impulse response (FIR) systems is presented. Here, we
-exploit directly the impeded Toeplitz channel structure in the signal linear
-model to build a quadratic form whose minimization leads to the desired channel
-estimation up to a scalar factor. This method can be extended to estimate any
-predefined linear structure, e.g. Hankel, that is usually encountered in linear
-systems. Simulation findings are provided to highlight the appealing advantages
-of the new structure-based subspace (SSS) method over the standard subspace
-(SS) method in certain adverse identification scenarii.
-",1,0,0,1,0,0
-17294,On Certain Analytical Representations of Cellular Automata,"  We extend a previously introduced semi-analytical representation of a
-decomposition of CA dynamics in arbitrary dimensions and neighborhood schemes
-via the use of certain universal maps in which CA rule vectors are derivable
-from the equivalent of superpotentials. The results justify the search for
-alternative analog models of computation and their possible physical
-connections.
-",0,1,0,0,0,0
-17295,Strong consistency and optimality for generalized estimating equations with stochastic covariates,"  In this article we study the existence and strong consistency of GEE
-estimators, when the generalized estimating functions are martingales with
-random coefficients. Furthermore, we characterize estimating functions which
-are asymptotically optimal.
-",0,0,1,1,0,0
-17296,Synthesis and electronic properties of Ruddlesden-Popper strontium iridate epitaxial thin films stabilized by control of growth kinetics,"  We report on the selective fabrication of high-quality Sr$_2$IrO$_4$ and
-SrIrO$_3$ epitaxial thin films from a single polycrystalline Sr$_2$IrO$_4$
-target by pulsed laser deposition. Using a combination of X-ray diffraction and
-photoemission spectroscopy characterizations, we discover that within a
-relatively narrow range of substrate temperature, the oxygen partial pressure
-plays a critical role in the cation stoichiometric ratio of the films, and
-triggers the stabilization of different Ruddlesden-Popper (RP) phases. Resonant
-X-ray absorption spectroscopy measurements taken at the Ir $L$-edge and the O
-$K$-edge demonstrate the presence of strong spin-orbit coupling, and reveal the
-electronic and orbital structures of both compounds. These results suggest that
-in addition to the conventional thermodynamics consideration, higher members of
-the Sr$_{n+1}$Ir$_n$O$_{3n+1}$ series can possibly be achieved by kinetic
-control away from the thermodynamic limit. These findings offer a new approach
-to the synthesis of ultra-thin films of the RP series of iridates and can be
-extended to other complex oxides with layered structure.
-",0,1,0,0,0,0
-17297,A proof on energy gap for Yang-Mills connection,"  In this note, we prove an ${L^{\frac{n}{2}}}$-energy gap result for
-Yang-Mills connections on a principal $G$-bundle over a compact manifold
-without using Lojasiewicz-Simon gradient inequality (arXiv:1502.00668).
-",0,0,1,0,0,0
-17298,Realisability of Pomsets via Communicating Automata,"  Pomsets are a model of concurrent computations introduced by Pratt. They can
-provide a syntax-oblivious description of semantics of coordination models
-based on asynchronous message-passing, such as Message Sequence Charts (MSCs).
-In this paper, we study conditions that ensure a specification expressed as a
-set of pomsets can be faithfully realised via communicating automata. Our main
-contributions are (i) the definition of a realisability condition accounting
-for termination soundness, (ii) conditions for global specifications with
-""multi-threaded"" participants, and (iii) the definition of realisability
-conditions that can be decided directly over pomsets. A positive by-product of
-our approach is the efficiency gain in the verification of the realisability
-conditions obtained when restricting to specific classes of choreographies
-characterisable in term of behavioural types.
-",1,0,0,0,0,0
-17299,Complex pattern formation driven by the interaction of stable fronts in a competition-diffusion system,"  The ecological invasion problem in which a weaker exotic species invades an
-ecosystem inhabited by two strongly competing native species is modelled by a
-three-species competition-diffusion system. It is known that for a certain
-range of parameter values competitor-mediated coexistence occurs and complex
-spatio-temporal patterns are observed in two spatial dimensions. In this paper
-we uncover the mechanism which generates such patterns. Under some assumptions
-on the parameters the three-species competition-diffusion system admits two
-planarly stable travelling waves. Their interaction in one spatial dimension
-may result in either reflection or merging into a single homoclinic wave,
-depending on the strength of the invading species. This transition can be
-understood by studying the bifurcation structure of the homoclinic wave. In
-particular, a time-periodic homoclinic wave (breathing wave) is born from a
-Hopf bifurcation and its unstable branch acts as a separator between the
-reflection and merging regimes. The same transition occurs in two spatial
-dimensions: the stable regular spiral associated to the homoclinic wave
-destabilizes, giving rise first to an oscillating breathing spiral and then
-breaking up producing a dynamic pattern characterized by many spiral cores. We
-find that these complex patterns are generated by the interaction of two
-planarly stable travelling waves, in contrast with many other well known cases
-of pattern formation where planar instability plays a central role.
-",0,0,0,0,1,0
-17300,Solitons with rings and vortex rings on solitons in nonlocal nonlinear media,"  Nonlocality is a key feature of many physical systems since it prevents a
-catastrophic collapse and a symmetry-breaking azimuthal instability of intense
-wave beams in a bulk self-focusing nonlinear media. This opens up an intriguing
-perspective for stabilization of complex topological structures such as
-higher-order solitons, vortex rings and vortex ring-on-line complexes. Using
-direct numerical simulations, we find a class of cylindrically-symmetric $n$-th
-order spatial solitons having the intensity distribution with a central bright
-spot surrounded by $n$ bright rings of varying size. We investigate dynamical
-properties of these higher-order solitons in a media with thermal nonlocal
-nonlinear response. We show theoretically that a vortex complex of vortex ring
-and vortex line, carrying two independent winding numbers, can be created by
-perturbation of the stable optical vortex soliton in nonlocal nonlinear media.
-",0,1,0,0,0,0
-17301,Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection,"  We introduce Deep-HiTS, a rotation invariant convolutional neural network
-(CNN) model for classifying images of transients candidates into artifacts or
-real sources for the High cadence Transient Survey (HiTS). CNNs have the
-advantage of learning the features automatically from the data while achieving
-high performance. We compare our CNN model against a feature engineering
-approach using random forests (RF). We show that our CNN significantly
-outperforms the RF model reducing the error by almost half. Furthermore, for a
-fixed number of approximately 2,000 allowed false transient candidates per
-night we are able to reduce the miss-classified real transients by
-approximately 1/5. To the best of our knowledge, this is the first time CNNs
-have been used to detect astronomical transient events. Our approach will be
-very useful when processing images from next generation instruments such as the
-Large Synoptic Survey Telescope (LSST). We have made all our code and data
-available to the community for the sake of allowing further developments and
-comparisons at this https URL.
-",1,1,0,0,0,0
-17302,"On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL","  In various approaches to learning, notably in domain adaptation, active
-learning, learning under covariate shift, semi-supervised learning, learning
-with concept drift, and the like, one often wants to compare a baseline
-classifier to one or more advanced (or at least different) strategies. In this
-chapter, we basically argue that if such classifiers, in their respective
-training phases, optimize a so-called surrogate loss that it may also be
-valuable to compare the behavior of this loss on the test set, next to the
-regular classification error rate. It can provide us with an additional view on
-the classifiers' relative performances that error rates cannot capture. As an
-example, limited but convincing empirical results demonstrates that we may be
-able to find semi-supervised learning strategies that can guarantee performance
-improvements with increasing numbers of unlabeled data in terms of
-log-likelihood. In contrast, the latter may be impossible to guarantee for the
-classification error rate.
-",1,0,0,1,0,0
-17303,Do Reichenbachian Common Cause Systems of Arbitrary Finite Size Exist?,"  The principle of common cause asserts that positive correlations between
-causally unrelated events ought to be explained through the action of some
-shared causal factors. Reichenbachian common cause systems are probabilistic
-structures aimed at accounting for cases where correlations of the aforesaid
-sort cannot be explained through the action of a single common cause. The
-existence of Reichenbachian common cause systems of arbitrary finite size for
-each pair of non-causally correlated events was allegedly demonstrated by
-Hofer-Szabó and Rédei in 2006. This paper shows that their proof is
-logically deficient, and we propose an improved proof.
-",1,1,0,1,0,0
-17304,Co-evolution of nodes and links: diversity driven coexistence in cyclic competition of three species,"  When three species compete cyclically in a well-mixed, stochastic system of
-$N$ individuals, extinction is known to typically occur at times scaling as the
-system size $N$. This happens, for example, in rock-paper-scissors games or
-conserved Lotka-Volterra models in which every pair of individuals can interact
-on a complete graph. Here we show that if the competing individuals also have a
-""social temperament"" to be either introverted or extroverted, leading them to
-cut or add links respectively, then long-living state in which all species
-coexist can occur when both introverts and extroverts are present. These states
-are non-equilibrium quasi-steady states, maintained by a subtle balance between
-species competition and network dynamcis. Remarkably, much of the phenomena is
-embodied in a mean-field description. However, an intuitive understanding of
-why diversity stabilizes the co-evolving node and link dynamics remains an open
-issue.
-",0,0,0,0,1,0
-17305,Online Learning with an Almost Perfect Expert,"  We study the multiclass online learning problem where a forecaster makes a
-sequence of predictions using the advice of $n$ experts. Our main contribution
-is to analyze the regime where the best expert makes at most $b$ mistakes and
-to show that when $b = o(\log_4{n})$, the expected number of mistakes made by
-the optimal forecaster is at most $\log_4{n} + o(\log_4{n})$. We also describe
-an adversary strategy showing that this bound is tight and that the worst case
-is attained for binary prediction.
-",0,0,0,1,0,0
-17306,Actively Learning what makes a Discrete Sequence Valid,"  Deep learning techniques have been hugely successful for traditional
-supervised and unsupervised machine learning problems. In large part, these
-techniques solve continuous optimization problems. Recently however, discrete
-generative deep learning models have been successfully used to efficiently
-search high-dimensional discrete spaces. These methods work by representing
-discrete objects as sequences, for which powerful sequence-based deep models
-can be employed. Unfortunately, these techniques are significantly hindered by
-the fact that these generative models often produce invalid sequences. As a
-step towards solving this problem, we propose to learn a deep recurrent
-validator model. Given a partial sequence, our model learns the probability of
-that sequence occurring as the beginning of a full valid sequence. Thus this
-identifies valid versus invalid sequences and crucially it also provides
-insight about how individual sequence elements influence the validity of
-discrete objects. To learn this model we propose an approach inspired by
-seminal work in Bayesian active learning. On a synthetic dataset, we
-demonstrate the ability of our model to distinguish valid and invalid
-sequences. We believe this is a key step toward learning generative models that
-faithfully produce valid discrete objects.
-",1,0,0,1,0,0
-17307,Symmetries and conservation laws of Hamiltonian systems,"  In this paper we study the infinitesimal symmetries, Newtonoid vector fields,
-infinitesimal Noether symmetries and conservation laws of Hamiltonian systems.
-Using the dynamical covariant derivative and Jacobi endomorphism on the
-cotangent bundle we find the invariant equations of infinitesimal symmetries
-and Newtonoid vector fields and prove that the canonical nonlinear connection
-induced by a regular Hamiltonian can be determined by these symmetries.
-Finally, an example from optimal control theory is given.
-",0,0,1,0,0,0
-17308,Fractional differential and fractional integral modified-Bloch equations for PFG anomalous diffusion and their general solutions,"  The studying of anomalous diffusion by pulsed field gradient (PFG) diffusion
-technique still faces challenges. Two different research groups have proposed
-modified Bloch equation for anomalous diffusion. However, these equations have
-different forms and, therefore, yield inconsistent results. The discrepancy in
-these reported modified Bloch equations may arise from different ways of
-combining the fractional diffusion equation with the precession equation where
-the time derivatives have different derivative orders and forms. Moreover, to
-the best of my knowledge, the general PFG signal attenuation expression
-including finite gradient pulse width (FGPW) effect for time-space fractional
-diffusion based on the fractional derivative has yet to be reported by other
-methods. Here, based on different combination strategy, two new modified Bloch
-equations are proposed, which belong to two significantly different types: a
-differential type based on the fractal derivative and an integral type based on
-the fractional derivative. The merit of the integral type modified Bloch
-equation is that the original properties of the contributions from linear or
-nonlinear processes remain unchanged at the instant of the combination. The
-general solutions including the FGPW effect were derived from these two
-equations as well as from two other methods: a method observing the signal
-intensity at the origin and the recently reported effective phase shift
-diffusion equation method. The relaxation effect was also considered. It is
-found that the relaxation behavior influenced by fractional diffusion based on
-the fractional derivative deviates from that of normal diffusion. The general
-solution agrees perfectly with continuous-time random walk (CTRW) simulations
-as well as reported literature results. The new modified Bloch equations is a
-valuable tool to describe PFG anomalous diffusion in NMR and MRI.
-",0,1,0,0,0,0
-17309,Change of the vortex core structure in two-band superconductors at impurity-scattering-driven $s_\pm/s_{++}$ crossover,"  We report a nontrivial transition in the core structure of vortices in
-two-band superconductors as a function of interband impurity scattering. We
-demonstrate that, in addition to singular zeros of the order parameter, the
-vortices there can acquire a circular nodal line around the singular point in
-one of the superconducting components. It results in the formation of the
-peculiar ""moat""-like profile in one of the superconducting gaps. The moat-core
-vortices occur generically in the vicinity of the impurity-induced crossover
-between $s_{\pm}$ and $s_{++}$ states.
-",0,1,0,0,0,0
-17310,Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit,"  Multi-armed bandit (MAB) is a class of online learning problems where a
-learning agent aims to maximize its expected cumulative reward while repeatedly
-selecting to pull arms with unknown reward distributions. We consider a
-scenario where the reward distributions may change in a piecewise-stationary
-fashion at unknown time steps. We show that by incorporating a simple
-change-detection component with classic UCB algorithms to detect and adapt to
-changes, our so-called M-UCB algorithm can achieve nearly optimal regret bound
-on the order of $O(\sqrt{MKT\log T})$, where $T$ is the number of time steps,
-$K$ is the number of arms, and $M$ is the number of stationary segments.
-Comparison with the best available lower bound shows that our M-UCB is nearly
-optimal in $T$ up to a logarithmic factor. We also compare M-UCB with the
-state-of-the-art algorithms in numerical experiments using a public Yahoo!
-dataset to demonstrate its superior performance.
-",0,0,0,1,0,0
-17311,An initial-boundary value problem of the general three-component nonlinear Schrodinger equation with a 4x4 Lax pair on a finite interval,"  We investigate the initial-boundary value problem for the general
-three-component nonlinear Schrodinger (gtc-NLS) equation with a 4x4 Lax pair on
-a finite interval by extending the Fokas unified approach. The solutions of the
-gtc-NLS equation can be expressed in terms of the solutions of a 4x4 matrix
-Riemann-Hilbert (RH) problem formulated in the complex k-plane. Moreover, the
-relevant jump matrices of the RH problem can be explicitly found via the three
-spectral functions arising from the initial data, the Dirichlet-Neumann
-boundary data. The global relation is also established to deduce two distinct
-but equivalent types of representations (i.e., one by using the large k of
-asymptotics of the eigenfunctions and another one in terms of the
-Gelfand-Levitan-Marchenko (GLM) method) for the Dirichlet and Neumann boundary
-value problems. Moreover, the relevant formulae for boundary value problems on
-the finite interval can reduce to ones on the half-line as the length of the
-interval approaches to infinity. Finally, we also give the linearizable
-boundary conditions for the GLM representation.
-",0,1,1,0,0,0
-17312,Deep Learning Microscopy,"  We demonstrate that a deep neural network can significantly improve optical
-microscopy, enhancing its spatial resolution over a large field-of-view and
-depth-of-field. After its training, the only input to this network is an image
-acquired using a regular optical microscope, without any changes to its design.
-We blindly tested this deep learning approach using various tissue samples that
-are imaged with low-resolution and wide-field systems, where the network
-rapidly outputs an image with remarkably better resolution, matching the
-performance of higher numerical aperture lenses, also significantly surpassing
-their limited field-of-view and depth-of-field. These results are
-transformative for various fields that use microscopy tools, including e.g.,
-life sciences, where optical microscopy is considered as one of the most widely
-used and deployed techniques. Beyond such applications, our presented approach
-is broadly applicable to other imaging modalities, also spanning different
-parts of the electromagnetic spectrum, and can be used to design computational
-imagers that get better and better as they continue to image specimen and
-establish new transformations among different modes of imaging.
-",1,1,0,0,0,0
-17313,Effects of pressure impulse and peak pressure of a shock wave on microjet velocity and the onset of cavitation in a microchannel,"  The development of needle-free injection systems utilizing high-speed
-microjets is of great importance to world healthcare. It is thus crucial to
-control the microjets, which are often induced by underwater shock waves. In
-this contribution from fluid-mechanics point of view, we experimentally
-investigate the effect of a shock wave on the velocity of a free surface
-(microjet) and underwater cavitation onset in a microchannel, focusing on the
-pressure impulse and peak pressure of the shock wave. The shock wave used had a
-non-spherically-symmetric peak pressure distribution and a spherically
-symmetric pressure impulse distribution [Tagawa et al., J. Fluid Mech., 2016,
-808, 5-18]. First, we investigate the effect of the shock wave on the jet
-velocity by installing a narrow tube and a hydrophone in different
-configurations in a large water tank, and measuring the shock wave pressure and
-the jet velocity simultaneously. The results suggest that the jet velocity
-depends only on the pressure impulse of the shock wave. We then investigate the
-effect of the shock wave on the cavitation onset by taking measurements in an
-L-shaped microchannel. The results suggest that the probability of cavitation
-onset depends only on the peak pressure of the shock wave. In addition, the jet
-velocity varies according to the presence or absence of cavitation. The above
-findings provide new insights for advancing a control method for high-speed
-microjets.
-",0,1,0,0,0,0
-17314,Clustering with Noisy Queries,"  In this paper, we initiate a rigorous theoretical study of clustering with
-noisy queries (or a faulty oracle). Given a set of $n$ elements, our goal is to
-recover the true clustering by asking minimum number of pairwise queries to an
-oracle. Oracle can answer queries of the form : ""do elements $u$ and $v$ belong
-to the same cluster?"" -- the queries can be asked interactively (adaptive
-queries), or non-adaptively up-front, but its answer can be erroneous with
-probability $p$. In this paper, we provide the first information theoretic
-lower bound on the number of queries for clustering with noisy oracle in both
-situations. We design novel algorithms that closely match this query complexity
-lower bound, even when the number of clusters is unknown. Moreover, we design
-computationally efficient algorithms both for the adaptive and non-adaptive
-settings. The problem captures/generalizes multiple application scenarios. It
-is directly motivated by the growing body of work that use crowdsourcing for
-{\em entity resolution}, a fundamental and challenging data mining task aimed
-to identify all records in a database referring to the same entity. Here crowd
-represents the noisy oracle, and the number of queries directly relates to the
-cost of crowdsourcing. Another application comes from the problem of {\em sign
-edge prediction} in social network, where social interactions can be both
-positive and negative, and one must identify the sign of all pair-wise
-interactions by querying a few pairs. Furthermore, clustering with noisy oracle
-is intimately connected to correlation clustering, leading to improvement
-therein. Finally, it introduces a new direction of study in the popular {\em
-stochastic block model} where one has an incomplete stochastic block model
-matrix to recover the clusters.
-",1,0,0,1,0,0
-17315,Divide-and-Conquer Checkpointing for Arbitrary Programs with No User Annotation,"  Classical reverse-mode automatic differentiation (AD) imposes only a small
-constant-factor overhead in operation count over the original computation, but
-has storage requirements that grow, in the worst case, in proportion to the
-time consumed by the original computation. This storage blowup can be
-ameliorated by checkpointing, a process that reorders application of classical
-reverse-mode AD over an execution interval to tradeoff space \vs\ time.
-Application of checkpointing in a divide-and-conquer fashion to strategically
-chosen nested execution intervals can break classical reverse-mode AD into
-stages which can reduce the worst-case growth in storage from linear to
-sublinear. Doing this has been fully automated only for computations of
-particularly simple form, with checkpoints spanning execution intervals
-resulting from a limited set of program constructs. Here we show how the
-technique can be automated for arbitrary computations. The essential innovation
-is to apply the technique at the level of the language implementation itself,
-thus allowing checkpoints to span any execution interval.
-",1,0,0,0,0,0
-17316,Bow Ties in the Sky II: Searching for Gamma-ray Halos in the Fermi Sky Using Anisotropy,"  Many-degree-scale gamma-ray halos are expected to surround extragalactic
-high-energy gamma ray sources. These arise from the inverse Compton emission of
-an intergalactic population of relativistic electron/positron pairs generated
-by the annihilation of >100 GeV gamma rays on the extragalactic background
-light. These are typically anisotropic due to the jetted structure from which
-they originate or the presence of intergalactic magnetic fields. Here we
-propose a novel method for detecting these inverse-Compton gamma-ray halos
-based upon this anisotropic structure. Specifically, we show that by stacking
-suitably defined angular power spectra instead of images it is possible to
-robustly detect gamma-ray halos with existing Fermi Large Area Telescope (LAT)
-observations for a broad class of intergalactic magnetic fields. Importantly,
-these are largely insensitive to systematic uncertainties within the LAT
-instrumental response or associated with contaminating astronomical sources.
-",0,1,0,0,0,0
-17317,Gain-loss-driven travelling waves in PT-symmetric nonlinear metamaterials,"  In this work we investigate a one-dimensional parity-time (PT)-symmetric
-magnetic metamaterial consisting of split-ring dimers having gain or loss.
-Employing a Melnikov analysis we study the existence of localized travelling
-waves, i.e. homoclinic or heteroclinic solutions. We find conditions under
-which the homoclinic or heteroclinic orbits persist. Our analytical results are
-found to be in good agreement with direct numerical computations. For the
-particular nonlinearity admitting travelling kinks, numerically we observe
-homoclinic snaking in the bifurcation diagram. The Melnikov analysis yields a
-good approximation to one of the boundaries of the snaking profile.
-",0,1,0,0,0,0
-17318,CapsuleGAN: Generative Adversarial Capsule Network,"  We present Generative Adversarial Capsule Network (CapsuleGAN), a framework
-that uses capsule networks (CapsNets) instead of the standard convolutional
-neural networks (CNNs) as discriminators within the generative adversarial
-network (GAN) setting, while modeling image data. We provide guidelines for
-designing CapsNet discriminators and the updated GAN objective function, which
-incorporates the CapsNet margin loss, for training CapsuleGAN models. We show
-that CapsuleGAN outperforms convolutional-GAN at modeling image data
-distribution on MNIST and CIFAR-10 datasets, evaluated on the generative
-adversarial metric and at semi-supervised image classification.
-",0,0,0,1,0,0
-17319,sourceR: Classification and Source Attribution of Infectious Agents among Heterogeneous Populations,"  Zoonotic diseases are a major cause of morbidity, and productivity losses in
-both humans and animal populations. Identifying the source of food-borne
-zoonoses (e.g. an animal reservoir or food product) is crucial for the
-identification and prioritisation of food safety interventions. For many
-zoonotic diseases it is difficult to attribute human cases to sources of
-infection because there is little epidemiological information on the cases.
-However, microbial strain typing allows zoonotic pathogens to be categorised,
-and the relative frequencies of the strain types among the sources and in human
-cases allows inference on the likely source of each infection. We introduce
-sourceR, an R package for quantitative source attribution, aimed at food-borne
-diseases. It implements a fully joint Bayesian model using strain-typed
-surveillance data from both human cases and source samples, capable of
-identifying important sources of infection. The model measures the force of
-infection from each source, allowing for varying survivability, pathogenicity
-and virulence of pathogen strains, and varying abilities of the sources to act
-as vehicles of infection. A Bayesian non-parametric (Dirichlet process)
-approach is used to cluster pathogen strain types by epidemiological behaviour,
-avoiding model overfitting and allowing detection of strain types associated
-with potentially high 'virulence'.
-sourceR is demonstrated using Campylobacter jejuni isolate data collected in
-New Zealand between 2005 and 2008. It enables straightforward attribution of
-cases of zoonotic infection to putative sources of infection by epidemiologists
-and public health decision makers. As sourceR develops, we intend it to become
-an important and flexible resource for food-borne disease attribution studies.
-",0,0,0,1,0,0
-17320,Low resistive edge contacts to CVD-grown graphene using a CMOS compatible metal,"  The exploitation of the excellent intrinsic electronic properties of graphene
-for device applications is hampered by a large contact resistance between the
-metal and graphene. The formation of edge contacts rather than top contacts is
-one of the most promising solutions for realizing low ohmic contacts. In this
-paper the fabrication and characterization of edge contacts to large area
-CVD-grown monolayer graphene by means of optical lithography using CMOS
-compatible metals, i.e. Nickel and Aluminum is reported. Extraction of the
-contact resistance by Transfer Line Method (TLM) as well as the direct
-measurement using Kelvin Probe Force Microscopy demonstrates a very low width
-specific contact resistance.
-",0,1,0,0,0,0
-17321,Uniqueness of planar vortex patch in incompressible steady flow,"  We investigate a steady planar flow of an ideal fluid in a bounded simple
-connected domain and focus on the vortex patch problem with prescribed
-vorticity strength. There are two methods to deal with the existence of
-solutions for this problem: the vorticity method and the stream function
-method. A long standing open problem is whether these two entirely different
-methods result in the same solution. In this paper, we will give a positive
-answer to this problem by studying the local uniqueness of the solutions.
-Another result obtained in this paper is that if the domain is convex, then the
-vortex patch problem has a unique solution.
-",0,0,1,0,0,0
-17322,An Equivalence of Fully Connected Layer and Convolutional Layer,"  This article demonstrates that convolutional operation can be converted to
-matrix multiplication, which has the same calculation way with fully connected
-layer. The article is helpful for the beginners of the neural network to
-understand how fully connected layer and the convolutional layer work in the
-backend. To be concise and to make the article more readable, we only consider
-the linear case. It can be extended to the non-linear case easily through
-plugging in a non-linear encapsulation to the values like this $\sigma(x)$
-denoted as $x^{\prime}$.
-",1,0,0,1,0,0
-17323,Critical Points of Neural Networks: Analytical Forms and Landscape Properties,"  Due to the success of deep learning to solving a variety of challenging
-machine learning tasks, there is a rising interest in understanding loss
-functions for training neural networks from a theoretical aspect. Particularly,
-the properties of critical points and the landscape around them are of
-importance to determine the convergence performance of optimization algorithms.
-In this paper, we provide full (necessary and sufficient) characterization of
-the analytical forms for the critical points (as well as global minimizers) of
-the square loss functions for various neural networks. We show that the
-analytical forms of the critical points characterize the values of the
-corresponding loss functions as well as the necessary and sufficient conditions
-to achieve global minimum. Furthermore, we exploit the analytical forms of the
-critical points to characterize the landscape properties for the loss functions
-of these neural networks. One particular conclusion is that: The loss function
-of linear networks has no spurious local minimum, while the loss function of
-one-hidden-layer nonlinear networks with ReLU activation function does have
-local minimum that is not global minimum.
-",1,0,0,1,0,0
-17324,When the Annihilator Graph of a Commutative Ring Is Planar or Toroidal?,"  Let $R$ be a commutative ring with identity, and let $Z(R)$ be the set of
-zero-divisors of $R$. The annihilator graph of $R$ is defined as the undirected
-graph $AG(R)$ with the vertex set $Z(R)^*=Z(R)\setminus\{0\}$, and two distinct
-vertices $x$ and $y$ are adjacent if and only if $ann_R(xy)\neq ann_R(x)\cup
-ann_R(y)$. In this paper, all rings whose annihilator graphs can be embed on
-the plane or torus are classified.
-",0,0,1,0,0,0
-17325,Econometric modelling and forecasting of intraday electricity prices,"  In the following paper we analyse the ID$_3$-Price on German Intraday
-Continuous Electricity Market using an econometric time series model. A
-multivariate approach is conducted for hourly and quarter-hourly products
-separately. We estimate the model using lasso and elastic net techniques and
-perform an out-of-sample very short-term forecasting study. The model's
-performance is compared with benchmark models and is discussed in detail.
-Forecasting results provide new insights to the German Intraday Continuous
-Electricity Market regarding its efficiency and to the ID$_3$-Price behaviour.
-The supplementary materials are available online.
-",0,0,0,0,0,1
-17326,Matrix-Based Characterization of the Motion and Wrench Uncertainties in Robotic Manipulators,"  Characterization of the uncertainty in robotic manipulators is the focus of
-this paper. Based on the random matrix theory (RMT), we propose uncertainty
-characterization schemes in which the uncertainty is modeled at the macro
-(system) level. This is different from the traditional approaches that model
-the uncertainty in the parametric space of micro (state) level. We show that
-perturbing the system matrices rather than the state of the system provides
-unique advantages especially for robotic manipulators. First, it requires only
-limited statistical information that becomes effective when dealing with
-complex systems where detailed information on their variability is not
-available. Second, the RMT-based models are aware of the system state and
-configuration that are significant factors affecting the level of uncertainty
-in system behavior. In this study, in addition to the motion uncertainty
-analysis that was first proposed in our earlier work, we also develop an
-RMT-based model for the quantification of the static wrench uncertainty in
-multi-agent cooperative systems. This model is aimed to be an alternative to
-the elaborate parametric formulation when only rough bounds are available on
-the system parameters. We discuss that how RMT-based model becomes advantageous
-when the complexity of the system increases. We perform experimental studies on
-a KUKA youBot arm to demonstrate the superiority of the RMT-based motion
-uncertainty models. We show that how these models outperform the traditional
-models built upon Gaussianity assumption in capturing real-system uncertainty
-and providing accurate bounds on the state estimation errors. In addition, to
-experimentally support our wrench uncertainty quantification model, we study
-the behavior of a cooperative system of mobile robots. It is shown that one can
-rely on less demanding RMT-based formulation and yet meets the acceptable
-accuracy.
-",1,0,0,1,0,0
-17327,Good Similar Patches for Image Denoising,"  Patch-based denoising algorithms like BM3D have achieved outstanding
-performance. An important idea for the success of these methods is to exploit
-the recurrence of similar patches in an input image to estimate the underlying
-image structures. However, in these algorithms, the similar patches used for
-denoising are obtained via Nearest Neighbour Search (NNS) and are sometimes not
-optimal. First, due to the existence of noise, NNS can select similar patches
-with similar noise patterns to the reference patch. Second, the unreliable
-noisy pixels in digital images can bring a bias to the patch searching process
-and result in a loss of color fidelity in the final denoising result. We
-observe that given a set of good similar patches, their distribution is not
-necessarily centered at the noisy reference patch and can be approximated by a
-Gaussian component. Based on this observation, we present a patch searching
-method that clusters similar patch candidates into patch groups using Gaussian
-Mixture Model-based clustering, and selects the patch group that contains the
-reference patch as the final patches for denoising. We also use an unreliable
-pixel estimation algorithm to pre-process the input noisy images to further
-improve the patch searching. Our experiments show that our approach can better
-capture the underlying patch structures and can consistently enable the
-state-of-the-art patch-based denoising algorithms, such as BM3D, LPCA and PLOW,
-to better denoise images by providing them with patches found by our approach
-while without modifying these algorithms.
-",1,0,0,0,0,0
-17328,Ginzburg - Landau expansion in strongly disordered attractive Anderson - Hubbard model,"  We have studied disordering effects on the coefficients of Ginzburg - Landau
-expansion in powers of superconducting order - parameter in attractive Anderson
-- Hubbard model within the generalized $DMFT+\Sigma$ approximation. We consider
-the wide region of attractive potentials $U$ from the weak coupling region,
-where superconductivity is described by BCS model, to the strong coupling
-region, where superconducting transition is related with Bose - Einstein
-condensation (BEC) of compact Cooper pairs formed at temperatures essentially
-larger than the temperature of superconducting transition, and the wide range
-of disorder - from weak to strong, where the system is in the vicinity of
-Anderson transition. In case of semi - elliptic bare density of states disorder
-influence upon the coefficients $A$ and $B$ before the square and the fourth
-power of the order - parameter is universal for any value of electron
-correlation and is related only to the general disorder widening of the bare
-band (generalized Anderson theorem). Such universality is absent for the
-gradient term expansion coefficient $C$. In the usual theory of ""dirty""
-superconductors the $C$ coefficient drops with the growth of disorder. In the
-limit of strong disorder in BCS limit the coefficient $C$ is very sensitive to
-the effects of Anderson localization, which lead to its further drop with
-disorder growth up to the region of Anderson insulator. In the region of BCS -
-BEC crossover and in BEC limit the coefficient $C$ and all related physical
-properties are weakly dependent on disorder. In particular, this leads to
-relatively weak disorder dependence of both penetration depth and coherence
-lengths, as well as of related slope of the upper critical magnetic field at
-superconducting transition, in the region of very strong coupling.
-",0,1,0,0,0,0
-17329,Reallocating and Resampling: A Comparison for Inference,"  Simulation-based inference plays a major role in modern statistics, and often
-employs either reallocating (as in a randomization test) or resampling (as in
-bootstrapping). Reallocating mimics random allocation to treatment groups,
-while resampling mimics random sampling from a larger population; does it
-matter whether the simulation method matches the data collection method?
-Moreover, do the results differ for testing versus estimation? Here we answer
-these questions in a simple setting by exploring the distribution of a sample
-difference in means under a basic two group design and four different
-scenarios: true random allocation, true random sampling, reallocating, and
-resampling. For testing a sharp null hypothesis, reallocating is superior in
-small samples, but reallocating and resampling are asymptotically equivalent.
-For estimation, resampling is generally superior, unless the effect is truly
-additive. Moreover, these results hold regardless of whether the data were
-collected by random sampling or random allocation.
-",0,0,1,1,0,0
-17330,An Efficient Algorithm for Bayesian Nearest Neighbours,"  K-Nearest Neighbours (k-NN) is a popular classification and regression
-algorithm, yet one of its main limitations is the difficulty in choosing the
-number of neighbours. We present a Bayesian algorithm to compute the posterior
-probability distribution for k given a target point within a data-set,
-efficiently and without the use of Markov Chain Monte Carlo (MCMC) methods or
-simulation - alongside an exact solution for distributions within the
-exponential family. The central idea is that data points around our target are
-generated by the same probability distribution, extending outwards over the
-appropriate, though unknown, number of neighbours. Once the data is projected
-onto a distance metric of choice, we can transform the choice of k into a
-change-point detection problem, for which there is an efficient solution: we
-recursively compute the probability of the last change-point as we move towards
-our target, and thus de facto compute the posterior probability distribution
-over k. Applying this approach to both a classification and a regression UCI
-data-sets, we compare favourably and, most importantly, by removing the need
-for simulation, we are able to compute the posterior probability of k exactly
-and rapidly. As an example, the computational time for the Ripley data-set is a
-few milliseconds compared to a few hours when using a MCMC approach.
-",1,0,0,1,0,0
-17331,In search of a new economic model determined by logistic growth,"  In this paper we extend the work by Ryuzo Sato devoted to the development of
-economic growth models within the framework of the Lie group theory. We propose
-a new growth model based on the assumption of logistic growth in factors. It is
-employed to derive new production functions and introduce a new notion of wage
-share. In the process it is shown that the new functions compare reasonably
-well against relevant economic data. The corresponding problem of maximization
-of profit under conditions of perfect competition is solved with the aid of one
-of these functions. In addition, it is explained in reasonably rigorous
-mathematical terms why Bowley's law no longer holds true in post-1960 data.
-",0,0,1,0,0,0
-17332,Limits on light WIMPs with a 1 kg-scale germanium detector at 160 eVee physics threshold at the China Jinping Underground Laboratory,"  We report results of a search for light weakly interacting massive particle
-(WIMP) dark matter from the CDEX-1 experiment at the China Jinping Underground
-Laboratory (CJPL). Constraints on WIMP-nucleon spin-independent (SI) and
-spin-dependent (SD) couplings are derived with a physics threshold of 160 eVee,
-from an exposure of 737.1 kg-days. The SI and SD limits extend the lower reach
-of light WIMPs to 2 GeV and improve over our earlier bounds at WIMP mass less
-than 6 GeV.
-",0,1,0,0,0,0
-17333,"A stellar census of the nearby, young 32 Orionis group","  The 32 Orionis group was discovered almost a decade ago and despite the fact
-that it represents the first northern, young (age ~ 25 Myr) stellar aggregate
-within 100 pc of the Sun ($d \simeq 93$ pc), a comprehensive survey for members
-and detailed characterisation of the group has yet to be performed. We present
-the first large-scale spectroscopic survey for new (predominantly M-type)
-members of the group after combining kinematic and photometric data to select
-candidates with Galactic space motion and positions in colour-magnitude space
-consistent with membership. We identify 30 new members, increasing the number
-of known 32 Ori group members by a factor of three and bringing the total
-number of identified members to 46, spanning spectral types B5 to L1. We also
-identify the lithium depletion boundary (LDB) of the group, i.e. the luminosity
-at which lithium remains unburnt in a coeval population. We estimate the age of
-the 32 Ori group independently using both isochronal fitting and LDB analyses
-and find it is essentially coeval with the {\beta} Pictoris moving group, with
-an age of $24\pm4$ Myr. Finally, we have also searched for circumstellar disc
-hosts utilising the AllWISE catalogue. Although we find no evidence for warm,
-dusty discs, we identify several stars with excess emission in the WISE W4-band
-at 22 {\mu}m. Based on the limited number of W4 detections we estimate a debris
-disc fraction of $32^{+12}_{-8}$ per cent for the 32 Ori group.
-",0,1,0,0,0,0
-17334,A High-Level Rule-based Language for Software Defined Network Programming based on OpenFlow,"  This paper proposes XML-Defined Network policies (XDNP), a new high-level
-language based on XML notation, to describe network control rules in Software
-Defined Network environments. We rely on existing OpenFlow controllers
-specifically Floodlight but the novelty of this project is to separate
-complicated language- and framework-specific APIs from policy descriptions.
-This separation makes it possible to extend the current work as a northbound
-higher level abstraction that can support a wide range of controllers who are
-based on different programming languages. By this approach, we believe that
-network administrators can develop and deploy network control policies easier
-and faster.
-",1,0,0,0,0,0
-17335,Domain Objects and Microservices for Systems Development: a roadmap,"  This paper discusses a roadmap to investigate Domain Objects being an
-adequate formalism to capture the peculiarity of microservice architecture, and
-to support Software development since the early stages. It provides a survey of
-both Microservices and Domain Objects, and it discusses plans and reflections
-on how to investigate whether a modeling approach suited to adaptable
-service-based components can also be applied with success to the microservice
-scenario.
-",1,0,0,0,0,0
-17336,Stabilization of prethermal Floquet steady states in a periodically driven dissipative Bose-Hubbard model,"  We discuss the effect of dissipation on heating which occurs in periodically
-driven quantum many body systems. We especially focus on a periodically driven
-Bose-Hubbard model coupled to an energy and particle reservoir. Without
-dissipation, this model is known to undergo parametric instabilities which can
-be considered as an initial stage of heating. By taking the weak on-site
-interaction limit as well as the weak system-reservoir coupling limit, we find
-that parametric instabilities are suppressed if the dissipation is stronger
-than the on-site interaction strength and stable steady states appear. Our
-results demonstrate that periodically-driven systems can emit energy, which is
-absorbed from external drivings, to the reservoir so that they can avoid
-heating.
-",0,1,0,0,0,0
-17337,Compressed Sensing using Generative Models,"  The goal of compressed sensing is to estimate a vector from an
-underdetermined system of noisy linear measurements, by making use of prior
-knowledge on the structure of vectors in the relevant domain. For almost all
-results in this literature, the structure is represented by sparsity in a
-well-chosen basis. We show how to achieve guarantees similar to standard
-compressed sensing but without employing sparsity at all. Instead, we suppose
-that vectors lie near the range of a generative model $G: \mathbb{R}^k \to
-\mathbb{R}^n$. Our main theorem is that, if $G$ is $L$-Lipschitz, then roughly
-$O(k \log L)$ random Gaussian measurements suffice for an $\ell_2/\ell_2$
-recovery guarantee. We demonstrate our results using generative models from
-published variational autoencoder and generative adversarial networks. Our
-method can use $5$-$10$x fewer measurements than Lasso for the same accuracy.
-",1,0,0,1,0,0
-17338,Two-part models with stochastic processes for modelling longitudinal semicontinuous data: computationally efficient inference and modelling the overall marginal mean,"  Several researchers have described two-part models with patient-specific
-stochastic processes for analysing longitudinal semicontinuous data. In theory,
-such models can offer greater flexibility than the standard two-part model with
-patient-specific random effects. However, in practice the high dimensional
-integrations involved in the marginal likelihood (i.e. integrated over the
-stochastic processes) significantly complicates model fitting. Thus
-non-standard computationally intensive procedures based on simulating the
-marginal likelihood have so far only been proposed. In this paper, we describe
-an efficient method of implementation by demonstrating how the high dimensional
-integrations involved in the marginal likelihood can be computed efficiently.
-Specifically, by using a property of the multivariate normal distribution and
-the standard marginal cumulative distribution function identity, we transform
-the marginal likelihood so that the high dimensional integrations are contained
-in the cumulative distribution function of a multivariate normal distribution,
-which can then be efficiently evaluated. Hence maximum likelihood estimation
-can be used to obtain parameter estimates and asymptotic standard errors (from
-the observed information matrix) of model parameters. We describe our proposed
-efficient implementation procedure for the standard two-part model
-parameterisation and when it is of interest to directly model the overall
-marginal mean. The methodology is applied on a psoriatic arthritis data set
-concerning functional disability.
-",0,0,0,1,0,0
-17339,Progressive Image Deraining Networks: A Better and Simpler Baseline,"  Along with the deraining performance improvement of deep networks, their
-structures and learning become more and more complicated and diverse, making it
-difficult to analyze the contribution of various network modules when
-developing new deraining networks. To handle this issue, this paper provides a
-better and simpler baseline deraining network by considering network
-architecture, input and output, and loss functions. Specifically, by repeatedly
-unfolding a shallow ResNet, progressive ResNet (PRN) is proposed to take
-advantage of recursive computation. A recurrent layer is further introduced to
-exploit the dependencies of deep features across stages, forming our
-progressive recurrent network (PReNet). Furthermore, intra-stage recursive
-computation of ResNet can be adopted in PRN and PReNet to notably reduce
-network parameters with graceful degradation in deraining performance. For
-network input and output, we take both stage-wise result and original rainy
-image as input to each ResNet and finally output the prediction of {residual
-image}. As for loss functions, single MSE or negative SSIM losses are
-sufficient to train PRN and PReNet. Experiments show that PRN and PReNet
-perform favorably on both synthetic and real rainy images. Considering its
-simplicity, efficiency and effectiveness, our models are expected to serve as a
-suitable baseline in future deraining research. The source codes are available
-at this https URL.
-",1,0,0,0,0,0
-17340,Optimal Nonparametric Inference under Quantization,"  Statistical inference based on lossy or incomplete samples is of fundamental
-importance in research areas such as signal/image processing, medical image
-storage, remote sensing, signal transmission. In this paper, we propose a
-nonparametric testing procedure based on quantized samples. In contrast to the
-classic nonparametric approach, our method lives on a coarse grid of sample
-information and are simple-to-use. Under mild technical conditions, we
-establish the asymptotic properties of the proposed procedures including
-asymptotic null distribution of the quantization test statistic as well as its
-minimax power optimality. Concrete quantizers are constructed for achieving the
-minimax optimality in practical use. Simulation results and a real data
-analysis are provided to demonstrate the validity and effectiveness of the
-proposed test. Our work bridges the classical nonparametric inference to modern
-lossy data setting.
-",1,0,1,1,0,0
-17341,Nearest neighbor imputation for general parameter estimation in survey sampling,"  Nearest neighbor imputation is popular for handling item nonresponse in
-survey sampling. In this article, we study the asymptotic properties of the
-nearest neighbor imputation estimator for general population parameters,
-including population means, proportions and quantiles. For variance estimation,
-the conventional bootstrap inference for matching estimators with fixed number
-of matches has been shown to be invalid due to the nonsmoothness nature of the
-matching estimator. We propose asymptotically valid replication variance
-estimation. The key strategy is to construct replicates of the estimator
-directly based on linear terms, instead of individual records of variables. A
-simulation study confirms that the new procedure provides valid variance
-estimation.
-",0,0,0,1,0,0
-17342,Time-delay signature suppression in a chaotic semiconductor laser by fiber random grating induced distributed feedback,"  We demonstrate that a semiconductor laser perturbed by the distributed
-feedback from a fiber random grating can emit light chaotically without the
-time delay signature. A theoretical model is developed based on the
-Lang-Kobayashi model in order to numerically explore the chaotic dynamics of
-the laser diode subjected to the random distributed feedback. It is predicted
-that the random distributed feedback is superior to the single reflection
-feedback in suppressing the time-delay signature. In experiments, a massive
-number of feedbacks with randomly varied time delays induced by a fiber random
-grating introduce large numbers of external cavity modes into the semiconductor
-laser, leading to the high dimension of chaotic dynamics and thus the
-concealment of the time delay signature. The obtained time delay signature with
-the maximum suppression is 0.0088, which is the smallest to date.
-",0,1,0,0,0,0
-17343,SAFS: A Deep Feature Selection Approach for Precision Medicine,"  In this paper, we propose a new deep feature selection method based on deep
-architecture. Our method uses stacked auto-encoders for feature representation
-in higher-level abstraction. We developed and applied a novel feature learning
-approach to a specific precision medicine problem, which focuses on assessing
-and prioritizing risk factors for hypertension (HTN) in a vulnerable
-demographic subgroup (African-American). Our approach is to use deep learning
-to identify significant risk factors affecting left ventricular mass indexed to
-body surface area (LVMI) as an indicator of heart damage risk. The results show
-that our feature learning and representation approach leads to better results
-in comparison with others.
-",1,0,0,1,0,0
-17344,Deep Reasoning with Multi-scale Context for Salient Object Detection,"  To detect and segment salient objects accurately, existing methods are
-usually devoted to designing complex network architectures to fuse powerful
-features from the backbone networks. However, they put much less efforts on the
-saliency inference module and only use a few fully convolutional layers to
-perform saliency reasoning from the fused features. However, should feature
-fusion strategies receive much attention but saliency reasoning be ignored a
-lot? In this paper, we find that weakness of the saliency reasoning unit limits
-salient object detection performance, and claim that saliency reasoning after
-multi-scale convolutional features fusion is critical. To verify our findings,
-we first extract multi-scale features with a fully convolutional network, and
-then directly reason from these comprehensive features using a deep yet
-light-weighted network, modified from ShuffleNet, to fast and precisely predict
-salient objects. Such simple design is shown to be capable of reasoning from
-multi-scale saliency features as well as giving superior saliency detection
-performance with less computation cost. Experimental results show that our
-simple framework outperforms the best existing method with 2.3\% and 3.6\%
-promotion for F-measure scores, 2.8\% reduction for MAE score on PASCAL-S,
-DUT-OMRON and SOD datasets respectively.
-",1,0,0,0,0,0
-17345,On Estimation of $L_{r}$-Norms in Gaussian White Noise Models,"  We provide a complete picture of asymptotically minimax estimation of
-$L_r$-norms (for any $r\ge 1$) of the mean in Gaussian white noise model over
-Nikolskii-Besov spaces. In this regard, we complement the work of Lepski,
-Nemirovski and Spokoiny (1999), who considered the cases of $r=1$ (with
-poly-logarithmic gap between upper and lower bounds) and $r$ even (with
-asymptotically sharp upper and lower bounds) over Hölder spaces. We
-additionally consider the case of asymptotically adaptive minimax estimation
-and demonstrate a difference between even and non-even $r$ in terms of an
-investigator's ability to produce asymptotically adaptive minimax estimators
-without paying a penalty.
-",1,0,1,1,0,0
-17346,Secure communications with cooperative jamming: Optimal power allocation and secrecy outage analysis,"  This paper studies the secrecy rate maximization problem of a secure wireless
-communication system, in the presence of multiple eavesdroppers. The security
-of the communication link is enhanced through cooperative jamming, with the
-help of multiple jammers. First, a feasibility condition is derived to achieve
-a positive secrecy rate at the destination. Then, we solve the original secrecy
-rate maximization problem, which is not convex in terms of power allocation at
-the jammers. To circumvent this non-convexity, the achievable secrecy rate is
-approximated for a given power allocation at the jammers and the approximated
-problem is formulated into a geometric programming one. Based on this
-approximation, an iterative algorithm has been developed to obtain the optimal
-power allocation at the jammers. Next, we provide a bisection approach, based
-on one-dimensional search, to validate the optimality of the proposed
-algorithm. In addition, by assuming Rayleigh fading, the secrecy outage
-probability (SOP) of the proposed cooperative jamming scheme is analyzed. More
-specifically, a single-integral form expression for SOP is derived for the most
-general case as well as a closed-form expression for the special case of two
-cooperative jammers and one eavesdropper. Simulation results have been provided
-to validate the convergence and the optimality of the proposed algorithm as
-well as the theoretical derivations of the presented SOP analysis.
-",1,0,1,0,0,0
-17347,Stochastic Calculus with respect to Gaussian Processes: Part I,"  Stochastic integration \textit{wrt} Gaussian processes has raised strong
-interest in recent years, motivated in particular by its applications in
-Internet traffic modeling, biomedicine and finance. The aim of this work is to
-define and develop a White Noise Theory-based anticipative stochastic calculus
-with respect to all Gaussian processes that have an integral representation
-over a real (maybe infinite) interval. Very rich, this class of Gaussian
-processes contains, among many others, Volterra processes (and thus fractional
-Brownian motion) as well as processes the regularity of which varies along the
-time (such as multifractional Brownian motion).A systematic comparison of the
-stochastic calculus (including It{ô} formula) we provide here, to the ones
-given by Malliavin calculus in
-\cite{nualart,MV05,NuTa06,KRT07,KrRu10,LN12,SoVi14,LN12}, and by It{ô}
-stochastic calculus is also made. Not only our stochastic calculus fully
-generalizes and extends the ones originally proposed in \cite{MV05} and in
-\cite{NuTa06} for Gaussian processes, but also the ones proposed in
-\cite{ell,bosw,ben1} for fractional Brownian motion (\textit{resp.} in
-\cite{JLJLV1,JL13,LLVH} for multifractional Brownian motion).
-",0,0,1,0,0,0
-17348,Path-like integrals of lenght on surfaces of constant curvature,"  We naturally associate a measurable space of paths to a couple of orthogonal
-vector fields over a surface and we integrate the length function over it. This
-integral is interpreted as a natural continuous generalization of indirect
-influences on finite graphs and can be thought as a tool to capture geometric
-information of the surface. As a byproduct we calculate volumes in different
-examples of spaces of paths.
-",0,0,1,0,0,0
-17349,Automated Synthesis of Divide and Conquer Parallelism,"  This paper focuses on automated synthesis of divide-and-conquer parallelism,
-which is a common parallel programming skeleton supported by many
-cross-platform multithreaded libraries. The challenges of producing (manually
-or automatically) a correct divide-and-conquer parallel program from a given
-sequential code are two-fold: (1) assuming that individual worker threads
-execute a code identical to the sequential code, the programmer has to provide
-the extra code for dividing the tasks and combining the computation results,
-and (2) sometimes, the sequential code may not be usable as is, and may need to
-be modified by the programmer. We address both challenges in this paper. We
-present an automated synthesis technique for the case where no modifications to
-the sequential code are required, and we propose an algorithm for modifying the
-sequential code to make it suitable for parallelization when some modification
-is necessary. The paper presents theoretical results for when this {\em
-modification} is efficiently possible, and experimental evaluation of the
-technique and the quality of the produced parallel programs.
-",1,0,0,0,0,0
-17350,"Nikol'ski\uı, Jackson and Ul'yanov type inequalities with Muckenhoupt weights","  In the present work we prove a Nikol'ski inequality for trigonometric
-polynomials and Ul'yanov type inequalities for functions in Lebesgue spaces
-with Muckenhoupt weights. Realization result and Jackson inequalities are
-obtained. Simultaneous approximation by polynomials is considered. Some uniform
-norm inequalities are transferred to weighted Lebesgue space.
-",0,0,1,0,0,0
-17351,CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks,"  Inferring model parameters from experimental data is a grand challenge in
-many sciences, including cosmology. This often relies critically on high
-fidelity numerical simulations, which are prohibitively computationally
-expensive. The application of deep learning techniques to generative modeling
-is renewing interest in using high dimensional density estimators as
-computationally inexpensive emulators of fully-fledged simulations. These
-generative models have the potential to make a dramatic shift in the field of
-scientific simulations, but for that shift to happen we need to study the
-performance of such generators in the precision regime needed for science
-applications. To this end, in this work we apply Generative Adversarial
-Networks to the problem of generating weak lensing convergence maps. We show
-that our generator network produces maps that are described by, with high
-statistical confidence, the same summary statistics as the fully simulated
-maps.
-",1,1,0,0,0,0
-17352,Gaussian approximation of maxima of Wiener functionals and its application to high-frequency data,"  This paper establishes an upper bound for the Kolmogorov distance between the
-maximum of a high-dimensional vector of smooth Wiener functionals and the
-maximum of a Gaussian random vector. As a special case, we show that the
-maximum of multiple Wiener-Itô integrals with common orders is
-well-approximated by its Gaussian analog in terms of the Kolmogorov distance if
-their covariance matrices are close to each other and the maximum of the fourth
-cumulants of the multiple Wiener-Itô integrals is close to zero. This may be
-viewed as a new kind of fourth moment phenomenon, which has attracted
-considerable attention in the recent studies of probability. This type of
-Gaussian approximation result has many potential applications to statistics. To
-illustrate this point, we present two statistical applications in
-high-frequency financial econometrics: One is the hypothesis testing problem
-for the absence of lead-lag effects and the other is the construction of
-uniform confidence bands for spot volatility.
-",0,0,1,1,0,0
-17353,A Kronecker-type identity and the representations of a number as a sum of three squares,"  By considering a limiting case of a Kronecker-type identity, we obtain an
-identity found by both Andrews and Crandall. We then use the Andrews-Crandall
-identity to give a new proof of a formula of Gauss for the representations of a
-number as a sum of three squares. From the Kronecker-type identity, we also
-deduce Gauss's theorem that every positive integer is representable as a sum of
-three triangular numbers.
-",0,0,1,0,0,0
-17354,DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction,"  In this paper, we consider the temporal pattern in traffic flow time series,
-and implement a deep learning model for traffic flow prediction. Detrending
-based methods decompose original flow series into trend and residual series, in
-which trend describes the fixed temporal pattern in traffic flow and residual
-series is used for prediction. Inspired by the detrending method, we propose
-DeepTrend, a deep hierarchical neural network used for traffic flow prediction
-which considers and extracts the time-variant trend. DeepTrend has two stacked
-layers: extraction layer and prediction layer. Extraction layer, a fully
-connected layer, is used to extract the time-variant trend in traffic flow by
-feeding the original flow series concatenated with corresponding simple average
-trend series. Prediction layer, an LSTM layer, is used to make flow prediction
-by feeding the obtained trend from the output of extraction layer and
-calculated residual series. To make the model more effective, DeepTrend needs
-first pre-trained layer-by-layer and then fine-tuned in the entire network.
-Experiments show that DeepTrend can noticeably boost the prediction performance
-compared with some traditional prediction models and LSTM with detrending based
-methods.
-",1,0,0,0,0,0
-17355,A new approach to Kaluza-Klein Theory,"  We propose in this paper a new approach to the Kaluza-Klein idea of a five
-dimensional space-time unifying gravitation and electromagnetism, and extension
-to higher-dimensional space-time. By considering a natural geometric definition
-of a matter fluid and abandoning the usual requirement of a Ricci-flat five
-dimensional space-time, we show that a unified geometrical frame can be set for
-gravitation and electromagnetism, giving, by projection on the classical
-4-dimensional space-time, the known Einstein-Maxwell-Lorentz equations for
-charged fluids. Thus, although not introducing new physics, we get a very
-aesthetic presentation of classical physics in the spirit of general
-relativity. The usual physical concepts, such as mass, energy, charge,
-trajectory, Maxwell-Lorentz law, are shown to be only various aspects of the
-geometry, for example curvature, of space-time considered as a Lorentzian
-manifold; that is no physical objects are introduced in space-time, no laws are
-given, everything is only geometry.
-We then extend these ideas to more than 5 dimensions, by considering
-spacetime as a generalization of a $(S^1\times W)$-fiber bundle, that we named
-multi-fibers bundle, where $S^1$ is the circle and $W$ a compact manifold. We
-will use this geometric structure as a possible way to model or encode
-deviations from standard 4-dimensional General Relativity, or ""dark"" effects
-such as dark matter or energy.
-",0,0,1,0,0,0
-17356,Density of orbits of dominant regular self-maps of semiabelian varieties,"  We prove a conjecture of Medvedev and Scanlon in the case of regular
-morphisms of semiabelian varieties. That is, if $G$ is a semiabelian variety
-defined over an algebraically closed field $K$ of characteristic $0$, and
-$\varphi\colon G\to G$ is a dominant regular self-map of $G$ which is not
-necessarily a group homomorphism, we prove that one of the following holds:
-either there exists a non-constant rational fibration preserved by $\varphi$,
-or there exists a point $x\in G(K)$ whose $\varphi$-orbit is Zariski dense in
-$G$.
-",0,0,1,0,0,0
-17357,Asymptotic coverage probabilities of bootstrap percentile confidence intervals for constrained parameters,"  The asymptotic behaviour of the commonly used bootstrap percentile confidence
-interval is investigated when the parameters are subject to linear inequality
-constraints. We concentrate on the important one- and two-sample problems with
-data generated from general parametric distributions in the natural exponential
-family. The focus of this paper is on quantifying the coverage probabilities of
-the parametric bootstrap percentile confidence intervals, in particular their
-limiting behaviour near boundaries. We propose a local asymptotic framework to
-study this subtle coverage behaviour. Under this framework, we discover that
-when the true parameters are on, or close to, the restriction boundary, the
-asymptotic coverage probabilities can always exceed the nominal level in the
-one-sample case; however, they can be, remarkably, both under and over the
-nominal level in the two-sample case. Using illustrative examples, we show that
-the results provide theoretical justification and guidance on applying the
-bootstrap percentile method to constrained inference problems.
-",0,0,1,1,0,0
-17358,Correlations and enlarged superconducting phase of $t$-$J_\perp$ chains of ultracold molecules on optical lattices,"  We compute physical properties across the phase diagram of the $t$-$J_\perp$
-chain with long-range dipolar interactions, which describe ultracold polar
-molecules on optical lattices. Our results obtained by the density-matrix
-renormalization group (DMRG) indicate that superconductivity is enhanced when
-the Ising component $J_z$ of the spin-spin interaction and the charge component
-$V$ are tuned to zero, and even further by the long-range dipolar interactions.
-At low densities, a substantially larger spin gap is obtained. We provide
-evidence that long-range interactions lead to algebraically decaying
-correlation functions despite the presence of a gap. Although this has recently
-been observed in other long-range interacting spin and fermion models, the
-correlations in our case have the peculiar property of having a small and
-continuously varying exponent. We construct simple analytic models and
-arguments to understand the most salient features.
-",0,1,0,0,0,0
-17359,MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural Networks,"  We introduce MinimalRNN, a new recurrent neural network architecture that
-achieves comparable performance as the popular gated RNNs with a simplified
-structure. It employs minimal updates within RNN, which not only leads to
-efficient learning and testing but more importantly better interpretability and
-trainability. We demonstrate that by endorsing the more restrictive update
-rule, MinimalRNN learns disentangled RNN states. We further examine the
-learning dynamics of different RNN structures using input-output Jacobians, and
-show that MinimalRNN is able to capture longer range dependencies than existing
-RNN architectures.
-",1,0,0,1,0,0
-17360,Boolean quadric polytopes are faces of linear ordering polytopes,"  Let $BQP(n)$ be a boolean quadric polytope, $LOP(m)$ be a linear ordering
-polytope. It is shown that $BQP(n)$ is linearly isomorphic to a face of
-$LOP(2n)$.
-",1,0,0,0,0,0
-17361,Sparse Matrix Code Dependence Analysis Simplification at Compile Time,"  Analyzing array-based computations to determine data dependences is useful
-for many applications including automatic parallelization, race detection,
-computation and communication overlap, verification, and shape analysis. For
-sparse matrix codes, array data dependence analysis is made more difficult by
-the use of index arrays that make it possible to store only the nonzero entries
-of the matrix (e.g., in A[B[i]], B is an index array). Here, dependence
-analysis is often stymied by such indirect array accesses due to the values of
-the index array not being available at compile time. Consequently, many
-dependences cannot be proven unsatisfiable or determined until runtime.
-Nonetheless, index arrays in sparse matrix codes often have properties such as
-monotonicity of index array elements that can be exploited to reduce the amount
-of runtime analysis needed. In this paper, we contribute a formulation of array
-data dependence analysis that includes encoding index array properties as
-universally quantified constraints. This makes it possible to leverage existing
-SMT solvers to determine whether such dependences are unsatisfiable and
-significantly reduces the number of dependences that require runtime analysis
-in a set of eight sparse matrix kernels. Another contribution is an algorithm
-for simplifying the remaining satisfiable data dependences by discovering
-equalities and/or subset relationships. These simplifications are essential to
-make a runtime-inspection-based approach feasible.
-",1,0,0,0,0,0
-17362,ICA based on the data asymmetry,"  Independent Component Analysis (ICA) - one of the basic tools in data
-analysis - aims to find a coordinate system in which the components of the data
-are independent. Most of existing methods are based on the minimization of the
-function of fourth-order moment (kurtosis). Skewness (third-order moment) has
-received much less attention.
-In this paper we present a competitive approach to ICA based on the Split
-Gaussian distribution, which is well adapted to asymmetric data. Consequently,
-we obtain a method which works better than the classical approaches, especially
-in the case when the underlying density is not symmetric, which is a typical
-situation in the color distribution in images.
-",0,0,1,1,0,0
-17363,Solid hulls of weighted Banach spaces of analytic functions on the unit disc with exponential weights,"  We study weighted $H^\infty$ spaces of analytic functions on the open unit
-disc in the case of non-doubling weights, which decrease rapidly with respect
-to the boundary distance. We characterize the solid hulls of such spaces and
-give quite explicit representations of them in the case of the most natural
-exponentially decreasing weights.
-",0,0,1,0,0,0
-17364,Line bundles defined by the Schwarz function,"  Cauchy and exponential transforms are characterized, and constructed, as
-canonical holomorphic sections of certain line bundles on the Riemann sphere
-defined in terms of the Schwarz function. A well known natural connection
-between Schwarz reflection and line bundles defined on the Schottky double of a
-planar domain is briefly discussed in the same context.
-",0,0,1,0,0,0
-17365,Collisional excitation of NH3 by atomic and molecular hydrogen,"  We report extensive theoretical calculations on the rotation-inversion
-excitation of interstellar ammonia (NH3) due to collisions with atomic and
-molecular hydrogen (both para- and ortho-H2). Close-coupling calculations are
-performed for total energies in the range 1-2000 cm-1 and rotational cross
-sections are obtained for all transitions among the lowest 17 and 34
-rotation-inversion levels of ortho- and para-NH3, respectively. Rate
-coefficients are deduced for kinetic temperatures up to 200 K. Propensity rules
-for the three colliding partners are discussed and we also compare the new
-results to previous calculations for the spherically symmetrical He and para-H2
-projectiles. Significant differences are found between the different sets of
-calculations. Finally, we test the impact of the new rate coefficients on the
-calibration of the ammonia thermometer. We find that the calibration curve is
-only weakly sensitive to the colliding partner and we confirm that the ammonia
-thermometer is robust.
-",0,1,0,0,0,0
-17366,Deterministic and Probabilistic Conditions for Finite Completability of Low-rank Multi-View Data,"  We consider the multi-view data completion problem, i.e., to complete a
-matrix $\mathbf{U}=[\mathbf{U}_1|\mathbf{U}_2]$ where the ranks of
-$\mathbf{U},\mathbf{U}_1$, and $\mathbf{U}_2$ are given. In particular, we
-investigate the fundamental conditions on the sampling pattern, i.e., locations
-of the sampled entries for finite completability of such a multi-view data
-given the corresponding rank constraints. In contrast with the existing
-analysis on Grassmannian manifold for a single-view matrix, i.e., conventional
-matrix completion, we propose a geometric analysis on the manifold structure
-for multi-view data to incorporate more than one rank constraint. We provide a
-deterministic necessary and sufficient condition on the sampling pattern for
-finite completability. We also give a probabilistic condition in terms of the
-number of samples per column that guarantees finite completability with high
-probability. Finally, using the developed tools, we derive the deterministic
-and probabilistic guarantees for unique completability.
-",1,0,1,0,0,0
-17367,Grid-forming Control for Power Converters based on Matching of Synchronous Machines,"  We consider the problem of grid-forming control of power converters in
-low-inertia power systems. Starting from an average-switch three-phase inverter
-model, we draw parallels to a synchronous machine (SM) model and propose a
-novel grid-forming converter control strategy which dwells upon the main
-characteristic of a SM: the presence of an internal rotating magnetic field. In
-particular, we augment the converter system with a virtual oscillator whose
-frequency is driven by the DC-side voltage measurement and which sets the
-converter pulse-width-modulation signal, thereby achieving exact matching
-between the converter in closed-loop and the SM dynamics. We then provide a
-sufficient condition assuring existence, uniqueness, and global asymptotic
-stability of equilibria in a coordinate frame attached to the virtual
-oscillator angle. By actuating the DC-side input of the converter we are able
-to enforce this sufficient condition. In the same setting, we highlight strict
-incremental passivity, droop, and power-sharing properties of the proposed
-framework, which are compatible with conventional requirements of power system
-operation. We subsequently adopt disturbance decoupling techniques to design
-additional control loops that regulate the DC-side voltage, as well as AC-side
-frequency and amplitude, while in the end validating them with numerical
-experiments.
-",0,0,1,0,0,0
-17368,Characterizing Dust Attenuation in Local Star-Forming Galaxies: Near-Infrared Reddening and Normalization,"  We characterize the near-infrared (NIR) dust attenuation for a sample of
-~5500 local (z<0.1) star-forming galaxies and obtain an estimate of their
-average total-to-selective attenuation $k(\lambda)$. We utilize data from the
-United Kingdom Infrared Telescope (UKIRT) and the Two Micron All-Sky Survey
-(2MASS), which is combined with previously measured UV-optical data for these
-galaxies. The average attenuation curve is slightly lower in the far-UV than
-local starburst galaxies, by roughly 15%, but appears similar at longer
-wavelengths with a total-to-selective normalization at V-band of
-$R_V=3.67\substack{+0.44 \\ -0.35}$. Under the assumption of energy balance,
-the total attenuated energy inferred from this curve is found to be broadly
-consistent with the observed infrared dust emission ($L_{\rm{TIR}}$) in a small
-sample of local galaxies for which far-IR measurements are available. However,
-the significant scatter in this quantity among the sample may reflect large
-variations in the attenuation properties of individual galaxies. We also derive
-the attenuation curve for sub-populations of the main sample, separated
-according to mean stellar population age (via $D_n4000$), specific star
-formation rate, stellar mass, and metallicity, and find that they show only
-tentative trends with low significance, at least over the range which is probed
-by our sample. These results indicate that a single curve is reasonable for
-applications seeking to broadly characterize large samples of galaxies in the
-local Universe, while applications to individual galaxies would yield large
-uncertainties and is not recommended.
-",0,1,0,0,0,0
-17369,Sequential Checking: Reallocation-Free Data-Distribution Algorithm for Scale-out Storage,"  Using tape or optical devices for scale-out storage is one option for storing
-a vast amount of data. However, it is impossible or almost impossible to
-rewrite data with such devices. Thus, scale-out storage using such devices
-cannot use standard data-distribution algorithms because they rewrite data for
-moving between servers constituting the scale-out storage when the server
-configuration is changed. Although using rewritable devices for scale-out
-storage, when server capacity is huge, rewriting data is very hard when server
-constitution is changed. In this paper, a data-distribution algorithm called
-Sequential Checking is proposed, which can be used for scale-out storage
-composed of devices that are hardly able to rewrite data. Sequential Checking
-1) does not need to move data between servers when the server configuration is
-changed, 2) distribute data, the amount of which depends on the server's
-volume, 3) select a unique server when datum is written, and 4) select servers
-when datum is read (there are few such server(s) in most cases) and find out a
-unique server that stores the newest datum from them. These basic
-characteristics were confirmed through proofs and simulations. Data can be read
-by accessing 1.98 servers on average from a storage comprising 256 servers
-under a realistic condition. And it is confirmed by evaluations in real
-environment that access time is acceptable. Sequential Checking makes selecting
-scale-out storage using tape or optical devices or using huge capacity servers
-realistic.
-",1,0,0,0,0,0
-17370,Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels,"  Noisy PN learning is the problem of binary classification when training
-examples may be mislabeled (flipped) uniformly with noise rate rho1 for
-positive examples and rho0 for negative examples. We propose Rank Pruning (RP)
-to solve noisy PN learning and the open problem of estimating the noise rates,
-i.e. the fraction of wrong positive and negative labels. Unlike prior
-solutions, RP is time-efficient and general, requiring O(T) for any
-unrestricted choice of probabilistic classifier with T fitting time. We prove
-RP has consistent noise estimation and equivalent expected risk as learning
-with uncorrupted labels in ideal conditions, and derive closed-form solutions
-when conditions are non-ideal. RP achieves state-of-the-art noise estimation
-and F1, error, and AUC-PR for both MNIST and CIFAR datasets, regardless of the
-amount of noise and performs similarly impressively when a large portion of
-training examples are noise drawn from a third distribution. To highlight, RP
-with a CNN classifier can predict if an MNIST digit is a ""one""or ""not"" with
-only 0.25% error, and 0.46 error across all digits, even when 50% of positive
-examples are mislabeled and 50% of observed positive labels are mislabeled
-negative examples.
-",1,0,0,1,0,0
-17371,code2vec: Learning Distributed Representations of Code,"  We present a neural model for representing snippets of code as continuous
-distributed vectors (""code embeddings""). The main idea is to represent a code
-snippet as a single fixed-length $\textit{code vector}$, which can be used to
-predict semantic properties of the snippet. This is performed by decomposing
-code to a collection of paths in its abstract syntax tree, and learning the
-atomic representation of each path $\textit{simultaneously}$ with learning how
-to aggregate a set of them. We demonstrate the effectiveness of our approach by
-using it to predict a method's name from the vector representation of its body.
-We evaluate our approach by training a model on a dataset of 14M methods. We
-show that code vectors trained on this dataset can predict method names from
-files that were completely unobserved during training. Furthermore, we show
-that our model learns useful method name vectors that capture semantic
-similarities, combinations, and analogies. Comparing previous techniques over
-the same data set, our approach obtains a relative improvement of over 75%,
-being the first to successfully predict method names based on a large,
-cross-project, corpus. Our trained model, visualizations and vector
-similarities are available as an interactive online demo at
-this http URL. The code, data, and trained models are available at
-this https URL.
-",1,0,0,1,0,0
-17372,Learning a Local Feature Descriptor for 3D LiDAR Scans,"  Robust data association is necessary for virtually every SLAM system and
-finding corresponding points is typically a preprocessing step for scan
-alignment algorithms. Traditionally, handcrafted feature descriptors were used
-for these problems but recently learned descriptors have been shown to perform
-more robustly. In this work, we propose a local feature descriptor for 3D LiDAR
-scans. The descriptor is learned using a Convolutional Neural Network (CNN).
-Our proposed architecture consists of a Siamese network for learning a feature
-descriptor and a metric learning network for matching the descriptors. We also
-present a method for estimating local surface patches and obtaining
-ground-truth correspondences. In extensive experiments, we compare our learned
-feature descriptor with existing 3D local descriptors and report highly
-competitive results for multiple experiments in terms of matching accuracy and
-computation time. \end{abstract}
-",1,0,0,0,0,0
-17373,Dynamical tides in exoplanetary systems containing Hot Jupiters: confronting theory and observations,"  We study the effect of dynamical tides associated with the excitation of
-gravity waves in an interior radiative region of the central star on orbital
-evolution in observed systems containing Hot Jupiters. We consider WASP-43,
-Ogle-tr-113, WASP-12, and WASP-18 which contain stars on the main sequence
-(MS). For these systems there are observational estimates regarding the rate of
-change of the orbital period. We also investigate Kepler-91 which contains an
-evolved giant star. We adopt the formalism of Ivanov et al. for calculating the
-orbital evolution.
-For the MS stars we determine expected rates of orbital evolution under
-different assumptions about the amount of dissipation acting on the tides,
-estimate the effect of stellar rotation for the two most rapidly rotating stars
-and compare results with observations. All cases apart from possibly WASP-43
-are consistent with a regime in which gravity waves are damped during their
-propagation over the star. However, at present this is not definitive as
-observational errors are large. We find that although it is expected to apply
-to Kepler-91, linear radiative damping cannot explain this dis- sipation regime
-applying to MS stars. Thus, a nonlinear mechanism may be needed.
-Kepler-91 is found to be such that the time scale for evolution of the star
-is comparable to that for the orbit. This implies that significant orbital
-circularisation may have occurred through tides acting on the star.
-Quasi-static tides, stellar winds, hydrodynamic drag and tides acting on the
-planet have likely played a minor role.
-",0,1,0,0,0,0
-17374,Metastability versus collapse following a quench in attractive Bose-Einstein condensates,"  We consider a Bose-Einstein condensate (BEC) with attractive two-body
-interactions in a cigar-shaped trap, initially prepared in its ground state for
-a given negative scattering length, which is quenched to a larger absolute
-value of the scattering length. Using the mean-field approximation, we compute
-numerically, for an experimentally relevant range of aspect ratios and initial
-strengths of the coupling, two critical values of quench: one corresponds to
-the weakest attraction strength the quench to which causes the system to
-collapse before completing even a single return from the narrow configuration
-(""perihelion"") in its breathing cycle. The other is a similar critical point
-for the occurrence of collapse before completing two returns. In the latter
-case, we also compute the limiting value, as we keep increasing the strength of
-the post-quench attraction towards its critical value, of the time interval
-between the first two perihelia. We also use a Gaussian variational model to
-estimate the critical quenched attraction strength below which the system is
-stable against the collapse for long times. These time intervals and critical
-attraction strengths---apart from being fundamental properties of nonlinear
-dynamics of self-attractive BECs---may provide clues to the design of upcoming
-experiments that are trying to create robust BEC breathers.
-",0,1,0,0,0,0
-17375,A similarity criterion for sequential programs using truth-preserving partial functions,"  The execution of sequential programs allows them to be represented using
-mathematical functions formed by the composition of statements following one
-after the other. Each such statement is in itself a partial function, which
-allows only inputs satisfying a particular Boolean condition to carry forward
-the execution and hence, the composition of such functions (as a result of
-sequential execution of the statements) strengthens the valid set of input
-state variables for the program to complete its execution and halt succesfully.
-With this thought in mind, this paper tries to study a particular class of
-partial functions, which tend to preserve the truth of two given Boolean
-conditions whenever the state variables satisfying one are mapped through such
-functions into a domain of state variables satisfying the other. The existence
-of such maps allows us to study isomorphism between different programs, based
-not only on their structural characteristics (e.g. the kind of programming
-constructs used and the overall input-output transformation), but also the
-nature of computation performed on seemingly different inputs. Consequently, we
-can now relate programs which perform a given type of computation, like a loop
-counting down indefinitely, without caring about the input sets they work on
-individually or the set of statements each program contains.
-",1,0,0,0,0,0
-17376,Subsampling large graphs and invariance in networks,"  Specify a randomized algorithm that, given a very large graph or network,
-extracts a random subgraph. What can we learn about the input graph from a
-single subsample? We derive laws of large numbers for the sampler output, by
-relating randomized subsampling to distributional invariance: Assuming an
-invariance holds is tantamount to assuming the sample has been generated by a
-specific algorithm. That in turn yields a notion of ergodicity. Sampling
-algorithms induce model classes---graphon models, sparse generalizations of
-exchangeable graphs, and random multigraphs with exchangeable edges can all be
-obtained in this manner, and we specialize our results to a number of examples.
-One class of sampling algorithms emerges as special: Roughly speaking, those
-defined as limits of random transformations drawn uniformly from certain
-sequences of groups. Some known pathologies of network models based on graphons
-are explained as a form of selection bias.
-",0,0,1,1,0,0
-17377,Taylor coefficients of non-holomorphic Jacobi forms and applications,"  In this paper, we prove modularity results of Taylor coefficients of certain
-non-holomorphic Jacobi forms. It is well-known that Taylor coefficients of
-holomorphic Jacobi forms are quasimoular forms. However recently there has been
-a wide interest for Taylor coefficients of non-holomorphic Jacobi forms for
-example arising in combinatorics. In this paper, we show that such coefficients
-still inherit modular properties. We then work out the precise spaces in which
-these coefficients lie for two examples.
-",0,0,1,0,0,0
-17378,Beamspace SU-MIMO for Future Millimeter Wave Wireless Communications,"  For future networks (i.e., the fifth generation (5G) wireless networks and
-beyond), millimeter-wave (mmWave) communication with large available unlicensed
-spectrum is a promising technology that enables gigabit multimedia
-applications. Thanks to the short wavelength of mmWave radio, massive antenna
-arrays can be packed into the limited dimensions of mmWave transceivers.
-Therefore, with directional beamforming (BF), both mmWave transmitters (MTXs)
-and mmWave receivers (MRXs) are capable of supporting multiple beams in 5G
-networks. However, for the transmission between an MTX and an MRX, most works
-have only considered a single beam, which means that they do not make full
-potential use of mmWave. Furthermore, the connectivity of single beam
-transmission can easily be blocked. In this context, we propose a single-user
-multi-beam concurrent transmission scheme for future mmWave networks with
-multiple reflected paths. Based on spatial spectrum reuse, the scheme can be
-described as a multiple-input multiple-output (MIMO) technique in beamspace
-(i.e., in the beam-number domain). Moreover, this study investigates the
-challenges and potential solutions for implementing this scheme, including
-multibeam selection, cooperative beam tracking, multi-beam power allocation and
-synchronization. The theoretical and numerical results show that the proposed
-beamspace SU-MIMO can largely improve the achievable rate of the transmission
-between an MTX and an MRX and, meanwhile, can maintain the connectivity.
-",1,0,0,0,0,0
-17379,Learning Robust Visual-Semantic Embeddings,"  Many of the existing methods for learning joint embedding of images and text
-use only supervised information from paired images and its textual attributes.
-Taking advantage of the recent success of unsupervised learning in deep neural
-networks, we propose an end-to-end learning framework that is able to extract
-more robust multi-modal representations across domains. The proposed method
-combines representation learning models (i.e., auto-encoders) together with
-cross-domain learning criteria (i.e., Maximum Mean Discrepancy loss) to learn
-joint embeddings for semantic and visual features. A novel technique of
-unsupervised-data adaptation inference is introduced to construct more
-comprehensive embeddings for both labeled and unlabeled data. We evaluate our
-method on Animals with Attributes and Caltech-UCSD Birds 200-2011 dataset with
-a wide range of applications, including zero and few-shot image recognition and
-retrieval, from inductive to transductive settings. Empirically, we show that
-our framework improves over the current state of the art on many of the
-considered tasks.
-",1,0,0,0,0,0
-17380,Quantitative estimates of the surface habitability of Kepler-452b,"  Kepler-452b is currently the best example of an Earth-size planet in the
-habitable zone of a sun-like star, a type of planet whose number of detections
-is expected to increase in the future. Searching for biosignatures in the
-supposedly thin atmospheres of these planets is a challenging goal that
-requires a careful selection of the targets. Under the assumption of a
-rocky-dominated nature for Kepler-452b, we considered it as a test case to
-calculate a temperature-dependent habitability index, $h_{050}$, designed to
-maximize the potential presence of biosignature-producing activity (Silva et
-al.\ 2016). The surface temperature has been computed for a broad range of
-climate factors using a climate model designed for terrestrial-type exoplanets
-(Vladilo et al.\ 2015). After fixing the planetary data according to the
-experimental results (Jenkins et al.\ 2015), we changed the surface gravity,
-CO$_2$ abundance, surface pressure, orbital eccentricity, rotation period, axis
-obliquity and ocean fraction within the range of validity of our model. For
-most choices of parameters we find habitable solutions with $h_{050}>0.2$ only
-for CO$_2$ partial pressure $p_\mathrm{CO_2} \lesssim 0.04$\,bar. At this
-limiting value of CO$_2$ abundance the planet is still habitable if the total
-pressure is $p \lesssim 2$\,bar. In all cases the habitability drops for
-eccentricity $e \gtrsim 0.3$. Changes of rotation period and obliquity affect
-the habitability through their impact on the equator-pole temperature
-difference rather than on the mean global temperature. We calculated the
-variation of $h_{050}$ resulting from the luminosity evolution of the host star
-for a wide range of input parameters. Only a small combination of parameters
-yield habitability-weighted lifetimes $\gtrsim 2$\,Gyr, sufficiently long to
-develop atmospheric biosignatures still detectable at the present time.
-",0,1,0,0,0,0
-17381,Design and implementation of dynamic logic gates and R-S flip-flop using quasiperiodically driven Murali-Lakshmanan-Chua circuit,"  We report the propagation of a square wave signal in a quasi-periodically
-driven Murali-Lakshmanan-Chua (QPDMLC) circuit system. It is observed that
-signal propagation is possible only above a certain threshold strength of the
-square wave or digital signal and all the values above the threshold amplitude
-are termed as 'region of signal propagation'. Then, we extend this region of
-signal propagation to perform various logical operations like AND/NAND/OR/NOR
-and hence it is also designated as the 'region of logical operation'. Based on
-this region, we propose implementing the dynamic logic gates, namely
-AND/NAND/OR/NOR, which can be decided by the asymmetrical input square waves
-without altering the system parameters. Further, we show that a single QPDMLC
-system will produce simultaneously two outputs which are complementary to each
-other. As a result, a single QPDMLC system yields either AND as well as NAND or
-OR as well as NOR gates simultaneously. Then we combine the corresponding two
-QPDMLC systems in a cross-coupled way and report that its dynamics mimics that
-of fundamental R-S flip-flop circuit. All these phenomena have been explained
-with analytical solutions of the circuit equations characterizing the system
-and finally the results are compared with the corresponding numerical and
-experimental analysis.
-",0,1,0,0,0,0
-17382,"Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects","  We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep
-generative model for videos of moving objects. It can reliably discover and
-track objects throughout the sequence of frames, and can also generate future
-frames conditioning on the current frame, thereby simulating expected motion of
-objects. This is achieved by explicitly encoding object presence, locations and
-appearances in the latent variables of the model. SQAIR retains all strengths
-of its predecessor, Attend, Infer, Repeat (AIR, Eslami et. al., 2016),
-including learning in an unsupervised manner, and addresses its shortcomings.
-We use a moving multi-MNIST dataset to show limitations of AIR in detecting
-overlapping or partially occluded objects, and show how SQAIR overcomes them by
-leveraging temporal consistency of objects. Finally, we also apply SQAIR to
-real-world pedestrian CCTV data, where it learns to reliably detect, track and
-generate walking pedestrians with no supervision.
-",0,0,0,1,0,0
-17383,Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks,"  We present a new local descriptor for 3D shapes, directly applicable to a
-wide range of shape analysis problems such as point correspondences, semantic
-segmentation, affordance prediction, and shape-to-scan matching. The descriptor
-is produced by a convolutional network that is trained to embed geometrically
-and semantically similar points close to one another in descriptor space. The
-network processes surface neighborhoods around points on a shape that are
-captured at multiple scales by a succession of progressively zoomed out views,
-taken from carefully selected camera positions. We leverage two extremely large
-sources of data to train our network. First, since our network processes
-rendered views in the form of 2D images, we repurpose architectures pre-trained
-on massive image datasets. Second, we automatically generate a synthetic dense
-point correspondence dataset by non-rigid alignment of corresponding shape
-parts in a large collection of segmented 3D models. As a result of these design
-choices, our network effectively encodes multi-scale local context and
-fine-grained surface detail. Our network can be trained to produce either
-category-specific descriptors or more generic descriptors by learning from
-multiple shape categories. Once trained, at test time, the network extracts
-local descriptors for shapes without requiring any part segmentation as input.
-Our method can produce effective local descriptors even for shapes whose
-category is unknown or different from the ones used while training. We
-demonstrate through several experiments that our learned local descriptors are
-more discriminative compared to state of the art alternatives, and are
-effective in a variety of shape analysis applications.
-",1,0,0,0,0,0
-17384,Alternating minimization for dictionary learning with random initialization,"  We present theoretical guarantees for an alternating minimization algorithm
-for the dictionary learning/sparse coding problem. The dictionary learning
-problem is to factorize vector samples $y^{1},y^{2},\ldots, y^{n}$ into an
-appropriate basis (dictionary) $A^*$ and sparse vectors $x^{1*},\ldots,x^{n*}$.
-Our algorithm is a simple alternating minimization procedure that switches
-between $\ell_1$ minimization and gradient descent in alternate steps.
-Dictionary learning and specifically alternating minimization algorithms for
-dictionary learning are well studied both theoretically and empirically.
-However, in contrast to previous theoretical analyses for this problem, we
-replace the condition on the operator norm (that is, the largest magnitude
-singular value) of the true underlying dictionary $A^*$ with a condition on the
-matrix infinity norm (that is, the largest magnitude term). This not only
-allows us to get convergence rates for the error of the estimated dictionary
-measured in the matrix infinity norm, but also ensures that a random
-initialization will provably converge to the global optimum. Our guarantees are
-under a reasonable generative model that allows for dictionaries with growing
-operator norms, and can handle an arbitrary level of overcompleteness, while
-having sparsity that is information theoretically optimal. We also establish
-upper bounds on the sample complexity of our algorithm.
-",1,0,0,1,0,0
-17385,Optimal Transmission Line Switching under Geomagnetic Disturbances,"  In recent years, there have been increasing concerns about how geomagnetic
-disturbances (GMDs) impact electrical power systems. Geomagnetically-induced
-currents (GICs) can saturate transformers, induce hot spot heating and increase
-reactive power losses. These effects can potentially cause catastrophic damage
-to transformers and severely impact the ability of a power system to deliver
-power. To address this problem, we develop a model of GIC impacts to power
-systems that includes 1) GIC thermal capacity of transformers as a function of
-normal Alternating Current (AC) and 2) reactive power losses as a function of
-GIC. We use this model to derive an optimization problem that protects power
-systems from GIC impacts through line switching, generator redispatch, and load
-shedding. We employ state-of-the-art convex relaxations of AC power flow
-equations to lower bound the objective. We demonstrate the approach on a
-modified RTS96 system and the UIUC 150-bus system and show that line switching
-is an effective means to mitigate GIC impacts. We also provide a sensitivity
-analysis of optimal switching decisions with respect to GMD direction.
-",1,0,0,0,0,0
-17386,Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks,"  In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and
-the segmentation-based multi-scale analysis to locate tampered areas in digital
-images. First, to deal with color input sliding windows of different scales, a
-unified CNN architecture is designed. Then, we elaborately design the training
-procedures of CNNs on sampled training patches. With a set of robust
-multi-scale tampering detectors based on CNNs, complementary tampering
-possibility maps can be generated. Last but not least, a segmentation-based
-method is proposed to fuse the maps and generate the final decision map. By
-exploiting the benefits of both the small-scale and large-scale analyses, the
-segmentation-based multi-scale analysis can lead to a performance leap in
-forgery localization of CNNs. Numerous experiments are conducted to demonstrate
-the effectiveness and efficiency of our method.
-",1,0,0,0,0,0
-17387,The QKP limit of the quantum Euler-Poisson equation,"  In this paper, we consider the derivation of the Kadomtsev-Petviashvili (KP)
-equation for cold ion-acoustic wave in the long wavelength limit of the
-two-dimensional quantum Euler-Poisson system, under different scalings for
-varying directions in the Gardner-Morikawa transform. It is shown that the
-types of the KP equation depend on the scaled quantum parameter $H>0$. The
-QKP-I is derived for $H>2$, QKP-II for $0<H<2$ and the dispersive-less KP (dKP)
-equation for the critical case $H=2$. The rigorous proof for these limits is
-given in the well-prepared initial data case, and the norm that is chosen to
-close the proof is anisotropic in the two directions, in accordance with the
-anisotropic structure of the KP equation as well as the Gardner-Morikawa
-transform. The results can be generalized in several directions.
-",0,0,1,0,0,0
-17388,Variational Implicit Processes,"  This paper introduces the variational implicit processes (VIPs), a Bayesian
-nonparametric method based on a class of highly flexible priors over functions.
-Similar to Gaussian processes (GPs), in implicit processes (IPs), an implicit
-multivariate prior (data simulators, Bayesian neural networks, etc.) is placed
-over any finite collections of random variables. A novel and efficient
-variational inference algorithm for IPs is derived using wake-sleep updates,
-which gives analytic solutions and allows scalable hyper-parameter learning
-with stochastic optimization. Experiments on real-world regression datasets
-demonstrate that VIPs return better uncertainty estimates and superior
-performance over existing inference methods for GPs and Bayesian neural
-networks. With a Bayesian LSTM as the implicit prior, the proposed approach
-achieves state-of-the-art results on predicting power conversion efficiency of
-molecules based on raw chemical formulas.
-",0,0,0,1,0,0
-17389,Silicon Micromachined High-contrast Artificial Dielectrics for Millimeter-wave Transformation Optics Antennas,"  Transformation optics methods and gradient index electromagnetic structures
-rely upon spatially varied arbitrary permittivity. This, along with recent
-interest in millimeter-wave lens-based antennas demands high spatial resolution
-dielectric variation. Perforated media have been used to fabricate gradient
-index structures from microwaves to THz but are often limited in contrast. We
-show that by employing regular polygon unit-cells (hexagon, square, and
-triangle) on matched lattices we can realize very high contrast permittivity
-ranging from 0.1-1.0 of the background permittivity. Silicon micromachining
-(Bosch process) is performed on high resistivity Silicon wafers to achieve a
-minimum permittivity of 1.25 (10% of Silicon) in the WR28 waveguide band,
-specifically targeting the proposed 39 GHz 5G communications band. The method
-is valid into the THz band.
-",0,1,0,0,0,0
-17390,Pseudogap and Fermi surface in the presence of spin-vortex checkerboard for 1/8-doped lanthanum cuprates,"  Lanthanum family of high-temperature cuprate superconductors is known to
-exhibit both spin and charge electronic modulations around doping level 1/8. We
-assume that these modulations have the character of two-dimensional spin-vortex
-checkerboard and investigate whether this assumption is consistent with the
-Fermi surface and the pseudogap measured by angle-resolved photo-emission
-spectroscopy. We also explore the possibility of observing quantum oscillations
-of transport coefficients in such a background. These investigations are based
-on a model of non-interacting spin-1/2 fermions hopping on a square lattice and
-coupled through spins to a magnetic field imitating spin-vortex checkerboard.
-The main results of this article include (i) calculation of Fermi surface
-containing Fermi arcs at the positions in the Brillouin zone largely consistent
-with experiments; (ii) identification of factors complicating the observations
-of quantum oscillations in the presence of spin modulations; and (iii)
-investigation of the symmetries of the resulting electronic energy bands,
-which, in particular, indicates that each band is double-degenerate and, in
-addition, has at least one conical point, where it touches another
-double-degenerate band. We discuss possible implications these cones may have
-for the transport properties and the pseudogap.
-",0,1,0,0,0,0
-17391,Revealing the cluster of slow transients behind a large slow slip event,"  Capable of reaching similar magnitudes to large megathrust earthquakes
-($M_w>7$), slow slip events play a major role in accommodating tectonic motion
-on plate boundaries. These slip transients are the slow release of built-up
-tectonic stress that are geodetically imaged as a predominantly aseismic
-rupture, which is smooth in both time and space. We demonstrate here that large
-slow slip events are in fact a cluster of short-duration slow transients. Using
-a dense catalog of low-frequency earthquakes as a guide, we investigate the
-$M_w7.5$ slow slip event that occurred in 2006 along the subduction interface
-40~km beneath Guerrero, Mexico. We show that while the long-period surface
-displacement as recorded by GPS suggests a six month duration, motion in the
-direction of tectonic release only sporadically occurs over 55 days and its
-surface signature is attenuated by rapid relocking of the plate interface.These
-results demonstrate that our current conceptual model of slow and continuous
-rupture is an artifact of low-resolution geodetic observations of a
-superposition of small, clustered slip events. Our proposed description of slow
-slip as a cluster of slow transients implies that we systematically
-overestimate the duration $T$ and underestimate the moment magnitude $M$ of
-large slow slip events.
-",0,1,0,0,0,0
-17392,General $N$-solitons and their dynamics in several nonlocal nonlinear Schrödinger equations,"  General $N$-solitons in three recently-proposed nonlocal nonlinear
-Schrödinger equations are presented. These nonlocal equations include the
-reverse-space, reverse-time, and reverse-space-time nonlinear Schrödinger
-equations, which are nonlocal reductions of the Ablowitz-Kaup-Newell-Segur
-(AKNS) hierarchy. It is shown that general $N$-solitons in these different
-equations can be derived from the same Riemann-Hilbert solutions of the AKNS
-hierarchy, except that symmetry relations on the scattering data are different
-for these equations. This Riemann-Hilbert framework allows us to identify new
-types of solitons with novel eigenvalue configurations in the spectral plane.
-Dynamics of $N$-solitons in these equations is also explored. In all the three
-nonlocal equations, a generic feature of their solutions is repeated
-collapsing. In addition, multi-solitons can behave very differently from
-fundamental solitons and may not correspond to a nonlinear superposition of
-fundamental solitons.
-",0,1,0,0,0,0
-17393,Revisiting wireless network jamming by SIR-based considerations and Multiband Robust Optimization,"  We revisit the mathematical models for wireless network jamming introduced by
-Commander et al.: we first point out the strong connections with classical
-wireless network design and then we propose a new model based on the explicit
-use of signal-to-interference quantities. Moreover, to address the intrinsic
-uncertain nature of the jamming problem and tackle the peculiar right-hand-side
-(RHS) uncertainty of the problem, we propose an original robust cutting-plane
-algorithm drawing inspiration from Multiband Robust Optimization. Finally, we
-assess the performance of the proposed cutting plane algorithm by experiments
-on realistic network instances.
-",1,0,1,0,0,0
-17394,New models for symbolic data analysis,"  Symbolic data analysis (SDA) is an emerging area of statistics based on
-aggregating individual level data into group-based distributional summaries
-(symbols), and then developing statistical methods to analyse them. It is ideal
-for analysing large and complex datasets, and has immense potential to become a
-standard inferential technique in the near future. However, existing SDA
-techniques are either non-inferential, do not easily permit meaningful
-statistical models, are unable to distinguish between competing models, and are
-based on simplifying assumptions that are known to be false. Further, the
-procedure for constructing symbols from the underlying data is erroneously not
-considered relevant to the resulting statistical analysis. In this paper we
-introduce a new general method for constructing likelihood functions for
-symbolic data based on a desired probability model for the underlying classical
-data, while only observing the distributional summaries. This approach resolves
-many of the conceptual and practical issues with current SDA methods, opens the
-door for new classes of symbol design and construction, in addition to
-developing SDA as a viable tool to enable and improve upon classical data
-analyses, particularly for very large and complex datasets. This work creates a
-new direction for SDA research, which we illustrate through several real and
-simulated data analyses.
-",0,0,0,1,0,0
-17395,Soft Methodology for Cost-and-error Sensitive Classification,"  Many real-world data mining applications need varying cost for different
-types of classification errors and thus call for cost-sensitive classification
-algorithms. Existing algorithms for cost-sensitive classification are
-successful in terms of minimizing the cost, but can result in a high error rate
-as the trade-off. The high error rate holds back the practical use of those
-algorithms. In this paper, we propose a novel cost-sensitive classification
-methodology that takes both the cost and the error rate into account. The
-methodology, called soft cost-sensitive classification, is established from a
-multicriteria optimization problem of the cost and the error rate, and can be
-viewed as regularizing cost-sensitive classification with the error rate. The
-simple methodology allows immediate improvements of existing cost-sensitive
-classification algorithms. Experiments on the benchmark and the real-world data
-sets show that our proposed methodology indeed achieves lower test error rates
-and similar (sometimes lower) test costs than existing cost-sensitive
-classification algorithms. We also demonstrate that the methodology can be
-extended for considering the weighted error rate instead of the original error
-rate. This extension is useful for tackling unbalanced classification problems.
-",1,0,0,0,0,0
-17396,Raman LIDARs and atmospheric calibration for the Cherenkov Telescope Array,"  The Cherenkov Telescope Array (CTA) is the next generation of Imaging
-Atmospheric Cherenkov Telescopes. It will reach a sensitivity and energy
-resolution never obtained until now by any other high energy gamma-ray
-experiment. Understanding the systematic uncertainties in general will be a
-crucial issue for the performance of CTA. It is well known that atmospheric
-conditions contribute particularly in this aspect.Within the CTA consortium
-several groups are currently building Raman LIDARs to be installed on the two
-sites. Raman LIDARs are devices composed of a powerful laser that shoots into
-the atmosphere, a collector that gathers the backscattered light from molecules
-and aerosols, a photo-sensor, an optical module that spectrally selects
-wavelengths of interest, and a read--out system.Unlike currently used elastic
-LIDARs, they can help reduce the systematic uncertainties of the molecular and
-aerosol components of the atmosphere to <5% so that CTA can achieve its energy
-resolution requirements of<10% uncertainty at 1 TeV.All the Raman LIDARs in
-this work have design features that make them different than typical Raman
-LIDARs used in atmospheric science and are characterized by large collecting
-mirrors (2.5m2) and reduced acquisition time.They provide both multiple elastic
-and Raman read-out channels and custom made optics design.In this paper, the
-motivation for Raman LIDARs, the design and the status of advance of these
-technologies are described.
-",0,1,0,0,0,0
-17397,Generalized notions of sparsity and restricted isometry property. Part II: Applications,"  The restricted isometry property (RIP) is a universal tool for data recovery.
-We explore the implication of the RIP in the framework of generalized sparsity
-and group measurements introduced in the Part I paper. It turns out that for a
-given measurement instrument the number of measurements for RIP can be improved
-by optimizing over families of Banach spaces. Second, we investigate the
-preservation of difference of two sparse vectors, which is not trivial in
-generalized models. Third, we extend the RIP of partial Fourier measurements at
-optimal scaling of number of measurements with random sign to far more general
-group structured measurements. Lastly, we also obtain RIP in infinite dimension
-in the context of Fourier measurement concepts with sparsity naturally replaced
-by smoothness assumptions.
-",0,0,0,1,0,0
-17398,Mellin-Meijer-kernel density estimation on $\mathbb{R}^+$,"  Nonparametric kernel density estimation is a very natural procedure which
-simply makes use of the smoothing power of the convolution operation. Yet, it
-performs poorly when the density of a positive variable is to be estimated
-(boundary issues, spurious bumps in the tail). So various extensions of the
-basic kernel estimator allegedly suitable for $\mathbb{R}^+$-supported
-densities, such as those using Gamma or other asymmetric kernels, abound in the
-literature. Those, however, are not based on any valid smoothing operation
-analogous to the convolution, which typically leads to inconsistencies. By
-contrast, in this paper a kernel estimator for $\mathbb{R}^+$-supported
-densities is defined by making use of the Mellin convolution, the natural
-analogue of the usual convolution on $\mathbb{R}^+$. From there, a very
-transparent theory flows and leads to new type of asymmetric kernels strongly
-related to Meijer's $G$-functions. The numerous pleasant properties of this
-`Mellin-Meijer-kernel density estimator' are demonstrated in the paper. Its
-pointwise and $L_2$-consistency (with optimal rate of convergence) is
-established for a large class of densities, including densities unbounded at 0
-and showing power-law decay in their right tail. Its practical behaviour is
-investigated further through simulations and some real data analyses.
-",0,0,1,1,0,0
-17399,Gene Ontology (GO) Prediction using Machine Learning Methods,"  We applied machine learning to predict whether a gene is involved in axon
-regeneration. We extracted 31 features from different databases and trained
-five machine learning models. Our optimal model, a Random Forest Classifier
-with 50 submodels, yielded a test score of 85.71%, which is 4.1% higher than
-the baseline score. We concluded that our models have some predictive
-capability. Similar methodology and features could be applied to predict other
-Gene Ontology (GO) terms.
-",1,0,0,1,0,0
-17400,Dimension Spectra of Lines,"  This paper investigates the algorithmic dimension spectra of lines in the
-Euclidean plane. Given any line L with slope a and vertical intercept b, the
-dimension spectrum sp(L) is the set of all effective Hausdorff dimensions of
-individual points on L. We draw on Kolmogorov complexity and geometrical
-arguments to show that if the effective Hausdorff dimension dim(a, b) is equal
-to the effective packing dimension Dim(a, b), then sp(L) contains a unit
-interval. We also show that, if the dimension dim(a, b) is at least one, then
-sp(L) is infinite. Together with previous work, this implies that the dimension
-spectrum of any line is infinite.
-",1,0,0,0,0,0
-17401,Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search,"  Inference in log-linear models scales linearly in the size of output space in
-the worst-case. This is often a bottleneck in natural language processing and
-computer vision tasks when the output space is feasibly enumerable but very
-large. We propose a method to perform inference in log-linear models with
-sublinear amortized cost. Our idea hinges on using Gumbel random variable
-perturbations and a pre-computed Maximum Inner Product Search data structure to
-access the most-likely elements in sublinear amortized time. Our method yields
-provable runtime and accuracy guarantees. Further, we present empirical
-experiments on ImageNet and Word Embeddings showing significant speedups for
-sampling, inference, and learning in log-linear models.
-",1,0,0,1,0,0
-17402,Temperature Dependence of Magnetic Excitations: Terahertz Magnons above the Curie Temperature,"  When an ordered spin system of a given dimensionality undergoes a second
-order phase transition the dependence of the order parameter i.e. magnetization
-on temperature can be well-described by thermal excitations of elementary
-collective spin excitations (magnons). However, the behavior of magnons
-themselves, as a function of temperature and across the transition temperature
-TC, is an unknown issue. Utilizing spin-polarized high resolution electron
-energy loss spectroscopy we monitor the high-energy (terahertz) magnons,
-excited in an ultrathin ferromagnet, as a function of temperature. We show that
-the magnons' energy and lifetime decrease with temperature. The
-temperature-induced renormalization of the magnons' energy and lifetime depends
-on the wave vector. We provide quantitative results on the temperature-induced
-damping and discuss the possible mechanism e.g., multi-magnon scattering. A
-careful investigation of physical quantities determining the magnons'
-propagation indicates that terahertz magnons sustain their propagating
-character even at temperatures far above TC.
-",0,1,0,0,0,0
-17403,On stable solitons and interactions of the generalized Gross-Pitaevskii equation with PT-and non-PT-symmetric potentials,"  We report the bright solitons of the generalized Gross-Pitaevskii (GP)
-equation with some types of physically relevant parity-time-(PT-) and
-non-PT-symmetric potentials. We find that the constant momentum coefficient can
-modulate the linear stability and complicated transverse power-flows (not
-always from the gain toward loss) of nonlinear modes. However, the varying
-momentum coefficient Gamma(x) can modulate both unbroken linear PT-symmetric
-phases and stability of nonlinear modes. Particularly, the nonlinearity can
-excite the unstable linear mode (i.e., broken linear PT-symmetric phase) to
-stable nonlinear modes. Moreover, we also find stable bright solitons in the
-presence of non-PT-symmetric harmonic-Gaussian potential. The interactions of
-two bright solitons are also illustrated in PT-symmetric potentials. Finally,
-we consider nonlinear modes and transverse power-flows in the three-dimensional
-(3D) GP equation with the generalized PT-symmetric Scarf-II potential
-",0,1,1,0,0,0
-17404,Mechanical properties of borophene films: A reactive molecular dynamics investigation,"  The most recent experimental advances could provide ways for the fabrication
-of several atomic thick and planar forms of boron atoms. For the first time, we
-explore the mechanical properties of five types of boron films with various
-vacancy ratios ranging from 0.1 to 0.15, using molecular dynamics simulations
-with ReaxFF force field. It is found that the Young's modulus and tensile
-strength decrease with increasing the temperature. We found that boron sheets
-exhibit an anisotropic mechanical response due to the different arrangement of
-atoms along the armchair and zigzag directions. At room temperature, 2D Young's
-modulus and fracture stress of these five sheets appear in the range 63 N/m and
-12 N/m, respectively. In addition, the strains at tensile strength are in the
-ranges of 9, 11, and 10 percent at 1, 300, and 600 K, respectively. This
-investigation not only reveals the remarkable stiffness of 2D boron, but
-establishes relations between the mechanical properties of the boron sheets to
-the loading direction, temperature and atomic structures.
-",0,1,0,0,0,0
-17405,Stronger selection can slow down evolution driven by recombination on a smooth fitness landscape,"  Stronger selection implies faster evolution---that is, the greater the force,
-the faster the change. This apparently self-evident proposition, however, is
-derived under the assumption that genetic variation within a population is
-primarily supplied by mutation (i.e.\ mutation-driven evolution). Here, we show
-that this proposition does not actually hold for recombination-driven
-evolution, i.e.\ evolution in which genetic variation is primarily created by
-recombination rather than mutation. By numerically investigating population
-genetics models of recombination, migration and selection, we demonstrate that
-stronger selection can slow down evolution on a perfectly smooth fitness
-landscape. Through simple analytical calculation, this apparently
-counter-intuitive result is shown to stem from two opposing effects of natural
-selection on the rate of evolution. On the one hand, natural selection tends to
-increase the rate of evolution by increasing the fixation probability of fitter
-genotypes. On the other hand, natural selection tends to decrease the rate of
-evolution by decreasing the chance of recombination between immigrants and
-resident individuals. As a consequence of these opposing effects, there is a
-finite selection pressure maximizing the rate of evolution. Hence, stronger
-selection can imply slower evolution if genetic variation is primarily supplied
-by recombination.
-",0,1,0,0,0,0
-17406,Differences Among Noninformative Stopping Rules Are Often Relevant to Bayesian Decisions,"  L.J. Savage once hoped to show that ""the superficially incompatible systems
-of ideas associated on the one hand with [subjective Bayesianism] and on the
-other hand with [classical statistics]...lend each other mutual support and
-clarification."" By 1972, however, he had largely ""lost faith in the devices"" of
-classical statistics. One aspect of those ""devices"" that he found objectionable
-is that differences among the ""stopping rules"" that are used to decide when to
-end an experiment which are ""noninformative"" from a Bayesian perspective can
-affect decisions made using a classical approach. Two experiments that produce
-the same data using different stopping rules seem to differ only in the
-intentions of the experimenters regarding whether or not they would have
-carried on if the data had been different, which seem irrelevant to the
-evidential import of the data and thus to facts about what actions the data
-warrant.
-I argue that classical and Bayesian ideas about stopping rules do in fact
-""lend each other"" the kind of ""mutual support and clarification"" that Savage
-had originally hoped to find. They do so in a kind of case that is common in
-scientific practice, in which those who design an experiment have different
-interests from those who will make decisions in light of its results. I show
-that, in cases of this kind, Bayesian principles provide qualified support for
-the classical statistical practice of ""penalizing"" ""biased"" stopping rules.
-However, they require this practice in a narrower range of circumstances than
-classical principles do, and for different reasons. I argue that classical
-arguments for this practice are compelling in precisely the class of cases in
-which Bayesian principles also require it, and thus that we should regard
-Bayesian principles as clarifying classical statistical ideas about stopping
-rules rather than the reverse.
-",0,0,1,1,0,0
-17407,CNNs are Globally Optimal Given Multi-Layer Support,"  Stochastic Gradient Descent (SGD) is the central workhorse for training
-modern CNNs. Although giving impressive empirical performance it can be slow to
-converge. In this paper we explore a novel strategy for training a CNN using an
-alternation strategy that offers substantial speedups during training. We make
-the following contributions: (i) replace the ReLU non-linearity within a CNN
-with positive hard-thresholding, (ii) reinterpret this non-linearity as a
-binary state vector making the entire CNN linear if the multi-layer support is
-known, and (iii) demonstrate that under certain conditions a global optima to
-the CNN can be found through local descent. We then employ a novel alternation
-strategy (between weights and support) for CNN training that leads to
-substantially faster convergence rates, nice theoretical properties, and
-achieving state of the art results across large scale datasets (e.g. ImageNet)
-as well as other standard benchmarks.
-",1,0,0,0,0,0
-17408,The Kontsevich integral for bottom tangles in handlebodies,"  The Kontsevich integral is a powerful link invariant, taking values in spaces
-of Jacobi diagrams. In this paper, we extend the Kontsevich integral to
-construct a functor on the category of bottom tangles in handlebodies. This
-functor gives a universal finite type invariant of bottom tangles, and refines
-a functorial version of the Le-Murakami-Ohtsuki 3-manifold invariant for
-Lagrangian cobordisms of surfaces.
-",0,0,1,0,0,0
-17409,Commutativity theorems for groups and semigroups,"  In this note we prove a selection of commutativity theorems for various
-classes of semigroups. For instance, if in a separative or completely regular
-semigroup $S$ we have $x^p y^p = y^p x^p$ and $x^q y^q = y^q x^q$ for all
-$x,y\in S$ where $p$ and $q$ are relatively prime, then $S$ is commutative. In
-a separative or inverse semigroup $S$, if there exist three consecutive
-integers $i$ such that $(xy)^i = x^i y^i$ for all $x,y\in S$, then $S$ is
-commutative. Finally, if $S$ is a separative or inverse semigroup satisfying
-$(xy)^3=x^3y^3$ for all $x,y\in S$, and if the cubing map $x\mapsto x^3$ is
-injective, then $S$ is commutative.
-",0,0,1,0,0,0
-17410,Content-based Approach for Vietnamese Spam SMS Filtering,"  Short Message Service (SMS) spam is a serious problem in Vietnam because of
-the availability of very cheap pre-paid SMS packages. There are some systems to
-detect and filter spam messages for English, most of which use machine learning
-techniques to analyze the content of messages and classify them. For
-Vietnamese, there is some research on spam email filtering but none focused on
-SMS. In this work, we propose the first system for filtering Vietnamese spam
-SMS. We first propose an appropriate preprocessing method since existing tools
-for Vietnamese preprocessing cannot give good accuracy on our dataset. We then
-experiment with vector representations and classifiers to find the best model
-for this problem. Our system achieves an accuracy of 94% when labelling spam
-messages while the misclassification rate of legitimate messages is relatively
-small, about only 0.4%. This is an encouraging result compared to that of
-English and can be served as a strong baseline for future development of
-Vietnamese SMS spam prevention systems.
-",1,0,0,0,0,0
-17411,Measuring Software Performance on Linux,"  Measuring and analyzing the performance of software has reached a high
-complexity, caused by more advanced processor designs and the intricate
-interaction between user programs, the operating system, and the processor's
-microarchitecture. In this report, we summarize our experience about how
-performance characteristics of software should be measured when running on a
-Linux operating system and a modern processor. In particular, (1) We provide a
-general overview about hardware and operating system features that may have a
-significant impact on timing and how they interact, (2) we identify sources of
-errors that need to be controlled in order to obtain unbiased measurement
-results, and (3) we propose a measurement setup for Linux to minimize errors.
-Although not the focus of this report, we describe the measurement process
-using hardware performance counters, which can faithfully reflect the real
-bottlenecks on a given processor. Our experiments confirm that our measurement
-setup has a large impact on the results. More surprisingly, however, they also
-suggest that the setup can be negligible for certain analysis methods.
-Furthermore, we found that our setup maintains significantly better performance
-under background load conditions, which means it can be used to improve
-software in high-performance applications.
-",1,0,0,0,0,0
-17412,A general method for calculating lattice Green functions on the branch cut,"  We present a method for calculating the complex Green function $G_{ij}
-(\omega)$ at any real frequency $\omega$ between any two sites $i$ and $j$ on a
-lattice. Starting from numbers of walks on square, cubic, honeycomb,
-triangular, bcc, fcc, and diamond lattices, we derive Chebyshev expansion
-coefficients for $G_{ij} (\omega)$. The convergence of the Chebyshev series can
-be accelerated by constructing functions $f(\omega)$ that mimic the van Hove
-singularities in $G_{ij} (\omega)$ and subtracting their Chebyshev coefficients
-from the original coefficients. We demonstrate this explicitly for the square
-lattice and bcc lattice. Our algorithm achieves typical accuracies of 6--9
-significant figures using 1000 series terms.
-",0,1,0,0,0,0
-17413,"Towards A Novel Unified Framework for Developing Formal, Network and Validated Agent-Based Simulation Models of Complex Adaptive Systems","  Literature on the modeling and simulation of complex adaptive systems (cas)
-has primarily advanced vertically in different scientific domains with
-scientists developing a variety of domain-specific approaches and applications.
-However, while cas researchers are inher-ently interested in an
-interdisciplinary comparison of models, to the best of our knowledge, there is
-currently no single unified framework for facilitating the development,
-comparison, communication and validation of models across different scientific
-domains. In this thesis, we propose first steps towards such a unified
-framework using a combination of agent-based and complex network-based modeling
-approaches and guidelines formulated in the form of a set of four levels of
-usage, which allow multidisciplinary researchers to adopt a suitable framework
-level on the basis of available data types, their research study objectives and
-expected outcomes, thus allowing them to better plan and conduct their
-respective re-search case studies.
-",1,1,0,0,0,0
-17414,Recover Fine-Grained Spatial Data from Coarse Aggregation,"  In this paper, we study a new type of spatial sparse recovery problem, that
-is to infer the fine-grained spatial distribution of certain density data in a
-region only based on the aggregate observations recorded for each of its
-subregions. One typical example of this spatial sparse recovery problem is to
-infer spatial distribution of cellphone activities based on aggregate mobile
-traffic volumes observed at sparsely scattered base stations. We propose a
-novel Constrained Spatial Smoothing (CSS) approach, which exploits the local
-continuity that exists in many types of spatial data to perform sparse recovery
-via finite-element methods, while enforcing the aggregated observation
-constraints through an innovative use of the ADMM algorithm. We also improve
-the approach to further utilize additional geographical attributes. Extensive
-evaluations based on a large dataset of phone call records and a demographical
-dataset from the city of Milan show that our approach significantly outperforms
-various state-of-the-art approaches, including Spatial Spline Regression (SSR).
-",1,0,0,0,0,0
-17415,The Power Allocation Game on Dynamic Networks: Subgame Perfection,"  In the game theory literature, there appears to be little research on
-equilibrium selection for normal-form games with an infinite strategy space and
-discontinuous utility functions. Moreover, many existing selection methods are
-not applicable to games involving both cooperative and noncooperative scenarios
-(e.g., ""games on signed graphs""). With the purpose of equilibrium selection,
-the power allocation game developed in \cite{allocation}, which is a static,
-resource allocation game on signed graphs, will be reformulated into an
-extensive form. Results about the subgame perfect Nash equilibria in the
-extensive-form game will be given. This appears to be the first time that
-subgame perfection based on time-varying graphs is used for equilibrium
-selection in network games. This idea of subgame perfection proposed in the
-paper may be extrapolated to other network games, which will be illustrated
-with a simple example of congestion games.
-",1,0,0,0,0,0
-17416,MIMO Graph Filters for Convolutional Neural Networks,"  Superior performance and ease of implementation have fostered the adoption of
-Convolutional Neural Networks (CNNs) for a wide array of inference and
-reconstruction tasks. CNNs implement three basic blocks: convolution, pooling
-and pointwise nonlinearity. Since the two first operations are well-defined
-only on regular-structured data such as audio or images, application of CNNs to
-contemporary datasets where the information is defined in irregular domains is
-challenging. This paper investigates CNNs architectures to operate on signals
-whose support can be modeled using a graph. Architectures that replace the
-regular convolution with a so-called linear shift-invariant graph filter have
-been recently proposed. This paper goes one step further and, under the
-framework of multiple-input multiple-output (MIMO) graph filters, imposes
-additional structure on the adopted graph filters, to obtain three new (more
-parsimonious) architectures. The proposed architectures result in a lower
-number of model parameters, reducing the computational complexity, facilitating
-the training, and mitigating the risk of overfitting. Simulations show that the
-proposed simpler architectures achieve similar performance as more complex
-models.
-",0,0,0,1,0,0
-17417,An Edge Driven Wavelet Frame Model for Image Restoration,"  Wavelet frame systems are known to be effective in capturing singularities
-from noisy and degraded images. In this paper, we introduce a new edge driven
-wavelet frame model for image restoration by approximating images as piecewise
-smooth functions. With an implicit representation of image singularities sets,
-the proposed model inflicts different strength of regularization on smooth and
-singular image regions and edges. The proposed edge driven model is robust to
-both image approximation and singularity estimation. The implicit formulation
-also enables an asymptotic analysis of the proposed models and a rigorous
-connection between the discrete model and a general continuous variational
-model. Finally, numerical results on image inpainting and deblurring show that
-the proposed model is compared favorably against several popular image
-restoration models.
-",1,0,1,0,0,0
-17418,An exact algorithm exhibiting RS-RSB/easy-hard correspondence for the maximum independent set problem,"  A recently proposed exact algorithm for the maximum independent set problem
-is analyzed. The typical running time is improved exponentially in some
-parameter regions compared to simple binary search. The algorithm also
-overcomes the core transition point, where the conventional leaf removal
-algorithm fails, and works up to the replica symmetry breaking (RSB) transition
-point. This suggests that a leaf removal core itself is not enough for typical
-hardness in the random maximum independent set problem, providing further
-evidence for RSB being the obstacle for algorithms in general.
-",1,1,0,0,0,0
-17419,Flexible Support for Fast Parallel Commutative Updates,"  Privatizing data is a useful strategy for increasing parallelism in a shared
-memory multithreaded program. Independent cores can compute independently on
-duplicates of shared data, combining their results at the end of their
-computations. Conventional approaches to privatization, however, rely on
-explicit static or dynamic memory allocation for duplicated state, increasing
-memory footprint and contention for cache resources, especially in shared
-caches. In this work, we describe CCache, a system for on-demand privatization
-of data manipulated by commutative operations. CCache garners the benefits of
-privatization, without the increase in memory footprint or cache occupancy.
-Each core in CCache dynamically privatizes commutatively manipulated data,
-operating on a copy. Periodically or at the end of its computation, the core
-merges its value with the value resident in memory, and when all cores have
-merged, the in-memory copy contains the up-to-date value. We describe a
-low-complexity architectural implementation of CCache that extends a
-conventional multicore to support on-demand privatization without using
-additional memory for private copies. We evaluate CCache on several high-value
-applications, including random access key-value store, clustering, breadth
-first search and graph ranking, showing speedups upto 3.2X.
-",1,0,0,0,0,0
-17420,Deep learning based supervised semantic segmentation of Electron Cryo-Subtomograms,"  Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for
-the 3D visualization of cellular structure and organization at submolecular
-resolution. It enables analyzing the native structures of macromolecular
-complexes and their spatial organization inside single cells. However, due to
-the high degree of structural complexity and practical imaging limitations,
-systematic macromolecular structural recovery inside CECT images remains
-challenging. Particularly, the recovery of a macromolecule is likely to be
-biased by its neighbor structures due to the high molecular crowding. To reduce
-the bias, here we introduce a novel 3D convolutional neural network inspired by
-Fully Convolutional Network and Encoder-Decoder Architecture for the supervised
-segmentation of macromolecules of interest in subtomograms. The tests of our
-models on realistically simulated CECT data demonstrate that our new approach
-has significantly improved segmentation performance compared to our baseline
-approach. Also, we demonstrate that the proposed model has generalization
-ability to segment new structures that do not exist in training data.
-",0,0,0,1,1,0
-17421,CNN-MERP: An FPGA-Based Memory-Efficient Reconfigurable Processor for Forward and Backward Propagation of Convolutional Neural Networks,"  Large-scale deep convolutional neural networks (CNNs) are widely used in
-machine learning applications. While CNNs involve huge complexity, VLSI (ASIC
-and FPGA) chips that deliver high-density integration of computational
-resources are regarded as a promising platform for CNN's implementation. At
-massive parallelism of computational units, however, the external memory
-bandwidth, which is constrained by the pin count of the VLSI chip, becomes the
-system bottleneck. Moreover, VLSI solutions are usually regarded as a lack of
-the flexibility to be reconfigured for the various parameters of CNNs. This
-paper presents CNN-MERP to address these issues. CNN-MERP incorporates an
-efficient memory hierarchy that significantly reduces the bandwidth
-requirements from multiple optimizations including on/off-chip data allocation,
-data flow optimization and data reuse. The proposed 2-level reconfigurability
-is utilized to enable fast and efficient reconfiguration, which is based on the
-control logic and the multiboot feature of FPGA. As a result, an external
-memory bandwidth requirement of 1.94MB/GFlop is achieved, which is 55% lower
-than prior arts. Under limited DRAM bandwidth, a system throughput of
-1244GFlop/s is achieved at the Vertex UltraScale platform, which is 5.48 times
-higher than the state-of-the-art FPGA implementations.
-",1,0,0,0,0,0
-17422,Enstrophy Cascade in Decaying Two-Dimensional Quantum Turbulence,"  We report evidence for an enstrophy cascade in large-scale point-vortex
-simulations of decaying two-dimensional quantum turbulence. Devising a method
-to generate quantum vortex configurations with kinetic energy narrowly
-localized near a single length scale, the dynamics are found to be
-well-characterised by a superfluid Reynolds number, $\mathrm{Re_s}$, that
-depends only on the number of vortices and the initial kinetic energy scale.
-Under free evolution the vortices exhibit features of a classical enstrophy
-cascade, including a $k^{-3}$ power-law kinetic energy spectrum, and steady
-enstrophy flux associated with inertial transport to small scales. Clear
-signatures of the cascade emerge for $N\gtrsim 500$ vortices. Simulating up to
-very large Reynolds numbers ($N = 32, 768$ vortices), additional features of
-the classical theory are observed: the Kraichnan-Batchelor constant is found to
-converge to $C' \approx 1.6$, and the width of the $k^{-3}$ range scales as
-$\mathrm{Re_s}^{1/2}$. The results support a universal phenomenology
-underpinning classical and quantum fluid turbulence.
-",0,1,0,0,0,0
-17423,Contextual Multi-armed Bandits under Feature Uncertainty,"  We study contextual multi-armed bandit problems under linear realizability on
-rewards and uncertainty (or noise) on features. For the case of identical noise
-on features across actions, we propose an algorithm, coined {\em NLinRel},
-having $O\left(T^{\frac{7}{8}} \left(\log{(dT)}+K\sqrt{d}\right)\right)$ regret
-bound for $T$ rounds, $K$ actions, and $d$-dimensional feature vectors. Next,
-for the case of non-identical noise, we observe that popular linear hypotheses
-including {\em NLinRel} are impossible to achieve such sub-linear regret.
-Instead, under assumption of Gaussian feature vectors, we prove that a greedy
-algorithm has $O\left(T^{\frac23}\sqrt{\log d}\right)$ regret bound with
-respect to the optimal linear hypothesis. Utilizing our theoretical
-understanding on the Gaussian case, we also design a practical variant of {\em
-NLinRel}, coined {\em Universal-NLinRel}, for arbitrary feature distributions.
-It first runs {\em NLinRel} for finding the `true' coefficient vector using
-feature uncertainties and then adjust it to minimize its regret using the
-statistical feature information. We justify the performance of {\em
-Universal-NLinRel} on both synthetic and real-world datasets.
-",1,0,0,1,0,0
-17424,Asymmetric Variational Autoencoders,"  Variational inference for latent variable models is prevalent in various
-machine learning problems, typically solved by maximizing the Evidence Lower
-Bound (ELBO) of the true data likelihood with respect to a variational
-distribution. However, freely enriching the family of variational distribution
-is challenging since the ELBO requires variational likelihood evaluations of
-the latent variables. In this paper, we propose a novel framework to enrich the
-variational family by incorporating auxiliary variables to the variational
-family. The resulting inference network doesn't require density evaluations for
-the auxiliary variables and thus complex implicit densities over the auxiliary
-variables can be constructed by neural networks. It can be shown that the
-actual variational posterior of the proposed approach is essentially modeling a
-rich probabilistic mixture of simple variational posterior indexed by auxiliary
-variables, thus a flexible inference model can be built. Empirical evaluations
-on several density estimation tasks demonstrates the effectiveness of the
-proposed method.
-",1,0,0,1,0,0
-17425,Dynamics of homogeneous shear turbulence: A key role of the nonlinear transverse cascade in the bypass concept,"  To understand the self-sustenance of subcritical turbulence in spectrally
-stable shear flows, we performed direct numerical simulations of homogeneous
-shear turbulence for different aspect ratios of the flow domain and analyzed
-the dynamical processes in Fourier space. There are no exponentially growing
-modes in such flows and the turbulence is energetically supported only by the
-linear growth of perturbation harmonics due to the shear flow non-normality.
-This non-normality-induced, or nonmodal growth is anisotropic in spectral
-space, which, in turn, leads to anisotropy of nonlinear processes in this
-space. As a result, a transverse (angular) redistribution of harmonics in
-Fourier space appears to be the main nonlinear process in these flows, rather
-than direct or inverse cascades. We refer to this type of nonlinear
-redistribution as the nonlinear transverse cascade. It is demonstrated that the
-turbulence is sustained by a subtle interplay between the linear nonmodal
-growth and the nonlinear transverse cascade that exemplifies a well-known
-bypass scenario of subcritical turbulence. These two basic processes mainly
-operate at large length scales, comparable to the domain size. Therefore, this
-central, small wave number area of Fourier space is crucial in the
-self-sustenance; we defined its size and labeled it as the vital area of
-turbulence. Outside the vital area, the nonmodal growth and the transverse
-cascade are of secondary importance. Although the cascades and the
-self-sustaining process of turbulence are qualitatively the same at different
-aspect ratios, the number of harmonics actively participating in this process
-varies, but always remains quite large. This implies that the self-sustenance
-of subcritical turbulence cannot be described by low-order models.
-",0,1,0,0,0,0
-17426,A taxonomy of learning dynamics in 2 x 2 games,"  Learning would be a convincing method to achieve coordination on an
-equilibrium. But does learning converge, and to what? We answer this question
-in generic 2-player, 2-strategy games, using Experience-Weighted Attraction
-(EWA), which encompasses many extensively studied learning algorithms. We
-exhaustively characterize the parameter space of EWA learning, for any payoff
-matrix, and we understand the generic properties that imply convergent or
-non-convergent behaviour in 2 x 2 games.
-Irrational choice and lack of incentives imply convergence to a mixed
-strategy in the centre of the strategy simplex, possibly far from the Nash
-Equilibrium (NE). In the opposite limit, in which the players quickly modify
-their strategies, the behaviour depends on the payoff matrix: (i) a strong
-discrepancy between the pure strategies describes dominance-solvable games,
-which show convergence to a unique fixed point close to the NE; (ii) a
-preference towards profiles of strategies along the main diagonal describes
-coordination games, with multiple stable fixed points corresponding to the NE;
-(iii) a cycle of best responses defines discoordination games, which commonly
-yield limit cycles or low-dimensional chaos.
-While it is well known that mixed strategy equilibria may be unstable, our
-approach is novel from several perspectives: we fully analyse EWA and provide
-explicit thresholds that define the onset of instability; we find an emerging
-taxonomy of the learning dynamics, without focusing on specific classes of
-games ex-ante; we show that chaos can occur even in the simplest games; we make
-a precise theoretical prediction that can be tested against data on
-experimental learning of discoordination games.
-",0,1,0,0,0,0
-17427,Dynamic attitude planning for trajectory tracking in underactuated VTOL UAVs,"  This paper addresses the trajectory tracking control problem for
-underactuated VTOL UAVs. According to the different actuation mechanisms, the
-most common UAV platforms can achieve only a partial decoupling of attitude and
-position tasks. Since position tracking is of utmost importance for
-applications involving aerial vehicles, we propose a control scheme in which
-position tracking is the primary objective. To this end, this work introduces
-the concept of attitude planner, a dynamical system through which the desired
-attitude reference is processed to guarantee the satisfaction of the primary
-objective: the attitude tracking task is considered as a secondary objective
-which can be realized as long as the desired trajectory satisfies specific
-trackability conditions. Two numerical simulations are performed by applying
-the proposed control law to a hexacopter with and without tilted propellers,
-which accounts for unmodeled dynamics and external disturbances not included in
-the control design model.
-",1,0,0,0,0,0
-17428,The JCMT Transient Survey: Data Reduction and Calibration Methods,"  Though there has been a significant amount of work investigating the early
-stages of low-mass star formation in recent years, the evolution of the mass
-assembly rate onto the central protostar remains largely unconstrained.
-Examining in depth the variation in this rate is critical to understanding the
-physics of star formation. Instabilities in the outer and inner circumstellar
-disk can lead to episodic outbursts. Observing these brightness variations at
-infrared or submillimetre wavelengths sets constraints on the current accretion
-models. The JCMT Transient Survey is a three-year project dedicated to studying
-the continuum variability of deeply embedded protostars in eight nearby
-star-forming regions at a one month cadence. We use the SCUBA-2 instrument to
-simultaneously observe these regions at wavelengths of 450 $\mu$m and 850
-$\mu$m. In this paper, we present the data reduction techniques, image
-alignment procedures, and relative flux calibration methods for 850 $\mu$m
-data. We compare the properties and locations of bright, compact emission
-sources fitted with Gaussians over time. Doing so, we achieve a spatial
-alignment of better than 1"" between the repeated observations and an
-uncertainty of 2-3\% in the relative peak brightness of significant, localised
-emission. This combination of imaging performance is unprecedented in
-ground-based, single dish submillimetre observations. Finally, we identify a
-few sources that show possible and confirmed brightness variations. These
-sources will be closely monitored and presented in further detail in additional
-studies throughout the duration of the survey.
-",0,1,0,0,0,0
-17429,"Synchronization Strings: Explicit Constructions, Local Decoding, and Applications","  This paper gives new results for synchronization strings, a powerful
-combinatorial object that allows to efficiently deal with insertions and
-deletions in various communication settings:
-$\bullet$ We give a deterministic, linear time synchronization string
-construction, improving over an $O(n^5)$ time randomized construction.
-Independently of this work, a deterministic $O(n\log^2\log n)$ time
-construction was just put on arXiv by Cheng, Li, and Wu. We also give a
-deterministic linear time construction of an infinite synchronization string,
-which was not known to be computable before. Both constructions are highly
-explicit, i.e., the $i^{th}$ symbol can be computed in $O(\log i)$ time.
-$\bullet$ This paper also introduces a generalized notion we call
-long-distance synchronization strings that allow for local and very fast
-decoding. In particular, only $O(\log^3 n)$ time and access to logarithmically
-many symbols is required to decode any index.
-We give several applications for these results:
-$\bullet$ For any $\delta<1$ and $\epsilon>0$ we provide an insdel correcting
-code with rate $1-\delta-\epsilon$ which can correct any $O(\delta)$ fraction
-of insdel errors in $O(n\log^3n)$ time. This near linear computational
-efficiency is surprising given that we do not even know how to compute the
-(edit) distance between the decoding input and output in sub-quadratic time. We
-show that such codes can not only efficiently recover from $\delta$ fraction of
-insdel errors but, similar to [Schulman, Zuckerman; TransInf'99], also from any
-$O(\delta/\log n)$ fraction of block transpositions and replications.
-$\bullet$ We show that highly explicitness and local decoding allow for
-infinite channel simulations with exponentially smaller memory and decoding
-time requirements. These simulations can be used to give the first near linear
-time interactive coding scheme for insdel errors.
-",1,0,0,0,0,0
-17430,Independent Component Analysis via Energy-based and Kernel-based Mutual Dependence Measures,"  We apply both distance-based (Jin and Matteson, 2017) and kernel-based
-(Pfister et al., 2016) mutual dependence measures to independent component
-analysis (ICA), and generalize dCovICA (Matteson and Tsay, 2017) to MDMICA,
-minimizing empirical dependence measures as an objective function in both
-deflation and parallel manners. Solving this minimization problem, we introduce
-Latin hypercube sampling (LHS) (McKay et al., 2000), and a global optimization
-method, Bayesian optimization (BO) (Mockus, 1994) to improve the initialization
-of the Newton-type local optimization method. The performance of MDMICA is
-evaluated in various simulation studies and an image data example. When the ICA
-model is correct, MDMICA achieves competitive results compared to existing
-approaches. When the ICA model is misspecified, the estimated independent
-components are less mutually dependent than the observed components using
-MDMICA, while they are prone to be even more mutually dependent than the
-observed components using other approaches.
-",0,0,0,1,0,0
-17431,The local geometry of testing in ellipses: Tight control via localized Kolmogorov widths,"  We study the local geometry of testing a mean vector within a
-high-dimensional ellipse against a compound alternative. Given samples of a
-Gaussian random vector, the goal is to distinguish whether the mean is equal to
-a known vector within an ellipse, or equal to some other unknown vector in the
-ellipse. Such ellipse testing problems lie at the heart of several
-applications, including non-parametric goodness-of-fit testing, signal
-detection in cognitive radio, and regression function testing in reproducing
-kernel Hilbert spaces. While past work on such problems has focused on the
-difficulty in a global sense, we study difficulty in a way that is localized to
-each vector within the ellipse. Our main result is to give sharp upper and
-lower bounds on the localized minimax testing radius in terms of an explicit
-formula involving the Kolmogorov width of the ellipse intersected with a
-Euclidean ball. When applied to particular examples, our general theorems yield
-interesting rates that were not known before: as a particular case, for testing
-in Sobolev ellipses of smoothness $\alpha$, we demonstrate rates that vary from
-$(\sigma^2)^{\frac{4 \alpha}{4 \alpha + 1}}$, corresponding to the classical
-global rate, to the faster rate $(\sigma^2)^{\frac{8
-\alpha}{8 \alpha + 1}}$, achievable for vectors at favorable locations within
-the ellipse. We also show that the optimal test for this problem is achieved by
-a linear projection test that is based on an explicit lower-dimensional
-projection of the observation vector.
-",0,0,1,1,0,0
-17432,A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe,"  Two of the most fundamental prototypes of greedy optimization are the
-matching pursuit and Frank-Wolfe algorithms. In this paper, we take a unified
-view on both classes of methods, leading to the first explicit convergence
-rates of matching pursuit methods in an optimization sense, for general sets of
-atoms. We derive sublinear ($1/t$) convergence for both classes on general
-smooth objectives, and linear convergence on strongly convex objectives, as
-well as a clear correspondence of algorithm variants. Our presented algorithms
-and rates are affine invariant, and do not need any incoherence or sparsity
-assumptions.
-",1,0,0,1,0,0
-17433,Subsampled Rényi Differential Privacy and Analytical Moments Accountant,"  We study the problem of subsampling in differential privacy (DP), a question
-that is the centerpiece behind many successful differentially private machine
-learning algorithms. Specifically, we provide a tight upper bound on the
-Rényi Differential Privacy (RDP) (Mironov, 2017) parameters for algorithms
-that: (1) subsample the dataset, and then (2) applies a randomized mechanism M
-to the subsample, in terms of the RDP parameters of M and the subsampling
-probability parameter. Our results generalize the moments accounting technique,
-developed by Abadi et al. (2016) for the Gaussian mechanism, to any subsampled
-RDP mechanism.
-",0,0,0,1,0,0
-17434,"Concurrency and Probability: Removing Confusion, Compositionally","  Assigning a satisfactory truly concurrent semantics to Petri nets with
-confusion and distributed decisions is a long standing problem, especially if
-one wants to fully replace nondeterminism with probability distributions and no
-stochastic structure is desired/allowed. Here we propose a general solution
-based on a recursive, static decomposition of (finite, occurrence) nets in loci
-of decision, called structural branching cells (s-cells). Each s-cell exposes a
-set of alternatives, called transactions, that can be equipped with a general
-probabilistic distribution. The solution is formalised as a transformation from
-a given Petri net to another net whose transitions are the transactions of the
-s-cells and whose places are the places of the original net, with some
-auxiliary structure for bookkeeping. The resulting net is confusion-free,
-namely if a transition is enabled, then all its conflicting alternatives are
-also enabled. Thus sets of conflicting alternatives can be equipped with
-probability distributions, while nonintersecting alternatives are purely
-concurrent and do not introduce any nondeterminism: they are Church-Rosser and
-their probability distributions are independent. The validity of the
-construction is witnessed by a tight correspondence result with the recent
-approach by Abbes and Benveniste (AB) based on recursively stopped
-configurations in event structures. Some advantages of our approach over AB's
-are that: i) s-cells are defined statically and locally in a compositional way,
-whereas AB's branching cells are defined dynamically and globally; ii) their
-recursively stopped configurations correspond to possible executions, but the
-existing concurrency is not made explicit. Instead, our resulting nets are
-equipped with an original concurrency structure exhibiting a so-called complete
-concurrency property.
-",1,0,0,0,0,0
-17435,Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration,"  We propose a technique for multi-task learning from demonstration that trains
-the controller of a low-cost robotic arm to accomplish several complex picking
-and placing tasks, as well as non-prehensile manipulation. The controller is a
-recurrent neural network using raw images as input and generating robot arm
-trajectories, with the parameters shared across the tasks. The controller also
-combines VAE-GAN-based reconstruction with autoregressive multimodal action
-prediction. Our results demonstrate that it is possible to learn complex
-manipulation tasks, such as picking up a towel, wiping an object, and
-depositing the towel to its previous position, entirely from raw images with
-direct behavior cloning. We show that weight sharing and reconstruction-based
-regularization substantially improve generalization and robustness, and
-training on multiple tasks simultaneously increases the success rate on all
-tasks.
-",1,0,0,0,0,0
-17436,Dynamic Word Embeddings,"  We present a probabilistic language model for time-stamped text data which
-tracks the semantic evolution of individual words over time. The model
-represents words and contexts by latent trajectories in an embedding space. At
-each moment in time, the embedding vectors are inferred from a probabilistic
-version of word2vec [Mikolov et al., 2013]. These embedding vectors are
-connected in time through a latent diffusion process. We describe two scalable
-variational inference algorithms--skip-gram smoothing and skip-gram
-filtering--that allow us to train the model jointly over all times; thus
-learning on all data while simultaneously allowing word and context vectors to
-drift. Experimental results on three different corpora demonstrate that our
-dynamic model infers word embedding trajectories that are more interpretable
-and lead to higher predictive likelihoods than competing methods that are based
-on static models trained separately on time slices.
-",0,0,0,1,0,0
-17437,Multiple scattering effect on angular distribution and polarization of radiation by relativistic electrons in a thin crystal,"  The multiple scattering of ultra relativistic electrons in an amorphous
-matter leads to the suppression of the soft part of radiation spectrum (the
-Landau-Pomeranchuk-Migdal effect), and also can change essentially the angular
-distribution of the emitted photons. A similar effect must take place in a
-crystal for the coherent radiation of relativistic electron. The results of the
-theoretical investigation of angular distributions and polarization of
-radiation by a relativistic electron passing through a thin (in comparison with
-a coherence length) crystal at a small angle to the crystal axis are presented.
-The electron trajectories in crystal were simulated using the binary collision
-model which takes into account both coherent and incoherent effects at
-scattering. The angular distribution of radiation and polarization were
-calculated as a sum of radiation from each electron. It is shown that there are
-nontrivial angular distributions of the emitted photons and their polarization
-that are connected to the superposition of the coherent scattering of electrons
-by atomic rows (""doughnut scattering"" effect) and the suppression of radiation
-(similar to the Landau-Pomeranchuk-Migdal effect in an amorphous matter). It is
-also shown that circular polarization of radiation in the considered case is
-identically zero.
-",0,1,0,0,0,0
-17438,Branched coverings of $CP^2$ and other basic 4-manifolds,"  We give necessary and sufficient conditions for a 4-manifold to be a branched
-covering of $CP^2$, $S^2\times S^2$, $S^2 \mathbin{\tilde\times} S^2$ and $S^3
-\times S^1$, which are expressed in terms of the Betti numbers and the
-intersection form of the 4-manifold.
-",0,0,1,0,0,0
-17439,Instantaneous effects of photons on electrons in semiconductors,"  The photoelectric effect established by Einstein is well known, which
-indicates that electrons on lower energy levels can jump up to higher levels by
-absorbing photons, or jump down from higher levels to lower levels and give out
-photons1-3. However, how do photons act on electrons and further on atoms have
-kept unknown up to now. Here we show the results that photons collide on
-electrons with energy-transmission in semiconductors and pass their momenta to
-electrons, which make the electrons jump up from lower energy levels to higher
-levels. We found that (i) photons have rest mass of 7.287exp(-38) kg and
-2.886exp(-35) kg, in vacuum and silicon respectively; (ii) excited by photons
-with energy of 1.12eV, electrons in silicon may jump up from the top of valance
-band to the bottom of conduction band with initial speed of 2.543exp(3) m/s and
-taking time of 4.977exp(-17) s; (iii) acted by photons with energy of 4.6eV,
-the atoms who lose electrons may be catapulted out of the semiconductors by the
-extruded neighbor atoms, and taking time of 2.224exp(-15) s. These results make
-reasonable explanation to rapid thermal annealing, laser ablation and laser
-cutting.
-",0,1,0,0,0,0
-17440,Mitigating the Impact of Speech Recognition Errors on Chatbot using Sequence-to-Sequence Model,"  We apply sequence-to-sequence model to mitigate the impact of speech
-recognition errors on open domain end-to-end dialog generation. We cast the
-task as a domain adaptation problem where ASR transcriptions and original text
-are in two different domains. In this paper, our proposed model includes two
-individual encoders for each domain data and make their hidden states similar
-to ensure the decoder predict the same dialog text. The method shows that the
-sequence-to-sequence model can learn the ASR transcriptions and original text
-pair having the same meaning and eliminate the speech recognition errors.
-Experimental results on Cornell movie dialog dataset demonstrate that the
-domain adaption system help the spoken dialog system generate more similar
-responses with the original text answers.
-",1,0,0,0,0,0
-17441,Simultaneous 183 GHz H2O Maser and SiO Observations Towards Evolved Stars Using APEX SEPIA Band 5,"  We investigate the use of 183 GHz H2O masers for characterization of the
-physical conditions and mass loss process in the circumstellar envelopes of
-evolved stars. We used APEX SEPIA Band 5 to observe the 183 GHz H2O line
-towards 2 Red Supergiant and 3 Asymptotic Giant Branch stars. Simultaneously,
-we observed lines in 28SiO v0, 1, 2 and 3, and for 29SiO v0 and 1. We detected
-the 183 GHz H2O line towards all the stars with peak flux densities greater
-than 100 Jy, including a new detection from VY CMa. Towards all 5 targets, the
-water line had indications of being due to maser emission and had higher peak
-flux densities than for the SiO lines. The SiO lines appear to originate from
-both thermal and maser processes. Comparison with simulations and models
-indicate that 183 GHz maser emission is likely to extend to greater radii in
-the circumstellar envelopes than SiO maser emission and to similar or greater
-radii than water masers at 22, 321 and 325 GHz. We speculate that a prominent
-blue-shifted feature in the W Hya 183 GHz spectrum is amplifying the stellar
-continuum, and is located at a similar distance from the star as mainline OH
-maser emission. From a comparison of the individual polarizations, we find that
-the SiO maser linear polarization fraction of several features exceeds the
-maximum fraction allowed under standard maser assumptions and requires strong
-anisotropic pumping of the maser transition and strongly saturated maser
-emission. The low polarization fraction of the H2O maser however, fits with the
-expectation for a non-saturated maser. 183 GHz H2O masers can provide strong
-probes of the mass loss process of evolved stars. Higher angular resolution
-observations of this line using ALMA Band 5 will enable detailed investigation
-of the emission location in circumstellar envelopes and can also provide
-information on magnetic field strength and structure.
-",0,1,0,0,0,0
-17442,What does the free energy principle tell us about the brain?,"  The free energy principle has been proposed as a unifying theory of brain
-function. It is closely related, and in some cases subsumes, earlier unifying
-ideas such as Bayesian inference, predictive coding, and active learning. This
-article clarifies these connections, teasing apart distinctive and shared
-predictions.
-",0,0,0,0,1,0
-17443,Learning with Changing Features,"  In this paper we study the setting where features are added or change
-interpretation over time, which has applications in multiple domains such as
-retail, manufacturing, finance. In particular, we propose an approach to
-provably determine the time instant from which the new/changed features start
-becoming relevant with respect to an output variable in an agnostic
-(supervised) learning setting. We also suggest an efficient version of our
-approach which has the same asymptotic performance. Moreover, our theory also
-applies when we have more than one such change point. Independent post analysis
-of a change point identified by our method for a large retailer revealed that
-it corresponded in time with certain unflattering news stories about a brand
-that resulted in the change in customer behavior. We also applied our method to
-data from an advanced manufacturing plant identifying the time instant from
-which downstream features became relevant. To the best of our knowledge this is
-the first work that formally studies change point detection in a distribution
-independent agnostic setting, where the change point is based on the changing
-relationship between input and output.
-",1,0,0,1,0,0
-17444,Estimation Considerations in Contextual Bandits,"  Contextual bandit algorithms are sensitive to the estimation method of the
-outcome model as well as the exploration method used, particularly in the
-presence of rich heterogeneity or complex outcome models, which can lead to
-difficult estimation problems along the path of learning. We study a
-consideration for the exploration vs. exploitation framework that does not
-arise in multi-armed bandits but is crucial in contextual bandits; the way
-exploration and exploitation is conducted in the present affects the bias and
-variance in the potential outcome model estimation in subsequent stages of
-learning. We develop parametric and non-parametric contextual bandits that
-integrate balancing methods from the causal inference literature in their
-estimation to make it less prone to problems of estimation bias. We provide the
-first regret bound analyses for contextual bandits with balancing in the domain
-of linear contextual bandits that match the state of the art regret bounds. We
-demonstrate the strong practical advantage of balanced contextual bandits on a
-large number of supervised learning datasets and on a synthetic example that
-simulates model mis-specification and prejudice in the initial training data.
-Additionally, we develop contextual bandits with simpler assignment policies by
-leveraging sparse model estimation methods from the econometrics literature and
-demonstrate empirically that in the early stages they can improve the rate of
-learning and decrease regret.
-",1,0,0,1,0,0
-17445,Using solar and load predictions in battery scheduling at the residential level,"  Smart solar inverters can be used to store, monitor and manage a home's solar
-energy. We describe a smart solar inverter system with battery which can either
-operate in an automatic mode or receive commands over a network to charge and
-discharge at a given rate. In order to make battery storage financially viable
-and advantageous to the consumers, effective battery scheduling algorithms can
-be employed. Particularly, when time-of-use tariffs are in effect in the region
-of the inverter, it is possible in some cases to schedule the battery to save
-money for the individual customer, compared to the ""automatic"" mode. Hence,
-this paper presents and evaluates the performance of a novel battery scheduling
-algorithm for residential consumers of solar energy. The proposed battery
-scheduling algorithm optimizes the cost of electricity over next 24 hours for
-residential consumers. The cost minimization is realized by controlling the
-charging/discharging of battery storage system based on the predictions for
-load and solar power generation values. The scheduling problem is formulated as
-a linear programming problem. We performed computer simulations over 83
-inverters using several months of hourly load and PV data. The simulation
-results indicate that key factors affecting the viability of optimization are
-the tariffs and the PV to Load ratio at each inverter. Depending on the tariff,
-savings of between 1% and 10% can be expected over the automatic approach. The
-prediction approach used in this paper is also shown to out-perform basic
-""persistence"" forecasting approaches. We have also examined the approaches for
-improving the prediction accuracy and optimization effectiveness.
-",1,0,0,0,0,0
-17446,Towards Large-Pose Face Frontalization in the Wild,"  Despite recent advances in face recognition using deep learning, severe
-accuracy drops are observed for large pose variations in unconstrained
-environments. Learning pose-invariant features is one solution, but needs
-expensively labeled large-scale data and carefully designed feature learning
-algorithms. In this work, we focus on frontalizing faces in the wild under
-various head poses, including extreme profile views. We propose a novel deep 3D
-Morphable Model (3DMM) conditioned Face Frontalization Generative Adversarial
-Network (GAN), termed as FF-GAN, to generate neutral head pose face images. Our
-framework differs from both traditional GANs and 3DMM based modeling.
-Incorporating 3DMM into the GAN structure provides shape and appearance priors
-for fast convergence with less training data, while also supporting end-to-end
-training. The 3DMM-conditioned GAN employs not only the discriminator and
-generator loss but also a new masked symmetry loss to retain visual quality
-under occlusions, besides an identity loss to recover high frequency
-information. Experiments on face recognition, landmark localization and 3D
-reconstruction consistently show the advantage of our frontalization method on
-faces in the wild datasets.
-",1,0,0,0,0,0
-17447,Managing the Public to Manage Data: Citizen Science and Astronomy,"  Citizen science projects recruit members of the public as volunteers to
-process and produce datasets. These datasets must win the trust of the
-scientific community. The task of securing credibility involves, in part,
-applying standard scientific procedures to clean these datasets. However,
-effective management of volunteer behavior also makes a significant
-contribution to enhancing data quality. Through a case study of Galaxy Zoo, a
-citizen science project set up to generate datasets based on volunteer
-classifications of galaxy morphologies, this paper explores how those involved
-in running the project manage volunteers. The paper focuses on how methods for
-crediting volunteer contributions motivate volunteers to provide higher quality
-contributions and to behave in a way that better corresponds to statistical
-assumptions made when combining volunteer contributions into datasets. These
-methods have made a significant contribution to the success of the project in
-securing trust in these datasets, which have been well used by other
-scientists. Implications for practice are then presented for citizen science
-projects, providing a list of considerations to guide choices regarding how to
-credit volunteer contributions to improve the quality and trustworthiness of
-citizen science-produced datasets.
-",1,1,0,0,0,0
-17448,Modular representations in type A with a two-row nilpotent central character,"  We study the category of representations of $\mathfrak{sl}_{m+2n}$ in
-positive characteristic, whose p-character is a nilpotent whose Jordan type is
-the two-row partition (m+n,n). In a previous paper with Anno, we used
-Bezrukavnikov-Mirkovic-Rumynin's theory of positive characteristic localization
-and exotic t-structures to give a geometric parametrization of the simples
-using annular crossingless matchings. Building on this, here we give
-combinatorial dimension formulae for the simple objects, and compute the
-Jordan-Holder multiplicities of the simples inside the baby Vermas (in special
-case where n=1, i.e. that a subregular nilpotent, these were known from work of
-Jantzen). We use Cautis-Kamnitzer's geometric categorification of the tangle
-calculus to study the images of the simple objects under the [BMR] equivalence.
-The dimension formulae may be viewed as a positive characteristic analogue of
-the combinatorial character formulae for simple objects in parabolic category O
-for $\mathfrak{sl}_{m+2n}$, due to Lascoux and Schutzenberger.
-",0,0,1,0,0,0
-17449,Knowledge Acquisition: A Complex Networks Approach,"  Complex networks have been found to provide a good representation of the
-structure of knowledge, as understood in terms of discoverable concepts and
-their relationships. In this context, the discovery process can be modeled as
-agents walking in a knowledge space. Recent studies proposed more realistic
-dynamics, including the possibility of agents being influenced by others with
-higher visibility or by their own memory. However, rather than dealing with
-these two concepts separately, as previously approached, in this study we
-propose a multi-agent random walk model for knowledge acquisition that
-incorporates both concepts. More specifically, we employed the true self
-avoiding walk alongside a new dynamics based on jumps, in which agents are
-attracted by the influence of others. That was achieved by using a Lévy
-flight influenced by a field of attraction emanating from the agents. In order
-to evaluate our approach, we use a set of network models and two real networks,
-one generated from Wikipedia and another from the Web of Science. The results
-were analyzed globally and by regions. In the global analysis, we found that
-most of the dynamics parameters do not significantly affect the discovery
-dynamics. The local analysis revealed a substantial difference of performance
-depending on the network regions where the dynamics are occurring. In
-particular, the dynamics at the core of networks tend to be more effective. The
-choice of the dynamics parameters also had no significant impact to the
-acquisition performance for the considered knowledge networks, even at the
-local scale.
-",1,1,0,0,0,0
-17450,Frequent flaring in the TRAPPIST-1 system - unsuited for life?,"  We analyze short cadence K2 light curve of the TRAPPIST-1 system. Fourier
-analysis of the data suggests $P_\mathrm{rot}=3.295\pm0.003$ days. The light
-curve shows several flares, of which we analyzed 42 events, these have
-integrated flare energies of $1.26\times10^{30}-1.24\times10^{33}$ ergs.
-Approximately 12% of the flares were complex, multi-peaked eruptions. The
-flaring and the possible rotational modulation shows no obvious correlation.
-The flaring activity of TRAPPIST-1 probably continuously alters the atmospheres
-of the orbiting exoplanets, making these less favorable for hosting life.
-",0,1,0,0,0,0
-17451,Playing Pairs with Pepper,"  As robots become increasingly prevalent in almost all areas of society, the
-factors affecting humans trust in those robots becomes increasingly important.
-This paper is intended to investigate the factor of robot attributes, looking
-specifically at the relationship between anthropomorphism and human development
-of trust. To achieve this, an interaction game, Matching the Pairs, was
-designed and implemented on two robots of varying levels of anthropomorphism,
-Pepper and Husky. Participants completed both pre- and post-test questionnaires
-that were compared and analyzed predominantly with the use of quantitative
-methods, such as paired sample t-tests. Post-test analyses suggested a positive
-relationship between trust and anthropomorphism with $80\%$ of participants
-confirming that the robots' adoption of facial features assisted in
-establishing trust. The results also indicated a positive relationship between
-interaction and trust with $90\%$ of participants confirming this for both
-robots post-test
-",1,0,0,0,0,0
-17452,Wild theories with o-minimal open core,"  Let $T$ be a consistent o-minimal theory extending the theory of densely
-ordered groups and let $T'$ be a consistent theory. Then there is a complete
-theory $T^*$ extending $T$ such that $T$ is an open core of $T^*$, but every
-model of $T^*$ interprets a model of $T'$. If $T'$ is NIP, $T^*$ can be chosen
-to be NIP as well. From this we deduce the existence of an NIP expansion of the
-real field that has no distal expansion.
-",0,0,1,0,0,0
-17453,Objective Bayesian inference with proper scoring rules,"  Standard Bayesian analyses can be difficult to perform when the full
-likelihood, and consequently the full posterior distribution, is too complex
-and difficult to specify or if robustness with respect to data or to model
-misspecifications is required. In these situations, we suggest to resort to a
-posterior distribution for the parameter of interest based on proper scoring
-rules. Scoring rules are loss functions designed to measure the quality of a
-probability distribution for a random variable, given its observed value.
-Important examples are the Tsallis score and the Hyvärinen score, which allow
-us to deal with model misspecifications or with complex models. Also the full
-and the composite likelihoods are both special instances of scoring rules.
-The aim of this paper is twofold. Firstly, we discuss the use of scoring
-rules in the Bayes formula in order to compute a posterior distribution, named
-SR-posterior distribution, and we derive its asymptotic normality. Secondly, we
-propose a procedure for building default priors for the unknown parameter of
-interest that can be used to update the information provided by the scoring
-rule in the SR-posterior distribution. In particular, a reference prior is
-obtained by maximizing the average $\alpha-$divergence from the SR-posterior
-distribution. For $0 \leq |\alpha|<1$, the result is a Jeffreys-type prior that
-is proportional to the square root of the determinant of the Godambe
-information matrix associated to the scoring rule. Some examples are discussed.
-",0,0,1,1,0,0
-17454,Large Area X-ray Proportional Counter (LAXPC) Instrument on AstroSat,"  Large Area X-ray Proportional Counter (LAXPC) is one of the major AstroSat
-payloads. LAXPC instrument will provide high time resolution X-ray observations
-in 3 to 80 keV energy band with moderate energy resolution. A cluster of three
-co-aligned identical LAXPC detectors is used in AstroSat to provide large
-collection area of more than 6000 cm2 . The large detection volume (15 cm
-depth) filled with xenon gas at about 2 atmosphere pressure, results in
-detection efficiency greater than 50%, above 30 keV. With its broad energy
-range and fine time resolution (10 microsecond), LAXPC instrument is well
-suited for timing and spectral studies of a wide variety of known and transient
-X-ray sources in the sky. We have done extensive calibration of all LAXPC
-detectors using radioactive sources as well as GEANT4 simulation of LAXPC
-detectors. We describe in brief some of the results obtained during the payload
-verification phase along with LXAPC capabilities.
-",0,1,0,0,0,0
-17455,"A Counterexample to the Vector Generalization of Costa's EPI, and Partial Resolution","  We give a counterexample to the vector generalization of Costa's entropy
-power inequality (EPI) due to Liu, Liu, Poor and Shamai. In particular, the
-claimed inequality can fail if the matix-valued parameter in the convex
-combination does not commute with the covariance of the additive Gaussian
-noise. Conversely, the inequality holds if these two matrices commute.
-",1,0,0,0,0,0
-17456,Models for Predicting Community-Specific Interest in News Articles,"  In this work, we ask two questions: 1. Can we predict the type of community
-interested in a news article using only features from the article content? and
-2. How well do these models generalize over time? To answer these questions, we
-compute well-studied content-based features on over 60K news articles from 4
-communities on reddit.com. We train and test models over three different time
-periods between 2015 and 2017 to demonstrate which features degrade in
-performance the most due to concept drift. Our models can classify news
-articles into communities with high accuracy, ranging from 0.81 ROC AUC to 1.0
-ROC AUC. However, while we can predict the community-specific popularity of
-news articles with high accuracy, practitioners should approach these models
-carefully. Predictions are both community-pair dependent and feature group
-dependent. Moreover, these feature groups generalize over time differently,
-with some only degrading slightly over time, but others degrading greatly.
-Therefore, we recommend that community-interest predictions are done in a
-hierarchical structure, where multiple binary classifiers can be used to
-separate community pairs, rather than a traditional multi-class model. Second,
-these models should be retrained over time based on accuracy goals and the
-availability of training data.
-",0,0,0,1,0,0
-17457,On subfiniteness of graded linear series,"  Hilbert's 14th problem studies the finite generation property of the
-intersection of an integral algebra of finite type with a subfield of the field
-of fractions of the algebra. It has a negative answer due to the counterexample
-of Nagata. We show that a subfinite version of Hilbert's 14th problem has a
-confirmative answer. We then establish a graded analogue of this result, which
-permits to show that the subfiniteness of graded linear series does not depend
-on the function field in which we consider it. Finally, we apply the
-subfiniteness result to the study of geometric and arithmetic graded linear
-series.
-",0,0,1,0,0,0
-17458,Natasha 2: Faster Non-Convex Optimization Than SGD,"  We design a stochastic algorithm to train any smooth neural network to
-$\varepsilon$-approximate local minima, using $O(\varepsilon^{-3.25})$
-backpropagations. The best result was essentially $O(\varepsilon^{-4})$ by SGD.
-More broadly, it finds $\varepsilon$-approximate local minima of any smooth
-nonconvex function in rate $O(\varepsilon^{-3.25})$, with only oracle access to
-stochastic gradients.
-",1,0,0,1,0,0
-17459,Evidence for a Dayside Thermal Inversion and High Metallicity for the Hot Jupiter WASP-18b,"  We find evidence for a strong thermal inversion in the dayside atmosphere of
-the highly irradiated hot Jupiter WASP-18b (T$_{eq}=2411K$, $M=10.3M_{J}$)
-based on emission spectroscopy from Hubble Space Telescope secondary eclipse
-observations and Spitzer eclipse photometry. We demonstrate a lack of water
-vapor in either absorption or emission at 1.4$\mu$m. However, we infer emission
-at 4.5$\mu$m and absorption at 1.6$\mu$m that we attribute to CO, as well as a
-non-detection of all other relevant species (e.g., TiO, VO). The most probable
-atmospheric retrieval solution indicates a C/O ratio of 1 and a high
-metallicity (C/H=$283^{+395}_{-138}\times$ solar). The derived composition and
-T/P profile suggest that WASP-18b is the first example of both a planet with a
-non-oxide driven thermal inversion and a planet with an atmospheric metallicity
-inconsistent with that predicted for Jupiter-mass planets at $>2\sigma$. Future
-observations are necessary to confirm the unusual planetary properties implied
-by these results.
-",0,1,0,0,0,0
-17460,$α$-$β$ and $β$-$γ$ phase boundaries of solid oxygen observed by adiabatic magnetocaloric effect,"  The magnetic-field-temperature phase diagram of solid oxygen is investigated
-by the adiabatic magnetocaloric effect (MCE) measurement with pulsed magnetic
-fields. Relatively large temperature decrease with hysteresis is observed at
-just below the $\beta$-$\gamma$ and $\alpha$-$\beta$ phase transition
-temperatures owing to the field-induced transitions. The magnetic field
-dependences of these phase boundaries are obtained as
-$T_\mathrm{\beta\gamma}(H)=43.8-1.55\times10^{-3}H^2$ K and
-$T_\mathrm{\alpha\beta}(H)=23.9-0.73\times10^{-3}H^2$ K. The magnetic
-Clausius-Clapeyron equation quantitatively explains the $H$ dependence of
-$T_\mathrm{\beta\gamma}$, meanwhile, does not $T_\mathrm{\alpha\beta}$. The MCE
-curve at $T_\mathrm{\beta\gamma}$ is of typical first-order, while the curve at
-$T_\mathrm{\alpha\beta}$ seems to have both characteristics of first- and
-second-order transitions. We discuss the order of the $\alpha$-$\beta$ phase
-transition and propose possible reasons for the unusual behavior.
-",0,1,0,0,0,0
-17461,Localization Algorithm with Circular Representation in 2D and its Similarity to Mammalian Brains,"  Extended Kalman filter (EKF) does not guarantee consistent mean and
-covariance under linearization, even though it is the main framework for
-robotic localization. While Lie group improves the modeling of the state space
-in localization, the EKF on Lie group still relies on the arbitrary Gaussian
-assumption in face of nonlinear models. We instead use von Mises filter for
-orientation estimation together with the conventional Kalman filter for
-position estimation, and thus we are able to characterize the first two moments
-of the state estimates. Since the proposed algorithm holds a solid
-probabilistic basis, it is fundamentally relieved from the inconsistency
-problem. Furthermore, we extend the localization algorithm to fully circular
-representation even for position, which is similar to grid patterns found in
-mammalian brains and in recurrent neural networks. The applicability of the
-proposed algorithms is substantiated not only by strong mathematical foundation
-but also by the comparison against other common localization methods.
-",1,0,0,0,1,0
-17462,"Lusin-type approximation of Sobolev by Lipschitz functions, in Gaussian and $RCD(K,\infty)$ spaces","  We establish new approximation results, in the sense of Lusin, of Sobolev
-functions by Lipschitz ones, in some classes of non-doubling metric measure
-structures. Our proof technique relies upon estimates for heat semigroups and
-applies to Gaussian and $RCD(K, \infty)$ spaces. As a consequence, we obtain
-quantitative stability for regular Lagrangian flows in Gaussian settings.
-",0,0,1,0,0,0
-17463,Distributed Coordination for a Class of Nonlinear Multi-agent Systems with Regulation Constraints,"  In this paper, a multi-agent coordination problem with steady-state
-regulation constraints is investigated for a class of nonlinear systems. Unlike
-existing leader-following coordination formulations, the reference signal is
-not given by a dynamic autonomous leader but determined as the optimal solution
-of a distributed optimization problem. Furthermore, we consider a global
-constraint having noisy data observations for the optimization problem, which
-implies that reference signal is not trivially available with existing
-optimization algorithms. To handle those challenges, we present a
-passivity-based analysis and design approach by using only local objective
-function, local data observation and exchanged information from their
-neighbors. The proposed distributed algorithms are shown to achieve the optimal
-steady-state regulation by rejecting the unknown observation disturbances for
-passive nonlinear agents, which are persuasive in various practical problems.
-Applications and simulation examples are then given to verify the effectiveness
-of our design.
-",1,0,1,0,0,0
-17464,Intermodulation distortion of actuated MEMS capacitive switches,"  For the first time, intermodulation distortion of micro-electromechanical
-capacitive switches in the actuated state was analyzed both theoretically and
-experimentally. The distortion, although higher than that of switches in the
-suspended state, was found to decrease with increasing bias voltage but to
-depend weakly on modulation frequencies between 55 kHz and 1.1 MHz. This
-dependence could be explained by the orders-of-magnitude increase of the spring
-constant when the switches were actuated. Additionally, the analysis suggested
-that increasing the spring constant and decreasing the contact roughness could
-improve the linearity of actuated switches. These results are critical to
-micro-electromechanical capacitive switches used in tuners, filters, phase
-shifters, etc. where the linearity of both suspended and actuated states are
-critical.
-",0,1,0,0,0,0
-17465,Parasitic Bipolar Leakage in III-V FETs: Impact of Substrate Architecture,"  InGaAs-based Gate-all-Around (GAA) FETs with moderate to high In content are
-shown experimentally and theoretically to be unsuitable for low-leakage
-advanced CMOS nodes. The primary cause for this is the large leakage penalty
-induced by the Parasitic Bipolar Effect (PBE), which is seen to be particularly
-difficult to remedy in GAA architectures. Experimental evidence of PBE in
-In70Ga30As GAA FETs is demonstrated, along with a simulation-based analysis of
-the PBE behavior. The impact of PBE is investigated by simulation for
-alternative device architectures, such as bulk FinFETs and
-FinFETs-on-insulator. PBE is found to be non-negligible in all standard InGaAs
-FET designs. Practical PBE metrics are introduced and the design of a substrate
-architecture for PBE suppression is elucidated. Finally, it is concluded that
-the GAA architecture is not suitable for low-leakage InGaAs FETs; a bulk FinFET
-is better suited for the role.
-",0,1,0,0,0,0
-17466,Properties of Ultra Gamma Function,"  In this paper we study the integral of type
-\[_{\delta,a}\Gamma_{\rho,b}(x)
-=\Gamma(\delta,a;\rho,b)(x)=\int_{0}^{\infty}t^{x-1}e^{-\frac{t^{\delta}}{a}-\frac{t^{-\rho}}{b}}dt.\]
-Different authors called this integral by different names like ultra gamma
-function, generalized gamma function, Kratzel integral, inverse Gaussian
-integral, reaction-rate probability integral, Bessel integral etc. We prove
-several identities and recurrence relation of above said integral, we called
-this integral as Four Parameter Gamma Function. Also we evaluate relation
-between Four Parameter Gamma Function, p-k Gamma Function and Classical Gamma
-Function. With some conditions we can evaluate Four Parameter Gamma Function in
-term of Hypergeometric function.
-",0,0,1,0,0,0
-17467,Further remarks on liftings of crossed modules,"  In this paper we define the notion of pullback lifting of a lifting crossed
-module over a crossed module morphism and interpret this notion in the category
-of group-groupoid actions as pullback action. Moreover, we give a criterion for
-the lifting of homotopic crossed module morphisms to be homotopic, which will
-be called homotopy lifting property for crossed module morphisms. Finally, we
-investigate some properties of derivations of lifting crossed modules according
-to base crossed module derivations.
-",0,0,1,0,0,0
-17468,Submodular Maximization through the Lens of Linear Programming,"  The simplex algorithm for linear programming is based on the fact that any
-local optimum with respect to the polyhedral neighborhood is also a global
-optimum. We show that a similar result carries over to submodular maximization.
-In particular, every local optimum of a constrained monotone submodular
-maximization problem yields a $1/2$-approximation, and we also present an
-appropriate extension to the non-monotone setting. However, reaching a local
-optimum quickly is a non-trivial task. Moreover, we describe a fast and very
-general local search procedure that applies to a wide range of constraint
-families, and unifies as well as extends previous methods. In our framework, we
-match known approximation guarantees while disentangling and simplifying
-previous approaches. Moreover, despite its generality, we are able to show that
-our local search procedure is slightly faster than previous specialized
-methods. Furthermore, we resolve an open question on the relation between
-linear optimization and submodular maximization; namely, whether a linear
-optimization oracle may be enough to obtain strong approximation algorithms for
-submodular maximization. We show that this is not the case by providing an
-example of a constraint family on a ground set of size $n$ for which, if only
-given a linear optimization oracle, any algorithm for submodular maximization
-with a polynomial number of calls to the linear optimization oracle will have
-an approximation ratio of only $O ( \frac{1}{\sqrt{n}} \cdot \frac{\log
-n}{\log\log n} )$.
-",1,0,0,0,0,0
-17469,Channel Estimation for Diffusive MIMO Molecular Communications,"  In diffusion-based communication, as for molecular systems, the achievable
-data rate is very low due to the slow nature of diffusion and the existence of
-severe inter-symbol interference (ISI). Multiple-input multiple-output (MIMO)
-technique can be used to improve the data rate. Knowledge of channel impulse
-response (CIR) is essential for equalization and detection in MIMO systems.
-This paper presents a training-based CIR estimation for diffusive MIMO (D-MIMO)
-channels. Maximum likelihood and least-squares estimators are derived, and the
-training sequences are designed to minimize the corresponding Cramér-Rao
-bound. Sub-optimal estimators are compared to Cramér-Rao bound to validate
-their performance.
-",1,0,0,1,0,0
-17470,Multi-stage splitting integrators for sampling with modified Hamiltonian Monte Carlo methods,"  Modified Hamiltonian Monte Carlo (MHMC) methods combine the ideas behind two
-popular sampling approaches: Hamiltonian Monte Carlo (HMC) and importance
-sampling. As in the HMC case, the bulk of the computational cost of MHMC
-algorithms lies in the numerical integration of a Hamiltonian system of
-differential equations. We suggest novel integrators designed to enhance
-accuracy and sampling performance of MHMC methods. The novel integrators belong
-to families of splitting algorithms and are therefore easily implemented. We
-identify optimal integrators within the families by minimizing the energy error
-or the average energy error. We derive and discuss in detail the modified
-Hamiltonians of the new integrators, as the evaluation of those Hamiltonians is
-key to the efficiency of the overall algorithms. Numerical experiments show
-that the use of the new integrators may improve very significantly the sampling
-performance of MHMC methods, in both statistical and molecular dynamics
-problems.
-",1,0,0,0,0,0
-17471,Proposal for a High Precision Tensor Processing Unit,"  This whitepaper proposes the design and adoption of a new generation of
-Tensor Processing Unit which has the performance of Google's TPU, yet performs
-operations on wide precision data. The new generation TPU is made possible by
-implementing arithmetic circuits which compute using a new general purpose,
-fractional arithmetic based on the residue number system.
-",1,0,0,0,0,0
-17472,DICOD: Distributed Convolutional Sparse Coding,"  In this paper, we introduce DICOD, a convolutional sparse coding algorithm
-which builds shift invariant representations for long signals. This algorithm
-is designed to run in a distributed setting, with local message passing, making
-it communication efficient. It is based on coordinate descent and uses locally
-greedy updates which accelerate the resolution compared to greedy coordinate
-selection. We prove the convergence of this algorithm and highlight its
-computational speed-up which is super-linear in the number of cores used. We
-also provide empirical evidence for the acceleration properties of our
-algorithm compared to state-of-the-art methods.
-",1,0,0,1,0,0
-17473,On uniqueness results for Dirichlet problems of elliptic systems without DeGiorgi-Nash-Moser regularity,"  We study uniqueness of Dirichlet problems of second order divergence-form
-elliptic systems with transversally independent coefficients on the upper
-half-space in absence of regularity of solutions. To this end, we develop a
-substitute for the fundamental solution used to invert elliptic operators on
-the whole space by means of a representation via abstract single layer
-potentials. We also show that such layer potentials are uniquely determined.
-",0,0,1,0,0,0
-17474,(LaTiO$_3$)$_n$/(LaVO$_3$)$_n$ as a model system for unconventional charge transfer and polar metallicity,"  At interfaces between oxide materials, lattice and electronic reconstructions
-always play important roles in exotic phenomena. In this study, the density
-functional theory and maximally localized Wannier functions are employed to
-investigate the (LaTiO$_3$)$_n$/(LaVO$_3$)$_n$ magnetic superlattices. The
-electron transfer from Ti$^{3+}$ to V$^{3+}$ is predicted, which violates the
-intuitive band alignment based on the electronic structures of LaTiO$_3$ and
-LaVO$_3$. Such unconventional charge transfer quenches the magnetism of
-LaTiO$_3$ layer mostly and leads to metal-insulator transition in the $n=1$
-superlattice when the stacking orientation is altered. In addition, the
-compatibility among the polar structure, ferrimagnetism, and metallicity is
-predicted in the $n=2$ superlattice.
-",0,1,0,0,0,0
-17475,Modeling sorption of emerging contaminants in biofilms,"  A mathematical model for emerging contaminants sorption in multispecies
-biofilms, based on a continuum approach and mass conservation principles is
-presented. Diffusion of contaminants within the biofilm is described using a
-diffusion-reaction equation. Binding sites formation and occupation are modeled
-by two systems of hyperbolic partial differential equations are mutually
-connected through the two growth rate terms. The model is completed with a
-system of hyperbolic equations governing the microbial species growth within
-the biofilm; a system of parabolic equations for substrates diffusion and
-reaction and a nonlinear ordinary differential equation describing the free
-boundary evolution. Two real special cases are modelled. The first one
-describes the dynamics of a free sorbent component diffusing and reacting in a
-multispecies biofilm. In the second illustrative case, the fate of two
-different contaminants has been modelled.
-",0,1,0,0,0,0
-17476,Stabilizing Training of Generative Adversarial Networks through Regularization,"  Deep generative models based on Generative Adversarial Networks (GANs) have
-demonstrated impressive sample quality but in order to work they require a
-careful choice of architecture, parameter initialization, and selection of
-hyper-parameters. This fragility is in part due to a dimensional mismatch or
-non-overlapping support between the model distribution and the data
-distribution, causing their density ratio and the associated f-divergence to be
-undefined. We overcome this fundamental limitation and propose a new
-regularization approach with low computational cost that yields a stable GAN
-training procedure. We demonstrate the effectiveness of this regularizer across
-several architectures trained on common benchmark image generation tasks. Our
-regularization turns GAN models into reliable building blocks for deep
-learning.
-",1,0,0,1,0,0
-17477,Spurious Vanishing Problem in Approximate Vanishing Ideal,"  Approximate vanishing ideal, which is a new concept from computer algebra, is
-a set of polynomials that almost takes a zero value for a set of given data
-points. The introduction of approximation to exact vanishing ideal has played a
-critical role in capturing the nonlinear structures of noisy data by computing
-the approximate vanishing polynomials. However, approximate vanishing has a
-theoretical problem, which is giving rise to the spurious vanishing problem
-that any polynomial turns into an approximate vanishing polynomial by
-coefficient scaling. In the present paper, we propose a general method that
-enables many basis construction methods to overcome this problem. Furthermore,
-a coefficient truncation method is proposed that balances the theoretical
-soundness and computational cost. The experiments show that the proposed method
-overcomes the spurious vanishing problem and significantly increases the
-accuracy of classification.
-",1,0,0,1,0,0
-17478,Opportunities for Two-color Experiments at the SASE3 undulator line of the European XFEL,"  X-ray Free Electron Lasers (XFELs) have been proven to generate short and
-powerful radiation pulses allowing for a wide class of novel experiments. If an
-XFEL facility supports the generation of two X-ray pulses with different
-wavelengths and controllable delay, the range of possible experiments is
-broadened even further to include X-ray-pump/X-ray-probe applications. In this
-work we discuss the possibility of applying a simple and cost-effective method
-for producing two-color pulses at the SASE3 soft X-ray beamline of the European
-XFEL. The technique is based on the installation of a magnetic chicane in the
-baseline undulator and can be accomplished in several steps. We discuss the
-scientific interest of this upgrade for the Small Quantum Systems (SQS)
-instrument, in connection with the high-repetition rate of the European XFEL,
-and we provide start-to-end simulations up to the radiation focus on the
-sample, proving the feasibility of our concept.
-",0,1,0,0,0,0
-17479,Braiding errors in interacting Majorana quantum wires,"  Avenues of Majorana bound states (MBSs) have become one of the primary
-directions towards a possible realization of topological quantum computation.
-For a Y-junction of Kitaev quantum wires, we numerically investigate the
-braiding of MBSs while considering the full quasi-particle background. The two
-central sources of braiding errors are found to be the fidelity loss due to the
-incomplete adiabaticity of the braiding operation as well as the hybridization
-of the MBS. The explicit extraction of the braiding phase in the low-energy
-Majorana sector from the full many-particle Hilbert space allows us to analyze
-the breakdown of the independent-particle picture of Majorana braiding.
-Furthermore, we find nearest-neighbor interactions to significantly affect the
-braiding performance to the better or worse, depending on the sign and
-magnitude of the coupling.
-",0,1,0,0,0,0
-17480,Machine Learning for Structured Clinical Data,"  Research is a tertiary priority in the EHR, where the priorities are patient
-care and billing. Because of this, the data is not standardized or formatted in
-a manner easily adapted to machine learning approaches. Data may be missing for
-a large variety of reasons ranging from individual input styles to differences
-in clinical decision making, for example, which lab tests to issue. Few
-patients are annotated at a research quality, limiting sample size and
-presenting a moving gold standard. Patient progression over time is key to
-understanding many diseases but many machine learning algorithms require a
-snapshot, at a single time point, to create a usable vector form. Furthermore,
-algorithms that produce black box results do not provide the interpretability
-required for clinical adoption. This chapter discusses these challenges and
-others in applying machine learning techniques to the structured EHR (i.e.
-Patient Demographics, Family History, Medication Information, Vital Signs,
-Laboratory Tests, Genetic Testing). It does not cover feature extraction from
-additional sources such as imaging data or free text patient notes but the
-approaches discussed can include features extracted from these sources.
-",1,0,0,0,0,0
-17481,Hidden order and symmetry protected topological states in quantum link ladders,"  We show that whereas spin-1/2 one-dimensional U(1) quantum-link models (QLMs)
-are topologically trivial, when implemented in ladder-like lattices these
-models may present an intriguing ground-state phase diagram, which includes a
-symmetry protected topological (SPT) phase that may be readily revealed by
-analyzing long-range string spin correlations along the ladder legs. We propose
-a simple scheme for the realization of spin-1/2 U(1) QLMs based on
-single-component fermions loaded in an optical lattice with s- and p-bands,
-showing that the SPT phase may be experimentally realized by adiabatic
-preparation.
-",0,1,0,0,0,0
-17482,The effect of prudence on the optimal allocation in possibilistic and mixed models,"  In this paper two portfolio choice models are studied: a purely possibilistic
-model, in which the return of a risky asset is a fuzzy number, and a mixed
-model in which a probabilistic background risk is added. For the two models an
-approximate formula of the optimal allocation is computed, with respect to the
-possibilistic moments associated with fuzzy numbers and the indicators of the
-investor risk preferences (risk aversion, prudence).
-",0,0,0,0,0,1
-17483,On universal operators and universal pairs,"  We study some basic properties of the class of universal operators on Hilbert
-space, and provide new examples of universal operators and universal pairs.
-",0,0,1,0,0,0
-17484,Interleaving Lattice for the APS Linac,"  To realize and test advanced accelerator concepts and hardware, a beamline is
-being reconfigured in the Linac Extension Area (LEA) of APS linac. A
-photo-cathode RF gun installed at the beginning of the APS linac will provide a
-low emittance electron beam into the LEA beamline. The thermionic RF gun beam
-for the APS storage ring, and the photo-cathode RF gun beam for LEA beamline
-will be accelerated through the linac in an interleaved fashion. In this paper,
-the design studies for interleaving lattice realization in APS linac is
-described with initial experiment result
-",0,1,0,0,0,0
-17485,\textit{Ab Initio} Study of the Magnetic Behavior of Metal Hydrides: A Comparison with the Slater-Pauling Curve,"  We investigated the magnetic behavior of metal hydrides FeH$_{x}$, CoH$_{x}$
-and NiH$_{x}$ for several concentrations of hydrogen ($x$) by using Density
-Functional Theory calculations. Several structural phases of the metallic host:
-bcc ($\alpha$), fcc ($\gamma$), hcp ($\varepsilon$), dhcp ($\varepsilon'$),
-tetragonal structure for FeH$_{x}$ and $\varepsilon$-$\gamma$ phases for
-CoH$_{x}$, were studied. We found that for CoH$_{x}$ and NiH$_{x}$ the magnetic
-moment ($m$) decreases regardless the concentration $x$. However, for FeH$_{x}$
-systems, $m$ increases or decreases depending on the variation in $x$. In order
-to find a general trend for these changes of $m$ in magnetic metal hydrides, we
-compare our results with the Slater-Pauling curve for ferromagnetic metallic
-binary alloys. It is found that the $m$ of metal hydrides made of Fe, Co and Ni
-fits the shape of the Slater-Pauling curve as a function of $x$. Our results
-indicate that there are two main effects that determine the $m$ value due to
-hydrogenation: an increase of volume causes $m$ to increase, and the addition
-of an extra electron to the metal always causes it to decrease. We discuss
-these behaviors in detail.
-",0,1,0,0,0,0
-17486,Secret Sharing for Cloud Data Security,"  Cloud computing helps reduce costs, increase business agility and deploy
-solutions with a high return on investment for many types of applications.
-However, data security is of premium importance to many users and often
-restrains their adoption of cloud technologies. Various approaches, i.e., data
-encryption, anonymization, replication and verification, help enforce different
-facets of data security. Secret sharing is a particularly interesting
-cryptographic technique. Its most advanced variants indeed simultaneously
-enforce data privacy, availability and integrity, while allowing computation on
-encrypted data. The aim of this paper is thus to wholly survey secret sharing
-schemes with respect to data security, data access and costs in the
-pay-as-you-go paradigm.
-",1,0,0,0,0,0
-17487,Design and Analysis of a Secure Three Factor User Authentication Scheme Using Biometric and Smart Card,"  Password security can no longer provide enough security in the area of remote
-user authentication. Considering this security drawback, researchers are trying
-to find solution with multifactor remote user authentication system. Recently,
-three factor remote user authentication using biometric and smart card has
-drawn a considerable attention of the researchers. However, most of the current
-proposed schemes have security flaws. They are vulnerable to attacks like user
-impersonation attack, server masquerading attack, password guessing attack,
-insider attack, denial of service attack, forgery attack, etc. Also, most of
-them are unable to provide mutual authentication, session key agreement and
-password, or smart card recovery system. Considering these drawbacks, we
-propose a secure three factor user authentication scheme using biometric and
-smart card. Through security analysis, we show that our proposed scheme can
-overcome drawbacks of existing systems and ensure high security in remote user
-authentication.
-",1,0,0,0,0,0
-17488,Real-World Modeling of a Pathfinding Robot Using Robot Operating System (ROS),"  This paper presents a practical approach towards implementing pathfinding
-algorithms on real-world and low-cost non- commercial hardware platforms. While
-using robotics simulation platforms as a test-bed for our algorithms we easily
-overlook real- world exogenous problems that are developed by external factors.
-Such problems involve robot wheel slips, asynchronous motors, abnormal sensory
-data or unstable power sources. The real-world dynamics tend to be very painful
-even for executing simple algorithms like a Wavefront planner or A-star search.
-This paper addresses designing techniques that tend to be robust as well as
-reusable for any hardware platforms; covering problems like controlling
-asynchronous drives, odometry offset issues and handling abnormal sensory
-feedback. The algorithm implementation medium and hardware design tools have
-been kept general in order to present our work as a serving platform for future
-researchers and robotics enthusiast working in the field of path planning
-robotics.
-",1,0,0,0,0,0
-17489,Understanding Convolution for Semantic Segmentation,"  Recent advances in deep learning, especially deep convolutional neural
-networks (CNNs), have led to significant improvement over previous semantic
-segmentation systems. Here we show how to improve pixel-wise semantic
-segmentation by manipulating convolution-related operations that are of both
-theoretical and practical value. First, we design dense upsampling convolution
-(DUC) to generate pixel-level prediction, which is able to capture and decode
-more detailed information that is generally missing in bilinear upsampling.
-Second, we propose a hybrid dilated convolution (HDC) framework in the encoding
-phase. This framework 1) effectively enlarges the receptive fields (RF) of the
-network to aggregate global information; 2) alleviates what we call the
-""gridding issue"" caused by the standard dilated convolution operation. We
-evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a
-state-of-art result of 80.1% mIOU in the test set at the time of submission. We
-also have achieved state-of-the-art overall on the KITTI road estimation
-benchmark and the PASCAL VOC2012 segmentation task. Our source code can be
-found at this https URL .
-",1,0,0,0,0,0
-17490,Mechanical Failure in Amorphous Solids: Scale Free Spinodal Criticality,"  The mechanical failure of amorphous media is a ubiquitous phenomenon from
-material engineering to geology. It has been noticed for a long time that the
-phenomenon is ""scale-free"", indicating some type of criticality. In spite of
-attempts to invoke ""Self-Organized Criticality"", the physical origin of this
-criticality, and also its universal nature, being quite insensitive to the
-nature of microscopic interactions, remained elusive. Recently we proposed that
-the precise nature of this critical behavior is manifested by a spinodal point
-of a thermodynamic phase transition. Moreover, at the spinodal point there
-exists a divergent correlation length which is associated with the
-system-spanning instabilities (known also as shear bands) which are typical to
-the mechanical yield. Demonstrating this requires the introduction of an ""order
-parameter"" that is suitable for distinguishing between disordered amorphous
-systems, and an associated correlation function, suitable for picking up the
-growing correlation length. The theory, the order parameter, and the
-correlation functions used are universal in nature and can be applied to any
-amorphous solid that undergoes mechanical yield. Critical exponents for the
-correlation length divergence and the system size dependence are estimated. The
-phenomenon is seen at its sharpest in athermal systems, as is explained below;
-in this paper we extend the discussion also to thermal systems, showing that at
-sufficiently high temperatures the spinodal phenomenon is destroyed by thermal
-fluctuations.
-",0,1,0,0,0,0
-17491,Moment conditions in strong laws of large numbers for multiple sums and random measures,"  The validity of the strong law of large numbers for multiple sums $S_n$ of
-independent identically distributed random variables $Z_k$, $k\leq n$, with
-$r$-dimensional indices is equivalent to the integrability of
-$|Z|(\log^+|Z|)^{r-1}$, where $Z$ is the typical summand. We consider the
-strong law of large numbers for more general normalisations, without assuming
-that the summands $Z_k$ are identically distributed, and prove a multiple sum
-generalisation of the Brunk--Prohorov strong law of large numbers. In the case
-of identical finite moments of irder $2q$ with integer $q\geq1$, we show that
-the strong law of large numbers holds with the normalisation $\|n_1\cdots
-n_r\|^{1/2}(\log n_1\cdots\log n_r)^{1/(2q)+\varepsilon}$ for any
-$\varepsilon>0$. The obtained results are also formulated in the setting of
-ergodic theorems for random measures, in particular those generated by marked
-point processes.
-",0,0,1,0,0,0
-17492,Connecting Clump Sizes in Turbulent Disk Galaxies to Instability Theory,"  In this letter we study the mean sizes of Halpha clumps in turbulent disk
-galaxies relative to kinematics, gas fractions, and Toomre Q. We use 100~pc
-resolution HST images, IFU kinematics, and gas fractions of a sample of rare,
-nearby turbulent disks with properties closely matched to z~1.5-2 main-sequence
-galaxies (the DYNAMO sample). We find linear correlations of normalized mean
-clump sizes with both the gas fraction and the velocity dispersion-to-rotation
-velocity ratio of the host galaxy. We show that these correlations are
-consistent with predictions derived from a model of instabilities in a
-self-gravitating disk (the so-called ""violent disk instability model""). We also
-observe, using a two-fluid model for Q, a correlation between the size of
-clumps and self-gravity driven unstable regions. These results are most
-consistent with the hypothesis that massive star forming clumps in turbulent
-disks are the result of instabilities in self-gravitating gas-rich disks, and
-therefore provide a direct connection between resolved clump sizes and this in
-situ mechanism.
-",0,1,0,0,0,0
-17493,Anomalous slowing down of individual human activity due to successive decision-making processes,"  Motivated by a host of empirical evidences revealing the bursty character of
-human dynamics, we develop a model of human activity based on successive
-switching between an hesitation state and a decision-realization state, with
-residency times in the hesitation state distributed according to a heavy-tailed
-Pareto distribution. This model is particularly reminiscent of an individual
-strolling through a randomly distributed human crowd. Using a stochastic model
-based on the concept of anomalous and non-Markovian Lévy walk, we show
-exactly that successive decision-making processes drastically slow down the
-progression of an individual faced with randomly distributed obstacles.
-Specifically, we prove exactly that the average displacement exhibits a
-sublinear scaling with time that finds its origins in: (i) the intrinsically
-non-Markovian character of human activity, and (ii) the power law distribution
-of hesitation times.
-",0,1,0,0,0,0
-17494,"The spectrum, radiation conditions and the Fredholm property for the Dirichlet Laplacian in a perforated plane with semi-infinite inclusions","  We consider the spectral Dirichlet problem for the Laplace operator in the
-plane $\Omega^{\circ}$ with double-periodic perforation but also in the domain
-$\Omega^{\bullet}$ with a semi-infinite foreign inclusion so that the
-Floquet-Bloch technique and the Gelfand transform do not apply directly. We
-describe waves which are localized near the inclusion and propagate along it.
-We give a formulation of the problem with radiation conditions that provides a
-Fredholm operator of index zero. The main conclusion concerns the spectra
-$\sigma^{\circ}$ and $\sigma^{\bullet}$ of the problems in $\Omega^{\circ}$ and
-$\Omega^{\bullet},$ namely we present a concrete geometry which supports the
-relation $\sigma^{\circ}\varsubsetneqq\sigma^{\bullet}$ due to a new non-empty
-spectral band caused by the semi-infinite inclusion called an open waveguide in
-the double-periodic medium.
-",0,0,1,0,0,0
-17495,On the Semantics and Complexity of Probabilistic Logic Programs,"  We examine the meaning and the complexity of probabilistic logic programs
-that consist of a set of rules and a set of independent probabilistic facts
-(that is, programs based on Sato's distribution semantics). We focus on two
-semantics, respectively based on stable and on well-founded models. We show
-that the semantics based on stable models (referred to as the ""credal
-semantics"") produces sets of probability models that dominate infinitely
-monotone Choquet capacities, we describe several useful consequences of this
-result. We then examine the complexity of inference with probabilistic logic
-programs. We distinguish between the complexity of inference when a
-probabilistic program and a query are given (the inferential complexity), and
-the complexity of inference when the probabilistic program is fixed and the
-query is given (the query complexity, akin to data complexity as used in
-database theory). We obtain results on the inferential and query complexity for
-acyclic, stratified, and cyclic propositional and relational programs,
-complexity reaches various levels of the counting hierarchy and even
-exponential levels.
-",1,0,0,0,0,0
-17496,Fitting Probabilistic Index Models on Large Datasets,"  Recently, Thas et al. (2012) introduced a new statistical model for the
-probability index. This index is defined as $P(Y \leq Y^*|X, X^*)$ where Y and
-Y* are independent random response variables associated with covariates X and
-X* [...] Crucially to estimate the parameters of the model, a set of
-pseudo-observations is constructed. For a sample size n, a total of $n(n-1)/2$
-pairwise comparisons between observations is considered. Consequently for large
-sample sizes, it becomes computationally infeasible or even impossible to fit
-the model as the set of pseudo-observations increases nearly quadratically. In
-this dissertation, we provide two solutions to fit a probabilistic index model.
-The first algorithm consists of splitting the entire data set into unique
-partitions. On each of these, we fit the model and then aggregate the
-estimates. A second algorithm is a subsampling scheme in which we select $K <<
-n$ observations without replacement and after B iterations aggregate the
-estimates. In Monte Carlo simulations, we show how the partitioning algorithm
-outperforms the latter [...] We illustrate the partitioning algorithm and the
-interpretation of the probabilistic index model on a real data set (Przybylski
-and Weinstein, 2017) of n = 116,630 where we compare it against the ordinary
-least squares method. By modelling the probabilistic index, we give an
-intuitive and meaningful quantification of the effect of the time adolescents
-spend using digital devices such as smartphones on self-reported mental
-well-being. We show how moderate usage is associated with an increased
-probability of reporting a higher mental well-being compared to random
-adolescents who do not use a smartphone. On the other hand, adolescents who
-excessively use their smartphone are associated with a higher probability of
-reporting a lower mental well-being than randomly chosen peers who do not use a
-smartphone.[...]
-",0,0,0,1,0,0
-17497,BICEP2 / Keck Array IX: New Bounds on Anisotropies of CMB Polarization Rotation and Implications for Axion-Like Particles and Primordial Magnetic Fields,"  We present the strongest constraints to date on anisotropies of CMB
-polarization rotation derived from $150$ GHz data taken by the BICEP2 & Keck
-Array CMB experiments up to and including the 2014 observing season (BK14). The
-definition of polarization angle in BK14 maps has gone through self-calibration
-in which the overall angle is adjusted to minimize the observed $TB$ and $EB$
-power spectra. After this procedure, the $QU$ maps lose sensitivity to a
-uniform polarization rotation but are still sensitive to anisotropies of
-polarization rotation. This analysis places constraints on the anisotropies of
-polarization rotation, which could be generated by CMB photons interacting with
-axion-like pseudoscalar fields or Faraday rotation induced by primordial
-magnetic fields. The sensitivity of BK14 maps ($\sim 3\mu$K-arcmin) makes it
-possible to reconstruct anisotropies of polarization rotation angle and measure
-their angular power spectrum much more precisely than previous attempts. Our
-data are found to be consistent with no polarization rotation anisotropies,
-improving the upper bound on the amplitude of the rotation angle spectrum by
-roughly an order of magnitude compared to the previous best constraints. Our
-results lead to an order of magnitude better constraint on the coupling
-constant of the Chern-Simons electromagnetic term $f_a \geq 1.7\times
-10^2\times (H_I/2\pi)$ ($2\sigma$) than the constraint derived from uniform
-rotation, where $H_I$ is the inflationary Hubble scale. The upper bound on the
-amplitude of the primordial magnetic fields is 30nG ($2\sigma$) from the
-polarization rotation anisotropies.
-",0,1,0,0,0,0
-17498,Unsupervised Object Discovery and Segmentation of RGBD-images,"  In this paper we introduce a system for unsupervised object discovery and
-segmentation of RGBD-images. The system models the sensor noise directly from
-data, allowing accurate segmentation without sensor specific hand tuning of
-measurement noise models making use of the recently introduced Statistical
-Inlier Estimation (SIE) method. Through a fully probabilistic formulation, the
-system is able to apply probabilistic inference, enabling reliable segmentation
-in previously challenging scenarios. In addition, we introduce new methods for
-filtering out false positives, significantly improving the signal to noise
-ratio. We show that the system significantly outperform state-of-the-art in on
-a challenging real-world dataset.
-",1,0,0,0,0,0
-17499,Enabling large-scale viscoelastic calculations via neural network acceleration,"  One of the most significant challenges involved in efforts to understand the
-effects of repeated earthquake cycle activity are the computational costs of
-large-scale viscoelastic earthquake cycle models. Computationally intensive
-viscoelastic codes must be evaluated thousands of times and locations, and as a
-result, studies tend to adopt a few fixed rheological structures and model
-geometries, and examine the predicted time-dependent deformation over short
-(<10 yr) time periods at a given depth after a large earthquake. Training a
-deep neural network to learn a computationally efficient representation of
-viscoelastic solutions, at any time, location, and for a large range of
-rheological structures, allows these calculations to be done quickly and
-reliably, with high spatial and temporal resolution. We demonstrate that this
-machine learning approach accelerates viscoelastic calculations by more than
-50,000%. This magnitude of acceleration will enable the modeling of
-geometrically complex faults over thousands of earthquake cycles across wider
-ranges of model parameters and at larger spatial and temporal scales than have
-been previously possible.
-",0,1,0,0,0,0
-17500,Speaker identification from the sound of the human breath,"  This paper examines the speaker identification potential of breath sounds in
-continuous speech. Speech is largely produced during exhalation. In order to
-replenish air in the lungs, speakers must periodically inhale. When inhalation
-occurs in the midst of continuous speech, it is generally through the mouth.
-Intra-speech breathing behavior has been the subject of much study, including
-the patterns, cadence, and variations in energy levels. However, an often
-ignored characteristic is the {\em sound} produced during the inhalation phase
-of this cycle. Intra-speech inhalation is rapid and energetic, performed with
-open mouth and glottis, effectively exposing the entire vocal tract to enable
-maximum intake of air. This results in vocal tract resonances evoked by
-turbulence that are characteristic of the speaker's speech-producing apparatus.
-Consequently, the sounds of inhalation are expected to carry information about
-the speaker's identity. Moreover, unlike other spoken sounds which are subject
-to active control, inhalation sounds are generally more natural and less
-affected by voluntary influences. The goal of this paper is to demonstrate that
-breath sounds are indeed bio-signatures that can be used to identify speakers.
-We show that these sounds by themselves can yield remarkably accurate speaker
-recognition with appropriate feature representations and classification
-frameworks.
-",1,0,0,1,0,0
-17501,Vector valued maximal Carleson type operators on the weighted Lorentz spaces,"  In this paper, by using the idea of linearizing maximal op-erators originated
-by Charles Fefferman and the TT* method of Stein-Wainger, we establish a
-weighted inequality for vector valued maximal Carleson type operators with
-singular kernels proposed by Andersen and John on the weighted Lorentz spaces
-with vector-valued functions.
-",0,0,1,0,0,0
-17502,Rigidity of square-tiled interval exchange transformations,"  We look at interval exchange transformations defined as first return maps on
-the set of diagonals of a flow of direction $\theta$ on a square-tiled surface:
-using a combinatorial approach, we show that, when the surface has at least one
-true singularity both the flow and the interval exchange are rigid if and only
-if tan $\theta$ has bounded partial quotients. Moreover, if all vertices of the
-squares are singularities of the flat metric, and tan $\theta$ has bounded
-partial quotients, the square-tiled interval exchange transformation T is not
-of rank one. Finally, for another class of surfaces, those defined by the
-unfolding of billiards in Veech triangles, we build an uncountable set of rigid
-directional flows and an uncountable set of rigid interval exchange
-transformations.
-",0,0,1,0,0,0
-17503,Global Marcinkiewicz estimates for nonlinear parabolic equations with nonsmooth coefficients,"  Consider the parabolic equation with measure data \begin{equation*} \left\{
-\begin{aligned} &u_t-{\rm div} \mathbf{a}(D u,x,t)=\mu&\text{in}& \quad
-\Omega_T, &u=0 \quad &\text{on}& \quad \partial_p\Omega_T, \end{aligned}\right.
-\end{equation*} where $\Omega$ is a bounded domain in $\mathbb{R}^n$,
-$\Omega_T=\Omega\times (0,T)$, $\partial_p\Omega_T=(\partial\Omega\times
-(0,T))\cup (\Omega\times\{0\})$, and $\mu$ is a signed Borel measure with
-finite total mass. Assume that the nonlinearity ${\bf a}$ satisfies a small
-BMO-seminorm condition, and $\Omega$ is a Reifenberg flat domain. This paper
-proves a global Marcinkiewicz estimate for the SOLA (Solution Obtained as
-Limits of Approximation) to the parabolic equation.
-",0,0,1,0,0,0
-17504,Can Who-Edits-What Predict Edit Survival?,"  As the number of contributors to online peer-production systems grows, it
-becomes increasingly important to predict whether the edits that users make
-will eventually be beneficial to the project. Existing solutions either rely on
-a user reputation system or consist of a highly specialized predictor that is
-tailored to a specific peer-production system. In this work, we explore a
-different point in the solution space that goes beyond user reputation but does
-not involve any content-based feature of the edits. We view each edit as a game
-between the editor and the component of the project. We posit that the
-probability that an edit is accepted is a function of the editor's skill, of
-the difficulty of editing the component and of a user-component interaction
-term. Our model is broadly applicable, as it only requires observing data about
-who makes an edit, what the edit affects and whether the edit survives or not.
-We apply our model on Wikipedia and the Linux kernel, two examples of
-large-scale peer-production systems, and we seek to understand whether it can
-effectively predict edit survival: in both cases, we provide a positive answer.
-Our approach significantly outperforms those based solely on user reputation
-and bridges the gap with specialized predictors that use content-based
-features. It is simple to implement, computationally inexpensive, and in
-addition it enables us to discover interesting structure in the data.
-",1,0,0,1,0,0
-17505,Introduction to intelligent computing unit 1,"  This brief note highlights some basic concepts required toward understanding
-the evolution of machine learning and deep learning models. The note starts
-with an overview of artificial intelligence and its relationship to biological
-neuron that ultimately led to the evolution of todays intelligent models.
-",1,0,0,1,0,0
-17506,Spatial heterogeneities shape collective behavior of signaling amoeboid cells,"  We present novel experimental results on pattern formation of signaling
-Dictyostelium discoideum amoeba in the presence of a periodic array of
-millimeter-sized pillars. We observe concentric cAMP waves that initiate almost
-synchronously at the pillars and propagate outwards. These waves have higher
-frequency than the other firing centers and dominate the system dynamics. The
-cells respond chemotactically to these circular waves and stream towards the
-pillars, forming periodic Voronoi domains that reflect the periodicity of the
-underlying lattice. We performed comprehensive numerical simulations of a
-reaction-diffusion model to study the characteristics of the boundary
-conditions given by the obstacles. Our simulations show that, the obstacles can
-act as the wave source depending on the imposed boundary condition.
-Interestingly, a critical minimum accumulation of cAMP around the obstacles is
-needed for the pillars to act as the wave source. This critical value is lower
-at smaller production rates of the intracellular cAMP which can be controlled
-in our experiments using caffeine. Experiments and simulations also show that
-in the presence of caffeine the number of firing centers is reduced which is
-crucial in our system for circular waves emitted from the pillars to
-successfully take over the dynamics. These results are crucial to understand
-the signaling mechanism of Dictyostelium cells that experience spatial
-heterogeneities in its natural habitat.
-",0,0,0,0,1,0
-17507,Yamabe Solitons on three-dimensional normal almost paracontact metric manifolds,"  The purpose of the paper is to study Yamabe solitons on three-dimensional
-para-Sasakian, paracosymplectic and para-Kenmotsu manifolds. Mainly, we proved
-that *If the semi-Riemannian metric of a three-dimensional para-Sasakian
-manifold is a Yamabe soliton, then it is of constant scalar curvature, and the
-flow vector field V is Killing. In the next step, we proved that either
-manifold has constant curvature -1 and reduces to an Einstein manifold, or V is
-an infinitesimal automorphism of the paracontact metric structure on the
-manifold. *If the semi-Riemannian metric of a three-dimensional
-paracosymplectic manifold is a Yamabe soliton, then it has constant scalar
-curvature. Furthermore either manifold is $\eta$-Einstein, or Ricci flat. *If
-the semi-Riemannian metric on a three-dimensional para-Kenmotsu manifold is a
-Yamabe soliton, then the manifold is of constant sectional curvature -1,
-reduces to an Einstein manifold. Furthermore, Yamabe soliton is expanding with
-$\lambda$=-6 and the vector field V is Killing. Finally, we construct examples
-to illustrate the results obtained in previous sections.
-",0,0,1,0,0,0
-17508,Clustering and Hitting Times of Threshold Exceedances and Applications,"  We investigate exceedances of the process over a sufficiently high threshold.
-The exceedances determine the risk of hazardous events like climate
-catastrophes, huge insurance claims, the loss and delay in telecommunication
-networks.
-Due to dependence such exceedances tend to occur in clusters. The cluster
-structure of social networks is caused by dependence (social relationships and
-interests) between nodes and possibly heavy-tailed distributions of the node
-degrees. A minimal time to reach a large node determines the first hitting
-time. We derive an asymptotically equivalent distribution and a limit
-expectation of the first hitting time to exceed the threshold $u_n$ as the
-sample size $n$ tends to infinity. The results can be extended to the second
-and, generally, to the $k$th ($k> 2$) hitting times. Applications in
-large-scale networks such as social, telecommunication and recommender systems
-are discussed.
-",0,0,1,1,0,0
-17509,Classification of Local Field Potentials using Gaussian Sequence Model,"  A problem of classification of local field potentials (LFPs), recorded from
-the prefrontal cortex of a macaque monkey, is considered. An adult macaque
-monkey is trained to perform a memory-based saccade. The objective is to decode
-the eye movement goals from the LFP collected during a memory period. The LFP
-classification problem is modeled as that of classification of smooth functions
-embedded in Gaussian noise. It is then argued that using minimax function
-estimators as features would lead to consistent LFP classifiers. The theory of
-Gaussian sequence models allows us to represent minimax estimators as finite
-dimensional objects. The LFP classifier resulting from this mathematical
-endeavor is a spectrum based technique, where Fourier series coefficients of
-the LFP data, followed by appropriate shrinkage and thresholding, are used as
-features in a linear discriminant classifier. The classifier is then applied to
-the LFP data to achieve high decoding accuracy. The function classification
-approach taken in the paper also provides a systematic justification for using
-Fourier series, with shrinkage and thresholding, as features for the problem,
-as opposed to using the power spectrum. It also suggests that phase information
-is crucial to the decision making.
-",0,0,0,1,0,0
-17510,A few explicit examples of complex dynamics of inertia groups on surfaces - a question of Professor Igor Dolgachev,"  We give a few explicit examples which answer an open minded question of
-Professor Igor Dolgachev on complex dynamics of the inertia group of a smooth
-rational curve on a projective K3 surface and its variants for a rational
-surface and a non-projective K3 surface.
-",0,0,1,0,0,0
-17511,Ancestral inference from haplotypes and mutations,"  We consider inference about the history of a sample of DNA sequences,
-conditional upon the haplotype counts and the number of segregating sites
-observed at the present time. After deriving some theoretical results in the
-coalescent setting, we implement rejection sampling and importance sampling
-schemes to perform the inference. The importance sampling scheme addresses an
-extension of the Ewens Sampling Formula for a configuration of haplotypes and
-the number of segregating sites in the sample. The implementations include both
-constant and variable population size models. The methods are illustrated by
-two human Y chromosome data sets.
-",0,0,1,1,0,0
-17512,Ellipsoid Method for Linear Programming made simple,"  In this paper, ellipsoid method for linear programming is derived using only
-minimal knowledge of algebra and matrices. Unfortunately, most authors first
-describe the algorithm, then later prove its correctness, which requires a good
-knowledge of linear algebra.
-",1,0,0,0,0,0
-17513,Collective strong coupling of cold atoms to an all-fiber ring cavity,"  We experimentally demonstrate a ring geometry all-fiber cavity system for
-cavity quantum electrodynamics with an ensemble of cold atoms. The fiber cavity
-contains a nanofiber section which mediates atom-light interactions through an
-evanescent field. We observe well-resolved, vacuum Rabi splitting of the cavity
-transmission spectrum in the weak driving limit due to a collective enhancement
-of the coupling rate by the ensemble of atoms within the evanescent field, and
-we present a simple theoretical model to describe this. In addition, we
-demonstrate a method to control and stabilize the resonant frequency of the
-cavity by utilizing the thermal properties of the nanofiber.
-",0,1,0,0,0,0
-17514,Robust Model-Based Clustering of Voting Records,"  We explore the possibility of discovering extreme voting patterns in the U.S.
-Congressional voting records by drawing ideas from the mixture of contaminated
-normal distributions. A mixture of latent trait models via contaminated normal
-distributions is proposed. We assume that the low dimensional continuous latent
-variable comes from a contaminated normal distribution and, therefore, picks up
-extreme patterns in the observed binary data while clustering. We consider in
-particular such model for the analysis of voting records. The model is applied
-to a U.S. Congressional Voting data set on 16 issues. Note this approach is the
-first instance within the literature of a mixture model handling binary data
-with possible extreme patterns.
-",0,0,0,1,0,0
-17515,The Quest for Solvable Multistate Landau-Zener Models,"  Recently, integrability conditions (ICs) in mutistate Landau-Zener (MLZ)
-theory were proposed [1]. They describe common properties of all known solved
-systems with linearly time-dependent Hamiltonians. Here we show that ICs enable
-efficient computer assisted search for new solvable MLZ models that span
-complexity range from several interacting states to mesoscopic systems with
-many-body dynamics and combinatorially large phase space. This diversity
-suggests that nontrivial solvable MLZ models are numerous. In addition, we
-refine the formulation of ICs and extend the class of solvable systems to
-models with points of multiple diabatic level crossing.
-",0,1,1,0,0,0
-17516,Bayesian Inference of the Multi-Period Optimal Portfolio for an Exponential Utility,"  We consider the estimation of the multi-period optimal portfolio obtained by
-maximizing an exponential utility. Employing Jeffreys' non-informative prior
-and the conjugate informative prior, we derive stochastic representations for
-the optimal portfolio weights at each time point of portfolio reallocation.
-This provides a direct access not only to the posterior distribution of the
-portfolio weights but also to their point estimates together with uncertainties
-and their asymptotic distributions. Furthermore, we present the posterior
-predictive distribution for the investor's wealth at each time point of the
-investment period in terms of a stochastic representation for the future wealth
-realization. This in turn makes it possible to use quantile-based risk measures
-or to calculate the probability of default. We apply the suggested Bayesian
-approach to assess the uncertainty in the multi-period optimal portfolio by
-considering assets from the FTSE 100 in the weeks after the British referendum
-to leave the European Union. The behaviour of the novel portfolio estimation
-method in a precarious market situation is illustrated by calculating the
-predictive wealth, the risk associated with the holding portfolio, and the
-default probability in each period.
-",0,0,1,1,0,0
-17517,Reconstructing the gravitational field of the local universe,"  Tests of gravity at the galaxy scale are in their infancy. As a first step to
-systematically uncovering the gravitational significance of galaxies, we map
-three fundamental gravitational variables -- the Newtonian potential,
-acceleration and curvature -- over the galaxy environments of the local
-universe to a distance of approximately 200 Mpc. Our method combines the
-contributions from galaxies in an all-sky redshift survey, halos from an N-body
-simulation hosting low-luminosity objects, and linear and quasi-linear modes of
-the density field. We use the ranges of these variables to determine the extent
-to which galaxies expand the scope of generic tests of gravity and are capable
-of constraining specific classes of model for which they have special
-significance. Finally, we investigate the improvements afforded by upcoming
-galaxy surveys.
-",0,1,0,0,0,0
-17518,Hierarchical organization of H. Eugene Stanley scientific collaboration community in weighted network representation,"  By mapping the most advanced elements of the contemporary social
-interactions, the world scientific collaboration network develops an extremely
-involved and heterogeneous organization. Selected characteristics of this
-heterogeneity are studied here and identified by focusing on the scientific
-collaboration community of H. Eugene Stanley - one of the most prolific world
-scholars at the present time. Based on the Web of Science records as of March
-28, 2016, several variants of networks are constructed. It is found that the
-Stanley #1 network - this in analogy to the Erdős # - develops a largely
-consistent hierarchical organization and Stanley himself obeys rules of the
-same hierarchy. However, this is seen exclusively in the weighted network
-representation. When such a weighted network is evolving, an existing relevant
-model indicates that the spread of weight gets stimulation to the
-multiplicative bursts over the neighbouring nodes, which leads to a balanced
-growth of interconnections among them. While not exclusive to Stanley, such a
-behaviour is not a rule, however. Networks of other outstanding scholars
-studied here more often develop a star-like form and the central hubs
-constitute the outliers. This study is complemented by a spectral analysis of
-the normalised Laplacian matrices derived from the weighted variants of the
-corresponding networks and, among others, it points to the efficiency of such a
-procedure for identifying the component communities and relations among them in
-the complex weighted networks.
-",1,1,0,0,0,0
-17519,Rational links and DT invariants of quivers,"  We prove that the generating functions for the colored HOMFLY-PT polynomials
-of rational links are specializations of the generating functions of the
-motivic Donaldson-Thomas invariants of appropriate quivers that we naturally
-associate with these links. This shows that the conjectural links-quivers
-correspondence of Kucharski-Reineke-Stošić-Su{\l}kowski as well as the
-LMOV conjecture hold for rational links. Along the way, we extend the
-links-quivers correspondence to tangles and, thus, explore elements of a skein
-theory for motivic Donaldson-Thomas invariants.
-",0,0,1,0,0,0
-17520,G2-structures for N=1 supersymmetric AdS4 solutions of M-theory,"  We study the N=1 supersymmetric solutions of D=11 supergravity obtained as a
-warped product of four-dimensional anti-de-Sitter space with a
-seven-dimensional Riemannian manifold M. Using the octonion bundle structure on
-M we reformulate the Killing spinor equations in terms of sections of the
-octonion bundle on M. The solutions then define a single complexified
-G2-structure on M or equivalently two real G2-structures. We then study the
-torsion of these G2-structures and the relationships between them.
-",0,0,1,0,0,0
-17521,A Neural Stochastic Volatility Model,"  In this paper, we show that the recent integration of statistical models with
-deep recurrent neural networks provides a new way of formulating volatility
-(the degree of variation of time series) models that have been widely used in
-time series analysis and prediction in finance. The model comprises a pair of
-complementary stochastic recurrent neural networks: the generative network
-models the joint distribution of the stochastic volatility process; the
-inference network approximates the conditional distribution of the latent
-variables given the observables. Our focus here is on the formulation of
-temporal dynamics of volatility over time under a stochastic recurrent neural
-network framework. Experiments on real-world stock price datasets demonstrate
-that the proposed model generates a better volatility estimation and prediction
-that outperforms mainstream methods, e.g., deterministic models such as GARCH
-and its variants, and stochastic models namely the MCMC-based model
-\emph{stochvol} as well as the Gaussian process volatility model \emph{GPVol},
-on average negative log-likelihood.
-",1,0,0,1,0,0
-17522,A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds,"  This paper proposes a segmentation-free, automatic and efficient procedure to
-detect general geometric quadric forms in point clouds, where clutter and
-occlusions are inevitable. Our everyday world is dominated by man-made objects
-which are designed using 3D primitives (such as planes, cones, spheres,
-cylinders, etc.). These objects are also omnipresent in industrial
-environments. This gives rise to the possibility of abstracting 3D scenes
-through primitives, thereby positions these geometric forms as an integral part
-of perception and high level 3D scene understanding.
-As opposed to state-of-the-art, where a tailored algorithm treats each
-primitive type separately, we propose to encapsulate all types in a single
-robust detection procedure. At the center of our approach lies a closed form 3D
-quadric fit, operating in both primal & dual spaces and requiring as low as 4
-oriented-points. Around this fit, we design a novel, local null-space voting
-strategy to reduce the 4-point case to 3. Voting is coupled with the famous
-RANSAC and makes our algorithm orders of magnitude faster than its conventional
-counterparts. This is the first method capable of performing a generic
-cross-type multi-object primitive detection in difficult scenes. Results on
-synthetic and real datasets support the validity of our method.
-",1,0,0,0,0,0
-17523,Mass-to-Light versus Color Relations for Dwarf Irregular Galaxies,"  We have determined new relations between $UBV$ colors and mass-to-light
-ratios ($M/L$) for dwarf irregular (dIrr) galaxies, as well as for transformed
-$g^\prime - r^\prime$. These $M/L$ to color relations (MLCRs) are based on
-stellar mass density profiles determined for 34 LITTLE THINGS dwarfs from
-spectral energy distribution fitting to multi-wavelength surface photometry in
-passbands from the FUV to the NIR. These relations can be used to determine
-stellar masses in dIrr galaxies for situations where other determinations of
-stellar mass are not possible. Our MLCRs are shallower than comparable MLCRs in
-the literature determined for spiral galaxies. We divided our dwarf data into
-four metallicity bins and found indications of a steepening of the MLCR with
-increased oxygen abundance, perhaps due to more line blanketing occurring at
-higher metallicity.
-",0,1,0,0,0,0
-17524,Insight into High-order Harmonic Generation from Solids: The Contributions of the Bloch Wave-packets Moving on the Group and Phase Velocities,"  We study numerically the Bloch electron wavepacket dynamics in periodic
-potentials to simulate laser-solid interactions. We introduce a new perspective
-in the coordinate space combined with the motion of the Bloch electron
-wavepackets moving at group and phase velocities under the laser fields. This
-model interprets the origins of the two contributions (intra- and interband
-transitions) of the high-order harmonic generation (HHG) by investigating the
-local and global behavior of the wavepackets. It also elucidates the underlying
-physical picture of the HHG intensity enhancement by means of carrier-envelope
-phase (CEP), chirp and inhomogeneous fields. It provides a deep insight into
-the emission of high-order harmonics from solids. This model is instructive for
-experimental measurements and provides a new avenue to distinguish mechanisms
-of the HHG from solids in diffrent laser fields.
-",0,1,0,0,0,0
-17525,Using deterministic approximations to accelerate SMC for posterior sampling,"  Sequential Monte Carlo has become a standard tool for Bayesian Inference of
-complex models. This approach can be computationally demanding, especially when
-initialized from the prior distribution. On the other hand, deter-ministic
-approximations of the posterior distribution are often available with no
-theoretical guaranties. We propose a bridge sampling scheme starting from such
-a deterministic approximation of the posterior distribution and targeting the
-true one. The resulting Shortened Bridge Sampler (SBS) relies on a sequence of
-distributions that is determined in an adaptive way. We illustrate the
-robustness and the efficiency of the methodology on a large simulation study.
-When applied to network datasets, SBS inference leads to different statistical
-conclusions from the one supplied by the standard variational Bayes
-approximation.
-",0,0,0,1,0,0
-17526,Evaluating (and improving) the correspondence between deep neural networks and human representations,"  Decades of psychological research have been aimed at modeling how people
-learn features and categories. The empirical validation of these theories is
-often based on artificial stimuli with simple representations. Recently, deep
-neural networks have reached or surpassed human accuracy on tasks such as
-identifying objects in natural images. These networks learn representations of
-real-world stimuli that can potentially be leveraged to capture psychological
-representations. We find that state-of-the-art object classification networks
-provide surprisingly accurate predictions of human similarity judgments for
-natural images, but fail to capture some of the structure represented by
-people. We show that a simple transformation that corrects these discrepancies
-can be obtained through convex optimization. We use the resulting
-representations to predict the difficulty of learning novel categories of
-natural images. Our results extend the scope of psychological experiments and
-computational modeling by enabling tractable use of large natural stimulus
-sets.
-",1,0,0,0,0,0
-17527,An approach to nonsolvable base change and descent,"  We present a collection of conjectural trace identities and explain why they
-are equivalent to base change and descent of automorphic representations of
-$\mathrm{GL}_n(\mathbb{A}_F)$ along nonsolvable extensions (under some
-simplifying hypotheses). The case $n=2$ is treated in more detail and
-applications towards the Artin conjecture for icosahedral Galois
-representations are given.
-",0,0,1,0,0,0
-17528,Towards Attack-Tolerant Networks: Concurrent Multipath Routing and the Butterfly Network,"  Targeted attacks against network infrastructure are notoriously difficult to
-guard against. In the case of communication networks, such attacks can leave
-users vulnerable to censorship and surveillance, even when cryptography is
-used. Much of the existing work on network fault-tolerance focuses on random
-faults and does not apply to adversarial faults (attacks). Centralized networks
-have single points of failure by definition, leading to a growing popularity in
-decentralized architectures and protocols for greater fault-tolerance. However,
-centralized network structure can arise even when protocols are decentralized.
-Despite their decentralized protocols, the Internet and World-Wide Web have
-been shown both theoretically and historically to be highly susceptible to
-attack, in part due to emergent structural centralization. When single points
-of failure exist, they are potentially vulnerable to non-technological (i.e.,
-coercive) attacks, suggesting the importance of a structural approach to
-attack-tolerance. We show how the assumption of partial trust transitivity,
-while more realistic than the assumption underlying webs of trust, can be used
-to quantify the effective redundancy of a network as a function of trust
-transitivity. We also prove that the effective redundancy of the wrap-around
-butterfly topology increases exponentially with trust transitivity and describe
-a novel concurrent multipath routing algorithm for constructing paths to
-utilize that redundancy. When portions of network structure can be dictated our
-results can be used to create scalable, attack-tolerant infrastructures. More
-generally, our results provide a theoretical formalism for evaluating the
-effects of network structure on adversarial fault-tolerance.
-",1,0,0,0,0,0
-17529,PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making,"  Reinforcement learning and symbolic planning have both been used to build
-intelligent autonomous agents. Reinforcement learning relies on learning from
-interactions with real world, which often requires an unfeasibly large amount
-of experience. Symbolic planning relies on manually crafted symbolic knowledge,
-which may not be robust to domain uncertainties and changes. In this paper we
-present a unified framework {\em PEORL} that integrates symbolic planning with
-hierarchical reinforcement learning (HRL) to cope with decision-making in a
-dynamic environment with uncertainties.
-Symbolic plans are used to guide the agent's task execution and learning, and
-the learned experience is fed back to symbolic knowledge to improve planning.
-This method leads to rapid policy search and robust symbolic plans in complex
-domains. The framework is tested on benchmark domains of HRL.
-",0,0,0,1,0,0
-17530,Structure of Native Two-dimensional Oxides on III--Nitride Surfaces,"  When pristine material surfaces are exposed to air, highly reactive broken
-bonds can promote the formation of surface oxides with structures and
-properties differing greatly from bulk. Determination of the oxide structure,
-however, is often elusive through the use of indirect diffraction methods or
-techniques that probe only the outer most layer. As a result, surface oxides
-forming on widely used materials, such as group III-nitrides, have not been
-unambiguously resolved, even though critical properties can depend sensitively
-on their presence. In this work, aberration corrected scanning transmission
-electron microscopy reveals directly, and with depth dependence, the structure
-of native two--dimensional oxides that form on AlN and GaN surfaces. Through
-atomic resolution imaging and spectroscopy, we show that the oxide layers are
-comprised of tetrahedra--octahedra cation--oxygen units, similar to bulk
-$\theta$--Al$_2$O$_3$ and $\beta$--Ga$_2$O$_3$. By applying density functional
-theory, we show that the observed structures are more stable than previously
-proposed surface oxide models. We place the impact of these observations in the
-context of key III-nitride growth, device issues, and the recent discovery of
-two-dimnesional nitrides.
-",0,1,0,0,0,0
-17531,Hydrodynamic charge and heat transport on inhomogeneous curved spaces,"  We develop the theory of hydrodynamic charge and heat transport in strongly
-interacting quasi-relativistic systems on manifolds with inhomogeneous spatial
-curvature. In solid-state physics, this is analogous to strain disorder in the
-underlying lattice. In the hydrodynamic limit, we find that the thermal and
-electrical conductivities are dominated by viscous effects, and that the
-thermal conductivity is most sensitive to this disorder. We compare the effects
-of inhomogeneity in the spatial metric to inhomogeneity in the chemical
-potential, and discuss the extent to which our hydrodynamic theory is relevant
-for experimentally realizable condensed matter systems, including suspended
-graphene at the Dirac point.
-",0,1,0,0,0,0
-17532,Simulation assisted machine learning,"  Predicting how a proposed cancer treatment will affect a given tumor can be
-cast as a machine learning problem, but the complexity of biological systems,
-the number of potentially relevant genomic and clinical features, and the lack
-of very large scale patient data repositories make this a unique challenge.
-""Pure data"" approaches to this problem are underpowered to detect
-combinatorially complex interactions and are bound to uncover false
-correlations despite statistical precautions taken (1). To investigate this
-setting, we propose a method to integrate simulations, a strong form of prior
-knowledge, into machine learning, a combination which to date has been largely
-unexplored. The results of multiple simulations (under various uncertainty
-scenarios) are used to compute similarity measures between every pair of
-samples: sample pairs are given a high similarity score if they behave
-similarly under a wide range of simulation parameters. These similarity values,
-rather than the original high dimensional feature data, are used to train
-kernelized machine learning algorithms such as support vector machines, thus
-handling the curse-of-dimensionality that typically affects genomic machine
-learning. Using four synthetic datasets of complex systems--three biological
-models and one network flow optimization model--we demonstrate that when the
-number of training samples is small compared to the number of features, the
-simulation kernel approach dominates over no-prior-knowledge methods. In
-addition to biology and medicine, this approach should be applicable to other
-disciplines, such as weather forecasting, financial markets, and agricultural
-management, where predictive models are sought and informative yet approximate
-simulations are available. The Python SimKern software, the models (in MATLAB,
-Octave, and R), and the datasets are made freely available at
-this https URL .
-",0,0,0,1,1,0
-17533,Rechargeable redox flow batteries: Maximum current density with electrolyte flow reactant penetration in a serpentine flow structure,"  Rechargeable redox flow batteries with serpentine flow field designs have
-been demonstrated to deliver higher current density and power density in medium
-and large-scale stationary energy storage applications. Nevertheless, the
-fundamental mechanisms involved with improved current density in flow batteries
-with flow field designs have not been understood. Here we report a maximum
-current density concept associated with stoichiometric availability of
-electrolyte reactant flow penetration through the porous electrode that can be
-achieved in a flow battery system with a ""zero-gap""serpentine flow field
-architecture. This concept can explain a higher current density achieved within
-allowing reactions of all species soluble in the electrolyte. Further
-validations with experimental data are confirmed by an example of a vanadium
-flow battery with a serpentine flow structure over carbon paper electrode.
-",0,1,0,0,0,0
-17534,Open problems in mathematical physics,"  We present a list of open questions in mathematical physics. After a
-historical introduction, a number of problems in a variety of different fields
-are discussed, with the intention of giving an overall impression of the
-current status of mathematical physics, particularly in the topical fields of
-classical general relativity, cosmology and the quantum realm. This list is
-motivated by the recent article proposing 42 fundamental questions (in physics)
-which must be answered on the road to full enlightenment. But paraphrasing a
-famous quote by the British football manager Bill Shankly, in response to the
-question of whether mathematics can answer the Ultimate Question of Life, the
-Universe, and Everything, mathematics is, of course, much more important than
-that.
-",0,1,1,0,0,0
-17535,Stochastic Bandit Models for Delayed Conversions,"  Online advertising and product recommendation are important domains of
-applications for multi-armed bandit methods. In these fields, the reward that
-is immediately available is most often only a proxy for the actual outcome of
-interest, which we refer to as a conversion. For instance, in web advertising,
-clicks can be observed within a few seconds after an ad display but the
-corresponding sale --if any-- will take hours, if not days to happen. This
-paper proposes and investigates a new stochas-tic multi-armed bandit model in
-the framework proposed by Chapelle (2014) --based on empirical studies in the
-field of web advertising-- in which each action may trigger a future reward
-that will then happen with a stochas-tic delay. We assume that the probability
-of conversion associated with each action is unknown while the distribution of
-the conversion delay is known, distinguishing between the (idealized) case
-where the conversion events may be observed whatever their delay and the more
-realistic setting in which late conversions are censored. We provide
-performance lower bounds as well as two simple but efficient algorithms based
-on the UCB and KLUCB frameworks. The latter algorithm, which is preferable when
-conversion rates are low, is based on a Poissonization argument, of independent
-interest in other settings where aggregation of Bernoulli observations with
-different success probabilities is required.
-",1,0,0,0,0,0
-17536,Fitting Analysis using Differential Evolution Optimization (FADO): Spectral population synthesis through genetic optimization under self-consistency boundary conditions,"  The goal of population spectral synthesis (PSS) is to decipher from the
-spectrum of a galaxy the mass, age and metallicity of its constituent stellar
-populations. This technique has been established as a fundamental tool in
-extragalactic research. It has been extensively applied to large spectroscopic
-data sets, notably the SDSS, leading to important insights into the galaxy
-assembly history. However, despite significant improvements over the past
-decade, all current PSS codes suffer from two major deficiencies that inhibit
-us from gaining sharp insights into the star-formation history (SFH) of
-galaxies and potentially introduce substantial biases in studies of their
-physical properties (e.g., stellar mass, mass-weighted stellar age and specific
-star formation rate). These are i) the neglect of nebular emission in spectral
-fits, consequently, ii) the lack of a mechanism that ensures consistency
-between the best-fitting SFH and the observed nebular emission characteristics
-of a star-forming (SF) galaxy. In this article, we present FADO (Fitting
-Analysis using Differential evolution Optimization): a conceptually novel,
-publicly available PSS tool with the distinctive capability of permitting
-identification of the SFH that reproduces the observed nebular characteristics
-of a SF galaxy. This so-far unique self-consistency concept allows us to
-significantly alleviate degeneracies in current spectral synthesis. The
-innovative character of FADO is further augmented by its mathematical
-foundation: FADO is the first PSS code employing genetic differential evolution
-optimization. This, in conjunction with other unique elements in its
-mathematical concept (e.g., optimization of the spectral library using
-artificial intelligence, convergence test, quasi-parallelization) results in
-key improvements with respect to computational efficiency and uniqueness of the
-best-fitting SFHs.
-",0,1,0,0,0,0
-17537,A Combinatoric Shortcut to Evaluate CHY-forms,"  In \cite{Chen:2016fgi} we proposed a differential operator for the evaluation
-of the multi-dimensional residues on isolated (zero-dimensional) poles.In this
-paper we discuss some new insight on evaluating the (generalized)
-Cachazo-He-Yuan (CHY) forms of the scattering amplitudes using this
-differential operator. We introduce a tableau representation for the
-coefficients appearing in the proposed differential operator. Combining the
-tableaux with the polynomial forms of the scattering equations, the evaluation
-of the generalized CHY form becomes a simple combinatoric problem. It is thus
-possible to obtain the coefficients arising in the differential operator in a
-straightforward way. We present the procedure for a complete solution of the
-$n$-gon amplitudes at one-loop level in a generalized CHY form. We also apply
-our method to fully evaluate the one-loop five-point amplitude in the maximally
-supersymmetric Yang-Mills theory; the final result is identical to the one
-obtained by Q-Cut.
-",0,0,1,0,0,0
-17538,ART: adaptive residual--time restarting for Krylov subspace matrix exponential evaluations,"  In this paper a new restarting method for Krylov subspace matrix exponential
-evaluations is proposed. Since our restarting technique essentially employs the
-residual, some convergence results for the residual are given. We also discuss
-how the restart length can be adjusted after each restart cycle, which leads to
-an adaptive restarting procedure. Numerical tests are presented to compare our
-restarting with three other restarting methods. Some of the algorithms
-described in this paper are a part of the Octave/Matlab package expmARPACK
-available at this http URL.
-",1,0,0,0,0,0
-17539,Nil extensions of simple regular ordered semigroup,"  In this paper, nil extensions of some special type of ordered semigroups,
-such as, simple regular ordered semigroups, left simple and right regular
-ordered semigroup. Moreover, we have characterized complete semilattice
-decomposition of all ordered semigroups which are nil extension of ordered
-semigroup.
-",0,0,1,0,0,0
-17540,The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings,"  We examine a class of embeddings based on structured random matrices with
-orthogonal rows which can be applied in many machine learning applications
-including dimensionality reduction and kernel approximation. For both the
-Johnson-Lindenstrauss transform and the angular kernel, we show that we can
-select matrices yielding guaranteed improved performance in accuracy and/or
-speed compared to earlier methods. We introduce matrices with complex entries
-which give significant further accuracy improvement. We provide geometric and
-Markov chain-based perspectives to help understand the benefits, and empirical
-results which suggest that the approach is helpful in a wider range of
-applications.
-",0,0,0,1,0,0
-17541,The Bayesian optimist's guide to adaptive immune receptor repertoire analysis,"  Probabilistic modeling is fundamental to the statistical analysis of complex
-data. In addition to forming a coherent description of the data-generating
-process, probabilistic models enable parameter inference about given data sets.
-This procedure is well-developed in the Bayesian perspective, in which one
-infers probability distributions describing to what extent various possible
-parameters agree with the data. In this paper we motivate and review
-probabilistic modeling for adaptive immune receptor repertoire data then
-describe progress and prospects for future work, from germline haplotyping to
-adaptive immune system deployment across tissues. The relevant quantities in
-immune sequence analysis include not only continuous parameters such as gene
-use frequency, but also discrete objects such as B cell clusters and lineages.
-Throughout this review, we unravel the many opportunities for probabilistic
-modeling in adaptive immune receptor analysis, including settings for which the
-Bayesian approach holds substantial promise (especially if one is optimistic
-about new computational methods). From our perspective the greatest prospects
-for progress in probabilistic modeling for repertoires concern ancestral
-sequence estimation for B cell receptor lineages, including uncertainty from
-germline genotype, rearrangement, and lineage development.
-",0,0,0,0,1,0
-17542,Predictive Indexing,"  There has been considerable research on automated index tuning in database
-management systems (DBMSs). But the majority of these solutions tune the index
-configuration by retrospectively making computationally expensive physical
-design changes all at once. Such changes degrade the DBMS's performance during
-the process, and have reduced utility during subsequent query processing due to
-the delay between a workload shift and the associated change. A better approach
-is to generate small changes that tune the physical design over time, forecast
-the utility of these changes, and apply them ahead of time to maximize their
-impact.
-This paper presents predictive indexing that continuously improves a
-database's physical design using lightweight physical design changes. It uses a
-machine learning model to forecast the utility of these changes, and
-continuously refines the index configuration of the database to handle evolving
-workloads. We introduce a lightweight hybrid scan operator with which a DBMS
-can make use of partially-built indexes for query processing. Our evaluation
-shows that predictive indexing improves the throughput of a DBMS by 3.5--5.2x
-compared to other state-of-the-art indexing approaches. We demonstrate that
-predictive indexing works seamlessly with other lightweight automated physical
-design tuning methods.
-",1,0,0,0,0,0
-17543,Making Deep Q-learning methods robust to time discretization,"  Despite remarkable successes, Deep Reinforcement Learning (DRL) is not robust
-to hyperparameterization, implementation details, or small environment changes
-(Henderson et al. 2017, Zhang et al. 2018). Overcoming such sensitivity is key
-to making DRL applicable to real world problems. In this paper, we identify
-sensitivity to time discretization in near continuous-time environments as a
-critical factor; this covers, e.g., changing the number of frames per second,
-or the action frequency of the controller. Empirically, we find that
-Q-learning-based approaches such as Deep Q- learning (Mnih et al., 2015) and
-Deep Deterministic Policy Gradient (Lillicrap et al., 2015) collapse with small
-time steps. Formally, we prove that Q-learning does not exist in continuous
-time. We detail a principled way to build an off-policy RL algorithm that
-yields similar performances over a wide range of time discretizations, and
-confirm this robustness empirically.
-",1,0,0,1,0,0
-17544,Anomalous current in diffusive ferromagnetic Josephson junctions,"  We demonstrate that in diffusive superconductor/ferromagnet/superconductor
-(S/F/S) junctions a finite, {\it anomalous}, Josephson current can flow even at
-zero phase difference between the S electrodes. The conditions for the
-observation of this effect are non-coplanar magnetization distribution and a
-broken magnetization inversion symmetry of the superconducting current. The
-latter symmetry is intrinsic for the widely used quasiclassical approximation
-and prevent previous works, based on this approximation, from obtaining the
-Josephson anomalous current. We show that this symmetry can be removed by
-introducing spin-dependent boundary conditions for the quasiclassical equations
-at the superconducting/ferromagnet interfaces in diffusive systems. Using this
-recipe we considered generic multilayer magnetic systems and determine the
-ideal experimental conditions in order to maximize the anomalous current.
-",0,1,0,0,0,0
-17545,Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates,"  Although a majority of the theoretical literature in high-dimensional
-statistics has focused on settings which involve fully-observed data, settings
-with missing values and corruptions are common in practice. We consider the
-problems of estimation and of constructing component-wise confidence intervals
-in a sparse high-dimensional linear regression model when some covariates of
-the design matrix are missing completely at random. We analyze a variant of the
-Dantzig selector [9] for estimating the regression model and we use a
-de-biasing argument to construct component-wise confidence intervals. Our first
-main result is to establish upper bounds on the estimation error as a function
-of the model parameters (the sparsity level s, the expected fraction of
-observed covariates $\rho_*$, and a measure of the signal strength
-$\|\beta^*\|_2$). We find that even in an idealized setting where the
-covariates are assumed to be missing completely at random, somewhat
-surprisingly and in contrast to the fully-observed setting, there is a
-dichotomy in the dependence on model parameters and much faster rates are
-obtained if the covariance matrix of the random design is known. To study this
-issue further, our second main contribution is to provide lower bounds on the
-estimation error showing that this discrepancy in rates is unavoidable in a
-minimax sense. We then consider the problem of high-dimensional inference in
-the presence of missing data. We construct and analyze confidence intervals
-using a de-biased estimator. In the presence of missing data, inference is
-complicated by the fact that the de-biasing matrix is correlated with the pilot
-estimator and this necessitates the design of a new estimator and a novel
-analysis. We also complement our mathematical study with extensive simulations
-on synthetic and semi-synthetic data that show the accuracy of our asymptotic
-predictions for finite sample sizes.
-",0,0,0,1,0,0
-17546,Applications of an algorithm for solving Fredholm equations of the first kind,"  In this paper we use an iterative algorithm for solving Fredholm equations of
-the first kind. The basic algorithm is known and is based on an EM algorithm
-when involved functions are non-negative and integrable. With this algorithm we
-demonstrate two examples involving the estimation of a mixing density and a
-first passage time density function involving Brownian motion. We also develop
-the basic algorithm to include functions which are not necessarily non-negative
-and again present illustrations under this scenario. A self contained proof of
-convergence of all the algorithms employed is presented.
-",0,0,1,1,0,0
-17547,Fully symmetric kernel quadrature,"  Kernel quadratures and other kernel-based approximation methods typically
-suffer from prohibitive cubic time and quadratic space complexity in the number
-of function evaluations. The problem arises because a system of linear
-equations needs to be solved. In this article we show that the weights of a
-kernel quadrature rule can be computed efficiently and exactly for up to tens
-of millions of nodes if the kernel, integration domain, and measure are fully
-symmetric and the node set is a union of fully symmetric sets. This is based on
-the observations that in such a setting there are only as many distinct weights
-as there are fully symmetric sets and that these weights can be solved from a
-linear system of equations constructed out of row sums of certain submatrices
-of the full kernel matrix. We present several numerical examples that show
-feasibility, both for a large number of nodes and in high dimensions, of the
-developed fully symmetric kernel quadrature rules. Most prominent of the fully
-symmetric kernel quadrature rules we propose are those that use sparse grids.
-",1,0,1,1,0,0
-17548,Conditional Accelerated Lazy Stochastic Gradient Descent,"  In this work we introduce a conditional accelerated lazy stochastic gradient
-descent algorithm with optimal number of calls to a stochastic first-order
-oracle and convergence rate $O\left(\frac{1}{\varepsilon^2}\right)$ improving
-over the projection-free, Online Frank-Wolfe based stochastic gradient descent
-of Hazan and Kale [2012] with convergence rate
-$O\left(\frac{1}{\varepsilon^4}\right)$.
-",1,0,0,1,0,0
-17549,MMD GAN: Towards Deeper Understanding of Moment Matching Network,"  Generative moment matching network (GMMN) is a deep generative model that
-differs from Generative Adversarial Network (GAN) by replacing the
-discriminator in GAN with a two-sample test based on kernel maximum mean
-discrepancy (MMD). Although some theoretical guarantees of MMD have been
-studied, the empirical performance of GMMN is still not as competitive as that
-of GAN on challenging and large benchmark datasets. The computational
-efficiency of GMMN is also less desirable in comparison with GAN, partially due
-to its requirement for a rather large batch size during the training. In this
-paper, we propose to improve both the model expressiveness of GMMN and its
-computational efficiency by introducing adversarial kernel learning techniques,
-as the replacement of a fixed Gaussian kernel in the original GMMN. The new
-approach combines the key ideas in both GMMN and GAN, hence we name it MMD GAN.
-The new distance measure in MMD GAN is a meaningful loss that enjoys the
-advantage of weak topology and can be optimized via gradient descent with
-relatively small batch sizes. In our evaluation on multiple benchmark datasets,
-including MNIST, CIFAR- 10, CelebA and LSUN, the performance of MMD-GAN
-significantly outperforms GMMN, and is competitive with other representative
-GAN works.
-",1,0,0,1,0,0
-17550,Multipartite entanglement after a quantum quench,"  We study the multipartite entanglement of a quantum many-body system
-undergoing a quantum quench. We quantify multipartite entanglement through the
-quantum Fisher information (QFI) density and we are able to express it after a
-quench in terms of a generalized response function. For pure state initial
-conditions and in the thermodynamic limit, we can express the QFI as the
-fluctuations of an observable computed in the so-called diagonal ensemble. We
-apply the formalism to the dynamics of a quantum Ising chain after a quench in
-the transverse field. In this model the asymptotic state is, in almost all
-cases, more than two-partite entangled. Moreover, starting from the
-ferromagnetic phase, we find a divergence of multipartite entanglement for
-small quenches closely connected to a corresponding divergence of the
-correlation length.
-",0,1,0,0,0,0
-17551,Thermal properties of graphene from path-integral simulations,"  Thermal properties of graphene monolayers are studied by path-integral
-molecular dynamics (PIMD) simulations, which take into account the quantization
-of vibrational modes in the crystalline membrane, and allow one to consider
-anharmonic effects in these properties. This system was studied at temperatures
-in the range from 12 to 2000~K and zero external stress, by describing the
-interatomic interactions through the LCBOPII effective potential. We analyze
-the internal energy and specific heat and compare the results derived from the
-simulations with those yielded by a harmonic approximation for the vibrational
-modes. This approximation turns out to be rather precise up to temperatures of
-about 400~K. At higher temperatures, we observe an influence of the elastic
-energy, due to the thermal expansion of the graphene sheet. Zero-point and
-thermal effects on the in-plane and ""real"" surface of graphene are discussed.
-The thermal expansion coefficient $\alpha$ of the real area is found to be
-positive at all temperatures, in contrast to the expansion coefficient
-$\alpha_p$ of the in-plane area, which is negative at low temperatures, and
-becomes positive for $T \gtrsim$ 1000~K.
-",0,1,0,0,0,0
-17552,Measuring Affectiveness and Effectiveness in Software Systems,"  The summary presented in this paper highlights the results obtained in a
-four-years project aiming at analyzing the development process of software
-artifacts from two points of view: Effectiveness and Affectiveness. The first
-attribute is meant to analyze the productivity of the Open Source Communities
-by measuring the time required to resolve an issue, while the latter provides a
-novel approach for studying the development process by analyzing the
-affectiveness ex-pressed by developers in their comments posted during the
-issue resolution phase. Affectivenes is obtained by measuring Sentiment,
-Politeness and Emotions. All the study presented in this summary are based on
-Jira, one of the most used software repositories.
-",1,0,0,0,0,0
-17553,Intertangled stochastic motifs in networks of excitatory-inhibitory units,"  A stochastic model of excitatory and inhibitory interactions which bears
-universality traits is introduced and studied. The endogenous component of
-noise, stemming from finite size corrections, drives robust inter-nodes
-correlations, that persist at large large distances. Anti-phase synchrony at
-small frequencies is resolved on adjacent nodes and found to promote the
-spontaneous generation of long-ranged stochastic patterns, that invade the
-network as a whole. These patterns are lacking under the idealized
-deterministic scenario, and could provide novel hints on how living systems
-implement and handle a large gallery of delicate computational tasks.
-",0,1,0,0,0,0
-17554,Accurate Computation of the Distribution of Sums of Dependent Log-Normals with Applications to the Black-Scholes Model,"  We present a new Monte Carlo methodology for the accurate estimation of the
-distribution of the sum of dependent log-normal random variables. The
-methodology delivers statistically unbiased estimators for three distributional
-quantities of significant interest in finance and risk management: the left
-tail, or cumulative distribution function, the probability density function,
-and the right tail, or complementary distribution function of the sum of
-dependent log-normal factors. In all of these three cases our methodology
-delivers fast and highly accurate estimators in settings for which existing
-methodology delivers estimators with large variance that tend to underestimate
-the true quantity of interest. We provide insight into the computational
-challenges using theory and numerical experiments, and explain their much wider
-implications for Monte Carlo statistical estimators of rare-event
-probabilities. In particular, we find that theoretically strongly-efficient
-estimators should be used with great caution in practice, because they may
-yield inaccurate results in the pre-limit. Further, this inaccuracy may not be
-detectable from the output of the Monte Carlo simulation, because the
-simulation output may severely underestimate the true variance of the
-estimator.
-",0,0,0,1,0,0
-17555,"The complete unitary dual of non-compact Lie superalgebra su(p,q|m) via the generalised oscillator formalism, and non-compact Young diagrams","  We study the unitary representations of the non-compact real forms of the
-complex Lie superalgebra sl(n|m). Among them, only the real form su(p,q|m)
-(p+q=n) admits nontrivial unitary representations, and all such representations
-are of the highest-weight type (or the lowest-weight type). We extend the
-standard oscillator construction of the unitary representations of non-compact
-Lie superalgebras over standard Fock spaces to generalised Fock spaces which
-allows us to define the action of oscillator determinants raised to non-integer
-powers. We prove that the proposed construction yields all the unitary
-representations including those with continuous labels. The unitary
-representations can be diagrammatically represented by non-compact Young
-diagrams. We apply our general results to the physically important case of
-four-dimensional conformal superalgebra su(2,2|4) and show how it yields
-readily its unitary representations including those corresponding to
-supermultiplets of conformal fields with continuous (anomalous) scaling
-dimensions.
-",0,0,1,0,0,0
-17556,DeepPainter: Painter Classification Using Deep Convolutional Autoencoders,"  In this paper we describe the problem of painter classification, and propose
-a novel approach based on deep convolutional autoencoder neural networks. While
-previous approaches relied on image processing and manual feature extraction
-from paintings, our approach operates on the raw pixel level, without any
-preprocessing or manual feature extraction. We first train a deep convolutional
-autoencoder on a dataset of paintings, and subsequently use it to initialize a
-supervised convolutional neural network for the classification phase.
-The proposed approach substantially outperforms previous methods, improving
-the previous state-of-the-art for the 3-painter classification problem from
-90.44% accuracy (previous state-of-the-art) to 96.52% accuracy, i.e., a 63%
-reduction in error rate.
-",1,0,0,1,0,0
-17557,"Sharp gradient estimate for heat kernels on $RCD^*(K,N)$ metric measure spaces","  In this paper, we will establish an elliptic local Li-Yau gradient estimate
-for weak solutions of the heat equation on metric measure spaces with
-generalized Ricci curvature bounded from below. One of its main applications is
-a sharp gradient estimate for the logarithm of heat kernels. These results seem
-new even for smooth Riemannian manifolds.
-",0,0,1,0,0,0
-17558,Thermodynamic properties of diatomic molecules systems under anharmonic Eckart potential,"  Due to one of the most representative contributions to the energy in diatomic
-molecules being the vibrational, we consider the generalized Morse potential
-(GMP) as one of the typical potential of interaction for one-dimensional
-microscopic systems, which describes local anharmonic effects. From Eckart
-potential (EP) model, it is possible to find a connection with the GMP model,
-as well as obtain the analytical expression for the energy spectrum because it
-is based on $S\,O\left(2,1\right)$ algebras. In this work we find the
-macroscopic properties such as vibrational mean energy $U$, specific heat $C$,
-Helmholtz free energy $F$ and entropy $S$ for a heteronuclear diatomic system,
-along with the exact partition function and its approximation for the high
-temperature region. Finally, we make a comparison between the graphs of some
-thermodynamic functions obtained with the GMP and the Morse potential (MP) for
-$H\,Cl$ molecules.
-",0,1,0,0,0,0
-17559,Effects of ultrasound waves intensity on the removal of Congo red color from the textile industry wastewater by Fe3O4@TiO2 core-shell nanospheres,"  Environmental pollutants, such as colors from the textile industry, affect
-water quality indicators like color, smell, and taste. These substances in the
-water cause the obstruction of filters and membranes and thereby reduce the
-efficiency of advanced water treatment processes. In addition, they are harmful
-to human health because of reaction with disinfectants and production of
-by-products. Iron oxide nanoparticles are considered effective absorbents for
-the removal of pollutants from aqueous environments. In order to increase the
-stability and dispersion, nanospheres with iron oxide core and titanium dioxide
-coating were used in this research and their ability to absorb Congo red color
-was evaluated. Iron oxide-titanium oxide nanospheres were prepared based on the
-coprecipitation method and then their physical properties were determined using
-a tunneling electron microscope (TEM) and an X-ray diffraction device.
-Morphological investigation of the absorbent surface showed that iron
-oxide-titanium oxide nanospheres sized about 5 to 10 nm. X-ray dispersion
-survey also suggested the high purity of the sample. In addition, the
-absorption rate was measured in the presence of ultrasound waves and the
-results indicated that the capacity of the synthesized sample to absorb Congo
-red is greatly dependent on the intensity power of ultrasound waves, as the
-absorption rate reaches 100% at powers above 30 watts.
-",0,1,0,0,0,0
-17560,Enumeration of Tree-like Maps with Arbitrary Number of Vertices,"  This paper provides the generating series for the embedding of tree-like
-graphs of arbitrary number of vertices, accourding to their genus. It applies
-and extends the techniques of Chan, where it was used to give an alternate
-proof of the Goulden and Slofstra formula. Furthermore, this greatly
-generalizes the famous Harer-Zagier formula, which computes the Euler
-characteristic of the moduli space of curves, and is equivalent to the
-computation of one vertex maps.
-",0,0,1,0,0,0
-17561,Depth resolved chemical speciation of a superlattice structure,"  We report results of simultaneous x-ray reflectivity and grazing incidence
-x-ray fluorescence measurements in combination with x-ray standing wave
-assisted depth resolved near edge x-ray absorption measurements to reveal new
-insights on chemical speciation of W in a W-B4C superlattice structure.
-Interestingly, our results show existence of various unusual electronic states
-for the W atoms especially those sitting at the surface and interface boundary
-of a thin film medium as compared to that of the bulk. These observations are
-found to be consistent with the results obtained using first principles
-calculations. Unlike the conventional x-ray absorption measurements the present
-approach has an advantage that it permits the determination of depth resolved
-chemical nature of an element in the thin layered materials at atomic length
-scale resolutions.
-",0,1,0,0,0,0
-17562,Optospintronics in graphene via proximity coupling,"  The observation of micron size spin relaxation makes graphene a promising
-material for applications in spintronics requiring long distance spin
-communication. However, spin dependent scatterings at the contact/graphene
-interfaces affect the spin injection efficiencies and hence prevent the
-material from achieving its full potential. While this major issue could be
-eliminated by nondestructive direct optical spin injection schemes, graphenes
-intrinsically low spin orbit coupling strength and optical absorption place an
-obstacle in their realization. We overcome this challenge by creating sharp
-artificial interfaces between graphene and WSe2 monolayers. Application of a
-circularly polarized light activates the spin polarized charge carriers in the
-WSe2 layer due to its spin coupled valley selective absorption. These carriers
-diffuse into the superjacent graphene layer, transport over a 3.5 um distance,
-and are finally detected electrically using BN/Co contacts in a non local
-geometry. Polarization dependent measurements confirm the spin origin of the
-non local signal.
-",0,1,0,0,0,0
-17563,Control strategy to limit duty cycle impact of earthquakes on the LIGO gravitational-wave detectors,"  Advanced gravitational-wave detectors such as the Laser Interferometer
-Gravitational-Wave Observatories (LIGO) require an unprecedented level of
-isolation from the ground. When in operation, they are expected to observe
-changes in the space-time continuum of less than one thousandth of the diameter
-of a proton. Strong teleseismic events like earthquakes disrupt the proper
-functioning of the detectors, and result in a loss of data until the detectors
-can be returned to their operating states. An earthquake early-warning system,
-as well as a prediction model have been developed to help understand the impact
-of earthquakes on LIGO. This paper describes a control strategy to use this
-early-warning system to reduce the LIGO downtime by 30%. It also presents a
-plan to implement this new earthquake configuration in the LIGO automation
-system.
-",0,1,0,0,0,0
-17564,Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO,"  The design of spacecraft trajectories for missions visiting multiple
-celestial bodies is here framed as a multi-objective bilevel optimization
-problem. A comparative study is performed to assess the performance of
-different Beam Search algorithms at tackling the combinatorial problem of
-finding the ideal sequence of bodies. Special focus is placed on the
-development of a new hybridization between Beam Search and the Population-based
-Ant Colony Optimization algorithm. An experimental evaluation shows all
-algorithms achieving exceptional performance on a hard benchmark problem. It is
-found that a properly tuned deterministic Beam Search always outperforms the
-remaining variants. Beam P-ACO, however, demonstrates lower parameter
-sensitivity, while offering superior worst-case performance. Being an anytime
-algorithm, it is then found to be the preferable choice for certain practical
-applications.
-",1,1,0,0,0,0
-17565,Types and unitary representations of reductive p-adic groups,"  We prove that for every Bushnell-Kutzko type that satisfies a certain
-rigidity assumption, the equivalence of categories between the corresponding
-Bernstein component and the category of modules for the Hecke algebra of the
-type induces a bijection between irreducible unitary representations in the two
-categories. This is a generalization of the unitarity criterion of Barbasch and
-Moy for representations with Iwahori fixed vectors.
-",0,0,1,0,0,0
-17566,Average values of L-functions in even characteristic,"  Let $k = \mathbb{F}_{q}(T)$ be the rational function field over a finite
-field $\mathbb{F}_{q}$, where $q$ is a power of $2$. In this paper we solve the
-problem of averaging the quadratic $L$-functions $L(s, \chi_{u})$ over
-fundamental discriminants. Any separable quadratic extension $K$ of $k$ is of
-the form $K = k(x_{u})$, where $x_{u}$ is a zero of $X^2+X+u=0$ for some $u\in
-k$. We characterize the family $\mathcal I$ (resp. $\mathcal F$, $\mathcal F'$)
-of rational functions $u\in k$ such that any separable quadratic extension $K$
-of $k$ in which the infinite prime $\infty = (1/T)$ of $k$ ramifies (resp.
-splits, is inert) can be written as $K = k(x_{u})$ with a unique $u\in\mathcal
-I$ (resp. $u\in\mathcal F$, $u\in\mathcal F'$). For almost all $s\in\mathbb C$
-with ${\rm Re}(s)\ge \frac{1}2$, we obtain the asymptotic formulas for the
-summation of $L(s,\chi_{u})$ over all $k(x_{u})$ with $u\in \mathcal I$, all
-$k(x_{u})$ with $u\in \mathcal F$ or all $k(x_{u})$ with $u\in \mathcal F'$ of
-given genus. As applications, we obtain the asymptotic mean value formulas of
-$L$-functions at $s=\frac{1}2$ and $s=1$ and the asymptotic mean value formulas
-of the class number $h_{u}$ or the class number times regulator $h_{u} R_{u}$.
-",0,0,1,0,0,0
-17567,"Decoupled molecules with binding polynomials of bidegree (n,2)","  We present a result on the number of decoupled molecules for systems binding
-two different types of ligands. In the case of $n$ and $2$ binding sites
-respectively, we show that, generically, there are $2(n!)^{2}$ decoupled
-molecules with the same binding polynomial. For molecules with more binding
-sites for the second ligand, we provide computational results.
-",1,1,0,0,0,0
-17568,Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization,"  Learning to remember long sequences remains a challenging task for recurrent
-neural networks. Register memory and attention mechanisms were both proposed to
-resolve the issue with either high computational cost to retain memory
-differentiability, or by discounting the RNN representation learning towards
-encoding shorter local contexts than encouraging long sequence encoding.
-Associative memory, which studies the compression of multiple patterns in a
-fixed size memory, were rarely considered in recent years. Although some recent
-work tries to introduce associative memory in RNN and mimic the energy decay
-process in Hopfield nets, it inherits the shortcoming of rule-based memory
-updates, and the memory capacity is limited. This paper proposes a method to
-learn the memory update rule jointly with task objective to improve memory
-capacity for remembering long sequences. Also, we propose an architecture that
-uses multiple such associative memory for more complex input encoding. We
-observed some interesting facts when compared to other RNN architectures on
-some well-studied sequence learning tasks.
-",1,0,0,1,0,0
-17569,McDiarmid Drift Detection Methods for Evolving Data Streams,"  Increasingly, Internet of Things (IoT) domains, such as sensor networks,
-smart cities, and social networks, generate vast amounts of data. Such data are
-not only unbounded and rapidly evolving. Rather, the content thereof
-dynamically evolves over time, often in unforeseen ways. These variations are
-due to so-called concept drifts, caused by changes in the underlying data
-generation mechanisms. In a classification setting, concept drift causes the
-previously learned models to become inaccurate, unsafe and even unusable.
-Accordingly, concept drifts need to be detected, and handled, as soon as
-possible. In medical applications and emergency response settings, for example,
-change in behaviours should be detected in near real-time, to avoid potential
-loss of life. To this end, we introduce the McDiarmid Drift Detection Method
-(MDDM), which utilizes McDiarmid's inequality in order to detect concept drift.
-The MDDM approach proceeds by sliding a window over prediction results, and
-associate window entries with weights. Higher weights are assigned to the most
-recent entries, in order to emphasize their importance. As instances are
-processed, the detection algorithm compares a weighted mean of elements inside
-the sliding window with the maximum weighted mean observed so far. A
-significant difference between the two weighted means, upper-bounded by the
-McDiarmid inequality, implies a concept drift. Our extensive experimentation
-against synthetic and real-world data streams show that our novel method
-outperforms the state-of-the-art. Specifically, MDDM yields shorter detection
-delays as well as lower false negative rates, while maintaining high
-classification accuracies.
-",1,0,0,1,0,0
-17570,Yield in Amorphous Solids: The Ant in the Energy Landscape Labyrinth,"  It has recently been shown that yield in amorphous solids under oscillatory
-shear is a dynamical transition from asymptotically periodic to asymptotically
-chaotic, diffusive dynamics. However, the type and universality class of this
-transition are still undecided. Here we show that the diffusive behavior of the
-vector of coordinates of the particles comprising an amorphous solid when
-subject to oscillatory shear, is analogous to that of a particle diffusing in a
-percolating lattice, the so-called ""ant in the labyrinth"" problem, and that
-yield corresponds to a percolation transition in the lattice. We explain this
-as a transition in the connectivity of the energy landscape, which affects the
-phase-space regions accessible to the coordinate vector for a given maximal
-strain amplitude. This transition provides a natural explanation to the
-observed limit-cycles, periods larger than one and diverging time-scales at
-yield.
-",0,1,0,0,0,0
-17571,Statistical methods in astronomy,"  We present a review of data types and statistical methods often encountered
-in astronomy. The aim is to provide an introduction to statistical applications
-in astronomy for statisticians and computer scientists. We highlight the
-complex, often hierarchical, nature of many astronomy inference problems and
-advocate for cross-disciplinary collaborations to address these challenges.
-",0,1,0,1,0,0
-17572,A note on some algebraic trapdoors for block ciphers,"  We provide sufficient conditions to guarantee that a translation based cipher
-is not vulnerable with respect to the partition-based trapdoor. This trapdoor
-has been introduced, recently, by Bannier et al. (2016) and it generalizes that
-introduced by Paterson in 1999. Moreover, we discuss the fact that studying the
-group generated by the round functions of a block cipher may not be sufficient
-to guarantee security against these trapdoors for the cipher.
-",1,0,1,0,0,0
-17573,Bi-Demographic Changes and Current Account using SVAR Modeling,"  The paper, as a new contribution, aims to explore the impacts of
-bi-demographic structure on the current account and growth. By using a SVAR
-modeling, we track the dynamic impacts between the underlying variables of the
-Saudi economy. New insights have been developed to study the interrelations
-between population growth, current account and economic growth inside the
-neoclassical theory of population. The long-run net impact on economic growth
-of the bi-population growth is negative, due to the typically lower skill sets
-of the immigrant labor population. Besides, the negative long-run contribution
-of immigrant workers to the current account growth largely exceeds that of
-contributions from the native population, because of the increasing levels of
-remittance outflows from the country. We find that a positive shock in
-immigration leads to a negative impact on native active age ratio. Thus, the
-immigrants appear to be more substitutes than complements for native workers.
-",0,0,0,0,0,1
-17574,Supervised Machine Learning for Signals Having RRC Shaped Pulses,"  Classification performances of the supervised machine learning techniques
-such as support vector machines, neural networks and logistic regression are
-compared for modulation recognition purposes. The simple and robust features
-are used to distinguish continuous-phase FSK from QAM-PSK signals. Signals
-having root-raised-cosine shaped pulses are simulated in extreme noisy
-conditions having joint impurities of block fading, lack of symbol and sampling
-synchronization, carrier offset, and additive white Gaussian noise. The
-features are based on sample mean and sample variance of the imaginary part of
-the product of two consecutive complex signal values.
-",1,0,0,0,0,0
-17575,End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks,"  We propose an autoencoding sequence-based transceiver for communication over
-dispersive channels with intensity modulation and direct detection (IM/DD),
-designed as a bidirectional deep recurrent neural network (BRNN). The receiver
-uses a sliding window technique to allow for efficient data stream estimation.
-We find that this sliding window BRNN (SBRNN), based on end-to-end deep
-learning of the communication system, achieves a significant bit-error-rate
-reduction at all examined distances in comparison to previous block-based
-autoencoders implemented as feed-forward neural networks (FFNNs), leading to an
-increase of the transmission distance. We also compare the end-to-end SBRNN
-with a state-of-the-art IM/DD solution based on two level pulse amplitude
-modulation with an FFNN receiver, simultaneously processing multiple received
-symbols and approximating nonlinear Volterra equalization. Our results show
-that the SBRNN outperforms such systems at both 42 and 84\,Gb/s, while training
-fewer parameters. Our novel SBRNN design aims at tailoring the end-to-end deep
-learning-based systems for communication over nonlinear channels with memory,
-such as the optical IM/DD fiber channel.
-",1,0,0,1,0,0
-17576,Effective difference elimination and Nullstellensatz,"  We prove effective Nullstellensatz and elimination theorems for difference
-equations in sequence rings. More precisely, we compute an explicit function of
-geometric quantities associated to a system of difference equations (and these
-geometric quantities may themselves be bounded by a function of the number of
-variables, the order of the equations, and the degrees of the equations) so
-that for any system of difference equations in variables $\mathbf{x} = (x_1,
-\ldots, x_m)$ and $\mathbf{u} = (u_1, \ldots, u_r)$, if these equations have
-any nontrivial consequences in the $\mathbf{x}$ variables, then such a
-consequence may be seen algebraically considering transforms up to the order of
-our bound. Specializing to the case of $m = 0$, we obtain an effective method
-to test whether a given system of difference equations is consistent.
-",0,0,1,0,0,0
-17577,A note on primitive $1-$normal elements over finite fields,"  Let $q$ be a prime power of a prime $p$, $n$ a positive integer and $\mathbb
-F_{q^n}$ the finite field with $q^n$ elements. The $k-$normal elements over
-finite fields were introduced and characterized by Huczynska et al (2013).
-Under the condition that $n$ is not divisible by $p$, they obtained an
-existence result on primitive $1-$normal elements of $\mathbb F_{q^n}$ over
-$\mathbb F_q$ for $q>2$. In this note, we extend their result to the excluded
-case $q=2$.
-",0,0,1,0,0,0
-17578,Sparse Coding Predicts Optic Flow Specificities of Zebrafish Pretectal Neurons,"  Zebrafish pretectal neurons exhibit specificities for large-field optic flow
-patterns associated with rotatory or translatory body motion. We investigate
-the hypothesis that these specificities reflect the input statistics of natural
-optic flow. Realistic motion sequences were generated using computer graphics
-simulating self-motion in an underwater scene. Local retinal motion was
-estimated with a motion detector and encoded in four populations of
-directionally tuned retinal ganglion cells, represented as two signed input
-variables. This activity was then used as input into one of two learning
-networks: a sparse coding network (competitive learning) and backpropagation
-network (supervised learning). Both simulations develop specificities for optic
-flow which are comparable to those found in a neurophysiological study (Kubo et
-al. 2014), and relative frequencies of the various neuronal responses are best
-modeled by the sparse coding approach. We conclude that the optic flow neurons
-in the zebrafish pretectum do reflect the optic flow statistics. The predicted
-vectorial receptive fields show typical optic flow fields but also ""Gabor"" and
-dipole-shaped patterns that likely reflect difference fields needed for
-reconstruction by linear superposition.
-",0,0,0,0,1,0
-17579,Revisiting the problem of audio-based hit song prediction using convolutional neural networks,"  Being able to predict whether a song can be a hit has impor- tant
-applications in the music industry. Although it is true that the popularity of
-a song can be greatly affected by exter- nal factors such as social and
-commercial influences, to which degree audio features computed from musical
-signals (whom we regard as internal factors) can predict song popularity is an
-interesting research question on its own. Motivated by the recent success of
-deep learning techniques, we attempt to ex- tend previous work on hit song
-prediction by jointly learning the audio features and prediction models using
-deep learning. Specifically, we experiment with a convolutional neural net-
-work model that takes the primitive mel-spectrogram as the input for feature
-learning, a more advanced JYnet model that uses an external song dataset for
-supervised pre-training and auto-tagging, and the combination of these two
-models. We also consider the inception model to characterize audio infor-
-mation in different scales. Our experiments suggest that deep structures are
-indeed more accurate than shallow structures in predicting the popularity of
-either Chinese or Western Pop songs in Taiwan. We also use the tags predicted
-by JYnet to gain insights into the result of different models.
-",1,0,0,1,0,0
-17580,Natural Language Multitasking: Analyzing and Improving Syntactic Saliency of Hidden Representations,"  We train multi-task autoencoders on linguistic tasks and analyze the learned
-hidden sentence representations. The representations change significantly when
-translation and part-of-speech decoders are added. The more decoders a model
-employs, the better it clusters sentences according to their syntactic
-similarity, as the representation space becomes less entangled. We explore the
-structure of the representation space by interpolating between sentences, which
-yields interesting pseudo-English sentences, many of which have recognizable
-syntactic structure. Lastly, we point out an interesting property of our
-models: The difference-vector between two sentences can be added to change a
-third sentence with similar features in a meaningful way.
-",0,0,0,1,0,0
-17581,Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data,"  Time series forecasting is a crucial component of many important
-applications, ranging from forecasting the stock markets to energy load
-prediction. The high-dimensionality, velocity and variety of the data collected
-in these applications pose significant and unique challenges that must be
-carefully addressed for each of them. In this work, a novel Temporal Logistic
-Neural Bag-of-Features approach, that can be used to tackle these challenges,
-is proposed. The proposed method can be effectively combined with deep neural
-networks, leading to powerful deep learning models for time series analysis.
-However, combining existing BoF formulations with deep feature extractors pose
-significant challenges: the distribution of the input features is not
-stationary, tuning the hyper-parameters of the model can be especially
-difficult and the normalizations involved in the BoF model can cause
-significant instabilities during the training process. The proposed method is
-capable of overcoming these limitations by a employing a novel adaptive scaling
-mechanism and replacing the classical Gaussian-based density estimation
-involved in the regular BoF model with a logistic kernel. The effectiveness of
-the proposed approach is demonstrated using extensive experiments on a
-large-scale financial time series dataset that consists of more than 4 million
-limit orders.
-",1,0,0,1,0,1
-17582,Extended Bose Hubbard model for two leg ladder systems in artificial magnetic fields,"  We investigate the ground state properties of ultracold atoms with long range
-interactions trapped in a two leg ladder configuration in the presence of an
-artificial magnetic field. Using a Gross-Pitaevskii approach and a mean field
-Gutzwiller variational method, we explore both the weakly interacting and
-strongly interacting regime, respectively. We calculate the boundaries between
-the density-wave/supersolid and the Mott-insulator/superfluid phases as a
-function of magnetic flux and uncover regions of supersolidity. The mean-field
-results are confirmed by numerical simulations using a cluster mean field
-approach.
-",0,1,0,0,0,0
-17583,Insensitivity of The Distance Ladder Hubble Constant Determination to Cepheid Calibration Modeling Choices,"  Recent determination of the Hubble constant via Cepheid-calibrated supernovae
-by \citet{riess_2.4_2016} (R16) find $\sim 3\sigma$ tension with inferences
-based on cosmic microwave background temperature and polarization measurements
-from $Planck$. This tension could be an indication of inadequacies in the
-concordance $\Lambda$CDM model. Here we investigate the possibility that the
-discrepancy could instead be due to systematic bias or uncertainty in the
-Cepheid calibration step of the distance ladder measurement by R16. We consider
-variations in total-to-selective extinction of Cepheid flux as a function of
-line-of-sight, hidden structure in the period-luminosity relationship, and
-potentially different intrinsic color distributions of Cepheids as a function
-of host galaxy. Considering all potential sources of error, our final
-determination of $H_0 = 73.3 \pm 1.7~{\rm km/s/Mpc}$ (not including systematic
-errors from the treatment of geometric distances or Type Ia Supernovae) shows
-remarkable robustness and agreement with R16. We conclude systematics from the
-modeling of Cepheid photometry, including Cepheid selection criteria, cannot
-explain the observed tension between Cepheid-variable and CMB-based inferences
-of the Hubble constant. Considering a `model-independent' approach to relating
-Cepheids in galaxies with known distances to Cepheids in galaxies hosting a
-Type Ia supernova and finding agreement with the R16 result, we conclude no
-generalization of the model relating anchor and host Cepheid magnitude
-measurements can introduce significant bias in the $H_0$ inference.
-",0,1,0,0,0,0
-17584,Phrase-based Image Captioning with Hierarchical LSTM Model,"  Automatic generation of caption to describe the content of an image has been
-gaining a lot of research interests recently, where most of the existing works
-treat the image caption as pure sequential data. Natural language, however
-possess a temporal hierarchy structure, with complex dependencies between each
-subsequence. In this paper, we propose a phrase-based hierarchical Long
-Short-Term Memory (phi-LSTM) model to generate image description. In contrast
-to the conventional solutions that generate caption in a pure sequential
-manner, our proposed model decodes image caption from phrase to sentence. It
-consists of a phrase decoder at the bottom hierarchy to decode noun phrases of
-variable length, and an abbreviated sentence decoder at the upper hierarchy to
-decode an abbreviated form of the image description. A complete image caption
-is formed by combining the generated phrases with sentence during the inference
-stage. Empirically, our proposed model shows a better or competitive result on
-the Flickr8k, Flickr30k and MS-COCO datasets in comparison to the state-of-the
-art models. We also show that our proposed model is able to generate more novel
-captions (not seen in the training data) which are richer in word contents in
-all these three datasets.
-",1,0,0,0,0,0
-17585,The three-dimensional structure of swirl-switching in bent pipe flow,"  Swirl-switching is a low-frequency oscillatory phenomenon which affects the
-Dean vortices in bent pipes and may cause fatigue in piping systems. Despite
-thirty years worth of research, the mechanism that causes these oscillations
-and the frequencies that characterise them remain unclear. Here we show that a
-three-dimensional wave-like structure is responsible for the low-frequency
-switching of the dominant Dean vortex. The present study, performed via direct
-numerical simulation, focuses on the turbulent flow through a 90 \degree pipe
-bend preceded and followed by straight pipe segments. A pipe with curvature 0.3
-(defined as ratio between pipe radius and bend radius) is studied for a bulk
-Reynolds number Re = 11 700, corresponding to a friction Reynolds number
-Re_\tau \approx 360. Synthetic turbulence is generated at the inflow section
-and used instead of the classical recycling method in order to avoid the
-interference between recycling and swirl-switching frequencies. The flow field
-is analysed by three-dimensional proper orthogonal decomposition (POD) which
-for the first time allows the identification of the source of swirl-switching:
-a wave-like structure that originates in the pipe bend. Contrary to some
-previous studies, the flow in the upstream pipe does not show any direct
-influence on the swirl-switching modes. Our analysis further shows that a
-three- dimensional characterisation of the modes is crucial to understand the
-mechanism, and that reconstructions based on 2D POD modes are incomplete.
-",0,1,0,0,0,0
-17586,InfoVAE: Information Maximizing Variational Autoencoders,"  A key advance in learning generative models is the use of amortized inference
-distributions that are jointly trained with the models. We find that existing
-training objectives for variational autoencoders can lead to inaccurate
-amortized inference distributions and, in some cases, improving the objective
-provably degrades the inference quality. In addition, it has been observed that
-variational autoencoders tend to ignore the latent variables when combined with
-a decoding distribution that is too flexible. We again identify the cause in
-existing training criteria and propose a new class of objectives (InfoVAE) that
-mitigate these problems. We show that our model can significantly improve the
-quality of the variational posterior and can make effective use of the latent
-features regardless of the flexibility of the decoding distribution. Through
-extensive qualitative and quantitative analyses, we demonstrate that our models
-outperform competing approaches on multiple performance metrics.
-",1,0,0,1,0,0
-17587,Between-class Learning for Image Classification,"  In this paper, we propose a novel learning method for image classification
-called Between-Class learning (BC learning). We generate between-class images
-by mixing two images belonging to different classes with a random ratio. We
-then input the mixed image to the model and train the model to output the
-mixing ratio. BC learning has the ability to impose constraints on the shape of
-the feature distributions, and thus the generalization ability is improved. BC
-learning is originally a method developed for sounds, which can be digitally
-mixed. Mixing two image data does not appear to make sense; however, we argue
-that because convolutional neural networks have an aspect of treating input
-data as waveforms, what works on sounds must also work on images. First, we
-propose a simple mixing method using internal divisions, which surprisingly
-proves to significantly improve performance. Second, we propose a mixing method
-that treats the images as waveforms, which leads to a further improvement in
-performance. As a result, we achieved 19.4% and 2.26% top-1 errors on
-ImageNet-1K and CIFAR-10, respectively.
-",1,0,0,1,0,0
-17588,Girsanov reweighting for path ensembles and Markov state models,"  The sensitivity of molecular dynamics on changes in the potential energy
-function plays an important role in understanding the dynamics and function of
-complex molecules.We present a method to obtain path ensemble averages of a
-perturbed dynamics from a set of paths generated by a reference dynamics. It is
-based on the concept of path probability measure and the Girsanov theorem, a
-result from stochastic analysis to estimate a change of measure of a path
-ensemble. Since Markov state models (MSM) of the molecular dynamics can be
-formulated as a combined phase-space and path ensemble average, the method can
-be extended toreweight MSMs by combining it with a reweighting of the Boltzmann
-distribution. We demonstrate how to efficiently implement the Girsanov
-reweighting in a molecular dynamics simulation program by calculating parts of
-the reweighting factor ""on the fly"" during the simulation, and we benchmark the
-method on test systems ranging from a two-dimensional diffusion process to an
-artificial many-body system and alanine dipeptide and valine dipeptide in
-implicit and explicit water. The method can be used to study the sensitivity of
-molecular dynamics on external perturbations as well as to reweight
-trajectories generated by enhanced sampling schemes to the original dynamics.
-",0,1,0,0,0,0
-17589,Coordination of multi-agent systems via asynchronous cloud communication,"  In this work we study a multi-agent coordination problem in which agents are
-only able to communicate with each other intermittently through a cloud server.
-To reduce the amount of required communication, we develop a self-triggered
-algorithm that allows agents to communicate with the cloud only when necessary
-rather than at some fixed period. Unlike the vast majority of similar works
-that propose distributed event- and/or self-triggered control laws, this work
-doesn't assume agents can be ""listening"" continuously. In other words, when an
-event is triggered by one agent, neighboring agents will not be aware of this
-until the next time they establish communication with the cloud themselves.
-Using a notion of ""promises"" about future control inputs, agents are able to
-keep track of higher quality estimates about their neighbors allowing them to
-stay disconnected from the cloud for longer periods of time while still
-guaranteeing a positive contribution to the global task. We prove that our
-self-triggered coordination algorithm guarantees that the system asymptotically
-reaches the set of desired states. Simulations illustrate our results.
-",1,0,1,0,0,0
-17590,PatternListener: Cracking Android Pattern Lock Using Acoustic Signals,"  Pattern lock has been widely used for authentication to protect user privacy
-on mobile devices (e.g., smartphones and tablets). Given its pervasive usage,
-the compromise of pattern lock could lead to serious consequences. Several
-attacks have been constructed to crack the lock. However, these approaches
-require the attackers to either be physically close to the target device or be
-able to manipulate the network facilities (e.g., WiFi hotspots) used by the
-victims. Therefore, the effectiveness of the attacks is significantly impacted
-by the environment of mobile devices. Also, these attacks are not scalable
-since they cannot easily infer unlock patterns of a large number of devices.
-Motivated by an observation that fingertip motions on the screen of a mobile
-device can be captured by analyzing surrounding acoustic signals on it, we
-propose PatternListener, a novel acoustic attack that cracks pattern lock by
-analyzing imperceptible acoustic signals reflected by the fingertip. It
-leverages speakers and microphones of the victim's device to play imperceptible
-audio and record the acoustic signals reflected by the fingertip. In
-particular, it infers each unlock pattern by analyzing individual lines that
-compose the pattern and are the trajectories of the fingertip. We propose
-several algorithms to construct signal segments according to the captured
-signals for each line and infer possible candidates of each individual line
-according to the signal segments. Finally, we map all line candidates into grid
-patterns and thereby obtain the candidates of the entire unlock pattern. We
-implement a PatternListener prototype by using off-the-shelf smartphones and
-thoroughly evaluate it using 130 unique patterns. The real experimental results
-demonstrate that PatternListener can successfully exploit over 90% patterns
-within five attempts.
-",1,0,0,0,0,0
-17591,Marginal Release Under Local Differential Privacy,"  Many analysis and machine learning tasks require the availability of marginal
-statistics on multidimensional datasets while providing strong privacy
-guarantees for the data subjects. Applications for these statistics range from
-finding correlations in the data to fitting sophisticated prediction models. In
-this paper, we provide a set of algorithms for materializing marginal
-statistics under the strong model of local differential privacy. We prove the
-first tight theoretical bounds on the accuracy of marginals compiled under each
-approach, perform empirical evaluation to confirm these bounds, and evaluate
-them for tasks such as modeling and correlation testing. Our results show that
-releasing information based on (local) Fourier transformations of the input is
-preferable to alternatives based directly on (local) marginals.
-",1,0,0,0,0,0
-17592,A possible flyby anomaly for Juno at Jupiter,"  In the last decades there have been an increasing interest in improving the
-accuracy of spacecraft navigation and trajectory data. In the course of this
-plan some anomalies have been found that cannot, in principle, be explained in
-the context of the most accurate orbital models including all known effects
-from classical dynamics and general relativity. Of particular interest for its
-puzzling nature, and the lack of any accepted explanation for the moment, is
-the flyby anomaly discovered in some spacecraft flybys of the Earth over the
-course of twenty years. This anomaly manifest itself as the impossibility of
-matching the pre and post-encounter Doppler tracking and ranging data within a
-single orbit but, on the contrary, a difference of a few mm$/$s in the
-asymptotic velocities is required to perform the fitting.
-Nevertheless, no dedicated missions have been carried out to elucidate the
-origin of this phenomenon with the objective either of revising our
-understanding of gravity or to improve the accuracy of spacecraft Doppler
-tracking by revealing a conventional origin.
-With the occasion of the Juno mission arrival at Jupiter and the close flybys
-of this planet, that are currently been performed, we have developed an orbital
-model suited to the time window close to the perijove. This model shows that an
-anomalous acceleration of a few mm$/$s$^2$ is also present in this case. The
-chance for overlooked conventional or possible unconventional explanations is
-discussed.
-",0,1,0,0,0,0
-17593,Crossover between various initial conditions in KPZ growth: flat to stationary,"  We conjecture the universal probability distribution at large time for the
-one-point height in the 1D Kardar-Parisi-Zhang (KPZ) stochastic growth
-universality class, with initial conditions interpolating from any one of the
-three main classes (droplet, flat, stationary) on the left, to another on the
-right, allowing for drifts and also for a step near the origin. The result is
-obtained from a replica Bethe ansatz calculation starting from the KPZ
-continuum equation, together with a ""decoupling assumption"" in the large time
-limit. Some cases are checked to be equivalent to previously known results from
-other models in the same class, which provides a test of the method, others
-appear to be new. In particular we obtain the crossover distribution between
-flat and stationary initial conditions (crossover from Airy$_1$ to Airy$_{\rm
-stat}$) in a simple compact form.
-",0,1,1,0,0,0
-17594,Multimodel Response Assessment for Monthly Rainfall Distribution in Some Selected Indian Cities Using Best Fit Probability as a Tool,"  We carry out a study of the statistical distribution of rainfall
-precipitation data for 20 cites in India. We have determined the best-fit
-probability distribution for these cities from the monthly precipitation data
-spanning 100 years of observations from 1901 to 2002. To fit the observed data,
-we considered 10 different distributions. The efficacy of the fits for these
-distributions was evaluated using four empirical non-parametric goodness-of-fit
-tests namely Kolmogorov-Smirnov, Anderson-Darling, Chi-Square, Akaike
-information criterion, and Bayesian Information criterion. Finally, the
-best-fit distribution using each of these tests were reported, by combining the
-results from the model comparison tests. We then find that for most of the
-cities, Generalized Extreme-Value Distribution or Inverse Gaussian Distribution
-most adequately fits the observed data.
-",0,1,0,1,0,0
-17595,Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing,"  Bin Packing problems have been widely studied because of their broad
-applications in different domains. Known as a set of NP-hard problems, they
-have different vari- ations and many heuristics have been proposed for
-obtaining approximate solutions. Specifically, for the 1D variable sized bin
-packing problem, the two key sets of optimization heuristics are the bin
-assignment and the bin allocation. Usually the performance of a single static
-optimization heuristic can not beat that of a dynamic one which is tailored for
-each bin packing instance. Building such an adaptive system requires modeling
-the relationship between bin features and packing perform profiles. The primary
-drawbacks of traditional AI machine learnings for this task are the natural
-limitations of feature engineering, such as the curse of dimensionality and
-feature selection quality. We introduce a deep learning approach to overcome
-the drawbacks by applying a large training data set, auto feature selection and
-fast, accurate labeling. We show in this paper how to build such a system by
-both theoretical formulation and engineering practices. Our prediction system
-achieves up to 89% training accuracy and 72% validation accuracy to select the
-best heuristic that can generate a better quality bin packing solution.
-",1,0,0,1,0,0
-17596,Surges of collective human activity emerge from simple pairwise correlations,"  Human populations exhibit complex behaviors---characterized by long-range
-correlations and surges in activity---across a range of social, political, and
-technological contexts. Yet it remains unclear where these collective behaviors
-come from, or if there even exists a set of unifying principles. Indeed,
-existing explanations typically rely on context-specific mechanisms, such as
-traffic jams driven by work schedules or spikes in online traffic induced by
-significant events. However, analogies with statistical mechanics suggest a
-more general mechanism: that collective patterns can emerge organically from
-fine-scale interactions within a population. Here, across four different modes
-of human activity, we show that the simplest correlations in a
-population---those between pairs of individuals---can yield accurate
-quantitative predictions for the large-scale behavior of the entire population.
-To quantify the minimal consequences of pairwise correlations, we employ the
-principle of maximum entropy, making our description equivalent to an Ising
-model whose interactions and external fields are notably calculated from past
-observations of population activity. In addition to providing accurate
-quantitative predictions, we show that the topology of learned Ising
-interactions resembles the network of inter-human communication within a
-population. Together, these results demonstrate that fine-scale correlations
-can be used to predict large-scale social behaviors, a perspective that has
-critical implications for modeling and resource allocation in human
-populations.
-",1,0,0,0,0,0
-17597,Semantically Enhanced Dynamic Bayesian Network for Detecting Sepsis Mortality Risk in ICU Patients with Infection,"  Although timely sepsis diagnosis and prompt interventions in Intensive Care
-Unit (ICU) patients are associated with reduced mortality, early clinical
-recognition is frequently impeded by non-specific signs of infection and
-failure to detect signs of sepsis-induced organ dysfunction in a constellation
-of dynamically changing physiological data. The goal of this work is to
-identify patient at risk of life-threatening sepsis utilizing a data-centered
-and machine learning-driven approach. We derive a mortality risk predictive
-dynamic Bayesian network (DBN) guided by a customized sepsis knowledgebase and
-compare the predictive accuracy of the derived DBN with the Sepsis-related
-Organ Failure Assessment (SOFA) score, the Quick SOFA (qSOFA) score, the
-Simplified Acute Physiological Score (SAPS-II) and the Modified Early Warning
-Score (MEWS) tools.
-A customized sepsis ontology was used to derive the DBN node structure and
-semantically characterize temporal features derived from both structured
-physiological data and unstructured clinical notes. We assessed the performance
-in predicting mortality risk of the DBN predictive model and compared
-performance to other models using Receiver Operating Characteristic (ROC)
-curves, area under curve (AUROC), calibration curves, and risk distributions.
-The derived dataset consists of 24,506 ICU stays from 19,623 patients with
-evidence of suspected infection, with 2,829 patients deceased at discharge. The
-DBN AUROC was found to be 0.91, which outperformed the SOFA (0.843), qSOFA
-(0.66), MEWS (0.73), and SAPS-II (0.77) scoring tools. Continuous Net
-Reclassification Index and Integrated Discrimination Improvement analysis
-supported the superiority DBN. Compared with conventional rule-based risk
-scoring tools, the sepsis knowledgebase-driven DBN algorithm offers improved
-performance for predicting mortality of infected patients in ICUs.
-",0,0,0,1,0,0
-17598,Hölder regularity of viscosity solutions of some fully nonlinear equations in the Heisenberg group,"  In this paper we prove the Hölder regularity of bounded, uniformly
-continuous, viscosity solutions of some degenerate fully nonlinear equations in
-the Heisenberg group.
-",0,0,1,0,0,0
-17599,Normal form for transverse instability of the line soliton with a nearly critical speed of propagation,"  There exists a critical speed of propagation of the line solitons in the
-Zakharov-Kuznetsov (ZK) equation such that small transversely periodic
-perturbations are unstable for line solitons with larger-than-critical speeds
-and orbitally stable for those with smaller-than-critical speeds. The normal
-form for transverse instability of the line soliton with a nearly critical
-speed of propagation is derived by means of symplectic projections and
-near-identity transformations. Justification of this normal form is provided
-with the energy method. The normal form predicts a transformation of the
-unstable line solitons with larger-than-critical speeds to the orbitally stable
-transversely modulated solitary waves.
-",0,1,1,0,0,0
-17600,The CLaC Discourse Parser at CoNLL-2016,"  This paper describes our submission ""CLaC"" to the CoNLL-2016 shared task on
-shallow discourse parsing. We used two complementary approaches for the task. A
-standard machine learning approach for the parsing of explicit relations, and a
-deep learning approach for non-explicit relations. Overall, our parser achieves
-an F1-score of 0.2106 on the identification of discourse relations (0.3110 for
-explicit relations and 0.1219 for non-explicit relations) on the blind
-CoNLL-2016 test set.
-",1,0,0,0,0,0
-17601,Ordinary differential equations in algebras of generalized functions,"  A local existence and uniqueness theorem for ODEs in the special algebra of
-generalized functions is established, as well as versions including parameters
-and dependence on initial values in the generalized sense. Finally, a Frobenius
-theorem is proved. In all these results, composition of generalized functions
-is based on the notion of c-boundedness.
-",0,0,1,0,0,0
-17602,Interesting Paths in the Mapper,"  The Mapper produces a compact summary of high dimensional data as a
-simplicial complex. We study the problem of quantifying the interestingness of
-subpopulations in a Mapper, which appear as long paths, flares, or loops.
-First, we create a weighted directed graph G using the 1-skeleton of the
-Mapper. We use the average values at the vertices of a target function to
-direct edges (from low to high). The difference between the average values at
-vertices (high-low) is set as the edge's weight. Covariation of the remaining h
-functions (independent variables) is captured by a h-bit binary signature
-assigned to the edge. An interesting path in G is a directed path whose edges
-all have the same signature. We define the interestingness score of such a path
-as a sum of its edge weights multiplied by a nonlinear function of their ranks
-in the path.
-Second, we study three optimization problems on this graph G. In the problem
-Max-IP, we seek an interesting path in G with the maximum interestingness
-score. We show that Max-IP is NP-complete. For the special case when G is a
-directed acyclic graph (DAG), we show that Max-IP can be solved in polynomial
-time - in O(mnd_i) where d_i is the maximum indegree of a vertex in G.
-In the more general problem IP, the goal is to find a collection of
-edge-disjoint interesting paths such that the overall sum of their
-interestingness scores is maximized. We also study a variant of IP termed k-IP,
-where the goal is to identify a collection of edge-disjoint interesting paths
-each with k edges, and their total interestingness score is maximized. While
-k-IP can be solved in polynomial time for k <= 2, we show k-IP is NP-complete
-for k >= 3 even when G is a DAG. We develop polynomial time heuristics for IP
-and k-IP on DAGs.
-",1,0,1,0,0,0
-17603,On a three dimensional vision based collision avoidance model,"  This paper presents a three dimensional collision avoidance approach for
-aerial vehicles inspired by coordinated behaviors in biological groups. The
-proposed strategy aims to enable a group of vehicles to converge to a common
-destination point avoiding collisions with each other and with moving obstacles
-in their environment. The interaction rules lead the agents to adapt their
-velocity vectors through a modification of the relative bearing angle and the
-relative elevation. Moreover the model satisfies the limited field of view
-constraints resulting from individual perception sensitivity. From the proposed
-individual based model, a mean-field kinetic model is derived. Simulations are
-performed to show the effectiveness of the proposed model.
-",0,0,1,0,0,0
-17604,Algorithms for Covering Multiple Barriers,"  In this paper, we consider the problems for covering multiple intervals on a
-line. Given a set $B$ of $m$ line segments (called ""barriers"") on a horizontal
-line $L$ and another set $S$ of $n$ horizontal line segments of the same length
-in the plane, we want to move all segments of $S$ to $L$ so that their union
-covers all barriers and the maximum movement of all segments of $S$ is
-minimized. Previously, an $O(n^3\log n)$-time algorithm was given for the case
-$m=1$. In this paper, we propose an $O(n^2\log n\log \log n+nm\log m)$-time
-algorithm for a more general setting with any $m\geq 1$, which also improves
-the previous work when $m=1$. We then consider a line-constrained version of
-the problem in which the segments of $S$ are all initially on the line $L$.
-Previously, an $O(n\log n)$-time algorithm was known for the case $m=1$. We
-present an algorithm of $O(m\log m+n\log m \log n)$ time for any $m\geq 1$.
-These problems may have applications in mobile sensor barrier coverage in
-wireless sensor networks.
-",1,0,0,0,0,0
-17605,Shattering the glass ceiling? How the institutional context mitigates the gender gap in entrepreneurship,"  We examine how the institutional context affects the relationship between
-gender and opportunity entrepreneurship. To do this, we develop a multi-level
-model that connects feminist theory at the micro-level to institutional theory
-at the macro-level. It is hypothesized that the gender gap in opportunity
-entrepreneurship is more pronounced in low-quality institutional contexts and
-less pronounced in high-quality institutional contexts. Using data from the
-Global Entrepreneurship Monitor (GEM) and regulation data from the economic
-freedom of the world index (EFW), we test our predictions and find evidence in
-support of our model. Our findings suggest that, while there is a gender gap in
-entrepreneurship, these disparities are reduced as the quality of the
-institutional context improves.
-",0,0,0,0,0,1
-17606,An Automated Text Categorization Framework based on Hyperparameter Optimization,"  A great variety of text tasks such as topic or spam identification, user
-profiling, and sentiment analysis can be posed as a supervised learning problem
-and tackle using a text classifier. A text classifier consists of several
-subprocesses, some of them are general enough to be applied to any supervised
-learning problem, whereas others are specifically designed to tackle a
-particular task, using complex and computational expensive processes such as
-lemmatization, syntactic analysis, etc. Contrary to traditional approaches, we
-propose a minimalistic and wide system able to tackle text classification tasks
-independent of domain and language, namely microTC. It is composed by some easy
-to implement text transformations, text representations, and a supervised
-learning algorithm. These pieces produce a competitive classifier even in the
-domain of informally written text. We provide a detailed description of microTC
-along with an extensive experimental comparison with relevant state-of-the-art
-methods. mircoTC was compared on 30 different datasets. Regarding accuracy,
-microTC obtained the best performance in 20 datasets while achieves competitive
-results in the remaining 10. The compared datasets include several problems
-like topic and polarity classification, spam detection, user profiling and
-authorship attribution. Furthermore, it is important to state that our approach
-allows the usage of the technology even without knowledge of machine learning
-and natural language processing.
-",1,0,0,1,0,0
-17607,Abdominal aortic aneurysms and endovascular sealing: deformation and dynamic response,"  Endovascular sealing is a new technique for the repair of abdominal aortic
-aneurysms. Commercially available in Europe since~2013, it takes a
-revolutionary approach to aneurysm repair through minimally invasive
-techniques. Although aneurysm sealing may be thought as more stable than
-conventional endovascular stent graft repairs, post-implantation movement of
-the endoprosthesis has been described, potentially leading to late
-complications. The paper presents for the first time a model, which explains
-the nature of forces, in static and dynamic regimes, acting on sealed abdominal
-aortic aneurysms, with references to real case studies. It is shown that
-elastic deformation of the aorta and of the endoprosthesis induced by static
-forces and vibrations during daily activities can potentially promote undesired
-movements of the endovascular sealing structure.
-",0,1,0,0,0,0
-17608,Affinity Scheduling and the Applications on Data Center Scheduling with Data Locality,"  MapReduce framework is the de facto standard in Hadoop. Considering the data
-locality in data centers, the load balancing problem of map tasks is a special
-case of affinity scheduling problem. There is a huge body of work on affinity
-scheduling, proposing heuristic algorithms which try to increase data locality
-in data centers like Delay Scheduling and Quincy. However, not enough attention
-has been put on theoretical guarantees on throughput and delay optimality of
-such algorithms. In this work, we present and compare different algorithms and
-discuss their shortcoming and strengths. To the best of our knowledge, most
-data centers are using static load balancing algorithms which are not efficient
-in any ways and results in wasting the resources and causing unnecessary delays
-for users.
-",1,0,0,0,0,0
-17609,Multivariate Regression with Gross Errors on Manifold-valued Data,"  We consider the topic of multivariate regression on manifold-valued output,
-that is, for a multivariate observation, its output response lies on a
-manifold. Moreover, we propose a new regression model to deal with the presence
-of grossly corrupted manifold-valued responses, a bottleneck issue commonly
-encountered in practical scenarios. Our model first takes a correction step on
-the grossly corrupted responses via geodesic curves on the manifold, and then
-performs multivariate linear regression on the corrected data. This results in
-a nonconvex and nonsmooth optimization problem on manifolds. To this end, we
-propose a dedicated approach named PALMR, by utilizing and extending the
-proximal alternating linearized minimization techniques. Theoretically, we
-investigate its convergence property, where it is shown to converge to a
-critical point under mild conditions. Empirically, we test our model on both
-synthetic and real diffusion tensor imaging data, and show that our model
-outperforms other multivariate regression models when manifold-valued responses
-contain gross errors, and is effective in identifying gross errors.
-",1,0,1,1,0,0
-17610,Computing an Approximately Optimal Agreeable Set of Items,"  We study the problem of finding a small subset of items that is
-\emph{agreeable} to all agents, meaning that all agents value the subset at
-least as much as its complement. Previous work has shown worst-case bounds,
-over all instances with a given number of agents and items, on the number of
-items that may need to be included in such a subset. Our goal in this paper is
-to efficiently compute an agreeable subset whose size approximates the size of
-the smallest agreeable subset for a given instance. We consider three
-well-known models for representing the preferences of the agents: ordinal
-preferences on single items, the value oracle model, and additive utilities. In
-each of these models, we establish virtually tight bounds on the approximation
-ratio that can be obtained by algorithms running in polynomial time.
-",1,0,0,0,0,0
-17611,3D Sketching using Multi-View Deep Volumetric Prediction,"  Sketch-based modeling strives to bring the ease and immediacy of drawing to
-the 3D world. However, while drawings are easy for humans to create, they are
-very challenging for computers to interpret due to their sparsity and
-ambiguity. We propose a data-driven approach that tackles this challenge by
-learning to reconstruct 3D shapes from one or more drawings. At the core of our
-approach is a deep convolutional neural network (CNN) that predicts occupancy
-of a voxel grid from a line drawing. This CNN provides us with an initial 3D
-reconstruction as soon as the user completes a single drawing of the desired
-shape. We complement this single-view network with an updater CNN that refines
-an existing prediction given a new drawing of the shape created from a novel
-viewpoint. A key advantage of our approach is that we can apply the updater
-iteratively to fuse information from an arbitrary number of viewpoints, without
-requiring explicit stroke correspondences between the drawings. We train both
-CNNs by rendering synthetic contour drawings from hand-modeled shape
-collections as well as from procedurally-generated abstract shapes. Finally, we
-integrate our CNNs in a minimal modeling interface that allows users to
-seamlessly draw an object, rotate it to see its 3D reconstruction, and refine
-it by re-drawing from another vantage point using the 3D reconstruction as
-guidance. The main strengths of our approach are its robustness to freehand
-bitmap drawings, its ability to adapt to different object categories, and the
-continuum it offers between single-view and multi-view sketch-based modeling.
-",1,0,0,0,0,0
-17612,Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier,"  We study the problem of ranking a set of items from nonactively chosen
-pairwise preferences where each item has feature information with it. We
-propose and characterize a very broad class of preference matrices giving rise
-to the Feature Low Rank (FLR) model, which subsumes several models ranging from
-the classic Bradley-Terry-Luce (BTL) (Bradley and Terry 1952) and Thurstone
-(Thurstone 1927) models to the recently proposed blade-chest (Chen and Joachims
-2016) and generic low-rank preference (Rajkumar and Agarwal 2016) models. We
-use the technique of matrix completion in the presence of side information to
-develop the Inductive Pairwise Ranking (IPR) algorithm that provably learns a
-good ranking under the FLR model, in a sample-efficient manner. In practice,
-through systematic synthetic simulations, we confirm our theoretical findings
-regarding improvements in the sample complexity due to the use of feature
-information. Moreover, on popular real-world preference learning datasets, with
-as less as 10% sampling of the pairwise comparisons, our method recovers a good
-ranking.
-",1,0,0,1,0,0
-17613,SAGA and Restricted Strong Convexity,"  SAGA is a fast incremental gradient method on the finite sum problem and its
-effectiveness has been tested on a vast of applications. In this paper, we
-analyze SAGA on a class of non-strongly convex and non-convex statistical
-problem such as Lasso, group Lasso, Logistic regression with $\ell_1$
-regularization, linear regression with SCAD regularization and Correct Lasso.
-We prove that SAGA enjoys the linear convergence rate up to the statistical
-estimation accuracy, under the assumption of restricted strong convexity (RSC).
-It significantly extends the applicability of SAGA in convex and non-convex
-optimization.
-",0,0,0,1,0,0
-17614,Characterization of Traps at Nitrided SiO$_2$/SiC Interfaces near the Conduction Band Edge by using Hall Effect Measurements,"  The effects of nitridation on the density of traps at SiO$_2$/SiC interfaces
-near the conduction band edge were qualitatively examined by a simple, newly
-developed characterization method that utilizes Hall effect measurements and
-split capacitance-voltage measurements. The results showed a significant
-reduction in the density of interface traps near the conduction band edge by
-nitridation, as well as the high density of interface traps that was not
-eliminated by nitridation.
-",0,1,0,0,0,0
-17615,Response theory of the ergodic many-body delocalized phase: Keldysh Finkel'stein sigma models and the 10-fold way,"  We derive the finite temperature Keldysh response theory for interacting
-fermions in the presence of quenched disorder, as applicable to any of the 10
-Altland-Zirnbauer classes in an Anderson delocalized phase with at least a U(1)
-continuous symmetry. In this formulation of the interacting Finkel'stein
-nonlinear sigma model, the statistics of one-body wave functions are encoded by
-the constrained matrix field, while physical correlations follow from the
-hydrodynamic density or spin response field, which decouples the interactions.
-Integrating out the matrix field first, we obtain weak (anti)localization and
-Altshuler-Aronov quantum conductance corrections from the hydrodynamic response
-function. This procedure automatically incorporates the correct infrared
-physics, and in particular gives the Altshuler-Aronov-Khmelnitsky (AAK)
-equations for dephasing of weak (anti)localization due to electron-electron
-collisions. We explicate the method by deriving known quantum corrections in
-two dimensions for the symplectic metal class AII, as well as the spin-SU(2)
-invariant superconductor classes C and CI. We show that conductance corrections
-due to the special modes at zero energy in nonstandard classes are
-automatically cut off by temperature, as previously expected, while the
-Wigner-Dyson class Cooperon modes that persist to all energies are cut by
-dephasing. We also show that for short-ranged interactions, the standard
-self-consistent solution for the dephasing rate is equivalent to a diagrammatic
-summation via the self-consistent Born approximation. This should be compared
-to the AAK solution for long-ranged Coulomb interactions, which exploits the
-Markovian noise correlations induced by thermal fluctuations of the
-electromagnetic field. We discuss prospects for exploring the many-body
-localization transition from the ergodic side as a dephasing catastrophe in
-short-range interacting models.
-",0,1,0,0,0,0
-17616,Classification via Tensor Decompositions of Echo State Networks,"  This work introduces a tensor-based method to perform supervised
-classification on spatiotemporal data processed in an echo state network.
-Typically when performing supervised classification tasks on data processed in
-an echo state network, the entire collection of hidden layer node states from
-the training dataset is shaped into a matrix, allowing one to use standard
-linear algebra techniques to train the output layer. However, the collection of
-hidden layer states is multidimensional in nature, and representing it as a
-matrix may lead to undesirable numerical conditions or loss of spatial and
-temporal correlations in the data.
-This work proposes a tensor-based supervised classification method on echo
-state network data that preserves and exploits the multidimensional nature of
-the hidden layer states. The method, which is based on orthogonal Tucker
-decompositions of tensors, is compared with the standard linear output weight
-approach in several numerical experiments on both synthetic and natural data.
-The results show that the tensor-based approach tends to outperform the
-standard approach in terms of classification accuracy.
-",1,0,0,1,0,0
-17617,Inference on Breakdown Frontiers,"  Given a set of baseline assumptions, a breakdown frontier is the boundary
-between the set of assumptions which lead to a specific conclusion and those
-which do not. In a potential outcomes model with a binary treatment, we
-consider two conclusions: First, that ATE is at least a specific value (e.g.,
-nonnegative) and second that the proportion of units who benefit from treatment
-is at least a specific value (e.g., at least 50\%). For these conclusions, we
-derive the breakdown frontier for two kinds of assumptions: one which indexes
-relaxations of the baseline random assignment of treatment assumption, and one
-which indexes relaxations of the baseline rank invariance assumption. These
-classes of assumptions nest both the point identifying assumptions of random
-assignment and rank invariance and the opposite end of no constraints on
-treatment selection or the dependence structure between potential outcomes.
-This frontier provides a quantitative measure of robustness of conclusions to
-relaxations of the baseline point identifying assumptions. We derive
-$\sqrt{N}$-consistent sample analog estimators for these frontiers. We then
-provide two asymptotically valid bootstrap procedures for constructing lower
-uniform confidence bands for the breakdown frontier. As a measure of
-robustness, estimated breakdown frontiers and their corresponding confidence
-bands can be presented alongside traditional point estimates and confidence
-intervals obtained under point identifying assumptions. We illustrate this
-approach in an empirical application to the effect of child soldiering on
-wages. We find that sufficiently weak conclusions are robust to simultaneous
-failures of rank invariance and random assignment, while some stronger
-conclusions are fairly robust to failures of rank invariance but not
-necessarily to relaxations of random assignment.
-",0,0,0,1,0,0
-17618,Sequential Detection of Three-Dimensional Signals under Dependent Noise,"  We study detection methods for multivariable signals under dependent noise.
-The main focus is on three-dimensional signals, i.e. on signals in the
-space-time domain. Examples for such signals are multifaceted. They include
-geographic and climatic data as well as image data, that are observed over a
-fixed time horizon. We assume that the signal is observed as a finite block of
-noisy samples whereby we are interested in detecting changes from a given
-reference signal. Our detector statistic is based on a sequential partial sum
-process, related to classical signal decomposition and reconstruction
-approaches applied to the sampled signal. We show that this detector process
-converges weakly under the no change null hypothesis that the signal coincides
-with the reference signal, provided that the spatial-temporal partial sum
-process associated to the random field of the noise terms disturbing the
-sampled signal con- verges to a Brownian motion. More generally, we also
-establish the limiting distribution under a wide class of local alternatives
-that allows for smooth as well as discontinuous changes. Our results also cover
-extensions to the case that the reference signal is unknown. We conclude with
-an extensive simulation study of the detection algorithm.
-",0,0,1,1,0,0
-17619,T-Branes at the Limits of Geometry,"  Singular limits of 6D F-theory compactifications are often captured by
-T-branes, namely a non-abelian configuration of intersecting 7-branes with a
-nilpotent matrix of normal deformations. The long distance approximation of
-such 7-branes is a Hitchin-like system in which simple and irregular poles
-emerge at marked points of the geometry. When multiple matter fields localize
-at the same point in the geometry, the associated Higgs field can exhibit
-irregular behavior, namely poles of order greater than one. This provides a
-geometric mechanism to engineer wild Higgs bundles. Physical constraints such
-as anomaly cancellation and consistent coupling to gravity also limit the order
-of such poles. Using this geometric formulation, we unify seemingly different
-wild Hitchin systems in a single framework in which orders of poles become
-adjustable parameters dictated by tuning gauge singlet moduli of the F-theory
-model.
-",0,0,1,0,0,0
-17620,Space-time crystal and space-time group,"  Crystal structures and the Bloch theorem play a fundamental role in condensed
-matter physics. We extend the static crystal to the dynamic ""space-time""
-crystal characterized by the general intertwined space-time periodicities in
-$D+1$ dimensions, which include both the static crystal and the Floquet crystal
-as special cases. A new group structure dubbed ""space-time"" group is
-constructed to describe the discrete symmetries of space-time crystal. Compared
-to space and magnetic groups, space-time group is augmented by ""time-screw""
-rotations and ""time-glide"" reflections involving fractional translations along
-the time direction. A complete classification of the 13 space-time groups in
-1+1D is performed. The Kramers-type degeneracy can arise from the glide
-time-reversal symmetry without the half-integer spinor structure, which
-constrains the winding number patterns of spectral dispersions. In 2+1D,
-non-symmorphic space-time symmetries enforce spectral degeneracies, leading to
-protected Floquet semi-metal states. Our work provides a general framework for
-further studying topological properties of the $D+1$ dimensional space-time
-crystal.
-",0,1,0,0,0,0
-17621,On the Performance of Zero-Forcing Processing in Multi-Way Massive MIMO Relay Networks,"  We consider a multi-way massive multiple-input multiple-output relay network
-with zero-forcing processing at the relay. By taking into account the
-time-division duplex protocol with channel estimation, we derive an analytical
-approximation of the spectral efficiency. This approximation is very tight and
-simple which enables us to analyze the system performance, as well as, to
-compare the spectral efficiency with zero-forcing and maximum-ratio processing.
-Our results show that by using a very large number of relay antennas and with
-the zero-forcing technique, we can simultaneously serve many active users in
-the same time-frequency resource, each with high spectral efficiency.
-",1,0,1,0,0,0
-17622,Motivic rational homotopy type,"  In this paper we introduce and study motives for rational homotopy types.
-",0,0,1,0,0,0
-17623,Preconditioner-free Wiener filtering with a dense noise matrix,"  This work extends the Elsner & Wandelt (2013) iterative method for efficient,
-preconditioner-free Wiener filtering to cases in which the noise covariance
-matrix is dense, but can be decomposed into a sum whose parts are sparse in
-convenient bases. The new method, which uses multiple messenger fields,
-reproduces Wiener filter solutions for test problems, and we apply it to a case
-beyond the reach of the Elsner & Wandelt (2013) method. We compute the Wiener
-filter solution for a simulated Cosmic Microwave Background map that contains
-spatially-varying, uncorrelated noise, isotropic $1/f$ noise, and large-scale
-horizontal stripes (like those caused by the atmospheric noise). We discuss
-simple extensions that can filter contaminated modes or inverse-noise filter
-the data. These techniques help to address complications in the noise
-properties of maps from current and future generations of ground-based
-Microwave Background experiments, like Advanced ACTPol, Simons Observatory, and
-CMB-S4.
-",0,1,0,0,0,0
-17624,"Order-unity argument for structure-generated ""extra"" expansion","  Self-consistent treatment of cosmological structure formation and expansion
-within the context of classical general relativity may lead to ""extra""
-expansion above that expected in a structureless universe. We argue that in
-comparison to an early-epoch, extrapolated Einstein-de Sitter model, about
-10-15% ""extra"" expansion is sufficient at the present to render superfluous the
-""dark energy"" 68% contribution to the energy density budget, and that this is
-observationally realistic.
-",0,1,0,0,0,0
-17625,The least unramified prime which does not split completely,"  Let $K/F$ be a finite extension of number fields of degree $n \geq 2$. We
-establish effective field-uniform unconditional upper bounds for the least norm
-of a prime ideal of $F$ which is degree 1 over $\mathbb{Q}$ and does not ramify
-or split completely in $K$. We improve upon the previous best known general
-estimates due to X. Li when $F = \mathbb{Q}$ and Murty-Patankar when $K/F$ is
-Galois. Our bounds are the first when $K/F$ is not assumed to be Galois and $F
-\neq \mathbb{Q}$.
-",0,0,1,0,0,0
-17626,Crosscorrelation of Rudin-Shapiro-Like Polynomials,"  We consider the class of Rudin-Shapiro-like polynomials, whose $L^4$ norms on
-the complex unit circle were studied by Borwein and Mossinghoff. The polynomial
-$f(z)=f_0+f_1 z + \cdots + f_d z^d$ is identified with the sequence
-$(f_0,f_1,\ldots,f_d)$ of its coefficients. From the $L^4$ norm of a
-polynomial, one can easily calculate the autocorrelation merit factor of its
-associated sequence, and conversely. In this paper, we study the
-crosscorrelation properties of pairs of sequences associated to
-Rudin-Shapiro-like polynomials. We find an explicit formula for the
-crosscorrelation merit factor. A computer search is then used to find pairs of
-Rudin-Shapiro-like polynomials whose autocorrelation and crosscorrelation merit
-factors are simultaneously high. Pursley and Sarwate proved a bound that limits
-how good this combined autocorrelation and crosscorrelation performance can be.
-We find infinite families of polynomials whose performance approaches quite
-close to this fundamental limit.
-",1,0,1,0,0,0
-17627,An Application of Rubi: Series Expansion of the Quark Mass Renormalization Group Equation,"  We highlight how Rule-based Integration (Rubi) is an enhanced method of
-symbolic integration which allows for the integration of many difficult
-integrals not accomplished by other computer algebra systems. Using Rubi, many
-integration techniques become tractable. Integrals are approached using
-step-wise simplification, hence distilling an integral (if the solution is
-unknown) into composite integrals which highlight yet undiscovered integration
-rules. The motivating example we use is the derivation of the updated series
-expansion of the quark mass renormalization group equation (RGE) to five-loop
-order. This series provides the relation between a light quark mass in the
-modified minimal subtraction ($\overline{\text{MS}}$) scheme defined at some
-given scale, e.g. at the tau-lepton mass scale, and another chosen energy
-scale, $s$. This relation explicitly depicts the renormalization scheme
-dependence of the running quark mass on the scale parameter, $s$, and is
-important in accurately determining a light quark mass at a chosen scale. The
-five-loop QCD $\beta(a_s)$ and $\gamma(a_s)$ functions are used in this
-determination.
-",1,0,0,0,0,0
-17628,On the Performance of Wireless Powered Communication With Non-linear Energy Harvesting,"  In this paper, we analyze the performance of a time-slotted multi-antenna
-wireless powered communication (WPC) system, where a wireless device first
-harvests radio frequency (RF) energy from a power station (PS) in the downlink
-to facilitate information transfer to an information receiving station (IRS) in
-the uplink. The main goal of this paper is to provide insights and guidelines
-for the design of practical WPC systems. To this end, we adopt a recently
-proposed parametric non-linear RF energy harvesting (EH) model, which has been
-shown to accurately model the end-to-end non-linearity of practical RF EH
-circuits. In order to enhance the RF power transfer efficiency, maximum ratio
-transmission is adopted at the PS to focus the energy signals on the wireless
-device. Furthermore, at the IRS, maximum ratio combining is used. We analyze
-the outage probability and the average throughput of information transfer,
-assuming Nakagami-$m$ fading uplink and downlink channels. Moreover, we study
-the system performance as a function of the number of PS transmit antennas, the
-number of IRS receive antennas, the transmit power of the PS, the fading
-severity, the transmission rate of the wireless device, and the EH time
-duration. In addition, we obtain a fixed point equation for the optimal
-transmission rate and the optimal EH time duration that maximize the asymptotic
-throughput for high PS transmit powers. All analytical results are corroborated
-by simulations.
-",1,0,0,0,0,0
-17629,Realizing polarization conversion and unidirectional transmission by using a uniaxial crystal plate,"  We show that polarization states of electromagnetic waves can be manipulated
-easily using a single thin uniaxial crystal plate. By performing a rotational
-transformation of the coordinates and controlling the thickness of the plate,
-we can achieve a complete polarization conversion between TE wave and TM wave
-in a spectral band. We show that the off-diagonal element of the permittivity
-is the key for polarization conversion. Our analysis can explain clearly the
-results found in experiments with metamaterials. Finally, we propose a simple
-device to realize unidirectional transmission based on polarization conversion
-and excitation of surface plasmon polaritons.
-",0,1,0,0,0,0
-17630,When Should You Adjust Standard Errors for Clustering?,"  In empirical work in economics it is common to report standard errors that
-account for clustering of units. Typically, the motivation given for the
-clustering adjustments is that unobserved components in outcomes for units
-within clusters are correlated. However, because correlation may occur across
-more than one dimension, this motivation makes it difficult to justify why
-researchers use clustering in some dimensions, such as geographic, but not
-others, such as age cohorts or gender. It also makes it difficult to explain
-why one should not cluster with data from a randomized experiment. In this
-paper, we argue that clustering is in essence a design problem, either a
-sampling design or an experimental design issue. It is a sampling design issue
-if sampling follows a two stage process where in the first stage, a subset of
-clusters were sampled randomly from a population of clusters, while in the
-second stage, units were sampled randomly from the sampled clusters. In this
-case the clustering adjustment is justified by the fact that there are clusters
-in the population that we do not see in the sample. Clustering is an
-experimental design issue if the assignment is correlated within the clusters.
-We take the view that this second perspective best fits the typical setting in
-economics where clustering adjustments are used. This perspective allows us to
-shed new light on three questions: (i) when should one adjust the standard
-errors for clustering, (ii) when is the conventional adjustment for clustering
-appropriate, and (iii) when does the conventional adjustment of the standard
-errors matter.
-",0,0,1,1,0,0
-17631,Approximate homomorphisms on lattices,"  We prove two results concerning an Ulam-type stability problem for
-homomorphisms between lattices. One of them involves estimates by quite general
-error functions; the other deals with approximate (join) homomorphisms in terms
-of certain systems of lattice neighborhoods. As a corollary, we obtain a
-stability result for approximately monotone functions.
-",0,0,1,0,0,0
-17632,Learning Latent Events from Network Message Logs: A Decomposition Based Approach,"  In this communication, we describe a novel technique for event mining using a
-decomposition based approach that combines non-parametric change-point
-detection with LDA. We prove theoretical guarantees about sample-complexity and
-consistency of the approach. In a companion paper, we will perform a thorough
-evaluation of our approach with detailed experiments.
-",0,0,0,1,0,0
-17633,Generating retinal flow maps from structural optical coherence tomography with artificial intelligence,"  Despite significant advances in artificial intelligence (AI) for computer
-vision, its application in medical imaging has been limited by the burden and
-limits of expert-generated labels. We used images from optical coherence
-tomography angiography (OCTA), a relatively new imaging modality that measures
-perfusion of the retinal vasculature, to train an AI algorithm to generate
-vasculature maps from standard structural optical coherence tomography (OCT)
-images of the same retinae, both exceeding the ability and bypassing the need
-for expert labeling. Deep learning was able to infer perfusion of
-microvasculature from structural OCT images with similar fidelity to OCTA and
-significantly better than expert clinicians (P < 0.00001). OCTA suffers from
-need of specialized hardware, laborious acquisition protocols, and motion
-artifacts; whereas our model works directly from standard OCT which are
-ubiquitous and quick to obtain, and allows unlocking of large volumes of
-previously collected standard OCT data both in existing clinical trials and
-clinical practice. This finding demonstrates a novel application of AI to
-medical imaging, whereby subtle regularities between different modalities are
-used to image the same body part and AI is used to generate detailed and
-accurate inferences of tissue function from structure imaging.
-",0,0,0,1,0,0
-17634,Varieties with Ample Tangent Sheaves,"  This paper generalises Mori's famous theorem about ""Projective manifolds with
-ample tangent bundles"" to normal projective varieties in the following way:
-A normal projective variety over $\mathbb{C}$ with ample tangent sheaf is
-isomorphic to the complex projective space.
-",0,0,1,0,0,0
-17635,Water sub-diffusion in membranes for fuel cells,"  We investigate the dynamics of water confined in soft ionic nano-assemblies,
-an issue critical for a general understanding of the multi-scale
-structure-function interplay in advanced materials. We focus in particular on
-hydrated perfluoro-sulfonic acid compounds employed as electrolytes in fuel
-cells. These materials form phase-separated morphologies that show outstanding
-proton-conducting properties, directly related to the state and dynamics of the
-absorbed water. We have quantified water motion and ion transport by combining
-Quasi Elastic Neutron Scattering, Pulsed Field Gradient Nuclear Magnetic
-Resonance, and Molecular Dynamics computer simulation. Effective water and ion
-diffusion coefficients have been determined together with their variation upon
-hydration at the relevant atomic, nanoscopic and macroscopic scales, providing
-a complete picture of transport. We demonstrate that confinement at the
-nanoscale and direct interaction with the charged interfaces produce anomalous
-sub-diffusion, due to a heterogeneous space-dependent dynamics within the ionic
-nanochannels. This is irrespective of the details of the chemistry of the
-hydrophobic confining matrix, confirming the statistical significance of our
-conclusions. Our findings turn out to indicate interesting connections and
-possibilities of cross-fertilization with other domains, including biophysics.
-They also establish fruitful correspondences with advanced topics in
-statistical mechanics, resulting in new possibilities for the analysis of
-Neutron scattering data.
-",0,1,0,0,0,0
-17636,Global Strong Solution of a 2D coupled Parabolic-Hyperbolic Magnetohydrodynamic System,"  The main objective of this paper is to study the global strong solution of
-the parabolic-hyperbolic incompressible magnetohydrodynamic (MHD) model in two
-dimensional space. Based on Agmon, Douglis and Nirenberg's estimates for the
-stationary Stokes equation and the Solonnikov's theorem of
-$L^p$-$L^q$-estimates for the evolution Stokes equation, it is shown that the
-mixed-type MHD equations exist a global strong solution.
-",0,0,1,0,0,0
-17637,Subcritical thermal convection of liquid metals in a rapidly rotating sphere,"  Planetary cores consist of liquid metals (low Prandtl number $Pr$) that
-convect as the core cools. Here we study nonlinear convection in a rotating
-(low Ekman number $Ek$) planetary core using a fully 3D direct numerical
-simulation. Near the critical thermal forcing (Rayleigh number $Ra$),
-convection onsets as thermal Rossby waves, but as the $Ra$ increases, this
-state is superceded by one dominated by advection. At moderate rotation, these
-states (here called the weak branch and strong branch, respectively) are
-smoothly connected. As the planetary core rotates faster, the smooth transition
-is replaced by hysteresis cycles and subcriticality until the weak branch
-disappears entirely and the strong branch onsets in a turbulent state at $Ek <
-10^{-6}$. Here the strong branch persists even as the thermal forcing drops
-well below the linear onset of convection ($Ra=0.7Ra_{crit}$ in this study). We
-highlight the importance of the Reynolds stress, which is required for
-convection to subsist below the linear onset. In addition, the Péclet number
-is consistently above 10 in the strong branch. We further note the presence of
-a strong zonal flow that is nonetheless unimportant to the convective state.
-Our study suggests that, in the asymptotic regime of rapid rotation relevant
-for planetary interiors, thermal convection of liquid metals in a sphere onsets
-through a subcritical bifurcation.
-",0,1,0,0,0,0
-17638,Decision-making processes underlying pedestrian behaviours at signalised crossings: Part 2. Do pedestrians show cultural herding behaviour ?,"  Followership is generally defined as a strategy that evolved to solve social
-coordination problems, and particularly those involved in group movement.
-Followership behaviour is particularly interesting in the context of
-road-crossing behaviour because it involves other principles such as
-risk-taking and evaluating the value of social information. This study sought
-to identify the cognitive mechanisms underlying decision-making by pedestrians
-who follow another person across the road at the green or at the red light in
-two different countries (France and Japan). We used agent-based modelling to
-simulate the road-crossing behaviours of pedestrians. This study showed that
-modelling is a reliable means to test different hypotheses and find the exact
-processes underlying decision-making when crossing the road. We found that two
-processes suffice to simulate pedestrian behaviours. Importantly, the study
-revealed differences between the two nationalities and between sexes in the
-decision to follow and cross at the green and at the red light. Japanese
-pedestrians are particularly attentive to the number of already departed
-pedestrians and the number of waiting pedestrians at the red light, whilst
-their French counterparts only consider the number of pedestrians that have
-already stepped off the kerb, thus showing the strong conformism of Japanese
-people. Finally, the simulations are revealed to be similar to observations,
-not only for the departure latencies but also for the number of crossing
-pedestrians and the rates of illegal crossings. The conclusion suggests new
-solutions for safety in transportation research.
-",0,0,0,0,1,0
-17639,Driven by Excess? Climatic Implications of New Global Mapping of Near-Surface Water-Equivalent Hydrogen on Mars,"  We present improved Mars Odyssey Neutron Spectrometer (MONS) maps of
-near-surface Water-Equivalent Hydrogen (WEH) on Mars that have intriguing
-implications for the global distribution of ""excess"" ice, which occurs when the
-mass fraction of water ice exceeds the threshold amount needed to saturate the
-pore volume in normal soils. We have refined the crossover technique of Feldman
-et al. (2011) by using spatial deconvolution and Gaussian weighting to create
-the first globally self-consistent map of WEH. At low latitudes, our new maps
-indicate that WEH exceeds 15% in several near-equatorial regions, such as
-Arabia Terra, which has important implications for the types of hydrated
-minerals present at low latitudes. At high latitudes, we demonstrate that the
-disparate MONS and Phoenix Robotic Arm (RA) observations of near surface WEH
-can be reconciled by a three-layer model incorporating dry soil over fully
-saturated pore ice over pure excess ice: such a three-layer model can also
-potentially explain the strong anticorrelation of subsurface ice content and
-ice table depth observed at high latitudes. At moderate latitudes, we show that
-the distribution of recently formed impact craters is also consistent with our
-latest MONS results, as both the shallowest ice-exposing crater and deepest
-non-ice-exposing crater at each impact site are in good agreement with our
-predictions of near-surface WEH. Overall, we find that our new mapping is
-consistent with the widespread presence at mid-to-high Martian latitudes of
-recently deposited shallow excess ice reservoirs that are not yet in
-equilibrium with the atmosphere.
-",0,1,0,0,0,0
-17640,On distribution of points with conjugate algebraic integer coordinates close to planar curves,"  Let $\varphi:\mathbb{R}\rightarrow \mathbb{R}$ be a continuously
-differentiable function on an interval $J\subset\mathbb{R}$ and let
-$\boldsymbol{\alpha}=(\alpha_1,\alpha_2)$ be a point with algebraic conjugate
-integer coordinates of degree $\leq n$ and of height $\leq Q$. Denote by
-$\tilde{M}^n_\varphi(Q,\gamma, J)$ the set of points $\boldsymbol{\alpha}$ such
-that $|\varphi(\alpha_1)-\alpha_2|\leq c_1 Q^{-\gamma}$. In this paper we show
-that for a real $0<\gamma<1$ and any sufficiently large $Q$ there exist
-positive values $c_2<c_3$, which are independent of $Q$, such that $c_2\cdot
-Q^{n-\gamma}<# \tilde{M}^n_\varphi(Q,\gamma, J)< c_3\cdot Q^{n-\gamma}$.
-",0,0,1,0,0,0
-17641,A Critical Investigation of Deep Reinforcement Learning for Navigation,"  The navigation problem is classically approached in two steps: an exploration
-step, where map-information about the environment is gathered; and an
-exploitation step, where this information is used to navigate efficiently. Deep
-reinforcement learning (DRL) algorithms, alternatively, approach the problem of
-navigation in an end-to-end fashion. Inspired by the classical approach, we ask
-whether DRL algorithms are able to inherently explore, gather and exploit
-map-information over the course of navigation. We build upon Mirowski et al.
-[2017] work and introduce a systematic suite of experiments that vary three
-parameters: the agent's starting location, the agent's target location, and the
-maze structure. We choose evaluation metrics that explicitly measure the
-algorithm's ability to gather and exploit map-information. Our experiments show
-that when trained and tested on the same maps, the algorithm successfully
-gathers and exploits map-information. However, when trained and tested on
-different sets of maps, the algorithm fails to transfer the ability to gather
-and exploit map-information to unseen maps. Furthermore, we find that when the
-goal location is randomized and the map is kept static, the algorithm is able
-to gather and exploit map-information but the exploitation is far from optimal.
-We open-source our experimental suite in the hopes that it serves as a
-framework for the comparison of future algorithms and leads to the discovery of
-robust alternatives to classical navigation methods.
-",1,0,0,0,0,0
-17642,Performance Evaluation of 3D Correspondence Grouping Algorithms,"  This paper presents a thorough evaluation of several widely-used 3D
-correspondence grouping algorithms, motived by their significance in vision
-tasks relying on correct feature correspondences. A good correspondence
-grouping algorithm is desired to retrieve as many as inliers from initial
-feature matches, giving a rise in both precision and recall. Towards this rule,
-we deploy the experiments on three benchmarks respectively addressing shape
-retrieval, 3D object recognition and point cloud registration scenarios. The
-variety in application context brings a rich category of nuisances including
-noise, varying point densities, clutter, occlusion and partial overlaps. It
-also results to different ratios of inliers and correspondence distributions
-for comprehensive evaluation. Based on the quantitative outcomes, we give a
-summarization of the merits/demerits of the evaluated algorithms from both
-performance and efficiency perspectives.
-",1,0,0,0,0,0
-17643,DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car,"  We present DeepPicar, a low-cost deep neural network based autonomous car
-platform. DeepPicar is a small scale replication of a real self-driving car
-called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN),
-which takes images from a front-facing camera as input and produces car
-steering angles as output. DeepPicar uses the same network architecture---9
-layers, 27 million connections and 250K parameters---and can drive itself in
-real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using
-DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end
-deep learning based real-time control of autonomous vehicles. We also
-systematically compare other contemporary embedded computing platforms using
-the DeepPicar's CNN-based real-time control workload. We find that all tested
-platforms, including the Pi 3, are capable of supporting the CNN-based
-real-time control, from 20 Hz up to 100 Hz, depending on hardware platform.
-However, we find that shared resource contention remains an important issue
-that must be considered in applying CNN models on shared memory based embedded
-computing platforms; we observe up to 11.6X execution time increase in the CNN
-based control loop due to shared resource contention. To protect the CNN
-workload, we also evaluate state-of-the-art cache partitioning and memory
-bandwidth throttling techniques on the Pi 3. We find that cache partitioning is
-ineffective, while memory bandwidth throttling is an effective solution.
-",1,0,0,0,0,0
-17644,Statistics of turbulence in the energy-containing range of Taylor-Couette compared to canonical wall-bounded flows,"  Considering structure functions of the streamwise velocity component in a
-framework akin to the extended self-similarity hypothesis (ESS), de Silva
-\textit{et al.} (\textit{J. Fluid Mech.}, vol. 823,2017, pp. 498-510) observed
-that remarkably the \textit{large-scale} (energy-containing range) statistics
-in canonical wall bounded flows exhibit universal behaviour. In the present
-study, we extend this universality, which was seen to encompass also flows at
-moderate Reynolds number, to Taylor-Couette flow. In doing so, we find that
-also the transversal structure function of the spanwise velocity component
-exhibits the same universal behaviour across all flow types considered. We
-further demonstrate that these observations are consistent with predictions
-developed based on an attached-eddy hypothesis. These considerations also yield
-a possible explanation for the efficacy of the ESS framework by showing that it
-relaxes the self-similarity assumption for the attached eddy contributions. By
-taking the effect of streamwise alignment into account, the attached eddy model
-predicts different behaviour for structure functions in the streamwise and in
-the spanwise directions and that this effect cancels in the ESS-framework ---
-both consistent with the data. Moreover, it is demonstrated here that also the
-additive constants, which were previously believed to be flow dependent, are
-indeed universal at least in turbulent boundary layers and pipe flow where
-high-Reynolds number data are currently available.
-",0,1,0,0,0,0
-17645,Physics-Based Modeling of TID Induced Global Static Leakage in Different CMOS Circuits,"  Compact modeling of inter-device radiation-induced leakage underneath the
-gateless thick STI oxide is presented and validated taking into account CMOS
-technology and hardness parameters, dose-rate and annealing effects, and
-dependence on electric modes under irradiation. It was shown that proposed
-approach can be applied for description of dose dependent static leakage
-currents in complex FPGA circuits.
-",0,1,0,0,0,0
-17646,A multiplier inclusion theorem on product domains,"  In this note it is shown that the class of all multipliers from the
-$d$-parameter Hardy space $H^1_{\mathrm{prod}} (\mathbb{T}^d)$ to $L^2
-(\mathbb{T}^d)$ is properly contained in the class of all multipliers from $L
-\log^{d/2} L (\mathbb{T}^d)$ to $L^2(\mathbb{T}^d)$.
-",0,0,1,0,0,0
-17647,Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains,"  We consider using a battery storage system simultaneously for peak shaving
-and frequency regulation through a joint optimization framework which captures
-battery degradation, operational constraints and uncertainties in customer load
-and regulation signals. Under this framework, using real data we show the
-electricity bill of users can be reduced by up to 15\%. Furthermore, we
-demonstrate that the saving from joint optimization is often larger than the
-sum of the optimal savings when the battery is used for the two individual
-applications. A simple threshold real-time algorithm is proposed and achieves
-this super-linear gain. Compared to prior works that focused on using battery
-storage systems for single applications, our results suggest that batteries can
-achieve much larger economic benefits than previously thought if they jointly
-provide multiple services.
-",1,0,1,0,0,0
-17648,Information Bottleneck in Control Tasks with Recurrent Spiking Neural Networks,"  The nervous system encodes continuous information from the environment in the
-form of discrete spikes, and then decodes these to produce smooth motor
-actions. Understanding how spikes integrate, represent, and process information
-to produce behavior is one of the greatest challenges in neuroscience.
-Information theory has the potential to help us address this challenge.
-Informational analyses of deep and feed-forward artificial neural networks
-solving static input-output tasks, have led to the proposal of the
-\emph{Information Bottleneck} principle, which states that deeper layers encode
-more relevant yet minimal information about the inputs. Such an analyses on
-networks that are recurrent, spiking, and perform control tasks is relatively
-unexplored. Here, we present results from a Mutual Information analysis of a
-recurrent spiking neural network that was evolved to perform the classic
-pole-balancing task. Our results show that these networks deviate from the
-\emph{Information Bottleneck} principle prescribed for feed-forward networks.
-",1,0,0,0,0,0
-17649,Physical Properties of Sub-galactic Clumps at 0.5 $\leq z \leq$ 1.5 in the UVUDF,"  We present an investigation of clumpy galaxies in the Hubble Ultra Deep Field
-at 0.5 $\leq z \leq$ 1.5 in the rest-frame far-ultraviolet (FUV) using HST WFC3
-broadband imaging in F225W, F275W, and F336W. An analysis of 1,404 galaxies
-yields 209 galaxies that host 403 kpc-scale clumps. These host galaxies appear
-to be typical star-forming galaxies, with an average of 2 clumps per galaxy and
-reaching a maximum of 8 clumps. We measure the photometry of the clumps, and
-determine the mass, age, and star formation rates (SFR) utilizing the
-SED-fitting code FAST. We find that clumps make an average contribution of 19%
-to the total rest-frame FUV flux of their host galaxy. Individually, clumps
-contribute a median of 5% to the host galaxy SFR and an average of $\sim$4% to
-the host galaxy mass, with total clump contributions to the host galaxy stellar
-mass ranging widely from less than 1% up to 93%. Clumps in the outskirts of
-galaxies are typically younger, with higher star formation rates, than clumps
-in the inner regions. The results are consistent with clump migration theories
-in which clumps form through violent gravitational instabilities in gas-rich
-turbulent disks, eventually migrate toward the center of the galaxies, and
-coalesce into the bulge.
-",0,1,0,0,0,0
-17650,Incomplete Dot Products for Dynamic Computation Scaling in Neural Network Inference,"  We propose the use of incomplete dot products (IDP) to dynamically adjust the
-number of input channels used in each layer of a convolutional neural network
-during feedforward inference. IDP adds monotonically non-increasing
-coefficients, referred to as a ""profile"", to the channels during training. The
-profile orders the contribution of each channel in non-increasing order. At
-inference time, the number of channels used can be dynamically adjusted to
-trade off accuracy for lowered power consumption and reduced latency by
-selecting only a beginning subset of channels. This approach allows for a
-single network to dynamically scale over a computation range, as opposed to
-training and deploying multiple networks to support different levels of
-computation scaling. Additionally, we extend the notion to multiple profiles,
-each optimized for some specific range of computation scaling. We present
-experiments on the computation and accuracy trade-offs of IDP for popular image
-classification models and datasets. We demonstrate that, for MNIST and
-CIFAR-10, IDP reduces computation significantly, e.g., by 75%, without
-significantly compromising accuracy. We argue that IDP provides a convenient
-and effective means for devices to lower computation costs dynamically to
-reflect the current computation budget of the system. For example, VGG-16 with
-50% IDP (using only the first 50% of channels) achieves 70% in accuracy on the
-CIFAR-10 dataset compared to the standard network which achieves only 35%
-accuracy when using the reduced channel set.
-",1,0,0,1,0,0
-17651,Graded components of Local cohomology modules II,"  Let $A$ be a commutative Noetherian ring containing a field $K$ of
-characteristic zero and let $R= A[X_1, \ldots, X_m]$. Consider $R$ as standard
-graded with $°A=0$ and $°X_i=1$ for all $i$. We present a few results
-about the behavior of the graded components of local cohomology modules
-$H_I^i(R)$ where $I$ is an arbitrary homogeneous ideal in $R$. We mostly
-restrict our attention to the Vanishing, Tameness and Rigidity problems.
-",0,0,1,0,0,0
-17652,Four-Dimensional Painlevé-Type Equations Associated with Ramified Linear Equations III: Garnier Systems and Fuji-Suzuki Systems,"  This is the last part of a series of three papers entitled ""Four-dimensional
-Painlevé-type equations associated with ramified linear equations"". In this
-series of papers we aim to construct the complete degeneration scheme of
-four-dimensional Painlevé-type equations. In the present paper, we consider
-the degeneration of the Garnier system in two variables and the Fuji-Suzuki
-system.
-",0,0,1,0,0,0
-17653,You Must Have Clicked on this Ad by Mistake! Data-Driven Identification of Accidental Clicks on Mobile Ads with Applications to Advertiser Cost Discounting and Click-Through Rate Prediction,"  In the cost per click (CPC) pricing model, an advertiser pays an ad network
-only when a user clicks on an ad; in turn, the ad network gives a share of that
-revenue to the publisher where the ad was impressed. Still, advertisers may be
-unsatisfied with ad networks charging them for ""valueless"" clicks, or so-called
-accidental clicks. [...] Charging advertisers for such clicks is detrimental in
-the long term as the advertiser may decide to run their campaigns on other ad
-networks. In addition, machine-learned click models trained to predict which ad
-will bring the highest revenue may overestimate an ad click-through rate, and
-as a consequence negatively impacting revenue for both the ad network and the
-publisher. In this work, we propose a data-driven method to detect accidental
-clicks from the perspective of the ad network. We collect observations of time
-spent by users on a large set of ad landing pages - i.e., dwell time. We notice
-that the majority of per-ad distributions of dwell time fit to a mixture of
-distributions, where each component may correspond to a particular type of
-clicks, the first one being accidental. We then estimate dwell time thresholds
-of accidental clicks from that component. Using our method to identify
-accidental clicks, we then propose a technique that smoothly discounts the
-advertiser's cost of accidental clicks at billing time. Experiments conducted
-on a large dataset of ads served on Yahoo mobile apps confirm that our
-thresholds are stable over time, and revenue loss in the short term is
-marginal. We also compare the performance of an existing machine-learned click
-model trained on all ad clicks with that of the same model trained only on
-non-accidental clicks. There, we observe an increase in both ad click-through
-rate (+3.9%) and revenue (+0.2%) on ads served by the Yahoo Gemini network when
-using the latter. [...]
-",0,0,0,1,0,0
-17654,Building Models for Biopathway Dynamics Using Intrinsic Dimensionality Analysis,"  An important task for many if not all the scientific domains is efficient
-knowledge integration, testing and codification. It is often solved with model
-construction in a controllable computational environment. In spite of that, the
-throughput of in-silico simulation-based observations become similarly
-intractable for thorough analysis. This is especially the case in molecular
-biology, which served as a subject for this study. In this project, we aimed to
-test some approaches developed to deal with the curse of dimensionality. Among
-these we found dimension reduction techniques especially appealing. They can be
-used to identify irrelevant variability and help to understand critical
-processes underlying high-dimensional datasets. Additionally, we subjected our
-data sets to nonlinear time series analysis, as those are well established
-methods for results comparison. To investigate the usefulness of dimension
-reduction methods, we decided to base our study on a concrete sample set. The
-example was taken from the domain of systems biology concerning dynamic
-evolution of sub-cellular signaling. Particularly, the dataset relates to the
-yeast pheromone pathway and is studied in-silico with a stochastic model. The
-model reconstructs signal propagation stimulated by a mating pheromone. In the
-paper, we elaborate on the reason of multidimensional analysis problem in the
-context of molecular signaling, and next, we introduce the model of choice,
-simulation details and obtained time series dynamics. A description of used
-methods followed by a discussion of results and their biological interpretation
-finalize the paper.
-",0,0,0,1,1,0
-17655,Equilibrium points and basins of convergence in the linear restricted four-body problem with angular velocity,"  The planar linear restricted four-body problem is used in order to determine
-the Newton-Raphson basins of convergence associated with the equilibrium
-points. The parametric variation of the position as well as of the stability of
-the libration points is monitored when the values of the mass parameter $b$ as
-well as of the angular velocity $\omega$ vary in predefined intervals. The
-regions on the configuration $(x,y)$ plane occupied by the basins of attraction
-are revealed using the multivariate version of the Newton-Raphson iterative
-scheme. The correlations between the attracting domains of the equilibrium
-points and the corresponding number of iterations needed for obtaining the
-desired accuracy are also illustrated. We perform a thorough and systematic
-numerical investigation by demonstrating how the parameters $b$ and $\omega$
-influence the shape, the geometry and of course the fractality of the
-converging regions. Our numerical outcomes strongly indicate that these two
-parameters are indeed two of the most influential factors in this dynamical
-system.
-",0,1,0,0,0,0
-17656,Nonmonotonic dependence of polymer glass mechanical response on chain bending stiffness,"  We investigate the mechanical properties of amorphous polymers by means of
-coarse-grained simulations and nonaffine lattice dynamics theory. A small
-increase of polymer chain bending stiffness leads first to softening of the
-material, while hardening happens only upon further strengthening of the
-backbones. This nonmonotonic variation of the storage modulus $G'$ with bending
-stiffness is caused by a competition between additional resistance to
-deformation offered by stiffer backbones and decreased density of the material
-due to a necessary decrease in monomer-monomer coordination. This
-counter-intuitive finding suggests that the strength of polymer glasses may in
-some circumstances be enhanced by softening the bending of constituent chains.
-",0,1,0,0,0,0
-17657,Convolutional Neural Network Committees for Melanoma Classification with Classical And Expert Knowledge Based Image Transforms Data Augmentation,"  Skin cancer is a major public health problem, as is the most common type of
-cancer and represents more than half of cancer diagnoses worldwide. Early
-detection influences the outcome of the disease and motivates our work. We
-investigate the composition of CNN committees and data augmentation for the the
-ISBI 2017 Melanoma Classification Challenge (named Skin Lesion Analysis towards
-Melanoma Detection) facing the peculiarities of dealing with such a small,
-unbalanced, biological database. For that, we explore committees of
-Convolutional Neural Networks trained over the ISBI challenge training dataset
-artificially augmented by both classical image processing transforms and image
-warping guided by specialist knowledge about the lesion axis and improve the
-final classifier invariance to common melanoma variations.
-",1,0,0,0,0,0
-17658,Arbitrage and Geometry,"  This article introduces the notion of arbitrage for a situation involving a
-collection of investments and a payoff matrix describing the return to an
-investor of each investment under each of a set of possible scenarios. We
-explain the Arbitrage Theorem, discuss its geometric meaning, and show its
-equivalence to Farkas' Lemma. We then ask a seemingly innocent question: given
-a random payoff matrix, what is the probability of an arbitrage opportunity?
-This question leads to some interesting geometry involving hyperplane
-arrangements and related topics.
-",0,0,1,1,0,0
-17659,ADE surfaces and their moduli,"  We define a class of surfaces and surface pairs corresponding to the ADE root
-lattices and construct compactifications of their moduli spaces, generalizing
-Losev-Manin spaces of curves.
-",0,0,1,0,0,0
-17660,An Efficient Descriptor Model for Designing Materials for Solar Cells,"  An efficient descriptor model for fast screening of potential materials for
-solar cell applications is presented. It works for both excitonic and
-non-excitonic solar cells materials, and in addition to the energy gap it
-includes the absorption spectrum ($\alpha(E)$) of the material. The charge
-transport properties of the explored materials are modeled using the
-characteristic diffusion length ($L_{d}$) determined for the respective family
-of compounds. The presented model surpasses the widely used Scharber model
-developed for bulk-heterojunction solar cells [Scharber \textit{et al.,
-Advanced Materials}, 2006, Vol. 18, 789]. Using published experimental data, we
-show that the presented model is more accurate in predicting the achievable
-efficiencies. Although the focus of this work is on organic photovoltaics
-(OPV), for which the original Scharber model was developed, the model presented
-here is applicable also to other solar cell technologies. To model both
-excitonic and non-excitonic systems, two different sets of parameters are used
-to account for the different modes of operation. The analysis of the presented
-descriptor model clearly shows the benefit of including $\alpha(E)$ and $L_{d}$
-in view of improved screening results.
-",0,1,0,0,0,0
-17661,Multiplication of a Schubert polynomial by a Stanley symmetric polynomial,"  We prove, combinatorially, that the product of a Schubert polynomial by a
-Stanley symmetric polynomial is a truncated Schubert polynomial. Using Monk's
-rule, we derive a nonnegative combinatorial formula for the Schubert polynomial
-expansion of a truncated Schubert polynomial. Combining these results, we give
-a nonnegative combinatorial rule for the product of a Schubert and a Schur
-polynomial in the Schubert basis.
-",0,0,1,0,0,0
-17662,Topological nodal line states and a potential catalyst of hydrogen evolution in the TiSi family,"  Topological nodal line (DNL) semimetals, formed by a closed loop of the
-inverted bands in the bulk, result in the nearly flat drumhead-like surface
-states with a high electronic density near the Fermi level. The high catalytic
-active sites associated with the high electronic densities, the good carrier
-mobility, and the proper thermodynamic stabilities with $\Delta
-G_{H^*}$$\approx$0 are currently the prerequisites to seek the alternative
-candidates to precious platinum for catalyzing electrochemical hydrogen (HER)
-production from water. Within this context, it is natural to consider whether
-or not the DNLs are a good candidate for the HER because its non-trivial
-surface states provide a robust platform to activate possibly chemical
-reactions. Here, through first-principles calculations we reported on a new DNL
-TiSi-type family with a closed Dirac nodal line consisting of the linear band
-crossings in the $k_y$ = 0 plane. The hydrogen adsorption on the (010) and
-(110) surfaces yields the $\Delta G_{H^*}$ to be almost zero. The topological
-charge carries have been revealed to participate in this HER. The results are
-highlighting that TiSi not only is a promising catalyst for the HER but also
-paves a new routine to design topological quantum catalyst utilizing the
-topological DNL-induced surface bands as active sites, rather than edge sites-,
-vacancy-, dopant-, strain-, or heterostructure-created active sites.
-",0,1,0,0,0,0
-17663,Boosting Variational Inference: an Optimization Perspective,"  Variational inference is a popular technique to approximate a possibly
-intractable Bayesian posterior with a more tractable one. Recently, boosting
-variational inference has been proposed as a new paradigm to approximate the
-posterior by a mixture of densities by greedily adding components to the
-mixture. However, as is the case with many other variational inference
-algorithms, its theoretical properties have not been studied. In the present
-work, we study the convergence properties of this approach from a modern
-optimization viewpoint by establishing connections to the classic Frank-Wolfe
-algorithm. Our analyses yields novel theoretical insights regarding the
-sufficient conditions for convergence, explicit rates, and algorithmic
-simplifications. Since a lot of focus in previous works for variational
-inference has been on tractability, our work is especially important as a much
-needed attempt to bridge the gap between probabilistic models and their
-corresponding theoretical properties.
-",1,0,0,1,0,0
-17664,Nature of carrier injection in metal/2D semiconductor interface and its implications to the limits of contact resistance,"  Monolayers of transition metal dichalcogenides (TMDCs) exhibit excellent
-electronic and optical properties. However, the performance of these
-two-dimensional (2D) devices are often limited by the large resistance offered
-by the metal contact interface. Till date, the carrier injection mechanism from
-metal to 2D TMDC layers remains unclear, with widely varying reports of
-Schottky barrier height (SBH) and contact resistance (Rc), particularly in the
-monolayer limit. In this work, we use a combination of theory and experiments
-in Au and Ni contacted monolayer MoS2 device to conclude the following points:
-(i) the carriers are injected at the source contact through a cascade of two
-potential barriers - the barrier heights being determined by the degree of
-interaction between the metal and the TMDC layer; (ii) the conventional
-Richardson equation becomes invalid due to the multi-dimensional nature of the
-injection barriers, and using Bardeen-Tersoff theory, we derive the appropriate
-form of the Richardson equation that describes such composite barrier; (iii) we
-propose a novel transfer length method (TLM) based SBH extraction methodology,
-to reliably extract SBH by eliminating any confounding effect of temperature
-dependent channel resistance variation; (iv) we derive the Landauer limit of
-the contact resistance achievable in such devices. A comparison of the limits
-with the experimentally achieved contact resistance reveals plenty of room for
-technological improvements.
-",0,1,0,0,0,0
-17665,Characteristics of stratified flows of Newtonian/non-Newtonian shear-thinning fluids,"  Exact solutions for laminar stratified flows of Newtonian/non-Newtonian
-shear-thinning fluids in horizontal and inclined channels are presented. An
-iterative algorithm is proposed to compute the laminar solution for the general
-case of a Carreau non-Newtonian fluid. The exact solution is used to study the
-effect of the rheology of the shear-thinning liquid on two-phase flow
-characteristics considering both gas/liquid and liquid/liquid systems.
-Concurrent and counter-current inclined systems are investigated, including the
-mapping of multiple solution boundaries. Aspects relevant to practical
-applications are discussed, such as the insitu hold-up, or lubrication effects
-achieved by adding a less viscous phase. A characteristic of this family of
-systems is that, even if the liquid has a complex rheology (Carreau fluid), the
-two-phase stratified flow can behave like the liquid is Newtonian for a wide
-range of operational conditions. The capability of the two-fluid model to yield
-satisfactory predictions in the presence of shear-thinning liquids is tested,
-and an algorithm is proposed to a priori predict if the Newtonian (zero shear
-rate viscosity) behaviour arises for a given operational conditions in order to
-avoid large errors in the predictions of flow characteristics when the
-power-law is considered for modelling the shear-thinning behaviour. Two-fluid
-model closures implied by the exact solution and the effect of a turbulent gas
-layer are also addressed.
-",0,1,0,0,0,0
-17666,Stochastic Chebyshev Gradient Descent for Spectral Optimization,"  A large class of machine learning techniques requires the solution of
-optimization problems involving spectral functions of parametric matrices, e.g.
-log-determinant and nuclear norm. Unfortunately, computing the gradient of a
-spectral function is generally of cubic complexity, as such gradient descent
-methods are rather expensive for optimizing objectives involving the spectral
-function. Thus, one naturally turns to stochastic gradient methods in hope that
-they will provide a way to reduce or altogether avoid the computation of full
-gradients. However, here a new challenge appears: there is no straightforward
-way to compute unbiased stochastic gradients for spectral functions. In this
-paper, we develop unbiased stochastic gradients for spectral-sums, an important
-subclass of spectral functions. Our unbiased stochastic gradients are based on
-combining randomized trace estimators with stochastic truncation of the
-Chebyshev expansions. A careful design of the truncation distribution allows us
-to offer distributions that are variance-optimal, which is crucial for fast and
-stable convergence of stochastic gradient methods. We further leverage our
-proposed stochastic gradients to devise stochastic methods for objective
-functions involving spectral-sums, and rigorously analyze their convergence
-rate. The utility of our methods is demonstrated in numerical experiments.
-",0,0,0,1,0,0
-17667,Poisson Bracket and Symplectic Structure of Covariant Canonical Formalism of Fields,"  The covariant canonical formalism is a covariant extension of the traditional
-canonical formalism of fields. In contrast to the traditional canonical theory,
-it has a remarkable feature that canonical equations of gauge theories or
-gravity are not only manifestly Lorentz covariant but also gauge covariant or
-diffeomorphism covariant. A mathematical peculiarity of the covariant canonical
-formalism is that its canonical coordinates are differential forms on a
-manifold. In the present paper, we find a natural Poisson bracket of this new
-canonical theory, and study symplectic structure behind it. The phase space of
-the theory is identified with a ringed space with the structure sheaf of the
-graded algebra of ""differentiable"" differential forms on the manifold. The
-Poisson and the symplectic structure we found can be even or odd, depending on
-the dimension of the manifold. Our Poisson structure is an example of physical
-application of Poisson structure defined on the graded algebra of differential
-forms.
-",0,0,1,0,0,0
-17668,Cascading Failures in Interdependent Systems: Impact of Degree Variability and Dependence,"  We study cascading failures in a system comprising interdependent
-networks/systems, in which nodes rely on other nodes both in the same system
-and in other systems to perform their function. The (inter-)dependence among
-nodes is modeled using a dependence graph, where the degree vector of a node
-determines the number of other nodes it can potentially cause to fail in each
-system through aforementioned dependency. In particular, we examine the impact
-of the variability and dependence properties of node degrees on the probability
-of cascading failures. We show that larger variability in node degrees hampers
-widespread failures in the system, starting with random failures. Similarly,
-positive correlations in node degrees make it harder to set off an epidemic of
-failures, thereby rendering the system more robust against random failures.
-",1,1,0,1,0,0
-17669,Critical exponent for geodesic currents,"  For any geodesic current we associated a quasi-metric space. For a subclass
-of geodesic currents, called filling, it defines a metric and we study the
-critical exponent associated to this space. We show that is is equal to the
-exponential growth rate of the intersection function for closed curves.
-",0,0,1,0,0,0
-17670,Human and Machine Speaker Recognition Based on Short Trivial Events,"  Trivial events are ubiquitous in human to human conversations, e.g., cough,
-laugh and sniff. Compared to regular speech, these trivial events are usually
-short and unclear, thus generally regarded as not speaker discriminative and so
-are largely ignored by present speaker recognition research. However, these
-trivial events are highly valuable in some particular circumstances such as
-forensic examination, as they are less subjected to intentional change, so can
-be used to discover the genuine speaker from disguised speech. In this paper,
-we collect a trivial event speech database that involves 75 speakers and 6
-types of events, and report preliminary speaker recognition results on this
-database, by both human listeners and machines. Particularly, the deep feature
-learning technique recently proposed by our group is utilized to analyze and
-recognize the trivial events, which leads to acceptable equal error rates
-(EERs) despite the extremely short durations (0.2-0.5 seconds) of these events.
-Comparing different types of events, 'hmm' seems more speaker discriminative.
-",1,0,0,0,0,0
-17671,AdiosStMan: Parallelizing Casacore Table Data System Using Adaptive IO System,"  In this paper, we investigate the Casacore Table Data System (CTDS) used in
-the casacore and CASA libraries, and methods to parallelize it. CTDS provides a
-storage manager plugin mechanism for third-party devel- opers to design and
-implement their own CTDS storage managers. Hav- ing this in mind, we looked
-into various storage backend techniques that can possibly enable parallel I/O
-for CTDS by implementing new storage managers. After carrying on benchmarks
-showing the excellent parallel I/O throughput of the Adaptive IO System
-(ADIOS), we implemented an ADIOS based parallel CTDS storage manager. We then
-applied the CASA MSTransform frequency split task to verify the ADIOS Storage
-Manager. We also ran a series of performance tests to examine the I/O
-throughput in a massively parallel scenario.
-",1,1,0,0,0,0
-17672,Direct Experimental Observation of the Gas Filamentation Effect using a Two-bunch X-ray FEL Beam,"  We report the experimental observation of the filamentation effect in gas
-devices designed for X-ray Free-electron Lasers. The measurements were carried
-out at the Linac Coherent Light Source on the X-ray Correlation Spectroscopy
-(XCS) instrument using a Two-bunch FEL beam at 6.5 keV with 122.5 ns separation
-passing through an Argon gas cell. The relative intensities of the two pulses
-of the Two-bunch beam were measured, after and before the gas cell, from the
-X-ray scattering off thin targets by using fast diodes with sufficient temporal
-resolution. It was found that the after-to-before ratio of the intensities of
-the second pulse was consistently higher than that of the first pulse,
-revealing lower effective attenuation of the gas cell due to the heating and
-subsequent gas density reduction in the beam path by the first pulse. This
-measurement is important in guiding the design and/or mitigating the adverse
-effect in gas devices for high repetition-rate FELs such as the LCLS-II and the
-European XFEL or other future high repetition-rate upgrade to existing FEL
-facilities
-",0,1,0,0,0,0
-17673,Coherence measurements of scattered incoherent light for lensless identification of an object's location and size,"  In absence of a lens to form an image, incoherent or partially coherent light
-scattering off an obstructive or reflective object forms a broad intensity
-distribution in the far field with only feeble spatial features. We show here
-that measuring the complex spatial coherence function can help in the
-identification of the size and location of a one-dimensional object placed in
-the path of a partially coherent light source. The complex coherence function
-is measured in the far field through wavefront sampling, which is performed via
-dynamically reconfigurable slits implemented on a digital micromirror device
-(DMD). The impact of an object -- parameterized by size and location -- that
-either intercepts or reflects incoherent light is studied. The experimental
-results show that measuring the spatial coherence function as a function of the
-separation between two slits located symmetrically around the optical axis can
-identify the object transverse location and angle subtended from the detection
-plane (the ratio of the object width to the axial distance from the detector).
-The measurements are in good agreement with numerical simulations of a forward
-model based on Fresnel propagators. The rapid refresh rate of DMDs may enable
-real-time operation of such a lensless coherency imaging scheme.
-",0,1,0,0,0,0
-17674,MVP2P: Layer-Dependency-Aware Live MVC Video Streaming over Peer-to-Peer Networks,"  Multiview video supports observing a scene from different viewpoints. The
-Joint Video Team (JVT) developed H.264/MVC to enhance the compression
-efficiency for multiview video, however, MVC encoded multiview video (MVC
-video) still requires high bitrates for transmission. This paper investigates
-live MVC video streaming over Peer-to-Peer (P2P) networks. The goal is to
-minimize the server bandwidth costs whist ensuring high streaming quality to
-peers. MVC employs intra-view and inter-view prediction structures, which leads
-to a complicated layer dependency relationship. As the peers' outbound
-bandwidth is shared while supplying all the MVC video layers, the bandwidth
-allocation to one MVC layer affects the available outbound bandwidth of the
-other layers. To optimise the utilisation of the peers' outbound bandwidth for
-providing video layers, a maximum flow based model is proposed which considers
-the MVC video layer dependency and the layer supplying relationship between
-peers. Based on the model, a layer dependency aware live MVC video streaming
-method over a BitTorrent-like P2P network is proposed, named MVP2P. The key
-components of MVP2P include a chunk scheduling strategy and a peer selection
-strategy for receiving peers, and a bandwidth scheduling algorithm for
-supplying peers. To evaluate the efficiency of the proposed solution, MVP2P is
-compared with existing methods considering the constraints of peer bandwidth,
-peer numbers, view switching rates, and peer churns. The test results show that
-MVP2P significantly outperforms the existing methods.
-",1,0,0,0,0,0
-17675,High order finite element simulations for fluid dynamics validated by experimental data from the fda benchmark nozzle model,"  The objective of the present work is to construct a sound mathematical,
-numerical and computational framework relevant to blood flow simulations and to
-assess it through a careful validation against experimental data. We perform
-simulations of a benchmark proposed by the FDA for fluid flow in an idealized
-medical device, under different flow regimes. The results are evaluated using
-metrics proposed in the literature and the findings are in very good agreement
-with the validation experiment.
-",0,1,1,0,0,0
-17676,Twin Networks: Matching the Future for Sequence Generation,"  We propose a simple technique for encouraging generative RNNs to plan ahead.
-We train a ""backward"" recurrent network to generate a given sequence in reverse
-order, and we encourage states of the forward model to predict cotemporal
-states of the backward model. The backward network is used only during
-training, and plays no role during sampling or inference. We hypothesize that
-our approach eases modeling of long-term dependencies by implicitly forcing the
-forward states to hold information about the longer-term future (as contained
-in the backward states). We show empirically that our approach achieves 9%
-relative improvement for a speech recognition task, and achieves significant
-improvement on a COCO caption generation task.
-",1,0,0,1,0,0
-17677,Structure and Randomness of Continuous-Time Discrete-Event Processes,"  Loosely speaking, the Shannon entropy rate is used to gauge a stochastic
-process' intrinsic randomness; the statistical complexity gives the cost of
-predicting the process. We calculate, for the first time, the entropy rate and
-statistical complexity of stochastic processes generated by finite unifilar
-hidden semi-Markov models---memoryful, state-dependent versions of renewal
-processes. Calculating these quantities requires introducing novel mathematical
-objects ({\epsilon}-machines of hidden semi-Markov processes) and new
-information-theoretic methods to stochastic processes.
-",0,1,1,1,0,0
-17678,In situ high resolution real-time quantum efficiency imaging for photocathodes,"  Aspects of the preparation process and performance degradation are two major
-problems of photocathodes. The lack of a means for dynamic quantum efficiency
-measurements results in the inability to observe the inhomogeneity of the
-cathode surface by fine structural analysis and in real time.Here we present a
-simple and scalable technique for in situ real-time quantum efficiency
-diagnosis. An incoherent light source provides uniform illumination on the
-cathode surface, and solenoid magnets are used as lens for focusing and imaging
-the emitted electron beam on a downstream scintillator screen, which converts
-the quantum efficiency information into fluorescence intensity distribution.
-The microscopic discontinuity and the dynamic changes of the quantum efficiency
-of a gallium arsenide photocathode are observed at a resolution of a few
-microns. An unexpected uneven decrease of the quantum efficiency is also
-recorded. The work demonstrates a new observation method for photoemission
-materials research.
-",0,1,0,0,0,0
-17679,Competitive division of a mixed manna,"  A mixed manna contains goods (that everyone likes), bads (that everyone
-dislikes), as well as items that are goods to some agents, but bads or satiated
-to others.
-If all items are goods and utility functions are homothetic, concave (and
-monotone), the Competitive Equilibrium with Equal Incomes maximizes the Nash
-product of utilities: hence it is welfarist (determined utility-wise by the
-feasible set of profiles), single-valued and easy to compute.
-We generalize the Gale-Eisenberg Theorem to a mixed manna. The Competitive
-division is still welfarist and related to the product of utilities or
-disutilities. If the zero utility profile (before any manna) is Pareto
-dominated, the competitive profile is unique and still maximizes the product of
-utilities. If the zero profile is unfeasible, the competitive profiles are the
-critical points of the product of disutilities on the efficiency frontier, and
-multiplicity is pervasive. In particular the task of dividing a mixed manna is
-either good news for everyone, or bad news for everyone.
-We refine our results in the practically important case of linear
-preferences, where the axiomatic comparison between the division of goods and
-that of bads is especially sharp. When we divide goods and the manna improves,
-everyone weakly benefits under the competitive rule; but no reasonable rule to
-divide bads can be similarly Resource Monotonic. Also, the much larger set of
-Non Envious and Efficient divisions of bads can be disconnected so that it will
-admit no continuous selection.
-",1,0,1,0,0,0
-17680,Understanding Black-box Predictions via Influence Functions,"  How can we explain the predictions of a black-box model? In this paper, we
-use influence functions -- a classic technique from robust statistics -- to
-trace a model's prediction through the learning algorithm and back to its
-training data, thereby identifying training points most responsible for a given
-prediction. To scale up influence functions to modern machine learning
-settings, we develop a simple, efficient implementation that requires only
-oracle access to gradients and Hessian-vector products. We show that even on
-non-convex and non-differentiable models where the theory breaks down,
-approximations to influence functions can still provide valuable information.
-On linear models and convolutional neural networks, we demonstrate that
-influence functions are useful for multiple purposes: understanding model
-behavior, debugging models, detecting dataset errors, and even creating
-visually-indistinguishable training-set attacks.
-",1,0,0,1,0,0
-17681,Linear and nonlinear photonic Jackiw-Rebbi states in waveguide arrays,"  We study analytically and numerically the optical analogue of the
-Jackiw-Rebbi states in quantum field theory. These solutions exist at the
-interface of two binary waveguide arrays which are described by two Dirac
-equations with opposite sign masses. We show that these special states are
-topologically robust not only in the linear regime, but also in nonlinear
-regimes (with both focusing and de-focusing nonlinearity). We also reveal that
-one can generate the Jackiw-Rebbi states starting from Dirac solitons.
-",0,1,0,0,0,0
-17682,"A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations","  Progress in science has advanced the development of human society across
-history, with dramatic revolutions shaped by information theory, genetic
-cloning, and artificial intelligence, among the many scientific achievements
-produced in the 20th century. However, the way that science advances itself is
-much less well-understood. In this work, we study the evolution of scientific
-development over the past century by presenting an anatomy of 89 million
-digitalized papers published between 1900 and 2015. We find that science has
-benefited from the shift from individual work to collaborative effort, with
-over 90% of the world-leading innovations generated by collaborations in this
-century, nearly four times higher than they were in the 1900s. We discover that
-rather than the frequent myopic- and self-referencing that was common in the
-early 20th century, modern scientists instead tend to look for literature
-further back and farther around. Finally, we also observe the globalization of
-scientific development from 1900 to 2015, including 25-fold and 7-fold
-increases in international collaborations and citations, respectively, as well
-as a dramatic decline in the dominant accumulation of citations by the US, the
-UK, and Germany, from ~95% to ~50% over the same period. Our discoveries are
-meant to serve as a starter for exploring the visionary ways in which science
-has developed throughout the past century, generating insight into and an
-impact upon the current scientific innovations and funding policies.
-",1,1,0,0,0,0
-17683,Interval-type theorems concerning means,"  Each family $\mathcal{M}$ of means has a natural, partial order (point-wise
-order), that is $M \le N$ iff $M(x) \le N(x)$ for all admissible $x$.
-In this setting we can introduce the notion of interval-type set (a subset
-$\mathcal{I} \subset \mathcal{M}$ such that whenever $M \le P \le N$ for some
-$M,\,N \in \mathcal{I}$ and $P \in \mathcal{M}$ then $P \in \mathcal{I}$). For
-example, in the case of power means there exists a natural isomorphism between
-interval-type sets and intervals contained in real numbers. Nevertheless there
-appear a number of interesting objects for a families which cannot be linearly
-ordered.
-In the present paper we consider this property for Gini means and Hardy
-means. Moreover some results concerning $L^\infty$ metric among (abstract)
-means will be obtained.
-",0,0,1,0,0,0
-17684,Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning,"  We propose a fully distributed actor-critic algorithm approximated by deep
-neural networks, named \textit{Diff-DAC}, with application to single-task and
-to average multitask reinforcement learning (MRL). Each agent has access to
-data from its local task only, but it aims to learn a policy that performs well
-on average for the whole set of tasks. During the learning process, agents
-communicate their value-policy parameters to their neighbors, diffusing the
-information across the network, so that they converge to a common policy, with
-no need for a central node. The method is scalable, since the computational and
-communication costs per agent grow with its number of neighbors. We derive
-Diff-DAC's from duality theory and provide novel insights into the standard
-actor-critic framework, showing that it is actually an instance of the dual
-ascent method that approximates the solution of a linear program. Experiments
-suggest that Diff-DAC can outperform the single previous distributed MRL
-approach (i.e., Dist-MTLPS) and even the centralized architecture.
-",1,0,0,1,0,0
-17685,On locally compact semitopological $0$-bisimple inverse $ω$-semigroups,"  We describe the structure of Hausdorff locally compact semitopological
-$0$-bisimple inverse $\omega$-semigroups with compact maximal subgroups. In
-particular, we show that a Hausdorff locally compact semitopological
-$0$-bisimple inverse $\omega$-semigroup with a compact maximal subgroup is
-either compact or topologically isomorphic to the topological sum of its
-$\mathscr{H}$-classes. We describe the structure of Hausdorff locally compact
-semitopological $0$-bisimple inverse $\omega$-semigroups with a monothetic
-maximal subgroups. In particular we prove the dichotomy for $T_1$ locally
-compact semitopological Reilly semigroup
-$\left(\textbf{B}(\mathbb{Z}_{+},\theta)^0,\tau\right)$ with adjoined zero and
-with a non-annihilating homomorphism $\theta\colon \mathbb{Z}_{+}\to
-\mathbb{Z}_{+}$: $\left(\textbf{B}(\mathbb{Z}_{+},\theta)^0,\tau\right)$ is
-either compact or discrete. At the end we discuss on the remainder under the
-closure of the discrete Reilly semigroup $\textbf{B}(\mathbb{Z}_{+},\theta)^0$
-in a semitopological semigroup.
-",0,0,1,0,0,0
-17686,Theory of Disorder-Induced Half-Integer Thermal Hall Conductance,"  Electrons that are confined to a single Landau level in a two dimensional
-electron gas realize the effects of strong electron-electron repulsion in its
-purest form. The kinetic energy of individual electrons is completely quenched
-and all physical properties are dictated solely by many-body effects. A
-remarkable consequence is the emergence of new quasiparticles with fractional
-charge and exotic quantum statistics of which the most exciting ones are
-non-Abelian quasiparticles. A non-integer quantized thermal Hall conductance
-$\kappa_{xy}$ (in units of temperature times the universal constant $\pi^2
-k_B^2 /3 h$; $h$ is the Planck constant and $k_B$ the Boltzmann constant)
-necessitates the existence of such quasiparticles. It has been predicted, and
-verified numerically, that such states are realized in the clean half-filled
-first Landau level of electrons with Coulomb repulsion, with $\kappa_{xy}$
-being either $3/2$ or $7/2$. Excitingly, a recent experiment has indeed
-observed a half-integer value, which was measured, however, to be
-$\kappa_{xy}=5/2$. We resolve this contradiction within a picture where smooth
-disorder results in the formation of mesoscopic puddles with locally
-$\kappa_{xy}=3/2$ or $7/2$. Interactions between these puddles generate a
-coherent macroscopic state, which is reflected in an extended plateau with
-quantized $\kappa_{xy}=5/2$. The topological properties of quasiparticles at
-large distances are determined by the macroscopic phase, and not by the
-microscopic puddle where they reside. In principle, the same mechanism might
-also allow non-Abelian quasiparticles to emerge from a system comprised of
-microscopic Abelian puddles.
-",0,1,0,0,0,0
-17687,On Security Research Towards Future Mobile Network Generations,"  Over the last decades, numerous security and privacy issues in all three
-active mobile network generations have been revealed that threaten users as
-well as network providers. In view of the newest generation (5G) currently
-under development, we now have the unique opportunity to identify research
-directions for the next generation based on existing security and privacy
-issues as well as already proposed defenses. This paper aims to unify security
-knowledge on mobile phone networks into a comprehensive overview and to derive
-pressing open research questions. To achieve this systematically, we develop a
-methodology that categorizes known attacks by their aim, proposed defenses,
-underlying causes, and root causes. Further, we assess the impact and the
-efficacy of each attack and defense. We then apply this methodology to existing
-literature on attacks and defenses in all three network generations. By doing
-so, we identify ten causes and four root causes of attacks. Mapping the attacks
-to proposed defenses and suggestions for the 5G specification enables us to
-uncover open research questions and challenges for the development of
-next-generation mobile networks. The problems of unsecured pre-authentication
-traffic and jamming attacks exist across all three mobile generations. They
-should be addressed in the future, in particular, to wipe out the class of
-downgrade attacks and, thereby, strengthen the users' privacy. Further advances
-are needed in the areas of inter-operator protocols as well as secure baseband
-implementations. Additionally, mitigations against denial-of-service attacks by
-smart protocol design represent an open research question.
-",1,0,0,0,0,0
-17688,Channel Simulation in Quantum Metrology,"  In this review we discuss how channel simulation can be used to simplify the
-most general protocols of quantum parameter estimation, where unlimited
-entanglement and adaptive joint operations may be employed. Whenever the
-unknown parameter encoded in a quantum channel is completely transferred in an
-environmental program state simulating the channel, the optimal adaptive
-estimation cannot beat the standard quantum limit. In this setting, we
-elucidate the crucial role of quantum teleportation as a primitive operation
-which allows one to completely reduce adaptive protocols over suitable
-teleportation-covariant channels and derive matching upper and lower bounds for
-parameter estimation. For these channels, we may express the quantum Cramér
-Rao bound directly in terms of their Choi matrices. Our review considers both
-discrete- and continuous-variable systems, also presenting some new results for
-bosonic Gaussian channels using an alternative sub-optimal simulation. It is an
-open problem to design simulations for quantum channels that achieve the
-Heisenberg limit.
-",0,1,0,0,0,0
-17689,Ranking Causal Influence of Financial Markets via Directed Information Graphs,"  A non-parametric method for ranking stock indices according to their mutual
-causal influences is presented. Under the assumption that indices reflect the
-underlying economy of a country, such a ranking indicates which countries exert
-the most economic influence in an examined subset of the global economy. The
-proposed method represents the indices as nodes in a directed graph, where the
-edges' weights are estimates of the pair-wise causal influences, quantified
-using the directed information functional. This method facilitates using a
-relatively small number of samples from each index. The indices are then ranked
-according to their net-flow in the estimated graph (sum of the incoming weights
-subtracted from the sum of outgoing weights). Daily and minute-by-minute data
-from nine indices (three from Asia, three from Europe and three from the US)
-were analyzed. The analysis of daily data indicates that the US indices are the
-most influential, which is consistent with intuition that the indices
-representing larger economies usually exert more influence. Yet, it is also
-shown that an index representing a small economy can strongly influence an
-index representing a large economy if the smaller economy is indicative of a
-larger phenomenon. Finally, it is shown that while inter-region interactions
-can be captured using daily data, intra-region interactions require more
-frequent samples.
-",0,0,0,0,0,1
-17690,Shear banding in metallic glasses described by alignments of Eshelby quadrupoles,"  Plastic deformation of metallic glasses performed well below the glass
-transition temperature leads to the formation of shear bands as a result of
-shear localization. It is believed that shear banding originates from
-individual stress concentrators having quadrupolar symmetry. To elucidate the
-underlying mechanisms of shear band formation, microstructural investigations
-were carried out on sheared zones using transmission electron microscopy. Here
-we show evidence of a characteristic signature present in shear bands
-manifested in the form of sinusoidal density variations. We present an
-analytical solution for the observed post-deformation state derived from
-continuum mechanics using an alignment of quadrupolar stress field
-perturbations for the plastic events. Since we observe qualitatively similar
-features for three different types of metallic glasses that span the entire
-range of characteristic properties of metallic glasses, we conclude that the
-reported deformation behavior is generic for all metallic glasses, and thus has
-far-reaching consequences for the deformation behavior of amorphous solids in
-general.
-",0,1,0,0,0,0
-17691,Detection of low dimensionality and data denoising via set estimation techniques,"  This work is closely related to the theories of set estimation and manifold
-estimation.
-Our object of interest is a, possibly lower-dimensional, compact set $S
-\subset {\mathbb R}^d$.
-The general aim is to identify (via stochastic procedures) some qualitative
-or quantitative features of $S$, of geometric or topological character. The
-available information is just a random sample of points drawn on $S$.
-The term ""to identify"" means here to achieve a correct answer almost surely
-(a.s.) when the sample size tends to infinity. More specifically the paper aims
-at giving some partial answers to the following questions: is $S$ full
-dimensional? Is $S$ ""close to a lower dimensional set"" $\mathcal{M}$? If so,
-can we estimate $\mathcal{M}$ or some functionals of $\mathcal{M}$ (in
-particular, the Minkowski content of $\mathcal{M}$)? As an important auxiliary
-tool in the answers of these questions, a denoising procedure is proposed in
-order to partially remove the noise in the original data. The theoretical
-results are complemented with some simulations and graphical illustrations.
-",0,0,1,1,0,0
-17692,Deep Networks with Shape Priors for Nucleus Detection,"  Detection of cell nuclei in microscopic images is a challenging research
-topic, because of limitations in cellular image quality and diversity of
-nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between
-multiple cell nuclei. This has been a topic of enduring interest with promising
-recent success shown by deep learning methods. These methods train for example
-convolutional neural networks (CNNs) with a training set of input images and
-known, labeled nuclei locations. Many of these methods are supplemented by
-spatial or morphological processing. We develop a new approach that we call
-Shape Priors with Convolutional Neural Networks (SP-CNN) to perform
-significantly enhanced nuclei detection. A set of canonical shapes is prepared
-with the help of a domain expert. Subsequently, we present a new network
-structure that can incorporate `expected behavior' of nucleus shapes via two
-components: {\em learnable} layers that perform the nucleus detection and a
-{\em fixed} processing part that guides the learning with prior information.
-Analytically, we formulate a new regularization term that is targeted at
-penalizing false positives while simultaneously encouraging detection inside
-cell nucleus boundary. Experimental results on a challenging dataset reveal
-that SP-CNN is competitive with or outperforms several state-of-the-art
-methods.
-",0,0,0,1,0,0
-17693,"A sheaf-theoretic model for SL(2,C) Floer homology","  Given a Heegaard splitting of a three-manifold Y, we consider the SL(2,C)
-character variety of the Heegaard surface, and two complex Lagrangians
-associated to the handlebodies. We focus on the smooth open subset
-corresponding to irreducible representations. On that subset, the intersection
-of the Lagrangians is an oriented d-critical locus in the sense of Joyce. Bussi
-associates to such an intersection a perverse sheaf of vanishing cycles. We
-prove that in our setting, the perverse sheaf is an invariant of Y, i.e., it is
-independent of the Heegaard splitting. The hypercohomology of this sheaf can be
-viewed as a model for (the dual of) SL(2,C) instanton Floer homology. We also
-present a framed version of this construction, which takes into account
-reducible representations. We give explicit computations for lens spaces and
-Brieskorn spheres, and discuss the connection to the Kapustin-Witten equations
-and Khovanov homology.
-",0,0,1,0,0,0
-17694,Discriminative models for multi-instance problems with tree-structure,"  Modeling network traffic is gaining importance in order to counter modern
-threats of ever increasing sophistication. It is though surprisingly difficult
-and costly to construct reliable classifiers on top of telemetry data due to
-the variety and complexity of signals that no human can manage to interpret in
-full. Obtaining training data with sufficiently large and variable body of
-labels can thus be seen as prohibitive problem. The goal of this work is to
-detect infected computers by observing their HTTP(S) traffic collected from
-network sensors, which are typically proxy servers or network firewalls, while
-relying on only minimal human input in model training phase. We propose a
-discriminative model that makes decisions based on all computer's traffic
-observed during predefined time window (5 minutes in our case). The model is
-trained on collected traffic samples over equally sized time window per large
-number of computers, where the only labels needed are human verdicts about the
-computer as a whole (presumed infected vs. presumed clean). As part of training
-the model itself recognizes discriminative patterns in traffic targeted to
-individual servers and constructs the final high-level classifier on top of
-them. We show the classifier to perform with very high precision, while the
-learned traffic patterns can be interpreted as Indicators of Compromise. In the
-following we implement the discriminative model as a neural network with
-special structure reflecting two stacked multi-instance problems. The main
-advantages of the proposed configuration include not only improved accuracy and
-ability to learn from gross labels, but also automatic learning of server types
-(together with their detectors) which are typically visited by infected
-computers.
-",1,0,0,0,0,0
-17695,Functional advantages offered by many-body coherences in biochemical systems,"  Quantum coherence phenomena driven by electronic-vibrational (vibronic)
-interactions, are being reported in many pulse (e.g. laser) driven chemical and
-biophysical systems. But what systems-level advantage(s) do such many-body
-coherences offer to future technologies? We address this question for pulsed
-systems of general size N, akin to the LHCII aggregates found in green plants.
-We show that external pulses generate vibronic states containing particular
-multipartite entanglements, and that such collective vibronic states increase
-the excitonic transfer efficiency. The strength of these many-body coherences
-and their robustness to decoherence, increase with aggregate size N and do not
-require strong electronic-vibrational coupling. The implications for energy and
-information transport are discussed.
-",0,1,0,0,0,0
-17696,Privacy Mining from IoT-based Smart Homes,"  Recently, a wide range of smart devices are deployed in a variety of
-environments to improve the quality of human life. One of the important
-IoT-based applications is smart homes for healthcare, especially for elders.
-IoT-based smart homes enable elders' health to be properly monitored and taken
-care of. However, elders' privacy might be disclosed from smart homes due to
-non-fully protected network communication or other reasons. To demonstrate how
-serious this issue is, we introduce in this paper a Privacy Mining Approach
-(PMA) to mine privacy from smart homes by conducting a series of deductions and
-analyses on sensor datasets generated by smart homes. The experimental results
-demonstrate that PMA is able to deduce a global sensor topology for a smart
-home and disclose elders' privacy in terms of their house layouts.
-",0,0,0,1,0,0
-17697,Idempotent ordered semigroup,"  An element e of an ordered semigroup $(S,\cdot,\leq)$ is called an ordered
-idempotent if $e\leq e^2$. We call an ordered semigroup $S$ idempotent ordered
-semigroup if every element of $S$ is an ordered idempotent. Every idempotent
-semigroup is a complete semilattice of rectangular idempotent semigroups and in
-this way we arrive to many other important classes of idempotent ordered
-semigroups.
-",0,0,1,0,0,0
-17698,Hybrid bounds for twists of $GL(3)$ $L$-functions,"  Let $\pi$ be a Hecke-Maass cusp form for $SL(3,\mathbb{Z})$ and
-$\chi=\chi_1\chi_2$ a Dirichlet character with $\chi_i$ primitive modulo $M_i$.
-Suppose that $M_1$, $M_2$ are primes such that
-$\max\{(M|t|)^{1/3+2\delta/3},M^{2/5}|t|^{-9/20},
-M^{1/2+2\delta}|t|^{-3/4+2\delta}\}(M|t|)^{\varepsilon}<M_1< \min\{
-(M|t|)^{2/5},(M|t|)^{1/2-8\delta}\}(M|t|)^{-\varepsilon}$ for any
-$\varepsilon>0$, where $M=M_1M_2$, $|t|\geq 1$ and $0<\delta< 1/52$. Then we
-have $$ L\left(\frac{1}{2}+it,\pi\otimes \chi\right)\ll_{\pi,\varepsilon}
-(M|t|)^{3/4-\delta+\varepsilon}. $$
-",0,0,1,0,0,0
-17699,Modeling stochastic skew of FX options using SLV models with stochastic spot/vol correlation and correlated jumps,"  It is known that the implied volatility skew of FX options demonstrates a
-stochastic behavior which is called stochastic skew. In this paper we create
-stochastic skew by assuming the spot/instantaneous variance correlation to be
-stochastic. Accordingly, we consider a class of SLV models with stochastic
-correlation where all drivers - the spot, instantaneous variance and their
-correlation are modeled by Levy processes. We assume all diffusion components
-to be fully correlated as well as all jump components. A new fully implicit
-splitting finite-difference scheme is proposed for solving forward PIDE which
-is used when calibrating the model to market prices of the FX options with
-different strikes and maturities. The scheme is unconditionally stable, of
-second order of approximation in time and space, and achieves a linear
-complexity in each spatial direction. The results of simulation obtained by
-using this model demonstrate capacity of the presented approach in modeling
-stochastic skew.
-",0,0,1,0,0,0
-17700,Computational Study of Amplitude-to-Phase Conversion in a Modified Uni-Traveling Carrier (MUTC) Photodetector,"  We calculate the amplitude-to-phase (AM-to-PM) noise conversion in a modified
-unitraveling carrier (MUTC) photodetector. We obtained two nulls as measured in
-the experiments, and we explain their origin. The nulls appear due to the
-transit time variation when the average photocurrent varies, and the transit
-time variation is due to the change of electron velocity when the average
-photocurrent varies. We also show that the AM-to-PM conversion coefficient
-depends only on the pulse energy and is independent of the pulse duration when
-the duration is less than 500 fs. When the pulse duration is larger than 500
-fs, the nulls of the AM-to-PM conversion coefficient shift to larger average
-photocurrents. This shift occurs because the increase in that pulse duration
-leads to a decrease in the peak photocurrent. The AM-to-PM noise conversion
-coefficient changes as the repetition rate varies. However, the repetition rate
-does not change the AM-to-PM conversion coefficient as a function of input
-optical pulse energy. The repetition rate changes the average photocurrent. We
-propose a design that would in theory improve the performance of the device.
-",0,1,0,0,0,0
-17701,Direct frequency-comb spectroscopy of $6S_{1/2}$-$8S_{1/2}$ transitions of atomic cesium,"  Direct frequency-comb spectroscopy is used to probe the absolute frequencies
-of $6S_{1/2}$-$8S_{1/2}$ two-photon transitions of atomic cesium in hot vapor
-environment. By utilizing the coherent control method of temporally splitting
-the laser spectrum above and below the two-photon resonance frequency,
-Doppler-free absorption is built in two spatially distinct locations and imaged
-for high-precision spectroscopy. Theoretical analysis finds that these
-transition lines are measured with uncertainty below $5\times10^{-10}$, mainly
-contributed from laser-induced AC Stark shift.
-",0,1,0,0,0,0
-17702,Aggregation and Disaggregation of Energetic Flexibility from Distributed Energy Resources,"  A variety of energy resources has been identified as being flexible in their
-electric energy consumption or generation. This energetic flexibility can be
-used for various purposes such as minimizing energy procurement costs or
-providing ancillary services to power grids. To fully leverage the flexibility
-available from distributed small-scale resources, their flexibility must be
-quantified and aggregated.
-This paper introduces a generic and scalable approach for flexible energy
-systems to quantitatively describe and price their flexibility based on
-zonotopic sets. The description proposed allows aggregators to efficiently pool
-the flexibility of large numbers of systems and to make control and market
-decisions on the aggregate level. In addition, an algorithm is presented that
-distributes aggregate-level control decisions among the individual systems of
-the pool in an economically fair and computationally efficient way. Finally, it
-is shown how the zonotopic description of flexibility enables an efficient
-computation of aggregate regulation power bid-curves.
-",1,0,0,0,0,0
-17703,Mapping Walls of Indoor Environment using RGB-D Sensor,"  Inferring walls configuration of indoor environment could help robot
-""understand"" the environment better. This allows the robot to execute a task
-that involves inter-room navigation, such as picking an object in the kitchen.
-In this paper, we present a method to inferring walls configuration from a
-moving RGB-D sensor. Our goal is to combine a simple wall configuration model
-and fast wall detection method in order to get a system that works online, is
-real-time, and does not need a Manhattan World assumption. We tested our
-preliminary work, i.e. wall detection and measurement from moving RGB-D sensor,
-with MIT Stata Center Dataset. The performance of our method is reported in
-terms of accuracy and speed of execution.
-",1,0,0,0,0,0
-17704,The Effects of Memory Replay in Reinforcement Learning,"  Experience replay is a key technique behind many recent advances in deep
-reinforcement learning. Allowing the agent to learn from earlier memories can
-speed up learning and break undesirable temporal correlations. Despite its
-wide-spread application, very little is understood about the properties of
-experience replay. How does the amount of memory kept affect learning dynamics?
-Does it help to prioritize certain experiences? In this paper, we address these
-questions by formulating a dynamical systems ODE model of Q-learning with
-experience replay. We derive analytic solutions of the ODE for a simple
-setting. We show that even in this very simple setting, the amount of memory
-kept can substantially affect the agent's performance. Too much or too little
-memory both slow down learning. Moreover, we characterize regimes where
-prioritized replay harms the agent's learning. We show that our analytic
-solutions have excellent agreement with experiments. Finally, we propose a
-simple algorithm for adaptively changing the memory buffer size which achieves
-consistently good empirical performance.
-",1,0,0,1,0,0
-17705,Portfolio Optimization for Cointelated Pairs: SDEs vs. Machine Learning,"  We investigate the problem of dynamic portfolio optimization in
-continuous-time, finite-horizon setting for a portfolio of two stocks and one
-risk-free asset. The stocks follow the Cointelation model. The proposed
-optimization methods are twofold. In what we call an Stochastic Differential
-Equation approach, we compute the optimal weights using mean-variance criterion
-and power utility maximization. We show that dynamically switching between
-these two optimal strategies by introducing a triggering function can further
-improve the portfolio returns. We contrast this with the machine learning
-clustering methodology inspired by the band-wise Gaussian mixture model. The
-first benefit of the machine learning over the Stochastic Differential Equation
-approach is that we were able to achieve the same results though a simpler
-channel. The second advantage is a flexibility to regime change.
-",0,0,0,0,0,1
-17706,High-mass Starless Clumps in the inner Galactic Plane: the Sample and Dust Properties,"  We report a sample of 463 high-mass starless clump (HMSC) candidates within
-$-60°<l<60°$ and $-1°<b<1°$. This sample has been singled out from
-10861 ATLASGAL clumps. All of these sources are not associated with any known
-star-forming activities collected in SIMBAD and young stellar objects
-identified using color-based criteria. We also make sure that the HMSC
-candidates have neither point sources at 24 and 70 \micron~nor strong extended
-emission at 24 $\mu$m. Most of the identified HMSCs are infrared ($\le24$
-$\mu$m) dark and some are even dark at 70 $\mu$m. Their distribution shows
-crowding in Galactic spiral arms and toward the Galactic center and some
-well-known star-forming complexes. Many HMSCs are associated with large-scale
-filaments. Some basic parameters were attained from column density and dust
-temperature maps constructed via fitting far-infrared and submillimeter
-continuum data to modified blackbodies. The HMSC candidates have sizes, masses,
-and densities similar to clumps associated with Class II methanol masers and
-HII regions, suggesting they will evolve into star-forming clumps. More than
-90% of the HMSC candidates have densities above some proposed thresholds for
-forming high-mass stars. With dust temperatures and luminosity-to-mass ratios
-significantly lower than that for star-forming sources, the HMSC candidates are
-externally heated and genuinely at very early stages of high-mass star
-formation. Twenty sources with equivalent radius $r_\mathrm{eq}<0.15$ pc and
-mass surface density $\Sigma>0.08$ g cm$^{-2}$ could be possible high-mass
-starless cores. Further investigations toward these HMSCs would undoubtedly
-shed light on comprehensively understanding the birth of high-mass stars.
-",0,1,0,0,0,0
-17707,Biologically Plausible Online Principal Component Analysis Without Recurrent Neural Dynamics,"  Artificial neural networks that learn to perform Principal Component Analysis
-(PCA) and related tasks using strictly local learning rules have been
-previously derived based on the principle of similarity matching: similar pairs
-of inputs should map to similar pairs of outputs. However, the operation of
-these networks (and of similar networks) requires a fixed-point iteration to
-determine the output corresponding to a given input, which means that dynamics
-must operate on a faster time scale than the variation of the input. Further,
-during these fast dynamics such networks typically ""disable"" learning, updating
-synaptic weights only once the fixed-point iteration has been resolved. Here,
-we derive a network for PCA-based dimensionality reduction that avoids this
-fast fixed-point iteration. The key novelty of our approach is a modification
-of the similarity matching objective to encourage near-diagonality of a
-synaptic weight matrix. We then approximately invert this matrix using a Taylor
-series approximation, replacing the previous fast iterations. In the offline
-setting, our algorithm corresponds to a dynamical system, the stability of
-which we rigorously analyze. In the online setting (i.e., with stochastic
-gradients), we map our algorithm to a familiar neural network architecture and
-give numerical results showing that our method converges at a competitive rate.
-The computational complexity per iteration of our online algorithm is linear in
-the total degrees of freedom, which is in some sense optimal.
-",0,0,0,1,1,0
-17708,Exploring to learn visual saliency: The RL-IAC approach,"  The problem of object localization and recognition on autonomous mobile
-robots is still an active topic. In this context, we tackle the problem of
-learning a model of visual saliency directly on a robot. This model, learned
-and improved on-the-fly during the robot's exploration provides an efficient
-tool for localizing relevant objects within their environment. The proposed
-approach includes two intertwined components. On the one hand, we describe a
-method for learning and incrementally updating a model of visual saliency from
-a depth-based object detector. This model of saliency can also be exploited to
-produce bounding box proposals around objects of interest. On the other hand,
-we investigate an autonomous exploration technique to efficiently learn such a
-saliency model. The proposed exploration, called Reinforcement
-Learning-Intelligent Adaptive Curiosity (RL-IAC) is able to drive the robot's
-exploration so that samples selected by the robot are likely to improve the
-current model of saliency. We then demonstrate that such a saliency model
-learned directly on a robot outperforms several state-of-the-art saliency
-techniques, and that RL-IAC can drastically decrease the required time for
-learning a reliable saliency model.
-",1,0,0,0,0,0
-17709,Fast and Robust Shortest Paths on Manifolds Learned from Data,"  We propose a fast, simple and robust algorithm for computing shortest paths
-and distances on Riemannian manifolds learned from data. This amounts to
-solving a system of ordinary differential equations (ODEs) subject to boundary
-conditions. Here standard solvers perform poorly because they require
-well-behaved Jacobians of the ODE, and usually, manifolds learned from data
-imply unstable and ill-conditioned Jacobians. Instead, we propose a fixed-point
-iteration scheme for solving the ODE that avoids Jacobians. This enhances the
-stability of the solver, while reduces the computational cost. In experiments
-involving both Riemannian metric learning and deep generative models we
-demonstrate significant improvements in speed and stability over both
-general-purpose state-of-the-art solvers as well as over specialized solvers.
-",1,0,0,1,0,0
-17710,Typed Graph Networks,"  Recently, the deep learning community has given growing attention to neural
-architectures engineered to learn problems in relational domains. Convolutional
-Neural Networks employ parameter sharing over the image domain, tying the
-weights of neural connections on a grid topology and thus enforcing the
-learning of a number of convolutional kernels. By instantiating trainable
-neural modules and assembling them in varied configurations (apart from grids),
-one can enforce parameter sharing over graphs, yielding models which can
-effectively be fed with relational data. In this context, vertices in a graph
-can be projected into a hyperdimensional real space and iteratively refined
-over many message-passing iterations in an end-to-end differentiable
-architecture. Architectures of this family have been referred to with several
-definitions in the literature, such as Graph Neural Networks, Message-passing
-Neural Networks, Relational Networks and Graph Networks. In this paper, we
-revisit the original Graph Neural Network model and show that it generalises
-many of the recent models, which in turn benefit from the insight of thinking
-about vertex \textbf{types}. To illustrate the generality of the original
-model, we present a Graph Neural Network formalisation, which partitions the
-vertices of a graph into a number of types. Each type represents an entity in
-the ontology of the problem one wants to learn. This allows - for instance -
-one to assign embeddings to edges, hyperedges, and any number of global
-attributes of the graph. As a companion to this paper we provide a
-Python/Tensorflow library to facilitate the development of such architectures,
-with which we instantiate the formalisation to reproduce a number of models
-proposed in the current literature.
-",1,0,0,1,0,0
-17711,The effect of different in-chain impurities on the magnetic properties of the spin chain compound SrCuO$_2$ probed by NMR,"  The S=1/2 Heisenberg spin chain compound SrCuO2 doped with different amounts
-of nickel (Ni), palladium (Pd), zinc (Zn) and cobalt (Co) has been studied by
-means of Cu nuclear magnetic resonance (NMR). Replacing only a few of the S=1/2
-Cu ions with Ni, Pd, Zn or Co has a major impact on the magnetic properties of
-the spin chain system. In the case of Ni, Pd and Zn an unusual line broadening
-in the low temperature NMR spectra reveals the existence of an impurity-induced
-local alternating magnetization (LAM), while exponentially decaying
-spin-lattice relaxation rates $T_1^{-1}$ towards low temperatures indicate the
-opening of spin gaps. A distribution of gap magnitudes is proven by a stretched
-spin-lattice relaxation and a variation of $T_1^{-1}$ within the broad
-resonance lines. These observations depend strongly on the impurity
-concentration and therefore can be understood using the model of finite
-segments of the spin 1/2 antiferromagnetic Heisenberg chain, i.e. pure chain
-segmentation due to S = 0 impurities. This is surprising for Ni as it was
-previously assumed to be a magnetic impurity with S = 1 which is screened by
-the neighboring copper spins. In order to confirm the S = 0 state of the Ni, we
-performed x-ray absorption spectroscopy (XAS) and compared the measurements to
-simulated XAS spectra based on multiplet ligand-field theory. Furthermore, Zn
-doping leads to much smaller effects on both the NMR spectra and the
-spin-lattice relaxation rates, indicating that Zn avoids occupying Cu sites.
-For magnetic Co impurities, $T_1^{-1}$ does not obey the gap like decrease, and
-the low-temperature spectra get very broad. This could be related to the
-increase of the Neel temperature which was observed by recent muSR and
-susceptibility measurements, and is most likely an effect of the impurity spin
-$S\neq0$.
-",0,1,0,0,0,0
-17712,L-groups and the Langlands program for covering groups: a historical introduction,"  In this joint introduction to an Asterisque volume, we give a short
-discussion of the historical developments in the study of nonlinear covering
-groups, touching on their structure theory, representation theory and the
-theory of automorphic forms. This serves as a historical motivation and sets
-the scene for the papers in the volume. Our discussion is necessarily
-subjective and will undoubtedly leave out the contributions of many authors, to
-whom we apologize in earnest.
-",0,0,1,0,0,0
-17713,Uncertainty Reduction for Stochastic Processes on Complex Networks,"  Many real-world systems are characterized by stochastic dynamical rules where
-a complex network of interactions among individual elements probabilistically
-determines their state. Even with full knowledge of the network structure and
-of the stochastic rules, the ability to predict system configurations is
-generally characterized by a large uncertainty. Selecting a fraction of the
-nodes and observing their state may help to reduce the uncertainty about the
-unobserved nodes. However, choosing these points of observation in an optimal
-way is a highly nontrivial task, depending on the nature of the stochastic
-process and on the structure of the underlying interaction pattern. In this
-paper, we introduce a computationally efficient algorithm to determine
-quasioptimal solutions to the problem. The method leverages network sparsity to
-reduce computational complexity from exponential to almost quadratic, thus
-allowing the straightforward application of the method to mid-to-large-size
-systems. Although the method is exact only for equilibrium stochastic processes
-defined on trees, it turns out to be effective also for out-of-equilibrium
-processes on sparse loopy networks.
-",1,1,0,0,0,0
-17714,Light propagation in Extreme Conditions - The role of optically clear tissues and scattering layers in optical biomedical imaging,"  The field of biomedical imaging has undergone a rapid growth in recent years,
-mostly due to the implementation of ad-hoc designed experimental setups,
-theoretical support methods and numerical reconstructions. Especially for
-biological samples, the high number of scattering events occurring during the
-photon propagation process limit the penetration depth and the possibility to
-outperform direct imaging in thicker and not transparent samples. In this
-thesis, we will examine theoretically and experimentally the scattering process
-from two opposite points of view, focusing also on the continuous stimulus
-offered by the will to tackle some specific challenges in the emerging optical
-imaging science. Firstly, we will discuss the light propagation in diffusive
-biological tissues considering the particular case of the presence of optically
-transparent regions enclosed in a highly scattering environment. The correct
-inclusion of this information, can ultimately lead to higher resolution
-reconstruction, especially in neuroimaging. On the other hand, we will examine
-the extreme case of the three-dimensional imaging of a totally hidden sample,
-in which the phase has been scrambled by a random scattering layer. By making
-use of appropriate numerical methods, we will prove how it is possible to
-outperform such hidden reconstruction in a very efficient way, opening the path
-toward the unexplored field of three-dimensional hidden imaging. Finally, we
-will present how, the properties noticed while addressing these problems,
-leaded us to the development of a novel alignment-free three-dimensional
-tomographic technique that we refer to as Phase-Retrieved Tomography.
-Ultimately, we used this technique for the study of the fluorescence
-distribution in a three-dimensional spherical tumor model, the cancer cell
-spheroid, one of the most important biological model for the study of such
-disease.
-",0,1,0,0,0,0
-17715,Design discussion on the ISDA Common Domain Model,"  A new initiative from the International Swaps and Derivatives Association
-(ISDA) aims to establish a ""Common Domain Model"" (ISDA CDM): a new standard for
-data and process representation across the full range of derivatives
-instruments. Design of the ISDA CDM is at an early stage and the draft
-definition contains considerable complexity. This paper contributes by offering
-insight, analysis and discussion relating to key topics in the design space
-such as data lineage, timestamps, consistency, operations, events, state and
-state transitions.
-",1,0,0,0,0,0
-17716,Preserving Intermediate Objectives: One Simple Trick to Improve Learning for Hierarchical Models,"  Hierarchical models are utilized in a wide variety of problems which are
-characterized by task hierarchies, where predictions on smaller subtasks are
-useful for trying to predict a final task. Typically, neural networks are first
-trained for the subtasks, and the predictions of these networks are
-subsequently used as additional features when training a model and doing
-inference for a final task. In this work, we focus on improving learning for
-such hierarchical models and demonstrate our method on the task of speaker
-trait prediction. Speaker trait prediction aims to computationally identify
-which personality traits a speaker might be perceived to have, and has been of
-great interest to both the Artificial Intelligence and Social Science
-communities. Persuasiveness prediction in particular has been of interest, as
-persuasive speakers have a large amount of influence on our thoughts, opinions
-and beliefs. In this work, we examine how leveraging the relationship between
-related speaker traits in a hierarchical structure can help improve our ability
-to predict how persuasive a speaker is. We present a novel algorithm that
-allows us to backpropagate through this hierarchy. This hierarchical model
-achieves a 25% relative error reduction in classification accuracy over current
-state-of-the art methods on the publicly available POM dataset.
-",1,0,0,0,0,0
-17717,Atomistic simulations of dislocation/precipitation interactions in Mg-Al alloys and implications for precipitation hardening,"  Atomistic simulations were carried out to analyze the interaction between $<
-a>$ basal dislocations and precipitates in Mg-Al alloys and the associated
-strengthening mechanisms.
-",0,1,0,0,0,0
-17718,Recovering Dense Tissue Multispectral Signal from in vivo RGB Images,"  Hyperspectral/multispectral imaging (HSI/MSI) contains rich information
-clinical applications, such as 1) narrow band imaging for vascular
-visualisation; 2) oxygen saturation for intraoperative perfusion monitoring and
-clinical decision making [1]; 3) tissue classification and identification of
-pathology [2]. The current systems which provide pixel-level HSI/MSI signal can
-be generally divided into two types: spatial scanning and spectral scanning.
-However, the trade-off between spatial/spectral resolution, the acquisition
-time, and the hardware complexity hampers implementation in real-world
-applications, especially intra-operatively. Acquiring high resolution images in
-real-time is important for HSI/MSI in intra-operative imaging, to alleviate the
-side effect caused by breathing, heartbeat, and other sources of motion.
-Therefore, we developed an algorithm to recover a pixel-level MSI stack using
-only the captured snapshot RGB images from a normal camera. We refer to this
-technique as ""super-spectral-resolution"". The proposed method enables recovery
-of pixel-level-dense MSI signals with 24 spectral bands at ~11 frames per
-second (FPS) on a GPU. Multispectral data captured from porcine bowel and
-sheep/rabbit uteri in vivo has been used for training, and the algorithm has
-been validated using unseen in vivo animal experiments.
-",1,0,0,0,0,0
-17719,Atomically thin gallium layers from solid-melt exfoliation,"  Among the large number of promising two-dimensional (2D) atomic layer
-crystals, true metallic layers are rare. Through combined theoretical and
-experimental approaches, we report on the stability and successful exfoliation
-of atomically thin gallenene sheets, having two distinct atomic arrangements
-along crystallographic twin directions of the parent alpha-gallium. Utilizing
-the weak interface between solid and molten phases of gallium, a solid-melt
-interface exfoliation technique is developed to extract these layers. Phonon
-dispersion calculations show that gallenene can be stabilized with bulk gallium
-lattice parameters. The electronic band structure of gallenene shows a
-combination of partially filled Dirac cone and the non-linear dispersive band
-near Fermi level suggesting that gallenene should behave as a metallic layer.
-Furthermore it is observed that strong interaction of gallenene with other 2D
-semiconductors induces semiconducting to metallic phase transitions in the
-latter paving the way for using gallenene as interesting metallic contacts in
-2D devices.
-",0,1,0,0,0,0
-17720,Novel Universality Classes in Ferroelectric Liquid Crystals,"  Starting from a Langevin formulation of a thermally perturbed nonlinear
-elastic model of the ferroelectric smectic-C$^*$ (SmC${*}$) liquid crystals in
-the presence of an electric field, this article characterizes the hitherto
-unexplored dynamical phase transition from a thermo-electrically forced
-ferroelectric SmC${}^{*}$ phase to a chiral nematic liquid crystalline phase
-and vice versa. The theoretical analysis is based on a combination of dynamic
-renormalization (DRG) and numerical simulation of the emergent model. While the
-DRG architecture predicts a generic transition to the Kardar-Parisi-Zhang (KPZ)
-universality class at dynamic equilibrium, in agreement with recent
-experiments, the numerical simulations of the model show simultaneous existence
-of two phases, one a ""subdiffusive"" (SD) phase characterized by a dynamical
-exponent value of 1, and the other a KPZ phase, characterized by a dynamical
-exponent value of 1.5. The SD phase flows over to the KPZ phase with increased
-external forcing, offering a new universality paradigm, hitherto unexplored in
-the context of ferroelectric liquid crystals.
-",0,1,0,0,0,0
-17721,Feature functional theory - binding predictor (FFT-BP) for the blind prediction of binding free energies,"  We present a feature functional theory - binding predictor (FFT-BP) for the
-protein-ligand binding affinity prediction. The underpinning assumptions of
-FFT-BP are as follows: i) representability: there exists a microscopic feature
-vector that can uniquely characterize and distinguish one protein-ligand
-complex from another; ii) feature-function relationship: the macroscopic
-features, including binding free energy, of a complex is a functional of
-microscopic feature vectors; and iii) similarity: molecules with similar
-microscopic features have similar macroscopic features, such as binding
-affinity. Physical models, such as implicit solvent models and quantum theory,
-are utilized to extract microscopic features, while machine learning algorithms
-are employed to rank the similarity among protein-ligand complexes. A large
-variety of numerical validations and tests confirms the accuracy and robustness
-of the proposed FFT-BP model. The root mean square errors (RMSEs) of FFT-BP
-blind predictions of a benchmark set of 100 complexes, the PDBBind v2007 core
-set of 195 complexes and the PDBBind v2015 core set of 195 complexes are 1.99,
-2.02 and 1.92 kcal/mol, respectively. Their corresponding Pearson correlation
-coefficients are 0.75, 0.80, and 0.78, respectively.
-",1,1,0,0,0,0
-17722,Combining and Steganography of 3D Face Textures,"  One of the serious issues in communication between people is hiding
-information from others, and the best way for this, is deceiving them. Since
-nowadays face images are mostly used in three dimensional format, in this paper
-we are going to steganography 3D face images, detecting which by curious people
-will be impossible. As in detecting face only its texture is important, we
-separate texture from shape matrices, for eliminating half of the extra
-information, steganography is done only for face texture, and for
-reconstructing 3D face, we can use any other shape. Moreover, we will indicate
-that, by using two textures, how two 3D faces can be combined. For a complete
-description of the process, first, 2D faces are used as an input for building
-3D faces, and then 3D textures are hidden within other images.
-",1,0,0,0,0,0
-17723,Degree of sequentiality of weighted automata,"  Weighted automata (WA) are an important formalism to describe quantitative
-properties. Obtaining equivalent deterministic machines is a longstanding
-research problem. In this paper we consider WA with a set semantics, meaning
-that the semantics is given by the set of weights of accepting runs. We focus
-on multi-sequential WA that are defined as finite unions of sequential WA. The
-problem we address is to minimize the size of this union. We call this minimum
-the degree of sequentiality of (the relation realized by) the WA. For a given
-positive integer k, we provide multiple characterizations of relations realized
-by a union of k sequential WA over an infinitary finitely generated group: a
-Lipschitz-like machine independent property, a pattern on the automaton (a new
-twinning property) and a subclass of cost register automata. When possible, we
-effectively translate a WA into an equivalent union of k sequential WA. We also
-provide a decision procedure for our twinning property for commutative
-computable groups thus allowing to compute the degree of sequentiality. Last,
-we show that these results also hold for word transducers and that the
-associated decision problem is Pspace-complete.
-",1,0,0,0,0,0
-17724,On the orders of the non-Frattini elements of a finite group,"  Let $G$ be a finite group and let $p_1,\dots,p_n$ be distinct primes. If $G$
-contains an element of order $p_1\cdots p_n,$ then there is an element in $G$
-which is not contained in the Frattini subgroup of $G$ and whose order is
-divisible by $p_1\cdots p_n.$
-",0,0,1,0,0,0
-17725,Varieties of general type with small volumes,"  Generalize Kobayashi's example for the Noether inequality in dimension three,
-we provide examples of n-folds of general type with small volumes.
-",0,0,1,0,0,0
-17726,Translations in the exponential Orlicz space with Gaussian weight,"  We study the continuity of space translations on non-parametric exponential
-families based on the exponential Orlicz space with Gaussian reference density.
-",0,0,1,1,0,0
-17727,Shrinkage Estimation Strategies in Generalized Ridge Regression Models Under Low/High-Dimension Regime,"  In this study, we propose shrinkage methods based on {\it generalized ridge
-regression} (GRR) estimation which is suitable for both multicollinearity and
-high dimensional problems with small number of samples (large $p$, small $n$).
-Also, it is obtained theoretical properties of the proposed estimators for
-Low/High Dimensional cases. Furthermore, the performance of the listed
-estimators is demonstrated by both simulation studies and real-data analysis,
-and compare its performance with existing penalty methods. We show that the
-proposed methods compare well to competing regularization techniques.
-",0,0,1,1,0,0
-17728,A path integral approach to Bayesian inference in Markov processes,"  We formulate Bayesian updates in Markov processes by means of path integral
-techniques and derive the imaginary-time Schrödinger equation with
-likelihood to direct the inference incorporated as a potential for the
-posterior probability distribution
-",0,0,1,1,0,0
-17729,Strong-coupling charge density wave in a one-dimensional topological metal,"  Scanning tunnelling microscopy and low energy electron diffraction show a
-dimerization-like reconstruction in the one-dimensional atomic chains on
-Bi(114) at low temperatures. While one-dimensional systems are generally
-unstable against such a distortion, its observation is not expected for this
-particular surface, since there are several factors that should prevent it: One
-is the particular spin texture of the Fermi surface, which resembles a
-one-dimensional topological state, and spin protection should hence prevent the
-formation of the reconstruction. The second is the very short nesting vector $2
-k_F$, which is inconsistent with the observed lattice distortion. A
-nesting-driven mechanism of the reconstruction is indeed excluded by the
-absence of any changes in the electronic structure near the Fermi surface, as
-observed by angle-resolved photoemission spectroscopy. However, distinct
-changes in the electronic structure at higher binding energies are found to
-accompany the structural phase transition. This, as well as the observed short
-correlation length of the pairing distortion, suggest that the transition is of
-the strong coupling type and driven by phonon entropy rather than electronic
-entropy.
-",0,1,0,0,0,0
-17730,$\mathfrak A$-principal Hopf hypersurfaces in complex quadrics,"  A real hypersurface in the complex quadric $Q^m=SO_{m+2}/SO_mSO_2$ is said to
-be $\mathfrak A$-principal if its unit normal vector field is singular of type
-$\mathfrak A$-principal everywhere. In this paper, we show that a $\mathfrak
-A$-principal Hopf hypersurface in $Q^m$, $m\geq3$ is an open part of a tube
-around a totally geodesic $Q^{m+1}$ in $Q^m$. We also show that such real
-hypersurfaces are the only contact real hypersurfaces in $Q^m$. %, this answers
-affirmatively a question posted by Berndt (cf. \cite{berndt1})}. The
-classification for pseudo-Einstein real hypersurfaces in $Q^m$, $m\geq3$, is
-also obtained.
-",0,0,1,0,0,0
-17731,On the stochastic phase stability of Ti2AlC-Cr2AlC,"  The quest towards expansion of the MAX design space has been accelerated with
-the recent discovery of several solid solution and ordered phases involving at
-least two MAX end members. Going beyond the nominal MAX compounds enables not
-only fine tuning of existing properties but also entirely new functionality.
-This search, however, has been mostly done through painstaking experiments as
-knowledge of the phase stability of the relevant systems is rather scarce. In
-this work, we report the first attempt to evaluate the finite-temperature
-pseudo-binary phase diagram of the Ti2AlC-Cr2AlC via first-principles-guided
-Bayesian CALPHAD framework that accounts for uncertainties not only in ab
-initio calculations and thermodynamic models but also in synthesis conditions
-in reported experiments. The phase stability analyses are shown to have good
-agreement with previous experiments. The work points towards a promising way of
-investigating phase stability in other MAX Phase systems providing the
-knowledge necessary to elucidate possible synthesis routes for MAX systems with
-unprecedented properties.
-",0,1,0,0,0,0
-17732,Note on Attacking Object Detectors with Adversarial Stickers,"  Deep learning has proven to be a powerful tool for computer vision and has
-seen widespread adoption for numerous tasks. However, deep learning algorithms
-are known to be vulnerable to adversarial examples. These adversarial inputs
-are created such that, when provided to a deep learning algorithm, they are
-very likely to be mislabeled. This can be problematic when deep learning is
-used to assist in safety critical decisions. Recent research has shown that
-classifiers can be attacked by physical adversarial examples under various
-physical conditions. Given the fact that state-of-the-art objection detection
-algorithms are harder to be fooled by the same set of adversarial examples,
-here we show that these detectors can also be attacked by physical adversarial
-examples. In this note, we briefly show both static and dynamic test results.
-We design an algorithm that produces physical adversarial inputs, which can
-fool the YOLO object detector and can also attack Faster-RCNN with relatively
-high success rate based on transferability. Furthermore, our algorithm can
-compress the size of the adversarial inputs to stickers that, when attached to
-the targeted object, result in the detector either mislabeling or not detecting
-the object a high percentage of the time. This note provides a small set of
-results. Our upcoming paper will contain a thorough evaluation on other object
-detectors, and will present the algorithm.
-",1,0,0,0,0,0
-17733,All or Nothing Caching Games with Bounded Queries,"  We determine the value of some search games where our goal is to find all of
-some hidden treasures using queries of bounded size. The answer to a query is
-either empty, in which case we lose, or a location, which contains a treasure.
-We prove that if we need to find $d$ treasures at $n$ possible locations with
-queries of size at most $k$, then our chance of winning is $\frac{k^d}{\binom
-nd}$ if each treasure is at a different location and
-$\frac{k^d}{\binom{n+d-1}d}$ if each location might hide several treasures for
-large enough $n$. Our work builds on some results by Csóka who has studied a
-continuous version of this problem, known as Alpern's Caching Game; we also
-prove that the value of Alpern's Caching Game is $\frac{k^d}{\binom{n+d-1}d}$
-for integer $k$ and large enough $n$.
-",1,0,1,0,0,0
-17734,"HATS-43b, HATS-44b, HATS-45b, and HATS-46b: Four Short Period Transiting Giant Planets in the Neptune-Jupiter Mass Range","  We report the discovery of four short period extrasolar planets transiting
-moderately bright stars from photometric measurements of the HATSouth network
-coupled to additional spectroscopic and photometric follow-up observations.
-While the planet masses range from 0.26 to 0.90 M$_J$, the radii are all
-approximately a Jupiter radii, resulting in a wide range of bulk densities. The
-orbital period of the planets range from 2.7d to 4.7d, with HATS-43b having an
-orbit that appears to be marginally non-circular (e= 0.173$\pm$0.089). HATS-44
-is notable for a high metallicity ([Fe/H]= 0.320$\pm$0.071). The host stars
-spectral types range from late F to early K, and all of them are moderately
-bright (13.3<V<14.4), allowing the execution of future detailed follow-up
-observations. HATS-43b and HATS-46b, with expected transmission signals of 2350
-ppm and 1500 ppm, respectively, are particularly well suited targets for
-atmospheric characterisation via transmission spectroscopy.
-",0,1,0,0,0,0
-17735,Well-balanced mesh-based and meshless schemes for the shallow-water equations,"  We formulate a general criterion for the exact preservation of the ""lake at
-rest"" solution in general mesh-based and meshless numerical schemes for the
-strong form of the shallow-water equations with bottom topography. The main
-idea is a careful mimetic design for the spatial derivative operators in the
-momentum flux equation that is paired with a compatible averaging rule for the
-water column height arising in the bottom topography source term. We prove
-consistency of the mimetic difference operators analytically and demonstrate
-the well-balanced property numerically using finite difference and RBF-FD
-schemes in the one- and two-dimensional cases.
-",0,1,1,0,0,0
-17736,An Enhanced Initial Margin Methodology to Manage Warehoused Credit Risk,"  The use of CVA to cover credit risk is widely spread, but has its
-limitations. Namely, dealers face the problem of the illiquidity of instruments
-used for hedging it, hence forced to warehouse credit risk. As a result,
-dealers tend to offer a limited OTC derivatives market to highly risky
-counterparties. Consequently, those highly risky entities rarely have access to
-hedging services precisely when they need them most. In this paper we propose a
-method to overcome this limitation. We propose to extend the CVA risk-neutral
-framework to compute an initial margin (IM) specific to each counterparty,
-which depends on the credit quality of the entity at stake, transforming the
-effective credit rating of a given netting set to AAA, regardless of the credit
-rating of the counterparty. By transforming CVA requirement into IM ones, as
-proposed in this paper, an institution could rely on the existing mechanisms
-for posting and calling of IM, hence ensuring the operational viability of this
-new form of managing warehoused risk. The main difference with the currently
-standard framework is the creation of a Specific Initial Margin, that depends
-in the credit rating of the counterparty and the characteristics of the netting
-set in question. In this paper we propose a methodology for such transformation
-in a sound manner, and hence this method overcomes some of the limitations of
-the CVA framework.
-",0,0,0,0,0,1
-17737,Ensemble Pruning based on Objection Maximization with a General Distributed Framework,"  Ensemble pruning, selecting a subset of individual learners from an original
-ensemble, alleviates the deficiencies of ensemble learning on the cost of time
-and space. Accuracy and diversity serve as two crucial factors while they
-usually conflict with each other. To balance both of them, we formalize the
-ensemble pruning problem as an objection maximization problem based on
-information entropy. Then we propose an ensemble pruning method including a
-centralized version and a distributed version, in which the latter is to speed
-up the former's execution. At last, we extract a general distributed framework
-for ensemble pruning, which can be widely suitable for most of existing
-ensemble pruning methods and achieve less time consuming without much accuracy
-decline. Experimental results validate the efficiency of our framework and
-methods, particularly with regard to a remarkable improvement of the execution
-speed, accompanied by gratifying accuracy performance.
-",0,0,0,1,0,0
-17738,Psychophysical laws as reflection of mental space properties,"  The paper is devoted to the relationship between psychophysics and physics of
-mind. The basic trends in psychophysics development are briefly discussed with
-special attention focused on Teghtsoonian's hypotheses. These hypotheses pose
-the concept of the universality of inner psychophysics and enable to speak
-about psychological space as an individual object with its own properties.
-Turning to the two-component description of human behavior (I. Lubashevsky,
-Physics of the Human Mind, Springer, 2017) the notion of mental space is
-formulated and human perception of external stimuli is treated as the emergence
-of the corresponding images in the mental space. On one hand, these images are
-caused by external stimuli and their magnitude bears the information about the
-intensity of the corresponding stimuli. On the other hand, the individual
-structure of such images as well as their subsistence after emergence is
-determined only by the properties of mental space on its own. Finally, the
-mental operations of image comparison and their scaling are defined in a way
-allowing for the bounded capacity of human cognition. As demonstrated, the
-developed theory of stimulus perception is able to explain the basic
-regularities of psychophysics, e.g., (i) the regression and range effects
-leading to the overestimation of weak stimuli and the underestimation of strong
-stimuli, (ii) scalar variability (Weber's and Ekman' laws), and (\textit{iii})
-the sequential (memory) effects. As the final result, a solution to the
-Fechner-Stevens dilemma is proposed. This solution posits that Fechner's
-logarithmic law is not a consequences of Weber's law but stems from the
-interplay of uncertainty in evaluating stimulus intensities and the multi-step
-scaling required to overcome the stimulus incommensurability.
-",0,0,0,0,1,0
-17739,Shimura curves in the Prym locus,"  We study Shimura curves of PEL type in $\mathsf{A}_g$ generically contained
-in the Prym locus. We study both the unramified Prym locus, obtained using
-étale double covers, and the ramified Prym locus, corresponding to double
-covers ramified at two points. In both cases we consider the family of all
-double covers compatible with a fixed group action on the base curve. We
-restrict to the case where the family is 1-dimensional and the quotient of the
-base curve by the group is $\mathbb{P}^1$. We give a simple criterion for the
-image of these families under the Prym map to be a Shimura curve. Using
-computer algebra we check all the examples gotten in this way up to genus 28.
-We obtain 43 Shimura curves generically contained in the unramified Prym locus
-and 9 families generically contained in the ramified Prym locus. Most of these
-curves are not generically contained in the Jacobian locus.
-",0,0,1,0,0,0
-17740,Latent Laplacian Maximum Entropy Discrimination for Detection of High-Utility Anomalies,"  Data-driven anomaly detection methods suffer from the drawback of detecting
-all instances that are statistically rare, irrespective of whether the detected
-instances have real-world significance or not. In this paper, we are interested
-in the problem of specifically detecting anomalous instances that are known to
-have high real-world utility, while ignoring the low-utility statistically
-anomalous instances. To this end, we propose a novel method called Latent
-Laplacian Maximum Entropy Discrimination (LatLapMED) as a potential solution.
-This method uses the EM algorithm to simultaneously incorporate the Geometric
-Entropy Minimization principle for identifying statistical anomalies, and the
-Maximum Entropy Discrimination principle to incorporate utility labels, in
-order to detect high-utility anomalies. We apply our method in both simulated
-and real datasets to demonstrate that it has superior performance over existing
-alternatives that independently pre-process with unsupervised anomaly detection
-algorithms before classifying.
-",1,0,0,1,0,0
-17741,View-Invariant Recognition of Action Style Self-Dissimilarity,"  Self-similarity was recently introduced as a measure of inter-class
-congruence for classification of actions. Herein, we investigate the dual
-problem of intra-class dissimilarity for classification of action styles. We
-introduce self-dissimilarity matrices that discriminate between same actions
-performed by different subjects regardless of viewing direction and camera
-parameters. We investigate two frameworks using these invariant style
-dissimilarity measures based on Principal Component Analysis (PCA) and Fisher
-Discriminant Analysis (FDA). Extensive experiments performed on IXMAS dataset
-indicate remarkably good discriminant characteristics for the proposed
-invariant measures for gender recognition from video data.
-",1,0,0,0,0,0
-17742,Conducting Highly Principled Data Science: A Statistician's Job and Joy,"  Highly Principled Data Science insists on methodologies that are: (1)
-scientifically justified, (2) statistically principled, and (3) computationally
-efficient. An astrostatistics collaboration, together with some reminiscences,
-illustrates the increased roles statisticians can and should play to ensure
-this trio, and to advance the science of data along the way.
-",0,0,0,1,0,0
-17743,Regularizing nonlinear Schroedinger equations through partial off-axis variations,"  We study a class of focusing nonlinear Schroedinger-type equations derived
-recently by Dumas, Lannes and Szeftel within the mathematical description of
-high intensity laser beams [7]. These equations incorporate the possibility of
-a (partial) off-axis variation of the group velocity of such laser beams
-through a second order partial differential operator acting in some, but not
-necessarily all, spatial directions. We study the well-posedness theory for
-such models and obtain a regularizing effect, even in the case of only partial
-off-axis dependence. This provides an answer to an open problem posed in [7].
-",0,0,1,0,0,0
-17744,On h-Lexicalized Restarting Automata,"  Following some previous studies on restarting automata, we introduce a
-refined model - the h-lexicalized restarting automaton (h-RLWW). We argue that
-this model is useful for expressing lexicalized syntax in computational
-linguistics. We compare the input languages, which are the languages
-traditionally considered in automata theory, to the so-called basic and
-h-proper languages, which are (implicitly) used by categorial grammars, the
-original tool for the description of lexicalized syntax. The basic and h-proper
-languages allow us to stress several nice properties of h-lexicalized
-restarting automata, and they are suitable for modeling the analysis by
-reduction and, subsequently, for the development of categories of a lexicalized
-syntax. Based on the fact that a two-way deterministic monotone restarting
-automaton can be transformed into an equivalent deterministic monotone
-RL-automaton in (Marcus) contextual form, we obtain a transformation from
-monotone RLWW-automata that recognize the class CFL of context-free languages
-as their input languages to deterministic monotone h-RLWW-automata that
-recognize CFL through their h-proper languages. Through this transformation we
-obtain automata with the complete correctness preserving property and an
-infinite hierarchy within CFL, based on the size of the read/write window.
-Additionally, we consider h-RLWW-automata that are allowed to perform multiple
-rewrite steps per cycle, and we establish another infinite hierarchy above CFL
-that is based on the number of rewrite steps that may be executed within a
-cycle. The corresponding separation results and their proofs illustrate the
-transparency of h-RLWW-automata that work with the (complete or cyclic)
-correctness preserving property
-",1,0,0,0,0,0
-17745,Bayesian Nonparametric Inference for M/G/1 Queueing Systems,"  In this work, nonparametric statistical inference is provided for the
-continuous-time M/G/1 queueing model from a Bayesian point of view. The
-inference is based on observations of the inter-arrival and service times.
-Beside other characteristics of the system, particular interest is in the
-waiting time distribution which is not accessible in closed form. Thus, we use
-an indirect statistical approach by exploiting the Pollaczek-Khinchine
-transform formula for the Laplace transform of the waiting time distribution.
-Due to this, an estimator is defined and its frequentist validation in terms of
-posterior consistency and posterior normality is studied. It will turn out that
-we can hereby make inference for the observables separately and compose the
-results subsequently by suitable techniques.
-",0,0,1,1,0,0
-17746,Symbol Invariant of Partition and the Construction,"  The symbol is used to describe the Springer correspondence for the classical
-groups. We propose equivalent definitions of symbols for rigid partitions in
-the $B_n$, $C_n$, and $D_n$ theories uniformly. Analysing the new definition of
-symbol in detail, we give rules to construct symbol of a partition, which are
-easy to remember and to operate on. We introduce formal operations of a
-partition, which reduce the difficulties in the proof of the construction
-rules. According these rules, we give a closed formula of symbols for different
-theories uniformly. As applications, previous results can be illustrated more
-clearly by the construction rules of symbol.
-",0,0,1,0,0,0
-17747,Fairness in representation: quantifying stereotyping as a representational harm,"  While harms of allocation have been increasingly studied as part of the
-subfield of algorithmic fairness, harms of representation have received
-considerably less attention. In this paper, we formalize two notions of
-stereotyping and show how they manifest in later allocative harms within the
-machine learning pipeline. We also propose mitigation strategies and
-demonstrate their effectiveness on synthetic datasets.
-",1,0,0,1,0,0
-17748,Probing the possibility of hotspots on the central neutron star in HESS J1731-347,"  The X-ray spectra of the neutron stars located in the centers of supernova
-remnants Cas A and HESS J1731-347 are well fit with carbon atmosphere models.
-These fits yield plausible neutron star sizes for the known or estimated
-distances to these supernova remnants. The evidence in favor of the presence of
-a pure carbon envelope at the neutron star surface is rather indirect and is
-based on the assumption that the emission is generated uniformly by the entire
-stellar surface. Although this assumption is supported by the absence of
-pulsations, the observational upper limit on the pulsed fraction is not very
-stringent. In an attempt to quantify this evidence, we investigate the
-possibility that the observed spectrum of the neutron star in HESS J1731-347 is
-a combination of the spectra produced in a hydrogen atmosphere of the hotspots
-and of the cooler remaining part of the neutron star surface. The lack of
-pulsations in this case has to be explained either by a sufficiently small
-angle between the neutron star spin axis and the line of sight, or by a
-sufficiently small angular distance between the hotspots and the neutron star
-rotation poles. As the observed flux from a non-uniformly emitting neutron star
-depends on the angular distribution of the radiation emerging from the
-atmosphere, we have computed two new grids of pure carbon and pure hydrogen
-atmosphere model spectra accounting for Compton scattering. Using new hydrogen
-models, we have evaluated the probability of a geometry that leads to a pulsed
-fraction below the observed upper limit to be about 8.2 %. Such a geometry thus
-seems to be rather improbable but cannot be excluded at this stage.
-",0,1,0,0,0,0
-17749,Some Elementary Partition Inequalities and Their Implications,"  We prove various inequalities between the number of partitions with the bound
-on the largest part and some restrictions on occurrences of parts. We explore
-many interesting consequences of these partition inequalities. In particular,
-we show that for $L\geq 1$, the number of partitions with $l-s \leq L$ and
-$s=1$ is greater than the number of partitions with $l-s\leq L$ and $s>1$. Here
-$l$ and $s$ are the largest part and the smallest part of the partition,
-respectively.
-",0,0,1,0,0,0
-17750,The terrestrial late veneer from core disruption of a lunar-sized impactor,"  Overabundances in highly siderophile elements (HSEs) of Earth's mantle can be
-explained by conveyance from a singular, immense (3000 km in a diameter) ""Late
-Veneer"" impactor of chondritic composition, subsequent to lunar formation and
-terrestrial core-closure. Such rocky objects of approximately lunar mass (about
-0.01 M_E) ought to be differentiated, such that nearly all of their HSE payload
-is sequestered into iron cores. Here, we analyze the mechanical and chemical
-fate of the core of such a Late Veneer impactor, and trace how its HSEs are
-suspended - and thus pollute - the mantle. For the statistically most-likely
-oblique collision (about 45degree), the impactor's core elongates and
-thereafter disintegrates into a metallic hail of small particles (about 10 m).
-Some strike the orbiting Moon as sesquinary impactors, but most re-accrete to
-Earth as secondaries with further fragmentation. We show that a single oblique
-impactor provides an adequate amount of HSEs to the primordial terrestrial
-silicate reservoirs via oxidation of (<m-sized) metal particles with a hydrous,
-pre-impact, early Hadean Earth.
-",0,1,0,0,0,0
-17751,A Bayesian model for lithology/fluid class prediction using a Markov mesh prior fitted from a training image,"  We consider a Bayesian model for inversion of observed amplitude variation
-with offset (AVO) data into lithology/fluid classes, and study in particular
-how the choice of prior distribution for the lithology/fluid classes influences
-the inversion results. Two distinct prior distributions are considered, a
-simple manually specified Markov random field prior with a first order
-neighborhood and a Markov mesh model with a much larger neighborhood estimated
-from a training image. They are chosen to model both horisontal connectivity
-and vertical thickness distribution of the lithology/fluid classes, and are
-compared on an offshore clastic oil reservoir in the North Sea. We combine both
-priors with the same linearised Gaussian likelihood function based on a
-convolved linearised Zoeppritz relation and estimate properties of the
-resulting two posterior distributions by simulating from these distributions
-with the Metropolis-Hastings algorithm.
-The influence of the prior on the marginal posterior probabilities for the
-lithology/fluid classes is clearly observable, but modest. The importance of
-the prior on the connectivity properties in the posterior realisations,
-however, is much stronger. The larger neighborhood of the Markov mesh prior
-enables it to identify and model connectivity and curvature much better than
-what can be done by the first order neighborhood Markov random field prior. As
-a result, we conclude that the posterior realisations based on the Markov mesh
-prior appear with much higher lateral connectivity, which is geologically
-plausible.
-",0,0,0,1,0,0
-17752,Correct Brillouin zone and electronic structure of BiPd,"  A promising route to the realization of Majorana fermions is in
-non-centrosymmetric superconductors, in which spin-orbit-coupling lifts the
-spin degeneracy of both bulk and surface bands. A detailed assessment of the
-electronic structure is critical to evaluate their suitability for this through
-establishing the topological properties of the electronic structure. This
-requires correct identification of the time-reversal-invariant momenta. One
-such material is BiPd, a recently rediscovered non-centrosymmetric
-superconductor which can be grown in large, high-quality single crystals and
-has been studied by several groups using angular resolved photoemission to
-establish its surface electronic structure. Many of the published electronic
-structure studies on this material are based on a reciprocal unit cell which is
-not the actual Brillouin zone of the material. We show here the consequences of
-this for the electronic structures and show how the inferred topological nature
-of the material is affected.
-",0,1,0,0,0,0
-17753,Even faster sorting of (not only) integers,"  In this paper we introduce RADULS2, the fastest parallel sorter based on
-radix algorithm. It is optimized to process huge amounts of data making use of
-modern multicore CPUs. The main novelties include: extremely optimized
-algorithm for handling tiny arrays (up to about a hundred of records) that
-could appear even billions times as subproblems to handle and improved
-processing of larger subarrays with better use of non-temporal memory stores.
-",1,0,0,0,0,0
-17754,Evidential Deep Learning to Quantify Classification Uncertainty,"  Deterministic neural nets have been shown to learn effective predictors on a
-wide range of machine learning problems. However, as the standard approach is
-to train the network to minimize a prediction loss, the resultant model remains
-ignorant to its prediction confidence. Orthogonally to Bayesian neural nets
-that indirectly infer prediction uncertainty through weight uncertainties, we
-propose explicit modeling of the same using the theory of subjective logic. By
-placing a Dirichlet distribution on the class probabilities, we treat
-predictions of a neural net as subjective opinions and learn the function that
-collects the evidence leading to these opinions by a deterministic neural net
-from data. The resultant predictor for a multi-class classification problem is
-another Dirichlet distribution whose parameters are set by the continuous
-output of a neural net. We provide a preliminary analysis on how the
-peculiarities of our new loss function drive improved uncertainty estimation.
-We observe that our method achieves unprecedented success on detection of
-out-of-distribution queries and endurance against adversarial perturbations.
-",0,0,0,1,0,0
-17755,Neutron interference in the Earth's gravitational field,"  This work relates to the famous experiments, performed in 1975 and 1979 by
-Werner et al., measuring neutron interference and neutron Sagnac effects in the
-earth's gravitational field. Employing the method of Stodolsky in its weak
-field approximation, explicit expressions are derived for the two phase shifts,
-which turn out to be in agreement with the experiments and with the previously
-obtained expressions derived from semi-classical arguments: these expressions
-are simply modified by relativistic correction factors.
-",0,1,0,0,0,0
-17756,Solve For Shortest Paths Problem Within Logarithm Runtime,"  The Shortest Paths Problem (SPP) is no longer unresolved. Just for a large
-scalar of instance on this problem, even we cannot know if an algorithm
-achieves the computing. Those cutting-edge methods are still in the low
-performance. If we go to a strategy the best-first-search to deal with
-computing, it is awkward that the technical barrier from another field: the
-database, which with the capable of Online Oriented. In this paper, we will
-introduce such a synthesis to solve for SPP which comprises various modules
-therein including such database leads to finish the task in a logarithm
-runtime.
-Through experiments taken on three typical instances on mega-scalar data for
-transaction in a common laptop, we show off a totally robust, tractable and
-practical applicability for other projects.
-",1,0,0,0,0,0
-17757,A short proof of the error term in Simpson's rule,"  In this paper we present a short and elementary proof for the error in
-Simpson's rule.
-",0,0,1,0,0,0
-17758,Neutrino mass and dark energy constraints from redshift-space distortions,"  Cosmology in the near future promises a measurement of the sum of neutrino
-masses, a fundamental Standard Model parameter, as well as
-substantially-improved constraints on the dark energy. We use the shape of the
-BOSS redshift-space galaxy power spectrum, in combination with CMB and
-supernova data, to constrain the neutrino masses and the dark energy. Essential
-to this calculation are several recent advances in non-linear cosmological
-perturbation theory, including FFT methods, redshift space distortions, and
-scale-dependent growth. Our 95% confidence upper bound of 200 meV on the sum of
-masses degrades substantially to 770 meV when the dark energy equation of state
-and its first derivative are also allowed to vary, representing a significant
-challenge to current constraints. We also study the impact of additional galaxy
-bias parameters, finding that a velocity bias or a more complicated
-scale-dependent density bias shift the preferred neutrino mass values 20%-30%
-lower while minimally impacting the other cosmological parameters.
-",0,1,0,0,0,0
-17759,Dielectrophoretic assembly of liquid-phase-exfoliated TiS3 nanoribbons for photodetecting applications,"  Liquid-phase-exfoliation is a technique capable of producing large quantities
-of two-dimensional material in suspension. Despite many efforts in the
-optimization of the exfoliation process itself not much has been done towards
-the integration of liquid-phase-exfoliated materials in working solid-state
-devices. In this article, we use dielectrophoresis to direct the assembly of
-liquid-phase-exfoliated TiS3 nanoribbons between two gold electrodes to produce
-photodetectors working in the visible. Through electrical and optical
-measurements we characterize the responsivity of the device and we find values
-as large as 3.8 mA/W, which improve of more than one order of magnitude on the
-state-of-the-art for devices based on liquid-phase-exfoliated two-dimensional
-materials assembled by drop-casting or ink-jet methods.
-",0,1,0,0,0,0
-17760,Motion optimization and parameter identification for a human and lower-back exoskeleton model,"  Designing an exoskeleton to reduce the risk of low-back injury during lifting
-is challenging. Computational models of the human-robot system coupled with
-predictive movement simulations can help to simplify this design process. Here,
-we present a study that models the interaction between a human model actuated
-by muscles and a lower-back exoskeleton. We provide a computational framework
-for identifying the spring parameters of the exoskeleton using an optimal
-control approach and forward-dynamics simulations. This is applied to generate
-dynamically consistent bending and lifting movements in the sagittal plane. Our
-computations are able to predict motions and forces of the human and
-exoskeleton that are within the torque limits of a subject. The identified
-exoskeleton could also yield a considerable reduction of the peak lower-back
-torques as well as the cumulative lower-back load during the movements. This
-work is relevant to the research communities working on human-robot
-interaction, and can be used as a basis for a better human-centered design
-process.
-",1,0,0,0,0,0
-17761,A dataset for Computer-Aided Detection of Pulmonary Embolism in CTA images,"  Todays, researchers in the field of Pulmonary Embolism (PE) analysis need to
-use a publicly available dataset to assess and compare their methods. Different
-systems have been designed for the detection of pulmonary embolism (PE), but
-none of them have used any public datasets. All papers have used their own
-private dataset. In order to fill this gap, we have collected 5160 slices of
-computed tomography angiography (CTA) images acquired from 20 patients, and
-after labeling the image by experts in this field, we provided a reliable
-dataset which is now publicly available. In some situation, PE detection can be
-difficult, for example when it occurs in the peripheral branches or when
-patients have pulmonary diseases (such as parenchymal disease). Therefore, the
-efficiency of CAD systems highly depends on the dataset. In the given dataset,
-66% of PE are located in peripheral branches, and different pulmonary diseases
-are also included.
-",1,0,0,0,0,0
-17762,"Comment on Ben-Amotz and Honig, ""Average entropy dissipation in irreversible mesoscopic processes,"" Phys. Rev. Lett. 96, 020602 (2006)","  We point out that most of the classical thermodynamics results in the paper
-have been known in the literature, see Kestin and Woods, for quite some time
-and are not new, contrary to what the authors imply. As shown by Kestin, these
-results are valid for quasistatic irreversible processes only and not for
-arbitrary irreversible processes as suggested in the paper. Thus, the
-application to the Jarzynski process is limited.
-",0,1,0,0,0,0
-17763,High-transmissivity Silicon Visible-wavelength Metasurface Designs based on Truncated-cone Nanoantennae,"  High-transmissivity all-dielectric metasurfaces have recently attracted
-attention towards the realization of ultra-compact optical devices and systems.
-Silicon based metasurfaces, in particular, are highly promising considering the
-possibility of monolithic integration with VLSI circuits. Realization of
-silicon based metasurfaces operational in the visible wavelengths remains a
-challenge. A numerical study of silicon metasurfaces based on stepped truncated
-cone shaped nanoantenna elements is presented. Metasurfaces based on the
-stepped conical geometry can be designed for operation in the 700nm to 800nm
-wavelength window and achieve full cycle phase response (0 to pi with an
-improved transmittance in comparison with previously reported cylindrical
-geometry [1]. A systematic parameter study of the influence of various
-geometrical parameters on the achievable amplitude and phase coverage is
-reported.
-",0,1,0,0,0,0
-17764,Probabilistic Multigraph Modeling for Improving the Quality of Crowdsourced Affective Data,"  We proposed a probabilistic approach to joint modeling of participants'
-reliability and humans' regularity in crowdsourced affective studies.
-Reliability measures how likely a subject will respond to a question seriously;
-and regularity measures how often a human will agree with other
-seriously-entered responses coming from a targeted population.
-Crowdsourcing-based studies or experiments, which rely on human self-reported
-affect, pose additional challenges as compared with typical crowdsourcing
-studies that attempt to acquire concrete non-affective labels of objects. The
-reliability of participants has been massively pursued for typical
-non-affective crowdsourcing studies, whereas the regularity of humans in an
-affective experiment in its own right has not been thoroughly considered. It
-has been often observed that different individuals exhibit different feelings
-on the same test question, which does not have a sole correct response in the
-first place. High reliability of responses from one individual thus cannot
-conclusively result in high consensus across individuals. Instead, globally
-testing consensus of a population is of interest to investigators. Built upon
-the agreement multigraph among tasks and workers, our probabilistic model
-differentiates subject regularity from population reliability. We demonstrate
-the method's effectiveness for in-depth robust analysis of large-scale
-crowdsourced affective data, including emotion and aesthetic assessments
-collected by presenting visual stimuli to human subjects.
-",1,0,0,1,0,0
-17765,Quantum key distribution protocol with pseudorandom bases,"  Quantum key distribution (QKD) offers a way for establishing
-information-theoretically secure communications. An important part of QKD
-technology is a high-quality random number generator (RNG) for quantum states
-preparation and for post-processing procedures. In the present work, we
-consider a novel class of prepare-and-measure QKD protocols, utilizing
-additional pseudorandomness in the preparation of quantum states. We study one
-of such protocols and analyze its security against the intercept-resend attack.
-We demonstrate that, for single-photon sources, the considered protocol gives
-better secret key rates than the BB84 and the asymmetric BB84 protocol.
-However, the protocol strongly requires single-photon sources.
-",1,0,0,0,0,0
-17766,Deep clustering of longitudinal data,"  Deep neural networks are a family of computational models that have led to a
-dramatical improvement of the state of the art in several domains such as
-image, voice or text analysis. These methods provide a framework to model
-complex, non-linear interactions in large datasets, and are naturally suited to
-the analysis of hierarchical data such as, for instance, longitudinal data with
-the use of recurrent neural networks. In the other hand, cohort studies have
-become a tool of importance in the research field of epidemiology. In such
-studies, variables are measured repeatedly over time, to allow the practitioner
-to study their temporal evolution as trajectories, and, as such, as
-longitudinal data. This paper investigates the application of the advanced
-modelling techniques provided by the deep learning framework in the analysis of
-the longitudinal data provided by cohort studies. Methods: A method for
-visualizing and clustering longitudinal dataset is proposed, and compared to
-other widely used approaches to the problem on both real and simulated
-datasets. Results: The proposed method is shown to be coherent with the
-preexisting procedures on simple tasks, and to outperform them on more complex
-tasks such as the partitioning of longitudinal datasets into non-spherical
-clusters. Conclusion: Deep artificial neural networks can be used to visualize
-longitudinal data in a low dimensional manifold that is much simpler to
-interpret than traditional longitudinal plots are. Consequently, practitioners
-should start considering the use of deep artificial neural networks for the
-analysis of their longitudinal data in studies to come.
-",0,0,0,1,0,0
-17767,Economic Implications of Blockchain Platforms,"  In an economy with asymmetric information, the smart contract in the
-blockchain protocol mitigates uncertainty. Since, as a new trading platform,
-the blockchain triggers segmentation of market and differentiation of agents in
-both the sell and buy sides of the market, it recomposes the asymmetric
-information and generates spreads in asset price and quality between itself and
-a traditional platform. We show that marginal innovation and sophistication of
-the smart contract have non-monotonic effects on the trading value in the
-blockchain platform, its fundamental value, the price of cryptocurrency, and
-consumers' welfare. Moreover, a blockchain manager who controls the level of
-the innovation of the smart contract has an incentive to keep it lower than the
-first best when the underlying information asymmetry is not severe, leading to
-welfare loss for consumers.
-",0,0,0,0,0,1
-17768,Proof Theory and Ordered Groups,"  Ordering theorems, characterizing when partial orders of a group extend to
-total orders, are used to generate hypersequent calculi for varieties of
-lattice-ordered groups (l-groups). These calculi are then used to provide new
-proofs of theorems arising in the theory of ordered groups. More precisely: an
-analytic calculus for abelian l-groups is generated using an ordering theorem
-for abelian groups; a calculus is generated for l-groups and new decidability
-proofs are obtained for the equational theory of this variety and extending
-finite subsets of free groups to right orders; and a calculus for representable
-l-groups is generated and a new proof is obtained that free groups are
-orderable.
-",0,0,1,0,0,0
-17769,DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features,"  As the foundation of driverless vehicle and intelligent robots, Simultaneous
-Localization and Mapping(SLAM) has attracted much attention these days.
-However, non-geometric modules of traditional SLAM algorithms are limited by
-data association tasks and have become a bottleneck preventing the development
-of SLAM. To deal with such problems, many researchers seek to Deep Learning for
-help. But most of these studies are limited to virtual datasets or specific
-environments, and even sacrifice efficiency for accuracy. Thus, they are not
-practical enough.
-We propose DF-SLAM system that uses deep local feature descriptors obtained
-by the neural network as a substitute for traditional hand-made features.
-Experimental results demonstrate its improvements in efficiency and stability.
-DF-SLAM outperforms popular traditional SLAM systems in various scenes,
-including challenging scenes with intense illumination changes. Its versatility
-and mobility fit well into the need for exploring new environments. Since we
-adopt a shallow network to extract local descriptors and remain others the same
-as original SLAM systems, our DF-SLAM can still run in real-time on GPU.
-",1,0,0,0,0,0
-17770,A-NICE-MC: Adversarial Training for MCMC,"  Existing Markov Chain Monte Carlo (MCMC) methods are either based on
-general-purpose and domain-agnostic schemes which can lead to slow convergence,
-or hand-crafting of problem-specific proposals by an expert. We propose
-A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to
-produce samples with desired properties. First, we propose an efficient
-likelihood-free adversarial training method to train a Markov chain and mimic a
-given data distribution. Then, we leverage flexible volume preserving flows to
-obtain parametric kernels for MCMC. Using a bootstrap approach, we show how to
-train efficient Markov chains to sample from a prescribed posterior
-distribution by iteratively improving the quality of both the model and the
-samples. A-NICE-MC provides the first framework to automatically design
-efficient domain-specific MCMC proposals. Empirical results demonstrate that
-A-NICE-MC combines the strong guarantees of MCMC with the expressiveness of
-deep neural networks, and is able to significantly outperform competing methods
-such as Hamiltonian Monte Carlo.
-",1,0,0,1,0,0
-17771,"Starobinsky-like Inflation, Supercosmology and Neutrino Masses in No-Scale Flipped SU(5)","  We embed a flipped ${\rm SU}(5) \times {\rm U}(1)$ GUT model in a no-scale
-supergravity framework, and discuss its predictions for cosmic microwave
-background observables, which are similar to those of the Starobinsky model of
-inflation. Measurements of the tilt in the spectrum of scalar perturbations in
-the cosmic microwave background, $n_s$, constrain significantly the model
-parameters. We also discuss the model's predictions for neutrino masses, and
-pay particular attention to the behaviours of scalar fields during and after
-inflation, reheating and the GUT phase transition. We argue in favor of strong
-reheating in order to avoid excessive entropy production which could dilute the
-generated baryon asymmetry.
-",0,1,0,0,0,0
-17772,"'Viral' Turing Machines, Computation from Noise and Combinatorial Hierarchies","  The interactive computation paradigm is reviewed and a particular example is
-extended to form the stochastic analog of a computational process via a
-transcription of a minimal Turing Machine into an equivalent asynchronous
-Cellular Automaton with an exponential waiting times distribution of effective
-transitions. Furthermore, a special toolbox for analytic derivation of
-recursive relations of important statistical and other quantities is introduced
-in the form of an Inductive Combinatorial Hierarchy.
-",1,1,0,0,0,0
-17773,Estimating model evidence using ensemble-based data assimilation with localization - The model selection problem,"  IIn recent years, there has been a growing interest in applying data
-assimilation (DA) methods, originally designed for state estimation, to the
-model selection problem. In this setting, Carrassi et al. (2017) introduced the
-contextual formulation of model evidence (CME) and showed that CME can be
-efficiently computed using a hierarchy of ensemble-based DA procedures.
-Although Carrassi et al. (2017) analyzed the DA methods most commonly used for
-operational atmospheric and oceanic prediction worldwide, they did not study
-these methods in conjunction with localization to a specific domain. Yet any
-application of ensemble DA methods to realistic geophysical models requires the
-implementation of some form of localization. The present study extends the
-theory for estimating CME to ensemble DA methods with domain localization. The
-domain-localized CME (DL-CME) developed herein is tested for model selection
-with two models: (i) the Lorenz 40-variable mid-latitude atmospheric dynamics
-model (L95); and (ii) the simplified global atmospheric SPEEDY model. The CME
-is compared to the root-mean-square-error (RMSE) as a metric for model
-selection. The experiments show that CME improves systematically over the RMSE,
-and that this skill improvement is further enhanced by applying localization in
-the estimate of the CME, using the DL-CME. The potential use and range of
-applications of the CME and DL-CME as a model selection metric are also
-discussed.
-",0,0,0,1,0,0
-17774,An efficient spectral-Galerkin approximation and error analysis for Maxwell transmission eigenvalue problems in spherical geometries,"  We propose and analyze an efficient spectral-Galerkin approximation for the
-Maxwell transmission eigenvalue problem in spherical geometry. Using a vector
-spherical harmonic expansion, we reduce the problem to a sequence of equivalent
-one-dimensional TE and TM modes that can be solved individually in parallel.
-For the TE mode, we derive associated generalized eigenvalue problems and
-corresponding pole conditions. Then we introduce weighted Sobolev spaces based
-on the pole condition and prove error estimates for the generalized eigenvalue
-problem. The TM mode is a coupled system with four unknown functions, which is
-challenging for numerical calculation. To handle it, we design an effective
-algorithm using Legendre-type vector basis functions. Finally, we provide some
-numerical experiments to validate our theoretical results and demonstrate the
-efficiency of the algorithms.
-",0,0,1,0,0,0
-17775,Mendelian randomization with fine-mapped genetic data: choosing from large numbers of correlated instrumental variables,"  Mendelian randomization uses genetic variants to make causal inferences about
-the effect of a risk factor on an outcome. With fine-mapped genetic data, there
-may be hundreds of genetic variants in a single gene region any of which could
-be used to assess this causal relationship. However, using too many genetic
-variants in the analysis can lead to spurious estimates and inflated Type 1
-error rates. But if only a few genetic variants are used, then the majority of
-the data is ignored and estimates are highly sensitive to the particular choice
-of variants. We propose an approach based on summarized data only (genetic
-association and correlation estimates) that uses principal components analysis
-to form instruments. This approach has desirable theoretical properties: it
-takes the totality of data into account and does not suffer from numerical
-instabilities. It also has good properties in simulation studies: it is not
-particularly sensitive to varying the genetic variants included in the analysis
-or the genetic correlation matrix, and it does not have greatly inflated Type 1
-error rates. Overall, the method gives estimates that are not so precise as
-those from variable selection approaches (such as using a conditional analysis
-or pruning approach to select variants), but are more robust to seemingly
-arbitrary choices in the variable selection step. Methods are illustrated by an
-example using genetic associations with testosterone for 320 genetic variants
-to assess the effect of sex hormone-related pathways on coronary artery disease
-risk, in which variable selection approaches give inconsistent inferences.
-",0,0,0,1,0,0
-17776,Building competitive direct acoustics-to-word models for English conversational speech recognition,"  Direct acoustics-to-word (A2W) models in the end-to-end paradigm have
-received increasing attention compared to conventional sub-word based automatic
-speech recognition models using phones, characters, or context-dependent hidden
-Markov model states. This is because A2W models recognize words from speech
-without any decoder, pronunciation lexicon, or externally-trained language
-model, making training and decoding with such models simple. Prior work has
-shown that A2W models require orders of magnitude more training data in order
-to perform comparably to conventional models. Our work also showed this
-accuracy gap when using the English Switchboard-Fisher data set. This paper
-describes a recipe to train an A2W model that closes this gap and is at-par
-with state-of-the-art sub-word based models. We achieve a word error rate of
-8.8%/13.9% on the Hub5-2000 Switchboard/CallHome test sets without any decoder
-or language model. We find that model initialization, training data order, and
-regularization have the most impact on the A2W model performance. Next, we
-present a joint word-character A2W model that learns to first spell the word
-and then recognize it. This model provides a rich output to the user instead of
-simple word hypotheses, making it especially useful in the case of words unseen
-or rarely-seen during training.
-",1,0,0,1,0,0
-17777,Convolutional Dictionary Learning: Acceleration and Convergence,"  Convolutional dictionary learning (CDL or sparsifying CDL) has many
-applications in image processing and computer vision. There has been growing
-interest in developing efficient algorithms for CDL, mostly relying on the
-augmented Lagrangian (AL) method or the variant alternating direction method of
-multipliers (ADMM). When their parameters are properly tuned, AL methods have
-shown fast convergence in CDL. However, the parameter tuning process is not
-trivial due to its data dependence and, in practice, the convergence of AL
-methods depends on the AL parameters for nonconvex CDL problems. To moderate
-these problems, this paper proposes a new practically feasible and convergent
-Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The
-BPG-M-based CDL is investigated with different block updating schemes and
-majorization matrix designs, and further accelerated by incorporating some
-momentum coefficient formulas and restarting techniques. All of the methods
-investigated incorporate a boundary artifacts removal (or, more generally,
-sampling) operator in the learning model. Numerical experiments show that,
-without needing any parameter tuning process, the proposed BPG-M approach
-converges more stably to desirable solutions of lower objective values than the
-existing state-of-the-art ADMM algorithm and its memory-efficient variant do.
-Compared to the ADMM approaches, the BPG-M method using a multi-block updating
-scheme is particularly useful in single-threaded CDL algorithm handling large
-datasets, due to its lower memory requirement and no polynomial computational
-complexity. Image denoising experiments show that, for relatively strong
-additive white Gaussian noise, the filters learned by BPG-M-based CDL
-outperform those trained by the ADMM approach.
-",1,0,1,0,0,0
-17778,The late-time light curve of the type Ia supernova SN 2011fe,"  We present late-time optical $R$-band imaging data from the Palomar Transient
-Factory (PTF) for the nearby type Ia supernova SN 2011fe. The stacked PTF light
-curve provides densely sampled coverage down to $R\simeq22$ mag over 200 to 620
-days past explosion. Combining with literature data, we estimate the
-pseudo-bolometric light curve for this event from 200 to 1600 days after
-explosion, and constrain the likely near-infrared contribution. This light
-curve shows a smooth decline consistent with radioactive decay, except over
-~450 to ~600 days where the light curve appears to decrease faster than
-expected based on the radioactive isotopes presumed to be present, before
-flattening at around 600 days. We model the 200-1600d pseudo-bolometric light
-curve with the luminosity generated by the radioactive decay chains of
-$^{56}$Ni, $^{57}$Ni and $^{55}$Co, and find it is not consistent with models
-that have full positron trapping and no infrared catastrophe (IRC); some
-additional energy escape other than optical/near-IR photons is required.
-However, the light curve is consistent with models that allow for positron
-escape (reaching 75% by day 500) and/or an IRC (with 85% of the flux emerging
-in non-optical wavelengths by day 600). The presence of the $^{57}$Ni decay
-chain is robustly detected, but the $^{55}$Co decay chain is not formally
-required, with an upper mass limit estimated at 0.014 M$_{\odot}$. The
-measurement of the $^{57}$Ni/$^{56}$Ni mass ratio is subject to significant
-systematic uncertainties, but all of our fits require a high ratio >0.031 (>1.3
-in solar abundances).
-",0,1,0,0,0,0
-17779,Convergence analysis of the block Gibbs sampler for Bayesian probit linear mixed models with improper priors,"  In this article, we consider Markov chain Monte Carlo(MCMC) algorithms for
-exploring the intractable posterior density associated with Bayesian probit
-linear mixed models under improper priors on the regression coefficients and
-variance components. In particular, we construct the two-block Gibbs sampler
-using the data augmentation (DA) techniques. Furthermore, we prove geometric
-ergodicity of the Gibbs sampler, which is the foundation for building central
-limit theorems for MCMC based estimators and subsequent inferences. The
-conditions for geometric convergence are similar to those guaranteeing
-posterior propriety. We also provide conditions for posterior propriety when
-the design matrices take commonly observed forms. In general, the Haar
-parameter expansion for DA (PX- DA) algorithm is an improvement of the DA
-algorithm and it has been shown that it is theoretically at least as good as
-the DA algorithm. Here we construct a Haar PX-DA algorithm, which has
-essentially the same computational cost as the two-block Gibbs sampler.
-",0,0,1,1,0,0
-17780,The complexity of the Multiple Pattern Matching Problem for random strings,"  We generalise a multiple string pattern matching algorithm, recently proposed
-by Fredriksson and Grabowski [J. Discr. Alg. 7, 2009], to deal with arbitrary
-dictionaries on an alphabet of size $s$. If $r_m$ is the number of words of
-length $m$ in the dictionary, and $\phi(r) = \max_m \ln(s\, m\, r_m)/m$, the
-complexity rate for the string characters to be read by this algorithm is at
-most $\kappa_{{}_\textrm{UB}}\, \phi(r)$ for some constant
-$\kappa_{{}_\textrm{UB}}$. On the other side, we generalise the classical lower
-bound of Yao [SIAM J. Comput. 8, 1979], for the problem with a single pattern,
-to deal with arbitrary dictionaries, and determine it to be at least
-$\kappa_{{}_\textrm{LB}}\, \phi(r)$. This proves the optimality of the
-algorithm, improving and correcting previous claims.
-",1,0,0,0,0,0
-17781,Functoriality and uniformity in Hrushovski's groupoid-cover correspondence,"  The correspondence between definable connected groupoids in a theory $T$ and
-internal generalised imaginary sorts of $T$, established by Hrushovski in
-[""Groupoids, imaginaries and internal covers,"" Turkish Journal of Mathematics,
-2012], is here extended in two ways: First, it is shown that the correspondence
-is in fact an equivalence of categories, with respect to appropriate notions of
-morphism. Secondly, the equivalence of categories is shown to vary uniformly in
-definable families, with respect to an appropriate relativisation of these
-categories. Some elaboration on Hrushovki's original constructions are also
-included.
-",0,0,1,0,0,0
-17782,Simple Conditions for Metastability of Continuous Markov Chains,"  A family $\{Q_{\beta}\}_{\beta \geq 0}$ of Markov chains is said to exhibit
-$\textit{metastable mixing}$ with $\textit{modes}$
-$S_{\beta}^{(1)},\ldots,S_{\beta}^{(k)}$ if its spectral gap (or some other
-mixing property) is very close to the worst conductance
-$\min(\Phi_{\beta}(S_{\beta}^{(1)}), \ldots, \Phi_{\beta}(S_{\beta}^{(k)}))$ of
-its modes. We give simple sufficient conditions for a family of Markov chains
-to exhibit metastability in this sense, and verify that these conditions hold
-for a prototypical Metropolis-Hastings chain targeting a mixture distribution.
-Our work differs from existing work on metastability in that, for the class of
-examples we are interested in, it gives an asymptotically exact formula for the
-spectral gap (rather than a bound that can be very far from sharp) while at the
-same time giving technical conditions that are easier to verify for many
-statistical examples. Our bounds from this paper are used in a companion paper
-to compare the mixing times of the Hamiltonian Monte Carlo algorithm and a
-random walk algorithm for multimodal target distributions.
-",0,0,0,1,0,0
-17783,Deterministic preparation of highly non-classical macroscopic quantum states,"  We present a scheme to deterministically prepare non-classical quantum states
-of a massive mirror including highly non-Gaussian states exhibiting sizeable
-negativity of the Wigner function. This is achieved by exploiting the
-non-linear light-matter interaction in an optomechanical cavity by driving the
-system with optimally designed frequency patterns. Our scheme reveals to be
-resilient against mechanical and optical damping, as well as mechanical thermal
-noise and imperfections in the driving scheme. Our proposal thus opens a
-promising route for table-top experiments to explore and exploit macroscopic
-quantum phenomena.
-",0,1,0,0,0,0
-17784,On Local Optimizers of Acquisition Functions in Bayesian Optimization,"  Bayesian optimization is a sample-efficient method for finding a global
-optimum of an expensive-to-evaluate black-box function. A global solution is
-found by accumulating a pair of query point and corresponding function value,
-repeating these two procedures: (i) learning a surrogate model for the
-objective function using the data observed so far; (ii) the maximization of an
-acquisition function to determine where next to query the objective function.
-Convergence guarantees are only valid when the global optimizer of the
-acquisition function is found and selected as the next query point. In
-practice, however, local optimizers of acquisition functions are also used,
-since searching the exact optimizer of the acquisition function is often a
-non-trivial or time-consuming task. In this paper we present an analysis on the
-behavior of local optimizers of acquisition functions, in terms of
-instantaneous regrets over global optimizers. We also present the performance
-analysis when multi-started local optimizers are used to find the maximum of
-the acquisition function. Numerical experiments confirm the validity of our
-theoretical analysis.
-",1,0,0,1,0,0
-17785,One Password: An Encryption Scheme for Hiding Users' Register Information,"  In recent years, the attack which leverages register information (e.g.
-accounts and passwords) leaked from 3rd party applications to try other
-applications is popular and serious. We call this attack ""database collision"".
-Traditionally, people have to keep dozens of accounts and passwords for
-different applications to prevent this attack. In this paper, we propose a
-novel encryption scheme for hiding users' register information and preventing
-this attack. Specifically, we first hash the register information using
-existing safe hash function. Then the hash string is hidden, instead a
-coefficient vector is stored for verification. Coefficient vectors of the same
-register information are generated randomly for different applications. Hence,
-the original information is hardly cracked by dictionary based attack or
-database collision in practice. Using our encryption scheme, each user only
-needs to keep one password for dozens of applications.
-",1,0,0,0,0,0
-17786,The RoPES project with HARPS and HARPS-N. I. A system of super-Earths orbiting the moderately active K-dwarf HD 176986,"  We report the discovery of a system of two super-Earths orbiting the
-moderately active K-dwarf HD 176986. This work is part of the RoPES RV program
-of G- and K-type stars, which combines radial velocities (RVs) from the HARPS
-and HARPS-N spectrographs to search for short-period terrestrial planets. HD
-176986 b and c are super-Earth planets with masses of 5.74 and 9.18
-M$_{\oplus}$, orbital periods of 6.49 and 16.82 days, and distances of 0.063
-and 0.119 AU in orbits that are consistent with circular. The host star is a
-K2.5 dwarf, and despite its modest level of chromospheric activity (log(R'hk) =
-- 4.90 +- 0.04), it shows a complex activity pattern. Along with the discovery
-of the planets, we study the magnetic cycle and rotation of the star. HD 176986
-proves to be suitable for testing the available RV analysis technique and
-further our understanding of stellar activity.
-",0,1,0,0,0,0
-17787,On the possibility of developing quasi-cw high-power high-pressure laser on 4p-4s transition of ArI with electron beam - optical pumping: quenching of 4s (3P2) lower laser level,"  A new electron beam-optical procedure is proposed for quasi-cw pumping of
-high-pressure large-volume He-Ar laser on 4p[1/2]1 - 4s[3/2]2 argon atom
-transition at the wavelength of 912.5 nm. It consists of creation and
-maintenance of a necessary density of 4s[3/2]2 metastable state in the gain
-medium by a fast electron beam and subsequent optically pumping of the upper
-laser level via the classical three-level scheme using a laser diode.
-Absorption probing is used to study collisional quenching of Ar* metastable in
-electron-beam-excited high-pressure He-Ar mixtures with a low content of argon.
-The rate constants for plasma-chemical reactions Ar*+He+Ar-Ar2*+He (3.6 +-
-0.4)x10-33 cm6/s, Ar+2He-HeAr*+He (4.4 +- 0.9)x10-36 cm6/s and
-Ar*+He-Products+He (2.4 +- 0.3)x10-15 cm3/s are for the first time measured.
-",0,1,0,0,0,0
-17788,An Adaptive Characteristic-wise Reconstruction WENOZ scheme for Gas Dynamic Euler Equations,"  Due to its excellent shock-capturing capability and high resolution, the WENO
-scheme family has been widely used in varieties of compressive flow simulation.
-However, for problems containing strong shocks and contact discontinuities,
-such as the Lax shock tube problem, the WENO scheme still produces numerical
-oscillations. To avoid such numerical oscillations, the characteristic-wise
-construction method should be applied. Compared to component-wise
-reconstruction, characteristic-wise reconstruction leads to much more
-computational cost and thus is not suite for large scale simulation such as
-direct numeric simulation of turbulence. In this paper, an adaptive
-characteristic-wise reconstruction WENO scheme, i.e. the AdaWENO scheme, is
-proposed to improve the computational efficiency of the characteristic-wise
-reconstruction method. The new scheme performs characteristic-wise
-reconstruction near discontinuities while switching to component-wise
-reconstruction for smooth regions. Meanwhile, a new calculation strategy for
-the WENO smoothness indicators is implemented to reduce over-all computational
-cost. Several one dimensional and two dimensional numerical tests are performed
-to validate and evaluate the AdaWENO scheme. Numerical results show that
-AdaWENO maintains essentially non-oscillatory flow field near discontinuities
-as the characteristic-wise reconstruction method. Besieds, compared to the
-component-wise reconstruction, AdaWENO is about 40\% faster which indicates its
-excellent efficiency.
-",0,1,0,0,0,0
-17789,A level set-based structural optimization code using FEniCS,"  This paper presents an educational code written using FEniCS, based on the
-level set method, to perform compliance minimization in structural
-optimization. We use the concept of distributed shape derivative to compute a
-descent direction for the compliance, which is defined as a shape functional.
-The use of the distributed shape derivative is facilitated by FEniCS, which
-allows to handle complicated partial differential equations with a simple
-implementation. The code is written for compliance minimization in the
-framework of linearized elasticity, and can be easily adapted to tackle other
-functionals and partial differential equations. We also provide an extension of
-the code for compliant mechanisms. We start by explaining how to compute shape
-derivatives, and discuss the differences between the distributed and boundary
-expressions of the shape derivative. Then we describe the implementation in
-details, and show the application of this code to some classical benchmarks of
-topology optimization. The code is available at
-this http URL, and the main file is also given in
-the appendix.
-",0,0,1,0,0,0
-17790,Transaction Support over Redis: An Overview,"  This document outlines the approach to supporting cross-node transactions
-over a Redis cluster.
-",1,0,0,0,0,0
-17791,Subgraphs and motifs in a dynamic airline network,"  How does the small-scale topological structure of an airline network behave
-as the network evolves? To address this question, we study the dynamic and
-spatial properties of small undirected subgraphs using 15 years of data on
-Southwest Airlines' domestic route service. We find that this real-world
-network has much in common with random graphs, and describe a possible
-power-law scaling between subgraph counts and the number of edges in the
-network, that appears to be quite robust to changes in network density and
-size. We use analytic formulae to identify statistically over- and
-under-represented subgraphs, known as motifs and anti-motifs, and discover the
-existence of substantial topology transitions. We propose a simple
-subgraph-based node ranking measure, that is not always highly correlated with
-standard node centrality, and can identify important nodes relative to specific
-topologies, and investigate the spatial ""distribution"" of the triangle subgraph
-using graphical tools. Our results have implications for the way in which
-subgraphs can be used to analyze real-world networks.
-",1,0,0,0,0,0
-17792,On the Glitch Phenomenon,"  The Principle of the Glitch states that for any device which makes a discrete
-decision based upon a continuous range of possible inputs, there are inputs for
-which it will take arbitrarily long to reach a decision. The appropriate
-mathematical setting for studying this principle is described. This involves
-defining the concept of continuity for mappings on sets of functions. It can
-then be shown that the glitch principle follows from the continuous behavior of
-the device.
-",1,0,1,0,0,0
-17793,Asymptotic properties and approximation of Bayesian logspline density estimators for communication-free parallel computing methods,"  In this article we perform an asymptotic analysis of Bayesian parallel
-density estimators which are based on logspline density estimation. The
-parallel estimator we introduce is in the spirit of a kernel density estimator
-introduced in recent studies. We provide a numerical procedure that produces
-the density estimator itself in place of the sampling algorithm. We then derive
-an error bound for the mean integrated squared error for the full data
-posterior density estimator. We also investigate the parameters that arise from
-logspline density estimation and the numerical approximation procedure. Our
-investigation identifies specific choices of parameters for logspline density
-estimation that result in the error bound scaling appropriately in relation to
-these choices.
-",0,0,1,1,0,0
-17794,Tunable Weyl and Dirac states in the nonsymmorphic compound $\rm\mathbf{CeSbTe}$,"  Recent interest in topological semimetals has lead to the proposal of many
-new topological phases that can be realized in real materials. Next to Dirac
-and Weyl systems, these include more exotic phases based on manifold band
-degeneracies in the bulk electronic structure. The exotic states in topological
-semimetals are usually protected by some sort of crystal symmetry and the
-introduction of magnetic order can influence these states by breaking time
-reversal symmetry. Here we show that we can realize a rich variety of different
-topological semimetal states in a single material, $\rm CeSbTe$. This compound
-can exhibit different types of magnetic order that can be accessed easily by
-applying a small field. It allows, therefore, for tuning the electronic
-structure and can drive it through a manifold of topologically distinct phases,
-such as the first nonsymmorphic magnetic topological material with an
-eight-fold band crossing at a high symmetry point. Our experimental results are
-backed by a full magnetic group theory analysis and ab initio calculations.
-This discovery introduces a realistic and promising platform for studying the
-interplay of magnetism and topology.
-",0,1,0,0,0,0
-17795,Personalised Query Suggestion for Intranet Search with Temporal User Profiling,"  Recent research has shown the usefulness of using collective user interaction
-data (e.g., query logs) to recommend query modification suggestions for
-Intranet search. However, most of the query suggestion approaches for Intranet
-search follow an ""one size fits all"" strategy, whereby different users who
-submit an identical query would get the same query suggestion list. This is
-problematic, as even with the same query, different users may have different
-topics of interest, which may change over time in response to the user's
-interaction with the system. We address the problem by proposing a personalised
-query suggestion framework for Intranet search. For each search session, we
-construct two temporal user profiles: a click user profile using the user's
-clicked documents and a query user profile using the user's submitted queries.
-We then use the two profiles to re-rank the non-personalised query suggestion
-list returned by a state-of-the-art query suggestion method for Intranet
-search. Experimental results on a large-scale query logs collection show that
-our personalised framework significantly improves the quality of suggested
-queries.
-",1,0,0,0,0,0
-17796,Navigability of Random Geometric Graphs in the Universe and Other Spacetimes,"  Random geometric graphs in hyperbolic spaces explain many common structural
-and dynamical properties of real networks, yet they fail to predict the correct
-values of the exponents of power-law degree distributions observed in real
-networks. In that respect, random geometric graphs in asymptotically de Sitter
-spacetimes, such as the Lorentzian spacetime of our accelerating universe, are
-more attractive as their predictions are more consistent with observations in
-real networks. Yet another important property of hyperbolic graphs is their
-navigability, and it remains unclear if de Sitter graphs are as navigable as
-hyperbolic ones. Here we study the navigability of random geometric graphs in
-three Lorentzian manifolds corresponding to universes filled only with dark
-energy (de Sitter spacetime), only with matter, and with a mixture of dark
-energy and matter as in our universe. We find that these graphs are navigable
-only in the manifolds with dark energy. This result implies that, in terms of
-navigability, random geometric graphs in asymptotically de Sitter spacetimes
-are as good as random hyperbolic graphs. It also establishes a connection
-between the presence of dark energy and navigability of the discretized causal
-structure of spacetime, which provides a basis for a different approach to the
-dark energy problem in cosmology.
-",1,1,0,0,0,0
-17797,Learning Geometric Concepts with Nasty Noise,"  We study the efficient learnability of geometric concept classes -
-specifically, low-degree polynomial threshold functions (PTFs) and
-intersections of halfspaces - when a fraction of the data is adversarially
-corrupted. We give the first polynomial-time PAC learning algorithms for these
-concept classes with dimension-independent error guarantees in the presence of
-nasty noise under the Gaussian distribution. In the nasty noise model, an
-omniscient adversary can arbitrarily corrupt a small fraction of both the
-unlabeled data points and their labels. This model generalizes well-studied
-noise models, including the malicious noise model and the agnostic (adversarial
-label noise) model. Prior to our work, the only concept class for which
-efficient malicious learning algorithms were known was the class of
-origin-centered halfspaces.
-Specifically, our robust learning algorithm for low-degree PTFs succeeds
-under a number of tame distributions -- including the Gaussian distribution
-and, more generally, any log-concave distribution with (approximately) known
-low-degree moments. For LTFs under the Gaussian distribution, we give a
-polynomial-time algorithm that achieves error $O(\epsilon)$, where $\epsilon$
-is the noise rate. At the core of our PAC learning results is an efficient
-algorithm to approximate the low-degree Chow-parameters of any bounded function
-in the presence of nasty noise. To achieve this, we employ an iterative
-spectral method for outlier detection and removal, inspired by recent work in
-robust unsupervised learning. Our aforementioned algorithm succeeds for a range
-of distributions satisfying mild concentration bounds and moment assumptions.
-The correctness of our robust learning algorithm for intersections of
-halfspaces makes essential use of a novel robust inverse independence lemma
-that may be of broader interest.
-",1,0,0,0,0,0
-17798,The relationship between the number of editorial board members and the scientific output of universities in the chemistry field,"  Editorial board members, who are considered the gatekeepers of scientific
-journals, play an important role in academia, and may directly or indirectly
-affect the scientific output of a university. In this article, we used the
-quantile regression method among a sample of 1,387 university in chemistry to
-characterize the correlation between the number of editorial board members and
-the scientific output of their universities. Furthermore, we used time-series
-data and the Granger causality test to explore the causal relationship between
-the number of editorial board members and the number of articles of some top
-universities. Our results suggest that the number of editorial board members is
-positively and significantly related to the scientific output (as measured by
-the number of articles, total number of citations, citations per paper, and h
-index) of their universities. However, the Granger causality test results
-suggest that the causal relationship between the number of editorial board
-members and the number of articles of some top universities is not obvious.
-Combining these findings with the results of qualitative interviews with
-editorial board members, we discuss the causal relationship between the number
-of editorial board members and the scientific output of their universities.
-",1,0,0,1,0,0
-17799,A Non-standard Standard Model,"  This paper examines the Standard Model under the strong-electroweak gauge
-group $SU_S(3)\times U_{EW}(2)$ subject to the condition $u_{EW}(2)\not\cong
-su_I(2)\oplus u_Y(1)$. Physically, the condition ensures that all electroweak
-gauge bosons interact with each other prior to symmetry breaking --- as one
-might expect from $U(2)$ invariance. This represents a crucial shift in the
-notion of physical gauge bosons: Unlike the Standard Model which posits a
-change of Lie algebra basis induced by spontaneous symmetry breaking, here the
-basis is unaltered and $A,\,Z^0,\,W^\pm$ represent (modulo $U_{EW}(2)$ gauge
-transformations) the physical bosons both \emph{before} and after spontaneous
-symmetry breaking.
-Our choice of $u_{EW}(2)$ basis requires some modification of the matter
-field sector of the Standard Model. Careful attention to the product group
-structure calls for strong-electroweak degrees of freedom in the
-$(\mathbf{3},\mathbf{2})$ and the $(\mathbf{3},\overline{\mathbf{2}})$ of
-$SU_S(3)\times U_{EW}(2)$ that possess integer electric charge just like
-leptons. These degrees of freedom play the role of quarks, and they lead to a
-modified Lagrangian that nevertheless reproduces transition rates and cross
-sections equivalent to the Standard Model.
-The close resemblance between quark and lepton electroweak doublets in this
-picture suggests a mechanism for a phase transition between quarks and leptons
-that stems from the product structure of the gauge group. Our hypothesis is
-that the strong and electroweak bosons see each other as a source of
-decoherence. In effect, leptons get identified with the $SU_S(3)$-trace of
-quark representations. This mechanism allows for possible extensions of the
-Standard Model that don't require large inclusive multiplets of matter fields.
-As an example, we propose and investigate a model that turns out to have some
-promising cosmological implications.
-",0,1,0,0,0,0
-17800,Robust functional regression model for marginal mean and subject-specific inferences,"  We introduce flexible robust functional regression models, using various
-heavy-tailed processes, including a Student $t$-process. We propose efficient
-algorithms in estimating parameters for the marginal mean inferences and in
-predicting conditional means as well interpolation and extrapolation for the
-subject-specific inferences. We develop bootstrap prediction intervals for
-conditional mean curves. Numerical studies show that the proposed model
-provides robust analysis against data contamination or distribution
-misspecification, and the proposed prediction intervals maintain the nominal
-confidence levels. A real data application is presented as an illustrative
-example.
-",0,0,0,1,0,0
-17801,Linear theory for single and double flap wavemakers,"  In this paper, we are concerned with deterministic wave generation in a
-hydrodynamic laboratory. A linear wavemaker theory is developed based on the
-fully dispersive water wave equations. The governing field equation is the
-Laplace equation for potential flow with several boundary conditions: the
-dynamic and kinematic boundary condition at the free surface, the lateral
-boundary condition at the wavemaker and the bottom boundary condition. In this
-work, we consider both single-flap and double-flap wavemakers. The velocity
-potential and surface wave elevation are derived, and the relation between the
-propagating wave height and wavemaker stroke is formulated. This formulation is
-then used to find how to operate the wavemaker in an efficient way to generate
-the desired propagating waves with minimal disturbances near the wavemaker.
-",0,1,0,0,0,0
-17802,Image-domain multi-material decomposition for dual-energy CT based on correlation and sparsity of material images,"  Dual energy CT (DECT) enhances tissue characterization because it can produce
-images of basis materials such as soft-tissue and bone. DECT is of great
-interest in applications to medical imaging, security inspection and
-nondestructive testing. Theoretically, two materials with different linear
-attenuation coefficients can be accurately reconstructed using DECT technique.
-However, the ability to reconstruct three or more basis materials is clinically
-and industrially important. Under the assumption that there are at most three
-materials in each pixel, there are a few methods that estimate multiple
-material images from DECT measurements by enforcing sum-to-one and a box
-constraint ([0 1]) derived from both the volume and mass conservation
-assumption. The recently proposed image-domain multi-material decomposition
-(MMD) method introduces edge-preserving regularization for each material image
-which neglects the relations among material images, and enforced the assumption
-that there are at most three materials in each pixel using a time-consuming
-loop over all possible material-triplet in each iteration of optimizing its
-cost function. We propose a new image-domain MMD method for DECT that considers
-the prior information that different material images have common edges and
-encourages sparsity of material composition in each pixel using regularization.
-",0,1,0,0,0,0
-17803,A tutorial on the synthesis and validation of a closed-loop wind farm controller using a steady-state surrogate model,"  In wind farms, wake interaction leads to losses in power capture and
-accelerated structural degradation when compared to freestanding turbines. One
-method to reduce wake losses is by misaligning the rotor with the incoming flow
-using its yaw actuator, thereby laterally deflecting the wake away from
-downstream turbines. However, this demands an accurate and computationally
-tractable model of the wind farm dynamics. This problem calls for a closed-loop
-solution. This tutorial paper fills the scientific gap by demonstrating the
-full closed-loop controller synthesis cycle using a steady-state surrogate
-model. Furthermore, a novel, computationally efficient and modular
-communication interface is presented that enables researchers to
-straight-forwardly test their control algorithms in large-eddy simulations.
-High-fidelity simulations of a 9-turbine farm show a power production increase
-of up to 11% using the proposed closed-loop controller compared to traditional,
-greedy wind farm operation.
-",1,0,0,0,0,0
-17804,UCB Exploration via Q-Ensembles,"  We show how an ensemble of $Q^*$-functions can be leveraged for more
-effective exploration in deep reinforcement learning. We build on well
-established algorithms from the bandit setting, and adapt them to the
-$Q$-learning setting. We propose an exploration strategy based on
-upper-confidence bounds (UCB). Our experiments show significant gains on the
-Atari benchmark.
-",1,0,0,1,0,0
-17805,Towards a Deep Improviser: a prototype deep learning post-tonal free music generator,"  Two modest-sized symbolic corpora of post-tonal and post-metric keyboard
-music have been constructed, one algorithmic, the other improvised. Deep
-learning models of each have been trained and largely optimised. Our purpose is
-to obtain a model with sufficient generalisation capacity that in response to a
-small quantity of separate fresh input seed material, it can generate outputs
-that are distinctive, rather than recreative of the learned corpora or the seed
-material. This objective has been first assessed statistically, and as judged
-by k-sample Anderson-Darling and Cramer tests, has been achieved. Music has
-been generated using the approach, and informal judgements place it roughly on
-a par with algorithmic and composed music in related forms. Future work will
-aim to enhance the model such that it can be evaluated in relation to
-expression, meaning and utility in real-time performance.
-",1,0,0,0,0,0
-17806,Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data,"  Existing urban boundaries are usually defined by government agencies for
-administrative, economic, and political purposes. Defining urban boundaries
-that consider socio-economic relationships and citizen commute patterns is
-important for many aspects of urban and regional planning. In this paper, we
-describe a method to delineate urban boundaries based upon human interactions
-with physical space inferred from social media. Specifically, we depicted the
-urban boundaries of Great Britain using a mobility network of Twitter user
-spatial interactions, which was inferred from over 69 million geo-located
-tweets. We define the non-administrative anthropographic boundaries in a
-hierarchical fashion based on different physical movement ranges of users
-derived from the collective mobility patterns of Twitter users in Great
-Britain. The results of strongly connected urban regions in the form of
-communities in the network space yield geographically cohesive, non-overlapping
-urban areas, which provide a clear delineation of the non-administrative
-anthropographic urban boundaries of Great Britain. The method was applied to
-both national (Great Britain) and municipal scales (the London metropolis).
-While our results corresponded well with the administrative boundaries, many
-unexpected and interesting boundaries were identified. Importantly, as the
-depicted urban boundaries exhibited a strong instance of spatial proximity, we
-employed a gravity model to understand the distance decay effects in shaping
-the delineated urban boundaries. The model explains how geographical distances
-found in the mobility patterns affect the interaction intensity among different
-non-administrative anthropographic urban areas, which provides new insights
-into human spatial interactions with urban space.
-",1,1,0,0,0,0
-17807,Avoiding a Tragedy of the Commons in the Peer Review Process,"  Peer review is the foundation of scientific publication, and the task of
-reviewing has long been seen as a cornerstone of professional service. However,
-the massive growth in the field of machine learning has put this community
-benefit under stress, threatening both the sustainability of an effective
-review process and the overall progress of the field. In this position paper,
-we argue that a tragedy of the commons outcome may be avoided by emphasizing
-the professional aspects of this service. In particular, we propose a rubric to
-hold reviewers to an objective standard for review quality. In turn, we also
-propose that reviewers be given appropriate incentive. As one possible such
-incentive, we explore the idea of financial compensation on a per-review basis.
-We suggest reasonable funding models and thoughts on long term effects.
-",1,0,0,0,0,0
-17808,Partially Recursive Acceptance Rejection,"  Generating random variates from high-dimensional distributions is often done
-approximately using Markov chain Monte Carlo. In certain cases, perfect
-simulation algorithms exist that allow one to draw exactly from the stationary
-distribution, but most require $O(n \ln(n))$ time, where $n$ measures the size
-of the input. In this work a new protocol for creating perfect simulation
-algorithms that runs in $O(n)$ time for a wider range of parameters on several
-models (such as Strauss, Ising, and random cluster) than was known previously.
-This work represents an extension of the popping algorithms due to Wilson.
-",1,0,1,0,0,0
-17809,Reifenberg Flatness and Oscillation of the Unit Normal Vector,"  We show (under mild topological assumptions) that small oscillation of the
-unit normal vector implies Reifenberg flatness. We then apply this observation
-to the study of chord-arc domains and to a quantitative version of a two-phase
-free boundary problem for harmonic measure previously studied by Kenig-Toro.
-",0,0,1,0,0,0
-17810,Stability and optimality of distributed secondary frequency control schemes in power networks,"  We present a systematic method for designing distributed generation and
-demand control schemes for secondary frequency regulation in power networks
-such that stability and an economically optimal power allocation can be
-guaranteed. A dissipativity condition is imposed on net power supply variables
-to provide stability guarantees. Furthermore, economic optimality is achieved
-by explicit decentralized steady state conditions on the generation and
-controllable demand. We discuss how various classes of dynamics used in recent
-studies fit within our framework and give examples of higher order generation
-and controllable demand dynamics that can be included within our analysis. In
-case of linear dynamics, we discuss how the proposed dissipativity condition
-can be efficiently verified using an appropriate linear matrix inequality.
-Moreover, it is shown how the addition of a suitable observer layer can relax
-the requirement for demand measurements in the employed controller. The
-efficiency and practicality of the proposed results are demonstrated with a
-simulation on the Northeast Power Coordinating Council (NPCC) 140-bus system.
-",1,0,1,0,0,0
-17811,A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks -- Prevention and Prediction for Combating Terrorism,"  Terrorism has become one of the most tedious problems to deal with and a
-prominent threat to mankind. To enhance counter-terrorism, several research
-works are developing efficient and precise systems, data mining is not an
-exception. Immense data is floating in our lives, though the scarce
-availability of authentic terrorist attack data in the public domain makes it
-complicated to fight terrorism. This manuscript focuses on data mining
-classification techniques and discusses the role of United Nations in
-counter-terrorism. It analyzes the performance of classifiers such as Lazy
-Tree, Multilayer Perceptron, Multiclass and Naïve Bayes classifiers for
-observing the trends for terrorist attacks around the world. The database for
-experiment purpose is created from different public and open access sources for
-years 1970-2015 comprising of 156,772 reported attacks causing massive losses
-of lives and property. This work enumerates the losses occurred, trends in
-attack frequency and places more prone to it, by considering the attack
-responsibilities taken as evaluation class.
-",1,0,0,1,0,0
-17812,OVI 6830Å Imaging Polarimetry of Symbiotic Stars,"  I present here the first results from an ongoing pilot project with the 1.6 m
-telescope at the OPD, Brasil, aimed at the detection of the OVI $\lambda$6830
-line via linear polarization in symbiotic stars. The main goal is to
-demonstrate that OVI imaging polarimetry is an efficient technique for
-discovering new symbiotic stars. The OVI $\lambda$6830 line is found in 5 out
-of 9 known symbiotic stars, in which the OVI line has already been
-spectroscopically confirmed, with at least 3-$\sigma$ detection. Three new
-symbiotic star candidates have also been found.
-",0,1,0,0,0,0
-17813,MMGAN: Manifold Matching Generative Adversarial Network,"  It is well-known that GANs are difficult to train, and several different
-techniques have been proposed in order to stabilize their training. In this
-paper, we propose a novel training method called manifold-matching, and a new
-GAN model called manifold-matching GAN (MMGAN). MMGAN finds two manifolds
-representing the vector representations of real and fake images. If these two
-manifolds match, it means that real and fake images are statistically
-identical. To assist the manifold-matching task, we also use i) kernel tricks
-to find better manifold structures, ii) moving-averaged manifolds across
-mini-batches, and iii) a regularizer based on correlation matrix to suppress
-mode collapse.
-We conduct in-depth experiments with three image datasets and compare with
-several state-of-the-art GAN models. 32.4% of images generated by the proposed
-MMGAN are recognized as fake images during our user study (16% enhancement
-compared to other state-of-the-art model). MMGAN achieved an unsupervised
-inception score of 7.8 for CIFAR-10.
-",1,0,0,0,0,0
-17814,Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts,"  Acute respiratory infections have epidemic and pandemic potential and thus
-are being studied worldwide, albeit in many different contexts and study
-formats. Predicting infection from symptom data is critical, though using
-symptom data from varied studies in aggregate is challenging because the data
-is collected in different ways. Accordingly, different symptom profiles could
-be more predictive in certain studies, or even symptoms of the same name could
-have different meanings in different contexts. We assess state-of-the-art
-transfer learning methods for improving prediction of infection from symptom
-data in multiple types of health care data ranging from clinical, to home-visit
-as well as crowdsourced studies. We show interesting characteristics regarding
-six different study types and their feature domains. Further, we demonstrate
-that it is possible to use data collected from one study to predict infection
-in another, at close to or better than using a single dataset for prediction on
-itself. We also investigate in which conditions specific transfer learning and
-domain adaptation methods may perform better on symptom data. This work has the
-potential for broad applicability as we show how it is possible to transfer
-learning from one public health study design to another, and data collected
-from one study may be used for prediction of labels for another, even collected
-through different study designs, populations and contexts.
-",0,0,0,1,1,0
-17815,Constrained empirical Bayes priors on regression coefficients,"  Under model uncertainty, empirical Bayes (EB) procedures can have undesirable
-properties such as extreme estimates of inclusion probabilities (Scott &
-Berger, 2010) or inconsistency under the null model (Liang et al., 2008). To
-avoid these issues, we define empirical Bayes priors with constraints that
-ensure that the estimates of the hyperparameters are at least as ""vague"" as
-those of proper default priors. In our examples, we observe that constrained EB
-procedures are better behaved than their unconstrained counterparts and that
-the Bayesian Information Criterion (BIC) is similar to an intuitively appealing
-constrained EB procedure.
-",0,0,1,1,0,0
-17816,A finite field analogue for Appell series F_3,"  In this paper we introduce a finite field analogue for the Appell series F_3
-and give some reduction formulae and certain generating functions for this
-function over finite fields.
-",0,0,1,0,0,0
-17817,Two-way Two-tape Automata,"  In this article we consider two-way two-tape (alternating) automata accepting
-pairs of words and we study some closure properties of this model. Our main
-result is that such alternating automata are not closed under complementation
-for non-unary alphabets. This improves a similar result of Kari and Moore for
-picture languages. We also show that these deterministic, non-deterministic and
-alternating automata are not closed under composition.
-",1,0,0,0,0,0
-17818,Making 360$^{\circ}$ Video Watchable in 2D: Learning Videography for Click Free Viewing,"  360$^{\circ}$ video requires human viewers to actively control ""where"" to
-look while watching the video. Although it provides a more immersive experience
-of the visual content, it also introduces additional burden for viewers;
-awkward interfaces to navigate the video lead to suboptimal viewing
-experiences. Virtual cinematography is an appealing direction to remedy these
-problems, but conventional methods are limited to virtual environments or rely
-on hand-crafted heuristics. We propose a new algorithm for virtual
-cinematography that automatically controls a virtual camera within a
-360$^{\circ}$ video. Compared to the state of the art, our algorithm allows
-more general camera control, avoids redundant outputs, and extracts its output
-videos substantially more efficiently. Experimental results on over 7 hours of
-real ""in the wild"" video show that our generalized camera control is crucial
-for viewing 360$^{\circ}$ video, while the proposed efficient algorithm is
-essential for making the generalized control computationally tractable.
-",1,0,0,0,0,0
-17819,Zero-Shot Learning by Generating Pseudo Feature Representations,"  Zero-shot learning (ZSL) is a challenging task aiming at recognizing novel
-classes without any training instances. In this paper we present a simple but
-high-performance ZSL approach by generating pseudo feature representations
-(GPFR). Given the dataset of seen classes and side information of unseen
-classes (e.g. attributes), we synthesize feature-level pseudo representations
-for novel concepts, which allows us access to the formulation of unseen class
-predictor. Firstly we design a Joint Attribute Feature Extractor (JAFE) to
-acquire understandings about attributes, then construct a cognitive repository
-of attributes filtered by confidence margins, and finally generate pseudo
-feature representations using a probability based sampling strategy to
-facilitate subsequent training process of class predictor. We demonstrate the
-effectiveness in ZSL settings and the extensibility in supervised recognition
-scenario of our method on a synthetic colored MNIST dataset (C-MNIST). For
-several popular ZSL benchmark datasets, our approach also shows compelling
-results on zero-shot recognition task, especially leading to tremendous
-improvement to state-of-the-art mAP on zero-shot retrieval task.
-",1,0,0,0,0,0
-17820,Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection,"  Speaker change detection (SCD) is an important task in dialog modeling. Our
-paper addresses the problem of text-based SCD, which differs from existing
-audio-based studies and is useful in various scenarios, for example, processing
-dialog transcripts where speaker identities are missing (e.g., OpenSubtitle),
-and enhancing audio SCD with textual information. We formulate text-based SCD
-as a matching problem of utterances before and after a certain decision point;
-we propose a hierarchical recurrent neural network (RNN) with static
-sentence-level attention. Experimental results show that neural networks
-consistently achieve better performance than feature-based approaches, and that
-our attention-based model significantly outperforms non-attention neural
-networks.
-",1,0,0,0,0,0
-17821,Bivariate Discrete Generalized Exponential Distribution,"  In this paper we develop a bivariate discrete generalized exponential
-distribution, whose marginals are discrete generalized exponential distribution
-as proposed by Nekoukhou, Alamatsaz and Bidram (""Discrete generalized
-exponential distribution of a second type"", Statistics, 47, 876 - 887, 2013).
-It is observed that the proposed bivariate distribution is a very flexible
-distribution and the bivariate geometric distribution can be obtained as a
-special case of this distribution. The proposed distribution can be seen as a
-natural discrete analogue of the bivariate generalized exponential distribution
-proposed by Kundu and Gupta (""Bivariate generalized exponential distribution"",
-Journal of Multivariate Analysis, 100, 581 - 593, 2009). We study different
-properties of this distribution and explore its dependence structures. We
-propose a new EM algorithm to compute the maximum likelihood estimators of the
-unknown parameters which can be implemented very efficiently, and discuss some
-inferential issues also. The analysis of one data set has been performed to
-show the effectiveness of the proposed model. Finally we propose some open
-problems and conclude the paper.
-",0,0,0,1,0,0
-17822,"Optimization of Ensemble Supervised Learning Algorithms for Increased Sensitivity, Specificity, and AUC of Population-Based Colorectal Cancer Screenings","  Over 150,000 new people in the United States are diagnosed with colorectal
-cancer each year. Nearly a third die from it (American Cancer Society). The
-only approved noninvasive diagnosis tools currently involve fecal blood count
-tests (FOBTs) or stool DNA tests. Fecal blood count tests take only five
-minutes and are available over the counter for as low as \$15. They are highly
-specific, yet not nearly as sensitive, yielding a high percentage (25%) of
-false negatives (Colon Cancer Alliance). Moreover, FOBT results are far too
-generalized, meaning that a positive result could mean much more than just
-colorectal cancer, and could just as easily mean hemorrhoids, anal fissure,
-proctitis, Crohn's disease, diverticulosis, ulcerative colitis, rectal ulcer,
-rectal prolapse, ischemic colitis, angiodysplasia, rectal trauma, proctitis
-from radiation therapy, and others. Stool DNA tests, the modern benchmark for
-CRC screening, have a much higher sensitivity and specificity, but also cost
-\$600, take two weeks to process, and are not for high-risk individuals or
-people with a history of polyps. To yield a cheap and effective CRC screening
-alternative, a unique ensemble-based classification algorithm is put in place
-that considers the FIT result, BMI, smoking history, and diabetic status of
-patients. This method is tested under ten-fold cross validation to have a .95
-AUC, 92% specificity, 89% sensitivity, .88 F1, and 90% precision. Once
-clinically validated, this test promises to be cheaper, faster, and potentially
-more accurate when compared to a stool DNA test.
-",1,0,0,1,0,0
-17823,More or Less? Predict the Social Influence of Malicious URLs on Social Media,"  Users of Online Social Networks (OSNs) interact with each other more than
-ever. In the context of a public discussion group, people receive, read, and
-write comments in response to articles and postings. In the absence of access
-control mechanisms, OSNs are a great environment for attackers to influence
-others, from spreading phishing URLs, to posting fake news. Moreover, OSN user
-behavior can be predicted by social science concepts which include conformity
-and the bandwagon effect. In this paper, we show how social recommendation
-systems affect the occurrence of malicious URLs on Facebook. We exploit
-temporal features to build a prediction framework, having greater than 75%
-accuracy, to predict whether the following group users' behavior will increase
-or not. Included in this work, we demarcate classes of URLs, including those
-malicious URLs classified as creating critical damage, as well as those of a
-lesser nature which only inflict light damage such as aggressive commercial
-advertisements and spam content. It is our hope that the data and analyses in
-this paper provide a better understanding of OSN user reactions to different
-categories of malicious URLs, thereby providing a way to mitigate the influence
-of these malicious URL attacks.
-",1,0,0,0,0,0
-17824,Path-Following through Control Funnel Functions,"  We present an approach to path following using so-called control funnel
-functions. Synthesizing controllers to ""robustly"" follow a reference trajectory
-is a fundamental problem for autonomous vehicles. Robustness, in this context,
-requires our controllers to handle a specified amount of deviation from the
-desired trajectory. Our approach considers a timing law that describes how fast
-to move along a given reference trajectory and a control feedback law for
-reducing deviations from the reference. We synthesize both feedback laws using
-""control funnel functions"" that jointly encode the control law as well as its
-correctness argument over a mathematical model of the vehicle dynamics. We
-adapt a previously described demonstration-based learning algorithm to
-synthesize a control funnel function as well as the associated feedback law. We
-implement this law on top of a 1/8th scale autonomous vehicle called the
-Parkour car. We compare the performance of our path following approach against
-a trajectory tracking approach by specifying trajectories of varying lengths
-and curvatures. Our experiments demonstrate the improved robustness obtained
-from the use of control funnel functions.
-",1,0,0,0,0,0
-17825,Tunable Anomalous Andreev Reflection and Triplet Pairings in Spin Orbit Coupled Graphene,"  We theoretically study scattering process and superconducting triplet
-correlations in a graphene junction comprised of ferromagnet-RSO-superconductor
-in which RSO stands for a region with Rashba spin orbit interaction. Our
-results reveal spin-polarized subgap transport through the system due to an
-anomalous equal-spin Andreev reflection in addition to conventional back
-scatterings. We calculate equal- and opposite-spin pair correlations near the
-F-RSO interface and demonstrate direct link of the anomalous Andreev reflection
-and equal-spin pairings arised due to the proximity effect in the presence of
-RSO interaction. Moreover, we show that the amplitude of anomalous Andreev
-reflection, and thus the triplet pairings, are experimentally controllable when
-incorporating the influences of both tunable strain and Fermi level in the
-nonsuperconducting region. Our findings can be confirmed by a conductance
-spectroscopy experiment and provide better insights into the proximity-induced
-RSO coupling in graphene layers reported in recent experiments.
-",0,1,0,0,0,0
-17826,Conditional Model Selection in Mixed-Effects Models with cAIC4,"  Model selection in mixed models based on the conditional distribution is
-appropriate for many practical applications and has been a focus of recent
-statistical research. In this paper we introduce the R-package cAIC4 that
-allows for the computation of the conditional Akaike Information Criterion
-(cAIC). Computation of the conditional AIC needs to take into account the
-uncertainty of the random effects variance and is therefore not
-straightforward. We introduce a fast and stable implementation for the
-calculation of the cAIC for linear mixed models estimated with lme4 and
-additive mixed models estimated with gamm4 . Furthermore, cAIC4 offers a
-stepwise function that allows for a fully automated stepwise selection scheme
-for mixed models based on the conditional AIC. Examples of many possible
-applications are presented to illustrate the practical impact and easy handling
-of the package.
-",0,0,0,1,0,0
-17827,Inequalities for the lowest magnetic Neumann eigenvalue,"  We study the ground state energy of the Neumann magnetic Laplacian on planar
-domains. For a constant magnetic field we consider the question whether, under
-an assumption of fixed area, the disc maximizes this eigenvalue. More
-generally, we discuss old and new bounds obtained on this problem.
-",0,0,1,0,0,0
-17828,Automated optimization of large quantum circuits with continuous parameters,"  We develop and implement automated methods for optimizing quantum circuits of
-the size and type expected in quantum computations that outperform classical
-computers. We show how to handle continuous gate parameters and report a
-collection of fast algorithms capable of optimizing large-scale quantum
-circuits. For the suite of benchmarks considered, we obtain substantial
-reductions in gate counts. In particular, we provide better optimization in
-significantly less time than previous approaches, while making minimal
-structural changes so as to preserve the basic layout of the underlying quantum
-algorithms. Our results help bridge the gap between the computations that can
-be run on existing hardware and those that are expected to outperform classical
-computers.
-",1,0,0,0,0,0
-17829,Testing the simplifying assumption in high-dimensional vine copulas,"  Testing the simplifying assumption in high-dimensional vine copulas is a
-difficult task because tests must be based on estimated observations and amount
-to checking constraints on high-dimensional distributions. So far,
-corresponding tests have been limited to single conditional copulas with a
-low-dimensional set of conditioning variables. We propose a novel testing
-procedure that is computationally feasible for high-dimensional data sets and
-that exhibits a power that decreases only slightly with the dimension. By
-discretizing the support of the conditioning variables and incorporating a
-penalty in the test statistic, we mitigate the curse of dimensions by looking
-for the possibly strongest deviation from the simplifying assumption. The use
-of a decision tree renders the test computationally feasible for large
-dimensions. We derive the asymptotic distribution of the test and analyze its
-finite sample performance in an extensive simulation study. The utility of the
-test is demonstrated by its application to 10 data sets with up to 49
-dimensions.
-",0,0,0,1,0,0
-17830,TSP With Locational Uncertainty: The Adversarial Model,"  In this paper we study a natural special case of the Traveling Salesman
-Problem (TSP) with point-locational-uncertainty which we will call the {\em
-adversarial TSP} problem (ATSP). Given a metric space $(X, d)$ and a set of
-subsets $R = \{R_1, R_2, ... , R_n\} : R_i \subseteq X$, the goal is to devise
-an ordering of the regions, $\sigma_R$, that the tour will visit such that when
-a single point is chosen from each region, the induced tour over those points
-in the ordering prescribed by $\sigma_R$ is as short as possible. Unlike the
-classical locational-uncertainty-TSP problem, which focuses on minimizing the
-expected length of such a tour when the point within each region is chosen
-according to some probability distribution, here, we focus on the {\em
-adversarial model} in which once the choice of $\sigma_R$ is announced, an
-adversary selects a point from each region in order to make the resulting tour
-as long as possible. In other words, we consider an offline problem in which
-the goal is to determine an ordering of the regions $R$ that is optimal with
-respect to the ""worst"" point possible within each region being chosen by an
-adversary, who knows the chosen ordering. We give a $3$-approximation when $R$
-is a set of arbitrary regions/sets of points in a metric space. We show how
-geometry leads to improved constant factor approximations when regions are
-parallel line segments of the same lengths, and a polynomial-time approximation
-scheme (PTAS) for the important special case in which $R$ is a set of disjoint
-unit disks in the plane.
-",1,0,0,0,0,0
-17831,Exact solutions to three-dimensional generalized nonlinear Schrodinger equations with varying potential and nonlinearities,"  It is shown that using the similarity transformations, a set of
-three-dimensional p-q nonlinear Schrodinger (NLS) equations with inhomogeneous
-coefficients can be reduced to one-dimensional stationary NLS equation with
-constant or varying coefficients, thus allowing for obtaining exact localized
-and periodic wave solutions. In the suggested reduction the original
-coordinates in the (1+3)-space are mapped into a set of one-parametric
-coordinate surfaces, whose parameter plays the role of the coordinate of the
-one-dimensional equation. We describe the algorithm of finding solutions and
-concentrate on power (linear and nonlinear) potentials presenting a number of
-case examples. Generalizations of the method are also discussed.
-",0,1,1,0,0,0
-17832,A Study of Reinforcement Learning for Neural Machine Translation,"  Recent studies have shown that reinforcement learning (RL) is an effective
-approach for improving the performance of neural machine translation (NMT)
-system. However, due to its instability, successfully RL training is
-challenging, especially in real-world systems where deep models and large
-datasets are leveraged. In this paper, taking several large-scale translation
-tasks as testbeds, we conduct a systematic study on how to train better NMT
-models using reinforcement learning. We provide a comprehensive comparison of
-several important factors (e.g., baseline reward, reward shaping) in RL
-training. Furthermore, to fill in the gap that it remains unclear whether RL is
-still beneficial when monolingual data is used, we propose a new method to
-leverage RL to further boost the performance of NMT systems trained with
-source/target monolingual data. By integrating all our findings, we obtain
-competitive results on WMT14 English- German, WMT17 English-Chinese, and WMT17
-Chinese-English translation tasks, especially setting a state-of-the-art
-performance on WMT17 Chinese-English translation task.
-",0,0,0,1,0,0
-17833,Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification,"  Artificial neural network (ANN) is a very useful tool in solving learning
-problems. Boosting the performances of ANN can be mainly concluded from two
-aspects: optimizing the architecture of ANN and normalizing the raw data for
-ANN. In this paper, a novel method which improves the effects of ANN by
-preprocessing the raw data is proposed. It totally leverages the fact that
-different features should play different roles. The raw data set is firstly
-preprocessed by principle component analysis (PCA), and then its principle
-components are weighted by their corresponding eigenvalues. Several aspects of
-analysis are carried out to analyze its theory and the applicable occasions.
-Three classification problems are launched by an active learning algorithm to
-verify the proposed method. From the empirical results, conclusion comes to the
-fact that the proposed method can significantly improve the performance of ANN.
-",1,0,0,1,0,0
-17834,Maximum likelihood estimation of determinantal point processes,"  Determinantal point processes (DPPs) have wide-ranging applications in
-machine learning, where they are used to enforce the notion of diversity in
-subset selection problems. Many estimators have been proposed, but surprisingly
-the basic properties of the maximum likelihood estimator (MLE) have received
-little attention. The difficulty is that it is a non-concave maximization
-problem, and such functions are notoriously difficult to understand in high
-dimensions, despite their importance in modern machine learning. Here we study
-both the local and global geometry of the expected log-likelihood function. We
-prove several rates of convergence for the MLE and give a complete
-characterization of the case where these are parametric. We also exhibit a
-potential curse of dimensionality where the asymptotic variance of the MLE
-scales exponentially with the dimension of the problem. Moreover, we exhibit an
-exponential number of saddle points, and give evidence that these may be the
-only critical points.
-",0,0,1,1,0,0
-17835,Orthogonal Machine Learning: Power and Limitations,"  Double machine learning provides $\sqrt{n}$-consistent estimates of
-parameters of interest even when high-dimensional or nonparametric nuisance
-parameters are estimated at an $n^{-1/4}$ rate. The key is to employ
-Neyman-orthogonal moment equations which are first-order insensitive to
-perturbations in the nuisance parameters. We show that the $n^{-1/4}$
-requirement can be improved to $n^{-1/(2k+2)}$ by employing a $k$-th order
-notion of orthogonality that grants robustness to more complex or
-higher-dimensional nuisance parameters. In the partially linear regression
-setting popular in causal inference, we show that we can construct second-order
-orthogonal moments if and only if the treatment residual is not normally
-distributed. Our proof relies on Stein's lemma and may be of independent
-interest. We conclude by demonstrating the robustness benefits of an explicit
-doubly-orthogonal estimation procedure for treatment effect.
-",1,0,1,1,0,0
-17836,Numerical investigation of supersonic shock-wave/boundary-layer interaction in transitional and turbulent regime,"  We perform direct numerical simulations of shock-wave/boundary-layer
-interactions (SBLI) at Mach number M = 1.7 to investigate the influence of the
-state of the incoming boundary layer on the interaction properties. We
-reproduce and extend the flow conditions of the experiments performed by
-Giepman et al., in which a spatially evolving laminar boundary layer over a
-flat plate is initially tripped by an array of distributed roughness elements
-and impinged further downstream by an oblique shock wave. Four SBLI cases are
-considered, based on two different shock impingement locations along the
-streamwise direction, corresponding to transitional and turbulent interactions,
-and two different shock strengths, corresponding to flow deflection angles 3
-degreees and 6 degrees. We find that, for all flow cases, shock induced
-separation is not observed, the boundary layer remains attached for the 3
-degrees case and close to incipient separation for the 6 degrees case,
-independent of the state of the incoming boundary layer. The findings of this
-work suggest that a transitional interaction might be the optimal solution for
-practical SBLI applications, as it removes the large separation bubble typical
-of laminar interactions and reduces the extent of the high-friction region
-associated with an incoming turbulent boundary layer.
-",0,1,0,0,0,0
-17837,Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations,"  Latent factor models for recommender systems represent users and items as low
-dimensional vectors. Privacy risks of such systems have previously been studied
-mostly in the context of recovery of personal information in the form of usage
-records from the training data. However, the user representations themselves
-may be used together with external data to recover private user information
-such as gender and age. In this paper we show that user vectors calculated by a
-common recommender system can be exploited in this way. We propose the
-privacy-adversarial framework to eliminate such leakage of private information,
-and study the trade-off between recommender performance and leakage both
-theoretically and empirically using a benchmark dataset. An advantage of the
-proposed method is that it also helps guarantee fairness of results, since all
-implicit knowledge of a set of attributes is scrubbed from the representations
-used by the model, and thus can't enter into the decision making. We discuss
-further applications of this method towards the generation of deeper and more
-insightful recommendations.
-",0,0,0,1,0,0
-17838,Asymptotics for high-dimensional covariance matrices and quadratic forms with applications to the trace functional and shrinkage,"  We establish large sample approximations for an arbitray number of bilinear
-forms of the sample variance-covariance matrix of a high-dimensional vector
-time series using $ \ell_1$-bounded and small $\ell_2$-bounded weighting
-vectors. Estimation of the asymptotic covariance structure is also discussed.
-The results hold true without any constraint on the dimension, the number of
-forms and the sample size or their ratios. Concrete and potential applications
-are widespread and cover high-dimensional data science problems such as tests
-for large numbers of covariances, sparse portfolio optimization and projections
-onto sparse principal components or more general spanning sets as frequently
-considered, e.g. in classification and dictionary learning. As two specific
-applications of our results, we study in greater detail the asymptotics of the
-trace functional and shrinkage estimation of covariance matrices. In shrinkage
-estimation, it turns out that the asymptotics differs for weighting vectors
-bounded away from orthogonaliy and nearly orthogonal ones in the sense that
-their inner product converges to 0.
-",0,0,1,1,0,0
-17839,Bayesian Paragraph Vectors,"  Word2vec (Mikolov et al., 2013) has proven to be successful in natural
-language processing by capturing the semantic relationships between different
-words. Built on top of single-word embeddings, paragraph vectors (Le and
-Mikolov, 2014) find fixed-length representations for pieces of text with
-arbitrary lengths, such as documents, paragraphs, and sentences. In this work,
-we propose a novel interpretation for neural-network-based paragraph vectors by
-developing an unsupervised generative model whose maximum likelihood solution
-corresponds to traditional paragraph vectors. This probabilistic formulation
-allows us to go beyond point estimates of parameters and to perform Bayesian
-posterior inference. We find that the entropy of paragraph vectors decreases
-with the length of documents, and that information about posterior uncertainty
-improves performance in supervised learning tasks such as sentiment analysis
-and paraphrase detection.
-",1,0,0,1,0,0
-17840,Subset Synchronization in Monotonic Automata,"  We study extremal and algorithmic questions of subset and careful
-synchronization in monotonic automata. We show that several synchronization
-problems that are hard in general automata can be solved in polynomial time in
-monotonic automata, even without knowing a linear order of the states preserved
-by the transitions. We provide asymptotically tight bounds on the maximum
-length of a shortest word synchronizing a subset of states in a monotonic
-automaton and a shortest word carefully synchronizing a partial monotonic
-automaton. We provide a complexity framework for dealing with problems for
-monotonic weakly acyclic automata over a three-letter alphabet, and use it to
-prove NP-completeness and inapproximability of problems such as {\sc Finite
-Automata Intersection} and the problem of computing the rank of a subset of
-states in this class. We also show that checking whether a monotonic partial
-automaton over a four-letter alphabet is carefully synchronizing is NP-hard.
-Finally, we give a simple necessary and sufficient condition when a strongly
-connected digraph with a selected subset of vertices can be transformed into a
-deterministic automaton where the corresponding subset of states is
-synchronizing.
-",1,0,0,0,0,0
-17841,Architecture of Text Mining Application in Analyzing Public Sentiments of West Java Governor Election using Naive Bayes Classification,"  The selection of West Java governor is one event that seizes the attention of
-the public is no exception to social media users. Public opinion on a
-prospective regional leader can help predict electability and tendency of
-voters. Data that can be used by the opinion mining process can be obtained
-from Twitter. Because the data is very varied form and very unstructured, it
-must be managed and uninformed using data pre-processing techniques into
-semi-structured data. This semi-structured information is followed by a
-classification stage to categorize the opinion into negative or positive
-opinions. The research methodology uses a literature study where the research
-will examine previous research on a similar topic. The purpose of this study is
-to find the right architecture to develop it into the application of twitter
-opinion mining to know public sentiments toward the election of the governor of
-west java. The result of this research is that Twitter opinion mining is part
-of text mining where opinions in Twitter if they want to be classified, must go
-through the preprocessing text stage first. The preprocessing step required
-from twitter data is cleansing, case folding, POS Tagging and stemming. The
-resulting text mining architecture is an architecture that can be used for text
-mining research with different topics.
-",1,0,0,0,0,0
-17842,Ray: A Distributed Framework for Emerging AI Applications,"  The next generation of AI applications will continuously interact with the
-environment and learn from these interactions. These applications impose new
-and demanding systems requirements, both in terms of performance and
-flexibility. In this paper, we consider these requirements and present Ray---a
-distributed system to address them. Ray implements a unified interface that can
-express both task-parallel and actor-based computations, supported by a single
-dynamic execution engine. To meet the performance requirements, Ray employs a
-distributed scheduler and a distributed and fault-tolerant store to manage the
-system's control state. In our experiments, we demonstrate scaling beyond 1.8
-million tasks per second and better performance than existing specialized
-systems for several challenging reinforcement learning applications.
-",1,0,0,1,0,0
-17843,A blockchain-based Decentralized System for proper handling of temporary Employment contracts,"  Temporary work is an employment situation useful and suitable in all
-occasions in which business needs to adjust more easily and quickly to workload
-fluctuations or maintain staffing flexibility. Temporary workers play therefore
-an important role in many companies, but this kind of activity is subject to a
-special form of legal protections and many aspects and risks must be taken into
-account both employers and employees. In this work we propose a
-blockchain-based system that aims to ensure respect for the rights for all
-actors involved in a temporary employment, in order to provide employees with
-the fair and legal remuneration (including taxes) of work performances and a
-protection in the case employer becomes insolvent. At the same time, our system
-wants to assist the employer in processing contracts with a fully automated and
-fast procedure. To resolve these problems we propose the D-ES (Decentralized
-Employment System). We first model the employment relationship as a state
-system. Then we describe the enabling technology that makes us able to realize
-the D-ES. In facts, we propose the implementation of a DLT (Decentralized
-Ledger Technology) based system, consisting in a blockchain system and of a
-web-based environment. Thanks the decentralized application platforms that
-makes us able to develop smart contracts, we define a discrete event control
-system that works inside the blockchain. In addition, we discuss the temporary
-work in agriculture as a interesting case of study.
-",1,0,0,0,0,0
-17844,Valley polarized relaxation and upconversion luminescence from Tamm-Plasmon Trion-Polaritons with a MoSe2 monolayer,"  Transition metal dichalcogenides represent an ideal testbed to study
-excitonic effects, spin-related phenomena and fundamental light-matter coupling
-in nanoscopic condensed matter systems. In particular, the valley degree of
-freedom, which is unique to such direct band gap monolayers with broken
-inversion symmetry, adds fundamental interest in these materials. Here, we
-implement a Tamm-plasmon structure with an embedded MoSe2 monolayer and study
-the formation of polaritonic quasi-particles. Strong coupling conditions
-between the Tamm-mode and the trion resonance of MoSe2 are established,
-yielding bright luminescence from the polaritonic ground state under
-non-resonant optical excitation. We demonstrate, that tailoring the
-electrodynamic environment of the monolayer results in a significantly
-increased valley polarization. This enhancement can be related to change in
-recombination dynamics shown in time-resolved photoluminescence measurements.
-We furthermore observe strong upconversion luminescence from resonantly excited
-polariton states in the lower polariton branch. This upconverted polariton
-luminescence is shown to preserve the valley polarization of the
-trion-polariton, which paves the way towards combining spin-valley physics and
-exciton scattering experiments.
-",0,1,0,0,0,0
-17845,Machine learning based localization and classification with atomic magnetometers,"  We demonstrate identification of position, material, orientation and shape of
-objects imaged by an $^{85}$Rb atomic magnetometer performing electromagnetic
-induction imaging supported by machine learning. Machine learning maximizes the
-information extracted from the images created by the magnetometer,
-demonstrating the use of hidden data. Localization 2.6 times better than the
-spatial resolution of the imaging system and successful classification up to
-97$\%$ are obtained. This circumvents the need of solving the inverse problem,
-and demonstrates the extension of machine learning to diffusive systems such as
-low-frequency electrodynamics in media. Automated collection of task-relevant
-information from quantum-based electromagnetic imaging will have a relevant
-impact from biomedicine to security.
-",0,1,0,0,0,0
-17846,On the difficulty of finding spines,"  We prove that the set of symplectic lattices in the Siegel space
-$\mathfrak{h}_g$ whose systoles generate a subspace of dimension at least 3 in
-$\mathbb{R}^{2g}$ does not contain any $\mathrm{Sp}(2g,\mathbb{Z})$-equivariant
-deformation retract of $\mathfrak{h}_g$.
-",0,0,1,0,0,0
-17847,Distributed Decoding of Convolutional Network Error Correction Codes,"  A Viterbi-like decoding algorithm is proposed in this paper for generalized
-convolutional network error correction coding. Different from classical Viterbi
-algorithm, our decoding algorithm is based on minimum error weight rather than
-the shortest Hamming distance between received and sent sequences. Network
-errors may disperse or neutralize due to network transmission and convolutional
-network coding. Therefore, classical decoding algorithm cannot be employed any
-more. Source decoding was proposed by multiplying the inverse of network
-transmission matrix, where the inverse is hard to compute. Starting from the
-Maximum A Posteriori (MAP) decoding criterion, we find that it is equivalent to
-the minimum error weight under our model. Inspired by Viterbi algorithm, we
-propose a Viterbi-like decoding algorithm based on minimum error weight of
-combined error vectors, which can be carried out directly at sink nodes and can
-correct any network errors within the capability of convolutional network error
-correction codes (CNECC). Under certain situations, the proposed algorithm can
-realize the distributed decoding of CNECC.
-",1,0,0,0,0,0
-17848,Temperature induced phase transition from cycloidal to collinear antiferromagnetism in multiferroic Bi$_{0.9}$Sm$_{0.1}$FeO$_3$ driven by $f$-$d$ induced magnetic anisotropy,"  In multiferroic BiFeO$_3$ a cycloidal antiferromagnetic structure is coupled
-to a large electric polarization at room temperature, giving rise to
-magnetoelectric functionality that may be exploited in novel multiferroic-based
-devices. In this paper, we demonstrate that by substituting samarium for 10% of
-the bismuth ions the periodicity of the room temperature cycloid is increased,
-and by cooling below $\sim15$ K the magnetic structure tends towards a simple
-G-type antiferromagnet, which is fully established at 1.5 K. We show that this
-transition results from $f-d$ exchange coupling, which induces a local
-anisotropy on the iron magnetic moments that destroys the cycloidal order - a
-result of general significance regarding the stability of non-collinear
-magnetic structures in the presence of multiple magnetic sublattices.
-",0,1,0,0,0,0
-17849,POSEYDON - Converting the DAFNE Collider into a double Positron Facility: a High Duty-Cycle pulse stretcher and a storage ring,"  This project proposes to reuse the DAFNE accelerator complex for producing a
-high intensity (up to 10^10), high-quality beam of high-energy (up to 500 MeV)
-positrons for HEP experiments, mainly - but not only - motivated by light dark
-particles searches. Such a facility would provide a unique source of
-ultra-relativistic, narrow-band and low-emittance positrons, with a high duty
-factor, without employing a cold technology, that would be an ideal facility
-for exploring the existence of light dark matter particles, produced in
-positron-on-target annihilations into a photon+missing mass, and using the
-bump-hunt technique. The PADME experiment, that will use the extracted beam
-from the DAFNE BTF, is indeed limited by the low duty-factor (10^-5=200 ns/20
-ms). The idea is to use a variant of the third of integer resonant extraction,
-with the aim of getting a <10^-6 m rad emittance and, at the same time,
-tailoring the scheme to the peculiar optics of the DAFNE machine. In
-alternative, the possibility of kicking the positrons by means of channelling
-effects in crystals can be evaluated. This would not only increase the
-extraction efficiency but also improve the beam quality, thanks to the high
-collimation of channelled particles.
-",0,1,0,0,0,0
-17850,Phase diagram of hydrogen and a hydrogen-helium mixture at planetary conditions by Quantum Monte Carlo simulations,"  Understanding planetary interiors is directly linked to our ability of
-simulating exotic quantum mechanical systems such as hydrogen (H) and
-hydrogen-helium (H-He) mixtures at high pressures and temperatures. Equations
-of State (EOSs) tables based on Density Functional Theory (DFT), are commonly
-used by planetary scientists, although this method allows only for a
-qualitative description of the phase diagram, due to an incomplete treatment of
-electronic interactions. Here we report Quantum Monte Carlo (QMC) molecular
-dynamics simulations of pure H and H-He mixture. We calculate the first QMC EOS
-at 6000 K for an H-He mixture of a proto-solar composition, and show the
-crucial influence of He on the H metallization pressure. Our results can be
-used to calibrate other EOS calculations and are very timely given the accurate
-determination of Jupiter's gravitational field from the NASA Juno mission and
-the effort to determine its structure.
-",0,1,0,0,0,0
-17851,Identifying exogenous and endogenous activity in social media,"  The occurrence of new events in a system is typically driven by external
-causes and by previous events taking place inside the system. This is a general
-statement, applying to a range of situations including, more recently, to the
-activity of users in Online social networks (OSNs). Here we develop a method
-for extracting from a series of posting times the relative contributions of
-exogenous, e.g. news media, and endogenous, e.g. information cascade. The
-method is based on the fitting of a generalized linear model (GLM) equipped
-with a self-excitation mechanism. We test the method with synthetic data
-generated by a nonlinear Hawkes process, and apply it to a real time series of
-tweets with a given hashtag. In the empirical dataset, the estimated
-contributions of exogenous and endogenous volumes are close to the amounts of
-original tweets and retweets respectively. We conclude by discussing the
-possible applications of the method, for instance in online marketing.
-",1,0,0,0,0,0
-17852,Water flow in Carbon and Silicon Carbide nanotubes,"  In this work the conduction of ion-water solution through two discrete
-bundles of armchair carbon and silicon carbide nanotubes, as useful membranes
-for water desalination, is studied. In order that studies on different types of
-nanotubes be comparable, the chiral vectors of C and Si-C nanotubes are
-selected as (7,7) and (5,5), respectively, so that a similar volume of fluid is
-investigated flowing through two similar dimension membranes. Different
-hydrostatic pressures are applied and the flow rates of water and ions are
-calculated through molecular dynamics simulations. Consequently, according to
-conductance of water per each nanotube, per nanosecond, it is perceived that at
-lower pressures (below 150 MPa) the Si-C nanotubes seem to be more applicable,
-while higher hydrostatic pressures make carbon nanotube membranes more suitable
-for water desalination.
-",0,1,0,0,0,0
-17853,Multidimensional extremal dependence coefficients,"  Extreme values modeling has attracting the attention of researchers in
-diverse areas such as the environment, engineering, or finance. Multivariate
-extreme value distributions are particularly suitable to model the tails of
-multidimensional phenomena. The analysis of the dependence among multivariate
-maxima is useful to evaluate risk. Here we present new multivariate extreme
-value models, as well as, coefficients to assess multivariate extremal
-dependence.
-",0,0,1,1,0,0
-17854,A general framework for data-driven uncertainty quantification under complex input dependencies using vine copulas,"  Systems subject to uncertain inputs produce uncertain responses. Uncertainty
-quantification (UQ) deals with the estimation of statistics of the system
-response, given a computational model of the system and a probabilistic model
-of its inputs. In engineering applications it is common to assume that the
-inputs are mutually independent or coupled by a Gaussian or elliptical
-dependence structure (copula). In this paper we overcome such limitations by
-modelling the dependence structure of multivariate inputs as vine copulas. Vine
-copulas are models of multivariate dependence built from simpler pair-copulas.
-The vine representation is flexible enough to capture complex dependencies.
-This paper formalises the framework needed to build vine copula models of
-multivariate inputs and to combine them with virtually any UQ method. The
-framework allows for a fully automated, data-driven inference of the
-probabilistic input model on available input data. The procedure is exemplified
-on two finite element models of truss structures, both subject to inputs with
-non-Gaussian dependence structures. For each case, we analyse the moments of
-the model response (using polynomial chaos expansions), and perform a
-structural reliability analysis to calculate the probability of failure of the
-system (using the first order reliability method and importance sampling).
-Reference solutions are obtained by Monte Carlo simulation. The results show
-that, while the Gaussian assumption yields biased statistics, the vine copula
-representation achieves significantly more precise estimates, even when its
-structure needs to be fully inferred from a limited amount of observations.
-",0,0,0,1,0,0
-17855,Pachinko Prediction: A Bayesian method for event prediction from social media data,"  The combination of large open data sources with machine learning approaches
-presents a potentially powerful way to predict events such as protest or social
-unrest. However, accounting for uncertainty in such models, particularly when
-using diverse, unstructured datasets such as social media, is essential to
-guarantee the appropriate use of such methods. Here we develop a Bayesian
-method for predicting social unrest events in Australia using social media
-data. This method uses machine learning methods to classify individual postings
-to social media as being relevant, and an empirical Bayesian approach to
-calculate posterior event probabilities. We use the method to predict events in
-Australian cities over a period in 2017/18.
-",1,0,0,0,0,0
-17856,A Digital Hardware Fast Algorithm and FPGA-based Prototype for a Novel 16-point Approximate DCT for Image Compression Applications,"  The discrete cosine transform (DCT) is the key step in many image and video
-coding standards. The 8-point DCT is an important special case, possessing
-several low-complexity approximations widely investigated. However, 16-point
-DCT transform has energy compaction advantages. In this sense, this paper
-presents a new 16-point DCT approximation with null multiplicative complexity.
-The proposed transform matrix is orthogonal and contains only zeros and ones.
-The proposed transform outperforms the well-know Walsh-Hadamard transform and
-the current state-of-the-art 16-point approximation. A fast algorithm for the
-proposed transform is also introduced. This fast algorithm is experimentally
-validated using hardware implementations that are physically realized and
-verified on a 40 nm CMOS Xilinx Virtex-6 XC6VLX240T FPGA chip for a maximum
-clock rate of 342 MHz. Rapid prototypes on FPGA for 8-bit input word size shows
-significant improvement in compressed image quality by up to 1-2 dB at the cost
-of only eight adders compared to the state-of-art 16-point DCT approximation
-algorithm in the literature [S. Bouguezel, M. O. Ahmad, and M. N. S. Swamy. A
-novel transform for image compression. In {\em Proceedings of the 53rd IEEE
-International Midwest Symposium on Circuits and Systems (MWSCAS)}, 2010].
-",1,0,0,1,0,0
-17857,Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice,"  It is well known that the initialization of weights in deep neural networks
-can have a dramatic impact on learning speed. For example, ensuring the mean
-squared singular value of a network's input-output Jacobian is $O(1)$ is
-essential for avoiding the exponential vanishing or explosion of gradients. The
-stronger condition that all singular values of the Jacobian concentrate near
-$1$ is a property known as dynamical isometry. For deep linear networks,
-dynamical isometry can be achieved through orthogonal weight initialization and
-has been shown to dramatically speed up learning; however, it has remained
-unclear how to extend these results to the nonlinear setting. We address this
-question by employing powerful tools from free probability theory to compute
-analytically the entire singular value distribution of a deep network's
-input-output Jacobian. We explore the dependence of the singular value
-distribution on the depth of the network, the weight initialization, and the
-choice of nonlinearity. Intriguingly, we find that ReLU networks are incapable
-of dynamical isometry. On the other hand, sigmoidal networks can achieve
-isometry, but only with orthogonal weight initialization. Moreover, we
-demonstrate empirically that deep nonlinear networks achieving dynamical
-isometry learn orders of magnitude faster than networks that do not. Indeed, we
-show that properly-initialized deep sigmoidal networks consistently outperform
-deep ReLU networks. Overall, our analysis reveals that controlling the entire
-distribution of Jacobian singular values is an important design consideration
-in deep learning.
-",1,0,0,1,0,0
-17858,Singular perturbation for abstract elliptic equations and application,"  Boundary value problem for complete second order elliptic equation is
-considered in Banach space. The equation and boundary conditions involve a
-small and spectral parameter. The uniform L_{p}-regularity properties with
-respect to space variable and parameters are established. Here, the explicit
-formula for the solution is given and behavior of solution is derived when the
-small parameter approaches zero. It used to obtain singular perturbation result
-for abstract elliptic equation
-",0,0,1,0,0,0
-17859,Pruning and Nonparametric Multiple Change Point Detection,"  Change point analysis is a statistical tool to identify homogeneity within
-time series data. We propose a pruning approach for approximate nonparametric
-estimation of multiple change points. This general purpose change point
-detection procedure `cp3o' applies a pruning routine within a dynamic program
-to greatly reduce the search space and computational costs. Existing
-goodness-of-fit change point objectives can immediately be utilized within the
-framework. We further propose novel change point algorithms by applying cp3o to
-two popular nonparametric goodness of fit measures: `e-cp3o' uses E-statistics,
-and `ks-cp3o' uses Kolmogorov-Smirnov statistics. Simulation studies highlight
-the performance of these algorithms in comparison with parametric and other
-nonparametric change point methods. Finally, we illustrate these approaches
-with climatological and financial applications.
-",0,0,0,1,0,0
-17860,Context encoding enables machine learning-based quantitative photoacoustics,"  Real-time monitoring of functional tissue parameters, such as local blood
-oxygenation, based on optical imaging could provide groundbreaking advances in
-the diagnosis and interventional therapy of various diseases. While
-photoacoustic (PA) imaging is a novel modality with great potential to measure
-optical absorption deep inside tissue, quantification of the measurements
-remains a major challenge. In this paper, we introduce the first machine
-learning based approach to quantitative PA imaging (qPAI), which relies on
-learning the fluence in a voxel to deduce the corresponding optical absorption.
-The method encodes relevant information of the measured signal and the
-characteristics of the imaging system in voxel-based feature vectors, which
-allow the generation of thousands of training samples from a single simulated
-PA image. Comprehensive in silico experiments suggest that context encoding
-(CE)-qPAI enables highly accurate and robust quantification of the local
-fluence and thereby the optical absorption from PA images.
-",1,1,0,0,0,0
-17861,Simulating the interaction between a falling super-quadric object and a soap film,"  The interaction that occurs between a light solid object and a horizontal
-soap film of a bamboo foam contained in a cylindrical tube is simulated in 3D.
-We vary the shape of the falling object from a sphere to a cube by changing a
-single shape parameter as well as varying the initial orientation and position
-of the object. We investigate in detail how the soap film deforms in all these
-cases, and determine the network and pressure forces that a foam exerts on a
-falling object, due to surface tension and bubble pressure respectively. We
-show that a cubic particle in a particular orientation experiences the largest
-drag force, and that this orientation is also the most likely outcome of
-dropping a cube from an arbitrary orientation through a bamboo foam.
-",0,1,0,0,0,0
-17862,Power Flow Analysis Using Graph based Combination of Iterative Methods and Vertex Contraction Approach,"  Compared with relational database (RDB), graph database (GDB) is a more
-intuitive expression of the real world. Each node in the GDB is a both storage
-and logic unit. Since it is connected to its neighboring nodes through edges,
-and its neighboring information could be easily obtained in one-step graph
-traversal. It is able to conduct local computation independently and all nodes
-can do their local work in parallel. Then the whole system can be maximally
-analyzed and assessed in parallel to largely improve the computation
-performance without sacrificing the precision of final results. This paper
-firstly introduces graph database, power system graph modeling and potential
-graph computing applications in power systems. Two iterative methods based on
-graph database and PageRank are presented and their convergence are discussed.
-Vertex contraction is proposed to improve the performance by eliminating
-zero-impedance branch. A combination of the two iterative methods is proposed
-to make use of their advantages. Testing results based on a provincial 1425-bus
-system demonstrate that the proposed comprehensive approach is a good candidate
-for power flow analysis.
-",1,0,0,0,0,0
-17863,A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm,"  In this paper, we present an algorithm for the sparse signal recovery problem
-that incorporates damped Gaussian generalized approximate message passing
-(GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning
-(SBL). In particular, GGAMP is used to implement the E-step in SBL in place of
-matrix inversion, leveraging the fact that GGAMP is guaranteed to converge with
-appropriate damping. The resulting GGAMP-SBL algorithm is much more robust to
-arbitrary measurement matrix $\boldsymbol{A}$ than the standard damped GAMP
-algorithm while being much lower complexity than the standard SBL algorithm. We
-then extend the approach from the single measurement vector (SMV) case to the
-temporally correlated multiple measurement vector (MMV) case, leading to the
-GGAMP-TSBL algorithm. We verify the robustness and computational advantages of
-the proposed algorithms through numerical experiments.
-",1,0,0,1,0,0
-17864,Sub-Nanometer Channels Embedded in Two-Dimensional Materials,"  Two-dimensional (2D) materials are among the most promising candidates for
-next-generation electronics due to their atomic thinness, allowing for flexible
-transparent electronics and ultimate length scaling. Thus far, atomically-thin
-p-n junctions, metal-semiconductor contacts, and metal-insulator barriers have
-been demonstrated. While 2D materials achieve the thinnest possible devices,
-precise nanoscale control over the lateral dimensions is also necessary. Here,
-we report the direct synthesis of sub-nanometer-wide 1D MoS2 channels embedded
-within WSe2 monolayers, using a dislocation-catalyzed approach. The 1D channels
-have edges free of misfit dislocations and dangling bonds, forming a coherent
-interface with the embedding 2D matrix. Periodic dislocation arrays produce 2D
-superlattices of coherent MoS2 1D channels in WSe2. Using molecular dynamics
-simulations, we have identified other combinations of 2D materials where 1D
-channels can also be formed. The electronic band structure of these 1D channels
-offer the promise of carrier confinement in a direct-gap material and charge
-separation needed to access the ultimate length scales necessary for future
-electronic applications.
-",0,1,0,0,0,0
-17865,Emotion in Reinforcement Learning Agents and Robots: A Survey,"  This article provides the first survey of computational models of emotion in
-reinforcement learning (RL) agents. The survey focuses on agent/robot emotions,
-and mostly ignores human user emotions. Emotions are recognized as functional
-in decision-making by influencing motivation and action selection. Therefore,
-computational emotion models are usually grounded in the agent's decision
-making architecture, of which RL is an important subclass. Studying emotions in
-RL-based agents is useful for three research fields. For machine learning (ML)
-researchers, emotion models may improve learning efficiency. For the
-interactive ML and human-robot interaction (HRI) community, emotions can
-communicate state and enhance user investment. Lastly, it allows affective
-modelling (AM) researchers to investigate their emotion theories in a
-successful AI agent class. This survey provides background on emotion theory
-and RL. It systematically addresses 1) from what underlying dimensions (e.g.,
-homeostasis, appraisal) emotions can be derived and how these can be modelled
-in RL-agents, 2) what types of emotions have been derived from these
-dimensions, and 3) how these emotions may either influence the learning
-efficiency of the agent or be useful as social signals. We also systematically
-compare evaluation criteria, and draw connections to important RL sub-domains
-like (intrinsic) motivation and model-based RL. In short, this survey provides
-both a practical overview for engineers wanting to implement emotions in their
-RL agents, and identifies challenges and directions for future emotion-RL
-research.
-",1,0,0,1,0,0
-17866,Tensor Networks in a Nutshell,"  Tensor network methods are taking a central role in modern quantum physics
-and beyond. They can provide an efficient approximation to certain classes of
-quantum states, and the associated graphical language makes it easy to describe
-and pictorially reason about quantum circuits, channels, protocols, open
-systems and more. Our goal is to explain tensor networks and some associated
-methods as quickly and as painlessly as possible. Beginning with the key
-definitions, the graphical tensor network language is presented through
-examples. We then provide an introduction to matrix product states. We conclude
-the tutorial with tensor contractions evaluating combinatorial counting
-problems. The first one counts the number of solutions for Boolean formulae,
-whereas the second is Penrose's tensor contraction algorithm, returning the
-number of $3$-edge-colorings of $3$-regular planar graphs.
-",0,1,0,0,0,0
-17867,Cheryl's Birthday,"  We present four logic puzzles and after that their solutions. Joseph Yeo
-designed 'Cheryl's Birthday'. Mike Hartley came up with a novel solution for
-'One Hundred Prisoners and a Light Bulb'. Jonathan Welton designed 'A Blind
-Guess' and 'Abby's Birthday'. Hans van Ditmarsch and Barteld Kooi authored the
-puzzlebook 'One Hundred Prisoners and a Light Bulb' that contains other
-knowledge puzzles, and that can also be found on the webpage
-this http URL dedicated to the book.
-",1,0,0,0,0,0
-17868,Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow,"  Exploration bonus derived from the novelty of the states in an environment
-has become a popular approach to motivate exploration for deep reinforcement
-learning agents in the past few years. Recent methods such as curiosity-driven
-exploration usually estimate the novelty of new observations by the prediction
-errors of their system dynamics models. Due to the capacity limitation of the
-models and difficulty of performing next-frame prediction, however, these
-methods typically fail to balance between exploration and exploitation in
-high-dimensional observation tasks, resulting in the agents forgetting the
-visited paths and exploring those states repeatedly. Such inefficient
-exploration behavior causes significant performance drops, especially in large
-environments with sparse reward signals. In this paper, we propose to introduce
-the concept of optical flow estimation from the field of computer vision to
-deal with the above issue. We propose to employ optical flow estimation errors
-to examine the novelty of new observations, such that agents are able to
-memorize and understand the visited states in a more comprehensive fashion. We
-compare our method against the previous approaches in a number of experimental
-experiments. Our results indicate that the proposed method appears to deliver
-superior and long-lasting performance than the previous methods. We further
-provide a set of comprehensive ablative analysis of the proposed method, and
-investigate the impact of optical flow estimation on the learning curves of the
-DRL agents.
-",1,0,0,0,0,0
-17869,Lenient Multi-Agent Deep Reinforcement Learning,"  Much of the success of single agent deep reinforcement learning (DRL) in
-recent years can be attributed to the use of experience replay memories (ERM),
-which allow Deep Q-Networks (DQNs) to be trained efficiently through sampling
-stored state transitions. However, care is required when using ERMs for
-multi-agent deep reinforcement learning (MA-DRL), as stored transitions can
-become outdated because agents update their policies in parallel [11]. In this
-work we apply leniency [23] to MA-DRL. Lenient agents map state-action pairs to
-decaying temperature values that control the amount of leniency applied towards
-negative policy updates that are sampled from the ERM. This introduces optimism
-in the value-function update, and has been shown to facilitate cooperation in
-tabular fully-cooperative multi-agent reinforcement learning problems. We
-evaluate our Lenient-DQN (LDQN) empirically against the related Hysteretic-DQN
-(HDQN) algorithm [22] as well as a modified version we call scheduled-HDQN,
-that uses average reward learning near terminal states. Evaluations take place
-in extended variations of the Coordinated Multi-Agent Object Transportation
-Problem (CMOTP) [8] which include fully-cooperative sub-tasks and stochastic
-rewards. We find that LDQN agents are more likely to converge to the optimal
-policy in a stochastic reward CMOTP compared to standard and scheduled-HDQN
-agents.
-",1,0,0,0,0,0
-17870,A New Framework for Synthetic Aperture Sonar Micronavigation,"  Synthetic aperture imaging systems achieve constant azimuth resolution by
-coherently summating the observations acquired along the aperture path. At this
-aim, their locations have to be known with subwavelength accuracy. In
-underwater Synthetic Aperture Sonar (SAS), the nature of propagation and
-navigation in water makes the retrieval of this information challenging.
-Inertial sensors have to be employed in combination with signal processing
-techniques, which are usually referred to as micronavigation. In this paper we
-propose a novel micronavigation approach based on the minimization of an error
-function between two contiguous pings having some mutual information. This
-error is obtained by comparing the vector space intersections between the pings
-orthogonal projectors. The effectiveness and generality of the proposed
-approach is demonstrated by means of simulations and by means of an experiment
-performed in a controlled environment.
-",1,0,0,0,0,0
-17871,Emotionalism within People-Oriented Software Design,"  In designing most software applications, much effort is placed upon the
-functional goals, which make a software system useful. However, the failure to
-consider emotional goals, which make a software system pleasurable to use, can
-result in disappointment and system rejection even if utilitarian goals are
-well implemented. Although several studies have emphasized the importance of
-people's emotional goals in developing software, there is little advice on how
-to address these goals in the software system development process. This paper
-proposes a theoretically-sound and practical method by combining the theories
-and techniques of software engineering, requirements engineering, and decision
-making. The outcome of this study is the Emotional Goal Systematic Analysis
-Technique (EG-SAT), which facilitates the process of finding software system
-capabilities to address emotional goals in software design. EG-SAT is easy to
-learn and easy to use technique that helps analysts to gain insights into how
-to address people's emotional goals. To demonstrate the method in use, a
-two-part evaluation is conducted. First, EG-SAT is used to analyze the
-emotional goals of potential users of a mobile learning application that
-provides information about low carbon living for tradespeople and professionals
-in the building industry in Australia. The results of using EG-SAT in this case
-study are compared with a professionally-developed baseline. Second, we ran a
-semi-controlled experiment in which 12 participants were asked to apply EG-SAT
-and another technique on part of our case study. The outcomes show that EG-SAT
-helped participants to both analyse emotional goals and gain valuable insights
-about the functional and non-functional goals for addressing people's emotional
-goals.
-",1,0,0,0,0,0
-17872,Quantifying the Estimation Error of Principal Components,"  Principal component analysis is an important pattern recognition and
-dimensionality reduction tool in many applications. Principal components are
-computed as eigenvectors of a maximum likelihood covariance $\widehat{\Sigma}$
-that approximates a population covariance $\Sigma$, and these eigenvectors are
-often used to extract structural information about the variables (or
-attributes) of the studied population. Since PCA is based on the
-eigendecomposition of the proxy covariance $\widehat{\Sigma}$ rather than the
-ground-truth $\Sigma$, it is important to understand the approximation error in
-each individual eigenvector as a function of the number of available samples.
-The recent results of Kolchinskii and Lounici yield such bounds. In the present
-paper we sharpen these bounds and show that eigenvectors can often be
-reconstructed to a required accuracy from a sample of strictly smaller size
-order.
-",0,0,1,1,0,0
-17873,On Convex Programming Relaxations for the Permanent,"  In recent years, several convex programming relaxations have been proposed to
-estimate the permanent of a non-negative matrix, notably in the works of
-Gurvits and Samorodnitsky. However, the origins of these relaxations and their
-relationships to each other have remained somewhat mysterious. We present a
-conceptual framework, implicit in the belief propagation literature, to
-systematically arrive at these convex programming relaxations for estimating
-the permanent -- as approximations to an exponential-sized max-entropy convex
-program for computing the permanent. Further, using standard convex programming
-techniques such as duality, we establish equivalence of these aforementioned
-relaxations to those based on capacity-like quantities studied by Gurvits and
-Anari et al.
-",1,0,1,0,0,0
-17874,Many cubic surfaces contain rational points,"  Building on recent work of Bhargava--Elkies--Schnidman and Kriz--Li, we
-produce infinitely many smooth cubic surfaces defined over the field of
-rational numbers that contain rational points.
-",0,0,1,0,0,0
-17875,Representation learning of drug and disease terms for drug repositioning,"  Drug repositioning (DR) refers to identification of novel indications for the
-approved drugs. The requirement of huge investment of time as well as money and
-risk of failure in clinical trials have led to surge in interest in drug
-repositioning. DR exploits two major aspects associated with drugs and
-diseases: existence of similarity among drugs and among diseases due to their
-shared involved genes or pathways or common biological effects. Existing
-methods of identifying drug-disease association majorly rely on the information
-available in the structured databases only. On the other hand, abundant
-information available in form of free texts in biomedical research articles are
-not being fully exploited. Word-embedding or obtaining vector representation of
-words from a large corpora of free texts using neural network methods have been
-shown to give significant performance for several natural language processing
-tasks. In this work we propose a novel way of representation learning to obtain
-features of drugs and diseases by combining complementary information available
-in unstructured texts and structured datasets. Next we use matrix completion
-approach on these feature vectors to learn projection matrix between drug and
-disease vector spaces. The proposed method has shown competitive performance
-with state-of-the-art methods. Further, the case studies on Alzheimer's and
-Hypertension diseases have shown that the predicted associations are matching
-with the existing knowledge.
-",1,0,0,0,0,0
-17876,Analysis of a remarkable singularity in a nonlinear DDE,"  In this work we investigate the dynamics of the nonlinear DDE
-(delay-differential equation)
-x''(t)+x(t-T)+x(t)^3=0
-where T is the delay. For T=0 this system is conservative and exhibits no
-limit cycles. For T>0, no matter how small, an infinite number of limit cycles
-exist, their amplitudes going to infinity in the limit as T approaches zero.
-We investigate this situation in three ways: 1) Harmonic Balance, 2)
-Melnikov's integral, and 3) Adding damping to regularize the singularity.
-",0,1,1,0,0,0
-17877,Characterization of Lipschitz functions in terms of variable exponent Lebesgue spaces,"  Our aim is to characterize the Lipschitz functions by variable exponent
-Lebesgue spaces. We give some characterizations of the boundedness of the
-maximal or nonlinear commutators of the Hardy-Littlewood maximal function and
-sharp maximal function in variable exponent Lebesgue spaces when the symbols
-$b$ belong to the Lipschitz spaces, by which some new characterizations of
-Lipschitz spaces and nonnegative Lipschitz functions are obtained. Some
-equivalent relations between the Lipschitz norm and the variable exponent
-Lebesgue norm are also given.
-",0,0,1,0,0,0
-17878,Superconductivity Induced by Interfacial Coupling to Magnons,"  We consider a thin normal metal sandwiched between two ferromagnetic
-insulators. At the interfaces, the exchange coupling causes electrons within
-the metal to interact with magnons in the insulators. This electron-magnon
-interaction induces electron-electron interactions, which, in turn, can result
-in p-wave superconductivity. In the weak-coupling limit, we solve the gap
-equation numerically and estimate the critical temperature. In YIG-Au-YIG
-trilayers, superconductivity sets in at temperatures somewhere in the interval
-between 1 and 10 K. EuO-Au-EuO trilayers require a lower temperature, in the
-range from 0.01 to 1 K.
-",0,1,0,0,0,0
-17879,Privacy Assessment of De-identified Opal Data: A report for Transport for NSW,"  We consider the privacy implications of public release of a de-identified
-dataset of Opal card transactions. The data was recently published at
-this https URL. It
-consists of tap-on and tap-off counts for NSW's four modes of public transport,
-collected over two separate week-long periods. The data has been further
-treated to improve privacy by removing small counts, aggregating some stops and
-routes, and perturbing the counts. This is a summary of our findings.
-",1,0,0,0,0,0
-17880,On the Importance of Correlations in Rational Choice: A Case for Non-Nashian Game Theory,"  The Nash equilibrium paradigm, and Rational Choice Theory in general, rely on
-agents acting independently from each other. This note shows how this
-assumption is crucial in the definition of Rational Choice Theory. It explains
-how a consistent Alternate Rational Choice Theory, as suggested by Jean-Pierre
-Dupuy, can be built on the exact opposite assumption, and how it provides a
-viable account for alternate, actually observed behavior of rational agents
-that is based on correlations between their decisions.
-The end goal of this note is three-fold: (i) to motivate that the Perfect
-Prediction Equilibrium, implementing Dupuy's notion of projected time and
-previously called ""projected equilibrium"", is a reasonable approach in certain
-real situations and a meaningful complement to the Nash paradigm, (ii) to
-summarize common misconceptions about this equilibrium, and (iii) to give a
-concise motivation for future research on non-Nashian game theory.
-",1,0,0,0,0,0
-17881,Reliable estimation of prediction uncertainty for physico-chemical property models,"  The predictions of parameteric property models and their uncertainties are
-sensitive to systematic errors such as inconsistent reference data, parametric
-model assumptions, or inadequate computational methods. Here, we discuss the
-calibration of property models in the light of bootstrapping, a sampling method
-akin to Bayesian inference that can be employed for identifying systematic
-errors and for reliable estimation of the prediction uncertainty. We apply
-bootstrapping to assess a linear property model linking the 57Fe Moessbauer
-isomer shift to the contact electron density at the iron nucleus for a diverse
-set of 44 molecular iron compounds. The contact electron density is calculated
-with twelve density functionals across Jacob's ladder (PWLDA, BP86, BLYP, PW91,
-PBE, M06-L, TPSS, B3LYP, B3PW91, PBE0, M06, TPSSh). We provide systematic-error
-diagnostics and reliable, locally resolved uncertainties for isomer-shift
-predictions. Pure and hybrid density functionals yield average prediction
-uncertainties of 0.06-0.08 mm/s and 0.04-0.05 mm/s, respectively, the latter
-being close to the average experimental uncertainty of 0.02 mm/s. Furthermore,
-we show that both model parameters and prediction uncertainty depend
-significantly on the composition and number of reference data points.
-Accordingly, we suggest that rankings of density functionals based on
-performance measures (e.g., the coefficient of correlation, r2, or the
-root-mean-square error, RMSE) should not be inferred from a single data set.
-This study presents the first statistically rigorous calibration analysis for
-theoretical Moessbauer spectroscopy, which is of general applicability for
-physico-chemical property models and not restricted to isomer-shift
-predictions. We provide the statistically meaningful reference data set MIS39
-and a new calibration of the isomer shift based on the PBE0 functional.
-",0,1,0,0,0,0
-17882,Rethinking Split Manufacturing: An Information-Theoretic Approach with Secure Layout Techniques,"  Split manufacturing is a promising technique to defend against fab-based
-malicious activities such as IP piracy, overbuilding, and insertion of hardware
-Trojans. However, a network flow-based proximity attack, proposed by Wang et
-al. (DAC'16) [1], has demonstrated that most prior art on split manufacturing
-is highly vulnerable. Here in this work, we present two practical layout
-techniques towards secure split manufacturing: (i) gate-level graph coloring
-and (ii) clustering of same-type gates. Our approach shows promising results
-against the advanced proximity attack, lowering its success rate by 5.27x,
-3.19x, and 1.73x on average compared to the unprotected layouts when splitting
-at metal layers M1, M2, and M3, respectively. Also, it largely outperforms
-previous defense efforts; we observe on average 8x higher resilience when
-compared to representative prior art. At the same time, extensive simulations
-on ISCAS'85 and MCNC benchmarks reveal that our techniques incur an acceptable
-layout overhead. Apart from this empirical study, we provide---for the first
-time---a theoretical framework for quantifying the layout-level resilience
-against any proximity-induced information leakage. Towards this end, we
-leverage the notion of mutual information and provide extensive results to
-validate our model.
-",1,0,0,0,0,0
-17883,Unsupervised robotic sorting: Towards autonomous decision making robots,"  Autonomous sorting is a crucial task in industrial robotics which can be very
-challenging depending on the expected amount of automation. Usually, to decide
-where to sort an object, the system needs to solve either an instance retrieval
-(known object) or a supervised classification (predefined set of classes)
-problem. In this paper, we introduce a new decision making module, where the
-robotic system chooses how to sort the objects in an unsupervised way. We call
-this problem Unsupervised Robotic Sorting (URS) and propose an implementation
-on an industrial robotic system, using deep CNN feature extraction and standard
-clustering algorithms. We carry out extensive experiments on various standard
-datasets to demonstrate the efficiency of the proposed image clustering
-pipeline. To evaluate the robustness of our URS implementation, we also
-introduce a complex real world dataset containing images of objects under
-various background and lighting conditions. This dataset is used to fine tune
-the design choices (CNN and clustering algorithm) for URS. Finally, we propose
-a method combining our pipeline with ensemble clustering to use multiple images
-of each object. This redundancy of information about the objects is shown to
-increase the clustering results.
-",1,0,0,0,0,0
-17884,Beyond Whittle: Nonparametric correction of a parametric likelihood with a focus on Bayesian time series analysis,"  The Whittle likelihood is widely used for Bayesian nonparametric estimation
-of the spectral density of stationary time series. However, the loss of
-efficiency for non-Gaussian time series can be substantial. On the other hand,
-parametric methods are more powerful if the model is well-specified, but may
-fail entirely otherwise. Therefore, we suggest a nonparametric correction of a
-parametric likelihood taking advantage of the efficiency of parametric models
-while mitigating sensitivities through a nonparametric amendment. Using a
-Bernstein-Dirichlet prior for the nonparametric spectral correction, we show
-posterior consistency and illustrate the performance of our procedure in a
-simulation study and with LIGO gravitational wave data.
-",0,0,0,1,0,0
-17885,A consistent approach to unstructured mesh generation for geophysical models,"  Geophysical model domains typically contain irregular, complex fractal-like
-boundaries and physical processes that act over a wide range of scales.
-Constructing geographically constrained boundary-conforming spatial
-discretizations of these domains with flexible use of anisotropically, fully
-unstructured meshes is a challenge. The problem contains a wide range of scales
-and a relatively large, heterogeneous constraint parameter space. Approaches
-are commonly ad hoc, model or application specific and insufficiently
-described. Development of new spatial domains is frequently time-consuming,
-hard to repeat, error prone and difficult to ensure consistent due to the
-significant human input required. As a consequence, it is difficult to
-reproduce simulations, ensure a provenance in model data handling and
-initialization, and a challenge to conduct model intercomparisons rigorously.
-Moreover, for flexible unstructured meshes, there is additionally a greater
-potential for inconsistencies in model initialization and forcing parameters.
-This paper introduces a consistent approach to unstructured mesh generation for
-geophysical models, that is automated, quick-to-draft and repeat, and provides
-a rigorous and robust approach that is consistent to the source data
-throughout. The approach is enabling further new research in complex
-multi-scale domains, difficult or not possible to achieve with existing
-methods. Examples being actively pursued in a range of geophysical modeling
-efforts are presented alongside the approach, together with the implementation
-library Shingle and a selection of its verification test cases.
-",1,1,0,0,0,0
-17886,Metric Map Merging using RFID Tags & Topological Information,"  A map merging component is crucial for the proper functionality of a
-multi-robot system performing exploration, since it provides the means to
-integrate and distribute the most important information carried by the agents:
-the explored-covered space and its exact (depending on the SLAM accuracy)
-morphology. Map merging is a prerequisite for an intelligent multi-robot team
-aiming to deploy a smart exploration technique. In the current work, a metric
-map merging approach based on environmental information is proposed, in
-conjunction with spatially scattered RFID tags localization. This approach is
-divided into the following parts: the maps approximate rotation calculation via
-the obstacles poses and localized RFID tags, the translation employing the best
-localized common RFID tag and finally the transformation refinement using an
-ICP algorithm.
-",1,0,0,0,0,0
-17887,Learning Local Feature Aggregation Functions with Backpropagation,"  This paper introduces a family of local feature aggregation functions and a
-novel method to estimate their parameters, such that they generate optimal
-representations for classification (or any task that can be expressed as a cost
-function minimization problem). To achieve that, we compose the local feature
-aggregation function with the classifier cost function and we backpropagate the
-gradient of this cost function in order to update the local feature aggregation
-function parameters. Experiments on synthetic datasets indicate that our method
-discovers parameters that model the class-relevant information in addition to
-the local feature space. Further experiments on a variety of motion and visual
-descriptors, both on image and video datasets, show that our method outperforms
-other state-of-the-art local feature aggregation functions, such as Bag of
-Words, Fisher Vectors and VLAD, by a large margin.
-",1,0,0,1,0,0
-17888,Sub-harmonic Injection Locking in Metronomes,"  In this paper, we demonstrate sub-harmonic injection locking (SHIL) in
-mechanical metronomes. To do so, we first formulate metronome's physical
-compact model, focusing on its nonlinear terms for friction and the escapement
-mechanism. Then we analyze metronomes using phase-macromodel-based techniques
-and show that the phase of their oscillation is in fact very immune to periodic
-perturbation at twice its natural frequency, making SHIL difficult. Guided by
-the phase-macromodel-based analysis, we are able to modify the escapement
-mechanism of metronomes such that SHIL can happen more easily. Then we verify
-the occurrence of SHIL in experiments. To our knowledge, this is the first
-demonstration of SHIL in metronomes; As such, it provides many valuable
-insights into the modelling, simulation, analysis and design of nonlinear
-oscillators. The demonstration is also suitable to use for teaching the subject
-of injection locking and SHIL.
-",0,1,0,0,0,0
-17889,Spin Seebeck effect in a polar antiferromagnet $α$-Cu$_{2}$V$_{2}$O$_{7}$,"  We have studied the longitudinal spin Seebeck effect in a polar
-antiferromagnet $\alpha$-Cu$_{2}$V$_{2}$O$_{7}$ in contact with a Pt film.
-Below the antiferromagnetic transition temperature of
-$\alpha$-Cu$_{2}$V$_{2}$O$_{7}$, spin Seebeck voltages whose magnetic field
-dependence is similar to that reported in antiferromagnetic MnF$_{2}$$\mid$Pt
-bilayers are observed. Though a small weak-ferromagnetic moment appears owing
-to the Dzyaloshinskii-Moriya interaction in $\alpha$-Cu$_{2}$V$_{2}$O$_{7}$,
-the magnetic field dependence of spin Seebeck voltages is found to be
-irrelevant to the weak ferromagnetic moments. The dependences of the spin
-Seebeck voltages on magnetic fields and temperature are analyzed by a magnon
-spin current theory. The numerical calculation of spin Seebeck voltages using
-magnetic parameters of $\alpha$-Cu$_{2}$V$_{2}$O$_{7}$ determined by previous
-neutron scattering studies reveals that the magnetic-field and temperature
-dependences of the spin Seebeck voltages for
-$\alpha$-Cu$_{2}$V$_{2}$O$_{7}$$\mid$Pt are governed by the changes in magnon
-lifetimes with magnetic fields and temperature.
-",0,1,0,0,0,0
-17890,Mackey algebras which are Gorenstein,"  We complete the picture available in the literature by showing that the
-integral Mackey algebra is Gorenstein if and only if the group order is
-square-free, in which case it must have Gorenstein dimension one. We illustrate
-this result by looking in details at the examples of the cyclic group of order
-four and the Klein four group.
-",0,0,1,0,0,0
-17891,Efficient acquisition rules for model-based approximate Bayesian computation,"  Approximate Bayesian computation (ABC) is a method for Bayesian inference
-when the likelihood is unavailable but simulating from the model is possible.
-However, many ABC algorithms require a large number of simulations, which can
-be costly. To reduce the computational cost, Bayesian optimisation (BO) and
-surrogate models such as Gaussian processes have been proposed. Bayesian
-optimisation enables one to intelligently decide where to evaluate the model
-next but common BO strategies are not designed for the goal of estimating the
-posterior distribution. Our paper addresses this gap in the literature. We
-propose to compute the uncertainty in the ABC posterior density, which is due
-to a lack of simulations to estimate this quantity accurately, and define a
-loss function that measures this uncertainty. We then propose to select the
-next evaluation location to minimise the expected loss. Experiments show that
-the proposed method often produces the most accurate approximations as compared
-to common BO strategies.
-",0,0,0,1,0,0
-17892,Active Learning for Regression Using Greedy Sampling,"  Regression problems are pervasive in real-world applications. Generally a
-substantial amount of labeled samples are needed to build a regression model
-with good generalization ability. However, many times it is relatively easy to
-collect a large number of unlabeled samples, but time-consuming or expensive to
-label them. Active learning for regression (ALR) is a methodology to reduce the
-number of labeled samples, by selecting the most beneficial ones to label,
-instead of random selection. This paper proposes two new ALR approaches based
-on greedy sampling (GS). The first approach (GSy) selects new samples to
-increase the diversity in the output space, and the second (iGS) selects new
-samples to increase the diversity in both input and output spaces. Extensive
-experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15
-subjects on EEG-based driver drowsiness estimation, verified their
-effectiveness and robustness.
-",0,0,0,1,0,0
-17893,Centroid estimation based on symmetric KL divergence for Multinomial text classification problem,"  We define a new method to estimate centroid for text classification based on
-the symmetric KL-divergence between the distribution of words in training
-documents and their class centroids. Experiments on several standard data sets
-indicate that the new method achieves substantial improvements over the
-traditional classifiers.
-",0,0,0,1,0,0
-17894,Rapid Near-Neighbor Interaction of High-dimensional Data via Hierarchical Clustering,"  Calculation of near-neighbor interactions among high dimensional, irregularly
-distributed data points is a fundamental task to many graph-based or
-kernel-based machine learning algorithms and applications. Such calculations,
-involving large, sparse interaction matrices, expose the limitation of
-conventional data-and-computation reordering techniques for improving space and
-time locality on modern computer memory hierarchies. We introduce a novel
-method for obtaining a matrix permutation that renders a desirable sparsity
-profile. The method is distinguished by the guiding principle to obtain a
-profile that is block-sparse with dense blocks. Our profile model and measure
-capture the essential properties affecting space and time locality, and permit
-variation in sparsity profile without imposing a restriction to a fixed
-pattern. The second distinction lies in an efficient algorithm for obtaining a
-desirable profile, via exploring and exploiting multi-scale cluster structure
-hidden in but intrinsic to the data. The algorithm accomplishes its task with
-key components for lower-dimensional embedding with data-specific principal
-feature axes, hierarchical data clustering, multi-level matrix compression
-storage, and multi-level interaction computations. We provide experimental
-results from case studies with two important data analysis algorithms. The
-resulting performance is remarkably comparable to the BLAS performance for the
-best-case interaction governed by a regularly banded matrix with the same
-sparsity.
-",1,0,0,0,0,0
-17895,Evolution and Recent Developments of the Gaseous Photon Detectors Technologies,"  The evolution and the present status of the gaseous photon detectors
-technologies are reviewed. The most recent developments in several branches of
-the field are described, in particular the installation and commissioning of
-the first large area MPGD-based detectors of single photons on COMPASS RICH-1.
-Investigation of novel detector architectures, different materials and various
-applications are reported, and the quest for visible light gaseous photon
-detectors is discussed. The progress on the use of gaseous photon detector
-related techniques in the field of cryogenic applications and gaseous or liquid
-scintillation imaging are presented.
-",0,1,0,0,0,0
-17896,Superconducting Qubit-Resonator-Atom Hybrid System,"  We propose a hybrid quantum system, where an $LC$ resonator inductively
-interacts with a flux qubit and is capacitively coupled to a Rydberg atom.
-Varying the external magnetic flux bias controls the flux-qubit flipping and
-the flux qubit-resonator interface. The atomic spectrum is tuned via an
-electrostatic field, manipulating the qubit-state transition of atom and the
-atom-resonator coupling. Different types of entanglement of superconducting,
-photonic, and atomic qubits can be prepared via simply tuning the flux bias and
-electrostatic field, leading to the implementation of three-qubit Toffoli logic
-gate.
-",0,1,0,0,0,0
-17897,Heroes and Zeroes: Predicting the Impact of New Video Games on Twitch.tv,"  Video games and the playing thereof have been a fixture of American culture
-since their introduction in the arcades of the 1980s. However, it was not until
-the recent proliferation of broadband connections robust and fast enough to
-handle live video streaming that players of video games have transitioned from
-a content consumer role to a content producer role. Simultaneously, the rise of
-social media has revealed how interpersonal connections drive user engagement
-and interest. In this work, we discuss the recent proliferation of video game
-streaming, particularly on Twitch.tv, analyze trends and patterns in video game
-viewing, and develop predictive models for determining if a new game will have
-substantial impact on the streaming ecosystem.
-",1,0,0,0,0,0
-17898,Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study,"  Learning the latent representation of data in unsupervised fashion is a very
-interesting process that provides relevant features for enhancing the
-performance of a classifier. For speech emotion recognition tasks, generating
-effective features is crucial. Currently, handcrafted features are mostly used
-for speech emotion recognition, however, features learned automatically using
-deep learning have shown strong success in many problems, especially in image
-processing. In particular, deep generative models such as Variational
-Autoencoders (VAEs) have gained enormous success for generating features for
-natural images. Inspired by this, we propose VAEs for deriving the latent
-representation of speech signals and use this representation to classify
-emotions. To the best of our knowledge, we are the first to propose VAEs for
-speech emotion classification. Evaluations on the IEMOCAP dataset demonstrate
-that features learned by VAEs can produce state-of-the-art results for speech
-emotion classification.
-",1,0,0,1,0,0
-17899,Insense: Incoherent Sensor Selection for Sparse Signals,"  Sensor selection refers to the problem of intelligently selecting a small
-subset of a collection of available sensors to reduce the sensing cost while
-preserving signal acquisition performance. The majority of sensor selection
-algorithms find the subset of sensors that best recovers an arbitrary signal
-from a number of linear measurements that is larger than the dimension of the
-signal. In this paper, we develop a new sensor selection algorithm for sparse
-(or near sparse) signals that finds a subset of sensors that best recovers such
-signals from a number of measurements that is much smaller than the dimension
-of the signal. Existing sensor selection algorithms cannot be applied in such
-situations. Our proposed Incoherent Sensor Selection (Insense) algorithm
-minimizes a coherence-based cost function that is adapted from recent results
-in sparse recovery theory. Using six datasets, including two real-world
-datasets on microbial diagnostics and structural health monitoring, we
-demonstrate the superior performance of Insense for sparse-signal sensor
-selection.
-",1,0,0,0,0,0
-17900,Discrete Distribution for a Wiener Process Range and its Properties,"  We introduce the discrete distribution of a Wiener process range. Rather than
-finding some basic distributional properties including hazard rate function,
-moments, Stress-strength parameter and order statistics of this distribution,
-this work studies some basic properties of the truncated version of this
-distribution. The effectiveness of this distribution is established using a
-data set.
-",0,0,1,1,0,0
-17901,Coherent control of flexural vibrations in dual-nanoweb fibers using phase-modulated two-frequency light,"  Coherent control of the resonant response in spatially extended
-optomechanical structures is complicated by the fact that the optical drive is
-affected by the back-action from the generated phonons. Here we report a new
-approach to coherent control based on stimulated Raman-like scattering, in
-which the optical pressure can remain unaffected by the induced vibrations even
-in the regime of strong optomechanical interactions. We demonstrate
-experimentally coherent control of flexural vibrations simultaneously along the
-whole length of a dual-nanoweb fiber, by imprinting steps in the relative phase
-between the components of a two-frequency pump signal,the beat frequency being
-chosen to match a flexural resonance. Furthermore, sequential switching of the
-relative phase at time intervals shorter than the lifetime of the vibrations
-reduces their amplitude to a constant value that is fully adjustable by tuning
-the phase-modulation depth and switching rate. The results may trigger new
-developments in silicon photonics, since such coherent control uniquely
-decouples the amplitude of optomechanical oscillations from power-dependent
-thermal effects and nonlinear optical loss.
-",0,1,0,0,0,0
-17902,Static and Dynamic Magnetic Properties of FeMn/Pt Multilayers,"  Recently we have demonstrated the presence of spin-orbit toque in FeMn/Pt
-multilayers which, in combination with the anisotropy field, is able to rotate
-its magnetization consecutively from 0o to 360o without any external field.
-Here, we report on an investigation of static and dynamic magnetic properties
-of FeMn/Pt multilayers using combined techniques of magnetometry, ferromagnetic
-resonance, inverse spin Hall effect and spin Hall magnetoresistance
-measurements. The FeMn/Pt multilayer was found to exhibit ferromagnetic
-properties, and its temperature dependence of saturation magnetization can be
-fitted well using a phenomenological model by including a finite distribution
-in Curie temperature due to subtle thickness variations across the multilayer
-samples. The non-uniformity in static magnetic properties is also manifested in
-the ferromagnetic resonance spectra, which typically exhibit a broad resonance
-peak. A damping parameter of around 0.106 is derived from the frequency
-dependence of ferromagnetic resonance linewidth, which is comparable to the
-reported values for other types of Pt-based multilayers. Clear inverse spin
-Hall signals and spin Hall magnetoresistance have been observed in all samples
-below the Curie temperature, which corroborate the strong spin-orbit torque
-effect observed previously.
-",0,1,0,0,0,0
-17903,Ultraproducts of crossed product von Neumann algebras,"  We study a relationship between the ultraproduct of a crossed product von
-Neumann algebra and the crossed product of an ultraproduct von Neumann algebra.
-As an application, the continuous core of an ultraproduct von Neumann algebra
-is described.
-",0,0,1,0,0,0
-17904,Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing,"  A fundamental issue for statistical classification models in a streaming
-environment is that the joint distribution between predictor and response
-variables changes over time (a phenomenon also known as concept drifts), such
-that their classification performance deteriorates dramatically. In this paper,
-we first present a hierarchical hypothesis testing (HHT) framework that can
-detect and also adapt to various concept drift types (e.g., recurrent or
-irregular, gradual or abrupt), even in the presence of imbalanced data labels.
-A novel concept drift detector, namely Hierarchical Linear Four Rates (HLFR),
-is implemented under the HHT framework thereafter. By substituting a
-widely-acknowledged retraining scheme with an adaptive training strategy, we
-further demonstrate that the concept drift adaptation capability of HLFR can be
-significantly boosted. The theoretical analysis on the Type-I and Type-II
-errors of HLFR is also performed. Experiments on both simulated and real-world
-datasets illustrate that our methods outperform state-of-the-art methods in
-terms of detection precision, detection delay as well as the adaptability
-across different concept drift types.
-",1,0,0,1,0,0
-17905,Interval-based Prediction Uncertainty Bound Computation in Learning with Missing Values,"  The problem of machine learning with missing values is common in many areas.
-A simple approach is to first construct a dataset without missing values simply
-by discarding instances with missing entries or by imputing a fixed value for
-each missing entry, and then train a prediction model with the new dataset. A
-drawback of this naive approach is that the uncertainty in the missing entries
-is not properly incorporated in the prediction. In order to evaluate prediction
-uncertainty, the multiple imputation (MI) approach has been studied, but the
-performance of MI is sensitive to the choice of the probabilistic model of the
-true values in the missing entries, and the computational cost of MI is high
-because multiple models must be trained. In this paper, we propose an
-alternative approach called the Interval-based Prediction Uncertainty Bounding
-(IPUB) method. The IPUB method represents the uncertainties due to missing
-entries as intervals, and efficiently computes the lower and upper bounds of
-the prediction results when all possible training sets constructed by imputing
-arbitrary values in the intervals are considered. The IPUB method can be
-applied to a wide class of convex learning algorithms including penalized
-least-squares regression, support vector machine (SVM), and logistic
-regression. We demonstrate the advantages of the IPUB method by comparing it
-with an existing method in numerical experiment with benchmark datasets.
-",0,0,0,1,0,0
-17906,Methods for Interpreting and Understanding Deep Neural Networks,"  This paper provides an entry point to the problem of interpreting a deep
-neural network model and explaining its predictions. It is based on a tutorial
-given at ICASSP 2017. It introduces some recently proposed techniques of
-interpretation, along with theory, tricks and recommendations, to make most
-efficient use of these techniques on real data. It also discusses a number of
-practical applications.
-",1,0,0,1,0,0
-17907,Passive Classification of Source Printer using Text-line-level Geometric Distortion Signatures from Scanned Images of Printed Documents,"  In this digital era, one thing that still holds the convention is a printed
-archive. Printed documents find their use in many critical domains such as
-contract papers, legal tenders and proof of identity documents. As more
-advanced printing, scanning and image editing techniques are becoming
-available, forgeries on these legal tenders pose a serious threat. Ability to
-easily and reliably identify source printer of a printed document can help a
-lot in reducing this menace. During printing procedure, printer hardware
-introduces certain distortions in printed characters' locations and shapes
-which are invisible to naked eyes. These distortions are referred as geometric
-distortions, their profile (or signature) is generally unique for each printer
-and can be used for printer classification purpose. This paper proposes a set
-of features for characterizing text-line-level geometric distortions, referred
-as geometric distortion signatures and presents a novel system to use them for
-identification of the origin of a printed document. Detailed experiments
-performed on a set of thirteen printers demonstrate that the proposed system
-achieves state of the art performance and gives much higher accuracy under
-small training size constraint. For four training and six test pages of three
-different fonts, the proposed method gives 99\% classification accuracy.
-",1,0,0,0,0,0
-17908,The duration of load effect in lumber as stochastic degradation,"  This paper proposes a gamma process for modelling the damage that accumulates
-over time in the lumber used in structural engineering applications when stress
-is applied. The model separates the stochastic processes representing features
-internal to the piece of lumber on the one hand, from those representing
-external forces due to applied dead and live loads. The model applies those
-external forces through a time-varying population level function designed for
-time-varying loads. The application of this type of model, which is standard in
-reliability analysis, is novel in this context, which has been dominated by
-accumulated damage models (ADMs) over more than half a century. The proposed
-model is compared with one of the traditional ADMs. Our statistical results
-based on a Bayesian analysis of experimental data highlight the limitations of
-using accelerated testing data to assess long-term reliability, as seen in the
-wide posterior intervals. This suggests the need for more comprehensive testing
-in future applications, or to encode appropriate expert knowledge in the priors
-used for Bayesian analysis.
-",0,0,0,1,0,0
-17909,Partial Bridging of Vaccine Efficacy to New Populations,"  Suppose one has data from one or more completed vaccine efficacy trials and
-wishes to estimate the efficacy in a new setting. Often logistical or ethical
-considerations make running another efficacy trial impossible. Fortunately, if
-there is a biomarker that is the primary modifier of efficacy, then the
-biomarker-conditional efficacy may be identical in the completed trials and the
-new setting, or at least informative enough to meaningfully bound this
-quantity. Given a sample of this biomarker from the new population, we might
-hope we can bridge the results of the completed trials to estimate the vaccine
-efficacy in this new population. Unfortunately, even knowing the true
-conditional efficacy in the new population fails to identify the marginal
-efficacy due to the unknown conditional unvaccinated risk. We define a curve
-that partially identifies (lower bounds) the marginal efficacy in the new
-population as a function of the population's marginal unvaccinated risk, under
-the assumption that one can identify bounds on the conditional unvaccinated
-risk in the new population. Interpreting the curve only requires identifying
-plausible regions of the marginal unvaccinated risk in the new population. We
-present a nonparametric estimator of this curve and develop valid lower
-confidence bounds that concentrate at a parametric rate. We use vaccine
-terminology throughout, but the results apply to general binary interventions
-and bounded outcomes.
-",0,0,0,1,0,0
-17910,An induced map between rationalized classifying spaces for fibrations,"  Let $B{ aut}_1X$ be the Dold-Lashof classifying space of orientable
-fibrations with fiber $X$. For a rationally weakly trivial map $f:X\to Y$, our
-strictly induced map $a_f: (Baut_1X)_0\to (Baut_1Y)_0$ induces a natural map
-from a $X_0$-fibration to a $Y_0$-fibration. It is given by a map between the
-differential graded Lie algebras of derivations of Sullivan models. We note
-some conditions that the map $a_f$ admits a section and note some relations
-with the Halperin conjecture. Furthermore we give the obstruction class for a
-lifting of a classifying map $h: B\to (Baut_1Y)_0$ and apply it for liftings of
-$G$-actions on $Y$ for a compact connected Lie group $G$ as the case of $B=BG$
-and evaluating of rational toral ranks as $r_0(Y)\leq r_0(X)$.
-",0,0,1,0,0,0
-17911,The Statistical Recurrent Unit,"  Sophisticated gated recurrent neural network architectures like LSTMs and
-GRUs have been shown to be highly effective in a myriad of applications. We
-develop an un-gated unit, the statistical recurrent unit (SRU), that is able to
-learn long term dependencies in data by only keeping moving averages of
-statistics. The SRU's architecture is simple, un-gated, and contains a
-comparable number of parameters to LSTMs; yet, SRUs perform favorably to more
-sophisticated LSTM and GRU alternatives, often outperforming one or both in
-various tasks. We show the efficacy of SRUs as compared to LSTMs and GRUs in an
-unbiased manner by optimizing respective architectures' hyperparameters in a
-Bayesian optimization scheme for both synthetic and real-world tasks.
-",1,0,0,1,0,0
-17912,Many-body localization in the droplet spectrum of the random XXZ quantum spin chain,"  We study many-body localization properties of the disordered XXZ spin chain
-in the Ising phase. Disorder is introduced via a random magnetic field in the
-$z$-direction. We prove a strong form of dynamical exponential clustering for
-eigenstates in the droplet spectrum: For any pair of local observables
-separated by a distance $\ell$, the sum of the associated correlators over
-these states decays exponentially in $\ell$, in expectation. This exponential
-clustering persists under the time evolution in the droplet spectrum. Our
-result applies to the large disorder regime as well as to the strong Ising
-phase at fixed disorder, with bounds independent of the support of the
-observables.
-",0,0,1,0,0,0
-17913,FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices,"  Deep neural networks show great potential as solutions to many sensing
-application problems, but their excessive resource demand slows down execution
-time, pausing a serious impediment to deployment on low-end devices. To address
-this challenge, recent literature focused on compressing neural network size to
-improve performance. We show that changing neural network size does not
-proportionally affect performance attributes of interest, such as execution
-time. Rather, extreme run-time nonlinearities exist over the network
-configuration space. Hence, we propose a novel framework, called FastDeepIoT,
-that uncovers the non-linear relation between neural network structure and
-execution time, then exploits that understanding to find network configurations
-that significantly improve the trade-off between execution time and accuracy on
-mobile and embedded devices. FastDeepIoT makes two key contributions. First,
-FastDeepIoT automatically learns an accurate and highly interpretable execution
-time model for deep neural networks on the target device. This is done without
-prior knowledge of either the hardware specifications or the detailed
-implementation of the used deep learning library. Second, FastDeepIoT informs a
-compression algorithm how to minimize execution time on the profiled device
-without impacting accuracy. We evaluate FastDeepIoT using three different
-sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus.
-FastDeepIoT further reduces the neural network execution time by $48\%$ to
-$78\%$ and energy consumption by $37\%$ to $69\%$ compared with the
-state-of-the-art compression algorithms.
-",1,0,0,0,0,0
-17914,Robust MPC for tracking of nonholonomic robots with additive disturbances,"  In this paper, two robust model predictive control (MPC) schemes are proposed
-for tracking control of nonholonomic systems with bounded disturbances:
-tube-MPC and nominal robust MPC (NRMPC). In tube-MPC, the control signal
-consists of a control action and a nonlinear feedback law based on the
-deviation of the actual states from the states of a nominal system. It renders
-the actual trajectory within a tube centered along the optimal trajectory of
-the nominal system. Recursive feasibility and input-to-state stability are
-established and the constraints are ensured by tightening the input domain and
-the terminal region. While in NRMPC, an optimal control sequence is obtained by
-solving an optimization problem based on the current state, and the first
-portion of this sequence is applied to the real system in an open-loop manner
-during each sampling period. The state of nominal system model is updated by
-the actual state at each step, which provides additional a feedback. By
-introducing a robust state constraint and tightening the terminal region,
-recursive feasibility and input-to-state stability are guaranteed. Simulation
-results demonstrate the effectiveness of both strategies proposed.
-",1,0,0,0,0,0
-17915,Investigation on the use of Hidden-Markov Models in automatic transcription of music,"  Hidden Markov Models (HMMs) are a ubiquitous tool to model time series data,
-and have been widely used in two main tasks of Automatic Music Transcription
-(AMT): note segmentation, i.e. identifying the played notes after a multi-pitch
-estimation, and sequential post-processing, i.e. correcting note segmentation
-using training data. In this paper, we employ the multi-pitch estimation method
-called Probabilistic Latent Component Analysis (PLCA), and develop AMT systems
-by integrating different HMM-based modules in this framework. For note
-segmentation, we use two different twostate on/o? HMMs, including a
-higher-order one for duration modeling. For sequential post-processing, we
-focused on a musicological modeling of polyphonic harmonic transitions, using a
-first- and second-order HMMs whose states are defined through candidate note
-mixtures. These different PLCA plus HMM systems have been evaluated
-comparatively on two different instrument repertoires, namely the piano (using
-the MAPS database) and the marovany zither. Our results show that the use of
-HMMs could bring noticeable improvements to transcription results, depending on
-the instrument repertoire.
-",1,0,0,1,0,0
-17916,Network archaeology: phase transition in the recoverability of network history,"  Network growth processes can be understood as generative models of the
-structure and history of complex networks. This point of view naturally leads
-to the problem of network archaeology: Reconstructing all the past states of a
-network from its structure---a difficult permutation inference problem. In this
-paper, we introduce a Bayesian formulation of network archaeology, with a
-generalization of preferential attachment as our generative mechanism. We
-develop a sequential importance sampling algorithm to evaluate the posterior
-averages of this model, as well as an efficient heuristic that uncovers the
-history of a network in linear time. We use these methods to identify and
-characterize a phase transition in the quality of the reconstructed history,
-when they are applied to artificial networks generated by the model itself.
-Despite the existence of a no-recovery phase, we find that non-trivial
-inference is possible in a large portion of the parameter space as well as on
-empirical data.
-",0,0,0,1,0,0
-17917,"Big Data, Data Science, and Civil Rights","  Advances in data analytics bring with them civil rights implications.
-Data-driven and algorithmic decision making increasingly determine how
-businesses target advertisements to consumers, how police departments monitor
-individuals or groups, how banks decide who gets a loan and who does not, how
-employers hire, how colleges and universities make admissions and financial aid
-decisions, and much more. As data-driven decisions increasingly affect every
-corner of our lives, there is an urgent need to ensure they do not become
-instruments of discrimination, barriers to equality, threats to social justice,
-and sources of unfairness. In this paper, we argue for a concrete research
-agenda aimed at addressing these concerns, comprising five areas of emphasis:
-(i) Determining if models and modeling procedures exhibit objectionable bias;
-(ii) Building awareness of fairness into machine learning methods; (iii)
-Improving the transparency and control of data- and model-driven decision
-making; (iv) Looking beyond the algorithm(s) for sources of bias and
-unfairness-in the myriad human decisions made during the problem formulation
-and modeling process; and (v) Supporting the cross-disciplinary scholarship
-necessary to do all of that well.
-",1,0,0,0,0,0
-17918,Global well-posedness for 2-D Boussinesq system with the temperature-dependent viscosity and supercritical dissipation,"  The present paper is dedicated to the global well-posedness issue for the
-Boussinesq system with the temperature-dependent viscosity in $\mathbb{R}^2.$
-We aim at extending the work by Abidi and Zhang ( Adv. Math. 2017 (305)
-1202--1249 ) to a supercritical dissipation for temperature.
-",0,0,1,0,0,0
-17919,"CO~($J = 1-0$) Observations of a Filamentary Molecular Cloud in the Galactic Region Centered at $l = 150\arcdeg, b = 3.5\arcdeg$","  We present large-field (4.25~$\times$~3.75 deg$^2$) mapping observations
-toward the Galactic region centered at $l = 150\arcdeg, b = 3.5\arcdeg$ in the
-$J = 1-0$ emission line of CO isotopologues ($^{12}$CO, $^{13}$CO, and
-C$^{18}$O), using the 13.7 m millimeter-wavelength telescope of the Purple
-Mountain Observatory. Based on the $^{13}$CO observations, we reveal a
-filamentary cloud in the Local Arm at a velocity range of $-$0.5 to
-6.5~km~s$^{-1}$. This molecular cloud contains 1 main filament and 11
-sub-filaments, showing the so-called ""ridge-nest"" structure. The main filament
-and three sub-filaments are also detected in the C$^{18}$O line. The velocity
-structures of most identified filaments display continuous distribution with
-slight velocity gradients. The measured median excitation temperature, line
-width, length, width, and linear mass of the filaments are $\sim$9.28~K,
-0.85~km~s$^{-1}$, 7.30~pc, 0.79~pc, and 17.92~$M_\sun$~pc$^{-1}$, respectively,
-assuming a distance of 400~pc. We find that the four filaments detected in the
-C$^{18}$O line are thermally supercritical, and two of them are in the
-virialized state, and thus tend to be gravitationally bound. We identify in
-total 146 $^{13}$CO clumps in the cloud, about 77$\%$ of the clumps are
-distributed along the filaments. About 56$\%$ of the virialized clumps are
-found to be associated with the supercritical filaments. Three young stellar
-object (YSO) candidates are also identified in the supercritical filaments,
-based on the complementary infrared (IR) data. These results indicate that the
-supercritical filaments, especially the virialized filaments, may contain
-star-forming activities.
-",0,1,0,0,0,0
-17920,Leveraging Deep Neural Network Activation Entropy to cope with Unseen Data in Speech Recognition,"  Unseen data conditions can inflict serious performance degradation on systems
-relying on supervised machine learning algorithms. Because data can often be
-unseen, and because traditional machine learning algorithms are trained in a
-supervised manner, unsupervised adaptation techniques must be used to adapt the
-model to the unseen data conditions. However, unsupervised adaptation is often
-challenging, as one must generate some hypothesis given a model and then use
-that hypothesis to bootstrap the model to the unseen data conditions.
-Unfortunately, reliability of such hypotheses is often poor, given the mismatch
-between the training and testing datasets. In such cases, a model hypothesis
-confidence measure enables performing data selection for the model adaptation.
-Underlying this approach is the fact that for unseen data conditions, data
-variability is introduced to the model, which the model propagates to its
-output decision, impacting decision reliability. In a fully connected network,
-this data variability is propagated as distortions from one layer to the next.
-This work aims to estimate the propagation of such distortion in the form of
-network activation entropy, which is measured over a short- time running window
-on the activation from each neuron of a given hidden layer, and these
-measurements are then used to compute summary entropy. This work demonstrates
-that such an entropy measure can help to select data for unsupervised model
-adaptation, resulting in performance gains in speech recognition tasks. Results
-from standard benchmark speech recognition tasks show that the proposed
-approach can alleviate the performance degradation experienced under unseen
-data conditions by iteratively adapting the model to the unseen datas acoustic
-condition.
-",1,0,0,1,0,0
-17921,Asymmetric Preheating,"  We study the generation of the matter-antimatter asymmetry during bosonic
-preheating, focusing on the sources of the asymmetry. If the asymmetry appears
-in the multiplication factor of the resonant particle production, the
-matter-antimatter ratio will grow during preheating. On the other hand, if the
-asymmetry does not grow during preheating, one has to find out another reason.
-We consider several scenarios for the asymmetric preheating to distinguish the
-sources of the asymmetry. We also discuss a new baryogenesis scenario, in which
-the asymmetry is generated without introducing neither loop corrections nor
-rotation of a field.
-",0,1,0,0,0,0
-17922,A multi-device dataset for urban acoustic scene classification,"  This paper introduces the acoustic scene classification task of DCASE 2018
-Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task,
-and evaluates the performance of a baseline system in the task. As in previous
-years of the challenge, the task is defined for classification of short audio
-samples into one of predefined acoustic scene classes, using a supervised,
-closed-set classification setup. The newly recorded TUT Urban Acoustic Scenes
-2018 dataset consists of ten different acoustic scenes and was recorded in six
-large European cities, therefore it has a higher acoustic variability than the
-previous datasets used for this task, and in addition to high-quality binaural
-recordings, it also includes data recorded with mobile devices. We also present
-the baseline system consisting of a convolutional neural network and its
-performance in the subtasks using the recommended cross-validation setup.
-",1,0,0,0,0,0
-17923,Asymptotically preserving particle-in-cell methods for inhomogenous strongly magnetized plasmas,"  We propose a class of Particle-In-Cell (PIC) methods for the Vlasov-Poisson
-system with a strong and inhomogeneous external magnetic field with fixed
-direction, where we focus on the motion of particles in the plane orthogonal to
-the magnetic field (so-called poloidal directions). In this regime, the time
-step can be subject to stability constraints related to the smallness of Larmor
-radius and plasma frequency. To avoid this limitation, our approach is based on
-first and higher-order semi-implicit numerical schemes already validated on
-dissipative systems [3] and for homogeneous magnetic fields [10]. Thus, when
-the magnitude of the external magnetic field becomes large, this method
-provides a consistent PIC discretization of the guiding-center system taking
-into account variations of the magnetic field. We carry out some theoretical
-proofs and perform several numerical experiments that establish a solid
-validation of the method and its underlying concepts.
-",0,0,1,0,0,0
-17924,Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates,"  Methodological research rarely generates a broad interest, yet our work on
-the validity of cluster inference methods for functional magnetic resonance
-imaging (fMRI) created intense discussion on both the minutia of our approach
-and its implications for the discipline. In the present work, we take on
-various critiques of our work and further explore the limitations of our
-original work. We address issues about the particular event-related designs we
-used, considering multiple event types and randomisation of events between
-subjects. We consider the lack of validity found with one-sample permutation
-(sign flipping) tests, investigating a number of approaches to improve the
-false positive control of this widely used procedure. We found that the
-combination of a two-sided test and cleaning the data using ICA FIX resulted in
-nominal false positive rates for all datasets, meaning that data cleaning is
-not only important for resting state fMRI, but also for task fMRI. Finally, we
-discuss the implications of our work on the fMRI literature as a whole,
-estimating that at least 10% of the fMRI studies have used the most problematic
-cluster inference method (P = 0.01 cluster defining threshold), and how
-individual studies can be interpreted in light of our findings. These
-additional results underscore our original conclusions, on the importance of
-data sharing and thorough evaluation of statistical methods on realistic null
-data.
-",0,0,0,1,0,0
-17925,An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning,"  Deep learning is an established framework for learning hierarchical data
-representations. While compute power is in abundance, one of the main
-challenges in applying this framework to robotic grasping has been obtaining
-the amount of data needed to learn these representations, and structuring the
-data to the task at hand. Among contemporary approaches in the literature, we
-highlight key properties that have encouraged the use of deep learning
-techniques, and in this paper, detail our experience in developing a simulator
-for collecting cylindrical precision grasps of a multi-fingered dexterous
-robotic hand.
-",1,0,0,1,0,0
-17926,A Recursive Bayesian Approach To Describe Retinal Vasculature Geometry,"  Demographic studies suggest that changes in the retinal vasculature geometry,
-especially in vessel width, are associated with the incidence or progression of
-eye-related or systemic diseases. To date, the main information source for
-width estimation from fundus images has been the intensity profile between
-vessel edges. However, there are many factors affecting the intensity profile:
-pathologies, the central light reflex and local illumination levels, to name a
-few. In this study, we introduce three information sources for width
-estimation. These are the probability profiles of vessel interior, centreline
-and edge locations generated by a deep network. The probability profiles
-provide direct access to vessel geometry and are used in the likelihood
-calculation for a Bayesian method, particle filtering. We also introduce a
-geometric model which can handle non-ideal conditions of the probability
-profiles. Our experiments conducted on the REVIEW dataset yielded consistent
-estimates of vessel width, even in cases when one of the vessel edges is
-difficult to identify. Moreover, our results suggest that the method is better
-than human observers at locating edges of low contrast vessels.
-",1,0,0,0,0,0
-17927,Quantum Harmonic Analysis of the Density Matrix: Basics,"  In this Review we will study rigorously the notion of mixed states and their
-density matrices. We mostly give complete proofs. We will also discuss the
-quantum-mechanical consequences of possible variations of Planck's constant h.
-This Review has been written having in mind two readerships: mathematical
-physicists and quantum physicists. The mathematical rigor is maximal, but the
-language and notation we use throughout should be familiar to physicists.
-",0,0,1,0,0,0
-17928,The bromodomain-containing protein Ibd1 links multiple chromatin related protein complexes to highly expressed genes in Tetrahymena thermophila,"  Background: The chromatin remodelers of the SWI/SNF family are critical
-transcriptional regulators. Recognition of lysine acetylation through a
-bromodomain (BRD) component is key to SWI/SNF function; in most eukaryotes,
-this function is attributed to SNF2/Brg1.
-Results: Using affinity purification coupled to mass spectrometry (AP-MS) we
-identified members of a SWI/SNF complex (SWI/SNFTt) in Tetrahymena thermophila.
-SWI/SNFTt is composed of 11 proteins, Snf5Tt, Swi1Tt, Swi3Tt, Snf12Tt, Brg1Tt,
-two proteins with potential chromatin interacting domains and four proteins
-without orthologs to SWI/SNF proteins in yeast or mammals. SWI/SNFTt subunits
-localize exclusively to the transcriptionally active macronucleus (MAC) during
-growth and development, consistent with a role in transcription. While
-Tetrahymena Brg1 does not contain a BRD, our AP-MS results identified a
-BRD-containing SWI/SNFTt component, Ibd1 that associates with SWI/SNFTt during
-growth but not development. AP-MS analysis of epitope-tagged Ibd1 revealed it
-to be a subunit of several additional protein complexes, including putative
-SWRTt, and SAGATt complexes as well as a putative H3K4-specific histone methyl
-transferase complex. Recombinant Ibd1 recognizes acetyl-lysine marks on
-histones correlated with active transcription. Consistent with our AP-MS and
-histone array data suggesting a role in regulation of gene expression, ChIP-Seq
-analysis of Ibd1 indicated that it primarily binds near promoters and within
-gene bodies of highly expressed genes during growth.
-Conclusions: Our results suggest that through recognizing specific histones
-marks, Ibd1 targets active chromatin regions of highly expressed genes in
-Tetrahymena where it subsequently might coordinate the recruitment of several
-chromatin remodeling complexes to regulate the transcriptional landscape of
-vegetatively growing Tetrahymena cells.
-",0,0,0,0,1,0
-17929,A Bayesian Perspective on Generalization and Stochastic Gradient Descent,"  We consider two questions at the heart of machine learning; how can we
-predict if a minimum will generalize to the test set, and why does stochastic
-gradient descent find minima that generalize well? Our work responds to Zhang
-et al. (2016), who showed deep neural networks can easily memorize randomly
-labeled training data, despite generalizing well on real labels of the same
-inputs. We show that the same phenomenon occurs in small linear models. These
-observations are explained by the Bayesian evidence, which penalizes sharp
-minima but is invariant to model parameterization. We also demonstrate that,
-when one holds the learning rate fixed, there is an optimum batch size which
-maximizes the test set accuracy. We propose that the noise introduced by small
-mini-batches drives the parameters towards minima whose evidence is large.
-Interpreting stochastic gradient descent as a stochastic differential equation,
-we identify the ""noise scale"" $g = \epsilon (\frac{N}{B} - 1) \approx \epsilon
-N/B$, where $\epsilon$ is the learning rate, $N$ the training set size and $B$
-the batch size. Consequently the optimum batch size is proportional to both the
-learning rate and the size of the training set, $B_{opt} \propto \epsilon N$.
-We verify these predictions empirically.
-",1,0,0,1,0,0
-17930,Higher Derivative Field Theories: Degeneracy Conditions and Classes,"  We provide a full analysis of ghost free higher derivative field theories
-with coupled degrees of freedom. Assuming the absence of gauge symmetries, we
-derive the degeneracy conditions in order to evade the Ostrogradsky ghosts, and
-analyze which (non)trivial classes of solutions this allows for. It is shown
-explicitly how Lorentz invariance avoids the propagation of ""half"" degrees of
-freedom. Moreover, for a large class of theories, we construct the field
-redefinitions and/or (extended) contact transformations that put the theory in
-a manifestly first order form. Finally, we identify which class of theories
-cannot be brought to first order form by such transformations.
-",0,1,0,0,0,0
-17931,3D Morphology Prediction of Progressive Spinal Deformities from Probabilistic Modeling of Discriminant Manifolds,"  We introduce a novel approach for predicting the progression of adolescent
-idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray
-images. Recent progress in machine learning have allowed to improve
-classification and prognosis rates, but lack a probabilistic framework to
-measure uncertainty in the data. We propose a discriminative probabilistic
-manifold embedding where locally linear mappings transform data points from
-high-dimensional space to corresponding low-dimensional coordinates. A
-discriminant adjacency matrix is constructed to maximize the separation between
-progressive and non-progressive groups of patients diagnosed with scoliosis,
-while minimizing the distance in latent variables belonging to the same class.
-To predict the evolution of deformation, a baseline reconstruction is projected
-onto the manifold, from which a spatiotemporal regression model is built from
-parallel transport curves inferred from neighboring exemplars. Rate of
-progression is modulated from the spine flexibility and curve magnitude of the
-3D spine deformation. The method was tested on 745 reconstructions from 133
-subjects using longitudinal 3D reconstructions of the spine, with results
-demonstrating the discriminatory framework can identify between progressive and
-non-progressive of scoliotic patients with a classification rate of 81% and
-prediction differences of 2.1$^{o}$ in main curve angulation, outperforming
-other manifold learning methods. Our method achieved a higher prediction
-accuracy and improved the modeling of spatiotemporal morphological changes in
-highly deformed spines compared to other learning methods.
-",1,0,0,1,0,0
-17932,The Galaxy's Veil of Excited Hydrogen,"  Many of the baryons in our Galaxy probably lie outside the well known disk
-and bulge components. Despite a wealth of evidence for the presence of some gas
-in galactic halos, including absorption line systems in the spectra of quasars,
-high velocity neutral hydrogen clouds in our Galaxy halo, line emitting ionised
-hydrogen originating from galactic winds in nearby starburst galaxies, and the
-X-ray coronas surrounding the most massive galaxies, accounting for the gas in
-the halo of any galaxy has been observationally challenging primarily because
-of its low density in the expansive halo. The most sensitive measurements come
-from detecting absorption by the intervening gas in the spectra of distant
-objects such as quasars or distant halo stars, but these have typically been
-limited to a few lines of sight to sufficiently bright objects. Massive
-spectroscopic surveys of millions of objects provide an alternative approach to
-the problem. Here, we present the first evidence for a widely distributed,
-neutral, excited hydrogen component of the Galaxy's halo. It is observed as the
-slight, (0.779 $\pm$ 0.006)\%, absorption of flux near the rest wavelength of
-H$\alpha$ in the combined spectra of hundreds of thousands of galaxy spectra
-and is ubiquitous in high latitude lines of sight. This observation provides an
-avenue to tracing, both spatially and kinematically, the majority of the gas in
-the halo of our Galaxy.
-",0,1,0,0,0,0
-17933,Photonic-chip supercontinuum with tailored spectra for precision frequency metrology,"  Supercontinuum generation using chip-integrated photonic waveguides is a
-powerful approach for spectrally broadening pulsed laser sources with very low
-pulse energies and compact form factors. When pumped with a mode-locked laser
-frequency comb, these waveguides can coherently expand the comb spectrum to
-more than an octave in bandwidth to enable self-referenced stabilization.
-However, for applications in frequency metrology and precision spectroscopy, it
-is desirable to not only support self-referencing, but also to generate
-low-noise combs with customizable broadband spectra. In this work, we
-demonstrate dispersion-engineered waveguides based on silicon nitride that are
-designed to meet these goals and enable precision optical metrology experiments
-across large wavelength spans. We perform a clock comparison measurement and
-report a clock-limited relative frequency instability of $3.8\times10^{-15}$ at
-$\tau = 2$ seconds between a 1550 nm cavity-stabilized reference laser and
-NIST's calcium atomic clock laser at 657 nm using a two-octave
-waveguide-supercontinuum comb.
-",0,1,0,0,0,0
-17934,Endogenizing Epistemic Actions,"  Through a series of examples, we illustrate some important drawbacks that the
-action logic framework suffers from in its ability to represent the dynamics of
-information updates. We argue that these problems stem from the fact that the
-action model, a central construct designed to encode agents' uncertainty about
-actions, is itself effectively common knowledge amongst the agents. In response
-to these difficulties, we motivate and propose an alternative semantics that
-avoids them by (roughly speaking) endogenizing the action model. We discuss the
-relationship to action logic, and provide a sound and complete axiomatization.
-",1,0,0,0,0,0
-17935,The Meaning of Memory Safety,"  We give a rigorous characterization of what it means for a programming
-language to be memory safe, capturing the intuition that memory safety supports
-local reasoning about state. We formalize this principle in two ways. First, we
-show how a small memory-safe language validates a noninterference property: a
-program can neither affect nor be affected by unreachable parts of the state.
-Second, we extend separation logic, a proof system for heap-manipulating
-programs, with a memory-safe variant of its frame rule. The new rule is
-stronger because it applies even when parts of the program are buggy or
-malicious, but also weaker because it demands a stricter form of separation
-between parts of the program state. We also consider a number of pragmatically
-motivated variations on memory safety and the reasoning principles they
-support. As an application of our characterization, we evaluate the security of
-a previously proposed dynamic monitor for memory safety of heap-allocated data.
-",1,0,0,0,0,0
-17936,The Flexible Group Spatial Keyword Query,"  We present a new class of service for location based social networks, called
-the Flexible Group Spatial Keyword Query, which enables a group of users to
-collectively find a point of interest (POI) that optimizes an aggregate cost
-function combining both spatial distances and keyword similarities. In
-addition, our query service allows users to consider the tradeoffs between
-obtaining a sub-optimal solution for the entire group and obtaining an
-optimimized solution but only for a subgroup.
-We propose algorithms to process three variants of the query: (i) the group
-nearest neighbor with keywords query, which finds a POI that optimizes the
-aggregate cost function for the whole group of size n, (ii) the subgroup
-nearest neighbor with keywords query, which finds the optimal subgroup and a
-POI that optimizes the aggregate cost function for a given subgroup size m (m
-<= n), and (iii) the multiple subgroup nearest neighbor with keywords query,
-which finds optimal subgroups and corresponding POIs for each of the subgroup
-sizes in the range [m, n]. We design query processing algorithms based on
-branch-and-bound and best-first paradigms. Finally, we provide theoretical
-bounds and conduct extensive experiments with two real datasets which verify
-the effectiveness and efficiency of the proposed algorithms.
-",1,0,0,0,0,0
-17937,Undersampled windowed exponentials and their applications,"  We characterize the completeness and frame/basis property of a union of
-under-sampled windowed exponentials of the form $$ {\mathcal F}(g): =\{e^{2\pi
-i n x}: n\ge 0\}\cup \{g(x)e^{2\pi i nx}: n<0\} $$ for $L^2[-1/2,1/2]$ by the
-spectra of the Toeplitz operators with symbol $g$. Using this characterization,
-we classify all real-valued functions $g$ such that ${\mathcal F}(g)$ is
-complete or forms a frame/basis. Conversely, we use the classical
-Kadec-1/4-theorem in non-harmonic Fourier series to determine all $\xi$ such
-that the Toeplitz operators with symbol $e^{2\pi i \xi x}$ is injective or
-invertible. These results demonstrate an elegant interaction between frame
-theory of windowed exponentials and Toeplitz operators. Finally, as an
-application, we use our results to answer some open questions in dynamical
-sampling, phase retrieval and derivative samplings on $\ell^2({\mathbb Z})$ and
-Paley-Wiener spaces of bandlimited functions.
-",0,0,1,0,0,0
-17938,Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs,"  Deep learning models are often successfully trained using gradient descent,
-despite the worst case hardness of the underlying non-convex optimization
-problem. The key question is then under what conditions can one prove that
-optimization will succeed. Here we provide a strong result of this kind. We
-consider a neural net with one hidden layer and a convolutional structure with
-no overlap and a ReLU activation function. For this architecture we show that
-learning is NP-complete in the general case, but that when the input
-distribution is Gaussian, gradient descent converges to the global optimum in
-polynomial time. To the best of our knowledge, this is the first global
-optimality guarantee of gradient descent on a convolutional neural network with
-ReLU activations.
-",0,0,1,1,0,0
-17939,Detection of irregular QRS complexes using Hermite Transform and Support Vector Machine,"  Computer based recognition and detection of abnormalities in ECG signals is
-proposed. For this purpose, the Support Vector Machines (SVM) are combined with
-the advantages of Hermite transform representation. SVM represent a special
-type of classification techniques commonly used in medical applications.
-Automatic classification of ECG could make the work of cardiologic departments
-faster and more efficient. It would also reduce the number of false diagnosis
-and, as a result, save lives. The working principle of the SVM is based on
-translating the data into a high dimensional feature space and separating it
-using a linear classificator. In order to provide an optimal representation for
-SVM application, the Hermite transform domain is used. This domain is proved to
-be suitable because of the similarity of the QRS complex with Hermite basis
-functions. The maximal signal information is obtained using a small set of
-features that are used for detection of irregular QRS complexes. The aim of the
-paper is to show that these features can be employed for automatic ECG signal
-analysis.
-",1,0,0,0,0,0
-17940,On the Lipschitz equivalence of self-affine sets,"  Let $A$ be an expanding $d\times d$ matrix with integer entries and
-${\mathcal D}\subset {\mathbb Z}^d$ be a finite digit set. Then the pair $(A,
-{\mathcal D})$ defines a unique integral self-affine set $K=A^{-1}(K+{\mathcal
-D})$. In this paper, by replacing the Euclidean norm with a pseudo-norm $w$ in
-terms of $A$, we construct a hyperbolic graph on $(A, {\mathcal D})$ and show
-that $K$ can be identified with the hyperbolic boundary. Moreover, if $(A,
-{\mathcal D})$ safisfies the open set condition, we also prove that two totally
-disconnected integral self-affine sets are Lipschitz equivalent if an only if
-they have the same $w$-Hausdorff dimension, that is, their digit sets have
-equal cardinality. We extends some well-known results in the self-similar sets
-to the self-affine sets.
-",0,0,1,0,0,0
-17941,The Gross-Pitaevskii equations of a static and spherically symmetric condensate of gravitons,"  In this paper we consider the Dvali and Gómez assumption that the end state
-of a gravitational collapse is a Bose-Einstein condensate of gravitons. We then
-construct the two Gross-Pitaevskii equations for a static and spherically
-symmetric configuration of the condensate. These two equations correspond to
-the constrained minimisation of the gravitational Hamiltonian with respect to
-the redshift and the Newtonian potential, per given number of gravitons. We
-find that the effective geometry of the condensate is the one of a gravastar (a
-DeSitter star) with a sub-Planckian cosmological constant, for masses larger
-than the Planck scale. Thus, a condensate corresponding to a semiclassical
-black hole, is always quantum and weakly coupled. Finally, we obtain that the
-boundary of our gravastar, although it is not the location of a horizon,
-corresponds to the Schwarzschild radius.
-",0,1,0,0,0,0
-17942,Stability and instability in saddle point dynamics - Part I,"  We consider the problem of convergence to a saddle point of a concave-convex
-function via gradient dynamics. Since first introduced by Arrow, Hurwicz and
-Uzawa in [1] such dynamics have been extensively used in diverse areas, there
-are, however, features that render their analysis non trivial. These include
-the lack of convergence guarantees when the function considered is not strictly
-concave-convex and also the non-smoothness of subgradient dynamics. Our aim in
-this two part paper is to provide an explicit characterization to the
-asymptotic behaviour of general gradient and subgradient dynamics applied to a
-general concave-convex function. We show that despite the nonlinearity and
-non-smoothness of these dynamics their $\omega$-limit set is comprised of
-trajectories that solve only explicit linear ODEs that are characterized within
-the paper.
-More precisely, in Part I an exact characterization is provided to the
-asymptotic behaviour of unconstrained gradient dynamics. We also show that when
-convergence to a saddle point is not guaranteed then the system behaviour can
-be problematic, with arbitrarily small noise leading to an unbounded variance.
-In Part II we consider a general class of subgradient dynamics that restrict
-trajectories in an arbitrary convex domain, and show that their limiting
-trajectories are solutions of subgradient dynamics on only affine subspaces.
-The latter is a smooth class of dynamics with an asymptotic behaviour exactly
-characterized in Part I, as solutions to explicit linear ODEs. These results
-are used to formulate corresponding convergence criteria and are demonstrated
-with several examples and applications presented in Part II.
-",1,0,1,0,0,0
-17943,Continual Prediction of Notification Attendance with Classical and Deep Network Approaches,"  We investigate to what extent mobile use patterns can predict -- at the
-moment it is posted -- whether a notification will be clicked within the next
-10 minutes. We use a data set containing the detailed mobile phone usage logs
-of 279 users, who over the course of 5 weeks received 446,268 notifications
-from a variety of apps. Besides using classical gradient-boosted trees, we
-demonstrate how to make continual predictions using a recurrent neural network
-(RNN). The two approaches achieve a similar AUC of ca. 0.7 on unseen users,
-with a possible operation point of 50% sensitivity and 80% specificity
-considering all notification types (an increase of 40% with respect to a
-probabilistic baseline). These results enable automatic, intelligent handling
-of mobile phone notifications without the need for user feedback or
-personalization. Furthermore, they showcase how forego feature-extraction by
-using RNNs for continual predictions directly on mobile usage logs. To the best
-of our knowledge, this is the first work that leverages mobile sensor data for
-continual, context-aware predictions of interruptibility using deep neural
-networks.
-",1,0,0,0,0,0
-17944,"Extensions of Operators, Liftings of Monads and Distributive Laws","  In a previous study, the algebraic formulation of the First Fundamental
-Theorem of Calculus (FFTC) is shown to allow extensions of differential and
-Rota-Baxter operators on the one hand, and to give rise to liftings of monads
-and comonads, and mixed distributive laws on the other. Generalizing the FFTC,
-we consider in this paper a class of constraints between a differential
-operator and a Rota-Baxter operator. For a given constraint, we show that the
-existences of extensions of differential and Rota-Baxter operators, of liftings
-of monads and comonads, and of mixed distributive laws are equivalent. We
-further give a classification of the constraints satisfying these equivalent
-conditions.
-",0,0,1,0,0,0
-17945,Cascaded Incremental Nonlinear Dynamic Inversion Control for MAV Disturbance Rejection,"  Micro Aerial Vehicles (MAVs) are limited in their operation outdoors near
-obstacles by their ability to withstand wind gusts. Currently widespread
-position control methods such as Proportional Integral Derivative control do
-not perform well under the influence of gusts. Incremental Nonlinear Dynamic
-Inversion (INDI) is a sensor-based control technique that can control nonlinear
-systems subject to disturbances. It was developed for the attitude control of
-manned aircraft or MAVs. In this paper we generalize this method to the outer
-loop control of MAVs under severe gust loads. Significant improvements over a
-traditional Proportional Integral Derivative (PID) controller are demonstrated
-in an experiment where the quadrotor flies in and out of a windtunnel exhaust
-at 10 m/s. The control method does not rely on frequent position updates, as is
-demonstrated in an outside experiment using a standard GPS module. Finally, we
-investigate the effect of using a linearization to calculate thrust vector
-increments, compared to a nonlinear calculation. The method requires little
-modeling and is computationally efficient.
-",1,0,0,0,0,0
-17946,"Numerical Evaluation of Elliptic Functions, Elliptic Integrals and Modular Forms","  We describe algorithms to compute elliptic functions and their relatives
-(Jacobi theta functions, modular forms, elliptic integrals, and the
-arithmetic-geometric mean) numerically to arbitrary precision with rigorous
-error bounds for arbitrary complex variables. Implementations in ball
-arithmetic are available in the open source Arb library. We discuss the
-algorithms from a concrete implementation point of view, with focus on
-performance at tens to thousands of digits of precision.
-",1,0,0,0,0,0
-17947,In situ Electric Field Skyrmion Creation in Magnetoelectric Cu$_2$OSeO$_3$,"  Magnetic skyrmions are localized nanometric spin textures with quantized
-winding numbers as the topological invariant. Rapidly increasing attention has
-been paid to the investigations of skyrmions since their experimental discovery
-in 2009, due both to the fundamental properties and the promising potential in
-spintronics based applications. However, controlled creation of skyrmions
-remains a pivotal challenge towards technological applications. Here, we report
-that skyrmions can be created locally by electric field in the magnetoelectric
-helimagnet Cu$\mathsf{_2}$OSeO$\mathsf{_3}$. Using Lorentz transmission
-electron microscopy, we successfully write skyrmions in situ from a helical
-spin background. Our discovery is highly coveted since it implies that
-skyrmionics can be integrated into contemporary field effect transistor based
-electronic technology, where very low energy dissipation can be achieved, and
-hence realizes a large step forward to its practical applications.
-",0,1,0,0,0,0
-17948,An online sequence-to-sequence model for noisy speech recognition,"  Generative models have long been the dominant approach for speech
-recognition. The success of these models however relies on the use of
-sophisticated recipes and complicated machinery that is not easily accessible
-to non-practitioners. Recent innovations in Deep Learning have given rise to an
-alternative - discriminative models called Sequence-to-Sequence models, that
-can almost match the accuracy of state of the art generative models. While
-these models are easy to train as they can be trained end-to-end in a single
-step, they have a practical limitation that they can only be used for offline
-recognition. This is because the models require that the entirety of the input
-sequence be available at the beginning of inference, an assumption that is not
-valid for instantaneous speech recognition. To address this problem, online
-sequence-to-sequence models were recently introduced. These models are able to
-start producing outputs as data arrives, and the model feels confident enough
-to output partial transcripts. These models, like sequence-to-sequence are
-causal - the output produced by the model until any time, $t$, affects the
-features that are computed subsequently. This makes the model inherently more
-powerful than generative models that are unable to change features that are
-computed from the data. This paper highlights two main contributions - an
-improvement to online sequence-to-sequence model training, and its application
-to noisy settings with mixed speech from two speakers.
-",1,0,0,1,0,0
-17949,Existence of infinite Viterbi path for pairwise Markov models,"  For hidden Markov models one of the most popular estimates of the hidden
-chain is the Viterbi path -- the path maximising the posterior probability. We
-consider a more general setting, called the pairwise Markov model, where the
-joint process consisting of finite-state hidden regime and observation process
-is assumed to be a Markov chain. We prove that under some conditions it is
-possible to extend the Viterbi path to infinity for almost every observation
-sequence which in turn enables to define an infinite Viterbi decoding of the
-observation process, called the Viterbi process. This is done by constructing a
-block of observations, called a barrier, which ensures that the Viterbi path
-goes trough a given state whenever this block occurs in the observation
-sequence.
-",0,0,1,1,0,0
-17950,Coding for Segmented Edit Channels,"  This paper considers insertion and deletion channels with the additional
-assumption that the channel input sequence is implicitly divided into segments
-such that at most one edit can occur within a segment. No segment markers are
-available in the received sequence. We propose code constructions for the
-segmented deletion, segmented insertion, and segmented insertion-deletion
-channels based on subsets of Varshamov-Tenengolts codes chosen with
-pre-determined prefixes and/or suffixes. The proposed codes, constructed for
-any finite alphabet, are zero-error and can be decoded segment-by-segment. We
-also derive an upper bound on the rate of any zero-error code for the segmented
-edit channel, in terms of the segment length. This upper bound shows that the
-rate scaling of the proposed codes as the segment length increases is the same
-as that of the maximal code.
-",1,0,0,0,0,0
-17951,Charge compensation at the interface between the polar NaCl(111) surface and a NaCl aqueous solution,"  Periodic supercell models of electric double layers formed at the interface
-between a charged surface and an electrolyte are subject to serious finite size
-errors and require certain adjustments in the treatment of the long-range
-electrostatic interactions. In a previous publication (C. Zhang, M. Sprik,
-Phys. Rev. B 94, 245309 (2016)) we have shown how this can be achieved using
-finite field methods. The test system was the familiar simple point charge
-model of a NaCl aqueous solution confined between two oppositely charged walls.
-Here this method is extended to the interface between the (111) polar surface
-of a NaCl crystal and a high concentration NaCl aqueous solution. The crystal
-is kept completely rigid and the compensating charge screening the polarization
-can only be provided by the electrolyte. We verify that the excess electrolyte
-ionic charge at the interface conforms to the Tasker 1/2 rule for compensating
-charge in the theory of polar rocksalt (111) surfaces. The interface can be
-viewed as an electric double layer with a net charge. We define a generalized
-Helmholtz capacitance $C_\text{H}$ which can be computed by varying the applied
-electric field. We find $C_\text{H} = 8.23 \, \mu \mathrm{Fcm}^{-2}$, which
-should be compared to the $4.23 \, \mu \mathrm{Fcm}^{-2}$ for the (100)
-non-polar surface of the same NaCl crystal. This is rationalized by the
-observation that compensating ions shed their first solvation shell adsorbing
-as contact ions pairs on the polar surface.
-",0,1,0,0,0,0
-17952,CrowdTone: Crowd-powered tone feedback and improvement system for emails,"  In this paper, we present CrowdTone, a system designed to help people set the
-appropriate tone in their email communication. CrowdTone utilizes the context
-and content of an email message to identify and set the appropriate tone
-through a consensus-building process executed by crowd workers. We evaluated
-CrowdTone with 22 participants, who provided a total of 29 emails that they had
-received in the past, and ran them through CrowdTone. Participants and
-professional writers assessed the quality of improvements finding a substantial
-increase in the percentage of emails deemed ""appropriate"" or ""very appropriate""
-- from 25% to more than 90% by recipients, and from 45% to 90% by professional
-writers. Additionally, the recipients' feedback indicated that more than 90% of
-the CrowdTone processed emails showed improvement.
-",1,0,0,0,0,0
-17953,A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines,"  Restricted Boltzmann machines (RBMs) are energy-based neural-networks which
-are commonly used as the building blocks for deep architectures neural
-architectures. In this work, we derive a deterministic framework for the
-training, evaluation, and use of RBMs based upon the Thouless-Anderson-Palmer
-(TAP) mean-field approximation of widely-connected systems with weak
-interactions coming from spin-glass theory. While the TAP approach has been
-extensively studied for fully-visible binary spin systems, our construction is
-generalized to latent-variable models, as well as to arbitrarily distributed
-real-valued spin systems with bounded support. In our numerical experiments, we
-demonstrate the effective deterministic training of our proposed models and are
-able to show interesting features of unsupervised learning which could not be
-directly observed with sampling. Additionally, we demonstrate how to utilize
-our TAP-based framework for leveraging trained RBMs as joint priors in
-denoising problems.
-",1,1,0,1,0,0
-17954,Integrability conditions for Compound Random Measures,"  Compound random measures (CoRM's) are a flexible and tractable framework for
-vectors of completely random measure. In this paper, we provide conditions to
-guarantee the existence of a CoRM. Furthermore, we prove some interesting
-properties of CoRM's when exponential scores and regularly varying Lévy
-intensities are considered.
-",0,0,1,1,0,0
-17955,Tunable low energy Ps beam for the anti-hydrogen free fall and for testing gravity with a Mach-Zehnder interferometer,"  The test of gravitational force on antimatter in the field of the matter
-gravitational field, produced by earth, can be done by a free fall experiment
-which involves only General Relativity, and with a Mach-Zehnder interferometer
-which involves Quantum Mechanics. This article presents a new method to produce
-a tunable low energy (Ps ) beam suitable for trapping the (Hbar + ) ion in a
-free fall experiment, and suitable for a gravity Mach-Zehnder interferometer
-with (Ps). The low energy (Ps) beam is tunable in the [10 eV, 100 eV] range.
-",0,1,0,0,0,0
-17956,An analysis of incorporating an external language model into a sequence-to-sequence model,"  Attention-based sequence-to-sequence models for automatic speech recognition
-jointly train an acoustic model, language model, and alignment mechanism. Thus,
-the language model component is only trained on transcribed audio-text pairs.
-This leads to the use of shallow fusion with an external language model at
-inference time. Shallow fusion refers to log-linear interpolation with a
-separately trained language model at each step of the beam search. In this
-work, we investigate the behavior of shallow fusion across a range of
-conditions: different types of language models, different decoding units, and
-different tasks. On Google Voice Search, we demonstrate that the use of shallow
-fusion with a neural LM with wordpieces yields a 9.1% relative word error rate
-reduction (WERR) over our competitive attention-based sequence-to-sequence
-model, obviating the need for second-pass rescoring.
-",1,0,0,0,0,0
-17957,Multi-Generator Generative Adversarial Nets,"  We propose a new approach to train the Generative Adversarial Nets (GANs)
-with a mixture of generators to overcome the mode collapsing problem. The main
-intuition is to employ multiple generators, instead of using a single one as in
-the original GAN. The idea is simple, yet proven to be extremely effective at
-covering diverse data modes, easily overcoming the mode collapse and delivering
-state-of-the-art results. A minimax formulation is able to establish among a
-classifier, a discriminator, and a set of generators in a similar spirit with
-GAN. Generators create samples that are intended to come from the same
-distribution as the training data, whilst the discriminator determines whether
-samples are true data or generated by generators, and the classifier specifies
-which generator a sample comes from. The distinguishing feature is that
-internal samples are created from multiple generators, and then one of them
-will be randomly selected as final output similar to the mechanism of a
-probabilistic mixture model. We term our method Mixture GAN (MGAN). We develop
-theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon
-divergence (JSD) between the mixture of generators' distributions and the
-empirical data distribution is minimal, whilst the JSD among generators'
-distributions is maximal, hence effectively avoiding the mode collapse. By
-utilizing parameter sharing, our proposed model adds minimal computational cost
-to the standard GAN, and thus can also efficiently scale to large-scale
-datasets. We conduct extensive experiments on synthetic 2D data and natural
-image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior
-performance of our MGAN in achieving state-of-the-art Inception scores over
-latest baselines, generating diverse and appealing recognizable objects at
-different resolutions, and specializing in capturing different types of objects
-by generators.
-",1,0,0,1,0,0
-17958,Neural Style Transfer: A Review,"  The seminal work of Gatys et al. demonstrated the power of Convolutional
-Neural Networks (CNNs) in creating artistic imagery by separating and
-recombining image content and style. This process of using CNNs to render a
-content image in different styles is referred to as Neural Style Transfer
-(NST). Since then, NST has become a trending topic both in academic literature
-and industrial applications. It is receiving increasing attention and a variety
-of approaches are proposed to either improve or extend the original NST
-algorithm. In this paper, we aim to provide a comprehensive overview of the
-current progress towards NST. We first propose a taxonomy of current algorithms
-in the field of NST. Then, we present several evaluation methods and compare
-different NST algorithms both qualitatively and quantitatively. The review
-concludes with a discussion of various applications of NST and open problems
-for future research. A list of papers discussed in this review, corresponding
-codes, pre-trained models and more comparison results are publicly available at
-this https URL.
-",1,0,0,1,0,0
-17959,Distribution of water in the G327.3-0.6 massive star-forming region,"  We aim at characterizing the large-scale distribution of H2O in G327.3-0.6, a
-massive star-forming region made of individual objects in different
-evolutionary phases. We investigate variations of H2O abundance as function of
-evolution. We present Herschel continuum maps at 89 and 179 $\mu$m of the whole
-region and an APEX map at 350 {\mu}m of the IRDC. New spectral HIFI maps toward
-the IRDC region covering low-energy H2O lines at 987 and 1113 GHz are also
-presented and combined with HIFI pointed observations of the G327 hot core. We
-infer the physical properties of the gas through optical depth analysis and
-radiative transfer modeling. The continuum emission at 89 and 179 {\mu}m
-follows the thermal continuum emission at longer wavelengths, with a peak at
-the position of the hot core, a secondary peak in the Hii region, and an
-arch-like layer of hot gas west of the Hii region. The same morphology is
-observed in the 1113 GHz line, in absorption toward all dust condensations.
-Optical depths of ~80 and 15 are estimated and correspond to column densities
-of 10^15 and 2 10^14 cm-2, for the hot core and IRDC position. These values
-indicate an H2O to H2 ratio of 3 10^-8 toward the hot core; the abundance of
-H2O does not change along the IRDC with values of some 10^-8. Infall (over ~
-20"") is detected toward the hot core position with a rate of 1-1.3 10^-2 M_sun
-/yr, high enough to overcome the radiation pressure due to the stellar
-luminosity. The source structure of the hot core region is complex, with a cold
-outer gas envelope in expansion, situated between the outflow and the observer,
-extending over 0.32 pc. The outflow is seen face-on and centered away from the
-hot core. The distribution of H2O along the IRDC is roughly constant with an
-abundance peak in the more evolved object. These water abundances are in
-agreement with previous studies in other massive objects and chemical models.
-",0,1,0,0,0,0
-17960,Straightening rule for an $m'$-truncated polynomial ring,"  We consider a certain quotient of a polynomial ring categorified by both the
-isomorphic Green rings of the symmetric groups and Schur algebras generated by
-the signed Young permutation modules and mixed powers respectively. They have
-bases parametrised by pairs of partitions whose second partitions are multiples
-of the odd prime $p$ the characteristic of the underlying field. We provide an
-explicit formula rewriting a signed Young permutation module (respectively,
-mixed power) in terms of signed Young permutation modules (respectively, mixed
-powers) labelled by those pairs of partitions. As a result, for each partition
-$\lambda$, we discovered the number of compositions $\delta$ such that $\delta$
-can be rearranged to $\lambda$ and whose partial sums of $\delta$ are not
-divisible by $p$.
-",0,0,1,0,0,0
-17961,Effective Blog Pages Extractor for Better UGC Accessing,"  Blog is becoming an increasingly popular media for information publishing.
-Besides the main content, most of blog pages nowadays also contain noisy
-information such as advertisements etc. Removing these unrelated elements can
-improves user experience, but also can better adapt the content to various
-devices such as mobile phones. Though template-based extractors are highly
-accurate, they may incur expensive cost in that a large number of template need
-to be developed and they will fail once the template is updated. To address
-these issues, we present a novel template-independent content extractor for
-blog pages. First, we convert a blog page into a DOM-Tree, where all elements
-including the title and body blocks in a page correspond to subtrees. Then we
-construct subtree candidate set for the title and the body blocks respectively,
-and extract both spatial and content features for elements contained in the
-subtree. SVM classifiers for the title and the body blocks are trained using
-these features. Finally, the classifiers are used to extract the main content
-from blog pages. We test our extractor on 2,250 blog pages crawled from nine
-blog sites with obviously different styles and templates. Experimental results
-verify the effectiveness of our extractor.
-",1,0,0,0,0,0
-17962,The Principle of Similitude in Biology: From Allometry to the Formulation of Dimensionally Homogenous `Laws',"  Meaningful laws of nature must be independent of the units employed to
-measure the variables. The principle of similitude (Rayleigh 1915) or
-dimensional homogeneity, states that only commensurable quantities (ones having
-the same dimension) may be compared, therefore, meaningful laws of nature must
-be homogeneous equations in their various units of measurement, a result which
-was formalized in the $\rm \Pi$ theorem (Vaschy 1892; Buckingham 1914).
-However, most relations in allometry do not satisfy this basic requirement,
-including the `3/4 Law' (Kleiber 1932) that relates the basal metabolic rate
-and body mass, which it is sometimes claimed to be the most fundamental
-biological rate (Brown et al. 2004) and the closest to a law in life sciences
-(West \& Brown 2004). Using the $\rm \Pi$ theorem, here we show that it is
-possible to construct a unique homogeneous equation for the metabolic rates, in
-agreement with data in the literature. We find that the variations in the
-dependence of the metabolic rates on body mass are secondary, coming from
-variations in the allometric dependence of the heart frequencies. This includes
-not only different classes of animals (mammals, birds, invertebrates) but also
-different exercise conditions (basal and maximal). Our results demonstrate that
-most of the differences found in the allometric exponents (White et al. 2007)
-are due to compare incommensurable quantities and that our dimensionally
-homogenous formula, unify these differences into a single formulation. We
-discuss the ecological implications of this new formulation in the context of
-the Malthusian's, Fenchel's and the total energy consumed in a lifespan
-relations.
-",0,1,0,0,0,0
-17963,New insight into the dynamics of rhodopsin photoisomerization from one-dimensional quantum-classical modeling,"  Characterization of the primary events involved in the $cis-trans$
-photoisomerization of the rhodopsin retinal chromophore was approximated by a
-minimum one-dimensional quantum-classical model. The developed mathematical
-model is identical to that obtained using conventional quantum-classical
-approaches, and multiparametric quantum-chemical or molecular dynamics (MD)
-computations were not required. The quantum subsystem of the model includes
-three electronic states for rhodopsin: (i) the ground state, (ii) the excited
-state, and (iii) the primary photoproduct in the ground state. The resultant
-model is in perfect agreement with experimental data in terms of the quantum
-yield, the time required to reach the conical intersection and to complete the
-quantum evolution, the range of the characteristic low frequencies active
-within the primary events of the $11-cis$ retinal isomerization, and the
-coherent character of the photoreaction. An effective redistribution of excess
-energy between the vibration modes of rhodopsin was revealed by analysis of the
-dissipation process. The results confirm the validity of the minimal model,
-despite its one-dimensional character. The fundamental nature of the
-photoreaction was therefore demonstrated using a minimum mathematical model for
-the first time.
-",0,1,0,0,0,0
-17964,High-order schemes for the Euler equations in cylindrical/spherical coordinates,"  We consider implementations of high-order finite difference Weighted
-Essentially Non-Oscillatory (WENO) schemes for the Euler equations in
-cylindrical and spherical coordinate systems with radial dependence only. The
-main concern of this work lies in ensuring both high-order accuracy and
-conservation. Three different spatial discretizations are assessed: one that is
-shown to be high-order accurate but not conservative, one conservative but not
-high-order accurate, and a new approach that is both high-order accurate and
-conservative. For cylindrical and spherical coordinates, we present convergence
-results for the advection equation and the Euler equations with an acoustics
-problem; we then use the Sod shock tube and the Sedov point-blast problems in
-cylindrical coordinates to verify our analysis and implementations.
-",0,1,1,0,0,0
-17965,Suppressing correlations in massively parallel simulations of lattice models,"  For lattice Monte Carlo simulations parallelization is crucial to make
-studies of large systems and long simulation time feasible, while sequential
-simulations remain the gold-standard for correlation-free dynamics. Here,
-various domain decomposition schemes are compared, concluding with one which
-delivers virtually correlation-free simulations on GPU Extensive simulations of
-the octahedron model for $2+1$ dimensional Karda--Parisi--Zhang surface growth,
-which is very sensitive to correlation in the site-selection dynamics, were
-performed to show self-consistency of the parallel runs and agreement with the
-sequential algorithm. We present a GPU implementation providing a speedup of
-about $30\times$ over a parallel CPU implementation on a single socket and at
-least $180\times$ with respect to the sequential reference.
-",0,1,0,0,0,0
-17966,New bounds on the strength of some restrictions of Hindman's Theorem,"  We prove upper and lower bounds on the effective content and logical strength
-for a variety of natural restrictions of Hindman's Finite Sums Theorem. For
-example, we show that Hindman's Theorem for sums of length at most 2 and 4
-colors implies $\mathsf{ACA}_0$. An emerging {\em leitmotiv} is that the known
-lower bounds for Hindman's Theorem and for its restriction to sums of at most 2
-elements are already valid for a number of restricted versions which have
-simple proofs and better computability- and proof-theoretic upper bounds than
-the known upper bound for the full version of the theorem. We highlight the
-role of a sparsity-like condition on the solution set, which we call apartness.
-",0,0,1,0,0,0
-17967,"3D Face Morphable Models ""In-the-Wild""","  3D Morphable Models (3DMMs) are powerful statistical models of 3D facial
-shape and texture, and among the state-of-the-art methods for reconstructing
-facial shape from single images. With the advent of new 3D sensors, many 3D
-facial datasets have been collected containing both neutral as well as
-expressive faces. However, all datasets are captured under controlled
-conditions. Thus, even though powerful 3D facial shape models can be learnt
-from such data, it is difficult to build statistical texture models that are
-sufficient to reconstruct faces captured in unconstrained conditions
-(""in-the-wild""). In this paper, we propose the first, to the best of our
-knowledge, ""in-the-wild"" 3DMM by combining a powerful statistical model of
-facial shape, which describes both identity and expression, with an
-""in-the-wild"" texture model. We show that the employment of such an
-""in-the-wild"" texture model greatly simplifies the fitting procedure, because
-there is no need to optimize with regards to the illumination parameters.
-Furthermore, we propose a new fast algorithm for fitting the 3DMM in arbitrary
-images. Finally, we have captured the first 3D facial database with relatively
-unconstrained conditions and report quantitative evaluations with
-state-of-the-art performance. Complementary qualitative reconstruction results
-are demonstrated on standard ""in-the-wild"" facial databases. An open source
-implementation of our technique is released as part of the Menpo Project.
-",1,0,0,0,0,0
-17968,Image Segmentation to Distinguish Between Overlapping Human Chromosomes,"  In medicine, visualizing chromosomes is important for medical diagnostics,
-drug development, and biomedical research. Unfortunately, chromosomes often
-overlap and it is necessary to identify and distinguish between the overlapping
-chromosomes. A segmentation solution that is fast and automated will enable
-scaling of cost effective medicine and biomedical research. We apply neural
-network-based image segmentation to the problem of distinguishing between
-partially overlapping DNA chromosomes. A convolutional neural network is
-customized for this problem. The results achieved intersection over union (IOU)
-scores of 94.7% for the overlapping region and 88-94% on the non-overlapping
-chromosome regions.
-",1,0,0,1,0,0
-17969,Implementing Large-Scale Agile Frameworks: Challenges and Recommendations,"  Based on 13 agile transformation cases over 15 years, this article identifies
-nine challenges associated with implementing SAFe, Scrum-at-Scale, Spotify,
-LeSS, Nexus, and other mixed or customised large-scale agile frameworks. These
-challenges should be considered by organizations aspiring to pursue a
-large-scale agile strategy. This article also provides recommendations for
-practitioners and agile researchers.
-",1,0,0,0,0,0
-17970,On The Limiting Distributions of the Total Height On Families of Trees,"  A symbolic-computational algorithm, fully implemented in Maple, is described,
-that computes explicit expressions for generating functions that enable the
-efficient computations of the expectation, variance, and higher moments, of the
-random variable `sum of distances to the root', defined on any given family of
-rooted ordered trees (defined by degree restrictions). Taking limits, we
-confirm, via elementary methods, the fact, due to David Aldous, and expanded by
-Svante Janson and others, that the limiting (scaled) distributions are all the
-same, and coincide with the limiting distribution of the same random variable,
-when it is defined on labeled rooted trees.
-",0,0,1,0,0,0
-17971,HESS J1826$-$130: A Very Hard $γ$-Ray Spectrum Source in the Galactic Plane,"  HESS J1826$-$130 is an unidentified hard spectrum source discovered by
-H.E.S.S. along the Galactic plane, the spectral index being $\Gamma$ = 1.6 with
-an exponential cut-off at about 12 TeV. While the source does not have a clear
-counterpart at longer wavelengths, the very hard spectrum emission at TeV
-energies implies that electrons or protons accelerated up to several hundreds
-of TeV are responsible for the emission. In the hadronic case, the VHE emission
-can be produced by runaway cosmic-rays colliding with the dense molecular
-clouds spatially coincident with the H.E.S.S. source.
-",0,1,0,0,0,0
-17972,Laser opacity in underdense preplasma of solid targets due to quantum electrodynamics effects,"  We investigate how next-generation laser pulses at 10 PW $-$ 200 PW interact
-with a solid target in the presence of a relativistically underdense preplasma
-produced by amplified spontaneous emission (ASE). Laser hole boring and
-relativistic transparency are strongly restrained due to the generation of
-electron-positron pairs and $\gamma$-ray photons via quantum electrodynamics
-(QED) processes. A pair plasma with a density above the initial preplasma
-density is formed, counteracting the electron-free channel produced by the hole
-boring. This pair-dominated plasma can block the laser transport and trigger an
-avalanche-like QED cascade, efficiently transfering the laser energy to
-photons. This renders a 1-$\rm\mu m$-scalelength, underdense preplasma
-completely opaque to laser pulses at this power level. The QED-induced opacity
-therefore sets much higher contrast requirements for such pulse in solid-target
-experiments than expected by classical plasma physics. Our simulations show for
-example, that proton acceleration from the rear of a solid with a preplasma
-would be strongly impaired.
-",0,1,0,0,0,0
-17973,Consequentialist conditional cooperation in social dilemmas with imperfect information,"  Social dilemmas, where mutual cooperation can lead to high payoffs but
-participants face incentives to cheat, are ubiquitous in multi-agent
-interaction. We wish to construct agents that cooperate with pure cooperators,
-avoid exploitation by pure defectors, and incentivize cooperation from the
-rest. However, often the actions taken by a partner are (partially) unobserved
-or the consequences of individual actions are hard to predict. We show that in
-a large class of games good strategies can be constructed by conditioning one's
-behavior solely on outcomes (ie. one's past rewards). We call this
-consequentialist conditional cooperation. We show how to construct such
-strategies using deep reinforcement learning techniques and demonstrate, both
-analytically and experimentally, that they are effective in social dilemmas
-beyond simple matrix games. We also show the limitations of relying purely on
-consequences and discuss the need for understanding both the consequences of
-and the intentions behind an action.
-",1,0,0,0,0,0
-17974,"Casimir free energy of dielectric films: Classical limit, low-temperature behavior and control","  The Casimir free energy of dielectric films, both free-standing in vacuum and
-deposited on metallic or dielectric plates, is investigated. It is shown that
-the values of the free energy depend considerably on whether the calculation
-approach used neglects or takes into account the dc conductivity of film
-material. We demonstrate that there are the material-dependent and universal
-classical limits in the former and latter cases, respectively. The analytic
-behavior of the Casimir free energy and entropy for a free-standing dielectric
-film at low temperature in found. According to our results, the Casimir entropy
-goes to zero when the temperature vanishes if the calculation approach with
-neglected dc conductivity of a film is employed. If the dc conductivity is
-taken into account, the Casimir entropy takes the positive value at zero
-temperature, depending on the parameters of a film, i.e., the Nernst heat
-theorem is violated. By considering the Casimir free energy of silica and
-sapphire films deposited on a Au plate in the framework of two calculation
-approaches, we argue that physically correct values are obtained by
-disregarding the role of dc conductivity. A comparison with the well known
-results for the configuration of two parallel plates is made. Finally, we
-compute the Casimir free energy of silica, sapphire and Ge films deposited on
-high-resistivity Si plates of different thicknesses and demonstrate that it can
-be positive, negative and equal to zero. Possible applications of the obtained
-results to thin films used in microelectronics are discussed.
-",0,1,0,0,0,0
-17975,Discriminant chronicles mining: Application to care pathways analytics,"  Pharmaco-epidemiology (PE) is the study of uses and effects of drugs in well
-defined populations. As medico-administrative databases cover a large part of
-the population, they have become very interesting to carry PE studies. Such
-databases provide longitudinal care pathways in real condition containing
-timestamped care events, especially drug deliveries. Temporal pattern mining
-becomes a strategic choice to gain valuable insights about drug uses. In this
-paper we propose DCM, a new discriminant temporal pattern mining algorithm. It
-extracts chronicle patterns that occur more in a studied population than in a
-control population. We present results on the identification of possible
-associations between hospitalizations for seizure and anti-epileptic drug
-switches in care pathway of epileptic patients.
-",1,0,0,0,0,0
-17976,Adversarial Symmetric Variational Autoencoder,"  A new form of variational autoencoder (VAE) is developed, in which the joint
-distribution of data and codes is considered in two (symmetric) forms: ($i$)
-from observed data fed through the encoder to yield codes, and ($ii$) from
-latent codes drawn from a simple prior and propagated through the decoder to
-manifest data. Lower bounds are learned for marginal log-likelihood fits
-observed data and latent codes. When learning with the variational bound, one
-seeks to minimize the symmetric Kullback-Leibler divergence of joint density
-functions from ($i$) and ($ii$), while simultaneously seeking to maximize the
-two marginal log-likelihoods. To facilitate learning, a new form of adversarial
-training is developed. An extensive set of experiments is performed, in which
-we demonstrate state-of-the-art data reconstruction and generation on several
-image benchmark datasets.
-",1,0,0,0,0,0
-17977,The singular locus of hypersurface sections containing a closed subscheme over finite fields,"  We prove that there exist hypersurfaces that contain a given closed subscheme
-$Z$ of the projective space over a finite field and intersect a given smooth
-scheme $X$ off of $Z$ smoothly, if the intersection $V = Z \cap X$ is smooth.
-Furthermore, we can give a bound on the dimension of the singular locus of the
-hypersurface section and prescribe finitely many local conditions on the
-hypersurface. This is an analogue of a Bertini theorem of Bloch over finite
-fields and is proved using Poonen's closed point sieve. We also show a similar
-theorem for the case where $V$ is not smooth.
-",0,0,1,0,0,0
-17978,A Statistical Perspective on Inverse and Inverse Regression Problems,"  Inverse problems, where in broad sense the task is to learn from the noisy
-response about some unknown function, usually represented as the argument of
-some known functional form, has received wide attention in the general
-scientific disciplines. How- ever, in mainstream statistics such inverse
-problem paradigm does not seem to be as popular. In this article we provide a
-brief overview of such problems from a statistical, particularly Bayesian,
-perspective.
-We also compare and contrast the above class of problems with the perhaps
-more statistically familiar inverse regression problems, arguing that this
-class of problems contains the traditional class of inverse problems. In course
-of our review we point out that the statistical literature is very scarce with
-respect to both the inverse paradigms, and substantial research work is still
-necessary to develop the fields.
-",0,0,1,1,0,0
-17979,Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling,"  This work presents a model reduction approach for problems with coherent
-structures that propagate over time such as convection-dominated flows and
-wave-type phenomena. Traditional model reduction methods have difficulties with
-these transport-dominated problems because propagating coherent structures
-typically introduce high-dimensional features that require high-dimensional
-approximation spaces. The approach proposed in this work exploits the locality
-in space and time of propagating coherent structures to derive efficient
-reduced models. First, full-model solutions are approximated locally in time
-via local reduced spaces that are adapted with basis updates during time
-stepping. The basis updates are derived from querying the full model at a few
-selected spatial coordinates. Second, the locality in space of the coherent
-structures is exploited via an adaptive sampling scheme that selects at which
-components to query the full model for computing the basis updates. Our
-analysis shows that, in probability, the more local the coherent structure is
-in space, the fewer full-model samples are required to adapt the reduced basis
-with the proposed adaptive sampling scheme. Numerical results on benchmark
-examples with interacting wave-type structures and time-varying transport
-speeds and on a model combustor of a single-element rocket engine demonstrate
-the wide applicability of our approach and the significant runtime speedups
-compared to full models and traditional reduced models.
-",1,0,0,0,0,0
-17980,Weighted parallel SGD for distributed unbalanced-workload training system,"  Stochastic gradient descent (SGD) is a popular stochastic optimization method
-in machine learning. Traditional parallel SGD algorithms, e.g., SimuParallel
-SGD, often require all nodes to have the same performance or to consume equal
-quantities of data. However, these requirements are difficult to satisfy when
-the parallel SGD algorithms run in a heterogeneous computing environment;
-low-performance nodes will exert a negative influence on the final result. In
-this paper, we propose an algorithm called weighted parallel SGD (WP-SGD).
-WP-SGD combines weighted model parameters from different nodes in the system to
-produce the final output. WP-SGD makes use of the reduction in standard
-deviation to compensate for the loss from the inconsistency in performance of
-nodes in the cluster, which means that WP-SGD does not require that all nodes
-consume equal quantities of data. We also analyze the theoretical feasibility
-of running two other parallel SGD algorithms combined with WP-SGD in a
-heterogeneous environment. The experimental results show that WP-SGD
-significantly outperforms the traditional parallel SGD algorithms on
-distributed training systems with an unbalanced workload.
-",1,0,0,1,0,0
-17981,Neural Lander: Stable Drone Landing Control using Learned Dynamics,"  Precise trajectory control near ground is difficult for multi-rotor drones,
-due to the complex ground effects caused by interactions between multi-rotor
-airflow and the environment. Conventional control methods often fail to
-properly account for these complex effects and fall short in accomplishing
-smooth landing. In this paper, we present a novel deep-learning-based robust
-nonlinear controller (Neural-Lander) that improves control performance of a
-quadrotor during landing. Our approach blends together a nominal dynamics model
-coupled with a Deep Neural Network (DNN) that learns the high-order
-interactions. We employ a novel application of spectral normalization to
-constrain the DNN to have bounded Lipschitz behavior. Leveraging this Lipschitz
-property, we design a nonlinear feedback linearization controller using the
-learned model and prove system stability with disturbance rejection. To the
-best of our knowledge, this is the first DNN-based nonlinear feedback
-controller with stability guarantees that can utilize arbitrarily large neural
-nets. Experimental results demonstrate that the proposed controller
-significantly outperforms a baseline linear proportional-derivative (PD)
-controller in both 1D and 3D landing cases. In particular, we show that
-compared to the PD controller, Neural-Lander can decrease error in z direction
-from 0.13m to zero, and mitigate average x and y drifts by 90% and 34%
-respectively, in 1D landing. Meanwhile, Neural-Lander can decrease z error from
-0.12m to zero, in 3D landing. We also empirically show that the DNN generalizes
-well to new test inputs outside the training domain.
-",1,0,0,0,0,0
-17982,Finite-Time Stabilization of Longitudinal Control for Autonomous Vehicles via a Model-Free Approach,"  This communication presents a longitudinal model-free control approach for
-computing the wheel torque command to be applied on a vehicle. This setting
-enables us to overcome the problem of unknown vehicle parameters for generating
-a suitable control law. An important parameter in this control setting is made
-time-varying for ensuring finite-time stability. Several convincing computer
-simulations are displayed and discussed. Overshoots become therefore smaller.
-The driving comfort is increased and the robustness to time-delays is improved.
-",1,0,1,0,0,0
-17983,"Review of Geraint F. Lewis and Luke A. Barnes, A Fortunate Universe: Life in a Finely Tuned Cosmos","  This new book by cosmologists Geraint F. Lewis and Luke A. Barnes is another
-entry in the long list of cosmology-centered physics books intended for a large
-audience. While many such books aim at advancing a novel scientific theory, A
-Fortunate Universe has no such scientific pretense. Its goals are to assert
-that the universe is fine-tuned for life, to defend that this fact can
-reasonably motivate further scientific inquiry as to why it is so, and to show
-that the multiverse and intelligent design hypotheses are reasonable proposals
-to explain this fine-tuning. This book's potential contribution, therefore,
-lies in how convincingly and efficiently it can make that case.
-",0,1,0,0,0,0
-17984,Introducing SPAIN (SParse Audion INpainter),"  A novel sparsity-based algorithm for audio inpainting is proposed by
-translating the SPADE algorithm by Kitić et. al.---the state-of-the-art for
-audio declipping---into the task of audio inpainting. SPAIN (SParse Audio
-INpainter) comes in synthesis and analysis variants. Experiments show that both
-A-SPAIN and S-SPAIN outperform other sparsity-based inpainting algorithms and
-that A-SPAIN performs on a par with the state-of-the-art method based on linear
-prediction.
-",1,0,0,0,0,0
-17985,Palindromic Decompositions with Gaps and Errors,"  Identifying palindromes in sequences has been an interesting line of research
-in combinatorics on words and also in computational biology, after the
-discovery of the relation of palindromes in the DNA sequence with the HIV
-virus. Efficient algorithms for the factorization of sequences into palindromes
-and maximal palindromes have been devised in recent years. We extend these
-studies by allowing gaps in decompositions and errors in palindromes, and also
-imposing a lower bound to the length of acceptable palindromes.
-We first present an algorithm for obtaining a palindromic decomposition of a
-string of length n with the minimal total gap length in time O(n log n * g) and
-space O(n g), where g is the number of allowed gaps in the decomposition. We
-then consider a decomposition of the string in maximal \delta-palindromes (i.e.
-palindromes with \delta errors under the edit or Hamming distance) and g
-allowed gaps. We present an algorithm to obtain such a decomposition with the
-minimal total gap length in time O(n (g + \delta)) and space O(n g).
-",1,0,0,0,0,0
-17986,End-of-Use Core Triage in Extreme Scenarios Based on a Threshold Approach,"  Remanufacturing is a significant factor in securing sustainability through a
-circular economy. Sorting plays a significant role in remanufacturing
-pre-processing inspections. Its significance can increase when remanufacturing
-facilities encounter extreme situations, such as abnormally huge core arrivals.
-Our main objective in this work is switching from less efficient to a more
-efficient model and to characterize extreme behavior of core arrival in
-remanufacturing and applying the developed model to triage cores. Central
-tendency core flow models are not sufficient to handle extreme situations,
-however, complementary Extreme Value (EV) approaches have shown to improve
-model efficiency. Extreme core flows to remanufacturing facilities are rare but
-still likely and can adversely affect remanufacturing business operations. In
-this investigation, extreme end-of-use core flow is modelled by a threshold
-approach using the Generalized Pareto Distribution (GPD). It is shown that GPD
-has better performance than its maxima-block GEV counterpart from practical and
-data efficiency perspectives. The model is validated by a synthesized big
-dataset, tested by sophisticated statistical Anderson Darling (AD) test, and is
-applied to a case of extreme flow to a valve shop in order to predict
-probability of over-capacity arrivals that is critical in remanufacturing
-business management. Finally, the GPD model combined with triage strategies is
-used to initiate investigations into the efficacy of different triage methods
-in remanufacturing operations.
-",0,0,0,1,0,0
-17987,A temperature-dependent implicit-solvent model of polyethylene glycol in aqueous solution,"  A temperature (T)-dependent coarse-grained (CG) Hamiltonian of polyethylene
-glycol/oxide (PEG/PEO) in aqueous solution is reported to be used in
-implicit-solvent material models in a wide temperature (i.e., solvent quality)
-range. The T-dependent nonbonded CG interactions are derived from a combined
-""bottom-up"" and ""top-down"" approach. The pair potentials calculated from
-atomistic replica-exchange molecular dynamics simulations in combination with
-the iterative Boltzmann inversion are post-refined by benchmarking to
-experimental data of the radius of gyration. For better handling and a fully
-continuous transferability in T-space, the pair potentials are conveniently
-truncated and mapped to an analytic formula with three structural parameters
-expressed as explicit continuous functions of T. It is then demonstrated that
-this model without further adjustments successfully reproduces other
-experimentally known key thermodynamic properties of semi-dilute PEG solutions
-such as the full equation of state (i.e., T-dependent osmotic pressure) for
-various chain lengths as well as their cloud point (or collapse) temperature.
-",0,1,0,0,0,0
-17988,Directional Statistics and Filtering Using libDirectional,"  In this paper, we present libDirectional, a MATLAB library for directional
-statistics and directional estimation. It supports a variety of commonly used
-distributions on the unit circle, such as the von Mises, wrapped normal, and
-wrapped Cauchy distributions. Furthermore, various distributions on
-higher-dimensional manifolds such as the unit hypersphere and the hypertorus
-are available. Based on these distributions, several recursive filtering
-algorithms in libDirectional allow estimation on these manifolds. The
-functionality is implemented in a clear, well-documented, and object-oriented
-structure that is both easy to use and easy to extend.
-",1,0,0,1,0,0
-17989,A solution of the dark energy and its coincidence problem based on local antigravity sources without fine-tuning or new scales,"  A novel idea is proposed for a natural solution of the dark energy and its
-cosmic coincidence problem. The existence of local antigravity sources,
-associated with astrophysical matter configurations distributed throughout the
-universe, can lead to a recent cosmic acceleration effect. Various physical
-theories can be compatible with this idea, but here, in order to test our
-proposal, we focus on quantum originated spherically symmetric metrics matched
-with the cosmological evolution through the simplest Swiss cheese model. In the
-context of asymptotically safe gravity, we have explained the observed amount
-of dark energy using Newton's constant, the galaxy or cluster length scales,
-and dimensionless order one parameters predicted by the theory, without
-fine-tuning or extra unproven energy scales. The interior modified
-Schwarzschild-de Sitter metric allows us to approximately interpret this result
-as that the standard cosmological constant is a composite quantity made of the
-above parameters, instead of a fundamental one.
-",0,1,0,0,0,0
-17990,Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples,"  Self-paced learning and hard example mining re-weight training instances to
-improve learning accuracy. This paper presents two improved alternatives based
-on lightweight estimates of sample uncertainty in stochastic gradient descent
-(SGD): the variance in predicted probability of the correct class across
-iterations of mini-batch SGD, and the proximity of the correct class
-probability to the decision threshold. Extensive experimental results on six
-datasets show that our methods reliably improve accuracy in various network
-architectures, including additional gains on top of other popular training
-techniques, such as residual learning, momentum, ADAM, batch normalization,
-dropout, and distillation.
-",0,0,0,1,0,0
-17991,"Optical signature of Weyl electronic structures in tantalum pnictides Ta$Pn$ ($Pn=$ P, As)","  To investigate the electronic structure of Weyl semimetals Ta$Pn$ ($Pn=$P,
-As), optical conductivity [$\sigma(\omega)$] spectra are measured over a wide
-range of photon energies and temperatures, and these measured values are
-compared with band calculations. Two significant structures can be observed: a
-bending structure at $\hbar\omega\sim$85 meV in TaAs, and peaks at
-$\hbar\omega\sim$ 50 meV (TaP) and $\sim$30 meV (TaAs). The bending structure
-can be explained by the interband transition between saddle points connecting a
-set of $W_2$ Weyl points. The temperature dependence of the peak intensity can
-be fitted by assuming the interband transition between saddle points connecting
-a set of $W_1$ Weyl points. Owing to the different temperature dependence of
-the Drude weight in both materials, it is found that the Weyl points of TaAs
-are located near the Fermi level, whereas those of TaP are further away.
-",0,1,0,0,0,0
-17992,"A study of cyber security in hospitality industry- threats and countermeasures: case study in Reno, Nevada","  The purpose of this study is to analyze cyber security and security practices
-of electronic information and network system, network threats, and techniques
-to prevent the cyber attacks in hotels. Helping the information technology
-directors and chief information officers (CIO) is the aim of this study to
-advance policy for security of electronic information in hotels and suggesting
-some techniques and tools to secure the computer networks. This research is
-completely qualitative while the case study and interviews have done in 5
-random hotels in Reno, Nevada, United States of America. The interview has done
-with 50 hotel guests, 10 front desk employees, 3 IT manager and 2 assistant of
-General manager. The results show that hotels' cyber security is very low and
-hotels are very vulnerable in this regard and at the end, the implications and
-contribution of the study is mentioned.
-",1,0,0,0,0,0
-17993,Weak Fraisse categories,"  We develop the theory of weak Fraisse categories, where the crucial concept
-is the weak amalgamation property, discovered relatively recently in model
-theory. We show that, in a suitable framework, every weak Fraisse category has
-its unique limit, a special object in a bigger category, characterized by
-certain variant of injectivity. This significantly extends the known theory of
-Fraisse limits.
-",0,0,1,0,0,0
-17994,Multiple Stakeholders in Music Recommender Systems,"  Music recommendation services collectively spin billions of songs for
-millions of listeners on a daily basis. Users can typically listen to a variety
-of songs tailored to their personal tastes and preferences. Music is not the
-only type of content encountered in these services, however. Advertisements are
-generally interspersed throughout the music stream to generate revenue for the
-business. Additional content may include artist messaging, ticketing, sports,
-news and weather. In this paper, we discuss issues that arise when multiple
-content providers are stakeholders in the recommendation process. These
-stakeholders each have their own objectives and must work in concert to sustain
-a healthy music recommendation service.
-",1,0,0,0,0,0
-17995,Large time behavior of solution to nonlinear Dirac equation in $1+1$ dimensions,"  This paper studies the large time behavior of solution for a class of
-nonlinear massless Dirac equations in $R^{1+1}$. It is shown that the solution
-will tend to travelling wave solution when time tends to infinity.
-",0,0,1,0,0,0
-17996,Compositions of Functions and Permutations Specified by Minimal Reaction Systems,"  This paper studies mathematical properties of reaction systems that was
-introduced by Enrenfeucht and Rozenberg as computational models inspired by
-biochemical reaction in the living cells. In particular, we continue the study
-on the generative power of functions specified by minimal reaction systems
-under composition initiated by Salomaa. Allowing degenerate reaction systems,
-functions specified by minimal reaction systems over a quarternary alphabet
-that are permutations generate the alternating group on the power set of the
-background set.
-",1,0,0,0,0,0
-17997,The center problem for the Lotka reactions with generalized mass-action kinetics,"  Chemical reaction networks with generalized mass-action kinetics lead to
-power-law dynamical systems. As a simple example, we consider the Lotka
-reactions and the resulting planar ODE. We characterize the parameters
-(positive coefficients and real exponents) for which the unique positive
-equilibrium is a center.
-",0,0,1,0,0,0
-17998,Recovery of Missing Samples Using Sparse Approximation via a Convex Similarity Measure,"  In this paper, we study the missing sample recovery problem using methods
-based on sparse approximation. In this regard, we investigate the algorithms
-used for solving the inverse problem associated with the restoration of missed
-samples of image signal. This problem is also known as inpainting in the
-context of image processing and for this purpose, we suggest an iterative
-sparse recovery algorithm based on constrained $l_1$-norm minimization with a
-new fidelity metric. The proposed metric called Convex SIMilarity (CSIM) index,
-is a simplified version of the Structural SIMilarity (SSIM) index, which is
-convex and error-sensitive. The optimization problem incorporating this
-criterion, is then solved via Alternating Direction Method of Multipliers
-(ADMM). Simulation results show the efficiency of the proposed method for
-missing sample recovery of 1D patch vectors and inpainting of 2D image signals.
-",1,0,0,1,0,0
-17999,Skin cancer reorganization and classification with deep neural network,"  As one kind of skin cancer, melanoma is very dangerous. Dermoscopy based
-early detection and recarbonization strategy is critical for melanoma therapy.
-However, well-trained dermatologists dominant the diagnostic accuracy. In order
-to solve this problem, many effort focus on developing automatic image analysis
-systems. Here we report a novel strategy based on deep learning technique, and
-achieve very high skin lesion segmentation and melanoma diagnosis accuracy: 1)
-we build a segmentation neural network (skin_segnn), which achieved very high
-lesion boundary detection accuracy; 2) We build another very deep neural
-network based on Google inception v3 network (skin_recnn) and its well-trained
-weight. The novel designed transfer learning based deep neural network
-skin_inceptions_v3_nn helps to achieve a high prediction accuracy.
-",1,0,0,0,0,0
-18000,Rescaled extrapolation for vector-valued functions,"  We extend Rubio de Francia's extrapolation theorem for functions valued in
-UMD Banach function spaces, leading to short proofs of some new and known
-results. In particular we prove Littlewood-Paley-Rubio de Francia-type
-estimates and boundedness of variational Carleson operators for Banach function
-spaces with UMD concavifications.
-",0,0,1,0,0,0
-18001,Separation of the charge density wave and superconducting states by an intermediate semimetal phase in pressurized TaTe2,"  In layered transition metal dichalcogenides (LTMDCs) that display both charge
-density waves (CDWs) and superconductivity, the superconducting state generally
-emerges directly on suppression of the CDW state. Here, however, we report a
-different observation for pressurized TaTe2, a non-superconducting CDW-bearing
-LTMDC at ambient pressure. We find that a superconducting state does not occur
-in TaTe2 after the full suppression of its CDW state, which we observe at about
-3 GPa, but, rather, a non-superconducting semimetal state is observed. At a
-higher pressure, ~21 GPa, where both the semimetal state and the corresponding
-positive magnetoresistance effect are destroyed, superconductivity finally
-emerges and remains present up to ~50 GPa, the high pressure limit of our
-measurements. Our pressure-temperature phase diagram for TaTe2 demonstrates
-that the CDW and the superconducting phases in TaTe2 do not directly transform
-one to the other, but rather are separated by a semimetal state, - the first
-experimental case where the CDW and superconducting states are separated by an
-intermediate phase in LTMDC systems.
-",0,1,0,0,0,0
-18002,Zero distribution for Angelesco Hermite--Padé polynomials,"  We consider the problem of zero distribution of the first kind
-Hermite--Padé polynomials associated with a vector function $\vec f = (f_1,
-\dots, f_s)$ whose components $f_k$ are functions with a finite number of
-branch points in plane. We assume that branch sets of component functions are
-well enough separated (which constitute the Angelesco case). Under this
-condition we prove a theorem on limit zero distribution for such polynomials.
-The limit measures are defined in terms of a known vector equilibrium problem.
-Proof of the theorem is based on the methods developed by H.~Stahl,
-A.~A.~Gonchar and the author. These methods obtained some further
-generalization in the paper in application to systems of polynomials defined by
-systems of complex orthogonality relations.
-Together with the characterization of the limit zero distributions of
-Hermite--Padé polynomials by a vector equilibrium problem we consider an
-alternative characterization using a Riemann surface $\mathcal R(\vec f)$
-associated with $\vec f$. In this terms we present a more general (without
-Angelesco condition) conjecture on the zero distribution of Hermite--Padé
-polynomials.
-Bibliography: 72 items.
-",0,0,1,0,0,0
-18003,Atomic-scale identification of novel planar defect phases in heteroepitaxial YBa$_2$Cu$_3$O$_{7-δ}$ thin films,"  We have discovered two novel types of planar defects that appear in
-heteroepitaxial YBa$_2$Cu$_3$O$_{7-\delta}$ (YBCO123) thin films, grown by
-pulsed-laser deposition (PLD) either with or without a
-La$_{2/3}$Ca$_{1/3}$MnO$_3$ (LCMO) overlayer, using the combination of
-high-angle annular dark-field scanning transmission electron microscopy
-(HAADF-STEM) imaging and electron energy loss spectroscopy (EELS) mapping for
-unambiguous identification. These planar lattice defects are based on the
-intergrowth of either a BaO plane between two CuO chains or multiple Y-O layers
-between two CuO$_2$ planes, resulting in non-stoichiometric layer sequences
-that could directly impact the high-$T_c$ superconductivity.
-",0,1,0,0,0,0
-18004,Self-consistent assessment of Englert-Schwinger model on atomic properties,"  Our manuscript investigates a self-consistent solution of the statistical
-atom model proposed by Berthold-Georg Englert and Julian Schwinger (the ES
-model) and benchmarks it against atomic Kohn-Sham and two orbital-free models
-of the Thomas-Fermi-Dirac (TFD)-$\lambda$vW family. Results show that the ES
-model generally offers the same accuracy as the well-known TFD-$\frac{1}{5}$vW
-model; however, the ES model corrects the failure in Pauli potential
-near-nucleus region. We also point to the inability of describing low-$Z$ atoms
-as the foremost concern in improving the present model.
-",0,1,0,0,0,0
-18005,Target volatility option pricing in lognormal fractional SABR model,"  We examine in this article the pricing of target volatility options in the
-lognormal fractional SABR model. A decomposition formula by Ito's calculus
-yields a theoretical replicating strategy for the target volatility option,
-assuming the accessibilities of all variance swaps and swaptions. The same
-formula also suggests an approximation formula for the price of target
-volatility option in small time by the technique of freezing the coefficient.
-Alternatively, we also derive closed formed expressions for a small volatility
-of volatility expansion of the price of target volatility option. Numerical
-experiments show accuracy of the approximations in a reasonably wide range of
-parameters.
-",0,0,0,0,0,1
-18006,Robust Blind Deconvolution via Mirror Descent,"  We revisit the Blind Deconvolution problem with a focus on understanding its
-robustness and convergence properties. Provable robustness to noise and other
-perturbations is receiving recent interest in vision, from obtaining immunity
-to adversarial attacks to assessing and describing failure modes of algorithms
-in mission critical applications. Further, many blind deconvolution methods
-based on deep architectures internally make use of or optimize the basic
-formulation, so a clearer understanding of how this sub-module behaves, when it
-can be solved, and what noise injection it can tolerate is a first order
-requirement. We derive new insights into the theoretical underpinnings of blind
-deconvolution. The algorithm that emerges has nice convergence guarantees and
-is provably robust in a sense we formalize in the paper. Interestingly, these
-technical results play out very well in practice, where on standard datasets
-our algorithm yields results competitive with or superior to the state of the
-art. Keywords: blind deconvolution, robust continuous optimization
-",1,0,0,1,0,0
-18007,One-Dimensional Symmetry Protected Topological Phases and their Transitions,"  We present a unified perspective on symmetry protected topological (SPT)
-phases in one dimension and address the open question of what characterizes
-their phase transitions. In the first part of this work we use symmetry as a
-guide to map various well-known fermionic and spin SPTs to a Kitaev chain with
-coupling of range $\alpha \in \mathbb Z$. This unified picture uncovers new
-properties of old models --such as how the cluster state is the fixed point
-limit of the Affleck-Kennedy-Lieb-Tasaki state in disguise-- and elucidates the
-connection between fermionic and bosonic phases --with the Hubbard chain
-interpolating between four Kitaev chains and a spin chain in the Haldane phase.
-In the second part, we study the topological phase transitions between these
-models in the presence of interactions. This leads us to conjecture that the
-critical point between any SPT with $d$-dimensional edge modes and the trivial
-phase has a central charge $c \geq \log_2 d$. We analytically verify this for
-many known transitions. This agrees with the intuitive notion that the phase
-transition is described by a delocalized edge mode, and that the central charge
-of a conformal field theory is a measure of the gapless degrees of freedom.
-",0,1,0,0,0,0
-18008,"Proceedings Eighth International Symposium on Games, Automata, Logics and Formal Verification","  This volume contains the proceedings of the Eighth International Symposium on
-Games, Automata, Logic and Formal Verification (GandALF 2017). The symposium
-took place in Roma, Italy, from the 20th to the 22nd of September 2017. The
-GandALF symposium was established by a group of Italian computer scientists
-interested in mathematical logic, automata theory, game theory, and their
-applications to the specification, design, and verification of complex systems.
-Its aim is to provide a forum where people from different areas, and possibly
-with different backgrounds, can fruitfully interact. GandALF has a truly
-international spirit, as witnessed by the composition of the program and
-steering committee and by the country distribution of the submitted papers.
-",1,0,0,0,0,0
-18009,Valence Bonds in Random Quantum Magnets: Theory and Application to YbMgGaO4,"  We analyze the effect of quenched disorder on spin-1/2 quantum magnets in
-which magnetic frustration promotes the formation of local singlets. Our
-results include a theory for 2d valence-bond solids subject to weak bond
-randomness, as well as extensions to stronger disorder regimes where we make
-connections with quantum spin liquids. We find, on various lattices, that the
-destruction of a valence-bond solid phase by weak quenched disorder leads
-inevitably to the nucleation of topological defects carrying spin-1/2 moments.
-This renormalizes the lattice into a strongly random spin network with
-interesting low-energy excitations. Similarly when short-ranged valence bonds
-would be pinned by stronger disorder, we find that this putative glass is
-unstable to defects that carry spin-1/2 magnetic moments, and whose residual
-interactions decide the ultimate low energy fate. Motivated by these results we
-conjecture Lieb-Schultz-Mattis-like restrictions on ground states for
-disordered magnets with spin-1/2 per statistical unit cell. These conjectures
-are supported by an argument for 1d spin chains. We apply insights from this
-study to the phenomenology of YbMgGaO$_4$, a recently discovered triangular
-lattice spin-1/2 insulator which was proposed to be a quantum spin liquid. We
-instead explore a description based on the present theory. Experimental
-signatures, including unusual specific heat, thermal conductivity, and
-dynamical structure factor, and their behavior in a magnetic field, are
-predicted from the theory, and compare favorably with existing measurements on
-YbMgGaO$_4$ and related materials.
-",0,1,0,0,0,0
-18010,TuckER: Tensor Factorization for Knowledge Graph Completion,"  Knowledge graphs are structured representations of real world facts. However,
-they typically contain only a small subset of all possible facts. Link
-prediction is a task of inferring missing facts based on existing ones. We
-propose TuckER, a relatively simple but powerful linear model based on Tucker
-decomposition of the binary tensor representation of knowledge graph triples.
-TuckER outperforms all previous state-of-the-art models across standard link
-prediction datasets. We prove that TuckER is a fully expressive model, deriving
-the bound on its entity and relation embedding dimensionality for full
-expressiveness which is several orders of magnitude smaller than the bound of
-previous state-of-the-art models ComplEx and SimplE. We further show that
-several previously introduced linear models can be viewed as special cases of
-TuckER.
-",1,0,0,1,0,0
-18011,Taming Non-stationary Bandits: A Bayesian Approach,"  We consider the multi armed bandit problem in non-stationary environments.
-Based on the Bayesian method, we propose a variant of Thompson Sampling which
-can be used in both rested and restless bandit scenarios. Applying discounting
-to the parameters of prior distribution, we describe a way to systematically
-reduce the effect of past observations. Further, we derive the exact expression
-for the probability of picking sub-optimal arms. By increasing the exploitative
-value of Bayes' samples, we also provide an optimistic version of the
-algorithm. Extensive empirical analysis is conducted under various scenarios to
-validate the utility of proposed algorithms. A comparison study with various
-state-of-the-arm algorithms is also included.
-",1,0,0,1,0,0
-18012,Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization,"  We propose a new randomized coordinate descent method for a convex
-optimization template with broad applications. Our analysis relies on a novel
-combination of four ideas applied to the primal-dual gap function: smoothing,
-acceleration, homotopy, and coordinate descent with non-uniform sampling. As a
-result, our method features the first convergence rate guarantees among the
-coordinate descent methods, that are the best-known under a variety of common
-structure assumptions on the template. We provide numerical evidence to support
-the theoretical results with a comparison to state-of-the-art algorithms.
-",0,0,0,1,0,0
-18013,Guarantees for Greedy Maximization of Non-submodular Functions with Applications,"  We investigate the performance of the standard Greedy algorithm for
-cardinality constrained maximization of non-submodular nondecreasing set
-functions. While there are strong theoretical guarantees on the performance of
-Greedy for maximizing submodular functions, there are few guarantees for
-non-submodular ones. However, Greedy enjoys strong empirical performance for
-many important non-submodular functions, e.g., the Bayesian A-optimality
-objective in experimental design. We prove theoretical guarantees supporting
-the empirical performance. Our guarantees are characterized by a combination of
-the (generalized) curvature $\alpha$ and the submodularity ratio $\gamma$. In
-particular, we prove that Greedy enjoys a tight approximation guarantee of
-$\frac{1}{\alpha}(1- e^{-\gamma\alpha})$ for cardinality constrained
-maximization. In addition, we bound the submodularity ratio and curvature for
-several important real-world objectives, including the Bayesian A-optimality
-objective, the determinantal function of a square submatrix and certain linear
-programs with combinatorial constraints. We experimentally validate our
-theoretical findings for both synthetic and real-world applications.
-",1,0,1,0,0,0
-18014,Affine Metrics and Associated Algebroid Structures: Application to General Relativity,"  In this paper, algebroid bundle associated to affine metrics provide an
-structure for unification of gravity and electromagnetism and, geometrization
-of matter.
-",0,0,1,0,0,0
-18015,Controllability of temporal networks: An analysis using higher-order networks,"  The control of complex networks is a significant challenge, especially when
-the network topology of the system to be controlled is dynamic. Addressing this
-challenge, here we introduce a novel approach which allows exploring the
-controllability of temporal networks. Studying six empirical data sets, we
-particularly show that order correlations in the sequence of interactions can
-both increase or decrease the time needed to achieve full controllability.
-Counter-intuitively, we find that this effect can be opposite than the effect
-of order correlations on other dynamical processes. Specifically, we show that
-order correlations that speed up a diffusion process in a given system can slow
-down the control of the same system, and vice-versa. Building on the
-higher-order graphical modeling framework introduced in recent works, we
-further demonstrate that spectral properties of higher-order network topologies
-can be used to analytically explain this phenomenon.
-",1,1,0,0,0,0
-18016,Mapping momentum-dependent electron-phonon coupling and non-equilibrium phonon dynamics with ultrafast electron diffuse scattering,"  Despite their fundamental role in determining material properties, detailed
-momentum-dependent information on the strength of electron-phonon and
-phonon-phonon coupling (EPC and PPC, respectively) across the entire Brillouin
-zone (BZ) has proved difficult to obtain. Here we demonstrate that ultrafast
-electron diffuse scattering (UEDS) directly provides such information. By
-exploiting symmetry-based selection rules and time-resolution, scattering from
-different phonon branches can be distinguished even without energy resolution.
-Using graphite as a model system, we show that UEDS patterns map the relative
-EPC and PPC strength through their profound sensitivity to photoinduced changes
-in phonon populations. We measure strong EPC to the $K$-point transverse
-optical phonon of $A_1'$ symmetry ($K-A_1'$) and along the entire longitudinal
-optical branch between $\Gamma-K$, not only to the $\Gamma-E_{2g}$ phonon as
-previously emphasized. We also determine that the subsequent phonon relaxation
-pathway involves three stages; decay via several identifiable channels to
-transverse acoustic (TA) and longitudinal acoustic (LA) phonons (1-2 ps),
-intraband thermalization of the non-equilibrium TA/LA phonon populations (30-40
-ps) and interband relaxation of the LA/TA modes (115 ps). Combining UEDS with
-ultrafast angle-resolved photoelectron spectroscopy will yield a complete
-picture of the dynamics within and between electron and phonon subsystems,
-helping to unravel complex phases in which the intertwined nature of these
-systems have a strong influence on emergent properties.
-",0,1,0,0,0,0
-18017,Meridional Circulation Dynamics in a Cyclic Convective Dynamo,"  Surface observations indicate that the speed of the solar meridional
-circulation in the photosphere varies in anti-phase with the solar cycle. The
-current explanation for the source of this variation is that inflows into
-active regions alter the global surface pattern of the meridional circulation.
-When these localized inflows are integrated over a full hemisphere, they
-contribute to the slow down of the axisymmetric poleward horizontal component.
-The behavior of this large scale flow deep inside the convection zone remains
-largely unknown. Present helioseismic techniques are not sensitive enough to
-capture the dynamics of this weak large scale flow. Moreover, the large time of
-integration needed to map the meridional circulation inside the convection
-zone, also masks some of the possible dynamics on shorter timescales. In this
-work we examine the dynamics of the meridional circulation that emerges from a
-3D MHD global simulation of the solar convection zone. Our aim is to assess and
-quantify the behavior of meridional circulation deep inside the convection
-zone, where the cyclic large-scale magnetic field can reach considerable
-strength. Our analyses indicate that the meridional circulation morphology and
-amplitude are both highly influenced by the magnetic field, via the impact of
-magnetic torques on the global angular momentum distribution. A dynamic feature
-induced by these magnetic torques is the development of a prominent upward flow
-at mid latitudes in the lower convection zone that occurs near the equatorward
-edge of the toroidal bands and that peaks during cycle maximum. Globally, the
-dynamo-generated large-scale magnetic field drives variations in the meridional
-flow, in stark contrast to the conventional kinematic flux transport view of
-the magnetic field being advected passively by the flow.
-",0,1,0,0,0,0
-18018,Bayesian analysis of 210Pb dating,"  In many studies of environmental change of the past few centuries, 210Pb
-dating is used to obtain chronologies for sedimentary sequences. One of the
-most commonly used approaches to estimate the ages of depths in a sequence is
-to assume a constant rate of supply (CRS) or influx of `unsupported' 210Pb from
-the atmosphere, together with a constant or varying amount of `supported'
-210Pb. Current 210Pb dating models do not use a proper statistical framework
-and thus provide poor estimates of errors. Here we develop a new model for
-210Pb dating, where both ages and values of supported and unsupported 210Pb
-form part of the parameters. We apply our model to a case study from Canada as
-well as to some simulated examples. Our model can extend beyond the current CRS
-approach, deal with asymmetric errors and mix 210Pb with other types of dating,
-thus obtaining more robust, realistic and statistically better defined
-estimates.
-",0,0,0,1,0,0
-18019,Analytic continuation with Padé decomposition,"  The ill-posed analytic continuation problem for Green's functions or
-self-energies can be done using the Padé rational polynomial approximation.
-However, to extract accurate results from this approximation, high precision
-input data of the Matsubara Green's function are needed. The calculation of the
-Matsubara Green's function generally involves a Matsubara frequency summation
-which cannot be evaluated analytically. Numerical summation is requisite but it
-converges slowly with the increase of the Matsubara frequency. Here we show
-that this slow convergence problem can be significantly improved by utilizing
-the Padé decomposition approach to replace the Matsubara frequency summation
-by a Padé frequency summation, and high precision input data can be obtained
-to successfully perform the Padé analytic continuation.
-",0,1,0,0,0,0
-18020,Imperative Functional Programs that Explain their Work,"  Program slicing provides explanations that illustrate how program outputs
-were produced from inputs. We build on an approach introduced in prior work by
-Perera et al., where dynamic slicing was defined for pure higher-order
-functional programs as a Galois connection between lattices of partial inputs
-and partial outputs. We extend this approach to imperative functional programs
-that combine higher-order programming with references and exceptions. We
-present proofs of correctness and optimality of our approach and a
-proof-of-concept implementation and experimental evaluation.
-",1,0,0,0,0,0
-18021,Synthetic dimensions in ultracold molecules: quantum strings and membranes,"  Synthetic dimensions alter one of the most fundamental properties in nature,
-the dimension of space. They allow, for example, a real three-dimensional
-system to act as effectively four-dimensional. Driven by such possibilities,
-synthetic dimensions have been engineered in ongoing experiments with ultracold
-matter. We show that rotational states of ultracold molecules can be used as
-synthetic dimensions extending to many - potentially hundreds of - synthetic
-lattice sites. Microwaves coupling rotational states drive fully controllable
-synthetic inter-site tunnelings, enabling, for example, topological band
-structures. Interactions leads to even richer behavior: when molecules are
-frozen in a real space lattice with uniform synthetic tunnelings, dipole
-interactions cause the molecules to aggregate to a narrow strip in the
-synthetic direction beyond a critical interaction strength, resulting in a
-quantum string or a membrane, with an emergent condensate that lives on this
-string or membrane. All these phases can be detected using measurements of
-rotational state populations.
-",0,1,0,0,0,0
-18022,Self-Organization and The Origins of Life: The Managed-Metabolism Hypothesis,"  The managed-metabolism hypothesis suggests that a cooperation barrier must be
-overcome if self-producing chemical organizations are to transition from
-non-life to life. This barrier prevents un-managed, self-organizing,
-autocatalytic networks of molecular species from individuating into complex,
-cooperative organizations. The barrier arises because molecular species that
-could otherwise make significant cooperative contributions to the success of an
-organization will often not be supported within the organization, and because
-side reactions and other free-riding processes will undermine cooperation. As a
-result, the barrier seriously limits the possibility space that can be explored
-by un-managed organizations, impeding individuation, complex functionality and
-the transition to life. The barrier can be overcome comprehensively by
-appropriate management which implements a system of evolvable constraints. The
-constraints support beneficial co-operators and suppress free riders. In this
-way management can manipulate the chemical processes of an autocatalytic
-organization, producing novel processes that serve the interests of the
-organization as a whole and that could not arise and persist spontaneously in
-an un-managed chemical organization. Management self-organizes because it is
-able to capture some of the benefits that are produced when its management of
-an autocatalytic organization promotes beneficial cooperation. Selection
-therefore favours the emergence of managers that take over and manage chemical
-organizations so as to overcome the cooperation barrier. The managed-metabolism
-hypothesis shows that if management is to overcome the cooperation barrier
-comprehensively, its interventions must be digitally coded. In this way, the
-hypothesis accounts for the two-tiered structure of all living cells in which a
-digitally-coded genetic apparatus manages an analogically-informed metabolism.
-",0,1,0,0,0,0
-18023,"Pile-up Reduction, Bayesian Decomposition and Applications of Silicon Drift Detectors at LCLS","  Silicon drift detectors (SDDs) revolutionized spectroscopy in fields as
-diverse as geology and dentistry. For a subset of experiments at ultra-fast,
-x-ray free-electron lasers (FELs), SDDs can make substantial contributions.
-Often the unknown spectrum is interesting, carrying science data, or the
-background measurement is useful to identify unexpected signals. Many
-measurements involve only several discrete photon energies known a priori. We
-designed a pulse function (a combination of gradual step and exponential decay
-function) and demonstrated that for individual pulses the signal amplitude,
-peaking time, and pulse amplitude are interrelated and the signal amplitude and
-peaking time are obtained for each pulse by fitting. Avoiding pulse shaping
-reduced peaking times to tens of nanoseconds, resulting in reduced pulse
-pile-up and allowing decomposition of remaining pulse pile-up at photon
-separation times down to 100~ns while yielding time-of-arrival information with
-precision of 10~nanoseconds. At pulsed sources or high photon rates, photon
-pile-up still occurs. We showed that the area of one photon peaks is not
-suitable for estimating high photon rates while pile-up spectrum fitting is
-relatively simple and preferable to pile-up spectrum deconvolution. We
-developed a photon pile-up model for constant intensity sources, extended it to
-variable intensity sources (typical for FELs) and used it to fit a complex
-pile-up spectrum, demonstrating its accuracy. Based on the pile-up model, we
-developed a Bayesian pile-up decomposition method that allows decomposing
-pile-up of single events with up to 6 photons from 6 monochromatic lines with
-99% accuracy. The usefulness of SDDs will continue into the x-ray FEL era of
-science. Their successors, the ePixS hybrid pixel detectors, already offer
-hundreds of pixels, each with similar performance to an SDD, in a compact,
-robust and affordable package.
-",0,1,0,0,0,0
-18024,Online Learning to Rank in Stochastic Click Models,"  Online learning to rank is a core problem in information retrieval and
-machine learning. Many provably efficient algorithms have been recently
-proposed for this problem in specific click models. The click model is a model
-of how the user interacts with a list of documents. Though these results are
-significant, their impact on practice is limited, because all proposed
-algorithms are designed for specific click models and lack convergence
-guarantees in other models. In this work, we propose BatchRank, the first
-online learning to rank algorithm for a broad class of click models. The class
-encompasses two most fundamental click models, the cascade and position-based
-models. We derive a gap-dependent upper bound on the $T$-step regret of
-BatchRank and evaluate it on a range of web search queries. We observe that
-BatchRank outperforms ranked bandits and is more robust than CascadeKL-UCB, an
-existing algorithm for the cascade model.
-",1,0,0,1,0,0
-18025,Transforming Coroutining Logic Programs into Equivalent CHR Programs,"  We extend a technique called Compiling Control. The technique transforms
-coroutining logic programs into logic programs that, when executed under the
-standard left-to-right selection rule (and not using any delay features) have
-the same computational behavior as the coroutining program. In recent work, we
-revised Compiling Control and reformulated it as an instance of Abstract
-Conjunctive Partial Deduction. This work was mostly focused on the program
-analysis performed in Compiling Control. In the current paper, we focus on the
-synthesis of the transformed program. Instead of synthesizing a new logic
-program, we synthesize a CHR(Prolog) program which mimics the coroutining
-program. The synthesis to CHR yields programs containing only simplification
-rules, which are particularly amenable to certain static analysis techniques.
-The programs are also more concise and readable and can be ported to CHR
-implementations embedded in other languages than Prolog.
-",1,0,0,0,0,0
-18026,Gap and rings carved by vortices in protoplanetary dust,"  Large-scale vortices in protoplanetary disks are thought to form and survive
-for long periods of time. Hence, they can significantly change the global disk
-evolution and particularly the distribution of the solid particles embedded in
-the gas, possibly explaining asymmetries and dust concentrations recently
-observed at sub-millimeter and millimeter wavelengths. We investigate the
-spatial distribution of dust grains using a simple model of protoplanetary disk
-hosted by a giant gaseous vortex. We explore the dependence of the results on
-grain size and deduce possible consequences and predictions for observations of
-the dust thermal emission at sub-millimeter and millimeter wavelengths. Global
-2D simulations with a bi-fluid code are used to follow the evolution of a
-single population of solid particles aerodynamically coupled to the gas.
-Possible observational signatures of the dust thermal emission are obtained
-using simulators of ALMA and ngVLA observations. We find that a giant vortex
-not only captures dust grains with Stokes number St < 1 but can also affect the
-distribution of larger grains (with St '~' 1) carving a gap associated to a
-ring composed of incompletely trapped particles. The results are presented for
-different particle size and associated to their possible signatures in disk
-observations. Gap clearing in the dust spatial distribution could be due to the
-interaction with a giant gaseous vortex and their associated spiral waves,
-without the gravitational assistance of a planet. Hence, strong dust
-concentrations at short sub-mm wavelengths associated with a gap and an
-irregular ring at longer mm and cm wavelengths could indicate the presence of
-an unseen gaseous vortex.
-",0,1,0,0,0,0
-18027,Wasserstein Dictionary Learning: Optimal Transport-based unsupervised non-linear dictionary learning,"  This paper introduces a new nonlinear dictionary learning method for
-histograms in the probability simplex. The method leverages optimal transport
-theory, in the sense that our aim is to reconstruct histograms using so-called
-displacement interpolations (a.k.a. Wasserstein barycenters) between dictionary
-atoms; such atoms are themselves synthetic histograms in the probability
-simplex. Our method simultaneously estimates such atoms, and, for each
-datapoint, the vector of weights that can optimally reconstruct it as an
-optimal transport barycenter of such atoms. Our method is computationally
-tractable thanks to the addition of an entropic regularization to the usual
-optimal transportation problem, leading to an approximation scheme that is
-efficient, parallel and simple to differentiate. Both atoms and weights are
-learned using a gradient-based descent method. Gradients are obtained by
-automatic differentiation of the generalized Sinkhorn iterations that yield
-barycenters with entropic smoothing. Because of its formulation relying on
-Wasserstein barycenters instead of the usual matrix product between dictionary
-and codes, our method allows for nonlinear relationships between atoms and the
-reconstruction of input data. We illustrate its application in several
-different image processing settings.
-",1,0,0,1,0,0
-18028,A Generalization of Smillie's Theorem on Strongly Cooperative Tridiagonal Systems,"  Smillie (1984) proved an interesting result on the stability of nonlinear,
-time-invariant, strongly cooperative, and tridiagonal dynamical systems. This
-result has found many applications in models from various fields including
-biology, ecology, and chemistry. Smith (1991) has extended Smillie's result and
-proved entrainment in the case where the vector field is time-varying and
-periodic. We use the theory of linear totally nonnegative differential systems
-developed by Schwarz (1970) to give a generalization of these two results. This
-is based on weakening the requirement for strong cooperativity to
-cooperativity, and adding an additional observability-type condition.
-",1,0,0,0,0,0
-18029,Geospatial Semantics,"  Geospatial semantics is a broad field that involves a variety of research
-areas. The term semantics refers to the meaning of things, and is in contrast
-with the term syntactics. Accordingly, studies on geospatial semantics usually
-focus on understanding the meaning of geographic entities as well as their
-counterparts in the cognitive and digital world, such as cognitive geographic
-concepts and digital gazetteers. Geospatial semantics can also facilitate the
-design of geographic information systems (GIS) by enhancing the
-interoperability of distributed systems and developing more intelligent
-interfaces for user interactions. During the past years, a lot of research has
-been conducted, approaching geospatial semantics from different perspectives,
-using a variety of methods, and targeting different problems. Meanwhile, the
-arrival of big geo data, especially the large amount of unstructured text data
-on the Web, and the fast development of natural language processing methods
-enable new research directions in geospatial semantics. This chapter,
-therefore, provides a systematic review on the existing geospatial semantic
-research. Six major research areas are identified and discussed, including
-semantic interoperability, digital gazetteers, geographic information
-retrieval, geospatial Semantic Web, place semantics, and cognitive geographic
-concepts.
-",1,0,0,0,0,0
-18030,Stability of the sum of two solitary waves for (gDNLS) in the energy space,"  In this paper, we continue the study in \cite{MiaoTX:DNLS:Stab}. We use the
-perturbation argument, modulational analysis and the energy argument in
-\cite{MartelMT:Stab:gKdV, MartelMT:Stab:NLS} to show the stability of the sum
-of two solitary waves with weak interactions for the generalized derivative
-Schrödinger equation (gDNLS) in the energy space. Here (gDNLS) hasn't the
-Galilean transformation invariance, the pseudo-conformal invariance and the
-gauge transformation invariance, and the case $\sigma>1$ we considered
-corresponds to the $L^2$-supercritical case.
-",0,0,1,0,0,0
-18031,A BERT Baseline for the Natural Questions,"  This technical note describes a new baseline for the Natural Questions. Our
-model is based on BERT and reduces the gap between the model F1 scores reported
-in the original dataset paper and the human upper bound by 30% and 50% relative
-for the long and short answer tasks respectively. This baseline has been
-submitted to the official NQ leaderboard at
-ai.google.com/research/NaturalQuestions and we plan to opensource the code for
-it in the near future.
-",1,0,0,0,0,0
-18032,Knowledge Evolution in Physics Research: An Analysis of Bibliographic Coupling Networks,"  Even as we advance the frontiers of physics knowledge, our understanding of
-how this knowledge evolves remains at the descriptive levels of Popper and
-Kuhn. Using the APS publications data sets, we ask in this letter how new
-knowledge is built upon old knowledge. We do so by constructing year-to-year
-bibliographic coupling networks, and identify in them validated communities
-that represent different research fields. We then visualize their evolutionary
-relationships in the form of alluvial diagrams, and show how they remain intact
-through APS journal splits. Quantitatively, we see that most fields undergo
-weak Popperian mixing, and it is rare for a field to remain isolated/undergo
-strong mixing. The sizes of fields obey a simple linear growth with
-recombination. We can also reliably predict the merging between two fields, but
-not for the considerably more complex splitting. Finally, we report a case
-study of two fields that underwent repeated merging and splitting around 1995,
-and how these Kuhnian events are correlated with breakthroughs on BEC, quantum
-teleportation, and slow light. This impact showed up quantitatively in the
-citations of the BEC field as a larger proportion of references from during and
-shortly after these events.
-",1,1,0,0,0,0
-18033,On a lower bound for the energy functional on a family of Hamiltonian minimal Lagrangian tori in $\mathbb{C}P^2$,"  We study the energy functional on the set of Lagrangian tori in
-$\mathbb{C}P^2$ . We prove that the value of the energy functional on a certain
-family of Hamiltonian minimal Lagrangian tori in $\mathbb{C}P^2$ is strictly
-larger than energy of the Clifford torus.
-",0,0,1,0,0,0
-18034,Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks,"  Viewing the trajectory of a patient as a dynamical system, a recurrent neural
-network was developed to learn the course of patient encounters in the
-Pediatric Intensive Care Unit (PICU) of a major tertiary care center. Data
-extracted from Electronic Medical Records (EMR) of about 12000 patients who
-were admitted to the PICU over a period of more than 10 years were leveraged.
-The RNN model ingests a sequence of measurements which include physiologic
-observations, laboratory results, administered drugs and interventions, and
-generates temporally dynamic predictions for in-ICU mortality at user-specified
-times. The RNN's ICU mortality predictions offer significant improvements over
-those from two clinically-used scores and static machine learning algorithms.
-",1,0,1,1,0,0
-18035,Global Strichartz estimates for the Schrödinger equation with non zero boundary conditions and applications,"  We consider the Schrödinger equation on a half space in any dimension with
-a class of nonhomogeneous boundary conditions including Dirichlet, Neuman and
-the so-called transparent boundary conditions. Building upon recent local in
-time Strichartz estimates (for Dirichlet boundary conditions), we obtain global
-Strichartz estimates for initial data in $H^s,\ 0\leq s\leq 2$ and boundary
-data in a natural space $\mathcal{H}^s$. For $s\geq 1/2$, the issue of
-compatibility conditions requires a thorough analysis of the $\mathcal{H}^s$
-space. As an application we solve nonlinear Schrödinger equations and
-construct global asymptotically linear solutions for small data. A discussion
-is included on the appropriate notion of scattering in this framework, and the
-optimality of the $\mathcal{H}^s$ space.
-",0,0,1,0,0,0
-18036,Structure of $^{20}$Ne states in the resonance $^{16}$O+$α$ elastic scattering,"  Background
-The nuclear structure of the cluster bands in $^{20}$Ne presents a challenge
-for different theoretical approaches. It is especially difficult to explain the
-broad 0$^+$, 2$^+$ states at 9 MeV excitation energy. Simultaneously, it is
-important to obtain more reliable experimental data for these levels in order
-to quantitatively assess the theoretical framework.
-Purpose
-To obtain new data on $^{20}$Ne $\alpha$ cluster structure. Method Thick
-target inverse kinematics technique was used to study the $^{16}$O+$\alpha$
-resonance elastic scattering and the data were analyzed using an \textit{R}
-matrix approach. The $^{20}$Ne spectrum, the cluster and nucleon spectroscopic
-factors were calculated using cluster-nucleon configuration interaction model
-(CNCIM).
-Results
-We determined the parameters of the broad resonances in
-\textsuperscript{20}Ne: 0$^+$ level at 8.77 $\pm$ 0.150 MeV with a width of 750
-(+500/-220) keV; 2$^+$ level at 8.75 $\pm$ 0.100 MeV with the width of 695
-$\pm$ 120 keV; the width of 9.48 MeV level of 65 $\pm$ 20 keV and showed that
-9.19 MeV, 2$^+$ level (if exists) should have width $\leq$ 10 keV. The detailed
-comparison of the theoretical CNCIM predictions with the experimental data on
-cluster states was made.
-Conclusions
-Our experimental results by the TTIK method generally confirm the adopted
-data on $\alpha$ cluster levels in $^{20}$Ne. The CNCIM gives a good
-description of the $^{20}$Ne positive parity states up to an excitation energy
-of $\sim$ 7 MeV, predicting reasonably well the excitation energy of the states
-and their cluster and single particle properties. At higher excitations, the
-qualitative disagreement with the experimentally observed structure is evident,
-especially for broad resonances.
-",0,1,0,0,0,0
-18037,Terminal-Pairability in Complete Bipartite Graphs,"  We investigate the terminal-pairibility problem in the case when the base
-graph is a complete bipartite graph, and the demand graph is also bipartite
-with the same color classes. We improve the lower bound on maximum value of
-$\Delta(D)$ which still guarantees that the demand graph $D$ is
-terminal-pairable in this setting. We also prove a sharp theorem on the maximum
-number of edges such a demand graph can have.
-",0,0,1,0,0,0
-18038,Rate-optimal Meta Learning of Classification Error,"  Meta learning of optimal classifier error rates allows an experimenter to
-empirically estimate the intrinsic ability of any estimator to discriminate
-between two populations, circumventing the difficult problem of estimating the
-optimal Bayes classifier. To this end we propose a weighted nearest neighbor
-(WNN) graph estimator for a tight bound on the Bayes classification error; the
-Henze-Penrose (HP) divergence. Similar to recently proposed HP estimators
-[berisha2016], the proposed estimator is non-parametric and does not require
-density estimation. However, unlike previous approaches the proposed estimator
-is rate-optimal, i.e., its mean squared estimation error (MSEE) decays to zero
-at the fastest possible rate of $O(1/M+1/N)$ where $M,N$ are the sample sizes
-of the respective populations. We illustrate the proposed WNN meta estimator
-for several simulated and real data sets.
-",0,0,0,1,0,0
-18039,"Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures","  Event detection is a critical feature in data-driven systems as it assists
-with the identification of nominal and anomalous behavior. Event detection is
-increasingly relevant in robotics as robots operate with greater autonomy in
-increasingly unstructured environments. In this work, we present an accurate,
-robust, fast, and versatile measure for skill and anomaly identification. A
-theoretical proof establishes the link between the derivative of the
-log-likelihood of the HMM filtered belief state and the latest emission
-probabilities. The key insight is the inverse relationship in which gradient
-analysis is used for skill and anomaly identification. Our measure showed
-better performance across all metrics than related state-of-the art works. The
-result is broadly applicable to domains that use HMMs for event detection.
-",1,0,0,0,0,0
-18040,Geometric features of Vessiot--Guldberg Lie algebras of conformal and Killing vector fields on $\mathbb{R}^2$,"  This paper locally classifies finite-dimensional Lie algebras of conformal
-and Killing vector fields on $\mathbb{R}^2$ relative to an arbitrary
-pseudo-Riemannian metric. Several results about their geometric properties are
-detailed, e.g. their invariant distributions and induced symplectic structures.
-Findings are illustrated with two examples of physical nature: the
-Milne--Pinney equation and the projective Schrödinger equation on the Riemann
-sphere.
-",0,1,0,0,0,0
-18041,Fermion inter-particle potentials in 5D and a dimensional restriction prescription to 4D,"  This work sets out to compute and discuss effects of spin, velocity and
-dimensionality on inter-particle potentials systematically derived from gauge
-field-theoretic models. We investigate the interaction of fermionic particles
-by the exchange of a vector field in a parity-preserving description in
-five-dimensional $(5D)$ space-time. A particular dimensional reduction
-prescription is adopted $-$ reduction by dimensional restriction $-$ and
-special effects, like a pseudo-spin dependence, show up in four dimensions
-$(4D)$. What we refer to as pseudo-spin shall be duly explained. The main idea
-we try to convey is that the calculation of the potentials in $5D$ and the
-consequent reduction to $4D$ exhibits new effects that are not present if the
-potential is calculated in $4D$ after the action has been reduced.
-",0,1,0,0,0,0
-18042,Characterizations of idempotent discrete uninorms,"  In this paper we provide an axiomatic characterization of the idempotent
-discrete uninorms by means of three conditions only: conservativeness,
-symmetry, and nondecreasing monotonicity. We also provide an alternative
-characterization involving the bisymmetry property. Finally, we provide a
-graphical characterization of these operations in terms of their contour plots,
-and we mention a few open questions for further research.
-",1,0,1,0,0,0
-18043,Constraints on the sum of neutrino masses using cosmological data including the latest extended Baryon Oscillation Spectroscopic Survey DR14 quasar sample,"  We investigate the constraints on the sum of neutrino masses ($\Sigma m_\nu$)
-using the most recent cosmological data, which combines the distance
-measurement from baryonic acoustic oscillation in the extended Baryon
-Oscillation Spectroscopic Survey DR14 quasar sample with the power spectra of
-temperature and polarization anisotropies in the cosmic microwave background
-from the Planck 2015 data release. We also use other low-redshift observations
-including the baryonic acoustic oscillation at relatively low redshifts, the
-supernovae of type Ia and the local measurement of Hubble constant. In the
-standard cosmological constant $\Lambda$ cold dark matter plus massive neutrino
-model, we obtain the $95\%$ \acl{CL} upper limit to be $\Sigma
-m_\nu<0.129~\mathrm{eV}$ for the degenerate mass hierarchy, $\Sigma
-m_{\nu}<0.159~\mathrm{eV}$ for the normal mass hierarchy, and $\Sigma
-m_{\nu}<0.189~\mathrm{eV}$ for the inverted mass hierarchy. Based on Bayesian
-evidence, we find that the degenerate hierarchy is positively supported, and
-the current data combination can not distinguish normal and inverted
-hierarchies. Assuming the degenerate mass hierarchy, we extend our study to
-non-standard cosmological models including the generic dark energy, the spatial
-curvature, and the extra relativistic degrees of freedom, respectively, but
-find these models not favored by the data.
-",0,1,0,0,0,0
-18044,The effect of the choice of neural network depth and breadth on the size of its hypothesis space,"  We show that the number of unique function mappings in a neural network
-hypothesis space is inversely proportional to $\prod_lU_l!$, where $U_{l}$ is
-the number of neurons in the hidden layer $l$.
-",0,0,0,1,0,0
-18045,Fairwashing: the risk of rationalization,"  Black-box explanation is the problem of explaining how a machine learning
-model -- whose internal logic is hidden to the auditor and generally complex --
-produces its outcomes. Current approaches for solving this problem include
-model explanation, outcome explanation as well as model inspection. While these
-techniques can be beneficial by providing interpretability, they can be used in
-a negative manner to perform fairwashing, which we define as promoting the
-perception that a machine learning model respects some ethical values while it
-might not be the case. In particular, we demonstrate that it is possible to
-systematically rationalize decisions taken by an unfair black-box model using
-the model explanation as well as the outcome explanation approaches with a
-given fairness metric. Our solution, LaundryML, is based on a regularized rule
-list enumeration algorithm whose objective is to search for fair rule lists
-approximating an unfair black-box model. We empirically evaluate our
-rationalization technique on black-box models trained on real-world datasets
-and show that one can obtain rule lists with high fidelity to the black-box
-model while being considerably less unfair at the same time.
-",1,0,0,1,0,0
-18046,Volume Dependence of N-Body Bound States,"  We derive the finite-volume correction to the binding energy of an N-particle
-quantum bound state in a cubic periodic volume. Our results are applicable to
-bound states with arbitrary composition and total angular momentum, and in any
-number of spatial dimensions. The only assumptions are that the interactions
-have finite range. The finite-volume correction is a sum of contributions from
-all possible breakup channels. In the case where the separation is into two
-bound clusters, our result gives the leading volume dependence up to
-exponentially small corrections. If the separation is into three or more
-clusters, there is a power-law factor that is beyond the scope of this work,
-however our result again determines the leading exponential dependence. We also
-present two independent methods that use finite-volume data to determine
-asymptotic normalization coefficients. The coefficients are useful to determine
-low-energy capture reactions into weakly bound states relevant for nuclear
-astrophysics. Using the techniques introduced here, one can even extract the
-infinite-volume energy limit using data from a single-volume calculation. The
-derived relations are tested using several exactly solvable systems and
-numerical examples. We anticipate immediate applications to lattice
-calculations of hadronic, nuclear, and cold atomic systems.
-",0,1,1,0,0,0
-18047,Tree-Structured Boosting: Connections Between Gradient Boosted Stumps and Full Decision Trees,"  Additive models, such as produced by gradient boosting, and full interaction
-models, such as classification and regression trees (CART), are widely used
-algorithms that have been investigated largely in isolation. We show that these
-models exist along a spectrum, revealing never-before-known connections between
-these two approaches. This paper introduces a novel technique called
-tree-structured boosting for creating a single decision tree, and shows that
-this method can produce models equivalent to CART or gradient boosted stumps at
-the extremes by varying a single parameter. Although tree-structured boosting
-is designed primarily to provide both the model interpretability and predictive
-performance needed for high-stake applications like medicine, it also can
-produce decision trees represented by hybrid models between CART and boosted
-stumps that can outperform either of these approaches.
-",1,0,0,1,0,0
-18048,Magnetic field influenced electron-impurity scattering and magnetotransport,"  We formulate a quasiclassical theory ($\omega_c\tau \lesssim 1$ with
-$\omega_c$ as the cyclotron frequency and $\tau$ as the relaxation time) to
-study the influence of magnetic field on electron-impurity scattering process
-in the two-dimensional electron gas. We introduce a general recipe based on an
-abstraction of the detailed impurity scattering process to define the
-scattering parameter such as the incoming and outgoing momentum and coordinate
-jump. In this picture, we can conveniently describe the skew scattering and
-coordinate jump, which will eventually modify the Boltzmann equation. We find
-an anomalous Hall resistivity different from the conventional Boltzmann-Drude
-result and a negative magnetoresistivity parabolic in magnetic field. The
-origin of these results has been analyzed. The relevance between our theory and
-recent simulation and experimental works is also discussed. Our theory
-dominates in dilute impurity system where the correlation effect is negligible.
-",0,1,0,0,0,0
-18049,Evidence for depletion of heavy silicon isotopes at comet 67P/Churyumov-Gerasimenko,"  Context. The Rosetta Orbiter Spectrometer for Ion and Neutral Analysis
-(ROSINA) was designed to measure the composition of the gas in the coma of
-comet 67P/Churyumov-Gerasimenko, the target of the European Space Agency's
-Rosetta mission. In addition to the volatiles, ROSINA measured refractories
-sputtered off the comet by the interaction of solar wind protons with the
-surface of the comet.
-Aims. The origin of different solar system materials is still heavily
-debated. Isotopic ratios can be used to distinguish between different
-reservoirs and investigate processes occurring during the formation of the
-solar system.
-Methods. ROSINA consisted of two mass spectrometers and a pressure sensor. In
-the ROSINA Double Focusing Mass Spectrometer (DFMS), the neutral gas of
-cometary origin was ionized and then deflected in an electric and a magnetic
-field that separated the ions based on their mass-to-charge ratio. The DFMS had
-a high mass resolution, dynamic range, and sensitivity that allowed detection
-of rare species and the known major volatiles.
-Results. We measured the relative abundance of all three stable silicon
-isotopes with the ROSINA instrument on board the Rosetta spacecraft.
-Furthermore, we measured $^{13}$C/$^{12}$C in C$_2$H$_4$, C$_2$H$_5$, and CO.
-The DFMS in situ measurements indicate that the average silicon isotopic
-composition shows depletion in the heavy isotopes $^{29}$Si and $^{30}$Si with
-respect to $^{28}$Si and solar abundances, while $^{13}$C to $^{12}$C is
-analytically indistinguishable from bulk planetary and meteorite compositions.
-Although the origin of the deficiency of the heavy silicon isotopes cannot be
-explained unambiguously, we discuss mechanisms that could have contributed to
-the measured depletion of the isotopes $^{29}$Si and $^{30}$Si.
-",0,1,0,0,0,0
-18050,"Design, Simulation, and Testing of a Flexible Actuated Spine for Quadruped Robots","  Walking quadruped robots face challenges in positioning their feet and
-lifting their legs during gait cycles over uneven terrain. The robot Laika is
-under development as a quadruped with a flexible, actuated spine designed to
-assist with foot movement and balance during these gaits. This paper presents
-the first set of hardware designs for the spine of Laika, a physical prototype
-of those designs, and tests in both hardware and simulations that show the
-prototype's capabilities. Laika's spine is a tensegrity structure, used for its
-advantages with weight and force distribution, and represents the first working
-prototype of a tensegrity spine for a quadruped robot. The spine bends by
-adjusting the lengths of the cables that separate its vertebrae, and twists
-using an actuated rotating vertebra at its center. The current prototype of
-Laika has stiff legs attached to the spine, and is used as a test setup for
-evaluation of the spine itself. This work shows the advantages of Laika's spine
-by demonstrating the spine lifting each of the robot's four feet, both as a
-form of balancing and as a precursor for a walking gait. These foot motions,
-using specific combinations of bending and rotation movements of the spine, are
-measured in both simulation and hardware experiments. Hardware data are used to
-calibrate the simulations, such that the simulations can be used for control of
-balancing or gait cycles in the future. Future work will attach actuated legs
-to Laika's spine, and examine balancing and gait cycles when combined with leg
-movements.
-",1,0,0,0,0,0
-18051,The landscape of NeuroImage-ing research,"  As the field of neuroimaging grows, it can be difficult for scientists within
-the field to gain and maintain a detailed understanding of its ever-changing
-landscape. While collaboration and citation networks highlight important
-contributions within the field, the roles of and relations among specific areas
-of study can remain quite opaque. Here, we apply techniques from network
-science to map the landscape of neuroimaging research documented in the journal
-NeuroImage over the past decade. We create a network in which nodes represent
-research topics, and edges give the degree to which these topics tend to be
-covered in tandem. The network displays small-world architecture, with
-communities characterized by common imaging modalities and medical
-applications, and with bridges that integrate these distinct subfields. Using
-node-level analysis, we quantify the structural roles of individual topics
-within the neuroimaging landscape, and find high levels of clustering within
-the structural MRI subfield as well as increasing participation among topics
-related to psychiatry. The overall prevalence of a topic is unrelated to the
-prevalence of its neighbors, but the degree to which a topic becomes more or
-less popular over time is strongly related to changes in the prevalence of its
-neighbors. Broadly, this work presents a cohesive model for understanding the
-landscape of neuroimaging research across the field, in broad subfields, and
-within specific topic areas.
-",1,0,0,1,0,0
-18052,To tune or not to tune the number of trees in random forest?,"  The number of trees T in the random forest (RF) algorithm for supervised
-learning has to be set by the user. It is controversial whether T should simply
-be set to the largest computationally manageable value or whether a smaller T
-may in some cases be better. While the principle underlying bagging is that
-""more trees are better"", in practice the classification error rate sometimes
-reaches a minimum before increasing again for increasing number of trees. The
-goal of this paper is four-fold: (i) providing theoretical results showing that
-the expected error rate may be a non-monotonous function of the number of trees
-and explaining under which circumstances this happens; (ii) providing
-theoretical results showing that such non-monotonous patterns cannot be
-observed for other performance measures such as the Brier score and the
-logarithmic loss (for classification) and the mean squared error (for
-regression); (iii) illustrating the extent of the problem through an
-application to a large number (n = 306) of datasets from the public database
-OpenML; (iv) finally arguing in favor of setting it to a computationally
-feasible large number, depending on convergence properties of the desired
-performance measure.
-",1,0,0,1,0,0
-18053,SPH Modeling of Short-crested Waves,"  This study investigates short-crested wave breaking over a planar beach by
-using the mesh-free Smoothed Particle Hydrodynamics model, GPUSPH. The
-short-crested waves are created by generating intersecting wave trains in a
-numerical wave basin. We examine the influence of beach slope, incident wave
-height, and incident wave angle on the generated short-crested waves.
-Short-crested wave breaking over a steeper beach generates stronger rip
-currents, and larger circulation cells in front of the beach. Intersecting wave
-trains with a larger incident wave height drive a more complicated
-short-crested wave field including isolated breakers and wave amplitude
-diffraction. Nearshore circulation induced by short-crested wave breaking is
-greatly influenced by the incident wave angle (or the rip current spacing).
-There is no secondary circulation cell between the nodal line and the antinodal
-line if the rip current spacing is narrow. However, there are multiple
-secondary circulation cells observed when the rip current spacing is relatively
-large.
-",0,1,0,0,0,0
-18054,Tunneling Field-Effect Junctions with WS$_2$ barrier,"  Transition metal dichalcogenides (TMDCs), with their two-dimensional
-structures and sizable bandgaps, are good candidates for barrier materials in
-tunneling field-effect transistor (TFET) formed from atomic precision vertical
-stacks of graphene and insulating crystals of a few atomic layers in thickness.
-We report first-principles study of the electronic properties of the
-Graphene/WS$_2$/Graphene sandwich structure revealing strong interface effects
-on dielectric properties and predicting a high ON/OFF ratio with an appropriate
-WS$_2$ thickness and a suitable range of the gate voltage. Both the band
-spin-orbit coupling splitting and the dielectric constant of the WS$_2$ layer
-depend on its thickness when in contact with the graphene electrodes,
-indicating strong influence from graphene across the interfaces. The dielectric
-constant is significantly reduced from the bulk WS$_2$ value. The effective
-barrier height varies with WS$_2$ thickness and can be tuned by a gate voltage.
-These results are critical for future nanoelectronic device designs.
-",0,1,0,0,0,0
-18055,Topological Larkin-Ovchinnikov phase and Majorana zero mode chain in bilayer superconducting topological insulator films,"  We theoretically study bilayer superconducting topological insulator film, in
-which superconductivity exists for both top and bottom surface states. We show
-that an in-plane magnetic field can drive the system into Larkin-Ovchinnikov
-(LO) phase, where electrons are paired with finite momenta. The LO phase is
-topologically non-trivial and characterized by a Z 2 topological invariant,
-leading to a Majorana zero mode chain along the edge perpendicular to in-plane
-magnetic fields.
-",0,1,0,0,0,0
-18056,Optimization and Analysis of Wireless Powered Multi-antenna Cooperative Systems,"  In this paper, we consider a three-node cooperative wireless powered
-communication system consisting of a multi-antenna hybrid access point (H-AP)
-and a single-antenna relay and a single-antenna user. The energy constrained
-relay and user first harvest energy in the downlink and then the relay assists
-the user using the harvested power for information transmission in the uplink.
-The optimal energy beamforming vector and the time split between harvest and
-cooperation are investigated. To reduce the computational complexity,
-suboptimal designs are also studied, where closed-form expressions are derived
-for the energy beamforming vector and the time split. For comparison purposes,
-we also present a detailed performance analysis in terms of the achievable
-outage probability and the average throughput of an intuitive energy
-beamforming scheme, where the H-AP directs all the energy towards the user. The
-findings of the paper suggest that implementing multiple antennas at the H-AP
-can significantly improve the system performance, and the closed-form
-suboptimal energy beamforming vector and time split yields near optimal
-performance. Also, for the intuitive beamforming scheme, a diversity order of
-(N+1)/2 can be achieved, where N is the number of antennas at the H-AP.
-",1,0,0,0,0,0
-18057,Asai cube L-functions and the local Langlands conjecture,"  Let $F$ be a non-archimedean locally compact field. We study a class of
-Langlands-Shahidi pairs $({\bf H},{\bf L})$, consisting of a quasi-split
-connected reductive group $\bf H$ over $F$ and a Levi subgroup $\bf L$ which is
-closely related to a product of restriction of scalars of ${\rm GL}_1$'s or
-${\rm GL}_2$'s. We prove the compatibility of the resulting local factors with
-the Langlands correspondence. In particular, let $E$ be a cubic separable
-extension of $F$. We consider a simply connected quasi-split semisimple group
-$\bf H$ over $F$ of type $D_4$, with triality corresponding to $E$, and let
-$\bf L$ be its Levi subgroup with derived group ${\rm Res}_{E/F} {\rm SL}_2$.
-In this way we obtain Asai cube local factors attached to irreducible smooth
-representations of ${\rm GL}_2(E)$; we prove that they are Weil-Deligne factors
-obtained via the local Langlands correspondence for ${\rm GL}_2(E)$ and tensor
-induction from $E$ to $F$. A consequence is that Asai cube $\gamma$- and
-$\varepsilon$-factors become stable under twists by highly ramified characters.
-",0,0,1,0,0,0
-18058,What's in a game? A theory of game models,"  Game semantics is a rich and successful class of denotational models for
-programming languages. Most game models feature a rather intuitive setup, yet
-surprisingly difficult proofs of such basic results as associativity of
-composition of strategies. We set out to unify these models into a basic
-abstract framework for game semantics, game settings. Our main contribution is
-the generic construction, for any game setting, of a category of games and
-strategies. Furthermore, we extend the framework to deal with innocence, and
-prove that innocent strategies form a subcategory. We finally show that our
-constructions cover many concrete cases, mainly among the early models and the
-very recent sheaf-based ones.
-",1,0,0,0,0,0
-18059,Proposal for the Detection of Magnetic Monopoles in Spin Ice via Nanoscale Magnetometry,"  We present a proposal for applying nanoscale magnetometry to the search for
-magnetic monopoles in the spin ice materials holmium and dysprosium titanate.
-Employing Monte Carlo simulations of the dipolar spin ice model, we find that
-when cooled to below $1.5\,$K these materials exhibit a sufficiently low
-monopole density to enable the direct observation of magnetic fields from
-individual monopoles. At these temperatures we demonstrate that noise
-spectroscopy can capture the intrinsic fluctuations associated with monopole
-dynamics, allowing one to isolate the qualitative effects associated with both
-the Coulomb interaction between monopoles and the topological constraints
-implied by Dirac strings. We describe in detail three different nanoscale
-magnetometry platforms (muon spin rotation, nitrogen vacancy defects, and
-nanoSQUID arrays) that can be used to detect monopoles in these experiments,
-and analyze the advantages of each.
-",0,1,0,0,0,0
-18060,Rule-Based Spanish Morphological Analyzer Built From Spell Checking Lexicon,"  Preprocessing tools for automated text analysis have become more widely
-available in major languages, but non-English tools are often still limited in
-their functionality. When working with Spanish-language text, researchers can
-easily find tools for tokenization and stemming, but may not have the means to
-extract more complex word features like verb tense or mood. Yet Spanish is a
-morphologically rich language in which such features are often identifiable
-from word form. Conjugation rules are consistent, but many special verbs and
-nouns take on different rules. While building a complete dictionary of known
-words and their morphological rules would be labor intensive, resources to do
-so already exist, in spell checkers designed to generate valid forms of known
-words. This paper introduces a set of tools for Spanish-language morphological
-analysis, built using the COES spell checking tools, to label person, mood,
-tense, gender and number, derive a word's root noun or verb infinitive, and
-convert verbs to their nominal form.
-",1,0,0,0,0,0
-18061,Learning Edge Representations via Low-Rank Asymmetric Projections,"  We propose a new method for embedding graphs while preserving directed edge
-information. Learning such continuous-space vector representations (or
-embeddings) of nodes in a graph is an important first step for using network
-information (from social networks, user-item graphs, knowledge bases, etc.) in
-many machine learning tasks.
-Unlike previous work, we (1) explicitly model an edge as a function of node
-embeddings, and we (2) propose a novel objective, the ""graph likelihood"", which
-contrasts information from sampled random walks with non-existent edges.
-Individually, both of these contributions improve the learned representations,
-especially when there are memory constraints on the total size of the
-embeddings. When combined, our contributions enable us to significantly improve
-the state-of-the-art by learning more concise representations that better
-preserve the graph structure.
-We evaluate our method on a variety of link-prediction task including social
-networks, collaboration networks, and protein interactions, showing that our
-proposed method learn representations with error reductions of up to 76% and
-55%, on directed and undirected graphs. In addition, we show that the
-representations learned by our method are quite space efficient, producing
-embeddings which have higher structure-preserving accuracy but are 10 times
-smaller.
-",1,0,0,1,0,0
-18062,"Inference, Prediction, and Control of Networked Epidemics","  We develop a feedback control method for networked epidemic spreading
-processes. In contrast to most prior works which consider mean field, open-loop
-control schemes, the present work develops a novel framework for feedback
-control of epidemic processes which leverages incomplete observations of the
-stochastic epidemic process in order to control the exact dynamics of the
-epidemic outbreak. We develop an observation model for the epidemic process,
-and demonstrate that if the set of observed nodes is sufficiently well
-structured, then the random variables which denote the process' infections are
-conditionally independent given the observations. We then leverage the attained
-conditional independence property to construct tractable mechanisms for the
-inference and prediction of the process state, avoiding the need to use mean
-field approximations or combinatorial representations. We conclude by
-formulating a one-step lookahead controller for the discrete-time
-Susceptible-Infected-Susceptible (SIS) epidemic process which leverages the
-developed Bayesian inference and prediction mechanisms, and causes the epidemic
-to die out at a chosen rate.
-",0,0,1,0,0,0
-18063,The Weisfeiler-Leman algorithm and the diameter of Schreier graphs,"  We prove that the number of iterations taken by the Weisfeiler-Leman
-algorithm for configurations coming from Schreier graphs is closely linked to
-the diameter of the graphs themselves: an upper bound is found for general
-Schreier graphs, and a lower bound holds for particular cases, such as for
-Schreier graphs with $G=\mbox{SL}_{n}({\mathbb F}_{q})$ ($q>2$) acting on
-$k$-tuples of vectors in ${\mathbb F}_{q}^{n}$; moreover, an exact expression
-is found in the case of Cayley graphs.
-",0,0,1,0,0,0
-18064,Correlations between primes in short intervals on curves over finite fields,"  We prove an analogue of the Hardy-Littlewood conjecture on the asymptotic
-distribution of prime constellations in the setting of short intervals in
-function fields of smooth projective curves over finite fields.
-",0,0,1,0,0,0
-18065,Extracting and Exploiting Inherent Sparsity for Efficient IoT Support in 5G: Challenges and Potential Solutions,"  Besides enabling an enhanced mobile broadband, next generation of mobile
-networks (5G) are envisioned for the support of massive connectivity of
-heterogeneous Internet of Things (IoT)s. These IoTs are envisioned for a large
-number of use-cases including smart cities, environment monitoring, smart
-vehicles, etc. Unfortunately, most IoTs have very limited computing and storage
-capabilities and need cloud services. Hence, connecting these devices through
-5G systems requires huge spectrum resources in addition to handling the massive
-connectivity and improved security. This article discusses the challenges
-facing the support of IoTs through 5G systems. The focus is devoted to
-discussing physical layer limitations in terms of spectrum resources and radio
-access channel connectivity. We show how sparsity can be exploited for
-addressing these challenges especially in terms of enabling wideband spectrum
-management and handling the connectivity by exploiting device-to-device
-communications and edge-cloud. Moreover, we identify major open problems and
-research directions that need to be explored towards enabling the support of
-massive heterogeneous IoTs through 5G systems.
-",1,0,0,0,0,0
-18066,Existence and convexity of local solutions to degenerate hessian equations,"  In this work, we prove the existence of local convex solution to the
-degenerate Hessian equation
-",0,0,1,0,0,0
-18067,Superexponential estimates and weighted lower bounds for the square function,"  We prove the following superexponential distribution inequality: for any
-integrable $g$ on $[0,1)^{d}$ with zero average, and any $\lambda>0$ \[ |\{ x
-\in [0,1)^{d} \; :\; g \geq\lambda \}| \leq e^{-
-\lambda^{2}/(2^{d}\|S(g)\|_{\infty}^{2})}, \] where $S(g)$ denotes the
-classical dyadic square function in $[0,1)^{d}$. The estimate is sharp when
-dimension $d$ tends to infinity in the sense that the constant $2^{d}$ in the
-denominator cannot be replaced by $C2^{d}$ with $0<C<1$ independent of $d$ when
-$d \to \infty$.
-For $d=1$ this is a classical result of Chang--Wilson--Wolff [4]; however, in
-the case $d>1$ they work with a special square function $S_\infty$, and their
-result does not imply the estimates for the classical square function.
-Using good $\lambda$ inequalities technique we then obtain unweighted and
-weighted $L^p$ lower bounds for $S$; to get the corresponding good $\lambda$
-inequalities we need to modify the classical construction.
-We also show how to obtain our superexponential distribution inequality
-(although with worse constants) from the weighted $L^2$ lower bounds for $S$,
-obtained in [5].
-",0,0,1,0,0,0
-18068,Reframing the S\&P500 Network of Stocks along the \nth{21} Century,"  Since the beginning of the new millennium, stock markets went through every
-state from long-time troughs, trade suspensions to all-time highs. The
-literature on asset pricing hence assumes random processes to be underlying the
-movement of stock returns. Observed procyclicality and time-varying correlation
-of stock returns tried to give the apparently random behavior some sort of
-structure. However, common misperceptions about the co-movement of asset prices
-in the years preceding the \emph{Great Recession} and the \emph{Global
-Commodity Crisis}, is said to have even fueled the crisis' economic impact.
-Here we show how a varying macroeconomic environment influences stocks'
-clustering into communities. From a sample of 296 stocks of the S\&P 500 index,
-distinct periods in between 2004 and 2011 are used to develop networks of
-stocks. The Minimal Spanning Tree analysis of those time-varying networks of
-stocks demonstrates that the crises of 2007-2008 and 2010-2011 drove the market
-to clustered community structures in both periods, helping to restore the stock
-market's ceased order of the pre-crises era. However, a comparison of the
-emergent clusters with the \textit{General Industry Classification Standard}
-conveys the impression that industry sectors do not play a major role in that
-order.
-",0,0,0,0,0,1
-18069,Point-contact spectroscopy of superconducting energy gap in $\rm DyNi_2B_2C$,"  The superconducting energy gap in $\rm DyNi_2B_2C$ has been investigated
-using a point-contact technique based on the Andreev reflection from a normal
-(N)-superconductor (S) boundary, where N is Ag. The observed differential
-resistance $dV/dI$ is well described by the Blonder-Tinkham-Klapwijk (BTK)
-theory based on the BSC density of states with zero broadening parameter.
-Typically, the intensity of the gap structure amounts to several percentage of
-the normal state resistance, which is an order of magnitude less than predicted
-by the theory. For $\rm DyNi_2B_2C$ with $T_c<T_N$ (the Neel temperature), we
-found gap values satisfying the ratio of $2\Delta_0/k_BT_c=3.63\pm 0.05$
-similar to other superconducting nickel-borocarbides, both nonmagnetic and
-magnetic with $T_c\geq T_N$. The superconducting gap nonlinearity is
-superimposed on the antiferromagnetic structure in $dV/dI(V)$ which is
-suppressed at the magnetic field of the order of 3T applied nominally in the
-$ab$-plane and temperature $\geq 11~K$. The observed superconducting properties
-depend on the exact composition and structure at the surface of the crystal.
-",0,1,0,0,0,0
-18070,Parameterized Complexity of Safe Set,"  In this paper we study the problem of finding a small safe set $S$ in a graph
-$G$, i.e. a non-empty set of vertices such that no connected component of
-$G[S]$ is adjacent to a larger component in $G - S$. We enhance our
-understanding of the problem from the viewpoint of parameterized complexity by
-showing that (1) the problem is W[2]-hard when parameterized by the pathwidth
-$pw$ and cannot be solved in time $n^{o(pw)}$ unless the ETH is false, (2) it
-admits no polynomial kernel parameterized by the vertex cover number $vc$
-unless $\mathrm{PH} = \Sigma^{\mathrm{p}}_{3}$, but (3) it is fixed-parameter
-tractable (FPT) when parameterized by the neighborhood diversity $nd$, and (4)
-it can be solved in time $n^{f(cw)}$ for some double exponential function $f$
-where $cw$ is the clique-width. We also present (5) a faster FPT algorithm when
-parameterized by solution size.
-",1,0,0,0,0,0
-18071,A Matrix Variate Skew-t Distribution,"  Although there is ample work in the literature dealing with skewness in the
-multivariate setting, there is a relative paucity of work in the matrix variate
-paradigm. Such work is, for example, useful for modelling three-way data. A
-matrix variate skew-t distribution is derived based on a mean-variance matrix
-normal mixture. An expectation-conditional maximization algorithm is developed
-for parameter estimation. Simulated data are used for illustration.
-",0,0,1,1,0,0
-18072,Status Updates Through Multicast Networks,"  Using age of information as the freshness metric, we examine a multicast
-network in which real-time status updates are generated by the source and sent
-to a group of $n$ interested receivers. We show that in order to keep the
-information freshness at each receiver, the source should terminate the
-transmission of the current update and start sending a new update packet as
-soon as it receives the acknowledgements back from any $k$ out of $n$ nodes. As
-the source stopping threshold $k$ increases, a node is more likely to get the
-latest generated update, but the age of the most recent update is more likely
-to become outdated. We derive the age minimized stopping threshold $k$ that
-balances the likelihood of getting the latest update and the freshness of the
-latest update for shifted exponential link delay. Through numerical evaluations
-for different stopping strategies, we find that waiting for the
-acknowledgements from the earliest $k$ out of $n$ nodes leads to lower average
-age than waiting for a pre-selected group of $k$ nodes. We also observe that a
-properly chosen threshold $k$ can prevent information staleness for increasing
-number of nodes $n$ in the multicast network.
-",1,0,0,0,0,0
-18073,Extension of Convolutional Neural Network with General Image Processing Kernels,"  We applied pre-defined kernels also known as filters or masks developed for
-image processing to convolution neural network. Instead of letting neural
-networks find its own kernels, we used 41 different general-purpose kernels of
-blurring, edge detecting, sharpening, discrete cosine transformation, etc. for
-the first layer of the convolution neural networks. This architecture, thus
-named as general filter convolutional neural network (GFNN), can reduce
-training time by 30% with a better accuracy compared to the regular
-convolutional neural network (CNN). GFNN also can be trained to achieve 90%
-accuracy with only 500 samples. Furthermore, even though these kernels are not
-specialized for the MNIST dataset, we achieved 99.56% accuracy without ensemble
-nor any other special algorithms.
-",1,0,0,1,0,0
-18074,Sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network,"  This paper proposes a neural network architecture and training scheme to
-learn the start and end time of sound events (strong labels) in an audio
-recording given just the list of sound events existing in the audio without
-time information (weak labels). We achieve this by using a stacked
-convolutional and recurrent neural network with two prediction layers in
-sequence one for the strong followed by the weak label. The network is trained
-using frame-wise log mel-band energy as the input audio feature, and weak
-labels provided in the dataset as labels for the weak label prediction layer.
-Strong labels are generated by replicating the weak labels as many number of
-times as the frames in the input audio feature, and used for strong label layer
-during training. We propose to control what the network learns from the weak
-and strong labels by different weighting for the loss computed in the two
-prediction layers. The proposed method is evaluated on a publicly available
-dataset of 155 hours with 17 sound event classes. The method achieves the best
-error rate of 0.84 for strong labels and F-score of 43.3% for weak labels on
-the unseen test split.
-",1,0,0,0,0,0
-18075,Categoricity and Universal Classes,"  Let $(\mathcal{K} ,\subseteq )$ be a universal class with
-$LS(\mathcal{K})=\lambda$ categorical in regular $\kappa >\lambda^+$ with
-arbitrarily large models, and let $\mathcal{K}^*$ be the class of all
-$\mathcal{A}\in\mathcal{K}_{>\lambda}$ for which there is $\mathcal{B} \in
-\mathcal{K}_{\ge\kappa}$ such that $\mathcal{A}\subseteq\mathcal{B}$. We prove
-that $\mathcal{K}^*$ is categorical in every $\xi >\lambda^+$,
-$\mathcal{K}_{\ge\beth_{(2^{\lambda^+})^+}} \subseteq \mathcal{K}^{*}$, and the
-models of $\mathcal{K}^*_{>\lambda^+}$ are essentially vector spaces (or
-trivial i.e. disintegrated).
-",0,0,1,0,0,0
-18076,"Water, High-Altitude Condensates, and Possible Methane Depletion in the Atmosphere of the Warm Super-Neptune WASP-107b","  The super-Neptune exoplanet WASP-107b is an exciting target for atmosphere
-characterization. It has an unusually large atmospheric scale height and a
-small, bright host star, raising the possibility of precise constraints on its
-current nature and formation history. We report the first atmospheric study of
-WASP-107b, a Hubble Space Telescope measurement of its near-infrared
-transmission spectrum. We determined the planet's composition with two
-techniques: atmospheric retrieval based on the transmission spectrum and
-interior structure modeling based on the observed mass and radius. The interior
-structure models set a $3\,\sigma$ upper limit on the atmospheric metallicity
-of $30\times$ solar. The transmission spectrum shows strong evidence for water
-absorption ($6.5\,\sigma$ confidence), and the retrieved water abundance is
-consistent with expectations for a solar abundance pattern. The inferred
-carbon-to-oxygen ratio is subsolar at $2.7\,\sigma$ confidence, which we
-attribute to possible methane depletion in the atmosphere. The spectral
-features are smaller than predicted for a cloud-free composition, crossing less
-than one scale height. A thick condensate layer at high altitudes (0.1 - 3
-mbar) is needed to match the observations. We find that physically motivated
-cloud models with moderate sedimentation efficiency ($f_\mathrm{sed} = 0.3$) or
-hazes with a particle size of 0.3 $\mu$m reproduce the observed spectral
-feature amplitude. Taken together, these findings serve as an illustration of
-the diversity and complexity of exoplanet atmospheres. The community can look
-forward to more such results with the high precision and wide spectral coverage
-afforded by future observing facilities.
-",0,1,0,0,0,0
-18077,Partial Order on the set of Boolean Regulatory Functions,"  Logical models have been successfully used to describe regulatory and
-signaling networks without requiring quantitative data. However, existing data
-is insufficient to adequately define a unique model, rendering the
-parametrization of a given model a difficult task.
-Here, we focus on the characterization of the set of Boolean functions
-compatible with a given regulatory structure, i.e. the set of all monotone
-nondegenerate Boolean functions. We then propose an original set of rules to
-locally explore the direct neighboring functions of any function in this set,
-without explicitly generating the whole set. Also, we provide relationships
-between the regulatory functions and their corresponding dynamics.
-Finally, we illustrate the usefulness of this approach by revisiting
-Probabilistic Boolean Networks with the model of T helper cell differentiation
-from Mendoza & Xenarios.
-",1,0,0,0,1,0
-18078,Scalable solvers for complex electromagnetics problems,"  In this work, we present scalable balancing domain decomposition by
-constraints methods for linear systems arising from arbitrary order edge finite
-element discretizations of multi-material and heterogeneous 3D problems. In
-order to enforce the continuity across subdomains of the method, we use a
-partition of the interface objects (edges and faces) into sub-objects
-determined by the variation of the physical coefficients of the problem. For
-multi-material problems, a constant coefficient condition is enough to define
-this sub-partition of the objects. For arbitrarily heterogeneous problems, a
-relaxed version of the method is defined, where we only require that the
-maximal contrast of the physical coefficient in each object is smaller than a
-predefined threshold. Besides, the addition of perturbation terms to the
-preconditioner is empirically shown to be effective in order to deal with the
-case where the two coefficients of the model problem jump simultaneously across
-the interface. The new method, in contrast to existing approaches for problems
-in curl-conforming spaces, preserves the simplicity of the original
-preconditioner, i.e., no spectral information is required, whilst providing
-robustness with regard to coefficient jumps and heterogeneous materials. A
-detailed set of numerical experiments, which includes the application of the
-preconditioner to 3D realistic cases, shows excellent weak scalability
-properties of the implementation of the proposed algorithms.
-",1,0,0,0,0,0
-18079,A flux-splitting method for hyperbolic-equation system of magnetized electron fluids in quasi-neutral plasmas,"  A flux-splitting method is proposed for the hyperbolic-equation system (HES)
-of magnetized electron fluids in quasi-neutral plasmas. The numerical fluxes
-are split into four categories, which are computed by using an upwind method
-which incorporates a flux-vector splitting (FVS) and advection upstream
-splitting method (AUSM). The method is applied to a test calculation condition
-of uniformly distributed and angled magnetic lines of force. All of the
-pseudo-time advancement terms converge monotonically and the conservation laws
-are strictly satisfied in the steady state. The calculation results are
-compared with those computed by using the elliptic-parabolic-equation system
-(EPES) approach using a magnetic-field-aligned mesh (MFAM). Both qualitative
-and quantitative comparisons yield good agreements of results, indicating that
-the HES approach with the flux-splitting method attains a high computational
-accuracy.
-",0,1,0,0,0,0
-18080,TransFlow: Unsupervised Motion Flow by Joint Geometric and Pixel-level Estimation,"  We address unsupervised optical flow estimation for ego-centric motion. We
-argue that optical flow can be cast as a geometrical warping between two
-successive video frames and devise a deep architecture to estimate such
-transformation in two stages. First, a dense pixel-level flow is computed with
-a geometric prior imposing strong spatial constraints. Such prior is typical of
-driving scenes, where the point of view is coherent with the vehicle motion. We
-show how such global transformation can be approximated with an homography and
-how spatial transformer layers can be employed to compute the flow field
-implied by such transformation. The second stage then refines the prediction
-feeding a second deeper network. A final reconstruction loss compares the
-warping of frame X(t) with the subsequent frame X(t+1) and guides both
-estimates. The model, which we named TransFlow, performs favorably compared to
-other unsupervised algorithms, and shows better generalization compared to
-supervised methods with a 3x reduction in error on unseen data.
-",1,0,0,0,0,0
-18081,A General Pipeline for 3D Detection of Vehicles,"  Autonomous driving requires 3D perception of vehicles and other objects in
-the in environment. Much of the current methods support 2D vehicle detection.
-This paper proposes a flexible pipeline to adopt any 2D detection network and
-fuse it with a 3D point cloud to generate 3D information with minimum changes
-of the 2D detection networks. To identify the 3D box, an effective model
-fitting algorithm is developed based on generalised car models and score maps.
-A two-stage convolutional neural network (CNN) is proposed to refine the
-detected 3D box. This pipeline is tested on the KITTI dataset using two
-different 2D detection networks. The 3D detection results based on these two
-networks are similar, demonstrating the flexibility of the proposed pipeline.
-The results rank second among the 3D detection algorithms, indicating its
-competencies in 3D detection.
-",0,0,0,1,0,0
-18082,Search for CII Emission on Cosmological Scales at Redshift Z~2.6,"  We present a search for CII emission over cosmological scales at
-high-redshifts. The CII line is a prime candidate to be a tracer of star
-formation over large-scale structure since it is one of the brightest emission
-lines from galaxies. Redshifted CII emission appears in the submillimeter
-regime, meaning it could potentially be present in the higher frequency
-intensity data from the Planck satellite used to measure the cosmic infrared
-background (CIB). We search for CII emission over redshifts z=2-3.2 in the
-Planck 545 GHz intensity map by cross-correlating the 3 highest frequency
-Planck maps with spectroscopic quasars and CMASS galaxies from the Sloan
-Digital Sky Survey III (SDSS-III), which we then use to jointly fit for CII
-intensity, CIB parameters, and thermal Sunyaev-Zeldovich (SZ) emission. We
-report a measurement of an anomalous emission
-$\mathrm{I_\nu}=6.6^{+5.0}_{-4.8}\times10^4$ Jy/sr at 95% confidence, which
-could be explained by CII emission, favoring collisional excitation models of
-CII emission that tend to be more optimistic than models based on CII
-luminosity scaling relations from local measurements; however, a comparison of
-Bayesian information criteria reveal that this model and the CIB & SZ only
-model are equally plausible. Thus, more sensitive measurements will be needed
-to confirm the existence of large-scale CII emission at high redshifts.
-Finally, we forecast that intensity maps from Planck cross-correlated with
-quasars from the Dark Energy Spectroscopic Instrument (DESI) would increase our
-sensitivity to CII emission by a factor of 5, while the proposed Primordial
-Inflation Explorer (PIXIE) could increase the sensitivity further.
-",0,1,0,0,0,0
-18083,Ricci flow and diffeomorphism groups of 3-manifolds,"  We complete the proof of the Generalized Smale Conjecture, apart from the
-case of $RP^3$, and give a new proof of Gabai's theorem for hyperbolic
-3-manifolds. We use an approach based on Ricci flow through singularities,
-which applies uniformly to spherical space forms other than $S^3$ and $RP^3$
-and hyperbolic manifolds, to prove that the moduli space of metrics of constant
-sectional curvature is contractible. As a corollary, for such a 3-manifold $X$,
-the inclusion $\text{Isom} (X,g)\to \text{Diff}(X)$ is a homotopy equivalence
-for any Riemannian metric $g$ of constant sectional curvature.
-",0,0,1,0,0,0
-18084,RLlib: Abstractions for Distributed Reinforcement Learning,"  Reinforcement learning (RL) algorithms involve the deep nesting of highly
-irregular computation patterns, each of which typically exhibits opportunities
-for distributed computation. We argue for distributing RL components in a
-composable way by adapting algorithms for top-down hierarchical control,
-thereby encapsulating parallelism and resource requirements within
-short-running compute tasks. We demonstrate the benefits of this principle
-through RLlib: a library that provides scalable software primitives for RL.
-These primitives enable a broad range of algorithms to be implemented with high
-performance, scalability, and substantial code reuse. RLlib is available at
-this https URL.
-",1,0,0,0,0,0
-18085,A Multiscale-Analysis of Stochastic Bistable Reaction-Diffusion Equations,"  A multiscale analysis of 1D stochastic bistable reaction-diffusion equations
-with additive noise is carried out w.r.t. travelling waves within the
-variational approach to stochastic partial differential equations. It is shown
-with explicit error estimates on appropriate function spaces that up to lower
-order w.r.t. the noise amplitude, the solution can be decomposed into the
-orthogonal sum of a travelling wave moving with random speed and into Gaussian
-fluctuations. A stochastic differential equation describing the speed of the
-travelling wave and a linear stochastic partial differential equation
-describing the fluctuations are derived in terms of the coefficients. Our
-results extend corresponding results obtained for stochastic neural field
-equations to the present class of stochastic dynamics.
-",0,0,1,0,0,0
-18086,MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis,"  Interpretability has emerged as a crucial aspect of machine learning, aimed
-at providing insights into the working of complex neural networks. However,
-existing solutions vary vastly based on the nature of the interpretability
-task, with each use case requiring substantial time and effort. This paper
-introduces MARGIN, a simple yet general approach to address a large set of
-interpretability tasks ranging from identifying prototypes to explaining image
-predictions. MARGIN exploits ideas rooted in graph signal analysis to determine
-influential nodes in a graph, which are defined as those nodes that maximally
-describe a function defined on the graph. By carefully defining task-specific
-graphs and functions, we demonstrate that MARGIN outperforms existing
-approaches in a number of disparate interpretability challenges.
-",1,0,0,1,0,0
-18087,SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability,"  We propose a new technique, Singular Vector Canonical Correlation Analysis
-(SVCCA), a tool for quickly comparing two representations in a way that is both
-invariant to affine transform (allowing comparison between different layers and
-networks) and fast to compute (allowing more comparisons to be calculated than
-with previous methods). We deploy this tool to measure the intrinsic
-dimensionality of layers, showing in some cases needless over-parameterization;
-to probe learning dynamics throughout training, finding that networks converge
-to final representations from the bottom up; to show where class-specific
-information in networks is formed; and to suggest new training regimes that
-simultaneously save computation and overfit less. Code:
-this https URL
-",1,0,0,1,0,0
-18088,Discrete Choice and Rational Inattention: a General Equivalence Result,"  This paper establishes a general equivalence between discrete choice and
-rational inattention models. Matejka and McKay (2015, AER) showed that when
-information costs are modelled using the Shannon entropy function, the
-resulting choice probabilities in the rational inattention model take the
-multinomial logit form. By exploiting convex-analytic properties of the
-discrete choice model, we show that when information costs are modelled using a
-class of generalized entropy functions, the choice probabilities in any
-rational inattention model are observationally equivalent to some additive
-random utility discrete choice model and vice versa. Thus any additive random
-utility model can be given an interpretation in terms of boundedly rational
-behavior. This includes empirically relevant specifications such as the probit
-and nested logit models.
-",0,0,0,1,0,0
-18089,A Hardy inequality for ultraspherical expansions with an application to the sphere,"  We prove a Hardy inequality for ultraspherical expansions by using a proper
-ground state representation. From this result we deduce some uncertainty
-principles for this kind of expansions. Our result also implies a Hardy
-inequality on spheres with a potential having a double singularity.
-",0,0,1,0,0,0
-18090,Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors,"  Bayesian Neural Networks (BNNs) have recently received increasing attention
-for their ability to provide well-calibrated posterior uncertainties. However,
-model selection---even choosing the number of nodes---remains an open question.
-Recent work has proposed the use of a horseshoe prior over node pre-activations
-of a Bayesian neural network, which effectively turns off nodes that do not
-help explain the data. In this work, we propose several modeling and inference
-advances that consistently improve the compactness of the model learned while
-maintaining predictive performance, especially in smaller-sample settings
-including reinforcement learning.
-",0,0,0,1,0,0
-18091,Efficient Reinforcement Learning via Initial Pure Exploration,"  In several realistic situations, an interactive learning agent can practice
-and refine its strategy before going on to be evaluated. For instance, consider
-a student preparing for a series of tests. She would typically take a few
-practice tests to know which areas she needs to improve upon. Based of the
-scores she obtains in these practice tests, she would formulate a strategy for
-maximizing her scores in the actual tests. We treat this scenario in the
-context of an agent exploring a fixed-horizon episodic Markov Decision Process
-(MDP), where the agent can practice on the MDP for some number of episodes (not
-necessarily known in advance) before starting to incur regret for its actions.
-During practice, the agent's goal must be to maximize the probability of
-following an optimal policy. This is akin to the problem of Pure Exploration
-(PE). We extend the PE problem of Multi Armed Bandits (MAB) to MDPs and propose
-a Bayesian algorithm called Posterior Sampling for Pure Exploration (PSPE),
-which is similar to its bandit counterpart. We show that the Bayesian simple
-regret converges at an optimal exponential rate when using PSPE.
-When the agent starts being evaluated, its goal would be to minimize the
-cumulative regret incurred. This is akin to the problem of Reinforcement
-Learning (RL). The agent uses the Posterior Sampling for Reinforcement Learning
-algorithm (PSRL) initialized with the posteriors of the practice phase. We
-hypothesize that this PSPE + PSRL combination is an optimal strategy for
-minimizing regret in RL problems with an initial practice phase. We show
-empirical results which prove that having a lower simple regret at the end of
-the practice phase results in having lower cumulative regret during evaluation.
-",1,0,0,1,0,0
-18092,Stochastic Evolution of Augmented Born--Infeld Equations,"  This paper compares the results of applying a recently developed method of
-stochastic uncertainty quantification designed for fluid dynamics to the
-Born-Infeld model of nonlinear electromagnetism. The similarities in the
-results are striking. Namely, the introduction of Stratonovich cylindrical
-noise into each of their Hamiltonian formulations introduces stochastic Lie
-transport into their dynamics in the same form for both theories. Moreover, the
-resulting stochastic partial differential equations (SPDE) retain their
-unperturbed form, except for an additional term representing induced Lie
-transport by the set of divergence-free vector fields associated with the
-spatial correlations of the cylindrical noise. The explanation for this
-remarkable similarity lies in the method of construction of the Hamiltonian for
-the Stratonovich stochastic contribution to the motion in both cases; which is
-done via pairing spatial correlation eigenvectors for cylindrical noise with
-the momentum map for the deterministic motion. This momentum map is responsible
-for the well-known analogy between hydrodynamics and electromagnetism. The
-momentum map for the Maxwell and Born-Infeld theories of electromagnetism
-treated here is the 1-form density known as the Poynting vector. Two Appendices
-treat the Hamiltonian structures underlying these results.
-",0,1,1,0,0,0
-18093,T-matrix evaluation of acoustic radiation forces on nonspherical objects in Bessel beams,"  Acoustical radiation force (ARF) induced by a single Bessel beam with
-arbitrary order and location on a nonspherical shape is studied with the
-emphasis on the physical mechanism and parameter conditions of negative
-(pulling) forces. Numerical experiments are conducted to verify the T-matrix
-method (TMM) for axial ARFs. This study may guide the experimental set-up to
-find negative axial ARF quickly and effectively based on the predicted
-parameters with TMM, and could be extended for lateral forces. The present work
-could help to design acoustic tweezers numerical toolbox, which provides an
-alternate to the optic tweezers.
-",0,1,0,0,0,0
-18094,On the commutative center of Moufang loops,"  We construct two infinite series of Moufang loops of exponent $3$ whose
-commutative center (i.e. the set of elements that commute with all elements of
-the loop) is not a normal subloop. In particular, we obtain examples of such
-loops of orders $3^8$ and $3^{11}$ one of which can be defined as the Moufang
-triplication of the free Burnside group $B(3,3)$.
-",0,0,1,0,0,0
-18095,The Kontsevich tetrahedral flow in 2D: a toy model,"  In the paper ""Formality conjecture"" (1996) Kontsevich designed a universal
-flow $\dot{\mathcal{P}}=\mathcal{Q}_{a:b}(\mathcal{P})=a\Gamma_{1}+b\Gamma_{2}$
-on the spaces of Poisson structures $\mathcal{P}$ on all affine manifolds of
-dimension $n \geqslant 2$. We prove a claim from $\textit{loc. cit.}$ stating
-that if $n=2$, the flow $\mathcal{Q}_{1:0}=\Gamma_{1}(\mathcal{P})$ is
-Poisson-cohomology trivial: $\Gamma_{1}(\mathcal{P})$ is the Schouten bracket
-of $\mathcal{P}$ with $\mathcal{X}$, for some vector field $\mathcal{X}$; we
-examine the structure of the space of solutions $\mathcal{X}$. Both the
-construction of differential polynomials $\Gamma_{1}(\mathcal{P})$ and
-$\Gamma_{2}(\mathcal{P})$ and the technique to study them remain valid in
-higher dimensions $n \geqslant 3$, but neither the trivializing vector field
-$\mathcal{X}$ nor the setting $b:=0$ survive at $n\geqslant 3$, where the
-balance is $a:b=1:6$.
-",0,0,1,0,0,0
-18096,Twin Learning for Similarity and Clustering: A Unified Kernel Approach,"  Many similarity-based clustering methods work in two separate steps including
-similarity matrix computation and subsequent spectral clustering. However,
-similarity measurement is challenging because it is usually impacted by many
-factors, e.g., the choice of similarity metric, neighborhood size, scale of
-data, noise and outliers. Thus the learned similarity matrix is often not
-suitable, let alone optimal, for the subsequent clustering. In addition,
-nonlinear similarity often exists in many real world data which, however, has
-not been effectively considered by most existing methods. To tackle these two
-challenges, we propose a model to simultaneously learn cluster indicator matrix
-and similarity information in kernel spaces in a principled way. We show
-theoretical relationships to kernel k-means, k-means, and spectral clustering
-methods. Then, to address the practical issue of how to select the most
-suitable kernel for a particular clustering task, we further extend our model
-with a multiple kernel learning ability. With this joint model, we can
-automatically accomplish three subtasks of finding the best cluster indicator
-matrix, the most accurate similarity relations and the optimal combination of
-multiple kernels. By leveraging the interactions between these three subtasks
-in a joint framework, each subtask can be iteratively boosted by using the
-results of the others towards an overall optimal solution. Extensive
-experiments are performed to demonstrate the effectiveness of our method.
-",1,0,0,1,0,0
-18097,Big Data Analysis Using Shrinkage Strategies,"  In this paper, we apply shrinkage strategies to estimate regression
-coefficients efficiently for the high-dimensional multiple regression model,
-where the number of samples is smaller than the number of predictors. We assume
-in the sparse linear model some of the predictors have very weak influence on
-the response of interest. We propose to shrink estimators more than usual.
-Specifically, we use integrated estimation strategies in sub and full models
-and shrink the integrated estimators by incorporating a bounded measurable
-function of some weights. The exhibited double shrunken estimators improve the
-prediction performance of sub models significantly selected from existing
-Lasso-type variable selection methods. Monte Carlo simulation studies as well
-as real examples of eye data and Riboavin data confirm the superior performance
-of the estimators in the high-dimensional regression model.
-",0,0,0,1,0,0
-18098,Interpreter fr topologists,"  Let M be a transitive model of set theory. There is a canonical
-interpretation functor between the category of regular Hausdorff, continuous
-open images of Cech-complete spaces of M and the same category in V, preserving
-many concepts of topology, functional analysis, and dynamics. The functor can
-be further canonically extended to the category of Borel subspaces. This
-greatly simplifies and extends similar results of Fremlin.
-",0,0,1,0,0,0
-18099,Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks,"  We propose a parallel-data-free voice-conversion (VC) method that can learn a
-mapping from source to target speech without relying on parallel data. The
-proposed method is general purpose, high quality, and parallel-data free and
-works without any extra data, modules, or alignment procedure. It also avoids
-over-smoothing, which occurs in many conventional statistical model-based VC
-methods. Our method, called CycleGAN-VC, uses a cycle-consistent adversarial
-network (CycleGAN) with gated convolutional neural networks (CNNs) and an
-identity-mapping loss. A CycleGAN learns forward and inverse mappings
-simultaneously using adversarial and cycle-consistency losses. This makes it
-possible to find an optimal pseudo pair from unpaired data. Furthermore, the
-adversarial loss contributes to reducing over-smoothing of the converted
-feature sequence. We configure a CycleGAN with gated CNNs and train it with an
-identity-mapping loss. This allows the mapping function to capture sequential
-and hierarchical structures while preserving linguistic information. We
-evaluated our method on a parallel-data-free VC task. An objective evaluation
-showed that the converted feature sequence was near natural in terms of global
-variance and modulation spectra. A subjective evaluation showed that the
-quality of the converted speech was comparable to that obtained with a Gaussian
-mixture model-based method under advantageous conditions with parallel and
-twice the amount of data.
-",1,0,0,1,0,0
-18100,Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDA,"  Least-squares models such as linear regression and Linear Discriminant
-Analysis (LDA) are amongst the most popular statistical learning techniques.
-However, since their computation time increases cubically with the number of
-features, they are inefficient in high-dimensional neuroimaging datasets.
-Fortunately, for k-fold cross-validation, an analytical approach has been
-developed that yields the exact cross-validated predictions in least-squares
-models without explicitly training the model. Its computation time grows with
-the number of test samples. Here, this approach is systematically investigated
-in the context of cross-validation and permutation testing. LDA is used
-exemplarily but results hold for all other least-squares methods. Furthermore,
-a non-trivial extension to multi-class LDA is formally derived. The analytical
-approach is evaluated using complexity calculations, simulations, and
-permutation testing of an EEG/MEG dataset. Depending on the ratio between
-features and samples, the analytical approach is up to 10,000x faster than the
-standard approach (retraining the model on each training set). This allows for
-a fast cross-validation of least-squares models and multi-class LDA in
-high-dimensional data, with obvious applications in multi-dimensional datasets,
-Representational Similarity Analysis, and permutation testing.
-",0,0,0,1,0,0
-18101,"A new, large-scale map of interstellar reddening derived from HI emission","  We present a new map of interstellar reddening, covering the 39\% of the sky
-with low {\rm HI} column densities ($N_{\rm HI} < 4\times10^{20}\,\rm cm^{-2}$
-or $E(B-V)\approx 45\rm\, mmag$) at $16\overset{'}{.}1$ resolution, based on
-all-sky observations of Galactic HI emission by the HI4PI Survey. In this low
-column density regime, we derive a characteristic value of $N_{\rm HI}/E(B-V) =
-8.8\times10^{21}\, \rm\, cm^{2}\, mag^{-1}$ for gas with $|v_{\rm LSR}| <
-90\,\rm km\, s^{-1}$ and find no significant reddening associated with gas at
-higher velocities. We compare our HI-based reddening map with the Schlegel,
-Finkbeiner, and Davis (1998, SFD) reddening map and find them consistent to
-within a scatter of $\simeq 5\,\rm mmag$. Further, the differences between our
-map and the SFD map are in excellent agreement with the low resolution
-($4\overset{\circ}{.}5$) corrections to the SFD map derived by Peek and Graves
-(2010) based on observed reddening toward passive galaxies. We therefore argue
-that our HI-based map provides the most accurate interstellar reddening
-estimates in the low column density regime to date. Our reddening map is made
-publicly available (this http URL).
-",0,1,0,0,0,0
-18102,Seasonal modulation of seismicity: the competing/collaborative effect of the snow and ice load on the lithosphere,"  Seasonal patterns associated with stress modulation, as evidenced by
-earthquake occurrence, have been detected in regions characterized by present
-day mountain building and glacial retreat in the Northern Hemisphere. In the
-Himalaya and the Alps, seismicity is peaking in spring and summer; opposite
-behaviour is observed in the Apennines. This diametrical behaviour, confirmed
-by recent strong earthquakes, well correlates with the dominant tectonic
-regime: peak in spring and summer in shortening areas, peak in fall and winter
-in extensional areas. The analysis of the seasonal effect is extended to
-several shortening (e.g. Zagros and Caucasus) and extensional regions, and
-counter-examples from regions where no seasonal modulation is expected (e.g.
-Tropical Atlantic Ridge). This study generalizes to different seismotectonic
-settings the early observations made about short-term (seasonal) and long-term
-(secular) modulation of seismicity and confirms, with some statistical
-significance, that snow and ice thaw may cause crustal deformations that
-modulate the occurrence of major earthquakes.
-",0,1,0,0,0,0
-18103,Existence and nonexistence of positive solutions to some fully nonlinear equation in one dimension,"  In this paper, we consider the existence (and nonexistence) of solutions to
-\[
--\mathcal{M}_{\lambda,\Lambda}^\pm (u'') + V(x) u = f(u) \quad {\rm in} \
-\mathbf{R}
-\] where $\mathcal{M}_{\lambda,\Lambda}^+$ and
-$\mathcal{M}_{\lambda,\Lambda}^-$ denote the Pucci operators with $0< \lambda
-\leq \Lambda < \infty$, $V(x)$ is a bounded function, $f(s)$ is a continuous
-function and its typical example is a power-type nonlinearity $f(s)
-=|s|^{p-1}s$ $(p>1)$. In particular, we are interested in positive solutions
-which decay at infinity, and the existence (and nonexistence) of such solutions
-is proved.
-",0,0,1,0,0,0
-18104,Shiba Bound States across the mobility edge in doped InAs nanowires,"  We present a study of Andreev Quantum Dots (QDots) fabricated with
-small-diameter (30 nm) Si-doped InAs nanowires where the Fermi level can be
-tuned across a mobility edge separating localized states from delocalized
-states. The transition to the insulating phase is identified by a drop in the
-amplitude and width of the excited levels and is found to have remarkable
-consequences on the spectrum of superconducting SubGap Resonances (SGRs). While
-at deeply localized levels, only quasiparticles co-tunneling is observed, for
-slightly delocalized levels, Shiba bound states form and a parity changing
-quantum phase transition is identified by a crossing of the bound states at
-zero energy. Finally, in the metallic regime, single Andreev resonances are
-observed.
-",0,1,0,0,0,0
-18105,The Helsinki Neural Machine Translation System,"  We introduce the Helsinki Neural Machine Translation system (HNMT) and how it
-is applied in the news translation task at WMT 2017, where it ranked first in
-both the human and automatic evaluations for English--Finnish. We discuss the
-success of English--Finnish translations and the overall advantage of NMT over
-a strong SMT baseline. We also discuss our submissions for English--Latvian,
-English--Chinese and Chinese--English.
-",1,0,0,0,0,0
-18106,Spatial distribution of nuclei in progressive nucleation: modeling and application,"  Phase transformations ruled by non-simultaneous nucleation and growth do not
-lead to random distribution of nuclei. Since nucleation is only allowed in the
-untransformed portion of space, positions of nuclei are correlated. In this
-article an analytical approach is presented for computing pair-correlation
-function of nuclei in progressive nucleation. This quantity is further employed
-for characterizing the spatial distribution of nuclei through the nearest
-neighbor distribution function. The modeling is developed for nucleation in 2D
-space with power growth law and it is applied to describe electrochemical
-nucleation where correlation effects are significant. Comparison with both
-computer simulations and experimental data lends support to the model which
-gives insights into the transition from Poissonian to correlated nearest
-neighbor probability density.
-",0,1,0,0,0,0
-18107,Finite-time Guarantees for Byzantine-Resilient Distributed State Estimation with Noisy Measurements,"  This work considers resilient, cooperative state estimation in unreliable
-multi-agent networks. A network of agents aims to collaboratively estimate the
-value of an unknown vector parameter, while an {\em unknown} subset of agents
-suffer Byzantine faults. Faulty agents malfunction arbitrarily and may send out
-{\em highly unstructured} messages to other agents in the network. As opposed
-to fault-free networks, reaching agreement in the presence of Byzantine faults
-is far from trivial. In this paper, we propose a computationally-efficient
-algorithm that is provably robust to Byzantine faults. At each iteration of the
-algorithm, a good agent (1) performs a gradient descent update based on noisy
-local measurements, (2) exchanges its update with other agents in its
-neighborhood, and (3) robustly aggregates the received messages using
-coordinate-wise trimmed means. Under mild technical assumptions, we establish
-that good agents learn the true parameter asymptotically in almost sure sense.
-We further complement our analysis by proving (high probability) {\em
-finite-time} convergence rate, encapsulating network characteristics.
-",1,0,0,0,0,0
-18108,Constraining Polarized Foregrounds for EOR Experiments II: Polarization Leakage Simulations in the Avoidance Scheme,"  A critical challenge in the observation of the redshifted 21-cm line is its
-separation from bright Galactic and extragalactic foregrounds. In particular,
-the instrumental leakage of polarized foregrounds, which undergo significant
-Faraday rotation as they propagate through the interstellar medium, may
-harmfully contaminate the 21-cm power spectrum. We develop a formalism to
-describe the leakage due to instrumental widefield effects in visibility-based
-power spectra measured with redundant arrays, extending the delay-spectrum
-approach presented in Parsons et al. (2012). We construct polarized sky models
-and propagate them through the instrument model to simulate realistic full-sky
-observations with the Precision Array to Probe the Epoch of Reionization. We
-find that the leakage due to a population of polarized point sources is
-expected to be higher than diffuse Galactic polarization at any $k$ mode for a
-30~m reference baseline. For the same reference baseline, a foreground-free
-window at $k > 0.3 \, h$~Mpc$^{-1}$ can be defined in terms of leakage from
-diffuse Galactic polarization even under the most pessimistic assumptions. If
-measurements of polarized foreground power spectra or a model of polarized
-foregrounds are given, our method is able to predict the polarization leakage
-in actual 21-cm observations, potentially enabling its statistical subtraction
-from the measured 21-cm power spectrum.
-",0,1,0,0,0,0
-18109,Collision Selective Visual Neural Network Inspired by LGMD2 Neurons in Juvenile Locusts,"  For autonomous robots in dynamic environments mixed with human, it is vital
-to detect impending collision quickly and robustly. The biological visual
-systems evolved over millions of years may provide us efficient solutions for
-collision detection in complex environments. In the cockpit of locusts, two
-Lobula Giant Movement Detectors, i.e. LGMD1 and LGMD2, have been identified
-which respond to looming objects rigorously with high firing rates. Compared to
-LGMD1, LGMD2 matures early in the juvenile locusts with specific selectivity to
-dark moving objects against bright background in depth while not responding to
-light objects embedded in dark background - a similar situation which ground
-vehicles and robots are facing with. However, little work has been done on
-modeling LGMD2, let alone its potential in robotics and other vision-based
-applications. In this article, we propose a novel way of modeling LGMD2 neuron,
-with biased ON and OFF pathways splitting visual streams into parallel channels
-encoding brightness increments and decrements separately to fulfill its
-selectivity. Moreover, we apply a biophysical mechanism of spike frequency
-adaptation to shape the looming selectivity in such a collision-detecting
-neuron model. The proposed visual neural network has been tested with
-systematic experiments, challenged against synthetic and real physical stimuli,
-as well as image streams from the sensor of a miniature robot. The results
-demonstrated this framework is able to detect looming dark objects embedded in
-bright backgrounds selectively, which make it ideal for ground mobile
-platforms. The robotic experiments also showed its robustness in collision
-detection - it performed well for near range navigation in an arena with many
-obstacles. Its enhanced collision selectivity to dark approaching objects
-versus receding and translating ones has also been verified via systematic
-experiments.
-",1,0,0,0,1,0
-18110,Multifractal invariant measures in expanding piecewise linear coupled maps,"  We analyze invariant measures of two coupled piecewise linear and everywhere
-expanding maps on the synchronization manifold. We observe that though the
-individual maps have simple and smooth functions as their stationary densities,
-they become multifractal as soon as two of them are coupled nonlinearly even
-with a small coupling. For some maps, the multifractal spectrum seems to be
-robust with the coupling or map parameters and for some other maps, there is a
-substantial variation. The origin of the multifractal spectrum here is
-intriguing as it does not seem to conform to the existing theory of
-multifractal functions.
-",0,1,0,0,0,0
-18111,Virtually free finite-normal-subgroup-free groups are strongly verbally closed,"  Any virtually free group $H$ containing no non-trivial finite normal subgroup
-(e.g., the infinite dihedral group) is a retract of any finitely generated
-group containing $H$ as a verbally closed subgroup.
-",0,0,1,0,0,0
-18112,Stochastic Geometry-based Comparison of Secrecy Enhancement Techniques in D2D Networks,"  This letter presents a performance comparison of two popular secrecy
-enhancement techniques in wireless networks: (i) creating guard zones by
-restricting transmissions of legitimate transmitters whenever any eavesdropper
-is detected in their vicinity, and (ii) adding artificial noise to the
-confidential messages to make it difficult for the eavesdroppers to decode
-them. Focusing on a noise-limited regime, we use tools from stochastic geometry
-to derive the secrecy outage probability at the eavesdroppers as well as the
-coverage probability at the legitimate users for both these techniques. Using
-these results, we derive a threshold on the density of the eavesdroppers below
-which no secrecy enhancing technique is required to ensure a target secrecy
-outage probability. For eavesdropper densities above this threshold, we
-concretely characterize the regimes in which each technique outperforms the
-other. Our results demonstrate that guard zone technique is better when the
-distances between the transmitters and their legitimate receivers are higher
-than a certain threshold.
-",1,0,1,0,0,0
-18113,Linear convergence of SDCA in statistical estimation,"  In this paper, we consider stochastic dual coordinate (SDCA) {\em without}
-strongly convex assumption or convex assumption. We show that SDCA converges
-linearly under mild conditions termed restricted strong convexity. This covers
-a wide array of popular statistical models including Lasso, group Lasso, and
-logistic regression with $\ell_1$ regularization, corrected Lasso and linear
-regression with SCAD regularizer. This significantly improves previous
-convergence results on SDCA for problems that are not strongly convex. As a by
-product, we derive a dual free form of SDCA that can handle general
-regularization term, which is of interest by itself.
-",1,0,0,1,0,0
-18114,Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibrium,"  Regularized inversion methods for image reconstruction are used widely due to
-their tractability and ability to combine complex physical sensor models with
-useful regularity criteria. Such methods motivated the recently developed
-Plug-and-Play prior method, which provides a framework to use advanced
-denoising algorithms as regularizers in inversion. However, the need to
-formulate regularized inversion as the solution to an optimization problem
-limits the possible regularity conditions and physical sensor models.
-In this paper, we introduce Consensus Equilibrium (CE), which generalizes
-regularized inversion to include a much wider variety of both forward
-components and prior components without the need for either to be expressed
-with a cost function. CE is based on the solution of a set of equilibrium
-equations that balance data fit and regularity. In this framework, the problem
-of MAP estimation in regularized inversion is replaced by the problem of
-solving these equilibrium equations, which can be approached in multiple ways.
-The key contribution of CE is to provide a novel framework for fusing
-multiple heterogeneous models of physical sensors or models learned from data.
-We describe the derivation of the CE equations and prove that the solution of
-the CE equations generalizes the standard MAP estimate under appropriate
-circumstances.
-We also discuss algorithms for solving the CE equations, including ADMM with
-a novel form of preconditioning and Newton's method. We give examples to
-illustrate consensus equilibrium and the convergence properties of these
-algorithms and demonstrate this method on some toy problems and on a denoising
-example in which we use an array of convolutional neural network denoisers,
-none of which is tuned to match the noise level in a noisy image but which in
-consensus can achieve a better result than any of them individually.
-",1,0,1,0,0,0
-18115,Using angular pair upweighting to improve 3D clustering measurements,"  Three dimensional galaxy clustering measurements provide a wealth of
-cosmological information. However, obtaining spectra of galaxies is expensive,
-and surveys often only measure redshifts for a subsample of a target galaxy
-population. Provided that the spectroscopic data is representative, we argue
-that angular pair upweighting should be used in these situations to improve the
-3D clustering measurements. We present a toy model showing mathematically how
-such a weighting can improve measurements, and provide a practical example of
-its application using mocks created for the Baryon Oscillation Spectroscopic
-Survey (BOSS). Our analysis of mocks suggests that, if an angular clustering
-measurement is available over twice the area covered spectroscopically,
-weighting gives a $\sim$10-20% reduction of the variance of the monopole
-correlation function on the BAO scale.
-",0,1,0,0,0,0
-18116,Maximal entries of elements in certain matrix monoids,"  Let $L_u=\begin{bmatrix}1 & 0\\u & 1\end{bmatrix}$ and $R_v=\begin{bmatrix}1
-& v\\0 & 1\end{bmatrix}$ be matrices in $SL_2(\mathbb Z)$ with $u, v\geq 1$.
-Since the monoid generated by $L_u$ and $R_v$ is free, we can associate a depth
-to each element based on its product representation. In the cases where $u=v=2$
-and $u=v=3$, Bromberg, Shpilrain, and Vdovina determined the depth $n$ matrices
-containing the maximal entry for each $n\geq 1$. By using ideas from our
-previous work on $(u,v)$-Calkin-Wilf trees, we extend their results for any $u,
-v\geq 1$ and in the process we recover the Fibonacci and some Lucas sequences.
-As a consequence we obtain bounds which guarantee collision resistance on a
-family of hashing functions based on $L_u$ and $R_v$.
-",0,0,1,0,0,0
-18117,Generative Modeling of Multimodal Multi-Human Behavior,"  This work presents a methodology for modeling and predicting human behavior
-in settings with N humans interacting in highly multimodal scenarios (i.e.
-where there are many possible highly-distinct futures). A motivating example
-includes robots interacting with humans in crowded environments, such as
-self-driving cars operating alongside human-driven vehicles or human-robot
-collaborative bin packing in a warehouse. Our approach to model human behavior
-in such uncertain environments is to model humans in the scene as nodes in a
-graphical model, with edges encoding relationships between them. For each
-human, we learn a multimodal probability distribution over future actions from
-a dataset of multi-human interactions. Learning such distributions is made
-possible by recent advances in the theory of conditional variational
-autoencoders and deep learning approximations of probabilistic graphical
-models. Specifically, we learn action distributions conditioned on interaction
-history, neighboring human behavior, and candidate future agent behavior in
-order to take into account response dynamics. We demonstrate the performance of
-such a modeling approach in modeling basketball player trajectories, a highly
-multimodal, multi-human scenario which serves as a proxy for many robotic
-applications.
-",1,0,0,0,0,0
-18118,Deep Sets,"  We study the problem of designing models for machine learning tasks defined
-on \emph{sets}. In contrast to traditional approach of operating on fixed
-dimensional vectors, we consider objective functions defined on sets that are
-invariant to permutations. Such problems are widespread, ranging from
-estimation of population statistics \cite{poczos13aistats}, to anomaly
-detection in piezometer data of embankment dams \cite{Jung15Exploration}, to
-cosmology \cite{Ntampaka16Dynamical,Ravanbakhsh16ICML1}. Our main theorem
-characterizes the permutation invariant functions and provides a family of
-functions to which any permutation invariant objective function must belong.
-This family of functions has a special structure which enables us to design a
-deep network architecture that can operate on sets and which can be deployed on
-a variety of scenarios including both unsupervised and supervised learning
-tasks. We also derive the necessary and sufficient conditions for permutation
-equivariance in deep models. We demonstrate the applicability of our method on
-population statistic estimation, point cloud classification, set expansion, and
-outlier detection.
-",1,0,0,1,0,0
-18119,Assortment Optimization under a Single Transition Model,"  In this paper, we consider a Markov chain choice model with single
-transition. In this model, customers arrive at each product with a certain
-probability. If the arrived product is unavailable, then the seller can
-recommend a subset of available products to the customer and the customer will
-purchase one of the recommended products or choose not to purchase with certain
-transition probabilities. The distinguishing features of the model are that the
-seller can control which products to recommend depending on the arrived product
-and that each customer either purchases a product or leaves the market after
-one transition.
-We study the assortment optimization problem under this model. Particularly,
-we show that this problem is generally NP-Hard even if each product could only
-transit to at most two products. Despite the complexity of the problem, we
-provide polynomial time algorithms for several special cases, such as when the
-transition probabilities are homogeneous with respect to the starting point, or
-when each product can only transit to one other product. We also provide a
-tight performance bound for revenue-ordered assortments. In addition, we
-propose a compact mixed integer program formulation that can solve this problem
-of large size. Through extensive numerical experiments, we show that the
-proposed algorithms can solve the problem efficiently and the obtained
-assortments could significantly improve the revenue of the seller than under
-the Markov chain choice model.
-",1,0,1,0,0,0
-18120,Deconstructing Type III,"  SAS introduced Type III methods to address difficulties in dummy-variable
-models for effects of multiple factors and covariates. Type III methods are
-widely used in practice; they are the default method in many statistical
-computing packages. Type III sums of squares (SSs) are defined by an algorithm,
-and an explicit mathematical formulation does not seem to exist. For that
-reason, their properties have not been rigorously proven. Some that are widely
-believed to be true are not always true. An explicit formulation is derived in
-this paper. It is used as a basis to prove fundamental properties of Type III
-estimable functions and SSs. It is shown that, in any given setting, Type III
-effects include all estimable ANOVA effects, and that if all of an ANOVA effect
-is estimable then the Type III SS tests it exactly. The setting for these
-results is general, comprising linear models for the mean vector of a response
-that include arbitrary sets of effects of factors and covariates.
-",0,0,0,1,0,0
-18121,The Digital Flynn Effect: Complexity of Posts on Social Media Increases over Time,"  Parents and teachers often express concern about the extensive use of social
-media by youngsters. Some of them see emoticons, undecipherable initialisms and
-loose grammar typical for social media as evidence of language degradation. In
-this paper, we use a simple measure of text complexity to investigate how the
-complexity of public posts on a popular social networking site changes over
-time. We analyze a unique dataset that contains texts posted by 942, 336 users
-from a large European city across nine years. We show that the chosen
-complexity measure is correlated with the academic performance of users: users
-from high-performing schools produce more complex texts than users from
-low-performing schools. We also find that complexity of posts increases with
-age. Finally, we demonstrate that overall language complexity of posts on the
-social networking site is constantly increasing. We call this phenomenon the
-digital Flynn effect. Our results may suggest that the worries about language
-degradation are not warranted.
-",1,0,0,0,0,0
-18122,Epistemic Modeling with Justifications,"  Existing logical models do not fairly represent epistemic situations with
-fallible justifications, e.g., Russell's Prime Minister example, though such
-scenarios have long been at the center of epistemic studies. We introduce
-justification epistemic models, JEM, which can handle such scenarios. JEM makes
-justifications prime objects and draws a distinction between accepted and
-knowledge-producing justifications; belief and knowledge become derived
-notions. Furthermore, Kripke models can be viewed as special cases of JEMs with
-additional assumptions of evidence insensitivity and common knowledge of the
-model. We argue that JEM can be applied to a range of epistemic scenarios in
-CS, AI, Game Theory, etc.
-",1,0,0,0,0,0
-18123,Emergent universal critical behavior of the 2D $N$-color Ashkin-Teller model in the presence of correlated disorder,"  We study the critical behavior of the 2D $N$-color Ashkin-Teller model in the
-presence of random bond disorder whose correlations decays with the distance
-$r$ as a power-law $r^{-a}$. We consider the case when the spins of different
-colors sitting at the same site are coupled by the same bond and map this
-problem onto the 2D system of $N/2$ flavors of interacting Dirac fermions in
-the presence of correlated disorder. Using renormalization group we show that
-for $N=2$, a ""weakly universal"" scaling behavior at the continuous transition
-becomes universal with new critical exponents. For $N>2$, the first-order phase
-transition is rounded by the correlated disorder and turns into a continuous
-one.
-",0,1,0,0,0,0
-18124,Transport signatures of topological superconductivity in a proximity-coupled nanowire,"  We study the conductance of a junction between the normal and superconducting
-segments of a nanowire, both of which are subjected to spin-orbit coupling and
-an external magnetic field. We directly compare the transport properties of the
-nanowire assuming two different models for the superconducting segment: one
-where we put superconductivity by hand into the wire, and one where
-superconductivity is induced through a tunneling junction with a bulk s-wave
-superconductor. While these two models are equivalent at low energies and at
-weak coupling between the nanowire and the superconductor, we show that there
-are several interesting qualitative differences away from these two limits. In
-particular, the tunneling model introduces an additional conductance peak at
-the energy corresponding to the bulk gap of the parent superconductor. By
-employing a combination of analytical methods at zero temperature and numerical
-methods at finite temperature, we show that the tunneling model of the
-proximity effect reproduces many more of the qualitative features that are seen
-experimentally in such a nanowire system.
-",0,1,0,0,0,0
-18125,An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation,"  Training of neural machine translation (NMT) models usually uses mini-batches
-for efficiency purposes. During the mini-batched training process, it is
-necessary to pad shorter sentences in a mini-batch to be equal in length to the
-longest sentence therein for efficient computation. Previous work has noted
-that sorting the corpus based on the sentence length before making mini-batches
-reduces the amount of padding and increases the processing speed. However,
-despite the fact that mini-batch creation is an essential step in NMT training,
-widely used NMT toolkits implement disparate strategies for doing so, which
-have not been empirically validated or compared. This work investigates
-mini-batch creation strategies with experiments over two different datasets.
-Our results suggest that the choice of a mini-batch creation strategy has a
-large effect on NMT training and some length-based sorting strategies do not
-always work well compared with simple shuffling.
-",1,0,0,0,0,0
-18126,Network analyses of 4D genome datasets automate detection of community-scale gene structure and plasticity,"  Chromosome conformation capture and Hi-C technologies provide gene-gene
-proximity datasets of stationary cells, revealing chromosome territories,
-topologically associating domains, and chromosome topology. Imaging of tagged
-DNA sequences in live cells through the lac operator reporter system provides
-dynamic datasets of chromosomal loci. Chromosome modeling explores the
-mechanisms underlying 3D genome structure and dynamics. Here, we automate 4D
-genome dataset analysis with network-based tools as an alternative to gene-gene
-proximity statistics and visual structure determination. Temporal network
-models and community detection algorithms are applied to 4D modeling of G1 in
-budding yeast with transient crosslinking of $5 kb$ domains in the nucleolus,
-analyzing datasets from four decades of transient binding timescales. Network
-tools detect and track transient gene communities (clusters) within the
-nucleolus, their size, number, persistence time, and frequency of gene
-exchanges. An optimal, weak binding affinity is revealed that maximizes
-community-scale plasticity whereby large communities persist, frequently
-exchanging genes.
-",0,0,0,0,1,0
-18127,Predicate Pairing for Program Verification,"  It is well-known that the verification of partial correctness properties of
-imperative programs can be reduced to the satisfiability problem for
-constrained Horn clauses (CHCs). However, state-of-the-art solvers for CHCs
-(CHC solvers) based on predicate abstraction are sometimes unable to verify
-satisfiability because they look for models that are definable in a given class
-A of constraints, called A-definable models. We introduce a transformation
-technique, called Predicate Pairing (PP), which is able, in many interesting
-cases, to transform a set of clauses into an equisatisfiable set whose
-satisfiability can be proved by finding an A-definable model, and hence can be
-effectively verified by CHC solvers. We prove that, under very general
-conditions on A, the unfold/fold transformation rules preserve the existence of
-an A-definable model, i.e., if the original clauses have an A-definable model,
-then the transformed clauses have an A-definable model. The converse does not
-hold in general, and we provide suitable conditions under which the transformed
-clauses have an A-definable model iff the original ones have an A-definable
-model. Then, we present the PP strategy which guides the application of the
-transformation rules with the objective of deriving a set of clauses whose
-satisfiability can be proved by looking for A-definable models. PP introduces a
-new predicate defined by the conjunction of two predicates together with some
-constraints. We show through some examples that an A-definable model may exist
-for the new predicate even if it does not exist for its defining atomic
-conjuncts. We also present some case studies showing that PP plays a crucial
-role in the verification of relational properties of programs (e.g., program
-equivalence and non-interference). Finally, we perform an experimental
-evaluation to assess the effectiveness of PP in increasing the power of CHC
-solving.
-",1,0,0,0,0,0
-18128,Entanglement entropy and computational complexity of the Anderson impurity model out of equilibrium I: quench dynamics,"  We study the growth of entanglement entropy in density matrix renormalization
-group calculations of the real-time quench dynamics of the Anderson impurity
-model. We find that with appropriate choice of basis, the entropy growth is
-logarithmic in both the interacting and noninteracting single-impurity models.
-The logarithmic entropy growth is understood from a noninteracting chain model
-as a critical behavior separating regimes of linear growth and saturation of
-entropy, corresponding respectively to an overlapping and gapped energy spectra
-of the set of bath states. We find that with an appropriate choices of basis
-(energy-ordered bath orbitals), logarithmic entropy growth is the generic
-behavior of quenched impurity models. A noninteracting calculation of a
-double-impurity Anderson model supports the conclusion in the multi-impurity
-case. The logarithmic growth of entanglement entropy enables studies of quench
-dynamics to very long times.
-",0,1,0,0,0,0
-18129,Be Careful What You Backpropagate: A Case For Linear Output Activations & Gradient Boosting,"  In this work, we show that saturating output activation functions, such as
-the softmax, impede learning on a number of standard classification tasks.
-Moreover, we present results showing that the utility of softmax does not stem
-from the normalization, as some have speculated. In fact, the normalization
-makes things worse. Rather, the advantage is in the exponentiation of error
-gradients. This exponential gradient boosting is shown to speed up convergence
-and improve generalization. To this end, we demonstrate faster convergence and
-better performance on diverse classification tasks: image classification using
-CIFAR-10 and ImageNet, and semantic segmentation using PASCAL VOC 2012. In the
-latter case, using the state-of-the-art neural network architecture, the model
-converged 33% faster with our method (roughly two days of training less) than
-with the standard softmax activation, and with a slightly better performance to
-boot.
-",1,0,0,0,0,0
-18130,A model theoretic Rieffel's theorem of quantum 2-torus,"  We defined a notion of quantum 2-torus $T_\theta$ in ""Masanori Itai and Boris
-Zilber, Notes on a model theory of quantum 2-torus $T_q^2$ for generic $q$,
-arXiv:1503.06045v1 [mathLO]"" and studied its model theoretic property. In this
-note we associate quantum 2-tori $T_\theta$ with the structure over ${\mathbb
-C}_\theta = ({\mathbb C}, +, \cdot, y = x^\theta),$ where $\theta \in {\mathbb
-R} \setminus {\mathbb Q}$, and introduce the notion of geometric isomorphisms
-between such quantum 2-tori.
-We show that this notion is closely connected with the fundamental notion of
-Morita equivalence of non-commutative geometry. Namely, we prove that the
-quantum 2-tori $T_{\theta_1}$ and $T_{\theta_2}$ are Morita equivalent if and
-only if $\theta_2 = {\displaystyle \frac{a \theta_1 + b}{c \theta_1 + d}}$ for
-some $ \left( \begin{array}{cc} a & b \\ c & d \end{array} \right)
-\in {\rm GL}_2({\mathbb Z})$ with $|ad - bc| = 1$. This is our version of
-Rieffel's Theorem in ""M. A. Rieffel and A. Schwarz, Morita equivalence of
-multidimensional noncummutative tori, Internat. J. Math. 10, 2 (1999) 289-299""
-which characterises Morita equivalence of quantum tori in the same terms.
-The result in essence confirms that the representation $T_\theta$ in terms of
-model-theoretic geometry \cite{IZ} is adequate to its original definition in
-terms of non-commutative geometry.
-",0,0,1,0,0,0
-18131,Introducing the Simulated Flying Shapes and Simulated Planar Manipulator Datasets,"  We release two artificial datasets, Simulated Flying Shapes and Simulated
-Planar Manipulator that allow to test the learning ability of video processing
-systems. In particular, the dataset is meant as a tool which allows to easily
-assess the sanity of deep neural network models that aim to encode, reconstruct
-or predict video frame sequences. The datasets each consist of 90000 videos.
-The Simulated Flying Shapes dataset comprises scenes showing two objects of
-equal shape (rectangle, triangle and circle) and size in which one object
-approaches its counterpart. The Simulated Planar Manipulator shows a 3-DOF
-planar manipulator that executes a pick-and-place task in which it has to place
-a size-varying circle on a squared platform. Different from other widely used
-datasets such as moving MNIST [1], [2], the two presented datasets involve
-goal-oriented tasks (e.g. the manipulator grasping an object and placing it on
-a platform), rather than showing random movements. This makes our datasets more
-suitable for testing prediction capabilities and the learning of sophisticated
-motions by a machine learning model. This technical document aims at providing
-an introduction into the usage of both datasets.
-",1,0,0,0,0,0
-18132,Couplings and quantitative contraction rates for Langevin dynamics,"  We introduce a new probabilistic approach to quantify convergence to
-equilibrium for (kinetic) Langevin processes. In contrast to previous analytic
-approaches that focus on the associated kinetic Fokker-Planck equation, our
-approach is based on a specific combination of reflection and synchronous
-coupling of two solutions of the Langevin equation. It yields contractions in a
-particular Wasserstein distance, and it provides rather precise bounds for
-convergence to equilibrium at the borderline between the overdamped and the
-underdamped regime. In particular, we are able to recover kinetic behavior in
-terms of explicit lower bounds for the contraction rate. For example, for a
-rescaled double-well potential with local minima at distance $a$, we obtain a
-lower bound for the contraction rate of order $\Omega (a^{-1})$ provided the
-friction coefficient is of order $\Theta (a^{-1})$.
-",0,0,1,0,0,0
-18133,Stacked transfer learning for tropical cyclone intensity prediction,"  Tropical cyclone wind-intensity prediction is a challenging task considering
-drastic changes climate patterns over the last few decades. In order to develop
-robust prediction models, one needs to consider different characteristics of
-cyclones in terms of spatial and temporal characteristics. Transfer learning
-incorporates knowledge from a related source dataset to compliment a target
-datasets especially in cases where there is lack or data. Stacking is a form of
-ensemble learning focused for improving generalization that has been recently
-used for transfer learning problems which is referred to as transfer stacking.
-In this paper, we employ transfer stacking as a means of studying the effects
-of cyclones whereby we evaluate if cyclones in different geographic locations
-can be helpful for improving generalization performs. Moreover, we use
-conventional neural networks for evaluating the effects of duration on cyclones
-in prediction performance. Therefore, we develop an effective strategy that
-evaluates the relationships between different types of cyclones through
-transfer learning and conventional learning methods via neural networks.
-",1,0,0,1,0,0
-18134,Generalized Gray codes with prescribed ends of small dimensions,"  Given pairwise distinct vertices $\{\alpha_i , \beta_i\}^k_{i=1}$ of the
-$n$-dimensional hypercube $Q_n$ such that the distance of $\alpha_i$ and
-$\beta_i$ is odd, are there paths $P_i$ between $\alpha_i$ and $\beta_i$ such
-that $\{V (P_i)\}^k_{i=1}$ partitions $V(Q_n)$? A positive solution for every
-$n\ge1$ and $k=1$ is known as a Gray code of dimension $n$. In this paper we
-settle this problem for small values of $n$.
-",1,0,0,0,0,0
-18135,Elementary-base cirquent calculus I: Parallel and choice connectives,"  Cirquent calculus is a proof system manipulating circuit-style constructs
-rather than formulas. Using it, this article constructs a sound and complete
-axiomatization CL16 of the propositional fragment of computability logic (the
-game-semantically conceived logic of computational problems - see
-this http URL ) whose logical vocabulary consists
-of negation and parallel and choice connectives, and whose atoms represent
-elementary, i.e. moveless, games.
-",1,0,1,0,0,0
-18136,"Targeted and Imaging-guided In Vivo Photodynamic Therapy of Tumors Using Dual-functional, Aggregation-induced Emission Nanoparticles","  Dual-functional nanoparticles, with the property of aggregation-induced
-emission and the capability of reactive oxygen species, were used to achieve
-passive/active targeting of tumor. Good contrast in in vivo imaging and obvious
-therapeutic efficiency were realized with a low dose of AIE nanoparticles as
-well as a low power density of light, resulting in negligible side effects.
-",0,1,0,0,0,0
-18137,TOSC: an algorithm for the tomography of spotted transit chords,"  Photometric observations of planetary transits may show localized bumps,
-called transit anomalies, due to the possible crossing of photospheric
-starspots. The aim of this work is to analyze the transit anomalies and derive
-the temperature profile inside the transit belt along the transit direction. We
-develop the algorithm TOSC, a tomographic inverse-approach tool which, by means
-of simple algebra, reconstructs the flux distribution along the transit belt.
-We test TOSC against some simulated scenarios. We find that TOSC provides
-robust results for light curves with photometric accuracies better than 1~mmag,
-returning the spot-photosphere temperature contrast with an accuracy better
-than 100~K. TOSC is also robust against the presence of unocculted spots,
-provided that the apparent planetary radius given by the fit of the transit
-light curve is used in place of the true radius. The analysis of real data with
-TOSC returns results consistent with previous studies.
-",0,1,0,0,0,0
-18138,Bayesian uncertainty quantification for epidemic spread on networks,"  While there exist a number of mathematical approaches to modeling the spread
-of disease on a network, analyzing such systems in the presence of uncertainty
-introduces significant complexity. In scenarios where system parameters must be
-inferred from limited observations, general approaches to uncertainty
-quantification can generate approximate distributions of the unknown
-parameters, but these methods often become computationally expensive if the
-underlying disease model is complex. In this paper, we apply the recent
-massively parallelizable Bayesian uncertainty quantification framework $\Pi4U$
-to a model of a disease spreading on a network of communities, showing that the
-method can accurately and tractably recover system parameters and select
-optimal models in this setting.
-",0,0,0,1,0,0
-18139,Switching divergences for spectral learning in blind speech dereverberation,"  When recorded in an enclosed room, a sound signal will most certainly get
-affected by reverberation. This not only undermines audio quality, but also
-poses a problem for many human-machine interaction technologies that use speech
-as their input. In this work, a new blind, two-stage dereverberation approach
-based in a generalized \beta-divergence as a fidelity term over a non-negative
-representation is proposed. The first stage consists of learning the spectral
-structure of the signal solely from the observed spectrogram, while the second
-stage is devoted to model reverberation. Both steps are taken by minimizing a
-cost function in which the aim is put either in constructing a dictionary or a
-good representation by changing the divergence involved. In addition, an
-approach for finding an optimal fidelity parameter for dictionary learning is
-proposed. An algorithm for implementing the proposed method is described and
-tested against state-of-the-art methods. Results show improvements for both
-artificial reverberation and real recordings.
-",1,0,0,0,0,0
-18140,The correlation between the sizes of globular cluster systems and their host dark matter haloes,"  The sizes of entire systems of globular clusters (GCs) depend on the
-formation and destruction histories of the GCs themselves, but also on the
-assembly, merger and accretion history of the dark matter (DM) haloes that they
-inhabit. Recent work has shown a linear relation between total mass of globular
-clusters in the globular cluster system and the mass of its host dark matter
-halo, calibrated from weak lensing. Here we extend this to GC system sizes, by
-studying the radial density profiles of GCs around galaxies in nearby galaxy
-groups. We find that radial density profiles of the GC systems are well fit
-with a de Vaucouleurs profile. Combining our results with those from the
-literature, we find tight relationship ($\sim 0.2$ dex scatter) between the
-effective radius of the GC system and the virial radius (or mass) of its host
-DM halo. The steep non-linear dependence of this relationship ($R_{e, GCS}
-\propto R_{200}^{2.5 - 3}$) is currently not well understood, but is an
-important clue regarding the assembly history of DM haloes and of the GC
-systems that they host.
-",0,1,0,0,0,0
-18141,Variation of field enhancement factor near the emitter tip,"  The field enhancement factor at the emitter tip and its variation in a close
-neighbourhood determines the emitter current in a Fowler-Nordheim like
-formulation. For an axially symmetric emitter with a smooth tip, it is shown
-that the variation can be accounted by a $\cos{\tilde{\theta}}$ factor in
-appropriately defined normalized co-ordinates. This is shown analytically for a
-hemi-ellipsoidal emitter and confirmed numerically for other emitter shapes
-with locally quadratic tips.
-",0,1,0,0,0,0
-18142,Benchmark Environments for Multitask Learning in Continuous Domains,"  As demand drives systems to generalize to various domains and problems, the
-study of multitask, transfer and lifelong learning has become an increasingly
-important pursuit. In discrete domains, performance on the Atari game suite has
-emerged as the de facto benchmark for assessing multitask learning. However, in
-continuous domains there is a lack of agreement on standard multitask
-evaluation environments which makes it difficult to compare different
-approaches fairly. In this work, we describe a benchmark set of tasks that we
-have developed in an extendable framework based on OpenAI Gym. We run a simple
-baseline using Trust Region Policy Optimization and release the framework
-publicly to be expanded and used for the systematic comparison of multitask,
-transfer, and lifelong learning in continuous domains.
-",1,0,0,0,0,0
-18143,A Multiple Linear Regression Approach For Estimating the Market Value of Football Players in Forward Position,"  In this paper, market values of the football players in the forward positions
-are estimated using multiple linear regression by including the physical and
-performance factors in 2017-2018 season. Players from 4 major leagues of Europe
-are examined, and by applying the test for homoscedasticity, a reasonable
-regression model within 0.10 significance level is built, and the most and the
-least affecting factors are explained in detail.
-",0,0,0,1,0,0
-18144,Application of the Huang-Hilbert transform and natural time to the analysis of Seismic Electric Signal activities,"  The Huang-Hilbert transform is applied to Seismic Electric Signal (SES)
-activities in order to decompose them into a number of Intrinsic Mode Functions
-(IMFs) and study which of these functions better represent the SES. The results
-are compared to those obtained from the analysis in a new time domain termed
-natural time after having subtracted the magnetotelluric background from the
-original signal. It is shown that the instantaneous amplitudes of the IMFs can
-be used for the distinction of SES from artificial noises when combined with
-the natural time analysis.
-",0,1,0,0,0,0
-18145,The Hurwitz Subgroups of $E_6(2)$,"  We prove that the exceptional group $E_6(2)$ is not a Hurwitz group. In the
-course of proving this, we complete the classification up to conjugacy of all
-Hurwitz subgroups of $E_6(2)$, in particular, those isomorphic to $L_2(8)$ and
-$L_3(2)$.
-",0,0,1,0,0,0
-18146,Three IQs of AI Systems and their Testing Methods,"  The rapid development of artificial intelligence has brought the artificial
-intelligence threat theory as well as the problem about how to evaluate the
-intelligence level of intelligent products. Both need to find a quantitative
-method to evaluate the intelligence level of intelligence systems, including
-human intelligence. Based on the standard intelligence system and the extended
-Von Neumann architecture, this paper proposes General IQ, Service IQ and Value
-IQ evaluation methods for intelligence systems, depending on different
-evaluation purposes. Among them, the General IQ of intelligence systems is to
-answer the question of whether the artificial intelligence can surpass the
-human intelligence, which is reflected in putting the intelligence systems on
-an equal status and conducting the unified evaluation. The Service IQ and Value
-IQ of intelligence systems are used to answer the question of how the
-intelligent products can better serve the human, reflecting the intelligence
-and required cost of each intelligence system as a product in the process of
-serving human.
-",1,0,0,0,0,0
-18147,Polynomial functors in manifold calculus,"  Let M be a smooth manifold, and let O(M) be the poset of open subsets of M.
-Manifold calculus, due to Goodwillie and Weiss, is a calculus of functors
-suitable for studying contravariant functors (cofunctors) F: O(M)--> Top from
-O(M) to the category of spaces. Weiss showed that polynomial cofunctors of
-degree <= k are determined by their values on O_k(M), where O_k(M) is the full
-subposet of O(M) whose objects are open subsets diffeomorphic to the disjoint
-union of at most k balls. Afterwards Pryor showed that one can replace O_k(M)
-by more general subposets and still recover the same notion of polynomial
-cofunctor. In this paper, we generalize these results to cofunctors from O(M)
-to any simplicial model category C. If conf(k, M) stands for the unordered
-configuration space of k points in M, we also show that the category of
-homogeneous cofunctors O(M) --> C of degree k is weakly equivalent to the
-category of linear cofunctors O(conf(k, M)) --> C provided that C has a zero
-object. Using a completely different approach, we also show that if C is a
-general model category and F: O_k(M) --> C is an isotopy cofunctor, then the
-homotopy right Kan extension of F along the inclusion O_k(M) --> O(M) is also
-an isotopy cofunctor.
-",0,0,1,0,0,0
-18148,Tikhonov Regularization for Long Short-Term Memory Networks,"  It is a well-known fact that adding noise to the input data often improves
-network performance. While the dropout technique may be a cause of memory loss,
-when it is applied to recurrent connections, Tikhonov regularization, which can
-be regarded as the training with additive noise, avoids this issue naturally,
-though it implies regularizer derivation for different architectures. In case
-of feedforward neural networks this is straightforward, while for networks with
-recurrent connections and complicated layers it leads to some difficulties. In
-this paper, a Tikhonov regularizer is derived for Long-Short Term Memory (LSTM)
-networks. Although it is independent of time for simplicity, it considers
-interaction between weights of the LSTM unit, which in theory makes it possible
-to regularize the unit with complicated dependences by using only one parameter
-that measures the input data perturbation. The regularizer that is proposed in
-this paper has three parameters: one to control the regularization process, and
-other two to maintain computation stability while the network is being trained.
-The theory developed in this paper can be applied to get such regularizers for
-different recurrent neural networks with Hadamard products and Lipschitz
-continuous functions.
-",1,0,0,1,0,0
-18149,On the shape operator of relatively parallel hypersurfaces in the $n$-dimensional relative differential geometry,"  We deal with hypersurfaces in the framework of the $n$-dimensional relative
-differential geometry. We consider a hypersurface $\varPhi$ of
-$\mathbb{R}^{n+1}$ with position vector field $\mathbf{x}$, which is relatively
-normalized by a relative normalization $\mathbf{y}$. Then $\mathbf{y}$ is also
-a relative normalization of every member of the one-parameter family
-$\mathcal{F}$ of hypersurfaces $\varPhi_\mu$ with position vector field
-$$\mathbf{x}_\mu = \mathbf{x} + \mu \, \mathbf{y},$$ where $\mu$ is a real
-constant. We call every hypersurface $\varPhi_\mu \in \mathcal{F}$ relatively
-parallel to $\varPhi$ at the ""relative distance"" $\mu$. In this paper we study
-(a) the shape (or Weingarten) operator,
-(b) the relative principal curvatures,
-(c) the relative mean curvature functions and
-(d) the affine normalization
-of a relatively parallel hypersurface $\left( \varPhi_\mu,\mathbf{y}\right)$
-to $\left(\varPhi,\mathbf{y}\right)$.
-",0,0,1,0,0,0
-18150,Soft Pneumatic Gelatin Actuator for Edible Robotics,"  We present a fully edible pneumatic actuator based on gelatin-glycerol
-composite. The actuator is monolithic, fabricated via a molding process, and
-measures 90 mm in length, 20 mm in width, and 17 mm in thickness. Thanks to the
-composite mechanical characteristics similar to those of silicone elastomers,
-the actuator exhibits a bending angle of 170.3 ° and a blocked force of
-0.34 N at the applied pressure of 25 kPa. These values are comparable to
-elastomer based pneumatic actuators. As a validation example, two actuators are
-integrated to form a gripper capable of handling various objects, highlighting
-the high performance and applicability of the edible actuator. These edible
-actuators, combined with other recent edible materials and electronics, could
-lay the foundation for a new type of edible robots.
-",1,0,0,0,0,0
-18151,Deep learning for extracting protein-protein interactions from biomedical literature,"  State-of-the-art methods for protein-protein interaction (PPI) extraction are
-primarily feature-based or kernel-based by leveraging lexical and syntactic
-information. But how to incorporate such knowledge in the recent deep learning
-methods remains an open question. In this paper, we propose a multichannel
-dependency-based convolutional neural network model (McDepCNN). It applies one
-channel to the embedding vector of each word in the sentence, and another
-channel to the embedding vector of the head of the corresponding word.
-Therefore, the model can use richer information obtained from different
-channels. Experiments on two public benchmarking datasets, AIMed and BioInfer,
-demonstrate that McDepCNN compares favorably to the state-of-the-art
-rich-feature and single-kernel based methods. In addition, McDepCNN achieves
-24.4% relative improvement in F1-score over the state-of-the-art methods on
-cross-corpus evaluation and 12% improvement in F1-score over kernel-based
-methods on ""difficult"" instances. These results suggest that McDepCNN
-generalizes more easily over different corpora, and is capable of capturing
-long distance features in the sentences.
-",1,0,0,0,0,0
-18152,Hierarchical VampPrior Variational Fair Auto-Encoder,"  Decision making is a process that is extremely prone to different biases. In
-this paper we consider learning fair representations that aim at removing
-nuisance (sensitive) information from the decision process. For this purpose,
-we propose to use deep generative modeling and adapt a hierarchical Variational
-Auto-Encoder to learn these fair representations. Moreover, we utilize the
-mutual information as a useful regularizer for enforcing fairness of a
-representation. In experiments on two benchmark datasets and two scenarios
-where the sensitive variables are fully and partially observable, we show that
-the proposed approach either outperforms or performs on par with the current
-best model.
-",0,0,0,1,0,0
-18153,MHD Models of Gamma-ray Emission in WR 11,"  Recent reports claiming tentative association of the massive star binary
-system gamma^2 Velorum (WR 11) with a high-energy gamma-ray source observed by
-Fermi-LAT contrast the so-far exclusive role of Eta Carinae as the hitherto
-only detected gamma-ray emitter in the source class of particle-accelerating
-colliding-wind binary systems. We aim to shed light on this claim of
-association by providing dedicated model predictions for the nonthermal photon
-emission spectrum of WR 11. We use three-dimensional magneto-hydrodynamic
-modeling to trace the structure and conditions of the wind-collision region of
-WR 11 throughout its 78.5 day orbit, including the important effect of
-radiative braking in the stellar winds. A transport equation is then solved in
-the wind-collision region to determine the population of relativistic electrons
-and protons which are subsequently used to compute nonthermal photon emission
-components. We find that - if WR 11 be indeed confirmed as the responsible
-object for the observed gamma-ray emission - its radiation will unavoidably be
-of hadronic origin owing to the strong radiation fields in the binary system
-which inhibit the acceleration of electrons to energies suffciently high for
-observable inverse Compton radiation. Different conditions in wind-collision
-region near the apastron and periastron configuration lead to significant
-variability on orbital time scales. The bulk of the hadronic gamma-ray emission
-originates at a 400 solar radii wide region at the apex.
-",0,1,0,0,0,0
-18154,Caulking the Leakage Effect in MEEG Source Connectivity Analysis,"  Simplistic estimation of neural connectivity in MEEG sensor space is
-impossible due to volume conduction. The only viable alternative is to carry
-out connectivity estimation in source space. Among the neuroscience community
-this is claimed to be impossible or misleading due to Leakage: linear mixing of
-the reconstructed sources. To address this problematic we propose a novel
-solution method that caulks the Leakage in MEEG source activity and
-connectivity estimates: BC-VARETA. It is based on a joint estimation of source
-activity and connectivity in the frequency domain representation of MEEG time
-series. To achieve this, we go beyond current methods that assume a fixed
-gaussian graphical model for source connectivity. In contrast we estimate this
-graphical model in a Bayesian framework by placing priors on it, which allows
-for highly optimized computations of the connectivity, via a new procedure
-based on the local quadratic approximation under quite general prior models. A
-further contribution of this paper is the rigorous definition of leakage via
-the Spatial Dispersion Measure and Earth Movers Distance based on the geodesic
-distances over the cortical manifold. Both measures are extended for the first
-time to quantify Connectivity Leakage by defining them on the cartesian product
-of cortical manifolds. Using these measures, we show that BC-VARETA outperforms
-most state of the art inverse solvers by several orders of magnitude.
-",0,0,0,0,1,0
-18155,New ADS Functionality for the Curator,"  In this paper we provide an update concerning the operations of the NASA
-Astrophysics Data System (ADS), its services and user interface, and the
-content currently indexed in its database. As the primary information system
-used by researchers in Astronomy, the ADS aims to provide a comprehensive index
-of all scholarly resources appearing in the literature. With the current effort
-in our community to support data and software citations, we discuss what steps
-the ADS is taking to provide the needed infrastructure in collaboration with
-publishers and data providers. A new API provides access to the ADS search
-interface, metrics, and libraries allowing users to programmatically automate
-discovery and curation tasks. The new ADS interface supports a greater
-integration of content and services with a variety of partners, including ORCID
-claiming, indexing of SIMBAD objects, and article graphics from a variety of
-publishers. Finally, we highlight how librarians can facilitate the ingest of
-gray literature that they curate into our system.
-",1,1,0,0,0,0
-18156,Survey of reasoning using Neural networks,"  Reason and inference require process as well as memory skills by humans.
-Neural networks are able to process tasks like image recognition (better than
-humans) but in memory aspects are still limited (by attention mechanism, size).
-Recurrent Neural Network (RNN) and it's modified version LSTM are able to solve
-small memory contexts, but as context becomes larger than a threshold, it is
-difficult to use them. The Solution is to use large external memory. Still, it
-poses many challenges like, how to train neural networks for discrete memory
-representation, how to describe long term dependencies in sequential data etc.
-Most prominent neural architectures for such tasks are Memory networks:
-inference components combined with long term memory and Neural Turing Machines:
-neural networks using external memory resources. Also, additional techniques
-like attention mechanism, end to end gradient descent on discrete memory
-representation are needed to support these solutions. Preliminary results of
-above neural architectures on simple algorithms (sorting, copying) and Question
-Answering (based on story, dialogs) application are comparable with the state
-of the art. In this paper, I explain these architectures (in general), the
-additional techniques used and the results of their application.
-",1,0,0,0,0,0
-18157,Recurrent Neural Network-based Model Predictive Control for Continuous Pharmaceutical Manufacturing,"  The pharmaceutical industry has witnessed exponential growth in transforming
-operations towards continuous manufacturing to effectively achieve increased
-profitability, reduced waste, and extended product range. Model Predictive
-Control (MPC) can be applied for enabling this vision, in providing superior
-regulation of critical quality attributes. For MPC, obtaining a workable model
-is of fundamental importance, especially in the presence of complex reaction
-kinetics and process dynamics. Whilst physics-based models are desirable, it is
-not always practical to obtain one effective and fit-for-purpose model.
-Instead, within industry, data-driven system-identification approaches have
-been found to be useful and widely deployed in MPC solutions. In this work, we
-demonstrated the applicability of Recurrent Neural Networks (RNNs) for MPC
-applications in continuous pharmaceutical manufacturing. We have shown that
-RNNs are especially well-suited for modeling dynamical systems due to their
-mathematical structure and satisfactory closed-loop control performance can be
-yielded for MPC in continuous pharmaceutical manufacturing.
-",1,0,0,0,0,0
-18158,"A unitary ""quantization commutes with reduction"" map for the adjoint action of a compact Lie group","  Let $K$ be a simply connected compact Lie group and $T^{\ast}(K)$ its
-cotangent bundle. We consider the problem of ""quantization commutes with
-reduction"" for the adjoint action of $K$ on $T^{\ast}(K).$ We quantize both
-$T^{\ast}(K)$ and the reduced phase space using geometric quantization with
-half-forms. We then construct a geometrically natural map from the space of
-invariant elements in the quantization of $T^{\ast}(K)$ to the quantization of
-the reduced phase space. We show that this map is a constant multiple of a
-unitary map.
-",0,0,1,0,0,0
-18159,Electron Paramagnetic Resonance Spectroscopy of Er$^{3+}$:Y$_2$SiO$_5$ Using Josephson Bifurcation Amplifier: Observation of Hyperfine and Quadrupole Structures,"  We performed magnetic field and frequency tunable electron paramagnetic
-resonance spectroscopy of an Er$^{3+}$ doped Y$_2$SiO$_5$ crystal by observing
-the change in flux induced on a direct current-superconducting quantum
-interference device (dc-SQUID) loop of a tunable Josephson bifurcation
-amplifer. The observed spectra show multiple transitions which agree well with
-the simulated energy levels, taking into account the hyperfine and quadrupole
-interactions of $^{167}$Er. The sensing volume is about 0.15 pl, and our
-inferred measurement sensitivity (limited by external flux noise) is
-approximately $1.5\times10^4$ electron spins for a 1 s measurement. The
-sensitivity value is two orders of magnitude better than similar schemes using
-dc-SQUID switching readout.
-",0,1,0,0,0,0
-18160,Step Detection Algorithm For Accurate Distance Estimation Using Dynamic Step Length,"  In this paper, a new Smartphone sensor based algorithm is proposed to detect
-accurate distance estimation. The algorithm consists of two phases, the first
-phase is for detecting the peaks from the Smartphone accelerometer sensor. The
-other one is for detecting the step length which varies from step to step. The
-proposed algorithm is tested and implemented in real environment and it showed
-promising results. Unlike the conventional approaches, the error of the
-proposed algorithm is fixed and is not affected by the long distance.
-Keywords distance estimation, peaks, step length, accelerometer.
-",1,0,0,0,0,0
-18161,Digging Into Self-Supervised Monocular Depth Estimation,"  Depth-sensing is important for both navigation and scene understanding.
-However, procuring RGB images with corresponding depth data for training deep
-models is challenging; large-scale, varied, datasets with ground truth training
-data are scarce. Consequently, several recent methods have proposed treating
-the training of monocular color-to-depth estimation networks as an image
-reconstruction problem, thus forgoing the need for ground truth depth.
-There are multiple concepts and design decisions for these networks that seem
-sensible, but give mixed or surprising results when tested. For example,
-binocular stereo as the source of self-supervision seems cumbersome and hard to
-scale, yet results are less blurry compared to training with monocular videos.
-Such decisions also interplay with questions about architectures, loss
-functions, image scales, and motion handling. In this paper, we propose a
-simple yet effective model, with several general architectural and loss
-innovations, that surpasses all other self-supervised depth estimation
-approaches on KITTI.
-",0,0,0,1,0,0
-18162,The Causality/Repair Connection in Databases: Causality-Programs,"  In this work, answer-set programs that specify repairs of databases are used
-as a basis for solving computational and reasoning problems about causes for
-query answers from databases.
-",1,0,0,0,0,0
-18163,Two-temperature logistic regression based on the Tsallis divergence,"  We develop a variant of multiclass logistic regression that achieves three
-properties: i) We minimize a non-convex surrogate loss which makes the method
-robust to outliers, ii) our method allows transitioning between non-convex and
-convex losses by the choice of the parameters, iii) the surrogate loss is Bayes
-consistent, even in the non-convex case. The algorithm has one weight vector
-per class and the surrogate loss is a function of the linear activations (one
-per class). The surrogate loss of an example with linear activation vector
-$\mathbf{a}$ and class $c$ has the form $-\log_{t_1} \exp_{t_2} (a_c -
-G_{t_2}(\mathbf{a}))$ where the two temperatures $t_1$ and $t_2$ ""temper"" the
-$\log$ and $\exp$, respectively, and $G_{t_2}$ is a generalization of the
-log-partition function. We motivate this loss using the Tsallis divergence. As
-the temperature of the logarithm becomes smaller than the temperature of the
-exponential, the surrogate loss becomes ""more quasi-convex"". Various tunings of
-the temperatures recover previous methods and tuning the degree of
-non-convexity is crucial in the experiments. The choice $t_1<1$ and $t_2>1$
-performs best experimentally. We explain this by showing that $t_1 < 1$ caps
-the surrogate loss and $t_2 >1$ makes the predictive distribution have a heavy
-tail.
-",1,0,0,1,0,0
-18164,Bootstrapped synthetic likelihood,"  Approximate Bayesian computation (ABC) and synthetic likelihood (SL)
-techniques have enabled the use of Bayesian inference for models that may be
-simulated, but for which the likelihood cannot be evaluated pointwise at values
-of an unknown parameter $\theta$. The main idea in ABC and SL is to, for
-different values of $\theta$ (usually chosen using a Monte Carlo algorithm),
-build estimates of the likelihood based on simulations from the model
-conditional on $\theta$. The quality of these estimates determines the
-efficiency of an ABC/SL algorithm. In standard ABC/SL, the only means to
-improve an estimated likelihood at $\theta$ is to simulate more times from the
-model conditional on $\theta$, which is infeasible in cases where the simulator
-is computationally expensive. In this paper we describe how to use
-bootstrapping as a means for improving SL estimates whilst using fewer
-simulations from the model, and also investigate its use in ABC. Further, we
-investigate the use of the bag of little bootstraps as a means for applying
-this approach to large datasets, yielding Monte Carlo algorithms that
-accurately approximate posterior distributions whilst only simulating
-subsamples of the full data. Examples of the approach applied to i.i.d.,
-temporal and spatial data are given.
-",1,0,0,1,0,0
-18165,The Weinstein conjecture for iterated planar contact structures,"  In this paper, we introduce the notions of an iterated planar Lefschetz
-fibration and an iterated planar open book decomposition and prove the
-Weinstein conjecture for contact manifolds supporting an open book that has
-iterated planar pages. For $n\geq 1$, we show that a $(2n+1)$-dimensional
-contact manifold $M$ supporting an iterated planar open book decomposition
-satisfies the Weinstein conjecture.
-",0,0,1,0,0,0
-18166,Privacy-Preserving Deep Learning via Weight Transmission,"  This paper considers the scenario that multiple data owners wish to apply a
-machine learning method over the combined dataset of all owners to obtain the
-best possible learning output but do not want to share the local datasets owing
-to privacy concerns. We design systems for the scenario that the stochastic
-gradient descent (SGD) algorithm is used as the machine learning method because
-SGD (or its variants) is at the heart of recent deep learning techniques over
-neural networks. Our systems differ from existing systems in the following
-features: {\bf (1)} any activation function can be used, meaning that no
-privacy-preserving-friendly approximation is required; {\bf (2)} gradients
-computed by SGD are not shared but the weight parameters are shared instead;
-and {\bf (3)} robustness against colluding parties even in the extreme case
-that only one honest party exists. We prove that our systems, while
-privacy-preserving, achieve the same learning accuracy as SGD and hence retain
-the merit of deep learning with respect to accuracy. Finally, we conduct
-several experiments using benchmark datasets, and show that our systems
-outperform previous system in terms of learning accuracies.
-",0,0,0,1,0,0
-18167,CORRECT: Code Reviewer Recommendation in GitHub Based on Cross-Project and Technology Experience,"  Peer code review locates common coding rule violations and simple logical
-errors in the early phases of software development, and thus reduces overall
-cost. However, in GitHub, identifying an appropriate code reviewer for a pull
-request is a non-trivial task given that reliable information for reviewer
-identification is often not readily available. In this paper, we propose a code
-reviewer recommendation technique that considers not only the relevant
-cross-project work history (e.g., external library experience) but also the
-experience of a developer in certain specialized technologies associated with a
-pull request for determining her expertise as a potential code reviewer. We
-first motivate our technique using an exploratory study with 10 commercial
-projects and 10 associated libraries external to those projects. Experiments
-using 17,115 pull requests from 10 commercial projects and six open source
-projects show that our technique provides 85%--92% recommendation accuracy,
-about 86% precision and 79%--81% recall in code reviewer recommendation, which
-are highly promising. Comparison with the state-of-the-art technique also
-validates the empirical findings and the superiority of our recommendation
-technique.
-",1,0,0,0,0,0
-18168,Charged Perfect Fluid Distribution for Cosmological Universe Interacting With Massive Scalar Field in Brans-Dicke Theory,"  Considering a spherically-symmetric non-static cosmological flat model of
-Robertson-Walker universe we have investigated the problem of perfect fluid
-distribution interacting with the gravitational field in presence of massive
-scalar field and electromagnetic field in B-D theory. Exact solutions have been
-obtained by using a general approach of solving the partial differential
-equations and it has been observed that the electromagnetic field cannot
-survive for the cosmological flat model due to the influence caused by the
-presence of massive scalar field.
-",0,1,0,0,0,0
-18169,Finite homogeneous geometries,"  This paper reproduces the text of a part of the Author's DPhil thesis. It
-gives a proof of the classification of non-trivial, finite homogeneous
-geometries of sufficiently high dimension which does not depend on the
-classification of the finite simple groups.
-",0,0,1,0,0,0
-18170,Weakly-Private Information Retrieval,"  Private information retrieval (PIR) protocols make it possible to retrieve a
-file from a database without disclosing any information about the identity of
-the file being retrieved. These protocols have been rigorously explored from an
-information-theoretic perspective in recent years. While existing protocols
-strictly impose that no information is leaked on the file's identity, this work
-initiates the study of the tradeoffs that can be achieved by relaxing the
-requirement of perfect privacy. In case the user is willing to leak some
-information on the identity of the retrieved file, we study how the PIR rate,
-as well as the upload cost and access complexity, can be improved. For the
-particular case of replicated servers, we propose two weakly-private
-information retrieval schemes based on two recent PIR protocols and a family of
-schemes based on partitioning. Lastly, we compare the performance of the
-proposed schemes.
-",1,0,0,0,0,0
-18171,Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget,"  Iterative algorithms, like gradient descent, are common tools for solving a
-variety of problems, such as model fitting. For this reason, there is interest
-in creating differentially private versions of them. However, their conversion
-to differentially private algorithms is often naive. For instance, a fixed
-number of iterations are chosen, the privacy budget is split evenly among them,
-and at each iteration, parameters are updated with a noisy gradient. In this
-paper, we show that gradient-based algorithms can be improved by a more careful
-allocation of privacy budget per iteration. Intuitively, at the beginning of
-the optimization, gradients are expected to be large, so that they do not need
-to be measured as accurately. However, as the parameters approach their optimal
-values, the gradients decrease and hence need to be measured more accurately.
-We add a basic line-search capability that helps the algorithm decide when more
-accurate gradient measurements are necessary. Our gradient descent algorithm
-works with the recently introduced zCDP version of differential privacy. It
-outperforms prior algorithms for model fitting and is competitive with the
-state-of-the-art for $(\epsilon,\delta)$-differential privacy, a strictly
-weaker definition than zCDP.
-",0,0,0,1,0,0
-18172,"""The universal meaning of the quantum of action"", by Jun Ishiwara","  Commented translation of the paper ""Universelle Bedeutung des
-Wirkungsquantums"", published by Jun Ishiwara in German in the Proceedings of
-Tokyo Mathematico-Physical Society 8 106-116 (1915). In his work, Ishiwara,
-tenured at Sendai University, Japan, proposed - simultaneously with Arnold
-Sommerfeld, William Wilson and Niels Bohr in Europe - the pase-space-integral
-quantization, a rule that would be incorporated into the old-quantum-mechanics
-formalism.
-",0,1,0,0,0,0
-18173,Barrier to recombination of oppositely charged large polarons,"  Electronic charge carriers in ionic materials can self-trap to form large
-polarons. Interference between the ionic displacements associated with
-oppositely charged large polarons increases as they approach one another.
-Initially this interference produces an attractive potential that fosters their
-merger. However, for small enough separations this interference generates a
-repulsive interaction between oppositely charged large polarons. In suitable
-circumstances this repulsion can overwhelm their direct Coulomb attraction.
-Then the resulting net repulsion between oppositely charged large polarons
-constitutes a potential barrier which impedes their recombination.
-",0,1,0,0,0,0
-18174,Metric Reduction and Generalized Holomorphic Structures,"  In this paper, metric reduction in generalized geometry is investigated. We
-show how the Bismut connections on the quotient manifold are obtained from
-those on the original manifold. The result facilitates the analysis of
-generalized K$\ddot{a}$hler reduction, which motivates the concept of metric
-generalized principal bundles and our approach to construct a family of
-generalized holomorphic line bundles over $\mathbb{C}P^2$ equipped with some
-non-trivial generalized K$\ddot{a}$hler structures.
-",0,0,1,0,0,0
-18175,Learning of Optimal Forecast Aggregation in Partial Evidence Environments,"  We consider the forecast aggregation problem in repeated settings, where the
-forecasts are done on a binary event. At each period multiple experts provide
-forecasts about an event. The goal of the aggregator is to aggregate those
-forecasts into a subjective accurate forecast. We assume that experts are
-Bayesian; namely they share a common prior, each expert is exposed to some
-evidence, and each expert applies Bayes rule to deduce his forecast. The
-aggregator is ignorant with respect to the information structure (i.e.,
-distribution over evidence) according to which experts make their prediction.
-The aggregator observes the experts' forecasts only. At the end of each period
-the actual state is realized. We focus on the question whether the aggregator
-can learn to aggregate optimally the forecasts of the experts, where the
-optimal aggregation is the Bayesian aggregation that takes into account all the
-information (evidence) in the system.
-We consider the class of partial evidence information structures, where each
-expert is exposed to a different subset of conditionally independent signals.
-Our main results are positive; We show that optimal aggregation can be learned
-in polynomial time in a quite wide range of instances of the partial evidence
-environments. We provide a tight characterization of the instances where
-learning is possible and impossible.
-",0,0,0,1,0,0
-18176,Distributed Unknown-Input-Observers for Cyber Attack Detection and Isolation in Formation Flying UAVs,"  In this paper, cyber attack detection and isolation is studied on a network
-of UAVs in a formation flying setup. As the UAVs communicate to reach consensus
-on their states while making the formation, the communication network among the
-UAVs makes them vulnerable to a potential attack from malicious adversaries.
-Two types of attacks pertinent to a network of UAVs have been considered: a
-node attack on the UAVs and a deception attack on the communication between the
-UAVs. UAVs formation control presented using a consensus algorithm to reach a
-pre-specified formation. A node and a communication path deception cyber
-attacks on the UAV's network are considered with their respective models in the
-formation setup. For these cyber attacks detection, a bank of Unknown Input
-Observer (UIO) based distributed fault detection scheme proposed to detect and
-identify the compromised UAV in the formation. A rule based on the residuals
-generated using the bank of UIOs are used to detect attacks and identify the
-compromised UAV in the formation. Further, an algorithm developed to remove the
-faulty UAV from the network once an attack detected and the compromised UAV
-isolated while maintaining the formation flight with a missing UAV node.
-",1,0,0,0,0,0
-18177,Exponential Decay of the lengths of Spectral Gaps for Extended Harper's Model with Liouvillean Frequency,"  In this paper, we study the non-self dual extended Harper's model with
-Liouvillean frequency. By establishing quantitative reducibility results
-together with the averaging method, we prove that the lengths of spectral gaps
-decay exponentially.
-",0,0,1,0,0,0
-18178,Topological Landau-Zener Bloch Oscillations in Photonic Floquet Lieb Lattices,"  The Lieb Lattice exhibits intriguing properties that are of general interest
-in both the fundamental physics and practical applications. Here, we
-investigate the topological Landau-Zener Bloch oscillation in a photonic
-Floquet Lieb lattice, where the dimerized helical waveguides is constructed to
-realize the synthetic spin-orbital interaction through the Floquet mechanism,
-rendering us to study the impacts of topological transition from trivial gaps
-to non-trivial ones. The compact localized states of flat bands supported by
-the local symmetry of Lieb lattice will be associated with other bands by
-topological invariants, Chern number, and involved into Landau-Zener transition
-during Bloch oscillation. Importantly, the non-trivial geometrical phases after
-topological transitions will be taken into account for constructive and
-destructive interferences of wave functions. The numerical calculations of
-continuum photonic medium demonstrate reasonable agreements with theoretical
-tight-binding model. Our results provide an ongoing effort to realize designed
-quantum materials with tailored properties.
-",0,1,0,0,0,0
-18179,Assessing the impact of bulk and shear viscosities on large scale structure formation,"  It is analyzed the effects of both bulk and shear viscosities on the
-perturbations, relevant for structure formation in late time cosmology. It is
-shown that shear viscosity can be as effective as the bulk viscosity on
-suppressing the growth of perturbations and delaying the nonlinear regime. A
-statistical analysis of the shear and bulk viscous effects is performed and
-some constraints on these viscous effects are given.
-",0,1,0,0,0,0
-18180,Correlations in eigenfunctions of quantum chaotic systems with sparse Hamiltonian matrices,"  In most realistic models for quantum chaotic systems, the Hamiltonian
-matrices in unperturbed bases have a sparse structure. We study correlations in
-eigenfunctions of such systems and derive explicit expressions for some of the
-correlation functions with respect to energy. The analytical results are tested
-in several models by numerical simulations. An application is given for a
-relation between transition probabilities.
-",0,1,0,0,0,0
-18181,Fast Trajectory Optimization for Legged Robots using Vertex-based ZMP Constraints,"  This paper combines the fast Zero-Moment-Point (ZMP) approaches that work
-well in practice with the broader range of capabilities of a Trajectory
-Optimization formulation, by optimizing over body motion, footholds and Center
-of Pressure simultaneously. We introduce a vertex-based representation of the
-support-area constraint, which can treat arbitrarily oriented point-, line-,
-and area-contacts uniformly. This generalization allows us to create motions
-such quadrupedal walking, trotting, bounding, pacing, combinations and
-transitions between these, limping, bipedal walking and push-recovery all with
-the same approach. This formulation constitutes a minimal representation of the
-physical laws (unilateral contact forces) and kinematic restrictions (range of
-motion) in legged locomotion, which allows us to generate various motion in
-less than a second. We demonstrate the feasibility of the generated motions on
-a real quadruped robot.
-",1,0,1,0,0,0
-18182,Electric Field Properties inside Central Gap of Dipole Micro/Nano Antennas Operating at 30 THz,"  This work investigates the influence of geometric variations in dipole
-micro/nano antennas, regarding their implications on the characteristics of the
-electric field inside the gap space of antenna monopoles. The gap is the
-interface for a metal-Insulator-Metal (MIM) rectifier diode and it needs to be
-carefully optimized, in order to allow better electric current generation by
-tunneling current mechanisms. The arrangement (antenna + diode or rectenna) was
-designed to operate around 30 Terahertz (THz).
-",0,1,0,0,0,0
-18183,Towards Understanding the Invertibility of Convolutional Neural Networks,"  Several recent works have empirically observed that Convolutional Neural Nets
-(CNNs) are (approximately) invertible. To understand this approximate
-invertibility phenomenon and how to leverage it more effectively, we focus on a
-theoretical explanation and develop a mathematical model of sparse signal
-recovery that is consistent with CNNs with random weights. We give an exact
-connection to a particular model of model-based compressive sensing (and its
-recovery algorithms) and random-weight CNNs. We show empirically that several
-learned networks are consistent with our mathematical analysis and then
-demonstrate that with such a simple theoretical framework, we can obtain
-reasonable re- construction results on real images. We also discuss gaps
-between our model assumptions and the CNN trained for classification in
-practical scenarios.
-",1,0,0,1,0,0
-18184,An effective algorithm for hyperparameter optimization of neural networks,"  A major challenge in designing neural network (NN) systems is to determine
-the best structure and parameters for the network given the data for the
-machine learning problem at hand. Examples of parameters are the number of
-layers and nodes, the learning rates, and the dropout rates. Typically, these
-parameters are chosen based on heuristic rules and manually fine-tuned, which
-may be very time-consuming, because evaluating the performance of a single
-parametrization of the NN may require several hours. This paper addresses the
-problem of choosing appropriate parameters for the NN by formulating it as a
-box-constrained mathematical optimization problem, and applying a
-derivative-free optimization tool that automatically and effectively searches
-the parameter space. The optimization tool employs a radial basis function
-model of the objective function (the prediction accuracy of the NN) to
-accelerate the discovery of configurations yielding high accuracy. Candidate
-configurations explored by the algorithm are trained to a small number of
-epochs, and only the most promising candidates receive full training. The
-performance of the proposed methodology is assessed on benchmark sets and in
-the context of predicting drug-drug interactions, showing promising results.
-The optimization tool used in this paper is open-source.
-",1,0,0,0,0,0
-18185,On estimation in varying coefficient models for sparse and irregularly sampled functional data,"  In this paper, we study a smoothness regularization method for a varying
-coefficient model based on sparse and irregularly sampled functional data which
-is contaminated with some measurement errors. We estimate the one-dimensional
-covariance and cross-covariance functions of the underlying stochastic
-processes based on a reproducing kernel Hilbert space approach. We then obtain
-least squares estimates of the coefficient functions. Simulation studies
-demonstrate that the proposed method has good performance. We illustrate our
-method by an analysis of longitudinal primary biliary liver cirrhosis data.
-",0,0,0,1,0,0
-18186,Large-degree asymptotics of rational Painleve-IV functions associated to generalized Hermite polynomials,"  The Painleve-IV equation has three families of rational solutions generated
-by the generalized Hermite polynomials. Each family is indexed by two positive
-integers m and n. These functions have applications to nonlinear wave
-equations, random matrices, fluid dynamics, and quantum mechanics. Numerical
-studies suggest the zeros and poles form a deformed n by m rectangular grid.
-Properly scaled, the zeros and poles appear to densely fill certain curvilinear
-rectangles as m and n tend to infinity with r=m/n fixed. Generalizing a method
-of Bertola and Bothner used to study rational Painleve-II functions, we express
-the generalized Hermite rational Painleve-IV functions in terms of certain
-orthogonal polynomials on the unit circle. Using the Deift-Zhou nonlinear
-steepest-descent method, we asymptotically analyze the associated
-Riemann-Hilbert problem in the limit as n tends to infinity with m=r*n for r
-fixed. We obtain an explicit characterization of the boundary curve and
-determine the leading-order asymptotic expansion of the functions in the
-pole-free region.
-",0,1,1,0,0,0
-18187,Lattice Boltzmann study of chemically-driven self-propelled droplets,"  We numerically study the behavior of self-propelled liquid droplets whose
-motion is triggered by a Marangoni-like flow. This latter is generated by
-variations of surfactant concentration which affect the droplet surface tension
-promoting its motion. In the present paper a model for droplets with a third
-amphiphilic component is adopted. The dynamics is described by Navier-Stokes
-and convection-diffusion equations, solved by lattice Boltzmann method coupled
-with finite-difference schemes. We focus on two cases. First the study of
-self-propulsion of an isolated droplet is carried on and, then, the interaction
-of two self-propelled droplets is investigated. In both cases, when the
-surfactant migrates towards the interface, a quadrupolar vortex of the velocity
-field forms inside the droplet and causes the motion. A weaker dipolar field
-emerges instead when the surfactant is mainly diluted in the bulk. The dynamics
-of two interacting droplets is more complex and strongly depends on their
-reciprocal distance. If, in a head-on collision, droplets are close enough, the
-velocity field initially attracts them until a motionless steady state is
-achieved. If the droplets are vertically shifted, the hydrodynamic field leads
-to an initial reciprocal attraction followed by a scattering along opposite
-directions. This hydrodynamic interaction acts on a separation of some droplet
-radii otherwise it becomes negligible and droplets motion is only driven by
-Marangoni effect. Finally, if one of the droplets is passive, this latter is
-generally advected by the fluid flow generated by the active one.
-",0,1,0,0,0,0
-18188,RDMAvisor: Toward Deploying Scalable and Simple RDMA as a Service in Datacenters,"  RDMA is increasingly adopted by cloud computing platforms to provide low CPU
-overhead, low latency, high throughput network services. On the other hand,
-however, it is still challenging for developers to realize fast deployment of
-RDMA-aware applications in the datacenter, since the performance is highly
-related to many lowlevel details of RDMA operations. To address this problem,
-we present a simple and scalable RDMA as Service (RaaS) to mitigate the impact
-of RDMA operational details. RaaS provides careful message buffer management to
-improve CPU/memory utilization and improve the scalability of RDMA operations.
-These optimized designs lead to simple and flexible programming model for
-common and knowledgeable users. We have implemented a prototype of RaaS, named
-RDMAvisor, and evaluated its performance on a cluster with a large number of
-connections. Our experiment results demonstrate that RDMAvisor achieves high
-throughput for thousand of connections and maintains low CPU and memory
-overhead through adaptive RDMA transport selection.
-",1,0,0,0,0,0
-18189,Towards a better understanding of the matrix product function approximation algorithm in application to quantum physics,"  We recently introduced a method to approximate functions of Hermitian Matrix
-Product Operators or Tensor Trains that are of the form $\mathsf{Tr} f(A)$.
-Functions of this type occur in several applications, most notably in quantum
-physics. In this work we aim at extending the theoretical understanding of our
-method by showing several properties of our algorithm that can be used to
-detect and correct errors in its results. Most importantly, we show that there
-exists a more computationally efficient version of our algorithm for certain
-inputs. To illustrate the usefulness of our finding, we prove that several
-classes of spin Hamiltonians in quantum physics fall into this input category.
-We finally support our findings with numerical results obtained for an example
-from quantum physics.
-",1,1,0,0,0,0
-18190,Explicit polynomial sequences with maximal spaces of partial derivatives and a question of K. Mulmuley,"  We answer a question of K. Mulmuley: In [Efremenko-Landsberg-Schenck-Weyman]
-it was shown that the method of shifted partial derivatives cannot be used to
-separate the padded permanent from the determinant. Mulmuley asked if this
-""no-go"" result could be extended to a model without padding. We prove this is
-indeed the case using the iterated matrix multiplication polynomial. We also
-provide several examples of polynomials with maximal space of partial
-derivatives, including the complete symmetric polynomials. We apply Koszul
-flattenings to these polynomials to have the first explicit sequence of
-polynomials with symmetric border rank lower bounds higher than the bounds
-attainable via partial derivatives.
-",1,0,1,0,0,0
-18191,Affine processes under parameter uncertainty,"  We develop a one-dimensional notion of affine processes under parameter
-uncertainty, which we call non-linear affine processes. This is done as
-follows: given a set of parameters for the process, we construct a
-corresponding non-linear expectation on the path space of continuous processes.
-By a general dynamic programming principle we link this non-linear expectation
-to a variational form of the Kolmogorov equation, where the generator of a
-single affine process is replaced by the supremum over all corresponding
-generators of affine processes with parameters in the parameter set. This
-non-linear affine process yields a tractable model for Knightian uncertainty,
-especially for modelling interest rates under ambiguity.
-We then develop an appropriate Ito-formula, the respective term-structure
-equations and study the non-linear versions of the Vasicek and the
-Cox-Ingersoll-Ross (CIR) model. Thereafter we introduce the non-linear
-Vasicek-CIR model. This model is particularly suitable for modelling interest
-rates when one does not want to restrict the state space a priori and hence the
-approach solves this modelling issue arising with negative interest rates.
-",0,0,0,0,0,1
-18192,Plane graphs without 4- and 5-cycles and without ext-triangular 7-cycles are 3-colorable,"  Listed as No. 53 among the one hundred famous unsolved problems in [J. A.
-Bondy, U. S. R. Murty, Graph Theory, Springer, Berlin, 2008] is Steinberg's
-conjecture, which states that every planar graph without 4- and 5-cycles is
-3-colorable. In this paper, we show that plane graphs without 4- and 5-cycles
-are 3-colorable if they have no ext-triangular 7-cycles. This implies that (1)
-planar graphs without 4-, 5-, 7-cycles are 3-colorable, and (2) planar graphs
-without 4-, 5-, 8-cycles are 3-colorable, which cover a number of known results
-in the literature motivated by Steinberg's conjecture.
-",0,0,1,0,0,0
-18193,Temporal Logic Task Planning and Intermittent Connectivity Control of Mobile Robot Networks,"  In this paper, we develop a distributed intermittent communication and task
-planning framework for mobile robot teams. The goal of the robots is to
-accomplish complex tasks, captured by local Linear Temporal Logic formulas, and
-share the collected information with all other robots and possibly also with a
-user. Specifically, we consider situations where the robot communication
-capabilities are not sufficient to form reliable and connected networks while
-the robots move to accomplish their tasks. In this case, intermittent
-communication protocols are necessary that allow the robots to temporarily
-disconnect from the network in order to accomplish their tasks free of
-communication constraints. We assume that the robots can only communicate with
-each other when they meet at common locations in space. Our distributed control
-framework jointly determines local plans that allow all robots fulfill their
-assigned temporal tasks, sequences of communication events that guarantee
-information exchange infinitely often, and optimal communication locations that
-minimize a desired distance metric. Simulation results verify the efficacy of
-the proposed controllers.
-",1,0,0,0,0,0
-18194,Second-oder analysis in second-oder cone programming,"  The paper conducts a second-order variational analysis for an important class
-of nonpolyhedral conic programs generated by the so-called
-second-order/Lorentz/ice-cream cone $Q$. From one hand, we prove that the
-indicator function of $Q$ is always twice epi-differentiable and apply this
-result to characterizing the uniqueness of Lagrange multipliers at stationary
-points together with an error bound estimate in the general second-order cone
-setting involving ${\cal C}^2$-smooth data. On the other hand, we precisely
-calculate the graphical derivative of the normal cone mapping to $Q$ under the
-weakest metric subregularity constraint qualification and then give an
-application of the latter result to a complete characterization of isolated
-calmness for perturbed variational systems associated with second-order cone
-programs. The obtained results seem to be the first in the literature in these
-directions for nonpolyhedral problems without imposing any nondegeneracy
-assumptions.
-",0,0,1,0,0,0
-18195,Understanding Deep Learning Performance through an Examination of Test Set Difficulty: A Psychometric Case Study,"  Interpreting the performance of deep learning models beyond test set accuracy
-is challenging. Characteristics of individual data points are often not
-considered during evaluation, and each data point is treated equally. We
-examine the impact of a test set question's difficulty to determine if there is
-a relationship between difficulty and performance. We model difficulty using
-well-studied psychometric methods on human response patterns. Experiments on
-Natural Language Inference (NLI) and Sentiment Analysis (SA) show that the
-likelihood of answering a question correctly is impacted by the question's
-difficulty. As DNNs are trained with more data, easy examples are learned more
-quickly than hard examples.
-",1,0,0,0,0,0
-18196,The homotopy theory of coalgebras over simplicial comonads,"  We apply the Acyclicity Theorem of Hess, Kerdziorek, Riehl, and Shipley
-(recently corrected by Garner, Kedziorek, and Riehl) to establishing the
-existence of model category structure on categories of coalgebras over comonads
-arising from simplicial adjunctions, under mild conditions on the adjunction
-and the associated comonad. We study three concrete examples of such
-adjunctions where the left adjoint is comonadic and show that in each case the
-component of the derived counit of the comparison adjunction at any fibrant
-object is an isomorphism, while the component of the derived unit at any
-1-connected object is a weak equivalence. To prove this last result, we explain
-how to construct explicit fibrant replacements for 1-connected coalgebras in
-the image of the canonical comparison functor from the Postnikov decompositions
-of their underlying simplicial sets. We also show in one case that the derived
-unit is precisely the Bousfield-Kan completion map.
-",0,0,1,0,0,0
-18197,Spatio-temporal analysis of regional unemployment rates: A comparison of model based approaches,"  This study aims to analyze the methodologies that can be used to estimate the
-total number of unemployed, as well as the unemployment rates for 28 regions of
-Portugal, designated as NUTS III regions, using model based approaches as
-compared to the direct estimation methods currently employed by INE (National
-Statistical Institute of Portugal). Model based methods, often known as small
-area estimation methods (Rao, 2003), ""borrow strength"" from neighbouring
-regions and in doing so, aim to compensate for the small sample sizes often
-observed in these areas. Consequently, it is generally accepted that model
-based methods tend to produce estimates which have lesser variation. Other
-benefit in employing model based methods is the possibility of including
-auxiliary information in the form of variables of interest and latent random
-structures. This study focuses on the application of Bayesian hierarchical
-models to the Portuguese Labor Force Survey data from the 1st quarter of 2011
-to the 4th quarter of 2013. Three different data modeling strategies are
-considered and compared: Modeling of the total unemployed through Poisson,
-Binomial and Negative Binomial models; modeling of rates using a Beta model;
-and modeling of the three states of the labor market (employed, unemployed and
-inactive) by a Multinomial model. The implementation of these models is based
-on the \textit{Integrated Nested Laplace Approximation} (INLA) approach, except
-for the Multinomial model which is implemented based on the method of Monte
-Carlo Markov Chain (MCMC). Finally, a comparison of the performance of these
-models, as well as the comparison of the results with those obtained by direct
-estimation methods at NUTS III level are given.
-",0,0,0,1,0,0
-18198,Accelerating solutions of one-dimensional unsteady PDEs with GPU-based swept time-space decomposition,"  The expedient design of precision components in aerospace and other high-tech
-industries requires simulations of physical phenomena often described by
-partial differential equations (PDEs) without exact solutions. Modern design
-problems require simulations with a level of resolution difficult to achieve in
-reasonable amounts of time---even in effectively parallelized solvers. Though
-the scale of the problem relative to available computing power is the greatest
-impediment to accelerating these applications, significant performance gains
-can be achieved through careful attention to the details of memory
-communication and access. The swept time-space decomposition rule reduces
-communication between sub-domains by exhausting the domain of influence before
-communicating boundary values. Here we present a GPU implementation of the
-swept rule, which modifies the algorithm for improved performance on this
-processing architecture by prioritizing use of private (shared) memory,
-avoiding interblock communication, and overwriting unnecessary values. It shows
-significant improvement in the execution time of finite-difference solvers for
-one-dimensional unsteady PDEs, producing speedups of 2--9$\times$ for a range
-of problem sizes, respectively, compared with simple GPU versions and
-7--300$\times$ compared with parallel CPU versions. However, for a more
-sophisticated one-dimensional system of equations discretized with a
-second-order finite-volume scheme, the swept rule performs 1.2--1.9$\times$
-worse than a standard implementation for all problem sizes.
-",1,1,0,0,0,0
-18199,Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks,"  This article presents the use of Answer Set Programming (ASP) to mine
-sequential patterns. ASP is a high-level declarative logic programming paradigm
-for high level encoding combinatorial and optimization problem solving as well
-as knowledge representation and reasoning. Thus, ASP is a good candidate for
-implementing pattern mining with background knowledge, which has been a data
-mining issue for a long time. We propose encodings of the classical sequential
-pattern mining tasks within two representations of embeddings (fill-gaps vs
-skip-gaps) and for various kinds of patterns: frequent, constrained and
-condensed. We compare the computational performance of these encodings with
-each other to get a good insight into the efficiency of ASP encodings. The
-results show that the fill-gaps strategy is better on real problems due to
-lower memory consumption. Finally, compared to a constraint programming
-approach (CPSM), another declarative programming paradigm, our proposal showed
-comparable performance.
-",1,0,0,1,0,0
-18200,Fukaya categories in Koszul duality theory,"  In this paper, we define $A_{\infty}$-Koszul duals for directed
-$A_{\infty}$-categories in terms of twists in their $A_{\infty}$-derived
-categories. Then, we compute a concrete formula of $A_{\infty}$-Koszul duals
-for path algebras with directed $A_n$-type Gabriel quivers. To compute an
-$A_\infty$-Koszul dual of such an algebra $A$, we construct a directed
-subcategory of a Fukaya category which are $A_\infty$-derived equivalent to the
-category of $A$-modules and compute Dehn twists as twists. The formula unveils
-all the ext groups of simple modules of the parh algebras and their higher
-composition structures.
-",0,0,1,0,0,0
-18201,BSDEs and SDEs with time-advanced and -delayed coefficients,"  This paper introduces a class of backward stochastic differential equations
-(BSDEs), whose coefficients not only depend on the value of its solutions of
-the present but also the past and the future. For a sufficiently small time
-delay or a sufficiently small Lipschitz constant, the existence and uniqueness
-of such BSDEs is obtained. As an adjoint process, a class of stochastic
-differential equations (SDEs) is introduced, whose coefficients also depend on
-the present, the past and the future of its solutions. The existence and
-uniqueness of such SDEs is proved for a sufficiently small time advance or a
-sufficiently small Lipschitz constant. A duality between such BSDEs and SDEs is
-established.
-",0,0,1,0,0,0
-18202,Characterizing information importance and the effect on the spread in various graph topologies,"  In this paper we present a thorough analysis of the nature of news in
-different mediums across the ages, introducing a unique mathematical model to
-fit the characteristics of information spread. This model enhances the
-information diffusion model to account for conflicting information and the
-topical distribution of news in terms of popularity for a given era. We
-translate this information to a separate graphical node model to determine the
-spread of a news item given a certain category and relevance factor. The two
-models are used as a base for a simulation of information dissemination for
-varying graph topoligies. The simulation is stress-tested and compared against
-real-world data to prove its relevancy. We are then able to use these
-simulations to deduce some conclusive statements about the optimization of
-information spread.
-",1,1,0,0,0,0
-18203,Quantum criticality at the superconductor to insulator transition revealed by specific heat measurements,"  The superconductor-insulator transition (SIT) is considered an excellent
-example of a quantum phase transition which is driven by quantum fluctuations
-at zero temperature. The quantum critical point is characterized by a diverging
-correlation length and a vanishing energy scale. Low energy fluctuations near
-quantum criticality may be experimentally detected by specific heat, $c_{\rm
-p}$, measurements. Here, we use a unique highly sensitive experiment to measure
-$c_{\rm p}$ of two-dimensional granular Pb films through the SIT. The specific
-heat shows the usual jump at the mean field superconducting transition
-temperature $T_{\rm c}^{\rm {mf}}$ marking the onset of Cooper pairs formation.
-As the film thickness is tuned toward the SIT, $T_{\rm c}^{\rm {mf}}$ is
-relatively unchanged, while the magnitude of the jump and low temperature
-specific heat increase significantly. This behaviour is taken as the
-thermodynamic fingerprint of quantum criticality in the vicinity of a quantum
-phase transition.
-",0,1,0,0,0,0
-18204,Continuous-Time Gaussian Process Motion Planning via Probabilistic Inference,"  We introduce a novel formulation of motion planning, for continuous-time
-trajectories, as probabilistic inference. We first show how smooth
-continuous-time trajectories can be represented by a small number of states
-using sparse Gaussian process (GP) models. We next develop an efficient
-gradient-based optimization algorithm that exploits this sparsity and GP
-interpolation. We call this algorithm the Gaussian Process Motion Planner
-(GPMP). We then detail how motion planning problems can be formulated as
-probabilistic inference on a factor graph. This forms the basis for GPMP2, a
-very efficient algorithm that combines GP representations of trajectories with
-fast, structure-exploiting inference via numerical optimization. Finally, we
-extend GPMP2 to an incremental algorithm, iGPMP2, that can efficiently replan
-when conditions change. We benchmark our algorithms against several
-sampling-based and trajectory optimization-based motion planning algorithms on
-planning problems in multiple environments. Our evaluation reveals that GPMP2
-is several times faster than previous algorithms while retaining robustness. We
-also benchmark iGPMP2 on replanning problems, and show that it can find
-successful solutions in a fraction of the time required by GPMP2 to replan from
-scratch.
-",1,0,0,0,0,0
-18205,Polynomially and Infinitesimally Injective Modules,"  The injective polynomial modules for a general linear group $G$ of degree $n$
-are labelled by the partitions with at most $n$ parts. Working over an
-algebraically closed field of characteristic $p$, we consider the question of
-which partitions correspond to polynomially injective modules that are also
-injective as modules for the restricted enveloping algebra of the Lie algebra
-of $G$. The question is related to the ""index of divisibility"" of a polynomial
-module in general, and an explicit answer is given for $n=2$.
-",0,0,1,0,0,0
-18206,Nonstandard Methods in Ramsey Theory and Combinatorial Number Theory,"  The goal of this present manuscript is to introduce the reader to the
-nonstandard method and to provide an overview of its most prominent
-applications in Ramsey theory and combinatorial number theory.
-",0,0,1,0,0,0
-18207,When is Network Lasso Accurate?,"  The ""least absolute shrinkage and selection operator"" (Lasso) method has been
-adapted recently for networkstructured datasets. In particular, this network
-Lasso method allows to learn graph signals from a small number of noisy signal
-samples by using the total variation of a graph signal for regularization.
-While efficient and scalable implementations of the network Lasso are
-available, only little is known about the conditions on the underlying network
-structure which ensure network Lasso to be accurate. By leveraging concepts of
-compressed sensing, we address this gap and derive precise conditions on the
-underlying network topology and sampling set which guarantee the network Lasso
-for a particular loss function to deliver an accurate estimate of the entire
-underlying graph signal. We also quantify the error incurred by network Lasso
-in terms of two constants which reflect the connectivity of the sampled nodes.
-",0,0,0,1,0,0
-18208,T-ROME: A Simple and Energy Efficient Tree Routing Protocol for Low-Power Wake-up Receivers,"  Wireless sensor networks are deployed in many monitoring applications but
-still suffer from short lifetimes originating from limited energy sources and
-storages. Due to their low-power consumption and their on-demand communication
-ability, wake-up receivers represent an energy efficient and simple enhancement
-to wireless sensor nodes and wireless sensor network protocols. In this
-context, wake-up receivers have the ability to increase the network lifetime.
-In this article, we present T-ROME, a simple and energy efficient cross-layer
-routing protocol for wireless sensor nodes containing wake-up receivers. The
-protocol makes use of the different transmission ranges of wake-up and main
-radios in order to save energy by skipping nodes during data transfer. With
-respect to energy consumption and latency, T-ROME outperforms existing
-protocols in many scenarios. Here, we describe and analyze the cross layer
-multi-hop protocol by means of a Markov chain model that we verify using a
-laboratory test setup.
-",1,0,0,0,0,0
-18209,Classifying the Correctness of Generated White-Box Tests: An Exploratory Study,"  White-box test generator tools rely only on the code under test to select
-test inputs, and capture the implementation's output as assertions. If there is
-a fault in the implementation, it could get encoded in the generated tests.
-Tool evaluations usually measure fault-detection capability using the number of
-such fault-encoding tests. However, these faults are only detected, if the
-developer can recognize that the encoded behavior is faulty. We designed an
-exploratory study to investigate how developers perform in classifying
-generated white-box test as faulty or correct. We carried out the study in a
-laboratory setting with 54 graduate students. The tests were generated for two
-open-source projects with the help of the IntelliTest tool. The performance of
-the participants were analyzed using binary classification metrics and by
-coding their observed activities. The results showed that participants
-incorrectly classified a large number of both fault-encoding and correct tests
-(with median misclassification rate 33% and 25% respectively). Thus the real
-fault-detection capability of test generators could be much lower than
-typically reported, and we suggest to take this human factor into account when
-evaluating generated white-box tests.
-",1,0,0,0,0,0
-18210,Numerical Simulations of Saturn's B Ring: Granular Friction as a Mediator between Self-Gravity and Viscous Overstability,"  We study the B ring's complex optical depth structure. The source of this
-structure may be the complex dynamics of the Keplerian shear and the
-self-gravity of the ring particles. The outcome of these dynamic effects
-depends sensitively on the collisional and physical properties of the
-particles. Two mechanisms can emerge that dominate the macroscopic physical
-structure of the ring: self-gravity wakes and viscous overstability. Here we
-study the interplay between these two mechanisms by using our recently
-developed particle collision method that allows us to better model the
-inter-particle contact physics. We find that for a constant ring surface
-density and particle internal density, particles with rough surfaces tend to
-produce axisymmetric ring features associated with the viscous overstability,
-while particles with smoother surfaces produce self-gravity wakes.
-",0,1,0,0,0,0
-18211,Shock-darkening in ordinary chondrites: determination of the pressure-temperature conditions by shock physics mesoscale modeling,"  We determined the shock-darkening pressure range in ordinary chondrites using
-the iSALE shock physics code. We simulated planar shock waves on a mesoscale in
-a sample layer at different nominal pressures. Iron and troilite grains were
-resolved in a porous olivine matrix in the sample layer. We used equations of
-state (Tillotson EoS and ANEOS) and basic strength and thermal properties to
-describe the material phases. We used Lagrangian tracers to record peak shock
-pressures in each material unit. The post-shock temperatures (and the fractions
-of tracers experiencing temperatures above the melting point) for each material
-were estimated after the passage of the shock wave and after reflections of the
-shock at grain boundaries in the heterogeneous materials. The results showed
-that shock-darkening, associated with troilite melt and the onset of olivine
-melt, happened between 40 and 50 GPa - with 52 GPa being the pressure at which
-all tracers in the troilite material reach the melting point. We demonstrate
-the difficulties of shock heating in iron and also the importance of porosity.
-Material impedances, grain shapes and the porosity models available in the
-iSALE code are discussed. We also discussed possible not-shock-related triggers
-for iron melt.
-",0,1,0,0,0,0
-18212,On the Global Fluctuations of Block Gaussian Matrices,"  In this paper we study the global fluctuations of block Gaussian matrices
-within the framework of second-order free probability theory. In order to
-compute the second-order Cauchy transform of these matrices, we introduce a
-matricial second-order conditional expectation and compute the matricial
-second-order Cauchy transform of a certain type of non-commutative random
-variables. As a by-product, using the linearization technique, we obtain the
-second-order Cauchy transform of non-commutative rational functions evaluated
-on selfadjoint Gaussian matrices.
-",0,0,1,0,0,0
-18213,Carnot Efficiency of Publication,"  This paper analyzes publication efficiency in terms of Hirsch-index or
-h-index and total citations, with an analogy to the Carnot efficiency used in
-thermodynamics. Such publication efficiency, with typical value of 30%, can be
-utilized to normalize the research output judgment, favoring quality outputs in
-reduced quantity, which is currently lacking in many discipline.
-",1,0,0,0,0,0
-18214,EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras,"  Event-based cameras have shown great promise in a variety of situations where
-frame based cameras suffer, such as high speed motions and high dynamic range
-scenes. However, developing algorithms for event measurements requires a new
-class of hand crafted algorithms. Deep learning has shown great success in
-providing model free solutions to many problems in the vision community, but
-existing networks have been developed with frame based images in mind, and
-there does not exist the wealth of labeled data for events as there does for
-images for supervised training. To these points, we present EV-FlowNet, a novel
-self-supervised deep learning pipeline for optical flow estimation for event
-based cameras. In particular, we introduce an image based representation of a
-given event stream, which is fed into a self-supervised neural network as the
-sole input. The corresponding grayscale images captured from the same camera at
-the same time as the events are then used as a supervisory signal to provide a
-loss function at training time, given the estimated flow from the network. We
-show that the resulting network is able to accurately predict optical flow from
-events only in a variety of different scenes, with performance competitive to
-image based networks. This method not only allows for accurate estimation of
-dense optical flow, but also provides a framework for the transfer of other
-self-supervised methods to the event-based domain.
-",1,0,0,0,0,0
-18215,Deep Active Learning for Named Entity Recognition,"  Deep learning has yielded state-of-the-art performance on many natural
-language processing tasks including named entity recognition (NER). However,
-this typically requires large amounts of labeled data. In this work, we
-demonstrate that the amount of labeled training data can be drastically reduced
-when deep learning is combined with active learning. While active learning is
-sample-efficient, it can be computationally expensive since it requires
-iterative retraining. To speed this up, we introduce a lightweight architecture
-for NER, viz., the CNN-CNN-LSTM model consisting of convolutional character and
-word encoders and a long short term memory (LSTM) tag decoder. The model
-achieves nearly state-of-the-art performance on standard datasets for the task
-while being computationally much more efficient than best performing models. We
-carry out incremental active learning, during the training process, and are
-able to nearly match state-of-the-art performance with just 25\% of the
-original training data.
-",1,0,0,0,0,0
-18216,Bipedal Walking with Corrective Actions in the Tilt Phase Space,"  Many methods exist for a bipedal robot to keep its balance while walking. In
-addition to step size and timing, other strategies are possible that influence
-the stability of the robot without interfering with the target direction and
-speed of locomotion. This paper introduces a multifaceted feedback controller
-that uses numerous different feedback mechanisms, collectively termed
-corrective actions, to stabilise a core keypoint-based gait. The feedback
-controller is experimentally effective, yet free of any physical model of the
-robot, very computationally inexpensive, and requires only a single 6-axis IMU
-sensor. Due to these low requirements, the approach is deemed to be highly
-portable between robots, and was specifically also designed to target lower
-cost robots that have suboptimal sensing, actuation and computational
-resources. The IMU data is used to estimate the yaw-independent tilt
-orientation of the robot, expressed in the so-called tilt phase space, and is
-the source of all feedback provided by the controller. Experimental validation
-is performed in simulation as well as on real robot hardware.
-",1,0,0,0,0,0
-18217,TimeNet: Pre-trained deep recurrent neural network for time series classification,"  Inspired by the tremendous success of deep Convolutional Neural Networks as
-generic feature extractors for images, we propose TimeNet: a deep recurrent
-neural network (RNN) trained on diverse time series in an unsupervised manner
-using sequence to sequence (seq2seq) models to extract features from time
-series. Rather than relying on data from the problem domain, TimeNet attempts
-to generalize time series representation across domains by ingesting time
-series from several domains simultaneously. Once trained, TimeNet can be used
-as a generic off-the-shelf feature extractor for time series. The
-representations or embeddings given by a pre-trained TimeNet are found to be
-useful for time series classification (TSC). For several publicly available
-datasets from UCR TSC Archive and an industrial telematics sensor data from
-vehicles, we observe that a classifier learned over the TimeNet embeddings
-yields significantly better performance compared to (i) a classifier learned
-over the embeddings given by a domain-specific RNN, as well as (ii) a nearest
-neighbor classifier based on Dynamic Time Warping.
-",1,0,0,0,0,0
-18218,Differentially Private Federated Learning: A Client Level Perspective,"  Federated learning is a recent advance in privacy protection. In this
-context, a trusted curator aggregates parameters optimized in decentralized
-fashion by multiple clients. The resulting model is then distributed back to
-all clients, ultimately converging to a joint representative model without
-explicitly having to share the data. However, the protocol is vulnerable to
-differential attacks, which could originate from any party contributing during
-federated optimization. In such an attack, a client's contribution during
-training and information about their data set is revealed through analyzing the
-distributed model. We tackle this problem and propose an algorithm for client
-sided differential privacy preserving federated optimization. The aim is to
-hide clients' contributions during training, balancing the trade-off between
-privacy loss and model performance. Empirical studies suggest that given a
-sufficiently large number of participating clients, our proposed procedure can
-maintain client-level differential privacy at only a minor cost in model
-performance.
-",1,0,0,1,0,0
-18219,"Okapi: Causally Consistent Geo-Replication Made Faster, Cheaper and More Available","  Okapi is a new causally consistent geo-replicated key- value store. Okapi
-leverages two key design choices to achieve high performance. First, it relies
-on hybrid logical/physical clocks to achieve low latency even in the presence
-of clock skew. Second, Okapi achieves higher resource efficiency and better
-availability, at the expense of a slight increase in update visibility latency.
-To this end, Okapi implements a new stabilization protocol that uses a
-combination of vector and scalar clocks and makes a remote update visible when
-its delivery has been acknowledged by every data center. We evaluate Okapi with
-different workloads on Amazon AWS, using three geographically distributed
-regions and 96 nodes. We compare Okapi with two recent approaches to causal
-consistency, Cure and GentleRain. We show that Okapi delivers up to two orders
-of magnitude better performance than GentleRain and that Okapi achieves up to
-3.5x lower latency and a 60% reduction of the meta-data overhead with respect
-to Cure.
-",1,0,0,0,0,0
-18220,Emergence of epithelial cell density waves,"  Epithelial cell monolayers exhibit traveling mechanical waves. We rationalize
-this observation thanks to a hydrodynamic description of the monolayer as a
-compressible, active and polar material. We show that propagating waves of the
-cell density, polarity, velocity and stress fields may be due to a Hopf
-bifurcation occurring above threshold values of active coupling coefficients.
-",0,1,0,0,0,0
-18221,Complex Analysis of Real Functions IV: Non-Integrable Real Functions,"  In the context of the complex-analytic structure within the unit disk
-centered at the origin of the complex plane, that was presented in a previous
-paper, we show that a certain class of non-integrable real functions can be
-represented within that same structure. In previous papers it was shown that
-essentially all integrable real functions, as well as all singular Schwartz
-distributions, can be represented within that same complex-analytic structure.
-The large class of non-integrable real functions which we analyze here can
-therefore be represented side by side with those other real objects, thus
-allowing all these objects to be treated in a unified way.
-",0,0,1,0,0,0
-18222,Convolution Aware Initialization,"  Initialization of parameters in deep neural networks has been shown to have a
-big impact on the performance of the networks (Mishkin & Matas, 2015). The
-initialization scheme devised by He et al, allowed convolution activations to
-carry a constrained mean which allowed deep networks to be trained effectively
-(He et al., 2015a). Orthogonal initializations and more generally orthogonal
-matrices in standard recurrent networks have been proved to eradicate the
-vanishing and exploding gradient problem (Pascanu et al., 2012). Majority of
-current initialization schemes do not take fully into account the intrinsic
-structure of the convolution operator. Using the duality of the Fourier
-transform and the convolution operator, Convolution Aware Initialization builds
-orthogonal filters in the Fourier space, and using the inverse Fourier
-transform represents them in the standard space. With Convolution Aware
-Initialization we noticed not only higher accuracy and lower loss, but faster
-convergence. We achieve new state of the art on the CIFAR10 dataset, and
-achieve close to state of the art on various other tasks.
-",1,0,0,1,0,0
-18223,A Multitask Diffusion Strategy with Optimized Inter-Cluster Cooperation,"  We consider a multitask estimation problem where nodes in a network are
-divided into several connected clusters, with each cluster performing a
-least-mean-squares estimation of a different random parameter vector. Inspired
-by the adapt-then-combine diffusion strategy, we propose a multitask diffusion
-strategy whose mean stability can be ensured whenever individual nodes are
-stable in the mean, regardless of the inter-cluster cooperation weights. In
-addition, the proposed strategy is able to achieve an asymptotically unbiased
-estimation, when the parameters have same mean. We also develop an
-inter-cluster cooperation weights selection scheme that allows each node in the
-network to locally optimize its inter-cluster cooperation weights. Numerical
-results demonstrate that our approach leads to a lower average steady-state
-network mean-square deviation, compared with using weights selected by various
-other commonly adopted methods in the literature.
-",1,0,0,0,0,0
-18224,High Resolution Observations of the Massive Protostar in IRAS18566+0408,"  We report 3 mm continuum, CH3CN(5-4) and 13CS(2-1) line observations with
-CARMA, in conjunction with 6 and 1.3 cm continuum VLA data, and 12 and 25
-micron broadband data from the Subaru Telescope toward the massive proto-star
-IRAS18566+0408. The VLA data resolve the ionized jet into 4 components aligned
-in the E-W direction. Radio components A, C, and D have flat cm SEDs indicative
-of optically thin emission from ionized gas, and component B has a spectral
-index alpha = 1.0, and a decreasing size with frequency proportional to
-frequency to the -0.5 power. Emission from the CARMA 3 mm continuum, and from
-the 13CS(2-1), and CH3CN(5-4) spectral lines is compact (i.e. < 6700 AU), and
-peaks near the position of VLA cm source, component B. Analysis of these lines
-indicates hot, and dense molecular gas, typical for HMCs. Our Subaru telescope
-observations detect a single compact source, coincident with radio component B,
-demonstrating that most of the energy in IRAS18566+0408 originates from a
-region of size < 2400 AU. We also present UKIRT near-infrared archival data for
-IRAS18566+0408 which show extended K-band emission along the jet direction. We
-detect an E-W velocity shift of about 10 km/sec over the HMC in the CH3CN lines
-possibly tracing the interface of the ionized jet with the surrounding core
-gas. Our data demonstrate the presence of an ionized jet at the base of the
-molecular outflow, and support the hypothesis that massive protostars with
-O-type luminosity form with a mechanism similar to lower mass stars.
-",0,1,0,0,0,0
-18225,MEDL and MEDLA: Methods for Assessment of Scaling by Medians of Log-Squared Nondecimated Wavelet Coefficients,"  High-frequency measurements and images acquired from various sources in the
-real world often possess a degree of self-similarity and inherent regular
-scaling. When data look like a noise, the scaling exponent may be the only
-informative feature that summarizes such data. Methods for the assessment of
-self-similarity by estimating Hurst exponent often involve analysis of rate of
-decay in a spectrum defined in various multiresolution domains. When this
-spectrum is calculated using discrete non-decimated wavelet transforms, due to
-increased autocorrelation in wavelet coefficients, the estimators of $H$ show
-increased bias compared to the estimators that use traditional orthogonal
-transforms. At the same time, non-decimated transforms have a number of
-advantages when employed for calculation of wavelet spectra and estimation of
-Hurst exponents: the variance of the estimator is smaller, input signals and
-images could be of arbitrary size, and due to the shift-invariance, the local
-scaling can be assessed as well. We propose two methods based on robust
-estimation and resampling that alleviate the effect of increased
-autocorrelation while maintaining all advantages of non-decimated wavelet
-transforms. The proposed methods extend the approaches in existing literature
-where the logarithmic transformation and pairing of wavelet coefficients are
-used for lowering the bias. In a simulation study we use fractional Brownian
-motions with a range of theoretical Hurst exponents. For such signals for which
-""true"" $H$ is known, we demonstrate bias reduction and overall reduction of the
-mean-squared error by the two proposed estimators. For fractional Brownian
-motions, both proposed methods yield estimators of $H$ that are asymptotically
-normal and unbiased.
-",0,0,0,1,0,0
-18226,A Two-Level Graph Partitioning Problem Arising in Mobile Wireless Communications,"  In the k-partition problem (k-PP), one is given an edge-weighted undirected
-graph, and one must partition the node set into at most k subsets, in order to
-minimise (or maximise) the total weight of the edges that have their end-nodes
-in the same cluster. Various hierarchical variants of this problem have been
-studied in the context of data mining. We consider a 'two-level' variant that
-arises in mobile wireless communications. We show that an exact algorithm based
-on intelligent preprocessing, cutting planes and symmetry-breaking is capable
-of solving small- and medium-size instances to proven optimality, and providing
-strong lower bounds for larger instances.
-",1,0,1,0,0,0
-18227,Emotion Specification from Musical Stimuli: An EEG Study with AFA and DFA,"  The present study reports interesting findings in regard to emotional arousal
-based activities while listening to two Hindustani classical ragas of contrast
-emotion. EEG data was taken on 5 naive listeners while they listened to two
-ragas Bahar and Mia ki Malhar which are conventionally known to portray
-contrast emotions. The EEG data were analyzed with the help of two robust non
-linear tools viz. Adaptive Fractal Analysis (AFA) and Detrended Fluctuation
-Analysis (DFA). A comparative study of the Hurst Exponents obtained from the
-two methods have been shown which shows that DFA provides more rigorous results
-compared to AFA when it comes to the scaling analysis of biosignal data. The
-results and implications have been discussed in detail.
-",0,1,0,0,0,0
-18228,The light pollution as a surrogate for urban population of the US cities,"  We show that the definition of the city boundaries can have a dramatic
-influence on the scaling behavior of the night-time light (NTL) as a function
-of population (POP) in the US. Precisely, our results show that the arbitrary
-geopolitical definition based on the Metropolitan/Consolidated Metropolitan
-Statistical Areas (MSA/CMSA) leads to a sublinear power-law growth of NTL with
-POP. On the other hand, when cities are defined according to a more natural
-agglomeration criteria, namely, the City Clustering Algorithm (CCA), an
-isometric relation emerges between NTL and population. This discrepancy is
-compatible with results from previous works showing that the scaling behaviors
-of various urban indicators with population can be substantially different for
-distinct definitions of city boundaries. Moreover, considering the CCA
-definition as more adequate than the MSA/CMSA one because the former does not
-violate the expected extensivity between land population and area of their
-generated clusters, we conclude that, without loss of generality, the CCA
-measures of light pollution and population could be interchangeably utilized in
-future studies.
-",0,1,0,0,0,0
-18229,"Analytic calculation of radio emission from parameterized extensive air showers, a tool to extract shower parameters","  The radio intensity and polarization footprint of a cosmic-ray induced
-extensive air shower is determined by the time-dependent structure of the
-current distribution residing in the plasma cloud at the shower front. In turn,
-the time dependence of the integrated charge-current distribution in the plasma
-cloud, the longitudinal shower structure, is determined by interesting physics
-which one would like to extract such as the location and multiplicity of the
-primary cosmic-ray collision or the values of electric fields in the atmosphere
-during thunderstorms. To extract the structure of a shower from its footprint
-requires solving a complicated inverse problem. For this purpose we have
-developed a code that semi-analytically calculates the radio footprint of an
-extensive air shower given an arbitrary longitudinal structure. This code can
-be used in a optimization procedure to extract the optimal longitudinal shower
-structure given a radio footprint. On the basis of air-shower universality we
-propose a simple parametrization of the structure of the plasma cloud. This
-parametrization is based on the results of Monte-Carlo shower simulations.
-Deriving the parametrization also teaches which aspects of the plasma cloud are
-important for understanding the features seen in the radio-emission footprint.
-The calculated radio footprints are compared with microscopic CoREAS
-simulations.
-",0,1,0,0,0,0
-18230,Akhmediev Breathers and Peregrine Solitary Waves in a Quadratic Medium,"  We investigate the formation of optical localized nonlinear structures,
-evolving upon a non-zero background plane wave, in a dispersive quadratic
-medium. We show the existence of quadratic Akhmediev breathers and Peregrine
-solitary waves, in the regime of cascading second-harmonic generation. This
-finding opens a novel path for the excitation of extreme rogue waves and for
-the description of modulation instability in quadratic nonlinear optics.
-",0,1,0,0,0,0
-18231,Novel approaches to spectral properties of correlated electron materials: From generalized Kohn-Sham theory to screened exchange dynamical mean field theory,"  The most intriguing properties of emergent materials are typically
-consequences of highly correlated quantum states of their electronic degrees of
-freedom. Describing those materials from first principles remains a challenge
-for modern condensed matter theory. Here, we review, apply and discuss novel
-approaches to spectral properties of correlated electron materials, assessing
-current day predictive capabilities of electronic structure calculations. In
-particular, we focus on the recent Screened Exchange Dynamical Mean-Field
-Theory scheme and its relation to generalized Kohn-Sham theory. These concepts
-are illustrated on the transition metal pnictide BaCo$_2$As$_2$ and elemental
-zinc and cadmium.
-",0,1,0,0,0,0
-18232,Flux-Rope Twist in Eruptive Flares and CMEs: due to Zipper and Main-Phase Reconnection,"  The nature of three-dimensional reconnection when a twisted flux tube erupts
-during an eruptive flare or coronal mass ejection is considered. The
-reconnection has two phases: first of all, 3D ""zipper reconnection"" propagates
-along the initial coronal arcade, parallel to the polarity inversion line
-(PIL), then subsequent quasi-2D ""main phase reconnection"" in the low corona
-around a flux rope during its eruption produces coronal loops and chromospheric
-ribbons that propagate away from the PIL in a direction normal to it.
-One scenario starts with a sheared arcade: the zipper reconnection creates a
-twisted flux rope of roughly one turn ($2\pi$ radians of twist), and then main
-phase reconnection builds up the bulk of the erupting flux rope with a
-relatively uniform twist of a few turns. A second scenario starts with a
-pre-existing flux rope under the arcade. Here the zipper phase can create a
-core with many turns that depend on the ratio of the magnetic fluxes in the
-newly formed flare ribbons and the new flux rope. Main phase reconnection then
-adds a layer of roughly uniform twist to the twisted central core. Both phases
-and scenarios are modeled in a simple way that assumes the initial magnetic
-flux is fragmented along the PIL. The model uses conservation of magnetic
-helicity and flux, together with equipartition of magnetic helicity, to deduce
-the twist of the erupting flux rope in terms the geometry of the initial
-configuration.
-Interplanetary observations show some flux ropes have a fairly uniform twist,
-which could be produced when the zipper phase and any pre-existing flux rope
-possess small or moderate twist (up to one or two turns). Other interplanetary
-flux ropes have highly twisted cores (up to five turns), which could be
-produced when there is a pre-existing flux rope and an active zipper phase that
-creates substantial extra twist.
-",0,1,0,0,0,0
-18233,On the classification of Kantor pairs and structurable algebras in characteristic 5,"  We observe that any finite-dimensional central simple 5-graded Lie algebra
-over over a field k of characteristic not 2,3 is necessarily classical, i.e. a
-twisted form of a Chevalley Lie algebra. Consequently, the classification of
-central simple structurable algebras and Kantor pairs over fields of
-characteristic 5 derives from the classification of simple algebraic groups.
-Using the classification of nilpotent conjugacy classes, we list all 5-gradings
-on Lie algebras of simple algebraic groups that give rise to simple
-structurable algebras over algebraically closed fields.
-",0,0,1,0,0,0
-18234,Cross-lingual Distillation for Text Classification,"  Cross-lingual text classification(CLTC) is the task of classifying documents
-written in different languages into the same taxonomy of categories. This paper
-presents a novel approach to CLTC that builds on model distillation, which
-adapts and extends a framework originally proposed for model compression. Using
-soft probabilistic predictions for the documents in a label-rich language as
-the (induced) supervisory labels in a parallel corpus of documents, we train
-classifiers successfully for new languages in which labeled training data are
-not available. An adversarial feature adaptation technique is also applied
-during the model training to reduce distribution mismatch. We conducted
-experiments on two benchmark CLTC datasets, treating English as the source
-language and German, French, Japan and Chinese as the unlabeled target
-languages. The proposed approach had the advantageous or comparable performance
-of the other state-of-art methods.
-",1,0,0,0,0,0
-18235,Characterization of Majorana-Ising phase transition in a helical liquid system,"  We map an interacting helical liquid system, coupled to an external magnetic
-field and s-wave superconductor, to an XYZ spin system, and it undergoes
-Majorana-Ising transition by tuning of parameters. In the Majorana state,
-lowest excitation gap decays exponentially with system size, and the system has
-degenerate ground state in the thermodynamic limit. On the contrary, the gap
-opens in the Ising phase even in the thermodynamic limit. We also study other
-criteria to characterize the transition, such as edge spin correlation with its
-neighbor $C(r=1)$, local susceptibility $\chi_i$, superconducting order
-parameter of edge spin $P(r=1)$, and longitudinal structure factor $S(k)$. The
-ground state degeneracy and three other criteria lead to the same critical
-value of parameters for Majorana-Ising phase transition in the thermodynamic
-limit. We study, for the first time, the entanglement spectrum of the reduced
-density matrix of the helical liquid system. The system shows finite Schmidt
-gap and non-degeneracy of the entanglement spectrum in the Ising limit. The
-Schmidt gap closes in the Majorana state, and all the eigenvalues are either
-doubly or multiply degenerate.
-",0,1,0,0,0,0
-18236,Monotonicity and symmetry of nonnegative solutions to $ -Δu=f(u) $ in half-planes and strips,"  We consider nonnegative solutions to $-\Delta u=f(u)$ in half-planes and
-strips, under zero Dirichlet boundary condition. Exploiting a
-rotating$\&$sliding line technique, we prove symmetry and monotonicity
-properties of the solutions, under very general assumptions on the nonlinearity
-$f$. In fact we provide a unified approach that works in all the cases
-$f(0)<0$, $f(0)= 0$ or $f(0)> 0$. Furthermore we make the effort to deal with
-nonlinearities $f$ that may be not locally-Lipschitz continuous. We also
-provide explicite examples showing the sharpness of our assumptions on the
-nonlinear function $f$.
-",0,0,1,0,0,0
-18237,Gini-regularized Optimal Transport with an Application to Spatio-Temporal Forecasting,"  Rapidly growing product lines and services require a finer-granularity
-forecast that considers geographic locales. However the open question remains,
-how to assess the quality of a spatio-temporal forecast? In this manuscript we
-introduce a metric to evaluate spatio-temporal forecasts. This metric is based
-on an Opti- mal Transport (OT) problem. The metric we propose is a constrained
-OT objec- tive function using the Gini impurity function as a regularizer. We
-demonstrate through computer experiments both the qualitative and the
-quantitative charac- teristics of the Gini regularized OT problem. Moreover, we
-show that the Gini regularized OT problem converges to the classical OT
-problem, when the Gini regularized problem is considered as a function of
-{\lambda}, the regularization parame-ter. The convergence to the classical OT
-solution is faster than the state-of-the-art Entropic-regularized OT[Cuturi,
-2013] and results in a numerically more stable algorithm.
-",1,0,0,1,0,0
-18238,Indicators of Conformal Field Theory: entanglement entropy and multiple-point correlators,"  The entanglement entropy (EE) of quantum systems is often used as a test of
-low-energy descriptions by conformal field theory (CFT). Here we point out that
-this is not a reliable indicator, as the EE often shows the same behavior even
-when a CFT description is not correct (as long as the system is asymptotically
-scale-invariant). We use constraints on the scaling dimension given by the CFT
-with SU(2) symmetry to provide alternative tests with two- and four-point
-correlation functions, showing examples for quantum spin models in 1+1
-dimensions. In the case of a critical amplitude-product state expressed in the
-valence-bond basis (where the amplitudes decay as a power law of the bond
-length and the wave function is the product of all bond amplitudes), we show
-that even though the EE exhibits the expected CFT behavior, there is no CFT
-description of this state. We provide numerical tests of the behavior predicted
-by CFT for the correlation functions in the critical transverse-field Ising
-chain and the $J$-$Q$ spin chain, where the conformal structure is well
-understood. That behavior is not reproduced in the amplitude-product state.
-",0,1,0,0,0,0
-18239,Classical Entanglement Structure in the Wavefunction of Inflationary Fluctuations,"  We argue that the preferred classical variables that emerge from a pure
-quantum state are determined by its entanglement structure in the form of
-redundant records: information shared between many subsystems. Focusing on the
-early universe, we ask how classical metric perturbations emerge from vacuum
-fluctuations in an inflationary background. We show that the squeezing of the
-quantum state for super-horizon modes, along with minimal gravitational
-interactions, leads to decoherence and to an exponential number of records of
-metric fluctuations on very large scales, $\lambda/\lambda_{\rm
-Hubble}>\Delta_\zeta^{-2/3}$, where $\Delta_\zeta\lesssim 10^{-5}$ is the
-amplitude of scalar metric fluctuations. This determines a preferred
-decomposition of the inflationary wavefunction into orthogonal ""branches""
-corresponding to classical metric perturbations, which defines an inflationary
-entropy production rate and accounts for the emergence of stochastic,
-inhomogeneous spacetime geometry.
-",0,1,0,0,0,0
-18240,Pulsejet engine dynamics in vertical motion using momentum conservation,"  The momentum conservation law is applied to analyse the dynamics of pulsejet
-engine in vertical motion in a uniform gravitational field in the absence of
-friction. The model predicts existence of a terminal speed given frequency of
-the short pulses. The conditions that the engine does not return to the
-starting position are identified. The number of short periodic pulses after
-which the engine returns to the starting position is found to be independent of
-the exhaust velocity and gravitational field intensity for certain frequency of
-the pulses. The pulsejet engine and turbojet engine aircraft models of dynamics
-are compared. Also the octopus dynamics is modelled. The paper is addressed to
-intermediate undergraduate students of classical mechanics and aerospace
-engineering.
-",0,1,0,0,0,0
-18241,Mitigating Blackout Risk via Maintenance : Inference from Simulation Data,"  Whereas maintenance has been recognized as an important and effective means
-for risk management in power systems, it turns out to be intractable if
-cascading blackout risk is considered due to the extremely high computational
-complexity. In this paper, based on the inference from the blackout simulation
-data, we propose a methodology to efficiently identify the most influential
-component(s) for mitigating cascading blackout risk in a large power system. To
-this end, we first establish an analytic relationship between maintenance
-strategies and blackout risk estimation by inferring from the data of cascading
-outage simulations. Then we formulate the component maintenance decision-making
-problem as a nonlinear 0-1 programming. Afterwards, we quantify the credibility
-of blackout risk estimation, leading to an adaptive method to determine the
-least required number of simulations, which servers as a crucial parameter of
-the optimization model. Finally, we devise two heuristic algorithms to find
-approximate optimal solutions to the model with very high efficiency. Numerical
-experiments well manifest the efficacy and high efficiency of our methodology.
-",0,0,0,1,0,0
-18242,Evolving to Non-round Weingarten Spheres: Integer Linear Hopf Flows,"  In the 1950's Hopf gave examples of non-round convex 2-spheres in Euclidean
-3-space with rotational symmetry that satisfy a linear relationship between
-their principal curvatures. In this paper we investigate conditions under which
-evolving a smooth rotationally symmetric sphere by a linear combination of its
-radii of curvature yields a Hopf sphere. When the coefficients of the flow have
-certain integer values, the fate of an initial sphere is entirely determined by
-the local geometry of its isolated umbilic points. A surprising variety of
-behaviours is uncovered: convergence to round spheres and non-round Hopf
-spheres, as well as divergence to infinity.
-The critical quantity is the rate of vanishing of the astigmatism - the
-difference of the radii of curvature - at the isolated umbilic points. It is
-proven that the size of this quantity versus the coefficient in the flow
-function determines the fate of the evolution.
-The geometric setting for the equation is Radius of Curvature space, viewed
-as a pair of hyperbolic/AdS half-planes joined along their boundary, the
-umbilic horizon. A rotationally symmetric sphere determines a parameterized
-curve in this plane with end-points on the umbilic horizon. The slope of the
-curve at the umbilic horizon is linked by the Codazzi-Mainardi equations to the
-rate of vanishing of astigmatism, and for generic initial conditions can be
-used to determine the outcome of the flow.
-The slope can jump during the flow, and a number of examples are given:
-instant jumps of the initial slope, as well as umbilic circles that contract to
-points in finite time and 'pop' the slope. Finally, we present soliton-like
-solutions: curves that evolve under linear flows by mutual hyperbolic/AdS
-isometries (dilation and translation) of Radius of Curvature space. A
-forthcoming paper will apply these geometric ideas to non-linear curvature
-flows.
-",0,0,1,0,0,0
-18243,On Infinite Linear Programming and the Moment Approach to Deterministic Infinite Horizon Discounted Optimal Control Problems,"  We revisit the linear programming approach to deterministic, continuous time,
-infinite horizon discounted optimal control problems. In the first part, we
-relax the original problem to an infinite-dimensional linear program over a
-measure space and prove equivalence of the two formulations under mild
-assumptions, significantly weaker than those found in the literature until now.
-The proof is based on duality theory and mollification techniques for
-constructing approximate smooth subsolutions to the associated
-Hamilton-Jacobi-Bellman equation. In the second part, we assume polynomial data
-and use Lasserre's hierarchy of primal-dual moment-sum-of-squares semidefinite
-relaxations to approximate the value function and design an approximate optimal
-feedback controller. We conclude with an illustrative example.
-",1,0,1,0,0,0
-18244,Granular permittivity representation in extremely near-field light-matter interactions processes,"  Light-matter interaction processes are significantly affected by surrounding
-electromagnetic environment. Dielectric materials are usually introduced into
-an interaction picture via their classical properties, e.g. permittivity,
-appearing in constitutive relations. While this approach was proven to be
-applicable in many occasions, it might face limitations when an emitter is
-situated very close to a material boundary. In this case nonlocal extend of a
-quantum wave function of an emitter becomes comparable with a distance to a
-boundary and a lattice constant of a material. Here a semi-classical model,
-taking into account material's granularity, is developed. In particular,
-spontaneous emission process in the vicinity of flat boundaries is considered.
-The material boundary is divided into a pair areas - far zone is modeled as a
-continuous phase, while the near zone next to a nonlocal emitter is represented
-with a discrete array of polarizable particles. This array resembles optical
-properties of the continuous phase under the standard homogenization procedure.
-Local field effects were shown to lead orders of magnitude corrections to
-spontaneous emission rates in the case of sub-nanometer emitter-surface
-separation distances. The developed mesoscopic model enables addressing few
-aspects of local field corrections in quite complex scenarios, where quantum ab
-initio techniques yet face challenges owing to involved computational
-complexity. The developed method could be utilized for designs of quantum
-sources and networks, enhanced with structured electromagnetic environment.
-",0,1,0,0,0,0
-18245,Application of Superhalogens in the Design of Organic Superconductors,"  Bechgaard salts, (TMTSF)2X (TMTSF = tetramethyl tetraselenafulvalene and X =
-complex anion), form the most popular series of organic superconductors. In
-these salts, TMTSF molecules act as super-electron donor and X as acceptor. We
-computationally examine the electronic structure and properties of X in
-commonly used (TMTSF)2X (X = NO3, BF4, ClO4, PF6) superconductors and notice
-that they belong to the class of superhalogens due to their higher vertical
-detachment energy than halogen anions. This prompted us to choose other
-superhalogens such as X = BO2, BH4, B2F7, AuF6 and study their (TMTSF)2X
-complexes. Our findings suggest that these complexes behave more or less
-similar to those of established (TMTSF)2X superconductors, particularly for X =
-BO2 and B2F7. We, therefore, believe that the concept of superhalogen can be
-successfully applied in the design of novel organic superconductors.
-",0,1,0,0,0,0
-18246,Inhomogeneous hard-core bosonic mixture with checkerboard supersolid phase: Quantum and thermal phase diagram,"  We introduce an inhomogeneous bosonic mixture composed of two kinds of
-hard-core and semi-hard-core bosons with different nilpotency conditions and
-demonstrate that in contrast with the standard hard-core Bose-Hubbard model,
-our bosonic mixture with nearest- and next-nearest-neighbor interactions on a
-square lattice develops the checkerboard supersolid phase characterized by the
-simultaneous superfluid and checkerboard solid orders. Our bosonic mixture is
-created from a two-orbital Bose-Hubbard model including two kinds of bosons: a
-single-orbital boson and a two-orbital boson. By mapping the bosonic mixture to
-an anisotropic inhomogeneous spin model in the presence of a magnetic field, we
-study the ground-state phase diagram of the model by means of cluster mean
-field theory and linear spin-wave theory and show that various phases such as
-solid, superfluid, supersolid, and Mott insulator appear in the phase diagram
-of the mixture. Competition between the interactions and magnetic field causes
-the mixture to undergo different kinds of first- and second-order phase
-transitions. By studying the behavior of the spin-wave excitations, we find the
-reasons of all first- and second-order phase transitions. We also obtain the
-temperature phase diagram of the system using cluster mean field theory. We
-show that the checkerboard supersolid phase persists at finite temperature
-comparable with the interaction energies of bosons.
-",0,1,0,0,0,0
-18247,Width-$k$ Generalizations of Classical Permutation Statistics,"  We introduce new natural generalizations of the classical descent and
-inversion statistics for permutations, called width-$k$ descents and width-$k$
-inversions. These variations induce generalizations of the excedance and major
-statistics, providing a framework in which the most well-known
-equidistributivity results for classical statistics are paralleled. We explore
-additional relationships among the statistics providing specific formulas in
-certain special cases. Moreover, we explore the behavior of these width-$k$
-statistics in the context of pattern avoidance.
-",0,0,1,0,0,0
-18248,Klout Topics for Modeling Interests and Expertise of Users Across Social Networks,"  This paper presents Klout Topics, a lightweight ontology to describe social
-media users' topics of interest and expertise. Klout Topics is designed to: be
-human-readable and consumer-friendly; cover multiple domains of knowledge in
-depth; and promote data extensibility via knowledge base entities. We discuss
-why this ontology is well-suited for text labeling and interest modeling
-applications, and how it compares to available alternatives. We show its
-coverage against common social media interest sets, and examples of how it is
-used to model the interests of over 780M social media users on Klout.com.
-Finally, we open the ontology for external use.
-",1,0,0,0,0,0
-18249,Set-Based Tests for Genetic Association Using the Generalized Berk-Jones Statistic,"  Studying the effects of groups of Single Nucleotide Polymorphisms (SNPs), as
-in a gene, genetic pathway, or network, can provide novel insight into complex
-diseases, above that which can be gleaned from studying SNPs individually.
-Common challenges in set-based genetic association testing include weak effect
-sizes, correlation between SNPs in a SNP-set, and scarcity of signals, with
-single-SNP effects often ranging from extremely sparse to moderately sparse in
-number. Motivated by these challenges, we propose the Generalized Berk-Jones
-(GBJ) test for the association between a SNP-set and outcome. The GBJ extends
-the Berk-Jones (BJ) statistic by accounting for correlation among SNPs, and it
-provides advantages over the Generalized Higher Criticism (GHC) test when
-signals in a SNP-set are moderately sparse. We also provide an analytic p-value
-calculation procedure for SNP-sets of any finite size. Using this p-value
-calculation, we illustrate that the rejection region for GBJ can be described
-as a compromise of those for BJ and GHC. We develop an omnibus statistic as
-well, and we show that this omnibus test is robust to the degree of signal
-sparsity. An additional advantage of our method is the ability to conduct
-inference using individual SNP summary statistics from a genome-wide
-association study. We evaluate the finite sample performance of the GBJ though
-simulation studies and application to gene-level association analysis of breast
-cancer risk.
-",0,0,0,1,0,0
-18250,Comprehensive Modeling of Three-Phase Distribution Systems via the Bus Admittance Matrix,"  The theme of this paper is three-phase distribution system modeling suitable
-for the Z-Bus load-flow. Detailed models of wye and delta constant-power,
-constant-current, and constant-impedance loads are presented. Models of
-transmission lines, voltage regulators, and transformers that build the bus
-admittance matrix (Y-Bus) are laid out. The Z-Bus load-flow is then reviewed
-and the singularity of the Y-Bus in case of certain transformer connections is
-rigorously discussed. Based on realistic assumptions and conventional
-modifications, the invertibility of the Y-Bus is proved. Last but not least,
-the MATLAB scripts that construct the detailed component models for the IEEE
-37-bus, IEEE 123-bus, and 8500-node feeders as well as the European 906-bus
-low-voltage feeder are provided.
-",1,0,1,0,0,0
-18251,$c$-vectors of 2-Calabi--Yau categories and Borel subalgebras of ${\mathfrak{sl}}_{\infty}$,"  We develop a general framework for $c$-vectors of 2-Calabi--Yau categories,
-which deals with cluster tilting subcategories that are not reachable from each
-other and contain infinitely many indecomposable objects. It does not rely on
-iterative sequences of mutations.
-We prove a categorical (infinite-rank) generalization of the
-Nakanishi--Zelevinsky duality for $c$-vectors and establish two formulae for
-the effective computation of $c$-vectors -- one in terms of indices and the
-other in terms of dimension vectors for cluster tilted algebras.
-In this framework, we construct a correspondence between the $c$-vectors of
-the cluster categories ${\mathscr{C}}(A_{\infty})$ of type $A_{\infty}$ due to
-Igusa--Todorov and the roots of the Borel subalgebras of
-${\mathfrak{sl}}_{\infty}$. Contrary to the finite dimensional case, the Borel
-subalgebras of ${\mathfrak{sl}}_{\infty}$ are not conjugate to each other. On
-the categorical side, the cluster tilting subcategories of
-${\mathscr{C}}(A_{\infty})$ exhibit different homological properties. The
-correspondence builds a bridge between the two classes of objects.
-",0,0,1,0,0,0
-18252,Electrostatic interaction between non-identical charged particles at an electrolyte interface,"  In this thesis we study the lateral electrostatic interaction between a pair
-of non-identical, moderately charged colloidal particles trapped at an
-electrolyte interface in the limit of short inter-particle separations. Using a
-simplified model system we solve the problem analytically within the framework
-of linearised Poisson-Boltzmann theory and classical density functional theory.
-In the first step, we calculate the electrostatic potential inside the system
-exactly as well as within the widely used superposition approximation. Then
-these results are used to calculate the surface and line interaction energy
-densities between the particles. Contrary to the case of identical particles,
-depending upon the parameters of the system, we obtain that both the surface
-and the line interaction can vary non-monotonically with varying separation
-between the particles and the superposition approximation fails to predict the
-correct qualitative behaviours in most cases. Additionally, the superposition
-approximation is unable to predict the energy contributions quantitatively even
-at large distances. We also provide expression for the constant (independent of
-the inter-particle separation) interaction parameters, i.e., the surface
-tension, the line tension and the interfacial tension. Our results are expected
-to be of use for modelling particle-interaction at fluid interfaces and, in
-particular, for emulsion stabilization using oppositely charged particles.
-",0,1,0,0,0,0
-18253,The weighted connection and sectional curvature for manifolds with density,"  In this paper we study sectional curvature bounds for Riemannian manifolds
-with density from the perspective of a weighted torsion free connection
-introduced recently by the last two authors. We develop two new tools for
-studying weighted sectional curvature bounds: a new weighted Rauch comparison
-theorem and a modified notion of convexity for distance functions. As
-applications we prove generalizations of theorems of Preissman and Byers for
-negative curvature, the (homeomorphic) quarter-pinched sphere theorem, and
-Cheeger's finiteness theorem. We also improve results of the first two authors
-for spaces of positive weighted sectional curvature and symmetry.
-",0,0,1,0,0,0
-18254,Control and Limit Enforcements for VSC Multi-Terminal HVDC in Newton Power Flow,"  This paper proposes a novel method to automatically enforce controls and
-limits for Voltage Source Converter (VSC) based multi-terminal HVDC in the
-Newton power flow iteration process. A general VSC MT-HVDC model with primary
-PQ or PV control and secondary voltage control is formulated. Both the
-dependent and independent variables are included in the propose formulation so
-that the algebraic variables of the VSC MT-HVDC are adjusted simultaneously.
-The proposed method also maintains the number of equations and the dimension of
-the Jacobian matrix unchanged so that, when a limit is reached and a control is
-released, the Jacobian needs no re-factorization. Simulations on the IEEE
-14-bus and Polish 9241-bus systems are performed to demonstrate the
-effectiveness of the method.
-",1,0,0,0,0,0
-18255,A.Ya. Khintchine's Work in Probability Theory,"  The paper is devoted to the contribution in the Probability Theory of the
-well-known Soviet mathematician Alexander Yakovlevich Khintchine (1894-1959).
-Several of his results are described, in particular those fundamental results
-on the infinitely divisible distributions. Attention is paid also to his
-interaction with Paul Levy. The content of the paper is related to our joint
-book The Legacy of A.Ya. Khintchine's Work in Probability Theory, published in
-2010 by Cambridge Scientific Publishers.
-",0,0,1,0,0,0
-18256,Dynamic Complexity under Definable Changes,"  This paper studies dynamic complexity under definable change operations in
-the DynFO framework by Patnaik and Immerman. It is shown that for changes
-definable by parameter-free first-order formulas, all (uniform) $AC^1$ queries
-can be maintained by first-order dynamic programs. Furthermore, many
-maintenance results for single-tuple changes are extended to more powerful
-change operations: (1) The reachability query for undirected graphs is
-first-order maintainable under single tuple changes and first-order defined
-insertions, likewise the reachability query for directed acyclic graphs under
-quantifier-free insertions. (2) Context-free languages are first-order
-maintainable under $\Sigma_1$-defined changes. These results are complemented
-by several inexpressibility results, for example, that the reachability query
-cannot be maintained by quantifier-free programs under definable,
-quantifier-free deletions.
-",1,0,0,0,0,0
-18257,The Planar Sandwich and Other 1D Planar Heat Flow Test Problems in ExactPack,"  This report documents the implementation of several related 1D heat flow
-problems in the verification package ExactPack. In particular, the planar
-sandwich class defined by Dawes et al., as well as the classes
-PlanarSandwichHot, PlanarSandwichHalf, and other generalizations of the planar
-sandwich problem, are defined and documented here. A rather general treatment
-of 1D heat flow is presented, whose main results have been implemented in the
-class Rod1D. All planar sandwich classes are derived from the parent class
-Rod1D.
-",0,1,0,0,0,0
-18258,Difficulties of Timestamping Archived Web Pages,"  We show that state-of-the-art services for creating trusted timestamps in
-blockchain-based networks do not adequately allow for timestamping of web
-pages. They accept data by value (e.g., images and text), but not by reference
-(e.g., URIs of web pages). Also, we discuss difficulties in repeatedly
-generating the same cryptographic hash value of an archived web page. We then
-introduce several requirements to be fulfilled in order to produce repeatable
-hash values for archived web pages.
-",1,0,0,0,0,0
-18259,Cops and robber on grids and tori,"  This paper is a contribution to the classical cops and robber problem on a
-graph, directed to two-dimensional grids and toroidal grids. These studies are
-generally aimed at determining the minimum number of cops needed to capture the
-robber and proposing algorithms for the capture. We apply some new concepts to
-propose a new solution to the problem on grids that was already solved under a
-different approach, and apply these concepts to give efficient algorithms for
-the capture on toroidal grids. As for grids, we show that two cops suffice even
-in a semi-torus (i.e. a grid with toroidal closure in one dimension) and three
-cops are necessary and sufficient in a torus. Then we treat the problem in
-function of any number k of cops, giving efficient algorithms for grids and
-tori and computing lower and upper bounds on the capture time. Conversely we
-determine the minimum value of k needed for any given capture time and study a
-possible speed-up phenomenon.
-",1,0,0,0,0,0
-18260,Measuring Gender Inequalities of German Professions on Wikipedia,"  Wikipedia is a community-created online encyclopedia; arguably, it is the
-most popular and largest knowledge resource on the Internet. Thus, reliability
-and neutrality are of high importance for Wikipedia. Previous research [3]
-reveals gender bias in Google search results for many professions and
-occupations. Also, Wikipedia was criticized for existing gender bias in
-biographies [4] and gender gap in the editor community [5, 6]. Thus, one could
-expect that gender bias related to professions and occupations may be present
-in Wikipedia. The term gender bias is used here in the sense of conscious or
-unconscious favoritism towards one gender over another [47] with respect to
-professions and occupations. The objective of this work is to identify and
-assess gender bias. To this end, the German Wikipedia articles about
-professions and occupations were analyzed on three dimensions: redirections,
-images, and people mentioned in the articles. This work provides evidence for
-systematic overrepresentation of men in all three dimensions; female bias is
-only present for a few professions.
-",1,0,0,0,0,0
-18261,Contact Adaption during Epidemics: A Multilayer Network Formulation Approach,"  People change their physical contacts as a preventive response to infectious
-disease propagations. Yet, only a few mathematical models consider the coupled
-dynamics of the disease propagation and the contact adaptation process. This
-paper presents a model where each agent has a default contact neighborhood set,
-and switches to a different contact set once she becomes alert about infection
-among her default contacts. Since each agent can adopt either of two possible
-neighborhood sets, the overall contact network switches among 2^N possible
-configurations. Notably, a two-layer network representation can fully model the
-underlying adaptive, state-dependent contact network. Contact adaptation
-influences the size of the disease prevalence and the epidemic threshold---a
-characteristic measure of a contact network robustness against epidemics---in a
-nonlinear fashion. Particularly, the epidemic threshold for the presented
-adaptive contact network belongs to the solution of a nonlinear
-Perron-Frobenius (NPF) problem, which does not depend on the contact adaptation
-rate monotonically. Furthermore, the network adaptation model predicts a
-counter-intuitive scenario where adaptively changing contacts may adversely
-lead to lower network robustness against epidemic spreading if the contact
-adaptation is not fast enough. An original result for a class of NPF problems
-facilitate the analytical developments in this paper.
-",1,0,0,0,0,0
-18262,Estimation in the convolution structure density model. Part I: oracle inequalities,"  We study the problem of nonparametric estimation under $\bL_p$-loss, $p\in
-[1,\infty)$, in the framework of the convolution structure density model on
-$\bR^d$. This observation scheme is a generalization of two classical
-statistical models, namely density estimation under direct and indirect
-observations. In Part I the original pointwise selection rule from a family of
-""kernel-type"" estimators is proposed. For the selected estimator, we prove an
-$\bL_p$-norm oracle inequality and several of its consequences. In Part II the
-problem of adaptive minimax estimation under $\bL_p$--loss over the scale of
-anisotropic Nikol'skii classes is addressed. We fully characterize the behavior
-of the minimax risk for different relationships between regularity parameters
-and norm indexes in the definitions of the functional class and of the risk. We
-prove that the selection rule proposed in Part I leads to the construction of
-an optimally or nearly optimally (up to logarithmic factor) adaptive estimator.
-",0,0,1,1,0,0
-18263,Candidate exoplanet host HD131399A: a nascent Am star,"  Direct imaging suggests that there is a Jovian exoplanet around the primary
-A-star in the triple-star system HD131399. We investigate a high-quality
-spectrum of the primary component HD131399A obtained with FEROS on the ESO/MPG
-2.2m telescope, aiming to characterise the star's atmospheric and fundamental
-parameters, and to determine elemental abundances at high precision and
-accuracy. The aim is to constrain the chemical composition of the birth cloud
-of the system and therefore the bulk composition of the putative planet. A
-hybrid non-local thermal equilibrium (non-LTE) model atmosphere technique is
-adopted for the quantitative spectral analysis. Comparison with the most recent
-stellar evolution models yields the fundamental parameters. The atmospheric and
-fundamental stellar parameters of HD131399A are constrained to Teff=9200+-100
-K, log g=4.37+-0.10, M=1.95+0.08-0.06 Msun, R=1.51+0.13-0.10 Rsun, and log
-L/Lsun=1.17+-0.07, locating the star on the zero-age main sequence. Non-LTE
-effects on the derived metal abundances are often smaller than 0.1dex, but can
-reach up to ~0.8dex for individual lines. The observed lighter elements up to
-calcium are overall consistent with present-day cosmic abundances, with a C/O
-ratio of 0.45$\pm$0.07 by number, while the heavier elements show mild
-overabundances. We conclude that the birth cloud of the system had a standard
-chemical composition, but we witness the onset of the Am phenomenon in the
-slowly rotating star. We furthermore show that non-LTE analyses have the
-potential to solve the remaining discrepancies between observed abundances and
-predictions by diffusion models for Am stars. Moreover, the present case allows
-mass loss, not turbulent mixing, to be identified as the main transport process
-competing with diffusion in very young Am stars.
-",0,1,0,0,0,0
-18264,Toward Quantum Combinatorial Games,"  In this paper, we propose a Quantum variation of combinatorial games,
-generalizing the Quantum Tic-Tac-Toe proposed by Allan Goff. A combinatorial
-game is a two-player game with no chance and no hidden information, such as Go
-or Chess. In this paper, we consider the possibility of playing superpositions
-of moves in such games. We propose different rulesets depending on when
-superposed moves should be played, and prove that all these rulesets may lead
-similar games to different outcomes. We then consider Quantum variations of the
-game of Nim. We conclude with some discussion on the relative interest of the
-different rulesets.
-",1,0,0,0,0,0
-18265,Markov $L_2$ inequality with the Gegenbauer weight,"  For the Gegenbauer weight function $w_{\lambda}(t)=(1-t^2)^{\lambda-1/2}$,
-$\lambda>-1/2$, we denote by $\Vert\cdot\Vert_{w_{\lambda}}$ the associated
-$L_2$-norm, $$ \Vert
-f\Vert_{w_{\lambda}}:=\Big(\int_{-1}^{1}w_{\lambda}(t)f^2(t)\,dt\Big)^{1/2}. $$
-We study the Markov inequality $$ \Vert p^{\prime}\Vert_{w_{\lambda}}\leq
-c_{n}(\lambda)\,\Vert p\Vert_{w_{\lambda}},\qquad p\in \mathcal{P}_n, $$ where
-$\mathcal{P}_n$ is the class of algebraic polynomials of degree not exceeding
-$n$. Upper and lower bounds for the best Markov constant $c_{n}(\lambda)$ are
-obtained, which are valid for all $n\in \mathbb{N}$ and $\lambda>-\frac{1}{2}$.
-",0,0,1,0,0,0
-18266,A Note on Some Approximation Kernels on the Sphere,"  We produce precise estimates for the Kogbetliantz kernel for the
-approximation of functions on the sphere. Furthermore, we propose and study a
-new approximation kernel, which has slightly better properties.
-",0,0,1,0,0,0
-18267,Machine Learning as Statistical Data Assimilation,"  We identify a strong equivalence between neural network based machine
-learning (ML) methods and the formulation of statistical data assimilation
-(DA), known to be a problem in statistical physics. DA, as used widely in
-physical and biological sciences, systematically transfers information in
-observations to a model of the processes producing the observations. The
-correspondence is that layer label in the ML setting is the analog of time in
-the data assimilation setting. Utilizing aspects of this equivalence we discuss
-how to establish the global minimum of the cost functions in the ML context,
-using a variational annealing method from DA. This provides a design method for
-optimal networks for ML applications and may serve as the basis for
-understanding the success of ""deep learning"". Results from an ML example are
-presented.
-When the layer label is taken to be continuous, the Euler-Lagrange equation
-for the ML optimization problem is an ordinary differential equation, and we
-see that the problem being solved is a two point boundary value problem. The
-use of continuous layers is denoted ""deepest learning"". The Hamiltonian version
-provides a direct rationale for back propagation as a solution method for the
-canonical momentum; however, it suggests other solution methods are to be
-preferred.
-",1,0,0,1,0,0
-18268,Nonparametric estimation of the kernel function of symmetric stable moving average random functions,"  We use the empirical normalized (smoothed) periodogram of a $S\alpha S$
-moving average random function to estimate its kernel function from high
-frequency observation data. The weak consistency of the estimator is shown. A
-simulation study of the performance of the estimates rounds up the paper.
-",0,0,1,1,0,0
-18269,Fermions in Two Dimensions: Scattering and Many-Body Properties,"  Ultracold atomic Fermi gases in two-dimensions (2D) are an increasingly
-popular topic of research. The interaction strength between spin-up and
-spin-down particles in two-component Fermi gases can be tuned in experiments,
-allowing for a strongly interacting regime where the gas properties are yet to
-be fully understood. We have probed this regime for 2D Fermi gases by
-performing T=0 ab initio diffusion Monte Carlo calculations. The many-body
-dynamics are largely dependent on the two-body interactions, therefore we start
-with an in-depth look at scattering theory in 2D. We show the partial-wave
-expansion and its relation to the scattering length and effective range. Then
-we discuss our numerical methods for determining these scattering parameters.
-We close out this discussion by illustrating the details of bound states in 2D.
-Transitioning to the many-body system, we use variationally optimized wave
-functions to calculate ground-state properties of the gas over a range of
-interaction strengths. We show results for the energy per particle and
-parametrize an equation of state. We then proceed to determine the chemical
-potential for the strongly interacting gas.
-",0,1,0,0,0,0
-18270,Moving Horizon Estimation for ARMAX process with t-Distribution Noise,"  In this paper, instead of the usual Gaussian noise assumption,
-$t$-distribution noise is assumed. A Maximum Likelihood Estimator using the
-most recent N measurements is proposed for the Auto-Regressive-Moving-Average
-with eXogenous input (ARMAX) process with this assumption. The proposed
-estimator is robust to outliers because the `thick tail' of the t-distribution
-reduces the effect of large errors in the likelihood function. Instead of
-solving the resulting nonlinear estimator numerically, the Influence Function
-is used to formulate a computationally efficient recursive solution, which
-reduces to the traditional Moving Horizon Estimator when the noise is Gaussian.
-The formula for the variance of the estimate is derived. This formula shows
-explicitly how the variance of the estimate is affected by the number of
-measurements and noise variance. The simulation results show that the proposed
-estimator has smaller variance and is more robust to outliers than the Moving
-Window Least-Squares Estimator. For the same accuracy, the proposed estimator
-is an order of magnitude faster than the particle filter.
-",1,0,0,0,0,0
-18271,Chimera states in brain networks: empirical neural vs. modular fractal connectivity,"  Complex spatiotemporal patterns, called chimera states, consist of coexisting
-coherent and incoherent domains and can be observed in networks of coupled
-oscillators. The interplay of synchrony and asynchrony in complex brain
-networks is an important aspect in studies of both brain function and disease.
-We analyse the collective dynamics of FitzHugh-Nagumo neurons in complex
-networks motivated by its potential application to epileptology and epilepsy
-surgery. We compare two topologies: an empirical structural neural connectivity
-derived from diffusion-weighted magnetic resonance imaging and a mathematically
-constructed network with modular fractal connectivity. We analyse the
-properties of chimeras and partially synchronized states, and obtain regions of
-their stability in the parameter planes. Furthermore, we qualitatively simulate
-the dynamics of epileptic seizures and study the influence of the removal of
-nodes on the network synchronizability, which can be useful for applications to
-epileptic surgery.
-",0,1,0,0,0,0
-18272,Bayesian surrogate learning in dynamic simulator-based regression problems,"  The estimation of unknown values of parameters (or hidden variables, control
-variables) that characterise a physical system often relies on the comparison
-of measured data with synthetic data produced by some numerical simulator of
-the system as the parameter values are varied. This process often encounters
-two major difficulties: the generation of synthetic data for each considered
-set of parameter values can be computationally expensive if the system model is
-complicated; and the exploration of the parameter space can be inefficient
-and/or incomplete, a typical example being when the exploration becomes trapped
-in a local optimum of the objection function that characterises the mismatch
-between the measured and synthetic data. A method to address both these issues
-is presented, whereby: a surrogate model (or proxy), which emulates the
-computationally expensive system simulator, is constructed using deep recurrent
-networks (DRN); and a nested sampling (NS) algorithm is employed to perform
-efficient and robust exploration of the parameter space. The analysis is
-performed in a Bayesian context, in which the samples characterise the full
-joint posterior distribution of the parameters, from which parameter estimates
-and uncertainties are easily derived. The proposed approach is compared with
-conventional methods in some numerical examples, for which the results
-demonstrate that one can accelerate the parameter estimation process by at
-least an order of magnitude.
-",1,0,0,1,0,0
-18273,Stationary solutions for the ellipsoidal BGK model in a slab,"  We address the boundary value problem for the ellipsoidal BGK model of the
-Boltzmann equation posed in a bounded interval. The existence of a unique mild
-solution is established under the assumption that the inflow boundary data does
-not concentrate too much around the zero velocity, and the gas is sufficiently
-rarefied.
-",0,0,1,0,0,0
-18274,Raman signatures of monoclinic distortion in (Ba$_{1-x}$Sr$_{x}$)$_{3}$CaNb$_{2}$O$_{9}$ complex perovskites,"  Octahedral tilting is most common distortion process observed in
-centrosymmetric perovskite compounds (ABO$_{3}$). Indeed, crucial physical
-properties of this oxide stem from the tilts of BO$_{6}$ rigid octahedra. In
-microwave ceramics with perovskite-type structure, there is a close relation
-between the temperature coefficient of resonant frequency and tilt system of
-the perovskite structure. However, in many cases, limited access facilities are
-needed to assign correctly the space group, including neutron scattering and
-transmission electron microscopy. Here, we combine the Raman scattering and
-group-theory calculations to probe the structural distortion in the perovskite
-(Ba$_{1-x}$Sr$_{x}$)$_{3}$CaNb$_{2}$O$_{9}$ solid solution, which exhibits a
-structural phase transition at $x$ $\geq$ 0.7, from D$_{3d}^{3}$ trigonal to
-C$_{2h}^{3}$ monoclinic cell. Both phases are related by an octahedral tilting
-distortion ($a^{0}b^{-}b^{-}$ in Glazer notation). Low temperature Raman
-spectra corroborate the group-theoretical predictions for
-Sr$_{3}$CaNb$_{2}$O$_{9}$ compound, since 36 modes detected at 25 K agree well
-with those 42 (25A$_{g}$ $\oplus$ 17B$_{g}$) predicted ones.
-",0,1,0,0,0,0
-18275,On Quadratic Penalties in Elastic Weight Consolidation,"  Elastic weight consolidation (EWC, Kirkpatrick et al, 2017) is a novel
-algorithm designed to safeguard against catastrophic forgetting in neural
-networks. EWC can be seen as an approximation to Laplace propagation (Eskin et
-al, 2004), and this view is consistent with the motivation given by Kirkpatrick
-et al (2017). In this note, I present an extended derivation that covers the
-case when there are more than two tasks. I show that the quadratic penalties in
-EWC are inconsistent with this derivation and might lead to double-counting
-data from earlier tasks.
-",1,0,0,1,0,0
-18276,The design of the ILD forward region,"  Following the decision to reduce the L* from 4.4 m to 4.1 m, the BeamCal had
-to be moved closer to the interaction point. Results of a study of how this
-affects the beamstrahlung and backward scattering backgrounds show that the
-e+e- pair background depositions from beamstrahlung at the BeamCal rises by
-20%. The background from backscattered electrons and positrons in the inner
-pixel layers rises almost by a factor two, so does the number of photons in the
-tracker.
-",0,1,0,0,0,0
-18277,Conduction Channel Formation and Dissolution Due to Oxygen Thermophoresis/Diffusion in Hafnium Oxide Memristors,"  Transition metal oxide memristors, or resistive random-access memory (RRAM)
-switches, are under intense development for storage-class memory because of
-their favorable operating power, endurance, speed, and density. Their
-commercial deployment critically depends on predictive compact models based on
-understanding nanoscale physico-chemical forces, which remains elusive and
-controversial owing to the difficulties in directly observing atomic motions
-during resistive switching, Here, using scanning transmission synchrotron x-ray
-spectromicroscopy to study in-situ switching of hafnium oxide memristors, we
-directly observed the formation of a localized oxygen-deficiency-derived
-conductive channel surrounded by a low-conductivity ring of excess oxygen.
-Subsequent thermal annealing homogenized the segregated oxygen, resetting the
-cells towards their as-grown resistance state. We show that the formation and
-dissolution of the conduction channel are successfully modeled by radial
-thermophoresis and Fick diffusion of oxygen atoms driven by Joule heating. This
-confirmation and quantification of two opposing nanoscale radial forces that
-affect bipolar memristor switching are important components for any future
-physics-based compact model for the electronic switching of these devices.
-",0,1,0,0,0,0
-18278,Improving Massive MIMO Belief Propagation Detector with Deep Neural Network,"  In this paper, deep neural network (DNN) is utilized to improve the belief
-propagation (BP) detection for massive multiple-input multiple-output (MIMO)
-systems. A neural network architecture suitable for detection task is firstly
-introduced by unfolding BP algorithms. DNN MIMO detectors are then proposed
-based on two modified BP detectors, damped BP and max-sum BP. The correction
-factors in these algorithms are optimized through deep learning techniques,
-aiming at improved detection performance. Numerical results are presented to
-demonstrate the performance of the DNN detectors in comparison with various BP
-modifications. The neural network is trained once and can be used for multiple
-online detections. The results show that, compared to other state-of-the-art
-detectors, the DNN detectors can achieve lower bit error rate (BER) with
-improved robustness against various antenna configurations and channel
-conditions at the same level of complexity.
-",0,0,0,1,0,0
-18279,Trend Analysis on the Metadata of Program Comprehension Papers,"  As program comprehension is a vast research area, it is necessary to get an
-overview of its rising and falling trends. We performed an n-gram frequency
-analysis on titles, abstracts and keywords of 1885 articles about program
-comprehension from the years 2000-2014. According to this analysis, the most
-rising trends are feature location and open source systems, the most falling
-ones are program slicing and legacy systems.
-",1,0,0,0,0,0
-18280,Combinatorial views on persistent characters in phylogenetics,"  The so-called binary perfect phylogeny with persistent characters has
-recently been thoroughly studied in computational biology as it is less
-restrictive than the well known binary perfect phylogeny. Here, we focus on the
-notion of (binary) persistent characters, i.e. characters that can be realized
-on a phylogenetic tree by at most one $0 \rightarrow 1$ transition followed by
-at most one $1 \rightarrow 0$ transition in the tree, and analyze these
-characters under different aspects. First, we illustrate the connection between
-persistent characters and Maximum Parsimony, where we characterize persistent
-characters in terms of the first phase of the famous Fitch algorithm.
-Afterwards we focus on the number of persistent characters for a given
-phylogenetic tree. We show that this number solely depends on the balance of
-the tree. To be precise, we develop a formula for counting the number of
-persistent characters for a given phylogenetic tree based on an index of tree
-balance, namely the Sackin index. Lastly, we consider the question of how many
-(carefully chosen) binary characters together with their persistence status are
-needed to uniquely determine a phylogenetic tree and provide an upper bound for
-the number of characters needed.
-",0,0,0,0,1,0
-18281,Distribution of residuals in the nonparametric IV model with application to separability testing,"  We develop a uniform asymptotic expansion for the empirical distribution
-function of residuals in the nonparametric IV regression. Such expansion opens
-a door for construction of a broad range of residual-based specification tests
-in nonparametric IV models. Building on obtained result, we develop a test for
-the separability of unobservables in econometric models with endogeneity. The
-test is based on verifying the independence condition between residuals of the
-NPIV estimator and the instrument and can distinguish between the non-separable
-and the separable specification under endogeneity.
-",0,0,1,1,0,0
-18282,Scene Graph Generation by Iterative Message Passing,"  Understanding a visual scene goes beyond recognizing individual objects in
-isolation. Relationships between objects also constitute rich semantic
-information about the scene. In this work, we explicitly model the objects and
-their relationships using scene graphs, a visually-grounded graphical structure
-of an image. We propose a novel end-to-end model that generates such structured
-scene representation from an input image. The model solves the scene graph
-inference problem using standard RNNs and learns to iteratively improves its
-predictions via message passing. Our joint inference model can take advantage
-of contextual cues to make better predictions on objects and their
-relationships. The experiments show that our model significantly outperforms
-previous methods for generating scene graphs using Visual Genome dataset and
-inferring support relations with NYU Depth v2 dataset.
-",1,0,0,0,0,0
-18283,On the Apparent Power Law in CDM Halo Pseudo Phase Space Density Profiles,"  We investigate the apparent power-law scaling of the pseudo phase space
-density (PPSD) in CDM halos. We study fluid collapse, using the close analogy
-between the gas entropy and the PPSD in the fluid approximation. Our
-hydrodynamic calculations allow for a precise evaluation of logarithmic
-derivatives. For scale-free initial conditions, entropy is a power law in
-Lagrangian (mass) coordinates, but not in Eulerian (radial) coordinates. The
-deviation from a radial power law arises from incomplete hydrostatic
-equilibrium (HSE), linked to bulk inflow and mass accretion, and the
-convergence to the asymptotic central power-law slope is very slow. For more
-realistic collapse, entropy is not a power law with either radius or mass due
-to deviations from HSE and scale-dependent initial conditions. Instead, it is a
-slowly rolling power law that appears approximately linear on a log-log plot.
-Our fluid calculations recover PPSD power-law slopes and residual amplitudes
-similar to N-body simulations, indicating that deviations from a power law are
-not numerical artefacts. In addition, we find that realistic collapse is not
-self-similar: scale lengths such as the shock radius and the turnaround radius
-are not power-law functions of time. We therefore argue that the apparent
-power-law PPSD cannot be used to make detailed dynamical inferences or
-extrapolate halo profiles inward, and that it does not indicate any hidden
-integrals of motion. We also suggest that the apparent agreement between the
-PPSD and the asymptotic Bertschinger slope is purely coincidental.
-",0,1,0,0,0,0
-18284,Heavy tailed approximate identities and $σ$-stable Markov kernels,"  The aim of this paper is to present some results relating the properties of
-stability, concentration and approximation to the identity of convolution
-through not necessarily mollification type families of heavy tailed Markov
-kernels. A particular case is provided by the kernels $K_t$ obtained as the $t$
-mollification of $L^{\sigma(t)}$ selected from the family
-$\mathcal{L}=\{L^{\sigma}:
-\widehat{L^{\sigma}}(\xi)=e^{-|\xi|^\sigma},0<\sigma<2\}$, by a given function
-$\sigma$ with values in the interval $(0,2)$. We show that a basic Harnack type
-inequality, introduced by C.~Calderón in the convolution case, becomes at
-once natural to the setting and useful to connect the concepts of stability,
-concentration and approximation of the identity. Some of the general results
-are extended to spaces of homogeneous type since most of the concepts involved
-in the theory are given in terms of metric and measure.
-",0,0,1,0,0,0
-18285,Spreading of correlations in the Falicov-Kimball model,"  We study dynamical properties of the one- and two-dimensional Falicov-Kimball
-model using lattice Monte Carlo simulations. In particular, we calculate the
-spreading of charge correlations in the equilibrium model and after an
-interaction quench. The results show a reduction of the light-cone velocity
-with interaction strength at low temperature, while the phase velocity
-increases. At higher temperature, the initial spreading is determined by the
-Fermi velocity of the noninteracting system and the maximum range of the
-correlations decreases with increasing interaction strength. Charge order
-correlations in the disorder potential enhance the range of the correlations.
-We also use the numerically exact lattice Monte Carlo results to benchmark the
-accuracy of equilibrium and nonequilibrium dynamical cluster approximation
-calculations. It is shown that the bias introduced by the mapping to a
-periodized cluster is substantial, and that from a numerical point of view, it
-is more efficient to simulate the lattice model directly.
-",0,1,0,0,0,0
-18286,Direct Estimation of Information Divergence Using Nearest Neighbor Ratios,"  We propose a direct estimation method for Rényi and f-divergence measures
-based on a new graph theoretical interpretation. Suppose that we are given two
-sample sets $X$ and $Y$, respectively with $N$ and $M$ samples, where
-$\eta:=M/N$ is a constant value. Considering the $k$-nearest neighbor ($k$-NN)
-graph of $Y$ in the joint data set $(X,Y)$, we show that the average powered
-ratio of the number of $X$ points to the number of $Y$ points among all $k$-NN
-points is proportional to Rényi divergence of $X$ and $Y$ densities. A
-similar method can also be used to estimate f-divergence measures. We derive
-bias and variance rates, and show that for the class of $\gamma$-Hölder
-smooth functions, the estimator achieves the MSE rate of
-$O(N^{-2\gamma/(\gamma+d)})$. Furthermore, by using a weighted ensemble
-estimation technique, for density functions with continuous and bounded
-derivatives of up to the order $d$, and some extra conditions at the support
-set boundary, we derive an ensemble estimator that achieves the parametric MSE
-rate of $O(1/N)$. Our estimators are more computationally tractable than other
-competing estimators, which makes them appealing in many practical
-applications.
-",1,0,0,1,0,0
-18287,Efficiently Learning Mixtures of Mallows Models,"  Mixtures of Mallows models are a popular generative model for ranking data
-coming from a heterogeneous population. They have a variety of applications
-including social choice, recommendation systems and natural language
-processing. Here we give the first polynomial time algorithm for provably
-learning the parameters of a mixture of Mallows models with any constant number
-of components. Prior to our work, only the two component case had been settled.
-Our analysis revolves around a determinantal identity of Zagier which was
-proven in the context of mathematical physics, which we use to show polynomial
-identifiability and ultimately to construct test functions to peel off one
-component at a time.
-To complement our upper bounds, we show information-theoretic lower bounds on
-the sample complexity as well as lower bounds against restricted families of
-algorithms that make only local queries. Together, these results demonstrate
-various impediments to improving the dependence on the number of components.
-They also motivate the study of learning mixtures of Mallows models from the
-perspective of beyond worst-case analysis. In this direction, we show that when
-the scaling parameters of the Mallows models have separation, there are much
-faster learning algorithms.
-",0,0,0,1,0,0
-18288,Optimal Scheduling of Electrolyzer in Power Market with Dynamic Prices,"  Optimal scheduling of hydrogen production in dynamic pricing power market can
-maximize the profit of hydrogen producer; however, it highly depends on the
-accurate forecast of hydrogen consumption. In this paper, we propose a deep
-leaning based forecasting approach for predicting hydrogen consumption of fuel
-cell vehicles in future taxi industry. The cost of hydrogen production is
-minimized by utilizing the proposed forecasting tool to reduce the hydrogen
-produced during high cost on-peak hours and guide hydrogen producer to store
-sufficient hydrogen during low cost off-peak hours.
-",0,0,0,1,0,0
-18289,Determination of the thermopower of microscale samples with an AC method,"  A modified AC method based on micro-fabricated heater and resistive
-thermometers has been applied to measure the thermopower of microscale samples.
-A sinusoidal current with frequency {\omega} is passed to the heater to
-generate an oscillatory temperature difference across the sample at a frequency
-2{\omega}, which simultaneously induces an AC thermoelectric voltage, also at
-the frequency 2{\omega}. A key step of the method is to extract amplitude and
-phase of the oscillatory temperature difference by probing the AC temperature
-variation at each individual thermometer. The sign of the thermopower is
-determined by examining the phase difference between the oscillatory
-temperature difference and the AC thermoelectric voltage. The technique has
-been compared with the popular DC method by testing both n-type and p-type thin
-film samples. Both methods yielded consistent results, which verified the
-reliability of the newly proposed AC method.
-",0,1,0,0,0,0
-18290,Food for Thought: Analyzing Public Opinion on the Supplemental Nutrition Assistance Program,"  This project explores public opinion on the Supplemental Nutrition Assistance
-Program (SNAP) in news and social media outlets, and tracks elected
-representatives' voting records on issues relating to SNAP and food insecurity.
-We used machine learning, sentiment analysis, and text mining to analyze
-national and state level coverage of SNAP in order to gauge perceptions of the
-program over time across these outlets. Results indicate that the majority of
-news coverage has negative sentiment, more partisan news outlets have more
-extreme sentiment, and that clustering of negative reporting on SNAP occurs in
-the Midwest. Our final results and tools will be displayed in an on-line
-application that the ACFB Advocacy team can use to inform their communication
-to relevant stakeholders.
-",1,0,0,0,0,0
-18291,"Hasse diagrams of non-isomorphic posets with $n$ elements, $2\leq n \leq 7,$ and the number of posets with $10$ elements, without the aid of any computer program","  Let $P(n)$ be the set of all posets with $n$ elements and $NIP(n)$ the set of
-non-isomorphic posets with $n$ elements. Let $P^{(j)}(n)$, $1\leq j\leq 2^n,$
-be the number of all posets with $n$ elements possessing exactly $j$
-antichains. We have determined the numbers $P^{(j)}(7),$ $1\leq j\leq 128$, and
-using a result of M. Erné \cite{EM4}, we compute $|P(10)|$ without the aid of
-any computer program. We include the Hasse diagrams of all the non-isomorphic
-posets of $P(7)$.
-",0,0,1,0,0,0
-18292,Variable screening with multiple studies,"  Advancement in technology has generated abundant high-dimensional data that
-allows integration of multiple relevant studies. Due to their huge
-computational advantage, variable screening methods based on marginal
-correlation have become promising alternatives to the popular regularization
-methods for variable selection. However, all these screening methods are
-limited to single study so far. In this paper, we consider a general framework
-for variable screening with multiple related studies, and further propose a
-novel two-step screening procedure using a self-normalized estimator for
-high-dimensional regression analysis in this framework. Compared to the
-one-step procedure and rank-based sure independence screening (SIS) procedure,
-our procedure greatly reduces false negative errors while keeping a low false
-positive rate. Theoretically, we show that our procedure possesses the sure
-screening property with weaker assumptions on signal strengths and allows the
-number of features to grow at an exponential rate of the sample size. In
-addition, we relax the commonly used normality assumption and allow
-sub-Gaussian distributions. Simulations and a real transcriptomic application
-illustrate the advantage of our method as compared to the rank-based SIS
-method.
-",0,0,1,1,0,0
-18293,Gaussian-Constrained training for speaker verification,"  Neural models, in particular the d-vector and x-vector architectures, have
-produced state-of-the-art performance on many speaker verification tasks.
-However, two potential problems of these neural models deserve more
-investigation. Firstly, both models suffer from `information leak', which means
-that some parameters participating in model training will be discarded during
-inference, i.e, the layers that are used as the classifier. Secondly, both
-models do not regulate the distribution of the derived speaker vectors. This
-`unconstrained distribution' may degrade the performance of the subsequent
-scoring component, e.g., PLDA. This paper proposes a Gaussian-constrained
-training approach that (1) discards the parametric classifier, and (2) enforces
-the distribution of the derived speaker vectors to be Gaussian. Our experiments
-on the VoxCeleb and SITW databases demonstrated that this new training approach
-produced more representative and regular speaker embeddings, leading to
-consistent performance improvement.
-",1,0,0,0,0,0
-18294,Structural Change in (Economic) Time Series,"  Methods for detecting structural changes, or change points, in time series
-data are widely used in many fields of science and engineering. This chapter
-sketches some basic methods for the analysis of structural changes in time
-series data. The exposition is confined to retrospective methods for univariate
-time series. Several recent methods for dating structural changes are compared
-using a time series of oil prices spanning more than 60 years. The methods
-broadly agree for the first part of the series up to the mid-1980s, for which
-changes are associated with major historical events, but provide somewhat
-different solutions thereafter, reflecting a gradual increase in oil prices
-that is not well described by a step function. As a further illustration, 1990s
-data on the volatility of the Hang Seng stock market index are reanalyzed.
-",0,1,0,1,0,0
-18295,Exploiting Hierarchy in the Abstraction-Based Verification of Statecharts Using SMT Solvers,"  Statecharts are frequently used as a modeling formalism in the design of
-state-based systems. Formal verification techniques are also often applied to
-prove certain properties about the behavior of the system. One of the most
-efficient techniques for formal verification is Counterexample-Guided
-Abstraction Refinement (CEGAR), which reduces the complexity of systems by
-automatically building and refining abstractions. In our paper we present a
-novel adaptation of the CEGAR approach to hierarchical statechart models. First
-we introduce an encoding of the statechart to logical formulas that preserves
-information about the state hierarchy. Based on this encoding we propose
-abstraction and refinement techniques that utilize the hierarchical structure
-of statecharts and also handle variables in the model. The encoding allows us
-to use SMT solvers for the systematic exploration and verification of the
-abstract model, including also bounded model checking. We demonstrate the
-applicability and efficiency of our abstraction techniques with measurements on
-an industry-motivated example.
-",1,0,0,0,0,0
-18296,Modulus consensus in discrete-time signed networks and properties of special recurrent inequalities,"  Recently the dynamics of signed networks, where the ties among the agents can
-be both positive (attractive) or negative (repulsive) have attracted
-substantial attention of the research community. Examples of such networks are
-models of opinion dynamics over signed graphs, recently introduced by Altafini
-(2012,2013) and extended to discrete-time case by Meng et al. (2014). It has
-been shown that under mild connectivity assumptions these protocols provide the
-convergence of opinions in absolute value, whereas their signs may differ. This
-""modulus consensus"" may correspond to the polarization of the opinions (or
-bipartite consensus, including the usual consensus as a special case), or their
-convergence to zero. In this paper, we demonstrate that the phenomenon of
-modulus consensus in the discrete-time Altafini model is a manifestation of a
-more general and profound fact, regarding the solutions of a special recurrent
-inequality. Although such a recurrent inequality does not provide the
-uniqueness of a solution, it can be shown that, under some natural assumptions,
-each of its bounded solutions has a limit and, moreover, converges to
-consensus. A similar property has previously been established for special
-continuous-time differential inequalities (Proskurnikov, Cao, 2016). Besides
-analysis of signed networks, we link the consensus properties of recurrent
-inequalities to the convergence analysis of distributed optimization algorithms
-and the problems of Schur stability of substochastic matrices.
-",1,1,1,0,0,0
-18297,Mobility Transition at Grain Boundaries in Two-Step Sintered 8 mol% Yttria Stabilized Zirconia,"  Stagnation of grain growth is often attributed to impurity segregation.
-Yttria-stabilized cubic zirconia does not evidence any segregation-induced
-slowdown, as its grain growth obeys the parabolic law when the grain size
-increases by more than one order of magnitude. However, lowering the
-temperature below 1300 oC triggers an abrupt slowdown, constraining the average
-grains to grow by less than 0.5 ${\mu}$m in 1000 h despite a relatively large
-driving force imparted in the fine grains of ~0.5 ${\mu}$m. Yet isolated
-pockets of abnormally large grains, along with pockets of abnormally small
-grains, emerge in the same latter sample. Such microstructure bifurcation has
-never been observed before, and can only be explained by an inhomogeneous
-distribution of immobile four-grain junctions. The implications of these
-findings for two-step sintering are discussed.
-",0,1,0,0,0,0
-18298,"Sufficient conditions for the forcing theorem, and turning proper classes into sets","  We present three natural combinatorial properties for class forcing notions,
-which imply the forcing theorem to hold. We then show that all known sufficent
-conditions for the forcing theorem (except for the forcing theorem itself),
-including the three properties presented in this paper, imply yet another
-regularity property for class forcing notions, namely that proper classes of
-the ground model cannot become sets in a generic extension, that is they do not
-have set-sized names in the ground model. We then show that over certain models
-of Gödel-Bernays set theory without the power set axiom, there is a notion of
-class forcing which turns a proper class into a set, however does not satisfy
-the forcing theorem. Moreover, we show that the property of not turning proper
-classes into sets can be used to characterize pretameness over such models of
-Gödel-Bernays set theory.
-",0,0,1,0,0,0
-18299,Cauchy problem for effectively hyperbolic operators with triple characteristics,"  We study the Cauchy problem for effectively hyperbolic operators $P$ with
-principal symbol $p(t, x,\tau,\xi)$ having triple characteristics on $t = 0$.
-Under a condition (E) we show that such operators are strongly hyperbolic, that
-is the Cauchy problem is well posed for $p(t, x,D_t, D_x) + Q(t, x, D_t, D_x)$
-with arbitrary lower order term $Q$. The proof is based on energy estimates
-with weight $t^{-N}$ for a first order pseudo-differential system, where $N$
-depends on lower order terms. For our analysis we construct a non-negative
-definite symmetrizer $S(t)$ and we prove a version of Fefferman-Phong type
-inequality for ${\rm Re}\, (S(t)U, U)_{L^2({\mathbb R}^n)}$ with a lower bound
-$-C t^{-1}\|\langle D \rangle^{-1}U\|_{L^2(\mathbb R^n)}$.
-",0,0,1,0,0,0
-18300,\textit{Ab Initio} results for the Static Structure Factor of the Warm Dense Electron Gas,"  The uniform electron gas at finite temperature is of high current interest
-for warm dense matter research. The complicated interplay of quantum degeneracy
-and Coulomb coupling effects is fully contained in the pair distribution
-function or, equivalently, the static strucutre factor. By combining exact
-quantum Monte Carlo results for large wave vectors with the long-range behavior
-from the Singwi-Tosi-Land-Sjölander approximation, we are able to obtain
-highly accurate data for the static structure factor over the entire $k$-range.
-This allows us to gauge the accuracy of previous approximations and discuss
-their respective shortcomings. Further, our new data will serve as valuable
-input for the computation of other quantities.
-",0,1,0,0,0,0
-18301,Delooping the functor calculus tower,"  We study a connection between mapping spaces of bimodules and of
-infinitesimal bimodules over an operad. As main application and motivation of
-our work, we produce an explicit delooping of the manifold calculus tower
-associated to the space of smooth maps $D^{m}\rightarrow D^{n}$ pf discs,
-$n\geq m$, avoiding any given multisingularity and coinciding with the standard
-inclusion near $\partial D^{m}$. In particular, we give a new proof of the
-delooping of the space of disc embeddings in terms of little discs operads maps
-with the advantage that it can be applied to more general mapping spaces.
-",0,0,1,0,0,0
-18302,Area Law Violations and Quantum Phase Transitions in Modified Motzkin Walk Spin Chains,"  Area law violations for entanglement entropy in the form of a square root has
-recently been studied for one-dimensional frustration-free quantum systems
-based on the Motzkin walks and their variations. Here we consider a Motzkin
-walk with a different Hilbert space on each step of the walk spanned by
-elements of a {\it Symmetric Inverse Semigroup} with the direction of each step
-governed by its algebraic structure. This change alters the number of paths
-allowed in the Motzkin walk and introduces a ground state degeneracy sensitive
-to boundary perturbations. We study the frustration-free spin chains based on
-three symmetric inverse semigroups, $\cS^3_1$, $\cS^3_2$ and $\cS^2_1$. The
-system based on $\cS^3_1$ and $\cS^3_2$ provide examples of quantum phase
-transitions in one dimensions with the former exhibiting a transition between
-the area law and a logarithmic violation of the area law and the latter
-providing an example of transition from logarithmic scaling to a square root
-scaling in the system size, mimicking a colored $\cS^3_1$ system. The system
-with $\cS^2_1$ is much simpler and produces states that continue to obey the
-area law.
-",0,1,0,0,0,0
-18303,Diffusion and confusion of chaotic iteration based hash functions,"  To guarantee the integrity and security of data transmitted through the
-Internet, hash functions are fundamental tools. But recent researches have
-shown that security flaws exist in the most widely used hash functions. So a
-new way to improve their security performance is urgently demanded. In this
-article, we propose new hash functions based on chaotic iterations, which have
-chaotic properties as defined by Devaney. The corresponding diffusion and
-confusion analyzes are provided and a comparative study between the proposed
-hash functions is carried out, to make their use more applicable in any
-security context.
-",1,1,0,0,0,0
-18304,L1188: a promising candidate of cloud-cloud collision triggering the formation of the low- and intermediate-mass stars,"  We present a new large-scale (4 square degrees) simultaneous $^{12}$CO,
-$^{13}$CO, and C$^{18}$O ($J$=1$-$0) mapping of L1188 with the PMO 13.7-m
-telescope. Our observations have revealed that L1188 consists of two nearly
-orthogonal filamentary molecular clouds at two clearly separated velocities.
-Toward the intersection showing large velocity spreads, we find several
-bridging features connecting the two clouds in velocity, and an open arc
-structure which exhibits high excitation temperatures, enhanced $^{12}$CO and
-$^{13}$CO emission, and broad $^{12}$CO line wings. This agrees with the
-scenario that the two clouds are colliding with each other. The distribution of
-young stellar object (YSO) candidates implies an enhancement of star formation
-in the intersection of the two clouds. We suggest that a cloud-cloud collision
-happened in L1188 about 1~Myr ago, possibly triggering the formation of low-
-and intermediate-mass YSOs in the intersection.
-",0,1,0,0,0,0
-18305,Asymmetric Matrix-Valued Covariances for Multivariate Random Fields on Spheres,"  Matrix-valued covariance functions are crucial to geostatistical modeling of
-multivariate spatial data. The classical assumption of symmetry of a
-multivariate covariance function is overlay restrictive and has been considered
-as unrealistic for most of real data applications. Despite of that, the
-literature on asymmetric covariance functions has been very sparse. In
-particular, there is some work related to asymmetric covariances on Euclidean
-spaces, depending on the Euclidean distance. However, for data collected over
-large portions of planet Earth, the most natural spatial domain is a sphere,
-with the corresponding geodesic distance being the natural metric. In this
-work, we propose a strategy based on spatial rotations to generate asymmetric
-covariances for multivariate random fields on the $d$-dimensional unit sphere.
-We illustrate through simulations as well as real data analysis that our
-proposal allows to achieve improvements in the predictive performance in
-comparison to the symmetric counterpart.
-",0,0,1,1,0,0
-18306,A dynamic stochastic blockmodel for interaction lengths,"  We propose a new dynamic stochastic blockmodel that focuses on the analysis
-of interaction lengths in networks. The model does not rely on a discretization
-of the time dimension and may be used to analyze networks that evolve
-continuously over time. The framework relies on a clustering structure on the
-nodes, whereby two nodes belonging to the same latent group tend to create
-interactions and non-interactions of similar lengths. We introduce a fast
-variational expectation-maximization algorithm to perform inference, and adapt
-a widely used clustering criterion to perform model choice. Finally, we test
-our methodology on artificial data, and propose a demonstration on a dataset
-concerning face-to-face interactions between students in a high-school.
-",0,0,0,1,0,0
-18307,Do we agree on user interface aesthetics of Android apps?,"  Context: Visual aesthetics is increasingly seen as an essential factor in
-perceived usability, interaction, and overall appraisal of user interfaces
-especially with respect to mobile applications. Yet, a question that remains is
-how to assess and to which extend users agree on visual aesthetics. Objective:
-This paper analyzes the inter-rater agreement on visual aesthetics of user
-interfaces of Android apps as a basis for guidelines and evaluation models.
-Method: We systematically collected ratings on the visual aesthetics of 100
-user interfaces of Android apps from 10 participants and analyzed the frequency
-distribution, reliability and influencing design aspects. Results: In general,
-user interfaces of Android apps are perceived more ugly than beautiful. Yet,
-raters only moderately agree on the visual aesthetics. Disagreements seem to be
-related to subtle differences with respect to layout, shapes, colors,
-typography, and background images. Conclusion: Visual aesthetics is a key
-factor for the success of apps. However, the considerable disagreement of
-raters on the perceived visual aesthetics indicates the need for a better
-understanding of this software quality with respect to mobile apps.
-",1,0,0,0,0,0
-18308,A Solution for Large-scale Multi-object Tracking,"  A large-scale multi-object tracker based on the generalised labeled
-multi-Bernoulli (GLMB) filter is proposed. The algorithm is capable of tracking
-a very large, unknown and time-varying number of objects simultaneously, in the
-presence of a high number of false alarms, as well as misdetections and
-measurement origin uncertainty due to closely spaced objects. The algorithm is
-demonstrated on a simulated large-scale tracking scenario, where the peak
-number objects appearing simultaneously exceeds one million. To evaluate the
-performance of the proposed tracker, we also introduce a new method of applying
-the optimal sub-pattern assignment (OSPA) metric, and an efficient strategy for
-its evaluation in large-scale scenarios.
-",0,0,0,1,0,0
-18309,Empirical Survival Jensen-Shannon Divergence as a Goodness-of-Fit Measure for Maximum Likelihood Estimation and Curve Fitting,"  The coefficient of determination, known as $R^2$, is commonly used as a
-goodness-of-fit criterion for fitting linear models. $R^2$ is somewhat
-controversial when fitting nonlinear models, although it may be generalised on
-a case-by-case basis to deal with specific models such as the logistic model.
-Assume we are fitting a parametric distribution to a data set using, say, the
-maximum likelihood estimation method. A general approach to measure the
-goodness-of-fit of the fitted parameters, which we advocate herein, is to use a
-nonparametric measure for model comparison between the raw data and the fitted
-model. In particular, for this purpose we put forward the {\em Survival
-Jensen-Shannon divergence} ($SJS$) and its empirical counterpart (${\cal
-E}SJS$) as a metric which is bounded, and is a natural generalisation of the
-Jensen-Shannon divergence. We demonstrate, via a straightforward procedure
-making use of the ${\cal E}SJS$, that it can be used as part of maximum
-likelihood estimation or curve fitting as a measure of goodness-of-fit,
-including the construction of a confidence interval for the fitted parametric
-distribution. Furthermore, we show the validity of the proposed method with
-simulated data, and three empirical data sets of interest to researchers in
-sociophysics and econophysics.
-",0,0,0,0,0,1
-18310,Cooperative Localisation of a GPS-Denied UAV using Direction of Arrival Measurements,"  A GPS-denied UAV (Agent B) is localised through INS alignment with the aid of
-a nearby GPS-equipped UAV (Agent A), which broadcasts its position at several
-time instants. Agent B measures the signals' direction of arrival with respect
-to Agent B's inertial navigation frame. Semidefinite programming and the
-Orthogonal Procrustes algorithm are employed, and accuracy is improved through
-maximum likelihood estimation. The method is validated using flight data and
-simulations. A three-agent extension is explored.
-",1,0,0,0,0,0
-18311,Optical Surface Properties and their RF Limitations of European XFEL Cavities,"  The inner surface of superconducting cavities plays a crucial role to achieve
-highest accelerating fields and low losses. The industrial fabrication of
-cavities for the European X-Ray Free Electron Laser (XFEL) and the
-International Linear Collider (ILC) HiGrade Research Project allowed for an
-investigation of this interplay. For the serial inspection of the inner
-surface, the optical inspection robot OBACHT was constructed and to analyze the
-large amount of data, represented in the images of the inner surface, an image
-processing and analysis code was developed and new variables to describe the
-cavity surface were obtained. This quantitative analysis identified vendor
-specific surface properties which allow to perform a quality control and
-assurance during the production. In addition, a strong negative correlation of
-$\rho= -0.93$ with a significance of $6\,\sigma$ of the integrated grain
-boundary area $\sum{\mathrm{A}}$ versus the maximal achievable accelerating
-field $\mathrm{E_{acc,max}}$ has been found.
-",0,1,0,0,0,0
-18312,A comment on `An improved macroscale model for gas slip flow in porous media',"  In a recent paper by Lasseux, Valdés-Parada and Porter (J.~Fluid~Mech.
-\textbf{805} (2016) 118-146), it is found that the apparent gas permeability of
-the porous media is a nonlinear function of the Knudsen number. However, this
-result is highly questionable, because the adopted Navier-Stokes equations and
-the first-order velocity-slip boundary condition are first-order (in terms of
-the Knudsen number) approximations of the Boltzmann equation and the kinetic
-boundary condition for rarefied gas flows. Our numerical simulations based on
-the Bhatnagar-Gross-Krook kinetic equation and regularized 20-moment equations
-prove that the Navier-Stokes equations with the first-order velocity-slip
-boundary condition are only accurate at a very small Knudsen number limit,
-where the apparent gas permeability is a linear function of the Knudsen number.
-",0,1,0,0,0,0
-18313,AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training,"  Highly distributed training of Deep Neural Networks (DNNs) on future compute
-platforms (offering 100 of TeraOps/s of computational capacity) is expected to
-be severely communication constrained. To overcome this limitation, new
-gradient compression techniques are needed that are computationally friendly,
-applicable to a wide variety of layers seen in Deep Neural Networks and
-adaptable to variations in network architectures as well as their
-hyper-parameters. In this paper we introduce a novel technique - the Adaptive
-Residual Gradient Compression (AdaComp) scheme. AdaComp is based on localized
-selection of gradient residues and automatically tunes the compression rate
-depending on local activity. We show excellent results on a wide spectrum of
-state of the art Deep Learning models in multiple domains (vision, speech,
-language), datasets (MNIST, CIFAR10, ImageNet, BN50, Shakespeare), optimizers
-(SGD with momentum, Adam) and network parameters (number of learners,
-minibatch-size etc.). Exploiting both sparsity and quantization, we demonstrate
-end-to-end compression rates of ~200X for fully-connected and recurrent layers,
-and ~40X for convolutional layers, without any noticeable degradation in model
-accuracies.
-",1,0,0,1,0,0
-18314,Halo-independent determination of the unmodulated WIMP signal in DAMA: the isotropic case,"  We present a halo-independent determination of the unmodulated signal
-corresponding to the DAMA modulation if interpreted as due to dark matter
-weakly interacting massive particles (WIMPs). First we show how a modulated
-signal gives information on the WIMP velocity distribution function in the
-Galactic rest frame, from which the unmodulated signal descends. Then we
-perform a mathematically-sound profile likelihood analysis in which we profile
-the likelihood over a continuum of nuisance parameters (namely, the WIMP
-velocity distribution). As a first application of the method, which is very
-general and valid for any class of velocity distributions, we restrict the
-analysis to velocity distributions that are isotropic in the Galactic frame. In
-this way we obtain halo-independent maximum-likelihood estimates and confidence
-intervals for the DAMA unmodulated signal. We find that the estimated
-unmodulated signal is in line with expectations for a WIMP-induced modulation
-and is compatible with the DAMA background+signal rate. Specifically, for the
-isotropic case we find that the modulated amplitude ranges between a few
-percent and about 25% of the unmodulated amplitude, depending on the WIMP mass.
-",0,1,0,0,0,0
-18315,Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?,"  Deep reinforcement learning has achieved many recent successes, but our
-understanding of its strengths and limitations is hampered by the lack of rich
-environments in which we can fully characterize optimal behavior, and
-correspondingly diagnose individual actions against such a characterization.
-Here we consider a family of combinatorial games, arising from work of Erdos,
-Selfridge, and Spencer, and we propose their use as environments for evaluating
-and comparing different approaches to reinforcement learning. These games have
-a number of appealing features: they are challenging for current learning
-approaches, but they form (i) a low-dimensional, simply parametrized
-environment where (ii) there is a linear closed form solution for optimal
-behavior from any state, and (iii) the difficulty of the game can be tuned by
-changing environment parameters in an interpretable way. We use these
-Erdos-Selfridge-Spencer games not only to compare different algorithms, but
-test for generalization, make comparisons to supervised learning, analyse
-multiagent play, and even develop a self play algorithm. Code can be found at:
-this https URL
-",1,0,0,1,0,0
-18316,Memristor equations: incomplete physics and undefined passivity/activity,"  In his seminal paper, Chua presented a fundamental physical claim by
-introducing the memristor, ""The missing circuit element"". The memristor
-equations were originally supposed to represent a passive circuit element
-because, with active circuitry, arbitrary elements can be realized without
-limitations. Therefore, if the memristor equations do not guarantee that the
-circuit element can be realized by a passive system, the fundamental physics
-claim about the memristor as ""missing circuit element"" loses all its weight.
-The question of passivity/activity belongs to physics thus we incorporate
-thermodynamics into the study of this problem. We show that the memristor
-equations are physically incomplete regarding the problem of
-passivity/activity. As a consequence, the claim that the present memristor
-functions describe a passive device lead to unphysical results, such as
-violating the Second Law of thermodynamics, in infinitely large number of
-cases. The seminal memristor equations cannot introduce a new physical circuit
-element without making the model more physical such as providing the
-Fluctuation Dissipation Theory of memristors.
-",1,0,0,0,0,0
-18317,Astrophysical uncertainties on the local dark matter distribution and direct detection experiments,"  The differential event rate in Weakly Interacting Massive Particle (WIMP)
-direct detection experiments depends on the local dark matter density and
-velocity distribution. Accurate modelling of the local dark matter distribution
-is therefore required to obtain reliable constraints on the WIMP particle
-physics properties. Data analyses typically use a simple Standard Halo Model
-which might not be a good approximation to the real Milky Way (MW) halo. We
-review observational determinations of the local dark matter density, circular
-speed and escape speed and also studies of the local dark matter distribution
-in simulated MW-like galaxies. We discuss the effects of the uncertainties in
-these quantities on the energy spectrum and its time and direction dependence.
-Finally we conclude with an overview of various methods for handling these
-astrophysical uncertainties.
-",0,1,0,0,0,0
-18318,On Reliability-Aware Server Consolidation in Cloud Datacenters,"  In the past few years, datacenter (DC) energy consumption has become an
-important issue in technology world. Server consolidation using virtualization
-and virtual machine (VM) live migration allows cloud DCs to improve resource
-utilization and hence energy efficiency. In order to save energy, consolidation
-techniques try to turn off the idle servers, while because of workload
-fluctuations, these offline servers should be turned on to support the
-increased resource demands. These repeated on-off cycles could affect the
-hardware reliability and wear-and-tear of servers and as a result, increase the
-maintenance and replacement costs. In this paper we propose a holistic
-mathematical model for reliability-aware server consolidation with the
-objective of minimizing total DC costs including energy and reliability costs.
-In fact, we try to minimize the number of active PMs and racks, in a
-reliability-aware manner. We formulate the problem as a Mixed Integer Linear
-Programming (MILP) model which is in form of NP-complete. Finally, we evaluate
-the performance of our approach in different scenarios using extensive
-numerical MATLAB simulations.
-",1,0,0,0,0,0
-18319,A novel quantum dynamical approach in electron microscopy combining wave-packet propagation with Bohmian trajectories,"  The numerical analysis of the diffraction features rendered by transmission
-electron microscopy (TEM) typically relies either on classical approximations
-(Monte Carlo simulations) or quantum paraxial tomography (the multislice method
-and any of its variants). Although numerically advan- tageous (relatively
-simple implementations and low computational costs), they involve important
-approximations and thus their range of applicability is limited. To overcome
-such limitations, an alternative, more general approach is proposed, based on
-an optimal combination of wave-packet propagation with the on-the-fly
-computation of associated Bohmian trajectories. For the sake of clarity, but
-without loss of generality, the approach is used to analyze the diffraction of
-an electron beam by a thin aluminum slab as a function of three different
-incidence (work) conditions which are of interest in electron microscopy: the
-probe width, the tilting angle, and the beam energy. Specifically, it is shown
-that, because there is a dependence on particular thresholds of the beam
-energy, this approach provides a clear description of the diffraction process
-at any energy, revealing at the same time any diversion of the beam inside the
-material towards directions that cannot be accounted for by other conventional
-methods, which is of much interest when dealing with relatively low energies
-and/or relatively large tilting angles.
-",0,1,0,0,0,0
-18320,Convex equipartitions of colored point sets,"  We show that any $d$-colored set of points in general position in
-$\mathbb{R}^d$ can be partitioned into $n$ subsets with disjoint convex hulls
-such that the set of points and all color classes are partitioned as evenly as
-possible. This extends results by Holmsen, Kynčl & Valculescu (2017) and
-establishes a special case of their general conjecture. Our proof utilizes a
-result obtained independently by Soberón and by Karasev in 2010, on
-simultaneous equipartitions of $d$ continuous measures in $\mathbb{R}^d$ by $n$
-convex regions. This gives a convex partition of $\mathbb{R}^d$ with the
-desired properties, except that points may lie on the boundaries of the
-regions. In order to resolve the ambiguous assignment of these points, we set
-up a network flow problem. The equipartition of the continuous measures gives a
-fractional flow. The existence of an integer flow then yields the desired
-partition of the point set.
-",0,0,1,0,0,0
-18321,Logic Programming Petri Nets,"  With the purpose of modeling, specifying and reasoning in an integrated
-fashion with procedural and declarative aspects (both commonly present in cases
-or scenarios), the paper introduces Logic Programming Petri Nets (LPPN), an
-extension to the Petri Net notation providing an interface to logic programming
-constructs. Two semantics are presented. First, a hybrid operational semantics
-that separates the process component, treated with Petri nets, from the
-constraint/terminological component, treated with Answer Set Programming (ASP).
-Second, a denotational semantics maps the notation to ASP fully, via Event
-Calculus. These two alternative specifications enable a preliminary evaluation
-in terms of reasoning efficiency.
-",1,0,0,0,0,0
-18322,Unique Continuation through Hyperplane for Higher Order Parabolic and Shrödinger Equations,"  We consider higher order parabolic operator $\partial_t+(-\Delta_x)^m$ and
-higher order Schrödinger operator $i^{-1}\partial_t+(-\Delta_x)^m$ in
-$X=\{(t,x)\in\mathbb{R}^{1+n};~|t|<A,|x_n|<B\}$ where $m$ is any positive
-integer. Under certain lower order and regularity assumptions, we prove that if
-the solution for linear problem vanishes when $x_n>0$, then the solution
-vanishes in $X$. Such results are given globally, and we also prove some
-related local results.
-",0,0,1,0,0,0
-18323,Fashion Conversation Data on Instagram,"  The fashion industry is establishing its presence on a number of
-visual-centric social media like Instagram. This creates an interesting clash
-as fashion brands that have traditionally practiced highly creative and
-editorialized image marketing now have to engage with people on the platform
-that epitomizes impromptu, realtime conversation. What kinds of fashion images
-do brands and individuals share and what are the types of visual features that
-attract likes and comments? In this research, we take both quantitative and
-qualitative approaches to answer these questions. We analyze visual features of
-fashion posts first via manual tagging and then via training on convolutional
-neural networks. The classified images were examined across four types of
-fashion brands: mega couture, small couture, designers, and high street. We
-find that while product-only images make up the majority of fashion
-conversation in terms of volume, body snaps and face images that portray
-fashion items more naturally tend to receive a larger number of likes and
-comments by the audience. Our findings bring insights into building an
-automated tool for classifying or generating influential fashion information.
-We make our novel dataset of {24,752} labeled images on fashion conversations,
-containing visual and textual cues, available for the research community.
-",1,0,0,1,0,0
-18324,Viscous flow in a soft valve,"  Fluid-structure interactions are ubiquitous in nature and technology.
-However, the systems are often so complex that numerical simulations or ad hoc
-assumptions must be used to gain insight into the details of the complex
-interactions between the fluid and solid mechanics. In this paper, we present
-experiments and theory on viscous flow in a simple bioinspired soft valve which
-illustrate essential features of interactions between hydrodynamic and elastic
-forces at low Reynolds numbers. The setup comprises a sphere connected to a
-spring located inside a tapering cylindrical channel. The spring is aligned
-with the central axis of the channel and a pressure drop is applied across the
-sphere, thus forcing the liquid through the narrow gap between the sphere and
-the channel walls. The sphere's equilibrium position is determined by a balance
-between spring and hydrodynamic forces. Since the gap thickness changes with
-the sphere's position, the system has a pressure-dependent hydraulic
-resistance. This leads to a non-linear relation between applied pressure and
-flow rate: flow initially increases with pressure, but decreases when the
-pressure exceeds a certain critical value as the gap closes. To rationalize
-these observations, we propose a mathematical model that reduced the complexity
-of the flow to a two-dimensional lubrication approximation. A closed-form
-expression for the pressure-drop/flow rate is obtained which reveals that the
-flow rate $Q$ depends on the pressure drop $\Delta p$, sphere radius $a$, gap
-thickness $h_0$, and viscosity $\eta$ as $Q\sim \eta^{-1}
-a^{1/2}h_0^{5/2}\left(\Delta p_c-\Delta p\right)^{5/2}\Delta p$, where the
-critical pressure $\Delta p_c$ scales with the spring constant $k$ and sphere
-radius $a$ as $\Delta p_c\sim k a^{-2}$. These predictions compared favorably
-to the results of our experiments with no free parameters.
-",0,1,0,0,0,0
-18325,Superheavy Thermal Dark Matter and Primordial Asymmetries,"  The early universe could feature multiple reheating events, leading to jumps
-in the visible sector entropy density that dilute both particle asymmetries and
-the number density of frozen-out states. In fact, late time entropy jumps are
-usually required in models of Affleck-Dine baryogenesis, which typically
-produces an initial particle-antiparticle asymmetry that is much too large. An
-important consequence of late time dilution, is that a smaller dark matter
-annihilation cross section is needed to obtain the observed dark matter relic
-density. For cosmologies with high scale baryogenesis, followed by
-radiation-dominated dark matter freeze-out, we show that the perturbative
-unitarity mass bound on thermal relic dark matter is relaxed to $10^{10}$ GeV.
-We proceed to study superheavy asymmetric dark matter models, made possible by
-a sizable entropy injection after dark matter freeze-out, and identify how the
-Affleck-Dine mechanism would generate the baryon and dark asymmetries.
-",0,1,0,0,0,0
-18326,Time-dependent population imaging for solid high harmonic generation,"  We propose an intuitive method, called time-dependent population imaging
-(TDPI), to map the dynamical processes of high harmonic generation (HHG) in
-solids by solving the time-dependent Schrödinger equation (TDSE). It is
-shown that the real-time dynamical characteristics of HHG in solids, such as
-the instantaneous photon energies of emitted harmonics, can be read directly
-from the energy-resolved population oscillations of electrons in the TDPIs.
-Meanwhile, the short and long trajectories of solid HHG are illustrated clearly
-from TDPI. By using the TDPI, we also investigate the effects of
-carrier-envelope phase (CEP) in few-cycle pulses and intuitively demonstrate
-the HHG dynamics driven by two-color fields. Our results show that the TDPI
-provides a powerful tool to study the ultrafast dynamics in strong fields for
-various laser-solid configurations and gain an insight into HHG processes in
-solids.
-",0,1,0,0,0,0
-18327,Group Sparse Bayesian Learning for Active Surveillance on Epidemic Dynamics,"  Predicting epidemic dynamics is of great value in understanding and
-controlling diffusion processes, such as infectious disease spread and
-information propagation. This task is intractable, especially when surveillance
-resources are very limited. To address the challenge, we study the problem of
-active surveillance, i.e., how to identify a small portion of system components
-as sentinels to effect monitoring, such that the epidemic dynamics of an entire
-system can be readily predicted from the partial data collected by such
-sentinels. We propose a novel measure, the gamma value, to identify the
-sentinels by modeling a sentinel network with row sparsity structure. We design
-a flexible group sparse Bayesian learning algorithm to mine the sentinel
-network suitable for handling both linear and non-linear dynamical systems by
-using the expectation maximization method and variational approximation. The
-efficacy of the proposed algorithm is theoretically analyzed and empirically
-validated using both synthetic and real-world data.
-",1,0,0,1,0,0
-18328,FNS: an event-driven spiking neural network framework for efficient simulations of large-scale brain models,"  Limitations in processing capabilities and memory of today's computers make
-spiking neuron-based (human) whole-brain simulations inevitably characterized
-by a compromise between bio-plausibility and computational cost. It translates
-into brain models composed of a reduced number of neurons and a simplified
-neuron's mathematical model. Taking advantage of the sparse character of
-brain-like computation, eventdriven technique allows us to carry out efficient
-simulation of large-scale Spiking Neural Networks (SNN). The recent Leaky
-Integrate-and-Fire with Latency (LIFL) spiking neuron model is event-driven
-compatible and exhibits some realistic neuronal features, opening new horizons
-in whole-brain modelling. In this paper we present FNS, a LIFL-based exact
-event-driven spiking neural network framework implemented in Java and oriented
-to wholebrain simulations. FNS combines spiking/synaptic whole-brain modelling
-with the event-driven approach, allowing us to define heterogeneous modules and
-multi-scale connectivity with delayed connections and plastic synapses,
-providing fast simulations at the same time. A novel parallelization strategy
-is also implemented in order to further speed up simulations. This paper
-presents mathematical models, software implementation and simulation routines
-on which FNS is based. Finally, a reduced brain network model (1400 neurons and
-45000 synapses) is synthesized on the basis of real brain structural data, and
-the resulting model activity is compared with associated brain functional
-(source-space MEG) data. The conducted test shows a good matching between the
-activity of model and that of the emulated subject, in outstanding simulation
-times (about 20s for simulating 4s of activity with a normal PC). Dedicated
-sections of stimuli editing and output synthesis allow the neuroscientist to
-introduce and extract brain-like signals, respectively...
-",0,0,0,0,1,0
-18329,"Synchronization, phase slips and coherent structures in area-preserving maps","  The problem of synchronization of coupled Hamiltonian systems exhibits
-interesting features due to the non-uniform or mixed nature (regular and
-chaotic) of the phase space. We study these features by investigating the
-synchronization of unidirectionally coupled area-preserving maps coupled by the
-Pecora-Carroll method. We find that coupled standard maps show complete
-synchronization for values of the nonlinearity parameter at which regular
-structures are still present in phase space. The distribution of
-synchronization times has a power law tail indicating long synchronization
-times for at least some of the synchronizing trajectories. With the
-introduction of coherent structures using parameter perturbation in the system,
-this distribution crosses over to exponential behavior, indicating shorter
-synchronization times, and the number of initial conditions which synchronize
-increases significantly, indicating an enhancement in the basin of
-synchronization. On the other hand, coupled blinking vortex maps display both
-phase synchronization and phase slips, depending on the location of the initial
-conditions. We discuss the implication of our results.
-",0,1,0,0,0,0
-18330,Design of Real-time Semantic Segmentation Decoder for Automated Driving,"  Semantic segmentation remains a computationally intensive algorithm for
-embedded deployment even with the rapid growth of computation power. Thus
-efficient network design is a critical aspect especially for applications like
-automated driving which requires real-time performance. Recently, there has
-been a lot of research on designing efficient encoders that are mostly task
-agnostic. Unlike image classification and bounding box object detection tasks,
-decoders are computationally expensive as well for semantic segmentation task.
-In this work, we focus on efficient design of the segmentation decoder and
-assume that an efficient encoder is already designed to provide shared features
-for a multi-task learning system. We design a novel efficient non-bottleneck
-layer and a family of decoders which fit into a small run-time budget using
-VGG10 as efficient encoder. We demonstrate in our dataset that experimentation
-with various design choices led to an improvement of 10\% from a baseline
-performance.
-",1,0,0,1,0,0
-18331,"Nonparametric covariance estimation for mixed longitudinal studies, with applications in midlife women's health","  Motivated by applications of mixed longitudinal studies, where a group of
-subjects entering the study at different ages (cross-sectional) are followed
-for successive years (longitudinal), we consider nonparametric covariance
-estimation with samples of noisy and partially-observed functional
-trajectories. To ensure model identifiability and estimation consistency, we
-introduce and carefully discuss the reduced rank and neighboring incoherence
-condition. The proposed algorithm is based on a sequential-aggregation scheme,
-which is non-iterative, with only basic matrix operations and closed-form
-solutions in each step. The good performance of the proposed method is
-supported by both theory and numerical experiments. We also apply the proposed
-procedure to a midlife women's working memory study based on the data from the
-Study of Women's Health Across the Nation (SWAN).
-",0,0,1,1,0,0
-18332,Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data,"  The recent Nobel-prize-winning detections of gravitational waves from merging
-black holes and the subsequent detection of the collision of two neutron stars
-in coincidence with electromagnetic observations have inaugurated a new era of
-multimessenger astrophysics. To enhance the scope of this emergent science, we
-proposed the use of deep convolutional neural networks for the detection and
-characterization of gravitational wave signals in real-time. This method, Deep
-Filtering, was initially demonstrated using simulated LIGO noise. In this
-article, we present the extension of Deep Filtering using real data from the
-first observing run of LIGO, for both detection and parameter estimation of
-gravitational waves from binary black hole mergers with continuous data streams
-from multiple LIGO detectors. We show for the first time that machine learning
-can detect and estimate the true parameters of a real GW event observed by
-LIGO. Our comparisons show that Deep Filtering is far more computationally
-efficient than matched-filtering, while retaining similar sensitivity and lower
-errors, allowing real-time processing of weak time-series signals in
-non-stationary non-Gaussian noise, with minimal resources, and also enables the
-detection of new classes of gravitational wave sources that may go unnoticed
-with existing detection algorithms. This approach is uniquely suited to enable
-coincident detection campaigns of gravitational waves and their multimessenger
-counterparts in real-time.
-",1,1,0,0,0,0
-18333,Note on local coisotropic Floer homology and leafwise fixed points,"  I outline a construction of a local Floer homology for a coisotropic
-submanifold of a symplectic manifold and explain how it can be used to show
-that leafwise fixed points of Hamiltonian diffeomorphisms exist.
-",0,0,1,0,0,0
-18334,Geometric Surface-Based Tracking Control of a Quadrotor UAV under Actuator Constraints,"  This paper presents contributions on nonlinear tracking control systems for a
-quadrotor unmanned micro aerial vehicle. New controllers are proposed based on
-nonlinear surfaces composed by tracking errors that evolve directly on the
-nonlinear configuration manifold thus inherently including in the control
-design the nonlinear characteristics of the SE(3) configuration space. In
-particular geometric surface-based controllers are developed, and through
-rigorous stability proofs they are shown to have desirable closed loop
-properties that are almost global. A region of attraction, independent of the
-position error, is produced and its effects are analyzed. A strategy allowing
-the quadrotor to achieve precise attitude tracking while simultaneously
-following a desired position command and complying to actuator constraints in a
-computationally inexpensive manner is derived. This important contribution
-differentiates this work from existing Geometric Nonlinear Control System
-solutions (GNCSs) since the commanded thrusts can be realized by the majority
-of quadrotors produced by the industry. The new features of the proposed GNCSs
-are illustrated by numerical simulations of aggressive maneuvers and a
-comparison with a GNCSs from the bibliography.
-",1,0,0,0,0,0
-18335,Estimating the rate of defects under imperfect sampling inspection - a new approach,"  We consider the problem of estimating the the rate of defects (mean number of
-defects per item), given counts of defects detected by two independent
-imperfect inspectors on a sample of items. In contrast with the well-known
-method of Capture-Recapture, here we {\it{do not}} have information regarding
-the number of defects jointly detected by {\it{both}} inspectors. We solve this
-problem by constructing two types of estimators - a simple moment-type
-estimator, and a more complicated maximum-likelihood estimator. The performance
-of these estimators is studied analytically and by means of simulations. It is
-shown that the maximum-likelihood estimator is superior to the moment-type
-estimator. A systematic comparison with the Capture-Recapture method is also
-made.
-",0,0,0,1,0,0
-18336,Measuring heavy-tailedness of distributions,"  Different questions related with analysis of extreme values and outliers
-arise frequently in practice. To exclude extremal observations and outliers is
-not a good decision because they contain important information about the
-observed distribution. The difficulties with their usage are usually related to
-the estimation of the tail index in case it exists. There are many measures for
-the center of the distribution, e.g. mean, mode, median. There are many
-measures of the variance, asymmetry, and kurtosis, but there is no easy
-characteristic for heavy-tailedness of the observed distribution. Here we
-propose such a measure, give some examples and explore some of its properties.
-This allows us to introduce a classification of the distributions, with respect
-to their heavy-tailedness. The idea is to help and navigate practitioners for
-accurate and easier work in the field of probability distributions.
-Using the properties of the defined characteristics some distribution
-sensitive extremal index estimators are proposed and their properties are
-partially investigated.
-",0,0,0,1,0,0
-18337,Inductive $k$-independent graphs and $c$-colorable subgraphs in scheduling: A review,"  Inductive $k$-independent graphs generalize chordal graphs and have recently
-been advocated in the context of interference-avoiding wireless communication
-scheduling. The NP-hard problem of finding maximum-weight induced $c$-colorable
-subgraphs, which is a generalization of finding maximum independent sets,
-naturally occurs when selecting $c$ sets of pairwise non-conflicting jobs
-(modeled as graph vertices). We investigate the parameterized complexity of
-this problem on inductive $k$-independent graphs. We show that the Independent
-Set problem is W[1]-hard even on 2-simplicial 3-minoes---a subclass of
-inductive 2-independent graphs. In contrast, we prove that the more general
-Maximum $c$-Colorable Subgraph problem is fixed-parameter tractable on
-edge-wise unions of cluster and chordal graphs, which are 2-simplicial. In both
-cases, the parameter is the solution size. Aside from this, we survey other
-graph classes between inductive 1-inductive and inductive 2-inductive graphs
-with applications in scheduling.
-",1,0,0,0,0,0
-18338,The word and order problems for self-similar and automata groups,"  We prove that the word problem is undecidable in functionally recursive
-groups, and that the order problem is undecidable in automata groups, even
-under the assumption that they are contracting.
-",1,0,1,0,0,0
-18339,Machine Teaching: A New Paradigm for Building Machine Learning Systems,"  The current processes for building machine learning systems require
-practitioners with deep knowledge of machine learning. This significantly
-limits the number of machine learning systems that can be created and has led
-to a mismatch between the demand for machine learning systems and the ability
-for organizations to build them. We believe that in order to meet this growing
-demand for machine learning systems we must significantly increase the number
-of individuals that can teach machines. We postulate that we can achieve this
-goal by making the process of teaching machines easy, fast and above all,
-universally accessible.
-While machine learning focuses on creating new algorithms and improving the
-accuracy of ""learners"", the machine teaching discipline focuses on the efficacy
-of the ""teachers"". Machine teaching as a discipline is a paradigm shift that
-follows and extends principles of software engineering and programming
-languages. We put a strong emphasis on the teacher and the teacher's
-interaction with data, as well as crucial components such as techniques and
-design principles of interaction and visualization.
-In this paper, we present our position regarding the discipline of machine
-teaching and articulate fundamental machine teaching principles. We also
-describe how, by decoupling knowledge about machine learning algorithms from
-the process of teaching, we can accelerate innovation and empower millions of
-new uses for machine learning models.
-",1,0,0,1,0,0
-18340,Totally geodesic submanifolds of Teichmuller space,"  We show that any totally geodesic submanifold of Teichmuller space of
-dimension greater than one covers a totally geodesic subvariety, and only
-finitely many totally geodesic subvarieties of dimension greater than one exist
-in each moduli space.
-",0,0,1,0,0,0
-18341,Towards a splitting of the $K(2)$-local string bordism spectrum,"  We show that $K(2)$-locally, the smash product of the string bordism spectrum
-and the spectrum $T_2$ splits into copies of Morava $E$-theories. Here, $T_2$
-is related to the Thom spectrum of the canonical bundle over $\Omega SU(4)$.
-",0,0,1,0,0,0
-18342,Osmotic and diffusio-osmotic flow generation at high solute concentration. I. Mechanical approaches,"  In this paper, we explore various forms of osmotic transport in the regime of
-high solute concentration. We consider both the osmosis across membranes and
-diffusio-osmosis at solid interfaces, driven by solute concentration gradients.
-We follow a mechanical point of view of osmotic transport, which allows us to
-gain much insight into the local mechanical balance underlying osmosis. We
-demonstrate in particular how the general expression of the osmotic pressure
-for mixtures, as obtained classically from the thermodynamic framework, emerges
-from the mechanical balance controlling non-equilibrium transport under solute
-gradients. Expressions for the rejection coefficient of osmosis and the
-diffusio-osmotic mobilities are accordingly obtained. These results generalize
-existing ones in the dilute solute regime to mixtures with arbitrary
-concentrations.
-",0,1,0,0,0,0
-18343,Accurate and Efficient Estimation of Small P-values with the Cross-Entropy Method: Applications in Genomic Data Analysis,"  Small $p$-values are often required to be accurately estimated in large scale
-genomic studies for the adjustment of multiple hypothesis tests and the ranking
-of genomic features based on their statistical significance. For those
-complicated test statistics whose cumulative distribution functions are
-analytically intractable, existing methods usually do not work well with small
-$p$-values due to lack of accuracy or computational restrictions. We propose a
-general approach for accurately and efficiently calculating small $p$-values
-for a broad range of complicated test statistics based on the principle of the
-cross-entropy method and Markov chain Monte Carlo sampling techniques. We
-evaluate the performance of the proposed algorithm through simulations and
-demonstrate its application to three real examples in genomic studies. The
-results show that our approach can accurately evaluate small to extremely small
-$p$-values (e.g. $10^{-6}$ to $10^{-100}$). The proposed algorithm is helpful
-to the improvement of existing test procedures and the development of new test
-procedures in genomic studies.
-",0,0,0,1,0,0
-18344,Does the Testing Level affect the Prevalence of Coincidental Correctness?,"  Researchers have previously shown that Coincidental Correctness (CC) is
-prevalent; however, the benchmarks they used are considered inadequate
-nowadays. They have also recognized the negative impact of CC on the
-effectiveness of fault localization and testing. The aim of this paper is to
-study Coincidental Correctness, using more realistic code, mainly from the
-perspective of unit testing. This stems from the fact that the practice of unit
-testing has grown tremendously in recent years due to the wide adoption of
-software development processes, such as Test-Driven Development. We quantified
-the presence of CC in unit testing using the Defects4J benchmark. This entailed
-manually injecting two code checkers for each of the 395 defects in Defects4J:
-1) a weak checker that detects weak CC tests by monitoring whether the defect
-was reached; and 2) a strong checker that detects strong CC tests by monitoring
-whether the defect was reached and the program has transitioned into an
-infectious state. We also conducted preliminary experiments (using Defects4J,
-NanoXML and JTidy) to assess the pervasiveness of CC at the unit testing level
-in comparison to that at the integration and system levels. Our study showed
-that unit testing is not immune to CC, as it exhibited 7.2x more strong CC
-tests than failing tests and 8.3x more weak CC tests than failing tests.
-However, our preliminary results suggested that it might be less prone to CC
-than integration testing and system testing.
-",1,0,0,0,0,0
-18345,How spread changes affect the order book: Comparing the price responses of order deletions and placements to trades,"  We observe the effects of the three different events that cause spread
-changes in the order book, namely trades, deletions and placement of limit
-orders. By looking at the frequencies of the relative amounts of price changing
-events, we discover that deletions of orders open the bid-ask spread of a stock
-more often than trades do. We see that once the amount of spread changes due to
-deletions exceeds the amount of the ones due to trades, other observables in
-the order book change as well. We then look at how these spread changing events
-affect the prices of stocks, by means of the price response. We not only see
-that the self-response of stocks is positive for both spread changing trades
-and deletions and negative for order placements, but also cross-response to
-other stocks and therefore the market as a whole. In addition, the
-self-response function of spread-changing trades is similar to that of all
-trades. This leads to the conclusion that spread changing deletions and order
-placements have a similar effect on the order book and stock prices over time
-as trades.
-",0,0,0,0,0,1
-18346,High-Dimensional Dependency Structure Learning for Physical Processes,"  In this paper, we consider the use of structure learning methods for
-probabilistic graphical models to identify statistical dependencies in
-high-dimensional physical processes. Such processes are often synthetically
-characterized using PDEs (partial differential equations) and are observed in a
-variety of natural phenomena, including geoscience data capturing atmospheric
-and hydrological phenomena. Classical structure learning approaches such as the
-PC algorithm and variants are challenging to apply due to their high
-computational and sample requirements. Modern approaches, often based on sparse
-regression and variants, do come with finite sample guarantees, but are usually
-highly sensitive to the choice of hyper-parameters, e.g., parameter $\lambda$
-for sparsity inducing constraint or regularization. In this paper, we present
-ACLIME-ADMM, an efficient two-step algorithm for adaptive structure learning,
-which estimates an edge specific parameter $\lambda_{ij}$ in the first step,
-and uses these parameters to learn the structure in the second step. Both steps
-of our algorithm use (inexact) ADMM to solve suitable linear programs, and all
-iterations can be done in closed form in an efficient block parallel manner. We
-compare ACLIME-ADMM with baselines on both synthetic data simulated by partial
-differential equations (PDEs) that model advection-diffusion processes, and
-real data (50 years) of daily global geopotential heights to study information
-flow in the atmosphere. ACLIME-ADMM is shown to be efficient, stable, and
-competitive, usually better than the baselines especially on difficult
-problems. On real data, ACLIME-ADMM recovers the underlying structure of global
-atmospheric circulation, including switches in wind directions at the equator
-and tropics entirely from the data.
-",1,0,0,1,0,0
-18347,Lax orthogonal factorisations in monad-quantale-enriched categories,"  We show that, for a quantale $V$ and a $\mathsf{Set}$-monad $\mathbb{T}$
-laxly extended to $V$-$\mathsf{Rel}$, the presheaf monad on the category of
-$(\mathbb{T},V)$-categories is simple, giving rise to a lax orthogonal
-factorisation system (lofs) whose corresponding weak factorisation system has
-embeddings as left part. In addition, we present presheaf submonads and study
-the LOFSs they define. This provides a method of constructing weak
-factorisation systems on some well-known examples of topological categories
-over $\mathsf{Set}$.
-",0,0,1,0,0,0
-18348,Radiation reaction for spinning bodies in effective field theory I: Spin-orbit effects,"  We compute the leading Post-Newtonian (PN) contributions at linear order in
-the spin to the radiation-reaction acceleration and spin evolution for binary
-systems, which enter at fourth PN order. The calculation is carried out, from
-first principles, using the effective field theory framework for spinning
-compact objects, in both the Newton-Wigner and covariant spin supplementary
-conditions. A non-trivial consistency check is performed on our results by
-showing that the energy loss induced by the resulting radiation-reaction force
-is equivalent to the total emitted power in the far zone, up to so-called
-""Schott terms."" We also find that, at this order, the radiation reaction has no
-net effect on the evolution of the spins. The spin-spin contributions to
-radiation reaction are reported in a companion paper.
-",0,1,0,0,0,0
-18349,On the geometric notion of connection and its expression in tangent categories,"  Tangent categories provide an axiomatic approach to key structural aspects of
-differential geometry that exist not only in the classical category of smooth
-manifolds but also in algebraic geometry, homological algebra, computer
-science, and combinatorics. Generalizing the notion of (linear) connection on a
-smooth vector bundle, Cockett and Cruttwell have defined a notion of connection
-on a differential bundle in an arbitrary tangent category. Herein, we establish
-equivalent formulations of this notion of connection that reduce the amount of
-specified structure required. Further, one of our equivalent formulations
-substantially reduces the number of axioms imposed, and others provide useful
-abstract conceptualizations of connections. In particular, we show that a
-connection on a differential bundle E over M is equivalently given by a single
-morphism K that induces a suitable decomposition of TE as a biproduct. We also
-show that a connection is equivalently given by a vertical connection K for
-which a certain associated diagram is a limit diagram.
-",0,0,1,0,0,0
-18350,Objective priors for the number of degrees of freedom of a multivariate t distribution and the t-copula,"  An objective Bayesian approach to estimate the number of degrees of freedom
-$(\nu)$ for the multivariate $t$ distribution and for the $t$-copula, when the
-parameter is considered discrete, is proposed. Inference on this parameter has
-been problematic for the multivariate $t$ and, for the absence of any method,
-for the $t$-copula. An objective criterion based on loss functions which allows
-to overcome the issue of defining objective probabilities directly is employed.
-The support of the prior for $\nu$ is truncated, which derives from the
-property of both the multivariate $t$ and the $t$-copula of convergence to
-normality for a sufficiently large number of degrees of freedom. The
-performance of the priors is tested on simulated scenarios. The R codes and the
-replication material are available as a supplementary material of the
-electronic version of the paper and on real data: daily logarithmic returns of
-IBM and of the Center for Research in Security Prices Database.
-",0,0,0,1,0,0
-18351,PROBE: Predictive Robust Estimation for Visual-Inertial Navigation,"  Navigation in unknown, chaotic environments continues to present a
-significant challenge for the robotics community. Lighting changes,
-self-similar textures, motion blur, and moving objects are all considerable
-stumbling blocks for state-of-the-art vision-based navigation algorithms. In
-this paper we present a novel technique for improving localization accuracy
-within a visual-inertial navigation system (VINS). We make use of training data
-to learn a model for the quality of visual features with respect to
-localization error in a given environment. This model maps each visual
-observation from a predefined prediction space of visual-inertial predictors
-onto a scalar weight, which is then used to scale the observation covariance
-matrix. In this way, our model can adjust the influence of each observation
-according to its quality. We discuss our choice of predictors and report
-substantial reductions in localization error on 4 km of data from the KITTI
-dataset, as well as on experimental datasets consisting of 700 m of indoor and
-outdoor driving on a small ground rover equipped with a Skybotix VI-Sensor.
-",1,0,0,0,0,0
-18352,Ethical Artificial Intelligence - An Open Question,"  Artificial Intelligence (AI) is an effective science which employs strong
-enough approaches, methods, and techniques to solve unsolvable real world based
-problems. Because of its unstoppable rise towards the future, there are also
-some discussions about its ethics and safety. Shaping an AI friendly
-environment for people and a people friendly environment for AI can be a
-possible answer for finding a shared context of values for both humans and
-robots. In this context, objective of this paper is to address the ethical
-issues of AI and explore the moral dilemmas that arise from ethical algorithms,
-from pre set or acquired values. In addition, the paper will also focus on the
-subject of AI safety. As general, the paper will briefly analyze the concerns
-and potential solutions to solving the ethical issues presented and increase
-readers awareness on AI safety as another related research interest.
-",1,0,0,0,0,0
-18353,Self-Attentive Model for Headline Generation,"  Headline generation is a special type of text summarization task. While the
-amount of available training data for this task is almost unlimited, it still
-remains challenging, as learning to generate headlines for news articles
-implies that the model has strong reasoning about natural language. To overcome
-this issue, we applied recent Universal Transformer architecture paired with
-byte-pair encoding technique and achieved new state-of-the-art results on the
-New York Times Annotated corpus with ROUGE-L F1-score 24.84 and ROUGE-2
-F1-score 13.48. We also present the new RIA corpus and reach ROUGE-L F1-score
-36.81 and ROUGE-2 F1-score 22.15 on it.
-",1,0,0,0,0,0
-18354,Localized Recombining Plasma in G166.0+4.3: A Supernova Remnant with an Unusual Morphology,"  We observed the Galactic mixed-morphology supernova remnant G166.0+4.3 with
-Suzaku. The X-ray spectrum in the western part of the remnant is well
-represented by a one-component ionizing plasma model. The spectrum in the
-northeastern region can be explained by two components. One is the Fe-rich
-component with the electron temperature $kT_e = 0.87_{-0.03}^{+0.02}$ keV. The
-other is the recombining plasma component of lighter elements with $kT_e =
-0.46\pm0.03$ keV, the initial temperature $kT_{init} = 3$ keV (fixed) and the
-ionization parameter $n_et = (6.1_{-0.4}^{+0.5}) \times 10^{11} \rm cm^{-3} s$.
-As the formation process of the recombining plasma, two scenarios, the
-rarefaction and thermal conduction, are considered. The former would not be
-favored since we found the recombining plasma only in the northeastern region
-whereas the latter would explain the origin of the RP. In the latter scenario,
-an RP is anticipated in a part of the remnant where blast waves are in contact
-with cool dense gas. The emission measure suggests higher ambient gas density
-in the northeastern region. The morphology of the radio shell and a GeV
-gamma-ray emission also suggest a molecular cloud in the region.
-",0,1,0,0,0,0
-18355,Visualizing Design Erosion: How Big Balls of Mud are Made,"  Software systems are not static, they have to undergo frequent changes to
-stay fit for purpose, and in the process of doing so, their complexity
-increases. It has been observed that this process often leads to the erosion of
-the systems design and architecture and with it, the decline of many desirable
-quality attributes, such as maintainability. This process can be captured in
-terms of antipatterns-atomic violations of widely accepted design principles.
-We present a visualisation that exposes the design of evolving Java programs,
-highlighting instances of selected antipatterns including their emergence and
-cancerous growth. This visualisation assists software engineers and architects
-in assessing, tracing and therefore combating design erosion. We evaluated the
-effectiveness of the visualisation in four case studies with ten participants.
-",1,0,0,0,0,0
-18356,Hierarchical Representations for Efficient Architecture Search,"  We explore efficient neural architecture search methods and show that a
-simple yet powerful evolutionary algorithm can discover new architectures with
-excellent performance. Our approach combines a novel hierarchical genetic
-representation scheme that imitates the modularized design pattern commonly
-adopted by human experts, and an expressive search space that supports complex
-topologies. Our algorithm efficiently discovers architectures that outperform a
-large number of manually designed models for image classification, obtaining
-top-1 error of 3.6% on CIFAR-10 and 20.3% when transferred to ImageNet, which
-is competitive with the best existing neural architecture search approaches. We
-also present results using random search, achieving 0.3% less top-1 accuracy on
-CIFAR-10 and 0.1% less on ImageNet whilst reducing the search time from 36
-hours down to 1 hour.
-",1,0,0,1,0,0
-18357,A return to eddy viscosity model for epistemic UQ in RANS closures,"  For the purpose of Uncertainty Quantification (UQ) of Reynolds-Averaged
-Navier-Stokes closures, we introduce a framework in which perturbations in the
-eigenvalues of the anisotropy tensor are made in order to bound a
-Quantity-of-Interest based on limiting states of turbulence. To make the
-perturbations representative of local flow features, we introduce two
-additional transport equations for linear combinations of these aforementioned
-eigenvalues. The location, magnitude and direction of the eigenvalue
-perturbations are now governed by the model transport equations. The general
-behavior of our discrepancy model is determined by two coefficients, resulting
-in a low-dimensional UQ problem. We will furthermore show that the behavior of
-the model is intuitive and rooted in the physical interpretation of
-misalignment between the mean strain and Reynolds stresses.
-",0,1,0,0,0,0
-18358,A Neural Network model with Bidirectional Whitening,"  We present here a new model and algorithm which performs an efficient Natural
-gradient descent for Multilayer Perceptrons. Natural gradient descent was
-originally proposed from a point of view of information geometry, and it
-performs the steepest descent updates on manifolds in a Riemannian space. In
-particular, we extend an approach taken by the ""Whitened neural networks""
-model. We make the whitening process not only in feed-forward direction as in
-the original model, but also in the back-propagation phase. Its efficacy is
-shown by an application of this ""Bidirectional whitened neural networks"" model
-to a handwritten character recognition data (MNIST data).
-",0,0,0,1,0,0
-18359,Regularity gradient estimates for weak solutions of singular quasi-linear parabolic equations,"  This paper studies the Sobolev regularity estimates of weak solutions of a
-class of singular quasi-linear elliptic problems of the form $u_t -
-\mbox{div}[\mathbb{A}(x,t,u,\nabla u)]= \mbox{div}[{\mathbf F}]$ with
-homogeneous Dirichlet boundary conditions over bounded spatial domains. Our
-main focus is on the case that the vector coefficients $\mathbb{A}$ are
-discontinuous and singular in $(x,t)$-variables, and dependent on the solution
-$u$. Global and interior weighted $W^{1,p}(\Omega, \omega)$-regularity
-estimates are established for weak solutions of these equations, where $\omega$
-is a weight function in some Muckenhoupt class of weights. The results obtained
-are even new for linear equations, and for $\omega =1$, because of the
-singularity of the coefficients in $(x,t)$-variables
-",0,0,1,0,0,0
-18360,Learning to Avoid Errors in GANs by Manipulating Input Spaces,"  Despite recent advances, large scale visual artifacts are still a common
-occurrence in images generated by GANs. Previous work has focused on improving
-the generator's capability to accurately imitate the data distribution
-$p_{data}$. In this paper, we instead explore methods that enable GANs to
-actively avoid errors by manipulating the input space. The core idea is to
-apply small changes to each noise vector in order to shift them away from areas
-in the input space that tend to result in errors. We derive three different
-architectures from that idea. The main one of these consists of a simple
-residual module that leads to significantly less visual artifacts, while only
-slightly decreasing diversity. The module is trivial to add to existing GANs
-and costs almost zero computation and memory.
-",1,0,0,1,0,0
-18361,Improving 6D Pose Estimation of Objects in Clutter via Physics-aware Monte Carlo Tree Search,"  This work proposes a process for efficiently searching over combinations of
-individual object 6D pose hypotheses in cluttered scenes, especially in cases
-involving occlusions and objects resting on each other. The initial set of
-candidate object poses is generated from state-of-the-art object detection and
-global point cloud registration techniques. The best-scored pose per object by
-using these techniques may not be accurate due to overlaps and occlusions.
-Nevertheless, experimental indications provided in this work show that object
-poses with lower ranks may be closer to the real poses than ones with high
-ranks according to registration techniques. This motivates a global
-optimization process for improving these poses by taking into account
-scene-level physical interactions between objects. It also implies that the
-Cartesian product of candidate poses for interacting objects must be searched
-so as to identify the best scene-level hypothesis. To perform the search
-efficiently, the candidate poses for each object are clustered so as to reduce
-their number but still keep a sufficient diversity. Then, searching over the
-combinations of candidate object poses is performed through a Monte Carlo Tree
-Search (MCTS) process that uses the similarity between the observed depth image
-of the scene and a rendering of the scene given the hypothesized pose as a
-score that guides the search procedure. MCTS handles in a principled way the
-tradeoff between fine-tuning the most promising poses and exploring new ones,
-by using the Upper Confidence Bound (UCB) technique. Experimental results
-indicate that this process is able to quickly identify in cluttered scenes
-physically-consistent object poses that are significantly closer to ground
-truth compared to poses found by point cloud registration methods.
-",1,0,0,0,0,0
-18362,On the benefits of output sparsity for multi-label classification,"  The multi-label classification framework, where each observation can be
-associated with a set of labels, has generated a tremendous amount of attention
-over recent years. The modern multi-label problems are typically large-scale in
-terms of number of observations, features and labels, and the amount of labels
-can even be comparable with the amount of observations. In this context,
-different remedies have been proposed to overcome the curse of dimensionality.
-In this work, we aim at exploiting the output sparsity by introducing a new
-loss, called the sparse weighted Hamming loss. This proposed loss can be seen
-as a weighted version of classical ones, where active and inactive labels are
-weighted separately. Leveraging the influence of sparsity in the loss function,
-we provide improved generalization bounds for the empirical risk minimizer, a
-suitable property for large-scale problems. For this new loss, we derive rates
-of convergence linear in the underlying output-sparsity rather than linear in
-the number of labels. In practice, minimizing the associated risk can be
-performed efficiently by using convex surrogates and modern convex optimization
-algorithms. We provide experiments on various real-world datasets demonstrating
-the pertinence of our approach when compared to non-weighted techniques.
-",1,0,1,1,0,0
-18363,"On wrapping number, adequacy and the crossing number of satellite knots","  In this work we establish the tightest lower bound up-to-date for the minimal
-crossing number of a satellite knot based on the minimal crossing number of the
-companion used to build the satellite. If $M$ is the wrapping number of the
-pattern knot, we essentially show that $c(Sat(P,C))>\frac{M^2}{2}c(C)$. The
-existence of this bound will be proven when the companion knot is adequate, and
-it will be further tuned in the case of the companion being alternating.
-",0,0,1,0,0,0
-18364,On the Applicability of Delicious for Temporal Search on Web Archives,"  Web archives are large longitudinal collections that store webpages from the
-past, which might be missing on the current live Web. Consequently, temporal
-search over such collections is essential for finding prominent missing
-webpages and tasks like historical analysis. However, this has been challenging
-due to the lack of popularity information and proper ground truth to evaluate
-temporal retrieval models. In this paper we investigate the applicability of
-external longitudinal resources to identify important and popular websites in
-the past and analyze the social bookmarking service Delicious for this purpose.
-The timestamped bookmarks on Delicious provide explicit cues about popular
-time periods in the past along with relevant descriptors. These are valuable to
-identify important documents in the past for a given temporal query. Focusing
-purely on recall, we analyzed more than 12,000 queries and find that using
-Delicious yields average recall values from 46% up to 100%, when limiting
-ourselves to the best represented queries in the considered dataset. This
-constitutes an attractive and low-overhead approach for quick access into Web
-archives by not dealing with the actual contents.
-",1,0,0,0,0,0
-18365,The Geometry and Topology of Data and Information for Analytics of Processes and Behaviours: Building on Bourdieu and Addressing New Societal Challenges,"  We begin by summarizing the relevance and importance of inductive analytics
-based on the geometry and topology of data and information. Contemporary issues
-are then discussed. These include how sampling data for representativity is
-increasingly to be questioned. While we can always avail of analytics from a
-""bag of tools and techniques"", in the application of machine learning and
-predictive analytics, nonetheless we present the case for Bourdieu and
-Benzécri-based science of data, as follows. This is to construct bridges
-between data sources and position-taking, and decision-making. There is summary
-presentation of a few case studies, illustrating and exemplifying application
-domains.
-",1,0,0,0,0,0
-18366,"Conformal Twists, Yang-Baxter $σ$-models & Holographic Noncommutativity","  Expanding upon earlier results [arXiv:1702.02861], we present a compendium of
-$\sigma$-models associated with integrable deformations of AdS$_5$ generated by
-solutions to homogenous classical Yang-Baxter equation. Each example we study
-from four viewpoints: conformal (Drinfeld) twists, closed string gravity
-backgrounds, open string parameters and proposed dual noncommutative (NC) gauge
-theory. Irrespective of whether the deformed background is a solution to
-supergravity or generalized supergravity, we show that the open string metric
-associated with each gravity background is undeformed AdS$_5$ with constant
-open string coupling and the NC structure $\Theta$ is directly related to the
-conformal twist. One novel feature is that $\Theta$ exhibits ""holographic
-noncommutativity"": while it may exhibit non-trivial dependence on the
-holographic direction, its value everywhere in the bulk is uniquely determined
-by its value at the boundary, thus facilitating introduction of a dual NC gauge
-theory. We show that the divergence of the NC structure $\Theta$ is directly
-related to the unimodularity of the twist. We discuss the implementation of an
-outer automorphism of the conformal algebra as a coordinate transformation in
-the AdS bulk and discuss its implications for Yang-Baxter $\sigma$-models and
-self-T-duality based on fermionic T-duality. Finally, we comment on
-implications of our results for the integrability of associated open strings
-and planar integrability of dual NC gauge theories.
-",0,1,0,0,0,0
-18367,AWEsome: An open-source test platform for airborne wind energy systems,"  In this paper we present AWEsome (Airborne Wind Energy Standardized
-Open-source Model Environment), a test platform for airborne wind energy
-systems that consists of low-cost hardware and is entirely based on open-source
-software. It can hence be used without the need of large financial investments,
-in particular by research groups and startups to acquire first experiences in
-their flight operations, to test novel control strategies or technical designs,
-or for usage in public relations. Our system consists of a modified
-off-the-shelf model aircraft that is controlled by the pixhawk autopilot
-hardware and the ardupilot software for fixed wing aircraft. The aircraft is
-attached to the ground by a tether. We have implemented new flight modes for
-the autonomous tethered flight of the aircraft along periodic patterns. We
-present the principal functionality of our algorithms. We report on first
-successful tests of these modes in real flights.
-",1,0,0,0,0,0
-18368,A one-dimensional mathematical model of collecting lymphatics coupled with an electro-fluid-mechanical contraction model and valve dynamics,"  We propose a one-dimensional model for collecting lymphatics coupled with a
-novel Electro-Fluid-Mechanical Contraction (EFMC) model for dynamical
-contractions, based on a modified FitzHugh-Nagumo model for action potentials.
-The one-dimensional model for a compliant lymphatic vessel is a set of
-hyperbolic Partial Differential Equations (PDEs). The EFMC model combines the
-electrical activity of lymphangions (action potentials) with fluid-mechanical
-feedback (stretch of the lymphatic wall and wall shear stress) and the
-mechanical variation of the lymphatic wall properties (contractions). The EFMC
-model is governed by four Ordinary Differential Equations (ODEs) and
-phenomenologically relies on: (1) environmental calcium influx, (2)
-stretch-activated calcium influx, and (3) contraction inhibitions induced by
-wall shear stresses. We carried out a complete mathematical analysis of the
-stability of the stationary state of the EFMC model. Overall, the EFMC model
-allows imitating the influence of pressure and wall shear stress on the
-frequency of contractions observed experimentally. Lymphatic valves are
-modelled using a well-established lumped-parameter model which allows
-simulating stenotic and regurgitant valves. We analysed several lymphodynamical
-indexes of a single lymphangion for a wide range of upstream and downstream
-pressure combinations. Stenotic and regurgitant valves were modelled, and their
-effects are here quantified. Results for stenotic valves showed in the
-downstream lymphangion that for low frequencies of contractions the Calculated
-Pump Flow (CPF) index remained almost unaltered, while for high frequencies the
-CPF dramatically decreased depending on the severity of the stenosis (up to 93%
-for a severe stenosis). Results for incompetent valves showed that the net flow
-during a lymphatic cycle tends to zero as the degree of incompetence increases.
-",0,1,1,0,0,0
-18369,$A_{n}$-type surface singularity and nondisplaceable Lagrangian tori,"  We prove the existence of a one-parameter family of nondisplaceable
-Lagrangian tori near a linear chain of Lagrangian 2-spheres in a symplectic
-4-manifold. When the symplectic structure is rational we prove that the
-deformed Floer cohomology groups of these tori are nontrivial. The proof uses
-the idea of toric degeneration to analyze the full potential functions with
-bulk deformations of these tori.
-",0,0,1,0,0,0
-18370,Cascade LSTM Based Visual-Inertial Navigation for Magnetic Levitation Haptic Interaction,"  Haptic feedback is essential to acquire immersive experience when interacting
-in virtual or augmented reality. Although the existing promising magnetic
-levitation (maglev) haptic system has advantages of none mechanical friction,
-its performance is limited by its navigation method, which mainly results from
-the challenge that it is difficult to obtain high precision, high frame rate
-and good stability with lightweight design at the same. In this study, we
-propose to perform the visual-inertial fusion navigation based on
-sequence-to-sequence learning for the maglev haptic interaction. Cascade LSTM
-based-increment learning method is first presented to progressively learn the
-increments of the target variables. Then, two cascade LSTM networks are
-separately trained for accomplishing the visual-inertial fusion navigation in a
-loosely-coupled mode. Additionally, we set up a maglev haptic platform as the
-system testbed. Experimental results show that the proposed cascade LSTM
-based-increment learning method can achieve high-precision prediction, and our
-cascade LSTM based visual-inertial fusion navigation method can reach 200Hz
-while maintaining high-precision (the mean absolute error of the position and
-orientation is respectively less than 1mm and 0.02°)navigation for the
-maglev haptic interaction application.
-",1,0,0,0,0,0
-18371,Local Feature Descriptor Learning with Adaptive Siamese Network,"  Although the recent progress in the deep neural network has led to the
-development of learnable local feature descriptors, there is no explicit answer
-for estimation of the necessary size of a neural network. Specifically, the
-local feature is represented in a low dimensional space, so the neural network
-should have more compact structure. The small networks required for local
-feature descriptor learning may be sensitive to initial conditions and learning
-parameters and more likely to become trapped in local minima. In order to
-address the above problem, we introduce an adaptive pruning Siamese
-Architecture based on neuron activation to learn local feature descriptors,
-making the network more computationally efficient with an improved recognition
-rate over more complex networks. Our experiments demonstrate that our learned
-local feature descriptors outperform the state-of-art methods in patch
-matching.
-",1,0,0,1,0,0
-18372,Unbounded cache model for online language modeling with open vocabulary,"  Recently, continuous cache models were proposed as extensions to recurrent
-neural network language models, to adapt their predictions to local changes in
-the data distribution. These models only capture the local context, of up to a
-few thousands tokens. In this paper, we propose an extension of continuous
-cache models, which can scale to larger contexts. In particular, we use a large
-scale non-parametric memory component that stores all the hidden activations
-seen in the past. We leverage recent advances in approximate nearest neighbor
-search and quantization algorithms to store millions of representations while
-searching them efficiently. We conduct extensive experiments showing that our
-approach significantly improves the perplexity of pre-trained language models
-on new distributions, and can scale efficiently to much larger contexts than
-previously proposed local cache models.
-",1,0,0,0,0,0
-18373,Finite-Range Coulomb Gas Models of Banded Random Matrices and Quantum Kicked Rotors,"  Dyson demonstrated an equivalence between infinite-range Coulomb gas models
-and classical random matrix ensembles for study of eigenvalue statistics. We
-introduce finite-range Coulomb gas (FRCG) models via a Brownian matrix process,
-and study them analytically and by Monte-Carlo simulations. These models yield
-new universality classes, and provide a theoretical framework for study of
-banded random matrices (BRM) and quantum kicked rotors (QKR). We demonstrate
-that, for a BRM of bandwidth b and a QKR of chaos parameter {\alpha}, the
-appropriate FRCG model has the effective range d = (b^2)/N = ({\alpha}^2)/N,
-for large N matrix dimensionality. As d increases, there is a transition from
-Poisson to classical random matrix statistics.
-",0,1,0,0,0,0
-18374,Dual Loomis-Whitney inequalities via information theory,"  We establish lower bounds on the volume and the surface area of a geometric
-body using the size of its slices along different directions. In the first part
-of the paper, we derive volume bounds for convex bodies using generalized
-subadditivity properties of entropy combined with entropy bounds for
-log-concave random variables. In the second part, we investigate a new notion
-of Fisher information which we call the $L_1$-Fisher information, and show that
-certain superadditivity properties of the $L_1$-Fisher information lead to
-lower bounds for the surface areas of polyconvex sets in terms of its slices.
-",1,0,0,0,0,0
-18375,Cubic lead perovskite PbMoO3 with anomalous metallic behavior,"  A previously unreported Pb-based perovskite PbMoO$_3$ is obtained by
-high-pressure and high-temperature synthesis. This material crystallizes in the
-$Pm\bar{3}m$ cubic structure at room temperature, making it distinct from
-typical Pb-based perovskite oxides with a structural distortion. PbMoO$_3$
-exhibits a metallic behavior down to 0.1 K with an unusual $T$-sub linear
-dependence of the electrical resistivity. Moreover, a large specific heat is
-observed at low temperatures accompanied by a peak in $C_P/T^3$ around 10 K, in
-marked contrast to the isostructural metallic system SrMoO$_3$. These transport
-and thermal properties for PbMoO$_3$, taking into account anomalously large Pb
-atomic displacements detected through diffraction experiments, are attributed
-to a low-energy vibrational mode, associated with incoherent off-centering of
-lone pair Pb$^{2+}$ cations. We discuss the unusual behavior of the electrical
-resistivity in terms of a polaron-like conduction, mediated by the strong
-coupling between conduction electrons and optical phonons of the local
-low-energy vibrational mode.
-",0,1,0,0,0,0
-18376,On Long Memory Origins and Forecast Horizons,"  Most long memory forecasting studies assume that the memory is generated by
-the fractional difference operator. We argue that the most cited theoretical
-arguments for the presence of long memory do not imply the fractional
-difference operator, and assess the performance of the autoregressive
-fractionally integrated moving average $(ARFIMA)$ model when forecasting series
-with long memory generated by nonfractional processes. We find that high-order
-autoregressive $(AR)$ models produce similar or superior forecast performance
-than $ARFIMA$ models at short horizons. Nonetheless, as the forecast horizon
-increases, the $ARFIMA$ models tend to dominate in forecast performance. Hence,
-$ARFIMA$ models are well suited for forecasts of long memory processes
-regardless of the long memory generating mechanism, particularly for medium and
-long forecast horizons. Additionally, we analyse the forecasting performance of
-the heterogeneous autoregressive ($HAR$) model which imposes restrictions on
-high-order $AR$ models. We find that the structure imposed by the $HAR$ model
-produces better long horizon forecasts than $AR$ models of the same order, at
-the price of inferior short horizon forecasts in some cases. Our results have
-implications for, among others, Climate Econometrics and Financial Econometrics
-models dealing with long memory series at different forecast horizons. We show
-in an example that while a short memory autoregressive moving average $(ARMA)$
-model gives the best performance when forecasting the Realized Variance of the
-S\&P 500 up to a month ahead, the $ARFIMA$ model gives the best performance for
-longer forecast horizons.
-",0,0,1,0,0,0
-18377,Detecting Disguised Plagiarism,"  Source code plagiarism detection is a problem that has been addressed several
-times before; and several tools have been developed for that purpose. In this
-research project we investigated a set of possible disguises that can be
-mechanically applied to plagiarized source code to defeat plagiarism detection
-tools. We propose a preprocessor to be used with existing plagiarism detection
-tools to ""normalize"" source code before checking it, thus making such disguises
-ineffective.
-",1,0,0,0,0,0
-18378,Stellar population synthesis based modelling of the Milky Way using asteroseismology of dwarfs and subgiants from Kepler,"  Early attempts to apply asteroseismology to study the Galaxy have already
-shown unexpected discrepancies for the mass distribution of stars between the
-Galactic models and the data; a result that is still unexplained. Here, we
-revisit the analysis of the asteroseismic sample of dwarf and subgiant stars
-observed by Kepler and investigate in detail the possible causes for the
-reported discrepancy. We investigate two models of the Milky Way based on
-stellar population synthesis, Galaxia and TRILEGAL. In agreement with previous
-results, we find that TRILEGAL predicts more massive stars compared to Galaxia,
-and that TRILEGAL predicts too many blue stars compared to 2MASS observations.
-Both models fail to match the distribution of the stellar sample in $(\log
-g,T_{\rm eff})$ space, pointing to inaccuracies in the models and/or the
-assumed selection function. When corrected for this mismatch in $(\log g,T_{\rm
-eff})$ space, the mass distribution calculated by Galaxia is broader and the
-mean is shifted toward lower masses compared to that of the observed stars.
-This behaviour is similar to what has been reported for the Kepler red giant
-sample. The shift between the mass distributions is equivalent to a change of
-2\% in $\nu_{\rm max}$, which is within the current uncertainty in the
-$\nu_{\rm max}$ scaling relation. Applying corrections to the $\Delta \nu$
-scaling relation predicted by the stellar models makes the observed mass
-distribution significantly narrower, but there is no change to the mean.
-",0,1,0,0,0,0
-18379,EmbNum: Semantic labeling for numerical values with deep metric learning,"  Semantic labeling for numerical values is a task of assigning semantic labels
-to unknown numerical attributes. The semantic labels could be numerical
-properties in ontologies, instances in knowledge bases, or labeled data that
-are manually annotated by domain experts. In this paper, we refer to semantic
-labeling as a retrieval setting where the label of an unknown attribute is
-assigned by the label of the most relevant attribute in labeled data. One of
-the greatest challenges is that an unknown attribute rarely has the same set of
-values with the similar one in the labeled data. To overcome the issue,
-statistical interpretation of value distribution is taken into account.
-However, the existing studies assume a specific form of distribution. It is not
-appropriate in particular to apply open data where there is no knowledge of
-data in advance. To address these problems, we propose a neural numerical
-embedding model (EmbNum) to learn useful representation vectors for numerical
-attributes without prior assumptions on the distribution of data. Then, the
-""semantic similarities"" between the attributes are measured on these
-representation vectors by the Euclidean distance. Our empirical experiments on
-City Data and Open Data show that EmbNum significantly outperforms
-state-of-the-art methods for the task of numerical attribute semantic labeling
-regarding effectiveness and efficiency.
-",0,0,0,1,0,0
-18380,A Network Epidemic Model for Online Community Commissioning Data,"  A statistical model assuming a preferential attachment network, which is
-generated by adding nodes sequentially according to a few simple rules, usually
-describes real-life networks better than a model assuming, for example, a
-Bernoulli random graph, in which any two nodes have the same probability of
-being connected, does. Therefore, to study the propogation of ""infection""
-across a social network, we propose a network epidemic model by combining a
-stochastic epidemic model and a preferential attachment model. A simulation
-study based on the subsequent Markov Chain Monte Carlo algorithm reveals an
-identifiability issue with the model parameters. Finally, the network epidemic
-model is applied to a set of online commissioning data.
-",1,0,0,1,0,0
-18381,Descent and Galois theory for Hopf categories,"  Descent theory for linear categories is developed. Given a linear category as
-an extension of a diagonal category, we introduce descent data, and the
-category of descent data is isomorphic to the category of representations of
-the diagonal category, if some flatness assumptions are satisfied. Then
-Hopf-Galois descent theory for linear Hopf categories, the Hopf algebra version
-of a linear category, is developed. This leads to the notion of Hopf-Galois
-category extension. We have a dual theory, where actions by dual linear Hopf
-categories on linear categories are considered. Hopf-Galois category extensions
-over groupoid algebras correspond to strongly graded linear categories.
-",0,0,1,0,0,0
-18382,On winning strategies for Banach-Mazur games,"  We give topological and game theoretic definitions and theorems nec- essary
-for defining a Banach-Mazur game, and apply these definitions to formalize the
-game. We then state and prove two theorems which give necessary conditions for
-existence of winning strategies for players in a Banach-Mazur game.
-",0,0,1,0,0,0
-18383,Dynamical instability of the electric transport in strongly fluctuating superconductors,"  Theory of the influence of the thermal fluctuations on the electric transport
-beyond linear response in superconductors is developed within the framework of
-the time dependent Ginzburg - Landau approach. The I - V curve is calculated
-using the dynamical self - consistent gaussian approximation. Under certain
-conditions it exhibits a reentrant behaviour acquiring an S - shape form. The
-unstable region below a critical temperature $T^{\ast }$ is determined for
-arbitrary dimensionality ($D=1,2,3$) of the thermal fluctuations. The results
-are applied to analyse the transport data on nanowires and several classes of
-2D superconductors: metallic thin films, layered and atomically thick novel
-materials.
-",0,1,0,0,0,0
-18384,Controlling Multimode Optomechanical Interactions via Interference,"  We demonstrate optomechanical interference in a multimode system, in which an
-optical mode couples to two mechanical modes. A phase-dependent
-excitation-coupling approach is developed, which enables the observation of
-constructive and destructive optomechanical interferences. The destructive
-interference prevents the coupling of the mechanical system to the optical
-mode, suppressing optically-induced mechanical damping. These studies establish
-optomechanical interference as an essential tool for controlling the
-interactions between light and mechanical oscillators.
-",0,1,0,0,0,0
-18385,Online Adaptive Principal Component Analysis and Its extensions,"  We propose algorithms for online principal component analysis (PCA) and
-variance minimization for adaptive settings. Previous literature has focused on
-upper bounding the static adversarial regret, whose comparator is the optimal
-fixed action in hindsight. However, static regret is not an appropriate metric
-when the underlying environment is changing. Instead, we adopt the adaptive
-regret metric from the previous literature and propose online adaptive
-algorithms for PCA and variance minimization, that have sub-linear adaptive
-regret guarantees. We demonstrate both theoretically and experimentally that
-the proposed algorithms can adapt to the changing environments.
-",1,0,0,1,0,0
-18386,Diffusion MRI measurements in challenging head and brain regions via cross-term spatiotemporally encoding,"  Cross-term spatiotemporal encoding (xSPEN) is a recently introduced imaging
-approach delivering single-scan 2D NMR images with unprecedented resilience to
-field inhomogeneities. The method relies on performing a pre-acquisition
-encoding and a subsequent image read out while using the disturbing frequency
-inhomogeneities as part of the image formation processes, rather than as
-artifacts to be overwhelmed by the application of external gradients. This
-study introduces the use of this new single-shot MRI technique as a
-diffusion-monitoring tool, for accessing regions that have hitherto been
-unapproachable by diffusion-weighted imaging (DWI) methods. In order to achieve
-this, xSPEN MRIs intrinsic diffusion weighting effects are formulated using a
-customized, spatially-localized b-matrix analysis; with this, we devise a novel
-diffusion-weighting scheme that both exploits and overcomes xSPENs strong
-intrinsic weighting effects. The ability to provide reliable and robust
-diffusion maps in challenging head and brain regions, including the eyes and
-the optic nerves, is thus demonstrated in humans at 3T; new avenues for imaging
-other body regions are also briefly discussed.
-",0,1,0,0,0,0
-18387,Game-Theoretic Capital Asset Pricing in Continuous Time,"  We derive formulas for the performance of capital assets in continuous time
-from an efficient market hypothesis, with no stochastic assumptions and no
-assumptions about the beliefs or preferences of investors. Our efficient market
-hypothesis says that a speculator with limited means cannot beat a particular
-index by a substantial factor. Our results include a formula that resembles the
-classical CAPM formula for the expected simple return of a security or
-portfolio.
-This version of the article was essentially written in December 2001 but
-remains a working paper.
-",0,0,0,0,0,1
-18388,Full Workspace Generation of Serial-link Manipulators by Deep Learning based Jacobian Estimation,"  Apart from solving complicated problems that require a certain level of
-intelligence, fine-tuned deep neural networks can also create fast algorithms
-for slow, numerical tasks. In this paper, we introduce an improved version of
-[1]'s work, a fast, deep-learning framework capable of generating the full
-workspace of serial-link manipulators. The architecture consists of two neural
-networks: an estimation net that approximates the manipulator Jacobian, and a
-confidence net that measures the confidence of the approximation. We also
-introduce M3 (Manipulability Maps of Manipulators), a MATLAB robotics library
-based on [2](RTB), the datasets generated by which are used by this work.
-Results have shown that not only are the neural networks significantly faster
-than numerical inverse kinematics, it also offers superior accuracy when
-compared to other machine learning alternatives. Implementations of the
-algorithm (based on Keras[3]), including benchmark evaluation script, are
-available at this https URL . The M3
-Library APIs and datasets are also available at
-this https URL .
-",1,0,0,0,0,0
-18389,Linear regression without correspondence,"  This article considers algorithmic and statistical aspects of linear
-regression when the correspondence between the covariates and the responses is
-unknown. First, a fully polynomial-time approximation scheme is given for the
-natural least squares optimization problem in any constant dimension. Next, in
-an average-case and noise-free setting where the responses exactly correspond
-to a linear function of i.i.d. draws from a standard multivariate normal
-distribution, an efficient algorithm based on lattice basis reduction is shown
-to exactly recover the unknown linear function in arbitrary dimension. Finally,
-lower bounds on the signal-to-noise ratio are established for approximate
-recovery of the unknown linear function by any estimator.
-",1,0,1,1,0,0
-18390,Toward sensitive document release with privacy guarantees,"  Privacy has become a serious concern for modern Information Societies. The
-sensitive nature of much of the data that are daily exchanged or released to
-untrusted parties requires that responsible organizations undertake appropriate
-privacy protection measures. Nowadays, much of these data are texts (e.g.,
-emails, messages posted in social media, healthcare outcomes, etc.) that,
-because of their unstructured and semantic nature, constitute a challenge for
-automatic data protection methods. In fact, textual documents are usually
-protected manually, in a process known as document redaction or sanitization.
-To do so, human experts identify sensitive terms (i.e., terms that may reveal
-identities and/or confidential information) and protect them accordingly (e.g.,
-via removal or, preferably, generalization). To relieve experts from this
-burdensome task, in a previous work we introduced the theoretical basis of
-C-sanitization, an inherently semantic privacy model that provides the basis to
-the development of automatic document redaction/sanitization algorithms and
-offers clear and a priori privacy guarantees on data protection; even though
-its potential benefits C-sanitization still presents some limitations when
-applied to practice (mainly regarding flexibility, efficiency and accuracy). In
-this paper, we propose a new more flexible model, named (C, g(C))-sanitization,
-which enables an intuitive configuration of the trade-off between the desired
-level of protection (i.e., controlled information disclosure) and the
-preservation of the utility of the protected data (i.e., amount of semantics to
-be preserved). Moreover, we also present a set of technical solutions and
-algorithms that provide an efficient and scalable implementation of the model
-and improve its practical accuracy, as we also illustrate through empirical
-experiments.
-",1,0,0,0,0,0
-18391,Remarks on the Birch-Swinnerton-Dyer conjecture,"  We give a brief description of the Birch-Swinnerton-Dyer conjecture and
-present related conjectures. We describe the relation between the nilpotent
-orbits of SL(2,R) and CM points.
-",0,0,1,0,0,0
-18392,Trajectory Optimization for Cooperative Dual-band UAV Swarms,"  Unmanned aerial vehicles (UAVs) have gained a lot of popularity in diverse
-wireless communication fields. They can act as high-altitude flying relays to
-support communications between ground nodes due to their ability to provide
-line-of-sight links. With the flourishing Internet of Things, several types of
-new applications are emerging. In this paper, we focus on bandwidth hungry and
-delay-tolerant applications where multiple pairs of transceivers require the
-support of UAVs to complete their transmissions. To do so, the UAVs have the
-possibility to employ two different bands namely the typical microwave and the
-high-rate millimeter wave bands. In this paper, we develop a generic framework
-to assign UAVs to supported transceivers and optimize their trajectories such
-that a weighted function of the total service time is minimized. Taking into
-account both the communication time needed to relay the message and the flying
-time of the UAVs, a mixed non-linear programming problem aiming at finding the
-stops at which the UAVs hover to forward the data to the receivers is
-formulated. An iterative approach is then developed to solve the problem.
-First, a mixed linear programming problem is optimally solved to determine the
-path of each available UAV. Then, a hierarchical iterative search is executed
-to enhance the UAV stops' locations and reduce the service time. The behavior
-of the UAVs and the benefits of the proposed framework are showcased for
-selected scenarios.
-",1,0,0,0,0,0
-18393,A monad for full ground reference cells,"  We present a denotational account of dynamic allocation of potentially cyclic
-memory cells using a monad on a functor category. We identify the collection of
-heaps as an object in a different functor category equipped with a monad for
-adding hiding/encapsulation capabilities to the heaps. We derive a monad for
-full ground references supporting effect masking by applying a state monad
-transformer to the encapsulation monad. To evaluate the monad, we present a
-denotational semantics for a call-by-value calculus with full ground
-references, and validate associated code transformations.
-",1,0,1,0,0,0
-18394,Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work,"  Deep networks thrive when trained on large scale data collections. This has
-given ImageNet a central role in the development of deep architectures for
-visual object classification. However, ImageNet was created during a specific
-period in time, and as such it is prone to aging, as well as dataset bias
-issues. Moving beyond fixed training datasets will lead to more robust visual
-systems, especially when deployed on robots in new environments which must
-train on the objects they encounter there. To make this possible, it is
-important to break free from the need for manual annotators. Recent work has
-begun to investigate how to use the massive amount of images available on the
-Web in place of manual image annotations. We contribute to this research thread
-with two findings: (1) a study correlating a given level of noisily labels to
-the expected drop in accuracy, for two deep architectures, on two different
-types of noise, that clearly identifies GoogLeNet as a suitable architecture
-for learning from Web data; (2) a recipe for the creation of Web datasets with
-minimal noise and maximum visual variability, based on a visual and natural
-language processing concept expansion strategy. By combining these two results,
-we obtain a method for learning powerful deep object models automatically from
-the Web. We confirm the effectiveness of our approach through object
-categorization experiments using our Web-derived version of ImageNet on a
-popular robot vision benchmark database, and on a lifelong object discovery
-task on a mobile robot.
-",1,0,0,0,0,0
-18395,Existence of either a periodic collisional orbit or infinitely many consecutive collision orbits in the planar circular restricted three-body problem,"  In the restricted three-body problem, consecutive collision orbits are those
-orbits which start and end at collisions with one of the primaries. Interests
-for such orbits arise not only from mathematics but also from various
-engineering problems. In this article, using Floer homology, we show that there
-are either a periodic collisional orbit, or infinitely many consecutive
-collision orbits in the planar circular restricted three-body problem on each
-bounded component of the energy hypersurface for Jacobi energy below the first
-critical value.
-",0,0,1,0,0,0
-18396,Finding AND-OR Hierarchies in Workflow Nets,"  This paper presents the notion of AND-OR reduction, which reduces a WF net to
-a smaller net by iteratively contracting certain well-formed subnets into
-single nodes until no more such contractions are possible. This reduction can
-reveal the hierarchical structure of a WF net, and since it preserves certain
-semantical properties such as soundness, it can help with analysing and
-understanding why a WF net is sound or not. The reduction can also be used to
-verify if a WF net is an AND-OR net. This class of WF nets was introduced in
-earlier work, and arguably describes nets that follow good hierarchical design
-principles. It is shown that the AND-OR reduction is confluent up to
-isomorphism, which means that despite the inherent non-determinism that comes
-from the choice of subnets that are contracted, the final result of the
-reduction is always the same up to the choice of the identity of the nodes.
-Based on this result, a polynomial-time algorithm is presented that computes
-this unique result of the AND-OR reduction. Finally, it is shown how this
-algorithm can be used to verify if a WF net is an AND-OR net.
-",1,0,0,0,0,0
-18397,Fusarium Damaged Kernels Detection Using Transfer Learning on Deep Neural Network Architecture,"  The present work shows the application of transfer learning for a pre-trained
-deep neural network (DNN), using a small image dataset ($\approx$ 12,000) on a
-single workstation with enabled NVIDIA GPU card that takes up to 1 hour to
-complete the training task and archive an overall average accuracy of $94.7\%$.
-The DNN presents a $20\%$ score of misclassification for an external test
-dataset. The accuracy of the proposed methodology is equivalent to ones using
-HSI methodology $(81\%-91\%)$ used for the same task, but with the advantage of
-being independent on special equipment to classify wheat kernel for FHB
-symptoms.
-",0,0,0,1,0,0
-18398,About simple variational splines from the Hamiltonian viewpoint,"  In this paper, we study simple splines on a Riemannian manifold $Q$ from the
-point of view of the Pontryagin maximum principle (PMP) in optimal control
-theory. The control problem consists in finding smooth curves matching two
-given tangent vectors with the control being the curve's acceleration, while
-minimizing a given cost functional. We focus on cubic splines (quadratic cost
-function) and on time-minimal splines (constant cost function) under bounded
-acceleration. We present a general strategy to solve for the optimal
-hamiltonian within the PMP framework based on splitting the variables by means
-of a linear connection. We write down the corresponding hamiltonian equations
-in intrinsic form and study the corresponding hamiltonian dynamics in the case
-$Q$ is the $2$-sphere. We also elaborate on possible applications, including
-landmark cometrics in computational anatomy.
-",0,0,1,0,0,0
-18399,Adaptive Grasp Control through Multi-Modal Interactions for Assistive Prosthetic Devices,"  The hand is one of the most complex and important parts of the human body.
-The dexterity provided by its multiple degrees of freedom enables us to perform
-many of the tasks of daily living which involve grasping and manipulating
-objects of interest. Contemporary prosthetic devices for people with
-transradial amputations or wrist disarticulation vary in complexity, from
-passive prosthetics to complex devices that are body or electrically driven.
-One of the important challenges in developing smart prosthetic hands is to
-create devices which are able to mimic all activities that a person might
-perform and address the needs of a wide variety of users. The approach explored
-here is to develop algorithms that permit a device to adapt its behavior to the
-preferences of the operator through interactions with the wearer. This device
-uses multiple sensing modalities including muscle activity from a myoelectric
-armband, visual information from an on-board camera, tactile input through a
-touchscreen interface, and speech input from an embedded microphone. Presented
-within this paper are the design, software and controls of a platform used to
-evaluate this architecture as well as results from experiments deigned to
-quantify the performance.
-",1,0,0,0,0,0
-18400,Generalization of Effective Conductance Centrality for Egonetworks,"  We study the popular centrality measure known as effective conductance or in
-some circles as information centrality. This is an important notion of
-centrality for undirected networks, with many applications, e.g., for random
-walks, electrical resistor networks, epidemic spreading, etc. In this paper, we
-first reinterpret this measure in terms of modulus (energy) of families of
-walks on the network. This modulus centrality measure coincides with the
-effective conductance measure on simple undirected networks, and extends it to
-much more general situations, e.g., directed networks as well. Secondly, we
-study a variation of this modulus approach in the egocentric network paradigm.
-Egonetworks are networks formed around a focal node (ego) with a specific order
-of neighborhoods. We propose efficient analytical and approximate methods for
-computing these measures on both undirected and directed networks. Finally, we
-describe a simple method inspired by the modulus point-of-view, called shell
-degree, which proved to be a useful tool for network science.
-",1,1,0,0,0,0
-18401,Three-dimensional oscillatory magnetic reconnection,"  Here we detail the dynamic evolution of localised reconnection regions about
-three-dimensional (3D) magnetic null points by using numerical simulation. We
-demonstrate for the first time that reconnection triggered by the localised
-collapse of a 3D null point due to an external MHD wave involves a
-self-generated oscillation, whereby the current sheet and outflow jets undergo
-a reconnection reversal process during which back-pressure formation at the jet
-heads acts to prise open the collapsed field before overshooting the
-equilibrium into an opposite-polarity configuration. The discovery that
-reconnection at fully 3D nulls can proceed naturally in a time-dependent and
-periodic fashion is suggestive that oscillatory reconnection mechanisms may
-play a role in explaining periodicity in astrophysical phenomena associated
-with magnetic reconnection, such as the observed quasi-periodicity of solar and
-stellar flare emission. Furthermore, we find a consequence of oscillatory
-reconnection is the generation of a plethora of freely-propagating MHD waves
-which escape the vicinity of the reconnection region
-",0,1,0,0,0,0
-18402,Limits of Risk Predictability in a Cascading Alternating Renewal Process Model,"  Most risk analysis models systematically underestimate the probability and
-impact of catastrophic events (e.g., economic crises, natural disasters, and
-terrorism) by not taking into account interconnectivity and interdependence of
-risks. To address this weakness, we propose the Cascading Alternating Renewal
-Process (CARP) to forecast interconnected global risks. However, assessments of
-the model's prediction precision are limited by lack of sufficient ground truth
-data. Here, we establish prediction precision as a function of input data size
-by using alternative long ground truth data generated by simulations of the
-CARP model with known parameters. We illustrate the approach on a model of
-fires in artificial cities assembled from basic city blocks with diverse
-housing. The results confirm that parameter recovery variance exhibits power
-law decay as a function of the length of available ground truth data. Using
-CARP, we also demonstrate estimation using a disparate dataset that also has
-dependencies: real-world prediction precision for the global risk model based
-on the World Economic Forum Global Risk Report. We conclude that the CARP model
-is an efficient method for predicting catastrophic cascading events with
-potential applications to emerging local and global interconnected risks.
-",1,1,0,0,0,0
-18403,Self-contracted curves have finite length,"  A curve $\theta$: $I\to E$ in a metric space $E$ equipped with the distance
-$d$, where $I\subset \R$ is a (possibly unbounded) interval, is called
-self-contracted, if for any triple of instances of time $\{t_i\}_{i=1}^3\subset
-I$ with $t_1\leq t_2\leq t_3$ one has $d(\theta(t_3),\theta(t_2))\leq
-d(\theta(t_3),\theta(t_1))$. We prove that if $E$ is a finite-dimensional
-normed space with an arbitrary norm, the trace of $\theta$ is bounded, then
-$\theta$ has finite length, i.e. is rectifiable, thus answering positively the
-question raised in~\cite{Lemenant16sc-rectif}.
-",0,0,1,0,0,0
-18404,Unsupervised robust nonparametric learning of hidden community properties,"  We consider learning of fundamental properties of communities in large noisy
-networks, in the prototypical situation where the nodes or users are split into
-two classes according to a binary property, e.g., according to their opinions
-or preferences on a topic. For learning these properties, we propose a
-nonparametric, unsupervised, and scalable graph scan procedure that is, in
-addition, robust against a class of powerful adversaries. In our setup, one of
-the communities can fall under the influence of a knowledgeable adversarial
-leader, who knows the full network structure, has unlimited computational
-resources and can completely foresee our planned actions on the network. We
-prove strong consistency of our results in this setup with minimal assumptions.
-In particular, the learning procedure estimates the baseline activity of normal
-users asymptotically correctly with probability 1; the only assumption being
-the existence of a single implicit community of asymptotically negligible
-logarithmic size. We provide experiments on real and synthetic data to
-illustrate the performance of our method, including examples with adversaries.
-",1,0,0,1,0,0
-18405,OPEB: Open Physical Environment Benchmark for Artificial Intelligence,"  Artificial Intelligence methods to solve continuous- control tasks have made
-significant progress in recent years. However, these algorithms have important
-limitations and still need significant improvement to be used in industry and
-real- world applications. This means that this area is still in an active
-research phase. To involve a large number of research groups, standard
-benchmarks are needed to evaluate and compare proposed algorithms. In this
-paper, we propose a physical environment benchmark framework to facilitate
-collaborative research in this area by enabling different research groups to
-integrate their designed benchmarks in a unified cloud-based repository and
-also share their actual implemented benchmarks via the cloud. We demonstrate
-the proposed framework using an actual implementation of the classical
-mountain-car example and present the results obtained using a Reinforcement
-Learning algorithm.
-",1,0,0,0,0,0
-18406,The First Measurement of the $2^{3}S_{1} \rightarrow 3^{3}P - 2^{3}P$ Tune-Out Wavelength in He*,"  The workhorse of atomic physics: quantum electrodynamics is one of the best
-tested theories in physics. However recent discrepancies have shed doubt on its
-accuracy for complex atomic systems. To facilitate the development of the
-theory further we aim to measure transition dipole matrix elements of
-metastable helium (He*) (the ideal 3 body test-bed) to the highest accuracy
-thus far. We have undertaken a measurement of the `tune-out wavelength' which
-occurs when the contributions to the dynamic polarizability from all atomic
-transitions sum to zero; thus illuminating an atom with this wavelength of
-light then produces no net energy shift. This provides a strict constraint on
-the transition dipole matrix elements without the complication and inaccuracy
-of other methods.
-Using a novel atom-laser based technique we have made the first measurement
-of the the tune-out wavelength in metastable helium between the
-$3^{3}P_{1,2,3}$ and $2^{3}P_{1,2,3}$ states at 413.07(2)nm which compares well
-with the predicted value\cite{Mitroy2013} of 413.02(9). We have additionally
-developed many of the methods necessary to improve this measurement to the
-100fm level of accuracy where it will form the most accurate determination of
-transition rate information ever made in He* and provide a stringent test for
-atomic QED simulations. We believe this measurement to be one of the most
-sensitive ever made of an optical dipole potential, able to detect changes in
-potentials of $\sim200pK$ and is widely applicable to other species and areas
-of atom optics.
-",0,1,0,0,0,0
-18407,WheelCon: A wheel control-based gaming platform for studying human sensorimotor control,"  Feedback control theory has been extensively implemented to theoretically
-model human sensorimotor control. However, experimental platforms capable of
-manipulating important components of multiple feedback loops lack development.
-This paper describes the WheelCon, which is an open source platform aimed at
-resolving such insufficiencies. WheelCon enables safely simulation of the
-canonical sensorimotor task such as riding a mountain bike down a steep,
-twisting, bumpy trail etc., with provided only a computer, standard display,
-and an inexpensive gaming steering wheel with a force feedback motor. The
-platform provides flexibility, as will be demonstrated in the demos provided,
-so that researchers may manipulate the disturbances, delay, and quantization
-(data rate) in the layered feedback loops, including a high-level advanced plan
-layer and a low-level delayed reflex layer. In this paper, we illustrate
-WheelCon's graphical user interface (GUI), the input and output of existing
-demos, and how to design new games. In addition, we present the basic feedback
-model, and we show the testing results from our demo games which align well
-with prediction from the model. In short, the platform is featured as cheap,
-simple to use, and flexible to program for effective sensorimotor neuroscience
-research and control engineering education.
-",0,0,0,0,1,0
-18408,Depression and Self-Harm Risk Assessment in Online Forums,"  Users suffering from mental health conditions often turn to online resources
-for support, including specialized online support communities or general
-communities such as Twitter and Reddit. In this work, we present a neural
-framework for supporting and studying users in both types of communities. We
-propose methods for identifying posts in support communities that may indicate
-a risk of self-harm, and demonstrate that our approach outperforms strong
-previously proposed methods for identifying such posts. Self-harm is closely
-related to depression, which makes identifying depressed users on general
-forums a crucial related task. We introduce a large-scale general forum dataset
-(""RSDD"") consisting of users with self-reported depression diagnoses matched
-with control users. We show how our method can be applied to effectively
-identify depressed users from their use of language alone. We demonstrate that
-our method outperforms strong baselines on this general forum dataset.
-",1,0,0,0,0,0
-18409,Quantum dilogarithm identities for the square product of A-type Dynkin quivers,"  The famous pentagon identity for quantum dilogarithms has a generalization
-for every Dynkin quiver, due to Reineke. A more advanced generalization is
-associated with a pair of alternating Dynkin quivers, due to Keller. The
-description and proof of Keller's identities involves cluster algebras and
-cluster categories, and the statement of the identity is implicit. In this
-paper we describe Keller's identities explicitly, and prove them by a dimension
-counting argument. Namely, we consider quiver representations
-$\boldsymbol{\mathrm{Rep}}_\gamma$ together with a superpotential function
-$W_\gamma$, and calculate the Betti numbers of the equivariant $W_\gamma$ rapid
-decay cohomology algebra of $\boldsymbol{\mathrm{Rep}}_\gamma$ in two different
-ways corresponding to two natural stratifications of
-$\boldsymbol{\mathrm{Rep}}_\gamma$. This approach is suggested by Kontsevich
-and Soibelman in relation with the Cohomological Hall Algebra of quivers, and
-the associated Donaldson-Thomas invariants.
-",0,0,1,0,0,0
-18410,A Probabilistic Linear Genetic Programming with Stochastic Context-Free Grammar for solving Symbolic Regression problems,"  Traditional Linear Genetic Programming (LGP) algorithms are based only on the
-selection mechanism to guide the search. Genetic operators combine or mutate
-random portions of the individuals, without knowing if the result will lead to
-a fitter individual. Probabilistic Model Building Genetic Programming (PMB-GP)
-methods were proposed to overcome this issue through a probability model that
-captures the structure of the fit individuals and use it to sample new
-individuals. This work proposes the use of LGP with a Stochastic Context-Free
-Grammar (SCFG), that has a probability distribution that is updated according
-to selected individuals. We proposed a method for adapting the grammar into the
-linear representation of LGP. Tests performed with the proposed probabilistic
-method, and with two hybrid approaches, on several symbolic regression
-benchmark problems show that the results are statistically better than the
-obtained by the traditional LGP.
-",1,0,0,1,0,0
-18411,"Trace-free ${\rm SL}(2,\mathbb{C})$-representations of arborescent links","  Given a link $L\subset S^3$, a representation $\pi_1(S^3-L)\to{\rm
-SL}(2,\mathbb{C})$ is {\it trace-free} if it sends each meridian to an element
-with trace zero. We present a method for completely determining trace-free
-${\rm SL}(2,\mathbb{C})$-representations for arborescent links. Concrete
-computations are done for a class of 3-bridge arborescent links.
-",0,0,1,0,0,0
-18412,Algorithms for Weighted Sums of Squares Decomposition of Non-negative Univariate Polynomials,"  It is well-known that every non-negative univariate real polynomial can be
-written as the sum of two polynomial squares with real coefficients. When one
-allows a weighted sum of finitely many squares instead of a sum of two squares,
-then one can choose all coefficients in the representation to lie in the field
-generated by the coefficients of the polynomial.
-In this article, we describe, analyze and compare both from the theoretical
-and practical points of view, two algorithms computing such a weighted sums of
-squares decomposition for univariate polynomials with rational coefficients.
-The first algorithm, due to the third author relies on real root isolation,
-quadratic approximations of positive polynomials and square-free decomposition
-but its complexity was not analyzed. We provide bit complexity estimates, both
-on runtime and output size of this algorithm. They are exponential in the
-degree of the input univariate polynomial and linear in the maximum bitsize of
-its complexity. This analysis is obtained using quantifier elimination and root
-isolation bounds.
-The second algorithm, due to Chevillard, Harrison, Joldes and Lauter, relies
-on complex root isolation and square-free decomposition and has been introduced
-for certifying positiveness of polynomials in the context of computer
-arithmetics. Again, its complexity was not analyzed. We provide bit complexity
-estimates, both on runtime and output size of this algorithm, which are
-polynomial in the degree of the input polynomial and linear in the maximum
-bitsize of its complexity. This analysis is obtained using Vieta's formula and
-root isolation bounds.
-Finally, we report on our implementations of both algorithms. While the
-second algorithm is, as expected from the complexity result, more efficient on
-most of examples, we exhibit families of non-negative polynomials for which the
-first algorithm is better.
-",1,0,0,0,0,0
-18413,The Calkin algebra is $\aleph_1$-universal,"  We discuss the existence of (injectively) universal C*-algebras and prove
-that all C*-algebras of density character $\aleph_1$ embed into the Calkin
-algebra, $Q(H)$. Together with other results, this shows that each of the
-following assertions is relatively consistent with ZFC: (i) $Q(H)$ is a
-$2^{\aleph_0}$-universal C*-algebra. (ii) There exists a
-$2^{\aleph_0}$-universal C*-algebra, but $Q(H)$ is not
-$2^{\aleph_0}$-universal. (iii) A $2^{\aleph_0}$-universal C*-algebra does not
-exist. We also prove that it is relatively consistent with ZFC that (iv) there
-is no $\aleph_1$-universal nuclear C*-algebra, and that (v) there is no
-$\aleph_1$-universal simple nuclear C*-algebra.
-",0,0,1,0,0,0
-18414,Learning Structured Semantic Embeddings for Visual Recognition,"  Numerous embedding models have been recently explored to incorporate semantic
-knowledge into visual recognition. Existing methods typically focus on
-minimizing the distance between the corresponding images and texts in the
-embedding space but do not explicitly optimize the underlying structure. Our
-key observation is that modeling the pairwise image-image relationship improves
-the discrimination ability of the embedding model. In this paper, we propose
-the structured discriminative and difference constraints to learn
-visual-semantic embeddings. First, we exploit the discriminative constraints to
-capture the intra- and inter-class relationships of image embeddings. The
-discriminative constraints encourage separability for image instances of
-different classes. Second, we align the difference vector between a pair of
-image embeddings with that of the corresponding word embeddings. The difference
-constraints help regularize image embeddings to preserve the semantic
-relationships among word embeddings. Extensive evaluations demonstrate the
-effectiveness of the proposed structured embeddings for single-label
-classification, multi-label classification, and zero-shot recognition.
-",1,0,0,0,0,0
-18415,Estimators for a Class of Bivariate Measures of Concordance for Copulas,"  In the present paper we propose and study estimators for a wide class of
-bivariate measures of concordance for copulas. These measures of concordance
-are generated by a copula and generalize Spearman's rho and Gini's gamma. In
-the case of Spearman's rho and Gini's gamma the estimators turn out to be the
-usual sample versions of these measures of concordance.
-",0,0,1,1,0,0
-18416,Variational treatment of electron-polyatomic molecule scattering calculations using adaptive overset grids,"  The Complex Kohn variational method for electron-polyatomic molecule
-scattering is formulated using an overset grid representation of the scattering
-wave function. The overset grid consists of a central grid and multiple dense,
-atom-centered subgrids that allow the simultaneous spherical expansions of the
-wave function about multiple centers. Scattering boundary conditions are
-enforced by using a basis formed by the repeated application of the free
-particle Green's function and potential, $\hat{G}^+_0\hat{V}$ on the overset
-grid in a ""Born-Arnoldi"" solution of the working equations. The theory is shown
-to be equivalent to a specific Padé approximant to the $T$-matrix, and has
-rapid convergence properties, both in the number of numerical basis functions
-employed and the number of partial waves employed in the spherical expansions.
-The method is demonstrated in calculations on methane and CF$_4$ in the
-static-exchange approximation, and compared in detail with calculations
-performed with the numerical Schwinger variational approach based on single
-center expansions. An efficient procedure for operating with the free-particle
-Green's function and exchange operators (to which no approximation is made) is
-also described.
-",0,1,0,0,0,0
-18417,Geometry of quantum dynamics in infinite dimension,"  We develop a geometric approach to quantum mechanics based on the concept of
-the Tulczyjew triple. Our approach is genuinely infinite-dimensional and
-including a Lagrangian formalism in which self-adjoint (Schroedinger) operators
-are obtained as Lagrangian submanifolds associated with the Lagrangian. As a
-byproduct we obtain also results concerning coadjoint orbits of the unitary
-group in infinite dimension, embedding of the Hilbert projective space of pure
-states in the unitary group, and an approach to self-adjoint extensions of
-symmetric relations.
-",0,0,1,0,0,0
-18418,Canonical tilting relative generators,"  Given a relatively projective birational morphism $f\colon X\to Y$ of smooth
-algebraic spaces with dimension of fibers bounded by 1, we construct tilting
-relative (over $Y$) generators $T_{X,f}$ and $S_{X,f}$ in $\mathcal{D}^b(X)$.
-We develop a piece of general theory of strict admissible lattice filtrations
-in triangulated categories and show that $\mathcal{D}^b(X)$ has such a
-filtration $\mathcal{L}$ where the lattice is the set of all birational
-decompositions $f \colon X \xrightarrow{g} Z \xrightarrow{h} Y$ with smooth
-$Z$. The $t$-structures related to $T_{X,f}$ and $S_{X,f}$ are proved to be
-glued via filtrations left and right dual to $\mathcal{L}$. We realise all such
-$Z$ as the fine moduli spaces of simple quotients of $\mathcal{O}_X$ in the
-heart of the $t$-structure for which $S_{X,g}$ is a relative projective
-generator over $Y$. This implements the program of interpreting relevant smooth
-contractions of $X$ in terms of a suitable system of $t$-structures on
-$\mathcal{D}^b(X)$.
-",0,0,1,0,0,0
-18419,Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models,"  Current theories hold that brain function is highly related to long-range
-physical connections through axonal bundles, namely extrinsic connectiv-ity.
-However, obtaining a groupwise cortical parcellation based on extrinsic
-connectivity remains challenging. Current parcellation methods are
-compu-tationally expensive; need tuning of several parameters or rely on ad-hoc
-constraints. Furthermore, none of these methods present a model for the
-cortical extrinsic connectivity of the cortex. To tackle these problems, we
-propose a parsimonious model for the extrinsic connectivity and an efficient
-parceling technique based on clustering of tractograms. Our technique allows
-the creation of single subject and groupwise parcellations of the whole cortex.
-The parcellations obtained with our technique are in agreement with structural
-and functional parcellations in the literature. In particular, the motor and
-sensory cortex are subdivided in agreement with the human ho-munculus of
-Penfield. We illustrate this by comparing our resulting parcels with the motor
-strip mapping included in the Human Connectome Project data.
-",0,0,0,1,0,0
-18420,Modeling Biological Problems in Computer Science: A Case Study in Genome Assembly,"  As computer scientists working in bioinformatics/computational biology, we
-often face the challenge of coming up with an algorithm to answer a biological
-question. This occurs in many areas, such as variant calling, alignment, and
-assembly. In this tutorial, we use the example of the genome assembly problem
-to demonstrate how to go from a question in the biological realm to a solution
-in the computer science realm. We show the modeling process step-by-step,
-including all the intermediate failed attempts.
-",1,0,0,0,0,0
-18421,MEC: Memory-efficient Convolution for Deep Neural Network,"  Convolution is a critical component in modern deep neural networks, thus
-several algorithms for convolution have been developed. Direct convolution is
-simple but suffers from poor performance. As an alternative, multiple indirect
-methods have been proposed including im2col-based convolution, FFT-based
-convolution, or Winograd-based algorithm. However, all these indirect methods
-have high memory-overhead, which creates performance degradation and offers a
-poor trade-off between performance and memory consumption. In this work, we
-propose a memory-efficient convolution or MEC with compact lowering, which
-reduces memory-overhead substantially and accelerates convolution process. MEC
-lowers the input matrix in a simple yet efficient/compact way (i.e., much less
-memory-overhead), and then executes multiple small matrix multiplications in
-parallel to get convolution completed. Additionally, the reduced memory
-footprint improves memory sub-system efficiency, improving performance. Our
-experimental results show that MEC reduces memory consumption significantly
-with good speedup on both mobile and server platforms, compared with other
-indirect convolution algorithms.
-",1,0,0,0,0,0
-18422,On age of 6070 Rheinland and 54827 (2001 NQ8) asteroid pair,"  In this paper we present results of our studying of famous very young pair of
-asteroids 6070 Rheinland and 54827 (2001 NQ8). We have done numeric integration
-of orbits of pair with only planet perturbations and include Ceres and Vesta
-effect. We have confirmed results of previous studying, obtained with different
-integrators. And we confirm significant effect of Ceres and Vesta perturbation
-on dynamic of this pair. We find that effect of other massive asteroids is
-insignificant. According our results, more probable age of 6070 Rheinland and
-54827 (2001 NQ8) pair is 16.2 kyrs. Our value of age is very close to most
-recent age determination by Vokrouhlicky et al [12], obtained with different
-method. After the compare our results, we can conclude, that non-gravitational
-forces are small and large number of clones is not necessary in studying of
-this pair. As an additional way of studying of close orbits dynamics, we
-calculate relative velocity in pair during numeric integration. Normal
-component of velocity show a very good convergence at epoch of closest
-encounter in pair.
-",0,1,0,0,0,0
-18423,First-Order Reversal Curves of the Magnetostructural Phase Transition in FeTe,"  We apply the first-order reversal curve (FORC) method, borrowed from studies
-of ferromagnetic materials, to the magneto-structural phase transition of FeTe.
-FORC measurements reveal two features in the hysteretic phase transition, even
-in samples where traditional temperature measurements display only a single
-transition. For Fe1.13Te, the influence of magnetic field suggests that the
-main feature is primarily structural while a smaller, slightly
-higher-temperature transition is magnetic in origin. By contrast Fe1.03Te has a
-single transition which shows a uniform response to magnetic field, indicating
-a stronger coupling of the magnetic and structural phase transitions. We also
-introduce uniaxial stress, which spreads the distribution width without
-changing the underlying energy barrier of the transformation. The work shows
-how FORC can help disentangle the roles of the magnetic and structural phase
-transitions in FeTe.
-",0,1,0,0,0,0
-18424,Data Analytics on Online Labor Markets: Opportunities and Challenges,"  The data-driven economy has led to a significant shortage of data scientists.
-To address this shortage, this study explores the prospects of outsourcing data
-analysis tasks to freelancers available on online labor markets (OLMs) by
-identifying the essential factors for this endeavor. Specifically, we explore
-the skills required from freelancers, collect information about the skills
-present on major OLMs, and identify the main hurdles for out-/crowd-sourcing
-data analysis. Adopting a sequential mixed-method approach, we interviewed 20
-data scientists and subsequently surveyed 80 respondents from OLMs. Besides
-confirming the need for expected skills such as technical/mathematical
-capabilities, it also identifies less known ones such as domain understanding,
-an eye for aesthetic data visualization, good communication skills, and a
-natural understanding of the possibilities/limitations of data analysis in
-general. Finally, it elucidates obstacles for crowdsourcing like the
-communication overhead, knowledge gaps, quality assurance, and data
-confidentiality, which need to be mitigated.
-",1,0,0,0,0,0
-18425,Analyzing the Robustness of Nearest Neighbors to Adversarial Examples,"  Motivated by safety-critical applications, test-time attacks on classifiers
-via adversarial examples has recently received a great deal of attention.
-However, there is a general lack of understanding on why adversarial examples
-arise; whether they originate due to inherent properties of data or due to lack
-of training samples remains ill-understood. In this work, we introduce a
-theoretical framework analogous to bias-variance theory for understanding these
-effects.
-We use our framework to analyze the robustness of a canonical non-parametric
-classifier - the k-nearest neighbors. Our analysis shows that its robustness
-properties depend critically on the value of k - the classifier may be
-inherently non-robust for small k, but its robustness approaches that of the
-Bayes Optimal classifier for fast-growing k. We propose a novel modified
-1-nearest neighbor classifier, and guarantee its robustness in the large sample
-limit. Our experiments suggest that this classifier may have good robustness
-properties even for reasonable data set sizes.
-",1,0,0,1,0,0
-18426,Non Uniform On Chip Power Delivery Network Synthesis Methodology,"  In this paper, we proposed a non-uniform power delivery network (PDN)
-synthesis methodology. It first constructs initial PDN using uniform approach.
-Then preliminary power integrity analysis is performed to derive IR-safe
-candidate window. Congestion map is obtained based global route congestion
-estimation. A self-adaptive non-uniform PDN synthesis is then performed to
-globally and locally optimize PDN over selected regions. The PDN synthesis is
-congestion-driven and IR- guarded. Experimental results show significant timing
-important in trade-off small PDN length reduction with no EM/IR impact. We
-further explored potential power savings using our non-uniform PDN synthesis
-methodology.
-",1,0,0,0,0,0
-18427,Translating ceRNA susceptibilities into correlation functions,"  Competition to bind microRNAs induces an effective positive crosstalk between
-their targets, therefore known as `competing endogenous RNAs' or ceRNAs. While
-such an effect is known to play a significant role in specific conditions,
-estimating its strength from data and, experimentally, in physiological
-conditions appears to be far from simple. Here we show that the susceptibility
-of ceRNAs to different types of perturbations affecting their competitors (and
-hence their tendency to crosstalk) can be encoded in quantities as intuitive
-and as simple to measure as correlation functions. We confirm this scenario by
-extensive numerical simulations and validate it by re-analyzing PTEN's
-crosstalk pattern from TCGA breast cancer dataset. These results clarify the
-links between different quantities used to estimate the intensity of ceRNA
-crosstalk and provide new keys to analyze transcriptional datasets and
-effectively probe ceRNA networks in silico.
-",0,1,0,0,0,0
-18428,Impact of energetic particle orbits on long range frequency chirping of BGK modes,"  Long range frequency chirping of Bernstein-Greene-Kruskal modes, whose
-existence is determined by the fast particles, is investigated in cases where
-these particles do not move freely and their motion is bounded to restricted
-orbits. An equilibrium oscillating potential, which creates different orbit
-topologies of energetic particles, is included into the bump-on-tail
-instability problem of a plasma wave. With respect to fast particles dynamics,
-the extended model captures the range of particles motion (trapped/passing)
-with energy and thus represents a more realistic 1D picture of the long range
-sweeping events observed for weakly damped modes, e.g. global Alfven
-eigenmodes, in tokamaks. The Poisson equation is solved numerically along with
-bounce averaging the Vlasov equation in the adiabatic regime. We demonstrate
-that the shape and the saturation amplitude of the nonlinear mode structure
-depends not only on the amount of deviation from the initial eigenfrequency but
-also on the initial energy of the resonant electrons in the equilibrium
-potential. Similarly, the results reveal that the resonant electrons following
-different equilibrium orbits in the electrostatic potential lead to different
-rates of frequency evolution. As compared to the previous model [Breizman B.N.
-2010 Nucl. Fusion 50 084014], it is shown that the frequency sweeps with lower
-rates. The additional physics included in the model enables a more complete 1D
-description of the range of phenomena observed in experiments.
-",0,1,0,0,0,0
-18429,An Executable Specification of Typing Rules for Extensible Records based on Row Polymorphism,"  Type inference is an application domain that is a natural fit for logic
-programming (LP). LP systems natively support unification, which serves as a
-basic building block of typical type inference algorithms. In particular,
-polymorphic type inference in the Hindley--Milner type system (HM) can be
-succinctly specified and executed in Prolog. In our previous work, we have
-demonstrated that more advanced features of parametric polymorphism beyond HM,
-such as type-constructor polymorphism and kind polymorphism, can be similarly
-specified in Prolog. Here, we demonstrate a specification for records, which is
-one of the most widely supported compound data structures in real-world
-programming languages, and discuss the advantages and limitations of Prolog as
-a specification language for type systems. Record types are specified as
-order-irrelevant collections of named fields mapped to their corresponding
-types. In addition, an open-ended collection is used to support row
-polymorphism for record types to be extensible.
-",1,0,0,0,0,0
-18430,Seasonal Stochastic Volatility and the Samuelson Effect in Agricultural Futures Markets,"  We introduce a multi-factor stochastic volatility model for commodities that
-incorporates seasonality and the Samuelson effect. Conditions on the seasonal
-term under which the corresponding volatility factor is well-defined are given,
-and five different specifications of the seasonality pattern are proposed. We
-calculate the joint characteristic function of two futures prices for different
-maturities in the risk-neutral measure. The model is then presented under the
-physical measure, and its state-space representation is derived, in order to
-estimate the parameters with the Kalman filter for time series of corn, cotton,
-soybean, sugar and wheat futures from 2007 to 2017. The seasonal model
-significantly outperforms the nested non-seasonal model in all five markets,
-and we show which seasonality patterns are particularly well-suited in each
-case. We also confirm the importance of correctly modelling the Samuelson
-effect in order to account for futures with different maturities. Our results
-are clearly confirmed in a robustness check carried out with an alternative
-dataset of constant maturity futures for the same agricultural markets.
-",0,0,0,0,0,1
-18431,A Computational Model of a Single-Photon Avalanche Diode Sensor for Transient Imaging,"  Single-Photon Avalanche Diodes (SPAD) are affordable photodetectors, capable
-to collect extremely fast low-energy events, due to their single-photon
-sensibility. This makes them very suitable for time-of-flight-based range
-imaging systems, allowing to reduce costs and power requirements, without
-sacrifizing much temporal resolution. In this work we describe a computational
-model to simulate the behaviour of SPAD sensors, aiming to provide a realistic
-camera model for time-resolved light transport simulation, with applications on
-prototyping new reconstructions techniques based on SPAD time-of-flight data.
-Our model accounts for the major effects of the sensor on the incoming signal.
-We compare our model against real-world measurements, and apply it to a variety
-of scenarios, including complex multiply-scattered light transport.
-",1,1,0,0,0,0
-18432,On Certain Tilting Modules for SL2,"  We give a complete picture of when the tensor product of an induced module
-and a Weyl module is a tilting module for the algebraic group $SL_2$ over an
-algebraically closed field of characteristic $p$. Whilst the result is
-recursive by nature, we give an explicit statement in terms of the $p$-adic
-expansions of the highest weight of each module.
-",0,0,1,0,0,0
-18433,First-order Methods Almost Always Avoid Saddle Points,"  We establish that first-order methods avoid saddle points for almost all
-initializations. Our results apply to a wide variety of first-order methods,
-including gradient descent, block coordinate descent, mirror descent and
-variants thereof. The connecting thread is that such algorithms can be studied
-from a dynamical systems perspective in which appropriate instantiations of the
-Stable Manifold Theorem allow for a global stability analysis. Thus, neither
-access to second-order derivative information nor randomness beyond
-initialization is necessary to provably avoid saddle points.
-",1,0,0,1,0,0
-18434,Faster Algorithms for Computing Maximal 2-Connected Subgraphs in Sparse Directed Graphs,"  Connectivity related concepts are of fundamental interest in graph theory.
-The area has received extensive attention over four decades, but many problems
-remain unsolved, especially for directed graphs. A directed graph is
-2-edge-connected (resp., 2-vertex-connected) if the removal of any edge (resp.,
-vertex) leaves the graph strongly connected. In this paper we present improved
-algorithms for computing the maximal 2-edge- and 2-vertex-connected subgraphs
-of a given directed graph. These problems were first studied more than 35 years
-ago, with $\widetilde{O}(mn)$ time algorithms for graphs with m edges and n
-vertices being known since the late 1980s. In contrast, the same problems for
-undirected graphs are known to be solvable in linear time. Henzinger et al.
-[ICALP 2015] recently introduced $O(n^2)$ time algorithms for the directed
-case, thus improving the running times for dense graphs. Our new algorithms run
-in time $O(m^{3/2})$, which further improves the running times for sparse
-graphs.
-The notion of 2-connectivity naturally generalizes to k-connectivity for
-$k>2$. For constant values of k, we extend one of our algorithms to compute the
-maximal k-edge-connected in time $O(m^{3/2} \log{n})$, improving again for
-sparse graphs the best known algorithm by Henzinger et al. [ICALP 2015] that
-runs in $O(n^2 \log n)$ time.
-",1,0,0,0,0,0
-18435,Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting,"  Generative adversarial networks (GANs) are an exciting alternative to
-algorithms for solving density estimation problems---using data to assess how
-likely samples are to be drawn from the same distribution. Instead of
-explicitly computing these probabilities, GANs learn a generator that can match
-the given probabilistic source. This paper looks particularly at this matching
-capability in the context of problems with one-dimensional outputs. We identify
-a class of function decompositions with properties that make them well suited
-to the critic role in a leading approach to GANs known as Wasserstein GANs. We
-show that Taylor and Fourier series decompositions belong to our class, provide
-examples of these critics outperforming standard GAN approaches, and suggest
-how they can be scaled to higher dimensional problems in the future.
-",1,0,0,1,0,0
-18436,Fingering instabilities and pattern formation in a two-component dipolar Bose-Einstein condensate,"  We study fingering instabilities and pattern formation at the interface of an
-oppositely polarized two-component Bose-Einstein condensate with strong
-dipole-dipole interactions in three dimensions. It is shown that the rotational
-symmetry is spontaneously broken by fingering instability when the
-dipole-dipole interactions are strengthened. Frog-shaped and mushroom-shaped
-patterns emerge during the dynamics due to the dipolar interactions. We also
-demonstrate the spontaneous density modulation and domain growth of a
-two-component dipolar BEC in the dynamics. Bogoliubov analyses in the
-two-dimensional approximation are performed, and the characteristic lengths of
-the domains are estimated analytically. Patterns resembling those in magnetic
-classical fluids are modulated when the number ratio of atoms, the trap ratio
-of the external potential, or tilted polarization with respect to the z
-direction is varied.
-",0,1,0,0,0,0
-18437,Fast Information-theoretic Bayesian Optimisation,"  Information-theoretic Bayesian optimisation techniques have demonstrated
-state-of-the-art performance in tackling important global optimisation
-problems. However, current information-theoretic approaches require many
-approximations in implementation, introduce often-prohibitive computational
-overhead and limit the choice of kernels available to model the objective. We
-develop a fast information-theoretic Bayesian Optimisation method, FITBO, that
-avoids the need for sampling the global minimiser, thus significantly reducing
-computational overhead. Moreover, in comparison with existing approaches, our
-method faces fewer constraints on kernel choice and enjoys the merits of
-dealing with the output space. We demonstrate empirically that FITBO inherits
-the performance associated with information-theoretic Bayesian optimisation,
-while being even faster than simpler Bayesian optimisation approaches, such as
-Expected Improvement.
-",0,0,0,1,0,0
-18438,ExoMol molecular line lists XX: a comprehensive line list for H$_3^+$,"  H$_3^+$ is a ubiquitous and important astronomical species whose spectrum has
-been observed in the interstellar medium, planets and tentatively in the
-remnants of supernova SN1897a. Its role as a cooler is important for gas giant
-planets and exoplanets, and possibly the early Universe. All this makes the
-spectral properties, cooling function and partition function of H$_3^+$ key
-parameters for astronomical models and analysis. A new high-accuracy, very
-extensive line list for H$_3^+$ called MiZATeP was computed as part of the
-ExoMol project alongside a temperature-dependent cooling function and partition
-function as well as lifetimes for %individual excited states. These data are
-made available in electronic form as supplementary data to this article and at
-this http URL
-",0,1,0,0,0,0
-18439,Robust and Sparse Regression in GLM by Stochastic Optimization,"  The generalized linear model (GLM) plays a key role in regression analyses.
-In high-dimensional data, the sparse GLM has been used but it is not robust
-against outliers. Recently, the robust methods have been proposed for the
-specific example of the sparse GLM. Among them, we focus on the robust and
-sparse linear regression based on the $\gamma$-divergence. The estimator of the
-$\gamma$-divergence has strong robustness under heavy contamination. In this
-paper, we extend the robust and sparse linear regression based on the
-$\gamma$-divergence to the robust and sparse GLM based on the
-$\gamma$-divergence with a stochastic optimization approach in order to obtain
-the estimate. We adopt the randomized stochastic projected gradient descent as
-a stochastic optimization approach and extend the established convergence
-property to the classical first-order necessary condition. By virtue of the
-stochastic optimization approach, we can efficiently estimate parameters for
-very large problems. Particularly, we show the linear regression, logistic
-regression and Poisson regression with $L_1$ regularization in detail as
-specific examples of robust and sparse GLM. In numerical experiments and real
-data analysis, the proposed method outperformed comparative methods.
-",0,0,0,1,0,0
-18440,A Hint-Based Technique for System Level Model-Based Test Case Prioritization,"  Test Case Prioritization (TCP) techniques aim at proposing new test case
-execution orders to favor the achievement of certain testing goal, such as
-fault detection. Current TCP research focus mainly on code-based regression
-testing; however in the Model-Based Testing (MBT) context, we still need more
-investigation. General TCP techniques do not use historical information, since
-this information is often unavailable. Therefore, techniques use different
-sources of information to guide prioritization. We propose a novel technique
-that guides its operation using provided hints, the Hint-Based Adaptive Random
-Prioritization - HARP. Hints are indications or suggestions provided by
-developers about error-prone functionalities. As hints may be hard to collect
-automatically, we also propose an approach of collecting them. To validate our
-findings, we performed an experiment measuring the effect of introducing hints
-to HARP. It shows that hints can improve HARP's performance comparing to its
-baseline. Then, we investigated the ability of developers/managers to provide
-good hints and used them in a case study. This analysis showed that developers
-and managers are able to provide useful hints, which improves HARP's fault
-detection comparing to its baseline. Nonetheless, the provided hints should be
-consensual among the development team members.
-",1,0,0,0,0,0
-18441,Control of birhythmicity: A self-feedback approach,"  Birhythmicity occurs in many natural and artificial systems. In this paper we
-propose a self-feedback scheme to control birhythmicity. To establish the
-efficacy and generality of the proposed control scheme, we apply it on three
-birhythmic oscillators from diverse fields of natural science, namely, an
-energy harvesting system, the p53-Mdm2 network for protein genesis (the OAK
-model) and a glycolysis model (modified Decroly-Goldbeter model). Using the
-harmonic decomposition technique and energy balance method we derive the
-analytical conditions for the control of birhythmicity. A detailed numerical
-bifurcation analysis in the parameter space establishes that the control scheme
-is capable of eliminating birhythmicity and it can also induce transitions
-between different forms of bistability. As the proposed control scheme is quite
-general, it can be applied for control of several real systems, particularly in
-biochemical and engineering systems.
-",0,1,0,0,0,0
-18442,Forecasting the magnitude and onset of El Nino based on climate network,"  El Nino is probably the most influential climate phenomenon on interannual
-time scales. It affects the global climate system and is associated with
-natural disasters and serious consequences in many aspects of human life.
-However, the forecasting of the onset and in particular the magnitude of El
-Nino are still not accurate, at least more than half a year in advance. Here,
-we introduce a new forecasting index based on network links representing the
-similarity of low frequency temporal temperature anomaly variations between
-different sites in the El Nino 3.4 region. We find that significant upward
-trends and peaks in this index forecast with high accuracy both the onset and
-magnitude of El Nino approximately 1 year ahead. The forecasting procedure we
-developed improves in particular the prediction of the magnitude of El Nino and
-is validated based on several, up to more than a century long, datasets.
-",0,1,0,0,0,0
-18443,Weighted $1\times1$ cut-and-project sets in bounded distance to a lattice,"  Recent results of Grepstad and Lev are used to show that weighted
-cut-and-project sets with one-dimensional physical space and one-dimensional
-internal space are bounded distance equivalent to some lattice if the weight
-function $h$ is continuous on the internal space, and if $h$ is either
-piecewise linear, or twice differentiable with bounded curvature.
-",0,0,1,0,0,0
-18444,On Ordinal Invariants in Well Quasi Orders and Finite Antichain Orders,"  We investigate the ordinal invariants height, length, and width of well quasi
-orders (WQO), with particular emphasis on width, an invariant of interest for
-the larger class of orders with finite antichain condition (FAC). We show that
-the width in the class of FAC orders is completely determined by the width in
-the class of WQOs, in the sense that if we know how to calculate the width of
-any WQO then we have a procedure to calculate the width of any given FAC order.
-We show how the width of WQO orders obtained via some classical constructions
-can sometimes be computed in a compositional way. In particular, this allows
-proving that every ordinal can be obtained as the width of some WQO poset. One
-of the difficult questions is to give a complete formula for the width of
-Cartesian products of WQOs. Even the width of the product of two ordinals is
-only known through a complex recursive formula. Although we have not given a
-complete answer to this question we have advanced the state of knowledge by
-considering some more complex special cases and in particular by calculating
-the width of certain products containing three factors. In the course of
-writing the paper we have discovered that some of the relevant literature was
-written on cross-purposes and some of the notions re-discovered several times.
-Therefore we also use the occasion to give a unified presentation of the known
-results.
-",0,0,1,0,0,0
-18445,Indices in XML Databases,"  With XML becoming a standard for business information representation and
-exchange, stor-ing, indexing, and querying XML documents have rapidly become
-major issues in database research. In this context, query processing and
-optimization are primordial, native-XML data-bases not being mature yet. Data
-structures such as indices, which help enhance performances substantially, are
-extensively researched, especially since XML data bear numerous specifici-ties
-with respect to relational data. In this paper, we survey state-of-the-art XML
-indices and discuss the main issues, tradeoffs and future trends in XML
-indexing. We also present an in-dex that we specifically designed for the
-particular architecture of XML data warehouses.
-",1,0,0,0,0,0
-18446,Kernel theorems for modulation spaces,"  We deal with kernel theorems for modulation spaces. We completely
-characterize the continuity of a linear operator on the modulation spaces $M^p$
-for every $1\leq p\leq\infty$, by the membership of its kernel to (mixed)
-modulation spaces. Whereas Feichtinger's kernel theorem (which we recapture as
-a special case) is the modulation space counterpart of Schwartz' kernel theorem
-for temperate distributions, our results do not have a couterpart in
-distribution theory. This reveals the superiority, in some respects, of the
-modulation space formalism upon distribution theory, as already emphasized in
-Feichtinger's manifesto for a post-modern harmonic analysis, tailored to the
-needs of mathematical signal processing. The proof uses in an essential way a
-discretization of the problem by means of Gabor frames. We also show the
-equivalence of the operator norm and the modulation space norm of the
-corresponding kernel. For operators acting on $M^{p,q}$ a similar
-characterization is not expected, but sufficient conditions for boundedness can
-be sated in the same spirit.
-",0,0,1,0,0,0
-18447,On the Performance of Network Parallel Training in Artificial Neural Networks,"  Artificial Neural Networks (ANNs) have received increasing attention in
-recent years with applications that span a wide range of disciplines including
-vital domains such as medicine, network security and autonomous transportation.
-However, neural network architectures are becoming increasingly complex and
-with an increasing need to obtain real-time results from such models, it has
-become pivotal to use parallelization as a mechanism for speeding up network
-training and deployment. In this work we propose an implementation of Network
-Parallel Training through Cannon's Algorithm for matrix multiplication. We show
-that increasing the number of processes speeds up training until the point
-where process communication costs become prohibitive; this point varies by
-network complexity. We also show through empirical efficiency calculations that
-the speedup obtained is superlinear.
-",1,0,0,1,0,0
-18448,Fair Forests: Regularized Tree Induction to Minimize Model Bias,"  The potential lack of fairness in the outputs of machine learning algorithms
-has recently gained attention both within the research community as well as in
-society more broadly. Surprisingly, there is no prior work developing
-tree-induction algorithms for building fair decision trees or fair random
-forests. These methods have widespread popularity as they are one of the few to
-be simultaneously interpretable, non-linear, and easy-to-use. In this paper we
-develop, to our knowledge, the first technique for the induction of fair
-decision trees. We show that our ""Fair Forest"" retains the benefits of the
-tree-based approach, while providing both greater accuracy and fairness than
-other alternatives, for both ""group fairness"" and ""individual fairness.'"" We
-also introduce new measures for fairness which are able to handle multinomial
-and continues attributes as well as regression problems, as opposed to binary
-attributes and labels only. Finally, we demonstrate a new, more robust
-evaluation procedure for algorithms that considers the dataset in its entirety
-rather than only a specific protected attribute.
-",1,0,0,1,0,0
-18449,Discretization of SU(2) and the Orthogonal Group Using Icosahedral Symmetries and the Golden Numbers,"  The vertices of the four dimensional $120$-cell form a non-crystallographic
-root system whose corresponding symmetry group is the Coxeter group $H_{4}$.
-There are two special coordinate representations of this root system in which
-they and their corresponding Coxeter groups involve only rational numbers and
-the golden ratio $\tau$. The two are related by the conjugation $\tau
-\mapsto\tau' = -1/\tau$. This paper investigates what happens when the two root
-systems are combined and the group generated by both versions of $H_{4}$ is
-allowed to operate on them. The result is a new, but infinite, `root system'
-$\Sigma$ which itself turns out to have a natural structure of the unitary
-group $SU(2,\mathcal R)$ over the ring $\mathcal R = \mathbb
-Z[\frac{1}{2},\tau]$ (called here golden numbers). Acting upon it is the
-naturally associated infinite reflection group $H^{\infty}$, which we prove is
-of index $2$ in the orthogonal group $O(4,\mathcal R)$. The paper makes
-extensive use of the quaternions over $\mathcal R$ and leads to highly
-structured discretized filtration of $SU(2)$. We use this to offer a simple and
-effective way to approximate any element of $SU(2)$ to any degree of accuracy
-required using the repeated actions of just five fixed reflections, a process
-that may find application in computational methods in quantum mechanics.
-",0,0,1,0,0,0
-18450,Raman spectra of crystalline secondary amides,"  The study of single-crystal Raman spectra of a series of crystalline
-secondary amides (acetanilide, methacetin, phenacetine, orthorhombic and
-monoclinic polymorphs of paracetamol) as well as simple amides formanilide and
-benzanilide in the temperature range 5-300 K was carried out. The series of
-compounds with the same molecular fragment, i.e. acetamide group, can serve as
-a model system to study the interrelation between the latter and the properties
-of the intermolecular ""peptide-type"" NH...O=C hydrogen bonds. For all the
-""acetamide family"" of compounds, similar changes in the Raman spectra were
-observed on cooling the samples: an appearance of new Amide I(-) and Amide I(+)
-bands that are red and blue shifted respectively from the conventional Amide I
-band by around of 5-10 inverse centimeters. An appropriated changes in the same
-temperature range were observed for the N-H out-of-plane bending (Amide V) and
-N-H stretching vibrations of the N-H...O=C hydrogen bond. All the spectral
-changes on cooling the samples can be supposed to result from delocalization of
-the Amide I and N-H modes and appearance of dynamical splitting at low
-temperature.
-",0,1,0,0,0,0
-18451,Open-World Visual Recognition Using Knowledge Graphs,"  In a real-world setting, visual recognition systems can be brought to make
-predictions for images belonging to previously unknown class labels. In order
-to make semantically meaningful predictions for such inputs, we propose a
-two-step approach that utilizes information from knowledge graphs. First, a
-knowledge-graph representation is learned to embed a large set of entities into
-a semantic space. Second, an image representation is learned to embed images
-into the same space. Under this setup, we are able to predict structured
-properties in the form of relationship triples for any open-world image. This
-is true even when a set of labels has been omitted from the training protocols
-of both the knowledge graph and image embeddings. Furthermore, we append this
-learning framework with appropriate smoothness constraints and show how prior
-knowledge can be incorporated into the model. Both these improvements combined
-increase performance for visual recognition by a factor of six compared to our
-baseline. Finally, we propose a new, extended dataset which we use for
-experiments.
-",1,0,0,1,0,0
-18452,Mass-Imbalanced Ionic Hubbard Chain,"  A repulsive Hubbard model with both spin-asymmetric hopping (${t_\uparrow\neq
-t_\downarrow}$) and a staggered potential (of strength $\Delta$) is studied in
-one dimension. The model is a compound of the mass-imbalanced (${t_\uparrow\neq
-t_\downarrow}$, ${\Delta=0}$) and ionic (${t_\uparrow = t_\downarrow}$,
-${\Delta>0}$) Hubbard models, and may be realized by cold atoms in engineered
-optical lattices. We use mostly mean-field theory to determine the phases and
-phase transitions in the ground state for a half-filled band (one particle per
-site). We find that a period-two modulation of the particle (or charge) density
-and an alternating spin density coexist for arbitrary Hubbard interaction
-strength, ${U\geqslant 0}$. The amplitude of the charge modulation is largest
-at ${U=0}$, decreases with increasing $U$ and tends to zero for
-${U\rightarrow\infty}$. The amplitude for spin alternation increases with $U$
-and tends to saturation for ${U\rightarrow\infty}$. Charge order dominates
-below a critical value $U_c$, whereas magnetic order dominates above. The
-mean-field Hamiltonian has two gap parameters, $\Delta_\uparrow$ and
-$\Delta_\downarrow$, which have to be determined self-consistently. For
-${U<U_c}$ both parameters are positive, for ${U>U_c}$ they have different
-signs, and for ${U=U_c}$ one gap parameter jumps from a positive to a negative
-value. The weakly first-order phase transition at $U_c$ can be interpreted in
-terms of an avoided criticality (or metallicity). The system is reluctant to
-restore a symmetry that has been broken explicitly.
-",0,1,0,0,0,0
-18453,Optical properties of Xe color centers in diamond,"  Optical properties of color centers in diamond have been the subject of
-intense research due to their promising applications in quantum photonics. In
-this work we study the optical properties of Xe related color centers implanted
-into nitrogen rich (type IIA) and an ultrapure, electronic grade diamond. The
-Xe defect has two zero phonon lines at ~ 794 and 811 nm, which can be
-effectively excited using both green and red excitation, however, its emission
-in the nitrogen rich diamond is brighter. Near resonant excitation is performed
-at cryogenic temperatures and luminescence is probed under strong magnetic
-field. Our results are important towards the understanding of the Xe related
-defect and other near infrared color centers in diamond.
-",0,1,0,0,0,0
-18454,"Julian Ernst Besag, 26 March 1945 -- 6 August 2010, a biographical memoir","  Julian Besag was an outstanding statistical scientist, distinguished for his
-pioneering work on the statistical theory and analysis of spatial processes,
-especially conditional lattice systems. His work has been seminal in
-statistical developments over the last several decades ranging from image
-analysis to Markov chain Monte Carlo methods. He clarified the role of
-auto-logistic and auto-normal models as instances of Markov random fields and
-paved the way for their use in diverse applications. Later work included
-investigations into the efficacy of nearest neighbour models to accommodate
-spatial dependence in the analysis of data from agricultural field trials,
-image restoration from noisy data, and texture generation using lattice models.
-",0,0,0,1,0,0
-18455,Wake fields in a rectangular dielectric-lined accelerating structure with transversal isotropic loading,"  Dielectric lined waveguides are under extensive study as accelerating
-structures that can be excited by electron beams. Rectangular dielectric
-structures are used both in proof of principle experiments for new accelerating
-schemes and for studying the electronic properties of the structure loading
-material. Analysis of Cherenkov radiation generated by high current
-relativistic electron bunch passing through a rectangular waveguide with
-transversal isotropic dielectric loading has been carried out. Some of the
-materials used for the waveguide loading of accelerating structures (sapphire,
-ceramic films) possess significant anisotropic properties. In turn, it can
-influence excitation parameters of the wakefields generated by an electron
-beam. General solutions for the fields generated by a relativistic electron
-beam propagating in a rectangular dielectric waveguide have been derived using
-the orthogonal eigenmode decomposition method for the transverse operators of
-the Helmholtz equation. The analytical expression for the combined Cherenkov
-and Coulomb fields in terms of a superposition of LSM and LSE-modes of
-rectangular waveguide with transversal isotropic dielectric loading has been
-obtained. Numerical modelling of the longitudinal and transverse (deflecting)
-wakefields has been carried out as well. It is shown that the dielectric
-anisotropy causes frequency shift in comparison to the dielectric-lined
-waveguide with the isotropic dielectric loading.
-",0,1,0,0,0,0
-18456,Hyperfine Structure of the $B^3Π_1$ State and Predictions of Optical Cycling Behavior in the $X\rightarrow B$ transition of TlF,"  The rotational and hyperfine spectrum of the $X^1\Sigma^+ \rightarrow
-B^3\Pi_1$ transition in TlF molecules was measured using laser-induced
-fluorescence from both a thermal and a cryogenic molecular beam. Rotational and
-hyperfine constants for the $B$ state are obtained. The large magnetic
-hyperfine interaction of the Tl nuclear spin leads to significant mixing of the
-lowest $B$ state rotational levels. Updated, more precise measurements of the
-$B\rightarrow X$ vibrational branching fractions are also presented. The
-combined rovibrational branching fractions allow for the prediction of the
-number of photons that can be scattered in a given TlF optical cycling scheme.
-",0,1,0,0,0,0
-18457,Prevalence of DNSSEC for hospital websites in Illinois,"  The domain name system translates human friendly web addresses to a computer
-readable internet protocol address. This basic infrastructure is insecure and
-can be manipulated. Deployment of technology to secure the DNS system has been
-slow, reaching about 20% of all web sites based in the USA. Little is known
-about the efforts hospitals and health systems make to secure the domain name
-system for their websites. To investigate the prevalence of implementing Domain
-Name System Security Extensions (DNSSEC), we analyzed the websites of the 210
-public hospitals in the state of Illinois, USA. Only one Illinois hospital
-website was found to have implemented DNSSEC by December, 2017.
-",1,0,0,0,0,0
-18458,Mathematical analysis of plasmonic resonance for 2-D photonic crystal,"  In this article, we study the plasmonic resonance of infinite photonic
-crystal mounted by the double negative nanoparticles in two dimensions. The
-corresponding physical model is described by the Helmholz equation with so
-called Bloch wave condition in a periodic domain. By using the quasi-periodic
-layer potential techniques and the spectral theorem of quasi-periodic
-Neumann-Poincar{é} operator, the quasi-static expansion of the near field in
-the presence of nanoparticles is derived. Furthermore, when the magnetic
-permeability of nanoparticles satisfies the Drude model, we give the conditions
-under which the plasmonic resonance occurs, and the rate of blow up of near
-field energy with respect to nanoparticle's bulk electron relaxation rate and
-filling factor are also obtained. It indicates that one can appropriately
-control the bulk electron relaxation rate or filling factor of nanoparticle in
-photonic crystal structure such that the near field energy attains its maximum,
-and enhancing the efficiency of energy utilization.
-",0,0,1,0,0,0
-18459,Thermo-Optical Chaos and Direct Soliton Generation in Microresonators,"  We investigate, numerically and experimentally, the effect of thermo-optical
-(TO) chaos on direct soliton generation (DSG) in microresonators. When the pump
-laser is scanned from blue to red and then stopped at a fixed wavelength, we
-find that the solitons generated sometimes remain (survive) and sometimes
-annihilate subsequent to the end of the scan. We refer to the possibility of
-these different outcomes arising under identical laser scan conditions as
-coexistence of soliton annihilation and survival. Numerical simulations that
-include the thermal dynamics show that the coexistence of soliton annihilation
-and survival is explained by TO chaos accompanying comb generation. The random
-fluctuations of the cavity resonance occurring under TO chaos are also found to
-trigger spontaneous soliton generation after the laser scan ends. The
-coexistence of soliton annihilation and survival as well as spontaneous soliton
-generation are observed experimentally in a silicon-nitride microresonator. The
-elucidation of the role of TO chaos provides important insights into the
-soliton generation dynamics in microresonators, which may eventually facilitate
-straightforward soliton generation in fully-integrated microresonators.
-",0,1,0,0,0,0
-18460,Rigidity of closed metric measure spaces with nonnegative curvature,"  We show that one-dimensional circle is the only case for closed smooth metric
-measure spaces with nonnegative Bakry-Émery Ricci curvature whose spectrum
-of the weighted Laplacian has an optimal positive upper bound. This result
-extends the work of Hang-Wang in the manifold case (Int. Math. Res. Not. 18
-(2007), Art. ID rnm064, 9pp).
-",0,0,1,0,0,0
-18461,Combining Agile with Traditional V Model for Enhancement of Maturity in Software Development,"  In the field of software engineering there are many new archetypes are
-introducing day to day Improve the efficiency and effectiveness of software
-development. Due to dynamic environment organizations are frequently exchanging
-their software constraint to meet their objectives. The propose research is a
-new approach by integrating the traditional V model and agile methodology to
-combining the strength of these models while minimizing their individual
-weakness.The fluctuating requirements of emerging a carried software system and
-accumulative cost of operational software are imposing researchers and experts
-to determine innovative and superior means for emerging software application at
-slight business or at enterprise level are viewing for. Agile methodology has
-its own benefits but there are deficiency several of the features of
-traditional software development methodologies that are essential for success.
-Thats why an embedded approach will be the right answer for software industry
-rather than a pure agile approach. This research shows how agile embedded
-traditional can play a vital role in development of software. A survey
-conducted to find the impact of this approach in industry. Both qualitative and
-quantitative analysis performed.
-",1,0,0,0,0,0
-18462,Histograms of Gaussian normal distribution for feature matching in clutter scenes,"  3D feature descriptor provide information between corresponding models and
-scenes. 3D objection recognition in cluttered scenes, however, remains a
-largely unsolved problem. Practical applications impose several challenges
-which are not fully addressed by existing methods. Especially in cluttered
-scenes there are many feature mismatches between scenes and models. We
-therefore propose Histograms of Gaussian Normal Distribution (HGND) for
-extracting salient features on a local reference frame (LRF) that enables us to
-solve this problem. We propose a LRF on each local surface patches using the
-scatter matrix's eigenvectors. Then the HGND information of each salient point
-is calculated on the LRF, for which we use both the mesh and point data of the
-depth image. Experiments on 45 cluttered scenes of the Bologna Dataset and 50
-cluttered scenes of the UWA Dataset are made to evaluate the robustness and
-descriptiveness of our HGND. Experiments carried out by us demonstrate that
-HGND obtains a more reliable matching rate than state-of-the-art approaches in
-cluttered situations.
-",1,0,0,0,0,0
-18463,The temporalized Massey's method,"  We propose and throughly investigate a temporalized version of the popular
-Massey's technique for rating actors in sport competitions. The method can be
-described as a dynamic temporal process in which team ratings are updated at
-every match according to their performance during the match and the strength of
-the opponent team. Using the Italian soccer dataset, we empirically show that
-the method has a good foresight prediction accuracy.
-",1,1,0,0,0,0
-18464,Introduction to Nonnegative Matrix Factorization,"  In this paper, we introduce and provide a short overview of nonnegative
-matrix factorization (NMF). Several aspects of NMF are discussed, namely, the
-application in hyperspectral imaging, geometry and uniqueness of NMF solutions,
-complexity, algorithms, and its link with extended formulations of polyhedra.
-In order to put NMF into perspective, the more general problem class of
-constrained low-rank matrix approximation problems is first briefly introduced.
-",1,0,1,1,0,0
-18465,Randomized Dynamic Mode Decomposition,"  This paper presents a randomized algorithm for computing the near-optimal
-low-rank dynamic mode decomposition (DMD). Randomized algorithms are emerging
-techniques to compute low-rank matrix approximations at a fraction of the cost
-of deterministic algorithms, easing the computational challenges arising in the
-area of `big data'. The idea is to derive a small matrix from the
-high-dimensional data, which is then used to efficiently compute the dynamic
-modes and eigenvalues. The algorithm is presented in a modular probabilistic
-framework, and the approximation quality can be controlled via oversampling and
-power iterations. The effectiveness of the resulting randomized DMD (rDMD)
-algorithm is demonstrated on several benchmark examples of increasing
-complexity, providing an accurate and efficient approach to extract
-spatiotemporal coherent structures from big data in a framework that scales
-with the intrinsic rank of the data, rather than the ambient measurement
-dimension.
-",1,0,1,0,0,0
-18466,On the convergence of a fully discrete scheme of LES type to physically relevant solutions of the incompressible Navier-Stokes,"  Obtaining reliable numerical simulations of turbulent fluids is a challenging
-problem in computational fluid mechanics. The Large Eddy Simulations (LES)
-models are efficient tools to approximate turbulent fluids and an important
-step in the validation of these models is the ability to reproduce relevant
-properties of the flow. In this paper we consider a fully discrete
-approximation of the Navier-Stokes-Voigt model by an implicit Euler algorithm
-(with respect to the time variable) and a Fourier-Galerkin method (in the space
-variables). We prove the convergence to weak solutions of the incompressible
-Navier-Stokes equations satisfying the natural local entropy condition, hence
-selecting the so-called physically relevant solutions
-",0,0,1,0,0,0
-18467,Hardy-Sobolev-Maz'ya inequalities for higher order derivatives on half spaces,"  By using, among other things, the Fourier analysis techniques on hyperbolic
-and symmetric spaces, we establish the Hardy-Sobolev-Maz'ya inequalities for
-higher order derivatives on half spaces. The proof relies on a
-Hardy-Littlewood-Sobolev inequality on hyperbolic spaces which is of its
-independent interest. We also give an alternative proof of Benguria, Frank and
-Loss' work concerning the sharp constant in the Hardy-Sobolev-Maz'ya inequality
-in the three dimensional upper half space. Finally, we show the sharp constant
-in the Hardy-Sobolev-Maz'ya inequality for bi-Laplacian in the upper half space
-of dimension five coincides with the Sobolev constant.
-",0,0,1,0,0,0
-18468,Smooth invariants of focus-focus singularities and obstructions to product decomposition,"  We study focus-focus singularities (also known as nodal singularities, or
-pinched tori) of Lagrangian fibrations on symplectic $4$-manifolds. We show
-that, in contrast to elliptic and hyperbolic singularities, there exist
-homeomorphic focus-focus singularities which are not diffeomorphic.
-Furthermore, we obtain an algebraic description of the moduli space of
-focus-focus singularities up to smooth equivalence, and show that for double
-pinched tori this space is one-dimensional. Finally, we apply our construction
-to disprove Zung's conjecture which says that any non-degenerate singularity
-can be smoothly decomposed into an almost direct product of standard
-singularities.
-",0,1,1,0,0,0
-18469,A note about Euler's inequality and automated reasoning with dynamic geometry,"  Using implicit loci in GeoGebra Euler's $R\geq 2r$ inequality can be
-investigated in a novel way. Some unavoidable side effects of the implicit
-locus computation introduce unexpected algebraic curves. By using a mixture of
-symbolic and numerical methods a possible approach is sketched up to
-investigate the situation.
-",1,0,1,0,0,0
-18470,The MUSE view of He 2-10: no AGN ionization but a sparkling starburst,"  We study the physical and dynamical properties of the ionized gas in the
-prototypical HII galaxy Henize 2-10 using MUSE integral field spectroscopy. The
-large scale dynamics is dominated by extended outflowing bubbles, probably the
-results of massive gas ejection from the central star forming regions. We
-derive a mass outflow rate dMout/dt~0.30 Msun/yr, corresponding to mass loading
-factor eta~0.4, in range with similar measurements in local LIRGs. Such a
-massive outflow has a total kinetic energy that is sustainable by the stellar
-winds and Supernova Remnants expected in the galaxy. We use classical emission
-line diagnostic to study the dust extinction, electron density and ionization
-conditions all across the galaxy, confirming the extreme nature of the highly
-star forming knots in the core of the galaxy, which show high density and high
-ionization parameter. We measure the gas phase metallicity in the galaxy taking
-into account the strong variation of the ionization parameter, finding that the
-external parts of the galaxy have abundances as low as 12 + log(O/H)~8.3, while
-the central star forming knots are highly enriched with super solar
-metallicity. We find no sign of AGN ionization in the galaxy, despite the
-recent claim of the presence of a super massive active Black Hole in the core
-of He~2-10. We therefore reanalyze the X-ray data that were used to propose the
-presence of the AGN, but we conclude that the observed X-ray emission can be
-better explained with sources of a different nature, such as a Supernova
-Remnant.
-",0,1,0,0,0,0
-18471,Modeling Oral Multispecies Biofilm Recovery After Antibacterial Treatment,"  Recovery of multispecies oral biofilms is investigated following treatment by
-chlorhexidine gluconate (CHX), iodine-potassium iodide (IPI) and Sodium
-hypochlorite (NaOCl) both experimentally and theoretically. Experimentally,
-biofilms taken from two donors were exposed to the three antibacterial
-solutions (irrigants) for 10 minutes, respectively. We observe that (a) live
-bacterial cell ratios decline for a week after the exposure and the trend
-reverses beyond a week; after fifteen weeks, live bacterial cell ratios in
-biofilms fully return to their pretreatment levels; (b) NaOCl is shown as the
-strongest antibacterial agent for the oral biofilms; (c) multispecies oral
-biofilms from different donors showed no difference in their susceptibility to
-all the bacterial solutions. Guided by the experiment, a mathematical model for
-biofilm dynamics is developed, accounting for multiple bacterial phenotypes,
-quorum sensing, and growth factor proteins, to describe the nonlinear time
-evolutionary behavior of the biofilms. The model captures time evolutionary
-dynamics of biofilms before and after antibacterial treatment very well. It
-reveals the crucial role played by quorum sensing molecules and growth factors
-in biofilm recovery and verifies that the source of biofilms has a minimal to
-their recovery. The model is also applied to describe the state of biofilms of
-various ages treated by CHX, IPI and NaOCl, taken from different donors. Good
-agreement with experimental data predicted by the model is obtained as well,
-confirming its applicability to modeling biofilm dynamics in general.
-",0,0,0,0,1,0
-18472,On constant multi-commodity flow-cut gaps for directed minor-free graphs,"  The multi-commodity flow-cut gap is a fundamental parameter that affects the
-performance of several divide \& conquer algorithms, and has been extensively
-studied for various classes of undirected graphs. It has been shown by Linial,
-London and Rabinovich \cite{linial1994geometry} and by Aumann and Rabani
-\cite{aumann1998log} that for general $n$-vertex graphs it is bounded by
-$O(\log n)$ and the Gupta-Newman-Rabinovich-Sinclair conjecture
-\cite{gupta2004cuts} asserts that it is $O(1)$ for any family of graphs that
-excludes some fixed minor.
-The flow-cut gap is poorly understood for the case of directed graphs. We
-show that for uniform demands it is $O(1)$ on directed series-parallel graphs,
-and on directed graphs of bounded pathwidth. These are the first constant upper
-bounds of this type for some non-trivial family of directed graphs. We also
-obtain $O(1)$ upper bounds for the general multi-commodity flow-cut gap on
-directed trees and cycles. These bounds are obtained via new embeddings and
-Lipschitz quasipartitions for quasimetric spaces, which generalize analogous
-results form the metric case, and could be of independent interest. Finally, we
-discuss limitations of methods that were developed for undirected graphs, such
-as random partitions, and random embeddings.
-",1,0,0,0,0,0
-18473,Molecular Gas during the Post-Starburst Phase: Low Gas Fractions in Green Valley Seyfert Post-Starburst Galaxies,"  Post-starbursts (PSBs) are candidate for rapidly transitioning from
-star-bursting to quiescent galaxies. We study the molecular gas evolution of
-PSBs at z ~ 0.03 - 0.2. We undertook new CO (2-1) observations of 22 Seyfert
-PSBs candidates using the ARO Submillimeter Telescope. This sample complements
-previous samples of PSBs by including green valley PSBs with Seyfert-like
-emission, allowing us to analyze for the first time the molecular gas
-properties of 116 PSBs with a variety of AGN properties. The distribution of
-molecular gas to stellar mass fractions in PSBs is significantly different than
-normal star-forming galaxies in the COLD GASS survey. The combined samples of
-PSBs with Seyfert-like emission line ratios have a gas fraction distribution
-which is even more significantly different and is broader (~ 0.03-0.3). Most of
-them have lower gas fractions than normal star-forming galaxies. We find a
-highly significant correlation between the WISE 12 micron to 4.6 micron flux
-ratios and molecular gas fractions in both PSBs and normal galaxies. We detect
-molecular gas in 27% of our Seyfert PSBs. Taking into account the upper limits,
-the mean and the dispersion of the distribution of the gas fraction in our
-Seyfert PSB sample are much smaller (mean = 0.025, std dev. = 0.018) than
-previous samples of Seyfert PSBs or PSBs in general (mean ~ 0.1 - 0.2, std dev.
-~ 0.1 - 0.2).
-",0,1,0,0,0,0
-18474,Bit-Reversible Version of Milne's Fourth-Order Time-Reversible Integrator for Molecular Dynamics,"  We point out that two of Milne's fourth-order integrators are well-suited to
-bit-reversible simulations. The fourth-order method improves on the accuracy of
-Levesque and Verlet's algorithm and simplifies the definition of the velocity
-$v$ and energy $e = (q^2 + v^2)/2$ . ( We use this one-dimensional oscillator
-problem as an illustration throughout this paper ). Milne's integrator is
-particularly useful for the analysis of Lyapunov ( exponential ) instability in
-dynamical systems, including manybody molecular dynamics. We include the
-details necessary to the implementation of Milne's Algorithms.
-",0,1,0,0,0,0
-18475,Solvable Hydrodynamics of Quantum Integrable Systems,"  The conventional theory of hydrodynamics describes the evolution in time of
-chaotic many-particle systems from local to global equilibrium. In a quantum
-integrable system, local equilibrium is characterized by a local generalized
-Gibbs ensemble or equivalently a local distribution of pseudo-momenta. We study
-time evolution from local equilibria in such models by solving a certain
-kinetic equation, the ""Bethe-Boltzmann"" equation satisfied by the local
-pseudo-momentum density. Explicit comparison with density matrix
-renormalization group time evolution of a thermal expansion in the XXZ model
-shows that hydrodynamical predictions from smooth initial conditions can be
-remarkably accurate, even for small system sizes. Solutions are also obtained
-in the Lieb-Liniger model for free expansion into vacuum and collisions between
-clouds of particles, which model experiments on ultracold one-dimensional Bose
-gases.
-",0,1,0,0,0,0
-18476,Compressed sensing and optimal denoising of monotone signals,"  We consider the problems of compressed sensing and optimal denoising for
-signals $\mathbf{x_0}\in\mathbb{R}^N$ that are monotone, i.e.,
-$\mathbf{x_0}(i+1) \geq \mathbf{x_0}(i)$, and sparsely varying, i.e.,
-$\mathbf{x_0}(i+1) > \mathbf{x_0}(i)$ only for a small number $k$ of indices
-$i$. We approach the compressed sensing problem by minimizing the total
-variation norm restricted to the class of monotone signals subject to equality
-constraints obtained from a number of measurements $A\mathbf{x_0}$. For random
-Gaussian sensing matrices $A\in\mathbb{R}^{m\times N}$ we derive a closed form
-expression for the number of measurements $m$ required for successful
-reconstruction with high probability. We show that the probability undergoes a
-phase transition as $m$ varies, and depends not only on the number of change
-points, but also on their location. For denoising we regularize with the same
-norm and derive a formula for the optimal regularizer weight that depends only
-mildly on $\mathbf{x_0}$. We obtain our results using the statistical dimension
-tool.
-",1,0,1,1,0,0
-18477,On minimax nonparametric estimation of signal in Gaussian noise,"  For the problem of nonparametric estimation of signal in Gaussian noise we
-point out the strong asymptotically minimax estimators on maxisets for linear
-estimators (see \cite{ker93,rio}). It turns out that the order of rates of
-convergence of Pinsker estimator on this maxisets is worse than the order of
-rates of convergence for the class of linear estimators considered on this
-maxisets. We show that balls in Sobolev spaces are maxisets for Pinsker
-estimators.
-",0,0,1,1,0,0
-18478,Implicit Bias of Gradient Descent on Linear Convolutional Networks,"  We show that gradient descent on full-width linear convolutional networks of
-depth $L$ converges to a linear predictor related to the $\ell_{2/L}$ bridge
-penalty in the frequency domain. This is in contrast to linearly fully
-connected networks, where gradient descent converges to the hard margin linear
-support vector machine solution, regardless of depth.
-",0,0,0,1,0,0
-18479,Oxidation of clofibric acid in aqueous solution using a non-thermal plasma discharge or gamma radiation,"  In this work, we study degradation of clofibric acid (CFA) in aqueous
-solution using either ionizing radiation from a $^{60}$Co source or a
-non-thermal plasma produced by discharges in the air above the solution. The
-results obtained with the two technologies are compared in terms of
-effectiveness of CFA degradation and its by-products. In both cases the CFA
-degradation follows a quasi-exponential decay in time well modelled by a
-kinetic scheme which considers the competition between CFA and all reaction
-intermediates for the reactive species generated in solution as well as the
-amount of the end product formed. A new degradation law is deduced to explain
-the results. Although the end-product CO$_2$ was detected and the CFA
-conversion found to be very high under the studied conditions, HPLC analysis
-reveals several degradation intermediates still bearing the aromatic ring with
-the chlorine substituent. The extent of mineralization is rather limited. The
-energy yield is found to be higher in the gamma radiation experiments.
-",0,1,0,0,0,0
-18480,Quenching current by flux-flow instability in iron-chalcogenides thin films,"  The stability against quench is one of the main issue to be pursued in a
-superconducting material which should be able to perform at very high levels of
-current densities. Here we focus on the connection between the critical current
-$I_c$ and the quenching current $I^*$ associated to the so-called flux-flow
-instability phenomenon, which sets in as an abrupt transition from the flux
-flow state to the normal state. To this purpose, we analyze several
-current-voltage characteristics of three types of iron-based thin films,
-acquired at different temperature and applied magnetic field values. For these
-samples, we discuss the impact of a possible coexistence of intrinsic
-electronic mechanisms and extrinsic thermal effects on the quenching current
-dependence upon the applied magnetic field. The differences between the
-quenching current and the critical current are reported also in the case of
-predominant intrinsic mechanisms. Carrying out a comparison with
-high-temperature cuprate superconductors, we suggest which material can be the
-best trade-off between maximum operating temperature, higher upper critical
-field and stability under high current bias.
-",0,1,0,0,0,0
-18481,New cardinality estimation algorithms for HyperLogLog sketches,"  This paper presents new methods to estimate the cardinalities of data sets
-recorded by HyperLogLog sketches. A theoretically motivated extension to the
-original estimator is presented that eliminates the bias for small and large
-cardinalities. Based on the maximum likelihood principle a second unbiased
-method is derived together with a robust and efficient numerical algorithm to
-calculate the estimate. The maximum likelihood approach can also be applied to
-more than a single HyperLogLog sketch. In particular, it is shown that it gives
-more precise cardinality estimates for union, intersection, or relative
-complements of two sets that are both represented by HyperLogLog sketches
-compared to the conventional technique using the inclusion-exclusion principle.
-All the new methods are demonstrated and verified by extensive simulations.
-",1,0,0,0,0,0
-18482,The EUSO@TurLab Project,"  The TurLab facility is a laboratory, equipped with a 5 m diameter and 1 m
-depth rotating tank, located in the Physics Department of the University of
-Turin. The tank has been built mainly to study problems where system rotation
-plays a key role in the fluid behaviour such as in atmospheric and oceanic
-flows at different scales. The tank can be filled with different fluids of
-variable density, which enables studies in layered conditions such as sea
-waves. The tank can be also used to simulate the terrestrial surface with the
-optical characteristics of different environments such as snow, grass, ocean,
-land with soil, stones etc., fogs and clouds. As it is located in an extremely
-dark place, the light intensity can be controlled artificially. Such
-capabilities of the TurLab facility are applied to perform experiments related
-to the observation of Extreme Energy Cosmic Rays (EECRs) from space using the
-fluorescence technique, as in the case of the JEM-EUSO mission, where the
-diffuse night brightness and artificial light sources can vary significantly in
-time and space inside the Field of View (FoV) of the telescope. Here we will
-report the currently ongoing activity at the TurLab facility in the framework
-of the JEM-EUSO mission (EUSO@TurLab).
-",0,1,0,0,0,0
-18483,A finite element method framework for modeling rotating machines with superconducting windings,"  Electrical machines employing superconductors are attractive solutions in a
-variety of application domains. Numerical models are powerful and necessary
-tools to optimize their design and predict their performance. The
-electromagnetic modeling of superconductors by finite-element method (FEM) is
-usually based on a power-law resistivity for their electrical behavior. The
-implementation of such constitutive law in conventional models of electrical
-machines is quite problematic: the magnetic vector potential directly gives the
-electric field and requires using a power-law depending on it. This power-law
-is a non-bounded function that can generate enormous uneven values in low
-electric field regions that can destroy the reliability of solutions. The
-method proposed here consists in separating the model of an electrical machine
-in two parts, where the magnetic field is calculated with the most appropriate
-formulation: the H-formulation in the part containing the superconductors and
-the A-formulation in the part containing conventional conductors (and possibly
-permanent magnets). The main goal of this work is to determine and to correctly
-apply the continuity conditions on the boundary separating the two regions.
-Depending on the location of such boundary -- in the fixed or rotating part of
-the machine -- the conditions that one needs to apply are different. In
-addition, the application of those conditions requires the use of Lagrange
-multipliers satisfying the field transforms of the electromagnetic quantities
-in the two reference systems, the fixed and the rotating one. In this article,
-several exemplary cases for the possible configurations are presented. In order
-to emphasize and capture the essential point of this modeling strategy, the
-discussed examples are rather simple. Nevertheless, they constitute a solid
-starting point for modeling more complex and realistic devices.
-",0,1,0,0,0,0
-18484,Triangular Decomposition of Matrices in a Domain,"  Deterministic recursive algorithms for the computation of matrix triangular
-decompositions with permutations like LU and Bruhat decomposition are presented
-for the case of commutative domains. This decomposition can be considered as a
-generalization of LU and Bruhat decompositions, because they both may be easily
-obtained from this triangular decomposition. Algorithms have the same
-complexity as the algorithm of matrix multiplication.
-",1,0,0,0,0,0
-18485,Practical Bayesian Optimization for Transportation Simulators,"  We provide a method to solve optimization problem when objective function is
-a complex stochastic simulator of an urban transportation system. To reach this
-goal, a Bayesian optimization framework is introduced. We show how the choice
-of prior and inference algorithm effect the outcome of our optimization
-procedure. We develop dimensionality reduction techniques that allow for our
-optimization techniques to be applicable for real-life problems. We develop a
-distributed, Gaussian Process Bayesian regression and active learning models
-that allow parallel execution of our algorithms and enable usage of high
-performance computing. We present a fully Bayesian approach that is more sample
-efficient and reduces computational budget. Our framework is supported by
-theoretical analysis and an empirical study. We demonstrate our framework on
-the problem of calibrating a multi-modal transportation network of city of
-Bloomington, Illinois. Finally, we discuss directions for further research.
-",0,0,0,1,0,0
-18486,The Ising distribution as a latent variable model,"  It is shown that the Ising distribution can be treated as a latent variable
-model, where a set of N real-valued, correlated random variables are drawn and
-used to generate N binary spins independently. This allows to approximate the
-Ising distribution by a simpler model where the latent variables follow a
-multivariate normal distribution, the so-called Cox distribution. The
-approximation is formally related to an advanced mean field technique known as
-adaptive TAP, and its domain of validity is similar. When valid, it allows a
-principled replacement of the Ising distribution by a distribution much easier
-to sample and manipulate.
-",0,0,0,1,1,0
-18487,Applying DCOP to User Association Problem in Heterogeneous Networks with Markov Chain Based Algorithm,"  Multi-agent systems (MAS) is able to characterize the behavior of individual
-agent and the interaction between agents. Thus, it motivates us to leverage the
-distributed constraint optimization problem (DCOP), a framework of modeling
-MAS, to solve the user association problem in heterogeneous networks (HetNets).
-Two issues we have to consider when we take DCOP into the application of HetNet
-including: (i) How to set up an effective model by DCOP taking account of the
-negtive impact of the increment of users on the modeling process (ii) Which
-kind of algorithms is more suitable to balance the time consumption and the
-quality of soltuion. Aiming to overcome these issues, we firstly come up with
-an ECAV-$\eta$ (Each Connection As Variable) model in which a parameter $\eta$
-with an adequate assignment ($\eta=3$ in this paper) is able to control the
-scale of the model. After that, a Markov chain (MC) based algorithm is proposed
-on the basis of log-sum-exp function. Experimental results show that the
-solution obtained by DCOP framework is better than the one obtained by the
-Max-SINR algorithm. Comparing with the Lagrange dual decomposition based method
-(LDD), the solution performance has been improved since there is no need to
-transform original problem into a satisfied one. In addition, it is also
-apparent that the DCOP based method has better robustness than LDD when the
-number of users increases but the available resource at base stations are
-limited.
-",1,0,0,0,0,0
-18488,Linear growth of streaming instability in pressure bumps,"  Streaming instability is a powerful mechanism which concentrates dust grains
-in pro- toplanetary discs, eventually up to the stage where they collapse
-gravitationally and form planetesimals. Previous studies inferred that it
-should be ineffective in viscous discs, too efficient in inviscid discs, and
-may not operate in local pressure maxima where solids accumulate. From a linear
-analysis of stability, we show that streaming instability behaves differently
-inside local pressure maxima. Under the action of the strong differential
-advection imposed by the bump, a novel unstable mode develops and grows even
-when gas viscosity is large. Hence, pressure bumps are found to be the only
-places where streaming instability occurs in viscous discs. This offers a
-promising way to conciliate models of planet formation with recent observations
-of young discs.
-",0,1,0,0,0,0
-18489,"On the Gevrey regularity for Sums of Squares of vector fields, study of some models","  The micro-local Gevrey regularity of a class of ""sums of squares"" with real
-analytic coefficients is studied in detail. Some partial regularity result is
-also given.
-",0,0,1,0,0,0
-18490,Structure theorems for star-commuting power partial isometries,"  We give a new formulation and proof of a theorem of Halmos and Wallen on the
-structure of power partial isometries on Hilbert space. We then use this
-theorem to give a structure theorem for a finite set of partial isometries
-which star-commute: each operator commutes with the others and with their
-adjoints.
-",0,0,1,0,0,0
-18491,On free Gelfand--Dorfman--Novikov superalgebras and a PBW type theorem,"  We construct a linear basis of a free GDN superalgebra over a field of
-characteristic $\neq 2$.
-As applications, we prove a PBW theorem, that is, any GDN superalgebra can be
-embedded into its universal enveloping commutative associative differential
-superalgebra. An Engel theorem under some assumptions is given.
-",0,0,1,0,0,0
-18492,An inexact iterative Bregman method for optimal control problems,"  In this article we investigate an inexact iterative regularization method
-based on generalized Bregman distances of an optimal control problem with
-control constraints. We show robustness and convergence of the inexact Bregman
-method under a regularity assumption, which is a combination of a source
-condition and a regularity assumption on the active sets. We also take the
-discretization error into account. Numerical results are presented to
-demonstrate the algorithm.
-",0,0,1,0,0,0
-18493,Mixture Models in Astronomy,"  Mixture models combine multiple components into a single probability density
-function. They are a natural statistical model for many situations in
-astronomy, such as surveys containing multiple types of objects, cluster
-analysis in various data spaces, and complicated distribution functions. This
-chapter in the CRC Handbook of Mixture Analysis is concerned with astronomical
-applications of mixture models for cluster analysis, classification, and
-semi-parametric density estimation. We present several classification examples
-from the literature, including identification of a new class, analysis of
-contaminants, and overlapping populations. In most cases, mixtures of normal
-(Gaussian) distributions are used, but it is sometimes necessary to use
-different distribution functions derived from astrophysical experience. We also
-address the use of mixture models for the analysis of spatial distributions of
-objects, like galaxies in redshift surveys or young stars in star-forming
-regions. In the case of galaxy clustering, mixture models may not be the
-optimal choice for understanding the homogeneous and isotropic structure of
-voids and filaments. However, we show that mixture models, using astrophysical
-models for star clusters, may provide a natural solution to the problem of
-subdividing a young stellar population into subclusters. Finally, we explore
-how mixture models can be used for mathematically advanced modeling of data
-with heteroscedastic uncertainties or missing values, providing two example
-algorithms, the measurement error regression model of Kelly (2007) and the
-Extreme Deconvolution model of Bovy et al. (2011). The challenges presented by
-astronomical science, aided by the public availability of catalogs from major
-surveys and missions, are a rich area for collaboration between statisticians
-and astronomers.
-",0,1,0,1,0,0
-18494,Safe Medicine Recommendation via Medical Knowledge Graph Embedding,"  Most of the existing medicine recommendation systems that are mainly based on
-electronic medical records (EMRs) are significantly assisting doctors to make
-better clinical decisions benefiting both patients and caregivers. Even though
-the growth of EMRs is at a lighting fast speed in the era of big data, content
-limitations in EMRs restrain the existed recommendation systems to reflect
-relevant medical facts, such as drug-drug interactions. Many medical knowledge
-graphs that contain drug-related information, such as DrugBank, may give hope
-for the recommendation systems. However, the direct use of these knowledge
-graphs in the systems suffers from robustness caused by the incompleteness of
-the graphs. To address these challenges, we stand on recent advances in graph
-embedding learning techniques and propose a novel framework, called Safe
-Medicine Recommendation (SMR), in this paper. Specifically, SMR first
-constructs a high-quality heterogeneous graph by bridging EMRs (MIMIC-III) and
-medical knowledge graphs (ICD-9 ontology and DrugBank). Then, SMR jointly
-embeds diseases, medicines, patients, and their corresponding relations into a
-shared lower dimensional space. Finally, SMR uses the embeddings to decompose
-the medicine recommendation into a link prediction process while considering
-the patient's diagnoses and adverse drug reactions. To our best knowledge, SMR
-is the first to learn embeddings of a patient-disease-medicine graph for
-medicine recommendation in the world. Extensive experiments on real datasets
-are conducted to evaluate the effectiveness of proposed framework.
-",1,0,0,0,0,0
-18495,Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression,"  Deep neural networks (DNNs) have achieved great success in solving a variety
-of machine learning (ML) problems, especially in the domain of image
-recognition. However, recent research showed that DNNs can be highly vulnerable
-to adversarially generated instances, which look seemingly normal to human
-observers, but completely confuse DNNs. These adversarial samples are crafted
-by adding small perturbations to normal, benign images. Such perturbations,
-while imperceptible to the human eye, are picked up by DNNs and cause them to
-misclassify the manipulated instances with high confidence. In this work, we
-explore and demonstrate how systematic JPEG compression can work as an
-effective pre-processing step in the classification pipeline to counter
-adversarial attacks and dramatically reduce their effects (e.g., Fast Gradient
-Sign Method, DeepFool). An important component of JPEG compression is its
-ability to remove high frequency signal components, inside square blocks of an
-image. Such an operation is equivalent to selective blurring of the image,
-helping remove additive perturbations. Further, we propose an ensemble-based
-technique that can be constructed quickly from a given well-performing DNN, and
-empirically show how such an ensemble that leverages JPEG compression can
-protect a model from multiple types of adversarial attacks, without requiring
-knowledge about the model.
-",1,0,0,0,0,0
-18496,Numerical Simulation of Bloch Equations for Dynamic Magnetic Resonance Imaging,"  Magnetic Resonance Imaging (MRI) is a widely applied non-invasive imaging
-modality based on non-ionizing radiation which gives excellent images and soft
-tissue contrast of living tissues. We consider the modified Bloch problem as a
-model of MRI for flowing spins in an incompressible flow field. After
-establishing the well-posedness of the corresponding evolution problem, we
-analyze its spatial semidiscretization using discontinuous Galerkin methods.
-The high frequency time evolution requires a proper explicit and adaptive
-temporal discretization. The applicability of the approach is shown for basic
-examples.
-",1,0,1,0,0,0
-18497,End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks,"  Speech enhancement model is used to map a noisy speech to a clean speech. In
-the training stage, an objective function is often adopted to optimize the
-model parameters. However, in most studies, there is an inconsistency between
-the model optimization criterion and the evaluation criterion on the enhanced
-speech. For example, in measuring speech intelligibility, most of the
-evaluation metric is based on a short-time objective intelligibility (STOI)
-measure, while the frame based minimum mean square error (MMSE) between
-estimated and clean speech is widely used in optimizing the model. Due to the
-inconsistency, there is no guarantee that the trained model can provide optimal
-performance in applications. In this study, we propose an end-to-end
-utterance-based speech enhancement framework using fully convolutional neural
-networks (FCN) to reduce the gap between the model optimization and evaluation
-criterion. Because of the utterance-based optimization, temporal correlation
-information of long speech segments, or even at the entire utterance level, can
-be considered when perception-based objective functions are used for the direct
-optimization. As an example, we implement the proposed FCN enhancement
-framework to optimize the STOI measure. Experimental results show that the STOI
-of test speech is better than conventional MMSE-optimized speech due to the
-consistency between the training and evaluation target. Moreover, by
-integrating the STOI in model optimization, the intelligibility of human
-subjects and automatic speech recognition (ASR) system on the enhanced speech
-is also substantially improved compared to those generated by the MMSE
-criterion.
-",1,0,0,1,0,0
-18498,The connectivity of graphs of graphs with self-loops and a given degree sequence,"  `Double edge swaps' transform one graph into another while preserving the
-graph's degree sequence, and have thus been used in a number of popular Markov
-chain Monte Carlo (MCMC) sampling techniques. However, while double edge-swaps
-can transform, for any fixed degree sequence, any two graphs inside the classes
-of simple graphs, multigraphs, and pseudographs, this is not true for graphs
-which allow self-loops but not multiedges (loopy graphs). Indeed, we exactly
-characterize the degree sequences where double edge swaps cannot reach every
-valid loopy graph and develop an efficient algorithm to determine such degree
-sequences. The same classification scheme to characterize degree sequences can
-be used to prove that, for all degree sequences, loopy graphs are connected by
-a combination of double and triple edge swaps. Thus, we contribute the first
-MCMC sampler that uniformly samples loopy graphs with any given sequence.
-",1,1,1,0,0,0
-18499,The role of the background in past and future X-ray missions,"  Background has played an important role in X-ray missions, limiting the
-exploitation of science data in several and sometimes unexpected ways. In this
-presentation I review past X-ray missions focusing on some important lessons we
-can learn from them. I then go on discussing prospects for overcoming
-background related limitations in future ones.
-",0,1,0,0,0,0
-18500,Securing Manufacturing Intelligence for the Industrial Internet of Things,"  Widespread interest in the emerging area of predictive analytics is driving
-industries such as manufacturing to explore new approaches to the collection
-and management of data provided from Industrial Internet of Things (IIoT)
-devices. Often, analytics processing for Business Intelligence (BI) is an
-intensive task, and it also presents both an opportunity for competitive
-advantage as well as a security vulnerability in terms of the potential for
-losing Intellectual Property (IP). This article explores two approaches to
-securing BI in the manufacturing domain. Simulation results indicate that a
-Unified Threat Management (UTM) model is simpler to maintain and has less
-potential vulnerabilities than a distributed security model. Conversely, a
-distributed model of security out-performs the UTM model and offers more scope
-for the use of existing hardware resources. In conclusion, a hybrid security
-model is proposed where security controls are segregated into a multi-cloud
-architecture.
-",1,0,0,0,0,0
-18501,Boson-vortex duality in compressible spin-orbit coupled BECs,"  Using a (1+2)-dimensional boson-vortex duality between non-linear
-electrodynamics and a two-component compressible Bose-Einstein condensate (BEC)
-with spin-orbit (SO) coupling, we obtain generalised versions of the
-hydrodynamic continuity and Euler equations where the phase defect and
-non-defect degrees of freedom enter separately. We obtain the generalised
-Magnus force on vortices under SO coupling, and associate the linear
-confinement of vortices due to SO coupling with instanton fluctuations of the
-dual theory.
-",0,1,0,0,0,0
-18502,Communicating Correlated Sources Over an Interference Channel,"  A new coding technique, based on \textit{fixed block-length} codes, is
-proposed for the problem of communicating a pair of correlated sources over a
-$2-$user interference channel. Its performance is analyzed to derive a new set
-of sufficient conditions. The latter is proven to be strictly less binding than
-the current known best, which is due to Liu and Chen [Dec, 2011]. Our findings
-are inspired by Dueck's example [March, 1981].
-",1,0,0,0,0,0
-18503,Simulating polaron biophysics with Rydberg atoms,"  Transport of excitations along proteins can be formulated in a quantum
-physics context, based on the periodicity and vibrational modes of the
-structures. Exact solutions are very challenging to obtain on classical
-computers, however, approximate solutions based on the Davydov ansatz have
-demonstrated the possibility of stabilized solitonic excitations along the
-protein. We propose an alternative study based on a chain of ultracold atoms.
-We investigate the experimental parameters to control such a quantum simulator
-based on dressed Rydberg atoms. We show that there is a feasible range of
-parameters where a quantum simulator can directly mimic the Davydov equations
-and their solutions. Such a quantum simulator opens up new directions for the
-study of transport phenomena in a biophysical context.
-",0,1,0,0,0,0
-18504,"Solubility limit of methyl red and methylene blue in microemulsions and liquid crystals of water, sds and pentanol systems","  Solubility of dyes in amphiphilic association structures of water, SDS and
-penthanol system (i.e. in the phases of microemulsions and liquid crystals) was
-attracted much interest due to its wide industrial and technological
-applications. This research was focused on understanding the solubility
-limitation of methyl red and methylene blue in microemulsion and liquid crystal
-phases. Experimental results showed that the highest solubility of methyl red
-was in LLC, followed by w/o microemulsion and o/w microemulsion, respectively,
-whereas the highest solubility of methylene blue was in w/o microemulsion,
-followed by o/w microemulsion and LLC, respectively. Hence, a chemical dynamics
-strongly played an important role in the solubility limitation of methyl red
-and methylene blue in microemulsions and liquid crystal phases.
-",0,1,0,0,0,0
-18505,Towards Visual Ego-motion Learning in Robots,"  Many model-based Visual Odometry (VO) algorithms have been proposed in the
-past decade, often restricted to the type of camera optics, or the underlying
-motion manifold observed. We envision robots to be able to learn and perform
-these tasks, in a minimally supervised setting, as they gain more experience.
-To this end, we propose a fully trainable solution to visual ego-motion
-estimation for varied camera optics. We propose a visual ego-motion learning
-architecture that maps observed optical flow vectors to an ego-motion density
-estimate via a Mixture Density Network (MDN). By modeling the architecture as a
-Conditional Variational Autoencoder (C-VAE), our model is able to provide
-introspective reasoning and prediction for ego-motion induced scene-flow.
-Additionally, our proposed model is especially amenable to bootstrapped
-ego-motion learning in robots where the supervision in ego-motion estimation
-for a particular camera sensor can be obtained from standard navigation-based
-sensor fusion strategies (GPS/INS and wheel-odometry fusion). Through
-experiments, we show the utility of our proposed approach in enabling the
-concept of self-supervised learning for visual ego-motion estimation in
-autonomous robots.
-",1,0,0,0,0,0
-18506,Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks,"  Prediction of popularity has profound impact for social media, since it
-offers opportunities to reveal individual preference and public attention from
-evolutionary social systems. Previous research, although achieves promising
-results, neglects one distinctive characteristic of social data, i.e.,
-sequentiality. For example, the popularity of online content is generated over
-time with sequential post streams of social media. To investigate the
-sequential prediction of popularity, we propose a novel prediction framework
-called Deep Temporal Context Networks (DTCN) by incorporating both temporal
-context and temporal attention into account. Our DTCN contains three main
-components, from embedding, learning to predicting. With a joint embedding
-network, we obtain a unified deep representation of multi-modal user-post data
-in a common embedding space. Then, based on the embedded data sequence over
-time, temporal context learning attempts to recurrently learn two adaptive
-temporal contexts for sequential popularity. Finally, a novel temporal
-attention is designed to predict new popularity (the popularity of a new
-user-post pair) with temporal coherence across multiple time-scales.
-Experiments on our released image dataset with about 600K Flickr photos
-demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms,
-with an average of 21.51% relative performance improvement in the popularity
-prediction (Spearman Ranking Correlation).
-",1,0,0,0,0,0
-18507,Key Management and Learning based Two Level Data Security for Metering Infrastructure of Smart Grid,"  In the smart grid, smart meters, and numerous control and monitoring
-applications employ bidirectional wireless communication, where security is a
-critical issue. In key management based encryption method for the smart grid,
-the Trusted Third Party (TTP), and links between the smart meter and the third
-party are assumed to be fully trusted and reliable. However, in wired/wireless
-medium, a man-in-middle may want to interfere, monitor and control the network,
-thus exposing its vulnerability. Acknowledging this, in this paper, we propose
-a novel two level encryption method based on two partially trusted simple
-servers (constitutes the TTP) which implement this method without increasing
-packet overhead. One server is responsible for data encryption between the
-meter and control center/central database, and the other server manages the
-random sequence of data transmission. Numerical calculation shows that the
-number of iterations required to decode a message is large which is quite
-impractical. Furthermore, we introduce One-class support vector machine
-(machine learning) algorithm for node-to-node authentication utilizing the
-location information and the data transmission history (node identity, packet
-size and frequency of transmission). This secures data communication privacy
-without increasing the complexity of the conventional key management scheme.
-",1,0,0,0,0,0
-18508,A numerical study of the homogeneous elliptic equation with fractional order boundary conditions,"  We consider the homogeneous equation ${\mathcal A} u=0$, where ${\mathcal A}$
-is a symmetric and coercive elliptic operator in $H^1(\Omega)$ with $\Omega$
-bounded domain in ${\mathbb R}^d$. The boundary conditions involve fractional
-power $\alpha$, $ 0 < \alpha <1$, of the Steklov spectral operator arising in
-Dirichlet to Neumann map. For such problems we discuss two different numerical
-methods: (1) a computational algorithm based on an approximation of the
-integral representation of the fractional power of the operator and (2)
-numerical technique involving an auxiliary Cauchy problem for an
-ultra-parabolic equation and its subsequent approximation by a time stepping
-technique. For both methods we present numerical experiment for a model
-two-dimensional problem that demonstrate the accuracy, efficiency, and
-stability of the algorithms.
-",0,0,1,0,0,0
-18509,Effects of soft interactions and bound mobility on diffusion in crowded environments: a model of sticky and slippery obstacles,"  Crowded environments modify the diffusion of macromolecules, generally
-slowing their movement and inducing transient anomalous subdiffusion. The
-presence of obstacles also modifies the kinetics and equilibrium behavior of
-tracers. While previous theoretical studies of particle diffusion have
-typically assumed either impenetrable obstacles or binding interactions that
-immobilize the particle, in many cellular contexts bound particles remain
-mobile. Examples include membrane proteins or lipids with some entry and
-diffusion within lipid domains and proteins that can enter into membraneless
-organelles or compartments such as the nucleolus. Using a lattice model, we
-studied the diffusive movement of tracer particles which bind to soft
-obstacles, allowing tracers and obstacles to occupy the same lattice site. For
-sticky obstacles, bound tracer particles are immobile, while for slippery
-obstacles, bound tracers can hop without penalty to adjacent obstacles. In both
-models, binding significantly alters tracer motion. The type and degree of
-motion while bound is a key determinant of the tracer mobility: slippery
-obstacles can allow nearly unhindered diffusion, even at high obstacle filling
-fraction. To mimic compartmentalization in a cell, we examined how obstacle
-size and a range of bound diffusion coefficients affect tracer dynamics. The
-behavior of the model is similar in two and three spatial dimensions. Our work
-has implications for protein movement and interactions within cells.
-",0,1,0,0,0,0
-18510,Thermodynamics of a Quantum Ising system coupled to a spin bath: Zero Temperature Results,"  We study the effect of coupling a spin bath environment to a system which, at
-low energies, can be modeled as a quantum Ising system. A field theoretic
-formalism incorporating both thermal and quantum fluctuations is developed to
-derive results for the thermodynamic properties and response functions, both
-for a toy model and for the $LiHoF_4$ system, in which spin-8 electronic spins
-couple to a spin-$7/2$ nuclear spin bath: the phase transition then occurs in a
-system of electronuclear degrees of freedom, coupled by long-range dipolar
-interactions. The quantum Ising phase transition still exists, and one
-hybridized mode of the Ising and bath spins always goes soft at the transition.
-",0,1,0,0,0,0
-18511,Observation of a Lamb band gap in a polymer waveguide with periodic cross-like cavities,"  The quest for large and low frequency band gaps is one of the principal
-objectives pursued in a number of engineering applications, ranging from noise
-absorption to vibration control, to seismic wave abatement. For this purpose, a
-plethora of complex architectures (including multi-phase materials) and
-multi-physics approaches have been proposed in the past, often involving
-difficulties in their practical realization.
-To address this issue, in this work we propose an easy-to-manufacture design
-able to open large, low frequency complete Lamb band gaps exploiting a suitable
-arrangement of masses and stiffnesses produced by cavities in a monolithic
-material. The performance of the designed structure is evaluated by numerical
-simulations and confirmed by Scanning Laser Doppler Vibrometer (SLDV)
-measurements on an isotropic polyvinyl chloride plate in which a square ring
-region of cross-like cavities is fabricated. The full wave field reconstruction
-clearly confirms the ability of even a limited number of unit cell rows of the
-proposed design to efficiently attenuate Lamb waves. In addition, numerical
-simulations show that the structure allows to shift of the central frequency of
-the BG through geometrical modifications. The design may be of interest for
-applications in which large BGs at low frequencies are required.
-",0,1,0,0,0,0
-18512,Search for nucleon decays with EXO-200,"  A search for instability of nucleons bound in $^{136}$Xe nuclei is reported
-with 223 kg$\cdot$yr exposure of $^{136}$Xe in the EXO-200 experiment. Lifetime
-limits of 3.3$\times 10^{23}$ and 1.9$\times 10^{23}$ yrs are established for
-nucleon decay to $^{133}$Sb and $^{133}$Te, respectively. These are the most
-stringent to date, exceeding the prior decay limits by a factor of 9 and 7,
-respectively.
-",0,1,0,0,0,0
-18513,The multiplicity of massive stars: a 2016 view,"  Massive stars like company. Here, we provide a brief overview of progresses
-made over the last 5 years by a number of medium and large surveys. These
-results provide new insights on the observed and intrinsic multiplicity
-properties of main sequence massive stars and on the initial conditions for
-their future evolution. They also bring new interesting constraints on the
-outcome of the massive star formation process.
-",0,1,0,0,0,0
-18514,Principal Eigenvalue of Mixed Problem for the Fractional Laplacian: Moving the Boundary Conditions,"  We analyze the behavior of the eigenvalues of the following non local mixed
-problem $\left\{ \begin{array}{rcll} (-\Delta)^{s} u &=& \lambda_1(D) \ u
-&\inn\Omega,\\ u&=&0&\inn D,\\ \mathcal{N}_{s}u&=&0&\inn N. \end{array}\right $
-Our goal is to construct different sequences of problems by modifying the
-configuration of the sets $D$ and $N$, and to provide sufficient and necessary
-conditions on the size and the location of these sets in order to obtain
-sequences of eigenvalues that in the limit recover the eigenvalues of the
-Dirichlet or Neumann problem. We will see that the non locality plays a crucial
-role here, since the sets $D$ and $N$ can have infinite measure, a phenomenon
-that does not appear in the local case (see for example \cite{D,D2,CP}).
-",0,0,1,0,0,0
-18515,Two-dimensional Bose and Fermi gases beyond weak coupling,"  Using a formalism based on the two-body S-matrix we study two-dimensional
-Bose and Fermi gases with both attractive and repulsive interactions.
-Approximate analytic expressions, valid at weak coupling and beyond, are
-developed and applied to the Berezinskii-Kosterlitz-Thouless (BKT) transition.
-We successfully recover the correct logarithmic functional form of the critical
-chemical potential and density for the Bose gas. For fermions, the BKT critical
-temperature is calculated in BCS and BEC regimes through consideration of Tan's
-contact.
-",0,1,0,0,0,0
-18516,Anisotropy of magnetic interactions and symmetry of the order parameter in unconventional superconductor Sr$_{2}$RuO$_{4}$,"  Sr$_2$RuO$_4$ is the best candidate for spin-triplet superconductivity, an
-unusual and elusive superconducting state of fundamental importance. In the
-last three decades Sr$_2$RuO$_4$ has been very carefully studied and despite
-its apparent simplicity when compared with strongly correlated high-$T_{c}$
-cuprates, for which the pairing symmetry is understood, there is no scenario
-that can explain all the major experimental observations, a conundrum that has
-generated tremendous interest. Here we present a density-functional based
-analysis of magnetic interactions in Sr$_{2}$RuO$_{4}$ and discuss the role of
-magnetic anisotropy in its unconventional superconductivity. Our goal is
-twofold. First, we access the possibility of the superconducting order
-parameter rotation in an external magnetic field of 200 Oe, and conclude that
-the spin-orbit interaction in this material is several orders of magnitude too
-strong to be consistent with this hypothesis. Thus, the observed invariance of
-the Knight shift across $T_{c}$ has no plausible explanation, and casts doubt
-on using the Knight shift as an ultimate litmus paper for the pairing symmetry.
-Second, we propose a quantitative double-exchange-like model for combining
-itinerant fermions with an anisotropic Heisenberg magnetic Hamiltonian. This
-model is complementary to the Hubbard-model-based calculations published so
-far, and forms an alternative framework for exploring superconducting symmetry
-in Sr$_{2}$RuO$_{4}.$ As an example, we use this model to analyze the
-degeneracy between various $p-$triplet states in the simplest mean-field
-approximation, and show that it splits into a single and two doublets with the
-ground state defined by the competition between the ""Ising"" and ""compass""
-anisotropic terms.
-",0,1,0,0,0,0
-18517,Loss of Regularity of Solutions of the Lighthill Problem for Shock Diffraction for Potential Flow,"  We are concerned with the regularity of solutions of the Lighthill problem
-for shock diffraction by a convex corned wedge, which can be formulated as a
-free boundary problem. In this paper, we prove that there is no regular
-solution that is subsonic up to the wedge corner for potential flow. This
-indicates that, if the solution is subsonic at the wedge corner, at least a
-characteristic discontinuity (vortex sheet or entropy wave) is expected to be
-generated, which is consistent with the experimental and computational results.
-In order to achieve the non-existence result, a weak maximum principle for the
-solution is established, and several other mathematical techniques are
-developed. The methods and techniques developed here are also useful to the
-other problems with similar difficulties.
-",0,0,1,0,0,0
-18518,"Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 1 Theory","  The European Space Agency (ESA) defines an Earth Observation (EO) Level 2
-product as a multispectral (MS) image corrected for geometric, atmospheric,
-adjacency and topographic effects, stacked with its scene classification map
-(SCM), whose legend includes quality layers such as cloud and cloud-shadow. No
-ESA EO Level 2 product has ever been systematically generated at the ground
-segment. To contribute toward filling an information gap from EO big data to
-the ESA EO Level 2 product, an original Stage 4 validation (Val) of the
-Satellite Image Automatic Mapper (SIAM) lightweight computer program was
-conducted by independent means on an annual Web-Enabled Landsat Data (WELD)
-image composite time-series of the conterminous U.S. The core of SIAM is a one
-pass prior knowledge based decision tree for MS reflectance space
-hyperpolyhedralization into static color names presented in literature in
-recent years. For the sake of readability this paper is split into two. The
-present Part 1 Theory provides the multidisciplinary background of a priori
-color naming in cognitive science, from linguistics to computer vision. To cope
-with dictionaries of MS color names and land cover class names that do not
-coincide and must be harmonized, an original hybrid guideline is proposed to
-identify a categorical variable pair relationship. An original quantitative
-measure of categorical variable pair association is also proposed. The
-subsequent Part 2 Validation discusses Stage 4 Val results collected by an
-original protocol for wall-to-wall thematic map quality assessment without
-sampling where the test and reference map legends can differ. Conclusions are
-that the SIAM-WELD maps instantiate a Level 2 SCM product whose legend is the 4
-class taxonomy of the FAO Land Cover Classification System at the Dichotomous
-Phase Level 1 vegetation/nonvegetation and Level 2 terrestrial/aquatic.
-",1,0,0,0,0,0
-18519,Performance Analysis of MEC Approach for Haplotype Assembly,"  The Minimum Error Correction (MEC) approach is used as a metric for
-reconstruction of haplotypes from NGS reads. In this paper, we show that the
-MEC may encounter with imprecise reconstructed haplotypes for some NGS devices.
-Specifically, using mathematical derivations, we evaluate this approach for the
-SOLiD, Illumina, 454, Ion, Pacific BioSciences, Oxford Nanopore, and 10X
-Genomics devices. Our results reveal that the MEC yields inexact haplotypes for
-the Illumina MiniSeq, 454 GS Junior+, Ion PGM 314, and Oxford Nanopore MK 1
-MinION.
-",0,0,0,0,1,0
-18520,Opinion Recommendation using Neural Memory Model,"  We present opinion recommendation, a novel task of jointly predicting a
-custom review with a rating score that a certain user would give to a certain
-product or service, given existing reviews and rating scores to the product or
-service by other users, and the reviews that the user has given to other
-products and services. A characteristic of opinion recommendation is the
-reliance of multiple data sources for multi-task joint learning, which is the
-strength of neural models. We use a single neural network to model users and
-products, capturing their correlation and generating customised product
-representations using a deep memory network, from which customised ratings and
-reviews are constructed jointly. Results show that our opinion recommendation
-system gives ratings that are closer to real user ratings on Yelp.com data
-compared with Yelp's own ratings, and our methods give better results compared
-to several pipelines baselines using state-of-the-art sentiment rating and
-summarization systems.
-",1,0,0,0,0,0
-18521,Distributed Optimization of Multi-Beam Directional Communication Networks,"  We formulate an optimization problem for maximizing the data rate of a common
-message transmitted from nodes within an airborne network broadcast to a
-central station receiver while maintaining a set of intra-network rate demands.
-Assuming that the network has full-duplex links with multi-beam directional
-capability, we obtain a convex multi-commodity flow problem and use a
-distributed augmented Lagrangian algorithm to solve for the optimal flows
-associated with each beam in the network. For each augmented Lagrangian
-iteration, we propose a scaled gradient projection method to minimize the local
-Lagrangian function that incorporates the local topology of each node in the
-network. Simulation results show fast convergence of the algorithm in
-comparison to simple distributed primal dual methods and highlight performance
-gains over standard minimum distance-based routing.
-",0,0,1,0,0,0
-18522,Mean conservation for density estimation via diffusion using the finite element method,"  We propose boundary conditions for the diffusion equation that maintain the
-initial mean and the total mass of a discrete data sample in the density
-estimation process. A complete study of this framework with numerical
-experiments using the finite element method is presented for the one
-dimensional diffusion equation, some possible applications of this results are
-presented as well. We also comment on a similar methodology for the
-two-dimensional diffusion equation for future applications in two-dimensional
-domains.
-",0,0,1,0,0,0
-18523,Asymptotic properties of maximum likelihood estimator for the growth rate of a stable CIR process based on continuous time observations,"  We consider a stable Cox--Ingersoll--Ross process driven by a standard Wiener
-process and a spectrally positive strictly stable Lévy process, and we study
-asymptotic properties of the maximum likelihood estimator (MLE) for its growth
-rate based on continuous time observations. We distinguish three cases:
-subcritical, critical and supercritical. In all cases we prove strong
-consistency of the MLE in question, in the subcritical case asymptotic
-normality, and in the supercritical case asymptotic mixed normality are shown
-as well. In the critical case the description of the asymptotic behavior of the
-MLE in question remains open.
-",0,0,1,1,0,0
-18524,Indoor Office Wideband Penetration Loss Measurements at 73 GHz,"  This paper presents millimeter wave (mmWave) penetration loss measurements
-and analysis at 73 GHz using a wideband sliding correlator channel sounder in
-an indoor office environment. Penetration loss was measured using a carefully
-controlled measurement setup for many common indoor building materials such as
-glass doors, glass windows, closet doors, steel doors, and whiteboard writing
-walls. Measurements were conducted using narrowbeam transmitter (TX) and
-receiver (RX) horn antennas that were boresight-aligned with a test material
-between the antennas. Overall, 21 different locations were measured for 6
-different materials such that the same type of material was tested in at least
-two locations in order to characterize the effect of penetration loss for
-materials with similar composition. As shown here, attenuation through common
-materials ranged between 0.8 dB/cm and 9.9 dB/cm for co-polarized antennas,
-while cross-polarized antennas exhibited similar attenuation for most
-materials, but up to 23.4 dB/cm of attenuation for others. The penetration loss
-results presented here are useful for site-specific planning tools that will
-model indoor mmWave networks, without the need for expensive measurement
-campaigns.
-",1,0,0,0,0,0
-18525,The SPEDE spectrometer,"  The electron spectrometer, SPEDE, has been developed and will be employed in
-conjunction with the Miniball spectrometer at the HIE-ISOLDE facility, CERN.
-SPEDE allows for direct measurement of internal conversion electrons emitted
-in-flight, without employing magnetic fields to transport or momentum filter
-the electrons. Together with the Miniball spectrometer, it enables simultaneous
-observation of {\gamma} rays and conversion electrons in Coulomb-excitation
-experiments using radioactive ion beams.
-",0,1,0,0,0,0
-18526,Global Stabilization of Triangular Systems with Time-Delayed Dynamic Input Perturbations,"  A control design approach is developed for a general class of uncertain
-strict-feedback-like nonlinear systems with dynamic uncertain input
-nonlinearities with time delays. The system structure considered in this paper
-includes a nominal uncertain strict-feedback-like subsystem, the input signal
-to which is generated by an uncertain nonlinear input unmodeled dynamics that
-is driven by the entire system state (including unmeasured state variables) and
-is also allowed to depend on time delayed versions of the system state variable
-and control input signals. The system also includes additive uncertain
-nonlinear functions, coupled nonlinear appended dynamics, and uncertain dynamic
-input nonlinearities with time-varying uncertain time delays. The proposed
-control design approach provides a globally stabilizing delay-independent
-robust adaptive output-feedback dynamic controller based on a dual dynamic
-high-gain scaling based structure.
-",1,0,1,0,0,0
-18527,Multifractal Analysis of Pulsar Timing Residuals: Assessment of Gravitational Wave Detection,"  We introduce a pipeline including multifractal detrended cross-correlation
-analysis (MF-DXA) modified by either singular value decomposition or the
-adaptive method to examine the statistical properties of the pulsar timing
-residual ($PTR$) induced by a gravitational wave (GW) signal. We propose a new
-algorithm, the so-called irregular-MF-DXA, to deal with irregular data
-sampling. Inspired by the quadrupolar nature of the spatial cross-correlation
-function of a gravitational wave background, a new cross-correlation function,
-$\bar{\sigma}_{\times}$, derived from irregular-MF-DXA will be introduced. We
-show that, this measure reveals the quadrupolar signature in the $PTRs$ induced
-by stochastic GWB. We propose four strategies based on the $y$-intercept of
-fluctuation functions, the generalized Hurst exponent, and the width of the
-singularity spectrum to determine the dimensionless amplitude and power-law
-exponent of the characteristic strain spectrum as
-$\mathcal{H}_c(f)\sim\mathcal{A}_{yr}(f/f_{yr})^{\zeta}$ for stochastic GWB.
-Using the value of Hurst exponent, one can clarify the type of GWs. We apply
-our pipeline to explore 20 millisecond pulsars observed by Parkes Pulsar Timing
-Array. The computed scaling exponents confirm that all data are classified into
-a nonstationary class implying the universality feature. The value of the Hurst
-exponent is in the range $H\in [0.56,0.87]$. The $q$-dependency of the
-generalized Hurst exponent demonstrates that the observed $PTRs$ have
-multifractal behavior, and the source of this multifractality is mainly
-attributed to the correlation of data which is another universality of the
-observed datasets. Multifractal analysis of available $PTRs$ datasets reveals
-an upper bound on the dimensionless amplitude of the GWB, $\mathcal{A}_{yr}<
-2.0\times 10^{-15}$.
-",0,1,0,0,0,0
-18528,Recovery of Bennu's Orientation for the OSIRIS-REx Mission: Implications for the Spin State Accuracy and Geolocation Errors,"  The goal of the OSIRIS-REx mission is to return a sample of asteroid material
-from Near-Earth Asteroid (101955) Bennu. The role of the navigation and flight
-dynamics team is critical for the spacecraft to execute a precisely planned
-sampling maneuver over a specifically-selected landing site. In particular, the
-orientation of Bennu needs to be recovered with good accuracy during orbital
-operations to contribute as small an error as possible to the landing error
-budget. Although Bennu is well characterized from Earth-based radar
-observations, its orientation dynamics are not sufficiently known to exclude
-the presence of a small wobble. To better understand this contingency and
-evaluate how well the orientation can be recovered in the presence of a large
-1$^{\circ}$ wobble, we conduct a comprehensive simulation with the NASA GSFC
-GEODYN orbit determination and geodetic parameter estimation software. We
-describe the dynamic orientation modeling implemented in GEODYN in support of
-OSIRIS-REx operations, and show how both altimetry and imagery data can be used
-as either undifferenced (landmark, direct altimetry) or differenced (image
-crossover, altimetry crossover) measurements. We find that these two different
-types of data contribute differently to the recovery of instrument pointing or
-planetary orientation. When upweighted, the absolute measurements help reduce
-the geolocation errors, despite poorer astrometric (inertial) performance. We
-find that with no wobble present, all the geolocation requirements are met.
-While the presence of a large wobble is detrimental, the recovery is still
-reliable thanks to the combined use of altimetry and imagery data.
-",0,1,0,0,0,0
-18529,Energy Scale of Lorentz Violation in Rainbow Gravity,"  We modify the standard relativistic dispersion relation in a way which breaks
-Lorentz symmetry - the effect is predicted in a high-energy regime of some
-modern theories of quantum gravity. We show that it is possible to realise this
-scenario within the framework of Rainbow Gravity which introduces two new
-energy-dependent functions $f_1(E)$ and $f_2(E)$ into the dispersion relation.
-Additionally, we assume that the gravitational constant $G$ and the
-cosmological constant $\Lambda$ also depend on energy $E$ and introduce the
-scaling function $h(E)$ in order to express this dependence. For cosmological
-applications we specify the functions $f_1$ and $f_2$ in order to fit massless
-particles which allows us to derive modified cosmological equations. Finally,
-by using Hubble+SNIa+BAO(BOSS+Lyman $\alpha$)+CMB data, we constrain the energy
-scale $E_{LV}$ to be at least of the order of $10^{16}$ GeV at $1\sigma$ which
-is the GUT scale or even higher $10^{17}$ GeV at $3\sigma$. Our claim is that
-this energy can be interpreted as the decoupling scale of massless particles
-from spacetime Lorentz violating effects.
-",0,1,0,0,0,0
-18530,How to construct wavelets on local fields of positive characteristic,"  We present an algorithm for construction step wavelets on local fields of
-positive characteristic.
-",0,0,1,0,0,0
-18531,Fates of the dense cores formed by fragmentation of filaments: do they fragment again or not?,"  Fragmentation of filaments into dense cores is thought to be an important
-step in forming stars. The bar-mode instability of spherically collapsing cores
-found in previous linear analysis invokes a possibility of re-fragmentation of
-the cores due to their ellipsoidal (prolate or oblate) deformation. To
-investigate this possibility, here we perform three-dimensional
-self-gravitational hydrodynamics simulations that follow all the way from
-filament fragmentation to subsequent core collapse. We assume the gas is
-polytropic with index \gamma, which determines the stability of the bar-mode.
-For the case that the fragmentation of isolated hydrostatic filaments is
-triggered by the most unstable fragmentation mode, we find the bar mode grows
-as collapse proceeds if \gamma < 1.1, in agreement with the linear analysis.
-However, it takes more than ten orders-of-magnitude increase in the central
-density for the distortion to become non-linear. In addition to this fiducial
-case, we also study non-fiducial ones such as the fragmentation is triggered by
-a fragmentation mode with a longer wavelength and it occurs during radial
-collapse of filaments and find the distortion rapidly grows. In most of
-astrophysical applications, the effective polytropic index of collapsing gas
-exceeds 1.1 before ten orders-of-magnitude increase in the central density.
-Thus, supposing the fiducial case of filament fragmentation, re-fragmentation
-of dense cores would not be likely and their final mass would be determined
-when the filaments fragment.
-",0,1,0,0,0,0
-18532,Inference Related to Common Breaks in a Multivariate System with Joined Segmented Trends with Applications to Global and Hemispheric Temperatures,"  What transpires from recent research is that temperatures and radiative
-forcing seem to be characterized by a linear trend with two changes in the rate
-of growth. The first occurs in the early 60s and indicates a very large
-increase in the rate of growth of both temperature and radiative forcing
-series. This was termed as the ""onset of sustained global warming"". The second
-is related to the more recent so-called hiatus period, which suggests that
-temperatures and total radiative forcing have increased less rapidly since the
-mid-90s compared to the larger rate of increase from 1960 to 1990. There are
-two issues that remain unresolved. The first is whether the breaks in the slope
-of the trend functions of temperatures and radiative forcing are common. This
-is important because common breaks coupled with the basic science of climate
-change would strongly suggest a causal effect from anthropogenic factors to
-temperatures. The second issue relates to establishing formally via a proper
-testing procedure that takes into account the noise in the series, whether
-there was indeed a `hiatus period' for temperatures since the mid 90s. This is
-important because such a test would counter the widely held view that the
-hiatus is the product of natural internal variability. Our paper provides tests
-related to both issues. The results show that the breaks in temperatures and
-radiative forcing are common and that the hiatus is characterized by a
-significant decrease in their rate of growth. The statistical results are of
-independent interest and applicable more generally.
-",0,0,0,1,0,0
-18533,"How to Stop Consensus Algorithms, locally?","  This paper studies problems on locally stopping distributed consensus
-algorithms over networks where each node updates its state by interacting with
-its neighbors and decides by itself whether certain level of agreement has been
-achieved among nodes. Since an individual node is unable to access the states
-of those beyond its neighbors, this problem becomes challenging. In this work,
-we first define the stopping problem for generic distributed algorithms. Then,
-a distributed algorithm is explicitly provided for each node to stop consensus
-updating by exploring the relationship between the so-called local and global
-consensus. Finally, we show both in theory and simulation that its
-effectiveness depends both on the network size and the structure.
-",1,0,0,0,0,0
-18534,Seoul National University Camera II (SNUCAM-II): The New SED Camera for the Lee Sang Gak Telescope (LSGT),"  We present the characteristics and the performance of the new CCD camera
-system, SNUCAM-II (Seoul National University CAMera system II) that was
-installed on the Lee Sang Gak Telescope (LSGT) at the Siding Spring Observatory
-in 2016. SNUCAM-II consists of a deep depletion chip covering a wide wavelength
-from 0.3 {\mu}m to 1.1 {\mu}m with high sensitivity (QE at > 80% over 0.4 to
-0.9 {\mu}m). It is equipped with the SDSS ugriz filters and 13 medium band
-width (50 nm) filters, enabling us to study spectral energy distributions
-(SEDs) of diverse objects from extragalactic sources to solar system objects.
-On LSGT, SNUCAM-II offers 15.7 {\times} 15.7 arcmin field-of-view (FOV) at a
-pixel scale of 0.92 arcsec and a limiting magnitude of g = 19.91 AB mag and
-z=18.20 AB mag at 5{\sigma} with 180 sec exposure time for point source
-detection.
-",0,1,0,0,0,0
-18535,A Bridge Between Hyperparameter Optimization and Larning-to-learn,"  We consider a class of a nested optimization problems involving inner and
-outer objectives. We observe that by taking into explicit account the
-optimization dynamics for the inner objective it is possible to derive a
-general framework that unifies gradient-based hyperparameter optimization and
-meta-learning (or learning-to-learn). Depending on the specific setting, the
-variables of the outer objective take either the meaning of hyperparameters in
-a supervised learning problem or parameters of a meta-learner. We show that
-some recently proposed methods in the latter setting can be instantiated in our
-framework and tackled with the same gradient-based algorithms. Finally, we
-discuss possible design patterns for learning-to-learn and present encouraging
-preliminary experiments for few-shot learning.
-",1,0,0,1,0,0
-18536,Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks,"  Designing a new drug is a lengthy and expensive process. As the space of
-potential molecules is very large (10^23-10^60), a common technique during drug
-discovery is to start from a molecule which already has some of the desired
-properties. An interdisciplinary team of scientists generates hypothesis about
-the required changes to the prototype. In this work, we develop an algorithmic
-unsupervised-approach that automatically generates potential drug molecules
-given a prototype drug. We show that the molecules generated by the system are
-valid molecules and significantly different from the prototype drug. Out of the
-compounds generated by the system, we identified 35 FDA-approved drugs. As an
-example, our system generated Isoniazid - one of the main drugs for
-Tuberculosis. The system is currently being deployed for use in collaboration
-with pharmaceutical companies to further analyze the additional generated
-molecules.
-",0,0,0,1,0,0
-18537,Convex and non-convex regularization methods for spatial point processes intensity estimation,"  This paper deals with feature selection procedures for spatial point
-processes intensity estimation. We consider regularized versions of estimating
-equations based on Campbell theorem derived from two classical functions:
-Poisson likelihood and logistic regression likelihood. We provide general
-conditions on the spatial point processes and on penalty functions which ensure
-consistency, sparsity and asymptotic normality. We discuss the numerical
-implementation and assess finite sample properties in a simulation study.
-Finally, an application to tropical forestry datasets illustrates the use of
-the proposed methods.
-",0,0,1,1,0,0
-18538,Glass Transition in Supercooled Liquids with Medium Range Crystalline Order,"  The origins of rapid dynamical slow down in glass forming liquids in the
-growth of static length scales, possibly associated with identifiable
-structural ordering, is a much debated issue. Growth of medium range
-crystalline order (MRCO) has been observed in various model systems to be
-associated with glassy behaviour. Such observations raise the question about
-the eventual state reached by a glass former, if allowed to relax for
-sufficiently long times. Is a slowly growing crystalline order responsible for
-slow dynamics? Are the molecular mechanisms for glass transition in liquids
-with and without MRCO the same? If yes, glass formers with MRCO provide a
-paradigm for understanding glassy behaviour generically. If not, systems with
-MRCO form a new class of glass forming materials whose molecular mechanism for
-slow dynamics may be easier to understand in terms of growing crystalline
-order, and should be approached in that manner, even while they will not
-provide generic insights. In this study we perform extensive molecular dynamics
-simulations of a number of glass forming liquids in two dimensions and show
-that the static and dynamic properties of glasses with MRCO are different from
-other glass forming liquids with no predominant local order. We also resolve an
-important issue regarding the so-called Point-to-set method for determining
-static length scales, and demonstrate it to be a robust, order agnostic, method
-for determining static correlation lengths in glass formers.
-",0,1,0,0,0,0
-18539,Natural and Artificial Spectral Edges in Exoplanets,"  Technological civilizations may rely upon large-scale photovoltaic arrays to
-harness energy from their host star. Photovoltaic materials, such as silicon,
-possess distinctive spectral features, including an ""artificial edge"" that is
-characteristically shifted in wavelength shortwards of the ""red edge"" of
-vegetation. Future observations of reflected light from exoplanets would be
-able to detect both natural and artificial edges photometrically, if a
-significant fraction of the planet's surface is covered by vegetation or
-photovoltaic arrays respectively. The stellar energy thus tapped can be
-utilized for terraforming activities by transferring heat and light from the
-day side to the night side on tidally locked exoplanets, thereby producing
-detectable artifacts.
-",0,1,0,0,0,0
-18540,The damage inflicted by a computer virus: A new estimation method,"  This paper addressed the issue of estimating the damage caused by a computer
-virus. First, an individual-level delayed SIR model capturing the spreading
-process of a digital virus is derived. Second, the damage inflicted by the
-virus is modeled as the sum of the economic losses and the cost for developing
-the antivirus. Next, the impact of different factors, including the delay and
-the network structure, on the damage is explored by means of computer
-simulations. Thereby some measures of reducing the damage of a virus are
-recommended. To our knowledge, this is the first time the antivirus-developing
-cost is taken into account when estimating the damage of a virus.
-",1,1,0,0,0,0
-18541,Deep Fault Analysis and Subset Selection in Solar Power Grids,"  Non-availability of reliable and sustainable electric power is a major
-problem in the developing world. Renewable energy sources like solar are not
-very lucrative in the current stage due to various uncertainties like weather,
-storage, land use among others. There also exists various other issues like
-mis-commitment of power, absence of intelligent fault analysis, congestion,
-etc. In this paper, we propose a novel deep learning-based system for
-predicting faults and selecting power generators optimally so as to reduce
-costs and ensure higher reliability in solar power systems. The results are
-highly encouraging and they suggest that the approaches proposed in this paper
-have the potential to be applied successfully in the developing world.
-",1,0,0,1,0,0
-18542,Hirzebruch L-polynomials and multiple zeta values,"  We express the coefficients of the Hirzebruch L-polynomials in terms of
-certain alternating multiple zeta values. In particular, we show that every
-monomial in the Pontryagin classes appears with a non-zero coefficient, with
-the expected sign. Similar results hold for the polynomials associated to the
-A-hat genus.
-",0,0,1,0,0,0
-18543,On Nontrivial Zeros of Riemann Zeta Function,"  Let {\Xi} be a function relating to the Riemann zeta function with . In this
-paper, we construct a function containing and {\Xi} , and prove that satisfies
-a nonadjoint boundary value problem to a nonsingular differential equation if
-is any nontrivial zero of {\Xi} . Inspecting properties of and using known
-results of nontrivial zeros of , we derive that nontrivial zeros of all have
-real part equal to , which concludes that Riemann Hypothesis is true.
-",0,0,1,0,0,0
-18544,The reliability of a nutritional meta-analysis study,"  Background: Many researchers have studied the relationship between diet and
-health. There are papers showing an association between the consumption of
-sugar-sweetened beverages and Type 2 diabetes. Many meta-analyses use
-individual studies that do not adjust for multiple testing or multiple modeling
-and thus provide biased estimates of effect. Hence the claims reported in a
-meta-analysis paper may be unreliable if the primary papers do not ensure
-unbiased estimates of effect. Objective: Determine the statistical reliability
-of 10 papers and indirectly the reliability of the meta-analysis study. Method:
-Ten primary papers used in a meta-analysis paper and counted the numbers of
-outcomes, predictors, and covariates. We estimated the size of the potential
-analysis search space available to the authors of these papers; i.e. the number
-of comparisons and models available. Since we noticed that there were
-differences between predictors and covariates cited in the abstract and in the
-text, we applied this formula to information found in the abstracts, Space A,
-as well as the text, Space T, of each primary paper. Results: The median and
-range of the number of comparisons possible across the primary papers are 6.5
-and (2-12,288) for abstracts, and 196,608 and (3,072-117,117,952) the texts.
-Note that the median of 6.5 for Space A is misleading as each primary study has
-60-165 foods not mentioned in the abstract. Conclusion: Given that testing is
-at the 0.05 level and the number of comparisons is very large, nominal
-statistical significance is very weak support for a claim. The claims in these
-papers are not statistically supported and hence are unreliable. Thus, the
-claims of the meta-analysis paper lack evidentiary confirmation.
-",0,0,0,1,0,0
-18545,Composite Fermions on a Torus,"  We achieve an explicit construction of the lowest Landau level (LLL)
-projected wave functions for composite fermions in the periodic (torus)
-geometry. To this end, we first demonstrate how the vortex attachment of the
-composite fermion (CF) theory can be accomplished in the torus geometry to
-produce the ""unprojected"" wave functions satisfying the correct
-(quasi-)periodic boundary conditions. We then consider two methods for
-projecting these wave functions into the LLL. The direct projection produces
-valid wave functions but can be implemented only for very small systems. The
-more powerful and more useful projection method of Jain and Kamilla fails in
-the torus geometry because it does not preserve the periodic boundary
-conditions and thus takes us out of the original Hilbert space. We have
-succeeded in constructing a modified projection method that is consistent with
-both the periodic boundary conditions and the general structure of the CF
-theory. This method is valid for a large class of states of composite fermions,
-called ""proper states,"" which includes the incompressible ground states at
-electron filling factors $\nu=\frac{n}{2pn+ 1}$, their charged and neutral
-excitations, and also the quasidegenerate ground states at arbitrary filling
-factors of the form $\nu=\frac{\nu^*}{2p\nu^*+ 1}$, where $n$ and $p$ are
-integers and $\nu^*$ is the CF filling factor. Comparison with exact results
-known for small systems for the ground and excited states at filling factors
-$\nu=1/3$, 2/5 and 3/7 demonstrates our LLL-projected wave functions to be
-extremely accurate representations of the actual Coulomb eigenstates. Our
-construction enables the study of large systems of composite fermions on the
-torus, thereby opening the possibility of investigating numerous interesting
-questions and phenomena.
-",0,1,0,0,0,0
-18546,ROCKER: A Refinement Operator for Key Discovery,"  The Linked Data principles provide a decentral approach for publishing
-structured data in the RDF format on the Web. In contrast to structured data
-published in relational databases where a key is often provided explicitly,
-finding a set of properties that allows identifying a resource uniquely is a
-non-trivial task. Still, finding keys is of central importance for manifold
-applications such as resource deduplication, link discovery, logical data
-compression and data integration. In this paper, we address this research gap
-by specifying a refinement operator, dubbed ROCKER, which we prove to be
-finite, proper and non-redundant. We combine the theoretical characteristics of
-this operator with two monotonicities of keys to obtain a time-efficient
-approach for detecting keys, i.e., sets of properties that describe resources
-uniquely. We then utilize a hash index to compute the discriminability score
-efficiently. Therewith, we ensure that our approach can scale to very large
-knowledge bases. Results show that ROCKER yields more accurate results, has a
-comparable runtime, and consumes less memory w.r.t. existing state-of-the-art
-techniques.
-",1,0,0,0,0,0
-18547,Double-slit Fraunhofer pattern as the signature of the Josephson effect between Berezinskii superconductors through the ferromagnetic vortex,"  I apply the recently developed formalism of generalized quasiclassical theory
-to show that using hybrid superconducting systems with non-collinear strong
-ferromagnets one can realize the Josephson junction between Berezinskii-type
-superconductors. The reported calculation reproduces main features observed in
-the recent experiment, namely the the slightly asymmetric double-slit
-Fraunhofer interference pattern of the Josephson current through the
-ferromagnetic vortex. The double-slit structure results from the spatially
-inhomogeneous Berezinskii state with the amplitude controlled by the local
-angle between magnetic moments in two ferromagnetic layers. The critical
-current asymmetry by the sign of magnetic field can signal the presence of
-spontaneous supercurrents generated by the non-coplanar magnetic texture near
-the core of the ferromagnetic vortex core. I demonstrate that ferromagnetic
-vortex can induce spontaneous vorticity in the odd-frequency order parameter
-manifesting the possibility of the emergent magnetic field to create
-topological defects.
-",0,1,0,0,0,0
-18548,A class of differential quadratic algebras and their symmetries,"  We study a multi-parametric family of quadratic algebras in four generators,
-which includes coordinate algebras of noncommutative four-planes and, as
-quotient algebras, noncommutative three spheres. Particular subfamilies
-comprise Sklyanin algebras and Connes--Dubois-Violette planes. We determine
-quantum groups of symmetries for the general algebras and construct
-finite-dimensional covariant differential calculi.
-",0,0,1,0,0,0
-18549,Introduction to compact and discrete quantum groups,"  These are notes from introductory lectures at the graduate school
-""Topological Quantum Groups"" in Będlewo (June 28--July 11, 2015). The notes
-present the passage from Hopf algebras to compact quantum groups and sketch the
-notion of discrete quantum groups viewed as duals of compact quantum groups.
-",0,0,1,0,0,0
-18550,An optimization approach to adaptive multi-dimensional capital management,"  Firms should keep capital to offer sufficient protection against the risks
-they are facing. In the insurance context methods have been developed to
-determine the minimum capital level required, but less so in the context of
-firms with multiple business lines including allocation. The individual capital
-reserve of each line can be represented by means of classical models, such as
-the conventional Cramér-Lundberg model, but the challenge lies in soundly
-modelling the correlations between the business lines. We propose a simple yet
-versatile approach that allows for dependence by introducing a common
-environmental factor. We present a novel Bayesian approach to calibrate the
-latent environmental state distribution based on observations concerning the
-claim processes. The calibration approach is adjusted for an environmental
-factor that changes over time. The convergence of the calibration procedure
-towards the true environmental state is deduced. We then point out how to
-determine the optimal initial capital of the different business lines under
-specific constraints on the ruin probability of subsets of business lines. Upon
-combining the above findings, we have developed an easy-to-implement approach
-to capital risk management in a multi-dimensional insurance risk model.
-",0,0,0,0,0,1
-18551,Low Energy Phonons in $Bi_2Sr_2CaCu_2O_{8+δ}$ and their Possible Interaction with Electrons Measured by Inelastic Neutron Scattering,"  Angle-resolved photoemission (ARPES) experiments on copper oxide
-superconductors revealed enigmatic kinks in electronic dispersions near 10 meV
-presumably due to phonons or impuritites. We used inelastic neutron scattering
-to measure phonon branches below 15 meV in a large single crystal sample of
-optimally-doped $Bi_2Sr_2CaCu_2O_{8+\delta}$ (BSCCO). The high quality dataset
-covered several Brilloiun zones with different final energies. In addition to
-acoustic branches, optic branches disperse from 4 meV and 7 meV zone center
-energies. The 4 meV branch interacts with acoustic phonons at small
-wavevectors, which destroys the LA character of the acoustic branch beyond
-~0.15 reciprocal lattice units. We propose a mechanism that explains the low
-energy electronic dispersion features based on this observation.
-",0,1,0,0,0,0
-18552,Spectral properties of complex Airy operator on the semi-axis,"  We prove the theorem on the completeness of the root functions of the
-Schroedinger operator $L=-d^2/dx^2+p(x)$ on the semi-axis $\mathbb R_+$ with a
-complex--valued potential $p(x)$. It is assumed that the potential $p = q \pm
-ir$ is such that the real functions $q$ and $r$ are subject the conditions $$
-q(x) \geqslant c r(x), \quad r(x) \geqslant c_0+ c_1 x^\alpha, \quad \alpha >0,
-$$ where the constants $c, \ c_0\in \mathbb R$, $c_1>0$ and $\arg(\pm i+c) <
-2\alpha\pi/(2+\alpha)$. For the case of the Airy operator $L_c=-d^2/dx^2+cx$,
-$c=const$, this theorem imply the completeness of the system of the
-eigenfunctions of this operator if $|\arg c|<2\pi/3$. Using another technique
-based on the asymptotic behavior of the Airy functions we prove that the
-completeness theorem for the operator $L_c$ remains valid, provided that $|\arg
-c|<5\pi/6$.
-",0,0,1,0,0,0
-18553,Webs and $q$-Howe dualities in types $\mathbf{B}\mathbf{C}\mathbf{D}$,"  We define web categories describing intertwiners for the orthogonal and
-symplectic Lie algebras, and, in the quantized setup, for certain orthogonal
-and symplectic coideal subalgebras. They generalize the Brauer category, and
-allow us to prove quantum versions of some classical type
-$\mathbf{B}\mathbf{C}\mathbf{D}$ Howe dualities.
-",0,0,1,0,0,0
-18554,Structural Controllability of Linear Time-invariant Systems,"  One version of the concept of structural controllability defined for
-single-input systems by Lin and subsequently generalized to multi-input systems
-by others, states that a parameterized matrix pair $(A, B)$ whose nonzero
-entries are distinct parameters, is structurally controllable if values can be
-assigned to the parameters which cause the resulting matrix pair to be
-controllable. In this paper the concept of structural controllability is
-broadened to allow for the possibility that a parameter may appear in more than
-one location in the pair $(A, B)$. Subject to a certain condition on the
-parameterization called the ""binary assumption"", an explicit graph-theoretic
-characterization of such matrix pairs is derived.
-",1,0,0,0,0,0
-18555,Structure of martingale transports in finite dimensions,"  We study the structure of martingale transports in finite dimensions. We
-consider the family $\mathcal{M}(\mu,\nu) $ of martingale measures on
-$\mathbb{R}^N \times \mathbb{R}^N$ with given marginals $\mu,\nu$, and
-construct a family of relatively open convex sets $\{C_x:x\in \mathbb{R}^N \}$,
-which forms a partition of $\mathbb{R}^N$, and such that any martingale
-transport in $\mathcal{M}(\mu,\nu) $ sends mass from $x$ to within
-$\overline{C_x}$, $\mu(dx)$--a.e. Our results extend the analogous
-one-dimensional results of M. Beiglböck and N. Juillet (2016) and M.
-Beiglböck, M. Nutz, and N. Touzi (2015). We conjecture that the decomposition
-is canonical and minimal in the sense that it allows to characterise the
-martingale polar sets, i.e. the sets which have zero mass under all measures in
-$\mathcal{M}(\mu,\nu)$, and offers the martingale analogue of the
-characterisation of transport polar sets proved in M. Beiglböck, M.
-Goldstern, G. Maresch, and W. Schachermayer (2009).
-",0,0,1,0,0,0
-18556,Simplifying branched covering surface-knots by chart moves involving black vertices,"  A branched covering surface-knot is a surface-knot in the form of a branched
-covering over an oriented surface-knot $F$, where we include the case when the
-covering has no branch points. A branched covering surface-knot is presented by
-a graph called a chart on a surface diagram of $F$. We can simplify a branched
-covering surface-knot by an addition of 1-handles with chart loops to a form
-such that its chart is the union of free edges and 1-handles with chart loops.
-We investigate properties of such simplifications for the case when branched
-covering surface-knots have a non-zero number of branch points, using chart
-moves involving black vertices.
-",0,0,1,0,0,0
-18557,$K$-surfaces with free boundaries,"  A well-known question in classical differential geometry and geometric
-analysis asks for a description of possible boundaries of $K$-surfaces, which
-are smooth, compact hypersurfaces in $\mathbb{R}^d$ having constant Gauss
-curvature equal to $K \geq 0$. This question generated a considerable amount of
-remarkable results in the last few decades. Motivated by these developments
-here we study the question of determining a $K$-surface when only part of its
-boundary is fixed, and in addition the surface hits a given manifold at some
-fixed angle. While this general setting is out of reach for us at the present,
-we settle a model case of the problem, which in its analytic formulation
-reduces to a Bernoulli type free boundary problem for the Monge-Ampère
-equation. We study both the cases of 0-curvature and of positive curvature. The
-formulation of the free boundary condition and its regularity are the most
-delicate and challenging questions addressed in this work. In this regard we
-introduce a notion of a Blaschke extension of a solution which might be of
-independent interest.
-The problem we study can also be interpreted as the Alt-Caffarelli problem
-for the Monge-Ampère equation. Moreover, it also relates to the problem of
-isometric embedding of a positive metric on the annulus with partially
-prescribed boundary and optimal transport with free mass.
-",0,0,1,0,0,0
-18558,The graphs of join-semilattices and the shape of congruence lattices of particle lattices,"  We attach to each $\langle 0, \vee \rangle$-semilattice a graph
-$\boldsymbol{G}_{\boldsymbol{S}}$ whose vertices are join-irreducible elements
-of $\boldsymbol{S}$ and whose edges correspond to the reflexive dependency
-relation. We study properties of the graph $\boldsymbol{G}_{\boldsymbol{S}}$
-both when $\boldsymbol{S}$ is a join-semilattice and when it is a lattice. We
-call a $\langle 0, \vee \rangle$-semilattice $\boldsymbol{S}$ particle provided
-that the set of its join-irreducible elements join-generates $\boldsymbol{S}$
-and it satisfies DCC. We prove that the congruence lattice of a particle
-lattice is anti-isomorphic to the lattice of hereditary subsets of the
-corresponding graph that are closed in a certain zero-dimensional topology.
-Thus we extend the result known for principally chain finite lattices.
-",0,0,1,0,0,0
-18559,Optimal Quasi-Gray Codes: The Alphabet Matters,"  A quasi-Gray code of dimension $n$ and length $\ell$ over an alphabet
-$\Sigma$ is a sequence of distinct words $w_1,w_2,\dots,w_\ell$ from $\Sigma^n$
-such that any two consecutive words differ in at most $c$ coordinates, for some
-fixed constant $c>0$. In this paper we are interested in the read and write
-complexity of quasi-Gray codes in the bit-probe model, where we measure the
-number of symbols read and written in order to transform any word $w_i$ into
-its successor $w_{i+1}$.
-We present construction of quasi-Gray codes of dimension $n$ and length $3^n$
-over the ternary alphabet $\{0,1,2\}$ with worst-case read complexity $O(\log
-n)$ and write complexity $2$. This generalizes to arbitrary odd-size alphabets.
-For the binary alphabet, we present quasi-Gray codes of dimension $n$ and
-length at least $2^n - 20n$ with worst-case read complexity $6+\log n$ and
-write complexity $2$. This complements a recent result by Raskin [Raskin '17]
-who shows that any quasi-Gray code over binary alphabet of length $2^n$ has
-read complexity $\Omega(n)$.
-Our results significantly improve on previously known constructions and for
-the odd-size alphabets we break the $\Omega(n)$ worst-case barrier for
-space-optimal (non-redundant) quasi-Gray codes with constant number of writes.
-We obtain our results via a novel application of algebraic tools together with
-the principles of catalytic computation [Buhrman et al. '14, Ben-Or and Cleve
-'92, Barrington '89, Coppersmith and Grossman '75].
-",1,0,0,0,0,0
-18560,Revisiting the pre-main-sequence evolution of stars I. Importance of accretion efficiency and deuterium abundance,"  Recent theoretical work has shown that the pre-main-sequence (PMS) evolution
-of stars is much more complex than previously envisioned. Instead of the
-traditional steady, one-dimensional solution, accretion may be episodic and not
-necessarily symmetrical, thereby affecting the energy deposited inside the star
-and its interior structure. Given this new framework, we want to understand
-what controls the evolution of accreting stars. We use the MESA stellar
-evolution code with various sets of conditions. In particular, we account for
-the (unknown) efficiency of accretion in burying gravitational energy into the
-protostar through a parameter, $\xi$, and we vary the amount of deuterium
-present. We confirm the findings of previous works that the evolution changes
-significantly with the amount of energy that is lost during accretion. We find
-that deuterium burning also regulates the PMS evolution. In the low-entropy
-accretion scenario, the evolutionary tracks in the H-R diagram are
-significantly different from the classical tracks and are sensitive to the
-deuterium content. A comparison of theoretical evolutionary tracks and
-observations allows us to exclude some cold accretion models ($\xi\sim 0$) with
-low deuterium abundances. We confirm that the luminosity spread seen in
-clusters can be explained by models with a somewhat inefficient injection of
-accretion heat. The resulting evolutionary tracks then become sensitive to the
-accretion heat efficiency, initial core entropy, and deuterium content. In this
-context, we predict that clusters with a higher D/H ratio should have less
-scatter in luminosity than clusters with a smaller D/H. Future work on this
-issue should include radiation-hydrodynamic simulations to determine the
-efficiency of accretion heating and further observations to investigate the
-deuterium content in star-forming regions. (abbrev.)
-",0,1,0,0,0,0
-18561,Surface-assisted carrier excitation in plasmonic nanostructure,"  We present a quantum-mechanical model for surface-assisted carrier excitation
-by optical fields in plasmonic nanostructures of arbitrary shape. We derive an
-explicit expression, in terms of local fields inside the metal structure, for
-surface absorbed power and surface scattering rate that determine the
-enhancement of carrier excitation efficiency near the metal-dielectric
-interface. We show that surface scattering is highly sensitive to the local
-field polarization, and can be incorporated into metal dielectric function
-along with phonon and impurity scattering. We also show that the obtained
-surface scattering rate describes surface-assisted plasmon decay (Landau
-damping) in nanostructures larger than the nonlocality scale. Our model can be
-used for calculations of plasmon-assisted hot carrier generation rates in
-photovoltaics and photochemistry applications.
-",0,1,0,0,0,0
-18562,Existence of global weak solutions to the kinetic Hookean dumbbell model for incompressible dilute polymeric fluids,"  We explore the existence of global weak solutions to the Hookean dumbbell
-model, a system of nonlinear partial differential equations that arises from
-the kinetic theory of dilute polymers, involving the unsteady incompressible
-Navier--Stokes equations in a bounded domain in two or three space dimensions,
-coupled to a Fokker--Planck-type parabolic equation. We prove the existence of
-large-data global weak solutions in the case of two space dimensions.
-Indirectly, our proof also rigorously demonstrates that, in two space
-dimensions at least, the Oldroyd-B model is the macroscopic closure of the
-Hookean dumbbell model. In three space dimensions, we prove the existence of
-large-data global weak subsolutions to the model, which are weak solutions with
-a defect measure, where the defect measure appearing in the Navier--Stokes
-momentum equation is the divergence of a symmetric positive semidefinite
-matrix-valued Radon measure.
-",0,0,1,0,0,0
-18563,Tight Bounds for Online Coloring of Basic Graph Classes,"  We resolve a number of long-standing open problems in online graph coloring.
-More specifically, we develop tight lower bounds on the performance of online
-algorithms for fundamental graph classes. An important contribution is that our
-bounds also hold for randomized online algorithms, for which hardly any results
-were known. Technically, we construct lower bounds for chordal graphs. The
-constructions then allow us to derive results on the performance of randomized
-online algorithms for the following further graph classes: trees, planar,
-bipartite, inductive, bounded-treewidth and disk graphs. It shows that the best
-competitive ratio of both deterministic and randomized online algorithms is
-$\Theta(\log n)$, where $n$ is the number of vertices of a graph. Furthermore,
-we prove that this guarantee cannot be improved if an online algorithm has a
-lookahead of size $O(n/\log n)$ or access to a reordering buffer of size
-$n^{1-\epsilon}$, for any $0<\epsilon\leq 1$. A consequence of our results is
-that, for all of the above mentioned graph classes except bipartite graphs, the
-natural $\textit{First Fit}$ coloring algorithm achieves an optimal
-performance, up to constant factors, among deterministic and randomized online
-algorithms.
-",1,0,0,0,0,0
-18564,Comparison of PCA with ICA from data distribution perspective,"  We performed an empirical comparison of ICA and PCA algorithms by applying
-them on two simulated noisy time series with varying distribution parameters
-and level of noise. In general, ICA shows better results than PCA because it
-takes into account higher moments of data distribution. On the other hand, PCA
-remains quite sensitive to the level of correlations among signals.
-",0,0,0,1,0,0
-18565,The Special Polarization Characteristic Features of a Three-Dimensional Terahertz Photonic Crystal with a Silicon Inverse Diamond Structure,"  The band structure of a Si inverse diamond structure whose lattice point
-shape was vacant regular octahedrons was calculated using plane wave expansion
-method and a complete photonic band gap was theoretically confirmed at around
-0.4 THz. It is said that three-dimensional photonic crystals have no
-polarization anisotropy in photonic band gap (stop gap, stop band) of high
-symmetry points in normal incidence. However, it was experimentally confirmed
-that the polarization orientation of a reflected light was different from that
-of a incident light, {I(X,Y)}, where (X,Y) is the coordinate system fixed in
-the photonic crystal. It was studied on a plane (001) at around X point's
-photonic band gap (0.36 - 0.44 THz) for incident light direction [001]
-($\Gamma$-X direction) by rotating a sample in the plane (001), relatively. The
-polarization orientation of the reflected light was parallel to that of the
-incident light for the incident polarization orientation I(1,1), I(1,-1). In
-contrast, the former was perpendicular to the latter for the incident
-polarization orientation I(1,0), I(0,-1) in the vicinity of 0.38 THz. As far as
-the photonic crystal in this work is concerned, method of resolution and
-synthesis of the incident polarization vector isn't apparently able to apply to
-the analysis of experimental results.
-",0,1,0,0,0,0
-18566,Toward Finding Latent Cities with Non-Negative Matrix Factorization,"  In the last decade, digital footprints have been used to cluster population
-activity into functional areas of cities.
-However, a key aspect has been overlooked: we experience our cities not only
-by performing activities at specific destinations, but also by moving from one
-place to another.
-In this paper, we propose to analyze and cluster the city based on how people
-move through it. Particularly, we introduce Mobilicities, automatically
-generated travel patterns inferred from mobile phone network data using NMF, a
-matrix factorization model.
-We evaluate our method in a large city and we find that mobilicities reveal
-latent but at the same time interpretable mobility structures of the city. Our
-results provide evidence on how clustering and visualization of aggregated
-phone logs could be used in planning systems to interactively analyze city
-structure and population activity.
-",1,0,0,0,0,0
-18567,Time-Sensitive Networking for robotics,"  We argue that Time-Sensitive Networking (TSN) will become the de facto
-standard for real-time communications in robotics. We present a review and
-classification of the different communication standards which are relevant for
-the field and introduce the typical problems with traditional switched Ethernet
-networks. We discuss some of the TSN features relevant for deterministic
-communications and evaluate experimentally one of the shaping mechanisms in an
-exemplary robotic scenario. In particular, and based on our results, we claim
-that many of the existing real-time industrial solutions will slowly be
-replaced by TSN. And that this will lead towards a unified landscape of
-physically interoperable robot and robot components.
-",1,0,0,0,0,0
-18568,Active Orthogonal Matching Pursuit for Sparse Subspace Clustering,"  Sparse Subspace Clustering (SSC) is a state-of-the-art method for clustering
-high-dimensional data points lying in a union of low-dimensional subspaces.
-However, while $\ell_1$ optimization-based SSC algorithms suffer from high
-computational complexity, other variants of SSC, such as Orthogonal Matching
-Pursuit-based SSC (OMP-SSC), lose clustering accuracy in pursuit of improving
-time efficiency. In this letter, we propose a novel Active OMP-SSC, which
-improves clustering accuracy of OMP-SSC by adaptively updating data points and
-randomly dropping data points in the OMP process, while still enjoying the low
-computational complexity of greedy pursuit algorithms. We provide heuristic
-analysis of our approach, and explain how these two active steps achieve a
-better tradeoff between connectivity and separation. Numerical results on both
-synthetic data and real-world data validate our analyses and show the
-advantages of the proposed active algorithm.
-",1,0,0,1,0,0
-18569,Experimental and theoretical study of AC losses in variable asymmetrical magnetic environments,"  Measurements of AC losses in a HTS-tape placed in between of two bulk
-magnetic shields of high permeability were performed by applying calorimetric
-techniques for various asymmetrical shielding arrangements. The experiment was
-supported by analytical calculations and finite-element simulations of the
-field and current distributions, based on the Bean model of the critical state.
-The simulated current and field profiles perfectly reproduce the analytic
-solutions known for certain shielding geometries. The evaluation of the
-consequent AC losses exhibits good agreement with measurements for the central
-position of the tape between the magnets but increasing discrepancy when the
-tape is approaching the shields. This can be explained by the increasing
-contribution of the eddy currents and magnetic hysteresis losses in the
-conducting shields.
-",0,1,0,0,0,0
-18570,Robustness of semiparametric efficiency in nearly-true models for two-phase samples,"  We examine the performance of efficient and AIPW estimators under two-phase
-sampling when the complete-data model is nearly correctly specified, in the
-sense that the misspecification is not reliably detectable from the data by any
-possible diagnostic or test. Asymptotic results for these nearly true models
-are obtained by representing them as sequences of misspecified models that are
-mutually contiguous with a correctly specified model. We find that for the
-least-favourable direction of model misspecification the bias in the efficient
-estimator induced can be comparable to the extra variability in the AIPW
-estimator, so that the mean squared error of the efficient estimator is no
-longer lower. This can happen when the most-powerful test for the model
-misspecification still has modest power. We verify that the theoretical results
-agree with simulation in three examples: a simple informative-sampling model
-for a Normal mean, logistic regression in the classical case-control design,
-and linear regression in a two-phase design.
-",0,0,1,1,0,0
-18571,A* CCG Parsing with a Supertag and Dependency Factored Model,"  We propose a new A* CCG parsing model in which the probability of a tree is
-decomposed into factors of CCG categories and its syntactic dependencies both
-defined on bi-directional LSTMs. Our factored model allows the precomputation
-of all probabilities and runs very efficiently, while modeling sentence
-structures explicitly via dependencies. Our model achieves the state-of-the-art
-results on English and Japanese CCG parsing.
-",1,0,0,0,0,0
-18572,Numerical studies of Thompson's group F and related groups,"  We have developed polynomial-time algorithms to generate terms of the
-cogrowth series for groups $\mathbb{Z}\wr \mathbb{Z},$ the lamplighter group,
-$(\mathbb{Z}\wr \mathbb{Z})\wr \mathbb{Z}$ and the Navas-Brin group $B.$ We
-have also given an improved algorithm for the coefficients of Thompson's group
-$F,$ giving 32 terms of the cogrowth series. We develop numerical techniques to
-extract the asymptotics of these various cogrowth series. We present improved
-rigorous lower bounds on the growth-rate of the cogrowth series for Thompson's
-group $F$ using the method from \cite{HHR15} applied to our extended series. We
-also generalise their method by showing that it applies to loops on any locally
-finite graph. Unfortunately, lower bounds less than 16 do not help in
-determining amenability.
-Again for Thompson's group $F$ we prove that, if the group is amenable, there
-cannot be a sub-dominant stretched exponential term in the
-asymptotics\footnote{ }. Yet the numerical data provides compelling evidence
-for the presence of such a term. This observation suggests a potential path to
-a proof of non-amenability: If the universality class of the cogrowth sequence
-can be determined rigorously, it will likely prove non-amenability.
-We estimate the asymptotics of the cogrowth coefficients of $F$ to be $$ c_n
-\sim c \cdot \mu^n \cdot \kappa^{n^\sigma \log^\delta{n}} \cdot n^g,$$ where
-$\mu \approx 15,$ $\kappa \approx 1/e,$ $\sigma \approx 1/2,$ $\delta \approx
-1/2,$ and $g \approx -1.$ The growth constant $\mu$ must be 16 for amenability.
-These two approaches, plus a third based on extrapolating lower bounds, support
-the conjecture \cite{ERvR15, HHR15} that the group is not amenable.
-",0,0,1,0,0,0
-18573,The modularity of action and perception revisited using control theory and active inference,"  The assumption that action and perception can be investigated independently
-is entrenched in theories, models and experimental approaches across the brain
-and mind sciences. In cognitive science, this has been a central point of
-contention between computationalist and 4Es (enactive, embodied, extended and
-embedded) theories of cognition, with the former embracing the ""classical
-sandwich"", modular, architecture of the mind and the latter actively denying
-this separation can be made. In this work we suggest that the modular
-independence of action and perception strongly resonates with the separation
-principle of control theory and furthermore that this principle provides formal
-criteria within which to evaluate the implications of the modularity of action
-and perception. We will also see that real-time feedback with the environment,
-often considered necessary for the definition of 4Es ideas, is not however a
-sufficient condition to avoid the ""classical sandwich"". Finally, we argue that
-an emerging framework in the cognitive and brain sciences, active inference,
-extends ideas derived from control theory to the study of biological systems
-while disposing of the separation principle, describing non-modular models of
-behaviour strongly aligned with 4Es theories of cognition.
-",0,0,0,0,1,0
-18574,Clique-based Method for Social Network Clustering,"  In this article, we develop a clique-based method for social network
-clustering. We introduce a new index to evaluate the quality of clustering
-results, and propose an efficient algorithm based on recursive bipartition to
-maximize an objective function of the proposed index. The optimization problem
-is NP-hard, so we approximate the semi-optimal solution via an implicitly
-restarted Lanczos method. One of the advantages of our algorithm is that the
-proposed index of each community in the clustering result is guaranteed to be
-higher than some predetermined threshold, $p$, which is completely controlled
-by users. We also account for the situation that $p$ is unknown. A statistical
-procedure of controlling both under-clustering and over-clustering errors
-simultaneously is carried out to select localized threshold for each
-subnetwork, such that the community detection accuracy is optimized.
-Accordingly, we propose a localized clustering algorithm based on binary tree
-structure. Finally, we exploit the stochastic blockmodels to conduct simulation
-studies and demonstrate the accuracy and efficiency of our algorithms, both
-numerically and graphically.
-",1,0,0,1,0,0
-18575,Fermionic Matrix Product States and One-Dimensional Short-Range Entangled Phases with Anti-Unitary Symmetries,"  We extend the formalism of Matrix Product States (MPS) to describe
-one-dimensional gapped systems of fermions with both unitary and anti-unitary
-symmetries. Additionally, systems with orientation-reversing spatial symmetries
-are considered. The short-ranged entangled phases of such systems are
-classified by three invariants, which characterize the projective action of the
-symmetry on edge states. We give interpretations of these invariants as
-properties of states on the closed chain. The relationship between fermionic
-MPS systems at an RG fixed point and equivariant algebras is exploited to
-derive a group law for the stacking of fermionic phases. The result generalizes
-known classifications to symmetry groups that are non-trivial extensions of
-fermion parity and time-reversal.
-",0,1,0,0,0,0
-18576,How tracer particles sample the complexity of turbulence,"  On their roller coaster ride through turbulence, tracer particles sample the
-fluctuations of the underlying fields in space and time. Quantitatively
-relating particle and field statistics remains a fundamental challenge in a
-large variety of turbulent flows. We quantify how tracer particles sample
-turbulence by expressing their temporal velocity fluctuations in terms of an
-effective probabilistic sampling of spatial velocity field fluctuations. To
-corroborate our theory, we investigate an extensive suite of direct numerical
-simulations of hydrodynamic turbulence covering a Taylor-scale Reynolds number
-range from 150 to 430. Our approach allows the assessment of particle
-statistics from the knowledge of flow field statistics only, therefore opening
-avenues to a new generation of models for transport in complex flows.
-",0,1,0,0,0,0
-18577,Rare Nash Equilibria and the Price of Anarchy in Large Static Games,"  We study a static game played by a finite number of agents, in which agents
-are assigned independent and identically distributed random types and each
-agent minimizes its objective function by choosing from a set of admissible
-actions that depends on its type. The game is anonymous in the sense that the
-objective function of each agent depends on the actions of other agents only
-through the empirical distribution of their type-action pairs. We study the
-asymptotic behavior of Nash equilibria, as the number of agents tends to
-infinity, first by deriving laws of large numbers characterizes almost sure
-limit points of Nash equilibria in terms of so-called Cournot-Nash equilibria
-of an associated nonatomic game. Our main results are large deviation
-principles that characterize the probability of rare Nash equilibria and
-associated conditional limit theorems describing the behavior of equilibria
-conditioned on a rare event. The results cover situations when neither the
-finite-player game nor the associated nonatomic game has a unique equilibrium.
-In addition, we study the asymptotic behavior of the price of anarchy,
-complementing existing worst-case bounds with new probabilistic bounds in the
-context of congestion games, which are used to model traffic routing in
-networks.
-",1,0,1,0,0,0
-18578,"Componentwise different tail solutions for bivariate stochastic recurrence equations -- with application to GARCH(1,1) processes --","  We study bivariate stochastic recurrence equations (SREs) motivated by
-applications to GARCH(1,1) processes. If coefficient matrices of SREs have
-strictly positive entries, then the Kesten result applies and it gives
-solutions with regularly varying tails. Moreover, the tail indices are the same
-for all coordinates. However, for applications, this framework is too
-restrictive. We study SREs when coefficients are triangular matrices and prove
-that the coordinates of the solution may exhibit regularly varying tails with
-different indices. We also specify each tail index together with its constant.
-The results are used to characterize regular variations of bivariate stationary
-GARCH(1,1) processes.
-",0,0,1,1,0,0
-18579,Partial constraint singularities in elastic rods,"  We present a unified classical treatment of partially constrained elastic
-rods. Partial constraints often entail singularities in both shapes and
-reactions. Our approach encompasses both sleeve and adhesion problems, and
-provides simple and unambiguous derivations of counterintuitive results in the
-literature. Relationships between reaction forces and moments, geometry, and
-adhesion energies follow from the balance of energy during quasistatic motion.
-We also relate our approach to the balance of material momentum and the concept
-of a driving traction. The theory is generalizable and can be applied to a wide
-array of contact, adhesion, gripping, and locomotion problems.
-",0,1,0,0,0,0
-18580,Personalization in Goal-Oriented Dialog,"  The main goal of modeling human conversation is to create agents which can
-interact with people in both open-ended and goal-oriented scenarios. End-to-end
-trained neural dialog systems are an important line of research for such
-generalized dialog models as they do not resort to any situation-specific
-handcrafting of rules. However, incorporating personalization into such systems
-is a largely unexplored topic as there are no existing corpora to facilitate
-such work. In this paper, we present a new dataset of goal-oriented dialogs
-which are influenced by speaker profiles attached to them. We analyze the
-shortcomings of an existing end-to-end dialog system based on Memory Networks
-and propose modifications to the architecture which enable personalization. We
-also investigate personalization in dialog as a multi-task learning problem,
-and show that a single model which shares features among various profiles
-outperforms separate models for each profile.
-",1,0,0,0,0,0
-18581,Maximum Margin Principal Components,"  Principal Component Analysis (PCA) is a very successful dimensionality
-reduction technique, widely used in predictive modeling. A key factor in its
-widespread use in this domain is the fact that the projection of a dataset onto
-its first $K$ principal components minimizes the sum of squared errors between
-the original data and the projected data over all possible rank $K$
-projections. Thus, PCA provides optimal low-rank representations of data for
-least-squares linear regression under standard modeling assumptions. On the
-other hand, when the loss function for a prediction problem is not the
-least-squares error, PCA is typically a heuristic choice of dimensionality
-reduction -- in particular for classification problems under the zero-one loss.
-In this paper we target classification problems by proposing a straightforward
-alternative to PCA that aims to minimize the difference in margin distribution
-between the original and the projected data. Extensive experiments show that
-our simple approach typically outperforms PCA on any particular dataset, in
-terms of classification error, though this difference is not always
-statistically significant, and despite being a filter method is frequently
-competitive with Partial Least Squares (PLS) and Lasso on a wide range of
-datasets.
-",1,0,0,1,0,0
-18582,A New self-propelled magnetic bearing with helical windings,"  In this work a design is proposed for an active, permanent magnet based,
-self-propelled magnetic bearing i.e. levitating motor having the following
-features : (a) simple winding structure, (b) high load supporting capacity, (c)
-no eccentricity sensors, (d) stable confinement in all translational
-dimensions, (e) stable confinement in all rotational dimensions and (f) high
-efficiency. This design uses an architecture consisting of a helically wound
-three-phase stator, and a rotor with the magnets also arranged in a helical
-manner. Active control is used to excite the rotor at a torque angle lying in
-the second quadrant. This torque angle is independent of the rotor's position
-inside the stator cavity hence the control algorithm is similar to that of a
-conventional permanent magnet synchronous motor. It is motivated through a
-physical argument that the bearing rotor develops a lift force proportional to
-the output torque and that it remains stably confined in space. These
-assertions are then proved rigorously through a calculation of the magnetic
-fields, forces and torques. The stiffness matrix of the system is presented and
-a discussion of stable and unstable operating regions is given.
-",0,1,0,0,0,0
-18583,A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection,"  How do we learn an object detector that is invariant to occlusions and
-deformations? Our current solution is to use a data-driven strategy -- collect
-large-scale datasets which have object instances under different conditions.
-The hope is that the final classifier can use these examples to learn
-invariances. But is it really possible to see all the occlusions in a dataset?
-We argue that like categories, occlusions and object deformations also follow a
-long-tail. Some occlusions and deformations are so rare that they hardly
-happen; yet we want to learn a model invariant to such occurrences. In this
-paper, we propose an alternative solution. We propose to learn an adversarial
-network that generates examples with occlusions and deformations. The goal of
-the adversary is to generate examples that are difficult for the object
-detector to classify. In our framework both the original detector and adversary
-are learned in a joint manner. Our experimental results indicate a 2.3% mAP
-boost on VOC07 and a 2.6% mAP boost on VOC2012 object detection challenge
-compared to the Fast-RCNN pipeline. We also release the code for this paper.
-",1,0,0,0,0,0
-18584,Non-thermalization in trapped atomic ion spin chains,"  Linear arrays of trapped and laser cooled atomic ions are a versatile
-platform for studying emergent phenomena in strongly-interacting many-body
-systems. Effective spins are encoded in long-lived electronic levels of each
-ion and made to interact through laser mediated optical dipole forces. The
-advantages of experiments with cold trapped ions, including high spatiotemporal
-resolution, decoupling from the external environment, and control over the
-system Hamiltonian, are used to measure quantum effects not always accessible
-in natural condensed matter samples. In this review we highlight recent work
-using trapped ions to explore a variety of non-ergodic phenomena in long-range
-interacting spin-models which are heralded by memory of out-of-equilibrium
-initial conditions. We observe long-lived memory in static magnetizations for
-quenched many-body localization and prethermalization, while memory is
-preserved in the periodic oscillations of a driven discrete time crystal state.
-",0,1,0,0,0,0
-18585,Generalized Uniformity Testing,"  In this work, we revisit the problem of uniformity testing of discrete
-probability distributions. A fundamental problem in distribution testing,
-testing uniformity over a known domain has been addressed over a significant
-line of works, and is by now fully understood.
-The complexity of deciding whether an unknown distribution is uniform over
-its unknown (and arbitrary) support, however, is much less clear. Yet, this
-task arises as soon as no prior knowledge on the domain is available, or
-whenever the samples originate from an unknown and unstructured universe. In
-this work, we introduce and study this generalized uniformity testing question,
-and establish nearly tight upper and lower bound showing that -- quite
-surprisingly -- its sample complexity significantly differs from the
-known-domain case. Moreover, our algorithm is intrinsically adaptive, in
-contrast to the overwhelming majority of known distribution testing algorithms.
-",1,0,1,1,0,0
-18586,"Refraction in exoplanet atmospheres: Photometric signatures, implications for transmission spectroscopy, and search in Kepler data","  Refraction deflects photons that pass through atmospheres, which affects
-transit light curves. Refraction thus provides an avenue to probe physical
-properties of exoplanet atmospheres and to constrain the presence of clouds and
-hazes. In addition, an effective surface can be imposed by refraction, thereby
-limiting the pressure levels probed by transmission spectroscopy. The main
-objective of the paper is to model the effects of refraction on photometric
-light curves for realistic planets and to explore the dependencies on
-atmospheric physical parameters. We also explore under which circumstances
-transmission spectra are significantly affected by refraction. Finally, we
-search for refraction signatures in photometric residuals in Kepler data. We
-use the model of Hui & Seager (2002) to compute deflection angles and
-refraction transit light curves, allowing us to explore the parameter space of
-atmospheric properties. The observational search is performed by stacking large
-samples of transit light curves from Kepler. We find that out-of-transit
-refraction shoulders are the most easily observable features, which can reach
-peak amplitudes of ~10 parts per million (ppm) for planets around Sun-like
-stars. More typical amplitudes are a few ppm or less for Jovians and at the
-sub-ppm level for super-Earths. Interestingly, the signal-to-noise ratio of any
-refraction residuals for planets orbiting Sun-like hosts are expected to be
-similar for planets orbiting red dwarfs. We also find that the maximum depth
-probed by transmission spectroscopy is not limited by refraction for weakly
-lensing planets, but that the incidence of refraction can vary significantly
-for strongly lensing planets. We find no signs of refraction features in the
-stacked Kepler light curves, which is in agreement with our model predictions.
-",0,1,0,0,0,0
-18587,Balancing Efficiency and Coverage in Human-Robot Dialogue Collection,"  We describe a multi-phased Wizard-of-Oz approach to collecting human-robot
-dialogue in a collaborative search and navigation task. The data is being used
-to train an initial automated robot dialogue system to support collaborative
-exploration tasks. In the first phase, a wizard freely typed robot utterances
-to human participants. For the second phase, this data was used to design a GUI
-that includes buttons for the most common communications, and templates for
-communications with varying parameters. Comparison of the data gathered in
-these phases show that the GUI enabled a faster pace of dialogue while still
-maintaining high coverage of suitable responses, enabling more efficient
-targeted data collection, and improvements in natural language understanding
-using GUI-collected data. As a promising first step towards interactive
-learning, this work shows that our approach enables the collection of useful
-training data for navigation-based HRI tasks.
-",1,0,0,0,0,0
-18588,Reinforcement Learning Based Argument Component Detection,"  Argument component detection (ACD) is an important sub-task in argumentation
-mining. ACD aims at detecting and classifying different argument components in
-natural language texts. Historical annotations (HAs) are important features the
-human annotators consider when they manually perform the ACD task. However, HAs
-are largely ignored by existing automatic ACD techniques. Reinforcement
-learning (RL) has proven to be an effective method for using HAs in some
-natural language processing tasks. In this work, we propose a RL-based ACD
-technique, and evaluate its performance on two well-annotated corpora. Results
-suggest that, in terms of classification accuracy, HAs-augmented RL outperforms
-plain RL by at most 17.85%, and outperforms the state-of-the-art supervised
-learning algorithm by at most 11.94%.
-",1,0,0,0,0,0
-18589,VUNet: Dynamic Scene View Synthesis for Traversability Estimation using an RGB Camera,"  We present VUNet, a novel view(VU) synthesis method for mobile robots in
-dynamic environments, and its application to the estimation of future
-traversability. Our method predicts future images for given virtual robot
-velocity commands using only RGB images at previous and current time steps. The
-future images result from applying two types of image changes to the previous
-and current images: 1) changes caused by different camera pose, and 2) changes
-due to the motion of the dynamic obstacles. We learn to predict these two types
-of changes disjointly using two novel network architectures, SNet and DNet. We
-combine SNet and DNet to synthesize future images that we pass to our
-previously presented method GONet to estimate the traversable areas around the
-robot. Our quantitative and qualitative evaluation indicate that our approach
-for view synthesis predicts accurate future images in both static and dynamic
-environments. We also show that these virtual images can be used to estimate
-future traversability correctly. We apply our view synthesis-based
-traversability estimation method to two applications for assisted
-teleoperation.
-",1,0,0,0,0,0
-18590,Bayesian hypothesis tests with diffuse priors: Can we have our cake and eat it too?,"  We introduce a new class of priors for Bayesian hypothesis testing, which we
-name ""cake priors"". These priors circumvent Bartlett's paradox (also called the
-Jeffreys-Lindley paradox); the problem associated with the use of diffuse
-priors leading to nonsensical statistical inferences. Cake priors allow the use
-of diffuse priors (having one's cake) while achieving theoretically justified
-inferences (eating it too). We demonstrate this methodology for Bayesian
-hypotheses tests for scenarios under which the one and two sample t-tests, and
-linear models are typically derived. The resulting Bayesian test statistic
-takes the form of a penalized likelihood ratio test statistic. By considering
-the sampling distribution under the null and alternative hypotheses we show for
-independent identically distributed regular parametric models that Bayesian
-hypothesis tests using cake priors are Chernoff-consistent, i.e., achieve zero
-type I and II errors asymptotically. Lindley's paradox is also discussed. We
-argue that a true Lindley's paradox will only occur with small probability for
-large sample sizes.
-",0,0,1,1,0,0
-18591,On a Novel Speech Representation Using Multitapered Modified Group Delay Function,"  In this paper, a novel multitaper modified group delay function-based
-representation for speech signals is proposed. With a set of phoneme-based
-experiments, it is shown that the proposed method performs better that an
-existing multitaper magnitude (MT-MAG) estimation technique, in terms of
-variance and MSE, both in spectral- and cepstral-domains. In particular, the
-performance of MT-MOGDF is found to be the best with the Thomson tapers.
-Additionally, the utility of the MT-MOGDF technique is highlighted in a speaker
-recognition experimental setup, where an improvement of around $20\%$ compared
-to the next-best technique is obtained. Moreover, the computational
-requirements of the proposed technique is comparable to that of MT-MAG. The
-proposed feature can be used in for many speech-related applications; in
-particular, it is best suited among those that require information of speaker
-and speech.
-",1,0,0,0,0,0
-18592,Persistent Flows and Non-Reciprocal Interactions in Deterministic Networks,"  This paper studies deterministic consensus networks with discrete-time
-dynamics under persistent flows and non-reciprocal agent interactions. An arc
-describing the interaction strength between two agents is said to be persistent
-if its weight function has an infinite $l_1$ norm. We discuss two balance
-conditions on the interactions between agents which generalize the arc-balance
-and cut-balance conditions in the literature respectively. The proposed
-conditions require that such a balance should be satisfied over each time
-window of a fixed length instead of at each time instant. We prove that in both
-cases global consensus is reached if and only if the persistent graph, which
-consists of all the persistent arcs, contains a directed spanning tree. The
-convergence rates of the system to consensus are also provided in terms of the
-interactions between agents having taken place. The results are obtained under
-a weak condition without assuming the existence of a positive lower bound of
-all the nonzero weights of arcs and are compared with the existing results.
-Illustrative examples are provided to show the critical importance of the
-nontrivial lower boundedness of the self-confidence of the agents.
-",1,0,0,0,0,0
-18593,Linking Fluid and Kinetic Scales in Solar Wind Turbulence,"  We investigate possible links between the large-scale and small-scale
-features of solar wind fluctuations across the frequency break separating fluid
-and kinetic regimes. The aim is to correlate the magnetic field fluctuations
-polarization at dissipative scales with the particular state of turbulence
-within the inertial range of fluctuations. We found clear correlations between
-each type of polarization within the kinetic regime and fluid parameters within
-the inertial range. Moreover, for the first time in literature, we showed that
-left-handed and right-handed polarized fluctuations occupy different areas of
-the plasma instabilities-temperature anisotropy plot, as expected for
-Alfv$\acute{\textrm{e}}$n Ion Cyclotron and Kinetic Alfv$\acute{\textrm{e}}$n
-waves, respectively.
-",0,1,0,0,0,0
-18594,Thresholds For Detecting An Anomalous Path From Noisy Environments,"  We consider the ""searching for a trail in a maze"" composite hypothesis
-testing problem, in which one attempts to detect an anomalous directed path in
-a lattice 2D box of side n based on observations on the nodes of the box. Under
-the signal hypothesis, one observes independent Gaussian variables of unit
-variance at all nodes, with zero, mean off the anomalous path and mean \mu_n on
-it. Under the null hypothesis, one observes i.i.d. standard Gaussians on all
-nodes. Arias-Castro et al. (2008) showed that if the unknown directed path
-under the signal hypothesis has known the initial location, then detection is
-possible (in the minimax sense) if \mu_n >> 1/\sqrt log n, while it is not
-possible if \mu_n << 1/ log n\sqrt log log n. In this paper, we show that this
-result continues to hold even when the initial location of the unknown path is
-not known. As is the case with Arias-Castro et al. (2008), the upper bound here
-also applies when the path is undirected. The improvement is achieved by
-replacing the linear detection statistic used in Arias-Castro et al. (2008)
-with a polynomial statistic, which is obtained by employing a multi-scale
-analysis on a quadratic statistic to bootstrap its performance. Our analysis is
-motivated by ideas developed in the context of the analysis of random polymers
-in Lacoin (2010).
-",0,0,1,1,0,0
-18595,On the topology of real Bott manifolds,"  The main aim of this article is to give a necessary and sufficient condition
-for a real Bott manifold to admit a spin structure and further give a
-combinatorial characterization for the spin structure in terms of the
-associated acyclic digraph.
-",0,0,1,0,0,0
-18596,Relative Error Tensor Low Rank Approximation,"  We consider relative error low rank approximation of $tensors$ with respect
-to the Frobenius norm: given an order-$q$ tensor $A \in
-\mathbb{R}^{\prod_{i=1}^q n_i}$, output a rank-$k$ tensor $B$ for which
-$\|A-B\|_F^2 \leq (1+\epsilon)$OPT, where OPT $= \inf_{\textrm{rank-}k~A'}
-\|A-A'\|_F^2$. Despite the success on obtaining relative error low rank
-approximations for matrices, no such results were known for tensors. One
-structural issue is that there may be no rank-$k$ tensor $A_k$ achieving the
-above infinum. Another, computational issue, is that an efficient relative
-error low rank approximation algorithm for tensors would allow one to compute
-the rank of a tensor, which is NP-hard. We bypass these issues via (1)
-bicriteria and (2) parameterized complexity solutions:
-(1) We give an algorithm which outputs a rank $k' = O((k/\epsilon)^{q-1})$
-tensor $B$ for which $\|A-B\|_F^2 \leq (1+\epsilon)$OPT in $nnz(A) + n \cdot
-\textrm{poly}(k/\epsilon)$ time in the real RAM model. Here $nnz(A)$ is the
-number of non-zero entries in $A$.
-(2) We give an algorithm for any $\delta >0$ which outputs a rank $k$ tensor
-$B$ for which $\|A-B\|_F^2 \leq (1+\epsilon)$OPT and runs in $ ( nnz(A) + n
-\cdot \textrm{poly}(k/\epsilon) + \exp(k^2/\epsilon) ) \cdot n^\delta$ time in
-the unit cost RAM model.
-For outputting a rank-$k$ tensor, or even a bicriteria solution with
-rank-$Ck$ for a certain constant $C > 1$, we show a $2^{\Omega(k^{1-o(1)})}$
-time lower bound under the Exponential Time Hypothesis.
-Our results give the first relative error low rank approximations for tensors
-for a large number of robust error measures for which nothing was known, as
-well as column row and tube subset selection. We also obtain new results for
-matrices, such as $nnz(A)$-time CUR decompositions, improving previous
-$nnz(A)\log n$-time algorithms, which may be of independent interest.
-",1,0,0,0,0,0
-18597,Bayesian Boolean Matrix Factorisation,"  Boolean matrix factorisation aims to decompose a binary data matrix into an
-approximate Boolean product of two low rank, binary matrices: one containing
-meaningful patterns, the other quantifying how the observations can be
-expressed as a combination of these patterns. We introduce the OrMachine, a
-probabilistic generative model for Boolean matrix factorisation and derive a
-Metropolised Gibbs sampler that facilitates efficient parallel posterior
-inference. On real world and simulated data, our method outperforms all
-currently existing approaches for Boolean matrix factorisation and completion.
-This is the first method to provide full posterior inference for Boolean Matrix
-factorisation which is relevant in applications, e.g. for controlling false
-positive rates in collaborative filtering and, crucially, improves the
-interpretability of the inferred patterns. The proposed algorithm scales to
-large datasets as we demonstrate by analysing single cell gene expression data
-in 1.3 million mouse brain cells across 11 thousand genes on commodity
-hardware.
-",1,0,0,1,0,0
-18598,Global existence and convergence of $Q$-curvature flow on manifolds of even dimension,"  Using a negative gradient flow approach, we generalize and unify some
-existence theorems for the problem of prescribing $Q$-curvature first by Baird,
-Fardoun, and Regbaoui (Calc. Var. 27 75-104) for $4$-manifolds with a possible
-sign-changing curvature candidate then by Brendle (Ann. Math. 158 323-343) for
-$n$-manifolds with even $n$ with positive curvature candidate to the case of
-$n$-manifolds of all even dimension with sign-changing curvature candidates.
-Making use of the \L ojasiewicz--Simon inequality, we also address the rate of
-the convergence.
-",0,0,1,0,0,0
-18599,Group Embeddings with Algorithmic Properties,"  We show that every countable group H with solvable word problem (=computable
-group) can be subnormally embedded into a 2-generated group G which also has
-solvable word problem. Moreover, the membership problem for H < G is also
-solvable. We also give estimates of time and space complexity of the word
-problem in G and of the membership problem for H < G.
-",0,0,1,0,0,0
-18600,General purpose graphics-processing-unit implementation of cosmological domain wall network evolution,"  Topological defects unavoidably form at symmetry breaking phase transitions
-in the early Universe. To probe the parameter space of theoretical models and
-set tighter experimental constraints (exploiting the recent advances in
-astrophysical observations), one requires more and more demanding simulations,
-and therefore more hardware resources and computation time. Improving the speed
-and efficiency of existing codes is essential. Here we present a General
-Purpose Graphics Processing Unit implementation of the canonical
-Press-Ryden-Spergel algorithm for the evolution of cosmological domain wall
-networks. This is ported to the Open Computing Language standard, and as a
-consequence significant speed-ups are achieved both in 2D and 3D simulations.
-",0,1,0,0,0,0
-18601,A Consistent Bayesian Formulation for Stochastic Inverse Problems Based on Push-forward Measures,"  We formulate, and present a numerical method for solving, an inverse problem
-for inferring parameters of a deterministic model from stochastic observational
-data (quantities of interest). The solution, given as a probability measure, is
-derived using a Bayesian updating approach for measurable maps that finds a
-posterior probability measure, that when propagated through the deterministic
-model produces a push-forward measure that exactly matches the observed
-probability measure on the data. Our approach for finding such posterior
-measures, which we call consistent Bayesian inference, is simple and only
-requires the computation of the push-forward probability measure induced by the
-combination of a prior probability measure and the deterministic model. We
-establish existence and uniqueness of observation-consistent posteriors and
-present stability and error analysis. We also discuss the relationships between
-consistent Bayesian inference, classical/statistical Bayesian inference, and a
-recently developed measure-theoretic approach for inference. Finally,
-analytical and numerical results are presented to highlight certain properties
-of the consistent Bayesian approach and the differences between this approach
-and the two aforementioned alternatives for inference.
-",0,0,1,1,0,0
-18602,Coherent State Mapping Ring-Polymer Molecular Dynamics for Non-Adiabatic quantum propagations,"  We introduce the coherent state mapping ring-polymer molecular dynamics
-(CS-RPMD), a new method that accurately describes electronic non-adiabatic
-dynamics with explicit nuclear quantization. This new approach is derived by
-using coherent state mapping representation for the electronic degrees of
-freedom (DOF) and the ring-polymer path-integral representation for the nuclear
-DOF. CS-RPMD Hamiltonian does not contain any inter-bead coupling term in the
-state-dependent potential, which is a key feature that ensures correct
-electronic Rabi oscillations. Hamilton's equation of motion is used to sample
-initial configurations and propagate the trajectories, preserving the
-distribution with classical symplectic evolution. In the special one-bead limit
-for mapping variables, CS-RPMD preserves the detailed balance. Numerical tests
-of this method with a two-state model system show a very good agreement with
-exact quantum results over a broad range of electronic couplings.
-",0,1,0,0,0,0
-18603,Stochastic Gradient Descent in Continuous Time: A Central Limit Theorem,"  Stochastic gradient descent in continuous time (SGDCT) provides a
-computationally efficient method for the statistical learning of
-continuous-time models, which are widely used in science, engineering, and
-finance. The SGDCT algorithm follows a (noisy) descent direction along a
-continuous stream of data. The parameter updates occur in continuous time and
-satisfy a stochastic differential equation. This paper analyzes the asymptotic
-convergence rate of the SGDCT algorithm by proving a central limit theorem
-(CLT) for strongly convex objective functions and, under slightly stronger
-conditions, for non-convex objective functions as well. An L$^p$ convergence
-rate is also proven for the algorithm in the strongly convex case. The
-mathematical analysis lies at the intersection of stochastic analysis and
-statistical learning.
-",0,0,1,1,0,0
-18604,KMS states on $C^*$-algebras associated to a family of $*$-commuting local homeomorphisms,"  We consider a family of $*$-commuting local homeomorphisms on a compact
-space, and build a compactly aligned product system of Hilbert bimodules (in
-the sense of Fowler). This product system has a Nica-Toeplitz algebra and a
-Cuntz-Pimsner algebra. Both algebras carry a gauge action of a
-higher-dimensional torus, and there are many possible dynamics obtained by
-composing with different embeddings of the real line in this torus. We study
-the KMS states of these dynamics. For large inverse temperatures, we describe
-the simplex of KMS states on the Nica-Toeplitz algebra. To study KMS states for
-smaller inverse temperature, we consider a preferred dynamics for which there
-is a single critical inverse temperature. We find a KMS state on the
-Nica-Toeplitz algebra at this critical inverse temperature which factors
-through the Cuntz-Pimsner algebra. We then illustrate our results by
-considering backward shifts on the infinite-path spaces of a class of
-$k$-graphs.
-",0,0,1,0,0,0
-18605,Multi-Labelled Value Networks for Computer Go,"  This paper proposes a new approach to a novel value network architecture for
-the game Go, called a multi-labelled (ML) value network. In the ML value
-network, different values (win rates) are trained simultaneously for different
-settings of komi, a compensation given to balance the initiative of playing
-first. The ML value network has three advantages, (a) it outputs values for
-different komi, (b) it supports dynamic komi, and (c) it lowers the mean
-squared error (MSE). This paper also proposes a new dynamic komi method to
-improve game-playing strength. This paper also performs experiments to
-demonstrate the merits of the architecture. First, the MSE of the ML value
-network is generally lower than the value network alone. Second, the program
-based on the ML value network wins by a rate of 67.6% against the program based
-on the value network alone. Third, the program with the proposed dynamic komi
-method significantly improves the playing strength over the baseline that does
-not use dynamic komi, especially for handicap games. To our knowledge, up to
-date, no handicap games have been played openly by programs using value
-networks. This paper provides these programs with a useful approach to playing
-handicap games.
-",1,0,0,0,0,0
-18606,Lifting high-dimensional nonlinear models with Gaussian regressors,"  We study the problem of recovering a structured signal $\mathbf{x}_0$ from
-high-dimensional data $\mathbf{y}_i=f(\mathbf{a}_i^T\mathbf{x}_0)$ for some
-nonlinear (and potentially unknown) link function $f$, when the regressors
-$\mathbf{a}_i$ are iid Gaussian. Brillinger (1982) showed that ordinary
-least-squares estimates $\mathbf{x}_0$ up to a constant of proportionality
-$\mu_\ell$, which depends on $f$. Recently, Plan & Vershynin (2015) extended
-this result to the high-dimensional setting deriving sharp error bounds for the
-generalized Lasso. Unfortunately, both least-squares and the Lasso fail to
-recover $\mathbf{x}_0$ when $\mu_\ell=0$. For example, this includes all even
-link functions. We resolve this issue by proposing and analyzing an alternative
-convex recovery method. In a nutshell, our method treats such link functions as
-if they were linear in a lifted space of higher-dimension. Interestingly, our
-error analysis captures the effect of both the nonlinearity and the problem's
-geometry in a few simple summary parameters.
-",0,0,0,1,0,0
-18607,Functional Conceptual Substratum as a New Cognitive Mechanism for Mathematical Creation,"  We describe a new cognitive ability, i.e., functional conceptual substratum,
-used implicitly in the generation of several mathematical proofs and
-definitions. Furthermore, we present an initial (first-order) formalization of
-this mechanism together with its relation to classic notions like primitive
-positive definability and Diophantiveness. Additionally, we analyze the
-semantic variability of functional conceptual substratum when small syntactic
-modifications are done. Finally, we describe mathematically natural inference
-rules for definitions inspired by functional conceptual substratum and show
-that they are sound and complete w.r.t. standard calculi.
-",0,0,1,0,0,0
-18608,Applying Gromov's Amenable Localization to Geodesic Flows,"  Let $M$ be a compact connected smooth Riemannian $n$-manifold with boundary.
-We combine Gromov's amenable localization technique with the Poincaré
-duality to study the traversally generic geodesic flows on $SM$, the space of
-the spherical tangent bundle. Such flows generate stratifications of $SM$,
-governed by rich universal combinatorics. The stratification reflects the ways
-in which the geodesic flow trajectories interact with the boundary $\d(SM)$.
-Specifically, we get lower estimates of the numbers of connected components of
-these flow-generated strata of any given codimension $k$. These universal
-bounds are expressed in terms of the normed homology $H_k(M; \R)$ and $H_k(DM;
-\R)$, where $DM = M\cup_{\d M} M$ denotes the double of $M$. The norms here are
-the Gromov simplicial semi-norms in homology. The more complex the metric on
-$M$ is, the more numerous the strata of $SM$ and $S(DM)$ are. So one may regard
-our estimates as analogues of the Morse inequalities for the geodesics on
-manifolds with boundary.
-It turns out that some close relatives of the normed homology spaces form
-obstructions to the existence of globally $k$-convex traversally generic
-metrics on $M$.
-",0,0,1,0,0,0
-18609,Modeling the Ellsberg Paradox by Argument Strength,"  We present a formal measure of argument strength, which combines the ideas
-that conclusions of strong arguments are (i) highly probable and (ii) their
-uncertainty is relatively precise. Likewise, arguments are weak when their
-conclusion probability is low or when it is highly imprecise. We show how the
-proposed measure provides a new model of the Ellsberg paradox. Moreover, we
-further substantiate the psychological plausibility of our approach by an
-experiment (N = 60). The data show that the proposed measure predicts human
-inferences in the original Ellsberg task and in corresponding argument strength
-tasks. Finally, we report qualitative data taken from structured interviews on
-folk psychological conceptions on what argument strength means.
-",1,0,1,0,0,0
-18610,"Correction to the paper ""Some remarks on Davie's uniqueness theorem""","  The property 4 in Proposition 2.3 from the paper ""Some remarks on Davie's
-uniqueness theorem"" is replaced with a weaker assertion which is sufficient for
-the proof of the main results. Technical details and improvements are given.
-",0,0,1,0,0,0
-18611,An Estimation and Analysis Framework for the Rasch Model,"  The Rasch model is widely used for item response analysis in applications
-ranging from recommender systems to psychology, education, and finance. While a
-number of estimators have been proposed for the Rasch model over the last
-decades, the available analytical performance guarantees are mostly asymptotic.
-This paper provides a framework that relies on a novel linear minimum
-mean-squared error (L-MMSE) estimator which enables an exact, nonasymptotic,
-and closed-form analysis of the parameter estimation error under the Rasch
-model. The proposed framework provides guidelines on the number of items and
-responses required to attain low estimation errors in tests or surveys. We
-furthermore demonstrate its efficacy on a number of real-world collaborative
-filtering datasets, which reveals that the proposed L-MMSE estimator performs
-on par with state-of-the-art nonlinear estimators in terms of predictive
-performance.
-",0,0,0,1,0,0
-18612,Do planets remember how they formed?,"  One of the most directly observable features of a transiting multi-planet
-system is their size-ordering when ranked in orbital separation. Kepler has
-revealed a rich diversity of outcomes, from perfectly ordered systems, like
-Kepler-80, to ostensibly disordered systems, like Kepler-20. Under the
-hypothesis that systems are born via preferred formation pathways, one might
-reasonably expect non-random size-orderings reflecting these processes.
-However, subsequent dynamical evolution, often chaotic and turbulent in nature,
-may erode this information and so here we ask - do systems remember how they
-formed? To address this, we devise a model to define the entropy of a planetary
-system's size-ordering, by first comparing differences between neighboring
-planets and then extending to accommodate differences across the chain. We
-derive closed-form solutions for many of the micro state occupancies and
-provide public code with look-up tables to compute entropy for up to ten-planet
-systems. All three proposed entropy definitions exhibit the expected property
-that their credible interval increases with respect to a proxy for time. We
-find that the observed Kepler multis display a highly significant deficit in
-entropy compared to a randomly generated population. Incorporating a filter for
-systems deemed likely to be dynamically packed, we show that this result is
-robust against the possibility of missing planets too. Put together, our work
-establishes that Kepler systems do indeed remember something of their younger
-years and highlights the value of information theory for exoplanetary science.
-",0,1,0,0,0,0
-18613,Demarcating circulation regimes of synchronously rotating terrestrial planets within the habitable zone,"  We investigate the atmospheric dynamics of terrestrial planets in synchronous
-rotation within the habitable zone of low-mass stars using the Community
-Atmosphere Model (CAM). The surface temperature contrast between day and night
-hemispheres decreases with an increase in incident stellar flux, which is
-opposite the trend seen on gas giants. We define three dynamical regimes in
-terms of the equatorial Rossby deformation radius and the Rhines length. The
-slow rotation regime has a mean zonal circulation that spans from day to night
-side, with both the Rossby deformation radius and the Rhines length exceeding
-planetary radius, which occurs for planets around stars with effective
-temperatures of 3300 K to 4500 K (rotation period > 20 days). Rapid rotators
-have a mean zonal circulation that partially spans a hemisphere and with banded
-cloud formation beneath the substellar point, with the Rossby deformation
-radius is less than planetary radius, which occurs for planets orbiting stars
-with effective temperatures of less than 3000 K (rotation period < 5 days). In
-between is the Rhines rotation regime, which retains a thermally-direct
-circulation from day to night side but also features midlatitude
-turbulence-driven zonal jets. Rhines rotators occur for planets around stars in
-the range of 3000 K to 3300 K (rotation period ~ 5 to 20 days), where the
-Rhines length is greater than planetary radius but the Rossby deformation
-radius is less than planetary radius. The dynamical state can be
-observationally inferred from comparing the morphology of the thermal emission
-phase curves of synchronously rotating planets.
-",0,1,0,0,0,0
-18614,Versatile Large-Area Custom-Feature van der Waals Epitaxy of Topological Insulators,"  As the focus of applied research in topological insulators (TI) evolves, the
-need to synthesize large-area TI films for practical device applications takes
-center stage. However, constructing scalable and adaptable processes for
-high-quality TI compounds remains a challenge. To this end, a versatile van der
-Waals epitaxy (vdWE) process for custom-feature Bismuth Telluro-Sulfide TI
-growth and fabrication is presented, achieved through selective-area
-fluorination and modification of surface free-energy on mica. The TI features
-grow epitaxially in large single-crystal trigonal domains, exhibiting armchair
-or zigzag crystalline edges highly oriented with the underlying mica lattice
-and only two preferred domain orientations mirrored at $180^\circ$. As-grown
-feature thickness dependence on lateral dimensions and denuded zones at
-boundaries are observed, as explained by a semi-empirical two-species surface
-migration model with robust estimates of growth parameters and elucidating the
-role of selective-area surface modification. Topological surface states
-contribute up to 60% of device conductance at room-temperature, indicating
-excellent electronic quality. High-yield microfabrication and the adaptable
-vdWE growth mechanism with readily alterable precursor and substrate
-combinations, lend the process versatility to realize crystalline TI synthesis
-in arbitrary shapes and arrays suitable for facile integration with processes
-ranging from rapid prototyping to scalable manufacturing.
-",0,1,0,0,0,0
-18615,Sharp off-diagonal weighted norm estimates for the Bergman projection,"  We prove that for $1<p\le q<\infty$, $qp\geq {p'}^2$ or $p'q'\geq q^2$,
-$\frac{1}{p}+\frac{1}{p'}=\frac{1}{q}+\frac{1}{q'}=1$, $$\|\omega
-P_\alpha(f)\|_{L^p(\mathcal{H},y^{\alpha+(2+\alpha)(\frac{q}{p}-1)}dxdy)}\le
-C_{p,q,\alpha}[\omega]_{B_{p,q,\alpha}}^{(\frac{1}{p'}+\frac{1}{q})\max\{1,\frac{p'}{q}\}}\|\omega
-f\|_{L^p(\mathcal{H},y^{\alpha}dxdy)}$$ where $P_\alpha$ is the weighted
-Bergman projection of the upper-half plane $\mathcal{H}$, and
-$$[\omega]_{B_{p,q,\alpha}}:=\sup_{I\subset
-\mathbb{R}}\left(\frac{1}{|I|^{2+\alpha}}\int_{Q_I}\omega^{q}dV_\alpha\right)\left(\frac{1}{|I|^{2+\alpha}}\int_{Q_I}\omega^{-p'}dV_\alpha\right)^{\frac{q}{p'}},$$
-with $Q_I=\{z=x+iy\in \mathbb{C}: x\in I, 0<y<|I|\}$.
-",0,0,1,0,0,0
-18616,An Automated Scalable Framework for Distributing Radio Astronomy Processing Across Clusters and Clouds,"  The Low Frequency Array (LOFAR) radio telescope is an international aperture
-synthesis radio telescope used to study the Universe at low frequencies. One of
-the goals of the LOFAR telescope is to conduct deep wide-field surveys. Here we
-will discuss a framework for the processing of the LOFAR Two Meter Sky Survey
-(LoTSS). This survey will produce close to 50 PB of data within five years.
-These data rates require processing at locations with high-speed access to the
-archived data. To complete the LoTSS project, the processing software needs to
-be made portable and moved to clusters with a high bandwidth connection to the
-data archive. This work presents a framework that makes the LOFAR software
-portable, and is used to scale out LOFAR data reduction. Previous work was
-successful in preprocessing LOFAR data on a cluster of isolated nodes. This
-framework builds upon it and and is currently operational. It is designed to be
-portable, scalable, automated and general. This paper describes its design and
-high level operation and the initial results processing LoTSS data.
-",0,1,0,0,0,0
-18617,Learning multiple visual domains with residual adapters,"  There is a growing interest in learning data representations that work well
-for many different types of problems and data. In this paper, we look in
-particular at the task of learning a single visual representation that can be
-successfully utilized in the analysis of very different types of images, from
-dog breeds to stop signs and digits. Inspired by recent work on learning
-networks that predict the parameters of another, we develop a tunable deep
-network architecture that, by means of adapter residual modules, can be steered
-on the fly to diverse visual domains. Our method achieves a high degree of
-parameter sharing while maintaining or even improving the accuracy of
-domain-specific representations. We also introduce the Visual Decathlon
-Challenge, a benchmark that evaluates the ability of representations to capture
-simultaneously ten very different visual domains and measures their ability to
-recognize well uniformly.
-",1,0,0,1,0,0
-18618,Cosmic quantum optical probing of quantum gravity through a gravitational lensLens,"  We consider the nonunitary quantum dynamics of neutral massless scalar
-particles used to model photons around a massive gravitational lens. The
-gravitational interaction between the lensing mass and asymptotically free
-particles is described by their second-quantized scattering wavefunctions.
-Remarkably, the zero-point spacetime fluctuations can induce significant
-decoherence of the scattered states with spontaneous emission of gravitons,
-thereby reducing the particles' coherence as well as energy. This new effect
-suggests that, when photon polarizations are negligible, such quantum gravity
-phenomena could lead to measurable anomalous redshift of recently studied
-astrophysical lasers through a gravitational lens in the range of black holes
-and galaxy clusters.
-",0,1,0,0,0,0
-18619,Combinatorial Miller-Hagberg Algorithm for Randomization of Dense Networks,"  We propose a slightly revised Miller-Hagberg (MH) algorithm that efficiently
-generates a random network from a given expected degree sequence. The revision
-was to replace the approximated edge probability between a pair of nodes with a
-combinatorically calculated edge probability that better captures the
-likelihood of edge presence especially where edges are dense. The computational
-complexity of this combinatorial MH algorithm is still in the same order as the
-original one. We evaluated the proposed algorithm through several numerical
-experiments. The results demonstrated that the proposed algorithm was
-particularly good at accurately representing high-degree nodes in dense,
-heterogeneous networks. This algorithm may be a useful alternative of other
-more established network randomization methods, given that the data are
-increasingly becoming larger and denser in today's network science research.
-",1,0,0,0,0,0
-18620,Topological Insulators in Random Lattices,"  Our understanding of topological insulators is based on an underlying
-crystalline lattice where the local electronic degrees of freedom at different
-sites hybridize with each other in ways that produce nontrivial band topology,
-and the search for material systems to realize such phases have been strongly
-influenced by this. Here we theoretically demonstrate topological insulators in
-systems with a random distribution of sites in space, i. e., a random lattice.
-This is achieved by constructing hopping models on random lattices whose ground
-states possess nontrivial topological nature (characterized e. g., by Bott
-indices) that manifests as quantized conductances in systems with a boundary.
-By tuning parameters such as the density of sites (for a given range of fermion
-hopping), we can achieve transitions from trivial to topological phases. We
-discuss interesting features of these transitions. In two spatial dimensions,
-we show this for all five symmetry classes (A, AII, D, DIII and C) that are
-known to host nontrivial topology in crystalline systems. We expect similar
-physics to be realizable in any dimension and provide an explicit example of a
-$Z_2$ topological insulator on a random lattice in three spatial dimensions.
-Our study not only provides a deeper understanding of the topological phases of
-non-interacting fermions, but also suggests new directions in the pursuit of
-the laboratory realization of topological quantum matter.
-",0,1,0,0,0,0
-18621,DiGrad: Multi-Task Reinforcement Learning with Shared Actions,"  Most reinforcement learning algorithms are inefficient for learning multiple
-tasks in complex robotic systems, where different tasks share a set of actions.
-In such environments a compound policy may be learnt with shared neural network
-parameters, which performs multiple tasks concurrently. However such compound
-policy may get biased towards a task or the gradients from different tasks
-negate each other, making the learning unstable and sometimes less data
-efficient. In this paper, we propose a new approach for simultaneous training
-of multiple tasks sharing a set of common actions in continuous action spaces,
-which we call as DiGrad (Differential Policy Gradient). The proposed framework
-is based on differential policy gradients and can accommodate multi-task
-learning in a single actor-critic network. We also propose a simple heuristic
-in the differential policy gradient update to further improve the learning. The
-proposed architecture was tested on 8 link planar manipulator and 27 degrees of
-freedom(DoF) Humanoid for learning multi-goal reachability tasks for 3 and 2
-end effectors respectively. We show that our approach supports efficient
-multi-task learning in complex robotic systems, outperforming related methods
-in continuous action spaces.
-",1,0,0,1,0,0
-18622,Narratives of Quantum Theory in the Age of Quantum Technologies,"  Quantum technologies can be presented to the public with or without
-introducing a strange trait of quantum theory responsible for their
-non-classical efficiency. Traditionally the message was centered on the
-superposition principle, while entanglement and properties such as
-contextuality have been gaining ground recently. A less theoretical approach is
-focused on simple protocols that enable technological applications. It results
-in a pragmatic narrative built with the help of the resource paradigm and
-principle-based reconstructions. I discuss the advantages and weaknesses of
-these methods. To illustrate the importance of new metaphors beyond the
-Schrödinger cat, I briefly describe a non-mathematical narrative about
-entanglement that conveys an idea of some of its unusual properties. If quantum
-technologists are to succeed in building trust in their work, they ought to
-provoke an aesthetic perception in the public commensurable with the
-mathematical beauty of quantum theory experienced by the physicist. The power
-of the narrative method lies in its capacity to do so.
-",0,1,0,0,0,0
-18623,Is Information in the Brain Represented in Continuous or Discrete Form?,"  The question of continuous-versus-discrete information representation in the
-brain is a fundamental yet unresolved physiological question. Historically,
-most analyses assume a continuous representation without considering the
-alternative possibility of a discrete representation. Our work explores the
-plausibility of both representations, and answers the question from a
-communications engineering perspective. Drawing on the well-established
-Shannon's communications theory, we posit that information in the brain is
-represented in a discrete form. Using a computer simulation, we show that
-information cannot be communicated reliably between neurons using a continuous
-representation, due to the presence of noise; neural information has to be in a
-discrete form. In addition, we designed 3 (human) behavioral experiments on
-probability estimation and analyzed the data using a novel discrete (quantized)
-model of probability. Under a discrete model of probability, two distinct
-probabilities (say, 0.57 and 0.58) are treated indifferently. We found that
-data from all participants were better fit to discrete models than continuous
-ones. Furthermore, we re-analyzed the data from a published (human) behavioral
-study on intertemporal choice using a novel discrete (quantized) model of
-intertemporal choice. Under such a model, two distinct time delays (say, 16
-days and 17 days) are treated indifferently. We found corroborating results,
-showing that data from all participants were better fit to discrete models than
-continuous ones. In summary, all results reported here support our discrete
-hypothesis of information representation in the brain, which signifies a major
-demarcation from the current understanding of the brain's physiology.
-",0,0,0,0,1,0
-18624,Real-Time Background Subtraction Using Adaptive Sampling and Cascade of Gaussians,"  Background-Foreground classification is a fundamental well-studied problem in
-computer vision. Due to the pixel-wise nature of modeling and processing in the
-algorithm, it is usually difficult to satisfy real-time constraints. There is a
-trade-off between the speed (because of model complexity) and accuracy.
-Inspired by the rejection cascade of Viola-Jones classifier, we decompose the
-Gaussian Mixture Model (GMM) into an adaptive cascade of classifiers. This way
-we achieve a good improvement in speed without compensating for accuracy. In
-the training phase, we learn multiple KDEs for different durations to be used
-as strong prior distribution and detect probable oscillating pixels which
-usually results in misclassifications. We propose a confidence measure for the
-classifier based on temporal consistency and the prior distribution. The
-confidence measure thus derived is used to adapt the learning rate and the
-thresholds of the model, to improve accuracy. The confidence measure is also
-employed to perform temporal and spatial sampling in a principled way. We
-demonstrate a speed-up factor of 5x to 10x and 17 percent average improvement
-in accuracy over several standard videos.
-",1,0,0,1,0,0
-18625,Multilevel nested simulation for efficient risk estimation,"  We investigate the problem of computing a nested expectation of the form
-$\mathbb{P}[\mathbb{E}[X|Y]
-\!\geq\!0]\!=\!\mathbb{E}[\textrm{H}(\mathbb{E}[X|Y])]$ where $\textrm{H}$ is
-the Heaviside function. This nested expectation appears, for example, when
-estimating the probability of a large loss from a financial portfolio. We
-present a method that combines the idea of using Multilevel Monte Carlo (MLMC)
-for nested expectations with the idea of adaptively selecting the number of
-samples in the approximation of the inner expectation, as proposed by (Broadie
-et al., 2011). We propose and analyse an algorithm that adaptively selects the
-number of inner samples on each MLMC level and prove that the resulting MLMC
-method with adaptive sampling has an $\mathcal{O}\left(
-\varepsilon^{-2}|\log\varepsilon|^2 \right)$ complexity to achieve a root
-mean-squared error $\varepsilon$. The theoretical analysis is verified by
-numerical experiments on a simple model problem. We also present a stochastic
-root-finding algorithm that, combined with our adaptive methods, can be used to
-compute other risk measures such as Value-at-Risk (VaR) and Conditional
-Value-at-Risk (CVaR), with the latter being achieved with
-$\mathcal{O}\left(\varepsilon^{-2}\right)$ complexity.
-",0,0,0,0,0,1
-18626,Construction of a relativistic Ornstein-Uhlenbeck process,"  Based on a version of Dudley's Wiener process on the mass shell in the
-momentum Minkowski space of a massive point particle, a model of a relativistic
-Ornstein--Uhlenbeck process is constructed by addition of a specific drift
-term. The invariant distribution of this momentum process as well as other
-associated processes are computed.
-",0,0,1,0,0,0
-18627,Safe Trajectory Synthesis for Autonomous Driving in Unforeseen Environments,"  Path planning for autonomous vehicles in arbitrary environments requires a
-guarantee of safety, but this can be impractical to ensure in real-time when
-the vehicle is described with a high-fidelity model. To address this problem,
-this paper develops a method to perform trajectory design by considering a
-low-fidelity model that accounts for model mismatch. The presented method
-begins by computing a conservative Forward Reachable Set (FRS) of a
-high-fidelity model's trajectories produced when tracking trajectories of a
-low-fidelity model over a finite time horizon. At runtime, the vehicle
-intersects this FRS with obstacles in the environment to eliminate trajectories
-that can lead to a collision, then selects an optimal plan from the remaining
-safe set. By bounding the time for this set intersection and subsequent path
-selection, this paper proves a lower bound for the FRS time horizon and sensing
-horizon to guarantee safety. This method is demonstrated in simulation using a
-kinematic Dubin's car as the low-fidelity model and a dynamic unicycle as the
-high-fidelity model.
-",1,0,0,0,0,0
-18628,"A Data and Model-Parallel, Distributed and Scalable Framework for Training of Deep Networks in Apache Spark","  Training deep networks is expensive and time-consuming with the training
-period increasing with data size and growth in model parameters. In this paper,
-we provide a framework for distributed training of deep networks over a cluster
-of CPUs in Apache Spark. The framework implements both Data Parallelism and
-Model Parallelism making it suitable to use for deep networks which require
-huge training data and model parameters which are too big to fit into the
-memory of a single machine. It can be scaled easily over a cluster of cheap
-commodity hardware to attain significant speedup and obtain better results
-making it quite economical as compared to farm of GPUs and supercomputers. We
-have proposed a new algorithm for training of deep networks for the case when
-the network is partitioned across the machines (Model Parallelism) along with
-detailed cost analysis and proof of convergence of the same. We have developed
-implementations for Fully-Connected Feedforward Networks, Convolutional Neural
-Networks, Recurrent Neural Networks and Long Short-Term Memory architectures.
-We present the results of extensive simulations demonstrating the speedup and
-accuracy obtained by our framework for different sizes of the data and model
-parameters with variation in the number of worker cores/partitions; thereby
-showing that our proposed framework can achieve significant speedup (upto 11X
-for CNN) and is also quite scalable.
-",1,0,0,1,0,0
-18629,"ICLabel: An automated electroencephalographic independent component classifier, dataset, and website","  The electroencephalogram (EEG) provides a non-invasive, minimally
-restrictive, and relatively low cost measure of mesoscale brain dynamics with
-high temporal resolution. Although signals recorded in parallel by multiple,
-near-adjacent EEG scalp electrode channels are highly-correlated and combine
-signals from many different sources, biological and non-biological, independent
-component analysis (ICA) has been shown to isolate the various source generator
-processes underlying those recordings. Independent components (IC) found by ICA
-decomposition can be manually inspected, selected, and interpreted, but doing
-so requires both time and practice as ICs have no particular order or intrinsic
-interpretations and therefore require further study of their properties.
-Alternatively, sufficiently-accurate automated IC classifiers can be used to
-classify ICs into broad source categories, speeding the analysis of EEG studies
-with many subjects and enabling the use of ICA decomposition in near-real-time
-applications. While many such classifiers have been proposed recently, this
-work presents the ICLabel project comprised of (1) an IC dataset containing
-spatiotemporal measures for over 200,000 ICs from more than 6,000 EEG
-recordings, (2) a website for collecting crowdsourced IC labels and educating
-EEG researchers and practitioners about IC interpretation, and (3) the
-automated ICLabel classifier. The classifier improves upon existing methods in
-two ways: by improving the accuracy of the computed label estimates and by
-enhancing its computational efficiency. The ICLabel classifier outperforms or
-performs comparably to the previous best publicly available method for all
-measured IC categories while computing those labels ten times faster than that
-classifier as shown in a rigorous comparison against all other publicly
-available EEG IC classifiers.
-",1,0,0,1,0,0
-18630,Inference on a New Class of Sample Average Treatment Effects,"  We derive new variance formulas for inference on a general class of estimands
-of causal average treatment effects in a Randomized Control Trial (RCT). We
-generalize Robins (1988) and show that when the estimand of interest is the
-Sample Average Treatment Effect of the Treated (SATT or SATC for controls), a
-consistent variance estimator exists. Although these estimands are equal to the
-Sample Average Treatment Effect (SATE) in expectation, potentially large
-differences in both accuracy and coverage can occur by the change of estimand,
-even asymptotically. Inference on the SATE, even using a conservative
-confidence interval, provides incorrect coverage of the SATT or SATC. We derive
-the variance and limiting distribution of a new and general class of
-estimands---any mixing between SATT and SATC---for which the SATE is a specific
-case. We demonstrate the applicability of the new theoretical results using
-Monte-Carlo simulations and an empirical application with hundreds of online
-experiments with an average sample size of approximately one hundred million
-observations per experiment. An R package, estCI, that implements all the
-proposed estimation procedures is available.
-",0,0,1,1,0,0
-18631,A split step Fourier/discontinuous Galerkin scheme for the Kadomtsev--Petviashvili equation,"  In this paper we propose a method to solve the Kadomtsev--Petviashvili
-equation based on splitting the linear part of the equation from the nonlinear
-part. The linear part is treated using FFTs, while the nonlinear part is
-approximated using a semi-Lagrangian discontinuous Galerkin approach of
-arbitrary order.
-We demonstrate the efficiency and accuracy of the numerical method by
-providing a range of numerical simulations. In particular, we find that our
-approach can outperform the numerical methods considered in the literature by
-up to a factor of five. Although we focus on the Kadomtsev--Petviashvili
-equation in this paper, the proposed numerical scheme can be extended to a
-range of related models as well.
-",0,1,1,0,0,0
-18632,Dense Transformer Networks,"  The key idea of current deep learning methods for dense prediction is to
-apply a model on a regular patch centered on each pixel to make pixel-wise
-predictions. These methods are limited in the sense that the patches are
-determined by network architecture instead of learned from data. In this work,
-we propose the dense transformer networks, which can learn the shapes and sizes
-of patches from data. The dense transformer networks employ an encoder-decoder
-architecture, and a pair of dense transformer modules are inserted into each of
-the encoder and decoder paths. The novelty of this work is that we provide
-technical solutions for learning the shapes and sizes of patches from data and
-efficiently restoring the spatial correspondence required for dense prediction.
-The proposed dense transformer modules are differentiable, thus the entire
-network can be trained. We apply the proposed networks on natural and
-biological image segmentation tasks and show superior performance is achieved
-in comparison to baseline methods.
-",1,0,0,1,0,0
-18633,Tetragonal CH3NH3PbI3 Is Ferroelectric,"  Halide perovskite (HaP) semiconductors are revolutionizing photovoltaic (PV)
-solar energy conversion by showing remarkable performance of solar cells made
-with esp. tetragonal methylammonium lead tri-iodide (MAPbI3). In particular,
-the low voltage loss of these cells implies a remarkably low recombination rate
-of photogenerated carriers. It was suggested that low recombination can be due
-to spatial separation of electrons and holes, a possibility if MAPbI3 is a
-semiconducting ferroelectric, which, however, requires clear experimental
-evidence. As a first step we show that, in operando, MAPbI3 (unlike MAPbBr3) is
-pyroelectric, which implies it can be ferroelectric. The next step, proving it
-is (not) ferroelectric, is challenging, because of the material s relatively
-high electrical conductance (a consequence of an optical band gap suitable for
-PV conversion!) and low stability under high applied bias voltage. This
-excludes normal measurements of a ferroelectric hysteresis loop to prove
-ferroelctricity s hallmark for switchable polarization. By adopting an approach
-suitable for electrically leaky materials as MAPbI3, we show here ferroelectric
-hysteresis from well-characterized single crystals at low temperature (still
-within the tetragonal phase, which is the room temperature stable phase). Using
-chemical etching, we also image polar domains, the structural fingerprint for
-ferroelectricity, periodically stacked along the polar axis of the crystal,
-which, as predicted by theory, scale with the overall crystal size. We also
-succeeded in detecting clear second-harmonic generation, direct evidence for
-the material s non-centrosymmetry. We note that the material s ferroelectric
-nature, can, but not obviously need to be important in a PV cell, operating
-around room temperature.
-",0,1,0,0,0,0
-18634,A Simple and Efficient MapReduce Algorithm for Data Cube Materialization,"  Data cube materialization is a classical database operator introduced in Gray
-et al.~(Data Mining and Knowledge Discovery, Vol.~1), which is critical for
-many analysis tasks. Nandi et al.~(Transactions on Knowledge and Data
-Engineering, Vol.~6) first studied cube materialization for large scale
-datasets using the MapReduce framework, and proposed a sophisticated
-modification of a simple broadcast algorithm to handle a dataset with a 216GB
-cube size within 25 minutes with 2k machines in 2012. We take a different
-approach, and propose a simple MapReduce algorithm which (1) minimizes the
-total number of copy-add operations, (2) leverages locality of computation, and
-(3) balances work evenly across machines. As a result, the algorithm shows
-excellent performance, and materialized a real dataset with a cube size of
-35.0G tuples and 1.75T bytes in 54 minutes, with 0.4k machines in 2014.
-",1,0,0,0,0,0
-18635,"Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations","  The success of deep convolutional architectures is often attributed in part
-to their ability to learn multiscale and invariant representations of natural
-signals. However, a precise study of these properties and how they affect
-learning guarantees is still missing. In this paper, we consider deep
-convolutional representations of signals; we study their invariance to
-translations and to more general groups of transformations, their stability to
-the action of diffeomorphisms, and their ability to preserve signal
-information. This analysis is carried by introducing a multilayer kernel based
-on convolutional kernel networks and by studying the geometry induced by the
-kernel mapping. We then characterize the corresponding reproducing kernel
-Hilbert space (RKHS), showing that it contains a large class of convolutional
-neural networks with homogeneous activation functions. This analysis allows us
-to separate data representation from learning, and to provide a canonical
-measure of model complexity, the RKHS norm, which controls both stability and
-generalization of any learned model. In addition to models in the constructed
-RKHS, our stability analysis also applies to convolutional networks with
-generic activations such as rectified linear units, and we discuss its
-relationship with recent generalization bounds based on spectral norms.
-",1,0,0,1,0,0
-18636,Completion of High Order Tensor Data with Missing Entries via Tensor-train Decomposition,"  In this paper, we aim at the completion problem of high order tensor data
-with missing entries. The existing tensor factorization and completion methods
-suffer from the curse of dimensionality when the order of tensor N>>3. To
-overcome this problem, we propose an efficient algorithm called TT-WOPT
-(Tensor-train Weighted OPTimization) to find the latent core tensors of tensor
-data and recover the missing entries. Tensor-train decomposition, which has the
-powerful representation ability with linear scalability to tensor order, is
-employed in our algorithm. The experimental results on synthetic data and
-natural image completion demonstrate that our method significantly outperforms
-the other related methods. Especially when the missing rate of data is very
-high, e.g., 85% to 99%, our algorithm can achieve much better performance than
-other state-of-the-art algorithms.
-",1,0,0,0,0,0
-18637,On the inner products of some Deligne--Lusztig type representations,"  In this paper we introduce a family of Deligne--Lusztig type varieties
-attached to connected reductive groups over quotients of discrete valuation
-rings, naturally generalising the higher Deligne--Lusztig varieties and some
-constructions related to the algebraisation problem raised by Lusztig. We
-establish the inner product formula between the representations associated to
-these varieties and the higher Deligne--Lusztig representations.
-",0,0,1,0,0,0
-18638,An invariant for embedded Fano manifolds covered by linear spaces,"  For an embedded Fano manifold $X$, we introduce a new invariant $S_X$ related
-to the dimension of covering linear spaces. The aim of this paper is to
-classify Fano manifolds $X$ which have large $S_X$.
-",0,0,1,0,0,0
-18639,Delivery Latency Trade-Offs of Heterogeneous Contents in Fog Radio Access Networks,"  A Fog Radio Access Network (F-RAN) is a cellular wireless system that enables
-content delivery via the caching of popular content at edge nodes (ENs) and
-cloud processing. The existing information-theoretic analyses of F-RAN systems,
-and special cases thereof, make the assumption that all requests should be
-guaranteed the same delivery latency, which results in identical latency for
-all files in the content library. In practice, however, contents may have
-heterogeneous timeliness requirements depending on the applications that
-operate on them. Given per-EN cache capacity constraint, there exists a
-fundamental trade-off among the delivery latencies of different users'
-requests, since contents that are allocated more cache space generally enjoy
-lower delivery latencies. For the case with two ENs and two users, the optimal
-latency trade-off is characterized in the high-SNR regime in terms of the
-Normalized Delivery Time (NDT) metric. The main results are illustrated by
-numerical examples.
-",1,0,0,0,0,0
-18640,Leaking Uninitialized Secure Enclave Memory via Structure Padding (Extended Abstract),"  Intel software guard extensions (SGX) aims to provide an isolated execution
-environment, known as an enclave, for a user-level process to maximize its
-confidentiality and integrity. In this paper, we study how uninitialized data
-inside a secure enclave can be leaked via structure padding. We found that,
-during ECALL and OCALL, proxy functions that are automatically generated by the
-Intel SGX Software Development Kit (SDK) fully copy structure variables from an
-enclave to the normal memory to return the result of an ECALL function and to
-pass input parameters to an OCALL function. If the structure variables contain
-padding bytes, uninitialized enclave memory, which might contain confidential
-data like a private key, can be copied to the normal memory through the padding
-bytes. We also consider potential countermeasures against these security
-threats.
-",1,0,0,0,0,0
-18641,Asymptotic and numerical analysis of a stochastic PDE model of volume transmission,"  Volume transmission is an important neural communication pathway in which
-neurons in one brain region influence the neurotransmitter concentration in the
-extracellular space of a distant brain region. In this paper, we apply
-asymptotic analysis to a stochastic partial differential equation model of
-volume transmission to calculate the neurotransmitter concentration in the
-extracellular space. Our model involves the diffusion equation in a
-three-dimensional domain with interior holes that randomly switch between being
-either sources or sinks. These holes model nerve varicosities that alternate
-between releasing and absorbing neurotransmitter, according to when they fire
-action potentials. In the case that the holes are small, we compute
-analytically the first two nonzero terms in an asymptotic expansion of the
-average neurotransmitter concentration. The first term shows that the
-concentration is spatially constant to leading order and that this constant is
-independent of many details in the problem. Specifically, this constant first
-term is independent of the number and location of nerve varicosities, neural
-firing correlations, and the size and geometry of the extracellular space. The
-second term shows how these factors affect the concentration at second order.
-Interestingly, the second term is also spatially constant under some mild
-assumptions. We verify our asymptotic results by high-order numerical
-simulation using radial basis function-generated finite differences.
-",0,0,0,0,1,0
-18642,Mixtures of Hidden Truncation Hyperbolic Factor Analyzers,"  The mixture of factor analyzers model was first introduced over 20 years ago
-and, in the meantime, has been extended to several non-Gaussian analogues. In
-general, these analogues account for situations with heavy tailed and/or skewed
-clusters. An approach is introduced that unifies many of these approaches into
-one very general model: the mixture of hidden truncation hyperbolic factor
-analyzers (MHTHFA) model. In the process of doing this, a hidden truncation
-hyperbolic factor analysis model is also introduced. The MHTHFA model is
-illustrated for clustering as well as semi-supervised classification using two
-real datasets.
-",0,0,0,1,0,0
-18643,"Representations on Partially Holomorphic Cohomology Spaces, Revisited","  This is a semi--expository update and rewrite of my 1974 AMS AMS Memoir
-describing Plancherel formulae and partial Dolbeault cohomology realizations
-for standard tempered representations for general real reductive Lie groups.
-Even after so many years, much of that Memoir is up to date, but of course
-there have been a number of refinements, advances and new developments, most of
-which have applied to smaller classes of real reductive Lie groups. Here we
-rewrite that AMS Memoir in in view of these advances and indicate the ties with
-some of the more recent (or at least less classical) approaches to geometric
-realization of unitary representations.
-",0,0,1,0,0,0
-18644,Underground tests of quantum mechanics. Whispers in the cosmic silence?,"  By performing X-rays measurements in the ""cosmic silence"" of the underground
-laboratory of Gran Sasso, LNGS-INFN, we test a basic principle of quantum
-mechanics: the Pauli Exclusion Principle (PEP), for electrons. We present the
-achieved results of the VIP experiment and the ongoing VIP2 measurement aiming
-to gain two orders of magnitude improvement in testing PEP. We also use a
-similar experimental technique to search for radiation (X and gamma) predicted
-by continuous spontaneous localization models, which aim to solve the
-""measurement problem"".
-",0,1,0,0,0,0
-18645,Implications for Post-Processing Nucleosynthesis of Core-Collapse Supernova Models with Lagrangian Particles,"  We investigate core-collapse supernova (CCSN) nucleosynthesis with
-self-consistent, axisymmetric (2D) simulations performed using the
-radiation-hydrodynamics code Chimera. Computational costs have traditionally
-constrained the evolution of the nuclear composition within multidimensional
-CCSN models to, at best, a 14-species $\alpha$-network capable of tracking only
-$(\alpha,\gamma)$ reactions from $^{4}$He to $^{60}$Zn. Such a simplified
-network limits the ability to accurately evolve detailed composition and
-neutronization or calculate the nuclear energy generation rate. Lagrangian
-tracer particles are commonly used to extend the nuclear network evolution by
-incorporating more realistic networks in post-processing nucleosynthesis
-calculations. However, limitations such as poor spatial resolution of the
-tracer particles, inconsistent thermodynamic evolution, including misestimation
-of expansion timescales, and uncertain determination of the multidimensional
-mass-cut at the end of the simulation impose uncertainties inherent to this
-approach. We present a detailed analysis of the impact of such uncertainties
-for four self-consistent axisymmetric CCSN models initiated from stellar
-metallicity, non-rotating progenitors of 12 $M_\odot$, 15 $M_\odot$, 20
-$M_\odot$, and 25 $M_\odot$ and evolved with the smaller $\alpha$-network to
-more than 1 s after the launch of an explosion.
-",0,1,0,0,0,0
-18646,High Performance Parallel Image Reconstruction for New Vacuum Solar Telescope,"  Many technologies have been developed to help improve spatial resolution of
-observational images for ground-based solar telescopes, such as adaptive optics
-(AO) systems and post-processing reconstruction. As any AO system correction is
-only partial, it is indispensable to use post-processing reconstruction
-techniques. In the New Vacuum Solar Telescope (NVST), speckle masking method is
-used to achieve the diffraction limited resolution of the telescope. Although
-the method is very promising, the computation is quite intensive, and the
-amount of data is tremendous, requiring several months to reconstruct
-observational data of one day on a high-end computer. To accelerate image
-reconstruction, we parallelize the program package on a high performance
-cluster. We describe parallel implementation details for several reconstruction
-procedures. The code is written in C language using Message Passing Interface
-(MPI) and optimized for parallel processing in a multi-processor environment.
-We show the excellent performance of parallel implementation, and the whole
-data processing speed is about 71 times faster than before. Finally, we analyze
-the scalability of the code to find possible bottlenecks, and propose several
-ways to further improve the parallel performance. We conclude that the
-presented program is capable of executing in real-time reconstruction
-applications at NVST.
-",0,1,0,0,0,0
-18647,Approximation of Bandwidth for the Interactive Operation in Video on Demand System,"  An interactive session of video-on-demand (VOD) streaming procedure deserves
-smooth data transportation for the viewer, irrespective of their geographic
-location. To access the required video, bandwidth management during the video
-objects transportation at any interactive session is a mandatory prerequisite.
-It has been observed in the domain likes movie on demand, electronic
-encyclopedia, interactive games, and educational resources. The required data
-is imported from the distributed storage servers through the high speed
-backbone network. This paper presents the viewer driven session based
-multi-user model with respect to the overlay mesh network. In virtue of
-reality, the direct implication of this work elaborately shows the required
-bandwidth is a causal part in the video on demand system. The analytic model of
-session based single viewer bandwidth requirement model presents the bandwidth
-requirement for any interactive session like, pause, move slow, rewind, skip
-some number of frames, or move fast with some constant number of frames. This
-work presents the bandwidth requirement model for any interactive session that
-brings the trade-off in data-transportation and storage costs for different
-system resources and also for the various system configurations.
-",1,0,0,0,0,0
-18648,Twisted Recurrence via Polynomial Walks,"  In this paper we show how polynomial walks can be used to establish a twisted
-recurrence for sets of positive density in $\mathbb{Z}^d$. In particular, we
-prove that if $\Gamma \leq \operatorname{GL}_d(\mathbb{Z})$ is finitely
-generated by unipotents and acts irreducibly on $\mathbb{R}^d$, then for any
-set $B \subset \mathbb{Z}^d$ of positive density, there exists $k \geq 1$ such
-that for any $v \in k \mathbb{Z}^d$ one can find $\gamma \in \Gamma$ with
-$\gamma v \in B - B$. Our method does not require the linearity of the action,
-and we prove a twisted recurrence for semigroups of maps from $\mathbb{Z}^d$ to
-$\mathbb{Z}^d$ satisfying some irreducibility and polynomial assumptions. As
-one of the consequences, we prove a non-linear analog of Bogolubov's theorem --
-for any set $B \subset \mathbb{Z}^2$ of positive density, and $p(n) \in
-\mathbb{Z}[n]$, with $p(0) = 0$ and $\operatorname{deg}(p) \geq 2$, there
-exists $k \geq 1$ such that $k \mathbb{Z} \subset \{ x - p(y) \, | \, (x,y) \in
-B-B \}$. Unlike the previous works on twisted recurrence that used recent
-results of Benoist-Quint and Bourgain-Furman-Lindenstrauss-Mozes on
-equidistribution of random walks on automorphism groups of tori, our method
-relies on the classical Weyl equidistribution for polynomial orbits on tori.
-",0,0,1,0,0,0
-18649,Well-posedness and scattering for the Boltzmann equations: Soft potential with cut-off,"  We prove the global existence of the unique mild solution for the Cauchy
-problem of the cut-off Boltzmann equation for soft potential model $\gamma=2-N$
-with initial data small in $L^N_{x,v}$ where $N=2,3$ is the dimension. The
-proof relies on the existing inhomogeneous Strichartz estimates for the kinetic
-equation by Ovcharov and convolution-like estimates for the gain term of the
-Boltzmann collision operator by Alonso, Carneiro and Gamba. The global dynamics
-of the solution is also characterized by showing that the small global solution
-scatters with respect to the kinetic transport operator in $L^N_{x,v}$. Also
-the connection between function spaces and cut-off soft potential model
-$-N<\gamma<2-N$ is characterized in the local well-posedness result for the
-Cauchy problem with large initial data.
-",0,0,1,0,0,0
-18650,Wikipedia in academia as a teaching tool: from averse to proactive faculty profiles,"  This study concerned the active use of Wikipedia as a teaching tool in the
-classroom in higher education, trying to identify different usage profiles and
-their characterization. A questionnaire survey was administrated to all
-full-time and part-time teachers at the Universitat Oberta de Catalunya and the
-Universitat Pompeu Fabra, both in Barcelona, Spain. The questionnaire was
-designed using the Technology Acceptance Model as a reference, including items
-about teachers web 2.0 profile, Wikipedia usage, expertise, perceived
-usefulness, easiness of use, visibility and quality, as well as Wikipedia
-status among colleagues and incentives to use it more actively. Clustering and
-statistical analysis were carried out using the k-medoids algorithm and
-differences between clusters were assessed by means of contingency tables and
-generalized linear models (logit). The respondents were classified in four
-clusters, from less to more likely to adopt and use Wikipedia in the classroom,
-namely averse (25.4%), reluctant (17.9%), open (29.5%) and proactive (27.2%).
-Proactive faculty are mostly men teaching part-time in STEM fields, mainly
-engineering, while averse faculty are mostly women teaching full-time in
-non-STEM fields. Nevertheless, questionnaire items related to visibility,
-quality, image, usefulness and expertise determine the main differences between
-clusters, rather than age, gender or domain. Clusters involving a positive view
-of Wikipedia and at least some frequency of use clearly outnumber those with a
-strictly negative stance. This goes against the common view that faculty
-members are mostly sceptical about Wikipedia. Environmental factors such as
-academic culture and colleagues opinion are more important than faculty
-personal characteristics, especially with respect to what they think about
-Wikipedia quality.
-",1,0,0,0,0,0
-18651,A trans-disciplinary review of deep learning research for water resources scientists,"  Deep learning (DL), a new-generation of artificial neural network research,
-has transformed industries, daily lives and various scientific disciplines in
-recent years. DL represents significant progress in the ability of neural
-networks to automatically engineer problem-relevant features and capture highly
-complex data distributions. I argue that DL can help address several major new
-and old challenges facing research in water sciences such as
-inter-disciplinarity, data discoverability, hydrologic scaling, equifinality,
-and needs for parameter regionalization. This review paper is intended to
-provide water resources scientists and hydrologists in particular with a simple
-technical overview, trans-disciplinary progress update, and a source of
-inspiration about the relevance of DL to water. The review reveals that various
-physical and geoscientific disciplines have utilized DL to address data
-challenges, improve efficiency, and gain scientific insights. DL is especially
-suited for information extraction from image-like data and sequential data.
-Techniques and experiences presented in other disciplines are of high relevance
-to water research. Meanwhile, less noticed is that DL may also serve as a
-scientific exploratory tool. A new area termed 'AI neuroscience,' where
-scientists interpret the decision process of deep networks and derive insights,
-has been born. This budding sub-discipline has demonstrated methods including
-correlation-based analysis, inversion of network-extracted features,
-reduced-order approximations by interpretable models, and attribution of
-network decisions to inputs. Moreover, DL can also use data to condition
-neurons that mimic problem-specific fundamental organizing units, thus
-revealing emergent behaviors of these units. Vast opportunities exist for DL to
-propel advances in water sciences.
-",1,0,0,1,0,0
-18652,A new NS3 Implementation of CCNx 1.0 Protocol,"  The ccns3Sim project is an open source implementation of the CCNx 1.0
-protocols for the NS3 simulator. We describe the implementation and several
-important features including modularity and process delay simulation. The
-ccns3Sim implementation is a fresh NS3-specific implementation. Like NS3
-itself, it uses C++98 standard, NS3 code style, NS3 smart pointers, NS3 xUnit,
-and integrates with the NS3 documentation and manual. A user or developer does
-not need to learn two systems. If one knows NS3, one should be able to get
-started with the CCNx code right away. A developer can easily use their own
-implementation of the layer 3 protocol, layer 4 protocol, forwarder, routing
-protocol, Pending Interest Table (PIT) or Forwarding Information Base (FIB) or
-Content Store (CS). A user may configure or specify a new implementation for
-any of these features at runtime in the simulation script. In this paper, we
-describe the software architecture and give examples of using the simulator. We
-evaluate the implementation with several example experiments on ICN caching.
-",1,0,0,0,0,0
-18653,MultiRefactor: Automated Refactoring To Improve Software Quality,"  In this paper, a new approach is proposed for automated software maintenance.
-The tool is able to perform 26 different refactorings. It also contains a large
-selection of metrics to measure the impact of the refactorings on the software
-and six different search based optimization algorithms to improve the software.
-This tool contains both mono-objective and multi-objective search techniques
-for software improvement and is fully automated. The paper describes the
-various capabilities of the tool, the unique aspects of it, and also presents
-some research results from experimentation. The individual metrics are tested
-across five different codebases to deduce the most effective metrics for
-general quality improvement. It is found that the metrics that relate to more
-specific elements of the code are more useful for driving change in the search.
-The mono-objective genetic algorithm is also tested against the multi-objective
-algorithm to see how comparable the results gained are with three separate
-objectives. When comparing the best solutions of each individual objective the
-multi-objective approach generates suitable improvements in quality in less
-time, allowing for rapid maintenance cycles.
-",1,0,0,0,0,0
-18654,Learning Postural Synergies for Categorical Grasping through Shape Space Registration,"  Every time a person encounters an object with a given degree of familiarity,
-he/she immediately knows how to grasp it. Adaptation of the movement of the
-hand according to the object geometry happens effortlessly because of the
-accumulated knowledge of previous experiences grasping similar objects. In this
-paper, we present a novel method for inferring grasp configurations based on
-the object shape. Grasping knowledge is gathered in a synergy space of the
-robotic hand built by following a human grasping taxonomy. The synergy space is
-constructed through human demonstrations employing a exoskeleton that provides
-force feedback, which provides the advantage of evaluating the quality of the
-grasp. The shape descriptor is obtained by means of a categorical non-rigid
-registration that encodes typical intra-class variations. This approach is
-especially suitable for on-line scenarios where only a portion of the object's
-surface is observable. This method is demonstrated through simulation and real
-robot experiments by grasping objects never seen before by the robot.
-",1,0,0,0,0,0
-18655,Perturbations of self-adjoint operators in semifinite von Neumann algebras: Kato-Rosenblum theorem,"  In the paper, we prove an analogue of the Kato-Rosenblum theorem in a
-semifinite von Neumann algebra. Let $\mathcal{M}$ be a countably decomposable,
-properly infinite, semifinite von Neumann algebra acting on a Hilbert space
-$\mathcal{H}$ and let $\tau$ be a faithful normal semifinite tracial weight of
-$\mathcal M$. Suppose that $H$ and $H_1$ are self-adjoint operators affiliated
-with $\mathcal{M}$. We show that if $H-H_1$ is in $\mathcal{M}\cap
-L^{1}\left(\mathcal{M},\tau\right)$, then the ${norm}$ absolutely continuous
-parts of $H$ and $H_1$ are unitarily equivalent. This implies that the real
-part of a non-normal hyponormal operator in $\mathcal M$ is not a perturbation
-by $\mathcal{M}\cap L^{1}\left(\mathcal{M},\tau\right)$ of a diagonal operator.
-Meanwhile, for $n\ge 2$ and $1\leq p<n$, by modifying Voiculescu's invariant we
-give examples of commuting $n$-tuples of self-adjoint operators in
-$\mathcal{M}$ that are not arbitrarily small perturbations of commuting
-diagonal operators modulo $\mathcal{M}\cap L^{p}\left(\mathcal{M},\tau\right)$.
-",0,0,1,0,0,0
-18656,Dust radiative transfer modelling of the infrared ring around the magnetar SGR 1900$+$14,"  A peculiar infrared ring-like structure was discovered by {\em Spitzer}
-around the strongly magnetised neutron star SGR 1900$+$14. This infrared
-structure was suggested to be due to a dust-free cavity, produced by the SGR
-Giant Flare occurred in 1998, and kept illuminated by surrounding stars. Using
-a 3D dust radiative transfer code, we aimed at reproducing the emission
-morphology and the integrated emission flux of this structure assuming
-different spatial distributions and densities for the dust, and different
-positions for the illuminating stars. We found that a dust-free ellipsoidal
-cavity can reproduce the shape, flux, and spectrum of the ring-like infrared
-emission, provided that the illuminating stars are inside the cavity and that
-the interstellar medium has high gas density ($n_H\sim$1000 cm$^{-3}$). We
-further constrain the emitting region to have a sharp inner boundary and to be
-significantly extended in the radial direction, possibly even just a cavity in
-a smooth molecular cloud. We discuss possible scenarios for the formation of
-the dustless cavity and the particular geometry that allows it to be IR-bright.
-",0,1,0,0,0,0
-18657,Application of transfer matrix and transfer function analysis to grating-type dielectric laser accelerators: ponderomotive focusing of electrons,"  The question of suitability of transfer matrix description of electrons
-traversing grating-type dielectric laser acceleration (DLA) structures is
-addressed. It is shown that although matrix considerations lead to interesting
-insights, the basic transfer properties of DLA cells cannot be described by a
-matrix. A more general notion of a transfer function is shown to be a simple
-and useful tool for formulating problems of particle dynamics in DLA. As an
-example, a focusing structure is proposed which works simultaneously for all
-electron phases.
-",0,1,0,0,0,0
-18658,Discretization-free Knowledge Gradient Methods for Bayesian Optimization,"  This paper studies Bayesian ranking and selection (R&S) problems with
-correlated prior beliefs and continuous domains, i.e. Bayesian optimization
-(BO). Knowledge gradient methods [Frazier et al., 2008, 2009] have been widely
-studied for discrete R&S problems, which sample the one-step Bayes-optimal
-point. When used over continuous domains, previous work on the knowledge
-gradient [Scott et al., 2011, Wu and Frazier, 2016, Wu et al., 2017] often rely
-on a discretized finite approximation. However, the discretization introduces
-error and scales poorly as the dimension of domain grows. In this paper, we
-develop a fast discretization-free knowledge gradient method for Bayesian
-optimization. Our method is not restricted to the fully sequential setting, but
-useful in all settings where knowledge gradient can be used over continuous
-domains. We show how our method can be generalized to handle (i) batch of
-points suggestion (parallel knowledge gradient); (ii) the setting where
-derivative information is available in the optimization process
-(derivative-enabled knowledge gradient). In numerical experiments, we
-demonstrate that the discretization-free knowledge gradient method finds global
-optima significantly faster than previous Bayesian optimization algorithms on
-both synthetic test functions and real-world applications, especially when
-function evaluations are noisy; and derivative-enabled knowledge gradient can
-further improve the performances, even outperforming the gradient-based
-optimizer such as BFGS when derivative information is available.
-",1,0,1,1,0,0
-18659,Measuring the Robustness of Graph Properties,"  In this paper, we propose a perturbation framework to measure the robustness
-of graph properties. Although there are already perturbation methods proposed
-to tackle this problem, they are limited by the fact that the strength of the
-perturbation cannot be well controlled. We firstly provide a perturbation
-framework on graphs by introducing weights on the nodes, of which the magnitude
-of perturbation can be easily controlled through the variance of the weights.
-Meanwhile, the topology of the graphs are also preserved to avoid
-uncontrollable strength in the perturbation. We then extend the measure of
-robustness in the robust statistics literature to the graph properties.
-",1,0,0,1,0,0
-18660,"Better Software Analytics via ""DUO"": Data Mining Algorithms Using/Used-by Optimizers","  This paper claims that a new field of empirical software engineering research
-and practice is emerging: data mining using/used-by optimizers for empirical
-studies, or DUO. For example, data miners can generate the models that are
-explored by optimizers.Also, optimizers can advise how to best adjust the
-control parameters of a data miner. This combined approach acts like an agent
-leaning over the shoulder of an analyst that advises ""ask this question next""
-or ""ignore that problem, it is not relevant to your goals"". Further, those
-agents can help us build ""better"" predictive models, where ""better"" can be
-either greater predictive accuracy, or faster modeling time (which, in turn,
-enables the exploration of a wider range of options). We also caution that the
-era of papers that just use data miners is coming to an end. Results obtained
-from an unoptimized data miner can be quickly refuted, just by applying an
-optimizer to produce a different (and better performing) model. Our conclusion,
-hence, is that for software analytics it is possible, useful and necessary to
-combine data mining and optimization using DUO.
-",1,0,0,0,0,0
-18661,Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks,"  We propose an automatic diabetic retinopathy (DR) analysis algorithm based on
-two-stages deep convolutional neural networks (DCNN). Compared to existing
-DCNN-based DR detection methods, the proposed algorithm have the following
-advantages: (1) Our method can point out the location and type of lesions in
-the fundus images, as well as giving the severity grades of DR. Moreover, since
-retina lesions and DR severity appear with different scales in fundus images,
-the integration of both local and global networks learn more complete and
-specific features for DR analysis. (2) By introducing imbalanced weighting map,
-more attentions will be given to lesion patches for DR grading, which
-significantly improve the performance of the proposed algorithm. In this study,
-we label 12,206 lesion patches and re-annotate the DR grades of 23,595 fundus
-images from Kaggle competition dataset. Under the guidance of clinical
-ophthalmologists, the experimental results show that our local lesion detection
-net achieve comparable performance with trained human observers, and the
-proposed imbalanced weighted scheme also be proved to significantly improve the
-capability of our DCNN-based DR grading algorithm.
-",1,0,0,0,0,0
-18662,DRYVR:Data-driven verification and compositional reasoning for automotive systems,"  We present the DRYVR framework for verifying hybrid control systems that are
-described by a combination of a black-box simulator for trajectories and a
-white-box transition graph specifying mode switches. The framework includes (a)
-a probabilistic algorithm for learning sensitivity of the continuous
-trajectories from simulation data, (b) a bounded reachability analysis
-algorithm that uses the learned sensitivity, and (c) reasoning techniques based
-on simulation relations and sequential composition, that enable verification of
-complex systems under long switching sequences, from the reachability analysis
-of a simpler system under shorter sequences. We demonstrate the utility of the
-framework by verifying a suite of automotive benchmarks that include powertrain
-control, automatic transmission, and several autonomous and ADAS features like
-automatic emergency braking, lane-merge, and auto-passing controllers.
-",1,0,0,0,0,0
-18663,Deep Reinforcement Learning for Programming Language Correction,"  Novice programmers often struggle with the formal syntax of programming
-languages. To assist them, we design a novel programming language correction
-framework amenable to reinforcement learning. The framework allows an agent to
-mimic human actions for text navigation and editing. We demonstrate that the
-agent can be trained through self-exploration directly from the raw input, that
-is, program text itself, without any knowledge of the formal syntax of the
-programming language. We leverage expert demonstrations for one tenth of the
-training data to accelerate training. The proposed technique is evaluated on
-6975 erroneous C programs with typographic errors, written by students during
-an introductory programming course. Our technique fixes 14% more programs and
-29% more compiler error messages relative to those fixed by a state-of-the-art
-tool, DeepFix, which uses a fully supervised neural machine translation
-approach.
-",1,0,0,0,0,0
-18664,Dispersionless and multicomponent BKP hierarchies with quantum torus symmetries,"  In this article, we will construct the additional perturbative quantum torus
-symmetry of the dispersionless BKP hierarchy basing on the $W_{\infty}$
-infinite dimensional Lie symmetry. These results show that the complete quantum
-torus symmetry is broken from the BKP hierarchy to its dispersionless
-hierarchy. Further a series of additional flows of the multicomponent BKP
-hierarchy will be defined and these flows constitute an $N$-folds direct
-product of the positive half of the quantum torus symmetries.
-",0,1,1,0,0,0
-18665,A Sampling Framework for Solving Physics-driven Inverse Source Problems,"  Partial differential equations are central to describing many physical
-phenomena. In many applications these phenomena are observed through a sensor
-network, with the aim of inferring their underlying properties. Leveraging from
-certain results in sampling and approximation theory, we present a new
-framework for solving a class of inverse source problems for physical fields
-governed by linear partial differential equations. Specifically, we demonstrate
-that the unknown field sources can be recovered from a sequence of, so called,
-generalised measurements by using multidimensional frequency estimation
-techniques. Next we show that---for physics-driven fields---this sequence of
-generalised measurements can be estimated by computing a linear weighted-sum of
-the sensor measurements; whereby the exact weights (of the sums) correspond to
-those that reproduce multidimensional exponentials, when used to linearly
-combine translates of a particular prototype function related to the Green's
-function of our underlying field. Explicit formulae are then derived for the
-sequence of weights, that map sensor samples to the exact sequence of
-generalised measurements when the Green's function satisfies the generalised
-Strang-Fix condition. Otherwise, the same mapping yields a close approximation
-of the generalised measurements. Based on this new framework we develop
-practical, noise robust, sensor network strategies for solving the inverse
-source problem, and then present numerical simulation results to verify their
-performance.
-",1,0,1,0,0,0
-18666,An Application of $h$-principle to Manifold Calculus,"  Manifold calculus is a form of functor calculus that analyzes contravariant
-functors from some categories of manifolds to topological spaces by providing
-analytic approximations to them. In this paper we apply the theory of
-h-principle to construct several examples of analytic functors in this sense.
-We prove that the analytic approximation of the Lagrangian embeddings functor
-$\mathrm{emb}_{\mathrm{Lag}}(-,N)$ is the totally real embeddings functor
-$\mathrm{emb}_{\mathrm{TR}}(-,N)$. Under certain conditions we provide a
-geometric construction for the homotopy fiber of $ \mathrm{emb}(M,N)
-\rightarrow \mathrm{imm}(M,N)$. This construction also provides an example of a
-functor which is itself empty when evaluated on most manifolds but it's
-analytic approximation is almost always non-empty.
-",0,0,1,0,0,0
-18667,Grasping Unknown Objects in Clutter by Superquadric Representation,"  In this paper, a quick and efficient method is presented for grasping unknown
-objects in clutter. The grasping method relies on real-time superquadric (SQ)
-representation of partial view objects and incomplete object modelling, well
-suited for unknown symmetric objects in cluttered scenarios which is followed
-by optimized antipodal grasping. The incomplete object models are processed
-through a mirroring algorithm that assumes symmetry to first create an
-approximate complete model and then fit for SQ representation. The grasping
-algorithm is designed for maximum force balance and stability, taking advantage
-of the quick retrieval of dimension and surface curvature information from the
-SQ parameters. The pose of the SQs with respect to the direction of gravity is
-calculated and used together with the parameters of the SQs and specification
-of the gripper, to select the best direction of approach and contact points.
-The SQ fitting method has been tested on custom datasets containing objects in
-isolation as well as in clutter. The grasping algorithm is evaluated on a PR2
-and real time results are presented. Initial results indicate that though the
-method is based on simplistic shape information, it outperforms other learning
-based grasping algorithms that also work in clutter in terms of time-efficiency
-and accuracy.
-",1,0,0,0,0,0
-18668,Learning to Play with Intrinsically-Motivated Self-Aware Agents,"  Infants are experts at playing, with an amazing ability to generate novel
-structured behaviors in unstructured environments that lack clear extrinsic
-reward signals. We seek to mathematically formalize these abilities using a
-neural network that implements curiosity-driven intrinsic motivation. Using a
-simple but ecologically naturalistic simulated environment in which an agent
-can move and interact with objects it sees, we propose a ""world-model"" network
-that learns to predict the dynamic consequences of the agent's actions.
-Simultaneously, we train a separate explicit ""self-model"" that allows the agent
-to track the error map of its own world-model, and then uses the self-model to
-adversarially challenge the developing world-model. We demonstrate that this
-policy causes the agent to explore novel and informative interactions with its
-environment, leading to the generation of a spectrum of complex behaviors,
-including ego-motion prediction, object attention, and object gathering.
-Moreover, the world-model that the agent learns supports improved performance
-on object dynamics prediction, detection, localization and recognition tasks.
-Taken together, our results are initial steps toward creating flexible
-autonomous agents that self-supervise in complex novel physical environments.
-",0,0,0,1,0,0
-18669,$\ell_1$-minimization method for link flow correction,"  A computational method, based on $\ell_1$-minimization, is proposed for the
-problem of link flow correction, when the available traffic flow data on many
-links in a road network are inconsistent with respect to the flow conservation
-law. Without extra information, the problem is generally ill-posed when a large
-portion of the link sensors are unhealthy. It is possible, however, to correct
-the corrupted link flows \textit{accurately} with the proposed method under a
-recoverability condition if there are only a few bad sensors which are located
-at certain links. We analytically identify the links that are robust to
-miscounts and relate them to the geometric structure of the traffic network by
-introducing the recoverability concept and an algorithm for computing it. The
-recoverability condition for corrupted links is simply the associated
-recoverability being greater than 1. In a more realistic setting, besides the
-unhealthy link sensors, small measurement noises may be present at the other
-sensors. Under the same recoverability condition, our method guarantees to give
-an estimated traffic flow fairly close to the ground-truth data and leads to a
-bound for the correction error. Both synthetic and real-world examples are
-provided to demonstrate the effectiveness of the proposed method.
-",1,1,0,0,0,0
-18670,Current Flow Group Closeness Centrality for Complex Networks,"  Current flow closeness centrality (CFCC) has a better discriminating ability
-than the ordinary closeness centrality based on shortest paths. In this paper,
-we extend this notion to a group of vertices in a weighted graph, and then
-study the problem of finding a subset $S$ of $k$ vertices to maximize its CFCC
-$C(S)$, both theoretically and experimentally. We show that the problem is
-NP-hard, but propose two greedy algorithms for minimizing the reciprocal of
-$C(S)$ with provable guarantees using the monotoncity and supermodularity. The
-first is a deterministic algorithm with an approximation factor
-$(1-\frac{k}{k-1}\cdot\frac{1}{e})$ and cubic running time; while the second is
-a randomized algorithm with a
-$(1-\frac{k}{k-1}\cdot\frac{1}{e}-\epsilon)$-approximation and nearly-linear
-running time for any $\epsilon > 0$. Extensive experiments on model and real
-networks demonstrate that our algorithms are effective and efficient, with the
-second algorithm being scalable to massive networks with more than a million
-vertices.
-",1,0,0,0,0,0
-18671,Visualization of Constraint Handling Rules: Semantics and Applications,"  The work in the paper presents an animation extension ($CHR^{vis}$) to
-Constraint Handling Rules (CHR). Visualizations have always helped programmers
-understand data and debug programs. A picture is worth a thousand words. It can
-help identify where a problem is or show how something works. It can even
-illustrate a relation that was not clear otherwise. $CHR^{vis}$ aims at
-embedding animation and visualization features into CHR programs. It thus
-enables users, while executing programs, to have such executions animated. The
-paper aims at providing the operational semantics for $CHR^{vis}$. The
-correctness of $CHR^{vis}$ programs is also discussed. Some applications of the
-new extension are also introduced.
-",1,0,0,0,0,0
-18672,Simplified Energy Landscape for Modularity Using Total Variation,"  Networks capture pairwise interactions between entities and are frequently
-used in applications such as social networks, food networks, and protein
-interaction networks, to name a few. Communities, cohesive groups of nodes,
-often form in these applications, and identifying them gives insight into the
-overall organization of the network. One common quality function used to
-identify community structure is modularity. In Hu et al. [SIAM J. App. Math.,
-73(6), 2013], it was shown that modularity optimization is equivalent to
-minimizing a particular nonconvex total variation (TV) based functional over a
-discrete domain. They solve this problem, assuming the number of communities is
-known, using a Merriman, Bence, Osher (MBO) scheme.
-We show that modularity optimization is equivalent to minimizing a convex
-TV-based functional over a discrete domain, again, assuming the number of
-communities is known. Furthermore, we show that modularity has no convex
-relaxation satisfying certain natural conditions. We therefore, find a
-manageable non-convex approximation using a Ginzburg Landau functional, which
-provably converges to the correct energy in the limit of a certain parameter.
-We then derive an MBO algorithm with fewer hand-tuned parameters than in Hu et
-al. and which is 7 times faster at solving the associated diffusion equation
-due to the fact that the underlying discretization is unconditionally stable.
-Our numerical tests include a hyperspectral video whose associated graph has
-2.9x10^7 edges, which is roughly 37 times larger than was handled in the paper
-of Hu et al.
-",0,0,1,1,0,0
-18673,"A Hard Look at the Neutron Stars and Accretion Disks in 4U 1636-53, GX 17+2, and 4U 1705-44 with $\emph{NuSTAR}$","  We present $\emph{NuSTAR}$ observations of neutron star (NS) low-mass X-ray
-binaries: 4U 1636-53, GX 17+2, and 4U 1705-44. We observed 4U 1636-53 in the
-hard state, with an Eddington fraction, $F_{\mathrm{Edd}}$, of 0.01; GX 17+2
-and 4U 1705-44 were in the soft state with fractions of 0.57 and 0.10,
-respectively. Each spectrum shows evidence for a relativistically broadened Fe
-K$_{\alpha}$ line. Through accretion disk reflection modeling, we constrain the
-radius of the inner disk in 4U 1636-53 to be $R_{in}=1.03\pm0.03$ ISCO
-(innermost stable circular orbit) assuming a dimensionless spin parameter
-$a_{*}=cJ/GM^{2}=0.0$, and $R_{in}=1.08\pm0.06$ ISCO for $a_{*}=0.3$ (errors
-quoted at 1 $\sigma$). This value proves to be model independent. For
-$a_{*}=0.3$ and $M=1.4\ M_{\odot}$, for example, $1.08\pm0.06$ ISCO translates
-to a physical radius of $R=10.8\pm0.6$ km, and the neutron star would have to
-be smaller than this radius (other outcomes are possible for allowed spin
-parameters and masses). For GX 17+2, $R_{in}=1.00-1.04$ ISCO for $a_{*}=0.0$
-and $R_{in}=1.03-1.30$ ISCO for $a_{*}=0.3$. For $a_{*}=0.3$ and $M=1.4\
-M_{\odot}$, $R_{in}=1.03-1.30$ ISCO translates to $R=10.3-13.0$ km. The inner
-accretion disk in 4U 1705-44 may be truncated just above the stellar surface,
-perhaps by a boundary layer or magnetosphere; reflection models give a radius
-of 1.46-1.64 ISCO for $a_{*}=0.0$ and 1.69-1.93 ISCO for $a_{*}=0.3$. We
-discuss the implications that our results may have on the equation of state of
-ultradense, cold matter and our understanding of the innermost accretion flow
-onto neutron stars with low surface magnetic fields, and systematic errors
-related to the reflection models and spacetime metric around less idealized
-neutron stars.
-",0,1,0,0,0,0
-18674,Approximating Weighted Duo-Preservation in Comparative Genomics,"  Motivated by comparative genomics, Chen et al. [9] introduced the Maximum
-Duo-preservation String Mapping (MDSM) problem in which we are given two
-strings $s_1$ and $s_2$ from the same alphabet and the goal is to find a
-mapping $\pi$ between them so as to maximize the number of duos preserved. A
-duo is any two consecutive characters in a string and it is preserved in the
-mapping if its two consecutive characters in $s_1$ are mapped to same two
-consecutive characters in $s_2$. The MDSM problem is known to be NP-hard and
-there are approximation algorithms for this problem [3, 5, 13], but all of them
-consider only the ""unweighted"" version of the problem in the sense that a duo
-from $s_1$ is preserved by mapping to any same duo in $s_2$ regardless of their
-positions in the respective strings. However, it is well-desired in comparative
-genomics to find mappings that consider preserving duos that are ""closer"" to
-each other under some distance measure [19]. In this paper, we introduce a
-generalized version of the problem, called the Maximum-Weight Duo-preservation
-String Mapping (MWDSM) problem that captures both duos-preservation and
-duos-distance measures in the sense that mapping a duo from $s_1$ to each
-preserved duo in $s_2$ has a weight, indicating the ""closeness"" of the two
-duos. The objective of the MWDSM problem is to find a mapping so as to maximize
-the total weight of preserved duos. In this paper, we give a polynomial-time
-6-approximation algorithm for this problem.
-",1,0,0,0,0,0
-18675,On the virtual singular braid monoid,"  We study the algebraic structures of the virtual singular braid monoid,
-$VSB_n$, and the virtual singular pure braid monoid, $VSP_n$. The monoid
-$VSB_n$ is the splittable extension of $VSP_n$ by the symmetric group $S_n$. We
-also construct a representation of $VSB_n$.
-",0,0,1,0,0,0
-18676,Discrete Games in Endogenous Networks: Equilibria and Policy,"  In games of friendship links and behaviors, I propose $k$-player Nash
-stability---a family of equilibria, indexed by a measure of robustness given by
-the number of permitted link changes, which is (ordinally and cardinally)
-ranked in a probabilistic sense. Application of the proposed framework to
-adolescents' tobacco smoking and friendship decisions suggests that: (a.)
-friendship networks respond to increases of tobacco prices and this response
-amplifies the intended policy effect on smoking, (b.) racially desegregating
-high-schools, via stimulating the social interactions of students with
-different intrinsic propensity to smoke, decreases the overall smoking
-prevalence, (c.) adolescents are averse to sharing friends so that there is a
-rivalry for friendships, (d.) when data on individuals' friendship network is
-not available, the importance of price centered policy tools is underestimated.
-",1,1,0,0,0,0
-18677,Evaluating regulatory reform of network industries: a survey of empirical models based on categorical proxies,"  Proxies for regulatory reforms based on categorical variables are
-increasingly used in empirical evaluation models. We surveyed 63 studies that
-rely on such indices to analyze the effects of entry liberalization,
-privatization, unbundling, and independent regulation of the electricity,
-natural gas, and telecommunications sectors. We highlight methodological issues
-related to the use of these proxies. Next, taking stock of the literature, we
-provide practical advice for the design of the empirical strategy and discuss
-the selection of control and instrumental variables to attenuate endogeneity
-problems undermining identification of the effects of regulatory reforms.
-",0,0,0,0,0,1
-18678,Theory of ground states for classical Heisenberg spin systems II,"  We apply the theory of ground states for classical, finite, Heisenberg spin
-systems previously published to a couple of spin systems that can be considered
-as finite models $K_{12},\,K_{15}$ and $K_{18}$ of the AF Kagome lattice. The
-model $K_{12}$ is isomorphic to the cuboctahedron. In particular, we find
-three-dimensional ground states that cannot be viewed as resulting from the
-well-known independent rotation of subsets of spin vectors. For a couple of
-ground states with translational symmetry we calculate the corresponding wave
-numbers. Finally we study the model $K_{12w}$ without boundary conditions which
-exhibits new phenomena as, e.~g., two-dimensional families of three-dimensional
-ground states.
-",0,1,0,0,0,0
-18679,Relative Property (T) for Nilpotent Subgroups,"  We show that relative Property (T) for the abelianization of a nilpotent
-normal subgroup implies relative Property (T) for the subgroup itself. This and
-other results are a consequence of a theorem of independent interest, which
-states that if $H$ is a closed subgroup of a locally compact group $G$, and $A$
-is a closed subgroup of the center of $H$, such that $A$ is normal in $G$, and
-$(G/A, H/A)$ has relative Property (T), then $(G, H^{(1)})$ has relative
-Property (T), where $H^{(1)}$ is the closure of the commutator subgroup of $H$.
-In fact, the assumption that $A$ is in the center of $H$ can be replaced with
-the weaker assumption that $A$ is abelian and every $H$-invariant finite
-measure on the unitary dual of $A$ is supported on the set of fixed points.
-",0,0,1,0,0,0
-18680,Discriminant analysis in small and large dimensions,"  We study the distributional properties of the linear discriminant function
-under the assumption of normality by comparing two groups with the same
-covariance matrix but different mean vectors. A stochastic representation for
-the discriminant function coefficients is derived which is then used to obtain
-their asymptotic distribution under the high-dimensional asymptotic regime. We
-investigate the performance of the classification analysis based on the
-discriminant function in both small and large dimensions. A stochastic
-representation is established which allows to compute the error rate in an
-efficient way. We further compare the calculated error rate with the optimal
-one obtained under the assumption that the covariance matrix and the two mean
-vectors are known. Finally, we present an analytical expression of the error
-rate calculated in the high-dimensional asymptotic regime. The finite-sample
-properties of the derived theoretical results are assessed via an extensive
-Monte Carlo study.
-",0,0,1,1,0,0
-18681,Sparse Bayesian Inference for Dense Semantic Mapping,"  Despite impressive advances in simultaneous localization and mapping, dense
-robotic mapping remains challenging due to its inherent nature of being a
-high-dimensional inference problem. In this paper, we propose a dense semantic
-robotic mapping technique that exploits sparse Bayesian models, in particular,
-the relevance vector machine, for high-dimensional sequential inference. The
-technique is based on the principle of automatic relevance determination and
-produces sparse models that use a small subset of the original dense training
-set as the dominant basis. The resulting map posterior is continuous, and
-queries can be made efficiently at any resolution. Moreover, the technique has
-probabilistic outputs per semantic class through Bayesian inference. We
-evaluate the proposed relevance vector semantic map using publicly available
-benchmark datasets, NYU Depth V2 and KITTI; and the results show promising
-improvements over the state-of-the-art techniques.
-",1,0,0,0,0,0
-18682,Singularities and Semistable Degenerations for Symplectic Topology,"  We overview our recent work defining and studying normal crossings varieties
-and subvarieties in symplectic topology. This work answers a question of Gromov
-on the feasibility of introducing singular (sub)varieties into symplectic
-topology in the case of normal crossings singularities. It also provides a
-necessary and sufficient condition for smoothing normal crossings symplectic
-varieties. In addition, we explain some connections with other areas of
-mathematics and discuss a few directions for further research.
-",0,0,1,0,0,0
-18683,One-step Local M-estimator for Integrated Jump-Diffusion Models,"  In this paper, robust nonparametric estimators, instead of local linear
-estimators, are adapted for infinitesimal coefficients associated with
-integrated jump-diffusion models to avoid the impact of outliers on accuracy.
-Furthermore, consider the complexity of iteration of the solution for local
-M-estimator, we propose the one-step local M-estimators to release the
-computation burden. Under appropriate regularity conditions, we prove that
-one-step local M-estimators and the fully iterative M-estimators have the same
-performance in consistency and asymptotic normality. Through simulation, our
-method present advantages in bias reduction, robustness and reducing
-computation cost. In addition, the estimators are illustrated empirically
-through stock index under different sampling frequency.
-",0,0,1,1,0,0
-18684,"Python Open Source Waveform Extractor (POWER): An open source, Python package to monitor and post-process numerical relativity simulations","  Numerical simulations of Einstein's field equations provide unique insights
-into the physics of compact objects moving at relativistic speeds, and which
-are driven by strong gravitational interactions. Numerical relativity has
-played a key role to firmly establish gravitational wave astrophysics as a new
-field of research, and it is now paving the way to establish whether
-gravitational wave radiation emitted from compact binary mergers is accompanied
-by electromagnetic and astro-particle counterparts. As numerical relativity
-continues to blend in with routine gravitational wave data analyses to validate
-the discovery of gravitational wave events, it is essential to develop open
-source tools to streamline these studies. Motivated by our own experience as
-users and developers of the open source, community software, the Einstein
-Toolkit, we present an open source, Python package that is ideally suited to
-monitor and post-process the data products of numerical relativity simulations,
-and compute the gravitational wave strain at future null infinity in high
-performance environments. We showcase the application of this new package to
-post-process a large numerical relativity catalog and extract higher-order
-waveform modes from numerical relativity simulations of eccentric binary black
-hole mergers and neutron star mergers. This new software fills a critical void
-in the arsenal of tools provided by the Einstein Toolkit Consortium to the
-numerical relativity community.
-",1,1,0,0,0,0
-18685,Uniform confidence bands for nonparametric errors-in-variables regression,"  This paper develops a method to construct uniform confidence bands for a
-nonparametric regression function where a predictor variable is subject to a
-measurement error. We allow for the distribution of the measurement error to be
-unknown, but assume that there is an independent sample from the measurement
-error distribution. The sample from the measurement error distribution need not
-be independent from the sample on response and predictor variables. The
-availability of a sample from the measurement error distribution is satisfied
-if, for example, either 1) validation data or 2) repeated measurements (panel
-data) on the latent predictor variable with measurement errors, one of which is
-symmetrically distributed, are available. The proposed confidence band builds
-on the deconvolution kernel estimation and a novel application of the
-multiplier (or wild) bootstrap method. We establish asymptotic validity of the
-proposed confidence band under ordinary smooth measurement error densities,
-showing that the proposed confidence band contains the true regression function
-with probability approaching the nominal coverage probability. To the best of
-our knowledge, this is the first paper to derive asymptotically valid uniform
-confidence bands for nonparametric errors-in-variables regression. We also
-propose a novel data-driven method to choose a bandwidth, and conduct
-simulation studies to verify the finite sample performance of the proposed
-confidence band. Applying our method to a combination of two empirical data
-sets, we draw confidence bands for nonparametric regressions of medical costs
-on the body mass index (BMI), accounting for measurement errors in BMI.
-Finally, we discuss extensions of our results to specification testing, cases
-with additional error-free regressors, and confidence bands for conditional
-distribution functions.
-",0,0,1,1,0,0
-18686,Unconventional superconductivity in the BiS$_2$-based layered superconductor NdO$_{0.71}$F$_{0.29}$BiS$_2$,"  We investigate the superconducting-gap anisotropy in one of the recently
-discovered BiS$_2$-based superconductors, NdO$_{0.71}$F$_{0.29}$BiS$_2$ ($T_c$
-$\sim$ 5 K), using laser-based angle-resolved photoemission spectroscopy.
-Whereas the previously discovered high-$T_c$ superconductors such as copper
-oxides and iron-based superconductors, which are believed to have
-unconventional superconducting mechanisms, have $3d$ electrons in their
-conduction bands, the conduction band of BiS$_2$-based superconductors mainly
-consists of Bi 6$p$ electrons, and hence the conventional superconducting
-mechanism might be expected. Contrary to this expectation, we observe a
-strongly anisotropic superconducting gap. This result strongly suggests that
-the pairing mechanism for NdO$_{0.71}$F$_{0.29}$BiS$_2$ is unconventional one
-and we attribute the observed anisotropy to competitive or cooperative multiple
-paring interactions.
-",0,1,0,0,0,0
-18687,Low noise sensitivity analysis of Lq-minimization in oversampled systems,"  The class of Lq-regularized least squares (LQLS) are considered for
-estimating a p-dimensional vector \b{eta} from its n noisy linear observations
-y = X\b{eta}+w. The performance of these schemes are studied under the
-high-dimensional asymptotic setting in which p grows linearly with n. In this
-asymptotic setting, phase transition diagrams (PT) are often used for comparing
-the performance of different estimators. Although phase transition analysis is
-shown to provide useful information for compressed sensing, the fact that it
-ignores the measurement noise not only limits its applicability in many
-application areas, but also may lead to misunderstandings. For instance,
-consider a linear regression problem in which n > p and the signal is not
-exactly sparse. If the measurement noise is ignored in such systems,
-regularization techniques, such as LQLS, seem to be irrelevant since even the
-ordinary least squares (OLS) returns the exact solution. However, it is
-well-known that if n is not much larger than p then the regularization
-techniques improve the performance of OLS. In response to this limitation of PT
-analysis, we consider the low-noise sensitivity analysis. We show that this
-analysis framework (i) reveals the advantage of LQLS over OLS, (ii) captures
-the difference between different LQLS estimators even when n > p, and (iii)
-provides a fair comparison among different estimators in high signal-to-noise
-ratios. As an application of this framework, we will show that under mild
-conditions LASSO outperforms other LQLS even when the signal is dense. Finally,
-by a simple transformation we connect our low-noise sensitivity framework to
-the classical asymptotic regime in which n/p goes to infinity and characterize
-how and when regularization techniques offer improvements over ordinary least
-squares, and which regularizer gives the most improvement when the sample size
-is large.
-",0,0,1,1,0,0
-18688,Robust Transceiver Design Based on Interference Alignment for Multi-User Multi-Cell MIMO Networks with Channel Uncertainty,"  In this paper, we firstly exploit the inter-user interference (IUI) and
-inter-cell interference (ICI) as useful references to develop a robust
-transceiver design based on interference alignment for a downlink multi-user
-multi-cell multiple-input multiple-output (MIMO) interference network under
-channel estimation error. At transmitters, we propose a two-tier transmit
-beamforming strategy, we first achieve the inner beamforming direction and
-allocated power by minimizing the interference leakage as well as maximizing
-the system energy efficiency, respectively. Then, for the outer beamformer
-design, we develop an efficient conjugate gradient Grassmann manifold subspace
-tracking algorithm to minimize the distances between the subspace spanned by
-interference and the interference subspace in the time varying channel. At
-receivers, we propose a practical interference alignment based on fast and
-robust fast data projection method (FDPM) subspace tracking algorithm, to
-achieve the receive beamformer under channel uncertainty. Numerical results
-show that our proposed robust transceiver design achieves better performance
-compared with some existing methods in terms of the sum rate and the energy
-efficiency.
-",1,0,0,0,0,0
-18689,Machine learning quantum mechanics: solving quantum mechanics problems using radial basis function networks,"  Inspired by the recent work of Carleo and Troyer[1], we apply machine
-learning methods to quantum mechanics in this article. The radial basis
-function network in a discrete basis is used as the variational wavefunction
-for the ground state of a quantum system. Variational Monte Carlo(VMC)
-calculations are carried out for some simple Hamiltonians. The results are in
-good agreements with theoretical values. The smallest eigenvalue of a Hermitian
-matrix can also be acquired using VMC calculations. Our results demonstrate
-that machine learning techniques are capable of solving quantum mechanical
-problems.
-",0,1,0,0,0,0
-18690,Scale-free networks are rare,"  A central claim in modern network science is that real-world networks are
-typically ""scale free,"" meaning that the fraction of nodes with degree $k$
-follows a power law, decaying like $k^{-\alpha}$, often with $2 < \alpha < 3$.
-However, empirical evidence for this belief derives from a relatively small
-number of real-world networks. We test the universality of scale-free structure
-by applying state-of-the-art statistical tools to a large corpus of nearly 1000
-network data sets drawn from social, biological, technological, and
-informational sources. We fit the power-law model to each degree distribution,
-test its statistical plausibility, and compare it via a likelihood ratio test
-to alternative, non-scale-free models, e.g., the log-normal. Across domains, we
-find that scale-free networks are rare, with only 4% exhibiting the
-strongest-possible evidence of scale-free structure and 52% exhibiting the
-weakest-possible evidence. Furthermore, evidence of scale-free structure is not
-uniformly distributed across sources: social networks are at best weakly scale
-free, while a handful of technological and biological networks can be called
-strongly scale free. These results undermine the universality of scale-free
-networks and reveal that real-world networks exhibit a rich structural
-diversity that will likely require new ideas and mechanisms to explain.
-",1,0,0,1,1,0
-18691,"Enlargeability, foliations, and positive scalar curvature","  We extend the deep and important results of Lichnerowicz, Connes, and
-Gromov-Lawson which relate geometry and characteristic numbers to the existence
-and non-existence of metrics of positive scalar curvature (PSC). In particular,
-we show: that a spin foliation with Hausdorff homotopy groupoid of an
-enlargeable manifold admits no PSC metric; that any metric of PSC on such a
-foliation is bounded by a multiple of the reciprocal of the foliation K-area of
-the ambient manifold; and that Connes' vanishing theorem for characteristic
-numbers of PSC foliations extends to a vanishing theorem for Haefliger
-cohomology classes.
-",0,0,1,0,0,0
-18692,A simple mathematical model for unemployment: a case study in Portugal with optimal control,"  We propose a simple mathematical model for unemployment. Despite its
-simpleness, we claim that the model is more realistic and useful than recent
-models available in the literature. A case study with real data from Portugal
-supports our claim. An optimal control problem is formulated and solved, which
-provides some non-trivial and interesting conclusions.
-",0,0,0,0,0,1
-18693,Interpreting Deep Neural Networks Through Variable Importance,"  While the success of deep neural networks (DNNs) is well-established across a
-variety of domains, our ability to explain and interpret these methods is
-limited. Unlike previously proposed local methods which try to explain
-particular classification decisions, we focus on global interpretability and
-ask a universally applicable question: given a trained model, which features
-are the most important? In the context of neural networks, a feature is rarely
-important on its own, so our strategy is specifically designed to leverage
-partial covariance structures and incorporate variable dependence into feature
-ranking. Our methodological contributions in this paper are two-fold. First, we
-propose an effect size analogue for DNNs that is appropriate for applications
-with highly collinear predictors (ubiquitous in computer vision). Second, we
-extend the recently proposed ""RelATive cEntrality"" (RATE) measure (Crawford et
-al., 2019) to the Bayesian deep learning setting. RATE applies an information
-theoretic criterion to the posterior distribution of effect sizes to assess
-feature significance. We apply our framework to three broad application areas:
-computer vision, natural language processing, and social science.
-",1,0,0,1,0,0
-18694,Conformality of $1/N$ corrections in SYK-like models,"  The Sachdev-Ye--Kitaev is a quantum mechanical model of $N$ Majorana fermions
-which displays a number of appealing features -- solvability in the strong
-coupling regime, near-conformal invariance and maximal chaos -- which make it a
-suitable model for black holes in the context of the AdS/CFT holography. In
-this paper we show for the colored SYK model and several of its tensor model
-cousins that the next-to-leading order in the $N$ expansion preserves the
-conformal invariance of the $2$-point function in the strong coupling regime,
-up to the contribution of the Goldstone bosons leading to the spontaneous
-breaking of the symmetry and which are already seen in the leading order
-$4$-point function. We also comment on the composite field approach for
-computing correlation functions in colored tensor models.
-",0,1,0,0,0,0
-18695,Segal-type models of higher categories,"  Higher category theory is an exceedingly active area of research, whose rapid
-growth has been driven by its penetration into a diverse range of scientific
-fields. Its influence extends through key mathematical disciplines, notably
-homotopy theory, algebraic geometry and algebra, mathematical physics, to
-encompass important applications in logic, computer science and beyond. Higher
-categories provide a unifying language whose greatest strength lies in its
-ability to bridge between diverse areas and uncover novel applications.
-In this foundational work we introduce a new approach to higher categories.
-It builds upon the theory of iterated internal categories, one of the simplest
-possible higher categorical structures available, by adopting a novel and
-remarkably simple ""weak globularity"" postulate and demonstrating that the
-resulting model provides a fully general theory of weak n-categories. The
-latter are among the most complex of the higher structures, and are crucial for
-applications. We show that this new model of ""weakly globular n-fold
-categories"" is suitably equivalent to the well studied model of weak
-n-categories due to Tamsamani and Simpson.
-",0,0,1,0,0,0
-18696,Low-Mass Dark Matter Search with CDMSlite,"  The SuperCDMS experiment is designed to directly detect weakly interacting
-massive particles (WIMPs) that may constitute the dark matter in our Galaxy.
-During its operation at the Soudan Underground Laboratory, germanium detectors
-were run in the CDMSlite mode to gather data sets with sensitivity specifically
-for WIMPs with masses ${<}$10 GeV/$c^2$. In this mode, a higher detector-bias
-voltage is applied to amplify the phonon signals produced by drifting charges.
-This paper presents studies of the experimental noise and its effect on the
-achievable energy threshold, which is demonstrated to be as low as 56
-eV$_{\text{ee}}$ (electron equivalent energy). The detector-biasing
-configuration is described in detail, with analysis corrections for voltage
-variations to the level of a few percent. Detailed studies of the
-electric-field geometry, and the resulting successful development of a fiducial
-parameter, eliminate poorly measured events, yielding an energy resolution
-ranging from ${\sim}$9 eV$_{\text{ee}}$ at 0 keV to 101 eV$_{\text{ee}}$ at
-${\sim}$10 eV$_{\text{ee}}$. New results are derived for astrophysical
-uncertainties relevant to the WIMP-search limits, specifically examining how
-they are affected by variations in the most probable WIMP velocity and the
-Galactic escape velocity. These variations become more important for WIMP
-masses below 10 GeV/$c^2$. Finally, new limits on spin-dependent low-mass
-WIMP-nucleon interactions are derived, with new parameter space excluded for
-WIMP masses $\lesssim$3 GeV/$c^2$
-",0,1,0,0,0,0
-18697,Gravity with free initial conditions: a solution to the cosmological constant problem testable by CMB B-mode polarization,"  In standard general relativity the universe cannot be started with arbitrary
-initial conditions, because four of the ten components of the Einstein's field
-equations (EFE) are constraints on initial conditions. In the previous work it
-was proposed to extend the gravity theory to allow free initial conditions,
-with a motivation to solve the cosmological constant problem. This was done by
-setting four constraints on metric variations in the action principle, which is
-reasonable because the gravity's physical degrees of freedom are at most six.
-However, there are two problems about this theory; the three constraints in
-addition to the unimodular condition were introduced without clear physical
-meanings, and the flat Minkowski spacetime is unstable against perturbations.
-Here a new set of gravitational field equations is derived by replacing the
-three constraints with new ones requiring that geodesic paths remain geodesic
-against metric variations. The instability problem is then naturally solved.
-Implications for the cosmological constant $\Lambda$ are unchanged; the theory
-converges into EFE with nonzero $\Lambda$ by inflation, but $\Lambda$ varies on
-scales much larger than the present Hubble horizon. Then galaxies are formed
-only in small $\Lambda$ regions, and the cosmological constant problem is
-solved by the anthropic argument. Because of the increased degrees of freedom
-in metric dynamics, the theory predicts new non-oscillatory modes of metric
-anisotropy generated by quantum fluctuation during inflation, and CMB B-mode
-polarization would be observed differently from the standard predictions by
-general relativity.
-",0,1,0,0,0,0
-18698,Gradual Learning of Recurrent Neural Networks,"  Recurrent Neural Networks (RNNs) achieve state-of-the-art results in many
-sequence-to-sequence modeling tasks. However, RNNs are difficult to train and
-tend to suffer from overfitting. Motivated by the Data Processing Inequality
-(DPI), we formulate the multi-layered network as a Markov chain, introducing a
-training method that comprises training the network gradually and using
-layer-wise gradient clipping. We found that applying our methods, combined with
-previously introduced regularization and optimization methods, resulted in
-improvements in state-of-the-art architectures operating in language modeling
-tasks.
-",1,0,0,1,0,0
-18699,Borel subsets of the real line and continuous reducibility,"  We study classes of Borel subsets of the real line $\mathbb{R}$ such as
-levels of the Borel hierarchy and the class of sets that are reducible to the
-set $\mathbb{Q}$ of rationals, endowed with the Wadge quasi-order of
-reducibility with respect to continuous functions on $\mathbb{R}$. Notably, we
-explore several structural properties of Borel subsets of $\mathbb{R}$ that
-diverge from those of Polish spaces with dimension zero. Our first main result
-is on the existence of embeddings of several posets into the restriction of
-this quasi-order to any Borel class that is strictly above the classes of open
-and closed sets, for instance the linear order $\omega_1$, its reverse
-$\omega_1^\star$ and the poset $\mathcal{P}(\omega)/\mathsf{fin}$ of inclusion
-modulo finite error. As a consequence of its proof, it is shown that there are
-no complete sets for these classes. We further extend the previous theorem to
-targets that are reducible to $\mathbb{Q}$. These non-structure results
-motivate the study of further restrictions of the Wadge quasi-order. In our
-second main theorem, we introduce a combinatorial property that is shown to
-characterize those $F_\sigma$ sets that are reducible to $\mathbb{Q}$. This is
-applied to construct a minimal set below $\mathbb{Q}$ and prove its uniqueness
-up to Wadge equivalence. We finally prove several results concerning gaps and
-cardinal characteristics of the Wadge quasi-order and thereby answer questions
-of Brendle and Geschke.
-",0,0,1,0,0,0
-18700,Pairing from dynamically screened Coulomb repulsion in bismuth,"  Recently, Prakash et. al. have discovered bulk superconductivity in single
-crystals of bismuth, which is a semi metal with extremely low carrier density.
-At such low density, we argue that conventional electron-phonon coupling is too
-weak to be responsible for the binding of electrons into Cooper pairs. We study
-a dynamically screened Coulomb interaction with effective attraction generated
-on the scale of the collective plasma modes. We model the electronic states in
-bismuth to include three Dirac pockets with high velocity and one hole pocket
-with a significantly smaller velocity. We find a weak coupling instability,
-which is greatly enhanced by the presence of the hole pocket. Therefore, we
-argue that bismuth is the first material to exhibit superconductivity driven by
-retardation effects of Coulomb repulsion alone. By using realistic parameters
-for bismuth we find that the acoustic plasma mode does not play the central
-role in pairing. We also discuss a matrix element effect, resulting from the
-Dirac nature of the conduction band, which may affect $T_c$ in the $s$-wave
-channel without breaking time-reversal symmetry.
-",0,1,0,0,0,0
-18701,Calabi-Yau metrics on canonical bundles of complex flag manifolds,"  In the present paper we provide a description of complete Calabi-Yau metrics
-on the canonical bundle of generalized complex flag manifolds. By means of Lie
-theory we give an explicit description of complete Ricci-flat Kähler metrics
-obtained through the Calabi ansatz technique. We use this approach to provide
-several explicit examples of noncompact complete Calabi-Yau manifolds, these
-examples include canonical bundles of non-toric flag manifolds (e.g. Grassmann
-manifolds and full flag manifolds).
-",0,0,1,0,0,0
-18702,The ratio of normalizing constants for Bayesian graphical Gaussian model selection,"  Many graphical Gaussian selection methods in a Bayesian framework use the
-G-Wishart as the conjugate prior on the precision matrix. The Bayes factor to
-compare a model governed by a graph G and a model governed by the neighboring
-graph G-e, derived from G by deleting an edge e, is a function of the ratios of
-prior and posterior normalizing constants of the G-Wishart for G and G-e.
-While more recent methods avoid the computation of the posterior ratio,
-computing the ratio of prior normalizing constants, (2) below, has remained a
-computational stumbling block. In this paper, we propose an explicit analytic
-approximation to (2) which is equal to the ratio of two Gamma functions
-evaluated at (delta+d)/2 and (delta+d+1)/2 respectively, where delta is the
-shape parameter of the G-Wishart and d is the number of paths of length two
-between the endpoints of e. This approximation allows us to avoid Monte Carlo
-methods, is computationally inexpensive and is scalable to high-dimensional
-problems. We show that the ratio of the approximation to the true value is
-always between zero and one and so, one cannot incur wild errors.
-In the particular case where the paths between the endpoints of e are
-disjoint, we show that the approximation is very good. When the paths between
-these two endpoints are not disjoint we give a sufficient condition for the
-approximation to be good. Numerical results show that the ratio of the
-approximation to the true value of the prior ratio is always between .55 and 1
-and very often close to 1. We compare the results obtained with a model search
-using our approximation and a search using the double Metropolis-Hastings
-algorithm to compute the prior ratio. The results are extremely close.
-",0,0,1,1,0,0
-18703,Angiogenic Factors produced by Hypoxic Cells are a leading driver of Anastomoses in Sprouting Angiogenesis---a computational study,"  Angiogenesis - the growth of new blood vessels from a pre-existing
-vasculature - is key in both physiological processes and on several
-pathological scenarios such as cancer progression or diabetic retinopathy. For
-the new vascular networks to be functional, it is required that the growing
-sprouts merge either with an existing functional mature vessel or with another
-growing sprout. This process is called anastomosis. We present a systematic 2D
-and 3D computational study of vessel growth in a tissue to address the
-capability of angiogenic factor gradients to drive anastomosis formation. We
-consider that these growth factors are produced only by tissue cells in
-hypoxia, i.e. until nearby vessels merge and become capable of carrying blood
-and irrigating their vicinity. We demonstrate that this increased production of
-angiogenic factors by hypoxic cells is able to promote vessel anastomoses
-events in both 2D and 3D. The simulations also verify that the morphology of
-these networks has an increased resilience toward variations in the endothelial
-cell's proliferation and chemotactic response. The distribution of tissue
-cell`s and the concentration of the growth factors they produce are the major
-factors in determining the final morphology of the network.
-",0,0,0,0,1,0
-18704,Henri Bénard: Thermal convection and vortex shedding,"  We present in this article the work of Henri Bénard (1874-1939), French
-physicist who began the systematic experimental study of two hydrodynamic
-systems: the thermal convection of fluids heated from below (the
-Rayleigh-Bénard convection and the Bénard-Marangoni convection) and the
-periodical vortex shedding behind a bluff body in a flow (the
-Bénard-Kármán vortex street). Across his scientific biography, we review
-the interplay between experiments and theory in these two major subjects of
-fluid mechanics.
-",0,1,0,0,0,0
-18705,Surjective H-Colouring: New Hardness Results,"  A homomorphism from a graph G to a graph H is a vertex mapping f from the
-vertex set of G to the vertex set of H such that there is an edge between
-vertices f(u) and f(v) of H whenever there is an edge between vertices u and v
-of G. The H-Colouring problem is to decide whether or not a graph G allows a
-homomorphism to a fixed graph H. We continue a study on a variant of this
-problem, namely the Surjective H-Colouring problem, which imposes the
-homomorphism to be vertex-surjective. We build upon previous results and show
-that this problem is NP-complete for every connected graph H that has exactly
-two vertices with a self-loop as long as these two vertices are not adjacent.
-As a result, we can classify the computational complexity of Surjective
-H-Colouring for every graph H on at most four vertices.
-",1,0,1,0,0,0
-18706,Subband adaptive filter trained by differential evolution for channel estimation,"  The normalized subband adaptive filter (NSAF) is widely accepted as a
-preeminent adaptive filtering algorithm because of its efficiency under the
-colored excitation. However, the convergence rate of NSAF is slow. To address
-this drawback, in this paper, a variant of the NSAF, called the differential
-evolution (DE)-NSAF (DE-NSAF), is proposed for channel estimation based on DE
-strategy. It is worth noticing that there are several papers concerning
-designing DE strategies for adaptive filter. But their signal models are still
-the single adaptive filter model rather than the fullband adaptive filter model
-considered in this paper. Thus, the problem considered in our work is quite
-different from those. The proposed DE-NSAF algorithm is based on real-valued
-manipulations and has fast convergence rate for searching the global solution
-of optimized weight vector. Moreover, a design step of new algorithm is given
-in detail. Simulation results demonstrate the improved performance of the
-proposed DE-NSAF algorithm in terms of the convergence rate.
-",1,0,0,0,0,0
-18707,Distributionally Robust Games: f-Divergence and Learning,"  In this paper we introduce the novel framework of distributionally robust
-games. These are multi-player games where each player models the state of
-nature using a worst-case distribution, also called adversarial distribution.
-Thus each player's payoff depends on the other players' decisions and on the
-decision of a virtual player (nature) who selects an adversarial distribution
-of scenarios. This paper provides three main contributions. Firstly, the
-distributionally robust game is formulated using the statistical notions of
-$f$-divergence between two distributions, here represented by the adversarial
-distribution, and the exact distribution. Secondly, the complexity of the
-problem is significantly reduced by means of triality theory. Thirdly,
-stochastic Bregman learning algorithms are proposed to speedup the computation
-of robust equilibria. Finally, the theoretical findings are illustrated in a
-convex setting and its limitations are tested with a non-convex non-concave
-function.
-",1,0,1,0,0,0
-18708,Modeling and Reasoning About Wireless Networks: A Graph-based Calculus Approach,"  We propose a graph-based process calculus for modeling and reasoning about
-wireless networks with local broadcasts. Graphs are used at syntactical level
-to describe the topological structures of networks. This calculus is equipped
-with a reduction semantics and a labelled transition semantics. The former is
-used to define weak barbed congruence. The latter is used to define a
-parameterized weak bisimulation emphasizing locations and local broadcasts. We
-prove that weak bisimilarity implies weak barbed congruence. The potential
-applications are illustrated by some examples and two case studies.
-",1,0,0,0,0,0
-18709,"Weyl's law on $RCD^*(K,N)$ metric measure spaces","  In this paper, we will prove the Weyl's law for the asymptotic formula of
-Dirichlet eigenvalues on metric measure spaces with generalized Ricci curvature
-bounded from below.
-",0,0,1,0,0,0
-18710,The area of the Mandelbrot set and Zagier's conjecture,"  We prove Zagier's conjecture regarding the 2-adic valuation of the
-coefficients $\{b_m\}$ that appear in Ewing and Schober's series formula for
-the area of the Mandelbrot set in the case where $m\equiv 2 \mod 4$.
-",0,0,1,0,0,0
-18711,Accelerating Cross-Validation in Multinomial Logistic Regression with $\ell_1$-Regularization,"  We develop an approximate formula for evaluating a cross-validation estimator
-of predictive likelihood for multinomial logistic regression regularized by an
-$\ell_1$-norm. This allows us to avoid repeated optimizations required for
-literally conducting cross-validation; hence, the computational time can be
-significantly reduced. The formula is derived through a perturbative approach
-employing the largeness of the data size and the model dimensionality. An
-extension to the elastic net regularization is also addressed. The usefulness
-of the approximate formula is demonstrated on simulated data and the ISOLET
-dataset from the UCI machine learning repository.
-",0,1,0,1,0,0
-18712,Realizing an optimization approach inspired from Piagets theory on cognitive development,"  The objective of this paper is to introduce an artificial intelligence based
-optimization approach, which is inspired from Piagets theory on cognitive
-development. The approach has been designed according to essential processes
-that an individual may experience while learning something new or improving his
-/ her knowledge. These processes are associated with the Piagets ideas on an
-individuals cognitive development. The approach expressed in this paper is a
-simple algorithm employing swarm intelligence oriented tasks in order to
-overcome single-objective optimization problems. For evaluating effectiveness
-of this early version of the algorithm, test operations have been done via some
-benchmark functions. The obtained results show that the approach / algorithm
-can be an alternative to the literature in terms of single-objective
-optimization. The authors have suggested the name: Cognitive Development
-Optimization Algorithm (CoDOA) for the related intelligent optimization
-approach.
-",1,0,1,0,0,0
-18713,Catalyst design using actively learned machine with non-ab initio input features towards CO2 reduction reactions,"  In conventional chemisorption model, the d-band center theory (augmented
-sometimes with the upper edge of d-band for imporved accuarcy) plays a central
-role in predicting adsorption energies and catalytic activity as a function of
-d-band center of the solid surfaces, but it requires density functional
-calculations that can be quite costly for large scale screening purposes of
-materials. In this work, we propose to use the d-band width of the muffin-tin
-orbital theory (to account for local coordination environment) plus
-electronegativity (to account for adsorbate renormalization) as a simple set of
-alternative descriptors for chemisorption, which do not demand the ab initio
-calculations. This pair of descriptors are then combined with machine learning
-methods, namely, artificial neural network (ANN) and kernel ridge regression
-(KRR), to allow large scale materials screenings. We show, for a toy set of 263
-alloy systems, that the CO adsorption energy can be predicted with a remarkably
-small mean absolute deviation error of 0.05 eV, a significantly improved result
-as compared to 0.13 eV obtained with descriptors including costly d-band center
-calculations in literature. We achieved this high accuracy by utilizing an
-active learning algorithm, without which the accuracy was 0.18 eV otherwise. As
-a practical application of this machine, we identified Cu3Y@Cu as a highly
-active and cost-effective electrochemical CO2 reduction catalyst to produce CO
-with the overpotential 0.37 V lower than Au catalyst.
-",0,1,0,1,0,0
-18714,Clustering with Temporal Constraints on Spatio-Temporal Data of Human Mobility,"  Extracting significant places or places of interest (POIs) using individuals'
-spatio-temporal data is of fundamental importance for human mobility analysis.
-Classical clustering methods have been used in prior work for detecting POIs,
-but without considering temporal constraints. Usually, the involved parameters
-for clustering are difficult to determine, e.g., the optimal cluster number in
-hierarchical clustering. Currently, researchers either choose heuristic values
-or use spatial distance-based optimization to determine an appropriate
-parameter set. We argue that existing research does not optimally address
-temporal information and thus leaves much room for improvement. Considering
-temporal constraints in human mobility, we introduce an effective clustering
-approach - namely POI clustering with temporal constraints (PC-TC) - to extract
-POIs from spatio-temporal data of human mobility. Following human mobility
-nature in modern society, our approach aims to extract both global POIs (e.g.,
-workplace or university) and local POIs (e.g., library, lab, and canteen).
-Based on two publicly available datasets including 193 individuals, our
-evaluation results show that PC-TC has much potential for next place prediction
-in terms of granularity (i.e., the number of extracted POIs) and
-predictability.
-",0,0,0,1,0,0
-18715,The maximum number of zeros of $r(z) - \overline{z}$ revisited,"  Generalizing several previous results in the literature on rational harmonic
-functions, we derive bounds on the maximum number of zeros of functions $f(z) =
-\frac{p(z)}{q(z)} - \overline{z}$, which depend on both $\mathrm{deg}(p)$ and
-$\mathrm{deg}(q)$. Furthermore, we prove that any function that attains one of
-these upper bounds is regular.
-",0,0,1,0,0,0
-18716,High Luminosity Large Hadron Collider HL-LHC,"  HL-LHC federates the efforts and R&D of a large international community
-towards the ambitious HL- LHC objectives and contributes to establishing the
-European Research Area (ERA) as a focal point of global research cooperation
-and a leader in frontier knowledge and technologies. HL-LHC relies on strong
-participation from various partners, in particular from leading US and Japanese
-laboratories. This participation will be required for the execution of the
-construction phase as a global project. In particular, the US LHC Accelerator
-R&D Program (LARP) has developed some of the key technologies for the HL-LHC,
-such as the large-aperture niobium-tin ($Nb_{3}Sn) quadrupoles and the crab
-cavities. The proposed governance model is tailored accordingly and should pave
-the way for the organization of the construction phase.
-",0,1,0,0,0,0
-18717,Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies,"  Various approaches have been proposed to learn visuo-motor policies for
-real-world robotic applications. One solution is first learning in simulation
-then transferring to the real world. In the transfer, most existing approaches
-need real-world images with labels. However, the labelling process is often
-expensive or even impractical in many robotic applications. In this paper, we
-propose an adversarial discriminative sim-to-real transfer approach to reduce
-the cost of labelling real data. The effectiveness of the approach is
-demonstrated with modular networks in a table-top object reaching task where a
-7 DoF arm is controlled in velocity mode to reach a blue cuboid in clutter
-through visual observations. The adversarial transfer approach reduced the
-labelled real data requirement by 50%. Policies can be transferred to real
-environments with only 93 labelled and 186 unlabelled real images. The
-transferred visuo-motor policies are robust to novel (not seen in training)
-objects in clutter and even a moving target, achieving a 97.8% success rate and
-1.8 cm control accuracy.
-",1,0,0,0,0,0
-18718,Algebraic Bethe ansatz for the trigonometric sl(2) Gaudin model with triangular boundary,"  In the derivation of the generating function of the Gaudin Hamiltonians with
-boundary terms, we follow the same approach used previously in the rational
-case, which in turn was based on Sklyanin's method in the periodic case. Our
-derivation is centered on the quasi-classical expansion of the linear
-combination of the transfer matrix of the XXZ Heisenberg spin chain and the
-central element, the so-called Sklyanin determinant. The corresponding Gaudin
-Hamiltonians with boundary terms are obtained as the residues of the generating
-function. By defining the appropriate Bethe vectors which yield strikingly
-simple off-shell action of the generating function, we fully implement the
-algebraic Bethe ansatz, obtaining the spectrum of the generating function and
-the corresponding Bethe equations.
-",0,1,0,0,0,0
-18719,Marked Temporal Dynamics Modeling based on Recurrent Neural Network,"  We are now witnessing the increasing availability of event stream data, i.e.,
-a sequence of events with each event typically being denoted by the time it
-occurs and its mark information (e.g., event type). A fundamental problem is to
-model and predict such kind of marked temporal dynamics, i.e., when the next
-event will take place and what its mark will be. Existing methods either
-predict only the mark or the time of the next event, or predict both of them,
-yet separately. Indeed, in marked temporal dynamics, the time and the mark of
-the next event are highly dependent on each other, requiring a method that
-could simultaneously predict both of them. To tackle this problem, in this
-paper, we propose to model marked temporal dynamics by using a mark-specific
-intensity function to explicitly capture the dependency between the mark and
-the time of the next event. Extensive experiments on two datasets demonstrate
-that the proposed method outperforms state-of-the-art methods at predicting
-marked temporal dynamics.
-",1,0,0,0,0,0
-18720,A Bayesian framework for distributed estimation of arrival rates in asynchronous networks,"  In this paper we consider a network of agents monitoring a spatially
-distributed arrival process. Each node measures the number of arrivals seen at
-its monitoring point in a given time-interval with the objective of estimating
-the unknown local arrival rate. We propose an asynchronous distributed approach
-based on a Bayesian model with unknown hyperparameter, where each node computes
-the minimum mean square error (MMSE) estimator of its local arrival rate in a
-distributed way. As a result, the estimation at each node ""optimally"" fuses the
-information from the whole network through a distributed optimization
-algorithm. Moreover, we propose an ad-hoc distributed estimator, based on a
-consensus algorithm for time-varying and directed graphs, which exhibits
-reduced complexity and exponential convergence. We analyze the performance of
-the proposed distributed estimators, showing that they: (i) are reliable even
-in presence of limited local data, and (ii) improve the estimation accuracy
-compared to the purely decentralized setup. Finally, we provide a statistical
-characterization of the proposed estimators. In particular, for the ad-hoc
-estimator, we show that as the number of nodes goes to infinity its mean square
-error converges to the optimal one. Numerical Monte Carlo simulations confirm
-the theoretical characterization and highlight the appealing performances of
-the estimators.
-",1,0,1,0,0,0
-18721,Rigidity of branching microstructures in shape memory alloys,"  We analyze generic sequences for which the geometrically linear energy
-\[E_\eta(u,\chi):= \eta^{-\frac{2}{3}}\int_{B_{0}(1)} \left| e(u)-
-\sum_{i=1}^3 \chi_ie_i\right|^2 d x+\eta^\frac{1}{3} \sum_{i=1}^3
-|D\chi_i|(B_{0}(1))\] remains bounded in the limit $\eta \to 0$. Here $ e(u)
-:=1/2(Du + Du^T)$ is the (linearized) strain of the displacement $u$, the
-strains $e_i$ correspond to the martensite strains of a shape memory alloy
-undergoing cubic-to-tetragonal transformations and $\chi_i:B_{0}(1) \to
-\{0,1\}$ is the partition into phases. In this regime it is known that in
-addition to simple laminates also branched structures are possible, which if
-austenite was present would enable the alloy to form habit planes.
-In an ansatz-free manner we prove that the alignment of macroscopic
-interfaces between martensite twins is as predicted by well-known rank-one
-conditions. Our proof proceeds via the non-convex, non-discrete-valued
-differential inclusion \[e(u) \in \bigcup_{1\leq i\neq j\leq 3}
-\operatorname{conv} \{e_i,e_j\}\] satisfied by the weak limits of bounded
-energy sequences and of which we classify all solutions. In particular, there
-exist no convex integration solutions of the inclusion with complicated
-geometric structures.
-",0,0,1,0,0,0
-18722,"Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository","  Machine learning qualifies computers to assimilate with data, without being
-solely programmed [1, 2]. Machine learning can be classified as supervised and
-unsupervised learning. In supervised learning, computers learn an objective
-that portrays an input to an output hinged on training input-output pairs [3].
-Most efficient and widely used supervised learning algorithms are K-Nearest
-Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor
-(LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this
-paper is to implement these elegant learning algorithms on eleven different
-datasets from the UCI machine learning repository to observe the variation of
-accuracies for each of the algorithms on all datasets. Analyzing the accuracy
-of the algorithms will give us a brief idea about the relationship of the
-machine learning algorithms and the data dimensionality. All the algorithms are
-developed in Matlab. Upon such accuracy observation, the comparison can be
-built among KNN, SVM, LMNN, and ENN regarding their performances on each
-dataset.
-",0,0,0,1,0,0
-18723,Network Classification in Temporal Networks Using Motifs,"  Network classification has a variety of applications, such as detecting
-communities within networks and finding similarities between those representing
-different aspects of the real world. However, most existing work in this area
-focus on examining static undirected networks without considering directed
-edges or temporality. In this paper, we propose a new methodology that utilizes
-feature representation for network classification based on the temporal motif
-distribution of the network and a null model for comparing against random
-graphs. Experimental results show that our method improves accuracy by up
-$10\%$ compared to the state-of-the-art embedding method in network
-classification, for tasks such as classifying network type, identifying
-communities in email exchange network, and identifying users given their
-app-switching behaviors.
-",1,0,0,0,0,0
-18724,The strength of Ramsey's theorem for pairs and arbitrarily many colors,"  In this paper, we show that $\mathrm{RT}^{2}+\mathsf{WKL}_0$ is a
-$\Pi^{1}_{1}$-conservative extension of $\mathrm{B}\Sigma^0_3$.
-",0,0,1,0,0,0
-18725,Joint Power Allocation and Beamforming for Energy-Efficient Two-Way Multi-Relay Communications,"  This paper considers the joint design of user power allocation and relay
-beamforming in relaying communications, in which multiple pairs of
-single-antenna users exchange information with each other via multiple-antenna
-relays in two time slots. All users transmit their signals to the relays in the
-first time slot while the relays broadcast the beamformed signals to all users
-in the second time slot. The aim is to maximize the system's energy efficiency
-(EE) subject to quality-of-service (QoS) constraints in terms of exchange
-throughput requirements. The QoS constraints are nonconvex with many nonlinear
-cross-terms, so finding a feasible point is already computationally
-challenging. The sum throughput appears in the numerator while the total
-consumption power appears in the denominator of the EE objective function. The
-former is a nonconcave function and the latter is a nonconvex function, making
-fractional programming useless for EE optimization. Nevertheless, efficient
-iterations of low complexity to obtain its optimized solutions are developed.
-The performances of the multiple-user and multiple-relay networks under various
-scenarios are evaluated to show the merit of the paper development.
-",1,0,0,0,0,0
-18726,Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen,"  Armed conflict has led to an unprecedented number of internally displaced
-persons (IDPs) - individuals who are forced out of their homes but remain
-within their country. IDPs often urgently require shelter, food, and
-healthcare, yet prediction of when large fluxes of IDPs will cross into an area
-remains a major challenge for aid delivery organizations. Accurate forecasting
-of IDP migration would empower humanitarian aid groups to more effectively
-allocate resources during conflicts. We show that monthly flow of IDPs from
-province to province in both Syria and Yemen can be accurately forecasted one
-month in advance, using publicly available data. We model monthly IDP flow
-using data on food price, fuel price, wage, geospatial, and news data. We find
-that machine learning approaches can more accurately forecast migration trends
-than baseline persistence models. Our findings thus potentially enable
-proactive aid allocation for IDPs in anticipation of forecasted arrivals.
-",0,0,0,1,0,0
-18727,Entropy? Honest!,"  Here we deconstruct, and then in a reasoned way reconstruct, the concept of
-""entropy of a system,"" paying particular attention to where the randomness may
-be coming from. We start with the core concept of entropy as a COUNT associated
-with a DESCRIPTION; this count (traditionally expressed in logarithmic form for
-a number of good reasons) is in essence the number of possibilities---specific
-instances or ""scenarios,"" that MATCH that description. Very natural (and
-virtually inescapable) generalizations of the idea of description are the
-probability distribution and of its quantum mechanical counterpart, the density
-operator.
-We track the process of dynamically updating entropy as a system evolves.
-Three factors may cause entropy to change: (1) the system's INTERNAL DYNAMICS;
-(2) unsolicited EXTERNAL INFLUENCES on it; and (3) the approximations one has
-to make when one tries to predict the system's future state. The latter task is
-usually hampered by hard-to-quantify aspects of the original description,
-limited data storage and processing resource, and possibly algorithmic
-inadequacy. Factors 2 and 3 introduce randomness into one's predictions and
-accordingly degrade them. When forecasting, as long as the entropy bookkeping
-is conducted in an HONEST fashion, this degradation will ALWAYS lead to an
-entropy increase.
-To clarify the above point we introduce the notion of HONEST ENTROPY, which
-coalesces much of what is of course already done, often tacitly, in responsible
-entropy-bookkeping practice. This notion, we believe, will help to fill an
-expressivity gap in scientific discourse. With its help we shall prove that ANY
-dynamical system---not just our physical universe---strictly obeys Clausius's
-original formulation of the second law of thermodynamics IF AND ONLY IF it is
-invertible. Thus this law is a TAUTOLOGICAL PROPERTY of invertible systems!
-",0,1,0,0,0,0
-18728,Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors,"  We study the problem of generating adversarial examples in a black-box
-setting in which only loss-oracle access to a model is available. We introduce
-a framework that conceptually unifies much of the existing work on black-box
-attacks, and we demonstrate that the current state-of-the-art methods are
-optimal in a natural sense. Despite this optimality, we show how to improve
-black-box attacks by bringing a new element into the problem: gradient priors.
-We give a bandit optimization-based algorithm that allows us to seamlessly
-integrate any such priors, and we explicitly identify and incorporate two
-examples. The resulting methods use two to four times fewer queries and fail
-two to five times less often than the current state-of-the-art.
-",0,0,0,1,0,0
-18729,A Dynamic Model of Central Counterparty Risk,"  We introduce a dynamic model of the default waterfall of derivatives CCPs and
-propose a risk sensitive method for sizing the initial margin (IM), and the
-default fund (DF) and its allocation among clearing members. Using a Markovian
-structure model of joint credit migrations, our evaluation of DF takes into
-account the joint credit quality of clearing members as they evolve over time.
-Another important aspect of the proposed methodology is the use of the time
-consistent dynamic risk measures for computation of IM and DF. We carry out a
-comprehensive numerical study, where, in particular, we analyze the advantages
-of the proposed methodology and its comparison with the currently prevailing
-methods used in industry.
-",0,0,0,0,0,1
-18730,Half-Duplex Base Station with Adaptive Scheduling of the in-Band Uplink-Receptions and Downlink-Transmissions,"  In this paper, we propose a novel reception/transmission scheme for
-half-duplex base stations (BSs). In particular, we propose a half-duplex BS
-that employes in-band uplink-receptions from user 1 and downlink-transmissions
-to user 2, which occur in different time slots. Furthermore, we propose optimal
-adaptive scheduling of the in-band uplink-receptions and downlink-transmissions
-of the BS such that the uplink-downlink rate/throughput region is maximized and
-the outage probabilities of the uplink and downlink channels are minimized.
-Practically, this results in selecting whether in a given time slot the BS
-should receive from user 1 or transmit to user 2 based on the qualities of the
-in-band uplink-reception and downlink-transmission channels. Compared to the
-performance achieved with a conventional full-duplex division (FDD) base
-station, two main gains can be highlighted: 1) Increased uplink-downlink
-rate/throughput region; 2) Doubling of the diversity gain of both the uplink
-and downlink channels.
-",1,0,0,0,0,0
-18731,HAT-P-26b: A Neptune-Mass Exoplanet with a Well Constrained Heavy Element Abundance,"  A correlation between giant-planet mass and atmospheric heavy elemental
-abundance was first noted in the past century from observations of planets in
-our own Solar System, and has served as a cornerstone of planet formation
-theory. Using data from the Hubble and Spitzer Space Telescopes from 0.5 to 5
-microns, we conducted a detailed atmospheric study of the transiting
-Neptune-mass exoplanet HAT-P-26b. We detected prominent H2O absorption bands
-with a maximum base-to-peak amplitude of 525ppm in the transmission spectrum.
-Using the water abundance as a proxy for metallicity, we measured HAT-P-26b's
-atmospheric heavy element content [4.8 (-4.0 +21.5) times solar]. This likely
-indicates that HAT-P-26b's atmosphere is primordial and obtained its gaseous
-envelope late in its disk lifetime, with little contamination from metal-rich
-planetesimals.
-",0,1,0,0,0,0
-18732,Robust Localization Using Range Measurements with Unknown and Bounded Errors,"  Cooperative geolocation has attracted significant research interests in
-recent years. A large number of localization algorithms rely on the
-availability of statistical knowledge of measurement errors, which is often
-difficult to obtain in practice. Compared with the statistical knowledge of
-measurement errors, it can often be easier to obtain the measurement error
-bound. This work investigates a localization problem assuming unknown
-measurement error distribution except for a bound on the error. We first
-formulate this localization problem as an optimization problem to minimize the
-worst-case estimation error, which is shown to be a non-convex optimization
-problem. Then, relaxation is applied to transform it into a convex one.
-Furthermore, we propose a distributed algorithm to solve the problem, which
-will converge in a few iterations. Simulation results show that the proposed
-algorithms are more robust to large measurement errors than existing algorithms
-in the literature. Geometrical analysis providing additional insights is also
-provided.
-",0,0,0,1,0,0
-18733,Intuitionistic Non-Normal Modal Logics: A general framework,"  We define a family of intuitionistic non-normal modal logics; they can bee
-seen as intuitionistic counterparts of classical ones. We first consider
-monomodal logics, which contain only one between Necessity and Possibility. We
-then consider the more important case of bimodal logics, which contain both
-modal operators. In this case we define several interactions between Necessity
-and Possibility of increasing strength, although weaker than duality. For all
-logics we provide both a Hilbert axiomatisation and a cut-free sequent
-calculus, on its basis we also prove their decidability. We then give a
-semantic characterisation of our logics in terms of neighbourhood models. Our
-semantic framework captures modularly not only our systems but also already
-known intuitionistic non-normal modal logics such as Constructive K (CK) and
-the propositional fragment of Wijesekera's Constructive Concurrent Dynamic
-Logic.
-",1,0,0,0,0,0
-18734,Externalities in Socially-Based Resource Sharing Network,"  This paper investigates the impact of link formation between a pair of agents
-on resource availability of other agents in a social cloud network, which is a
-special case of socially-based resource sharing systems. Specifically, we study
-the correlation between externalities, network size, and network density.
-We first conjecture and experimentally support that if an agent experiences
-positive externalities, then its closeness (harmonic centrality measure) should
-increase. Next, we show the following for ring networks: in less populated
-networks no agent experiences positive externalities; in more populated
-networks a set of agents experience positive externalities, and larger the
-distance between agents forming a link, more the number of beneficiaries; and
-the number of beneficiaries is always less than the number of
-non-beneficiaries. Finally, we show that network density is inversely
-proportional to positive externalities, and further, it plays a crucial role in
-determining the kind of externalities.
-",1,0,0,0,0,0
-18735,Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits,"  Consider the problem: given data pair $(\mathbf{x}, \mathbf{y})$ drawn from a
-population with $f_*(x) = \mathbf{E}[\mathbf{y} | \mathbf{x} = x]$, specify a
-neural network and run gradient flow on the weights over time until reaching
-any stationarity. How does $f_t$, the function computed by the neural network
-at time $t$, relate to $f_*$, in terms of approximation and representation?
-What are the provable benefits of the adaptive representation by neural
-networks compared to the pre-specified fixed basis representation in the
-classical nonparametric literature? We answer the above questions via a dynamic
-reproducing kernel Hilbert space (RKHS) approach indexed by the training
-process of neural networks. We show that when reaching any local stationarity,
-gradient flow learns an adaptive RKHS representation, and performs the global
-least squares projection onto the adaptive RKHS, simultaneously. In addition,
-we prove that as the RKHS is data-adaptive and task-specific, the residual for
-$f_*$ lies in a subspace that is smaller than the orthogonal complement of the
-RKHS, formalizing the representation and approximation benefits of neural
-networks.
-",1,0,1,1,0,0
-18736,On utility maximization without passing by the dual problem,"  We treat utility maximization from terminal wealth for an agent with utility
-function $U:\mathbb{R}\to\mathbb{R}$ who dynamically invests in a
-continuous-time financial market and receives a possibly unbounded random
-endowment. We prove the existence of an optimal investment without introducing
-the associated dual problem. We rely on a recent result of Orlicz space theory,
-due to Delbaen and Owari which leads to a simple and transparent proof.
-Our results apply to non-smooth utilities and even strict concavity can be
-relaxed. We can handle certain random endowments with non-hedgeable risks,
-complementing earlier papers. Constraints on the terminal wealth can also be
-incorporated.
-As examples, we treat frictionless markets with finitely many assets and
-large financial markets.
-",0,0,1,0,0,0
-18737,Stochastic Generative Hashing,"  Learning-based binary hashing has become a powerful paradigm for fast search
-and retrieval in massive databases. However, due to the requirement of discrete
-outputs for the hash functions, learning such functions is known to be very
-challenging. In addition, the objective functions adopted by existing hashing
-techniques are mostly chosen heuristically. In this paper, we propose a novel
-generative approach to learn hash functions through Minimum Description Length
-principle such that the learned hash codes maximally compress the dataset and
-can also be used to regenerate the inputs. We also develop an efficient
-learning algorithm based on the stochastic distributional gradient, which
-avoids the notorious difficulty caused by binary output constraints, to jointly
-optimize the parameters of the hash function and the associated generative
-model. Extensive experiments on a variety of large-scale datasets show that the
-proposed method achieves better retrieval results than the existing
-state-of-the-art methods.
-",1,0,0,1,0,0
-18738,Nonlinear learning and learning advantages in evolutionary games,"  The idea of incompetence as a learning or adaptation function was introduced
-in the context of evolutionary games as a fixed parameter. However, live
-organisms usually perform different nonlinear adaptation functions such as a
-power law or exponential fitness growth. Here, we examine how the functional
-form of the learning process may affect the social competition between
-different behavioral types. Further, we extend our results for the evolutionary
-games where fluctuations in the environment affect the behavioral adaptation of
-competing species and demonstrate importance of the starting level of
-incompetence for survival. Hence, we define a new concept of learning
-advantages that becomes crucial when environments are constantly changing and
-requiring rapid adaptation from species. This may lead to the evolutionarily
-weak phase when even evolutionary stable populations become vulnerable to
-invasions.
-",0,0,0,0,1,0
-18739,Information-theoretic Limits for Community Detection in Network Models,"  We analyze the information-theoretic limits for the recovery of node labels
-in several network models. This includes the Stochastic Block Model, the
-Exponential Random Graph Model, the Latent Space Model, the Directed
-Preferential Attachment Model, and the Directed Small-world Model. For the
-Stochastic Block Model, the non-recoverability condition depends on the
-probabilities of having edges inside a community, and between different
-communities. For the Latent Space Model, the non-recoverability condition
-depends on the dimension of the latent space, and how far and spread are the
-communities in the latent space. For the Directed Preferential Attachment Model
-and the Directed Small-world Model, the non-recoverability condition depends on
-the ratio between homophily and neighborhood size. We also consider dynamic
-versions of the Stochastic Block Model and the Latent Space Model.
-",1,0,0,1,0,0
-18740,Proofs of life: molecular-biology reasoning simulates cell behaviors from first principles,"  We axiomatize the molecular-biology reasoning style, verify compliance of the
-standard reference: Ptashne, A Genetic Switch, and present proof-theory-induced
-technologies to predict phenotypes and life cycles from genotypes. The key is
-to note that `reductionist discipline' entails constructive reasoning, i.e.,
-that any argument for a compound property is constructed from more basic
-arguments. Proof theory makes explicit the inner structure of the axiomatized
-reasoning style and allows the permissible dynamics to be presented as a mode
-of computation that can be executed and analyzed. Constructivity and
-executability guarantee simulation when working over domain-specific languages.
-Here, we exhibit phenotype properties for genotype reasons: a molecular-biology
-argument is an open-system concurrent computation that results in compartment
-changes and is performed among processes of physiology change as determined
-from the molecular programming of given DNA. Life cycles are the possible
-sequentializations of the processes. A main implication of our construction is
-that technical correctness provides a complementary perspective on science that
-is as fundamental there as it is for pure mathematics, provided mature
-reductionism exists.
-",0,0,0,0,1,0
-18741,Beyond recursion operators,"  We briefly recall the history of the Nijenhuis torsion of (1,1)-tensors on
-manifolds and of the lesser-known Haantjes torsion. We then show how the
-Haantjes manifolds of Magri and the symplectic-Haantjes structures of Tempesta
-and Tondo generalize the classical approach to integrable systems in the
-bi-hamiltonian and symplectic-Nijenhuis formalisms, the sequence of powers of
-the recursion operator being replaced by a family of commuting Haantjes
-operators.
-",0,0,1,0,0,0
-18742,On Classical Integrability of the Hydrodynamics of Quantum Integrable Systems,"  Recently, a hydrodynamic description of local equilibrium dynamics in quantum
-integrable systems was discovered. In the diffusionless limit, this is
-equivalent to a certain ""Bethe-Boltzmann"" kinetic equation, which has the form
-of an integro-differential conservation law in $(1+1)$D. The purpose of the
-present work is to investigate the sense in which the Bethe-Boltzmann equation
-defines an ""integrable kinetic equation"". To this end, we study a class of $N$
-dimensional systems of evolution equations that arise naturally as
-finite-dimensional approximations to the Bethe-Boltzmann equation. We obtain
-non-local Poisson brackets and Hamiltonian densities for these equations and
-derive an infinite family of first integrals, parameterized by $N$ functional
-degrees of freedom. We find that the conserved charges arising from quantum
-integrability map to Casimir invariants of the hydrodynamic bracket and their
-group velocities map to Hamiltonian flows. Some results from the
-finite-dimensional setting extend to the underlying integro-differential
-equation, providing evidence for its integrability in the hydrodynamic sense.
-",0,1,0,0,0,0
-18743,Transition to turbulence when the Tollmien-Schlichting and bypass routes coexist,"  Plane Poiseuille flow, the pressure driven flow between parallel plates,
-shows a route to turbulence connected with a linear instability to
-Tollmien-Schlichting (TS) waves, and another one, the bypass transition, that
-is triggered with finite amplitude perturbation. We use direct numerical
-simulations to explore the arrangement of the different routes to turbulence
-among the set of initial conditions. For plates that are a distance $2H$ apart
-and in a domain of width $2\pi H$ and length $2\pi H$ the subcritical
-instability to TS waves sets in at $Re_{c}=5815$ that extends down to
-$Re_{TS}\approx4884$. The bypass route becomes available above $Re_E=459$ with
-the appearance of three-dimensional finite-amplitude traveling waves. The
-bypass transition covers a large set of finite amplitude perturbations. Below
-$Re_c$, TS appear for a tiny set of initial conditions that grows with
-increasing Reynolds number. Above $Re_c$ the previously stable region becomes
-unstable via TS waves, but a sharp transition to the bypass route can still be
-identified. Both routes lead to the same turbulent in the final stage of the
-transition, but on different time scales. Similar phenomena can be expected in
-other flows where two or more routes to turbulence compete.
-",0,1,0,0,0,0
-18744,Interactive Exploration and Discovery of Scientific Publications with PubVis,"  With an exponentially growing number of scientific papers published each
-year, advanced tools for exploring and discovering publications of interest are
-becoming indispensable. To empower users beyond a simple keyword search
-provided e.g. by Google Scholar, we present the novel web application PubVis.
-Powered by a variety of machine learning techniques, it combines essential
-features to help researchers find the content most relevant to them. An
-interactive visualization of a large collection of scientific publications
-provides an overview of the field and encourages the user to explore articles
-beyond a narrow research focus. This is augmented by personalized content based
-article recommendations as well as an advanced full text search to discover
-relevant references. The open sourced implementation of the app can be easily
-set up and run locally on a desktop computer to provide access to content
-tailored to the specific needs of individual users. Additionally, a PubVis demo
-with access to a collection of 10,000 papers can be tested online.
-",1,0,0,0,0,0
-18745,Improving phase II oncology trials using best observed RECIST response as an endpoint by modelling continuous tumour measurements,"  In many phase II trials in solid tumours, patients are assessed using
-endpoints based on the Response Evaluation Criteria in Solid Tumours (RECIST)
-scale. Often, analyses are based on the response rate. This is the proportion
-of patients who have an observed tumour shrinkage above a pre-defined level and
-no new tumour lesions. The augmented binary method has been proposed to improve
-the precision of the estimator of the response rate. The method involves
-modelling the tumour shrinkage to avoid dichotomising it. However, in many
-trials the best observed response is used as the primary outcome. In such
-trials, patients are followed until progression, and their best observed RECIST
-outcome is used as the primary endpoint. In this paper, we propose a method
-that extends the augmented binary method so that it can be used when the
-outcome is best observed response. We show through simulated data and data from
-a real phase II cancer trial that this method improves power in both single-arm
-and randomised trials. The average gain in power compared to the traditional
-analysis is equivalent to approximately a 35% increase in sample size. A
-modified version of the method is proposed to reduce the computational effort
-required. We show this modified method maintains much of the efficiency
-advantages.
-",0,0,0,1,0,0
-18746,Evans-Selberg potential on planar domains,"  We provide explicit formulas of Evans kernels, Evans-Selberg potentials and
-fundamental metrics on potential-theoretically parabolic planar domains.
-",0,0,1,0,0,0
-18747,Bridging the Gap Between Computational Photography and Visual Recognition,"  What is the current state-of-the-art for image restoration and enhancement
-applied to degraded images acquired under less than ideal circumstances? Can
-the application of such algorithms as a pre-processing step to improve image
-interpretability for manual analysis or automatic visual recognition to
-classify scene content? While there have been important advances in the area of
-computational photography to restore or enhance the visual quality of an image,
-the capabilities of such techniques have not always translated in a useful way
-to visual recognition tasks. Consequently, there is a pressing need for the
-development of algorithms that are designed for the joint problem of improving
-visual appearance and recognition, which will be an enabling factor for the
-deployment of visual recognition tools in many real-world scenarios. To address
-this, we introduce the UG^2 dataset as a large-scale benchmark composed of
-video imagery captured under challenging conditions, and two enhancement tasks
-designed to test algorithmic impact on visual quality and automatic object
-recognition. Furthermore, we propose a set of metrics to evaluate the joint
-improvement of such tasks as well as individual algorithmic advances, including
-a novel psychophysics-based evaluation regime for human assessment and a
-realistic set of quantitative measures for object recognition performance. We
-introduce six new algorithms for image restoration or enhancement, which were
-created as part of the IARPA sponsored UG^2 Challenge workshop held at CVPR
-2018. Under the proposed evaluation regime, we present an in-depth analysis of
-these algorithms and a host of deep learning-based and classic baseline
-approaches. From the observed results, it is evident that we are in the early
-days of building a bridge between computational photography and visual
-recognition, leaving many opportunities for innovation in this area.
-",1,0,0,0,0,0
-18748,On some conjectures of Samuels and Feige,"  Let $\mu_1 \ge \dotsc \ge \mu_n > 0$ and $\mu_1 + \dotsm + \mu_n = 1$. Let
-$X_1, \dotsc, X_n$ be independent non-negative random variables with $EX_1 =
-\dotsc = EX_n = 1$, and let $Z = \sum_{i=1}^n \mu_i X_i$. Let $M = \max_{1 \le
-i \le n} \mu_i = \mu_1$, and let $\delta > 0$ and $T = 1 + \delta$. Both
-Samuels and Feige formulated conjectures bounding the probability $P(Z < T)$
-from above. We prove that Samuels' conjecture implies a conjecture of Feige.
-",0,0,1,0,0,0
-18749,Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States,"  Making the right decision in traffic is a challenging task that is highly
-dependent on individual preferences as well as the surrounding environment.
-Therefore it is hard to model solely based on expert knowledge. In this work we
-use Deep Reinforcement Learning to learn maneuver decisions based on a compact
-semantic state representation. This ensures a consistent model of the
-environment across scenarios as well as a behavior adaptation function,
-enabling on-line changes of desired behaviors without re-training. The input
-for the neural network is a simulated object list similar to that of Radar or
-Lidar sensors, superimposed by a relational semantic scene description. The
-state as well as the reward are extended by a behavior adaptation function and
-a parameterization respectively. With little expert knowledge and a set of
-mid-level actions, it can be seen that the agent is capable to adhere to
-traffic rules and learns to drive safely in a variety of situations.
-",1,0,0,1,0,0
-18750,Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows,"  We present Sequential Neural Likelihood (SNL), a new method for Bayesian
-inference in simulator models, where the likelihood is intractable but
-simulating data from the model is possible. SNL trains an autoregressive flow
-on simulated data in order to learn a model of the likelihood in the region of
-high posterior density. A sequential training procedure guides simulations and
-reduces simulation cost by orders of magnitude. We show that SNL is more
-robust, more accurate and requires less tuning than related neural-based
-methods, and we discuss diagnostics for assessing calibration, convergence and
-goodness-of-fit.
-",0,0,0,1,0,0
-18751,Creativity: Generating Diverse Questions using Variational Autoencoders,"  Generating diverse questions for given images is an important task for
-computational education, entertainment and AI assistants. Different from many
-conventional prediction techniques is the need for algorithms to generate a
-diverse set of plausible questions, which we refer to as ""creativity"". In this
-paper we propose a creative algorithm for visual question generation which
-combines the advantages of variational autoencoders with long short-term memory
-networks. We demonstrate that our framework is able to generate a large set of
-varying questions given a single input image.
-",1,0,0,0,0,0
-18752,Cooling dynamics of a single trapped ion via elastic collisions with small-mass atoms,"  We demonstrated sympathetic cooling of a single ion in a buffer gas of
-ultracold atoms with small mass. Efficient collisional cooling was realized by
-suppressing collision-induced heating. We attempt to explain the experimental
-results with a simple rate equation model and provide a quantitative discussion
-of the cooling efficiency per collision. The knowledge we obtained in this work
-is an important ingredient for advancing the technique of sympathetic cooling
-of ions with neutral atoms.
-",0,1,0,0,0,0
-18753,Modulational instability in the full-dispersion Camassa-Holm equation,"  We determine the stability and instability of a sufficiently small and
-periodic traveling wave to long wavelength perturbations, for a nonlinear
-dispersive equation which extends a Camassa-Holm equation to include all the
-dispersion of water waves and the Whitham equation to include nonlinearities of
-medium amplitude waves. In the absence of the effects of surface tension, the
-result qualitatively agrees with the Benjamin-Feir instability of a Stokes
-wave. In the presence of the effects of surface tension, it qualitatively
-agrees with those from formal asymptotic expansions of the physical problem and
-it improves upon that for the Whitham equation, correctly predicting the limit
-of strong surface tension. We discuss the modulational stability and
-instability in the Camassa-Holm equation and related models.
-",0,1,1,0,0,0
-18754,Evaluating and Modelling Hanabi-Playing Agents,"  Agent modelling involves considering how other agents will behave, in order
-to influence your own actions. In this paper, we explore the use of agent
-modelling in the hidden-information, collaborative card game Hanabi. We
-implement a number of rule-based agents, both from the literature and of our
-own devising, in addition to an Information Set Monte Carlo Tree Search
-(IS-MCTS) agent. We observe poor results from IS-MCTS, so construct a new,
-predictor version that uses a model of the agents with which it is paired. We
-observe a significant improvement in game-playing strength from this agent in
-comparison to IS-MCTS, resulting from its consideration of what the other
-agents in a game would do. In addition, we create a flawed rule-based agent to
-highlight the predictor's capabilities with such an agent.
-",1,0,0,0,0,0
-18755,Rank-related dimension bounds for subspaces of bilinear forms over finite fields,"  Let q be a power of a prime and let V be a vector space of finite dimension n
-over the field of order q. Let Bil(V) denote the set of all bilinear forms
-defined on V x V, let Symm(V) denote the subspace of Bil(V) consisting of
-symmetric bilinear forms, and Alt(V) denote the subspace of alternating
-bilinear forms. Let M denote a subspace of any of the spaces Bil(V), Symm(V),
-or Alt(V). In this paper we investigate hypotheses on the rank of the non-zero
-elements of M which lead to reasonable bounds for dim M. Typically, we look at
-the case where exactly two or three non-zero ranks occur, one of which is
-usually n. In the case that M achieves the maximal dimension predicted by the
-dimension bound, we try to enumerate the number of forms of a given rank in M
-and describe geometric properties of the radicals of the degenerate elements of
-M.
-",0,0,1,0,0,0
-18756,Multi-objective optimization to explicitly account for model complexity when learning Bayesian Networks,"  Bayesian Networks have been widely used in the last decades in many fields,
-to describe statistical dependencies among random variables. In general,
-learning the structure of such models is a problem with considerable
-theoretical interest that still poses many challenges. On the one hand, this is
-a well-known NP-complete problem, which is practically hardened by the huge
-search space of possible solutions. On the other hand, the phenomenon of
-I-equivalence, i.e., different graphical structures underpinning the same set
-of statistical dependencies, may lead to multimodal fitness landscapes further
-hindering maximum likelihood approaches to solve the task. Despite all these
-difficulties, greedy search methods based on a likelihood score coupled with a
-regularization term to account for model complexity, have been shown to be
-surprisingly effective in practice. In this paper, we consider the formulation
-of the task of learning the structure of Bayesian Networks as an optimization
-problem based on a likelihood score. Nevertheless, our approach do not adjust
-this score by means of any of the complexity terms proposed in the literature;
-instead, it accounts directly for the complexity of the discovered solutions by
-exploiting a multi-objective optimization procedure. To this extent, we adopt
-NSGA-II and define the first objective function to be the likelihood of a
-solution and the second to be the number of selected arcs. We thoroughly
-analyze the behavior of our method on a wide set of simulated data, and we
-discuss the performance considering the goodness of the inferred solutions both
-in terms of their objective functions and with respect to the retrieved
-structure. Our results show that NSGA-II can converge to solutions
-characterized by better likelihood and less arcs than classic approaches,
-although paradoxically frequently characterized by a lower similarity to the
-target network.
-",0,0,0,1,0,0
-18757,Simultaneous Localization and Layout Model Selection in Manhattan Worlds,"  In this paper, we will demonstrate how Manhattan structure can be exploited
-to transform the Simultaneous Localization and Mapping (SLAM) problem, which is
-typically solved by a nonlinear optimization over feature positions, into a
-model selection problem solved by a convex optimization over higher order
-layout structures, namely walls, floors, and ceilings. Furthermore, we show how
-our novel formulation leads to an optimization procedure that automatically
-performs data association and loop closure and which ultimately produces the
-simplest model of the environment that is consistent with the available
-measurements. We verify our method on real world data sets collected with
-various sensing modalities.
-",1,0,0,0,0,0
-18758,Robust method for finding sparse solutions to linear inverse problems using an L2 regularization,"  We analyzed the performance of a biologically inspired algorithm called the
-Corrected Projections Algorithm (CPA) when a sparseness constraint is required
-to unambiguously reconstruct an observed signal using atoms from an
-overcomplete dictionary. By changing the geometry of the estimation problem,
-CPA gives an analytical expression for a binary variable that indicates the
-presence or absence of a dictionary atom using an L2 regularizer. The
-regularized solution can be implemented using an efficient real-time
-Kalman-filter type of algorithm. The smoother L2 regularization of CPA makes it
-very robust to noise, and CPA outperforms other methods in identifying known
-atoms in the presence of strong novel atoms in the signal.
-",1,0,0,1,0,0
-18759,Humanoid Robot-Application and Influence,"  Application of humanoid robots has been common in the field of healthcare and
-education. It has been recurrently used to improve social behavior and mollify
-distress level among children with autism, cancer and cerebral palsy. This
-article discusses the same from a human factors perspective. It shows how
-people of different age and gender have a different opinion towards the
-application and acceptance of humanoid robots. Additionally, this article
-highlights the influence of cerebral condition and social interaction on a user
-behavior and attitude towards humanoid robots. Our study performed a literature
-review and found that (a) children and elderly individuals prefer humanoid
-robots due to inactive social interaction, (b) The deterministic behavior of
-humanoid robots can be acknowledged to improve social behavior of autistic
-children, (c) Trust on humanoid robots is highly driven by its application and
-a user age, gender, and social life.
-",1,0,0,0,0,0
-18760,Onsager's Conjecture for the Incompressible Euler Equations in Bounded Domains,"  The goal of this note is to show that, also in a bounded domain $\Omega
-\subset \mathbb{R}^n$, with $\partial \Omega\in C^2$, any weak solution,
-$(u(x,t),p(x,t))$, of the Euler equations of ideal incompressible fluid in
-$\Omega\times (0,T) \subset \mathbb{R}^n\times\mathbb{R}_t$, with the
-impermeability boundary condition: $u\cdot \vec n =0$ on
-$\partial\Omega\times(0,T)$, is of constant energy on the interval $(0,T)$
-provided the velocity field $u \in L^3((0,T);
-C^{0,\alpha}(\overline{\Omega}))$, with $\alpha>\frac13\,.$
-",0,1,1,0,0,0
-18761,Fuzzy logic based approaches for gene regulatory network inference,"  The rapid advancement in high-throughput techniques has fueled the generation
-of large volume of biological data rapidly with low cost. Some of these
-techniques are microarray and next generation sequencing which provides genome
-level insight of living cells. As a result, the size of most of the biological
-databases, such as NCBI-GEO, NCBI-SRA, is exponentially growing. These
-biological data are analyzed using computational techniques for knowledge
-discovery - which is one of the objectives of bioinformatics research. Gene
-regulatory network (GRN) is a gene-gene interaction network which plays pivotal
-role in understanding gene regulation process and disease studies. From the
-last couple of decades, the researchers are interested in developing
-computational algorithms for GRN inference (GRNI) using high-throughput
-experimental data. Several computational approaches have been applied for
-inferring GRN from gene expression data including statistical techniques
-(correlation coefficient), information theory (mutual information), regression
-based approaches, probabilistic approaches (Bayesian networks, naive byes),
-artificial neural networks, and fuzzy logic. The fuzzy logic, along with its
-hybridization with other intelligent approach, is well studied in GRNI due to
-its several advantages. In this paper, we present a consolidated review on
-fuzzy logic and its hybrid approaches for GRNI developed during last two
-decades.
-",0,0,0,0,1,0
-18762,Instrumentation for nuclear magnetic resonance in zero and ultralow magnetic field,"  We review instrumentation for nuclear magnetic resonance (NMR) in zero and
-ultra-low magnetic field (ZULF, below 0.1 $\mu$T) where detection is based on a
-low-cost, non-cryogenic, spin-exchange relaxation free (SERF) $^{87}$Rb atomic
-magnetometer. The typical sensitivity is 20-30 fT/Hz$^{1/2}$ for signal
-frequencies below 1 kHz and NMR linewidths range from Hz all the way down to
-tens of mHz. These features enable precision measurements of chemically
-informative nuclear spin-spin couplings as well as nuclear spin precession in
-ultra-low magnetic fields.
-",0,1,0,0,0,0
-18763,Countable dense homogeneity and the Cantor set,"  It is shown that CH implies the existence of a compact Hausdorff space that
-is countable dense homogeneous, crowded and does not contain topological copies
-of the Cantor set. This contrasts with a previous result by the author which
-says that for any crowded Hausdorff space $X$ of countable $\pi$-weight, if
-${}^\omega{X}$ is countable dense homogeneous, then $X$ must contain a
-topological copy of the Cantor set.
-",0,0,1,0,0,0
-18764,Dimension Estimation Using Random Connection Models,"  Information about intrinsic dimension is crucial to perform dimensionality
-reduction, compress information, design efficient algorithms, and do
-statistical adaptation. In this paper we propose an estimator for the intrinsic
-dimension of a data set. The estimator is based on binary neighbourhood
-information about the observations in the form of two adjacency matrices, and
-does not require any explicit distance information. The underlying graph is
-modelled according to a subset of a specific random connection model, sometimes
-referred to as the Poisson blob model. Computationally the estimator scales
-like n log n, and we specify its asymptotic distribution and rate of
-convergence. A simulation study on both real and simulated data shows that our
-approach compares favourably with some competing methods from the literature,
-including approaches that rely on distance information.
-",0,0,1,1,0,0
-18765,Optimal projection of observations in a Bayesian setting,"  Optimal dimensionality reduction methods are proposed for the Bayesian
-inference of a Gaussian linear model with additive noise in presence of
-overabundant data. Three different optimal projections of the observations are
-proposed based on information theory: the projection that minimizes the
-Kullback-Leibler divergence between the posterior distributions of the original
-and the projected models, the one that minimizes the expected Kullback-Leibler
-divergence between the same distributions, and the one that maximizes the
-mutual information between the parameter of interest and the projected
-observations. The first two optimization problems are formulated as the
-determination of an optimal subspace and therefore the solution is computed
-using Riemannian optimization algorithms on the Grassmann manifold. Regarding
-the maximization of the mutual information, it is shown that there exists an
-optimal subspace that minimizes the entropy of the posterior distribution of
-the reduced model; a basis of the subspace can be computed as the solution to a
-generalized eigenvalue problem; an a priori error estimate on the mutual
-information is available for this particular solution; and that the
-dimensionality of the subspace to exactly conserve the mutual information
-between the input and the output of the models is less than the number of
-parameters to be inferred. Numerical applications to linear and nonlinear
-models are used to assess the efficiency of the proposed approaches, and to
-highlight their advantages compared to standard approaches based on the
-principal component analysis of the observations.
-",0,0,1,1,0,0
-18766,Notes on the Multiplicative Ergodic Theorem,"  The Oseledets Multiplicative Ergodic theorem is a basic result with numerous
-applications throughout dynamical systems. These notes provide an introduction
-to this theorem, as well as subsequent generalizations. They are based on
-lectures at summer schools in Brazil, France, and Russia.
-",0,0,1,0,0,0
-18767,Targeted Learning with Daily EHR Data,"  Electronic health records (EHR) data provide a cost and time-effective
-opportunity to conduct cohort studies of the effects of multiple time-point
-interventions in the diverse patient population found in real-world clinical
-settings. Because the computational cost of analyzing EHR data at daily (or
-more granular) scale can be quite high, a pragmatic approach has been to
-partition the follow-up into coarser intervals of pre-specified length. Current
-guidelines suggest employing a 'small' interval, but the feasibility and
-practical impact of this recommendation has not been evaluated and no formal
-methodology to inform this choice has been developed. We start filling these
-gaps by leveraging large-scale EHR data from a diabetes study to develop and
-illustrate a fast and scalable targeted learning approach that allows to follow
-the current recommendation and study its practical impact on inference. More
-specifically, we map daily EHR data into four analytic datasets using 90, 30,
-15 and 5-day intervals. We apply a semi-parametric and doubly robust estimation
-approach, the longitudinal TMLE, to estimate the causal effects of four dynamic
-treatment rules with each dataset, and compare the resulting inferences. To
-overcome the computational challenges presented by the size of these data, we
-propose a novel TMLE implementation, the 'long-format TMLE', and rely on the
-latest advances in scalable data-adaptive machine-learning software, xgboost
-and h2o, for estimation of the TMLE nuisance parameters.
-",0,0,0,1,0,0
-18768,Stripe-Based Fragility Analysis of Concrete Bridge Classes Using Machine Learning Techniques,"  A framework for the generation of bridge-specific fragility utilizing the
-capabilities of machine learning and stripe-based approach is presented in this
-paper. The proposed methodology using random forests helps to generate or
-update fragility curves for a new set of input parameters with less
-computational effort and expensive re-simulation. The methodology does not
-place any assumptions on the demand model of various components and helps to
-identify the relative importance of each uncertain variable in their seismic
-demand model. The methodology is demonstrated through the case studies of
-multi-span concrete bridges in California. Geometric, material and structural
-uncertainties are accounted for in the generation of bridge models and
-fragility curves. It is also noted that the traditional lognormality assumption
-on the demand model leads to unrealistic fragility estimates. Fragility results
-obtained the proposed methodology curves can be deployed in risk assessment
-platform such as HAZUS for regional loss estimation.
-",0,0,0,1,0,0
-18769,Accountability of AI Under the Law: The Role of Explanation,"  The ubiquity of systems using artificial intelligence or ""AI"" has brought
-increasing attention to how those systems should be regulated. The choice of
-how to regulate AI systems will require care. AI systems have the potential to
-synthesize large amounts of data, allowing for greater levels of
-personalization and precision than ever before---applications range from
-clinical decision support to autonomous driving and predictive policing. That
-said, there exist legitimate concerns about the intentional and unintentional
-negative consequences of AI systems. There are many ways to hold AI systems
-accountable. In this work, we focus on one: explanation. Questions about a
-legal right to explanation from AI systems was recently debated in the EU
-General Data Protection Regulation, and thus thinking carefully about when and
-how explanation from AI systems might improve accountability is timely. In this
-work, we review contexts in which explanation is currently required under the
-law, and then list the technical considerations that must be considered if we
-desired AI systems that could provide kinds of explanations that are currently
-required of humans.
-",1,0,0,1,0,0
-18770,Frequency-oriented sub-sampling by photonic Fourier transform and I/Q demodulation,"  Sub-sampling can acquire directly a passband within a broad radio frequency
-(RF) range, avoiding down-conversion and low-phase-noise tunable local
-oscillation (LO). However, sub-sampling suffers from band folding and
-self-image interference. In this paper we propose a frequency-oriented
-sub-sampling to solve the two problems. With ultrashort optical pulse and a
-pair of chromatic dispersions, the broadband RF signal is firstly short-time
-Fourier-transformed to a spectrum-spread pulse. Then a time slot, corresponding
-to the target spectrum slice, is coherently optical-sampled with
-in-phase/quadrature (I/Q) demodulation. We demonstrate the novel bandpass
-sampling by a numerical example, which shows the desired uneven intensity
-response, i.e. pre-filtering. We show in theory that appropriate time-stretch
-capacity from dispersion can result in pre-filtering bandwidth less than
-sampling rate. Image rejection due to I/Q sampling is also analyzed. A
-proof-of-concept experiment, which is based on a time-lens sampling source and
-chirped fiber Bragg gratings (CFBGs), shows the center-frequency-tunable
-pre-filtered sub-sampling with bandwidth of 6 GHz around, as well as imaging
-rejection larger than 26 dB. Our technique may benefit future broadband RF
-receivers for frequency-agile Radar or channelization.
-",0,1,0,0,0,0
-18771,A moment map picture of relative balanced metrics on extremal Kähler manifolds,"  We give a moment map interpretation of some relatively balanced metrics. As
-an application, we extend a result of S. K. Donaldson on constant scalar
-curvature Kähler metrics to the case of extremal metrics. Namely, we show
-that a given extremal metric is the limit of some specific relatively balanced
-metrics. As a corollary, we recover uniqueness and splitting results for
-extremal metrics in the polarized case.
-",0,0,1,0,0,0
-18772,Traffic models with adversarial vehicle behaviour,"  We examine the impact of adversarial actions on vehicles in traffic. Current
-advances in assisted/autonomous driving technologies are supposed to reduce the
-number of casualties, but this seems to be desired despite the recently proved
-insecurity of in-vehicle communication buses or components. Fortunately to some
-extent, while compromised cars have become a reality, the numerous attacks
-reported so far on in-vehicle electronics are exclusively concerned with
-impairments of a single target. In this work we put adversarial behavior under
-a more complex scenario where driving decisions deluded by corrupted
-electronics can affect more than one vehicle. Particularly, we focus our
-attention on chain collisions involving multiple vehicles that can be amplified
-by simple adversarial interventions, e.g., delaying taillights or falsifying
-speedometer readings. We provide metrics for assessing adversarial impact and
-consider safety margins against adversarial actions. Moreover, we discuss
-intelligent adversarial behaviour by which the creation of rogue platoons is
-possible and speed manipulations become stealthy to human drivers. We emphasize
-that our work does not try to show the mere fact that imprudent speeds and
-headways lead to chain-collisions, but points out that an adversary may favour
-such scenarios (eventually keeping his actions stealthy for human drivers) and
-further asks for quantifying the impact of adversarial activity or whether
-existing traffic regulations are prepared for such situations.
-",1,0,0,0,0,0
-18773,Robust Cooperative Manipulation without Force/Torque Measurements: Control Design and Experiments,"  This paper presents two novel control methodologies for the cooperative
-manipulation of an object by N robotic agents. Firstly, we design an adaptive
-control protocol which employs quaternion feedback for the object orientation
-to avoid potential representation singularities. Secondly, we propose a control
-protocol that guarantees predefined transient and steady-state performance for
-the object trajectory. Both methodologies are decentralized, since the agents
-calculate their own signals without communicating with each other, as well as
-robust to external disturbances and model uncertainties. Moreover, we consider
-that the grasping points are rigid, and avoid the need for force/torque
-measurements. Load distribution is also included via a grasp matrix
-pseudo-inverse to account for potential differences in the agents' power
-capabilities. Finally, simulation and experimental results with two robotic
-arms verify the theoretical findings.
-",1,0,0,0,0,0
-18774,Conditionally conjugate mean-field variational Bayes for logistic models,"  Variational Bayes (VB) is a common strategy for approximate Bayesian
-inference, but simple methods are only available for specific classes of models
-including, in particular, representations having conditionally conjugate
-constructions within an exponential family. Models with logit components are an
-apparently notable exception to this class, due to the absence of conjugacy
-between the logistic likelihood and the Gaussian priors for the coefficients in
-the linear predictor. To facilitate approximate inference within this widely
-used class of models, Jaakkola and Jordan (2000) proposed a simple variational
-approach which relies on a family of tangent quadratic lower bounds of logistic
-log-likelihoods, thus restoring conjugacy between these approximate bounds and
-the Gaussian priors. This strategy is still implemented successfully, but less
-attempts have been made to formally understand the reasons underlying its
-excellent performance. To cover this key gap, we provide a formal connection
-between the above bound and a recent Pólya-gamma data augmentation for
-logistic regression. Such a result places the computational methods associated
-with the aforementioned bounds within the framework of variational inference
-for conditionally conjugate exponential family models, thereby allowing recent
-advances for this class to be inherited also by the methods relying on Jaakkola
-and Jordan (2000).
-",0,0,1,1,0,0
-18775,A $\frac{3}{2}$-Approximation Algorithm for Tree Augmentation via Chvátal-Gomory Cuts,"  The weighted tree augmentation problem (WTAP) is a fundamental network design
-problem. We are given an undirected tree $G = (V,E)$, an additional set of
-edges $L$ called links and a cost vector $c \in \mathbb{R}^L_{\geq 1}$. The
-goal is to choose a minimum cost subset $S \subseteq L$ such that $G = (V, E
-\cup S)$ is $2$-edge-connected. In the unweighted case, that is, when we have
-$c_\ell = 1$ for all $\ell \in L$, the problem is called the tree augmentation
-problem (TAP).
-Both problems are known to be APX-hard, and the best known approximation
-factors are $2$ for WTAP by (Frederickson and JáJá, '81) and $\tfrac{3}{2}$
-for TAP due to (Kortsarz and Nutov, TALG '16). In the case where all link costs
-are bounded by a constant $M$, (Adjiashvili, SODA '17) recently gave a $\approx
-1.96418+\varepsilon$-approximation algorithm for WTAP under this assumption.
-This is the first approximation with a better guarantee than $2$ that does not
-require restrictions on the structure of the tree or the links.
-In this paper, we improve Adjiashvili's approximation to a
-$\frac{3}{2}+\varepsilon$-approximation for WTAP under the bounded cost
-assumption. We achieve this by introducing a strong LP that combines
-$\{0,\frac{1}{2}\}$-Chvátal-Gomory cuts for the standard LP for the problem
-with bundle constraints from Adjiashvili. We show that our LP can be solved
-efficiently and that it is exact for some instances that arise at the core of
-Adjiashvili's approach. This results in the improved guarantee of
-$\frac{3}{2}+\varepsilon$. For TAP, this is the best known LP-based result, and
-matches the bound of $\frac{3}{2}+\varepsilon$ achieved by the best SDP-based
-algorithm due to (Cheriyan and Gao, arXiv '15).
-",1,0,0,0,0,0
-18776,On Identifying Disaster-Related Tweets: Matching-based or Learning-based?,"  Social media such as tweets are emerging as platforms contributing to
-situational awareness during disasters. Information shared on Twitter by both
-affected population (e.g., requesting assistance, warning) and those outside
-the impact zone (e.g., providing assistance) would help first responders,
-decision makers, and the public to understand the situation first-hand.
-Effective use of such information requires timely selection and analysis of
-tweets that are relevant to a particular disaster. Even though abundant tweets
-are promising as a data source, it is challenging to automatically identify
-relevant messages since tweet are short and unstructured, resulting to
-unsatisfactory classification performance of conventional learning-based
-approaches. Thus, we propose a simple yet effective algorithm to identify
-relevant messages based on matching keywords and hashtags, and provide a
-comparison between matching-based and learning-based approaches. To evaluate
-the two approaches, we put them into a framework specifically proposed for
-analyzing disaster-related tweets. Analysis results on eleven datasets with
-various disaster types show that our technique provides relevant tweets of
-higher quality and more interpretable results of sentiment analysis tasks when
-compared to learning approach.
-",1,0,0,0,0,0
-18777,Self-Stabilizing Disconnected Components Detection and Rooted Shortest-Path Tree Maintenance in Polynomial Steps,"  We deal with the problem of maintaining a shortest-path tree rooted at some
-process r in a network that may be disconnected after topological changes. The
-goal is then to maintain a shortest-path tree rooted at r in its connected
-component, V\_r, and make all processes of other components detecting that r is
-not part of their connected component. We propose, in the composite atomicity
-model, a silent self-stabilizing algorithm for this problem working in
-semi-anonymous networks, where edges have strictly positive weights. This
-algorithm does not require any a priori knowledge about global parameters of
-the network. We prove its correctness assuming the distributed unfair daemon,
-the most general daemon. Its stabilization time in rounds is at most 3nmax+D,
-where nmax is the maximum number of non-root processes in a connected component
-and D is the hop-diameter of V\_r. Furthermore, if we additionally assume that
-edge weights are positive integers, then it stabilizes in a polynomial number
-of steps: namely, we exhibit a bound in O(maxi nmax^3 n), where maxi is the
-maximum weight of an edge and n is the number of processes.
-",1,0,0,0,0,0
-18778,Optimizing wearable assistive devices with neuromuscular models and optimal control,"  The coupling of human movement dynamics with the function and design of
-wearable assistive devices is vital to better understand the interaction
-between the two. Advanced neuromuscular models and optimal control formulations
-provide the possibility to study and improve this interaction. In addition,
-optimal control can also be used to generate predictive simulations that
-generate novel movements for the human model under varying optimization
-criterion.
-",1,0,0,0,0,0
-18779,Preferential placement for community structure formation,"  Various models have been recently proposed to reflect and predict different
-properties of complex networks. However, the community structure, which is one
-of the most important properties, is not well studied and modeled. In this
-paper, we suggest a principle called ""preferential placement"", which allows to
-model a realistic clustering structure. We provide an extensive empirical
-analysis of the obtained structure as well as some theoretical results.
-",1,1,0,0,0,0
-18780,Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization,"  In this paper, we consider the problem of sequentially optimizing a black-box
-function $f$ based on noisy samples and bandit feedback. We assume that $f$ is
-smooth in the sense of having a bounded norm in some reproducing kernel Hilbert
-space (RKHS), yielding a commonly-considered non-Bayesian form of Gaussian
-process bandit optimization. We provide algorithm-independent lower bounds on
-the simple regret, measuring the suboptimality of a single point reported after
-$T$ rounds, and on the cumulative regret, measuring the sum of regrets over the
-$T$ chosen points. For the isotropic squared-exponential kernel in $d$
-dimensions, we find that an average simple regret of $\epsilon$ requires $T =
-\Omega\big(\frac{1}{\epsilon^2} (\log\frac{1}{\epsilon})^{d/2}\big)$, and the
-average cumulative regret is at least $\Omega\big( \sqrt{T(\log T)^{d/2}}
-\big)$, thus matching existing upper bounds up to the replacement of $d/2$ by
-$2d+O(1)$ in both cases. For the Matérn-$\nu$ kernel, we give analogous
-bounds of the form $\Omega\big( (\frac{1}{\epsilon})^{2+d/\nu}\big)$ and
-$\Omega\big( T^{\frac{\nu + d}{2\nu + d}} \big)$, and discuss the resulting
-gaps to the existing upper bounds.
-",1,0,0,1,0,0
-18781,Hirota bilinear equations for Painlevé transcendents,"  We present some observations on the tau-function for the fourth Painlevé
-equation. By considering a Hirota bilinear equation of order four for this
-tau-function, we describe the general form of the Taylor expansion around an
-arbitrary movable zero. The corresponding Taylor series for the tau-functions
-of the first and second Painlevé equations, as well as that for the
-Weierstrass sigma function, arise naturally as special cases, by setting
-certain parameters to zero.
-",0,1,1,0,0,0
-18782,Gradient-based Representational Similarity Analysis with Searchlight for Analyzing fMRI Data,"  Representational Similarity Analysis (RSA) aims to explore similarities
-between neural activities of different stimuli. Classical RSA techniques employ
-the inverse of the covariance matrix to explore a linear model between the
-neural activities and task events. However, calculating the inverse of a
-large-scale covariance matrix is time-consuming and can reduce the stability
-and robustness of the final analysis. Notably, it becomes severe when the
-number of samples is too large. For facing this shortcoming, this paper
-proposes a novel RSA method called gradient-based RSA (GRSA). Moreover, the
-proposed method is not restricted to a linear model. In fact, there is a
-growing interest in finding more effective ways of using multi-subject and
-whole-brain fMRI data. Searchlight technique can extend RSA from the localized
-brain regions to the whole-brain regions with smaller memory footprint in each
-process. Based on Searchlight, we propose a new method called Spatiotemporal
-Searchlight GRSA (SSL-GRSA) that generalizes our ROI-based GRSA algorithm to
-the whole-brain data. Further, our approach can handle some computational
-challenges while dealing with large-scale, multi-subject fMRI data.
-Experimental studies on multi-subject datasets confirm that both proposed
-approaches achieve superior performance to other state-of-the-art RSA
-algorithms.
-",0,0,0,1,1,0
-18783,Diffeomorphic random sampling using optimal information transport,"  In this article we explore an algorithm for diffeomorphic random sampling of
-nonuniform probability distributions on Riemannian manifolds. The algorithm is
-based on optimal information transport (OIT)---an analogue of optimal mass
-transport (OMT). Our framework uses the deep geometric connections between the
-Fisher-Rao metric on the space of probability densities and the right-invariant
-information metric on the group of diffeomorphisms. The resulting sampling
-algorithm is a promising alternative to OMT, in particular as our formulation
-is semi-explicit, free of the nonlinear Monge--Ampere equation. Compared to
-Markov Chain Monte Carlo methods, we expect our algorithm to stand up well when
-a large number of samples from a low dimensional nonuniform distribution is
-needed.
-",0,0,1,1,0,0
-18784,On approximations by trigonometric polynomials of classes of functions defined by moduli of smoothness,"  In this paper, we give a characterization of Nikol'ski\u{\i}-Besov type
-classes of functions, given by integral representations of moduli of
-smoothness, in terms of series over the moduli of smoothness. Also, necessary
-and sufficient conditions in terms of monotone or lacunary Fourier coefficients
-for a function to belong to a such a class are given. In order to prove our
-results, we make use of certain recent reverse Copson- and Leindler-type
-inequalities.
-",0,0,1,0,0,0
-18785,Topologically protected Dirac plasmons in graphene,"  Topological optical states exhibit unique immunity to defects and the ability
-to propagate without losses rendering them ideal for photonic applications.A
-powerful class of such states is based on time-reversal symmetry breaking of
-the optical response.However, existing proposals either involve sophisticated
-and bulky structural designs or can only operate in the microwave regime. Here,
-we propose and provide a theoretical proof-of-principle demonstration for
-highly confined topologically protected optical states to be realized at
-infrared frequencies in a simple 2D material structure-a periodically patterned
-graphene monolayer-subject to a magnetic field below 1 tesla. In our graphene
-honeycomb superlattice structures plasmons exhibit substantial nonreciprocal
-behavior at the superlattice junctions, leading to the emergence of
-topologically protected edge states and localized bulk modes enabled by the
-strong magneto-optical response of this material, which leads to
-time-reversal-symmetry breaking already at moderate static magnetic fields. The
-proposed approach is simple and robust for realizing topologically nontrivial
-2D optical states, not only in graphene, but also in other 2D atomic layers,
-and could pave the way for realizing fast, nanoscale, defect-immune devices for
-integrated photonics applications.
-",0,1,0,0,0,0
-18786,The K-Nearest Neighbour UCB algorithm for multi-armed bandits with covariates,"  In this paper we propose and explore the k-Nearest Neighbour UCB algorithm
-for multi-armed bandits with covariates. We focus on a setting where the
-covariates are supported on a metric space of low intrinsic dimension, such as
-a manifold embedded within a high dimensional ambient feature space. The
-algorithm is conceptually simple and straightforward to implement. The
-k-Nearest Neighbour UCB algorithm does not require prior knowledge of the
-either the intrinsic dimension of the marginal distribution or the time
-horizon. We prove a regret bound for the k-Nearest Neighbour UCB algorithm
-which is minimax optimal up to logarithmic factors. In particular, the
-algorithm automatically takes advantage of both low intrinsic dimensionality of
-the marginal distribution over the covariates and low noise in the data,
-expressed as a margin condition. In addition, focusing on the case of bounded
-rewards, we give corresponding regret bounds for the k-Nearest Neighbour KL-UCB
-algorithm, which is an analogue of the KL-UCB algorithm adapted to the setting
-of multi-armed bandits with covariates. Finally, we present empirical results
-which demonstrate the ability of both the k-Nearest Neighbour UCB and k-Nearest
-Neighbour KL-UCB to take advantage of situations where the data is supported on
-an unknown sub-manifold of a high-dimensional feature space.
-",0,0,0,1,0,0
-18787,Proportional Mean Residual Life Model with Censored Survival Data under Case-cohort Design,"  Proportional mean residual life model is studied for analysing survival data
-from the case-cohort design. To simultaneously estimate the regression
-parameters and the baseline mean residual life function, weighted estimating
-equations based on an inverse selection probability are proposed. The resulting
-regression coefficients estimates are shown to be consistent and asymptotic
-normal with easily estimated variance-covariance. Simulation studies show that
-the proposed estimators perform very well. An application to a real dataset
-from the South Welsh nickel refiners study is also given to illustrate the
-methodology.
-",0,0,1,1,0,0
-18788,Shape differentiation of a steady-state reaction-diffusion problem arising in Chemical Engineering: the case of non-smooth kinetic with dead core,"  In this paper we consider an extension of the results in shape
-differentiation of semilinear equations with smooth nonlinearity presented in
-J.I. Díaz and D. Gómez-Castro: An Application of Shape Differentiation to
-the Effectiveness of a Steady State Reaction-Diffusion Problem Arising in
-Chemical Engineering. Electron. J. Differ. Equations in 2015 to the case in
-which the nonlinearities might be less smooth. Namely we will show that Gateaux
-shape derivatives exists when the nonlinearity is only Lipschitz continuous,
-and we will give a definition of the derivative when the nonlinearity has a
-blow up. In this direction, we will study the case of root-type nonlinearities.
-",0,0,1,0,0,0
-18789,"Analytic evaluation of Coulomb integrals for one, two and three-electron distance operators, $R_{C1}^{-n}R_{D1}^{-m}$, $R_{C1}^{-n}r_{12}^{-m}$ and $r_{12}^{-n}r_{13}^{-m}$ with $n, m=0,1,2$","  The state of the art for integral evaluation is that analytical solutions to
-integrals are far more useful than numerical solutions. We evaluate certain
-integrals analytically that are necessary in some approaches in quantum
-chemistry. In the title, where R stands for nucleus-electron and r for
-electron-electron distances, the $(n,m)=(0,0)$ case is trivial, the
-$(n,m)=(1,0)$ and (0,1) cases are well known, fundamental milestone in
-integration and widely used in computation chemistry, as well as based on
-Laplace transformation with integrand exp(-$a^2t^2$). The rest of the cases are
-new and need the other Laplace transformation with integrand exp(-$a^2t$) also,
-as well as the necessity of a two dimensional version of Boys function comes up
-in case. These analytic expressions (up to Gaussian function integrand) are
-useful for manipulation with higher moments of inter-electronic distances, for
-example in correlation calculations.
-",0,1,0,0,0,0
-18790,Herschel-PACS photometry of faint stars,"  Our aims are to determine flux densities and their photometric accuracy for a
-set of seventeen stars that range in flux from intermediately bright (<2.5 Jy)
-to faint (>5 mJy) in the far-infrared (FIR). We also aim to derive
-signal-to-noise dependence with flux and time, and compare the results with
-predictions from the Herschel exposure-time calculation tool. The PACS faint
-star sample has allowed a comprehensive sensitivity assessment of the PACS
-photometer. Accurate photometry allows us to establish a set of five FIR
-primary standard candidates, namely alpha Ari, epsilon Lep, omega,Cap, HD41047
-and 42Dra, which are 2 -- 20 times fainter than the faintest PACS fiducial
-standard (gamma Dra) with absolute accuracy of <6%. For three of these primary
-standard candidates, essential stellar parameters are known, meaning that a
-dedicated flux model code may be run.
-",0,1,0,0,0,0
-18791,Role of zero synapses in unsupervised feature learning,"  Synapses in real neural circuits can take discrete values, including zero
-(silent or potential) synapses. The computational role of zero synapses in
-unsupervised feature learning of unlabeled noisy data is still unclear, thus it
-is important to understand how the sparseness of synaptic activity is shaped
-during learning and its relationship with receptive field formation. Here, we
-formulate this kind of sparse feature learning by a statistical mechanics
-approach. We find that learning decreases the fraction of zero synapses, and
-when the fraction decreases rapidly around a critical data size, an
-intrinsically structured receptive field starts to develop. Further increasing
-the data size refines the receptive field, while a very small fraction of zero
-synapses remain to act as contour detectors. This phenomenon is discovered not
-only in learning a handwritten digits dataset, but also in learning retinal
-neural activity measured in a natural-movie-stimuli experiment.
-",1,1,0,0,0,0
-18792,On the Pervasiveness of Difference-Convexity in Optimization and Statistics,"  With the increasing interest in applying the methodology of
-difference-of-convex (dc) optimization to diverse problems in engineering and
-statistics, this paper establishes the dc property of many well-known functions
-not previously known to be of this class. Motivated by a quadratic programming
-based recourse function in two-stage stochastic programming, we show that the
-(optimal) value function of a copositive (thus not necessarily convex)
-quadratic program is dc on the domain of finiteness of the program when the
-matrix in the objective function's quadratic term and the constraint matrix are
-fixed. The proof of this result is based on a dc decomposition of a piecewise
-LC1 function (i.e., functions with Lipschitz gradients). Armed with these new
-results and known properties of dc functions existed in the literature, we show
-that many composite statistical functions in risk analysis, including the
-value-at-risk (VaR), conditional value-at-risk (CVaR), expectation-based,
-VaR-based, and CVaR-based random deviation functions are all dc. Adding the
-known class of dc surrogate sparsity functions that are employed as
-approximations of the l_0 function in statistical learning, our work
-significantly expands the family of dc functions and positions them for
-fruitful applications.
-",0,0,1,0,0,0
-18793,Stochastic Dynamic Optimal Power Flow in Distribution Network with Distributed Renewable Energy and Battery Energy Storage,"  The penetration of distributed renewable energy (DRE) greatly raises the risk
-of distribution network operation such as peak shaving and voltage stability.
-Battery energy storage (BES) has been widely accepted as the most potential
-application to cope with the challenge of high penetration of DRE. To cope with
-the uncertainties and variability of DRE, a stochastic day-ahead dynamic
-optimal power flow (DOPF) and its algorithm are proposed. The overall economy
-is achieved by fully considering the DRE, BES, electricity purchasing and
-active power losses. The rainflow algorithm-based cycle counting method of BES
-is incorporated in the DOPF model to capture the cell degradation, greatly
-extending the expected BES lifetime and achieving a better economy. DRE
-scenarios are generated to consider the uncertainties and correlations based on
-the Copula theory. To solve the DOPF model, we propose a Lagrange
-relaxation-based algorithm, which has a significantly reduced complexity with
-respect to the existing techniques. For this reason, the proposed algorithm
-enables much more scenarios incorporated in the DOPF model and better captures
-the DRE uncertainties and correlations. Finally, numerical studies for the
-day-ahead DOPF in the IEEE 123-node test feeder are presented to demonstrate
-the merits of the proposed method. Results show that the actual BES life
-expectancy of the proposed model has increased to 4.89 times compared with the
-traditional ones. The problems caused by DRE are greatly alleviated by fully
-capturing the uncertainties and correlations with the proposed method.
-",0,0,1,0,0,0
-18794,Achievable Rate Region of the Zero-Forcing Precoder in a 2 X 2 MU-MISO Broadcast VLC Channel with Per-LED Peak Power Constraint and Dimming Control,"  In this paper, we consider the 2 X 2 multi-user multiple-input-single-output
-(MU-MISO) broadcast visible light communication (VLC) channel with two light
-emitting diodes (LEDs) at the transmitter and a single photo diode (PD) at each
-of the two users. We propose an achievable rate region of the Zero-Forcing (ZF)
-precoder in this 2 X 2 MU-MISO VLC channel under a per-LED peak and average
-power constraint, where the average optical power emitted from each LED is
-fixed for constant lighting, but is controllable (referred to as dimming
-control in IEEE 802.15.7 standard on VLC). We analytically characterize the
-proposed rate region boundary and show that it is Pareto-optimal. Further
-analysis reveals that the largest rate region is achieved when the fixed
-per-LED average optical power is half of the allowed per-LED peak optical
-power. We also propose a novel transceiver architecture where the channel
-encoder and dimming control are separated which greatly simplifies the
-complexity of the transceiver. A case study of an indoor VLC channel with the
-proposed transceiver reveals that the achievable information rates are
-sensitive to the placement of the LEDs and the PDs. An interesting observation
-is that for a given placement of LEDs in a 5 m X 5 m X 3 m room, even with a
-substantial displacement of the users from their optimum placement, reduction
-in the achievable rates is not significant. This observation could therefore be
-used to define ""coverage zones"" within a room where the reduction in the
-information rates to the two users is within an acceptable tolerance limit.
-",1,0,1,0,0,0
-18795,Autonomous Electric Race Car Design,"  Autonomous driving and electric vehicles are nowadays very active research
-and development areas. In this paper we present the conversion of a standard
-Kyburz eRod into an autonomous vehicle that can be operated in challenging
-environments such as Swiss mountain passes. The overall hardware and software
-architectures are described in detail with a special emphasis on the sensor
-requirements for autonomous vehicles operating in partially structured
-environments. Furthermore, the design process itself and the finalized system
-architecture are presented. The work shows state of the art results in
-localization and controls for self-driving high-performance electric vehicles.
-Test results of the overall system are presented, which show the importance of
-generalizable state estimation algorithms to handle a plethora of conditions.
-",1,0,0,0,0,0
-18796,Temporal Stable Community in Time-Varying Networks,"  Identifying community structure of a complex network provides insight to the
-interdependence between the network topology and emergent collective behaviors
-of networks, while detecting such invariant communities in a time-varying
-network is more challenging. In this paper, we define the temporal stable
-community and newly propose the concept of dynamic modularity to evaluate the
-stable community structures in time-varying networks, which is robust against
-small changes as verified by several empirical time-varying network datasets.
-Besides, using the volatility features of temporal stable communities in
-functional brain networks, we successfully differentiate the ADHD (Attention
-Deficit Hyperactivity Disorder) patients and healthy controls efficiently.
-",0,0,0,0,1,0
-18797,The shape of a rapidly rotating polytrope with index unity,"  We show that the solutions obtained in the paper `An exact solution for
-arbitrarily rotating gaseous polytropes with index unity' by Kong, Zhang, and
-Schubert represent only approximate solutions of the free-boundary
-Euler-Poisson system of equations describing uniformly rotating,
-self-gravitating polytropes with index unity. We discuss the quality of such
-solutions as approximations to the rigidly rotating equilibrium polytropic
-configurations.
-",0,1,0,0,0,0
-18798,Measurement of the Planck constant at the National Institute of Standards and Technology from 2015 to 2017,"  Researchers at the National Institute of Standards and Technology(NIST) have
-measured the value of the Planck constant to be $h =6.626\,069\,934(89)\times
-10^{-34}\,$J$\,$s (relative standard uncertainty $13\times 10^{-9}$). The
-result is based on over 10$\,$000 weighings of masses with nominal values
-ranging from 0.5$\,$kg to 2$\,$kg with the Kibble balance NIST-4. The
-uncertainty has been reduced by more than twofold relative to a previous
-determination because of three factors: (1) a much larger data set than
-previously available, allowing a more realistic, and smaller, Type A
-evaluation; (2) a more comprehensive measurement of the back action of the
-weighing current on the magnet by weighing masses up to 2$\,$kg, decreasing the
-uncertainty associated with magnet non-linearity; (3) a rigorous investigation
-of the dependence of the geometric factor on the coil velocity reducing the
-uncertainty assigned to time-dependent leakage of current in the coil.
-",0,1,0,0,0,0
-18799,Event Stream-Based Process Discovery using Abstract Representations,"  The aim of process discovery, originating from the area of process mining, is
-to discover a process model based on business process execution data. A
-majority of process discovery techniques relies on an event log as an input. An
-event log is a static source of historical data capturing the execution of a
-business process. In this paper we focus on process discovery relying on online
-streams of business process execution events. Learning process models from
-event streams poses both challenges and opportunities, i.e. we need to handle
-unlimited amounts of data using finite memory and, preferably, constant time.
-We propose a generic architecture that allows for adopting several classes of
-existing process discovery techniques in context of event streams. Moreover, we
-provide several instantiations of the architecture, accompanied by
-implementations in the process mining tool-kit ProM (this http URL).
-Using these instantiations, we evaluate several dimensions of stream-based
-process discovery. The evaluation shows that the proposed architecture allows
-us to lift process discovery to the streaming domain.
-",1,0,0,1,0,0
-18800,"On $C$-bases, partition pairs and filtrations for induced or restricted Specht modules","  We obtain alternative explicit Specht filtrations for the induced and the
-restricted Specht modules in the Hecke algebra of the symmetric group (defined
-over the ring $A=\mathbb Z[q^{1/2},q^{-1/2}]$ where $q$ is an indeterminate)
-using $C$-bases for these modules. Moreover, we provide a link between a
-certain $C$-basis for the induced Specht module and the notion of pairs of
-partitions.
-",0,0,1,0,0,0
-18801,Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment,"  Network embeddings, which learn low-dimensional representations for each
-vertex in a large-scale network, have received considerable attention in recent
-years. For a wide range of applications, vertices in a network are typically
-accompanied by rich textual information such as user profiles, paper abstracts,
-etc. We propose to incorporate semantic features into network embeddings by
-matching important words between text sequences for all pairs of vertices. We
-introduce a word-by-word alignment framework that measures the compatibility of
-embeddings between word pairs, and then adaptively accumulates these alignment
-features with a simple yet effective aggregation function. In experiments, we
-evaluate the proposed framework on three real-world benchmarks for downstream
-tasks, including link prediction and multi-label vertex classification. Results
-demonstrate that our model outperforms state-of-the-art network embedding
-methods by a large margin.
-",1,0,0,0,0,0
-18802,Spin Precession Experiments for Light Axionic Dark Matter,"  Axion-like particles are promising candidates to make up the dark matter of
-the universe, but it is challenging to design experiments that can detect them
-over their entire allowed mass range. Dark matter in general, and in particular
-axion-like particles and hidden photons, can be as light as roughly $10^{-22}
-\;\rm{eV}$ ($\sim 10^{-8} \;\rm{Hz}$), with astrophysical anomalies providing
-motivation for the lightest masses (""fuzzy dark matter""). We propose
-experimental techniques for direct detection of axion-like dark matter in the
-mass range from roughly $10^{-13} \;\rm{eV}$ ($\sim 10^2 \;\rm{Hz}$) down to
-the lowest possible masses. In this range, these axion-like particles act as a
-time-oscillating magnetic field coupling only to spin, inducing effects such as
-a time-oscillating torque and periodic variations in the spin-precession
-frequency with the frequency and direction set by fundamental physics. We show
-how these signals can be measured using existing experimental technology,
-including torsion pendulums, atomic magnetometers, and atom interferometry.
-These experiments demonstrate a strong discovery capability, with future
-iterations of these experiments capable of pushing several orders of magnitude
-past current astrophysical bounds.
-",0,1,0,0,0,0
-18803,Persistence barcodes and Laplace eigenfunctions on surfaces,"  We obtain restrictions on the persistence barcodes of Laplace-Beltrami
-eigenfunctions and their linear combinations on compact surfaces with
-Riemannian metrics. Some applications to uniform approximation by linear
-combinations of Laplace eigenfunctions are also discussed.
-",0,0,1,0,0,0
-18804,DFT study of ionic liquids adsorption on circumcoronene shaped graphene,"  Carbon materials have a range of properties such as high electrical
-conductivity, high specific surface area, and mechanical flexibility are
-relevant for electrochemical applications. Carbon materials are utilised in
-energy conversion-and-storage devices along with electrolytes of complementary
-properties. In this work, we study the interaction of highly concentrated
-electrolytes (ionic liquids) at a model carbon surface (circumcoronene) using
-density functional theory methods. Our results indicate the decisive role of
-the dispersion interactions that noticeably strengthen the circumcoronene-ion
-interaction. Also, we focus on the adsorption of halide anions as the
-electrolytes containing these ions are promising for practical use in
-supercapacitors and solar cells.
-",0,1,0,0,0,0
-18805,On the overestimation of the largest eigenvalue of a covariance matrix,"  In this paper, we use a new approach to prove that the largest eigenvalue of
-the sample covariance matrix of a normally distributed vector is bigger than
-the true largest eigenvalue with probability 1 when the dimension is infinite.
-We prove a similar result for the smallest eigenvalue.
-",0,0,1,1,0,0
-18806,Thermodynamic Mechanism of Life and Aging,"  Life is a complex biological phenomenon represented by numerous chemical,
-physical and biological processes performed by a biothermodynamic
-system/cell/organism. Both living organisms and inanimate objects are subject
-to aging, a biological and physicochemical process characterized by changes in
-biological and thermodynamic state. Thus, the same physical laws govern
-processes in both animate and inanimate matter. All life processes lead to
-change of an organism's state. The change of biological and thermodynamic state
-of an organism in time underlies all of three kinds of aging (chronological,
-biological and thermodynamic). Life and aging of an organism both start at the
-moment of fertilization and continue through entire lifespan. Fertilization
-represents formation of a new organism. The new organism represents a new
-thermodynamic system. From the very beginning, it changes its state by changing
-thermodynamic parameters. The change of thermodynamic parameters is observed as
-aging and can be related to change in entropy. Entropy is thus the parameter
-that is related to all others and describes aging in the best manner. In the
-beginning, entropy change appears as a consequence of accumulation of matter
-(growth). Later, decomposition and configurational changes dominate, as a
-consequence of various chemical reactions (free radical, decomposition,
-fragmentation, accumulation of lipofuscin-like substances...).
-",0,0,0,0,1,0
-18807,Flat families of point schemes for connected graded algebras,"  We study truncated point schemes of connected graded algebras as families
-over the parameter space of varying relations for the algebras, proving that
-the families are flat over the open dense locus where the point schemes achieve
-the expected (i.e. minimal) dimension.
-When the truncated point scheme is zero-dimensional we obtain its number of
-points counted with multiplicity via a Chow ring computation. This latter
-application in particular confirms a conjecture of Brazfield to the effect that
-a generic two-generator, two-relator 4-dimensional Artin-Schelter regular
-algebra has seventeen truncated point modules of length six.
-",0,0,1,0,0,0
-18808,Multi-task Learning in the Computerized Diagnosis of Breast Cancer on DCE-MRIs,"  Hand-crafted features extracted from dynamic contrast-enhanced magnetic
-resonance images (DCE-MRIs) have shown strong predictive abilities in
-characterization of breast lesions. However, heterogeneity across medical image
-datasets hinders the generalizability of these features. One of the sources of
-the heterogeneity is the variation of MR scanner magnet strength, which has a
-strong influence on image quality, leading to variations in the extracted image
-features. Thus, statistical decision algorithms need to account for such data
-heterogeneity. Despite the variations, we hypothesize that there exist
-underlying relationships between the features extracted from the datasets
-acquired with different magnet strength MR scanners. We compared the use of a
-multi-task learning (MTL) method that incorporates those relationships during
-the classifier training to support vector machines run on a merged dataset that
-includes cases with various MRI strength images. As a result, higher predictive
-power is achieved with the MTL method.
-",0,1,0,0,0,0
-18809,Real-time Distracted Driver Posture Classification,"  In this paper, we present a new dataset for ""distracted driver"" posture
-estimation. In addition, we propose a novel system that achieves 95.98% driving
-posture estimation classification accuracy. The system consists of a
-genetically-weighted ensemble of Convolutional Neural Networks (CNNs). We show
-that a weighted ensemble of classifiers using a genetic algorithm yields in
-better classification confidence. We also study the effect of different visual
-elements (i.e. hands and face) in distraction detection and classification by
-means of face and hand localizations. Finally, we present a thinned version of
-our ensemble that could achieve a 94.29% classification accuracy and operate in
-a realtime environment.
-",1,0,0,0,0,0
-18810,Time Series Cube Data Model,"  The purpose of this document is to create a data model and its serialization
-for expressing generic time series data. Already existing IVOA data models are
-reused as much as possible. The model is also made as generic as possible to be
-open to new extensions but at the same time closed for modifications. This
-enables maintaining interoperability throughout different versions of the data
-model. We define the necessary building blocks for metadata discovery,
-serialization of time series data and understanding it by clients. We present
-several categories of time series science cases with examples of
-implementation. We also take into account the most pressing topics for time
-series providers like tracking original images for every individual point of a
-light curve or time-derived axes like frequency for gravitational wave
-analysis. The main motivation for the creation of a new model is to provide a
-unified time series data publishing standard - not only for light curves but
-also more generic time series data, e.g., radial velocity curves, power
-spectra, hardness ratio, provenance linkage, etc. The flexibility is the most
-crucial part of our model - we are not dependent on any physical domain or
-frame models. While images or spectra are already stable and standardized
-products, the time series related domains are still not completely evolved and
-new ones will likely emerge in near future. That is why we need to keep models
-like Time Series Cube DM independent of any underlying physical models. In our
-opinion, this is the only correct and sustainable way for future development of
-IVOA standards.
-",1,1,0,0,0,0
-18811,Markov modeling of peptide folding in the presence of protein crowders,"  We use Markov state models (MSMs) to analyze the dynamics of a
-$\beta$-hairpin-forming peptide in Monte Carlo (MC) simulations with
-interacting protein crowders, for two different types of crowder proteins
-[bovine pancreatic trypsin inhibitor (BPTI) and GB1]. In these systems, at the
-temperature used, the peptide can be folded or unfolded and bound or unbound to
-crowder molecules. Four or five major free-energy minima can be identified. To
-estimate the dominant MC relaxation times of the peptide, we build MSMs using a
-range of different time resolutions or lag times. We show that stable
-relaxation-time estimates can be obtained from the MSM eigenfunctions through
-fits to autocorrelation data. The eigenfunctions remain sufficiently accurate
-to permit stable relaxation-time estimation down to small lag times, at which
-point simple estimates based on the corresponding eigenvalues have large
-systematic uncertainties. The presence of the crowders have a stabilizing
-effect on the peptide, especially with BPTI crowders, which can be attributed
-to a reduced unfolding rate $k_\text{u}$, while the folding rate $k_\text{f}$
-is left largely unchanged.
-",0,0,0,0,1,0
-18812,Ultra-fast magnetization manipulation using single femtosecond light and hot-electrons pulse,"  Current induced magnetization manipulation is a key issue for spintronic
-application. Therefore, deterministic switching of the magnetization at the
-picoseconds timescale with a single electronic pulse represents a major step
-towards the future developments of ultrafast spintronic. Here, we have studied
-the ultrafast magnetization dynamics in engineered Gdx[FeCo]1-x based structure
-to compare the effect of femtosecond laser and hot-electrons pulses. We
-demonstrate that a single femtosecond hot-electrons pulse allows a
-deterministic magnetization reversal in either Gd-rich and FeCo-rich alloys
-similarly to a femtosecond laser pulse. In addition, we show that the limiting
-factor of such manipulation for perpendicular magnetized films arises from the
-multi-domain formation due to dipolar interaction. By performing time resolved
-measurements under various field, we demonstrate that the same magnetization
-dynamics is observed for both light and hot-electrons excitation and that the
-full magnetization reversal take place within 5 ps. The energy efficiency of
-the ultra-fast current induced magnetization manipulation is optimized thanks
-to the ballistic transport of hot-electrons before reaching the GdFeCo magnetic
-layer.
-",0,1,0,0,0,0
-18813,Grain Boundary Resistance in Copper Interconnects from an Atomistic Model to a Neural Network,"  Orientation effects on the resistivity of copper grain boundaries are studied
-systematically with two different atomistic tight binding methods. A
-methodology is developed to model the resistivity of grain boundaries using the
-Embedded Atom Model, tight binding methods and non-equilibrum Green's functions
-(NEGF). The methodology is validated against first principles calculations for
-small, ultra-thin body grain boundaries (<5nm) with 6.4% deviation in the
-resistivity. A statistical ensemble of 600 large, random structures with grains
-is studied. For structures with three grains, it is found that the distribution
-of resistivities is close to normal. Finally, a compact model for grain
-boundary resistivity is constructed based on a neural network.
-",0,1,0,0,0,0
-18814,Some remarkable infinite product identities involving Fibonacci and Lucas numbers,"  By applying the classic telescoping summation formula and its variants to
-identities involving inverse hyperbolic tangent functions having inverse powers
-of the golden ratio as arguments and employing subtle properties of the
-Fibonacci and Lucas numbers, we derive interesting general infinite product
-identities involving these numbers.
-",0,0,1,0,0,0
-18815,Limit Theorems in Mallows Distance for Processes with Gibssian Dependence,"  In this paper, we explore the connection between convergence in distribution
-and Mallows distance in the context of positively associated random variables.
-Our results extend some known invariance principles for sequences with FKG
-property. Applications for processes with Gibbssian dependence structures are
-included.
-",0,0,1,0,0,0
-18816,Localized-endemic state transition in the susceptible-infected-susceptible model on networks,"  It is a longstanding debate concerning the absence of threshold for the
-susceptible-infected-susceptible spreading model on networks with localized
-state. The key to resolve this controversy is the dynamical interaction
-pattern, which has not been uncovered. Here we show that the interaction
-driving the localized-endemic state transition is not the global interaction
-between a node and all the other nodes on the network, but exists at the level
-of super node composed of highly connected node and its neighbors. The internal
-interactions within a super node induce localized state with limited lifetime,
-while the interactions between neighboring super nodes via a path of two hops
-enable them to avoid trapping in the absorbing state, marking the onset of
-endemic state. The hybrid interactions render highly connected nodes
-exponentially increasing infection density, which truly account for the null
-threshold. These results are crucial for correctly understanding diverse
-recurrent contagion phenomena
-",0,1,0,0,0,0
-18817,Analytic approximation of solutions of parabolic partial differential equations with variable coefficients,"  A complete family of solutions for the one-dimensional reaction-diffusion
-equation \[ u_{xx}(x,t)-q(x)u(x,t) = u_t(x,t) \] with a coefficient $q$
-depending on $x$ is constructed. The solutions represent the images of the heat
-polynomials under the action of a transmutation operator. Their use allows one
-to obtain an explicit solution of the noncharacteristic Cauchy problem for the
-considered equation with sufficiently regular Cauchy data as well as to solve
-numerically initial boundary value problems. In the paper the Dirichlet
-boundary conditions are considered however the proposed method can be easily
-extended onto other standard boundary conditions. The proposed numerical method
-is shown to reveal good accuracy.
-",0,0,1,0,0,0
-18818,Final-State Constrained Optimal Control via a Projection Operator Approach,"  In this paper we develop a numerical method to solve nonlinear optimal
-control problems with final-state constraints. Specifically, we extend the
-PRojection Operator based Netwon's method for Trajectory Optimization (PRONTO),
-which was proposed by Hauser for unconstrained optimal control problems. While
-in the standard method final-state constraints can be only approximately
-handled by means of a terminal penalty, in this work we propose a methodology
-to meet the constraints exactly. Moreover, our method guarantees recursive
-feasibility of the final-state constraint. This is an appealing property
-especially in realtime applications in which one would like to be able to stop
-the computation even if the desired tolerance has not been reached, but still
-satisfy the constraints. Following the same conceptual idea of PRONTO, the
-proposed strategy is based on two main steps which (differently from the
-standard scheme) preserve the feasibility of the final-state constraints: (i)
-solve a quadratic approximation of the nonlinear problem to find a descent
-direction, and (ii) get a (feasible) trajectory by means of a feedback law
-(which turns out to be a nonlinear projection operator). To find the (feasible)
-descent direction we take advantage of final-state constrained Linear Quadratic
-optimal control methods, while the second step is performed by suitably
-designing a constrained version of the trajectory tracking projection operator.
-The effectiveness of the proposed strategy is tested on the optimal state
-transfer of an inverted pendulum.
-",1,0,0,0,0,0
-18819,Robust Tracking with Model Mismatch for Fast and Safe Planning: an SOS Optimization Approach,"  In the pursuit of real-time motion planning, a commonly adopted practice is
-to compute a trajectory by running a planning algorithm on a simplified,
-low-dimensional dynamical model, and then employ a feedback tracking controller
-that tracks such a trajectory by accounting for the full, high-dimensional
-system dynamics. While this strategy of planning with model mismatch generally
-yields fast computation times, there are no guarantees of dynamic feasibility,
-which hampers application to safety-critical systems. Building upon recent work
-that addressed this problem through the lens of Hamilton-Jacobi (HJ)
-reachability, we devise an algorithmic framework whereby one computes, offline,
-for a pair of ""planner"" (i.e., low-dimensional) and ""tracking"" (i.e.,
-high-dimensional) models, a feedback tracking controller and associated
-tracking bound. This bound is then used as a safety margin when generating
-motion plans via the low-dimensional model. Specifically, we harness the
-computational tool of sum-of-squares (SOS) programming to design a bilinear
-optimization algorithm for the computation of the feedback tracking controller
-and associated tracking bound. The algorithm is demonstrated via numerical
-experiments, with an emphasis on investigating the trade-off between the
-increased computational scalability afforded by SOS and its intrinsic
-conservativeness. Collectively, our results enable scaling the appealing
-strategy of planning with model mismatch to systems that are beyond the reach
-of HJ analysis, while maintaining safety guarantees.
-",1,0,0,0,0,0
-18820,Robust Gaussian Stochastic Process Emulation,"  We consider estimation of the parameters of a Gaussian Stochastic Process
-(GaSP), in the context of emulation (approximation) of computer models for
-which the outcomes are real-valued scalars. The main focus is on estimation of
-the GaSP parameters through various generalized maximum likelihood methods,
-mostly involving finding posterior modes; this is because full Bayesian
-analysis in computer model emulation is typically prohibitively expensive. The
-posterior modes that are studied arise from objective priors, such as the
-reference prior. These priors have been studied in the literature for the
-situation of an isotropic covariance function or under the assumption of
-separability in the design of inputs for model runs used in the GaSP
-construction. In this paper, we consider more general designs (e.g., a Latin
-Hypercube Design) with a class of commonly used anisotropic correlation
-functions, which can be written as a product of isotropic correlation
-functions, each having an unknown range parameter and a fixed roughness
-parameter. We discuss properties of the objective priors and marginal
-likelihoods for the parameters of the GaSP and establish the posterior
-propriety of the GaSP parameters, but our main focus is to demonstrate that
-certain parameterizations result in more robust estimation of the GaSP
-parameters than others, and that some parameterizations that are in common use
-should clearly be avoided. These results are applicable to many frequently used
-covariance functions, e.g., power exponential, Mat{é}rn, rational quadratic
-and spherical covariance. We also generalize the results to the GaSP model with
-a nugget parameter. Both theoretical and numerical evidence is presented
-concerning the performance of the studied procedures.
-",0,0,1,1,0,0
-18821,Observing the Atmospheres of Known Temperate Earth-sized Planets with JWST,"  Nine transiting Earth-sized planets have recently been discovered around
-nearby late M dwarfs, including the TRAPPIST-1 planets and two planets
-discovered by the MEarth survey, GJ 1132b and LHS 1140b. These planets are the
-smallest known planets that may have atmospheres amenable to detection with
-JWST. We present model thermal emission and transmission spectra for each
-planet, varying composition and surface pressure of the atmosphere. We base
-elemental compositions on those of Earth, Titan, and Venus and calculate the
-molecular compositions assuming chemical equilibrium, which can strongly depend
-on temperature. Both thermal emission and transmission spectra are sensitive to
-the atmospheric composition; thermal emission spectra are sensitive to surface
-pressure and temperature. We predict the observability of each planet's
-atmosphere with JWST. GJ 1132b and TRAPPIST-1b are excellent targets for
-emission spectroscopy with JWST/MIRI, requiring fewer than 10 eclipse
-observations. Emission photometry for TRAPPIST-1c requires 5-15 eclipses; LHS
-1140b and TRAPPIST-1d, TRAPPIST-1e, and TRAPPIST-1f, which could possibly have
-surface liquid water, may be accessible with photometry. Seven of the nine
-planets are strong candidates for transmission spectroscopy measurements with
-JWST, though the number of transits required depends strongly on the planets'
-actual masses. Using the measured masses, fewer than 20 transits are required
-for a 5 sigma detection of spectral features for GJ 1132b and six of the
-TRAPPIST-1 planets. Dedicated campaigns to measure the atmospheres of these
-nine planets will allow us, for the first time, to probe formation and
-evolution processes of terrestrial planetary atmospheres beyond our solar
-system.
-",0,1,0,0,0,0
-18822,Front Propagation for Nonlocal KPP Reaction-Diffusion Equations in Periodic Media,"  We study front propagation phenomena for a large class of nonlocal KPP-type
-reaction-diffusion equations in oscillatory environments, which model various
-forms of population growth with periodic dependence. The nonlocal diffusion is
-an anisotropic integro-differential operator of order $\alpha \in (0,2)$.
-",0,0,1,0,0,0
-18823,Distributed Functional Observers for LTI Systems,"  We study the problem of designing distributed functional observers for LTI
-systems. Specifically, we consider a setting consisting of a state vector that
-evolves over time according to a dynamical process. A set of nodes distributed
-over a communication network wish to collaboratively estimate certain functions
-of the state. We first show that classical existence conditions for the design
-of centralized functional observers do not directly translate to the
-distributed setting, due to the coupling that exists between the dynamics of
-the functions of interest and the diverse measurements at the various nodes.
-Accordingly, we design transformations that reveal such couplings and identify
-portions of the corresponding dynamics that are locally detectable at each
-sensor node. We provide sufficient conditions on the network, along with state
-estimate update and exchange rules for each node, that guarantee asymptotic
-reconstruction of the functions at each sensor node.
-",0,0,1,0,0,0
-18824,Wavelet eigenvalue regression for $n$-variate operator fractional Brownian motion,"  In this contribution, we extend the methodology proposed in Abry and Didier
-(2017) to obtain the first joint estimator of the real parts of the Hurst
-eigenvalues of $n$-variate OFBM. The procedure consists of a wavelet regression
-on the log-eigenvalues of the sample wavelet spectrum. The estimator is shown
-to be consistent for any time reversible OFBM and, under stronger assumptions,
-also asymptotically normal starting from either continuous or discrete time
-measurements. Simulation studies establish the finite sample effectiveness of
-the methodology and illustrate its benefits compared to univariate-like
-(entrywise) analysis. As an application, we revisit the well-known self-similar
-character of Internet traffic by applying the proposed methodology to 4-variate
-time series of modern, high quality Internet traffic data. The analysis reveals
-the presence of a rich multivariate self-similarity structure.
-",0,0,1,1,0,0
-18825,Deep Neural Network for Analysis of DNA Methylation Data,"  Many researches demonstrated that the DNA methylation, which occurs in the
-context of a CpG, has strong correlation with diseases, including cancer. There
-is a strong interest in analyzing the DNA methylation data to find how to
-distinguish different subtypes of the tumor. However, the conventional
-statistical methods are not suitable for analyzing the highly dimensional DNA
-methylation data with bounded support. In order to explicitly capture the
-properties of the data, we design a deep neural network, which composes of
-several stacked binary restricted Boltzmann machines, to learn the low
-dimensional deep features of the DNA methylation data. Experiments show these
-features perform best in breast cancer DNA methylation data cluster analysis,
-comparing with some state-of-the-art methods.
-",0,0,0,1,1,0
-18826,One look at the rating of scientific publications and corresponding toy-model,"  A toy-model of publications and citations processes is proposed. The model
-shows that the role of randomness in the processes is essential and cannot be
-ignored. Some other aspects of scientific publications rating are discussed.
-",1,0,0,1,0,0
-18827,"Alignment, Orientation, and Coulomb Explosion of Difluoroiodobenzene Studied with the Pixel Imaging Mass Spectrometry (PImMS) Camera","  Laser-induced adiabatic alignment and mixed-field orientation of
-2,6-difluoroiodobenzene (C6H3F2I) molecules are probed by Coulomb explosion
-imaging following either near-infrared strong-field ionization or
-extreme-ultraviolet multi-photon inner-shell ionization using free-electron
-laser pulses. The resulting photoelectrons and fragment ions are captured by a
-double-sided velocity map imaging spectrometer and projected onto two
-position-sensitive detectors. The ion side of the spectrometer is equipped with
-the Pixel Imaging Mass Spectrometry (PImMS) camera, a time-stamping pixelated
-detector that can record the hit positions and arrival times of up to four ions
-per pixel per acquisition cycle. Thus, the time-of-flight trace and ion
-momentum distributions for all fragments can be recorded simultaneously. We
-show that we can obtain a high degree of one- and three-dimensional alignment
-and mixed- field orientation, and compare the Coulomb explosion process induced
-at both wavelengths.
-",0,1,0,0,0,0
-18828,Unsupervised Machine Learning of Open Source Russian Twitter Data Reveals Global Scope and Operational Characteristics,"  We developed and used a collection of statistical methods (unsupervised
-machine learning) to extract relevant information from a Twitter supplied data
-set consisting of alleged Russian trolls who (allegedly) attempted to influence
-the 2016 US Presidential election. These unsupervised statistical methods allow
-fast identification of (i) emergent language communities within the troll
-population, (ii) the transnational scope of the operation and (iii) operational
-characteristics of trolls that can be used for future identification. Using
-natural language processing, manifold learning and Fourier analysis, we
-identify an operation that includes not only the 2016 US election, but also the
-French National and both local and national German elections. We show the
-resulting troll population is composed of users with common, but clearly
-customized, behavioral characteristics.
-",1,0,0,0,0,0
-18829,"Distributed, scalable and gossip-free consensus optimization with application to data analysis","  Distributed algorithms for solving additive or consensus optimization
-problems commonly rely on first-order or proximal splitting methods. These
-algorithms generally come with restrictive assumptions and at best enjoy a
-linear convergence rate. Hence, they can require many iterations or
-communications among agents to converge. In many cases, however, we do not seek
-a highly accurate solution for consensus problems. Based on this we propose a
-controlled relaxation of the coupling in the problem which allows us to compute
-an approximate solution, where the accuracy of the approximation can be
-controlled by the level of relaxation. The relaxed problem can be efficiently
-solved in a distributed way using a combination of primal-dual interior-point
-methods (PDIPMs) and message-passing. This algorithm purely relies on
-second-order methods and thus requires far fewer iterations and communications
-to converge. This is illustrated in numerical experiments, showing its superior
-performance compared to existing methods.
-",0,0,1,0,0,0
-18830,Consistency of Maximum Likelihood for Continuous-Space Network Models,"  Network analysis needs tools to infer distributions over graphs of arbitrary
-size from a single graph. Assuming the distribution is generated by a
-continuous latent space model which obeys certain natural symmetry and
-smoothness properties, we establish three levels of consistency for
-non-parametric maximum likelihood inference as the number of nodes grows: (i)
-the estimated locations of all nodes converge in probability on their true
-locations; (ii) the distribution over locations in the latent space converges
-on the true distribution; and (iii) the distribution over graphs of arbitrary
-size converges.
-",0,0,1,1,0,0
-18831,Consistency Results for Stationary Autoregressive Processes with Constrained Coefficients,"  We consider stationary autoregressive processes with coefficients restricted
-to an ellipsoid, which includes autoregressive processes with absolutely
-summable coefficients. We provide consistency results under different norms for
-the estimation of such processes using constrained and penalized estimators. As
-an application we show some weak form of universal consistency. Simulations
-show that directly including the constraint in the estimation can lead to more
-robust results.
-",0,0,0,1,0,0
-18832,Higher order mobile coverage control with application to localization,"  Most current results on coverage control using mobile sensors require that
-one partitioned cell is associated with precisely one sensor. In this paper, we
-consider a class of coverage control problems involving higher order Voronoi
-partitions, motivated by applications where more than one sensor is required to
-monitor and cover one cell. Such applications are frequent in scenarios
-requiring the sensors to localize targets. We introduce a framework depending
-on a coverage performance function incorporating higher order Voronoi cells and
-then design a gradient-based controller which allows the multi-sensor system to
-achieve a local equilibrium in a distributed manner. The convergence properties
-are studied and related to Lloyd algorithm. We study also the extension to
-coverage of a discrete set of points. In addition, we provide a number of real
-world scenarios where our framework can be applied. Simulation results are also
-provided to show the controller performance.
-",1,0,0,0,0,0
-18833,Borg's Periodicity Theorems for first order self-adjoint systems with complex potentials,"  A self-adjoint first order system with Hermitian $\pi$-periodic potential
-$Q(z)$, integrable on compact sets, is considered. It is shown that all zeros
-of $\Delta + 2e^{-i\int_0^\pi \Im q dt}$ are double zeros if and only if this
-self-adjoint system is unitarily equivalent to one in which $Q(z)$ is
-$\frac{\pi}{2}$-periodic. Furthermore, the zeros of $\Delta - 2e^{-i\int_0^\pi
-\Im q dt}$ are all double zeros if and only if the associated self-adjoint
-system is unitarily equivalent to one in which $Q(z) = \sigma_2 Q(z) \sigma_2$.
-Here $\Delta$ denotes the discriminant of the system and $\sigma_0$, $\sigma_2$
-are Pauli matrices. Finally, it is shown that all instability intervals vanish
-if and only if $Q = r\sigma_0 + q\sigma_2$, for some real valued $\pi$-periodic
-functions $r$ and $q$ integrable on compact sets.
-",0,0,1,0,0,0
-18834,On the Ubiquity of Information Inconsistency for Conjugate Priors,"  Informally, ""Information Inconsistency"" is the property that has been
-observed in many Bayesian hypothesis testing and model selection procedures
-whereby the Bayesian conclusion does not become definitive when the data seems
-to become definitive. An example is that, when performing a t-test using
-standard conjugate priors, the Bayes factor of the alternative hypothesis to
-the null hypothesis remains bounded as the t statistic grows to infinity. This
-paper shows that information inconsistency is ubiquitous in Bayesian hypothesis
-testing under conjugate priors. Yet the title does not fully describe the
-paper, since we also show that theoretically recommended priors, including
-scale mixtures of conjugate priors and adaptive priors, are information
-consistent. Hence the paper is simply a forceful warning that use of conjugate
-priors in testing and model selection is highly problematical, and should be
-replaced by the information consistent alternatives.
-",0,0,1,1,0,0
-18835,Competition evolution of Rayleigh-Taylor bubbles,"  Material mixing induced by a Rayleigh-Taylor instability occurs ubiquitously
-in either nature or engineering when a light fluid pushes against a heavy
-fluid, accompanying with the formation and evolution of chaotic bubbles. Its
-general evolution involves two mechanisms: bubble-merge and bubble-competition.
-The former obeys a universa1 evolution law and has been well-studied, while the
-latter depends on many factors and has not been well-recognized. In this paper,
-we establish a theory for the latter to clarify and quantify the longstanding
-open question: the dependence of bubbles evolution on the dominant factors of
-arbitrary density ratio, broadband initial perturbations and various material
-properties (e.g., viscosity, miscibility, surface tensor). Evolution of the
-most important characteristic quantities, i.e., the diameter of dominant bubble
-$D$ and the height of bubble zone $h$, is derived: (i) the $D$ expands
-self-similarly with steady aspect ratio $\beta \equiv D/h \thickapprox (1{\rm{
-+ }}A)/4$, depending only on dimensionless density ratio $A$, and (ii) the $h$
-grows quadratically with constant growth coefficient $\alpha \equiv h/(Ag{t^2})
-\thickapprox [2\phi/{\ln}(2{\eta _{\rm{0}}})]^2$, depending on both
-dimensionless initial perturbation amplitude ${\eta _{\rm{0}}}$ and
-material-property-associated linear growth rate ratio
-$\phi\equiv\Gamma_{actual}/\Gamma_{ideal}\leqslant1$. The theory successfully
-explains the continued puzzle about the widely varying $\alpha\in (0.02,0.12)$
-in experiments and simulations, conducted at all value of $A \in (0,1)$ and
-widely varying value of ${\eta _{\rm{0}}} \in [{10^{ - 7}},{10^{ - 2}}]$ with
-different materials. The good agreement between theory and experiments implies
-that majority of actual mixing depends on initial perturbations and material
-properties, to which more attention should be paid in either natural or
-engineering problems.
-",0,1,0,0,0,0
-18836,Nonlinear oblique projections,"  We construct nonlinear oblique projections along subalgebras of nilpotent Lie
-algebras in terms of the Baker-Campbell-Hausdorff multiplication. We prove that
-these nonlinear projections are real analytic on every Schubert cell of the
-Grassmann manifold whose points are the subalgebras of the nilpotent Lie
-algebra under consideration.
-",0,0,1,0,0,0
-18837,Granger Mediation Analysis of Multiple Time Series with an Application to fMRI,"  It becomes increasingly popular to perform mediation analysis for complex
-data from sophisticated experimental studies. In this paper, we present Granger
-Mediation Analysis (GMA), a new framework for causal mediation analysis of
-multiple time series. This framework is motivated by a functional magnetic
-resonance imaging (fMRI) experiment where we are interested in estimating the
-mediation effects between a randomized stimulus time series and brain activity
-time series from two brain regions. The stable unit treatment assumption for
-causal mediation analysis is thus unrealistic for this type of time series
-data. To address this challenge, our framework integrates two types of models:
-causal mediation analysis across the variables and vector autoregressive models
-across the temporal observations. We further extend this framework to handle
-multilevel data to address individual variability and correlated errors between
-the mediator and the outcome variables. These models not only provide valid
-causal mediation for time series data but also model the causal dynamics across
-time. We show that the modeling parameters in our models are identifiable, and
-we develop computationally efficient methods to maximize the likelihood-based
-optimization criteria. Simulation studies show that our method reduces the
-estimation bias and improve statistical power, compared to existing approaches.
-On a real fMRI data set, our approach not only infers the causal effects of
-brain pathways but accurately captures the feedback effect of the outcome
-region on the mediator region.
-",0,0,0,1,0,0
-18838,Parameterization of Sequence of MFCCs for DNN-based voice disorder detection,"  In this article a DNN-based system for detection of three common voice
-disorders (vocal nodules, polyps and cysts; laryngeal neoplasm; unilateral
-vocal paralysis) is presented. The input to the algorithm is (at least 3-second
-long) audio recording of sustained vowel sound /a:/. The algorithm was
-developed as part of the ""2018 FEMH Voice Data Challenge"" organized by Far
-Eastern Memorial Hospital and obtained score value (defined in the challenge
-specification) of 77.44. This was the second best result before final
-submission. Final challenge results are not yet known during writing of this
-document. The document also reports changes that were made for the final
-submission which improved the score value in cross-validation by 0.6% points.
-",1,0,0,0,0,0
-18839,Near-Optimal Adversarial Policy Switching for Decentralized Asynchronous Multi-Agent Systems,"  A key challenge in multi-robot and multi-agent systems is generating
-solutions that are robust to other self-interested or even adversarial parties
-who actively try to prevent the agents from achieving their goals. The
-practicality of existing works addressing this challenge is limited to only
-small-scale synchronous decision-making scenarios or a single agent planning
-its best response against a single adversary with fixed, procedurally
-characterized strategies. In contrast this paper considers a more realistic
-class of problems where a team of asynchronous agents with limited observation
-and communication capabilities need to compete against multiple strategic
-adversaries with changing strategies. This problem necessitates agents that can
-coordinate to detect changes in adversary strategies and plan the best response
-accordingly. Our approach first optimizes a set of stratagems that represent
-these best responses. These optimized stratagems are then integrated into a
-unified policy that can detect and respond when the adversaries change their
-strategies. The near-optimality of the proposed framework is established
-theoretically as well as demonstrated empirically in simulation and hardware.
-",1,0,0,0,0,0
-18840,Structural Connectome Validation Using Pairwise Classification,"  In this work, we study the extent to which structural connectomes and
-topological derivative measures are unique to individual changes within human
-brains. To do so, we classify structural connectome pairs from two large
-longitudinal datasets as either belonging to the same individual or not. Our
-data is comprised of 227 individuals from the Alzheimer's Disease Neuroimaging
-Initiative (ADNI) and 226 from the Parkinson's Progression Markers Initiative
-(PPMI). We achieve 0.99 area under the ROC curve score for features which
-represent either weights or network structure of the connectomes (node degrees,
-PageRank and local efficiency). Our approach may be useful for eliminating
-noisy features as a preprocessing step in brain aging studies and early
-diagnosis classification problems.
-",1,0,0,0,0,0
-18841,Deep Robust Kalman Filter,"  A Robust Markov Decision Process (RMDP) is a sequential decision making model
-that accounts for uncertainty in the parameters of dynamic systems. This
-uncertainty introduces difficulties in learning an optimal policy, especially
-for environments with large state spaces. We propose two algorithms, RTD-DQN
-and Deep-RoK, for solving large-scale RMDPs using nonlinear approximation
-schemes such as deep neural networks. The RTD-DQN algorithm incorporates the
-robust Bellman temporal difference error into a robust loss function, yielding
-robust policies for the agent. The Deep-RoK algorithm is a robust Bayesian
-method, based on the Extended Kalman Filter (EKF), that accounts for both the
-uncertainty in the weights of the approximated value function and the
-uncertainty in the transition probabilities, improving the robustness of the
-agent. We provide theoretical results for our approach and test the proposed
-algorithms on a continuous state domain.
-",1,0,0,1,0,0
-18842,Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach,"  The Fisher information matrix (FIM) is a fundamental quantity to represent
-the characteristics of a stochastic model, including deep neural networks
-(DNNs). The present study reveals novel statistics of FIM that are universal
-among a wide class of DNNs. To this end, we use random weights and large width
-limits, which enables us to utilize mean field theories. We investigate the
-asymptotic statistics of the FIM's eigenvalues and reveal that most of them are
-close to zero while the maximum takes a huge value. This implies that the
-eigenvalue distribution has a long tail. Because the landscape of the parameter
-space is defined by the FIM, it is locally flat in most dimensions, but
-strongly distorted in others. We also demonstrate the potential usage of the
-derived statistics through two exercises. First, small eigenvalues that induce
-flatness can be connected to a norm-based capacity measure of generalization
-ability. Second, the maximum eigenvalue that induces the distortion enables us
-to quantitatively estimate an appropriately sized learning rate for gradient
-methods to converge.
-",0,0,0,1,0,0
-18843,An extension problem and trace Hardy inequality for the sublaplacian on $H$-type groups,"  In this paper we study the extension problem for the sublaplacian on a
-$H$-type group and use the solutions to prove trace Hardy and Hardy
-inequalities for fractional powers of the sublaplacian.
-",0,0,1,0,0,0
-18844,Centered Isotonic Regression: Point and Interval Estimation for Dose-Response Studies,"  Univariate isotonic regression (IR) has been used for nonparametric
-estimation in dose-response and dose-finding studies. One undesirable property
-of IR is the prevalence of piecewise-constant stretches in its estimates,
-whereas the dose-response function is usually assumed to be strictly
-increasing. We propose a simple modification to IR, called centered isotonic
-regression (CIR). CIR's estimates are strictly increasing in the interior of
-the dose range. In the absence of monotonicity violations, CIR and IR both
-return the original observations. Numerical examination indicates that for
-sample sizes typical of dose-response studies and with realistic dose-response
-curves, CIR provides a substantial reduction in estimation error compared with
-IR when monotonicity violations occur. We also develop analytical interval
-estimates for IR and CIR, with good coverage behavior. An R package implements
-these point and interval estimates.
-",0,0,0,1,0,0
-18845,Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection,"  Measuring domain relevance of data and identifying or selecting well-fit
-domain data for machine translation (MT) is a well-studied topic, but denoising
-is not yet. Denoising is concerned with a different type of data quality and
-tries to reduce the negative impact of data noise on MT training, in
-particular, neural MT (NMT) training. This paper generalizes methods for
-measuring and selecting data for domain MT and applies them to denoising NMT
-training. The proposed approach uses trusted data and a denoising curriculum
-realized by online data selection. Intrinsic and extrinsic evaluations of the
-approach show its significant effectiveness for NMT to train on data with
-severe noise.
-",0,0,0,1,0,0
-18846,Discovering Signals from Web Sources to Predict Cyber Attacks,"  Cyber attacks are growing in frequency and severity. Over the past year alone
-we have witnessed massive data breaches that stole personal information of
-millions of people and wide-scale ransomware attacks that paralyzed critical
-infrastructure of several countries. Combating the rising cyber threat calls
-for a multi-pronged strategy, which includes predicting when these attacks will
-occur. The intuition driving our approach is this: during the planning and
-preparation stages, hackers leave digital traces of their activities on both
-the surface web and dark web in the form of discussions on platforms like
-hacker forums, social media, blogs and the like. These data provide predictive
-signals that allow anticipating cyber attacks. In this paper, we describe
-machine learning techniques based on deep neural networks and autoregressive
-time series models that leverage external signals from publicly available Web
-sources to forecast cyber attacks. Performance of our framework across ground
-truth data over real-world forecasting tasks shows that our methods yield a
-significant lift or increase of F1 for the top signals on predicted cyber
-attacks. Our results suggest that, when deployed, our system will be able to
-provide an effective line of defense against various types of targeted cyber
-attacks.
-",1,0,0,1,0,0
-18847,Higher-degree Smoothness of Perturbations I,"  In this paper and its sequels, we give an unified treatment of the
-higher-degree smoothness of admissible perturbations and related results used
-in the global perturbation method for GW and Floer theories.
-",0,0,1,0,0,0
-18848,Space Telescope and Optical Reverberation Mapping Project. V. Optical Spectroscopic Campaign and Emission-Line Analysis for NGC 5548,"  We present the results of an optical spectroscopic monitoring program
-targeting NGC 5548 as part of a larger multi-wavelength reverberation mapping
-campaign. The campaign spanned six months and achieved an almost daily cadence
-with observations from five ground-based telescopes. The H$\beta$ and He II
-$\lambda$4686 broad emission-line light curves lag that of the 5100 $\AA$
-optical continuum by $4.17^{+0.36}_{-0.36}$ days and $0.79^{+0.35}_{-0.34}$
-days, respectively. The H$\beta$ lag relative to the 1158 $\AA$ ultraviolet
-continuum light curve measured by the Hubble Space Telescope is roughly
-$\sim$50% longer than that measured against the optical continuum, and the lag
-difference is consistent with the observed lag between the optical and
-ultraviolet continua. This suggests that the characteristic radius of the
-broad-line region is $\sim$50% larger than the value inferred from optical data
-alone. We also measured velocity-resolved emission-line lags for H$\beta$ and
-found a complex velocity-lag structure with shorter lags in the line wings,
-indicative of a broad-line region dominated by Keplerian motion. The responses
-of both the H$\beta$ and He II $\lambda$4686 emission lines to the driving
-continuum changed significantly halfway through the campaign, a phenomenon also
-observed for C IV, Ly $\alpha$, He II(+O III]), and Si IV(+O IV]) during the
-same monitoring period. Finally, given the optical luminosity of NGC 5548
-during our campaign, the measured H$\beta$ lag is a factor of five shorter than
-the expected value implied by the $R_\mathrm{BLR} - L_\mathrm{AGN}$ relation
-based on the past behavior of NGC 5548.
-",0,1,0,0,0,0
-18849,An Inexact Regularized Newton Framework with a Worst-Case Iteration Complexity of $\mathcal{O}(ε^{-3/2})$ for Nonconvex Optimization,"  An algorithm for solving smooth nonconvex optimization problems is proposed
-that, in the worst-case, takes $\mathcal{O}(\epsilon^{-3/2})$ iterations to
-drive the norm of the gradient of the objective function below a prescribed
-positive real number $\epsilon$ and can take $\mathcal{O}(\epsilon^{-3})$
-iterations to drive the leftmost eigenvalue of the Hessian of the objective
-above $-\epsilon$. The proposed algorithm is a general framework that covers a
-wide range of techniques including quadratically and cubically regularized
-Newton methods, such as the Adaptive Regularisation using Cubics (ARC) method
-and the recently proposed Trust-Region Algorithm with Contractions and
-Expansions (TRACE). The generality of our method is achieved through the
-introduction of generic conditions that each trial step is required to satisfy,
-which in particular allow for inexact regularized Newton steps to be used.
-These conditions center around a new subproblem that can be approximately
-solved to obtain trial steps that satisfy the conditions. A new instance of the
-framework, distinct from ARC and TRACE, is described that may be viewed as a
-hybrid between quadratically and cubically regularized Newton methods.
-Numerical results demonstrate that our hybrid algorithm outperforms a cublicly
-regularized Newton method.
-",0,0,1,0,0,0
-18850,An inverse problem for Maxwell's equations with Lipschitz parameters,"  We consider an inverse boundary value problem for Maxwell's equations, which
-aims to recover the electromagnetic material properties of a body from
-measurements on the boundary. We show that a Lipschitz continuous conductivity,
-electric permittivity, and magnetic permeability are uniquely determined by
-knowledge of all tangential electric and magnetic fields on the boundary of the
-body at a fixed frequency.
-",0,0,1,0,0,0
-18851,Context Aware Robot Navigation using Interactively Built Semantic Maps,"  We discuss the process of building semantic maps, how to interactively label
-entities in them, and how to use them to enable context-aware navigation
-behaviors in human environments. We utilize planar surfaces, such as walls and
-tables, and static objects, such as door signs, as features for our semantic
-mapping approach. Users can interactively annotate these features by having the
-robot follow him/her, entering the label through a mobile app, and performing a
-pointing gesture toward the landmark of interest. Our gesture based approach
-can reliably estimate which object is being pointed at and detect ambiguous
-gestures with probabilistic modeling. Our person following method attempts to
-maximize future utility by a search for future actions assuming constant
-velocity model for the human. We describe a method to extract metric goals from
-a semantic map landmark and to plan a human aware path that takes into account
-the personal spaces of people. Finally, we demonstrate context-awareness for
-person following in two scenarios: interactive labeling and door passing. We
-believe that future navigation approaches and service robotics applications can
-be made more effective by further exploiting the structure of human
-environments.
-",1,0,0,0,0,0
-18852,Disentangling by Partitioning: A Representation Learning Framework for Multimodal Sensory Data,"  Multimodal sensory data resembles the form of information perceived by humans
-for learning, and are easy to obtain in large quantities. Compared to unimodal
-data, synchronization of concepts between modalities in such data provides
-supervision for disentangling the underlying explanatory factors of each
-modality. Previous work leveraging multimodal data has mainly focused on
-retaining only the modality-invariant factors while discarding the rest. In
-this paper, we present a partitioned variational autoencoder (PVAE) and several
-training objectives to learn disentangled representations, which encode not
-only the shared factors, but also modality-dependent ones, into separate latent
-variables. Specifically, PVAE integrates a variational inference framework and
-a multimodal generative model that partitions the explanatory factors and
-conditions only on the relevant subset of them for generation. We evaluate our
-model on two parallel speech/image datasets, and demonstrate its ability to
-learn disentangled representations by qualitatively exploring within-modality
-and cross-modality conditional generation with semantics and styles specified
-by examples. For quantitative analysis, we evaluate the classification accuracy
-of automatically discovered semantic units. Our PVAE can achieve over 99%
-accuracy on both modalities.
-",0,0,0,1,0,0
-18853,Blind Gain and Phase Calibration via Sparse Spectral Methods,"  Blind gain and phase calibration (BGPC) is a bilinear inverse problem
-involving the determination of unknown gains and phases of the sensing system,
-and the unknown signal, jointly. BGPC arises in numerous applications, e.g.,
-blind albedo estimation in inverse rendering, synthetic aperture radar
-autofocus, and sensor array auto-calibration. In some cases, sparse structure
-in the unknown signal alleviates the ill-posedness of BGPC. Recently there has
-been renewed interest in solutions to BGPC with careful analysis of error
-bounds. In this paper, we formulate BGPC as an eigenvalue/eigenvector problem,
-and propose to solve it via power iteration, or in the sparsity or joint
-sparsity case, via truncated power iteration. Under certain assumptions, the
-unknown gains, phases, and the unknown signal can be recovered simultaneously.
-Numerical experiments show that power iteration algorithms work not only in the
-regime predicted by our main results, but also in regimes where theoretical
-analysis is limited. We also show that our power iteration algorithms for BGPC
-compare favorably with competing algorithms in adversarial conditions, e.g.,
-with noisy measurement or with a bad initial estimate.
-",1,0,0,0,0,0
-18854,"Reconciling cooperation, biodiversity and stability in complex ecological communities","  Empirical observations show that ecological communities can have a huge
-number of coexisting species, also with few or limited number of resources.
-These ecosystems are characterized by multiple type of interactions, in
-particular displaying cooperative behaviors. However, standard modeling of
-population dynamics based on Lotka-Volterra type of equations predicts that
-ecosystem stability should decrease as the number of species in the community
-increases and that cooperative systems are less stable than communities with
-only competitive and/or exploitative interactions. Here we propose a stochastic
-model of population dynamics, which includes exploitative interactions as well
-as cooperative interactions induced by cross-feeding. The model is exactly
-solved and we obtain results for relevant macro-ecological patterns, such as
-species abundance distributions and correlation functions. In the large system
-size limit, any number of species can coexist for a very general class of
-interaction networks and stability increases as the number of species grows.
-For pure mutualistic/commensalistic interactions we determine the topological
-properties of the network that guarantee species coexistence. We also show that
-the stationary state is globally stable and that inferring species interactions
-through species abundance correlation analysis may be misleading. Our
-theoretical approach thus show that appropriate models of cooperation naturally
-leads to a solution of the long-standing question about complexity-stability
-paradox and on how highly biodiverse communities can coexist.
-",0,0,0,0,1,0
-18855,The Young L Dwarf 2MASS J11193254-1137466 is a Planetary-Mass Binary,"  We have discovered that the extremely red, low-gravity L7 dwarf 2MASS
-J11193254-1137466 is a 0.14"" (3.6 AU) binary using Keck laser guide star
-adaptive optics imaging. 2MASS J11193254-1137466 has previously been identified
-as a likely member of the TW Hydrae Association (TWA). Using our updated
-photometric distance and proper motion, a kinematic analysis based on the
-BANYAN II model gives an 82% probability of TWA membership. At TWA's 10$\pm$3
-Myr age and using hot-start evolutionary models, 2MASS J11193254-1137466AB is a
-pair of $3.7^{+1.2}_{-0.9}$ $M_{\rm Jup}$ brown dwarfs, making it the
-lowest-mass binary discovered to date. We estimate an orbital period of
-$90^{+80}_{-50}$ years. One component is marginally brighter in $K$ band but
-fainter in $J$ band, making this a probable flux-reversal binary, the first
-discovered with such a young age. We also imaged the spectrally similar TWA L7
-dwarf WISEA J114724.10-204021.3 with Keck and found no sign of binarity. Our
-evolutionary model-derived $T_{\rm eff}$ estimate for WISEA J114724.10-204021.3
-is $\approx$230 K higher than for 2MASS J11193254-1137466AB, at odds with their
-spectral similarity. This discrepancy suggests that WISEA J114724.10-204021.3
-may actually be a tight binary with masses and temperatures very similar to
-2MASS J11193254-1137466AB, or further supporting the idea that near-infrared
-spectra of young ultracool dwarfs are shaped by factors other than temperature
-and gravity. 2MASS J11193254-1137466AB will be an essential benchmark for
-testing evolutionary and atmospheric models in the young planetary-mass regime.
-",0,1,0,0,0,0
-18856,Diversity of Abundance Patterns of Light Neutron-capture Elements in Very-metal-poor Stars,"  We determine the abundances of neutron-capture elements from Sr to Eu for
-five very-metal-poor stars (-3<[Fe/H]<-2) in the Milky Way halo to reveal the
-origin of light neutron-capture elements. Previous spectroscopic studies have
-shown evidence of at least two components in the r-process; one referred to as
-the ""main r-process"" and the other as the ""weak r-process,"" which is mainly
-responsible for producing heavy and light neutron-capture elements,
-respectively. Observational studies of metal-poor stars suggest that there is a
-universal pattern in the main r-process, similar to the abundance pattern of
-the r-process component of solar-system material. Still, it is uncertain
-whether the abundance pattern of the weak r-process shows universality or
-diversity, due to the sparseness of measured light neutron-capture elements. We
-have detected the key elements, Mo, Ru, and Pd, in five target stars to give an
-answer to this question. The abundance patterns of light neutron-capture
-elements from Sr to Pd suggest a diversity in the weak r-process. In
-particular, scatter in the abundance ratio between Ru and Pd is significant
-when the abundance patterns are normalized at Zr. Our results are compared with
-the elemental abundances predicted by nucleosynthesis models of supernovae with
-parameters such as electron fraction or proto-neutron-star mass, to investigate
-sources of such diversity in the abundance patterns of light neutron-capture
-elements. This paper presents that the variation in the abundances of observed
-stars can be explained with a small range of parameters, which can serve as
-constraints on future modeling of supernova models.
-",0,1,0,0,0,0
-18857,Hypergames and Cyber-Physical Security for Control Systems,"  The identification of the Stuxnet worm in 2010 provided a highly publicized
-example of a cyber attack used to damage an industrial control system
-physically. This raised public awareness about the possibility of similar
-attacks against other industrial targets -- including critical infrastructure.
-In this paper, we use hypergames to analyze how adversarial perturbations can
-be used to manipulate a system using optimal control. Hypergames form an
-extension of game theory that enables us to model strategic interactions where
-the players may have significantly different perceptions of the game(s) they
-are playing. Past work with hypergames has been limited to relatively simple
-interactions consisting of a small set of discrete choices for each player, but
-here, we apply hypergames to larger systems with continuous variables. We find
-that manipulating constraints can be a more effective attacker strategy than
-directly manipulating objective function parameters. Moreover, the attacker
-need not change the underlying system to carry out a successful attack -- it
-may be sufficient to deceive the defender controlling the system. It is
-possible to scale our approach up to even larger systems, but the ability to do
-so will depend on the characteristics of the system in question, and we
-identify several characteristics that will make those systems amenable to
-hypergame analysis.
-",1,0,0,0,0,0
-18858,Measuring the academic reputation through citation networks via PageRank,"  The objective assessment of the prestige of an academic institution is a
-difficult and hotly debated task. In the last few years, different types of
-University Rankings have been proposed to quantify the excellence of different
-research institutions in the world. Albeit met with criticism in some cases,
-the relevance of university rankings is being increasingly acknowledged:
-indeed, rankings are having a major impact on the design of research policies,
-both at the institutional and governmental level. Yet, the debate on what
-rankings are {\em exactly} measuring is enduring. Here, we address the issue by
-measuring a quantitive and reliable proxy of the academic reputation of a given
-institution and by evaluating its correlation with different university
-rankings. Specifically, we study citation patterns among universities in five
-different Web of Science Subject Categories and use the \pr~algorithm on the
-five resulting citation networks. The rationale behind our work is that
-scientific citations are driven by the reputation of the reference so that the
-PageRank algorithm is expected to yield a rank which reflects the reputation of
-an academic institution in a specific field. Our results allow to quantifying
-the prestige of a set of institutions in a certain research field based only on
-hard bibliometric data. Given the volume of the data analysed, our findings are
-statistically robust and less prone to bias, at odds with ad--hoc surveys often
-employed by ranking bodies in order to attain similar results. Because our
-findings are found to correlate extremely well with the ARWU Subject rankings,
-the approach we propose in our paper may open the door to new, Academic Ranking
-methodologies that go beyond current methods by reconciling the qualitative
-evaluation of Academic Prestige with its quantitative measurements via
-publication impact.
-",1,0,0,0,0,0
-18859,A Model Order Reduction Algorithm for Estimating the Absorption Spectrum,"  The ab initio description of the spectral interior of the absorption spectrum
-poses both a theoretical and computational challenge for modern electronic
-structure theory. Due to the often spectrally dense character of this domain in
-the quantum propagator's eigenspectrum for medium-to-large sized systems,
-traditional approaches based on the partial diagonalization of the propagator
-often encounter oscillatory and stagnating convergence. Electronic structure
-methods which solve the molecular response problem through the solution of
-spectrally shifted linear systems, such as the complex polarization propagator,
-offer an alternative approach which is agnostic to the underlying spectral
-density or domain location. This generality comes at a seemingly high
-computational cost associated with solving a large linear system for each
-spectral shift in some discretization of the spectral domain of interest. We
-present a novel, adaptive solution based on model order reduction techniques
-via interpolation. Model order reduction reduces the computational complexity
-of mathematical models and is ubiquitous in the simulation of dynamical
-systems. The efficiency and effectiveness of the proposed algorithm in the ab
-initio prediction of X-Ray absorption spectra is demonstrated using a test set
-of challenging water clusters which are spectrally dense in the neighborhood of
-the oxygen K-edge. Based on a single, user defined tolerance we automatically
-determine the order of the reduced models and approximate the absorption
-spectrum up to the given tolerance. We also illustrate that the automatically
-determined model order increases logarithmically with the problem dimension,
-compared to a linear increase of the number of eigenvalues within the energy
-window. Furthermore, we observed that the computational cost of the proposed
-algorithm only scales quadratically with respect to the problem dimension.
-",1,1,0,0,0,0
-18860,"Uniform rank gradient, cost and local-global convergence","  We analyze the rank gradient of finitely generated groups with respect to
-sequences of subgroups of finite index that do not necessarily form a chain, by
-connecting it to the cost of p.m.p. actions. We generalize several results that
-were only known for chains before. The connection is made by the notion of
-local-global convergence.
-In particular, we show that for a finitely generated group $\Gamma$ with
-fixed price $c$, every Farber sequence has rank gradient $c-1$. By adapting
-Lackenby's trichotomy theorem to this setting, we also show that in a finitely
-presented amenable group, every sequence of subgroups with index tending to
-infinity has vanishing rank gradient.
-",0,0,1,0,0,0
-18861,Optimal design of a model energy conversion device,"  Fuel cells, batteries, thermochemical and other energy conversion devices
-involve the transport of a number of (electro-)chemical species through
-distinct materials so that they can meet and react at specified multi-material
-interfaces. Therefore, morphology or arrangement of these different materials
-can be critical in the performance of an energy conversion device. In this
-paper, we study a model problem motivated by a solar-driven thermochemical
-conversion device that splits water into hydrogen and oxygen. We formulate the
-problem as a system of coupled multi-material reaction-diffusion equations
-where each species diffuses selectively through a given material and where the
-reaction occurs at multi-material interfaces. We express the problem of optimal
-design of the material arrangement as a saddle point problem and obtain an
-effective functional which shows that regions with very fine phase mixtures of
-the material arise naturally. To explore this further, we introduce a
-phase-field formulation of the optimal design problem, and numerically study
-selected examples.
-",0,1,1,0,0,0
-18862,Mining Communication Data in a Music Community: A Preliminary Analysis,"  Comments play an important role within online creative communities because
-they make it possible to foster the production and improvement of authors'
-artifacts. We investigate how comment-based communication help shape members'
-behavior within online creative communities. In this paper, we report the
-results of a preliminary study aimed at mining the communication network of a
-music community for collaborative songwriting, where users collaborate online
-by first uploading new songs and then by adding new tracks and providing
-feedback in forms of comments.
-",1,0,0,0,0,0
-18863,Multidimensional VlasovPoisson Simulations with High-order Monotonicity- and Positivity-preserving Schemes,"  We develop new numerical schemes for Vlasov--Poisson equations with
-high-order accuracy. Our methods are based on a spatially
-monotonicity-preserving (MP) scheme and are modified suitably so that
-positivity of the distribution function is also preserved. We adopt an
-efficient semi-Lagrangian time integration scheme that is more accurate and
-computationally less expensive than the three-stage TVD Runge-Kutta
-integration. We apply our spatially fifth- and seventh-order schemes to a suite
-of simulations of collisionless self-gravitating systems and electrostatic
-plasma simulations, including linear and nonlinear Landau damping in one
-dimension and Vlasov--Poisson simulations in a six-dimensional phase space. The
-high-order schemes achieve a significantly improved accuracy in comparison with
-the third-order positive-flux-conserved scheme adopted in our previous study.
-With the semi-Lagrangian time integration, the computational cost of our
-high-order schemes does not significantly increase, but remains roughly the
-same as that of the third-order scheme. Vlasov--Poisson simulations on $128^3
-\times 128^3$ mesh grids have been successfully performed on a massively
-parallel computer.
-",0,1,0,0,0,0
-18864,Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy,"  Markov Chain Monte Carlo based Bayesian data analysis has now become the
-method of choice for analyzing and interpreting data in almost all disciplines
-of science. In astronomy, over the last decade, we have also seen a steady
-increase in the number of papers that employ Monte Carlo based Bayesian
-analysis. New, efficient Monte Carlo based methods are continuously being
-developed and explored. In this review, we first explain the basics of Bayesian
-theory and discuss how to set up data analysis problems within this framework.
-Next, we provide an overview of various Monte Carlo based methods for
-performing Bayesian data analysis. Finally, we discuss advanced ideas that
-enable us to tackle complex problems and thus hold great promise for the
-future. We also distribute downloadable computer software (available at
-this https URL ) that implements some of the algorithms and
-examples discussed here.
-",0,1,0,1,0,0
-18865,Stable splitting of mapping spaces via nonabelian Poincaré duality,"  We use nonabelian Poincaré duality to recover the stable splitting of
-compactly supported mapping spaces, $\rm{Map_c}$$(M,\Sigma^nX)$, where $M$ is a
-parallelizable $n$-manifold. Our method for deriving this splitting is new, and
-naturally extends to give a more general stable splitting of the space of
-compactly supported sections of a certain bundle on $M$ with fibers
-$\Sigma^nX$, twisted by the tangent bundle of $M$. This generalization
-incorporates possible $O(n)$-actions on $X$ as well as accommodating
-non-parallelizable manifolds.
-",0,0,1,0,0,0
-18866,Static Gesture Recognition using Leap Motion,"  In this report, an automated bartender system was developed for making orders
-in a bar using hand gestures. The gesture recognition of the system was
-developed using Machine Learning techniques, where the model was trained to
-classify gestures using collected data. The final model used in the system
-reached an average accuracy of 95%. The system raised ethical concerns both in
-terms of user interaction and having such a system in a real world scenario,
-but it could initially work as a complement to a real bartender.
-",1,0,0,1,0,0
-18867,B-spline-like bases for $C^2$ cubics on the Powell-Sabin 12-split,"  For spaces of constant, linear, and quadratic splines of maximal smoothness
-on the Powell-Sabin 12-split of a triangle, the so-called S-bases were recently
-introduced. These are simplex spline bases with B-spline-like properties on the
-12-split of a single triangle, which are tied together across triangles in a
-Bézier-like manner.
-In this paper we give a formal definition of an S-basis in terms of certain
-basic properties. We proceed to investigate the existence of S-bases for the
-aforementioned spaces and additionally the cubic case, resulting in an
-exhaustive list. From their nature as simplex splines, we derive simple
-differentiation and recurrence formulas to other S-bases. We establish a
-Marsden identity that gives rise to various quasi-interpolants and domain
-points forming an intuitive control net, in terms of which conditions for
-$C^0$-, $C^1$-, and $C^2$-smoothness are derived.
-",1,0,0,0,0,0
-18868,Synthesizing Correlations with Computational Likelihood Approach: Vitamin C Data,"  It is known that the primary source of dietary vitamin C is fruit and
-vegetables and the plasma level of vitamin C has been considered a good
-surrogate biomarker of vitamin C intake by fruit and vegetable consumption. To
-combine the information about association between vitamin C intake and the
-plasma level of vitamin C, numerical approximation methods for likelihood
-function of correlation coefficient are studied. The least squares approach is
-used to estimate a log-likelihood function by a function from a space of
-B-splines having desirable mathematical properties. The likelihood interval
-from the Highest Likelihood Regions (HLR) is used for further inference. This
-approach can be easily extended to the realm of meta-analysis involving sample
-correlations from different studies by use of an approximated combined
-likelihood function. The sample correlations between vitamin C intake and serum
-level of vitamin C from many studies are used to illustrate application of this
-approach.
-",0,0,0,1,0,0
-18869,Axion detection via Topological Casimir Effect,"  We propose a new table-top experimental configuration for the direct
-detection of dark matter axions with mass in the $(10^{-6} \rm eV - 10^{-2} \rm
-eV)$ range using non-perturbative effects in a system with non-trivial spatial
-topology. Different from most experimental setups found in literature on direct
-dark matter axion detection, which relies on $\dot{\theta}$ or
-$\vec{\nabla}\theta$, we found that our system is in principle sensitive to a
-static $\theta\geq 10^{-14}$ and can also be used to set limit on the
-fundamental constant $\theta_{\rm QED}$ which becomes the fundamental
-observable parameter of the Maxwell system if some conditions are met.
-Connection with Witten effect when the induced electric charge $e'$ is
-proportional to $\theta$ and the magnetic monopole becomes the dyon with
-non-vanishing $e'=-e \frac{\theta}{2\pi}$ is also discussed.
-",0,1,0,0,0,0
-18870,Novel Feature-Based Clustering of Micro-Panel Data (CluMP),"  Micro-panel data are collected and analysed in many research and industry
-areas. Cluster analysis of micro-panel data is an unsupervised learning
-exploratory method identifying subgroup clusters in a data set which include
-homogeneous objects in terms of the development dynamics of monitored
-variables. The supply of clustering methods tailored to micro-panel data is
-limited. The present paper focuses on a feature-based clustering method,
-introducing a novel two-step characteristic-based approach designed for this
-type of data. The proposed CluMP method aims to identify clusters that are at
-least as internally homogeneous and externally heterogeneous as those obtained
-by alternative methods already implemented in the statistical system R. We
-compare the clustering performance of the devised algorithm with two extant
-methods using simulated micro-panel data sets. Our approach has yielded similar
-or better outcomes than the other methods, the advantage of the proposed
-algorithm being time efficiency which makes it applicable for large data sets.
-",0,0,0,1,0,0
-18871,Ab initio effective Hamiltonians for cuprate superconductors,"  Ab initio low-energy effective Hamiltonians of two typical high-temperature
-copper-oxide superconductors, whose mother compounds are La$_2$CuO$_4$ and
-HgBa$_2$CuO$_4$, are derived by utilizing the multi-scale ab initio scheme for
-correlated electrons (MACE). The effective Hamiltonians obtained in the present
-study serve as platforms of future studies to accurately solve the low-energy
-effective Hamiltonians beyond the density functional theory. It allows further
-study on the superconducting mechanism from the first principles and
-quantitative basis without adjustable parameters not only for the available
-cuprates but also for future design of higher Tc in general. More concretely,
-we derive effective Hamiltonians for three variations, 1)one-band Hamiltonian
-for the antibonding orbital generated from strongly hybridized Cu
-$3d_{x^2-y^2}$ and O $2p_\sigma$ orbitals 2)two-band Hamiltonian constructed
-from the antibonding orbital and Cu $3d_{3z^2-r^2}$ orbital hybridized mainly
-with the apex oxygen $p_z$ orbital 3)three-band Hamiltonian consisting mainly
-of Cu $3d_{x^2-y^2}$ orbitals and two O $2p_\sigma$ orbitals. Differences
-between the Hamiltonians for La$_2$CuO$_4$ and HgBa$_2$CuO$_4$, which have
-relatively low and high critical temperatures, respectively, at optimally doped
-compounds, are elucidated. The main differences are summarized as i) the oxygen
-$2p_\sigma$ orbitals are farther(~3.7eV) below from the Cu $d_{x^2-y^2}$
-orbital for the La compound than the Hg compound(~2.4eV) in the three-band
-Hamiltonian. This causes a substantial difference in the character of the
-$d_{x^2-y^2}-2p_\sigma$ antibonding band at the Fermi level and makes the
-effective onsite Coulomb interaction U larger for the La compound than the Hg
-compound for the two- and one-band Hamiltonians. ii)The ratio of the
-second-neighbor to the nearest transfer t'/t is also substantially different
-(~0.26) for the Hg and ~0.15 for the La compound in the one-band Hamiltonian.
-",0,1,0,0,0,0
-18872,Longitudinal data analysis using matrix completion,"  In clinical practice and biomedical research, measurements are often
-collected sparsely and irregularly in time while the data acquisition is
-expensive and inconvenient. Examples include measurements of spine bone mineral
-density, cancer growth through mammography or biopsy, a progression of defect
-of vision, or assessment of gait in patients with neurological disorders. Since
-the data collection is often costly and inconvenient, estimation of progression
-from sparse observations is of great interest for practitioners.
-From the statistical standpoint, such data is often analyzed in the context
-of a mixed-effect model where time is treated as both random and fixed effect.
-Alternatively, researchers analyze Gaussian processes or functional data where
-observations are assumed to be drawn from a certain distribution of processes.
-These models are flexible but rely on probabilistic assumptions and require
-very careful implementation.
-In this study, we propose an alternative elementary framework for analyzing
-longitudinal data, relying on matrix completion. Our method yields point
-estimates of progression curves by iterative application of the SVD. Our
-framework covers multivariate longitudinal data, regression and can be easily
-extended to other settings.
-We apply our methods to understand trends of progression of motor impairment
-in children with Cerebral Palsy. Our model approximates individual progression
-curves and explains 30% of the variability. Low-rank representation of
-progression trends enables discovering that subtypes of Cerebral Palsy exhibit
-different progression trends.
-",0,0,0,1,0,0
-18873,Emission-line Diagnostics of Nearby HII Regions Including Supernova Hosts,"  We present a new model of the optical nebular emission from HII regions by
-combin- ing the results of the Binary Population and Spectral Synthesis (bpass)
-code with the photoion- ization code cloudy (Ferland et al. 1998). We explore a
-variety of emission-line diagnostics of these star-forming HII regions and
-examine the effects of metallicity and interacting binary evo- lution on the
-nebula emission-line production. We compare the line emission properties of HII
-regions with model stellar populations, and provide new constraints on their
-stellar populations and supernova progenitors. We find that models including
-massive binary stars can successfully match all the observational constraints
-and provide reasonable age and mass estimation of the HII regions and supernova
-progenitors.
-",0,1,0,0,0,0
-18874,On monomial linearisation and supercharacters of pattern subgroups,"  Column closed pattern subgroups $U$ of the finite upper unitriangular groups
-$U_n(q)$ are defined as sets of matrices in $U_n(q)$ having zeros in a
-prescribed set of columns besides the diagonal ones. We explain Jedlitschky's
-construction of monomial linearisation and apply this to $C U$ yielding a
-generalisation of Yan's coadjoint cluster representations. Then we give a
-complete classification of the resulting supercharacters, by describing the
-resulting orbits and determining the Hom-spaces between orbit modules.
-",0,0,1,0,0,0
-18875,The Structural Fate of Individual Multicomponent Metal-Oxide Nanoparticles in Polymer Nanoreactors,"  Multicomponent nanoparticles can be synthesized with either homogeneous or
-phase-segregated architectures depending on the synthesis conditions and
-elements incorporated. To understand the parameters that determine their
-structural fate, multicomponent metal-oxide nanoparticles consisting of
-combinations of Co, Ni, and Cu were synthesized via scanning probe block
-copolymer lithography and characterized using correlated electron microscopy.
-These studies revealed that the miscibility, ratio of the metallic components,
-and the synthesis temperature determine the crystal structure and architecture
-of the nanoparticles. A Co-Ni-O system forms a rock salt structure largely due
-to the miscibility of CoO and NiO, while Cu-Ni-O, which has large miscibility
-gaps, forms either homogeneous oxides, heterojunctions, or alloys depending on
-the annealing temperature and composition. Moreover, a higher ordered
-structure, Co-Ni-Cu-O, was found to follow the behavior of lower ordered
-systems.
-",0,1,0,0,0,0
-18876,On Compression of Unsupervised Neural Nets by Pruning Weak Connections,"  Unsupervised neural nets such as Restricted Boltzmann Machines(RBMs) and Deep
-Belif Networks(DBNs), are powerful in automatic feature extraction,unsupervised
-weight initialization and density estimation. In this paper,we demonstrate that
-the parameters of these neural nets can be dramatically reduced without
-affecting their performance. We describe a method to reduce the parameters
-required by RBM which is the basic building block for deep architectures.
-Further we propose an unsupervised sparse deep architectures selection
-algorithm to form sparse deep neural networks.Experimental results show that
-there is virtually no loss in either generative or discriminative performance.
-",1,0,0,0,0,0
-18877,A matrix generalization of a theorem of Fine,"  In 1947 Nathan Fine gave a beautiful product for the number of binomial
-coefficients $\binom{n}{m}$, for $m$ in the range $0 \leq m \leq n$, that are
-not divisible by $p$. We give a matrix product that generalizes Fine's formula,
-simultaneously counting binomial coefficients with $p$-adic valuation $\alpha$
-for each $\alpha \geq 0$. For each $n$ this information is naturally encoded in
-a polynomial generating function, and the sequence of these polynomials is
-$p$-regular in the sense of Allouche and Shallit. We also give a further
-generalization to multinomial coefficients.
-",1,0,1,0,0,0
-18878,Training Multi-Task Adversarial Network For Extracting Noise-Robust Speaker Embedding,"  Under noisy environments, to achieve the robust performance of speaker
-recognition is still a challenging task. Motivated by the promising performance
-of multi-task training in a variety of image processing tasks, we explore the
-potential of multi-task adversarial training for learning a noise-robust
-speaker embedding. In this paper we present a novel framework which consists of
-three components: an encoder that extracts noise-robust speaker embedding; a
-classifier that classifies the speakers; a discriminator that discriminates the
-noise type of the speaker embedding. Besides, we propose a training strategy
-using the training accuracy as an indicator to stabilize the multi-class
-adversarial optimization process. We conduct our experiments on the English and
-Mandarin corpus and the experimental results demonstrate that our proposed
-multi-task adversarial training method could greatly outperform the other
-methods without adversarial training in noisy environments. Furthermore,
-experiments indicate that our method is also able to improve the speaker
-verification performance the clean condition.
-",1,0,0,0,0,0
-18879,Playing Games with Bounded Entropy,"  In this paper, we consider zero-sum repeated games in which the maximizer is
-restricted to strategies requiring no more than a limited amount of randomness.
-Particularly, we analyze the maxmin payoff of the maximizer in two models: the
-first model forces the maximizer to randomize her action in each stage just by
-conditioning her decision to outcomes of a given sequence of random source,
-whereas, in the second model, the maximizer is a team of players who are free
-to privately randomize their corresponding actions but do not have access to
-any explicit source of shared randomness needed for cooperation. The works of
-Gossner and Vieille, and Gossner and Tomala adopted the method of types to
-establish their results; however, we utilize the idea of random hashing which
-is the core of randomness extractors in the information theory literature. In
-addition, we adopt the well-studied tool of simulation of a source from another
-source. By utilizing these tools, we are able to simplify the prior results and
-generalize them as well. We characterize the maxmin payoff of the maximizer in
-the repeated games under study. Particularly, the maxmin payoff of the first
-model is fully described by the function $J(h)$ which is the maximum payoff
-that the maximizer can secure in a one-shot game by choosing mixed strategies
-of entropy at most $h$. In the second part of the paper, we study the
-computational aspects of $J(h)$. We offer three explicit lower bounds on the
-entropy-payoff trade-off curve. To do this, we provide and utilize new results
-for the set of distributions that guarantee a certain payoff for Alice. In
-particular, we study how this set of distributions shrinks as we increase the
-security level. While the use of total variation distance is common in game
-theory, our derivation indicates the suitability of utilizing the
-Renyi-divergence of order two.
-",1,0,0,0,0,0
-18880,Exact Hausdorff and packing measures for random self-similar code-trees with necks,"  Random code-trees with necks were introduced recently to generalise the
-notion of $V$-variable and random homogeneous sets. While it is known that the
-Hausdorff and packing dimensions coincide irrespective of overlaps, their exact
-Hausdorff and packing measure has so far been largely ignored. In this article
-we consider the general question of an appropriate gauge function for positive
-and finite Hausdorff and packing measure. We first survey the current state of
-knowledge and establish some bounds on these gauge functions. We then show that
-self-similar code-trees do not admit a gauge functions that simultaneously give
-positive and finite Hausdorff measure almost surely. This surprising result is
-in stark contrast to the random recursive model and sheds some light on the
-question of whether $V$-variable sets interpolate between random homogeneous
-and random recursive sets. We conclude by discussing implications of our
-results.
-",0,0,1,0,0,0
-18881,On Modules over a G-set,"  Let R be a commutative ring with unity, M a module over R and let S be a
-G-set for a finite group G. We define a set MS to be the set of elements
-expressed as the formal finite sum of the form similar to the elements of group
-ring RG. The set MS is a module over the group ring RG under the addition and
-the scalar multiplication similar to the RG-module MG. With this notion, we not
-only generalize but also unify the theories of both of the group algebra and
-the group module, and we also establish some significant properties of MS. In
-particular, we describe a method for decomposing a given RG-module MS as a
-direct sum of RG-submodules. Furthermore, we prove the semisimplicity problem
-of MS with regard to the properties of M, S and G.
-",0,0,1,0,0,0
-18882,Polynomial-time algorithms for the Longest Induced Path and Induced Disjoint Paths problems on graphs of bounded mim-width,"  We give the first polynomial-time algorithms on graphs of bounded maximum
-induced matching width (mim-width) for problems that are not locally checkable.
-In particular, we give $n^{\mathcal{O}(w)}$-time algorithms on graphs of
-mim-width at most $w$, when given a decomposition, for the following problems:
-Longest Induced Path, Induced Disjoint Paths and $H$-Induced Topological Minor
-for fixed $H$. Our results imply that the following graph classes have
-polynomial-time algorithms for these three problems: Interval and Bi-Interval
-graphs, Circular Arc, Permutation and Circular Permutation graphs, Convex
-graphs, $k$-Trapezoid, Circular $k$-Trapezoid, $k$-Polygon, Dilworth-$k$ and
-Co-$k$-Degenerate graphs for fixed $k$.
-",1,0,0,0,0,0
-18883,Bug or Not? Bug Report Classification Using N-Gram IDF,"  Previous studies have found that a significant number of bug reports are
-misclassified between bugs and non-bugs, and that manually classifying bug
-reports is a time-consuming task. To address this problem, we propose a bug
-reports classification model with N-gram IDF, a theoretical extension of
-Inverse Document Frequency (IDF) for handling words and phrases of any length.
-N-gram IDF enables us to extract key terms of any length from texts, these key
-terms can be used as the features to classify bug reports. We build
-classification models with logistic regression and random forest using features
-from N-gram IDF and topic modeling, which is widely used in various software
-engineering tasks. With a publicly available dataset, our results show that our
-N-gram IDF-based models have a superior performance than the topic-based models
-on all of the evaluated cases. Our models show promising results and have a
-potential to be extended to other software engineering tasks.
-",1,0,0,0,0,0
-18884,Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection,"  Cell nuclei detection is a challenging research topic because of limitations
-in cellular image quality and diversity of nuclear morphology, i.e. varying
-nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been
-a topic of enduring interest with promising recent success shown by deep
-learning methods. These methods train Convolutional Neural Networks (CNNs) with
-a training set of input images and known, labeled nuclei locations. Many such
-methods are supplemented by spatial or morphological processing. Using a set of
-canonical cell nuclei shapes, prepared with the help of a domain expert, we
-develop a new approach that we call Shape Priors with Convolutional Neural
-Networks (SP-CNN). We further extend the network to introduce a shape prior
-(SP) layer and then allowing it to become trainable (i.e. optimizable). We call
-this network tunable SP-CNN (TSP-CNN). In summary, we present new network
-structures that can incorporate 'expected behavior' of nucleus shapes via two
-components: learnable layers that perform the nucleus detection and a fixed
-processing part that guides the learning with prior information. Analytically,
-we formulate two new regularization terms that are targeted at: 1) learning the
-shapes, 2) reducing false positives while simultaneously encouraging detection
-inside the cell nucleus boundary. Experimental results on two challenging
-datasets reveal that the proposed SP-CNN and TSP-CNN can outperform
-state-of-the-art alternatives.
-",1,0,0,1,0,0
-18885,Anisotropic hydrodynamic turbulence in accretion disks,"  Recently, the vertical shear instability (VSI) has become an attractive
-purely hydrodynamic candidate for the anomalous angular momentum transport
-required for weakly ionized accretion disks. In direct three-dimensional
-numerical simulations of VSI turbulence in disks, a meridional circulation
-pattern was observed that is opposite to the usual viscous flow behavior. Here,
-we investigate whether this feature can possibly be explained by an anisotropy
-of the VSI turbulence. Using three-dimensional hydrodynamical simulations, we
-calculate the turbulent Reynolds stresses relevant for angular momentum
-transport for a representative section of a disk.
-We find that the vertical stress is significantly stronger than the radial
-stress. Using our results in viscous disk simulations with different viscosity
-coefficients for the radial and vertical direction, we find good agreement with
-the VSI turbulence for the stresses and meridional flow; this provides
-additional evidence for the anisotropy. The results are important with respect
-to the transport of small embedded particles in disks.
-",0,1,0,0,0,0
-18886,Optimal Algorithms for Distributed Optimization,"  In this paper, we study the optimal convergence rate for distributed convex
-optimization problems in networks. We model the communication restrictions
-imposed by the network as a set of affine constraints and provide optimal
-complexity bounds for four different setups, namely: the function $F(\xb)
-\triangleq \sum_{i=1}^{m}f_i(\xb)$ is strongly convex and smooth, either
-strongly convex or smooth or just convex. Our results show that Nesterov's
-accelerated gradient descent on the dual problem can be executed in a
-distributed manner and obtains the same optimal rates as in the centralized
-version of the problem (up to constant or logarithmic factors) with an
-additional cost related to the spectral gap of the interaction matrix. Finally,
-we discuss some extensions to the proposed setup such as proximal friendly
-functions, time-varying graphs, improvement of the condition numbers.
-",1,0,0,1,0,0
-18887,Evaluation of Classical Features and Classifiers in Brain-Computer Interface Tasks,"  Brain-Computer Interface (BCI) uses brain signals in order to provide a new
-method for communication between human and outside world. Feature extraction,
-selection and classification are among the main matters of concerns in signal
-processing stage of BCI. In this article, we present our findings about the
-most effective features and classifiers in some brain tasks. Six different
-groups of classical features and twelve classifiers have been examined in nine
-datasets of brain signal. The results indicate that energy of brain signals in
-{\alpha} and \b{eta} frequency bands, together with some statistical parameters
-are more effective, comparing to the other types of extracted features. In
-addition, Bayesian classifier with Gaussian distribution assumption and also
-Support Vector Machine (SVM) show to classify different BCI datasets more
-accurately than the other classifiers. We believe that the results can give an
-insight about a strategy for blind classification of brain signals in
-brain-computer interface.
-",1,0,0,1,0,0
-18888,On exceptional compact homogeneous geometries of type C3,"  We provide a uniform framework to study the exceptional homogeneous compact
-geometries of type C3. This framework is then used to show that these are
-simply connected, answering a question by Kramer and Lytchak, and to calculate
-the full automorphism groups.
-",0,0,1,0,0,0
-18889,Intensity estimation of transaction arrivals on the intraday electricity market,"  In the following paper we present a simple intensity estimation method of
-transaction arrivals on the intraday electricity market. Assuming the
-interarrival times distribution, we utilize a maximum likelihood estimation.
-The method's performance is briefly tested using German Intraday Continuous
-data. Despite the simplicity of the method, the results are encouraging. The
-supplementary materials containing the R-codes and the data are attached to
-this paper.
-",0,0,0,1,0,1
-18890,Capacitive Mechanism of Oxygen Functional Groups on Carbon Surface in Supercapacitors,"  Oxygen functional groups are one of the most important subjects in the study
-of electrochemical properties of carbon materials which can change the
-wettability, conductivity and pore size distributions of carbon materials, and
-can occur redox reactions. In the electrode materials of carbon-based
-supercapacitors, the oxygen functional groups have widely been used to improve
-the capacitive performance. In this paper, we not only analyzed the reasons for
-the increase of the capacity that promoted by oxygen functional groups in the
-charge-discharge cycling tests, but also analyzed the mechanism how the
-pseudocapacitance was provided by the oxygen functional groups in the
-acid/alkaline aqueous electrolyte. Moreover, we also discussed the effect of
-the oxygen functional groups in electrochemical impedance spectroscopy.
-",0,1,0,0,0,0
-18891,On conditional least squares estimation for affine diffusions based on continuous time observations,"  We study asymptotic properties of conditional least squares estimators for
-the drift parameters of two-factor affine diffusions based on continuous time
-observations. We distinguish three cases: subcritical, critical and
-supercritical. For all the drift parameters, in the subcritical and
-supercritical cases, asymptotic normality and asymptotic mixed normality is
-proved, while in the critical case, non-standard asymptotic behavior is
-described.
-",0,0,1,1,0,0
-18892,Discretization error estimates for penalty formulations of a linearized Canham-Helfrich type energy,"  This paper is concerned with minimization of a fourth-order linearized
-Canham-Helfrich energy subject to Dirichlet boundary conditions on curves
-inside the domain. Such problems arise in the modeling of the mechanical
-interaction of biomembranes with embedded particles. There, the curve
-conditions result from the imposed particle--membrane coupling. We prove
-almost-$H^{\frac{5}{2}}$ regularity of the solution and then consider two
-possible penalty formulations. For the combination of these penalty
-formulations with a Bogner-Fox-Schmit finite element discretization we prove
-discretization error estimates which are optimal in view of the solution's
-reduced regularity. The error estimates are based on a general estimate for
-linear penalty problems in Hilbert spaces. Finally, we illustrate the
-theoretical results by numerical computations. An important feature of the
-presented discretization is that it does not require to resolve the particle
-boundary. This is crucial in order to avoid re-meshing if the presented problem
-arises as subproblem in a model where particles are allowed to move or rotate.
-",0,0,1,0,0,0
-18893,On the Humphreys conjecture on support varieties of tilting modules,"  Let $G$ be a simply-connected semisimple algebraic group over an
-algebraically closed field of characteristic $p$, assumed to be larger than the
-Coxeter number. The ""support variety"" of a $G$-module $M$ is a certain closed
-subvariety of the nilpotent cone of $G$, defined in terms of cohomology for the
-first Frobenius kernel $G_1$. In the 1990s, Humphreys proposed a conjectural
-description of the support varieties of tilting modules; this conjecture has
-been proved for $G = \mathrm{SL}_n$ in earlier work of the second author.
-In this paper, we show that for any $G$, the support variety of a tilting
-module always contains the variety predicted by Humphreys, and that they
-coincide (i.e., the Humphreys conjecture is true) when $p$ is sufficiently
-large. We also prove variants of these statements involving ""relative support
-varieties.""
-",0,0,1,0,0,0
-18894,Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks,"  Machine learning models have been widely used in security applications such
-as intrusion detection, spam filtering, and virus or malware detection.
-However, it is well-known that adversaries are always trying to adapt their
-attacks to evade detection. For example, an email spammer may guess what
-features spam detection models use and modify or remove those features to avoid
-detection. There has been some work on making machine learning models more
-robust to such attacks. However, one simple but promising approach called {\em
-randomization} is underexplored. This paper proposes a novel
-randomization-based approach to improve robustness of machine learning models
-against evasion attacks. The proposed approach incorporates randomization into
-both model training time and model application time (meaning when the model is
-used to detect attacks). We also apply this approach to random forest, an
-existing ML method which already has some degree of randomness. Experiments on
-intrusion detection and spam filtering data show that our approach further
-improves robustness of random-forest method. We also discuss how this approach
-can be applied to other ML models.
-",0,0,0,1,0,0
-18895,Further extension of the generalized Hurwitz-Lerch Zeta function of two variables,"  The main aim of this paper is to give a new generalization of Hurwitz-Lerch
-Zeta function of two variables.Also, we investigate several interesting
-properties such as integral representations, summation formula and a connection
-with generalized hypergeometric function. To strengthen the main results we
-also consider many important special cases.
-",0,0,1,0,0,0
-18896,A Physarum-inspired model for the probit-based stochastic user equilibrium problem,"  Stochastic user equilibrium is an important issue in the traffic assignment
-problems, tradition models for the stochastic user equilibrium problem are
-designed as mathematical programming problems. In this article, a
-Physarum-inspired model for the probit-based stochastic user equilibrium
-problem is proposed. There are two main contributions of our work. On the one
-hand, the origin Physarum model is modified to find the shortest path in
-traffic direction networks with the properties of two-way traffic
-characteristic. On the other hand, the modified Physarum-inspired model could
-get the equilibrium flows when traveller's perceived transportation cost
-complies with normal distribution. The proposed method is constituted with a
-two-step procedure. First, the modified Physarum model is applied to get the
-auxiliary flows. Second, the auxiliary flows are averaged to obtain the
-equilibrium flows. Numerical examples are conducted to illustrate the
-performance of the proposed method, which is compared with the Method of
-Successive Average method.
-",1,0,0,0,0,0
-18897,Going Higher in First-Order Quantifier Alternation Hierarchies on Words,"  We investigate quantifier alternation hierarchies in first-order logic on
-finite words. Levels in these hierarchies are defined by counting the number of
-quantifier alternations in formulas. We prove that one can decide membership of
-a regular language in the levels $\mathcal{B}{\Sigma}_2$ (finite boolean
-combinations of formulas having only one alternation) and ${\Sigma}_3$
-(formulas having only two alternations and beginning with an existential
-block). Our proofs work by considering a deeper problem, called separation,
-which, once solved for lower levels, allows us to solve membership for higher
-levels.
-",1,0,0,0,0,0
-18898,Invariance of Ideal Limit Points,"  Let $\mathcal{I}$ be an analytic P-ideal [respectively, a summable ideal] on
-the positive integers and let $(x_n)$ be a sequence taking values in a metric
-space $X$. First, it is shown that the set of ideal limit points of $(x_n)$ is
-an $F_\sigma$-set [resp., a closet set]. Let us assume that $X$ is also
-separable and the ideal $\mathcal{I}$ satisfies certain additional assumptions,
-which however includes several well-known examples, e.g., the collection of
-sets with zero asymptotic density, sets with zero logarithmic density, and some
-summable ideals. Then, it is shown that the set of ideal limit points of
-$(x_n)$ is equal to the set of ideal limit points of almost all its
-subsequences.
-",0,0,1,0,0,0
-18899,"Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching","  The unprecedented demand for large amount of data has catalyzed the trend of
-combining human insights with machine learning techniques, which facilitate the
-use of crowdsourcing to enlist label information both effectively and
-efficiently. The classic work on crowdsourcing mainly focuses on the label
-inference problem under the categorization setting. However, inferring the true
-label requires sophisticated aggregation models that usually can only perform
-well under certain assumptions. Meanwhile, no matter how complicated the
-aggregation model is, the true model that generated the crowd labels remains
-unknown. Therefore, the label inference problem can never infer the ground
-truth perfectly. Based on the fact that the crowdsourcing labels are abundant
-and utilizing aggregation will lose such kind of rich annotation information
-(e.g., which worker provided which labels), we believe that it is critical to
-take the diverse labeling abilities of the crowdsourcing workers as well as
-their correlations into consideration. To address the above challenge, we
-propose to tackle three research problems, namely inference, learning, and
-teaching.
-",0,0,0,1,0,0
-18900,Learning With Errors and Extrapolated Dihedral Cosets,"  The hardness of the learning with errors (LWE) problem is one of the most
-fruitful resources of modern cryptography. In particular, it is one of the most
-prominent candidates for secure post-quantum cryptography. Understanding its
-quantum complexity is therefore an important goal. We show that under quantum
-polynomial time reductions, LWE is equivalent to a relaxed version of the
-dihedral coset problem (DCP), which we call extrapolated DCP (eDCP). The extent
-of extrapolation varies with the LWE noise rate. By considering different
-extents of extrapolation, our result generalizes Regev's famous proof that if
-DCP is in BQP (quantum poly-time) then so is LWE (FOCS'02). We also discuss a
-connection between eDCP and Childs and Van Dam's algorithm for generalized
-hidden shift problems (SODA'07). Our result implies that a BQP solution for LWE
-might not require the full power of solving DCP, but rather only a solution for
-its relaxed version, eDCP, which could be easier.
-",1,0,0,0,0,0
-18901,Fermi bubbles: high latitude X-ray supersonic shell,"  The nature of the bipolar, $\gamma$-ray Fermi bubbles (FB) is still unclear,
-in part because their faint, high-latitude X-ray counterpart has until now
-eluded a clear detection. We stack ROSAT data at varying distances from the FB
-edges, thus boosting the signal and identifying an expanding shell behind the
-southwest, southeast, and northwest edges, albeit not in the dusty northeast
-sector near Loop I. A Primakoff-like model for the underlying flow is invoked
-to show that the signals are consistent with halo gas heated by a strong,
-forward shock to $\sim$keV temperatures. Assuming ion--electron thermal
-equilibrium then implies a $\sim10^{56}$ erg event near the Galactic centre
-$\sim7$ Myr ago. However, the reported high absorption-line velocities suggest
-a preferential shock-heating of ions, and thus more energetic ($\sim 10^{57}$
-erg), younger ($\lesssim 3$ Myr) FBs.
-",0,1,0,0,0,0
-18902,Passivity-Based Generalization of Primal-Dual Dynamics for Non-Strictly Convex Cost Functions,"  In this paper, we revisit primal-dual dynamics for convex optimization and
-present a generalization of the dynamics based on the concept of passivity. It
-is then proved that supplying a stable zero to one of the integrators in the
-dynamics allows one to eliminate the assumption of strict convexity on the cost
-function based on the passivity paradigm together with the invariance principle
-for Caratheodory systems. We then show that the present algorithm is also a
-generalization of existing augmented Lagrangian-based primal-dual dynamics, and
-discuss the benefit of the present generalization in terms of noise reduction
-and convergence speed.
-",1,0,0,0,0,0
-18903,"An integral formula for the powered sum of the independent, identically and normally distributed random variables","  The distribution of the sum of r-th power of standard normal random variables
-is a generalization of the chi-squared distribution. In this paper, we
-represent the probability density function of the random variable by an
-one-dimensional absolutely convergent integral with the characteristic
-function. Our integral formula is expected to be applied for evaluation of the
-density function. Our integral formula is based on the inversion formula, and
-we utilize a summation method. We also discuss on our formula in the view point
-of hyperfunctions.
-",0,0,1,0,0,0
-18904,A Dynamic Boosted Ensemble Learning Method Based on Random Forest,"  We propose a dynamic boosted ensemble learning method based on random forest
-(DBRF), a novel ensemble algorithm that incorporates the notion of hard example
-mining into Random Forest (RF) and thus combines the high accuracy of Boosting
-algorithm with the strong generalization of Bagging algorithm. Specifically, we
-propose to measure the quality of each leaf node of every decision tree in the
-random forest to determine hard examples. By iteratively training and then
-removing easy examples from training data, we evolve the random forest to focus
-on hard examples dynamically so as to learn decision boundaries better. Data
-can be cascaded through these random forests learned in each iteration in
-sequence to generate predictions, thus making RF deep. We also propose to use
-evolution mechanism and smart iteration mechanism to improve the performance of
-the model. DBRF outperforms RF on three UCI datasets and achieved
-state-of-the-art results compared to other deep models. Moreover, we show that
-DBRF is also a new way of sampling and can be very useful when learning from
-imbalanced data.
-",0,0,0,1,0,0
-18905,"Inter-Operator Resource Management for Millimeter Wave, Multi-Hop Backhaul Networks","  In this paper, a novel framework is proposed for optimizing the operation and
-performance of a large-scale, multi-hop millimeter wave (mmW) backhaul within a
-wireless small cell network (SCN) that encompasses multiple mobile network
-operators (MNOs). The proposed framework enables the small base stations (SBSs)
-to jointly decide on forming the multi-hop, mmW links over backhaul
-infrastructure that belongs to multiple, independent MNOs, while properly
-allocating resources across those links. In this regard, the problem is
-addressed using a novel framework based on matching theory that is composed to
-two, highly inter-related stages: a multi-hop network formation stage and a
-resource management stage. One unique feature of this framework is that it
-jointly accounts for both wireless channel characteristics and economic factors
-during both network formation and resource management. The multi-hop network
-formation stage is formulated as a one-to-many matching game which is solved
-using a novel algorithm, that builds on the so-called deferred acceptance
-algorithm and is shown to yield a stable and Pareto optimal multi-hop mmW
-backhaul network. Then, a one-to-many matching game is formulated to enable
-proper resource allocation across the formed multi-hop network. This game is
-then shown to exhibit peer effects and, as such, a novel algorithm is developed
-to find a stable and optimal resource management solution that can properly
-cope with these peer effects. Simulation results show that the proposed
-framework yields substantial gains, in terms of the average sum rate, reaching
-up to 27% and 54%, respectively, compared to a non-cooperative scheme in which
-inter-operator sharing is not allowed and a random allocation approach. The
-results also show that our framework provides insights on how to manage pricing
-and the cost of the cooperative mmW backhaul network for the MNOs.
-",1,0,0,0,0,0
-18906,An Online Learning Approach to Generative Adversarial Networks,"  We consider the problem of training generative models with a Generative
-Adversarial Network (GAN). Although GANs can accurately model complex
-distributions, they are known to be difficult to train due to instabilities
-caused by a difficult minimax optimization problem. In this paper, we view the
-problem of training GANs as finding a mixed strategy in a zero-sum game.
-Building on ideas from online learning we propose a novel training method named
-Chekhov GAN 1 . On the theory side, we show that our method provably converges
-to an equilibrium for semi-shallow GAN architectures, i.e. architectures where
-the discriminator is a one layer network and the generator is arbitrary. On the
-practical side, we develop an efficient heuristic guided by our theoretical
-results, which we apply to commonly used deep GAN architectures. On several
-real world tasks our approach exhibits improved stability and performance
-compared to standard GAN training.
-",1,0,0,1,0,0
-18907,Weakly supervised CRNN system for sound event detection with large-scale unlabeled in-domain data,"  Sound event detection (SED) is typically posed as a supervised learning
-problem requiring training data with strong temporal labels of sound events.
-However, the production of datasets with strong labels normally requires
-unaffordable labor cost. It limits the practical application of supervised SED
-methods. The recent advances in SED approaches focuses on detecting sound
-events by taking advantages of weakly labeled or unlabeled training data. In
-this paper, we propose a joint framework to solve the SED task using
-large-scale unlabeled in-domain data. In particular, a state-of-the-art general
-audio tagging model is first employed to predict weak labels for unlabeled
-data. On the other hand, a weakly supervised architecture based on the
-convolutional recurrent neural network (CRNN) is developed to solve the strong
-annotations of sound events with the aid of the unlabeled data with predicted
-labels. It is found that the SED performance generally increases as more
-unlabeled data is added into the training. To address the noisy label problem
-of unlabeled data, an ensemble strategy is applied to increase the system
-robustness. The proposed system is evaluated on the SED dataset of DCASE 2018
-challenge. It reaches a F1-score of 21.0%, resulting in an improvement of 10%
-over the baseline system.
-",1,0,0,0,0,0
-18908,Revealing the Coulomb interaction strength in a cuprate superconductor,"  We study optimally doped
-Bi$_{2}$Sr$_{2}$Ca$_{0.92}$Y$_{0.08}$Cu$_{2}$O$_{8+\delta}$ (Bi2212) using
-angle-resolved two-photon photoemission spectroscopy. Three spectral features
-are resolved near 1.5, 2.7, and 3.6 eV above the Fermi level. By tuning the
-photon energy, we determine that the 2.7-eV feature arises predominantly from
-unoccupied states. The 1.5- and 3.6-eV features reflect unoccupied states whose
-spectral intensities are strongly modulated by the corresponding occupied
-states. These unoccupied states are consistent with the prediction from a
-cluster perturbation theory based on the single-band Hubbard model. Through
-this comparison, a Coulomb interaction strength U of 2.7 eV is extracted. Our
-study complements equilibrium photoemission spectroscopy and provides a direct
-spectroscopic measurement of the unoccupied states in cuprates. The determined
-Coulomb U indicates that the charge-transfer gap of optimally doped Bi2212 is
-1.1 eV.
-",0,1,0,0,0,0
-18909,Ordering dynamics of self-propelled particles in an inhomogeneous medium,"  Ordering dynamics of self-propelled particles in an inhomogeneous medium in
-two-dimensions is studied. We write coarse-grained hydrodynamic equations of
-motion for coarse-grained density and velocity fields in the presence of an
-external random disorder field, which is quenched in time. The strength of
-inhomogeneity is tuned from zero disorder (clean system) to large disorder. In
-the clean system, the velocity field grows algebraically as $L_{\rm V} \sim
-t^{0.5}$. The density field does not show clean power-law growth; however, it
-follows $L_{\rm \rho} \sim t^{0.8}$ approximately. In the inhomogeneous system,
-we find a disorder dependent growth. For both the density and the velocity,
-growth slow down with increasing strength of disorder. The velocity shows a
-disorder dependent power-law growth $L_{\rm V}(t,\Delta) \sim t^{1/\bar z_{\rm
-V}(\Delta)}$ for intermediate times. At late times, there is a crossover to
-logarithmic growth $L_{\rm V}(t,\Delta) \sim (\ln t)^{1/\varphi}$, where
-$\varphi$ is a disorder independent exponent. Two-point correlation functions
-for the velocity shows dynamical scaling, but the density does not.
-",0,1,0,0,0,0
-18910,Inflationary preheating dynamics with ultracold atoms,"  We discuss the amplification of loop corrections in quantum many-body systems
-through dynamical instabilities. As an example, we investigate both
-analytically and numerically a two-component ultracold atom system in one
-spatial dimension. The model features a tachyonic instability, which
-incorporates characteristic aspects of the mechanisms for particle production
-in early-universe inflaton models. We establish a direct correspondence between
-measureable macroscopic growth rates for occupation numbers of the ultracold
-Bose gas and the underlying microscopic processes in terms of Feynman loop
-diagrams. We analyze several existing ultracold atom setups featuring dynamical
-instabilities and propose optimized protocols for their experimental
-realization. We demonstrate that relevant dynamical processes can be enhanced
-using a seeding procedure for unstable modes and clarify the role of initial
-quantum fluctuations and the generation of a non-linear secondary stage for the
-amplification of modes.
-",0,1,0,0,0,0
-18911,Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning,"  Reinforcement learning (RL), while often powerful, can suffer from slow
-learning speeds, particularly in high dimensional spaces. The autonomous
-decomposition of tasks and use of hierarchical methods hold the potential to
-significantly speed up learning in such domains. This paper proposes a novel
-practical method that can autonomously decompose tasks, by leveraging
-association rule mining, which discovers hidden relationship among entities in
-data mining. We introduce a novel method called ARM-HSTRL (Association Rule
-Mining to extract Hierarchical Structure of Tasks in Reinforcement Learning).
-It extracts temporal and structural relationships of sub-goals in RL, and
-multi-task RL. In particular,it finds sub-goals and relationship among them. It
-is shown the significant efficiency and performance of the proposed method in
-two main topics of RL.
-",1,0,0,0,0,0
-18912,Quantification of tumour evolution and heterogeneity via Bayesian epiallele detection,"  Motivation: Epigenetic heterogeneity within a tumour can play an important
-role in tumour evolution and the emergence of resistance to treatment. It is
-increasingly recognised that the study of DNA methylation (DNAm) patterns along
-the genome -- so-called `epialleles' -- offers greater insight into epigenetic
-dynamics than conventional analyses which examine DNAm marks individually.
-Results: We have developed a Bayesian model to infer which epialleles are
-present in multiple regions of the same tumour. We apply our method to reduced
-representation bisulfite sequencing (RRBS) data from multiple regions of one
-lung cancer tumour and a matched normal sample. The model borrows information
-from all tumour regions to leverage greater statistical power. The total number
-of epialleles, the epiallele DNAm patterns, and a noise hyperparameter are all
-automatically inferred from the data. Uncertainty as to which epiallele an
-observed sequencing read originated from is explicitly incorporated by
-marginalising over the appropriate posterior densities. The degree to which
-tumour samples are contaminated with normal tissue can be estimated and
-corrected for. By tracing the distribution of epialleles throughout the tumour
-we can infer the phylogenetic history of the tumour, identify epialleles that
-differ between normal and cancer tissue, and define a measure of global
-epigenetic disorder.
-",0,0,1,1,0,0
-18913,A bootstrap test to detect prominent Granger-causalities across frequencies,"  Granger-causality in the frequency domain is an emerging tool to analyze the
-causal relationship between two time series. We propose a bootstrap test on
-unconditional and conditional Granger-causality spectra, as well as on their
-difference, to catch particularly prominent causality cycles in relative terms.
-In particular, we consider a stochastic process derived applying independently
-the stationary bootstrap to the original series. Our null hypothesis is that
-each causality or causality difference is equal to the median across
-frequencies computed on that process. In this way, we are able to disambiguate
-causalities which depart significantly from the median one obtained ignoring
-the causality structure. Our test shows power one as the process tends to
-non-stationarity, thus being more conservative than parametric alternatives. As
-an example, we infer about the relationship between money stock and GDP in the
-Euro Area via our approach, considering inflation, unemployment and interest
-rates as conditioning variables. We point out that during the period 1999-2017
-the money stock aggregate M1 had a significant impact on economic output at all
-frequencies, while the opposite relationship is significant only at high
-frequencies.
-",0,0,0,1,0,1
-18914,Optimal segregation of proteins: phase transitions and symmetry breaking,"  Asymmetric segregation of key proteins at cell division -- be it a beneficial
-or deleterious protein -- is ubiquitous in unicellular organisms and often
-considered as an evolved trait to increase fitness in a stressed environment.
-Here, we provide a general framework to describe the evolutionary origin of
-this asymmetric segregation. We compute the population fitness as a function of
-the protein segregation asymmetry $a$, and show that the value of $a$ which
-optimizes the population growth manifests a phase transition between symmetric
-and asymmetric partitioning phases. Surprisingly, the nature of phase
-transition is different for the case of beneficial proteins as opposed to
-proteins which decrease the single-cell growth rate. Our study elucidates the
-optimization problem faced by evolution in the context of protein segregation,
-and motivates further investigation of asymmetric protein segregation in
-biological systems.
-",0,0,0,0,1,0
-18915,Optimal make-take fees for market making regulation,"  We consider an exchange who wishes to set suitable make-take fees to attract
-liquidity on its platform. Using a principal-agent approach, we are able to
-describe in quasi-explicit form the optimal contract to propose to a market
-maker. This contract depends essentially on the market maker inventory
-trajectory and on the volatility of the asset. We also provide the optimal
-quotes that should be displayed by the market maker. The simplicity of our
-formulas allows us to analyze in details the effects of optimal contracting
-with an exchange, compared to a situation without contract. We show in
-particular that it leads to higher quality liquidity and lower trading costs
-for investors.
-",0,0,0,0,0,1
-18916,Spontaneous symmetry breaking due to the trade-off between attractive and repulsive couplings,"  Spontaneous symmetry breaking (SSB) is an important phenomenon observed in
-various fields including physics and biology. In this connection, we here show
-that the trade-off between attractive and repulsive couplings can induce
-spontaneous symmetry breaking in a homogeneous system of coupled oscillators.
-With a simple model of a system of two coupled Stuart-Landau oscillators, we
-demonstrate how the tendency of attractive coupling in inducing in-phase
-synchronized (IPS) oscillations and the tendency of repulsive coupling in
-inducing out-of-phase synchronized (OPS) oscillations compete with each other
-and give rise to symmetry breaking oscillatory (SBO) states and interesting
-multistabilities. Further, we provide explicit expressions for synchronized and
-anti-synchronized oscillatory states as well as the so called oscillation death
-(OD) state and study their stability. If the Hopf bifurcation parameter
-(${\lambda}$) is greater than the natural frequency ($\omega$) of the system,
-the attractive coupling favours the emergence of an anti-symmetric OD state via
-a Hopf bifurcation whereas the repulsive coupling favours the emergence of a
-similar state through a saddle-node bifurcation. We show that an increase in
-the repulsive coupling not only destabilizes the IPS state but also facilitates
-the re-entrance of the IPS state.
-",0,1,0,0,0,0
-18917,Evolutionary Image Composition Using Feature Covariance Matrices,"  Evolutionary algorithms have recently been used to create a wide range of
-artistic work. In this paper, we propose a new approach for the composition of
-new images from existing ones, that retain some salient features of the
-original images. We introduce evolutionary algorithms that create new images
-based on a fitness function that incorporates feature covariance matrices
-associated with different parts of the images. This approach is very flexible
-in that it can work with a wide range of features and enables targeting
-specific regions in the images. For the creation of the new images, we propose
-a population-based evolutionary algorithm with mutation and crossover operators
-based on random walks. Our experimental results reveal a spectrum of
-aesthetically pleasing images that can be obtained with the aid of our
-evolutionary process.
-",1,0,0,0,0,0
-18918,Community Detection with Colored Edges,"  In this paper, we prove a sharp limit on the community detection problem with
-colored edges. We assume two equal-sized communities and there are $m$
-different types of edges. If two vertices are in the same community, the
-distribution of edges follows $p_i=\alpha_i\log{n}/n$ for $1\leq i \leq m$,
-otherwise the distribution of edges is $q_i=\beta_i\log{n}/n$ for $1\leq i \leq
-m$, where $\alpha_i$ and $\beta_i$ are positive constants and $n$ is the total
-number of vertices. Under these assumptions, a fundamental limit on community
-detection is characterized using the Hellinger distance between the two
-distributions. If $\sum_{i=1}^{m} {(\sqrt{\alpha_i} - \sqrt{\beta_i})}^2 >2$,
-then the community detection via maximum likelihood (ML) estimator is possible
-with high probability. If $\sum_{i=1}^m {(\sqrt{\alpha_i} - \sqrt{\beta_i})}^2
-< 2$, the probability that the ML estimator fails to detect the communities
-does not go to zero.
-",1,1,0,0,0,0
-18919,"Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling","  Nonlocal neural networks have been proposed and shown to be effective in
-several computer vision tasks, where the nonlocal operations can directly
-capture long-range dependencies in the feature space. In this paper, we study
-the nature of diffusion and damping effect of nonlocal networks by doing
-spectrum analysis on the weight matrices of the well-trained networks, and then
-propose a new formulation of the nonlocal block. The new block not only learns
-the nonlocal interactions but also has stable dynamics, thus allowing deeper
-nonlocal structures. Moreover, we interpret our formulation from the general
-nonlocal modeling perspective, where we make connections between the proposed
-nonlocal network and other nonlocal models, such as nonlocal diffusion process
-and Markov jump process.
-",0,0,0,1,0,0
-18920,Proof of FLT by Algebra Identities and Linear Algebra,"  The main aim of the present paper is to represent an exact and simple proof
-for FLT by using properties of the algebra identities and linear algebra.
-",0,0,1,0,0,0
-18921,An Observational Diagnostic for Distinguishing Between Clouds and Haze in Hot Exoplanet Atmospheres,"  The nature of aerosols in hot exoplanet atmospheres is one of the primary
-vexing questions facing the exoplanet field. The complex chemistry, multiple
-formation pathways, and lack of easily identifiable spectral features
-associated with aerosols make it especially challenging to constrain their key
-properties. We propose a transmission spectroscopy technique to identify the
-primary aerosol formation mechanism for the most highly irradiated hot Jupiters
-(HIHJs). The technique is based on the expectation that the two key types of
-aerosols -- photochemically generated hazes and equilibrium condensate clouds
--- are expected to form and persist in different regions of a highly irradiated
-planet's atmosphere. Haze can only be produced on the permanent daysides of
-tidally-locked hot Jupiters, and will be carried downwind by atmospheric
-dynamics to the evening terminator (seen as the trailing limb during transit).
-Clouds can only form in cooler regions on the night side and morning terminator
-of HIHJs (seen as the leading limb during transit). Because opposite limbs are
-expected to be impacted by different types of aerosols, ingress and egress
-spectra, which primarily probe opposing sides of the planet, will reveal the
-dominant aerosol formation mechanism. We show that the benchmark HIHJ,
-WASP-121b, has a transmission spectrum consistent with partial aerosol coverage
-and that ingress-egress spectroscopy would constrain the location and formation
-mechanism of those aerosols. In general, using this diagnostic we find that
-observations with JWST and potentially with HST should be able to distinguish
-between clouds and haze for currently known HIHJs.
-",0,1,0,0,0,0
-18922,$\mathcal{P}$-schemes and Deterministic Polynomial Factoring over Finite Fields,"  We introduce a family of mathematical objects called $\mathcal{P}$-schemes,
-where $\mathcal{P}$ is a poset of subgroups of a finite group $G$. A
-$\mathcal{P}$-scheme is a collection of partitions of the right coset spaces
-$H\backslash G$, indexed by $H\in\mathcal{P}$, that satisfies a list of axioms.
-These objects generalize the classical notion of association schemes as well as
-the notion of $m$-schemes (Ivanyos et al. 2009).
-Based on $\mathcal{P}$-schemes, we develop a unifying framework for the
-problem of deterministic factoring of univariate polynomials over finite fields
-under the generalized Riemann hypothesis (GRH).
-",1,0,1,0,0,0
-18923,Similarity Preserving Representation Learning for Time Series Analysis,"  A considerable amount of machine learning algorithms take instance-feature
-matrices as their inputs. As such, they cannot directly analyze time series
-data due to its temporal nature, usually unequal lengths, and complex
-properties. This is a great pity since many of these algorithms are effective,
-robust, efficient, and easy to use. In this paper, we bridge this gap by
-proposing an efficient representation learning framework that is able to
-convert a set of time series with equal or unequal lengths to a matrix format.
-In particular, we guarantee that the pairwise similarities between time series
-are well preserved after the transformation. The learned feature representation
-is particularly suitable to the class of learning problems that are sensitive
-to data similarities. Given a set of $n$ time series, we first construct an
-$n\times n$ partially observed similarity matrix by randomly sampling $O(n \log
-n)$ pairs of time series and computing their pairwise similarities. We then
-propose an extremely efficient algorithm that solves a highly non-convex and
-NP-hard problem to learn new features based on the partially observed
-similarity matrix. We use the learned features to conduct experiments on both
-data classification and clustering tasks. Our extensive experimental results
-demonstrate that the proposed framework is both effective and efficient.
-",1,0,0,0,0,0
-18924,Modeling Social Organizations as Communication Networks,"  We identify the ""organization"" of a human social group as the communication
-network(s) within that group. We then introduce three theoretical approaches to
-analyzing what determines the structures of human organizations. All three
-approaches adopt a group-selection perspective, so that the group's network
-structure is (approximately) optimal, given the information-processing
-limitations of agents within the social group, and the exogenous welfare
-function of the overall group. In the first approach we use a new sub-field of
-telecommunications theory called network coding, and focus on a welfare
-function that involves the ability of the organization to convey information
-among the agents. In the second approach we focus on a scenario where agents
-within the organization must allocate their future communication resources when
-the state of the future environment is uncertain. We show how this formulation
-can be solved with a linear program. In the third approach, we introduce an
-information synthesis problem in which agents within an organization receive
-information from various sources and must decide how to transform such
-information and transmit the results to other agents in the organization. We
-propose leveraging the computational power of neural networks to solve such
-problems. These three approaches formalize and synthesize work in fields
-including anthropology, archeology, economics and psychology that deal with
-organization structure, theory of the firm, span of control and cognitive
-limits on communication.
-",1,1,0,0,0,0
-18925,The Ricci flow on solvmanifolds of real type,"  We show that for any solvable Lie group of real type, any homogeneous Ricci
-flow solution converges in Cheeger-Gromov topology to a unique non-flat
-solvsoliton, which is independent of the initial left-invariant metric. As an
-application, we obtain results on the isometry groups of non-flat solvsoliton
-metrics and Einstein solvmanifolds.
-",0,0,1,0,0,0
-18926,Model-based clustering of multi-tissue gene expression data,"  Recently, it has become feasible to generate large-scale, multi-tissue gene
-expression data, where expression profiles are obtained from multiple tissues
-or organs sampled from dozens to hundreds of individuals. When traditional
-clustering methods are applied to this type of data, important information is
-lost, because they either require all tissues to be analyzed independently,
-ignoring dependencies and similarities between tissues, or to merge tissues in
-a single, monolithic dataset, ignoring individual characteristics of tissues.
-We developed a Bayesian model-based multi-tissue clustering algorithm, revamp,
-which can incorporate prior information on physiological tissue similarity, and
-which results in a set of clusters, each consisting of a core set of genes
-conserved across tissues as well as differential sets of genes specific to one
-or more subsets of tissues. Using data from seven vascular and metabolic
-tissues from over 100 individuals in the STockholm Atherosclerosis Gene
-Expression (STAGE) study, we demonstrate that multi-tissue clusters inferred by
-revamp are more enriched for tissue-dependent protein-protein interactions
-compared to alternative approaches. We further demonstrate that revamp results
-in easily interpretable multi-tissue gene expression associations to key
-coronary artery disease processes and clinical phenotypes in the STAGE
-individuals. Revamp is implemented in the Lemon-Tree software, available at
-this https URL
-",0,0,0,0,1,0
-18927,Fastest Convergence for Q-learning,"  The Zap Q-learning algorithm introduced in this paper is an improvement of
-Watkins' original algorithm and recent competitors in several respects. It is a
-matrix-gain algorithm designed so that its asymptotic variance is optimal.
-Moreover, an ODE analysis suggests that the transient behavior is a close match
-to a deterministic Newton-Raphson implementation. This is made possible by a
-two time-scale update equation for the matrix gain sequence.
-The analysis suggests that the approach will lead to stable and efficient
-computation even for non-ideal parameterized settings. Numerical experiments
-confirm the quick convergence, even in such non-ideal cases.
-A secondary goal of this paper is tutorial. The first half of the paper
-contains a survey on reinforcement learning algorithms, with a focus on minimum
-variance algorithms.
-",1,0,1,0,0,0
-18928,New Bounds on the Field Size for Maximally Recoverable Codes Instantiating Grid-like Topologies,"  In recent years, the rapidly increasing amounts of data created and processed
-through the internet resulted in distributed storage systems employing erasure
-coding based schemes. Aiming to balance the tradeoff between data recovery for
-correlated failures and efficient encoding and decoding, distributed storage
-systems employing maximally recoverable codes came up. Unifying a number of
-topologies considered both in theory and practice, Gopalan \cite{Gopalan2017}
-initiated the study of maximally recoverable codes for grid-like topologies.
-In this paper, we focus on the maximally recoverable codes that instantiate
-grid-like topologies $T_{m\times n}(1,b,0)$. To characterize the property of
-codes for these topologies, we introduce the notion of \emph{pseudo-parity
-check matrix}. Then, using the hypergraph independent set approach, we
-establish the first polynomial upper bound on the field size needed for
-achieving the maximal recoverability in topologies $T_{m\times n}(1,b,0)$, when
-$n$ is large enough. And we further improve this general upper bound for
-topologies $T_{4\times n}(1,2,0)$ and $T_{3\times n}(1,3,0)$. By relating the
-problem to generalized \emph{Sidon sets} in $\mathbb{F}_q$, we also obtain
-non-trivial lower bounds on the field size for maximally recoverable codes that
-instantiate topologies $T_{4\times n}(1,2,0)$ and $T_{3\times n}(1,3,0)$.
-",1,0,0,0,0,0
-18929,Two-Step Disentanglement for Financial Data,"  In this work, we address the problem of disentanglement of factors that
-generate a given data into those that are correlated with the labeling and
-those that are not. Our solution is simpler than previous solutions and employs
-adversarial training in a straightforward manner. We demonstrate the new method
-on visual datasets as well as on financial data. In order to evaluate the
-latter, we developed a hypothetical trading strategy whose performance is
-affected by the performance of the disentanglement, namely, it trades better
-when the factors are better separated.
-",1,0,0,1,0,0
-18930,Optimal control of a Vlasov-Poisson plasma by an external magnetic field - Analysis of a tracking type optimal control problem,"  In the paper ""Optimal control of a Vlasov-Poisson plasma by an external
-magnetic field - The basics for variational calculus"" [arXiv:1708.02464] we
-have already introduced a set of admissible magnetic fields and we have proved
-that each of those fields induces a unique strong solution of the
-Vlasov-Poisson system. We have also established that the field-state operator
-that maps any admissible field onto its corresponding solution is continuous
-and weakly compact. In this paper we will show that this operator is also
-Fréchet differentiable and we will continue to analyze the optimal control
-problem that was introduced in [arXiv:1708.02464]. More precisely, we will
-establish necessary and sufficient conditions for local optimality and we will
-show that an optimal solution is unique under certain conditions.
-",0,0,1,0,0,0
-18931,Towards an algebraic natural proofs barrier via polynomial identity testing,"  We observe that a certain kind of algebraic proof - which covers essentially
-all known algebraic circuit lower bounds to date - cannot be used to prove
-lower bounds against VP if and only if what we call succinct hitting sets exist
-for VP. This is analogous to the Razborov-Rudich natural proofs barrier in
-Boolean circuit complexity, in that we rule out a large class of lower bound
-techniques under a derandomization assumption. We also discuss connections
-between this algebraic natural proofs barrier, geometric complexity theory, and
-(algebraic) proof complexity.
-",1,0,1,0,0,0
-18932,In Defense of the Indefensible: A Very Naive Approach to High-Dimensional Inference,"  In recent years, a great deal of interest has focused on conducting inference
-on the parameters in a linear model in the high-dimensional setting. In this
-paper, we consider a simple and very naïve two-step procedure for this
-task, in which we (i) fit a lasso model in order to obtain a subset of the
-variables; and (ii) fit a least squares model on the lasso-selected set.
-Conventional statistical wisdom tells us that we cannot make use of the
-standard statistical inference tools for the resulting least squares model
-(such as confidence intervals and $p$-values), since we peeked at the data
-twice: once in running the lasso, and again in fitting the least squares model.
-However, in this paper, we show that under a certain set of assumptions, with
-high probability, the set of variables selected by the lasso is deterministic.
-Consequently, the naïve two-step approach can yield confidence intervals
-that have asymptotically correct coverage, as well as p-values with proper
-Type-I error control. Furthermore, this two-step approach unifies two existing
-camps of work on high-dimensional inference: one camp has focused on inference
-based on a sub-model selected by the lasso, and the other has focused on
-inference using a debiased version of the lasso estimator.
-",0,0,1,1,0,0
-18933,Asymptotic Analysis of Plausible Tree Hash Modes for SHA-3,"  Discussions about the choice of a tree hash mode of operation for a
-standardization have recently been undertaken. It appears that a single tree
-mode cannot address adequately all possible uses and specifications of a
-system. In this paper, we review the tree modes which have been proposed, we
-discuss their problems and propose remedies. We make the reasonable assumption
-that communicating systems have different specifications and that software
-applications are of different types (securing stored content or live-streamed
-content). Finally, we propose new modes of operation that address the resource
-usage problem for the three most representative categories of devices and we
-analyse their asymptotic behavior.
-",1,0,0,0,0,0
-18934,Neurally Plausible Model of Robot Reaching Inspired by Infant Motor Babbling,"  In this paper we present a neurally plausible model of robot reaching
-inspired by human infant reaching that is based on embodied artificial
-intelligence, which emphasizes the importance of the sensory-motor interaction
-of an agent and the world. This model encompasses both learning sensory-motor
-correlations through motor babbling and also arm motion planning using
-spreading activation. This model is organized in three layers of neural maps
-with parallel structures representing the same sensory-motor space. The motor
-babbling period shapes the structure of the three neural maps as well as the
-connections within and between them. We describe an implementation of this
-model and an investigation of this implementation using a simple reaching task
-on a humanoid robot. The robot has learned successfully to plan reaching
-motions from a test set with high accuracy and smoothness.
-",1,0,0,0,0,0
-18935,When Will AI Exceed Human Performance? Evidence from AI Experts,"  Advances in artificial intelligence (AI) will transform modern life by
-reshaping transportation, health, science, finance, and the military. To adapt
-public policy, we need to better anticipate these advances. Here we report the
-results from a large survey of machine learning researchers on their beliefs
-about progress in AI. Researchers predict AI will outperform humans in many
-activities in the next ten years, such as translating languages (by 2024),
-writing high-school essays (by 2026), driving a truck (by 2027), working in
-retail (by 2031), writing a bestselling book (by 2049), and working as a
-surgeon (by 2053). Researchers believe there is a 50% chance of AI
-outperforming humans in all tasks in 45 years and of automating all human jobs
-in 120 years, with Asian respondents expecting these dates much sooner than
-North Americans. These results will inform discussion amongst researchers and
-policymakers about anticipating and managing trends in AI.
-",1,0,0,0,0,0
-18936,E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks,"  Recurrent Neural Networks (RNNs) are a key technology for emerging
-applications such as automatic speech recognition, machine translation or image
-description. Long Short Term Memory (LSTM) networks are the most successful RNN
-implementation, as they can learn long term dependencies to achieve high
-accuracy. Unfortunately, the recurrent nature of LSTM networks significantly
-constrains the amount of parallelism and, hence, multicore CPUs and many-core
-GPUs exhibit poor efficiency for RNN inference. In this paper, we present
-E-PUR, an energy-efficient processing unit tailored to the requirements of LSTM
-computation. The main goal of E-PUR is to support large recurrent neural
-networks for low-power mobile devices. E-PUR provides an efficient hardware
-implementation of LSTM networks that is flexible to support diverse
-applications. One of its main novelties is a technique that we call Maximizing
-Weight Locality (MWL), which improves the temporal locality of the memory
-accesses for fetching the synaptic weights, reducing the memory requirements by
-a large extent. Our experimental results show that E-PUR achieves real-time
-performance for different LSTM networks, while reducing energy consumption by
-orders of magnitude with respect to general-purpose processors and GPUs, and it
-requires a very small chip area. Compared to a modern mobile SoC, an NVIDIA
-Tegra X1, E-PUR provides an average energy reduction of 92x.
-",1,0,0,0,0,0
-18937,Increasing Geminid meteor shower activity,"  Mathematical modelling has shown that activity of the Geminid meteor shower
-should rise with time, and that was confirmed by analysis of visual
-observations 1985--2016. We do not expect any outburst activity of the Geminid
-shower in 2017, even though the asteroid (3200) Phaethon has close approach to
-Earth in December of 2017. A small probability to observe dust ejected at
-perihelia 2009--2016 still exists.
-",0,1,0,0,0,0
-18938,The Bennett-Orlicz norm,"  Lederer and van de Geer (2013) introduced a new Orlicz norm, the
-Bernstein-Orlicz norm, which is connected to Bernstein type inequalities. Here
-we introduce another Orlicz norm, the Bennett-Orlicz norm, which is connected
-to Bennett type inequalities. The new Bennett-Orlicz norm yields inequalities
-for expectations of maxima which are potentially somewhat tighter than those
-resulting from the Bernstein-Orlicz norm when they are both applicable. We
-discuss cross connections between these norms, exponential inequalities of the
-Bernstein, Bennett, and Prokhorov types, and make comparisons with results of
-Talagrand (1989, 1994), and Boucheron, Lugosi, and Massart (2013).
-",0,0,1,1,0,0
-18939,Temporal Justification Logic,"  Justification logics are modal-like logics with the additional capability of
-recording the reason, or justification, for modalities in syntactic structures,
-called justification terms. Justification logics can be seen as explicit
-counterparts to modal logics. The behavior and interaction of agents in
-distributed system is often modeled using logics of knowledge and time. In this
-paper, we sketch some preliminary ideas on how the modal knowledge part of such
-logics of knowledge and time could be replaced with an appropriate
-justification logic.
-",1,0,0,0,0,0
-18940,Simultaneous Multiparty Communication Complexity of Composed Functions,"  In the Number On the Forehead (NOF) multiparty communication model, $k$
-players want to evaluate a function $F : X_1 \times\cdots\times X_k\rightarrow
-Y$ on some input $(x_1,\dots,x_k)$ by broadcasting bits according to a
-predetermined protocol. The input is distributed in such a way that each player
-$i$ sees all of it except $x_i$. In the simultaneous setting, the players
-cannot speak to each other but instead send information to a referee. The
-referee does not know the players' input, and cannot give any information back.
-At the end, the referee must be able to recover $F(x_1,\dots,x_k)$ from what
-she obtained.
-A central open question, called the $\log n$ barrier, is to find a function
-which is hard to compute for $polylog(n)$ or more players (where the $x_i$'s
-have size $poly(n)$) in the simultaneous NOF model. This has important
-applications in circuit complexity, as it could help to separate $ACC^0$ from
-other complexity classes. One of the candidates belongs to the family of
-composed functions. The input to these functions is represented by a $k\times
-(t\cdot n)$ boolean matrix $M$, whose row $i$ is the input $x_i$ and $t$ is a
-block-width parameter. A symmetric composed function acting on $M$ is specified
-by two symmetric $n$- and $kt$-variate functions $f$ and $g$, that output
-$f\circ g(M)=f(g(B_1),\dots,g(B_n))$ where $B_j$ is the $j$-th block of width
-$t$ of $M$. As the majority function $MAJ$ is conjectured to be outside of
-$ACC^0$, Babai et. al. suggested to study $MAJ\circ MAJ_t$, with $t$ large
-enough.
-So far, it was only known that $t=1$ is not enough for $MAJ\circ MAJ_t$ to
-break the $\log n$ barrier in the simultaneous deterministic NOF model. In this
-paper, we extend this result to any constant block-width $t>1$, by giving a
-protocol of cost $2^{O(2^t)}\log^{2^{t+1}}(n)$ for any symmetric composed
-function when there are $2^{\Omega(2^t)}\log n$ players.
-",1,0,0,0,0,0
-18941,Online Learning for Changing Environments using Coin Betting,"  A key challenge in online learning is that classical algorithms can be slow
-to adapt to changing environments. Recent studies have proposed ""meta""
-algorithms that convert any online learning algorithm to one that is adaptive
-to changing environments, where the adaptivity is analyzed in a quantity called
-the strongly-adaptive regret. This paper describes a new meta algorithm that
-has a strongly-adaptive regret bound that is a factor of $\sqrt{\log(T)}$
-better than other algorithms with the same time complexity, where $T$ is the
-time horizon. We also extend our algorithm to achieve a first-order (i.e.,
-dependent on the observed losses) strongly-adaptive regret bound for the first
-time, to our knowledge. At its heart is a new parameter-free algorithm for the
-learning with expert advice (LEA) problem in which experts sometimes do not
-output advice for consecutive time steps (i.e., \emph{sleeping} experts). This
-algorithm is derived by a reduction from optimal algorithms for the so-called
-coin betting problem. Empirical results show that our algorithm outperforms
-state-of-the-art methods in both learning with expert advice and metric
-learning scenarios.
-",1,0,0,1,0,0
-18942,On the global sup-norm of GL(3) cusp forms,"  Let $\phi$ be a spherical Hecke-Maass cusp form on the non-compact space
-$\mathrm{PGL}_3(\mathbb{Z})\backslash\mathrm{PGL}_3(\mathbb{R})$. We establish
-various pointwise upper bounds for $\phi$ in terms of its Laplace eigenvalue
-$\lambda_\phi$. These imply, for $\phi$ arithmetically normalized and tempered
-at the archimedean place, the bound $\|\phi\|_\infty\ll_\epsilon
-\lambda_{\phi}^{39/40+\epsilon}$ for the global sup-norm (without restriction
-to a compact subset). On the way, we derive a new uniform upper bound for the
-$\mathrm{GL}_3$ Jacquet-Whittaker function.
-",0,0,1,0,0,0
-18943,Four revolutions in physics and the second quantum revolution -- a unification of force and matter by quantum information,"  Newton's mechanical revolution unifies the motion of planets in the sky and
-falling of apple on earth. Maxwell's electromagnetic revolution unifies
-electricity, magnetism, and light. Einstein's relativity revolution unifies
-space with time, and gravity with space-time distortion. The quantum revolution
-unifies particle with waves, and energy with frequency. Each of those
-revolution changes our world view. In this article, we will describe a
-revolution that is happening now: the second quantum revolution which unifies
-matter/space with information. In other words, the new world view suggests that
-elementary particles (the bosonic force particles and fermionic matter
-particles) all originated from quantum information (qubits): they are
-collective excitations of an entangled qubit ocean that corresponds to our
-space. The beautiful geometric Yang-Mills gauge theory and the strange Fermi
-statistics of matter particles now have a common algebraic quantum
-informational origin.
-",0,1,0,0,0,0
-18944,Concept Drift Learning with Alternating Learners,"  Data-driven predictive analytics are in use today across a number of
-industrial applications, but further integration is hindered by the requirement
-of similarity among model training and test data distributions. This paper
-addresses the need of learning from possibly nonstationary data streams, or
-under concept drift, a commonly seen phenomenon in practical applications. A
-simple dual-learner ensemble strategy, alternating learners framework, is
-proposed. A long-memory model learns stable concepts from a long relevant time
-window, while a short-memory model learns transient concepts from a small
-recent window. The difference in prediction performance of these two models is
-monitored and induces an alternating policy to select, update and reset the two
-models. The method features an online updating mechanism to maintain the
-ensemble accuracy, and a concept-dependent trigger to focus on relevant data.
-Through empirical studies the method demonstrates effective tracking and
-prediction when the steaming data carry abrupt and/or gradual changes.
-",1,0,0,1,0,0
-18945,A new proof of the competitive exclusion principle in the chemostat,"  We give an new proof of the well-known competitive exclusion principle in the
-chemostat model with $n$ species competing for a single resource, for any set
-of increasing growth functions. The proof is constructed by induction on the
-number of the species, after being ordered. It uses elementary analysis and
-comparisons of solutions of ordinary differential equations.
-",0,0,1,0,0,0
-18946,From Random Differential Equations to Structural Causal Models: the stochastic case,"  Random Differential Equations provide a natural extension of Ordinary
-Differential Equations to the stochastic setting. We show how, and under which
-conditions, every equilibrium state of a Random Differential Equation (RDE) can
-be described by a Structural Causal Model (SCM), while pertaining the causal
-semantics. This provides an SCM that captures the stochastic and causal
-behavior of the RDE, which can model both cycles and confounders. This enables
-the study of the equilibrium states of the RDE by applying the theory and
-statistical tools available for SCMs, for example, marginalizations and Markov
-properties, as we illustrate by means of an example. Our work thus provides a
-direct connection between two fields that so far have been developing in
-isolation.
-",0,0,0,1,0,0
-18947,Ties That Bind - Characterizing Classes by Attributes and Social Ties,"  Given a set of attributed subgraphs known to be from different classes, how
-can we discover their differences? There are many cases where collections of
-subgraphs may be contrasted against each other. For example, they may be
-assigned ground truth labels (spam/not-spam), or it may be desired to directly
-compare the biological networks of different species or compound networks of
-different chemicals.
-In this work we introduce the problem of characterizing the differences
-between attributed subgraphs that belong to different classes. We define this
-characterization problem as one of partitioning the attributes into as many
-groups as the number of classes, while maximizing the total attributed quality
-score of all the given subgraphs.
-We show that our attribute-to-class assignment problem is NP-hard and an
-optimal $(1 - 1/e)$-approximation algorithm exists. We also propose two
-different faster heuristics that are linear-time in the number of attributes
-and subgraphs. Unlike previous work where only attributes were taken into
-account for characterization, here we exploit both attributes and social ties
-(i.e. graph structure).
-Through extensive experiments, we compare our proposed algorithms, show
-findings that agree with human intuition on datasets from Amazon co-purchases,
-Congressional bill sponsorships, and DBLP co-authorships. We also show that our
-approach of characterizing subgraphs is better suited for sense-making than
-discriminating classification approaches.
-",1,1,0,0,0,0
-18948,Probing dark matter with star clusters: a dark matter core in the ultra-faint dwarf Eridanus II,"  We present a new technique to probe the central dark matter (DM) density
-profile of galaxies that harnesses both the survival and observed properties of
-star clusters. As a first application, we apply our method to the `ultra-faint'
-dwarf Eridanus II (Eri II) that has a lone star cluster ~45 pc from its centre.
-Using a grid of collisional $N$-body simulations, incorporating the effects of
-stellar evolution, external tides and dynamical friction, we show that a DM
-core for Eri II naturally reproduces the size and the projected position of its
-star cluster. By contrast, a dense cusped galaxy requires the cluster to lie
-implausibly far from the centre of Eri II (>1 kpc), with a high inclination
-orbit that must be observed at a particular orbital phase. Our results,
-therefore, favour a dark matter core. This implies that either a cold DM cusp
-was `heated up' at the centre of Eri II by bursty star formation, or we are
-seeing an evidence for physics beyond cold DM.
-",0,1,0,0,0,0
-18949,Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s),"  We propose the ambiguity problem for the foreground object segmentation task
-and motivate the importance of estimating and accounting for this ambiguity
-when designing vision systems. Specifically, we distinguish between images
-which lead multiple annotators to segment different foreground objects
-(ambiguous) versus minor inter-annotator differences of the same object. Taking
-images from eight widely used datasets, we crowdsource labeling the images as
-""ambiguous"" or ""not ambiguous"" to segment in order to construct a new dataset
-we call STATIC. Using STATIC, we develop a system that automatically predicts
-which images are ambiguous. Experiments demonstrate the advantage of our
-prediction system over existing saliency-based methods on images from vision
-benchmarks and images taken by blind people who are trying to recognize objects
-in their environment. Finally, we introduce a crowdsourcing system to achieve
-cost savings for collecting the diversity of all valid ""ground truth""
-foreground object segmentations by collecting extra segmentations only when
-ambiguity is expected. Experiments show our system eliminates up to 47% of
-human effort compared to existing crowdsourcing methods with no loss in
-capturing the diversity of ground truths.
-",1,0,0,0,0,0
-18950,On Some Generalized Polyhedral Convex Constructions,"  Generalized polyhedral convex sets, generalized polyhedral convex functions
-on locally convex Hausdorff topological vector spaces, and the related
-constructions such as sum of sets, sum of functions, directional derivative,
-infimal convolution, normal cone, conjugate function, subdifferential, are
-studied thoroughly in this paper. Among other things, we show how a generalized
-polyhedral convex set can be characterized via the finiteness of the number of
-its faces. In addition, it is proved that the infimal convolution of a
-generalized polyhedral convex function and a polyhedral convex function is a
-polyhedral convex function. The obtained results can be applied to scalar
-optimization problems described by generalized polyhedral convex sets and
-generalized polyhedral convex functions.
-",0,0,1,0,0,0
-18951,mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion,"  Proteins are commonly used by biochemical industry for numerous processes.
-Refining these proteins' properties via mutations causes stability effects as
-well. Accurate computational method to predict how mutations affect protein
-stability are necessary to facilitate efficient protein design. However,
-accuracy of predictive models is ultimately constrained by the limited
-availability of experimental data. We have developed mGPfusion, a novel
-Gaussian process (GP) method for predicting protein's stability changes upon
-single and multiple mutations. This method complements the limited experimental
-data with large amounts of molecular simulation data. We introduce a Bayesian
-data fusion model that re-calibrates the experimental and in silico data
-sources and then learns a predictive GP model from the combined data. Our
-protein-specific model requires experimental data only regarding the protein of
-interest and performs well even with few experimental measurements. The
-mGPfusion models proteins by contact maps and infers the stability effects
-caused by mutations with a mixture of graph kernels. Our results show that
-mGPfusion outperforms state-of-the-art methods in predicting protein stability
-on a dataset of 15 different proteins and that incorporating molecular
-simulation data improves the model learning and prediction accuracy.
-",0,0,0,1,1,0
-18952,How Do Software Startups Pivot? Empirical Results from a Multiple Case Study,"  In order to handle intense time pressure and survive in dynamic market,
-software startups have to make crucial decisions constantly on whether to
-change directions or stay on chosen courses, or in the terms of Lean Startup,
-to pivot or to persevere. The existing research and knowledge on software
-startup pivots are very limited. In this study, we focused on understanding the
-pivoting processes of software startups, and identified the triggering factors
-and pivot types. To achieve this, we employed a multiple case study approach,
-and analyzed the data obtained from four software startups. The initial
-findings show that different software startups make different types of pivots
-related to business and technology during their product development life cycle.
-The pivots are triggered by various factors including negative customer
-feedback.
-",1,0,0,0,0,0
-18953,Some criteria for Wind Riemannian completeness and existence of Cauchy hypersurfaces,"  Recently, a link between Lorentzian and Finslerian Geometries has been
-carried out, leading to the notion of wind Riemannian structure (WRS), a
-generalization of Finslerian Randers metrics. Here, we further develop this
-notion and its applications to spacetimes, by introducing some
-characterizations and criteria for the completeness of WRS's.
-As an application, we consider a general class of spacetimes admitting a time
-function $t$ generated by the flow of a complete Killing vector field
-(generalized standard stationary spacetimes or, more precisely, SSTK ones) and
-derive simple criteria ensuring that its slices $t=$ constant are Cauchy.
-Moreover, a brief summary on the Finsler/Lorentz link for readers with some
-acquaintance in Lorentzian Geometry, plus some simple examples in Mathematical
-Relativity, are provided.
-",0,0,1,0,0,0
-18954,A generalization of Schönemann's theorem via a graph theoretic method,"  Recently, Grynkiewicz et al. [{\it Israel J. Math.} {\bf 193} (2013),
-359--398], using tools from additive combinatorics and group theory, proved
-necessary and sufficient conditions under which the linear congruence
-$a_1x_1+\cdots +a_kx_k\equiv b \pmod{n}$, where $a_1,\ldots,a_k,b,n$ ($n\geq
-1$) are arbitrary integers, has a solution $\langle x_1,\ldots,x_k \rangle \in
-\Z_{n}^k$ with all $x_i$ distinct modulo $n$. So, it would be an interesting
-problem to give an explicit formula for the number of such solutions. Quite
-surprisingly, this problem was first considered, in a special case, by
-Schönemann almost two centuries ago(!) but his result seems to have been
-forgotten. Schönemann [{\it J. Reine Angew. Math.} {\bf 1839} (1839),
-231--243] proved an explicit formula for the number of such solutions when
-$b=0$, $n=p$ a prime, and $\sum_{i=1}^k a_i \equiv 0 \pmod{p}$ but $\sum_{i \in
-I} a_i \not\equiv 0 \pmod{p}$ for all $I\varsubsetneq \lbrace 1, \ldots,
-k\rbrace$. In this paper, we generalize Schönemann's theorem using a result
-on the number of solutions of linear congruences due to D. N. Lehmer and also a
-result on graph enumeration recently obtained by Ardila et al. [{\it Int. Math.
-Res. Not.} {\bf 2015} (2015), 3830--3877]. This seems to be a rather uncommon
-method in the area; besides, our proof technique or its modifications may be
-useful for dealing with other cases of this problem (or even the general case)
-or other relevant problems.
-",1,0,0,0,0,0
-18955,Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning,"  In order for robots to perform mission-critical tasks, it is essential that
-they are able to quickly adapt to changes in their environment as well as to
-injuries and or other bodily changes. Deep reinforcement learning has been
-shown to be successful in training robot control policies for operation in
-complex environments. However, existing methods typically employ only a single
-policy. This can limit the adaptability since a large environmental
-modification might require a completely different behavior compared to the
-learning environment. To solve this problem, we propose Map-based Multi-Policy
-Reinforcement Learning (MMPRL), which aims to search and store multiple
-policies that encode different behavioral features while maximizing the
-expected reward in advance of the environment change. Thanks to these policies,
-which are stored into a multi-dimensional discrete map according to its
-behavioral feature, adaptation can be performed within reasonable time without
-retraining the robot. An appropriate pre-trained policy from the map can be
-recalled using Bayesian optimization. Our experiments show that MMPRL enables
-robots to quickly adapt to large changes without requiring any prior knowledge
-on the type of injuries that could occur. A highlight of the learned behaviors
-can be found here: this https URL .
-",1,0,0,0,0,0
-18956,Testing convexity of a discrete distribution,"  Based on the convex least-squares estimator, we propose two different
-procedures for testing convexity of a probability mass function supported on N
-with an unknown finite support. The procedures are shown to be asymptotically
-calibrated.
-",0,0,1,1,0,0
-18957,Small and Strong Formulations for Unions of Convex Sets from the Cayley Embedding,"  There is often a significant trade-off between formulation strength and size
-in mixed integer programming (MIP). When modeling convex disjunctive
-constraints (e.g. unions of convex sets), adding auxiliary continuous variables
-can sometimes help resolve this trade-off. However, standard formulations that
-use such auxiliary continuous variables can have a worse-than-expected
-computational effectiveness, which is often attributed precisely to these
-auxiliary continuous variables. For this reason, there has been considerable
-interest in constructing strong formulations that do not use continuous
-auxiliary variables. We introduce a technique to construct formulations without
-these detrimental continuous auxiliary variables. To develop this technique we
-introduce a natural non-polyhedral generalization of the Cayley embedding of a
-family of polytopes and show it inherits many geometric properties of the
-original embedding. We then show how the associated formulation technique can
-be used to construct small and strong formulation for a wide range of
-disjunctive constraints. In particular, we show it can recover and generalize
-all known strong formulations without continuous auxiliary variables.
-",0,0,1,0,0,0
-18958,Safe Robotic Grasping: Minimum Impact-Force Grasp Selection,"  This paper addresses the problem of selecting from a choice of possible
-grasps, so that impact forces will be minimised if a collision occurs while the
-robot is moving the grasped object along a post-grasp trajectory. Such
-considerations are important for safety in human-robot interaction, where even
-a certified ""human-safe"" (e.g. compliant) arm may become hazardous once it
-grasps and begins moving an object, which may have significant mass, sharp
-edges or other dangers. Additionally, minimising collision forces is critical
-to preserving the longevity of robots which operate in uncertain and hazardous
-environments, e.g. robots deployed for nuclear decommissioning, where removing
-a damaged robot from a contaminated zone for repairs may be extremely difficult
-and costly. Also, unwanted collisions between a robot and critical
-infrastructure (e.g. pipework) in such high-consequence environments can be
-disastrous. In this paper, we investigate how the safety of the post-grasp
-motion can be considered during the pre-grasp approach phase, so that the
-selected grasp is optimal in terms applying minimum impact forces if a
-collision occurs during a desired post-grasp manipulation. We build on the
-methods of augmented robot-object dynamics models and ""effective mass"" and
-propose a method for combining these concepts with modern grasp and trajectory
-planners, to enable the robot to achieve a grasp which maximises the safety of
-the post-grasp trajectory, by minimising potential collision forces. We
-demonstrate the effectiveness of our approach through several experiments with
-both simulated and real robots.
-",1,0,0,0,0,0
-18959,Nonparametric Kernel Density Estimation for Univariate Curent Status Data,"  We derive estimators of the density of the event times of current status
-data. The estimators are derived for the situations where the distribution of
-the observation times is known and where this distribution is unknown. The
-density estimators are constructed from kernel estimators of the density of
-transformed current status data, which have a distribution similar to uniform
-deconvolution data. Expansions of the expectation and variance as well as
-asymptotic normality are derived. A reference density based bandwidth selection
-method is proposed. A simulated example is presented.
-",0,0,1,1,0,0
-18960,Non-Kähler Mirror Symmetry of the Iwasawa Manifold,"  We propose a new approach to the Mirror Symmetry Conjecture in a form
-suitable to possibly non-Kähler compact complex manifolds whose canonical
-bundle is trivial. We apply our methods by proving that the Iwasawa manifold
-$X$, a well-known non-Kähler compact complex manifold of dimension $3$, is
-its own mirror dual to the extent that its Gauduchon cone, replacing the
-classical Kähler cone that is empty in this case, corresponds to what we call
-the local universal family of essential deformations of $X$. These are obtained
-by removing from the Kuranishi family the two ""superfluous"" dimensions of
-complex parallelisable deformations that have a similar geometry to that of the
-Iwasawa manifold. The remaining four dimensions are shown to have a clear
-geometric meaning including in terms of the degeneration at $E_2$ of the
-Frölicher spectral sequence. On the local moduli space of ""essential"" complex
-structures, we obtain a canonical Hodge decomposition of weight $3$ and a
-variation of Hodge structures, construct coordinates and Yukawa couplings while
-implicitly proving a local Torelli theorem. On the metric side of the mirror,
-we construct a variation of Hodge structures parametrised by a subset of the
-complexified Gauduchon cone of the Iwasawa manifold using the sGG property of
-all the small deformations of this manifold proved in earlier joint work of the
-author with L. Ugarte. Finally, we define a mirror map linking the two
-variations of Hodge structures and we highlight its properties.
-",0,0,1,0,0,0
-18961,Estimation and Inference for Moments of Ratios with Robustness against Large Trimming Bias,"  Empirical researchers often trim observations with small denominator A when
-they estimate moments of the form E[B/A]. Large trimming is a common practice
-to mitigate variance, but it incurs large trimming bias. This paper provides a
-novel method of correcting large trimming bias. If a researcher is willing to
-assume that the joint distribution between A and B is smooth, then a large
-trimming bias may be estimated well. With the bias correction, we also develop
-a valid and robust inference result for E[B/A].
-",0,0,0,1,0,0
-18962,FPGA-based real-time 105-channel data acquisition platform for imaging system,"  In this paper, a real-time 105-channel data acquisition platform based on
-FPGA for imaging will be implemented for mm-wave imaging systems. PC platform
-is also realized for imaging results monitoring purpose. Mm-wave imaging
-expands our vision by letting us see things under poor visibility conditions.
-With this extended vision ability, a wide range of military imaging missions
-would benefit, such as surveillance, precision targeting, navigation, and
-rescue. Based on the previously designed imager modules, this project would go
-on finishing the PCB design (both schematic and layout) of the following signal
-processing systems consisting of Programmable Gain Amplifier(PGA) (4 PGA for
-each ADC) and 16-channel Analog to Digital Converter (ADC) (7 ADC in total).
-Then the system verification would be performed on the Artix-7 35T Arty FPGA
-with the developing of proper controlling code to configure the ADC and realize
-the communication between the FPGA and the PC (through both UART and Ethernet).
-For the verification part, a simple test on a breadboard with a simple analog
-input (generated from a resistor divider) would first be performed. After the
-PCB design is finished, the whole system would be tested again with a precise
-reference and analog input.
-",1,0,0,0,0,0
-18963,Zn-induced in-gap electronic states in La214 probed by uniform magnetic susceptibility: relevance to the suppression of superconducting Tc,"  Substitution of isovalent non-magnetic defects, such as Zn, in CuO2 plane
-strongly modifies the magnetic properties of strongly electron correlated hole
-doped cuprate superconductors. The reason for enhanced uniform magnetic
-susceptibility, \c{hi}, in Zn substituted cuprates is debatable. So far, the
-observed magnetic behavior has been analyzed mainly in terms of two somewhat
-contrasting scenarios, (a) that due to independent localized moments appearing
-in the vicinity of Zn arising because of the strong electronic/magnetic
-correlations present in the host compound and (b) that due to transfer of
-quasiparticle spectral weight and creation of weakly localized low energy
-electronic states associated with each Zn atom in place of an in-plane Cu. If
-the second scenario is correct, one should expect a direct correspondence
-between Zn induced suppression of superconducting transition temperature, Tc,
-and the extent of the enhanced magnetic susceptibility at low temperature. In
-this case, the low-T enhancement of \c{hi} would be due to weakly localized
-quasiparticle states at low energy and these electronic states will be
-precluded from taking part in Cooper pairing. We explore this second
-possibility by analyzing the \c{hi}(T) data for La2-xSrxCu1-yZnyO4 with
-different hole contents, p (= x), and Zn concentrations (y) in this paper.
-Results of our analysis support this scenario.
-",0,1,0,0,0,0
-18964,Predicate Specialization for Definitional Higher-order Logic Programs,"  Higher-order logic programming is an interesting extension of traditional
-logic programming that allows predicates to appear as arguments and variables
-to be used where predicates typically occur. Higher-order characteristics are
-indeed desirable but on the other hand they are also usually more expensive to
-support. In this paper we propose a program specialization technique based on
-partial evaluation that can be applied to a modest but useful class of
-higher-order logic programs and can transform them into first-order programs
-without introducing additional data structures. The resulting first-order
-programs can be executed by conventional logic programming interpreters and
-benefit from other optimizations that might be available. We provide an
-implementation and experimental results that suggest the efficiency of the
-transformation.
-",1,0,0,0,0,0
-18965,Metastability and bifurcation in superconducting nanorings,"  We describe an approach, based on direct numerical solution of the Usadel
-equation, to finding stationary points of the free energy of superconducting
-nanorings. We consider both uniform (equilibrium) solutions and the critical
-droplets that mediate activated transitions between them. For the uniform
-solutions, we compute the critical current as a function of the temperature,
-thus obtaining a correction factor to Bardeen's 1962 interpolation formula. For
-the droplets, we present a metastability chart that shows the activation energy
-as a function of the temperature and current. A comparison of the activation
-energy for a ring to experimental results for a wire connected to
-superconducting leads reveals a discrepancy at large currents. We discuss
-possible reasons for it. We also discuss the nature of the bifurcation point at
-which the droplet merges with the uniform solution.
-",0,1,0,0,0,0
-18966,Origin of life in a digital microcosm,"  While all organisms on Earth descend from a common ancestor, there is no
-consensus on whether the origin of this ancestral self-replicator was a one-off
-event or whether it was only the final survivor of multiple origins. Here we
-use the digital evolution system Avida to study the origin of self-replicating
-computer programs. By using a computational system, we avoid many of the
-uncertainties inherent in any biochemical system of self-replicators (while
-running the risk of ignoring a fundamental aspect of biochemistry). We
-generated the exhaustive set of minimal-genome self-replicators and analyzed
-the network structure of this fitness landscape. We further examined the
-evolvability of these self-replicators and found that the evolvability of a
-self-replicator is dependent on its genomic architecture. We studied the
-differential ability of replicators to take over the population when competed
-against each other (akin to a primordial-soup model of biogenesis) and found
-that the probability of a self-replicator out-competing the others is not
-uniform. Instead, progenitor (most-recent common ancestor) genotypes are
-clustered in a small region of the replicator space. Our results demonstrate
-how computational systems can be used as test systems for hypotheses concerning
-the origin of life.
-",1,1,0,0,0,0
-18967,The equivalence of two tax processes,"  We introduce two models of taxation, the latent and natural tax processes,
-which have both been used to represent loss-carry-forward taxation on the
-capital of an insurance company. In the natural tax process, the tax rate is a
-function of the current level of capital, whereas in the latent tax process,
-the tax rate is a function of the capital that would have resulted if no tax
-had been paid. Whereas up to now these two types of tax processes have been
-treated separately, we show that, in fact, they are essentially equivalent.
-This allows a unified treatment, translating results from one model to the
-other. Significantly, we solve the question of existence and uniqueness for the
-natural tax process, which is defined via an integral equation. Our results
-clarify the existing literature on processes with tax.
-",0,0,0,0,0,1
-18968,Space-Time Geostatistical Models with both Linear and Seasonal Structures in the Temporal Components,"  We provide a novel approach to model space-time random fields where the
-temporal argument is decomposed into two parts. The former captures the linear
-argument, which is related, for instance, to the annual evolution of the field.
-The latter is instead a circular variable describing, for instance, monthly
-observations. The basic intuition behind this construction is to consider a
-random field defined over space (a compact set of the $d$-dimensional Euclidean
-space) across time, which is considered as the product space $\mathbb{R} \times
-\mathbb{S}^1$, with $\mathbb{S}^1$ being the unit circle. Under such framework,
-we derive new parametric families of covariance functions. In particular, we
-focus on two classes of parametric families. The former being parenthetical to
-the Gneiting class of covariance functions. The latter is instead obtained by
-proposing a new Lagrangian framework for the space-time domain considered in
-the manuscript. Our findings are illustrated through a real dataset of surface
-air temperatures. We show that the incorporation of both temporal variables can
-produce significant improvements in the predictive performances of the model.
-We also discuss the extension of this approach for fields defined spatially on
-a sphere, which allows to model space-time phenomena over large portions of
-planet Earth.
-",0,0,1,1,0,0
-18969,Stability and Robust Regulation of Passive Linear Systems,"  We study the stability of coupled impedance passive regular linear systems
-under power-preserving interconnections. We present new conditions for strong,
-exponential, and non-uniform stability of the closed-loop system. We apply the
-stability results to the construction of passive error feedback controllers for
-robust output tracking and disturbance rejection for strongly stabilizable
-passive systems. In the case of nonsmooth reference and disturbance signals we
-present conditions for non-uniform rational and logarithmic rates of
-convergence of the output. The results are illustrated with examples on
-designing controllers for linear wave and heat equations, and on studying the
-stability of a system of coupled partial differential equations.
-",0,0,1,0,0,0
-18970,Bayes Minimax Competitors of Preliminary Test Estimators in k Sample Problems,"  In this paper, we consider the estimation of a mean vector of a multivariate
-normal population where the mean vector is suspected to be nearly equal to mean
-vectors of $k-1$ other populations. As an alternative to the preliminary test
-estimator based on the test statistic for testing hypothesis of equal means, we
-derive empirical and hierarchical Bayes estimators which shrink the sample mean
-vector toward a pooled mean estimator given under the hypothesis. The
-minimaxity of those Bayesian estimators are shown, and their performances are
-investigated by simulation.
-",0,0,1,1,0,0
-18971,Non-orthogonal Multiple Access for High-reliable and Low-latency V2X Communications,"  In this paper, we consider a dense vehicular communication network where each
-vehicle broadcasts its safety information to its neighborhood in each
-transmission period. Such applications require low latency and high
-reliability, and thus, we propose a non-orthogonal multiple access scheme to
-reduce the latency and to improve the packet reception probability. In the
-proposed scheme, the BS performs the semi-persistent scheduling to optimize the
-time scheduling and allocate frequency resources in a non-orthogonal manner
-while the vehicles autonomously perform distributed power control. We formulate
-the centralized scheduling and resource allocation problem as equivalent to a
-multi-dimensional stable roommate matching problem, in which the users and
-time/frequency resources are considered as disjoint sets of players to be
-matched with each other. We then develop a novel rotation matching algorithm,
-which converges to a q-exchange stable matching after a limited number of
-iterations. Simulation results show that the proposed scheme outperforms the
-traditional orthogonal multiple access scheme in terms of the latency and
-reliability.
-",1,0,0,0,0,0
-18972,Cohomology monoids of monoids with coefficients in semimodules II,"  We relate the old and new cohomology monoids of an arbitrary monoid $M$ with
-coefficients in semimodules over $M$, introduced in the author's previous
-papers, to monoid and group extensions. More precisely, the old and new second
-cohomology monoids describe Schreier extensions of semimodules by monoids, and
-the new third cohomology monoid is related to a certain group extension
-problem.
-",0,0,1,0,0,0
-18973,Lower Bounds for Higher-Order Convex Optimization,"  State-of-the-art methods in convex and non-convex optimization employ
-higher-order derivative information, either implicitly or explicitly. We
-explore the limitations of higher-order optimization and prove that even for
-convex optimization, a polynomial dependence on the approximation guarantee and
-higher-order smoothness parameters is necessary. As a special case, we show
-Nesterov's accelerated cubic regularization method to be nearly tight.
-",1,0,0,1,0,0
-18974,Superconductivity in La1-xCexOBiSSe: carrier doping by mixed valence of Ce ions,"  We report the effects of Ce substitution on structural, electronic, and
-magnetic properties of layered bismuth-chalcogenide La1-xCexOBiSSe (x = 0-0.9),
-which are newly obtained in this study. Metallic conductivity was observed for
-x > 0.1 because of electron carriers induced by mixed valence of Ce ions, as
-revealed by bond valence sum calculation and magnetization measurements. Zero
-resistivity and clear diamagnetic susceptibility were obtained for x = 0.2-0.6,
-indicating the emergence of bulk superconductivity in these compounds.
-Dome-shaped superconductivity phase diagram with the highest transition
-temperature (Tc) of 3.1 K, which is slightly lower than that of F-doped
-LaOBiSSe (Tc = 3.7 K), was established. The present study clearly shows that
-the mixed valence of Ce ions can be utilized as an alternative approach for
-electron-doping in layered bismuth-chalcogenides to induce superconductivity.
-",0,1,0,0,0,0
-18975,Gaussian Processes Over Graphs,"  We propose Gaussian processes for signals over graphs (GPG) using the apriori
-knowledge that the target vectors lie over a graph. We incorporate this
-information using a graph- Laplacian based regularization which enforces the
-target vectors to have a specific profile in terms of graph Fourier transform
-coeffcients, for example lowpass or bandpass graph signals. We discuss how the
-regularization affects the mean and the variance in the prediction output. In
-particular, we prove that the predictive variance of the GPG is strictly
-smaller than the conventional Gaussian process (GP) for any non-trivial graph.
-We validate our concepts by application to various real-world graph signals.
-Our experiments show that the performance of the GPG is superior to GP for
-small training data sizes and under noisy training.
-",0,0,0,1,0,0
-18976,Differences of Type I error rates for ANOVA and Multilevel-Linear-Models using SAS and SPSS for repeated measures designs,"  To derive recommendations on how to analyze longitudinal data, we examined
-Type I error rates of Multilevel Linear Models (MLM) and repeated measures
-Analysis of Variance (rANOVA) using SAS and SPSS.We performed a simulation with
-the following specifications: To explore the effects of high numbers of
-measurement occasions and small sample sizes on Type I error, measurement
-occasions of m = 9 and 12 were investigated as well as sample sizes of n = 15,
-20, 25 and 30. Effects of non-sphericity in the population on Type I error were
-also inspected: 5,000 random samples were drawn from two populations containing
-neither a within-subject nor a between-group effect. They were analyzed
-including the most common options to correct rANOVA and MLM-results: The
-Huynh-Feldt-correction for rANOVA (rANOVA-HF) and the Kenward-Roger-correction
-for MLM (MLM-KR), which could help to correct progressive bias of MLM with an
-unstructured covariance matrix (MLM-UN). Moreover, uncorrected rANOVA and MLM
-assuming a compound symmetry covariance structure (MLM-CS) were also taken into
-account. The results showed a progressive bias for MLM-UN for small samples
-which was stronger in SPSS than in SAS. Moreover, an appropriate bias
-correction for Type I error via rANOVA-HF and an insufficient correction by
-MLM-UN-KR for n < 30 were found. These findings suggest MLM-CS or rANOVA if
-sphericity holds and a correction of a violation via rANOVA-HF. If an analysis
-requires MLM, SPSS yields more accurate Type I error rates for MLM-CS and SAS
-yields more accurate Type I error rates for MLM-UN.
-",0,0,0,1,0,0
-18977,Efficient algorithms for Bayesian Nearest Neighbor Gaussian Processes,"  We consider alternate formulations of recently proposed hierarchical Nearest
-Neighbor Gaussian Process (NNGP) models (Datta et al., 2016a) for improved
-convergence, faster computing time, and more robust and reproducible Bayesian
-inference. Algorithms are defined that improve CPU memory management and
-exploit existing high-performance numerical linear algebra libraries.
-Computational and inferential benefits are assessed for alternate NNGP
-specifications using simulated datasets and remotely sensed light detection and
-ranging (LiDAR) data collected over the US Forest Service Tanana Inventory Unit
-(TIU) in a remote portion of Interior Alaska. The resulting data product is the
-first statistically robust map of forest canopy for the TIU.
-",0,0,0,1,0,0
-18978,Variegation and space weathering on asteroid 21 Lutetia,"  During the flyby in 2010, the OSIRIS camera on-board Rosetta acquired
-hundreds of high-resolution images of asteroid Lutetia's surface through a
-range of narrow-band filters. While Lutetia appears very bland in the visible
-wavelength range, Magrin et al. (2012) tentatively identified UV color
-variations in the Baetica cluster, a group of relatively young craters close to
-the north pole. As Lutetia remains a poorly understood asteroid, such color
-variations may provide clues to the nature of its surface. We take the color
-analysis one step further. First we orthorectify the images using a shape model
-and improved camera pointing, then apply a variety of techniques (photometric
-correction, principal component analysis) to the resulting color cubes. We
-characterize variegation in the Baetica crater cluster at high spatial
-resolution, identifying crater rays and small, fresh impact craters. We argue
-that at least some of the color variation is due to space weathering, which
-makes Lutetia's regolith redder and brighter.
-",0,1,0,0,0,0
-18979,Rokhlin dimension for compact quantum group actions,"  We show that, for a given compact or discrete quantum group $G$, the class of
-actions of $G$ on C*-algebras is first-order axiomatizable in the logic for
-metric structures. As an application, we extend the notion of Rokhlin property
-for $G$-C*-algebra, introduced by Barlak, Szabó, and Voigt in the case when
-$G$ is second countable and coexact, to an arbitrary compact quantum group $G$.
-All the the preservations and rigidity results for Rokhlin actions of second
-countable coexact compact quantum groups obtained by Barlak, Szabó, and
-Voigt are shown to hold in this general context. As a further application, we
-extend the notion of equivariant order zero dimension for equivariant
-*-homomorphisms, introduced in the classical setting by the first and third
-authors, to actions of compact quantum groups. This allows us to define the
-Rokhlin dimension of an action of a compact quantum group on a C*-algebra,
-recovering the Rokhlin property as Rokhlin dimension zero. We conclude by
-establishing a preservation result for finite nuclear dimension and finite
-decomposition rank when passing to fixed point algebras and crossed products by
-compact quantum group actions with finite Rokhlin dimension.
-",0,0,1,0,0,0
-18980,Parametric Identification Using Weighted Null-Space Fitting,"  In identification of dynamical systems, the prediction error method using a
-quadratic cost function provides asymptotically efficient estimates under
-Gaussian noise and additional mild assumptions, but in general it requires
-solving a non-convex optimization problem. An alternative class of methods uses
-a non-parametric model as intermediate step to obtain the model of interest.
-Weighted null-space fitting (WNSF) belongs to this class. It is a weighted
-least-squares method consisting of three steps. In the first step, a high-order
-ARX model is estimated. In a second least-squares step, this high-order
-estimate is reduced to a parametric estimate. In the third step, weighted least
-squares is used to reduce the variance of the estimates. The method is flexible
-in parametrization and suitable for both open- and closed-loop data. In this
-paper, we show that WNSF provides estimates with the same asymptotic properties
-as PEM with a quadratic cost function when the model orders are chosen
-according to the true system. Also, simulation studies indicate that WNSF may
-be competitive with state-of-the-art methods.
-",1,0,0,0,0,0
-18981,Learning Large-Scale Bayesian Networks with the sparsebn Package,"  Learning graphical models from data is an important problem with wide
-applications, ranging from genomics to the social sciences. Nowadays datasets
-often have upwards of thousands---sometimes tens or hundreds of thousands---of
-variables and far fewer samples. To meet this challenge, we have developed a
-new R package called sparsebn for learning the structure of large, sparse
-graphical models with a focus on Bayesian networks. While there are many
-existing software packages for this task, this package focuses on the unique
-setting of learning large networks from high-dimensional data, possibly with
-interventions. As such, the methods provided place a premium on scalability and
-consistency in a high-dimensional setting. Furthermore, in the presence of
-interventions, the methods implemented here achieve the goal of learning a
-causal network from data. Additionally, the sparsebn package is fully
-compatible with existing software packages for network analysis.
-",1,0,0,1,0,0
-18982,An Asymptotically Optimal Index Policy for Finite-Horizon Restless Bandits,"  We consider restless multi-armed bandit (RMAB) with a finite horizon and
-multiple pulls per period. Leveraging the Lagrangian relaxation, we approximate
-the problem with a collection of single arm problems. We then propose an
-index-based policy that uses optimal solutions of the single arm problems to
-index individual arms, and offer a proof that it is asymptotically optimal as
-the number of arms tends to infinity. We also use simulation to show that this
-index-based policy performs better than the state-of-art heuristics in various
-problem settings.
-",0,0,1,0,0,0
-18983,Effective holographic theory of charge density waves,"  We use Gauge/Gravity duality to write down an effective low energy
-holographic theory of charge density waves. We consider a simple gravity model
-which breaks translations spontaneously in the dual field theory in a
-homogeneous manner, capturing the low energy dynamics of phonons coupled to
-conserved currents. We first focus on the leading two-derivative action, which
-leads to excited states with non-zero strain. We show that including subleading
-quartic derivative terms leads to dynamical instabilities of AdS$_2$
-translation invariant states and to stable phases breaking translations
-spontaneously. We compute analytically the real part of the electric
-conductivity. The model allows to construct Lifshitz-like hyperscaling
-violating quantum critical ground states breaking translations spontaneously.
-At these critical points, the real part of the dc conductivity can be metallic
-or insulating.
-",0,1,0,0,0,0
-18984,Interactive Certificates for Polynomial Matrices with Sub-Linear Communication,"  We develop and analyze new protocols to verify the correctness of various
-computations on matrices over F[x], where F is a field. The properties we
-verify concern an F[x]-module and therefore cannot simply rely on
-previously-developed linear algebra certificates which work only for vector
-spaces. Our protocols are interactive certificates, often randomized, and
-featuring a constant number of rounds of communication between the prover and
-verifier. We seek to minimize the communication cost so that the amount of data
-sent during the protocol is significantly smaller than the size of the result
-being verified, which can be useful when combining protocols or in some
-multi-party settings. The main tools we use are reductions to existing linear
-algebra certificates and a new protocol to verify that a given vector is in the
-F[x]-linear span of a given matrix.
-",1,0,0,0,0,0
-18985,Cost Functions for Robot Motion Style,"  We focus on autonomously generating robot motion for day to day physical
-tasks that is expressive of a certain style or emotion. Because we seek
-generalization across task instances and task types, we propose to capture
-style via cost functions that the robot can use to augment its nominal task
-cost and task constraints in a trajectory optimization process. We compare two
-approaches to representing such cost functions: a weighted linear combination
-of hand-designed features, and a neural network parameterization operating on
-raw trajectory input. For each cost type, we learn weights for each style from
-user feedback. We contrast these approaches to a nominal motion across
-different tasks and for different styles in a user study, and find that they
-both perform on par with each other, and significantly outperform the baseline.
-Each approach has its advantages: featurized costs require learning fewer
-parameters and can perform better on some styles, but neural network
-representations do not require expert knowledge to design features and could
-even learn more complex, nuanced costs than an expert can easily design.
-",1,0,0,0,0,0
-18986,Revealing the basins of convergence in the planar equilateral restricted four-body problem,"  The planar equilateral restricted four-body problem where two of the
-primaries have equal masses is used in order to determine the Newton-Raphson
-basins of convergence associated with the equilibrium points. The parametric
-variation of the position of the libration points is monitored when the value
-of the mass parameter $m_3$ varies in predefined intervals. The regions on the
-configuration $(x,y)$ plane occupied by the basins of attraction are revealed
-using the multivariate version of the Newton-Raphson iterative scheme. The
-correlations between the attracting domains of the equilibrium points and the
-corresponding number of iterations needed for obtaining the desired accuracy
-are also illustrated. We perform a thorough and systematic numerical
-investigation by demonstrating how the dynamical parameter $m_3$ influences the
-shape, the geometry and the degree of fractality of the converging regions. Our
-numerical outcomes strongly indicate that the mass parameter is indeed one of
-the most influential factors in this dynamical system.
-",0,1,0,0,0,0
-18987,The Eccentric Kozai-Lidov mechanism for Outer Test Particle,"  The secular approximation of the hierarchical three body systems has been
-proven to be very useful in addressing many astrophysical systems, from
-planets, stars to black holes. In such a system two objects are on a tight
-orbit, and the tertiary is on a much wider orbit. Here we study the dynamics of
-a system by taking the tertiary mass to zero and solve the hierarchical three
-body system up to the octupole level of approximation. We find a rich dynamics
-that the outer orbit undergoes due to gravitational perturbations from the
-inner binary. The nominal result of the precession of the nodes is mostly
-limited for the lowest order of approximation, however, when the octupole-level
-of approximation is introduced the system becomes chaotic, as expected, and the
-tertiary oscillates below and above 90deg, similarly to the non-test particle
-flip behavior (e.g., Naoz 2016). We provide the Hamiltonian of the system and
-investigate the dynamics of the system from the quadrupole to the octupole
-level of approximations. We also analyze the chaotic and quasi-periodic orbital
-evolution by studying the surfaces of sections. Furthermore, including general
-relativity, we show case the long term evolution of individual debris disk
-particles under the influence of a far away interior eccentric planet. We show
-that this dynamics can naturally result in retrograde objects and a puffy disk
-after a long timescale evolution (few Gyr) for initially aligned configuration.
-",0,1,0,0,0,0
-18988,A more symmetric picture for Kasparov's KK-bifunctor,"  For C*-algebras $A$ and $B$, we generalize the notion of a quasihomomorphism
-from $A$ to $B$, due to Cuntz, by considering quasihomomorphisms from some
-C*-algebra $C$ to $B$ such that $C$ surjects onto $A$, and the two maps forming
-a quasihomomorphism agree on the kernel of this surjection. Under an additional
-assumption, the group of homotopy classes of such generalized
-quasihomomorphisms coincides with $KK(A,B)$. This makes the definition of
-Kasparov's bifunctor slightly more symmetric and gives more flexibility for
-constructing elements of $KK$-groups. These generalized quasihomomorphisms can
-be viewed as pairs of maps directly from $A$ (instead of various $C$'s), but
-these maps need not be $*$-homomorphisms.
-",0,0,1,0,0,0
-18989,Spot dynamics in a reaction-diffusion model of plant root hair initiation,"  We study pattern formation in a 2-D reaction-diffusion (RD) sub-cellular
-model characterizing the effect of a spatial gradient of a plant hormone
-distribution on a family of G-proteins associated with root-hair (RH)
-initiation in the plant cell Arabidopsis thaliana. The activation of these
-G-proteins, known as the Rho of Plants (ROPs), by the plant hormone auxin, is
-known to promote certain protuberances on root hair cells, which are crucial
-for both anchorage and the uptake of nutrients from the soil. Our mathematical
-model for the activation of ROPs by the auxin gradient is an extension of the
-model of Payne and Grierson [PLoS ONE, 12(4), (2009)], and consists of a
-two-component Schnakenberg-type RD system with spatially heterogeneous
-coefficients on a 2-D domain. The nonlinear kinetics in this RD system model
-the nonlinear interactions between the active and inactive forms of ROPs. By
-using a singular perturbation analysis to study 2-D localized spatial patterns
-of active ROPs, it is shown that the spatial variations in the nonlinear
-reaction kinetics, due to the auxin gradient, lead to a slow spatial alignment
-of the localized regions of active ROPs along the longitudinal midline of the
-plant cell. Numerical bifurcation analysis, together with time-dependent
-numerical simulations of the RD system are used to illustrate both 2-D
-localized patterns in the model, and the spatial alignment of localized
-structures.
-",0,1,0,0,0,0
-18990,Incorporating Covariates into Integrated Factor Analysis of Multi-View Data,"  In modern biomedical research, it is ubiquitous to have multiple data sets
-measured on the same set of samples from different views (i.e., multi-view
-data). For example, in genetic studies, multiple genomic data sets at different
-molecular levels or from different cell types are measured for a common set of
-individuals to investigate genetic regulation. Integration and reduction of
-multi-view data have the potential to leverage information in different data
-sets, and to reduce the magnitude and complexity of data for further
-statistical analysis and interpretation. In this paper, we develop a novel
-statistical model, called supervised integrated factor analysis (SIFA), for
-integrative dimension reduction of multi-view data while incorporating
-auxiliary covariates. The model decomposes data into joint and individual
-factors, capturing the joint variation across multiple data sets and the
-individual variation specific to each set respectively. Moreover, both joint
-and individual factors are partially informed by auxiliary covariates via
-nonparametric models. We devise a computationally efficient
-Expectation-Maximization (EM) algorithm to fit the model under some
-identifiability conditions. We apply the method to the Genotype-Tissue
-Expression (GTEx) data, and provide new insights into the variation
-decomposition of gene expression in multiple tissues. Extensive simulation
-studies and an additional application to a pediatric growth study demonstrate
-the advantage of the proposed method over competing methods.
-",0,0,0,1,0,0
-18991,Batch-normalized joint training for DNN-based distant speech recognition,"  Improving distant speech recognition is a crucial step towards flexible
-human-machine interfaces. Current technology, however, still exhibits a lack of
-robustness, especially when adverse acoustic conditions are met. Despite the
-significant progress made in the last years on both speech enhancement and
-speech recognition, one potential limitation of state-of-the-art technology
-lies in composing modules that are not well matched because they are not
-trained jointly. To address this concern, a promising approach consists in
-concatenating a speech enhancement and a speech recognition deep neural network
-and to jointly update their parameters as if they were within a single bigger
-network. Unfortunately, joint training can be difficult because the output
-distribution of the speech enhancement system may change substantially during
-the optimization procedure. The speech recognition module would have to deal
-with an input distribution that is non-stationary and unnormalized. To mitigate
-this issue, we propose a joint training approach based on a fully
-batch-normalized architecture. Experiments, conducted using different datasets,
-tasks and acoustic conditions, revealed that the proposed framework
-significantly overtakes other competitive solutions, especially in challenging
-environments.
-",1,0,0,0,0,0
-18992,Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects,"  This paper describes a preliminary study for producing and distributing a
-large-scale database of embeddings from the Portuguese Twitter stream. We start
-by experimenting with a relatively small sample and focusing on three
-challenges: volume of training data, vocabulary size and intrinsic evaluation
-metrics. Using a single GPU, we were able to scale up vocabulary size from 2048
-words embedded and 500K training examples to 32768 words over 10M training
-examples while keeping a stable validation loss and approximately linear trend
-on training time per epoch. We also observed that using less than 50\% of the
-available training examples for each vocabulary size might result in
-overfitting. Results on intrinsic evaluation show promising performance for a
-vocabulary size of 32768 words. Nevertheless, intrinsic evaluation metrics
-suffer from over-sensitivity to their corresponding cosine similarity
-thresholds, indicating that a wider range of metrics need to be developed to
-track progress.
-",1,0,0,0,0,0
-18993,A temperate exo-Earth around a quiet M dwarf at 3.4 parsecs,"  The combination of high-contrast imaging and high-dispersion spectroscopy,
-which has successfully been used to detect the atmosphere of a giant planet, is
-one of the most promising potential probes of the atmosphere of Earth-size
-worlds. The forthcoming generation of extremely large telescopes (ELTs) may
-obtain sufficient contrast with this technique to detect O$_2$ in the
-atmosphere of those worlds that orbit low-mass M dwarfs. This is strong
-motivation to carry out a census of planets around cool stars for which
-habitable zones can be resolved by ELTs, i.e. for M dwarfs within $\sim$5
-parsecs. Our HARPS survey has been a major contributor to that sample of nearby
-planets. Here we report on our radial velocity observations of Ross 128
-(Proxima Virginis, GJ447, HIP 57548), an M4 dwarf just 3.4 parsec away from our
-Sun. This source hosts an exo-Earth with a projected mass $m \sin i = 1.35
-M_\oplus$ and an orbital period of 9.9 days. Ross 128 b receives $\sim$1.38
-times as much flux as Earth from the Sun and its equilibrium ranges in
-temperature between 269 K for an Earth-like albedo and 213 K for a Venus-like
-albedo. Recent studies place it close to the inner edge of the conventional
-habitable zone. An 80-day long light curve from K2 campaign C01 demonstrates
-that Ross~128~b does not transit. Together with the All Sky Automated Survey
-(ASAS) photometry and spectroscopic activity indices, the K2 photometry shows
-that Ross 128 rotates slowly and has weak magnetic activity. In a habitability
-context, this makes survival of its atmosphere against erosion more likely.
-Ross 128 b is the second closest known exo-Earth, after Proxima Centauri b (1.3
-parsec), and the closest temperate planet known around a quiet star. The 15 mas
-planet-star angular separation at maximum elongation will be resolved by ELTs
-($>$ 3$\lambda/D$) in the optical bands of O$_2$.
-",0,1,0,0,0,0
-18994,Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning,"  Low-rank structures play important role in recent advances of many problems
-in image science and data science. As a natural extension of low-rank
-structures for data with nonlinear structures, the concept of the
-low-dimensional manifold structure has been considered in many data processing
-problems. Inspired by this concept, we consider a manifold based low-rank
-regularization as a linear approximation of manifold dimension. This
-regularization is less restricted than the global low-rank regularization, and
-thus enjoy more flexibility to handle data with nonlinear structures. As
-applications, we demonstrate the proposed regularization to classical inverse
-problems in image sciences and data sciences including image inpainting, image
-super-resolution, X-ray computer tomography (CT) image reconstruction and
-semi-supervised learning. We conduct intensive numerical experiments in several
-image restoration problems and a semi-supervised learning problem of
-classifying handwritten digits using the MINST data. Our numerical tests
-demonstrate the effectiveness of the proposed methods and illustrate that the
-new regularization methods produce outstanding results by comparing with many
-existing methods.
-",1,0,1,0,0,0
-18995,History-aware Autonomous Exploration in Confined Environments using MAVs,"  Many scenarios require a robot to be able to explore its 3D environment
-online without human supervision. This is especially relevant for inspection
-tasks and search and rescue missions. To solve this high-dimensional path
-planning problem, sampling-based exploration algorithms have proven successful.
-However, these do not necessarily scale well to larger environments or spaces
-with narrow openings. This paper presents a 3D exploration planner based on the
-principles of Next-Best Views (NBVs). In this approach, a Micro-Aerial Vehicle
-(MAV) equipped with a limited field-of-view depth sensor randomly samples its
-configuration space to find promising future viewpoints. In order to obtain
-high sampling efficiency, our planner maintains and uses a history of visited
-places, and locally optimizes the robot's orientation with respect to
-unobserved space. We evaluate our method in several simulated scenarios, and
-compare it against a state-of-the-art exploration algorithm. The experiments
-show substantial improvements in exploration time ($2\times$ faster),
-computation time, and path length, and advantages in handling difficult
-situations such as escaping dead-ends (up to $20\times$ faster). Finally, we
-validate the on-line capability of our algorithm on a computational constrained
-real world MAV.
-",1,0,0,0,0,0
-18996,A factor-model approach for correlation scenarios and correlation stress-testing,"  In 2012, JPMorgan accumulated a USD~6.2 billion loss on a credit derivatives
-portfolio, the so-called `London Whale', partly as a consequence of
-de-correlations of non-perfectly correlated positions that were supposed to
-hedge each other. Motivated by this case, we devise a factor model for
-correlations that allows for scenario-based stress testing of correlations. We
-derive a number of analytical results related to a portfolio of homogeneous
-assets. Using the concept of Mahalanobis distance, we show how to identify
-adverse scenarios of correlation risk. In addition, we demonstrate how
-correlation and volatility stress tests can be combined. As an example, we
-apply the factor-model approach to the ""London Whale"" portfolio and determine
-the value-at-risk impact from correlation changes. Since our findings are
-particularly relevant for large portfolios, where even small correlation
-changes can have a large impact, a further application would be to stress test
-portfolios of central counterparties, which are of systemically relevant size.
-",0,0,0,0,0,1
-18997,Topology data analysis of critical transitions in financial networks,"  We develop a topology data analysis-based method to detect early signs for
-critical transitions in financial data. From the time-series of multiple stock
-prices, we build time-dependent correlation networks, which exhibit topological
-structures. We compute the persistent homology associated to these structures
-in order to track the changes in topology when approaching a critical
-transition. As a case study, we investigate a portfolio of stocks during a
-period prior to the US financial crisis of 2007-2008, and show the presence of
-early signs of the critical transition.
-",0,1,1,0,0,0
-18998,Construction of exact constants of motion and effective models for many-body localized systems,"  One of the defining features of many-body localization is the presence of
-extensively many quasi-local conserved quantities. These constants of motion
-constitute a corner-stone to an intuitive understanding of much of the
-phenomenology of many-body localized systems arising from effective
-Hamiltonians. They may be seen as local magnetization operators smeared out by
-a quasi-local unitary. However, accurately identifying such constants of motion
-remains a challenging problem. Current numerical constructions often capture
-the conserved operators only approximately restricting a conclusive
-understanding of many-body localization. In this work, we use methods from the
-theory of quantum many-body systems out of equilibrium to establish a new
-approach for finding a complete set of exact constants of motion which are in
-addition guaranteed to represent Pauli-$z$ operators. By this we are able to
-construct and investigate the proposed effective Hamiltonian using exact
-diagonalization. Hence, our work provides an important tool expected to further
-boost inquiries into the breakdown of transport due to quenched disorder.
-",0,1,0,0,0,0
-18999,Toric manifolds over cyclohedra,"  We study the action of the dihedral group on the (equivariant) cohomology of
-the toric manifolds associated with cycle graphs.
-",0,0,1,0,0,0
-19000,Use of Genome Information-Based Potentials to Characterize Human Adaptation,"  As a living information and communications system, the genome encodes
-patterns in single nucleotide polymorphisms (SNPs) reflecting human adaption
-that optimizes population survival in differing environments. This paper
-mathematically models environmentally induced adaptive forces that quantify
-changes in the distribution of SNP frequencies between populations. We make
-direct connections between biophysical methods (e.g. minimizing genomic free
-energy) and concepts in population genetics. Our unbiased computer program
-scanned a large set of SNPs in the major histocompatibility complex region, and
-flagged an altitude dependency on a SNP associated with response to oxygen
-deprivation. The statistical power of our double-blind approach is demonstrated
-in the flagging of mathematical functional correlations of SNP
-information-based potentials in multiple populations with specific
-environmental parameters. Furthermore, our approach provides insights for new
-discoveries on the biology of common variants. This paper demonstrates the
-power of biophysical modeling of population diversity for better understanding
-genome-environment interactions in biological phenomenon.
-",0,0,0,0,1,0
-19001,Communication-Efficient Algorithms for Decentralized and Stochastic Optimization,"  We present a new class of decentralized first-order methods for nonsmooth and
-stochastic optimization problems defined over multiagent networks. Considering
-that communication is a major bottleneck in decentralized optimization, our
-main goal in this paper is to develop algorithmic frameworks which can
-significantly reduce the number of inter-node communications. We first propose
-a decentralized primal-dual method which can find an $\epsilon$-solution both
-in terms of functional optimality gap and feasibility residual in
-$O(1/\epsilon)$ inter-node communication rounds when the objective functions
-are convex and the local primal subproblems are solved exactly. Our major
-contribution is to present a new class of decentralized primal-dual type
-algorithms, namely the decentralized communication sliding (DCS) methods, which
-can skip the inter-node communications while agents solve the primal
-subproblems iteratively through linearizations of their local objective
-functions. By employing DCS, agents can still find an $\epsilon$-solution in
-$O(1/\epsilon)$ (resp., $O(1/\sqrt{\epsilon})$) communication rounds for
-general convex functions (resp., strongly convex functions), while maintaining
-the $O(1/\epsilon^2)$ (resp., $O(1/\epsilon)$) bound on the total number of
-intra-node subgradient evaluations. We also present a stochastic counterpart
-for these algorithms, denoted by SDCS, for solving stochastic optimization
-problems whose objective function cannot be evaluated exactly. In comparison
-with existing results for decentralized nonsmooth and stochastic optimization,
-we can reduce the total number of inter-node communication rounds by orders of
-magnitude while still maintaining the optimal complexity bounds on intra-node
-stochastic subgradient evaluations. The bounds on the subgradient evaluations
-are actually comparable to those required for centralized nonsmooth and
-stochastic optimization.
-",1,0,1,0,0,0
-19002,A Machine Learning Approach to Shipping Box Design,"  Having the right assortment of shipping boxes in the fulfillment warehouse to
-pack and ship customer's online orders is an indispensable and integral part of
-nowadays eCommerce business, as it will not only help maintain a profitable
-business but also create great experiences for customers. However, it is an
-extremely challenging operations task to strategically select the best
-combination of tens of box sizes from thousands of feasible ones to be
-responsible for hundreds of thousands of orders daily placed on millions of
-inventory products. In this paper, we present a machine learning approach to
-tackle the task by formulating the box design problem prescriptively as a
-generalized version of weighted $k$-medoids clustering problem, where the
-parameters are estimated through a variety of descriptive analytics. We test
-this machine learning approach on fulfillment data collected from Walmart U.S.
-eCommerce, and our approach is shown to be capable of improving the box
-utilization rate by more than $10\%$.
-",0,0,0,1,0,0
-19003,Precise but Natural Specification for Robot Tasks,"  We present Flipper, a natural language interface for describing high-level
-task specifications for robots that are compiled into robot actions. Flipper
-starts with a formal core language for task planning that allows expressing
-rich temporal specifications and uses a semantic parser to provide a natural
-language interface. Flipper provides immediate visual feedback by executing an
-automatically constructed plan of the task in a graphical user interface. This
-allows the user to resolve potentially ambiguous interpretations. Flipper
-extends itself via naturalization: its users can add definitions for
-utterances, from which Flipper induces new rules and adds them to the core
-language, gradually growing a more and more natural task specification
-language. Flipper improves the naturalization by generalizing the definition
-provided by users. Unlike other task-specification systems, Flipper enables
-natural language interactions while maintaining the expressive power and formal
-precision of a programming language. We show through an initial user study that
-natural language interactions and generalization can considerably ease the
-description of tasks. Moreover, over time, users employ more and more concepts
-outside of the initial core language. Such extensions are available to the
-Flipper community, and users can use concepts that others have defined.
-",1,0,0,0,0,0
-19004,Rigorous Analysis for Efficient Statistically Accurate Algorithms for Solving Fokker-Planck Equations in Large Dimensions,"  This article presents a rigorous analysis for efficient statistically
-accurate algorithms for solving the Fokker-Planck equations associated with
-high-dimensional nonlinear turbulent dynamical systems with conditional
-Gaussian structures. Despite the conditional Gaussianity, these nonlinear
-systems contain many strong non-Gaussian features such as intermittency and
-fat-tailed probability density functions (PDFs). The algorithms involve a
-hybrid strategy that requires only a small number of samples $L$ to capture
-both the transient and the equilibrium non-Gaussian PDFs with high accuracy.
-Here, a conditional Gaussian mixture in a high-dimensional subspace via an
-extremely efficient parametric method is combined with a judicious Gaussian
-kernel density estimation in the remaining low-dimensional subspace. Rigorous
-analysis shows that the mean integrated squared error in the recovered PDFs in
-the high-dimensional subspace is bounded by the inverse square root of the
-determinant of the conditional covariance, where the conditional covariance is
-completely determined by the underlying dynamics and is independent of $L$.
-This is fundamentally different from a direct application of kernel methods to
-solve the full PDF, where $L$ needs to increase exponentially with the
-dimension of the system and the bandwidth shrinks. A detailed comparison
-between different methods justifies that the efficient statistically accurate
-algorithms are able to overcome the curse of dimensionality. It is also shown
-with mathematical rigour that these algorithms are robust in long time provided
-that the system is controllable and stochastically stable. Particularly,
-dynamical systems with energy-conserving quadratic nonlinearity as in many
-geophysical and engineering turbulence are proved to have these properties.
-",0,0,1,1,0,0
-19005,Rapid processing of 85Kr/Kr ratios using Atom Trap Trace Analysis,"  We report a methodology for measuring 85Kr/Kr isotopic abundances using Atom
-Trap Trace Analysis (ATTA) that increases sample measurement throughput by over
-an order of magnitude to 6 samples per 24 hours. The noble gas isotope 85Kr
-(half-life = 10.7 yr) is a useful tracer for young groundwater in the age range
-of 5-50 years. ATTA, an efficient and selective laser-based atom counting
-method, has recently been applied to 85Kr/Kr isotopic abundance measurements,
-requiring 5-10 microliters of krypton gas at STP extracted from 50-100 L of
-water. Previously a single such measurement required 48 hours. Our new method
-demonstrates that we can measure 85Kr/Kr ratios with 3-5% relative uncertainty
-every 4 hours, on average, with the same sample requirements.
-",0,1,0,0,0,0
-19006,Adapting Everyday Manipulation Skills to Varied Scenarios,"  We address the problem of executing tool-using manipulation skills in
-scenarios where the objects to be used may vary. We assume that point clouds of
-the tool and target object can be obtained, but no interpretation or further
-knowledge about these objects is provided. The system must interpret the point
-clouds and decide how to use the tool to complete a manipulation task with a
-target object; this means it must adjust motion trajectories appropriately to
-complete the task. We tackle three everyday manipulations: scraping material
-from a tool into a container, cutting, and scooping from a container. Our
-solution encodes these manipulation skills in a generic way, with parameters
-that can be filled in at run-time via queries to a robot perception module; the
-perception module abstracts the functional parts for the tool and extracts key
-parameters that are needed for the task. The approach is evaluated in
-simulation and with selected examples on a PR2 robot.
-",1,0,0,0,0,0
-19007,Effect of Particle Number Conservation on the Berry Phase Resulting from Transport of a Bound Quasiparticle around a Superfluid Vortex,"  Motivated by understanding Majorana zero modes in topological superfluids in
-particle-number conserving framework beyond the present framework, we study the
-effect of particle number conservation on the Berry phase resulting from
-transport of a bound quasiparticle around a superfluid vortex. We find that
-particle-number non-conserving calculations based on Bogoliubov-de Gennes (BdG)
-equations are unable to capture the correct physics when the quasiparticle is
-within the penetration depth of the vortex core where the superfluid velocity
-is non-zero. Particle number conservation is crucial for deriving the correct
-Berry phase in this context, and the Berry phase takes non-universal values
-depending on the system parameters and the external trap imposed to bind the
-quasiparticle. Of particular relevance to Majorana physics are the findings
-that superfluid condensate affects the part of the Berry phase not accounted
-for in the standard BdG framework, and that the superfluid many-body ground
-state of odd number of fermions involves superfluid condensate deformation due
-to the presence of the bound quasiparticle - an effect which is beyond the
-description of the BdG equations.
-",0,1,0,0,0,0
-19008,A new topological insulator - β-InTe strained in the layer plane,"  We have investigated the band structure of the bulk crystal and the (001)
-surface of the \beta-InTe layered crystal subjected to biaxial stretching in
-the layer plane. The calculation has been carried out using the full-potential
-linearized augmented plane wave method (FP LAPW) implemented in WIEN2k. It has
-been shown that at the strain \Deltaa/a=0.06, where a is the lattice parameter
-in the layer plane, the band gap in the electronic spectrum collapses. With
-further strain increase a band inversion occurs. The inclusion of the
-spin-orbit interaction reopens the gap in the electronic spectrum of a bulk
-crystal, and our calculations show that the spectrum of the surface states has
-the form of a Dirac cone, typical for topological insulators.
-",0,1,0,0,0,0
-19009,Stock management (Gestão de estoques),"  There is a great need to stock materials for production, but storing
-materials comes at a cost. Lack of organization in the inventory can result in
-a very high cost for the final product, in addition to generating other
-problems in the production chain. In this work we present mathematical and
-statistical methods applicable to stock management. The stock analysis using
-ABC curves serves to identify which are the priority items, the most expensive
-and with the highest turnover (demand), and thus determine, through stock
-control models, the purchase lot size and the periodicity that minimize the
-total costs of storing these materials. Using the Economic Order Quantity (EOQ)
-model and the (Q,R) model, the inventory costs of a company were minimized. The
-comparison of the results provided by the models was performed.
-",0,0,0,1,0,0
-19010,From Principal Subspaces to Principal Components with Linear Autoencoders,"  The autoencoder is an effective unsupervised learning model which is widely
-used in deep learning. It is well known that an autoencoder with a single
-fully-connected hidden layer, a linear activation function and a squared error
-cost function trains weights that span the same subspace as the one spanned by
-the principal component loading vectors, but that they are not identical to the
-loading vectors. In this paper, we show how to recover the loading vectors from
-the autoencoder weights.
-",0,0,0,1,0,0
-19011,A critical nonlinear elliptic equation with non local regional diffusion,"  In this article we are interested in the nonlocal regional Schrödinger
-equation with critical exponent \begin{eqnarray*} &\epsilon^{2\alpha}
-(-\Delta)_{\rho}^{\alpha}u + u = \lambda u^q + u^{2_{\alpha}^{*}-1} \mbox{ in }
-\mathbb{R}^{N}, \\ & u \in H^{\alpha}(\mathbb{R}^{N}), \end{eqnarray*} where
-$\epsilon$ is a small positive parameter, $\alpha \in (0,1)$, $q\in
-(1,2_{\alpha}^{*}-1)$, $2_{\alpha}^{*} = \frac{2N}{N-2\alpha}$ is the critical
-Sobolev exponent, $\lambda >0$ is a parameter and $(-\Delta)_{\rho}^{\alpha}$
-is a variational version of the regional laplacian, whose range of scope is a
-ball with radius $\rho(x)>0$. We study the existence of a ground state and we
-analyze the behavior of semi-classical solutions as $\varepsilon\to 0$.
-",0,0,1,0,0,0
-19012,"Don't Decay the Learning Rate, Increase the Batch Size","  It is common practice to decay the learning rate. Here we show one can
-usually obtain the same learning curve on both training and test sets by
-instead increasing the batch size during training. This procedure is successful
-for stochastic gradient descent (SGD), SGD with momentum, Nesterov momentum,
-and Adam. It reaches equivalent test accuracies after the same number of
-training epochs, but with fewer parameter updates, leading to greater
-parallelism and shorter training times. We can further reduce the number of
-parameter updates by increasing the learning rate $\epsilon$ and scaling the
-batch size $B \propto \epsilon$. Finally, one can increase the momentum
-coefficient $m$ and scale $B \propto 1/(1-m)$, although this tends to slightly
-reduce the test accuracy. Crucially, our techniques allow us to repurpose
-existing training schedules for large batch training with no hyper-parameter
-tuning. We train ResNet-50 on ImageNet to $76.1\%$ validation accuracy in under
-30 minutes.
-",1,0,0,1,0,0
-19013,Collisions of Dark Matter Axion Stars with Astrophysical Sources,"  If QCD axions form a large fraction of the total mass of dark matter, then
-axion stars could be very abundant in galaxies. As a result, collisions with
-each other, and with other astrophysical bodies, can occur. We calculate the
-rate and analyze the consequences of three classes of collisions, those
-occurring between a dilute axion star and: another dilute axion star, an
-ordinary star, or a neutron star. In all cases we attempt to quantify the most
-important astrophysical uncertainties; we also pay particular attention to
-scenarios in which collisions lead to collapse of otherwise stable axion stars,
-and possible subsequent decay through number changing interactions. Collisions
-between two axion stars can occur with a high total rate, but the low relative
-velocity required for collapse to occur leads to a very low total rate of
-collapses. On the other hand, collisions between an axion star and an ordinary
-star have a large rate, $\Gamma_\odot \sim 3000$ collisions/year/galaxy, and
-for sufficiently heavy axion stars, it is plausible that most or all such
-collisions lead to collapse. We identify in this case a parameter space which
-has a stable region and a region in which collision triggers collapse, which
-depend on the axion number ($N$) in the axion star, and a ratio of mass to
-radius cubed characterizing the ordinary star ($M_s/R_s^3$). Finally, we
-revisit the calculation of collision rates between axion stars and neutron
-stars, improving on previous estimates by taking cylindrical symmetry of the
-neutron star distribution into account. Collapse and subsequent decay through
-collision processes, if occurring with a significant rate, can affect dark
-matter phenomenology and the axion star mass distribution.
-",0,1,0,0,0,0
-19014,Practical Algorithms for Best-K Identification in Multi-Armed Bandits,"  In the Best-$K$ identification problem (Best-$K$-Arm), we are given $N$
-stochastic bandit arms with unknown reward distributions. Our goal is to
-identify the $K$ arms with the largest means with high confidence, by drawing
-samples from the arms adaptively. This problem is motivated by various
-practical applications and has attracted considerable attention in the past
-decade. In this paper, we propose new practical algorithms for the Best-$K$-Arm
-problem, which have nearly optimal sample complexity bounds (matching the lower
-bound up to logarithmic factors) and outperform the state-of-the-art algorithms
-for the Best-$K$-Arm problem (even for $K=1$) in practice.
-",1,0,0,1,0,0
-19015,Hyperfield Grassmannians,"  In a recent paper Baker and Bowler introduced matroids over hyperfields,
-offering a common generalization of matroids, oriented matroids, and linear
-subspaces of based vector spaces. This paper introduces the notion of a
-topological hyperfield and explores the generalization of Grassmannians and
-realization spaces to this context, particularly in relating the (hyper)fields
-R and C to hyperfields arising in matroid theory and in tropical geometry.
-",0,0,1,0,0,0
-19016,Statistical estimation in a randomly structured branching population,"  We consider a binary branching process structured by a stochastic trait that
-evolves according to a diffusion process that triggers the branching events, in
-the spirit of Kimmel's model of cell division with parasite infection. Based on
-the observation of the trait at birth of the first n generations of the
-process, we construct nonparametric estimator of the transition of the
-associated bifurcating chain and study the parametric estimation of the
-branching rate. In the limit, as n tends to infinity, we obtain asymptotic
-efficiency in the parametric case and minimax optimality in the nonparametric
-case.
-",0,0,1,0,0,0
-19017,Discovering Eastern European PCs by hacking them. Today,"  Computer science would not be the same without personal computers. In the
-West the so called PC revolution started in the late '70s and has its roots in
-hobbyists and do-it-yourself clubs. In the following years the diffusion of
-home and personal computers has made the discipline closer to many people. A
-bit later, to a lesser extent, yet in a similar way, the revolution took place
-also in East European countries. Today, the scenario of personal computing has
-completely changed, however the computers of the '80s are still objects of
-fascination for a number of retrocomputing fans who enjoy using, programming
-and hacking the old 8-bits. The paper highlights the continuity between
-yesterday's hobbyists and today's retrocomputing enthusiasts, particularly
-focusing on East European PCs. Besides the preservation of old hardware and
-software, the community is engaged in the development of emulators and cross
-compilers. Such tools can be used for historical investigation, for example to
-trace the origins of the BASIC interpreters loaded in the ROMs of East European
-PCs.
-",1,0,0,0,0,0
-19018,Neural Rating Regression with Abstractive Tips Generation for Recommendation,"  Recently, some E-commerce sites launch a new interaction box called Tips on
-their mobile apps. Users can express their experience and feelings or provide
-suggestions using short texts typically several words or one sentence. In
-essence, writing some tips and giving a numerical rating are two facets of a
-user's product assessment action, expressing the user experience and feelings.
-Jointly modeling these two facets is helpful for designing a better
-recommendation system. While some existing models integrate text information
-such as item specifications or user reviews into user and item latent factors
-for improving the rating prediction, no existing works consider tips for
-improving recommendation quality. We propose a deep learning based framework
-named NRT which can simultaneously predict precise ratings and generate
-abstractive tips with good linguistic quality simulating user experience and
-feelings. For abstractive tips generation, gated recurrent neural networks are
-employed to ""translate"" user and item latent representations into a concise
-sentence. Extensive experiments on benchmark datasets from different domains
-show that NRT achieves significant improvements over the state-of-the-art
-methods. Moreover, the generated tips can vividly predict the user experience
-and feelings.
-",1,0,0,0,0,0
-19019,Calibration Uncertainty for Advanced LIGO's First and Second Observing Runs,"  Calibration of the Advanced LIGO detectors is the quantification of the
-detectors' response to gravitational waves. Gravitational waves incident on the
-detectors cause phase shifts in the interferometer laser light which are read
-out as intensity fluctuations at the detector output. Understanding this
-detector response to gravitational waves is crucial to producing accurate and
-precise gravitational wave strain data. Estimates of binary black hole and
-neutron star parameters and tests of general relativity require well-calibrated
-data, as miscalibrations will lead to biased results. We describe the method of
-producing calibration uncertainty estimates for both LIGO detectors in the
-first and second observing runs.
-",0,1,0,0,0,0
-19020,Entanglement transitions induced by large deviations,"  The probability of large deviations of the smallest Schmidt eigenvalue for
-random pure states of bipartite systems, denoted as $A$ and $B$, is computed
-analytically using a Coulomb gas method. It is shown that this probability, for
-large $N$, goes as $\exp[-\beta N^2\Phi(\zeta)]$, where the parameter $\beta$
-is the Dyson index of the ensemble, $\zeta$ is the large deviation parameter
-while the rate function $\Phi(\zeta)$ is calculated exactly. Corresponding
-equilibrium Coulomb charge density is derived for its large deviations. Effects
-of the large deviations of the extreme (largest and smallest) Schmidt
-eigenvalues on the bipartite entanglement are studied using the von Neumann
-entropy. Effect of these deviations is also studied on the entanglement between
-subsystems $1$ and $2$, obtained by further partitioning the subsystem $A$,
-using the properties of the density matrix's partial transpose
-$\rho_{12}^\Gamma$. The density of states of $\rho_{12}^\Gamma$ is found to be
-close to the Wigner's semicircle law with these large deviations. The
-entanglement properties are captured very well by a simple random matrix model
-for the partial transpose. The model predicts the entanglement transition
-across a critical large deviation parameter $\zeta$. Log negativity is used to
-quantify the entanglement between subsystems $1$ and $2$. Analytical formulas
-for it are derived using the simple model. Numerical simulations are in
-excellent agreement with the analytical results.
-",0,0,1,1,0,0
-19021,"Understanding System Characteristics of Online Erasure Coding on Scalable, Distributed and Large-Scale SSD Array Systems","  Large-scale systems with arrays of solid state disks (SSDs) have become
-increasingly common in many computing segments. To make such systems resilient,
-we can adopt erasure coding such as Reed-Solomon (RS) code as an alternative to
-replication because erasure coding can offer a significantly lower storage cost
-than replication. To understand the impact of using erasure coding on system
-performance and other system aspects such as CPU utilization and network
-traffic, we build a storage cluster consisting of approximately one hundred
-processor cores with more than fifty high-performance SSDs, and evaluate the
-cluster with a popular open-source distributed parallel file system, Ceph. Then
-we analyze behaviors of systems adopting erasure coding from the following five
-viewpoints, compared with those of systems using replication: (1) storage
-system I/O performance; (2) computing and software overheads; (3) I/O
-amplification; (4) network traffic among storage nodes; (5) the impact of
-physical data layout on performance of RS-coded SSD arrays. For all these
-analyses, we examine two representative RS configurations, which are used by
-Google and Facebook file systems, and compare them with triple replication that
-a typical parallel file system employs as a default fault tolerance mechanism.
-Lastly, we collect 54 block-level traces from the cluster and make them
-available for other researchers.
-",1,0,0,0,0,0
-19022,Three Questions on Special Homeomorphisms on Subgroups of $R$ and $R^\infty$,"  We provide justifications for two questions on special maps on subgroups of
-the reals. We will show that the questions can be treated from different points
-of view. We also discuss two versions of Anderson's Involution Conjecture.
-",0,0,1,0,0,0
-19023,Nearly Semiparametric Efficient Estimation of Quantile Regression,"  As a competitive alternative to least squares regression, quantile regression
-is popular in analyzing heterogenous data. For quantile regression model
-specified for one single quantile level $\tau$, major difficulties of
-semiparametric efficient estimation are the unavailability of a parametric
-efficient score and the conditional density estimation. In this paper, with the
-help of the least favorable submodel technique, we first derive the
-semiparametric efficient scores for linear quantile regression models that are
-assumed for a single quantile level, multiple quantile levels and all the
-quantile levels in $(0,1)$ respectively. Our main discovery is a one-step
-(nearly) semiparametric efficient estimation for the regression coefficients of
-the quantile regression models assumed for multiple quantile levels, which has
-several advantages: it could be regarded as an optimal way to pool information
-across multiple/other quantiles for efficiency gain; it is computationally
-feasible and easy to implement, as the initial estimator is easily available;
-due to the nature of quantile regression models under investigation, the
-conditional density estimation is straightforward by plugging in an initial
-estimator. The resulting estimator is proved to achieve the corresponding
-semiparametric efficiency lower bound under regularity conditions. Numerical
-studies including simulations and an example of birth weight of children
-confirms that the proposed estimator leads to higher efficiency compared with
-the Koenker-Bassett quantile regression estimator for all quantiles of
-interest.
-",0,0,0,1,0,0
-19024,Relevance of backtracking paths in epidemic spreading on networks,"  The understanding of epidemics on networks has greatly benefited from the
-recent application of message-passing approaches, which allow to derive exact
-results for irreversible spreading (i.e. diseases with permanent acquired
-immunity) in locally-tree like topologies. This success has suggested the
-application of the same approach to reversible epidemics, for which an
-individual can contract the epidemic and recover repeatedly. The underlying
-assumption is that backtracking paths (i.e. an individual is reinfected by a
-neighbor he/she previously infected) do not play a relevant role. In this paper
-we show that this is not the case for reversible epidemics, since the neglect
-of backtracking paths leads to a formula for the epidemic threshold that is
-qualitatively incorrect in the large size limit. Moreover we define a modified
-reversible dynamics which explicitly forbids direct backtracking events and
-show that this modification completely upsets the phenomenology.
-",1,0,0,0,0,0
-19025,Hybrid graphene tunneling photoconductor with interface engineering towards fast photoresponse and high responsivity,"  Hybrid graphene photoconductor/phototransistor has achieved giant
-photoresponsivity, but its response speed dramatically degrades as the expense
-due to the long lifetime of trapped interfacial carriers. In this work, by
-intercalating a large-area atomically thin MoS2 film into a hybrid graphene
-photoconductor, we have developed a prototype tunneling photoconductor, which
-exhibits a record-fast response (rising time ~17 ns) and a high responsivity
-(~$3\times10^4$ A/W at 635 nm and 16.8 nW illumination) across the broad
-spectral range. We demonstrate that the photo-excited carriers generated in
-silicon are transferred into graphene through a tunneling process rather than
-carrier drift. The atomically thin MoS2 film not only serves as tunneling layer
-but also passivates surface states, which in combination delivers a superior
-response speed (~3 order of magnitude improved than a device without MoS2
-layer), while the responsivity remains high. This intriguing tunneling
-photoconductor integrates both fast response and high responsivity and thus has
-significant potential in practical applications of optoelectronic devices.
-",0,1,0,0,0,0
-19026,Embedded Real-Time Fall Detection Using Deep Learning For Elderly Care,"  This paper proposes a real-time embedded fall detection system using a
-DVS(Dynamic Vision Sensor) that has never been used for traditional fall
-detection, a dataset for fall detection using that, and a DVS-TN(DVS-Temporal
-Network). The first contribution is building a DVS Falls Dataset, which made
-our network to recognize a much greater variety of falls than the existing
-datasets that existed before and solved privacy issues using the DVS. Secondly,
-we introduce the DVS-TN : optimized deep learning network to detect falls using
-DVS. Finally, we implemented a fall detection system which can run on
-low-computing H/W with real-time, and tested on DVS Falls Dataset that takes
-into account various falls situations. Our approach achieved 95.5% on the
-F1-score and operates at 31.25 FPS on NVIDIA Jetson TX1 board.
-",1,0,0,1,0,0
-19027,A Distributed Algorithm for Solving Linear Algebraic Equations Over Random Networks,"  In this paper, we consider the problem of solving linear algebraic equations
-of the form $Ax=b$ among multi agents which seek a solution by using local
-information in presence of random communication topologies. The equation is
-solved by $m$ agents where each agent only knows a subset of rows of the
-partitioned matrix $[A,b]$. We formulate the problem such that this formulation
-does not need the distribution of random interconnection graphs. Therefore,
-this framework includes asynchronous updates or unreliable communication
-protocols without B-connectivity assumption. We apply the random
-Krasnoselskii-Mann iterative algorithm which converges almost surely and in
-mean square to a solution of the problem for any matrices $A$ and $b$ and any
-initial conditions of agents' states. We demonestrate that the limit point to
-which the agents' states converge is determined by the unique solution of a
-convex optimization problem regardless of the distribution of random
-communication graphs. Eventually, we show by two numerical examples that the
-rate of convergence of the algorithm cannot be guaranteed.
-",1,0,0,0,0,0
-19028,Doping-induced quantum cross-over in Er$_2$Ti$_{2-x}$Sn$_x$O$_7$,"  We present the results of the investigation of magnetic properties of the
-Er$_2$Ti$_{2-x}$Sn$_x$O$_7$ series. For small doping values the ordering
-temperature decreases linearly with $x$ while the moment configuration remains
-the same as in the $x = 0$ parent compound. Around $x = 1.7$ doping level we
-observe a change in the behavior, where the ordering temperature starts to
-increase and new magnetic Bragg peaks appear. For the first time we present
-evidence of a long-range order (LRO) in Er$_2$Sn$_2$O$_7$ ($x = 2.0$) below
-$T_N = 130$ mK. It is revealed that the moment configuration corresponds to a
-Palmer-Chalker type with a value of the magnetic moment significantly
-renormalized compared to $x = 0$. We discuss our results in the framework of a
-possible quantum phase transition occurring close to $x = 1.7$.
-",0,1,0,0,0,0
-19029,Stochastic Block Models with Multiple Continuous Attributes,"  The stochastic block model (SBM) is a probabilistic model for community
-structure in networks. Typically, only the adjacency matrix is used to perform
-SBM parameter inference. In this paper, we consider circumstances in which
-nodes have an associated vector of continuous attributes that are also used to
-learn the node-to-community assignments and corresponding SBM parameters. While
-this assumption is not realistic for every application, our model assumes that
-the attributes associated with the nodes in a network's community can be
-described by a common multivariate Gaussian model. In this augmented,
-attributed SBM, the objective is to simultaneously learn the SBM connectivity
-probabilities with the multivariate Gaussian parameters describing each
-community. While there are recent examples in the literature that combine
-connectivity and attribute information to inform community detection, our model
-is the first augmented stochastic block model to handle multiple continuous
-attributes. This provides the flexibility in biological data to, for example,
-augment connectivity information with continuous measurements from multiple
-experimental modalities. Because the lack of labeled network data often makes
-community detection results difficult to validate, we highlight the usefulness
-of our model for two network prediction tasks: link prediction and
-collaborative filtering. As a result of fitting this attributed stochastic
-block model, one can predict the attribute vector or connectivity patterns for
-a new node in the event of the complementary source of information
-(connectivity or attributes, respectively). We also highlight two biological
-examples where the attributed stochastic block model provides satisfactory
-performance in the link prediction and collaborative filtering tasks.
-",1,0,0,1,0,0
-19030,Asymptotic Independence of Bivariate Order Statistics,"  It is well known that an extreme order statistic and a central order
-statistic (os) as well as an intermediate os and a central os from a sample of
-iid univariate random variables get asymptotically independent as the sample
-size increases. We extend this result to bivariate random variables, where the
-os are taken componentwise. An explicit representation of the conditional
-distribution of bivariate os turns out to be a powerful tool.
-",0,0,1,1,0,0
-19031,Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks,"  Material recognition enables robots to incorporate knowledge of material
-properties into their interactions with everyday objects. For example, material
-recognition opens up opportunities for clearer communication with a robot, such
-as ""bring me the metal coffee mug"", and recognizing plastic versus metal is
-crucial when using a microwave or oven. However, collecting labeled training
-data with a robot is often more difficult than unlabeled data. We present a
-semi-supervised learning approach for material recognition that uses generative
-adversarial networks (GANs) with haptic features such as force, temperature,
-and vibration. Our approach achieves state-of-the-art results and enables a
-robot to estimate the material class of household objects with ~90% accuracy
-when 92% of the training data are unlabeled. We explore how well this approach
-can recognize the material of new objects and we discuss challenges facing
-generalization. To motivate learning from unlabeled training data, we also
-compare results against several common supervised learning classifiers. In
-addition, we have released the dataset used for this work which consists of
-time-series haptic measurements from a robot that conducted thousands of
-interactions with 72 household objects.
-",1,0,0,1,0,0
-19032,Level set shape and topology optimization of finite strain bilateral contact problems,"  This paper presents a method for the optimization of multi-component
-structures comprised of two and three materials considering large motion
-sliding contact and separation along interfaces. The structural geometry is
-defined by an explicit level set method, which allows for both shape and
-topology changes. The mechanical model assumes finite strains, a nonlinear
-elastic material behavior, and a quasi-static response. Identification of
-overlapping surface position is handled by a coupled parametric representation
-of contact surfaces. A stabilized Lagrange method and an active set strategy
-are used to model frictionless contact and separation. The mechanical model is
-discretized by the extended finite element method which maintains a clear
-definition of geometry. Face-oriented ghost penalization and dynamic relaxation
-are implemented to improve the stability of the physical response prediction. A
-nonlinear programming scheme is used to solve the optimization problem, which
-is regularized by introducing a perimeter penalty into the objective function.
-Sensitivities are determined by the adjoint method. The main characteristics of
-the proposed method are studied by numerical examples in two dimensions. The
-numerical results demonstrate improved design performance when compared to
-models optimized with a small strain assumption. Additionally, examples with
-load path dependent objectives display non-intuitive designs.
-",0,0,1,0,0,0
-19033,Thermal distortions of non-Gaussian beams in Fabry-Perot cavities,"  Thermal effects are already important in currently operating interferometric
-gravitational wave detectors. Planned upgrades of these detectors involve
-increasing optical power to combat quantum shot noise. We consider the
-ramifications of this increased power for one particular class of laser
-beams--wide, flat-topped, mesa beams. In particular we model a single mesa beam
-Fabry-Perot cavity having thermoelastically deformed mirrors. We calculate the
-intensity profile of the fundamental cavity eigenmode in the presence of
-thermal perturbations, and the associated changes in thermal noise. We also
-outline an idealized method of correcting for such effects. At each stage we
-contrast our results with those of a comparable Gaussian beam cavity. Although
-we focus on mesa beams the techniques described are applicable to any
-azimuthally symmetric system.
-",0,1,0,0,0,0
-19034,Rigidity and trace properties of divergence-measure vector fields,"  We show some rigidity properties of divergence-free vector fields defined on
-half-spaces. As an application, we prove the existence of the classical trace
-for a bounded, divergence-measure vector field $\xi$ defined on the Euclidean
-plane, at almost every point of a locally oriented rectifiable set $S$, under
-the assumption that its weak normal trace $[\xi\cdot \nu_S]$ attains a local
-maximum for the norm of $\xi$ at the point.
-",0,0,1,0,0,0
-19035,A note on the Almansi property,"  The first goal of this note is to study the Almansi property on an
-m-dimensional model in the sense of Greene and Wu and, more generally, in a
-Riemannian geometric setting. In particular, we shall prove that the only model
-on which the Almansi property is verified is the Euclidean space R^m. In the
-second part of the paper we shall study Almansi's property and biharmonicity
-for functions which depend on the distance from a given submanifold. Finally,
-in the last section we provide an extension to the semi-Euclidean case R^{p,q}
-which includes the proof of the classical Almansi property in R^m as a special
-instance.
-",0,0,1,0,0,0
-19036,The Quantum Complexity of Computing Schatten $p$-norms,"  We consider the quantum complexity of computing Schatten $p$-norms and
-related quantities, and find that the problem of estimating these quantities is
-closely related to the one clean qubit model of computation. We show that the
-problem of approximating $\text{Tr}\, (|A|^p)$ for a log-local $n$-qubit
-Hamiltonian $A$ and $p=\text{poly}(n)$, up to a suitable level of accuracy, is
-contained in DQC1; and that approximating this quantity up to a somewhat higher
-level of accuracy is DQC1-hard. In some cases the level of accuracy achieved by
-the quantum algorithm is substantially better than a natural classical
-algorithm for the problem. The same problem can be solved for arbitrary sparse
-matrices in BQP. One application of the algorithm is the approximate
-computation of the energy of a graph.
-",1,0,0,0,0,0
-19037,Bounds for fidelity of semiclassical Lagrangian states in K{ä}hler quantization,"  We define mixed states associated with submanifolds with probability
-densities in quantizable closed K{ä}hler manifolds. Then, we address the
-problem of comparing two such states via their fidelity. Firstly, we estimate
-the sub-fidelity and super-fidelity of two such states, giving lower and upper
-bounds for their fidelity, when the underlying submanifolds are two Lagrangian
-submanifolds intersecting transversally at a finite number of points, in the
-semiclassical limit. Secondly, we investigate a family of examples on the
-sphere, for which we manage to obtain a better upper bound for the fidelity. We
-conclude by stating a conjecture regarding the fidelity in the general case.
-",0,0,1,0,0,0
-19038,Precision measurement of antiproton to proton ratio with the Alpha Magnetic Spectrometer on the International Space Station,"  A precision measurement by AMS of the antiproton-to-proton flux ratio in
-primary cosmic rays in the absolute rigidity range from 1 to 450 GV is
-presented based on $3.49\times10^5$ antiproton events and $2.42\times10^9$
-proton events. Above $\sim60$ GV the antiproton to proton flux ratio is
-consistent with being rigidity independent. A decreasing behaviour is expected
-for this ratio considering the traditional models for the secondary antiproton
-flux.
-",0,1,0,0,0,0
-19039,Stability criteria for the 2D $α$-Euler equations,"  We derive analogues of the classical Rayleigh, Fjortoft and Arnold stability
-and instability theorems in the context of the 2D $\alpha$-Euler equations.
-",0,1,0,0,0,0
-19040,Superconducting spin valves controlled by spiral re-orientation in B20-family magnets,"  We propose a superconducting spin-triplet valve, which consists of a
-superconductor and an itinerant magnetic material, with the magnet showing an
-intrinsic non-collinear order characterized by a wave vector that may be
-aligned in a few equivalent preferred directions under control of a weak
-external magnetic field. Re-orienting the spiral direction allows one to
-controllably modify long-range spin-triplet superconducting correlations,
-leading to spin-valve switching behavior. Our results indicate that the
-spin-valve effect may be noticeable. This bilayer may be used as a magnetic
-memory element for cryogenic nanoelectronics. It has the following advantages
-in comparison to superconducting spin valves proposed previously: (i) it
-contains only one magnetic layer, which may be more easily fabricated and
-controlled, (ii) its ground states are separated by a potential barrier, which
-solves the ""half-select"" problem of the addressed switch of memory elements.
-",0,1,0,0,0,0
-19041,Experience Recommendation for Long Term Safe Learning-based Model Predictive Control in Changing Operating Conditions,"  Learning has propelled the cutting edge of performance in robotic control to
-new heights, allowing robots to operate with high performance in conditions
-that were previously unimaginable. The majority of the work, however, assumes
-that the unknown parts are static or slowly changing. This limits them to
-static or slowly changing environments. However, in the real world, a robot may
-experience various unknown conditions. This paper presents a method to extend
-an existing single mode GP-based safe learning controller to learn an
-increasing number of non-linear models for the robot dynamics. We show that
-this approach enables a robot to re-use past experience from a large number of
-previously visited operating conditions, and to safely adapt when a new and
-distinct operating condition is encountered. This allows the robot to achieve
-safety and high performance in an large number of operating conditions that do
-not have to be specified ahead of time. Our approach runs independently from
-the controller, imposing no additional computation time on the control loop
-regardless of the number of previous operating conditions considered. We
-demonstrate the effectiveness of our approach in experiment on a 900\,kg ground
-robot with both physical and artificial changes to its dynamics. All of our
-experiments are conducted using vision for localization.
-",1,0,0,0,0,0
-19042,Calderón-type inequalities for affine frames,"  We prove sharp upper and lower bounds for generalized Calderón's sums
-associated to frames on LCA groups generated by affine actions of cocompact
-subgroup translations and general measurable families of automorphisms. The
-proof makes use of techniques of analysis on metric spaces, and relies on a
-counting estimate of lattice points inside metric balls. We will deduce as
-special cases Calderón-type inequalities for families of expanding
-automorphisms as well as for LCA-Gabor systems.
-",0,0,1,0,0,0
-19043,Magnetism in Semiconducting Molybdenum Dichalcogenides,"  Transition metal dichalcogenides (TMDs) are interesting for understanding
-fundamental physics of two-dimensional materials (2D) as well as for many
-emerging technologies, including spin electronics. Here, we report the
-discovery of long-range magnetic order below TM = 40 K and 100 K in bulk
-semiconducting TMDs 2H-MoTe2 and 2H-MoSe2, respectively, by means of muon
-spin-rotation (muSR), scanning tunneling microscopy (STM), as well as density
-functional theory (DFT) calculations. The muon spin rotation measurements show
-the presence of a large and homogeneous internal magnetic fields at low
-temperatures in both compounds indicative of long-range magnetic order. DFT
-calculations show that this magnetism is promoted by the presence of defects in
-the crystal. The STM measurements show that the vast majority of defects in
-these materials are metal vacancies and chalcogen-metal antisites which are
-randomly distributed in the lattice at the sub-percent level. DFT indicates
-that the antisite defects are magnetic with a magnetic moment in the range of
-0.9-2.8 mu_B. Further, we find that the magnetic order stabilized in 2H-MoTe2
-and 2H-MoSe2 is highly sensitive to hydrostatic pressure. These observations
-establish 2H-MoTe2 and 2H-MoSe2 as a new class of magnetic semiconductors and
-opens a path to studying the interplay of 2D physics and magnetism in these
-interesting semiconductors.
-",0,1,0,0,0,0
-19044,Characteristic Polynomial of Certain Hyperplane Arrangements through Graph Theory,"  We give a formula for computing the characteristic polynomial for certain
-hyperplane arrangements in terms of the number of bipartite graphs of given
-rank and cardinality.
-",0,0,1,0,0,0
-19045,Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models,"  The process to certify highly Automated Vehicles has not yet been defined by
-any country in the world. Currently, companies test Automated Vehicles on
-public roads, which is time-consuming and inefficient. We proposed the
-Accelerated Evaluation concept, which uses a modified statistics of the
-surrounding vehicles and the Importance Sampling theory to reduce the
-evaluation time by several orders of magnitude, while ensuring the evaluation
-results are statistically accurate. In this paper, we further improve the
-accelerated evaluation concept by using Piecewise Mixture Distribution models,
-instead of Single Parametric Distribution models. We developed and applied this
-idea to forward collision control system reacting to vehicles making cut-in
-lane changes. The behavior of the cut-in vehicles was modeled based on more
-than 403,581 lane changes collected by the University of Michigan Safety Pilot
-Model Deployment Program. Simulation results confirm that the accuracy and
-efficiency of the Piecewise Mixture Distribution method outperformed single
-parametric distribution methods in accuracy and efficiency, and accelerated the
-evaluation process by almost four orders of magnitude.
-",1,0,0,0,0,0
-19046,Phase shift's influence of two strong pulsed laser waves on effective interaction of electrons,"  The phase shift's influence of two strong pulsed laser waves on effective
-interaction of electrons was studied. Considerable amplification of electrons
-repulsion in the certain range of phase shifts and waves intensities is shown.
-That leads to electrons scatter on greater distances than without an external
-field. The value of the distance can be greater on 2-3 order of magnitude. Also
-considerable influence of the phase shift of pulses of waves on the possibility
-of effective attraction of electrons is shown.
-",0,1,0,0,0,0
-19047,Topological Sieving of Rings According to their Rigidity,"  We present a novel mechanism for resolving the mechanical rigidity of
-nanoscopic circular polymers that flow in a complex environment. The emergence
-of a regime of negative differential mobility induced by topological
-interactions between the rings and the substrate is the key mechanism for
-selective sieving of circular polymers with distinct flexibilities. A simple
-model accurately describes the sieving process observed in molecular dynamics
-simulations and yields experimentally verifiable analytical predictions, which
-can be used as a reference guide for improving filtration procedures of
-circular filaments. The topological sieving mechanism we propose ought to be
-relevant also in probing the microscopic details of complex substrates.
-",0,0,0,0,1,0
-19048,A structural Markov property for decomposable graph laws that allows control of clique intersections,"  We present a new kind of structural Markov property for probabilistic laws on
-decomposable graphs, which allows the explicit control of interactions between
-cliques, so is capable of encoding some interesting structure. We prove the
-equivalence of this property to an exponential family assumption, and discuss
-identifiability, modelling, inferential and computational implications.
-",0,0,0,1,0,0
-19049,Fisher consistency for prior probability shift,"  We introduce Fisher consistency in the sense of unbiasedness as a desirable
-property for estimators of class prior probabilities. Lack of Fisher
-consistency could be used as a criterion to dismiss estimators that are
-unlikely to deliver precise estimates in test datasets under prior probability
-and more general dataset shift. The usefulness of this unbiasedness concept is
-demonstrated with three examples of classifiers used for quantification:
-Adjusted Classify & Count, EM-algorithm and CDE-Iterate. We find that Adjusted
-Classify & Count and EM-algorithm are Fisher consistent. A counter-example
-shows that CDE-Iterate is not Fisher consistent and, therefore, cannot be
-trusted to deliver reliable estimates of class probabilities.
-",1,0,0,1,0,0
-19050,Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders,"  The notion of disentangled autoencoders was proposed as an extension to the
-variational autoencoder by introducing a disentanglement parameter $\beta$,
-controlling the learning pressure put on the possible underlying latent
-representations. For certain values of $\beta$ this kind of autoencoders is
-capable of encoding independent input generative factors in separate elements
-of the code, leading to a more interpretable and predictable model behaviour.
-In this paper we quantify the effects of the parameter $\beta$ on the model
-performance and disentanglement. After training multiple models with the same
-value of $\beta$, we establish the existence of consistent variance in one of
-the disentanglement measures, proposed in literature. The negative consequences
-of the disentanglement to the autoencoder's discriminative ability are also
-asserted while varying the amount of examples available during training.
-",1,0,0,1,0,0
-19051,An omnibus test for the global null hypothesis,"  Global hypothesis tests are a useful tool in the context of, e.g, clinical
-trials, genetic studies or meta analyses, when researchers are not interested
-in testing individual hypotheses, but in testing whether none of the hypotheses
-is false. There are several possibilities how to test the global null
-hypothesis when the individual null hypotheses are independent. If it is
-assumed that many of the individual null hypotheses are false, combinations
-tests have been recommended to maximise power. If, however, it is assumed that
-only one or a few null hypotheses are false, global tests based on individual
-test statistics are more powerful (e.g., Bonferroni or Simes test). However,
-usually there is no a-priori knowledge on the number of false individual null
-hypotheses. We therefore propose an omnibus test based on the combination of
-p-values. We show that this test yields an impressive overall performance. The
-proposed method is implemented in the R-package omnibus.
-",0,0,0,1,0,0
-19052,Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification,"  We prove that the ordinary least-squares (OLS) estimator attains nearly
-minimax optimal performance for the identification of linear dynamical systems
-from a single observed trajectory. Our upper bound relies on a generalization
-of Mendelson's small-ball method to dependent data, eschewing the use of
-standard mixing-time arguments. Our lower bounds reveal that these upper bounds
-match up to logarithmic factors. In particular, we capture the correct
-signal-to-noise behavior of the problem, showing that more unstable linear
-systems are easier to estimate. This behavior is qualitatively different from
-arguments which rely on mixing-time calculations that suggest that unstable
-systems are more difficult to estimate. We generalize our technique to provide
-bounds for a more general class of linear response time-series.
-",0,0,0,1,0,0
-19053,Positive and Unlabeled Learning through Negative Selection and Imbalance-aware Classification,"  Motivated by applications in protein function prediction, we consider a
-challenging supervised classification setting in which positive labels are
-scarce and there are no explicit negative labels. The learning algorithm must
-thus select which unlabeled examples to use as negative training points,
-possibly ending up with an unbalanced learning problem. We address these issues
-by proposing an algorithm that combines active learning (for selecting negative
-examples) with imbalance-aware learning (for mitigating the label imbalance).
-In our experiments we observe that these two techniques operate
-synergistically, outperforming state-of-the-art methods on standard protein
-function prediction benchmarks.
-",0,0,0,0,1,0
-19054,Superconductivity in quantum wires: A symmetry analysis,"  We study properties of quantim wires with spin-orbit coupling and time
-reversal symmetry breaking, in normal and superconducting states. Electronic
-band structures are classified according to quasi-one-dimensional magnetic
-point groups, or magnetic classes. The latter belong to one of three distinct
-types, depending on the way the time reversal operation appears in the group
-elements. The superconducting gap functions are constructed using antiunitary
-operations and have different symmetry properties depending on the type of the
-magnetic point group. We obtain the spectrum of the Andreev boundary modes near
-the end of the wire in a model-independent way, using the semiclassical
-approach with the boundary conditions described by a phenomenological
-scattering matrix. Explicit expressions for the bulk topological invariants
-controlling the number of the boundary zero modes are presented in the general
-multiband case for two types of the magnetic point groups, corresponding to
-DIII and BDI symmetry classes.
-",0,1,0,0,0,0
-19055,Exponentially convergent data assimilation algorithm for Navier-Stokes equations,"  The paper presents a new state estimation algorithm for a bilinear equation
-representing the Fourier- Galerkin (FG) approximation of the Navier-Stokes (NS)
-equations on a torus in R2. This state equation is subject to uncertain but
-bounded noise in the input (Kolmogorov forcing) and initial conditions, and its
-output is incomplete and contains bounded noise. The algorithm designs a
-time-dependent gain such that the estimation error converges to zero
-exponentially. The sufficient condition for the existence of the gain are
-formulated in the form of algebraic Riccati equations. To demonstrate the
-results we apply the proposed algorithm to the reconstruction a chaotic fluid
-flow from incomplete and noisy data.
-",0,1,0,0,0,0
-19056,A Multi-Modal Approach to Infer Image Affect,"  The group affect or emotion in an image of people can be inferred by
-extracting features about both the people in the picture and the overall makeup
-of the scene. The state-of-the-art on this problem investigates a combination
-of facial features, scene extraction and even audio tonality. This paper
-combines three additional modalities, namely, human pose, text-based tagging
-and CNN extracted features / predictions. To the best of our knowledge, this is
-the first time all of the modalities were extracted using deep neural networks.
-We evaluate the performance of our approach against baselines and identify
-insights throughout this paper.
-",0,0,0,1,0,0
-19057,Joint Tilt Angle Adaptation and Beamforming in Multicell Multiuser Cellular Networks,"  3D beamforming is a promising approach for interference coordination in
-cellular networks which brings significant improvements in comparison with
-conventional 2D beamforming techniques. This paper investigates the problem of
-joint beamforming design and tilt angle adaptation of the BS antenna array for
-maximizing energy efficiency (EE) in downlink of multi-cell multi-user
-coordinated cellular networks. An iterative algorithm based on fractional
-programming approach is introduced to solve the resulting non-convex
-optimization problem. In each iteration, users are clustered based on their
-elevation angle. Then, optimization of the tilt angle is carried out through a
-reduced complexity greedy search to find the best tilt angle for a given
-placement of the users. Numerical results show that the proposed algorithm
-achieves higher EE compared to the 2D beamforming techniques.
-",1,0,1,0,0,0
-19058,Testing Degree Corrections in Stochastic Block Models,"  We study sharp detection thresholds for degree corrections in Stochastic
-Block Models in the context of a goodness of fit problem. When degree
-corrections are relatively dense, a simple test based on the total number of
-edges is asymptotically optimal. For sparse degree corrections in non-dense
-graphs, simple degree based Higher Criticism Test (Mukherjee, Mukherjee, Sen
-2016) is optimal with sharp constants. In contrast, for dense graphs, the
-optimal procedure runs in two stages. It involves running a suitable community
-recovery algorithm in stage 1, followed by a Higher Criticism Test based on a
-linear combination of within and across (estimated) community degrees in stage
-2. The necessity of the two step procedure is demonstrated by the failure of
-the ordinary Maximum Degree Test in achieving sharp constants. As necessary
-tools we also derive asymptotic distribution of the Maximum Degree in
-Stochastic Block Models along with moderate deviation and local central limit
-type asymptotics of positive linear combinations of independent Binomial random
-variables.
-",0,0,1,1,0,0
-19059,A Survey on Mobile Edge Computing: The Communication Perspective,"  Driven by the visions of Internet of Things and 5G communications, recent
-years have seen a paradigm shift in mobile computing, from the centralized
-Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of
-MEC is to push mobile computing, network control and storage to the network
-edges (e.g., base stations and access points) so as to enable
-computation-intensive and latency-critical applications at the resource-limited
-mobile devices. MEC promises dramatic reduction in latency and mobile energy
-consumption, tackling the key challenges for materializing 5G vision. The
-promised gains of MEC have motivated extensive efforts in both academia and
-industry on developing the technology. A main thrust of MEC research is to
-seamlessly merge the two disciplines of wireless communications and mobile
-computing, resulting in a wide-range of new designs ranging from techniques for
-computation offloading to network architectures. This paper provides a
-comprehensive survey of the state-of-the-art MEC research with a focus on joint
-radio-and-computational resource management. We also present a research outlook
-consisting of a set of promising directions for MEC research, including MEC
-system deployment, cache-enabled MEC, mobility management for MEC, green MEC,
-as well as privacy-aware MEC. Advancements in these directions will facilitate
-the transformation of MEC from theory to practice. Finally, we introduce recent
-standardization efforts on MEC as well as some typical MEC application
-scenarios.
-",1,0,1,0,0,0
-19060,Deep Learning Approximation: Zero-Shot Neural Network Speedup,"  Neural networks offer high-accuracy solutions to a range of problems, but are
-costly to run in production systems because of computational and memory
-requirements during a forward pass. Given a trained network, we propose a
-techique called Deep Learning Approximation to build a faster network in a tiny
-fraction of the time required for training by only manipulating the network
-structure and coefficients without requiring re-training or access to the
-training data. Speedup is achieved by by applying a sequential series of
-independent optimizations that reduce the floating-point operations (FLOPs)
-required to perform a forward pass. First, lossless optimizations are applied,
-followed by lossy approximations using singular value decomposition (SVD) and
-low-rank matrix decomposition. The optimal approximation is chosen by weighing
-the relative accuracy loss and FLOP reduction according to a single parameter
-specified by the user. On PASCAL VOC 2007 with the YOLO network, we show an
-end-to-end 2x speedup in a network forward pass with a 5% drop in mAP that can
-be re-gained by finetuning.
-",0,0,0,1,0,0
-19061,Frequency truncated discrete-time system norm,"  Multirate digital signal processing and model reduction applications require
-computation of the frequency truncated norm of a discrete-time system. This
-paper explains how to compute the frequency truncated norm of a discrete-time
-system. To this end, a much-generalized problem of integrating a transfer
-function of a discrete-time system given in the descriptor form over an
-interval of limited frequencies is also discussed along with its computation.
-",1,0,0,0,0,0
-19062,Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation,"  Recent work has shown that state-of-the-art classifiers are quite brittle, in
-the sense that a small adversarial change of an originally with high confidence
-correctly classified input leads to a wrong classification again with high
-confidence. This raises concerns that such classifiers are vulnerable to
-attacks and calls into question their usage in safety-critical systems. We show
-in this paper for the first time formal guarantees on the robustness of a
-classifier by giving instance-specific lower bounds on the norm of the input
-manipulation required to change the classifier decision. Based on this analysis
-we propose the Cross-Lipschitz regularization functional. We show that using
-this form of regularization in kernel methods resp. neural networks improves
-the robustness of the classifier without any loss in prediction performance.
-",1,0,0,1,0,0
-19063,Complete DFM Model for High-Performance Computing SoCs with Guard Ring and Dummy Fill Effect,"  For nanotechnology, the semiconductor device is scaled down dramatically with
-additional strain engineering for device enhancement, the overall device
-characteristic is no longer dominated by the device size but also circuit
-layout. The higher order layout effects, such as well proximity effect (WPE),
-oxide spacing effect (OSE) and poly spacing effect (PSE), play an important
-role for the device performance, it is critical to understand Design for
-Manufacturability (DFM) impacts with various layout topology toward the overall
-circuit performance. Currently, the layout effects (WPE, OSE and PSE) are
-validated through digital standard cell and analog differential pair test
-structure. However, two analog layout structures: the guard ring and dummy fill
-impact are not well studied yet, then, this paper describes the current mirror
-test circuit to examine the guard ring and dummy fills DFM impacts using TSMC
-28nm HPM process.
-",1,0,0,0,0,0
-19064,Investigating the potential of social network data for transport demand models,"  Location-based social network data offers the promise of collecting the data
-from a large base of users over a longer span of time at negligible cost. While
-several studies have applied social network data to activity and mobility
-analysis, a comparison with travel diaries and general statistics has been
-lacking. In this paper, we analysed geo-referenced Twitter activities from a
-large number of users in Singapore and neighbouring countries. By combining
-this data, population statistics and travel diaries and applying clustering
-techniques, we addressed detection of activity locations, as well as spatial
-separation and transitions between these locations. Kernel density estimation
-performs best to detect activity locations due to the scattered nature of the
-twitter data; more activity locations are detected per user than reported in
-the travel survey. The descriptive analysis shows that determining home
-locations is more difficult than detecting work locations for most planning
-zones. Spatial separations between detected activity locations from Twitter
-data - as reported in a travel survey and captured by public transport smart
-card data - are mostly similarly distributed, but also show relevant
-differences for very short and very long distances. This also holds for the
-transitions between zones. Whether the differences between Twitter data and
-other data sources stem from differences in the population sub-sample,
-clustering methodology, or whether social networks are being used significantly
-more at specific locations must be determined by further research. Despite
-these shortcomings, location-based social network data offers a promising data
-source for insights into activity locations and mobility patterns, especially
-for regions where travel survey data is not readily available.
-",1,1,0,0,0,0
-19065,Generalized Internal Boundaries (GIB),"  Representing large-scale motions and topological changes in the finite volume
-(FV) framework, while at the same time preserving the accuracy of the numerical
-solution, is difficult. In this paper, we present a robust, highly efficient
-method designed to achieve this capability. The proposed approach conceptually
-shares many of the characteristics of the cut-cell interface tracking method,
-but without the need for complex cell splitting/merging operations. The heart
-of the new technique is to align existing mesh facets with the geometry to be
-represented. We then modify the matrix contributions from these facets such
-that they are represented in an identical fashion to traditional boundary
-conditions. The collection of such faces is named a Generalised Internal
-Boundary (GIB). In order to introduce motion into the system, we rely on the
-classical ALE (Arbitrary Lagrangian-Eulerian) approach, but with the caveat
-that the non-time-dependent motion of elements instantaneously crossing the
-interface is handled separately from the time dependent component. The new
-methodology is validated through comparison with: a) a body-fitted grid
-simulation of an oscillating two dimensional cylinder and b) experimental
-results of a butterfly valve.
-",1,1,0,0,0,0
-19066,Reducing Certification Granularity to Increase Adaptability of Avionics Software,"  A strong certification process is required to insure the safety of airplanes,
-and more specifically the robustness of avionics applications. To implement
-this process, the development of avionics software must follow long and costly
-procedures. Most of these procedures have to be reexecuted each time the
-software is modified. In this paper, we propose a framework to reduce the cost
-and time impact of a software modification. With this new approach, the piece
-of software likely to change is isolated from the rest of the application, so
-it can be certified independently. This helps the system integrator to adapt an
-avionics application to the specificities of the target airplane, without the
-need for a new certification of the application.
-",1,0,0,0,0,0
-19067,Evolutionary multiplayer games on graphs with edge diversity,"  Evolutionary game dynamics in structured populations has been extensively
-explored in past decades. However, most previous studies assume that payoffs of
-individuals are fully determined by the strategic behaviors of interacting
-parties and social ties between them only serve as the indicator of the
-existence of interactions. This assumption neglects important information
-carried by inter-personal social ties such as genetic similarity, geographic
-proximity, and social closeness, which may crucially affect the outcome of
-interactions. To model these situations, we present a framework of evolutionary
-multiplayer games on graphs with edge diversity, where different types of edges
-describe diverse social ties. Strategic behaviors together with social ties
-determine the resulting payoffs of interactants. Under weak selection, we
-provide a general formula to predict the success of one behavior over the
-other. We apply this formula to various examples which cannot be dealt with
-using previous models, including the division of labor and relationship- or
-edge-dependent games. We find that labor division facilitates collective
-cooperation by decomposing a many-player game into several games of smaller
-sizes. The evolutionary process based on relationship-dependent games can be
-approximated by interactions under a transformed and unified game. Our work
-stresses the importance of social ties and provides effective methods to reduce
-the calculating complexity in analyzing the evolution of realistic systems.
-",0,0,0,0,1,0
-19068,Robust Consensus for Multi-Agent Systems Communicating over Stochastic Uncertain Networks,"  In this paper, we study the robust consensus problem for a set of
-discrete-time linear agents to coordinate over an uncertain communication
-network, which is to achieve consensus against the transmission errors and
-noises resulted from the information exchange between the agents. We model the
-network by means of communication links subject to multiplicative stochastic
-uncertainties, which are susceptible to describing packet dropout, random
-delay, and fading phenomena. Different communication topologies, such as
-undirected graphs and leader-follower graphs, are considered. We derive
-sufficient conditions for robust consensus in the mean square sense. This
-results unveil intrinsic constraints on consensus attainment imposed by the
-network synchronizability, the unstable agent dynamics, and the channel
-uncertainty variances. Consensus protocols are designed based on the state
-information transmitted over the uncertain channels, by solving a modified
-algebraic Riccati equation.
-",1,0,0,0,0,0
-19069,Fractional Topological Elasticity and Fracton Order,"  We analyze the ""higher rank"" gauge theories, that capture some of the
-phenomenology of the Fracton order. It is shown that these theories loose gauge
-invariance when arbitrarily weak and smooth curvature is introduced. We propose
-a resolution to this problem by introducing a theory invariant under
-area-preserving diffeomorphisms, which reduce to the ""higher rank"" gauge
-transformations upon linearization around a flat background. The proposed
-theory is \emph{geometric} in nature and is interpreted as a theory of
-\emph{fractional topological elasticity}. This theory exhibits the Fracton
-phenomenology. We explore the conservation laws, topological excitations,
-linear response, various kinematical constraints, and canonical structure of
-the theory. Finally, we emphasize that the very structure of Riemann-Cartan
-geometry, which we use to formulate the theory, encodes the Fracton
-phenomenology, suggesting that the Fracton order itself is \emph{geometric} in
-nature.
-",0,1,0,0,0,0
-19070,Perturbing Eisenstein polynomials over local fields,"  Let $K$ be a local field whose residue field has characteristic $p$ and let
-$L/K$ be a finite separable totally ramified extension. Let $\pi_L$ be a
-uniformizer for $L$ and let $f(X)$ be the minimum polynomial for $\pi_L$ over
-$K$. Suppose $\tilde{\pi}_L$ is another uniformizer for $L$ such that
-$\tilde{\pi}_L\equiv\pi_L+r\pi_L^{\ell+1} \pmod{\pi_L^{\ell+2}}$ for some
-$\ell\ge1$ and $r\in O_K$. Let $\tilde{f}(X)$ be the minimum polynomial for
-$\tilde{\pi}_L$ over $K$. In this paper we give congruences for the
-coefficients of $\tilde{f}(X)$ in terms of $r$ and the coefficients of $f(X)$.
-These congruences improve and extend work of Krasner.
-",0,0,1,0,0,0
-19071,Galactic Orbits of Globular Clusters in the Region of the Galactic Bulge,"  Galactic orbits have been constructed over long time intervals for ten
-globular clusters located near the Galactic center. A model with an axially
-symmetric gravitational potential for the Galaxy was initially applied, after
-which a non-axially symmetric potential corresponding to the central bar was
-added. Variations in the trajectories of all these globular clusters in the XY
-plane due to the influence of the bar were detected. These were greatest for
-the cluster Terzan 4 in the meridional (RZ) plane. The globular clusters Terzan
-1, Terzan 2, Terzan 4, Terzan 9, NGC 6522, and NGC 6558 always remained within
-the Galactic bulge, no farther than 4 kpc from the Galactic center.
-",0,1,0,0,0,0
-19072,Streamlines for Motion Planning in Underwater Currents,"  Motion planning for underwater vehicles must consider the effect of ocean
-currents. We present an efficient method to compute reachability and cost
-between sample points in sampling-based motion planning that supports
-long-range planning over hundreds of kilometres in complicated flows. The idea
-is to search a reduced space of control inputs that consists of stream
-functions whose level sets, or streamlines, optimally connect two given points.
-Such stream functions are generated by superimposing a control input onto the
-underlying current flow. A streamline represents the resulting path that a
-vehicle would follow as it is carried along by the current given that control
-input. We provide rigorous analysis that shows how our method avoids exhaustive
-search of the control space, and demonstrate simulated examples in complicated
-flows including a traversal along the east coast of Australia, using actual
-current predictions, between Sydney and Brisbane.
-",1,0,0,0,0,0
-19073,"Eddington-Limited Accretion in z~2 WISE-selected Hot, Dust-Obscured Galaxies","  Hot, Dust-Obscured Galaxies, or ""Hot DOGs"", are a rare, dusty, hyperluminous
-galaxy population discovered by the WISE mission. Predominantly at redshifts
-2-3, they include the most luminous known galaxies in the universe. Their high
-luminosities likely come from accretion onto highly obscured super massive
-black holes (SMBHs). We have conducted a pilot survey to measure the SMBH
-masses of five z~2 Hot DOGs via broad H_alpha emission lines, using
-Keck/MOSFIRE and Gemini/FLAMINGOS-2. We detect broad H_alpha emission in all
-five Hot DOGs. We find substantial corresponding SMBH masses for these Hot DOGs
-(~ 10^{9} M_sun), and their derived Eddington ratios are close to unity. These
-z~2 Hot DOGs are the most luminous AGNs at given BH masses, suggesting they are
-accreting at the maximum rates for their BHs. A similar property is found for
-known z~6 quasars. Our results are consistent with scenarios in which Hot DOGs
-represent a transitional, high-accretion phase between obscured and unobscured
-quasars. Hot DOGs may mark a special evolutionary stage before the red quasar
-and optical quasar phases, and they may be present at other cosmic epochs.
-",0,1,0,0,0,0
-19074,Boltzmann Transport in Nanostructures as a Friction Effect,"  Surface scattering is the key limiting factor to thermal transport in
-dielectric crystals as the length scales are reduced or when temperature is
-lowered. To explain this phenomenon, it is commonly assumed that the mean free
-paths of heat carriers are bound by the crystal size and that thermal
-conductivity is reduced in a manner proportional to such mean free paths. We
-show here that these conclusions rely on simplifying assumptions and
-approximated transport models. Instead, starting from the linearized Boltzmann
-transport equation in the relaxon basis, we show how the problem can be reduced
-to a set of decoupled linear differential equations. Then, the heat flow can be
-interpreted as a hydrodynamic phenomenon, with the relaxon gas being slowed
-down in proximity of a surface by friction effects, similar to the flux of a
-viscous fluid in a pipe. As an example, we study a ribbon and a trench of
-monolayer molybdenum disulphide, describing the procedure to reconstruct the
-temperature and thermal conductivity profile in the sample interior and showing
-how to estimate the effect of nanostructuring. The approach is general and
-could be extended to other transport carriers, such as electrons, or extended
-to materials of higher dimensionality and to different geometries, such as thin
-films.
-",0,1,0,0,0,0
-19075,Agile Software Engineering and Systems Engineering at SKA Scale,"  Systems Engineering (SE) is the set of processes and documentation required
-for successfully realising large-scale engineering projects, but the classical
-approach is not a good fit for software-intensive projects, especially when the
-needs of the different stakeholders are not fully known from the beginning, and
-requirement priorities might change. The SKA is the ultimate software-enabled
-telescope, with enormous amounts of computing hardware and software required to
-perform its data reduction. We give an overview of the system and software
-engineering processes in the SKA1 development, and the tension between
-classical and agile SE.
-",1,1,0,0,0,0
-19076,Prime geodesic theorem of Gallagher type,"  We reduce the exponent in the error term of the prime geodesic theorem for
-compact Riemann surfaces from $\frac{3}{4}$ to $\frac{7}{10}$ outside a set of
-finite logarithmic measure.
-",0,0,1,0,0,0
-19077,PriMaL: A Privacy-Preserving Machine Learning Method for Event Detection in Distributed Sensor Networks,"  This paper introduces PriMaL, a general PRIvacy-preserving MAchine-Learning
-method for reducing the privacy cost of information transmitted through a
-network. Distributed sensor networks are often used for automated
-classification and detection of abnormal events in high-stakes situations, e.g.
-fire in buildings, earthquakes, or crowd disasters. Such networks might
-transmit privacy-sensitive information, e.g. GPS location of smartphones, which
-might be disclosed if the network is compromised. Privacy concerns might slow
-down the adoption of the technology, in particular in the scenario of social
-sensing where participation is voluntary, thus solutions are needed which
-improve privacy without compromising on the event detection accuracy. PriMaL is
-implemented as a machine-learning layer that works on top of an existing event
-detection algorithm. Experiments are run in a general simulation framework, for
-several network topologies and parameter values. The privacy footprint of
-state-of-the-art event detection algorithms is compared within the proposed
-framework. Results show that PriMaL is able to reduce the privacy cost of a
-distributed event detection algorithm below that of the corresponding
-centralized algorithm, within the bounds of some assumptions about the
-protocol. Moreover the performance of the distributed algorithm is not
-statistically worse than that of the centralized algorithm.
-",1,0,0,0,0,0
-19078,Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU,"  The online problem of computing the top eigenvector is fundamental to machine
-learning. In both adversarial and stochastic settings, previous results (such
-as matrix multiplicative weight update, follow the regularized leader, follow
-the compressed leader, block power method) either achieve optimal regret but
-run slow, or run fast at the expense of loosing a $\sqrt{d}$ factor in total
-regret where $d$ is the matrix dimension.
-We propose a $\textit{follow-the-compressed-leader (FTCL)}$ framework which
-achieves optimal regret without sacrificing the running time. Our idea is to
-""compress"" the matrix strategy to dimension 3 in the adversarial setting, or
-dimension 1 in the stochastic setting. These respectively resolve two open
-questions regarding the design of optimal and efficient algorithms for the
-online eigenvector problem.
-",1,0,1,1,0,0
-19079,Solving satisfiability using inclusion-exclusion,"  Using Maple, we implement a SAT solver based on the principle of
-inclusion-exclusion and the Bonferroni inequalities. Using randomly generated
-input, we investigate the performance of our solver as a function of the number
-of variables and number of clauses. We also test it against Maple's built-in
-tautology procedure. Finally, we implement the Lovász local lemma with Maple
-and discuss its applicability to SAT.
-",1,0,0,0,0,0
-19080,Isometric immersions into manifolds with metallic structures,"  We consider submanifolds into Riemannian manifold with metallic structures.
-We obtain some new results for hypersurfaces in these spaces and we express the
-fundamental theorem of submanifolds into products spaces in terms of metallic
-structures. Moreover, we define new structures called complex metallic
-structures. We show that these structures are linked with complex structures.
-Then, we consider submanifolds into Riemannian manifold with such structures
-with a focus on invariant submanifolds and hypersurfaces. We also express in
-particular the fundamental theorem of submanifolds of complex space form in
-terms of complex metallic structures.
-",0,0,1,0,0,0
-19081,Demonstration of an ac Josephson junction laser,"  Superconducting electronic devices have re-emerged as contenders for both
-classical and quantum computing due to their fast operation speeds, low
-dissipation and long coherence times. An ultimate demonstration of coherence is
-lasing. We use one of the fundamental aspects of superconductivity, the ac
-Josephson effect, to demonstrate a laser made from a Josephson junction
-strongly coupled to a multi-mode superconducting cavity. A dc voltage bias to
-the junction provides a source of microwave photons, while the circuit's
-nonlinearity allows for efficient down-conversion of higher order Josephson
-frequencies down to the cavity's fundamental mode. The simple fabrication and
-operation allows for easy integration with a range of quantum devices, allowing
-for efficient on-chip generation of coherent microwave photons at low
-temperatures.
-",0,1,0,0,0,0
-19082,"Integrable systems, symmetries and quantization","  These notes correspond to a mini-course given at the Poisson 2016 conference
-in Geneva. Starting from classical integrable systems in the sense of
-Liouville, we explore the notion of non-degenerate singularity and expose
-recent research in connection with semi-toric systems. The quantum and
-semiclassical counterpart will also be presented, in the viewpoint of the
-inverse question: from the quantum mechanical spectrum, can you recover the
-classical system?
-",0,1,1,0,0,0
-19083,Detecting Friedel oscillations in ultracold Fermi gases,"  Investigating Friedel oscillations in ultracold gases would complement the
-studies performed on solid state samples with scanning-tunneling microscopes.
-In atomic quantum gases interactions and external potentials can be tuned
-freely and the inherently slower dynamics allow to access non-equilibrium
-dynamics following a potential or interaction quench. Here, we examine how
-Friedel oscillations can be observed in current ultracold gas experiments under
-realistic conditions. To this aim we numerically calculate the amplitude of the
-Friedel oscillations which a potential barrier provokes in a 1D Fermi gas and
-compare it to the expected atomic and photonic shot noise in a density
-measurement. We find that to detect Friedel oscillations the signal from
-several thousand one-dimensional systems has to be averaged. However, as up to
-100 parallel one-dimensional systems can be prepared in a single run with
-present experiments, averaging over about 100 images is sufficient.
-",0,1,0,0,0,0
-19084,Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems,"  While variational methods have been among the most powerful tools for solving
-linear inverse problems in imaging, deep (convolutional) neural networks have
-recently taken the lead in many challenging benchmarks. A remaining drawback of
-deep learning approaches is their requirement for an expensive retraining
-whenever the specific problem, the noise level, noise type, or desired measure
-of fidelity changes. On the contrary, variational methods have a plug-and-play
-nature as they usually consist of separate data fidelity and regularization
-terms.
-In this paper we study the possibility of replacing the proximal operator of
-the regularization used in many convex energy minimization algorithms by a
-denoising neural network. The latter therefore serves as an implicit natural
-image prior, while the data term can still be chosen independently. Using a
-fixed denoising neural network in exemplary problems of image deconvolution
-with different blur kernels and image demosaicking, we obtain state-of-the-art
-reconstruction results. These indicate the high generalizability of our
-approach and a reduction of the need for problem-specific training.
-Additionally, we discuss novel results on the analysis of possible optimization
-algorithms to incorporate the network into, as well as the choices of algorithm
-parameters and their relation to the noise level the neural network is trained
-on.
-",1,0,0,0,0,0
-19085,Heated-Up Softmax Embedding,"  Metric learning aims at learning a distance which is consistent with the
-semantic meaning of the samples. The problem is generally solved by learning an
-embedding for each sample such that the embeddings of samples of the same
-category are compact while the embeddings of samples of different categories
-are spread-out in the feature space. We study the features extracted from the
-second last layer of a deep neural network based classifier trained with the
-cross entropy loss on top of the softmax layer. We show that training
-classifiers with different temperature values of softmax function leads to
-features with different levels of compactness. Leveraging these insights, we
-propose a ""heating-up"" strategy to train a classifier with increasing
-temperatures, leading the corresponding embeddings to achieve state-of-the-art
-performance on a variety of metric learning benchmarks.
-",0,0,0,1,0,0
-19086,Fast Matrix Inversion and Determinant Computation for Polarimetric Synthetic Aperture Radar,"  This paper introduces a fast algorithm for simultaneous inversion and
-determinant computation of small sized matrices in the context of fully
-Polarimetric Synthetic Aperture Radar (PolSAR) image processing and analysis.
-The proposed fast algorithm is based on the computation of the adjoint matrix
-and the symmetry of the input matrix. The algorithm is implemented in a general
-purpose graphical processing unit (GPGPU) and compared to the usual approach
-based on Cholesky factorization. The assessment with simulated observations and
-data from an actual PolSAR sensor show a speedup factor of about two when
-compared to the usual Cholesky factorization. Moreover, the expressions
-provided here can be implemented in any platform.
-",1,0,0,1,0,0
-19087,The imprint of neutrinos on clustering in redshift-space,"  (abridged) We investigate the signatures left by the cosmic neutrino
-background on the clustering of matter, CDM+baryons and halos in redshift-space
-using a set of more than 1000 N-body and hydrodynamical simulations with
-massless and massive neutrinos. We find that the effect neutrinos induce on the
-clustering of CDM+baryons in redshift-space on small scales is almost entirely
-due to the change in $\sigma_8$. Neutrinos imprint a characteristic signature
-in the quadrupole of the matter (CDM+baryons+neutrinos) field on small scales,
-that can be used to disentangle the effect of $\sigma_8$ and $M_\nu$. We show
-that the effect of neutrinos on the clustering of halos is very different, on
-all scales, to the one induced by $\sigma_8$. We find that the effects of
-neutrinos of the growth rate of CDM+baryons ranges from $\sim0.3\%$ to $2\%$ on
-scales $k\in[0.01, 0.5]~h{\rm Mpc}^{-1}$ for neutrinos with masses $M_\nu
-\leqslant 0.15$ eV. We compute the bias between the momentum of halos and the
-momentum of CDM+baryon and find it to be 1 on large scales for all models with
-massless and massive neutrinos considered. This point towards a velocity bias
-between halos and total matter on large scales that it is important to account
-for in order to extract unbiased neutrino information from velocity/momentum
-surveys such as kSZ observations. We show that baryonic effects can affect the
-clustering of matter and CDM+baryons in redshift-space by up to a few percent
-down to $k=0.5~h{\rm Mpc}^{-1}$. We find that hydrodynamics and astrophysical
-processes, as implemented in our simulations, only distort the relative effect
-that neutrinos induce on the anisotropic clustering of matter, CDM+baryons and
-halos in redshift-space by less than $1\%$. Thus, the effect of neutrinos in
-the fully non-linear regime can be written as a transfer function with very
-weak dependence on astrophysics.
-",0,1,0,0,0,0
-19088,Quantum Privacy-Preserving Data Analytics,"  Data analytics (such as association rule mining and decision tree mining) can
-discover useful statistical knowledge from a big data set. But protecting the
-privacy of the data provider and the data user in the process of analytics is a
-serious issue. Usually, the privacy of both parties cannot be fully protected
-simultaneously by a classical algorithm. In this paper, we present a quantum
-protocol for data mining that can much better protect privacy than the known
-classical algorithms: (1) if both the data provider and the data user are
-honest, the data user can know nothing about the database except the
-statistical results, and the data provider can get nearly no information about
-the results mined by the data user; (2) if the data user is dishonest and tries
-to disclose private information of the other, she/he will be detected with a
-high probability; (3) if the data provider tries to disclose the privacy of the
-data user, she/he cannot get any useful information since the data user hides
-his privacy among noises.
-",1,0,0,0,0,0
-19089,Magnetic Properties of Transition-Metal Adsorbed ot-Phosphorus Monolayer: A First-principles and Monte Carlo Study,"  Using the first-principles and Monte Carlo methods, here we systematically
-study magnetic properties of monolayer octagonal-tetragonal phosphorus with 3d
-transition-metal (TM) adatoms. Different from the puckered hexagonal black
-phosphorus monolayer (phosphorene or $\alpha$-P), the octagonal-tetragonal
-phase of 2D phosphorus (named as ot-P or $\epsilon$-P in this article) is
-buckled with octagon-tetragon structure. Our calculations show that all TMs,
-except the closed-shell Zn atom, are able to strongly bind onto monolayer
-$ot$-P with significant binding energies. Local magnetic moments (up to 6
-$\mu$B) on adatoms of Sc, Ti, V, Cr, Mn, Fe and Co originate from the exchange
-and crystal-field splitting of TM 3d orbitals. The magnetic coupling between
-localized magnetic states of adatoms is dependent on adatomic distances and
-directions. Lastly, the uniformly magnetic order is investigated to screening
-two-dimensional dilute ferromagnets with high Curie temperature for
-applications of spintronics. It is found that ot-P with V atoms homogeneously
-adsorbed at the centre of octagons with a concentration of 5% has the most
-stable ferromagnetic ground state. Its Curie temperature is estimated to be 173
-K using the Monte Carlo method.
-",0,1,0,0,0,0
-19090,A bootstrap for the number of $\mathbb{F}_{q^r}$-rational points on a curve over $\mathbb{F}_q$,"  In this note we present a fast algorithm that finds for any $r$ the number
-$N_r$ of $\mathbb{F}_{q^r}$ rational points on a smooth absolutely irreducible
-curve $C$ defined over $\mathbb{F}_{q}$ assuming that we know $N_1,\cdots,N_g$,
-where $g$ is the genus of $C$. The proof of its validity is given in detail and
-its working are illustrated with several examples. In an Appendix we list the
-Python function in which we have implemented the algorithm together with other
-routines used in the examples.
-",0,0,1,0,0,0
-19091,Doping anatase TiO2 with group V-b and VI-b transition metal atoms: a hybrid functional first-principles study,"  We investigate the role of transition metal atoms of group V-b (V, Nb, and
-Ta) and VI-b (Cr, Mo, and W) as n- or p-type dopants in anatase TiO2 using
-thermodynamic principles and density functional theory with the
-Heyd-Scuseria-Ernzerhof HSE06 hybrid functional. The HSE06 functional provides
-a realistic value for the band gap, which ensures a correct classification of
-dopants as shallow or deep donors or acceptors. Defect formation energies and
-thermodynamic transition levels are calculated taking into account the
-constraints imposed by the stability of TiO2 and the solubility limit of the
-impurities. Nb, Ta, W and Mo are identified as shallow donors. Although W
-provides two electrons, Nb and Ta show a considerably lower formation energy,
-in particular under O-poor conditions. Mo donates in principle one electron,
-but under specific conditions can turn into a double donor. V impurities are
-deep donors and Cr shows up as an amphoteric defect, thereby acting as an
-electron trapping center in n-type TiO2 especially under O-rich conditions. A
-comparison with the available experimental data yields excellent agreement.
-",0,1,0,0,0,0
-19092,Patch-planting spin-glass solution for benchmarking,"  We introduce an algorithm to generate (not solve) spin-glass instances with
-planted solutions of arbitrary size and structure. First, a set of small
-problem patches with open boundaries is solved either exactly or with a
-heuristic, and then the individual patches are stitched together to create a
-large problem with a known planted solution. Because in these problems
-frustration is typically smaller than in random problems, we first assess the
-typical computational complexity of the individual patches using population
-annealing Monte Carlo, and introduce an approach that allows one to fine-tune
-the typical computational complexity of the patch-planted system. The scaling
-of the typical computational complexity of these planted instances with various
-numbers of patches and patch sizes is investigated and compared to random
-instances.
-",0,1,0,0,0,0
-19093,Evidence for the formation of comet 67P/Churyumov-Gerasimenko through gravitational collapse of a bound clump of pebbles,"  The processes that led to the formation of the planetary bodies in the Solar
-System are still not fully understood. Using the results obtained with the
-comprehensive suite of instruments on-board ESA's Rosetta mission, we present
-evidence that comet 67P/Churyumov-Gerasimenko likely formed through the gentle
-gravitational collapse of a bound clump of mm-sized dust aggregates
-(""pebbles""), intermixed with microscopic ice particles. This formation scenario
-leads to a cometary make-up that is simultaneously compatible with the global
-porosity, homogeneity, tensile strength, thermal inertia, vertical temperature
-profiles, sizes and porosities of emitted dust, and the steep increase in
-water-vapour production rate with decreasing heliocentric distance, measured by
-the instruments on-board the Rosetta spacecraft and the Philae lander. Our
-findings suggest that the pebbles observed to be abundant in protoplanetary
-discs around young stars provide the building material for comets and other
-minor bodies.
-",0,1,0,0,0,0
-19094,Detection of virial shocks in stacked Fermi-LAT clusters,"  Galaxy clusters are thought to grow by accreting mass through large-scale,
-strong, yet elusive, virial shocks. Such a shock is expected to accelerate
-relativistic electrons, thus generating a spectrally-flat leptonic virial-ring.
-However, until now, only the nearby Coma cluster has shown evidence for a
-$\gamma$-ray virial ring. We stack Fermi-LAT data for the 112 most massive,
-high latitude, extended clusters, enhancing the ring sensitivity by rescaling
-clusters to their virial radii and utilizing the expected flat energy spectrum.
-In addition to a central unresolved, hard signal (detected at the $\sim
-5.8\sigma$ confidence level), probably dominated by AGN, we identify (at the
-$5.8\sigma$ confidence level) a bright, spectrally-flat $\gamma$-ray ring at
-the expected virial shock position. The ring signal implies that the shock
-deposits $\sim 0.6\%$ (with an interpretation uncertainty factor $\sim2$) of
-the thermal energy in relativistic electrons over a Hubble time. This result,
-consistent with the Coma signal, validates and calibrates the virial shock
-model, and indicates that the cumulative emission from such shocks
-significantly contributes to the diffuse extragalactic $\gamma$-ray and
-low-frequency radio backgrounds.
-",0,1,0,0,0,0
-19095,Closed-form formulae of hyperbolic metamaterial made by stacked hole-array layers working at terahertz or microwave radiation,"  A metamaterial made by stacked hole-array layers known as a fishnet
-metamaterial behaves as a hyperbolic metamaterial at wavelength much longer
-than hole-array period. However, the analytical formulae of effective
-parameters of a fishnet metamaterial have not been reported hindering the
-design of deep-subwavelength imaging devices using this structure. We report
-the new closed-form formulae of effective parameters comprising anisotropic
-dispersion relation of a fishnet metamaterial working at terahertz or microwave
-frequency. These effective parameters of a fishnet metamaterial are consistent
-with those obtained by quasi-full solutions using known effective parameters of
-a hole-array layer working at frequency below its spoof plasma frequency with
-the superlattice period much smaller than the hole-array period. We also
-theoretically demonstrate the deep-subwavelength focusing at {\lambda}/83 using
-the composite structure of a slit-array layer and a fishnet metamaterial. It is
-found that the focused intensity inside a fishnet metamaterial is several times
-larger than that without the fishnet metamaterial, but the transmitted
-intensity is still restricted by large-wavevector difference in air and a
-fishnet metamaterial. Our effective parameters may aid the next-generation
-deep-subwavelength imaging devices working at terahertz or microwave radiation.
-",0,1,0,0,0,0
-19096,RMPflow: A Computational Graph for Automatic Motion Policy Generation,"  We develop a novel policy synthesis algorithm, RMPflow, based on
-geometrically consistent transformations of Riemannian Motion Policies (RMPs).
-RMPs are a class of reactive motion policies designed to parameterize
-non-Euclidean behaviors as dynamical systems in intrinsically nonlinear task
-spaces. Given a set of RMPs designed for individual tasks, RMPflow can
-consistently combine these local policies to generate an expressive global
-policy, while simultaneously exploiting sparse structure for computational
-efficiency. We study the geometric properties of RMPflow and provide sufficient
-conditions for stability. Finally, we experimentally demonstrate that
-accounting for the geometry of task policies can simplify classically difficult
-problems, such as planning through clutter on high-DOF manipulation systems.
-",1,0,0,0,0,0
-19097,Long-range correlations and fractal dynamics in C. elegans: changes with aging and stress,"  Reduced motor control is one of the most frequent features associated with
-aging and disease. Nonlinear and fractal analyses have proved to be useful in
-investigating human physiological alterations with age and disease. Similar
-findings have not been established for any of the model organisms typically
-studied by biologists, though. If the physiology of a simpler model organism
-displays the same characteristics, this fact would open a new research window
-on the control mechanisms that organisms use to regulate physiological
-processes during aging and stress. Here, we use a recently introduced animal
-tracking technology to simultaneously follow tens of Caenorhabdits elegans for
-several hours and use tools from fractal physiology to quantitatively evaluate
-the effects of aging and temperature stress on nematode motility. Similarly to
-human physiological signals, scaling analysis reveals long-range correlations
-in numerous motility variables, fractal properties in behavioral shifts, and
-fluctuation dynamics over a wide range of timescales. These properties change
-as a result of a superposition of age and stress-related adaptive mechanisms
-that regulate motility.
-",0,1,0,1,0,0
-19098,A Computational Approach to Extinction Events in Chemical Reaction Networks with Discrete State Spaces,"  Recent work of M.D. Johnston et al. has produced sufficient conditions on the
-structure of a chemical reaction network which guarantee that the corresponding
-discrete state space system exhibits an extinction event. The conditions
-consist of a series of systems of equalities and inequalities on the edges of a
-modified reaction network called a domination-expanded reaction network. In
-this paper, we present a computational implementation of these conditions
-written in Python and apply the program on examples drawn from the biochemical
-literature, including a model of polyamine metabolism in mammals and a model of
-the pentose phosphate pathway in Trypanosoma brucei. We also run the program on
-458 models from the European Bioinformatics Institute's BioModels Database and
-report our results.
-",0,0,1,0,0,0
-19099,Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media,"  A person's weight status can have profound implications on their life,
-ranging from mental health, to longevity, to financial income. At the societal
-level, ""fat shaming"" and other forms of ""sizeism"" are a growing concern, while
-increasing obesity rates are linked to ever raising healthcare costs. For these
-reasons, researchers from a variety of backgrounds are interested in studying
-obesity from all angles. To obtain data, traditionally, a person would have to
-accurately self-report their body-mass index (BMI) or would have to see a
-doctor to have it measured. In this paper, we show how computer vision can be
-used to infer a person's BMI from social media images. We hope that our tool,
-which we release, helps to advance the study of social aspects related to body
-weight.
-",1,0,0,0,0,0
-19100,Recursive simplex stars,"  This paper proposes a new method which builds a simplex based approximation
-of a $d-1$-dimensional manifold $M$ separating a $d$-dimensional compact set
-into two parts, and an efficient algorithm classifying points according to this
-approximation. In a first variant, the approximation is made of simplices that
-are defined in the cubes of a regular grid covering the compact set, from
-boundary points that approximate the intersection between $M$ and the edges of
-the cubes. All the simplices defined in a cube share the barycentre of the
-boundary points located in the cube and include simplices similarly defined in
-cube facets, and so on recursively. In a second variant, the Kuhn triangulation
-is used to break the cubes into simplices and the approximation is defined in
-these simplices from the boundary points computed on their edges, with the same
-principle. Both the approximation in cubes and in simplices define a separating
-surface on the whole grid and classifying a point on one side or the other of
-this surface requires only a small number (at most $d$) of simple tests. Under
-some conditions on the definition of the boundary points and on the reach of
-$M$, for both variants the Hausdorff distance between $M$ and its approximation
-decreases like $\mathcal{O}(d n_G^{-2})$, where $n_G$ is the number of points
-on each axis of the grid. The approximation in cubes requires computing less
-boundary points than the approximation in simplices but the latter is always a
-manifold and is more accurate for a given value of $n_G$. The paper reports
-tests of the method when varying $n_G$ and the dimensionality of the space (up
-to 9).
-",1,0,0,0,0,0
-19101,An Analytic Formula for Numbers of Restricted Partitions from Conformal Field Theory,"  We study the correlators of irregular vertex operators in two-dimensional
-conformal field theory (CFT) in order to propose an exact analytic formula for
-calculating numbers of partitions, that is:
-1) for given $N,k$, finding the total number $\lambda(N|k)$ of length $k$
-partitions of $N$: $N=n_1+...+n_k;0<n_1\leq{n_2}...\leq{n_k}$.
-2) finding the total number $\lambda(N)=\sum_{k=1}^N\lambda(N|k)$ of
-partitions of a natural number $N$
-We propose an exact analytic expression for $\lambda(N|k)$ by relating
-two-point short-distance correlation functions of irregular vertex operators in
-$c=1$ conformal field theory ( the form of the operators is established in this
-paper): with the first correlator counting the partitions in the upper
-half-plane and the second one obtained from the first correlator by conformal
-transformations of the form $f(z)=h(z)e^{-{i\over{z}}}$ where $h(z)$ is regular
-and non-vanishing at $z=0$. The final formula for $\lambda(N|k)$ is given in
-terms of regularized ($\epsilon$-ordered) finite series in the generalized
-higher-derivative Schwarzians and incomplete Bell polynomials of the above
-conformal transformation at $z=i\epsilon$ ($\epsilon\rightarrow{0}$)
-",0,0,1,0,0,0
-19102,Hyperopic Cops and Robbers,"  We introduce a new variant of the game of Cops and Robbers played on graphs,
-where the robber is invisible unless outside the neighbor set of a cop. The
-hyperopic cop number is the corresponding analogue of the cop number, and we
-investigate bounds and other properties of this parameter. We characterize the
-cop-win graphs for this variant, along with graphs with the largest possible
-hyperopic cop number. We analyze the cases of graphs with diameter 2 or at
-least 3, focusing on when the hyperopic cop number is at most one greater than
-the cop number. We show that for planar graphs, as with the usual cop number,
-the hyperopic cop number is at most 3. The hyperopic cop number is considered
-for countable graphs, and it is shown that for connected chains of graphs, the
-hyperopic cop density can be any real number in $[0,1/2].$
-",1,0,0,0,0,0
-19103,New pinching estimates for Inverse curvature flows in space forms,"  We prove new pinching estimate for the inverse curvature flow of strictly
-convex hypersurfaces in the space form $N$ of constant sectional curvature
-$K_N$ with speed given by $F^{-\alpha}$, where $\alpha\in (0,1]$ for $K_N=0,-1$
-and $\alpha=1$ for $K_N=1$, $F$ is a smooth, symmetric homogeneous of degree
-one function which is inverse concave and has dual $F_*$ approaching zero on
-the boundary of the positive cone $\Gamma_+$. We show that the ratio of the
-largest principal curvature to the smallest principal curvature of the flow
-hypersurface is controlled by its initial value. This can be used to prove the
-smooth convergence of the flow.
-",0,0,1,0,0,0
-19104,"automan: a simple, Python-based, automation framework for numerical computing","  We present an easy-to-use, Python-based framework that allows a researcher to
-automate their computational simulations. In particular the framework
-facilitates assembling several long-running computations and producing various
-plots from the data produced by these computations. The framework makes it
-possible to reproduce every figure made for a publication with a single
-command. It also allows one to distribute the computations across a network of
-computers. The framework has been used to write research papers in numerical
-computing. This paper discusses the design of the framework, and the benefits
-of using it. The ideas presented are general and should help researchers
-organize their computations for better reproducibility.
-",1,0,0,0,0,0
-19105,Extensive characterization of a high Reynolds number decelerating boundary layer using advanced optical metrology,"  An experiment conducted in the framework of the EUHIT project and designed to
-characterize large scale structures in an adverse pressure gradient boundary
-layer flow is presented. Up to 16 sCMOS cameras were used in order to perform
-large scale turbulent boundary layer PIV measurements with a large field of
-view and appropriate spatial resolution. To access the span-wise / wall-normal
-signature of the structures as well, stereoscopic PIV measurements in
-span-wise/wall-normal planes were performed at specific stream-wise locations.
-To complement these large field of view measurements, long-range micro-PIV,
-time resolved near wall velocity profiles and film-based measurements were
-performed in order to determine the wall-shear stress and its fluctuations at
-some specific locations along the model.
-",0,1,0,0,0,0
-19106,Q-analogues of the Fibo-Stirling numbers,"  Let $F_n$ denote the $n^{th}$ Fibonacci number relative to the initial
-conditions $F_0=0$ and $F_1=1$. Bach, Paudyal, and Remmel introduced Fibonacci
-analogues of the Stirling numbers called Fibo-Stirling numbers of the first and
-second kind. These numbers serve as the connection coefficients between the
-Fibo-falling factorial basis $\{(x)_{\downarrow_{F,n}}:n \geq 0\}$ and the
-Fibo-rising factorial basis $\{(x)_{\uparrow_{F,n}}:n \geq 0\}$ which are
-defined by $(x)_{\downarrow_{F,0}} = (x)_{\uparrow_{F,0}} = 1$ and for $k \geq
-1$, $(x)_{\downarrow_{F,k}} = x(x-F_1) \cdots (x-F_{k-1})$ and
-$(x)_{\uparrow_{F,k}} = x(x+F_1) \cdots (x+F_{k-1})$. We gave a general rook
-theory model which allowed us to give combinatorial interpretations of the
-Fibo-Stirling numbers of the first and second kind.
-There are two natural $q$-analogues of the falling and rising Fibo-factorial
-basis. That is, let $[x]_q = \frac{q^x-1}{q-1}$. Then we let
-$[x]_{\downarrow_{q,F,0}} = \overline{[x]}_{\downarrow_{q,F,0}} =
-[x]_{\uparrow_{q,F,0}} = \overline{[x]}_{\uparrow_{q,F,0}}=1$ and, for $k > 0$,
-we let $[x]_{\downarrow_{q,F,k}} = [x]_q [x-F_1]_q \cdots [x-F_{k-1}]_q$,
-$\overline{[x]}_{\downarrow_{q,F,k}}= [x]_q ([x]_q-[F_1]_q) \cdots
-([x]_q-[F_{k-1}]_q)$, $[x]_{\uparrow_{q,F,k}}= [x]_q [x+F_1]_q \cdots
-[x+F_{k-1}]_q$, and $\overline{[x]}_{\uparrow_{q,F,k}}= [x]_q ([x]_q+[F_1]_q)
-\cdots ([x]_q+[F_{k-1}]_q)$.
-In this paper, we show we can modify the rook theory model of Bach, Paudyal,
-and Remmel to give combinatorial interpretations for the two different types
-$q$-analogues of the Fibo-Stirling numbers which arise as the connection
-coefficients between the two different $q$-analogues of the Fibonacci falling
-and rising factorial bases. \end{abstract}
-",0,0,1,0,0,0
-19107,Hybrid Dirac Semimetal in CaAgBi Materials Family,"  Based on their formation mechanisms, Dirac points in three-dimensional
-systems can be classified as accidental or essential. The former can be further
-distinguished into type-I and type-II, depending on whether the Dirac cone
-spectrum is completely tipped over along certain direction. Here, we predict
-the coexistence of all three kinds of Dirac points in the low-energy band
-structure of CaAgBi-family materials with a stuffed Wurtzite structure. Two
-pairs of accidental Dirac points reside on the rotational axis, with one pair
-being type-I and the other pair type-II; while another essential Dirac point is
-pinned at the high symmetry point on the Brillouin zone boundary. Due to broken
-inversion symmetry, the band degeneracy around accidental Dirac points is
-completely lifted except along the rotational axis, which may enable the
-splitting of chiral carriers at a ballistic p-n junction with a double negative
-refraction effect. We clarify their symmetry protections, and find both the
-Dirac-cone and Fermi arc topological surface states.
-",0,1,0,0,0,0
-19108,On the martingale property in the rough Bergomi model,"  We consider a class of fractional stochastic volatility models (including the
-so-called rough Bergomi model), where the volatility is a superlinear function
-of a fractional Gaussian process. We show that the stock price is a true
-martingale if and only if the correlation $\rho$ between the driving Brownian
-motions of the stock and the volatility is nonpositive. We also show that for
-each $\rho<0$ and $m> \frac{1}{1-\rho^2}$, the $m$-th moment of the stock
-price is infinite at each positive time.
-",0,0,0,0,0,1
-19109,Weighted Surface Algebras,"  A finite-dimensional algebra $A$ over an algebraically closed field $K$ is
-called periodic if it is periodic under the action of the syzygy operator in
-the category of $A-A-$ bimodules. The periodic algebras are self-injective and
-occur naturally in the study of tame blocks of group algebras, actions of
-finite groups on spheres, hypersurface singularities of finite Cohen-Macaulay
-type, and Jacobian algebras of quivers with potentials. Recently, the tame
-periodic algebras of polynomial growth have been classified and it is natural
-to attempt to classify all tame periodic algebras. We introduce the weighted
-surface algebras of triangulated surfaces with arbitrarily oriented triangles
-and describe their basic properties. In particular, we prove that all these
-algebras, except the singular tetrahedral algebras, are symmetric tame periodic
-algebras of period $4$. Moreover, we describe the socle deformations of the
-weighted surface algebras and prove that all these algebras are symmetric tame
-periodic algebras of period $4$. The main results of the paper form an
-important step towards a classification of all periodic symmetric tame algebras
-of non-polynomial growth, and lead to a complete description of all algebras of
-generalized quaternion type. Further, the orbit closures of the weighted
-surface algebras (and their socle deformations) in the affine varieties of
-associative $K$-algebra structures contain wide classes of tame symmetric
-algebras related to algebras of dihedral and semidihedral types, which occur in
-the study of blocks of group algebras with dihedral and semidihedral defect
-groups.
-",0,0,1,0,0,0
-19110,A Statistical Learning Approach to Modal Regression,"  This paper studies the nonparametric modal regression problem systematically
-from a statistical learning view. Originally motivated by pursuing a
-theoretical understanding of the maximum correntropy criterion based regression
-(MCCR), our study reveals that MCCR with a tending-to-zero scale parameter is
-essentially modal regression. We show that nonparametric modal regression
-problem can be approached via the classical empirical risk minimization. Some
-efforts are then made to develop a framework for analyzing and implementing
-modal regression. For instance, the modal regression function is described, the
-modal regression risk is defined explicitly and its \textit{Bayes} rule is
-characterized; for the sake of computational tractability, the surrogate modal
-regression risk, which is termed as the generalization risk in our study, is
-introduced. On the theoretical side, the excess modal regression risk, the
-excess generalization risk, the function estimation error, and the relations
-among the above three quantities are studied rigorously. It turns out that
-under mild conditions, function estimation consistency and convergence may be
-pursued in modal regression as in vanilla regression protocols, such as mean
-regression, median regression, and quantile regression. However, it outperforms
-these regression models in terms of robustness as shown in our study from a
-re-descending M-estimation view. This coincides with and in return explains the
-merits of MCCR on robustness. On the practical side, the implementation issues
-of modal regression including the computational algorithm and the tuning
-parameters selection are discussed. Numerical assessments on modal regression
-are also conducted to verify our findings empirically.
-",0,0,1,1,0,0
-19111,Teaching a Machine to Read Maps with Deep Reinforcement Learning,"  The ability to use a 2D map to navigate a complex 3D environment is quite
-remarkable, and even difficult for many humans. Localization and navigation is
-also an important problem in domains such as robotics, and has recently become
-a focus of the deep reinforcement learning community. In this paper we teach a
-reinforcement learning agent to read a map in order to find the shortest way
-out of a random maze it has never seen before. Our system combines several
-state-of-the-art methods such as A3C and incorporates novel elements such as a
-recurrent localization cell. Our agent learns to localize itself based on 3D
-first person images and an approximate orientation angle. The agent generalizes
-well to bigger mazes, showing that it learned useful localization and
-navigation capabilities.
-",1,0,0,1,0,0
-19112,The fan beam model for the pulse evolution of PSR J0737-3039B,"  Average radio pulse profile of a pulsar B in a double pulsar system PSR
-J0737-3039A/B exhibits an interesting behaviour. During the observation period
-between 2003 and 2009, the profile evolves from a single-peaked to a
-double-peaked form, following disappearance in 2008 indicating that the
-geodetic precession of the pulsar is a possible origin of such behaviour. The
-known pulsar beam models can be used to determine the geometry of PSR
-J0737-3039B in the context of the precession. We study how the fan-beam
-geometry performs in explaining the observed variations of the radio profile
-morphology. It is shown that the fan beam can successfully reproduce the
-observed evolution of the pulse width, and should be considered as a serious
-alternative for the conal-like models.
-",0,1,0,0,0,0
-19113,Resilience of Core-Periphery Networks in the Case of Rich-Club,"  Core-periphery networks are structures that present a set of central and
-densely connected nodes, namely the core, and a set of non-central and sparsely
-connected nodes, namely the periphery. The rich-club refers to a set in which
-the highest degree nodes show a high density of connections. Thus, a network
-that displays a rich-club can be interpreted as a core-periphery network in
-which the core is made up by a number of hubs. In this paper, we test the
-resilience of networks showing a progressively denser rich-club and we observe
-how this structure is able to affect the network measures in terms of both
-cohesion and efficiency in information flow. Additionally, we consider the case
-in which, instead of making the core denser, we add links to the periphery.
-These two procedures of core and periphery thickening delineate a decision
-process in the placement of new links and allow us to conduct a scenario
-analysis that can be helpful in the comprehension and supervision of complex
-networks under the resilience perspective. The advantages of the two
-procedures, as well as their implications, are discussed in relation to both
-network effciency and node heterogeneity.
-",1,0,0,0,0,0
-19114,Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams,"  The last decade has seen a surge of interest in adaptive learning algorithms
-for data stream classification, with applications ranging from predicting ozone
-level peaks, learning stock market indicators, to detecting computer security
-violations. In addition, a number of methods have been developed to detect
-concept drifts in these streams. Consider a scenario where we have a number of
-classifiers with diverse learning styles and different drift detectors.
-Intuitively, the current 'best' (classifier, detector) pair is application
-dependent and may change as a result of the stream evolution. Our research
-builds on this observation. We introduce the $\mbox{Tornado}$ framework that
-implements a reservoir of diverse classifiers, together with a variety of drift
-detection algorithms. In our framework, all (classifier, detector) pairs
-proceed, in parallel, to construct models against the evolving data streams. At
-any point in time, we select the pair which currently yields the best
-performance. We further incorporate two novel stacking-based drift detection
-methods, namely the $\mbox{FHDDMS}$ and $\mbox{FHDDMS}_{add}$ approaches. The
-experimental evaluation confirms that the current 'best' (classifier, detector)
-pair is not only heavily dependent on the characteristics of the stream, but
-also that this selection evolves as the stream flows. Further, our
-$\mbox{FHDDMS}$ variants detect concept drifts accurately in a timely fashion
-while outperforming the state-of-the-art.
-",1,0,0,1,0,0
-19115,Extremes in Random Graphs Models of Complex Networks,"  Regarding the analysis of Web communication, social and complex networks the
-fast finding of most influential nodes in a network graph constitutes an
-important research problem. We use two indices of the influence of those nodes,
-namely, PageRank and a Max-linear model. We consider the PageRank %both as
-%Galton-Watson branching process and as an autoregressive process with a random
-number of random coefficients that depend on ranks of incoming nodes and their
-out-degrees and assume that the coefficients are independent and distributed
-with regularly varying tail and with the same tail index. Then it is proved
-that the tail index and the extremal index are the same for both PageRank and
-the Max-linear model and the values of these indices are found. The
-achievements are based on the study of random sequences of a random length and
-the comparison of the distribution of their maxima and linear combinations.
-",0,0,1,1,0,0
-19116,Estimates of the Reconstruction Error in Partially Redressed Warped Frames Expansions,"  In recent work, redressed warped frames have been introduced for the analysis
-and synthesis of audio signals with non-uniform frequency and time resolutions.
-In these frames, the allocation of frequency bands or time intervals of the
-elements of the representation can be uniquely described by means of a warping
-map. Inverse warping applied after time-frequency sampling provides the key to
-reduce or eliminate dispersion of the warped frame elements in the conjugate
-variable, making it possible, e.g., to construct frequency warped frames with
-synchronous time alignment through frequency. The redressing procedure is
-however exact only when the analysis and synthesis windows have compact support
-in the domain where warping is applied. This implies that frequency warped
-frames cannot have compact support in the time domain. This property is
-undesirable when online computation is required. Approximations in which the
-time support is finite are however possible, which lead to small reconstruction
-errors. In this paper we study the approximation error for compactly supported
-frequency warped analysis-synthesis elements, providing a few examples and case
-studies.
-",1,0,0,0,0,0
-19117,On the Optimality of Secret Key Agreement via Omniscience,"  For the multiterminal secret key agreement problem under a private source
-model, it is known that the maximum key rate, i.e., the secrecy capacity, can
-be achieved through communication for omniscience, but the omniscience strategy
-can be strictly suboptimal in terms of minimizing the public discussion rate.
-While a single-letter characterization is not known for the minimum discussion
-rate needed for achieving the secrecy capacity, we derive single-letter lower
-and upper bounds that yield some simple conditions for omniscience to be
-discussion-rate optimal. These conditions turn out to be enough to deduce the
-optimality of omniscience for a large class of sources including the
-hypergraphical sources. Through conjectures and examples, we explore other
-source models to which our methods do not easily extend.
-",1,0,0,0,0,0
-19118,Learning Motion Predictors for Smart Wheelchair using Autoregressive Sparse Gaussian Process,"  Constructing a smart wheelchair on a commercially available powered
-wheelchair (PWC) platform avoids a host of seating, mechanical design and
-reliability issues but requires methods of predicting and controlling the
-motion of a device never intended for robotics. Analog joystick inputs are
-subject to black-box transformations which may produce intuitive and adaptable
-motion control for human operators, but complicate robotic control approaches;
-furthermore, installation of standard axle mounted odometers on a commercial
-PWC is difficult. In this work, we present an integrated hardware and software
-system for predicting the motion of a commercial PWC platform that does not
-require any physical or electronic modification of the chair beyond plugging
-into an industry standard auxiliary input port. This system uses an RGB-D
-camera and an Arduino interface board to capture motion data, including visual
-odometry and joystick signals, via ROS communication. Future motion is
-predicted using an autoregressive sparse Gaussian process model. We evaluate
-the proposed system on real-world short-term path prediction experiments.
-Experimental results demonstrate the system's efficacy when compared to a
-baseline neural network model.
-",1,0,0,0,0,0
-19119,Kernelized Hashcode Representations for Relation Extraction,"  Kernel methods have produced state-of-the-art results for a number of NLP
-tasks such as relation extraction, but suffer from poor scalability due to the
-high cost of computing kernel similarities between natural language structures.
-A recently proposed technique, kernelized locality-sensitive hashing (KLSH),
-can significantly reduce the computational cost, but is only applicable to
-classifiers operating on kNN graphs. Here we propose to use random subspaces of
-KLSH codes for efficiently constructing an explicit representation of NLP
-structures suitable for general classification methods. Further, we propose an
-approach for optimizing the KLSH model for classification problems by
-maximizing an approximation of mutual information between the KLSH codes
-(feature vectors) and the class labels. We evaluate the proposed approach on
-biomedical relation extraction datasets, and observe significant and robust
-improvements in accuracy w.r.t. state-of-the-art classifiers, along with
-drastic (orders-of-magnitude) speedup compared to conventional kernel methods.
-",1,0,0,1,0,0
-19120,Can Adversarial Networks Hallucinate Occluded People With a Plausible Aspect?,"  When you see a person in a crowd, occluded by other persons, you miss visual
-information that can be used to recognize, re-identify or simply classify him
-or her. You can imagine its appearance given your experience, nothing more.
-Similarly, AI solutions can try to hallucinate missing information with
-specific deep learning architectures, suitably trained with people with and
-without occlusions. The goal of this work is to generate a complete image of a
-person, given an occluded version in input, that should be a) without occlusion
-b) similar at pixel level to a completely visible people shape c) capable to
-conserve similar visual attributes (e.g. male/female) of the original one. For
-the purpose, we propose a new approach by integrating the state-of-the-art of
-neural network architectures, namely U-nets and GANs, as well as discriminative
-attribute classification nets, with an architecture specifically designed to
-de-occlude people shapes. The network is trained to optimize a Loss function
-which could take into account the aforementioned objectives. As well we propose
-two datasets for testing our solution: the first one, occluded RAP, created
-automatically by occluding real shapes of the RAP dataset (which collects also
-attributes of the people aspect); the second is a large synthetic dataset, AiC,
-generated in computer graphics with data extracted from the GTA video game,
-that contains 3D data of occluded objects by construction. Results are
-impressive and outperform any other previous proposal. This result could be an
-initial step to many further researches to recognize people and their behavior
-in an open crowded world.
-",1,0,0,0,0,0
-19121,"John, the semi-conductor : a tool for comprovisation","  This article presents ""John"", an open-source software designed to help
-collective free improvisation. It provides generated screen-scores running on
-distributed, reactive web-browsers. The musicians can then concurrently edit
-the scores in their own browser. John is used by ONE, a septet playing
-improvised electro-acoustic music with digital musical instruments (DMI). One
-of the original features of John is that its design takes care of leaving the
-musician's attention as free as possible. Firstly, a quick review of the
-context of screen-based scores will help situate this research in the history
-of contemporary music notation. Then I will trace back how improvisation
-sessions led to John's particular ""notational perspective"". A brief description
-of the software will precede a discussion about the various aspects guiding its
-design.
-",1,0,0,0,0,0
-19122,Eigenvalue and Eigenfunction for the $PT$-symmetric Potential $V = - (ix)^N$,"  The real energy spectrum from the $PT$-symmetric Hamiltonian $H = p^2 -
-(ix)^N$ with $x\in\mathbb{C}$ was examined within one pair of Stokes wedges in
-1998 by Bender and Boettcher. For this Hamiltonian we discuss the following
-three questions. First, since their paper used a Runge-Kutta method to
-integrate along a path at the center of the Stokes wedges to calculate
-eigenvalues $E$ with high accuracy, we wonder if the same eigenvalues can be
-obtained if integrate along some other paths in different shapes. Second, what
-the corresponding eigenfunctions look like? Should the eigenfunctions be
-independent from the shapes of path or not? Third, since for large $N$ the
-Hamiltonian contains many pairs of Stokes wedges symmetric with respect to the
-imaginary axis of $x$, thus multiple families of real energy spectrum can be
-obtained. What do they look like? Any relation among them?
-",0,0,1,0,0,0
-19123,Random walks on activity-driven networks with attractiveness,"  Virtually all real-world networks are dynamical entities. In social networks,
-the propensity of nodes to engage in social interactions (activity) and their
-chances to be selected by active nodes (attractiveness) are heterogeneously
-distributed. Here, we present a time-varying network model where each node and
-the dynamical formation of ties are characterised by these two features. We
-study how these properties affect random walk processes unfolding on the
-network when the time scales describing the process and the network evolution
-are comparable. We derive analytical solutions for the stationary state and the
-mean first passage time of the process and we study cases informed by empirical
-observations of social networks. Our work shows that previously disregarded
-properties of real social systems such heterogeneous distributions of activity
-and attractiveness as well as the correlations between them, substantially
-affect the dynamical process unfolding on the network.
-",1,1,0,0,0,0
-19124,Predicting B Cell Receptor Substitution Profiles Using Public Repertoire Data,"  B cells develop high affinity receptors during the course of affinity
-maturation, a cyclic process of mutation and selection. At the end of affinity
-maturation, a number of cells sharing the same ancestor (i.e. in the same
-""clonal family"") are released from the germinal center, their amino acid
-frequency profile reflects the allowed and disallowed substitutions at each
-position. These clonal-family-specific frequency profiles, called ""substitution
-profiles"", are useful for studying the course of affinity maturation as well as
-for antibody engineering purposes. However, most often only a single sequence
-is recovered from each clonal family in a sequencing experiment, making it
-impossible to construct a clonal-family-specific substitution profile. Given
-the public release of many high-quality large B cell receptor datasets, one may
-ask whether it is possible to use such data in a prediction model for
-clonal-family-specific substitution profiles. In this paper, we present the
-method ""Substitution Profiles Using Related Families"" (SPURF), a penalized
-tensor regression framework that integrates information from a rich assemblage
-of datasets to predict the clonal-family-specific substitution profile for any
-single input sequence. Using this framework, we show that substitution profiles
-from similar clonal families can be leveraged together with simulated
-substitution profiles and germline gene sequence information to improve
-prediction. We fit this model on a large public dataset and validate the
-robustness of our approach on an external dataset. Furthermore, we provide a
-command-line tool in an open-source software package
-(this https URL) implementing these ideas and providing easy
-prediction using our pre-fit models.
-",0,0,0,0,1,0
-19125,Energy Efficient Mobile Edge Computing in Dense Cellular Networks,"  Merging Mobile Edge Computing (MEC), which is an emerging paradigm to meet
-the increasing computation demands from mobile devices, with the dense
-deployment of Base Stations (BSs), is foreseen as a key step towards the next
-generation mobile networks. However, new challenges arise for designing energy
-efficient networks since radio access resources and computing resources of BSs
-have to be jointly managed, and yet they are complexly coupled with traffic in
-both spatial and temporal domains. In this paper, we address the challenge of
-incorporating MEC into dense cellular networks, and propose an efficient online
-algorithm, called ENGINE (ENErgy constrained offloadINg and slEeping) which
-makes joint computation offloading and BS sleeping decisions in order to
-maximize the quality of service while keeping the energy consumption low. Our
-algorithm leverages Lyapunov optimization technique, works online and achieves
-a close-to-optimal performance without using future information. Our simulation
-results show that our algorithm can effectively reduce energy consumption
-without sacrificing the user quality of service.
-",1,0,0,0,0,0
-19126,Delone dynamical systems and spectral convergence,"  In the realm of Delone sets in locally compact, second countable, Hausdorff
-groups, we develop a dynamical systems approach in order to study the
-continuity behavior of measured quantities arising from point sets. A special
-focus is both on the autocorrelation, as well as on the density of states for
-random bounded operators. It is shown that for uniquely ergodic limit systems,
-the latter measures behave continuously with respect to the Chabauty-Fell
-convergence of hulls. In the special situation of Euclidean spaces, our results
-complement recent developments in describing spectra as topological limits: we
-show that the measured quantities under consideration can be approximated via
-periodic analogs.
-",0,0,1,0,0,0
-19127,On biconservative surfaces in Euclidean spaces,"  In this paper, we study biconservative surfaces with parallel normalized mean
-curvature vector in $\mathbb{E}^4$. We obtain complete local classification in
-$\mathbb{E}^4$ for a biconservative PNMCV surface. We also give an example to
-show the existence of PNMCV biconservative surfaces in $\mathbb{E}^4$.
-",0,0,1,0,0,0
-19128,Automatic implementation of material laws: Jacobian calculation in a finite element code with TAPENADE,"  In an effort to increase the versatility of finite element codes, we explore
-the possibility of automatically creating the Jacobian matrix necessary for the
-gradient-based solution of nonlinear systems of equations. Particularly, we aim
-to assess the feasibility of employing the automatic differentiation tool
-TAPENADE for this purpose on a large Fortran codebase that is the result of
-many years of continuous development. As a starting point we will describe the
-special structure of finite element codes and the implications that this code
-design carries for an efficient calculation of the Jacobian matrix. We will
-also propose a first approach towards improving the efficiency of such a
-method. Finally, we will present a functioning method for the automatic
-implementation of the Jacobian calculation in a finite element software, but
-will also point out important shortcomings that will have to be addressed in
-the future.
-",1,1,0,0,0,0
-19129,Asymptotic analysis for Hamilton-Jacobi equations with large drift term,"  We investigate the asymptotic behavior of solutions of Hamilton-Jacobi
-equations with large drift term in an open subset of two-dimensional Euclidean
-space. When the drift is given by $\varepsilon^{-1} (H_{x_2}, -H_{x_1})$ of a
-Hamiltonian $H$, with $\varepsilon > 0$, we establish the convergence, as
-$\varepsilon \to 0+$, of solutions of the Hamilton-Jacobi equations and
-identify the limit of the solutions as the solution of systems of ordinary
-differential equations on a graph. This result generalizes the previous one
-obtained by the author to the case where the Hamiltonian $H$ admits a
-degenerate critical point and, as a consequence, the graph may have segments
-more than four at a node.
-",0,0,1,0,0,0
-19130,An Upper Bound of the Minimal Dispersion via Delta Covers,"  For a point set of $n$ elements in the $d$-dimensional unit cube and a class
-of test sets we are interested in the largest volume of a test set which does
-not contain any point. For all natural numbers $n$, $d$ and under the
-assumption of a $delta$-cover with cardinality $\vert \Gamma_\delta \vert$ we
-prove that there is a point set, such that the largest volume of such a test
-set without any point is bounded by $\frac{\log \vert \Gamma_\delta \vert}{n} +
-\delta$. For axis-parallel boxes on the unit cube this leads to a volume of at
-most $\frac{4d}{n}\log(\frac{9n}{d})$ and on the torus to $\frac{4d}{n}\log
-(2n)$.
-",1,0,1,0,0,0
-19131,Three-dimensional band structure of LaSb and CeSb:Absence of band inversion,"  We have performed angle-resolved photoemission spectroscopy (ARPES) of LaSb
-and CeSb, a candidate of topological insulator. Using soft-x-ray photons, we
-have accurately determined the three-dimensional bulk band structure and
-revealed that the band inversion at the Brillouin-zone corner - a prerequisite
-for realizing topological-insulator phase - is absent in both LaSb and CeSb.
-Moreover, unlike the ARPES data obtained with soft-x-ray photons, those with
-vacuum ultraviolet (VUV) photons were found to suffer significant $k_z$
-broadening. These results suggest that LaSb and CeSb are topologically trivial
-semimetals, and unusual Dirac-cone-like states observed with VUV photons are
-not of the topological origin.
-",0,1,0,0,0,0
-19132,Stellar-to-halo mass relation of cluster galaxies,"  In the hierarchical formation model, galaxy clusters grow by accretion of
-smaller groups or isolated galaxies. During the infall into the centre of a
-cluster, the properties of accreted galaxies change. In particular, both
-observations and numerical simulations suggest that its dark matter halo is
-stripped by the tidal forces of the host.
-We use galaxy-galaxy weak lensing to measure the average mass of dark matter
-haloes of satellite galaxies as a function of projected distance to the centre
-of the host, for different stellar mass bins. Assuming that the stellar
-component of the galaxy is less disrupted by tidal stripping, stellar mass can
-be used as a proxy of the infall mass. We study the stellar to halo mass
-relation of satellites as a function of the cluster-centric distance to measure
-tidal stripping.
-We use the shear catalogues of the DES science verification archive, the
-CFHTLenS and the CFHT Stripe 82 (CS82) surveys, and we select satellites from
-the redMaPPer catalogue of clusters. For galaxies located in the outskirts of
-clusters, we find a stellar to halo mass relation in good agreement with the
-theoretical expectations from \citet{moster2013} for central galaxies. In the
-centre of the cluster, we find that this relation is shifted to smaller halo
-mass for a given stellar mass. We interpret this finding as further evidence
-for tidal stripping of dark matter haloes in high density environments.
-",0,1,0,0,0,0
-19133,Undecidability and Finite Automata,"  Using a novel rewriting problem, we show that several natural decision
-problems about finite automata are undecidable (i.e., recursively unsolvable).
-In contrast, we also prove three related problems are decidable. We apply one
-result to prove the undecidability of a related problem about k-automatic sets
-of rational numbers.
-",1,0,0,0,0,0
-19134,A globally stable attractor that is locally unstable everywhere,"  We construct two examples of invariant manifolds that despite being locally
-unstable at every point in the transverse direction are globally stable. Using
-numerical simulations we show that these invariant manifolds temporarily repel
-nearby trajectories but act as global attractors. We formulate an explanation
-for such global stability in terms of the `rate of rotation' of the stable and
-unstable eigenvectors spanning the normal subspace associated with each point
-of the invariant manifold. We discuss the role of this rate of rotation on the
-transitions between the stable and unstable regimes.
-",0,1,0,0,0,0
-19135,On Centers and Central Lines of Triangles in the Elliptic Plane,"  We determine barycentric coordinates of triangle centers in the elliptic
-plane. The main focus is put on centers that lie on lines whose euclidean limit
-(triangle excess --> 0) is the Euler line or the Brocard line. We also
-investigate curves which can serve in elliptic geometry as substitutes for the
-euclidean nine-point-circle, the first Lemoine circle or the apollonian
-circles.
-",0,0,1,0,0,0
-19136,Stopping Active Learning based on Predicted Change of F Measure for Text Classification,"  During active learning, an effective stopping method allows users to limit
-the number of annotations, which is cost effective. In this paper, a new
-stopping method called Predicted Change of F Measure will be introduced that
-attempts to provide the users an estimate of how much performance of the model
-is changing at each iteration. This stopping method can be applied with any
-base learner. This method is useful for reducing the data annotation bottleneck
-encountered when building text classification systems.
-",1,0,0,1,0,0
-19137,Statistical properties of random clique networks,"  In this paper, a random clique network model to mimic the large clustering
-coefficient and the modular structure that exist in many real complex networks,
-such as social networks, artificial networks, and protein interaction networks,
-is introduced by combining the random selection rule of the Erdös and Rényi
-(ER) model and the concept of cliques. We find that random clique networks
-having a small average degree differ from the ER network in that they have a
-large clustering coefficient and a power law clustering spectrum, while
-networks having a high average degree have similar properties as the ER model.
-In addition, we find that the relation between the clustering coefficient and
-the average degree shows a non-monotonic behavior and that the degree
-distributions can be fit by multiple Poisson curves; we explain the origin of
-such novel behaviors and degree distributions.
-",1,1,0,0,0,0
-19138,"Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset","  The paucity of videos in current action classification datasets (UCF-101 and
-HMDB-51) has made it difficult to identify good video architectures, as most
-methods obtain similar performance on existing small-scale benchmarks. This
-paper re-evaluates state-of-the-art architectures in light of the new Kinetics
-Human Action Video dataset. Kinetics has two orders of magnitude more data,
-with 400 human action classes and over 400 clips per class, and is collected
-from realistic, challenging YouTube videos. We provide an analysis on how
-current architectures fare on the task of action classification on this dataset
-and how much performance improves on the smaller benchmark datasets after
-pre-training on Kinetics.
-We also introduce a new Two-Stream Inflated 3D ConvNet (I3D) that is based on
-2D ConvNet inflation: filters and pooling kernels of very deep image
-classification ConvNets are expanded into 3D, making it possible to learn
-seamless spatio-temporal feature extractors from video while leveraging
-successful ImageNet architecture designs and even their parameters. We show
-that, after pre-training on Kinetics, I3D models considerably improve upon the
-state-of-the-art in action classification, reaching 80.9% on HMDB-51 and 98.0%
-on UCF-101.
-",1,0,0,0,0,0
-19139,Selfish Cops and Active Robber: Multi-Player Pursuit Evasion on Graphs,"  We introduce and study the game of ""Selfish Cops and Active Robber"" (SCAR)
-which can be seen as an multiplayer variant of the ""classic"" two-player Cops
-and Robbers (CR) game. In classic CR all cops are controlled by a single
-player, who has no preference over which cop captures the robber. In SCAR, on
-the other hand, each of N-1 cops is controlled by a separate player, and a
-single robber is controlled by the N-th player; and the capturing cop player
-receives a higher reward than the non-capturing ones. Consequently, SCAR is an
-N-player pursuit game on graphs, in which each cop player has an increased
-motive to be the one who captures the robber. The focus of our study is the
-existence and properties of SCAR Nash Equilibria (NE). In particular, we prove
-that SCAR always has one NE in deterministic positional strategies and (for N
-greater than two) another in deterministic nonpositional strategies.
-Furthermore, we study conditions which, at equilibrium, guarantee either
-capture or escape of the robber and show that (because of the antagonism
-between the ""selfish"" cop players) the robber may, in certain SCAR
-configurations, be captured later than he would be in classic CR, or even not
-captured at all. Finally we define the selfish cop number of a graph and study
-its connection to the classic cop number.
-",1,0,0,0,0,0
-19140,Singly-Thermostated Ergodicity in Gibbs' Canonical Ensemble and the 2016 Ian Snook Prize Award,"  The 2016 Snook Prize has been awarded to Diego Tapias, Alessandro Bravetti,
-and David Sanders for their paper -- Ergodicity of One-Dimensional Systems
-Coupled to the Logistic Thermostat. They introduced a relatively stiff
-hyperbolic tangent thermostat force and successfully tested its ability to
-reproduce Gibbs' canonical distribution for the harmonic oscillator, the
-quartic oscillator, and the Mexican Hat potentials. Their work constitutes an
-effective response to the 2016 Ian Snook Prize Award goal -- Finding ergodic
-algorithms for Gibbs' canonical ensemble using a single thermostat variable. We
-confirm their work here and highlight an interesting feature of the Mexican Hat
-problem when it is solved with an adaptive integrator.
-",0,1,0,0,0,0
-19141,Robust Estimation via Robust Gradient Estimation,"  We provide a new computationally-efficient class of estimators for risk
-minimization. We show that these estimators are robust for general statistical
-models: in the classical Huber epsilon-contamination model and in heavy-tailed
-settings. Our workhorse is a novel robust variant of gradient descent, and we
-provide conditions under which our gradient descent variant provides accurate
-estimators in a general convex risk minimization problem. We provide specific
-consequences of our theory for linear regression, logistic regression and for
-estimation of the canonical parameters in an exponential family. These results
-provide some of the first computationally tractable and provably robust
-estimators for these canonical statistical models. Finally, we study the
-empirical performance of our proposed methods on synthetic and real datasets,
-and find that our methods convincingly outperform a variety of baselines.
-",0,0,0,1,0,0
-19142,SMT Solving for Vesicle Traffic Systems in Cells,"  In biology, there are several questions that translate to combinatorial
-search. For example, vesicle traffic systems that move cargo within eukaryotic
-cells have been proposed to exhibit several graph properties such as three
-connectivity. These properties are consequences of underlying biophysical
-constraints. A natural question for biologists is: what are the possible
-networks for various combinations of those properties? In this paper, we
-present novel SMT based encodings of the properties over vesicle traffic
-systems and a tool that searches for the networks that satisfies the properties
-using SMT solvers. In our experiments, we show that our tool can search for
-networks of sizes that are considered to be relevant by biologists.
-",1,0,0,0,0,0
-19143,Endemicity and prevalence of multipartite viruses under heterogeneous between-host transmission,"  Multipartite viruses replicate through a puzzling evolutionary strategy.
-Their genome is segmented into two or more parts, and encapsidated in separate
-particles that appear to propagate independently. Completing the replication
-cycle, however, requires the full genome, so that a persistent infection of a
-host requires the concurrent presence of several particles. This represents an
-apparent evolutionary drawback of multipartitism, while its advantages remain
-unclear. A transition from monopartite to multipartite viral forms has been
-described in vitro under conditions of high multiplicity of infection,
-suggesting that cooperation between defective mutants is a plausible
-evolutionary pathway towards multipartitism. However, it is unknown how the
-putative advantages that multipartitism might enjoy affect its epidemiology, or
-if an explicit advantage is needed to explain its ecological persistence. To
-disentangle which mechanisms might contribute to the rise and fixation of
-multipartitism, we here investigate the interaction between viral spreading
-dynamics and host population structure. We set up a compartmental model of the
-spread of a virus in its different forms and explore its epidemiology using
-both analytical and numerical techniques. We uncover that the impact of host
-contact structure on spreading dynamics entails a rich phenomenology of
-ecological relationships that includes cooperation, competition, and
-commensality. Furthermore, we find out that multipartitism might rise to
-fixation even in the absence of explicit microscopic advantages. Multipartitism
-allows the virus to colonize environments that could not be invaded by the
-monopartite form, while homogeneous contacts between hosts facilitate its
-spread. We conjecture that there might have been an increase in the diversity
-and prevalence of multipartite viral forms concomitantly with the expansion of
-agricultural practices.
-",0,0,0,0,1,0
-19144,Correlating Cell Shape and Cellular Stress in Motile Confluent Tissues,"  Collective cell migration is a highly regulated process involved in wound
-healing, cancer metastasis and morphogenesis. Mechanical interactions among
-cells provide an important regulatory mechanism to coordinate such collective
-motion. Using a Self-Propelled Voronoi (SPV) model that links cell mechanics to
-cell shape and cell motility, we formulate a generalized mechanical inference
-method to obtain the spatio-temporal distribution of cellular stresses from
-measured traction forces in motile tissues and show that such traction-based
-stresses match those calculated from instantaneous cell shapes. We additionally
-use stress information to characterize the rheological properties of the
-tissue. We identify a motility-induced swim stress that adds to the interaction
-stress to determine the global contractility or extensibility of epithelia. We
-further show that the temporal correlation of the interaction shear stress
-determines an effective viscosity of the tissue that diverges at the
-liquid-solid transition, suggesting the possibility of extracting rheological
-information directly from traction data.
-",0,1,0,0,0,0
-19145,Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks,"  We consider the problem of learning function classes computed by neural
-networks with various activations (e.g. ReLU or Sigmoid), a task believed to be
-computationally intractable in the worst-case. A major open problem is to
-understand the minimal assumptions under which these classes admit provably
-efficient algorithms. In this work we show that a natural distributional
-assumption corresponding to {\em eigenvalue decay} of the Gram matrix yields
-polynomial-time algorithms in the non-realizable setting for expressive classes
-of networks (e.g. feed-forward networks of ReLUs). We make no assumptions on
-the structure of the network or the labels. Given sufficiently-strong
-polynomial eigenvalue decay, we obtain {\em fully}-polynomial time algorithms
-in {\em all} the relevant parameters with respect to square-loss. Milder decay
-assumptions also lead to improved algorithms. This is the first purely
-distributional assumption that leads to polynomial-time algorithms for networks
-of ReLUs, even with one hidden layer. Further, unlike prior distributional
-assumptions (e.g., the marginal distribution is Gaussian), eigenvalue decay has
-been observed in practice on common data sets.
-",1,0,0,0,0,0
-19146,A New Steganographic Technique Matching the Secret Message and Cover image Binary Value,"  Steganography involves hiding a secret message or image inside another cover
-image. Changes are made in the cover image without affecting visual quality of
-the image. In contrast to cryptography, Steganography provides complete secrecy
-of the communication. Security of very sensitive data can be enhanced by
-combining cryptography and steganography. A new technique that uses the concept
-of Steganography to obtain the position values from an image is suggested. This
-paper proposes a new method where no change is made to the cover image, only
-the pixel position LSB (Least Significant Bit) values that match with the
-secret message bit values are noted in a separate position file. At the sending
-end the position file along with the cover image is sent. At the receiving end
-the position file is opened only with a secret key. The bit positions are taken
-from the position file and the LSB values from the positions are combined to
-get ASCII values and then form characters of the secret message
-",1,0,0,0,0,0
-19147,On topological fluid mechanics of non-ideal systems and virtual frozen-in dynamics,"  Euler and Navier-Stokes have variant systems with dynamical invariance of
-helicity and thus (weak) topological equivalence, allowing a strong `frozen-in'
-(to, or, dually, `Lie-carried' by the \textit{virtual} velocity $V$)
-formulation of the vorticity with a flavor of `inverse Helmholtz theorem'. We
-remark on the non-ideal (statistical) topological fluid mechanics (TFM) for (1)
-the Constantin-Iyer formulation of Navier-Stokes, (2) our own extension of the
-Gallavotti-Cohen type dynamical ensembles of modified Navier-Stokes with
-energy-helicity constraints and (3) the Galerkin truncated Euler, as the
-typical case variants with dynamical time reversibility and helicity
-invariance. Ideal TFM is thus bridged with non-ideal flows. An example virtual
-(Lie-)carrier of the vorticity in a Galerkin-truncated Euler system is
-calculated to demonstrate the issue of determining $V$.
-",0,1,0,0,0,0
-19148,Topological AdS/CFT,"  We define a holographic dual to the Donaldson-Witten topological twist of
-$\mathcal{N}=2$ gauge theories on a Riemannian four-manifold. This is described
-by a class of asymptotically locally hyperbolic solutions to $\mathcal{N}=4$
-gauged supergravity in five dimensions, with the four-manifold as conformal
-boundary. Under AdS/CFT, minus the logarithm of the partition function of the
-gauge theory is identified with the holographically renormalized supergravity
-action. We show that the latter is independent of the metric on the boundary
-four-manifold, as required for a topological theory. Supersymmetric solutions
-in the bulk satisfy first order differential equations for a twisted $Sp(1)$
-structure, which extends the quaternionic Kahler structure that exists on any
-Riemannian four-manifold boundary. We comment on applications and extensions,
-including generalizations to other topological twists.
-",0,0,1,0,0,0
-19149,Using highly uniform and smooth Selenium colloids as low-loss magnetodielectric building blocks of optical metafluids,"  We systematically analyzed magnetodielectric resonances of Se colloids for
-the first time to exploit the possibility for use as building blocks of
-all-dielectric optical metafluids. By taking synergistic advantages of Se
-colloids, including (i) high-refractive-index at optical frequencies, (ii)
-unprecedented structural uniformity, and (iii) versatile access to copious
-quantities, the Kerker-type directional light scattering resulting from
-efficient coupling between strong electric and magnetic resonances were
-observed directly from Se colloidal suspension. Thus, the use of Se colloid as
-a generic magnetodielectric building block highlights an opportunity for the
-fluidic low-loss optical antenna, which can be processed via spin-coating and
-painting.
-",0,1,0,0,0,0
-19150,A Vector Field Method for Radiating Black Hole Spacetimes,"  We develop a commuting vector field method for a general class of radiating
-spacetimes. The metrics considered are certain long range perturbations of
-Minkowski space including those constructed from global stability problems in
-general relativity. Our method provides sharp peeling estimates for solutions
-to both linear and nonlinear (null form) scalar fields.
-",0,0,1,0,0,0
-19151,Experimental Constraint on an Exotic Spin- and Velocity-Dependent Interaction in the Sub-meV Range of Axion Mass with a Spin-Exchange Relaxation-Free Magnetometer,"  We conducted a search for an exotic spin- and velocity-dependent interaction
-for polarized electrons with an experimental approach based on a
-high-sensitivity spin-exchange relaxation-free (SERF) magnetometer, which
-serves as both a source of polarized electrons and a magnetic-field sensor. The
-experiment aims to sensitively detect magnetic-fieldlike effects from the
-exotic interaction between the polarized electrons in a SERF vapor cell and
-unpolarized nucleons of a closely located solid-state mass. We report
-experimental results on the interaction with 82 h of data averaging, which sets
-an experimental limit on the coupling strength around $10^{-19}$ for the axion
-mass $m_a \lesssim 10^{-3}$ eV, within the important axion window.
-",0,1,0,0,0,0
-19152,Generalization Error in Deep Learning,"  Deep learning models have lately shown great performance in various fields
-such as computer vision, speech recognition, speech translation, and natural
-language processing. However, alongside their state-of-the-art performance, it
-is still generally unclear what is the source of their generalization ability.
-Thus, an important question is what makes deep neural networks able to
-generalize well from the training set to new data. In this article, we provide
-an overview of the existing theory and bounds for the characterization of the
-generalization error of deep neural networks, combining both classical and more
-recent theoretical and empirical results.
-",0,0,0,1,0,0
-19153,Block Compressive Sensing of Image and Video with Nonlocal Lagrangian Multiplier and Patch-based Sparse Representation,"  Although block compressive sensing (BCS) makes it tractable to sense
-large-sized images and video, its recovery performance has yet to be
-significantly improved because its recovered images or video usually suffer
-from blurred edges, loss of details, and high-frequency oscillatory artifacts,
-especially at a low subrate. This paper addresses these problems by designing a
-modified total variation technique that employs multi-block gradient
-processing, a denoised Lagrangian multiplier, and patch-based sparse
-representation. In the case of video, the proposed recovery method is able to
-exploit both spatial and temporal similarities. Simulation results confirm the
-improved performance of the proposed method for compressive sensing of images
-and video in terms of both objective and subjective qualities.
-",1,0,0,0,0,0
-19154,A multi-phase-field method for surface tension-induced elasticity,"  A consistent treatment of the coupling of surface energy and elasticity
-within the multi-phase- field framework is presented. The model accurately
-reproduces stress distribution in a number of analytically tractable, yet
-non-trivial, cases including different types of spherical heterogeneities and a
-thin plate suspending in a gas environment. It is then used to study the stress
-distribution inside elastic bodies with non-spherical geometries, such as a
-solid ellipsoid and a sintered structure. In these latter cases, it is shown
-that the interplay between deformation and spatially variable surface curvature
-leads to heterogeneous stress distribution across the specimen.
-",0,1,0,0,0,0
-19155,On the restriction theorem for paraboloid in $\mathbb R^4$,"  We prove that recent breaking by Zahl of the $\frac32$ barrier in Wolff's
-estimate on the Kakeya maximal operator in $\mathbb R^4$ leads to improving the
-$\frac{14}{5}$ threshold for the restriction problem for the paraboloid in
-$\mathbb R^4$. One of the ingredients is a new trilinear estimate. The proofs
-are deliberately presented in a nontechnical and concise format, so as to make
-the arguments more readable and focus attention on the key tools.
-",0,0,1,0,0,0
-19156,"Existence of Stein Kernels under a Spectral Gap, and Discrepancy Bound","  We establish existence of Stein kernels for probability measures on
-$\mathbb{R}^d$ satisfying a Poincaré inequality, and obtain bounds on the
-Stein discrepancy of such measures. Applications to quantitative central limit
-theorems are discussed, including a new CLT in Wasserstein distance $W_2$ with
-optimal rate and dependence on the dimension. As a byproduct, we obtain a
-stability version of an estimate of the Poincaré constant of probability
-measures under a second moment constraint. The results extend more generally to
-the setting of converse weighted Poincaré inequalities. The proof is based on
-simple arguments of calculus of variations.
-Further, we establish two general properties enjoyed by the Stein
-discrepancy, holding whenever a Stein kernel exists: Stein discrepancy is
-strictly decreasing along the CLT, and it controls the skewness of a random
-vector.
-",1,0,1,0,0,0
-19157,High-order asynchrony-tolerant finite difference schemes for partial differential equations,"  Synchronizations of processing elements (PEs) in massively parallel
-simulations, which arise due to communication or load imbalances between PEs,
-significantly affect the scalability of scientific applications. We have
-recently proposed a method based on finite-difference schemes to solve partial
-differential equations in an asynchronous fashion -- synchronization between
-PEs is relaxed at a mathematical level. While standard schemes can maintain
-their stability in the presence of asynchrony, their accuracy is drastically
-affected. In this work, we present a general methodology to derive
-asynchrony-tolerant (AT) finite difference schemes of arbitrary order of
-accuracy, which can maintain their accuracy when synchronizations are relaxed.
-We show that there are several choices available in selecting a stencil to
-derive these schemes and discuss their effect on numerical and computational
-performance. We provide a simple classification of schemes based on the stencil
-and derive schemes that are representative of different classes. Their
-numerical error is rigorously analyzed within a statistical framework to obtain
-the overall accuracy of the solution. Results from numerical experiments are
-used to validate the performance of the schemes.
-",0,1,0,0,0,0
-19158,Hazard Analysis and Risk Assessment for an Automated Unmanned Protective Vehicle,"  For future application of automated vehicles in public traffic, ensuring
-functional safety is essential. In this context, a hazard analysis and risk
-assessment is an important input for designing functionally vehicle automation
-systems. In this contribution, we present a detailed hazard analysis and risk
-assessment (HARA) according to the ISO 26262 standard for a specific Level 4
-application, namely an unmanned protective vehicle operated without human
-supervision for motorway hard shoulder roadworks.
-",1,0,0,0,0,0
-19159,Detailed experimental and numerical analysis of a cylindrical cup deep drawing: pros and cons of using solid-shell elements,"  The Swift test was originally proposed as a formability test to reproduce the
-conditions observed in deep drawing operations. This test consists on forming a
-cylindrical cup from a circular blank, using a flat bottom cylindrical punch
-and has been extensively studied using both analytical and numerical methods.
-This test can also be combined with the Demeri test, which consists in cutting
-a ring from the wall of a cylindrical cup, in order to open it afterwards to
-measure the springback. This combination allows their use as benchmark test, in
-order to improve the knowledge concerning the numerical simulation models,
-through the comparison between experimental and numerical results. The focus of
-this study is the experimental and numerical analyses of the Swift cup test,
-followed by the Demeri test, performed with an AA5754-O alloy at room
-temperature. In this context, a detailed analysis of the punch force evolution,
-the thickness evolution along the cup wall, the earing profile, the strain
-paths and their evolution and the ring opening is performed. The numerical
-simulation is performed using the finite element code ABAQUS, with solid and
-solid-shell elements, in order to compare the computational efficiency of these
-type of elements. The results show that the solid-shell element is more
-cost-effective than the solid, presenting global accurate predictions, excepted
-for the thinning zones. Both the von Mises and the Hill48 yield criteria
-predict the strain distributions in the final cup quite accurately. However,
-improved knowledge concerning the stress states is still required, because the
-Hill48 criterion showed difficulties in the correct prediction of the
-springback, whatever the type of finite element adopted.
-",0,1,0,0,0,0
-19160,Hubble Frontier Fields: systematic errors in strong lensing models of galaxy clusters - Implications for cosmography,"  Strong gravitational lensing by galaxy clusters is a fundamental tool to
-study dark matter and constrain the geometry of the Universe. Recently, the
-Hubble Space Telescope Frontier Fields programme has allowed a significant
-improvement of mass and magnification measurements but lensing models still
-have a residual root mean square between 0.2 arcsec and few arcsec- onds, not
-yet completely understood. Systematic errors have to be better understood and
-treated in order to use strong lensing clusters as reliable cosmological
-probes. We have analysed two simulated Hubble-Frontier-Fields-like clusters
-from the Hubble Frontier Fields Comparison Challenge, Ares and Hera. We use
-several estimators (relative bias on magnification, den- sity profiles,
-ellipticity and orientation) to quantify the goodness of our reconstructions by
-comparing our multiple models, optimized with the parametric software LENSTOOL
-, with the input models. We have quantified the impact of systematic errors
-arising, first, from the choice of different density profiles and
-configurations and, secondly, from the availability of con- straints
-(spectroscopic or photometric redshifts, redshift ranges of the background
-sources) in the parametric modelling of strong lensing galaxy clusters and
-therefore on the retrieval of cosmological parameters. We find that
-substructures in the outskirts have a significant im- pact on the position of
-the multiple images, yielding tighter cosmological contours. The need for
-wide-field imaging around massive clusters is thus reinforced. We show that
-competitive cosmological constraints can be obtained also with complex
-multimodal clusters and that photometric redshifts improve the constraints on
-cosmological parameters when considering a narrow range of (spectroscopic)
-redshifts for the sources.
-",0,1,0,0,0,0
-19161,A Note on the Spectral Transfer Morphisms for Affine Hecke Algebras,"  E. Opdam introduced the tool of spectral transfer morphism (STM) of affine
-Hecke algebras to study the formal degrees of unipotent discrete series
-representations. He established a uniqueness property of STM for the affine
-Hecke algebras associated of unipotent discrete series representations. Based
-on this result, Opdam gave an explanation for Lusztig's arithmetic/geometric
-correspondence (in Lusztig's classification of unipotent representations of
-$p$-adic adjoint simple groups) in terms of harmonic analysis, and partitioned
-the unipotent discrete series representations into $L$-packets based on the
-Lusztig-Langlands parameters. The present paper provides some omitted details
-for the argument of the uniqueness property of STM. In the last section, we
-prove that three finite morphisms of algebraic tori are spectral transfer
-morphisms, and hence complete the proof of the uniqueness property.
-",0,0,1,0,0,0
-19162,Batch-Expansion Training: An Efficient Optimization Framework,"  We propose Batch-Expansion Training (BET), a framework for running a batch
-optimizer on a gradually expanding dataset. As opposed to stochastic
-approaches, batches do not need to be resampled i.i.d. at every iteration, thus
-making BET more resource efficient in a distributed setting, and when
-disk-access is constrained. Moreover, BET can be easily paired with most batch
-optimizers, does not require any parameter-tuning, and compares favorably to
-existing stochastic and batch methods. We show that when the batch size grows
-exponentially with the number of outer iterations, BET achieves optimal
-$O(1/\epsilon)$ data-access convergence rate for strongly convex objectives.
-Experiments in parallel and distributed settings show that BET performs better
-than standard batch and stochastic approaches.
-",1,0,0,0,0,0
-19163,Coordinate Descent with Bandit Sampling,"  Coordinate descent methods usually minimize a cost function by updating a
-random decision variable (corresponding to one coordinate) at a time. Ideally,
-we would update the decision variable that yields the largest decrease in the
-cost function. However, finding this coordinate would require checking all of
-them, which would effectively negate the improvement in computational
-tractability that coordinate descent is intended to afford. To address this, we
-propose a new adaptive method for selecting a coordinate. First, we find a
-lower bound on the amount the cost function decreases when a coordinate is
-updated. We then use a multi-armed bandit algorithm to learn which coordinates
-result in the largest lower bound by interleaving this learning with
-conventional coordinate descent updates except that the coordinate is selected
-proportionately to the expected decrease. We show that our approach improves
-the convergence of coordinate descent methods both theoretically and
-experimentally.
-",1,0,0,1,0,0
-19164,Closing the gap for pseudo-polynomial strip packing,"  The set of 2-dimensional packing problems builds an important class of
-optimization problems and Strip Packing together with 2-dimensional Bin Packing
-and 2-dimensional Knapsack is one of the most famous of these problems. Given a
-set of rectangular axis parallel items and a strip with bounded width and
-infinite height the objective is to find a packing of the items into the strip
-which minimizes the packing height. We speak of pseudo-polynomial Strip Packing
-if we consider algorithms with pseudo-polynomial running time with respect to
-the width of the strip.
-It is known that there is no pseudo-polynomial algorithm for Strip Packing
-with a ratio better than $5/4$ unless $\mathrm{P} = \mathrm{NP}$. The best
-algorithm so far has a ratio of $(4/3 + \varepsilon)$. In this paper, we close
-this gap between inapproximability result and best known algorithm by
-presenting an algorithm with approximation ratio $(5/4 + \varepsilon)$ and thus
-categorize the problem accurately. The algorithm uses a structural result which
-states that each optimal solution can be transformed such that it has one of a
-polynomial number of different forms. The strength of this structural result is
-that it applies to other problem settings as well for example to Strip Packing
-with rotations (90 degrees) and Contiguous Moldable Task Scheduling. This fact
-enabled us to present algorithms with approximation ratio $(5/4 + \varepsilon)$
-for these problems as well.
-",1,0,0,0,0,0
-19165,"Notes on the replica symmetric solution of the classical and quantum SK model, including the matrix of second derivatives and the spin glass susceptibility","  A review of the replica symmetric solution of the classical and quantum,
-infinite-range, Sherrington-Kirkpatrick spin glass is presented.
-",0,1,0,0,0,0
-19166,Self-supervised Knowledge Distillation Using Singular Value Decomposition,"  To solve deep neural network (DNN)'s huge training dataset and its high
-computation issue, so-called teacher-student (T-S) DNN which transfers the
-knowledge of T-DNN to S-DNN has been proposed. However, the existing T-S-DNN
-has limited range of use, and the knowledge of T-DNN is insufficiently
-transferred to S-DNN. To improve the quality of the transferred knowledge from
-T-DNN, we propose a new knowledge distillation using singular value
-decomposition (SVD). In addition, we define a knowledge transfer as a
-self-supervised task and suggest a way to continuously receive information from
-T-DNN. Simulation results show that a S-DNN with a computational cost of 1/5 of
-the T-DNN can be up to 1.1\% better than the T-DNN in terms of classification
-accuracy. Also assuming the same computational cost, our S-DNN outperforms the
-S-DNN driven by the state-of-the-art distillation with a performance advantage
-of 1.79\%. code is available on this https URL\_SVD.
-",0,0,0,1,0,0
-19167,Can justice be fair when it is blind? How social network structures can promote or prevent the evolution of despotism,"  Hierarchy is an efficient way for a group to organize, but often goes along
-with inequality that benefits leaders. To control despotic behaviour, followers
-can assess leaders decisions by aggregating their own and their neighbours
-experience, and in response challenge despotic leaders. But in hierarchical
-social networks, this interactional justice can be limited by (i) the high
-influence of a small clique who are treated better, and (ii) the low
-connectedness of followers. Here we study how the connectedness of a social
-network affects the co-evolution of despotism in leaders and tolerance to
-despotism in followers. We simulate the evolution of a population of agents,
-where the influence of an agent is its number of social links. Whether a leader
-remains in power is controlled by the overall satisfaction of group members, as
-determined by their joint assessment of the leaders behaviour. We demonstrate
-that centralization of a social network around a highly influential clique
-greatly increases the level of despotism. This is because the clique is more
-satisfied, and their higher influence spreads their positive opinion of the
-leader throughout the network. Finally, our results suggest that increasing the
-connectedness of followers limits despotism while maintaining hierarchy.
-",0,0,0,0,1,0
-19168,GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization,"  Bioinformatics tools have been developed to interpret gene expression data at
-the gene set level, and these gene set based analyses improve the biologists'
-capability to discover functional relevance of their experiment design. While
-elucidating gene set individually, inter gene sets association is rarely taken
-into consideration. Deep learning, an emerging machine learning technique in
-computational biology, can be used to generate an unbiased combination of gene
-set, and to determine the biological relevance and analysis consistency of
-these combining gene sets by leveraging large genomic data sets. In this study,
-we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model
-with the incorporation of a priori defined gene sets that retain the crucial
-biological features in the latent layer. We introduced the concept of the gene
-superset, an unbiased combination of gene sets with weights trained by the
-autoencoder, where each node in the latent layer is a superset. Trained with
-genomic data from TCGA and evaluated with their accompanying clinical
-parameters, we showed gene supersets' ability of discriminating tumor subtypes
-and their prognostic capability. We further demonstrated the biological
-relevance of the top component gene sets in the significant supersets. Using
-autoencoder model and gene superset at its latent layer, we demonstrated that
-gene supersets retain sufficient biological information with respect to tumor
-subtypes and clinical prognostic significance. Superset also provides high
-reproducibility on survival analysis and accurate prediction for cancer
-subtypes.
-",0,0,0,1,1,0
-19169,Natasha: Faster Non-Convex Stochastic Optimization Via Strongly Non-Convex Parameter,"  Given a nonconvex function that is an average of $n$ smooth functions, we
-design stochastic first-order methods to find its approximate stationary
-points. The convergence of our new methods depends on the smallest (negative)
-eigenvalue $-\sigma$ of the Hessian, a parameter that describes how nonconvex
-the function is.
-Our methods outperform known results for a range of parameter $\sigma$, and
-can be used to find approximate local minima. Our result implies an interesting
-dichotomy: there exists a threshold $\sigma_0$ so that the currently fastest
-methods for $\sigma>\sigma_0$ and for $\sigma<\sigma_0$ have different
-behaviors: the former scales with $n^{2/3}$ and the latter scales with
-$n^{3/4}$.
-",1,0,1,1,0,0
-19170,Higher-Order Bounded Model Checking,"  We present a Bounded Model Checking technique for higher-order programs. The
-vehicle of our study is a higher-order calculus with general references. Our
-technique is a symbolic state syntactical translation based on SMT solvers,
-adapted to a setting where the values passed and stored during computation can
-be functions of arbitrary order. We prove that our algorithm is sound, and
-devise an optimisation based on points-to analysis to improve scalability. We
-moreover provide a prototype implementation of the algorithm with experimental
-results showcasing its performance.
-",1,0,0,0,0,0
-19171,Smoothing for the fractional Schrodinger equation on the torus and the real line,"  In this paper we study the cubic fractional nonlinear Schrodinger equation
-(NLS) on the torus and on the real line. Combining the normal form and the
-restricted norm methods we prove that the nonlinear part of the solution is
-smoother than the initial data. Our method applies to both focusing and
-defocusing nonlinearities. In the case of full dispersion (NLS) and on the
-torus, the gain is a full derivative, while on the real line we get a
-derivative smoothing with an $\epsilon$ loss. Our result lowers the regularity
-requirement of a recent theorem of Kappeler et al. on the periodic defocusing
-cubic NLS, and extends it to the focusing case and to the real line. We also
-obtain estimates on the higher order Sobolev norms of the global smooth
-solutions in the defocusing case.
-",0,0,1,0,0,0
-19172,Deep Learning Based Cryptographic Primitive Classification,"  Cryptovirological augmentations present an immediate, incomparable threat.
-Over the last decade, the substantial proliferation of crypto-ransomware has
-had widespread consequences for consumers and organisations alike. Established
-preventive measures perform well, however, the problem has not ceased. Reverse
-engineering potentially malicious software is a cumbersome task due to platform
-eccentricities and obfuscated transmutation mechanisms, hence requiring
-smarter, more efficient detection strategies. The following manuscript presents
-a novel approach for the classification of cryptographic primitives in compiled
-binary executables using deep learning. The model blueprint, a DCNN, is
-fittingly configured to learn from variable-length control flow diagnostics
-output from a dynamic trace. To rival the size and variability of contemporary
-data compendiums, hence feeding the model cognition, a methodology for the
-procedural generation of synthetic cryptographic binaries is defined, utilising
-core primitives from OpenSSL with multivariate obfuscation, to draw a vastly
-scalable distribution. The library, CryptoKnight, rendered an algorithmic pool
-of AES, RC4, Blowfish, MD5 and RSA to synthesis combinable variants which are
-automatically fed in its core model. Converging at 91% accuracy, CryptoKnight
-is successfully able to classify the sample algorithms with minimal loss.
-",1,0,0,0,0,0
-19173,"The Alexandrov-Fenchel type inequalities, revisited","  Various Alexandrov-Fenchel type inequalities have appeared and played
-important roles in convex geometry, matrix theory and complex algebraic
-geometry. It has been noticed for some time that they share some striking
-analogies and have intimate relationships. The purpose of this article is to
-shed new light on this by comparatively investigating them in several aspects.
-\emph{The principal result} in this article is a complete solution to the
-equality characterization problem of various Alexandrov-Fenchel type
-inequalities for intersection numbers of nef and big classes on compact
-Kähler manifolds, extending earlier results of Boucksom-Favre-Jonsson,
-Fu-Xiao and Xiao-Lehmann. Our proof combines a result of Dinh-Nguyên on
-Kähler geometry and an idea in convex geometry tracing back to Shephard. In
-addition to this central result, we also give a geometric proof of the complex
-version of the Alexandrov-Fenchel type inequality for mixed discriminants and a
-determinantal type generalization of various Alexandrov-Fenchel type
-inequalities.
-",0,0,1,0,0,0
-19174,Development of non-modal shear induced instabilities in atmospheric tornadoes,"  In this paper we consider the role of nonmodal instabilities in the dynamics
-of atmospheric tornadoes. For this purpose we consider the Euler equation,
-continuity equation and the equation of state and linearise them. As an example
-we study several different velocity profiles: the so-called Rankine vortex
-model; the Burgers-Rott vortex model; Sullivan and modified Sullivan vortex
-models. It has been shown that in the two dimensional Rankine vortex model no
-instability appears in the inner region of a tornado. On the contrary, outside
-this area the physical system undergoes strong exponential instability. We have
-found that initially perturbed velocity components lead to amplified sound wave
-excitations. The similar results have been shown in Burgers-Rott vortex model
-as well. As it was numerically estimated, in this case, the unstable wave
-increases its energy by a factor of $400$ only in $\sim 0.5$min. According to
-the numerical study, in Sullivan and modified Sullivan models, the instability
-does not differ much by the growth. Despite the fact that in the inner area the
-exponential instability does not appear in a purely two dimensional case, we
-have found that in the modified Sullivan vortex even a small contribution from
-vertical velocities can drive unstable nonmodal waves.
-",0,1,0,0,0,0
-19175,DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling,"  Face modeling has been paid much attention in the field of visual computing.
-There exist many scenarios, including cartoon characters, avatars for social
-media, 3D face caricatures as well as face-related art and design, where
-low-cost interactive face modeling is a popular approach especially among
-amateur users. In this paper, we propose a deep learning based sketching system
-for 3D face and caricature modeling. This system has a labor-efficient
-sketching interface, that allows the user to draw freehand imprecise yet
-expressive 2D lines representing the contours of facial features. A novel CNN
-based deep regression network is designed for inferring 3D face models from 2D
-sketches. Our network fuses both CNN and shape based features of the input
-sketch, and has two independent branches of fully connected layers generating
-independent subsets of coefficients for a bilinear face representation. Our
-system also supports gesture based interactions for users to further manipulate
-initial face models. Both user studies and numerical results indicate that our
-sketching system can help users create face models quickly and effectively. A
-significantly expanded face database with diverse identities, expressions and
-levels of exaggeration is constructed to promote further research and
-evaluation of face modeling techniques.
-",1,0,0,0,0,0
-19176,Time-Inhomogeneous Branching Processes Conditioned on Non-Extinction,"  In this paper, we consider time-inhomogeneous branching processes and
-time-inhomogeneous birth-and-death processes, in which the offspring
-distribution and birth and death rates (respectively) vary in time. A classical
-result of branching processes states that in the critical regime, a process
-conditioned on non-extinction and normalized will converge in distribution to a
-standard exponential. In a paper of Jagers, time-inhomogeneous branching
-processes are shown to exhibit this convergence as well. In this paper, the
-hypotheses of Jagers' result are relaxed, further hypotheses are presented for
-convergence in moments, and the result is extended to the continuous-time
-analogue of time-inhomogeneous birth-and-death processes. In particular, the
-new hypotheses suggest a simple characterization of the critical regime.
-",0,0,1,0,0,0
-19177,The Dirichlet-to-Neumann operator for quantum graphs,"  For a compact, connected metric graphs with a boundary that consists of $k$
-vertices, we prove that an arbitrary symmetric $k\times k$ matrix with real
-entries can be realized as the Dirichlet-to-Neumann operator for the Laplacian
-plus a constant.
-",0,0,1,0,0,0
-19178,A study of the dual problem of the one-dimensional L-infinity optimal transport problem with applications,"  The Monge-Kantorovich problem for the infinite Wasserstein distance presents
-several peculiarities. Among them the lack of convexity and then of a direct
-duality. We study in dimension 1 the dual problem introduced by Barron, Bocea
-and Jensen. We construct a couple of Kantorovich potentials which is ""as less
-trivial as possible"". More precisely, we build a potential which is non
-constant around any point that the plan which is locally optimal moves at
-maximal distance. As an application, we show that the set of points which are
-displaced to maximal distance by a locally optimal transport plan is minimal.
-",0,0,1,0,0,0
-19179,Strong coupling Bose polarons in a BEC,"  We use a non-perturbative renormalization group approach to develop a unified
-picture of the Bose polaron problem, where a mobile impurity is strongly
-interacting with a surrounding Bose-Einstein condensate (BEC). A detailed
-theoretical analysis of the phase diagram is presented and the
-polaron-to-molecule transition is discussed. For attractive polarons we argue
-that a description in terms of an effective Fröhlich Hamiltonian with
-renormalized parameters is possible. Its strong coupling regime is realized
-close to a Feshbach resonance, where we predict a sharp increase of the
-effective mass. Already for weaker interactions, before the polaron mass
-diverges, we predict a transition to a regime where states exist below the
-polaron energy and the attractive polaron is no longer the ground state. On the
-repulsive side of the Feshbach resonance we recover the repulsive polaron,
-which has a finite lifetime because it can decay into low-lying molecular
-states. We show for the entire range of couplings that the polaron energy has
-logarithmic corrections in comparison with predictions by the mean-field
-approach. We demonstrate that they are a consequence of the polaronic mass
-renormalization which is due to quantum fluctuations of correlated phonons in
-the polaron cloud.
-",0,1,0,0,0,0
-19180,Triangle packing in (sparse) tournaments: approximation and kernelization,"  Given a tournament T and a positive integer k, the C_3-Pakcing-T problem asks
-if there exists a least k (vertex-)disjoint directed 3-cycles in T. This is the
-dual problem in tournaments of the classical minimal feedback vertex set
-problem. Surprisingly C_3-Pakcing-T did not receive a lot of attention in the
-literature. We show that it does not admit a PTAS unless P=NP, even if we
-restrict the considered instances to sparse tournaments, that is tournaments
-with a feedback arc set (FAS) being a matching. Focusing on sparse tournaments
-we provide a (1+6/(c-1)) approximation algorithm for sparse tournaments having
-a linear representation where all the backward arcs have ""length"" at least c.
-Concerning kernelization, we show that C_3-Pakcing-T admits a kernel with O(m)
-vertices, where m is the size of a given feedback arc set. In particular, we
-derive a O(k) vertices kernel for C_3-Pakcing-T when restricted to sparse
-instances. On the negative size, we show that C_3-Pakcing-T does not admit a
-kernel of (total bit) size O(k^{2-\epsilon}) unless NP is a subset of coNP /
-Poly. The existence of a kernel in O(k) vertices for C_3-Pakcing-T remains an
-open question.
-",1,0,0,0,0,0
-19181,"Asymptotics of Chebyshev Polynomials, II. DCT Subsets of $\mathbb{R}$","  We prove Szegő-Widom asymptotics for the Chebyshev polynomials of a
-compact subset of $\mathbb{R}$ which is regular for potential theory and obeys
-the Parreau-Widom and DCT conditions.
-",0,0,1,0,0,0
-19182,Tracking Urban Human Activity from Mobile Phone Calling Patterns,"  Timings of human activities are marked by circadian clocks which in turn are
-entrained to different environmental signals. In an urban environment the
-presence of artificial lighting and various social cues tend to disrupt the
-natural entrainment with the sunlight. However, it is not completely understood
-to what extent this is the case. Here we exploit the large-scale data analysis
-techniques to study the mobile phone calling activity of people in large cities
-to infer the dynamics of urban daily rhythms. From the calling patterns of
-about 1,000,000 users spread over different cities but lying inside the same
-time-zone, we show that the onset and termination of the calling activity
-synchronizes with the east-west progression of the sun. We also find that the
-onset and termination of the calling activity of users follows a yearly
-dynamics, varying across seasons, and that its timings are entrained to solar
-midnight. Furthermore, we show that the average mid-sleep time of people living
-in urban areas depends on the age and gender of each cohort as a result of
-biological and social factors.
-",0,1,0,0,0,0
-19183,Efficient conversion from rotating matrix to rotation axis and angle by extending Rodrigues' formula,"  In computational 3D geometric problems involving rotations, it is often that
-people have to convert back and forth between a rotational matrix and a
-rotation described by an axis and a corresponding angle. For this purpose,
-Rodrigues' rotation formula is a very popular expression to use because of its
-simplicity and efficiency. Nevertheless, while converting a rotation matrix to
-an axis of rotation and the rotation angle, there exists ambiguity. Further
-judgement or even manual interference may be necessary in some situations. An
-extension of the Rodrigues' formula helps to find the sine and cosine values of
-the rotation angle with respect to a given rotation axis is found and this
-simple extension may help to accelerate many applications.
-",1,0,0,0,0,0
-19184,Statistical methods for characterizing transfusion-related changes in regional oxygenation using Near-infrared spectroscopy (NIRS) in preterm infants,"  Near infrared spectroscopy (NIRS) is an imaging-based diagnostic tool that
-provides non-invasive and continuous evaluation of regional tissue oxygenation
-in real-time. In recent years, NIRS has show promise as a useful monitoring
-technology to help detect relative tissue ischemia that could lead to
-significant morbidity and mortality in preterm infants. However, some issues
-inherent in NIRS technology use on neonates, such as wide fluctuation in
-signals, signal dropout and low limit of detection of the device, pose
-challenges that may obscure reliable interpretation of the NIRS measurements
-using current methods of analysis. In this paper, we propose new statistical
-methods to analyse mesenteric rSO2 (regional oxygenation) produced by NIRS to
-evaluate oxygenation in intestinal tissues and investigate oxygenation response
-to red blood cell transfusion (RBC) in preterm infants. We present a mean area
-under the curve (MAUC) measure and a slope measure to capture the mean rSO2
-level and temporal trajectory of rSO2, respectively. Estimation methods are
-developed for these measures and nonparametric testing procedures are proposed
-to detect RBC-related changes in mesenteric oxygenation in preterm infants.
-Through simulation studies, we show that the proposed methods demonstrate
-improved accuracy in characterizing the mean level and changing pattern of
-mesenteric rSO2 and also increased statistical power in detecting RBC-related
-changes, as compared with standard approaches. We apply our methods to a NIRS
-study in preterm infants receiving RBC transfusion from Emory Univerity to
-evaluate the pre- and post-transfusion mesenteric oxygenation in preterm
-infants.
-",0,0,0,1,0,0
-19185,To understand deep learning we need to understand kernel learning,"  Generalization performance of classifiers in deep learning has recently
-become a subject of intense study. Deep models, typically over-parametrized,
-tend to fit the training data exactly. Despite this ""overfitting"", they perform
-well on test data, a phenomenon not yet fully understood.
-The first point of our paper is that strong performance of overfitted
-classifiers is not a unique feature of deep learning. Using six real-world and
-two synthetic datasets, we establish experimentally that kernel machines
-trained to have zero classification or near zero regression error perform very
-well on test data, even when the labels are corrupted with a high level of
-noise. We proceed to give a lower bound on the norm of zero loss solutions for
-smooth kernels, showing that they increase nearly exponentially with data size.
-We point out that this is difficult to reconcile with the existing
-generalization bounds. Moreover, none of the bounds produce non-trivial results
-for interpolating solutions.
-Second, we show experimentally that (non-smooth) Laplacian kernels easily fit
-random labels, a finding that parallels results for ReLU neural networks. In
-contrast, fitting noisy data requires many more epochs for smooth Gaussian
-kernels. Similar performance of overfitted Laplacian and Gaussian classifiers
-on test, suggests that generalization is tied to the properties of the kernel
-function rather than the optimization process.
-Certain key phenomena of deep learning are manifested similarly in kernel
-methods in the modern ""overfitted"" regime. The combination of the experimental
-and theoretical results presented in this paper indicates a need for new
-theoretical ideas for understanding properties of classical kernel methods. We
-argue that progress on understanding deep learning will be difficult until more
-tractable ""shallow"" kernel methods are better understood.
-",0,0,0,1,0,0
-19186,"Atomic Data Revisions for Transitions Relevant to Observations of Interstellar, Circumgalactic, and Intergalactic Matter","  Measurements of element abundances in galaxies from astrophysical
-spectroscopy depend sensitively on the atomic data used. With the goal of
-making the latest atomic data accessible to the community, we present a
-compilation of selected atomic data for resonant absorption lines at
-wavelengths longward of 911.753 {\AA} (the \ion{H}{1} Lyman limit), for key
-heavy elements (heavier than atomic number 5) of astrophysical interest. In
-particular, we focus on the transitions of those ions that have been observed
-in the Milky Way interstellar medium (ISM), the circumgalactic medium (CGM) of
-the Milky Way and/or other galaxies, and the intergalactic medium (IGM).
-We provide wavelengths, oscillator strengths, associated accuracy grades, and
-references to the oscillator strength determinations. We also attempt to
-compare and assess the recent oscillator strength determinations. For about
-22\% of the lines that have updated oscillator strength values, the differences
-between the former values and the updated ones are $\gtrsim$~0.1 dex.
-Our compilation will be a useful resource for absorption line studies of the
-ISM, as well as studies of the CGM and IGM traced by sight lines to quasars and
-gamma-ray bursts. Studies (including those enabled by future generations of
-extremely large telescopes) of absorption by galaxies against the light of
-background galaxies will also benefit from our compilation.
-",0,1,0,0,0,0
-19187,Using Nonlinear Normal Modes for Execution of Efficient Cyclic Motions in Soft Robots,"  With the aim of getting closer to the performance of the animal
-muscleskeletal system, elastic elements are purposefully introduced in the
-mechanical structure of soft robots. Indeed, previous works have extensively
-shown that elasticity can endow robots with the ability of performing tasks
-with increased efficiency, peak performances, and mechanical robustness.
-However, despite the many achievements, a general theory of efficient motions
-in soft robots is still lacking. Most of the literature focuses on specific
-examples, or imposes a prescribed behavior through dynamic cancellations, thus
-defeating the purpose of introducing elasticity in the first place. This paper
-aims at making a step towards establishing such a general framework. To this
-end, we leverage on the theory of oscillations in nonlinear dynamical systems,
-and we take inspiration from state of the art theories about how the human
-central nervous system manages the muscleskeletal system. We propose to
-generate regular and efficient motions in soft robots by stabilizing
-sub-manifolds of the state space on which the system would naturally evolve. We
-select these sub-manifolds as the nonlinear continuation of linear eigenspaces,
-called nonlinear normal modes. In such a way, efficient oscillatory behaviors
-can be excited. We show the effectiveness of the methods in simulations on an
-elastic inverted pendulum, and experimentally on a segmented elastic leg.
-",1,0,0,0,0,0
-19188,Efficient Deep Learning on Multi-Source Private Data,"  Machine learning models benefit from large and diverse datasets. Using such
-datasets, however, often requires trusting a centralized data aggregator. For
-sensitive applications like healthcare and finance this is undesirable as it
-could compromise patient privacy or divulge trade secrets. Recent advances in
-secure and privacy-preserving computation, including trusted hardware enclaves
-and differential privacy, offer a way for mutually distrusting parties to
-efficiently train a machine learning model without revealing the training data.
-In this work, we introduce Myelin, a deep learning framework which combines
-these privacy-preservation primitives, and use it to establish a baseline level
-of performance for fully private machine learning.
-",0,0,0,1,0,0
-19189,Analytical solution of the integral equation for partial wave Coulomb t-matrices at excited-state energy,"  Starting from the integral representation of the three-dimensional Coulomb
-transition matrix elaborated by us formerly with the use of specific symmetry
-of the interaction in a four-dimensional Euclidean space introduced by Fock,
-the possibility of the analytical solving of the integral equation for the
-partial wave transition matrices at the excited bound state energy has been
-studied. New analytical expressions for the partial s-, p- and d-wave Coulomb
-t-matrices for like-charged particles and the expression for the partial d-wave
-t-matrix for unlike-charged particles at the energy of the first excited bound
-state have been derived.
-",0,1,0,0,0,0
-19190,Equilateral $p$-gons in $\mathbb R^d$ and deformed spheres and mod $p$ Fadell-Husseini index,"  We introduce the concept of $r$-equilateral $m$-gons. We prove the existence
-of $r$-equilateral $p$-gons in $\mathbb R^d$ if $r<d$ and the existence of
-equilateral $p$-gons in the image of continuous injective maps $f:S^d\to
-\mathbb R^{d+1}$. Our ideas are based mainly in the paper of Y. Soibelman
-\cite{soibelman}, in which the topological Borsuk number of $\mathbb{R}^2$ is
-calculated by means of topological methods and the paper of P. Blagojević and
-G. Ziegler \cite{blagojevictetrahedra} where Fadell-Husseini index is used for
-solving a problem related to the topological Borsuk problem for $\mathbb{R}^3$.
-",0,0,1,0,0,0
-19191,Jónsson posets,"  According to Kearnes and Oman (2013), an ordered set $P$ is \emph{Jónsson}
-if it is infinite and the cardinality of every proper initial segment of $P$ is
-strictly less than the cardinaliy of $P$. We examine the structure of Jónsson
-posets.
-",0,0,1,0,0,0
-19192,Hypotheses testing on infinite random graphs,"  Drawing on some recent results that provide the formalism necessary to
-definite stationarity for infinite random graphs, this paper initiates the
-study of statistical and learning questions pertaining to these objects.
-Specifically, a criterion for the existence of a consistent test for complex
-hypotheses is presented, generalizing the corresponding results on time series.
-As an application, it is shown how one can test that a tree has the Markov
-property, or, more generally, to estimate its memory.
-",1,0,1,1,0,0
-19193,On compact splitting complex submanifolds of quotients of bounded symmetric domains,"  In the current article our primary objects of study are compact complex
-submanifolds of quotient manifolds of irreducible bounded symmetric domains by
-torsion free discrete lattices of automorphisms. We are interested in the
-characterization of the totally geodesic submanifolds among compact splitting
-complex submanifolds, i.e. under the assumption that the tangent sequence
-splits holomorphically over the submanifold.
-",0,0,1,0,0,0
-19194,Octupolar Tensors for Liquid Crystals,"  A third-order three-dimensional symmetric traceless tensor, called the
-\emph{octupolar} tensor, has been introduced to study tetrahedratic nematic
-phases in liquid crystals. The octupolar \emph{potential}, a scalar-valued
-function generated on the unit sphere by that tensor, should ideally have four
-maxima capturing the most probable molecular orientations (on the vertices of a
-tetrahedron), but it was recently found to possess an equally generic variant
-with \emph{three} maxima instead of four. It was also shown that the
-irreducible admissible region for the octupolar tensor in a three-dimensional
-parameter space is bounded by a dome-shaped surface, beneath which is a
-\emph{separatrix} surface connecting the two generic octupolar states. The
-latter surface, which was obtained through numerical continuation, may be
-physically interpreted as marking a possible \emph{intra-octupolar} transition.
-In this paper, by using the resultant theory of algebraic geometry and the
-E-characteristic polynomial of spectral theory of tensors, we give a
-closed-form, algebraic expression for both the dome-shaped surface and the
-separatrix surface. This turns the envisaged intra-octupolar transition into a
-quantitative, possibly observable prediction. Some other properties of
-octupolar tensors are also studied.
-",0,0,1,0,0,0
-19195,Efficient boundary corrected Strang splitting,"  Strang splitting is a well established tool for the numerical integration of
-evolution equations. It allows the application of tailored integrators for
-different parts of the vector field. However, it is also prone to order
-reduction in the case of non-trivial boundary conditions. This order reduction
-can be remedied by correcting the boundary values of the intermediate splitting
-step. In this paper, three different approaches for constructing such a
-correction in the case of inhomogeneous Dirichlet, Neumann, and mixed boundary
-conditions are presented. Numerical examples that illustrate the effectivity
-and benefits of these corrections are included.
-",0,1,0,0,0,0
-19196,SOS-convex Semi-algebraic Programs and its Applications to Robust Optimization: A Tractable Class of Nonsmooth Convex Optimization,"  In this paper, we introduce a new class of nonsmooth convex functions called
-SOS-convex semialgebraic functions extending the recently proposed notion of
-SOS-convex polynomials. This class of nonsmooth convex functions covers many
-common nonsmooth functions arising in the applications such as the Euclidean
-norm, the maximum eigenvalue function and the least squares functions with
-$\ell_1$-regularization or elastic net regularization used in statistics and
-compressed sensing. We show that, under commonly used strict feasibility
-conditions, the optimal value and an optimal solution of SOS-convex
-semi-algebraic programs can be found by solving a single semi-definite
-programming problem (SDP). We achieve the results by using tools from
-semi-algebraic geometry, convex-concave minimax theorem and a recently
-established Jensen inequality type result for SOS-convex polynomials. As an
-application, we outline how the derived results can be applied to show that
-robust SOS-convex optimization problems under restricted spectrahedron data
-uncertainty enjoy exact SDP relaxations. This extends the existing exact SDP
-relaxation result for restricted ellipsoidal data uncertainty and answers the
-open questions left in [Optimization Letters 9, 1-18(2015)] on how to recover a
-robust solution from the semi-definite programming relaxation in this broader
-setting.
-",0,0,1,0,0,0
-19197,An Uncertainty Principle for Estimates of Floquet Multipliers,"  We derive a Cramér-Rao lower bound for the variance of Floquet multiplier
-estimates that have been constructed from stable limit cycles perturbed by
-noise. To do so, we consider perturbed periodic orbits in the plane. We use a
-periodic autoregressive process to model the intersections of these orbits with
-cross sections, then passing to the limit of a continuum of sections to obtain
-a bound that depends on the continuous flow restricted to the (nontrivial)
-Floquet mode. We compare our bound against the empirical variance of estimates
-constructed using several cross sections. The section-based estimates are close
-to being optimal. We posit that the utility of our bound persists in higher
-dimensions when computed along Floquet modes for real and distinct multipliers.
-Our bound elucidates some of the empirical observations noted in the
-literature; e.g., (a) it is the number of cycles (as opposed to the frequency
-of observations) that drives the variance of estimates to zero, and (b) the
-estimator variance has a positive lower bound as the noise amplitude tends to
-zero.
-",0,0,0,1,0,0
-19198,Shielding Google's language toxicity model against adversarial attacks,"  Lack of moderation in online communities enables participants to incur in
-personal aggression, harassment or cyberbullying, issues that have been
-accentuated by extremist radicalisation in the contemporary post-truth politics
-scenario. This kind of hostility is usually expressed by means of toxic
-language, profanity or abusive statements. Recently Google has developed a
-machine-learning-based toxicity model in an attempt to assess the hostility of
-a comment; unfortunately, it has been suggested that said model can be deceived
-by adversarial attacks that manipulate the text sequence of the comment. In
-this paper we firstly characterise such adversarial attacks as using
-obfuscation and polarity transformations. The former deceives by corrupting
-toxic trigger content with typographic edits, whereas the latter deceives by
-grammatical negation of the toxic content. Then, we propose a two--stage
-approach to counter--attack these anomalies, bulding upon a recently proposed
-text deobfuscation method and the toxicity scoring model. Lastly, we conducted
-an experiment with approximately 24000 distorted comments, showing how in this
-way it is feasible to restore toxicity of the adversarial variants, while
-incurring roughly on a twofold increase in processing time. Even though novel
-adversary challenges would keep coming up derived from the versatile nature of
-written language, we anticipate that techniques combining machine learning and
-text pattern recognition methods, each one targeting different layers of
-linguistic features, would be needed to achieve robust detection of toxic
-language, thus fostering aggression--free digital interaction.
-",1,0,0,0,0,0
-19199,Diagrammatic Monte-Carlo for weak-coupling expansion of non-Abelian lattice field theories: large-N U(N)xU(N) principal chiral model,"  We develop numerical tools for Diagrammatic Monte-Carlo simulations of
-non-Abelian lattice field theories in the t'Hooft large-N limit based on the
-weak-coupling expansion. First we note that the path integral measure of such
-theories contributes a bare mass term in the effective action which is
-proportional to the bare coupling constant. This mass term renders the
-perturbative expansion infrared-finite and allows to study it directly in the
-large-N and infinite-volume limits using the Diagrammatic Monte-Carlo approach.
-On the exactly solvable example of a large-N O(N) sigma model in D=2 dimensions
-we show that this infrared-finite weak-coupling expansion contains, in addition
-to powers of bare coupling, also powers of its logarithm, reminiscent of
-re-summed perturbation theory in thermal field theory and resurgent
-trans-series without exponential terms. We numerically demonstrate the
-convergence of these double series to the manifestly non-perturbative dynamical
-mass gap. We then develop a Diagrammatic Monte-Carlo algorithm for sampling
-planar diagrams in the large-N matrix field theory, and apply it to study this
-infrared-finite weak-coupling expansion for large-N U(N)xU(N) nonlinear sigma
-model (principal chiral model) in D=2. We sample up to 12 leading orders of the
-weak-coupling expansion, which is the practical limit set by the increasingly
-strong sign problem at high orders. Comparing Diagrammatic Monte-Carlo with
-conventional Monte-Carlo simulations extrapolated to infinite N, we find a good
-agreement for the energy density as well as for the critical temperature of the
-""deconfinement"" transition. Finally, we comment on the applicability of our
-approach to planar QCD at zero and finite density.
-",0,1,0,0,0,0
-19200,Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks,"  There is an increasing interest on accelerating neural networks for real-time
-applications. We study the student-teacher strategy, in which a small and fast
-student network is trained with the auxiliary information learned from a large
-and accurate teacher network. We propose to use conditional adversarial
-networks to learn the loss function to transfer knowledge from teacher to
-student. The proposed method is particularly effective for relatively small
-student networks. Moreover, experimental results show the effect of network
-size when the modern networks are used as student. We empirically study the
-trade-off between inference time and classification accuracy, and provide
-suggestions on choosing a proper student network.
-",1,0,0,0,0,0
-19201,Transductive Zero-Shot Learning with Adaptive Structural Embedding,"  Zero-shot learning (ZSL) endows the computer vision system with the
-inferential capability to recognize instances of a new category that has never
-seen before. Two fundamental challenges in it are visual-semantic embedding and
-domain adaptation in cross-modality learning and unseen class prediction steps,
-respectively. To address both challenges, this paper presents two corresponding
-methods named Adaptive STructural Embedding (ASTE) and Self-PAsed Selective
-Strategy (SPASS), respectively. Specifically, ASTE formulates the
-visualsemantic interactions in a latent structural SVM framework to adaptively
-adjust the slack variables to embody the different reliableness among training
-instances. In this way, the reliable instances are imposed with small
-punishments, wheras the less reliable instances are imposed with more severe
-punishments. Thus, it ensures a more discriminative embedding. On the other
-hand, SPASS offers a framework to alleviate the domain shift problem in ZSL,
-which exploits the unseen data in an easy to hard fashion. Particularly, SPASS
-borrows the idea from selfpaced learning by iteratively selecting the unseen
-instances from reliable to less reliable to gradually adapt the knowledge from
-the seen domain to the unseen domain. Subsequently, by combining SPASS and
-ASTE, we present a self-paced Transductive ASTE (TASTE) method to progressively
-reinforce the classification capacity. Extensive experiments on three benchmark
-datasets (i.e., AwA, CUB, and aPY) demonstrate the superiorities of ASTE and
-TASTE. Furthermore, we also propose a fast training (FT) strategy to improve
-the efficiency of most of existing ZSL methods. The FT strategy is surprisingly
-simple and general enough, which can speed up the training time of most
-existing methods by 4~300 times while holding the previous performance.
-",1,0,0,0,0,0
-19202,A geometric attractor mechanism for self-organization of entorhinal grid modules,"  Grid cells in the medial entorhinal cortex (mEC) respond when an animal
-occupies a periodic lattice of ""grid fields"" in the environment. The grids are
-organized in modules with spatial periods clustered around discrete values
-separated by constant ratios reported in the range 1.3-1.8. We propose a
-mechanism for dynamical self-organization in the mEC that can produce this
-modular structure. In attractor network models of grid formation, the period of
-a single module is set by the length scale of recurrent inhibition between
-neurons. We show that grid cells will instead form a hierarchy of discrete
-modules if a continuous increase in inhibition distance along the dorso-ventral
-axis of the mEC is accompanied by excitatory interactions along this axis.
-Moreover, constant scale ratios between successive modules arise through
-geometric relationships between triangular grids, whose lattice constants are
-separated by $\sqrt{3} \approx 1.7$, $\sqrt{7}/2 \approx 1.3$, or other ratios.
-We discuss how the interactions required by our model might be tested
-experimentally and realized by circuits in the mEC.
-",0,0,0,0,1,0
-19203,Attacking Strategies and Temporal Analysis Involving Facebook Discussion Groups,"  Online social network (OSN) discussion groups are exerting significant
-effects on political dialogue. In the absence of access control mechanisms, any
-user can contribute to any OSN thread. Individuals can exploit this
-characteristic to execute targeted attacks, which increases the potential for
-subsequent malicious behaviors such as phishing and malware distribution. These
-kinds of actions will also disrupt bridges among the media, politicians, and
-their constituencies.
-For the concern of Security Management, blending malicious cyberattacks with
-online social interactions has introduced a brand new challenge. In this paper
-we describe our proposal for a novel approach to studying and understanding the
-strategies that attackers use to spread malicious URLs across Facebook
-discussion groups. We define and analyze problems tied to predicting the
-potential for attacks focused on threads created by news media organizations.
-We use a mix of macro static features and the micro dynamic evolution of posts
-and threads to identify likely targets with greater than 90% accuracy. One of
-our secondary goals is to make such predictions within a short (10 minute) time
-frame. It is our hope that the data and analyses presented in this paper will
-support a better understanding of attacker strategies and footprints, thereby
-developing new system management methodologies in handing cyber attacks on
-social networks.
-",1,0,0,0,0,0
-19204,Perception-based energy functions in seam-cutting,"  Image stitching is challenging in consumer-level photography, due to
-alignment difficulties in unconstrained shooting environment. Recent studies
-show that seam-cutting approaches can effectively relieve artifacts generated
-by local misalignment. Normally, seam-cutting is described in terms of energy
-minimization, however, few of existing methods consider human perception in
-their energy functions, which sometimes causes that a seam with minimum energy
-is not most invisible in the overlapping region. In this paper, we propose a
-novel perception-based energy function in the seam-cutting framework, which
-considers the nonlinearity and the nonuniformity of human perception in energy
-minimization. Our perception-based approach adopts a sigmoid metric to
-characterize the perception of color discrimination, and a saliency weight to
-simulate that human eyes incline to pay more attention to salient objects. In
-addition, our seam-cutting composition can be easily implemented into other
-stitching pipelines. Experiments show that our method outperforms the
-seam-cutting method of the normal energy function, and a user study
-demonstrates that our composed results are more consistent with human
-perception.
-",1,0,0,0,0,0
-19205,Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo,"  Probabilistic modeling provides the capability to represent and manipulate
-uncertainty in data, models, predictions and decisions. We are concerned with
-the problem of learning probabilistic models of dynamical systems from measured
-data. Specifically, we consider learning of probabilistic nonlinear state-space
-models. There is no closed-form solution available for this problem, implying
-that we are forced to use approximations. In this tutorial we will provide a
-self-contained introduction to one of the state-of-the-art methods---the
-particle Metropolis--Hastings algorithm---which has proven to offer a practical
-approximation. This is a Monte Carlo based method, where the particle filter is
-used to guide a Markov chain Monte Carlo method through the parameter space.
-One of the key merits of the particle Metropolis--Hastings algorithm is that it
-is guaranteed to converge to the ""true solution"" under mild assumptions,
-despite being based on a particle filter with only a finite number of
-particles. We will also provide a motivating numerical example illustrating the
-method using a modeling language tailored for sequential Monte Carlo methods.
-The intention of modeling languages of this kind is to open up the power of
-sophisticated Monte Carlo methods---including particle
-Metropolis--Hastings---to a large group of users without requiring them to know
-all the underlying mathematical details.
-",1,0,0,1,0,0
-19206,Deep Projective 3D Semantic Segmentation,"  Semantic segmentation of 3D point clouds is a challenging problem with
-numerous real-world applications. While deep learning has revolutionized the
-field of image semantic segmentation, its impact on point cloud data has been
-limited so far. Recent attempts, based on 3D deep learning approaches
-(3D-CNNs), have achieved below-expected results. Such methods require
-voxelizations of the underlying point cloud data, leading to decreased spatial
-resolution and increased memory consumption. Additionally, 3D-CNNs greatly
-suffer from the limited availability of annotated datasets.
-In this paper, we propose an alternative framework that avoids the
-limitations of 3D-CNNs. Instead of directly solving the problem in 3D, we first
-project the point cloud onto a set of synthetic 2D-images. These images are
-then used as input to a 2D-CNN, designed for semantic segmentation. Finally,
-the obtained prediction scores are re-projected to the point cloud to obtain
-the segmentation results. We further investigate the impact of multiple
-modalities, such as color, depth and surface normals, in a multi-stream network
-architecture. Experiments are performed on the recent Semantic3D dataset. Our
-approach sets a new state-of-the-art by achieving a relative gain of 7.9 %,
-compared to the previous best approach.
-",1,0,0,0,0,0
-19207,Dynamic Pricing with Finitely Many Unknown Valuations,"  Motivated by posted price auctions where buyers are grouped in an unknown
-number of latent types characterized by their private values for the good on
-sale, we investigate revenue maximization in stochastic dynamic pricing when
-the distribution of buyers' private values is supported on an unknown set of
-points in [0,1] of unknown cardinality K. This setting can be viewed as an
-instance of a stochastic K-armed bandit problem where the location of the arms
-(the K unknown valuations) must be learned as well. In the distribution-free
-case, we show that our setting is just as hard as K-armed stochastic bandits:
-we prove that no algorithm can achieve a regret significantly better than
-$\sqrt{KT}$, (where T is the time horizon) and present an efficient algorithm
-matching this lower bound up to logarithmic factors. In the
-distribution-dependent case, we show that for all K>2 our setting is strictly
-harder than K-armed stochastic bandits by proving that it is impossible to
-obtain regret bounds that grow logarithmically in time or slower. On the other
-hand, when a lower bound $\gamma>0$ on the smallest drop in the demand curve is
-known, we prove an upper bound on the regret of order $(1/\Delta+(\log \log
-T)/\gamma^2)(K\log T)$. This is a significant improvement on previously known
-regret bounds for discontinuous demand curves, that are at best of order
-$(K^{12}/\gamma^8)\sqrt{T}$. When K=2 in the distribution-dependent case, the
-hardness of our setting reduces to that of a stochastic 2-armed bandit: we
-prove that an upper bound of order $(\log T)/\Delta$ (up to $\log\log$ factors)
-on the regret can be achieved with no information on the demand curve. Finally,
-we show a $O(\sqrt{T})$ upper bound on the regret for the setting in which the
-buyers' decisions are nonstochastic, and the regret is measured with respect to
-the best between two fixed valuations one of which is known to the seller.
-",0,0,0,1,0,0
-19208,"Haar systems, KMS states on von Neumann algebras and $C^*$-algebras on dynamically defined groupoids and Noncommutative Integration","  We analyse certain Haar systems associated to groupoids obtained by certain
-natural equivalence relations of dynamical nature on sets like
-$\{1,2,...,d\}^\mathbb{Z}$, $\{1,2,...,d\}^\mathbb{N}$, $S^1\times S^1$, or
-$(S^1)^\mathbb{N}$, where $S^1$ is the unitary circle. We also describe
-properties of transverse functions, quasi-invariant probabilities and KMS
-states for some examples of von Neumann algebras (and also $C^*$-Algebras)
-associated to these groupoids. We relate some of these KMS states with Gibbs
-states of Thermodynamic Formalism. While presenting new results, we will also
-describe in detail several examples and basic results on the above topics. In
-other words it is also a survey paper. Some known results on non-commutative
-integration are presented, more precisely, the relation of transverse measures,
-cocycles and quasi-invariant probabilities.
-We describe the results in a language which is more familiar to people in
-Dynamical Systems. Our intention is to study Haar systems, quasi-invariant
-probabilities and von Neumann algebras as a topic on measure theory
-(intersected with ergodic theory) avoiding questions of algebraic nature
-(which, of course, are also extremely important).
-",0,0,1,0,0,0
-19209,Restoring a smooth function from its noisy integrals,"  Numerical (and experimental) data analysis often requires the restoration of
-a smooth function from a set of sampled integrals over finite bins. We present
-the bin hierarchy method that efficiently computes the maximally smooth
-function from the sampled integrals using essentially all the information
-contained in the data. We perform extensive tests with different classes of
-functions and levels of data quality, including Monte Carlo data suffering from
-a severe sign problem and physical data for the Green's function of the
-Fröhlich polaron.
-",0,1,0,1,0,0
-19210,Adversarial Active Learning for Deep Networks: a Margin Based Approach,"  We propose a new active learning strategy designed for deep neural networks.
-The goal is to minimize the number of data annotation queried from an oracle
-during training. Previous active learning strategies scalable for deep networks
-were mostly based on uncertain sample selection. In this work, we focus on
-examples lying close to the decision boundary. Based on theoretical works on
-margin theory for active learning, we know that such examples may help to
-considerably decrease the number of annotations. While measuring the exact
-distance to the decision boundaries is intractable, we propose to rely on
-adversarial examples. We do not consider anymore them as a threat instead we
-exploit the information they provide on the distribution of the input space in
-order to approximate the distance to decision boundaries. We demonstrate
-empirically that adversarial active queries yield faster convergence of CNNs
-trained on MNIST, the Shoe-Bag and the Quick-Draw datasets.
-",0,0,0,1,0,0
-19211,Improving SIEM capabilities through an enhanced probe for encrypted Skype traffic detection,"  Nowadays, the Security Information and Event Management (SIEM) systems take
-on great relevance in handling security issues for critical infrastructures as
-Internet Service Providers. Basically, a SIEM has two main functions: i) the
-collection and the aggregation of log data and security information from
-disparate network devices (routers, firewalls, intrusion detection systems, ad
-hoc probes and others) and ii) the analysis of the gathered data by
-implementing a set of correlation rules aimed at detecting potential suspicious
-events as the presence of encrypted real-time traffic. In the present work, the
-authors propose an enhanced implementation of a SIEM where a particular focus
-is given to the detection of encrypted Skype traffic by using an ad-hoc
-developed enhanced probe (ESkyPRO) conveniently governed by the SIEM itself.
-Such enhanced probe, able to interact with an agent counterpart deployed into
-the SIEM platform, is designed by exploiting some machine learning concepts.
-The main purpose of the proposed ad-hoc SIEM is to correlate the information
-received by ESkyPRO and other types of data obtained by an Intrusion Detection
-System (IDS) probe in order to make the encrypted Skype traffic detection as
-accurate as possible.
-",1,0,0,0,0,0
-19212,Escaping Saddle Points with Adaptive Gradient Methods,"  Adaptive methods such as Adam and RMSProp are widely used in deep learning
-but are not well understood. In this paper, we seek a crisp, clean and precise
-characterization of their behavior in nonconvex settings. To this end, we first
-provide a novel view of adaptive methods as preconditioned SGD, where the
-preconditioner is estimated in an online manner. By studying the preconditioner
-on its own, we elucidate its purpose: it rescales the stochastic gradient noise
-to be isotropic near stationary points, which helps escape saddle points.
-Furthermore, we show that adaptive methods can efficiently estimate the
-aforementioned preconditioner. By gluing together these two components, we
-provide the first (to our knowledge) second-order convergence result for any
-adaptive method. The key insight from our analysis is that, compared to SGD,
-adaptive methods escape saddle points faster, and can converge faster overall
-to second-order stationary points.
-",1,0,0,1,0,0
-19213,The Effect of Temperature on Cu-K-In-Se Thin Films,"  Films of Cu-K-In-Se were co-evaporated at varied K/(K+Cu) compositions and
-substrate temperatures (with constant (K+Cu)/In ~ 0.85). Increased Na
-composition on the substrate's surface and decreased growth temperature were
-both found to favor Cu1-xKxInSe2 (CKIS) alloy formation, relative to
-mixed-phase CuInSe2 + KInSe2 formation. Structures from X-ray diffraction
-(XRD), band gaps, resistivities, minority carrier lifetimes and carrier
-concentrations from time-resolved photoluminescence were in agreement with
-previous reports, where low K/(K+Cu) composition films exhibited properties
-promising for photovoltaic (PV) absorbers. Films grown at 400-500 C were then
-annealed to 600 C under Se, which caused K loss by evaporation in proportion to
-initial K/(K+Cu) composition. Similar to growth temperature, annealing drove
-CKIS alloy consumption and CuInSe2 + KInSe2 production, as evidenced by high
-temperature XRD. Annealing also decomposed KInSe2 and formed K2In12Se19. At
-high temperature the KInSe2 crystal lattice gradually contracted as temperature
-and time increased, as well as just time. Evaporative loss of K during
-annealing could accompany the generation of vacancies on K lattice sites, and
-may explain the KInSe2 lattice contraction. This knowledge of Cu-K-In-Se
-material chemistry may be used to predict and control minor phase impurities in
-Cu(In,Ga)(Se,S)2 PV absorbers-where impurities below typical detection limits
-may have played a role in recent world record PV efficiencies that utilized KF
-post-deposition treatments.
-",0,1,0,0,0,0
-19214,Simulations of the Solar System's Early Dynamical Evolution with a Self-Gravitating Planetesimal Disk,"  Over the course of last decade, the Nice model has dramatically changed our
-view of the solar system's formation and early evolution. Within the context of
-this model, a transient period of planet-planet scattering is triggered by
-gravitational interactions between the giant planets and a massive primordial
-planetesimal disk, leading to a successful reproduction of the solar system's
-present-day architecture. In typical realizations of the Nice model,
-self-gravity of the planetesimal disk is routinely neglected, as it poses a
-computational bottleneck to the calculations. Recent analyses have shown,
-however, that a self-gravitating disk can exhibit behavior that is dynamically
-distinct, and this disparity may have significant implications for the solar
-system's evolutionary path. In this work, we explore this discrepancy utilizing
-a large suite of Nice odel simulations with and without a self-gravitating
-planetesimal disk, taking advantage of the inherently parallel nature of
-graphic processing units. Our simulations demonstrate that self-consistent
-modeling of particle interactions does not lead to significantly different
-final planetary orbits from those obtained within conventional simulations.
-Moreover, self-gravitating calculations show similar planetesimal evolution to
-non-self-gravitating numerical experiments after dynamical instability is
-triggered, suggesting that the orbital clustering observed in the distant
-Kuiper belt is unlikely to have a self-gravitational origin.
-",0,1,0,0,0,0
-19215,On weak Fraisse limits,"  Using the natural action of $S_\infty$ we show that a countable hereditary
-class $\cC$ of finitely generated structures has the joint embedding property
-(JEP) and the weak amalgamation property (WAP) if and only if there is a
-structure $M$ whose isomorphism type is comeager in the space of all countable,
-infinitely generated structures with age in $\cC$. In this case, $M$ is the
-weak Fraïssé limit of $\cC$.
-This applies in particular to countable structures with generic automorphisms
-and recovers a result by Kechris and Rosendal [\textit{Proc.\ Lond.\ Math.\
-Soc.,\ 2007}].
-",0,0,1,0,0,0
-19216,Promoting Saving for College Through Data Science,"  The cost of attending college has been steadily rising and in 10 years is
-estimated to reach $140,000 for a 4-year public university. Recent surveys
-estimate just over half of US families are saving for college. State-operated
-529 college savings plans are an effective way for families to plan and save
-for future college costs, but only 3% of families currently use them. The
-Office of the Illinois State Treasurer (Treasurer) administers two 529 plans to
-help its residents save for college. In order to increase the number of
-families saving for college, the Treasurer and Civis Analytics used data
-science techniques to identify the people most likely to sign up for a college
-savings plan. In this paper, we will discuss the use of person matching to join
-accountholder data from the Treasurer to the Civis National File, as well as
-the use of lookalike modeling to identify new potential signups. In order to
-avoid reinforcing existing demographic imbalances in who saves for college, the
-lookalike models used were ensured to be racially and economically balanced. We
-will also discuss how these new signup targets were then individually served
-digital ads to encourage opening college savings accounts.
-",1,0,0,0,0,0
-19217,Rotation Blurring: Use of Artificial Blurring to Reduce Cybersickness in Virtual Reality First Person Shooters,"  Users of Virtual Reality (VR) systems often experience vection, the
-perception of self-motion in the absence of any physical movement. While
-vection helps to improve presence in VR, it often leads to a form of motion
-sickness called cybersickness. Cybersickness is a major deterrent to large
-scale adoption of VR.
-Prior work has discovered that changing vection (changing the perceived speed
-or moving direction) causes more severe cybersickness than steady vection
-(walking at a constant speed or in a constant direction). Based on this idea,
-we try to reduce the cybersickness caused by character movements in a First
-Person Shooter (FPS) game in VR. We propose Rotation Blurring (RB), uniformly
-blurring the screen during rotational movements to reduce cybersickness. We
-performed a user study to evaluate the impact of RB in reducing cybersickness.
-We found that the blurring technique led to an overall reduction in sickness
-levels of the participants and delayed its onset. Participants who experienced
-acute levels of cybersickness benefited significantly from this technique.
-",1,0,0,0,0,0
-19218,Comparative Benchmarking of Causal Discovery Techniques,"  In this paper we present a comprehensive view of prominent causal discovery
-algorithms, categorized into two main categories (1) assuming acyclic and no
-latent variables, and (2) allowing both cycles and latent variables, along with
-experimental results comparing them from three perspectives: (a) structural
-accuracy, (b) standard predictive accuracy, and (c) accuracy of counterfactual
-inference. For (b) and (c) we train causal Bayesian networks with structures as
-predicted by each causal discovery technique to carry out counterfactual or
-standard predictive inference. We compare causal algorithms on two pub- licly
-available and one simulated datasets having different sample sizes: small,
-medium and large. Experiments show that structural accuracy of a technique does
-not necessarily correlate with higher accuracy of inferencing tasks. Fur- ther,
-surveyed structure learning algorithms do not perform well in terms of
-structural accuracy in case of datasets having large number of variables.
-",1,0,0,1,0,0
-19219,Sensitivity of Love and quasi-Rayleigh waves to model parameters,"  We examine the sensitivity of the Love and the quasi-Rayleigh waves to model
-parameters. Both waves are guided waves that propagate in the same model of an
-elastic layer above an elastic halfspace. We study their dispersion curves
-without any simplifying assumptions, beyond the standard approach of elasticity
-theory in isotropic media. We examine the sensitivity of both waves to
-elasticity parameters, frequency and layer thickness, for varying frequency and
-different modes. In the case of Love waves, we derive and plot the absolute
-value of a dimensionless sensitivity coefficient in terms of partial
-derivatives, and perform an analysis to find the optimum frequency for
-determining the layer thickness. For a coherency of the background information,
-we briefly review the Love-wave dispersion relation and provide details of the
-less common derivation of the quasi-Rayleigh relation in an appendix. We
-compare that derivation to past results in the literature, finding certain
-discrepancies among them.
-",0,1,0,0,0,0
-19220,The image size of iterated rational maps over finite fields,"  Let $\varphi:\mathbb{F}_q\to\mathbb{F}_q$ be a rational map on a fixed finite
-field. We give explicit asymptotic formulas for the size of image sets
-$\varphi^n(\mathbb{F}_q)$ as a function of $n$. This is done by using
-properties of the Galois groups of iterated maps, whose connection to the
-question of the size of image sets is established via Chebotarev's Density
-Theorem. We then apply these results to provide explicit bounds on the
-proportion of periodic points in $\mathbb{F}_q$ in terms of $q$ for certain
-rational maps.
-",0,0,1,0,0,0
-19221,Noncommutative modular symbols and Eisenstein series,"  We form real-analytic Eisenstein series twisted by Manin's noncommutative
-modular symbols. After developing their basic properties, these series are
-shown to have meromorphic continuations to the entire complex plane and satisfy
-functional equations in some cases. This theory neatly contains and generalizes
-earlier work in the literature on the properties of Eisenstein series twisted
-by classical modular symbols.
-",0,0,1,0,0,0
-19222,Some rigidity characterizations on critical metrics for quadratic curvature functionals,"  We study closed $n$-dimensional manifolds of which the metrics are critical
-for quadratic curvature functionals involving the Ricci curvature, the scalar
-curvature and the Riemannian curvature tensor on the space of Riemannian
-metrics with unit volume. Under some additional integral conditions, we
-classify such manifolds. Moreover, under some curvature conditions, the result
-that a critical metric must be Einstein is proved.
-",0,0,1,0,0,0
-19223,Profinite completions of Burnside-type quotients of surface groups,"  Using quantum representations of mapping class groups we prove that profinite
-completions of Burnside-type surface group quotients are not virtually
-prosolvable, in general. Further, we construct infinitely many finite simple
-characteristic quotients of surface groups.
-",0,0,1,0,0,0
-19224,Learning Infinite RBMs with Frank-Wolfe,"  In this work, we propose an infinite restricted Boltzmann machine~(RBM),
-whose maximum likelihood estimation~(MLE) corresponds to a constrained convex
-optimization. We consider the Frank-Wolfe algorithm to solve the program, which
-provides a sparse solution that can be interpreted as inserting a hidden unit
-at each iteration, so that the optimization process takes the form of a
-sequence of finite models of increasing complexity. As a side benefit, this can
-be used to easily and efficiently identify an appropriate number of hidden
-units during the optimization. The resulting model can also be used as an
-initialization for typical state-of-the-art RBM training algorithms such as
-contrastive divergence, leading to models with consistently higher test
-likelihood than random initialization.
-",1,0,0,1,0,0
-19225,New low-mass eclipsing binary systems in Praesepe discovered by K2,"  We present the discovery of four low-mass ($M<0.6$ $M_\odot$) eclipsing
-binary (EB) systems in the sub-Gyr old Praesepe open cluster using Kepler/K2
-time-series photometry and Keck/HIRES spectroscopy. We present a new Gaussian
-process eclipsing binary model, GP-EBOP, as well as a method of simultaneously
-determining effective temperatures and distances for EBs. Three of the reported
-systems (AD 3814, AD 2615 and AD 1508) are detached and double-lined, and
-precise solutions are presented for the first two. We determine masses and
-radii to 1-3% precision for AD 3814 and to 5-6% for AD 2615. Together with
-effective temperatures determined to $\sim$50 K precision, we test the PARSEC
-v1.2 and BHAC15 stellar evolution models. Our EB parameters are more consistent
-with the PARSEC models, primarily because the BHAC15 temperature scale is
-hotter than our data over the mid M-dwarf mass range probed. Both ADs 3814 and
-2615, which have orbital periods of 6.0 and 11.6 days, are circularized but not
-synchronized. This suggests that either synchronization proceeds more slowly in
-fully convective stars than the theory of equilibrium tides predicts or
-magnetic braking is currently playing a more important role than tidal forces
-in the spin evolution of these binaries. The fourth system (AD 3116) comprises
-a brown dwarf transiting a mid M-dwarf, which is the first such system
-discovered in a sub-Gyr open cluster. Finally, these new discoveries increase
-the number of characterized EBs in sub-Gyr open clusters by 20% (40%) below
-$M<1.5$ $M_{\odot}$ ($M<0.6$ $M_{\odot}$).
-",0,1,0,0,0,0
-19226,The impact of the halide cage on the electronic properties of fully inorganic caesium lead halide perovskites,"  Perovskite solar cells with record power conversion efficiency are fabricated
-by alloying both hybrid and fully inorganic compounds. While the basic
-electronic properties of the hybrid perovskites are now well understood, key
-electronic parameters for solar cell performance, such as the exciton binding
-energy of fully inorganic perovskites, are still unknown. By performing magneto
-transmission measurements, we determine with high accuracy the exciton binding
-energy and reduced mass of fully inorganic CsPbX$_3$ perovskites (X=I, Br, and
-an alloy of these). The well behaved (continuous) evolution of the band gap
-with temperature in the range $4-270$\,K suggests that fully inorganic
-perovskites do not undergo structural phase transitions like their hybrid
-counterparts. The experimentally determined dielectric constants indicate that
-at low temperature, when the motion of the organic cation is frozen, the
-dielectric screening mechanism is essentially the same both for hybrid and
-inorganic perovskites, and is dominated by the relative motion of atoms within
-the lead-halide cage.
-",0,1,0,0,0,0
-19227,Discovery of Giant Radio Galaxies from NVSS: Radio & Infrared Properties,"  Giant radio galaxies (GRGs) are one of the largest astrophysical sources in
-the Universe with an overall projected linear size of ~0.7 Mpc or more. Last
-six decades of radio astronomy research has led to the detection of thousands
-of radio galaxies. But only ~ 300 of them can be classified as GRGs. The
-reasons behind their large size and rarity are unknown. We carried out a
-systematic search for these radio giants and found a large sample of GRGs. In
-this paper, we report the discovery of 25 GRGs from NVSS, in the redshift range
-(z) ~ 0.07 to 0.67. Their physical sizes range from ~0.8 Mpc to ~4 Mpc. Eight
-of these GRGs have sizes greater than 2Mpc which is a rarity. In this paper,
-for the first time, we investigate the mid-IR properties of the optical hosts
-of the GRGs and classify them securely into various AGN types using the WISE
-mid-IR colours. Using radio and IR data, four of the hosts of GRGs were
-observed to be radio loud quasars that extend up to 2 Mpc in radio size. These
-GRGs missed detection in earlier searches possibly because of their highly
-diffuse nature, low surface brightness and lack of optical data. The new GRGs
-are a significant addition to the existing sample that will contribute to
-better understanding of the physical properties of radio giants.
-",0,1,0,0,0,0
-19228,SGD: General Analysis and Improved Rates,"  We propose a general yet simple theorem describing the convergence of SGD
-under the arbitrary sampling paradigm. Our theorem describes the convergence of
-an infinite array of variants of SGD, each of which is associated with a
-specific probability law governing the data selection rule used to form
-mini-batches. This is the first time such an analysis is performed, and most of
-our variants of SGD were never explicitly considered in the literature before.
-Our analysis relies on the recently introduced notion of expected smoothness
-and does not rely on a uniform bound on the variance of the stochastic
-gradients. By specializing our theorem to different mini-batching strategies,
-such as sampling with replacement and independent sampling, we derive exact
-expressions for the stepsize as a function of the mini-batch size. With this we
-can also determine the mini-batch size that optimizes the total complexity, and
-show explicitly that as the variance of the stochastic gradient evaluated at
-the minimum grows, so does the optimal mini-batch size. For zero variance, the
-optimal mini-batch size is one. Moreover, we prove insightful
-stepsize-switching rules which describe when one should switch from a constant
-to a decreasing stepsize regime.
-",1,0,0,1,0,0
-19229,Nonconvex One-bit Single-label Multi-label Learning,"  We study an extreme scenario in multi-label learning where each training
-instance is endowed with a single one-bit label out of multiple labels. We
-formulate this problem as a non-trivial special case of one-bit rank-one matrix
-sensing and develop an efficient non-convex algorithm based on alternating
-power iteration. The proposed algorithm is able to recover the underlying
-low-rank matrix model with linear convergence. For a rank-$k$ model with $d_1$
-features and $d_2$ classes, the proposed algorithm achieves $O(\epsilon)$
-recovery error after retrieving $O(k^{1.5}d_1 d_2/\epsilon)$ one-bit labels
-within $O(kd)$ memory. Our bound is nearly optimal in the order of
-$O(1/\epsilon)$. This significantly improves the state-of-the-art sampling
-complexity of one-bit multi-label learning. We perform experiments to verify
-our theory and evaluate the performance of the proposed algorithm.
-",1,0,0,1,0,0
-19230,Two Categories of Indoor Interactive Dynamics of a Large-scale Human Population in a WiFi covered university campus,"  To explore large-scale population indoor interactions, we analyze 18,715
-users' WiFi access logs recorded in a Chinese university campus during 3
-months, and define two categories of human interactions, the event interaction
-(EI) and the temporal interaction (TI). The EI helps construct a transmission
-graph, and the TI helps build an interval graph. The dynamics of EIs show that
-their active durations are truncated power-law distributed, which is
-independent on the number of involved individuals. The transmission duration
-presents a truncated power-law behavior at the daily timescale with weekly
-periodicity. Besides, those `leaf' individuals in the aggregated contact
-network may participate in the `super-connecting cliques' in the aggregated
-transmission graph. Analyzing the dynamics of the interval graph, we find that
-the probability distribution of TIs' inter-event duration also displays a
-truncated power-law pattern at the daily timescale with weekly periodicity,
-while the pairwise individuals with burst interactions are prone to randomly
-select their interactive locations, and those individuals with periodic
-interactions have preferred interactive locations.
-",1,1,0,0,0,0
-19231,Robust estimators for generalized linear models with a dispersion parameter,"  Highly robust and efficient estimators for the generalized linear model with
-a dispersion parameter are proposed. The estimators are based on three steps.
-In the first step the maximum rank correlation estimator is used to
-consistently estimate the slopes up to a scale factor. In the second step, the
-scale factor, the intercept, and the dispersion parameter are consistently
-estimated using a MT-estimator of a simple regression model. The combined
-estimator is highly robust but inefficient. Then, randomized quantile residuals
-based on the initial estimators are used to detect outliers to be rejected and
-to define a set S of observations to be retained. Finally, a conditional
-maximum likelihood (CML) estimator given the observations in S is computed. We
-show that, under the model, S tends to the complete sample for increasing
-sample size. Therefore, the CML tends to the unconditional maximum likelihood
-estimator. It is therefore highly efficient, while maintaining the high degree
-of robustness of the initial estimator. The case of the negative binomial
-regression model is studied in detail.
-",0,0,0,1,0,0
-19232,Abstracting Event-Driven Systems with Lifestate Rules,"  We present lifestate rules--an approach for abstracting event-driven object
-protocols. Developing applications against event-driven software frameworks is
-notoriously difficult. One reason why is that to create functioning
-applications, developers must know about and understand the complex protocols
-that abstract the internal behavior of the framework. Such protocols intertwine
-the proper registering of callbacks to receive control from the framework with
-appropriate application programming interface (API) calls to delegate back to
-it. Lifestate rules unify lifecycle and typestate constraints in one common
-specification language. Our primary contribution is a model of event-driven
-systems from which lifestate rules can be derived. We then apply specification
-mining techniques to learn lifestate specifications for Android framework
-types. In the end, our implementation is able to find several rules that
-characterize actual behavior of the Android framework.
-",1,0,0,0,0,0
-19233,Boltzmann Encoded Adversarial Machines,"  Restricted Boltzmann Machines (RBMs) are a class of generative neural network
-that are typically trained to maximize a log-likelihood objective function. We
-argue that likelihood-based training strategies may fail because the objective
-does not sufficiently penalize models that place a high probability in regions
-where the training data distribution has low probability. To overcome this
-problem, we introduce Boltzmann Encoded Adversarial Machines (BEAMs). A BEAM is
-an RBM trained against an adversary that uses the hidden layer activations of
-the RBM to discriminate between the training data and the probability
-distribution generated by the model. We present experiments demonstrating that
-BEAMs outperform RBMs and GANs on multiple benchmarks.
-",0,0,0,1,0,0
-19234,"A Non-Gaussian, Nonparametric Structure for Gene-Gene and Gene-Environment Interactions in Case-Control Studies Based on Hierarchies of Dirichlet Processes","  It is becoming increasingly clear that complex interactions among genes and
-environmental factors play crucial roles in triggering complex diseases. Thus,
-understanding such interactions is vital, which is possible only through
-statistical models that adequately account for such intricate, albeit unknown,
-dependence structures. Bhattacharya & Bhattacharya (2016b) attempt such
-modeling, relating finite mixtures composed of Dirichlet processes that
-represent unknown number of genetic sub-populations through a hierarchical
-matrix-normal structure that incorporates gene-gene interactions, and possible
-mutations, induced by environmental variables. However, the product dependence
-structure implied by their matrix-normal model seems to be too simple to be
-appropriate for general complex, realistic situations. In this article, we
-propose and develop a novel nonparametric Bayesian model for case-control
-genotype data using hierarchies of Dirichlet processes that offers a more
-realistic and nonparametric dependence structure between the genes, induced by
-the environmental variables. In this regard, we propose a novel and highly
-parallelisable MCMC algorithm that is rendered quite efficient by the
-combination of modern parallel computing technology, effective Gibbs sampling
-steps, retrospective sampling and Transformation based Markov Chain Monte Carlo
-(TMCMC). We use appropriate Bayesian hypothesis testing procedures to detect
-the roles of genes and environment in case-control studies. We apply our ideas
-to 5 biologically realistic case-control genotype datasets simulated under
-distinct set-ups, and obtain encouraging results in each case. We finally apply
-our ideas to a real, myocardial infarction dataset, and obtain interesting
-results on gene-gene and gene-environment interaction, while broadly agreeing
-with the results reported in the literature.
-",0,0,0,1,0,0
-19235,An Efficient Bayesian Robust Principal Component Regression,"  Principal component regression is a linear regression model with principal
-components as regressors. This type of modelling is particularly useful for
-prediction in settings with high-dimensional covariates. Surprisingly, the
-existing literature treating of Bayesian approaches is relatively sparse. In
-this paper, we aim at filling some gaps through the following practical
-contribution: we introduce a Bayesian approach with detailed guidelines for a
-straightforward implementation. The approach features two characteristics that
-we believe are important. First, it effectively involves the relevant principal
-components in the prediction process. This is achieved in two steps. The first
-one is model selection; the second one is to average out the predictions
-obtained from the selected models according to model averaging mechanisms,
-allowing to account for model uncertainty. The model posterior probabilities
-are required for model selection and model averaging. For this purpose, we
-include a procedure leading to an efficient reversible jump algorithm. The
-second characteristic of our approach is whole robustness, meaning that the
-impact of outliers on inference gradually vanishes as they approach plus or
-minus infinity. The conclusions obtained are consequently consistent with the
-majority of observations (the bulk of the data).
-",0,0,0,1,0,0
-19236,Bayesian estimation from few samples: community detection and related problems,"  We propose an efficient meta-algorithm for Bayesian estimation problems that
-is based on low-degree polynomials, semidefinite programming, and tensor
-decomposition. The algorithm is inspired by recent lower bound constructions
-for sum-of-squares and related to the method of moments. Our focus is on sample
-complexity bounds that are as tight as possible (up to additive lower-order
-terms) and often achieve statistical thresholds or conjectured computational
-thresholds.
-Our algorithm recovers the best known bounds for community detection in the
-sparse stochastic block model, a widely-studied class of estimation problems
-for community detection in graphs. We obtain the first recovery guarantees for
-the mixed-membership stochastic block model (Airoldi et el.) in constant
-average degree graphs---up to what we conjecture to be the computational
-threshold for this model. We show that our algorithm exhibits a sharp
-computational threshold for the stochastic block model with multiple
-communities beyond the Kesten--Stigum bound---giving evidence that this task
-may require exponential time.
-The basic strategy of our algorithm is strikingly simple: we compute the
-best-possible low-degree approximation for the moments of the posterior
-distribution of the parameters and use a robust tensor decomposition algorithm
-to recover the parameters from these approximate posterior moments.
-",1,0,0,1,0,0
-19237,X-ray Transform and Boundary Rigidity for Asymptotically Hyperbolic Manifolds,"  We consider the boundary rigidity problem for asymptotically hyperbolic
-manifolds. We show injectivity of the X-ray transform in several cases and
-consider the non-linear inverse problem which consists of recovering a metric
-from boundary measurements for the geodesic flow.
-",0,0,1,0,0,0
-19238,Pinning of longitudinal phonons in holographic spontaneous helices,"  We consider the spontaneous breaking of translational symmetry and identify
-the associated Goldstone mode -- a longitudinal phonon -- in a holographic
-model with Bianchi VII helical symmetry. For the first time in holography, we
-observe the pinning of this mode after introducing a source for explicit
-breaking compatible with the helical symmetry of our setup. We study the
-dispersion relation of the resulting pseudo-Goldstone mode, uncovering how its
-speed and mass gap depend on the amplitude of the source and temperature. In
-addition, we extract the optical conductivity as a function of frequency, which
-reveals a metal-insulator transition as a consequence of the pinning.
-",0,1,0,0,0,0
-19239,An Extension of the Method of Brackets. Part 1,"  The method of brackets is an efficient method for the evaluation of a large
-class of definite integrals on the half-line. It is based on a small collection
-of rules, some of which are heuristic. The extension discussed here is based on
-the concepts of null and divergent series. These are formal representations of
-functions, whose coefficients $a_{n}$ have meromorphic representations for $n
-\in \mathbb{C}$, but might vanish or blow up when $n \in \mathbb{N}$. These
-ideas are illustrated with the evaluation of a variety of entries from the
-classical table of integrals by Gradshteyn and Ryzhik.
-",0,0,1,0,0,0
-19240,optimParallel: an R Package Providing Parallel Versions of the Gradient-Based Optimization Methods of optim(),"  The R package optimParallel provides a parallel version of the gradient-based
-optimization methods of optim(). The main function of the package is
-optimParallel(), which has the same usage and output as optim(). Using
-optimParallel() can significantly reduce optimization times. We introduce the R
-package and illustrate its implementation, which takes advantage of the lexical
-scoping mechanism of R.
-",0,0,0,1,0,0
-19241,The barocaloric effect: A Spin-off of the Discovery of High-Temperature Superconductivity,"  Some key results obtained in joint research projects with Alex Müller are
-summarized, concentrating on the invention of the barocaloric effect and its
-application for cooling as well as on important findings in the field of
-high-temperature superconductivity resulting from neutron scattering
-experiments.
-",0,1,0,0,0,0
-19242,Spontaneous currents in superconducting systems with strong spin-orbit coupling,"  We show that Rashba spin-orbit coupling at the interface between a
-superconductor and a ferromagnet should produce a spontaneous current in the
-atomic thickness region near the interface. This current is counter-balanced by
-the superconducting screening current flowing in the region of the width of the
-London penetration depth near the interface. Such current carrying state
-creates a magnetic field near the superconductor surface, generates a stray
-magnetic field outside the sample edges, changes the slope of the temperature
-dependence of the critical field $H_{c3}$ and may generate the spontaneous
-Abrikosov vortices near the interface.
-",0,1,0,0,0,0
-19243,Catching Loosely Synchronized Behavior in Face of Camouflage,"  Fraud has severely detrimental impacts on the business of social networks and
-other online applications. A user can become a fake celebrity by purchasing
-""zombie followers"" on Twitter. A merchant can boost his reputation through fake
-reviews on Amazon. This phenomenon also conspicuously exists on Facebook, Yelp
-and TripAdvisor, etc. In all the cases, fraudsters try to manipulate the
-platform's ranking mechanism by faking interactions between the fake accounts
-they control and the target customers.
-",1,0,0,0,0,0
-19244,Three-dimensional localized-delocalized Anderson transition in the time domain,"  Systems which can spontaneously reveal periodic evolution are dubbed time
-crystals. This is in analogy with space crystals that display periodic behavior
-in configuration space. While space crystals are modelled with the help of
-space periodic potentials, crystalline phenomena in time can be modelled by
-periodically driven systems. Disorder in the periodic driving can lead to
-Anderson localization in time: the probability for detecting a system at a
-fixed point of configuration space becomes exponentially localized around a
-certain moment in time. We here show that a three-dimensional system exposed to
-a properly disordered pseudo-periodic driving may display a
-localized-delocalized Anderson transition in the time domain, in strong analogy
-with the usual three-dimensional Anderson transition in disordered systems.
-Such a transition could be experimentally observed with ultra-cold atomic
-gases.
-",0,1,0,0,0,0
-19245,Socio-economic constraints to maximum human lifespan,"  The analysis of the demographic transition of the past century and a half,
-using both empirical data and mathematical models, has rendered a wealth of
-well-established facts, including the dramatic increases in life expectancy.
-Despite these insights, such analyses have also occasionally triggered debates
-which spill over many disciplines, from genetics, to biology, or demography.
-Perhaps the hottest discussion is happening around the question of maximum
-human lifespan, which --besides its fascinating historical and philosophical
-interest-- poses urgent pragmatic warnings on a number of issues in public and
-private decision-making. In this paper, we add to the controversy some results
-which, based on purely statistical grounds, suggest that the maximum human
-lifespan is not fixed, or has not reached yet a plateau. Quite the contrary,
-analysis on reliable data for over 150 years in more than 20 industrialized
-countries point at a sustained increase in the maximum age at death.
-Furthermore, were this trend to continue, a limitless lifespan could be
-achieved by 2102. Finally, we quantify the dependence of increases in the
-maximum lifespan on socio-economic factors. Our analysis indicates that in some
-countries the observed rising patterns can only be sustained by progressively
-larger increases in GDP, setting the problem of longevity in a context of
-diminishing returns.
-",0,0,0,1,1,0
-19246,SD-CPS: Taming the Challenges of Cyber-Physical Systems with a Software-Defined Approach,"  Cyber-Physical Systems (CPS) revolutionize various application domains with
-integration and interoperability of networking, computing systems, and
-mechanical devices. Due to its scale and variety, CPS faces a number of
-challenges and opens up a few research questions in terms of management,
-fault-tolerance, and scalability. We propose a software-defined approach
-inspired by Software-Defined Networking (SDN), to address the challenges for a
-wider CPS adoption. We thus design a middleware architecture for the correct
-and resilient operation of CPS, to manage and coordinate the interacting
-devices centrally in the cyberspace whilst not sacrificing the functionality
-and performance benefits inherent to a distributed execution.
-",1,0,0,0,0,0
-19247,Dual Ore's theorem on distributive intervals of finite groups,"  This paper gives a self-contained group-theoretic proof of a dual version of
-a theorem of Ore on distributive intervals of finite groups. We deduce a bridge
-between combinatorics and representations in finite group theory.
-",0,0,1,0,0,0
-19248,A construction of trivial Beltrami coefficients,"  A measurable function $\mu$ on the unit disk $\mathbb{D}$ of the complex
-plane with $\|\mu\|_\infty<1$ is sometimes called a Beltrami coefficient. We
-say that $\mu$ is trivial if it is the complex dilatation $f_{\bar z}/f_z$ of a
-quasiconformal automorphism $f$ of $\mathbb{D}$ satisfying the trivial boundary
-condition $f(z)=z,~|z|=1.$ Since it is not easy to solve the Beltrami equation
-explicitly, to detect triviality of a given Beltrami coefficient is a hard
-problem, in general. In the present article, we offer a sufficient condition
-for a Beltrami coefficient to be trivial. Our proof is based on Betker's
-theorem on Löwner chains.
-",0,0,1,0,0,0
-19249,A GNS construction of three-dimensional abelian Dijkgraaf-Witten theories,"  We give a detailed account of the so-called ""universal construction"" that
-aims to extend invariants of closed manifolds, possibly with additional
-structure, to topological field theories and show that it amounts to a
-generalization of the GNS construction. We apply this construction to an
-invariant defined in terms of the groupoid cardinality of groupoids of bundles
-to recover Dijkgraaf-Witten theories, including the vector spaces obtained as a
-linearization of spaces of principal bundles.
-",0,0,1,0,0,0
-19250,Long-path formation in a deformed microdisk laser,"  An asymmetric resonant cavity can be used to form a path that is much longer
-than the cavity size. We demonstrate this capability for a deformed microdisk
-equipped with two linear waveguides, by constructing a multiply reflected
-periodic orbit that is confined by total internal reflection within the
-deformed microdisk and outcoupled by the two linear waveguides. Resonant mode
-analysis reveals that the modes corresponding to the periodic orbit are
-characterized by high quality factors. From measured spectral and far-field
-data, we confirm that the fabricated devices can form a path about 9.3 times
-longer than the average diameter of the deformed microdisk.
-",0,1,0,0,0,0
-19251,Spitzer Observations of Large Amplitude Variables in the LMC and IC 1613,"  The 3.6 and 4.5 micron characteristics of AGB variables in the LMC and IC1613
-are discussed. For C-rich Mira variables there is a very clear
-period-luminosity-colour relation, where the [3.6]-[4.5] colour is associated
-with the amount of circumstellar material and correlated with the pulsation
-amplitude. The [4.5] period-luminosity relation for dusty stars is
-approximately one mag brighter than for their naked counterparts with
-comparable periods.
-",0,1,0,0,0,0
-19252,Synergistic Team Composition,"  Effective teams are crucial for organisations, especially in environments
-that require teams to be constantly created and dismantled, such as software
-development, scientific experiments, crowd-sourcing, or the classroom. Key
-factors influencing team performance are competences and personality of team
-members. Hence, we present a computational model to compose proficient and
-congenial teams based on individuals' personalities and their competences to
-perform tasks of different nature. With this purpose, we extend Wilde's
-post-Jungian method for team composition, which solely employs individuals'
-personalities. The aim of this study is to create a model to partition agents
-into teams that are balanced in competences, personality and gender. Finally,
-we present some preliminary empirical results that we obtained when analysing
-student performance. Results show the benefits of a more informed team
-composition that exploits individuals' competences besides information about
-their personalities.
-",1,0,0,0,0,0
-19253,The inverse hull of 0-left cancellative semigroups,"  Given a semigroup S with zero, which is left-cancellative in the sense that
-st=sr \neq 0 implies that t=r, we construct an inverse semigroup called the
-inverse hull of S, denoted H(S). When S admits least common multiples, in a
-precise sense defined below, we study the idempotent semilattice of H(S), with
-a focus on its spectrum. When S arises as the language semigroup for a subsift
-X on a finite alphabet, we discuss the relationship between H(S) and several
-C*-algebras associated to X appearing in the literature.
-",0,0,1,0,0,0
-19254,The Hadamard Determinant Inequality - Extensions to Operators on a Hilbert Space,"  A generalization of classical determinant inequalities like Hadamard's
-inequality and Fischer's inequality is studied. For a version of the
-inequalities originally proved by Arveson for positive operators in von Neumann
-algebras with a tracial state, we give a different proof. We also improve and
-generalize to the setting of finite von Neumann algebras, some `Fischer-type'
-inequalities by Matic for determinants of perturbed positive-definite matrices.
-In the process, a conceptual framework is established for viewing these
-inequalities as manifestations of Jensen's inequality in conjunction with the
-theory of operator monotone and operator convex functions on $[0,\infty)$. We
-place emphasis on documenting necessary and sufficient conditions for equality
-to hold.
-",0,0,1,0,0,0
-19255,Experimental results : Reinforcement Learning of POMDPs using Spectral Methods,"  We propose a new reinforcement learning algorithm for partially observable
-Markov decision processes (POMDP) based on spectral decomposition methods.
-While spectral methods have been previously employed for consistent learning of
-(passive) latent variable models such as hidden Markov models, POMDPs are more
-challenging since the learner interacts with the environment and possibly
-changes the future observations in the process. We devise a learning algorithm
-running through epochs, in each epoch we employ spectral techniques to learn
-the POMDP parameters from a trajectory generated by a fixed policy. At the end
-of the epoch, an optimization oracle returns the optimal memoryless planning
-policy which maximizes the expected reward based on the estimated POMDP model.
-We prove an order-optimal regret bound with respect to the optimal memoryless
-policy and efficient scaling with respect to the dimensionality of observation
-and action spaces.
-",1,0,0,1,0,0
-19256,Discreteness of silting objects and t-structures in triangulated categories,"  We introduce the notion of ST-pairs of triangulated subcategories, a
-prototypical example of which is the pair of the bound homotopy category and
-the bound derived category of a finite-dimensional algebra. For an ST-pair
-$(\C,\D)$, we construct an order-preserving map from silting objects in $\C$ to
-bounded $t$-structures on $\D$ and show that the map is bijective if and only
-if $\C$ is silting-discrete if and only if $\D$ is $t$-discrete. Based on a
-work of Qiu and Woolf, the above result is applied to show that if $\C$ is
-silting-discrete then the stability space of $\D$ is contractible. This is used
-to obtain the contractibility of the stability spaces of some Calabi--Yau
-triangulated categories associated to Dynkin quivers.
-",0,0,1,0,0,0
-19257,Sketching the order of events,"  We introduce features for massive data streams. These stream features can be
-thought of as ""ordered moments"" and generalize stream sketches from ""moments of
-order one"" to ""ordered moments of arbitrary order"". In analogy to classic
-moments, they have theoretical guarantees such as universality that are
-important for learning algorithms.
-",1,0,1,1,0,0
-19258,Deep Multi-view Learning to Rank,"  We study the problem of learning to rank from multiple sources. Though
-multi-view learning and learning to rank have been studied extensively leading
-to a wide range of applications, multi-view learning to rank as a synergy of
-both topics has received little attention. The aim of the paper is to propose a
-composite ranking method while keeping a close correlation with the individual
-rankings simultaneously. We propose a multi-objective solution to ranking by
-capturing the information of the feature mapping from both within each view as
-well as across views using autoencoder-like networks. Moreover, a novel
-end-to-end solution is introduced to enhance the joint ranking with minimum
-view-specific ranking loss, so that we can achieve the maximum global view
-agreements within a single optimization process. The proposed method is
-validated on a wide variety of ranking problems, including university ranking,
-multi-view lingual text ranking and image data ranking, providing superior
-results.
-",0,0,0,1,0,0
-19259,One-Shot Visual Imitation Learning via Meta-Learning,"  In order for a robot to be a generalist that can perform a wide range of
-jobs, it must be able to acquire a wide variety of skills quickly and
-efficiently in complex unstructured environments. High-capacity models such as
-deep neural networks can enable a robot to represent complex skills, but
-learning each skill from scratch then becomes infeasible. In this work, we
-present a meta-imitation learning method that enables a robot to learn how to
-learn more efficiently, allowing it to acquire new skills from just a single
-demonstration. Unlike prior methods for one-shot imitation, our method can
-scale to raw pixel inputs and requires data from significantly fewer prior
-tasks for effective learning of new skills. Our experiments on both simulated
-and real robot platforms demonstrate the ability to learn new tasks,
-end-to-end, from a single visual demonstration.
-",1,0,0,0,0,0
-19260,Second variation of Selberg zeta functions and curvature asymptotics,"  We give an explicit formula for the second variation of the logarithm of the
-Selberg zeta function, $Z(s)$, on Teichmüller space. We then use this formula
-to determine the asymptotic behavior as $s \to \infty$ of the second variation.
-As a consequence, we determine the signature of the Hessian of $\log Z(s)$ for
-sufficiently large $s$. As a further consequence, the asymptotic behavior of
-the second variation of $\log Z(s)$ shows that the Ricci curvature of the Hodge
-bundle $H^0(\mathcal K^m_t)\mapsto t$ over Teichmüller space agrees with the
-Quillen curvature up to a term of exponential decay, $O(s^2 e^{-l_0 s}),$ where
-$l_0$ is the length of the shortest closed hyperbolic geodesic.
-",0,0,1,0,0,0
-19261,Magnetic Fields Threading Black Holes: restrictions from general relativity and implications for astrophysical black holes,"  The idea that black hole spin is instrumental in the generation of powerful
-jets in active galactic nuclei and X-ray binaries is arguably the most
-contentious claim in black hole astrophysics. Because jets are thought to
-originate in the context of electromagnetism, and the modeling of Maxwell
-fields in curved spacetime around black holes is challenging, various
-approximations are made in numerical simulations that fall under the guise of
-'ideal magnetohydrodynamics'. But the simplifications of this framework may
-struggle to capture relevant details of real astrophysical environments near
-black holes. In this work, we highlight tension between analytic and numerical
-results, specifically between the analytically derived conserved Noether
-currents for rotating black hole spacetimes and the results of general
-relativistic numerical simulations (GRMHD). While we cannot definitively
-attribute the issue to any specific approximation used in the numerical
-schemes, there seem to be natural candidates, which we explore. GRMHD
-notwithstanding, if electromagnetic fields around rotating black holes are
-brought to the hole by accretion, we show from first principles that prograde
-accreting disks likely experience weaker large-scale black hole-threading
-fields, implying weaker jets than in retrograde configurations.
-",0,1,0,0,0,0
-19262,Distance covariance for stochastic processes,"  The distance covariance of two random vectors is a measure of their
-dependence. The empirical distance covariance and correlation can be used as
-statistical tools for testing whether two random vectors are independent. We
-propose an analogs of the distance covariance for two stochastic processes
-defined on some interval. Their empirical analogs can be used to test the
-independence of two processes.
-",0,0,1,1,0,0
-19263,On Nonparametric Regression using Data Depth,"  We investigate nonparametric regression methods based on statistical depth
-functions. These nonparametric regression procedures can be used in situations,
-where the response is multivariate and the covariate is a random element in a
-metric space. This includes regression with functional covariate as a special
-case. Our objective is to study different features of the conditional
-distribution of the response given the covariate. We construct measures of the
-center and the spread of the conditional distribution using depth based
-nonparametric regression procedures. We establish the asymptotic consistency of
-those measures and develop a test for heteroscedasticity based on the measure
-of conditional spread. The usefulness of the methodology is demonstrated in
-some real datasets. In one dataset consisting of Italian household expenditure
-data for the period 1973 to 1992, we regress the expenditure for different
-items on their prices. In another dataset, our responses are the nutritional
-contents of different meat samples measured by their protein, fat and moisture
-contents, and the functional covariate is the absorbance spectra of the meat
-samples.
-",0,0,0,1,0,0
-19264,A Backward Simulation Method for Stochastic Optimal Control Problems,"  A number of optimal decision problems with uncertainty can be formulated into
-a stochastic optimal control framework. The Least-Squares Monte Carlo (LSMC)
-algorithm is a popular numerical method to approach solutions of such
-stochastic control problems as analytical solutions are not tractable in
-general. This paper generalizes the LSMC algorithm proposed in Shen and Weng
-(2017) to solve a wide class of stochastic optimal control models. Our
-algorithm has three pillars: a construction of auxiliary stochastic control
-model, an artificial simulation of the post-action value of state process, and
-a shape-preserving sieve estimation method which equip the algorithm with a
-number of merits including bypassing forward simulation and control
-randomization, evading extrapolating the value function, and alleviating
-computational burden of the tuning parameter selection. The efficacy of the
-algorithm is corroborated by an application to pricing equity-linked insurance
-products.
-",0,0,0,0,0,1
-19265,Damped Posterior Linearization Filter,"  The iterated posterior linearization filter (IPLF) is an algorithm for
-Bayesian state estimation that performs the measurement update using iterative
-statistical regression. The main result behind IPLF is that the posterior
-approximation is more accurate when the statistical regression of measurement
-function is done in the posterior instead of the prior as is done in
-non-iterative Kalman filter extensions. In IPLF, each iteration in principle
-gives a better posterior estimate to obtain a better statistical regression and
-more accurate posterior estimate in the next iteration. However, IPLF may
-diverge. IPLF's fixed- points are not described as solutions to an optimization
-problem, which makes it challenging to improve its convergence properties. In
-this letter, we introduce a double-loop version of IPLF, where the inner loop
-computes the posterior mean using an optimization algorithm. Simulation results
-are presented to show that the proposed algorithm has better convergence than
-IPLF and its accuracy is similar to or better than other state-of-the-art
-algorithms.
-",0,0,1,1,0,0
-19266,Status updates through M/G/1/1 queues with HARQ,"  We consider a system where randomly generated updates are to be transmitted
-to a monitor, but only a single update can be in the transmission service at a
-time. Therefore, the source has to prioritize between the two possible
-transmission policies: preempting the current update or discarding the new one.
-We consider Poisson arrivals and general service time, and refer to this system
-as the M/G/1/1 queue. We start by studying the average status update age and
-the optimal update arrival rate for these two schemes under general service
-time distribution. We then apply these results on two practical scenarios in
-which updates are sent through an erasure channel using (a) an infinite
-incremental redundancy (IIR) HARQ system and (b) a fixed redundancy (FR) HARQ
-system. We show that in both schemes the best strategy would be not to preempt.
-Moreover, we also prove that, from an age point of view, IIR is better than FR.
-",1,0,0,0,0,0
-19267,A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials,"  Most analyses of randomised trials with incomplete outcomes make untestable
-assumptions and should therefore be subjected to sensitivity analyses. However,
-methods for sensitivity analyses are not widely used. We propose a mean score
-approach for exploring global sensitivity to departures from missing at random
-or other assumptions about incomplete outcome data in a randomised trial. We
-assume a single outcome analysed under a generalised linear model. One or more
-sensitivity parameters, specified by the user, measure the degree of departure
-from missing at random in a pattern mixture model. Advantages of our method are
-that its sensitivity parameters are relatively easy to interpret and so can be
-elicited from subject matter experts; it is fast and non-stochastic; and its
-point estimate, standard error and confidence interval agree perfectly with
-standard methods when particular values of the sensitivity parameters make
-those standard methods appropriate. We illustrate the method using data from a
-mental health trial.
-",0,0,1,1,0,0
-19268,On the union complexity of families of axis-parallel rectangles with a low packing number,"  Let R be a family of n axis-parallel rectangles with packing number p-1,
-meaning that among any p of the rectangles, there are two with a non-empty
-intersection. We show that the union complexity of R is at most O(n+p^2), and
-that the (<=k)-level complexity of R is at most O(kn+k^2p^2). Both upper bounds
-are tight.
-",1,0,1,0,0,0
-19269,Using photo-ionisation models to derive carbon and oxygen gas-phase abundances in the rest UV,"  We present a new method to derive oxygen and carbon abundances using the
-ultraviolet (UV) lines emitted by the gas-phase ionised by massive stars. The
-method is based on the comparison of the nebular emission-line ratios with
-those predicted by a large grid of photo-ionisation models. Given the large
-dispersion in the O/H - C/O plane, our method firstly fixes C/O using ratios of
-appropriate emission lines and, in a second step, calculates O/H and the
-ionisation parameter from carbon lines in the UV. We find abundances totally
-consistent with those provided by the direct method when we apply this method
-to a sample of objects with an empirical determination of the electron
-temperature using optical emission lines. The proposed methodology appears as a
-powerful tool for systematic studies of nebular abundances in star-forming
-galaxies at high redshift.
-",0,1,0,0,0,0
-19270,A Probabilistic Framework for Location Inference from Social Media,"  We study the extent to which we can infer users' geographical locations from
-social media. Location inference from social media can benefit many
-applications, such as disaster management, targeted advertising, and news
-content tailoring. In recent years, a number of algorithms have been proposed
-for identifying user locations on social media platforms such as Twitter and
-Facebook from message contents, friend networks, and interactions between
-users. In this paper, we propose a novel probabilistic model based on factor
-graphs for location inference that offers several unique advantages for this
-task. First, the model generalizes previous methods by incorporating content,
-network, and deep features learned from social context. The model is also
-flexible enough to support both supervised learning and semi-supervised
-learning. Second, we explore several learning algorithms for the proposed
-model, and present a Two-chain Metropolis-Hastings (MH+) algorithm, which
-improves the inference accuracy. Third, we validate the proposed model on three
-different genres of data - Twitter, Weibo, and Facebook - and demonstrate that
-the proposed model can substantially improve the inference accuracy (+3.3-18.5%
-by F1-score) over that of several state-of-the-art methods.
-",1,0,0,0,0,0
-19271,"The Novel ABALONE Photosensor Technology: 4-Year Long Tests of Vacuum Integrity, Internal Pumping and Afterpulsing","  The ABALONE Photosensor Technology (U.S. Patent 9064678 2015) has the
-capability of supplanting the expensive 80 year old Photomultiplier Tube (PMT)
-manufacture by providing a modern and cost effective alternative product. An
-ABALONE Photosensor comprises only three monolithic glass components, sealed
-together by our new thin film adhesive. In 2013, we left one of the early
-ABALONE Photosensor prototypes intact for continuous stress testing, and here
-we report its long term vacuum integrity. The exceptionally low ion
-afterpulsing rate (approximately two orders of magnitude lower than in PMTs)
-has been constantly improving. We explain the physical and technological
-reasons for this achievement. Due to the cost-effectiveness and the specific
-combination of features, including low level of radioactivity, integration into
-large-area panels, and robustness, this technology can open new horizons in the
-fields of fundamental physics, functional medical imaging, and nuclear
-security.
-",0,1,0,0,0,0
-19272,Multi-robot Dubins Coverage with Autonomous Surface Vehicles,"  In large scale coverage operations, such as marine exploration or aerial
-monitoring, single robot approaches are not ideal, as they may take too long to
-cover a large area. In such scenarios, multi-robot approaches are preferable.
-Furthermore, several real world vehicles are non-holonomic, but can be modeled
-using Dubins vehicle kinematics. This paper focuses on environmental monitoring
-of aquatic environments using Autonomous Surface Vehicles (ASVs). In
-particular, we propose a novel approach for solving the problem of complete
-coverage of a known environment by a multi-robot team consisting of Dubins
-vehicles. It is worth noting that both multi-robot coverage and Dubins vehicle
-coverage are NP-complete problems. As such, we present two heuristics methods
-based on a variant of the traveling salesman problem -- k-TSP -- formulation
-and clustering algorithms that efficiently solve the problem. The proposed
-methods are tested both in simulations to assess their scalability and with a
-team of ASVs operating on a lake to ensure their applicability in real world.
-",1,0,0,0,0,0
-19273,New Fairness Metrics for Recommendation that Embrace Differences,"  We study fairness in collaborative-filtering recommender systems, which are
-sensitive to discrimination that exists in historical data. Biased data can
-lead collaborative filtering methods to make unfair predictions against
-minority groups of users. We identify the insufficiency of existing fairness
-metrics and propose four new metrics that address different forms of
-unfairness. These fairness metrics can be optimized by adding fairness terms to
-the learning objective. Experiments on synthetic and real data show that our
-new metrics can better measure fairness than the baseline, and that the
-fairness objectives effectively help reduce unfairness.
-",1,0,0,0,0,0
-19274,Graph heat mixture model learning,"  Graph inference methods have recently attracted a great interest from the
-scientific community, due to the large value they bring in data interpretation
-and analysis. However, most of the available state-of-the-art methods focus on
-scenarios where all available data can be explained through the same graph, or
-groups corresponding to each graph are known a priori. In this paper, we argue
-that this is not always realistic and we introduce a generative model for mixed
-signals following a heat diffusion process on multiple graphs. We propose an
-expectation-maximisation algorithm that can successfully separate signals into
-corresponding groups, and infer multiple graphs that govern their behaviour. We
-demonstrate the benefits of our method on both synthetic and real data.
-",1,0,0,1,0,0
-19275,Technical Report: Reactive Navigation in Partially Known Non-Convex Environments,"  This paper presents a provably correct method for robot navigation in 2D
-environments cluttered with familiar but unexpected non-convex, star-shaped
-obstacles as well as completely unknown, convex obstacles. We presuppose a
-limited range onboard sensor, capable of recognizing, localizing and
-(leveraging ideas from constructive solid geometry) generating online from its
-catalogue of the familiar, non-convex shapes an implicit representation of each
-one. These representations underlie an online change of coordinates to a
-completely convex model planning space wherein a previously developed online
-construction yields a provably correct reactive controller that is pulled back
-to the physically sensed representation to generate the actual robot commands.
-We extend the construction to differential drive robots, and suggest the
-empirical utility of the proposed control architecture using both formal proofs
-and numerical simulations.
-",1,0,0,0,0,0
-19276,Automatic smoothness detection of the resolvent Krylov subspace method for the approximation of $C_0$-semigroups,"  The resolvent Krylov subspace method builds approximations to operator
-functions $f(A)$ times a vector $v$. For the semigroup and related operator
-functions, this method is proved to possess the favorable property that the
-convergence is automatically faster when the vector $v$ is smoother. The user
-of the method does not need to know the presented theory and alterations of the
-method are not necessary in order to adapt to the (possibly unknown) smoothness
-of $v$. The findings are illustrated by numerical experiments.
-",0,0,1,0,0,0
-19277,On the observability of Pauli crystals,"  The best known manifestation of the Fermi-Dirac statistics is the Pauli
-exclusion principle: no two identical fermions can occupy the same one-particle
-state. This principle enforces high order correlations in systems of many
-identical fermions and is responsible for a particular geometric arrangement of
-trapped particles even when all mutual interactions are absent [1]. These
-geometric structures, called Pauli crystals, are predicted for a system of $N$
-identical atoms trapped in a harmonic potential. They emerge as the most
-frequent configurations in a collection of single-shot pictures of the system.
-Here we study how fragile Pauli crystals are when realistic experimental
-limitations are taken into account. The influence of the number of single-shots
-pictures available to analysis, thermal fluctuations and finite efficiency of
-detection are considered. The role of these sources of noise on the possibility
-of experimental observation of Pauli crystals is shown and conditions necessary
-for the detection of the geometrical arrangements of particles are identified.
-",0,1,0,0,0,0
-19278,Interpolating Between Choices for the Approximate Intermediate Value Theorem,"  This paper proves the approximate intermediate value theorem, constructively
-and from notably weak hypotheses: from pointwise rather than uniform
-continuity, without assuming that reals are presented with rational
-approximants, and without using countable choice. The theorem is that if a
-pointwise continuous function has both a negative and a positive value, then it
-has values arbitrarily close to 0. The proof builds on the usual classical
-proof by bisection, which repeatedly selects the left or right half of an
-interval; the algorithm here selects an interval of half the size in a
-continuous way, interpolating between those two possibilities.
-",1,0,1,0,0,0
-19279,HAZMAT II: Ultraviolet Variability of Low-Mass Stars in the GALEX Archive,"  The ultraviolet (UV) light from a host star influences a planet's atmospheric
-photochemistry and will affect interpretations of exoplanetary spectra from
-future missions like the James Webb Space Telescope. These effects will be
-particularly critical in the study of planetary atmospheres around M dwarfs,
-including Earth-sized planets in the habitable zone. Given the higher activity
-levels of M dwarfs compared to Sun-like stars, time resolved UV data are needed
-for more accurate input conditions for exoplanet atmospheric modeling. The
-Galaxy Evolution Explorer (\emph{GALEX}) provides multi-epoch photometric
-observations in two UV bands: near-ultraviolet (NUV; 1771 -- 2831 \AA) and
-far-ultraviolet (FUV; 1344 -- 1786 \AA). Within 30 pc of Earth, there are 357
-and 303 M dwarfs in the NUV and FUV bands, respectively, with multiple\GALEX
-observations. Simultaneous NUV and FUV detections exist for 145 stars in
-both\GALEX bands. Our analyses of these data show that low-mass stars are
-typically more variable in the FUV than the NUV. Median variability increases
-with later spectral types in the NUV with no clear trend in the FUV. We find
-evidence that flares increase the FUV flux density far more than the NUV flux
-density, leading to variable FUV to NUV flux density ratios in the \GALEX\
-bandpasses.The ratio of FUV to NUV flux is important for interpreting the
-presence of atmospheric molecules in planetary atmospheres such as oxygen and
-methane as a high FUV to NUV ratio may cause false-positive biosignature
-detections. This ratio of flux density in the\GALEX\ bands spans three orders
-of magnitude in our sample, from 0.008 to 4.6, and is 1 to 2 orders of
-magnitude higher than for G dwarfs like the Sun. These results characterize the
-UV behavior for the largest set of low-mass stars to date.
-",0,1,0,0,0,0
-19280,Exactly solvable Schrödinger equation with double-well potential for hydrogen bond,"  We construct a double-well potential for which the Schrödinger equation can
-be exactly solved via reducing to the confluent Heun's one. Thus the wave
-function is expressed via the confluent Heun's function. The latter is
-tabulated in {\sl {Maple}} so that the obtained solution is easily treated. The
-potential is infinite at the boundaries of the final interval that makes it to
-be highly suitable for modeling hydrogen bonds (both ordinary and low-barrier
-ones). We exemplify theoretical results by detailed treating the hydrogen bond
-in $KHCO_3$ and show their good agreement with literature experimental data.
-",0,1,0,0,0,0
-19281,The co-evolution of emotional well-being with weak and strong friendship ties,"  Social ties are strongly related to well-being. But what characterizes this
-relationship? This study investigates social mechanisms explaining how social
-ties affect well-being through social integration and social influence, and how
-well-being affects social ties through social selection. We hypothesize that
-highly integrated individuals - those with more extensive and dense friendship
-networks - report higher emotional well-being than others. Moreover, emotional
-well-being should be influenced by the well-being of close friends. Finally,
-well-being should affect friendship selection when individuals prefer others
-with higher levels of well-being, and others whose well-being is similar to
-theirs. We test our hypotheses using longitudinal social network and well-being
-data of 117 individuals living in a graduate housing community. The application
-of a novel extension of Stochastic Actor-Oriented Models for ordered networks
-(ordered SAOMs) allows us to detail and test our hypotheses for weak- and
-strong-tied friendship networks simultaneously. Results do not support our
-social integration and social influence hypotheses but provide evidence for
-selection: individuals with higher emotional well-being tend to have more
-strong-tied friends, and there are homophily processes regarding emotional
-well-being in strong-tied networks. Our study highlights the two-directional
-relationship between social ties and well-being, and demonstrates the
-importance of considering different tie strengths for various social processes.
-",1,0,0,1,0,0
-19282,A magnetic version of the Smilansky-Solomyak model,"  We analyze spectral properties of two mutually related families of magnetic
-Schrödinger operators, $H_{\mathrm{Sm}}(A)=(i \nabla +A)^2+\omega^2
-y^2+\lambda y \delta(x)$ and $H(A)=(i \nabla +A)^2+\omega^2 y^2+ \lambda y^2
-V(x y)$ in $L^2(R^2)$, with the parameters $\omega>0$ and $\lambda<0$, where
-$A$ is a vector potential corresponding to a homogeneous magnetic field
-perpendicular to the plane and $V$ is a regular nonnegative and compactly
-supported potential. We show that the spectral properties of the operators
-depend crucially on the one-dimensional Schrödinger operators $L=
--\frac{\mathrm{d}^2}{\mathrm{d}x^2} +\omega^2 +\lambda \delta (x)$ and $L (V)=
-- \frac{\mathrm{d}^2}{\mathrm{d}x^2} +\omega^2 +\lambda V(x)$, respectively.
-Depending on whether the operators $L$ and $L(V)$ are positive or not, the
-spectrum of $H_{\mathrm{Sm}}(A)$ and $H(V)$ exhibits a sharp transition.
-",0,0,1,0,0,0
-19283,John-Nirenberg Radius and Collapse in Conformal Geometry,"  Given a positive function $u\in W^{1,n}$, we define its John-Nirenberg radius
-at point $x$ to be the supreme of the radius such that $\int_{B_t}|\nabla\log
-u|^n<\epsilon_0^n$ when $n>2$, and $\int_{B_t}|\nabla u|^2<\epsilon_0^2$ when
-$n=2$. We will show that for a collapsing sequence in a fixed conformal class
-under some curvature conditions, the radius is bounded below by a positive
-constant. As applications, we will study the convergence of a conformal metric
-sequence on a $4$-manifold with bounded $\|K\|_{W^{1,2}}$, and prove a
-generalized Hélein's Convergence Theorem.
-",0,0,1,0,0,0
-19284,Deep Prior,"  The recent literature on deep learning offers new tools to learn a rich
-probability distribution over high dimensional data such as images or sounds.
-In this work we investigate the possibility of learning the prior distribution
-over neural network parameters using such tools. Our resulting variational
-Bayes algorithm generalizes well to new tasks, even when very few training
-examples are provided. Furthermore, this learned prior allows the model to
-extrapolate correctly far from a given task's training data on a meta-dataset
-of periodic signals.
-",1,0,0,1,0,0
-19285,Generalized two-dimensional linear discriminant analysis with regularization,"  Recent advances show that two-dimensional linear discriminant analysis
-(2DLDA) is a successful matrix based dimensionality reduction method. However,
-2DLDA may encounter the singularity issue theoretically and the sensitivity to
-outliers. In this paper, a generalized Lp-norm 2DLDA framework with
-regularization for an arbitrary $p>0$ is proposed, named G2DLDA. There are
-mainly two contributions of G2DLDA: one is G2DLDA model uses an arbitrary
-Lp-norm to measure the between-class and within-class scatter, and hence a
-proper $p$ can be selected to achieve the robustness. The other one is that by
-introducing an extra regularization term, G2DLDA achieves better generalization
-performance, and solves the singularity problem. In addition, G2DLDA can be
-solved through a series of convex problems with equality constraint, and it has
-closed solution for each single problem. Its convergence can be guaranteed
-theoretically when $1\leq p\leq2$. Preliminary experimental results on three
-contaminated human face databases show the effectiveness of the proposed
-G2DLDA.
-",0,0,0,1,0,0
-19286,Time-Frequency Audio Features for Speech-Music Classification,"  Distinct striation patterns are observed in the spectrograms of speech and
-music. This motivated us to propose three novel time-frequency features for
-speech-music classification. These features are extracted in two stages. First,
-a preset number of prominent spectral peak locations are identified from the
-spectra of each frame. These important peak locations obtained from each frame
-are used to form Spectral peak sequences (SPS) for an audio interval. In second
-stage, these SPS are treated as time series data of frequency locations. The
-proposed features are extracted as periodicity, average frequency and
-statistical attributes of these spectral peak sequences. Speech-music
-categorization is performed by learning binary classifiers on these features.
-We have experimented with Gaussian mixture models, support vector machine and
-random forest classifiers. Our proposal is validated on four datasets and
-benchmarked against three baseline approaches. Experimental results establish
-the validity of our proposal.
-",1,0,0,0,0,0
-19287,Kinodynamic Planning on Constraint Manifolds,"  This paper presents a motion planner for systems subject to kinematic and
-dynamic constraints. The former appear when kinematic loops are present in the
-system, such as in parallel manipulators, in robots that cooperate to achieve a
-given task, or in situations involving contacts with the environment. The
-latter are necessary to obtain realistic trajectories, taking into account the
-forces acting on the system. The kinematic constraints make the state space
-become an implicitly-defined manifold, which complicates the application of
-common motion planning techniques. To address this issue, the planner
-constructs an atlas of the state space manifold incrementally, and uses this
-atlas both to generate random states and to dynamically simulate the steering
-of the system towards such states. The resulting tools are then exploited to
-construct a rapidly-exploring random tree (RRT) over the state space. To the
-best of our knowledge, this is the first randomized kinodynamic planner for
-implicitly-defined state spaces. The test cases presented in this paper
-validate the approach in significantly-complex systems.
-",1,0,0,0,0,0
-19288,Binary Classification from Positive-Confidence Data,"  Can we learn a binary classifier from only positive data, without any
-negative data or unlabeled data? We show that if one can equip positive data
-with confidence (positive-confidence), one can successfully learn a binary
-classifier, which we name positive-confidence (Pconf) classification. Our work
-is related to one-class classification which is aimed at ""describing"" the
-positive class by clustering-related methods, but one-class classification does
-not have the ability to tune hyper-parameters and their aim is not on
-""discriminating"" positive and negative classes. For the Pconf classification
-problem, we provide a simple empirical risk minimization framework that is
-model-independent and optimization-independent. We theoretically establish the
-consistency and an estimation error bound, and demonstrate the usefulness of
-the proposed method for training deep neural networks through experiments.
-",1,0,0,1,0,0
-19289,Skewing Methods for Variance-Stabilizing Local Linear Regression Estimation,"  It is well-known that kernel regression estimators do not produce a constant
-estimator variance over a domain. To correct this problem, Nishida and Kanazawa
-(2015) proposed a variance-stabilizing (VS) local variable bandwidth for Local
-Linear (LL) regression estimator. In contrast, Choi and Hall (1998) proposed
-the skewing (SK) methods for a univariate LL estimator and constructed a convex
-combination of one LL estimator and two SK estimators that are symmetrically
-placed on both sides of the LL estimator (the convex combination (CC)
-estimator) to eliminate higher-order terms in its asymptotic bias. To obtain a
-CC estimator with a constant estimator variance without employing the VS local
-variable bandwidth, the weight in the convex combination must be determined
-locally to produce a constant estimator variance. In this study, we compare the
-performances of two VS methods for a CC estimator and find cases in which the
-weighting method can superior to the VS bandwidth method in terms of the degree
-of variance stabilization.
-",0,0,0,1,0,0
-19290,Proximodistal Exploration in Motor Learning as an Emergent Property of Optimization,"  To harness the complexity of their high-dimensional bodies during
-sensorimotor development, infants are guided by patterns of freezing and
-freeing of degrees of freedom. For instance, when learning to reach, infants
-free the degrees of freedom in their arm proximodistally, i.e. from joints that
-are closer to the body to those that are more distant. Here, we formulate and
-study computationally the hypothesis that such patterns can emerge
-spontaneously as the result of a family of stochastic optimization processes
-(evolution strategies with covariance-matrix adaptation), without an innate
-encoding of a maturational schedule. In particular, we present simulated
-experiments with an arm where a computational learner progressively acquires
-reaching skills through adaptive exploration, and we show that a proximodistal
-organization appears spontaneously, which we denote PDFF (ProximoDistal
-Freezing and Freeing of degrees of freedom). We also compare this emergent
-organization between different arm morphologies -- from human-like to quite
-unnatural ones -- to study the effect of different kinematic structures on the
-emergence of PDFF. Keywords: human motor learning; proximo-distal exploration;
-stochastic optimization; modelling; evolution strategies; cross-entropy
-methods; policy search; morphology.}
-",1,0,0,0,0,0
-19291,Near-optimal Sample Complexity Bounds for Robust Learning of Gaussians Mixtures via Compression Schemes,"  We prove that $\tilde{\Theta}(k d^2 / \varepsilon^2)$ samples are necessary
-and sufficient for learning a mixture of $k$ Gaussians in $\mathbb{R}^d$, up to
-error $\varepsilon$ in total variation distance. This improves both the known
-upper bounds and lower bounds for this problem. For mixtures of axis-aligned
-Gaussians, we show that $\tilde{O}(k d / \varepsilon^2)$ samples suffice,
-matching a known lower bound. Moreover, these results hold in the
-agnostic-learning/robust-estimation setting as well, where the target
-distribution is only approximately a mixture of Gaussians.
-The upper bound is shown using a novel technique for distribution learning
-based on a notion of `compression.' Any class of distributions that allows such
-a compression scheme can also be learned with few samples. Moreover, if a class
-of distributions has such a compression scheme, then so do the classes of
-products and mixtures of those distributions. The core of our main result is
-showing that the class of Gaussians in $\mathbb{R}^d$ admits a small-sized
-compression scheme.
-",1,0,1,0,0,0
-19292,Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME,"  We present a heuristic based algorithm to induce \textit{nonmonotonic} logic
-programs that will explain the behavior of XGBoost trained classifiers. We use
-the technique based on the LIME approach to locally select the most important
-features contributing to the classification decision. Then, in order to explain
-the model's global behavior, we propose the LIME-FOLD algorithm ---a
-heuristic-based inductive logic programming (ILP) algorithm capable of learning
-non-monotonic logic programs---that we apply to a transformed dataset produced
-by LIME. Our proposed approach is agnostic to the choice of the ILP algorithm.
-Our experiments with UCI standard benchmarks suggest a significant improvement
-in terms of classification evaluation metrics. Meanwhile, the number of induced
-rules dramatically decreases compared to ALEPH, a state-of-the-art ILP system.
-",0,0,0,1,0,0
-19293,Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation,"  Manipulation of deformable objects, such as ropes and cloth, is an important
-but challenging problem in robotics. We present a learning-based system where a
-robot takes as input a sequence of images of a human manipulating a rope from
-an initial to goal configuration, and outputs a sequence of actions that can
-reproduce the human demonstration, using only monocular images as input. To
-perform this task, the robot learns a pixel-level inverse dynamics model of
-rope manipulation directly from images in a self-supervised manner, using about
-60K interactions with the rope collected autonomously by the robot. The human
-demonstration provides a high-level plan of what to do and the low-level
-inverse model is used to execute the plan. We show that by combining the high
-and low-level plans, the robot can successfully manipulate a rope into a
-variety of target shapes using only a sequence of human-provided images for
-direction.
-",1,0,0,0,0,0
-19294,Online learning with graph-structured feedback against adaptive adversaries,"  We derive upper and lower bounds for the policy regret of $T$-round online
-learning problems with graph-structured feedback, where the adversary is
-nonoblivious but assumed to have a bounded memory. We obtain upper bounds of
-$\widetilde O(T^{2/3})$ and $\widetilde O(T^{3/4})$ for strongly-observable and
-weakly-observable graphs, respectively, based on analyzing a variant of the
-Exp3 algorithm. When the adversary is allowed a bounded memory of size 1, we
-show that a matching lower bound of $\widetilde\Omega(T^{2/3})$ is achieved in
-the case of full-information feedback. We also study the particular loss
-structure of an oblivious adversary with switching costs, and show that in such
-a setting, non-revealing strongly-observable feedback graphs achieve a lower
-bound of $\widetilde\Omega(T^{2/3})$, as well.
-",0,0,0,1,0,0
-19295,Dynamical characteristics of electromagnetic field under conditions of total reflection,"  The dynamical characteristics of electromagnetic fields include energy,
-momentum, angular momentum (spin) and helicity. We analyze their spatial
-distributions near the planar interface between two transparent and
-non-dispersive media, when the incident monochromatic plane wave with arbitrary
-polarization is totally reflected, and an evanescent wave is formed in the
-medium with lower optical density. Based on the recent arguments in favor of
-the Minkowski definition of the electromagnetic momentum in a material medium
-[Phys. Rev. A 83, 013823 (2011); 86, 055802 (2012); Phys. Rev. Lett. 119,
-073901 (2017)], we derive the explicit expressions for the dynamical
-characteristics in both media, with special attention to their behavior at the
-interface. Especially, the ""extraordinary"" spin and momentum components
-orthogonal to the plane of incidence are described, and the canonical (spin -
-orbital) momentum decomposition is performed that contains no singular terms.
-The field energy, helicity, the spin momentum and orbital momentum components
-are everywhere regular but experience discontinuities at the interface; the
-spin components parallel to the interface appear to be continuous, which
-testifies for the consistency of the adopted Minkowski picture. The results
-supply a meaningful example of the electromagnetic momentum decomposition, with
-separation of spatial and polarization degrees of freedom, in inhomogeneous
-media, and can be used in engineering the structured fields designed for
-optical sorting, dispatching and micromanipulation.
-",0,1,0,0,0,0
-19296,Functors induced by Cauchy extension of C*-algebras,"  In this paper we give three functors $\mathfrak{P}$, $[\cdot]_K$ and
-$\mathfrak{F}$ on the category of C$^\ast$-algebras. The functor $\mathfrak{P}$
-assigns to each C$^\ast$-algebra $\mathcal{A}$ a pre-C$^\ast$-algebra
-$\mathfrak{P}(\mathcal{A})$ with completion $[\mathcal{A}]_K$. The functor
-$[\cdot]_K$ assigns to each C$^\ast$-algebra $\mathcal{A}$ the Cauchy extension
-$[\mathcal{A}]_K$ of $\mathcal{A}$ by a non-unital C$^\ast$-algebra
-$\mathfrak{F}(\mathcal{A})$. Some properties of these functors are also given.
-In particular, we show that the functors $[\cdot]_K$ and $\mathfrak{F}$ are
-exact and the functor $\mathfrak{P}$ is normal exact.
-",0,0,1,0,0,0
-19297,Hierarchically cocompact classifying spaces for mapping class groups of surfaces,"  We define the notion of a hierarchically cocompact classifying space for a
-family of subgroups of a group. Our main application is to show that the
-mapping class group $\mbox{Mod}(S)$ of any connected oriented compact surface
-$S$, possibly with punctures and boundary components and with negative Euler
-characteristic has a hierarchically cocompact model for the family of virtually
-cyclic subgroups of dimension at most $\mbox{vcd} \mbox{Mod}(S)+1$. When the
-surface is closed, we prove that this bound is optimal. In particular, this
-answers a question of Lück for mapping class groups of surfaces.
-",0,0,1,0,0,0
-19298,A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks,"  An explosion of high-throughput DNA sequencing in the past decade has led to
-a surge of interest in population-scale inference with whole-genome data.
-Recent work in population genetics has centered on designing inference methods
-for relatively simple model classes, and few scalable general-purpose inference
-techniques exist for more realistic, complex models. To achieve this, two
-inferential challenges need to be addressed: (1) population data are
-exchangeable, calling for methods that efficiently exploit the symmetries of
-the data, and (2) computing likelihoods is intractable as it requires
-integrating over a set of correlated, extremely high-dimensional latent
-variables. These challenges are traditionally tackled by likelihood-free
-methods that use scientific simulators to generate datasets and reduce them to
-hand-designed, permutation-invariant summary statistics, often leading to
-inaccurate inference. In this work, we develop an exchangeable neural network
-that performs summary statistic-free, likelihood-free inference. Our framework
-can be applied in a black-box fashion across a variety of simulation-based
-tasks, both within and outside biology. We demonstrate the power of our
-approach on the recombination hotspot testing problem, outperforming the
-state-of-the-art.
-",0,0,0,1,1,0
-19299,Coherent single-atom superradiance,"  Quantum effects, prevalent in the microscopic scale, generally elusive in
-macroscopic systems due to dissipation and decoherence. Quantum phenomena in
-large systems emerge only when particles are strongly correlated as in
-superconductors and superfluids. Cooperative interaction of correlated atoms
-with electromagnetic fields leads to superradiance, the enhanced quantum
-radiation phenomenon, exhibiting novel physics such as quantum Dicke phase and
-ultranarrow linewidth for optical clocks. Recent researches to imprint atomic
-correlation directly demonstrated controllable collective atom-field
-interactions. Here, we report cavity-mediated coherent single-atom
-superradiance. Single atoms with predefined correlation traverse a high-Q
-cavity one by one, emitting photons cooperatively with the atoms already gone
-through the cavity. Such collective behavior of time-separated atoms is
-mediated by the long-lived cavity field. As a result, a coherent field is
-generated in the steady state, whose intensity varies as the square of the
-number of traversing atoms during the cavity decay time, exhibiting more than
-ten-fold enhancement from noncollective cases. The correlation among single
-atoms is prepared with the aligned atomic phase achieved by nanometer-precision
-position control of atoms with a nanohole-array aperture. The present work
-deepens our understanding of the collective matter-light interaction and
-provides an advanced platform for phase-controlled atom-field interactions.
-",0,1,0,0,0,0
-19300,Recurrent Deterministic Policy Gradient Method for Bipedal Locomotion on Rough Terrain Challenge,"  This paper presents a deep learning framework that is capable of solving
-partially observable locomotion tasks based on our novel interpretation of
-Recurrent Deterministic Policy Gradient (RDPG). We study on bias of sampled
-error measure and its variance induced by the partial observability of
-environment and subtrajectory sampling, respectively. Three major improvements
-are introduced in our RDPG based learning framework: tail-step bootstrap of
-interpolated temporal difference, initialisation of hidden state using past
-trajectory scanning, and injection of external experiences learned by other
-agents. The proposed learning framework was implemented to solve the
-Bipedal-Walker challenge in OpenAI's gym simulation environment where only
-partial state information is available. Our simulation study shows that the
-autonomous behaviors generated by the RDPG agent are highly adaptive to a
-variety of obstacles and enables the agent to effectively traverse rugged
-terrains for long distance with higher success rate than leading contenders.
-",1,0,0,0,0,0
-19301,Diversified essential properties in halogenated graphenes,"  The significant halogenation effects on the essential properties of graphene
-are investigated by the first-principles method. The geometric structures,
-electronic properties, and magnetic configurations are greatly diversified
-under the various halogen adsorptions. Fluorination, with the strong
-multi-orbital chemical bondings, can create the buckled graphene structure,
-while the other halogenations do not change the planar {\sigma} bonding in the
-presence of single-orbital hybridization. Electronic structures consist of the
-carbon-, adatom- and (carbon, adatom)-dominated energy bands. All halogenated
-graphenes belong to hole-doped metals except that fluorinated systems are
-middle-gap semiconductors at sufficiently high concentration. Moreover, the
-metallic ferromagnetism is revealed in certain adatom distributions. The
-unusual hybridization-induced features are clearly evidenced in many van Hove
-singularities of the density of states. The structure- and adatom-enriched
-essential properties are compared with the measured results, and potential
-applications are also discussed.
-",0,1,0,0,0,0
-19302,Quantifying genuine multipartite correlations and their pattern complexity,"  We propose an information-theoretic framework to quantify multipartite
-correlations in classical and quantum systems, answering questions such as:
-what is the amount of seven-partite correlations in a given state of ten
-particles? We identify measures of genuine multipartite correlations, i.e.
-statistical dependencies which cannot be ascribed to bipartite correlations,
-satisfying a set of desirable properties. Inspired by ideas developed in
-complexity science, we then introduce the concept of weaving to classify states
-which display different correlation patterns, but cannot be distinguished by
-correlation measures. The weaving of a state is defined as the weighted sum of
-correlations of every order. Weaving measures are good descriptors of the
-complexity of correlation structures in multipartite systems.
-",0,1,0,0,0,0
-19303,Pulsating low-mass white dwarfs in the frame of new evolutionary sequences: IV. The secular rate of period change,"  We present a theoretical assessment of the expected temporal rates of change
-of periods ($\dot{\Pi}$) for low-mass ($M_{\star}/M_{\sun} \lesssim 0.45$) and
-extremely low-mass (ELM, $M_{\star}/M_{\sun} \lesssim 0.18-0.20$) white-dwarf
-stars, based on fully evolutionary low-mass He-core white dwarf and pre-white
-dwarf models. Our analysis is based on a large set of adiabatic periods of
-radial and nonradial pulsation modes computed on a suite of low-mass He-core
-white dwarf and pre-white dwarf models with masses ranging from $0.1554$ to
-$0.4352 M_{\sun}$. We compute the secular rates of period change of radial
-($\ell= 0$) and nonradial ($\ell= 1, 2$) $g$ and $p$ modes for stellar models
-representative of ELMV and pre-ELMV stars, as well as for stellar objects that
-are evolving just before the occurrence of CNO flashes at the early cooling
-branches. We found that the theoretically expected magnitude of $\dot{\Pi}$ of
-$g$ modes for pre-ELMVs are by far larger than for ELMVs. In turn, $\dot{\Pi}$
-of $g$ modes for models evolving before the occurrence of CNO flashes are
-larger than the maximum values of the rates of period change predicted for
-pre-ELMV stars. Regarding $p$ and radial modes, we found that the larger
-absolute values of $\dot{\Pi}$ correspond to pre-ELMV models. We conclude that
-any eventual measurement of a rate of period change for a given pulsating
-low-mass pre-white dwarf or white dwarf star could shed light about its
-evolutionary status. Also, in view of the systematic difficulties in the
-spectroscopic classification of stars of the ELM Survey, an eventual
-measurement of $\dot{\Pi}$ could help to confirm that a given pulsating star is
-an authentic low-mass white dwarf and not a star from another stellar
-population.
-",0,1,0,0,0,0
-19304,Normalized Maximum Likelihood with Luckiness for Multivariate Normal Distributions,"  The normalized maximum likelihood (NML) is one of the most important
-distribution in coding theory and statistics. NML is the unique solution (if
-exists) to the pointwise minimax regret problem. However, NML is not defined
-even for simple family of distributions such as the normal distributions. Since
-there does not exist any meaningful minimax-regret distribution for such case,
-it is pointed out that NML with luckiness (LNML) can be employed as an
-alternative to NML. In this paper, we develop the closed form of LNMLs for
-multivariate normal distributions.
-",0,0,1,1,0,0
-19305,Tackling Diversity and Heterogeneity by Vertical Memory Management,"  Existing memory management mechanisms used in commodity computing machines
-typically adopt hardware based address interleaving and OS directed random
-memory allocation to service generic application requests. These conventional
-memory management mechanisms are challenged by contention at multiple memory
-levels, a daunting variety of workload behaviors, and an increasingly
-complicated memory hierarchy. Our ISCA-41 paper proposes vertical partitioning
-to eliminate shared resource contention at multiple levels in the memory
-hierarchy. Combined with horizontal memory management policies, our framework
-supports a flexible policy space for tackling diverse application needs in
-production environment and is suitable for future heterogeneous memory systems.
-",1,0,0,0,0,0
-19306,From the Icosahedron to E8,"  The regular icosahedron is connected to many exceptional objects in
-mathematics. Here we describe two constructions of the $\mathrm{E}_8$ lattice
-from the icosahedron. One uses a subring of the quaternions called the
-""icosians"", while the other uses du Val's work on the resolution of Kleinian
-singularities. Together they link the golden ratio, the quaternions, the
-quintic equation, the 600-cell, and the Poincare homology 3-sphere. We leave it
-as a challenge to the reader to find the connection between these two
-constructions.
-",0,0,1,0,0,0
-19307,Sequential Discrete Kalman Filter for Real-Time State Estimation in Power Distribution Systems: Theory and Implementation,"  This paper demonstrates the feasibility of implementing Real-Time State
-Estimators (RTSEs) for Active Distribution Networks (ADNs) in
-Field-Programmable Gate Arrays (FPGAs) by presenting an operational prototype.
-The prototype is based on a Linear State Estimator (LSE) that uses
-synchrophasor measurements from Phasor Measurement Units (PMUs). The underlying
-algorithm is the Sequential Discrete Kalman Filter (SDKF), an equivalent
-formulation of the Discrete Kalman Filter (DKF) for the case of uncorrelated
-measurement noise. In this regard, this work formally proves the equivalence
-the SDKF and the DKF, and highlights the suitability of the SDKF for an FPGA
-implementation by means of a computational complexity analysis. The developed
-prototype is validated using a case study adapted from the IEEE 34-node
-distribution test feeder.
-",0,0,0,1,0,0
-19308,Game-Theoretic Semantics for ATL+ with Applications to Model Checking,"  We develop game-theoretic semantics (GTS) for the fragment ATL+ of the full
-Alternating-time Temporal Logic ATL*, essentially extending a recently
-introduced GTS for ATL. We first show that the new game-theoretic semantics is
-equivalent to the standard semantics of ATL+ (based on perfect recall
-strategies). We then provide an analysis, based on the new semantics, of the
-memory and time resources needed for model checking ATL+. Based on that, we
-establish that strategies that use only a very limited amount of memory suffice
-for ATL+. Furthermore, using the GTS we provide a new algorithm for model
-checking of ATL+ and identify a natural hierarchy of tractable fragments of
-ATL+ that extend ATL.
-",1,0,1,0,0,0
-19309,Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification,"  The wide usage of Machine Learning (ML) has lead to research on the attack
-vectors and vulnerability of these systems. The defenses in this area are
-however still an open problem, and often lead to an arms race. We define a
-naive, secure classifier at test time and show that a Gaussian Process (GP) is
-an instance of this classifier given two assumptions: one concerns the
-distances in the training data, the other rejection at test time. Using these
-assumptions, we are able to show that a classifier is either secure, or
-generalizes and thus learns. Our analysis also points towards another factor
-influencing robustness, the curvature of the classifier. This connection is not
-unknown for linear models, but GP offer an ideal framework to study this
-relationship for nonlinear classifiers. We evaluate on five security and two
-computer vision datasets applying test and training time attacks and membership
-inference. We show that we only change which attacks are needed to succeed,
-instead of alleviating the threat. Only for membership inference, there is a
-setting in which attacks are unsuccessful (<10% increase in accuracy over
-random guess). Given these results, we define a classification scheme based on
-voting, ParGP. This allows us to decide how many points vote and how large the
-agreement on a class has to be. This ensures a classification output only in
-cases when there is evidence for a decision, where evidence is parametrized. We
-evaluate this scheme and obtain promising results.
-",0,0,0,1,0,0
-19310,Approximate message passing for nonconvex sparse regularization with stability and asymptotic analysis,"  We analyse a linear regression problem with nonconvex regularization called
-smoothly clipped absolute deviation (SCAD) under an overcomplete Gaussian basis
-for Gaussian random data. We propose an approximate message passing (AMP)
-algorithm considering nonconvex regularization, namely SCAD-AMP, and
-analytically show that the stability condition corresponds to the de
-Almeida--Thouless condition in spin glass literature. Through asymptotic
-analysis, we show the correspondence between the density evolution of SCAD-AMP
-and the replica symmetric solution. Numerical experiments confirm that for a
-sufficiently large system size, SCAD-AMP achieves the optimal performance
-predicted by the replica method. Through replica analysis, a phase transition
-between replica symmetric (RS) and replica symmetry breaking (RSB) region is
-found in the parameter space of SCAD. The appearance of the RS region for a
-nonconvex penalty is a significant advantage that indicates the region of
-smooth landscape of the optimization problem. Furthermore, we analytically show
-that the statistical representation performance of the SCAD penalty is better
-than that of L1-based methods, and the minimum representation error under RS
-assumption is obtained at the edge of the RS/RSB phase. The correspondence
-between the convergence of the existing coordinate descent algorithm and RS/RSB
-transition is also indicated.
-",1,0,0,1,0,0
-19311,Concurrence Topology of Some Cancer Genomics Data,"  The topological data analysis method ""concurrence topology"" is applied to
-mutation frequencies in 69 genes in glioblastoma data. In dimension 1 some
-apparent ""mutual exclusivity"" is found. By simulation of data having
-approximately the same second order dependence structure as that found in the
-data, it appears that one triple of mutations, PTEN, RB1, TP53, exhibits mutual
-exclusivity that depends on special features of the third order dependence and
-may reflect global dependence among a larger group of genes. A bootstrap
-analysis suggests that this form of mutual exclusivity is not uncommon in the
-population from which the data were drawn.
-",0,0,0,1,0,0
-19312,Quasimomentum of an elementary excitation for a system of point bosons with zero boundary conditions,"  As is known, an elementary excitation of a many-particle system with
-boundaries is not characterized by a definite momentum. Here, we obtain the
-formula for the quasimomentum of an elementary excitation for a one-dimensional
-system of $N$ spinless point bosons with zero boundary conditions (BCs). We
-also find that the dispersion law $E(p)$ of the system with zero BCs coincides
-with that of a system with periodic BCs. The elementary excitations are defined
-within a new approach proposed earlier by the author. This approach is
-mathematically equivalent to the traditional approach by Lieb, but differs from
-it by a simpler way of enumeration of excited states and leads to a single
-dispersion law (instead of two ones in the Lieb's approach).
-",0,1,0,0,0,0
-19313,Moving to VideoKifu: the last steps toward a fully automatic record-keeping of a Go game,"  In a previous paper [ arXiv:1508.03269 ] we described the techniques we
-successfully employed for automatically reconstructing the whole move sequence
-of a Go game by means of a set of pictures. Now we describe how it is possible
-to reconstruct the move sequence by means of a video stream (which may be
-provided by an unattended webcam), possibly in real-time. Although the basic
-algorithms remain the same, we will discuss the new problems that arise when
-dealing with videos, with special care for the ones that could block a
-real-time analysis and require an improvement of our previous techniques or
-even a completely brand new approach. Eventually we present a number of
-preliminary but positive experimental results supporting the effectiveness of
-the software we are developing, built on the ideas here outlined.
-",1,0,0,0,0,0
-19314,Malware Detection by Eating a Whole EXE,"  In this work we introduce malware detection from raw byte sequences as a
-fruitful research area to the larger machine learning community. Building a
-neural network for such a problem presents a number of interesting challenges
-that have not occurred in tasks such as image processing or NLP. In particular,
-we note that detection from raw bytes presents a sequence problem with over two
-million time steps and a problem where batch normalization appear to hinder the
-learning process. We present our initial work in building a solution to tackle
-this problem, which has linear complexity dependence on the sequence length,
-and allows for interpretable sub-regions of the binary to be identified. In
-doing so we will discuss the many challenges in building a neural network to
-process data at this scale, and the methods we used to work around them.
-",1,0,0,1,0,0
-19315,On the Complexity of Model Checking for Syntactically Maximal Fragments of the Interval Temporal Logic HS with Regular Expressions,"  In this paper, we investigate the model checking (MC) problem for Halpern and
-Shoham's interval temporal logic HS. In the last years, interval temporal logic
-MC has received an increasing attention as a viable alternative to the
-traditional (point-based) temporal logic MC, which can be recovered as a
-special case. Most results have been obtained under the homogeneity assumption,
-that constrains a proposition letter to hold over an interval if and only if it
-holds over each component state. Recently, Lomuscio and Michaliszyn proposed a
-way to relax such an assumption by exploiting regular expressions to define the
-behaviour of proposition letters over intervals in terms of their component
-states. When homogeneity is assumed, the exact complexity of MC is a difficult
-open question for full HS and for its two syntactically maximal fragments
-AA'BB'E' and AA'EB'E'. In this paper, we provide an asymptotically optimal
-bound to the complexity of these two fragments under the more expressive
-semantic variant based on regular expressions by showing that their MC problem
-is AEXP_pol-complete, where AEXP_pol denotes the complexity class of problems
-decided by exponential-time bounded alternating Turing Machines making a
-polynomially bounded number of alternations.
-",1,0,0,0,0,0
-19316,Deforming 3-manifolds of bounded geometry and uniformly positive scalar curvature,"  We prove that the moduli space of complete Riemannian metrics of bounded
-geometry and uniformly positive scalar curvature on an orientable 3-manifold is
-path-connected. This generalizes the main result of the fourth author [Mar12]
-in the compact case. The proof uses Ricci flow with surgery as well as
-arguments involving performing infinite connected sums with control on the
-geometry.
-",0,0,1,0,0,0
-19317,Optimal and Myopic Information Acquisition,"  We consider the problem of optimal dynamic information acquisition from many
-correlated information sources. Each period, the decision-maker jointly takes
-an action and allocates a fixed number of observations across the available
-sources. His payoff depends on the actions taken and on an unknown state. In
-the canonical setting of jointly normal information sources, we show that the
-optimal dynamic information acquisition rule proceeds myopically after finitely
-many periods. If signals are acquired in large blocks each period, then the
-optimal rule turns out to be myopic from period 1. These results demonstrate
-the possibility of robust and ""simple"" optimal information acquisition, and
-simplify the analysis of dynamic information acquisition in a widely used
-informational environment.
-",1,0,1,1,0,0
-19318,Infinitely many minimal classes of graphs of unbounded clique-width,"  The celebrated theorem of Robertson and Seymour states that in the family of
-minor-closed graph classes, there is a unique minimal class of graphs of
-unbounded tree-width, namely, the class of planar graphs. In the case of
-tree-width, the restriction to minor-closed classes is justified by the fact
-that the tree-width of a graph is never smaller than the tree-width of any of
-its minors. This, however, is not the case with respect to clique-width, as the
-clique-width of a graph can be (much) smaller than the clique-width of its
-minor. On the other hand, the clique-width of a graph is never smaller than the
-clique-width of any of its induced subgraphs, which allows us to be restricted
-to hereditary classes (that is, classes closed under taking induced subgraphs),
-when we study clique-width. Up to date, only finitely many minimal hereditary
-classes of graphs of unbounded clique-width have been discovered in the
-literature. In the present paper, we prove that the family of such classes is
-infinite. Moreover, we show that the same is true with respect to linear
-clique-width.
-",1,0,1,0,0,0
-19319,A Passivity-Based Approach to Nash Equilibrium Seeking over Networks,"  In this paper we consider the problem of distributed Nash equilibrium (NE)
-seeking over networks, a setting in which players have limited local
-information. We start from a continuous-time gradient-play dynamics that
-converges to an NE under strict monotonicity of the pseudo-gradient and assumes
-perfect information, i.e., instantaneous all-to-all player communication. We
-consider how to modify this gradient-play dynamics in the case of partial, or
-networked information between players. We propose an augmented gradient-play
-dynamics with correction in which players communicate locally only with their
-neighbours to compute an estimate of the other players' actions. We derive the
-new dynamics based on the reformulation as a multi-agent coordination problem
-over an undirected graph. We exploit incremental passivity properties and show
-that a synchronizing, distributed Laplacian feedback can be designed using
-relative estimates of the neighbours. Under a strict monotonicity property of
-the pseudo-gradient, we show that the augmented gradient-play dynamics
-converges to consensus on the NE of the game. We further discuss two cases that
-highlight the tradeoff between properties of the game and the communication
-graph.
-",0,0,1,0,0,0
-19320,Magnetic and dielectric investigations of $γ$ - Fe${_2}$WO${_6}$,"  The magnetic, thermodynamic and dielectric properties of the $\gamma$ -
-Fe${_2}$WO${_6}$ system is reported. Crystallizing in the centrosymmetric
-$Pbcn$ space group, this particular polymorph exhibits a number of different
-magnetic transitions, all of which are seen to exhibit a finite
-magneto-dielectric coupling. At the lowest measured temperatures, the magnetic
-ground state appears to be glass-like, as evidenced by the waiting time
-dependence of the magnetic relaxation. Also reflected in the frequency
-dependent dielectric measurements, these signatures possibly arise as a
-consequence of the oxygen non-stoichiometry, which promotes an inhomogeneous
-magnetic and electronic ground state.
-",0,1,0,0,0,0
-19321,Linear centralization classifier,"  A classification algorithm, called the Linear Centralization Classifier
-(LCC), is introduced. The algorithm seeks to find a transformation that best
-maps instances from the feature space to a space where they concentrate towards
-the center of their own classes, while maximimizing the distance between class
-centers. We formulate the classifier as a quadratic program with quadratic
-constraints. We then simplify this formulation to a linear program that can be
-solved effectively using a linear programming solver (e.g., simplex-dual). We
-extend the formulation for LCC to enable the use of kernel functions for
-non-linear classification applications. We compare our method with two standard
-classification methods (support vector machine and linear discriminant
-analysis) and four state-of-the-art classification methods when they are
-applied to eight standard classification datasets. Our experimental results
-show that LCC is able to classify instances more accurately (based on the area
-under the receiver operating characteristic) in comparison to other tested
-methods on the chosen datasets. We also report the results for LCC with a
-particular kernel to solve for synthetic non-linear classification problems.
-",1,0,0,1,0,0
-19322,Spatio-temporal Person Retrieval via Natural Language Queries,"  In this paper, we address the problem of spatio-temporal person retrieval
-from multiple videos using a natural language query, in which we output a tube
-(i.e., a sequence of bounding boxes) which encloses the person described by the
-query. For this problem, we introduce a novel dataset consisting of videos
-containing people annotated with bounding boxes for each second and with five
-natural language descriptions. To retrieve the tube of the person described by
-a given natural language query, we design a model that combines methods for
-spatio-temporal human detection and multimodal retrieval. We conduct
-comprehensive experiments to compare a variety of tube and text representations
-and multimodal retrieval methods, and present a strong baseline in this task as
-well as demonstrate the efficacy of our tube representation and multimodal
-feature embedding technique. Finally, we demonstrate the versatility of our
-model by applying it to two other important tasks.
-",1,0,0,0,0,0
-19323,Thermo-elasto-plastic simulations of femtosecond laser-induced multiple-cavity in fused silica,"  The formation and the interaction of multiple cavities, induced by tightly
-focused femtosecond laser pulses, are studied by using a developed numerical
-tool, including the thermo-elasto-plastic material response. Simulations are
-performed in fused silica in cases of one, two, and four spots of laser energy
-deposition. The relaxation of the heated matter, launching shock waves in the
-surrounding cold material, leads to cavity formation and emergence of areas
-where cracks may be induced. Results show that the laser-induced structure
-shape depends on the energy deposition configuration and demonstrate the
-potential of the used numerical tool to obtain the desired designed structure
-or technological process.
-",0,1,0,0,0,0
-19324,Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species,"  Machine-learning potentials (MLPs) for atomistic simulations are a promising
-alternative to conventional classical potentials. Current approaches rely on
-descriptors of the local atomic environment with dimensions that increase
-quadratically with the number of chemical species. In this article, we
-demonstrate that such a scaling can be avoided in practice. We show that a
-mathematically simple and computationally efficient descriptor with constant
-complexity is sufficient to represent transition-metal oxide compositions and
-biomolecules containing 11 chemical species with a precision of around 3
-meV/atom. This insight removes a perceived bound on the utility of MLPs and
-paves the way to investigate the physics of previously inaccessible materials
-with more than ten chemical species.
-",0,1,0,0,0,0
-19325,Randomized Constraints Consensus for Distributed Robust Linear Programming,"  In this paper we consider a network of processors aiming at cooperatively
-solving linear programming problems subject to uncertainty. Each node only
-knows a common cost function and its local uncertain constraint set. We propose
-a randomized, distributed algorithm working under time-varying, asynchronous
-and directed communication topology. The algorithm is based on a local
-computation and communication paradigm. At each communication round, nodes
-perform two updates: (i) a verification in which they check-in a randomized
-setup-the robust feasibility (and hence optimality) of the candidate optimal
-point, and (ii) an optimization step in which they exchange their candidate
-bases (minimal sets of active constraints) with neighbors and locally solve an
-optimization problem whose constraint set includes: a sampled constraint
-violating the candidate optimal point (if it exists), agent's current basis and
-the collection of neighbor's basis. As main result, we show that if a processor
-successfully performs the verification step for a sufficient number of
-communication rounds, it can stop the algorithm since a consensus has been
-reached. The common solution is-with high confidence-feasible (and hence
-optimal) for the entire set of uncertainty except a subset having arbitrary
-small probability measure. We show the effectiveness of the proposed
-distributed algorithm on a multi-core platform in which the nodes communicate
-asynchronously.
-",0,0,1,0,0,0
-19326,Continuum limit of the vibrational properties of amorphous solids,"  The low-frequency vibrational and low-temperature thermal properties of
-amorphous solids are markedly different from those of crystalline solids. This
-situation is counter-intuitive because any solid material is expected to behave
-as a homogeneous elastic body in the continuum limit, in which vibrational
-modes are phonons following the Debye law. A number of phenomenological
-explanations have been proposed, which assume elastic heterogeneities, soft
-localized vibrations, and so on. Recently, the microscopic mean-field theories
-have been developed to predict the universal non-Debye scaling law. Considering
-these theoretical arguments, it is absolutely necessary to directly observe the
-nature of the low-frequency vibrations of amorphous solids and determine the
-laws that such vibrations obey. Here, we perform an extremely large-scale
-vibrational mode analysis of a model amorphous solid. We find that the scaling
-law predicted by the mean-field theory is violated at low frequency, and in the
-continuum limit, the vibrational modes converge to a mixture of phonon modes
-following the Debye law and soft localized modes following another universal
-non-Debye scaling law.
-",0,1,0,0,0,0
-19327,Existence of smooth solutions of multi-term Caputo-type fractional differential equations,"  This paper deals with the initial value problem for the multi-term fractional
-differential equation. The fractional derivative is defined in the Caputo
-sense. Firstly the initial value problem is transformed into a equivalent
-Volterra-type integral equation under appropriate assumptions. Then new
-existence results for smooth solutions are established by using the Schauder
-fixed point theorem.
-",0,0,1,0,0,0
-19328,Classes of elementary function solutions to the CEV model. I,"  The CEV model subsumes some of the previous option pricing models. An
-important parameter in the model is the parameter b, the elasticity of
-volatility. For b=0, b=-1/2, and b=-1 the CEV model reduces respectively to the
-BSM model, the square-root model of Cox and Ross, and the Bachelier model. Both
-in the case of the BSM model and in the case of the CEV model it has become
-traditional to begin a discussion of option pricing by starting with the
-vanilla European calls and puts. In the case of BSM model simpler solutions are
-the log and power solutions. These contracts, despite the simplicity of their
-mathematical description, are attracting increasing attention as a trading
-instrument. Similar simple solutions have not been studied so far in a
-systematic fashion for the CEV model. We use Kovacic's algorithm to derive, for
-all half-integer values of b, all solutions ""in quadratures"" of the CEV
-ordinary differential equation. These solutions give rise, by separation of
-variables, to simple solutions to the CEV partial differential equation. In
-particular, when b=...,-5/2,-2,-3/2,-1, 1, 3/2, 2, 5/2,..., we obtain four
-classes of denumerably infinite elementary function solutions, when b=-1/2 and
-b=1/2 we obtain two classes of denumerably infinite elementary function
-solutions, whereas, when b=0 we find two elementary function solutions. In the
-derived solutions we have also dispensed with the unnecessary assumption made
-in the the BSM model asserting that the underlying asset pays no dividends
-during the life of the option.
-",0,0,0,0,0,1
-19329,Congruent families and invariant tensors,"  Classical results of Chentsov and Campbell state that -- up to constant
-multiples -- the only $2$-tensor field of a statistical model which is
-invariant under congruent Markov morphisms is the Fisher metric and the only
-invariant $3$-tensor field is the Amari-Chentsov tensor. We generalize this
-result for arbitrary degree $n$, showing that any family of $n$-tensors which
-is invariant under congruent Markov morphisms is algebraically generated by the
-canonical tensor fields defined in an earlier paper.
-",0,0,1,1,0,0
-19330,Quality of Information in Mobile Crowdsensing: Survey and Research Challenges,"  Smartphones have become the most pervasive devices in people's lives, and are
-clearly transforming the way we live and perceive technology. Today's
-smartphones benefit from almost ubiquitous Internet connectivity and come
-equipped with a plethora of inexpensive yet powerful embedded sensors, such as
-accelerometer, gyroscope, microphone, and camera. This unique combination has
-enabled revolutionary applications based on the mobile crowdsensing paradigm,
-such as real-time road traffic monitoring, air and noise pollution, crime
-control, and wildlife monitoring, just to name a few. Differently from prior
-sensing paradigms, humans are now the primary actors of the sensing process,
-since they become fundamental in retrieving reliable and up-to-date information
-about the event being monitored. As humans may behave unreliably or
-maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
-more important than ever. In this paper, we provide a new framework for
-defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
-current state-of-the-art on the topic. We also outline novel research
-challenges, along with possible directions of future work.
-",1,0,0,0,0,0
-19331,SimBlock: A Blockchain Network Simulator,"  Blockchain, which is a technology for distributedly managing ledger
-information over multiple nodes without a centralized system, has elicited
-increasing attention. Performing experiments on actual blockchains are
-difficult because a large number of nodes in wide areas are necessary. In this
-study, we developed a blockchain network simulator SimBlock for such
-experiments. Unlike the existing simulators, SimBlock can easily change
-behavior of node, so that it enables to investigate the influence of nodes'
-behavior on blockchains. We compared some simulation results with the measured
-values in actual blockchains to demonstrate the validity of this simulator.
-Furthermore, to show practical usage, we conducted two experiments which
-clarify the influence of neighbor node selection algorithms and relay networks
-on the block propagation time. The simulator could depict the effects of the
-two techniques on block propagation time. The simulator will be publicly
-available in a few months.
-",1,0,0,0,0,0
-19332,Bayesian Fused Lasso regression for dynamic binary networks,"  We propose a multinomial logistic regression model for link prediction in a
-time series of directed binary networks. To account for the dynamic nature of
-the data we employ a dynamic model for the model parameters that is strongly
-connected with the fused lasso penalty. In addition to promoting sparseness,
-this prior allows us to explore the presence of change points in the structure
-of the network. We introduce fast computational algorithms for estimation and
-prediction using both optimization and Bayesian approaches. The performance of
-the model is illustrated using simulated data and data from a financial trading
-network in the NYMEX natural gas futures market. Supplementary material
-containing the trading network data set and code to implement the algorithms is
-available online.
-",0,0,0,1,0,0
-19333,Indefinite Kernel Logistic Regression,"  Traditionally, kernel learning methods requires positive definitiveness on
-the kernel, which is too strict and excludes many sophisticated similarities,
-that are indefinite, in multimedia area. To utilize those indefinite kernels,
-indefinite learning methods are of great interests. This paper aims at the
-extension of the logistic regression from positive semi-definite kernels to
-indefinite kernels. The model, called indefinite kernel logistic regression
-(IKLR), keeps consistency to the regular KLR in formulation but it essentially
-becomes non-convex. Thanks to the positive decomposition of an indefinite
-matrix, IKLR can be transformed into a difference of two convex models, which
-follows the use of concave-convex procedure. Moreover, we employ an inexact
-solving scheme to speed up the sub-problem and develop a concave-inexact-convex
-procedure (CCICP) algorithm with theoretical convergence analysis. Systematical
-experiments on multi-modal datasets demonstrate the superiority of the proposed
-IKLR method over kernel logistic regression with positive definite kernels and
-other state-of-the-art indefinite learning based algorithms.
-",1,0,0,1,0,0
-19334,Microstructure and thickening of dense suspensions under extensional and shear flows,"  Dense suspensions are non-Newtonian fluids which exhibit strong shear
-thickening and normal stress differences. Using numerical simulation of
-extensional and shear flows, we investigate how rheological properties are
-determined by the microstructure which is built under flows and by the
-interactions between particles. By imposing extensional and shear flows, we can
-assess the degree of flow-type dependence in regimes below and above
-thickening. Even when the flow-type dependence is hindered, nondissipative
-responses, such as normal stress differences, are present and characterise the
-non-Newtonian behaviour of dense suspensions.
-",0,1,0,0,0,0
-19335,Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots,"  One of the open challenges in designing robots that operate successfully in
-the unpredictable human environment is how to make them able to predict what
-actions they can perform on objects, and what their effects will be, i.e., the
-ability to perceive object affordances. Since modeling all the possible world
-interactions is unfeasible, learning from experience is required, posing the
-challenge of collecting a large amount of experiences (i.e., training data).
-Typically, a manipulative robot operates on external objects by using its own
-hands (or similar end-effectors), but in some cases the use of tools may be
-desirable, nevertheless, it is reasonable to assume that while a robot can
-collect many sensorimotor experiences using its own hands, this cannot happen
-for all possible human-made tools.
-Therefore, in this paper we investigate the developmental transition from
-hand to tool affordances: what sensorimotor skills that a robot has acquired
-with its bare hands can be employed for tool use? By employing a visual and
-motor imagination mechanism to represent different hand postures compactly, we
-propose a probabilistic model to learn hand affordances, and we show how this
-model can generalize to estimate the affordances of previously unseen tools,
-ultimately supporting planning, decision-making and tool selection tasks in
-humanoid robots. We present experimental results with the iCub humanoid robot,
-and we publicly release the collected sensorimotor data in the form of a hand
-posture affordances dataset.
-",1,0,0,1,0,0
-19336,Rewriting in Free Hypergraph Categories,"  We study rewriting for equational theories in the context of symmetric
-monoidal categories where there is a separable Frobenius monoid on each object.
-These categories, also called hypergraph categories, are increasingly relevant:
-Frobenius structures recently appeared in cross-disciplinary applications,
-including the study of quantum processes, dynamical systems and natural
-language processing. In this work we give a combinatorial characterisation of
-arrows of a free hypergraph category as cospans of labelled hypergraphs and
-establish a precise correspondence between rewriting modulo Frobenius structure
-on the one hand and double-pushout rewriting of hypergraphs on the other. This
-interpretation allows to use results on hypergraphs to ensure decidability of
-confluence for rewriting in a free hypergraph category. Our results generalise
-previous approaches where only categories generated by a single object (props)
-were considered.
-",1,0,0,0,0,0
-19337,Weighting Scheme for a Pairwise Multi-label Classifier Based on the Fuzzy Confusion Matrix,"  In this work we addressed the issue of applying a stochastic classifier and a
-local, fuzzy confusion matrix under the framework of multi-label
-classification. We proposed a novel solution to the problem of correcting label
-pairwise ensembles. The main step of the correction procedure is to compute
-classifier-specific competence and cross-competence measures, which estimates
-error pattern of the underlying classifier. At the fusion phase we employed two
-weighting approaches based on information theory. The classifier weights
-promote base classifiers which are the most susceptible to the correction based
-on the fuzzy confusion matrix. During the experimental study, the proposed
-approach was compared against two reference methods. The comparison was made in
-terms of six different quality criteria. The conducted experiments reveals that
-the proposed approach eliminates one of main drawbacks of the original
-FCM-based approach i.e. the original approach is vulnerable to the imbalanced
-class/label distribution. What is more, the obtained results shows that the
-introduced method achieves satisfying classification quality under all
-considered quality criteria. Additionally, the impact of fluctuations of data
-set characteristics is reduced.
-",1,0,0,1,0,0
-19338,Wilcoxon Rank-Based Tests for Clustered Data with R Package clusrank,"  Wilcoxon Rank-based tests are distribution-free alternatives to the popular
-two-sample and paired t-tests. For independent data, they are available in
-several R packages such as stats and coin. For clustered data, in spite of the
-recent methodological developments, there did not exist an R package that makes
-them available at one place. We present a package clusrank where the latest
-developments are implemented and wrapped under a unified user-friendly
-interface. With different methods dispatched based on the inputs, this package
-offers great flexibility in rank-based tests for various clustered data. Exact
-tests based on permutations are also provided for some methods. Details of the
-major schools of different methods are briefly reviewed. Usages of the package
-clusrank are illustrated with simulated data as well as a real dataset from an
-ophthalmological study. The package also enables convenient comparison between
-selected methods under settings that have not been studied before and the
-results are discussed.
-",0,0,0,1,0,0
-19339,Latent heterogeneous multilayer community detection,"  We propose a method for simultaneously detecting shared and unshared
-communities in heterogeneous multilayer weighted and undirected networks. The
-multilayer network is assumed to follow a generative probabilistic model that
-takes into account the similarities and dissimilarities between the
-communities. We make use of a variational Bayes approach for jointly inferring
-the shared and unshared hidden communities from multilayer network
-observations. We show the robustness of our approach compared to state-of-the
-art algorithms in detecting disparate (shared and private) communities on
-synthetic data as well as on real genome-wide fibroblast proliferation dataset.
-",1,0,0,1,0,0
-19340,Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection,"  In this paper, we analyze the behavior of the multivariate symmetric
-uncertainty (MSU) measure through the use of statistical simulation techniques
-under various mixes of informative and non-informative randomly generated
-features. Experiments show how the number of attributes, their cardinalities,
-and the sample size affect the MSU. We discovered a condition that preserves
-good quality in the MSU under different combinations of these three factors,
-providing a new useful criterion to help drive the process of dimension
-reduction.
-",1,0,0,1,0,0
-19341,Approximation Dynamics,"  We describe the approximation of a continuous dynamical system on a p. l.
-manifold or Cantor set by a tractable system. A system is tractable when it has
-a finite number of chain components and, with respect to a given full
-background measure, almost every point is generic for one of a finite number of
-ergodic invariant measures with non-overlapping supports. The approximations
-use non-degenerate simplicial dynamical systems for p. l. manifolds and
-shift-like dynamical systems for Cantor Sets.
-",0,0,1,0,0,0
-19342,Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance,"  The Wasserstein distance between two probability measures on a metric space
-is a measure of closeness with applications in statistics, probability, and
-machine learning. In this work, we consider the fundamental question of how
-quickly the empirical measure obtained from $n$ independent samples from $\mu$
-approaches $\mu$ in the Wasserstein distance of any order. We prove sharp
-asymptotic and finite-sample results for this rate of convergence for general
-measures on general compact metric spaces. Our finite-sample results show the
-existence of multi-scale behavior, where measures can exhibit radically
-different rates of convergence as $n$ grows.
-",0,0,1,1,0,0
-19343,Ensuring patients privacy in a cryptographic-based-electronic health records using bio-cryptography,"  Several recent works have proposed and implemented cryptography as a means to
-preserve privacy and security of patients health data. Nevertheless, the
-weakest point of electronic health record (EHR) systems that relied on these
-cryptographic schemes is key management. Thus, this paper presents the
-development of privacy and security system for cryptography-based-EHR by taking
-advantage of the uniqueness of fingerprint and iris characteristic features to
-secure cryptographic keys in a bio-cryptography framework. The results of the
-system evaluation showed significant improvements in terms of time efficiency
-of this approach to cryptographic-based-EHR. Both the fuzzy vault and fuzzy
-commitment demonstrated false acceptance rate (FAR) of 0%, which reduces the
-likelihood of imposters gaining successful access to the keys protecting
-patients protected health information. This result also justifies the
-feasibility of implementing fuzzy key binding scheme in real applications,
-especially fuzzy vault which demonstrated a better performance during key
-reconstruction.
-",1,0,0,0,0,0
-19344,A Dynamically Reconfigurable Terahertz Array Antenna for Near-field Imaging Applications,"  A proof of concept for high speed near-field imaging with sub-wavelength
-resolution using SLM is presented. An 8 channel THz detector array antenna with
-an electrode gap of 100 um and length of 5 mm is fabricated using the
-commercially available GaAs semiconductor substrate. Each array antenna can be
-excited simultaneously by spatially reconfiguring the optical probe beam and
-the THz electric field can be recorded using 8 channel lock-in amplifiers. By
-scanning the probe beam along the length of the array antenna, a 2D image can
-be obtained with amplitude, phase and frequency information.
-",0,1,0,0,0,0
-19345,Stationary C*-dynamical systems,"  We introduce the notion of stationary actions in the context of C*-algebras.
-We develop the basics of the theory, and provide applications to several
-ergodic theoretical and operator algebraic rigidity problems.
-",0,0,1,0,0,0
-19346,The impact of imbalanced training data on machine learning for author name disambiguation,"  In supervised machine learning for author name disambiguation, negative
-training data are often dominantly larger than positive training data. This
-paper examines how the ratios of negative to positive training data can affect
-the performance of machine learning algorithms to disambiguate author names in
-bibliographic records. On multiple labeled datasets, three classifiers -
-Logistic Regression, Naïve Bayes, and Random Forest - are trained through
-representative features such as coauthor names, and title words extracted from
-the same training data but with various positive-negative training data ratios.
-Results show that increasing negative training data can improve disambiguation
-performance but with a few percent of performance gains and sometimes degrade
-it. Logistic Regression and Naïve Bayes learn optimal disambiguation models
-even with a base ratio (1:1) of positive and negative training data. Also, the
-performance improvement by Random Forest tends to quickly saturate roughly
-after 1:10 ~ 1:15. These findings imply that contrary to the common practice
-using all training data, name disambiguation algorithms can be trained using
-part of negative training data without degrading much disambiguation
-performance while increasing computational efficiency. This study calls for
-more attention from author name disambiguation scholars to methods for machine
-learning from imbalanced data.
-",0,0,0,1,0,0
-19347,SIGNet: Scalable Embeddings for Signed Networks,"  Recent successes in word embedding and document embedding have motivated
-researchers to explore similar representations for networks and to use such
-representations for tasks such as edge prediction, node label prediction, and
-community detection. Such network embedding methods are largely focused on
-finding distributed representations for unsigned networks and are unable to
-discover embeddings that respect polarities inherent in edges. We propose
-SIGNet, a fast scalable embedding method suitable for signed networks. Our
-proposed objective function aims to carefully model the social structure
-implicit in signed networks by reinforcing the principles of social balance
-theory. Our method builds upon the traditional word2vec family of embedding
-approaches and adds a new targeted node sampling strategy to maintain
-structural balance in higher-order neighborhoods. We demonstrate the
-superiority of SIGNet over state-of-the-art methods proposed for both signed
-and unsigned networks on several real world datasets from different domains. In
-particular, SIGNet offers an approach to generate a richer vocabulary of
-features of signed networks to support representation and reasoning.
-",1,0,0,1,0,0
-19348,Spread of hate speech in online social media,"  The present online social media platform is afflicted with several issues,
-with hate speech being on the predominant forefront. The prevalence of online
-hate speech has fueled horrific real-world hate-crime such as the mass-genocide
-of Rohingya Muslims, communal violence in Colombo and the recent massacre in
-the Pittsburgh synagogue. Consequently, It is imperative to understand the
-diffusion of such hateful content in an online setting. We conduct the first
-study that analyses the flow and dynamics of posts generated by hateful and
-non-hateful users on Gab (gab.com) over a massive dataset of 341K users and 21M
-posts. Our observations confirms that hateful content diffuse farther, wider
-and faster and have a greater outreach than those of non-hateful users. A
-deeper inspection into the profiles and network of hateful and non-hateful
-users reveals that the former are more influential, popular and cohesive. Thus,
-our research explores the interesting facets of diffusion dynamics of hateful
-users and broadens our understanding of hate speech in the online world.
-",1,0,0,0,0,0
-19349,GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams,"  Designing adaptive classifiers for an evolving data stream is a challenging
-task due to the data size and its dynamically changing nature. Combining
-individual classifiers in an online setting, the ensemble approach, is a
-well-known solution. It is possible that a subset of classifiers in the
-ensemble outperforms others in a time-varying fashion. However, optimum weight
-assignment for component classifiers is a problem which is not yet fully
-addressed in online evolving environments. We propose a novel data stream
-ensemble classifier, called Geometrically Optimum and Online-Weighted Ensemble
-(GOOWE), which assigns optimum weights to the component classifiers using a
-sliding window containing the most recent data instances. We map vote scores of
-individual classifiers and true class labels into a spatial environment. Based
-on the Euclidean distance between vote scores and ideal-points, and using the
-linear least squares (LSQ) solution, we present a novel, dynamic, and online
-weighting approach. While LSQ is used for batch mode ensemble classifiers, it
-is the first time that we adapt and use it for online environments by providing
-a spatial modeling of online ensembles. In order to show the robustness of the
-proposed algorithm, we use real-world datasets and synthetic data generators
-using the MOA libraries. First, we analyze the impact of our weighting system
-on prediction accuracy through two scenarios. Second, we compare GOOWE with 8
-state-of-the-art ensemble classifiers in a comprehensive experimental
-environment. Our experiments show that GOOWE provides improved reactions to
-different types of concept drift compared to our baselines. The statistical
-tests indicate a significant improvement in accuracy, with conservative time
-and memory requirements.
-",1,0,0,0,0,0
-19350,Graph Product Multilayer Networks: Spectral Properties and Applications,"  This paper aims to establish theoretical foundations of graph product
-multilayer networks (GPMNs), a family of multilayer networks that can be
-obtained as a graph product of two or more factor networks. Cartesian, direct
-(tensor), and strong product operators are considered, and then generalized. We
-first describe mathematical relationships between GPMNs and their factor
-networks regarding their degree/strength, adjacency, and Laplacian spectra, and
-then show that those relationships can still hold for nonsimple and generalized
-GPMNs. Applications of GPMNs are discussed in three areas: predicting epidemic
-thresholds, modeling propagation in nontrivial space and time, and analyzing
-higher-order properties of self-similar networks. Directions of future research
-are also discussed.
-",1,1,0,0,0,0
-19351,Multilayer flows in molecular networks identify biological modules in the human proteome,"  A variety of complex systems exhibit different types of relationships
-simultaneously that can be modeled by multiplex networks. A typical problem is
-to determine the community structure of such systems that, in general, depend
-on one or more parameters to be tuned. In this study we propose one measure,
-grounded on information theory, to find the optimal value of the relax rate
-characterizing Multiplex Infomap, the generalization of the Infomap algorithm
-to the realm of multilayer networks. We evaluate our methodology on synthetic
-networks, to show that the most representative community structure can be
-reliably identified when the most appropriate relax rate is used. Capitalizing
-on these results, we use this measure to identify the most reliable meso-scale
-functional organization in the human protein-protein interaction multiplex
-network and compare the observed clusters against a collection of independently
-annotated gene sets from the Molecular Signatures Database (MSigDB). Our
-analysis reveals that modules obtained with the optimal value of the relax rate
-are biologically significant and, remarkably, with higher functional content
-than the ones obtained from the aggregate representation of the human proteome.
-Our framework allows us to characterize the meso-scale structure of those
-multilayer systems whose layers are not explicitly interconnected each other --
-as in the case of edge-colored models -- the ones describing most biological
-networks, from proteomes to connectomes.
-",0,0,0,0,1,0
-19352,Using Artificial Neural Networks (ANN) to Control Chaos,"  Controlling Chaos could be a big factor in getting great stable amounts of
-energy out of small amounts of not necessarily stable resources. By definition,
-Chaos is getting huge changes in the system's output due to unpredictable small
-changes in initial conditions, and that means we could take advantage of this
-fact and select the proper control system to manipulate system's initial
-conditions and inputs in general and get a desirable output out of otherwise a
-Chaotic system. That was accomplished by first building some known chaotic
-circuit (Chua circuit) and the NI's MultiSim was used to simulate the ANN
-control system. It was shown that this technique can also be used to stabilize
-some hard to stabilize electronic systems.
-",1,1,0,0,0,0
-19353,Glassy quantum dynamics in translation invariant fracton models,"  We investigate relaxation in the recently discovered ""fracton"" models and
-discover that these models naturally host glassy quantum dynamics in the
-absence of quenched disorder. We begin with a discussion of ""type I"" fracton
-models, in the taxonomy of Vijay, Haah, and Fu. We demonstrate that in these
-systems, the mobility of charges is suppressed exponentially in the inverse
-temperature. We further demonstrate that when a zero temperature type I fracton
-model is placed in contact with a finite temperature heat bath, the approach to
-equilibrium is a logarithmic function of time over an exponentially wide window
-of time scales. Generalizing to the more complex ""type II"" fracton models, we
-find that the charges exhibit subdiffusion upto a relaxation time that diverges
-at low temperatures as a super-exponential function of inverse temperature.
-This behaviour is reminiscent of ""nearly localized"" disordered systems, but
-occurs with a translation invariant three-dimensional Hamiltonian. We also
-conjecture that fracton models with conserved charge may support a phase which
-is a thermal metal but a charge insulator.
-",0,1,0,0,0,0
-19354,Saliency Guided Hierarchical Robust Visual Tracking,"  A saliency guided hierarchical visual tracking (SHT) algorithm containing
-global and local search phases is proposed in this paper. In global search, a
-top-down saliency model is novelly developed to handle abrupt motion and
-appearance variation problems. Nineteen feature maps are extracted first and
-combined with online learnt weights to produce the final saliency map and
-estimated target locations. After the evaluation of integration mechanism, the
-optimum candidate patch is passed to the local search. In local search, a
-superpixel based HSV histogram matching is performed jointly with an L2-RLS
-tracker to take both color distribution and holistic appearance feature of the
-object into consideration. Furthermore, a linear refinement search process with
-fast iterative solver is implemented to attenuate the possible negative
-influence of dominant particles. Both qualitative and quantitative experiments
-are conducted on a series of challenging image sequences. The superior
-performance of the proposed method over other state-of-the-art algorithms is
-demonstrated by comparative study.
-",1,0,0,0,0,0
-19355,Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images,"  The present study shows that the performance of CNN is not significantly
-different from the best classical methods and human doctors for classifying
-mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does
-not need tumor segmentation or feature calculation, it is more convenient and
-more objective than the classical methods. However, CNN does not make use of
-the import diagnostic features, which have been proved more discriminative than
-the texture features for classifying small-sized lymph nodes. Therefore,
-incorporating the diagnostic features into CNN is a promising direction for
-future research.
-",1,1,0,0,0,0
-19356,A Semantics for Probabilistic Control-Flow Graphs,"  This article develops a novel operational semantics for probabilistic
-control-flow graphs (pCFGs) of probabilistic imperative programs with random
-assignment and ""observe"" (or conditioning) statements. The semantics transforms
-probability distributions (on stores) as control moves from one node to another
-in pCFGs. We relate this semantics to a standard, expectation-transforming,
-denotational semantics of structured probabilistic imperative programs, by
-translating structured programs into (unstructured) pCFGs, and proving adequacy
-of the translation. This shows that the operational semantics can be used
-without loss of information, and is faithful to the ""intended"" semantics and
-hence can be used to reason about, for example, the correctness of
-transformations (as we do in a companion article).
-",1,0,0,0,0,0
-19357,Generalized Slow Roll in the Unified Effective Field Theory of Inflation,"  We provide a compact and unified treatment of power spectrum observables for
-the effective field theory (EFT) of inflation with the complete set of
-operators that lead to second-order equations of motion in metric perturbations
-in both space and time derivatives, including Horndeski and GLPV theories. We
-relate the EFT operators in ADM form to the four additional free functions of
-time in the scalar and tensor equations. Using the generalized slow roll
-formalism, we show that each power spectrum can be described by an integral
-over a single source that is a function of its respective sound horizon. With
-this correspondence, existing model independent constraints on the source
-function can be simply reinterpreted in the more general inflationary context.
-By expanding these sources around an optimized freeze-out epoch, we also
-provide characterizations of these spectra in terms of five slow-roll
-hierarchies whose leading order forms are compact and accurate as long as EFT
-coefficients vary only on timescales greater than an efold. We also clarify the
-relationship between the unitary gauge observables employed in the EFT and the
-comoving gauge observables of the post-inflationary universe.
-",0,1,0,0,0,0
-19358,MgO thickness-induced spin reorientation transition in Co0.9Fe0.1/MgO/Co0.9Fe0.1 structure,"  The magnetic anisotropy (MA) of Mo/Au/Co0.9Fe0.1/Au/MgO(0.7 - 3
-nm)/Au/Co0.9Fe0.1/Au heterostructure has been investigated at room temperature
-as a function of MgO layer thickness (tMgO). Our studies show that while the MA
-of the top layer does not change its character upon variation of tMgO, the
-uniaxial out-of-plane MA of the bottom one undergoes a spin reorientation
-transition at tMgO of about 0.8 nm, switching to the regime where the
-coexistence of in- and out-of-plane magnetization alignments is observed. The
-magnitudes of the magnetic anisotropy constants have been determined from
-ferromagnetic resonance and dc-magnetometry measurements. The origin of MA
-evolution has been attributed to a presence of an interlayer exchange coupling
-(IEC) between Co0.9Fe0.1 layers through the thin MgO film.
-",0,1,0,0,0,0
-19359,Bjerrum Pairs in Ionic Solutions: a Poisson-Boltzmann Approach,"  Ionic solutions are often regarded as fully dissociated ions dispersed in a
-polar solvent. While this picture holds for dilute solutions, at higher ionic
-concentrations, oppositely charged ions can associate into dimers, referred to
-as Bjerrum pairs. We consider the formation of such pairs within the nonlinear
-Poisson-Boltzmann framework, and investigate their effects on bulk and
-interfacial properties of electrolytes. Our findings show that pairs can reduce
-the magnitude of the dielectric decrement of ionic solutions as the ionic
-concentration increases. We describe the effect of pairs on the Debye screening
-length, and relate our results to recent surface-force experiments.
-Furthermore, we show that Bjerrum pairs reduce the ionic concentration in bulk
-electrolyte and at the proximity of charged surfaces, while they enhance the
-attraction between oppositely charged surfaces.
-",0,1,0,0,0,0
-19360,Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding,"  Network embedding methodologies, which learn a distributed vector
-representation for each vertex in a network, have attracted considerable
-interest in recent years. Existing works have demonstrated that vertex
-representation learned through an embedding method provides superior
-performance in many real-world applications, such as node classification, link
-prediction, and community detection. However, most of the existing methods for
-network embedding only utilize topological information of a vertex, ignoring a
-rich set of nodal attributes (such as, user profiles of an online social
-network, or textual contents of a citation network), which is abundant in all
-real-life networks. A joint network embedding that takes into account both
-attributional and relational information entails a complete network information
-and could further enrich the learned vector representations. In this work, we
-present Neural-Brane, a novel Neural Bayesian Personalized Ranking based
-Attributed Network Embedding. For a given network, Neural-Brane extracts latent
-feature representation of its vertices using a designed neural network model
-that unifies network topological information and nodal attributes; Besides, it
-utilizes Bayesian personalized ranking objective, which exploits the proximity
-ordering between a similar node-pair and a dissimilar node-pair. We evaluate
-the quality of vertex embedding produced by Neural-Brane by solving the node
-classification and clustering tasks on four real-world datasets. Experimental
-results demonstrate the superiority of our proposed method over the
-state-of-the-art existing methods.
-",0,0,0,1,0,0
-19361,Pairwise $k$-Semi-Stratifiable Bispaces and Topological Ordered Spaces,"  In this paper, we continue to study pairwise ($k$-semi-)stratifiable
-bitopological spaces. Some new characterizations of pairwise
-$k$-semi-stratifiable bitopological spaces are provided. Relationships between
-pairwise stratifiable and pairwise $k$-semi-stratifiable bitopological spaces
-are further investigated, and an open question recently posed by Li and Lin in
-\cite{LL} is completely solved. We also study the quasi-pseudo-metrizability of
-a topological ordered space $(X, \tau, \preccurlyeq)$. It is shown that if $(X,
-\tau, \preccurlyeq)$ is a ball transitive topological ordered $C$- and
-$I$-space such that $\tau$ is metrizable, then its associated bitopological
-space $(X,\tau^{\flat},\tau^{\natural})$ is quasi-pseudo-metrizable. This
-result provides a partial affirmative answer to a problem in \cite{KM}.
-",0,0,1,0,0,0
-19362,Ontology based system to guide internship assignment process,"  Internship assignment is a complicated process for universities since it is
-necessary to take into account a multiplicity of variables to establish a
-compromise between companies' requirements and student competencies acquired
-during the university training. These variables build up a complex relations
-map that requires the formulation of an exhaustive and rigorous conceptual
-scheme. In this research a domain ontological model is presented as support to
-the student's decision making for opportunities of University studies level of
-the University Lumiere Lyon 2 (ULL) education system. The ontology is designed
-and created using methodological approach offering the possibility of improving
-the progressive creation, capture and knowledge articulation. In this paper, we
-draw a balance taking the demands of the companies across the capabilities of
-the students. This will be done through the establishment of an ontological
-model of an educational learners' profile and the internship postings which are
-written in a free text and using uncontrolled vocabulary. Furthermore, we
-outline the process of semantic matching which improves the quality of query
-results.
-",1,0,0,0,0,0
-19363,Vacancy-driven extended stability of cubic metastable Ta-Al-N and Nb-Al-N phases,"  Quantum mechanical calculations had been previously applied to predict phase
-stability in many ternary and multinary nitride systems. While the predictions
-were very accurate for the Ti-Al-N system, some discrepancies between theory
-and experiment were obtained in the case of other systems. Namely, in the case
-of Ta-Al-N, the calculations tend to overestimate the minimum Al content
-necessary to obtain a metastable solid solution with a cubic structure. In this
-work, we present a comprehensive study of the impact of vacancies on the phase
-fields in quasi-binary TaN-AlN and NbN-AlN systems. Our calculations clearly
-show that presence of point defects strongly enlarges the cubic phase field in
-the TaN-AlN system, while the effect is less pronounced in the NbN-AlN case.
-The present phase stability predictions agree better with experimental
-observations of physical vapour deposited thin films reported in the literature
-than that based on perfect, non-defected structures. This study shows that a
-representative structural model is crucial for a meaningful comparison with
-experimental data.
-",0,1,0,0,0,0
-19364,Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation,"  This paper addresses the question: Why do neural dialog systems generate
-short and meaningless replies? We conjecture that, in a dialog system, an
-utterance may have multiple equally plausible replies, causing the deficiency
-of neural networks in the dialog application. We propose a systematic way to
-mimic the dialog scenario in a machine translation system, and manage to
-reproduce the phenomenon of generating short and less meaningful sentences in
-the translation setting, showing evidence of our conjecture.
-",1,0,0,0,0,0
-19365,Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain,"  In this paper we tackle the problem of visually predicting surface friction
-for environments with diverse surfaces, and integrating this knowledge into
-biped robot locomotion planning. The problem is essential for autonomous robot
-locomotion since diverse surfaces with varying friction abound in the real
-world, from wood to ceramic tiles, grass or ice, which may cause difficulties
-or huge energy costs for robot locomotion if not considered. We propose to
-estimate friction and its uncertainty from visual estimation of material
-classes using convolutional neural networks, together with probability
-distribution functions of friction associated with each material. We then
-robustly integrate the friction predictions into a hierarchical (footstep and
-full-body) planning method using chance constraints, and optimize the same
-trajectory costs at both levels of the planning method for consistency. Our
-solution achieves fully autonomous perception and locomotion on slippery
-terrain, which considers not only friction and its uncertainty, but also
-collision, stability and trajectory cost. We show promising friction prediction
-results in real pictures of outdoor scenarios, and planning experiments on a
-real robot facing surfaces with different friction.
-",1,0,0,0,0,0
-19366,Non-Archimedean Replicator Dynamics and Eigen's Paradox,"  We present a new non-Archimedean model of evolutionary dynamics, in which the
-genomes are represented by p-adic numbers. In this model the genomes have a
-variable length, not necessarily bounded, in contrast with the classical models
-where the length is fixed. The time evolution of the concentration of a given
-genome is controlled by a p-adic evolution equation. This equation depends on a
-fitness function f and on mutation measure Q. By choosing a mutation measure of
-Gibbs type, and by using a p-adic version of the Maynard Smith Ansatz, we show
-the existence of threshold function M_{c}(f,Q), such that the long term
-survival of a genome requires that its length grows faster than M_{c}(f,Q).
-This implies that Eigen's paradox does not occur if the complexity of genomes
-grows at the right pace. About twenty years ago, Scheuring and Poole, Jeffares,
-Penny proposed a hypothesis to explain Eigen's paradox. Our mathematical model
-shows that this biological hypothesis is feasible, but it requires p-adic
-analysis instead of real analysis. More exactly, the Darwin-Eigen cycle
-proposed by Poole et al. takes place if the length of the genomes exceeds
-M_{c}(f,Q).
-",0,0,0,0,1,0
-19367,D-optimal design for multivariate polynomial regression via the Christoffel function and semidefinite relaxations,"  We present a new approach to the design of D-optimal experiments with
-multivariate polynomial regressions on compact semi-algebraic design spaces. We
-apply the moment-sum-of-squares hierarchy of semidefinite programming problems
-to solve numerically and approximately the optimal design problem. The geometry
-of the design is recovered with semidefinite programming duality theory and the
-Christoffel polynomial.
-",0,0,1,1,0,0
-19368,A dynamic network model to measure exposure diversification in the Austrian interbank market,"  We propose a statistical model for weighted temporal networks capable of
-measuring the level of heterogeneity in a financial system. Our model focuses
-on the level of diversification of financial institutions; that is, whether
-they are more inclined to distribute their assets equally among partners, or if
-they rather concentrate their commitment towards a limited number of
-institutions. Crucially, a Markov property is introduced to capture time
-dependencies and to make our measures comparable across time. We apply the
-model on an original dataset of Austrian interbank exposures. The temporal span
-encompasses the onset and development of the financial crisis in 2008 as well
-as the beginnings of European sovereign debt crisis in 2011. Our analysis
-highlights an overall increasing trend for network homogeneity, whereby core
-banks have a tendency to distribute their market exposures more equally across
-their partners.
-",0,0,0,1,0,1
-19369,Universal scaling in the Knight shift anomaly of doped periodic Anderson model,"  We report a Dynamical Cluster Approximation (DCA) investigation of the doped
-periodic Anderson model (PAM) to explain the universal scaling in the Knight
-shift anomaly predicted by the phenomenological two-fluid model and confirmed
-in many heavy-fermion compounds. We calculate the quantitative evolution of the
-orbital-dependent magnetic susceptibility and reproduce correctly the two-fluid
-prediction in a large range of doping and hybridization. Our results confirm
-the presence of a temperature/energy scale $T^{\ast}$ for the universal scaling
-and show distinctive behavors of the Knight shift anomaly in response to other
-""orders"" at low temperatures. However, comparison with the temperature
-evolution of the calculated resistivity and quasiparticle spectral peak
-indicates a different characteristic temperature from $T^*$, in contradiction
-with the experimental observation in CeCoIn$_5$ and other compounds. This
-reveals a missing piece in the current model calculations in explaining the
-two-fluid phenomenology.
-",0,1,0,0,0,0
-19370,Reinforcement Learning Algorithm Selection,"  This paper formalises the problem of online algorithm selection in the
-context of Reinforcement Learning. The setup is as follows: given an episodic
-task and a finite number of off-policy RL algorithms, a meta-algorithm has to
-decide which RL algorithm is in control during the next episode so as to
-maximize the expected return. The article presents a novel meta-algorithm,
-called Epochal Stochastic Bandit Algorithm Selection (ESBAS). Its principle is
-to freeze the policy updates at each epoch, and to leave a rebooted stochastic
-bandit in charge of the algorithm selection. Under some assumptions, a thorough
-theoretical analysis demonstrates its near-optimality considering the
-structural sampling budget limitations. ESBAS is first empirically evaluated on
-a dialogue task where it is shown to outperform each individual algorithm in
-most configurations. ESBAS is then adapted to a true online setting where
-algorithms update their policies after each transition, which we call SSBAS.
-SSBAS is evaluated on a fruit collection task where it is shown to adapt the
-stepsize parameter more efficiently than the classical hyperbolic decay, and on
-an Atari game, where it improves the performance by a wide margin.
-",1,0,1,1,0,0
-19371,"On periodic solutions of nonlinear wave equations, including Einstein equations with a negative cosmological constant","  We construct periodic solutions of nonlinear wave equations using analytic
-continuation. The construction applies in particular to Einstein equations,
-leading to infinite-dimensional families of time-periodic solutions of the
-vacuum, or of the Einstein-Maxwell-dilaton-scalar
-fields-Yang-Mills-Higgs-Chern-Simons-$f(R)$ equations, with a negative
-cosmological constant.
-",0,0,1,0,0,0
-19372,"Automatic Discovery, Association Estimation and Learning of Semantic Attributes for a Thousand Categories","  Attribute-based recognition models, due to their impressive performance and
-their ability to generalize well on novel categories, have been widely adopted
-for many computer vision applications. However, usually both the attribute
-vocabulary and the class-attribute associations have to be provided manually by
-domain experts or large number of annotators. This is very costly and not
-necessarily optimal regarding recognition performance, and most importantly, it
-limits the applicability of attribute-based models to large scale data sets. To
-tackle this problem, we propose an end-to-end unsupervised attribute learning
-approach. We utilize online text corpora to automatically discover a salient
-and discriminative vocabulary that correlates well with the human concept of
-semantic attributes. Moreover, we propose a deep convolutional model to
-optimize class-attribute associations with a linguistic prior that accounts for
-noise and missing data in text. In a thorough evaluation on ImageNet, we
-demonstrate that our model is able to efficiently discover and learn semantic
-attributes at a large scale. Furthermore, we demonstrate that our model
-outperforms the state-of-the-art in zero-shot learning on three data sets:
-ImageNet, Animals with Attributes and aPascal/aYahoo. Finally, we enable
-attribute-based learning on ImageNet and will share the attributes and
-associations for future research.
-",1,0,0,0,0,0
-19373,Variational Bayesian Inference For A Scale Mixture Of Normal Distributions Handling Missing Data,"  In this paper, a scale mixture of Normal distributions model is developed for
-classification and clustering of data having outliers and missing values. The
-classification method, based on a mixture model, focuses on the introduction of
-latent variables that gives us the possibility to handle sensitivity of model
-to outliers and to allow a less restrictive modelling of missing data.
-Inference is processed through a Variational Bayesian Approximation and a
-Bayesian treatment is adopted for model learning, supervised classification and
-clustering.
-",0,0,0,1,0,0
-19374,Energy stable discretization of Allen-Cahn type problems modeling the motion of phase boundaries,"  We study the systematic numerical approximation of a class of Allen-Cahn type
-problems modeling the motion of phase interfaces. The common feature of these
-models is an underlying gradient flow structure which gives rise to a decay of
-an associated energy functional along solution trajectories. We first study the
-discretization in space by a conforming Galerkin approximation of a variational
-principle which characterizes smooth solutions of the problem. Well-posedness
-of the resulting semi-discretization is established and the energy decay along
-discrete solution trajectories is proven. A problem adapted implicit
-time-stepping scheme is then proposed and we establish its well-posed and decay
-of the free energy for the fully discrete scheme. Some details about the
-numerical realization by finite elements are discussed, in particular the
-iterative solution of the nonlinear problems arising in every time-step. The
-theoretical results are illustrated by numerical tests which also provide
-further evidence for asymptotic expansions of the interface velocities derived
-by Alber et al.
-",0,0,1,0,0,0
-19375,Hidden space reconstruction inspires link prediction in complex networks,"  As a fundamental challenge in vast disciplines, link prediction aims to
-identify potential links in a network based on the incomplete observed
-information, which has broad applications ranging from uncovering missing
-protein-protein interaction to predicting the evolution of networks. One of the
-most influential methods rely on similarity indices characterized by the common
-neighbors or its variations. We construct a hidden space mapping a network into
-Euclidean space based solely on the connection structures of a network.
-Compared with real geographical locations of nodes, our reconstructed locations
-are in conformity with those real ones. The distances between nodes in our
-hidden space could serve as a novel similarity metric in link prediction. In
-addition, we hybrid our hidden space method with other state-of-the-art
-similarity methods which substantially outperforms the existing methods on the
-prediction accuracy. Hence, our hidden space reconstruction model provides a
-fresh perspective to understand the network structure, which in particular
-casts a new light on link prediction.
-",1,1,0,0,0,0
-19376,Maximum principles for the fractional p-Laplacian and symmetry of solutions,"  In this paper, we consider nonlinear equations involving the fractional
-p-Laplacian $$ (-\lap)_p^s u(x)) \equiv C_{n,s,p} PV \int_{\mathbb{R}^n}
-\frac{|u(x)-u(y)|^{p-2}[u(x)-u(y)]}{|x-z|^{n+ps}} dz= f(x,u).$$
-We prove a {\em maximum principle for anti-symmetric functions} and obtain
-other key ingredients for carrying on the method of moving planes, such as {\em
-a key boundary estimate lemma}. Then we establish radial symmetry and
-monotonicity for positive solutions to semilinear equations involving the
-fractional p-Laplacian in a unit ball and in the whole space. We believe that
-the methods developed here can be applied to a variety of problems involving
-nonlinear nonlocal operators.
-",0,0,1,0,0,0
-19377,On slowly rotating axisymmetric solutions of the Einstein-Euler equations,"  In recent works we have constructed axisymmetric solutions to the
-Euler-Poisson equations which give mathematical models of slowly uniformly
-rotating gaseous stars. We try to extend this result to the study of solutions
-of the Einstein-Euler equations in the framework of the general theory of
-relativity. Although many interesting studies have been done about axisymmetric
-metric in the general theory of relativity, they are restricted to the region
-of the vacuum. Mathematically rigorous existence theorem of the axisymmetric
-interior solutions of the stationary metric corresponding to the
-energy-momentum tensor of the perfect fluid with non-zero pressure may be not
-yet established until now except only one found in the pioneering work by U.
-Heilig done in 1993. In this article, along a different approach to that of
-Heilig's work, axisymmetric stationary solutions of the Einstein-Euler
-equations are constructed near those of the Euler-Poisson equations when the
-speed of light is sufficiently large in the considered system of units, or,
-when the gravitational field is sufficiently weak.
-",0,0,1,0,0,0
-19378,Gapped paramagnetic state in a frustrated spin-$\frac{1}{2}$ Heisenberg antiferromagnet on the cross-striped square lattice,"  We implement the coupled cluster method to very high orders of approximation
-to study the spin-$\frac{1}{2}$ $J_{1}$--$J_{2}$ Heisenberg model on a
-cross-striped square lattice. Every nearest-neighbour pair of sites on the
-square lattice has an isotropic antiferromagnetic exchange bond of strength
-$J_{1}>0$, while the basic square plaquettes in alternate columns have either
-both or neither next-nearest-neighbour (diagonal) pairs of sites connected by
-an equivalent frustrating bond of strength $J_{2} \equiv \alpha J_{1} > 0$. By
-studying the magnetic order parameter (i.e., the average local on-site
-magnetization) in the range $0 \leq \alpha \leq 1$ of the frustration parameter
-we find that the quasiclassical antiferromagnetic Néel and (so-called)
-double Néel states form the stable ground-state phases in the respective
-regions $\alpha < \alpha_{1a}^{c} = 0.46(1)$ and $\alpha > \alpha_{1b}^{c} =
-0.615(5)$. The double Néel state has Néel
-($\cdots\uparrow\downarrow\uparrow\downarrow\cdots$) ordering along the
-(column) direction parallel to the stripes of squares with both or no $J_{2}$
-bonds, and spins alternating in a pairwise
-($\cdots\uparrow\uparrow\downarrow\downarrow\uparrow\uparrow\downarrow\downarrow\cdots$)
-fashion along the perpendicular (row) direction, so that the parallel pairs
-occur on squares with both $J_{2}$ bonds present. Further explicit calculations
-of both the triplet spin gap and the zero-field uniform transverse magnetic
-susceptibility provide compelling evidence that the ground-state phase over all
-or most of the intermediate regime $\alpha_{1a}^{c} < \alpha < \alpha_{1b}^{c}$
-is a gapped state with no discernible long-range magnetic order.
-",0,1,0,0,0,0
-19379,Clouds in the atmospheres of extrasolar planets. V. The impact of CO2 ice clouds on the outer boundary of the habitable zone,"  Clouds have a strong impact on the climate of planetary atmospheres. The
-potential scattering greenhouse effect of CO2 ice clouds in the atmospheres of
-terrestrial extrasolar planets is of particular interest because it might
-influence the position and thus the extension of the outer boundary of the
-classic habitable zone around main sequence stars. Here, the impact of CO2 ice
-clouds on the surface temperatures of terrestrial planets with CO2 dominated
-atmospheres, orbiting different types of stars is studied. Additionally, their
-corresponding effect on the position of the outer habitable zone boundary is
-evaluated. For this study, a radiative-convective atmospheric model is used the
-calculate the surface temperatures influenced by CO2 ice particles. The clouds
-are included using a parametrised cloud model. The atmospheric model includes a
-general discrete ordinate radiative transfer that can describe the anisotropic
-scattering by the cloud particles accurately. A net scattering greenhouse
-effect caused by CO2 clouds is only obtained in a rather limited parameter
-range which also strongly depends on the stellar effective temperature. For
-cool M-stars, CO2 clouds only provide about 6 K of additional greenhouse
-heating in the best case scenario. On the other hand, the surface temperature
-for a planet around an F-type star can be increased by 30 K if carbon dioxide
-clouds are present. Accordingly, the extension of the habitable zone due to
-clouds is quite small for late-type stars. Higher stellar effective
-temperatures, on the other hand, can lead to outer HZ boundaries about 0.5 au
-farther out than the corresponding clear-sky values.
-",0,1,0,0,0,0
-19380,Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks,"  Magnetic resonance imaging (MRI) has been proposed as a complimentary method
-to measure bone quality and assess fracture risk. However, manual segmentation
-of MR images of bone is time-consuming, limiting the use of MRI measurements in
-the clinical practice. The purpose of this paper is to present an automatic
-proximal femur segmentation method that is based on deep convolutional neural
-networks (CNNs). This study had institutional review board approval and written
-informed consent was obtained from all subjects. A dataset of volumetric
-structural MR images of the proximal femur from 86 subject were
-manually-segmented by an expert. We performed experiments by training two
-different CNN architectures with multiple number of initial feature maps and
-layers, and tested their segmentation performance against the gold standard of
-manual segmentations using four-fold cross-validation. Automatic segmentation
-of the proximal femur achieved a high dice similarity score of 0.94$\pm$0.05
-with precision = 0.95$\pm$0.02, and recall = 0.94$\pm$0.08 using a CNN
-architecture based on 3D convolution exceeding the performance of 2D CNNs. The
-high segmentation accuracy provided by CNNs has the potential to help bring the
-use of structural MRI measurements of bone quality into clinical practice for
-management of osteoporosis.
-",1,0,0,1,0,0
-19381,Identifying On-time Reward Delivery Projects with Estimating Delivery Duration on Kickstarter,"  In Crowdfunding platforms, people turn their prototype ideas into real
-products by raising money from the crowd, or invest in someone else's projects.
-In reward-based crowdfunding platforms such as Kickstarter and Indiegogo,
-selecting accurate reward delivery duration becomes crucial for creators,
-backers, and platform providers to keep the trust between the creators and the
-backers, and the trust between the platform providers and users. According to
-Kickstarter, 35% backers did not receive rewards on time. Unfortunately, little
-is known about on-time and late reward delivery projects, and there is no prior
-work to estimate reward delivery duration. To fill the gap, in this paper, we
-(i) extract novel features that reveal latent difficulty levels of project
-rewards; (ii) build predictive models to identify whether a creator will
-deliver all rewards in a project on time or not; and (iii) build a regression
-model to estimate accurate reward delivery duration (i.e., how long it will
-take to produce and deliver all the rewards). Experimental results show that
-our models achieve good performance -- 82.5% accuracy, 78.1 RMSE, and 0.108
-NRMSE at the first 5% of the longest reward delivery duration.
-",1,0,0,0,0,0
-19382,Bayesian Model-Agnostic Meta-Learning,"  Learning to infer Bayesian posterior from a few-shot dataset is an important
-step towards robust meta-learning due to the model uncertainty inherent in the
-problem. In this paper, we propose a novel Bayesian model-agnostic
-meta-learning method. The proposed method combines scalable gradient-based
-meta-learning with nonparametric variational inference in a principled
-probabilistic framework. During fast adaptation, the method is capable of
-learning complex uncertainty structure beyond a point estimate or a simple
-Gaussian approximation. In addition, a robust Bayesian meta-update mechanism
-with a new meta-loss prevents overfitting during meta-update. Remaining an
-efficient gradient-based meta-learner, the method is also model-agnostic and
-simple to implement. Experiment results show the accuracy and robustness of the
-proposed method in various tasks: sinusoidal regression, image classification,
-active learning, and reinforcement learning.
-",0,0,0,1,0,0
-19383,Distributed matching scheme and a flexible deterministic matching algorithm for arbitrary systems,"  We discuss the distributed matching scheme in accelerators where control of
-transverse beam phase space, oscillation, and transport is accomplished by
-flexible distribution of focusing elements beyond dedicated matching sections.
-Besides freeing accelerator design from fixed matching sections, such a scheme
-has many operational advantages, and enables fluid optics manipulation not
-possible in conventional schemes. Combined with an interpolation scheme this
-can bring about a new paradigm for efficient, flexible, and robust optics
-control. A rigorous and deterministic algorithm is developed for its
-realization. The algorithm is a matching tool in its own right with unique
-characteristics in robustness and determinism. The beam phase space dynamics is
-naturally integrated into the algorithm, instead of being treated as generic
-numerical parameters as in traditional schemes. It is applicable to a wider
-range of problems, such as trading-off between competing options for desired
-machine states.
-",0,1,0,0,0,0
-19384,Group Metrics for Graph Products of Cyclic Groups,"  We complement the characterization of the graph products of cyclic groups
-$G(\Gamma, \mathfrak{p})$ admitting a Polish group topology of [9] with the
-following result. Let $G = G(\Gamma, \mathfrak{p})$, then the following are
-equivalent: (i) there is a metric on $\Gamma$ which induces a separable
-topology in which $E_{\Gamma}$ is closed; (ii) $G(\Gamma, \mathfrak{p})$ is
-embeddable into a Polish group; (iii) $G(\Gamma, \mathfrak{p})$ is embeddable
-into a non-Archimedean Polish group. We also construct left-invariant separable
-group ultrametrics for $G = G(\Gamma, \mathfrak{p})$ and $\Gamma$ a closed
-graph on the Baire space, which is of independent interest.
-",0,0,1,0,0,0
-19385,Robust Online Multi-Task Learning with Correlative and Personalized Structures,"  Multi-Task Learning (MTL) can enhance a classifier's generalization
-performance by learning multiple related tasks simultaneously. Conventional MTL
-works under the offline or batch setting, and suffers from expensive training
-cost and poor scalability. To address such inefficiency issues, online learning
-techniques have been applied to solve MTL problems. However, most existing
-algorithms of online MTL constrain task relatedness into a presumed structure
-via a single weight matrix, which is a strict restriction that does not always
-hold in practice. In this paper, we propose a robust online MTL framework that
-overcomes this restriction by decomposing the weight matrix into two
-components: the first one captures the low-rank common structure among tasks
-via a nuclear norm and the second one identifies the personalized patterns of
-outlier tasks via a group lasso. Theoretical analysis shows the proposed
-algorithm can achieve a sub-linear regret with respect to the best linear model
-in hindsight. Even though the above framework achieves good performance, the
-nuclear norm that simply adds all nonzero singular values together may not be a
-good low-rank approximation. To improve the results, we use a log-determinant
-function as a non-convex rank approximation. The gradient scheme is applied to
-optimize log-determinant function and can obtain a closed-form solution for
-this refined problem. Experimental results on a number of real-world
-applications verify the efficacy of our method.
-",1,0,0,1,0,0
-19386,Cobordism maps on PFH induced by Lefschetz fibration over higher genus base,"  In this note, we discuss the cobordism maps on periodic Floer homology(PFH)
-induced by Lefschetz fibration. In the first part of the note, we define the
-cobordism maps on PFH induced by Lefschetz fibration via Seiberg Witten theory
-and the isomorphism between PFH and Seiberg Witten cohomology. The second part
-is to define the cobordism maps induced by Lefschetz fibration provided that
-the cobordism satisfies certain conditions. Under certain monotone assumptions,
-we show that these two definitions in fact are equivalent.
-",0,0,1,0,0,0
-19387,A short note on the order of the Zhang-Liu matrices over arbitrary fields,"  We give necessary and sufficient conditions for the Zhang-Liu matrices to be
-diagonalizable over arbitrary fields and provide the eigen-decomposition when
-it is possible. We use this result to calculate the order of these matrices
-over any arbitrary field. This generalizes a result of the second author.
-",0,0,1,0,0,0
-19388,Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017),"  This is the Proceedings of the 2017 ICML Workshop on Human Interpretability
-in Machine Learning (WHI 2017), which was held in Sydney, Australia, August 10,
-2017. Invited speakers were Tony Jebara, Pang Wei Koh, and David Sontag.
-",1,0,0,1,0,0
-19389,Ferromagnetic transition in a one-dimensional spin-orbit-coupled metal and its mapping to a critical point in smectic liquid crystals,"  We study the quantum phase transition between a paramagnetic and
-ferromagnetic metal in the presence of Rashba spin-orbit coupling in one
-dimension. Using bosonization, we analyze the transition by means of
-renormalization group, controlled by an $\varepsilon$-expansion around the
-upper critical dimension of two. We show that the presence of Rashba spin-orbit
-coupling allows for a new nonlinear term in the bosonized action, which
-generically leads to a fluctuation driven first-order transition. We further
-demonstrate that the Euclidean action of this system maps onto a classical
-smectic-A -- C phase transition in a magnetic field in two dimensions. We show
-that the smectic transition is second-order and is controlled by a new critical
-point.
-",0,1,0,0,0,0
-19390,Riemannian almost product manifolds generated by a circulant structure,"  A 4-dimensional Riemannian manifold equipped with a circulant structure,
-which is an isometry with respect to the metric and its fourth power is the
-identity, is considered. The almost product manifold associated with the
-considered manifold is studied. The relation between the covariant derivatives
-of the almost product structure and the circulant structure is obtained. The
-conditions for the covariant derivative of the circulant structure, which imply
-that an almost product manifold belongs to each of the basic classes of the
-Staikova-Gribachev classification, are given.
-",0,0,1,0,0,0
-19391,Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis,"  Modern software systems provide many configuration options which
-significantly influence their non-functional properties. To understand and
-predict the effect of configuration options, several sampling and learning
-strategies have been proposed, albeit often with significant cost to cover the
-highly dimensional configuration space. Recently, transfer learning has been
-applied to reduce the effort of constructing performance models by transferring
-knowledge about performance behavior across environments. While this line of
-research is promising to learn more accurate models at a lower cost, it is
-unclear why and when transfer learning works for performance modeling. To shed
-light on when it is beneficial to apply transfer learning, we conducted an
-empirical study on four popular software systems, varying software
-configurations and environmental conditions, such as hardware, workload, and
-software versions, to identify the key knowledge pieces that can be exploited
-for transfer learning. Our results show that in small environmental changes
-(e.g., homogeneous workload change), by applying a linear transformation to the
-performance model, we can understand the performance behavior of the target
-environment, while for severe environmental changes (e.g., drastic workload
-change) we can transfer only knowledge that makes sampling more efficient,
-e.g., by reducing the dimensionality of the configuration space.
-",1,0,0,1,0,0
-19392,"Critique of Barbosa's ""P != NP Proof""","  We review André Luiz Barbosa's paper ""P != NP Proof,"" in which the classes
-P and NP are generalized and claimed to be proven separate. We highlight
-inherent ambiguities in Barbosa's definitions, and show that attempts to
-resolve this ambiguity lead to flaws in the proof of his main result.
-",1,0,0,0,0,0
-19393,"Two-Armed Bandit Problem, Data Processing, and Parallel Version of the Mirror Descent Algorithm","  We consider the minimax setup for the two-armed bandit problem as applied to
-data processing if there are two alternative processing methods available with
-different a priori unknown efficiencies. One should determine the most
-effective method and provide its predominant application. To this end we use
-the mirror descent algorithm (MDA). It is well-known that corresponding minimax
-risk has the order $N^{1/2}$ with $N$ being the number of processed data. We
-improve significantly the theoretical estimate of the factor using Monte-Carlo
-simulations. Then we propose a parallel version of the MDA which allows
-processing of data by packets in a number of stages. The usage of parallel
-version of the MDA ensures that total time of data processing depends mostly on
-the number of packets but not on the total number of data. It is quite
-unexpectedly that the parallel version behaves unlike the ordinary one even if
-the number of packets is large. Moreover, the parallel version considerably
-improves control performance because it provides significantly smaller value of
-the minimax risk. We explain this result by considering another parallel
-modification of the MDA which behavior is close to behavior of the ordinary
-version. Our estimates are based on invariant descriptions of the algorithms.
-All estimates are obtained by Monte-Carlo simulations. It's worth noting that
-parallel version performs well only for methods with close efficiencies. If
-efficiencies differ significantly then one should use the combined algorithm
-which at initial sufficiently short control horizon uses ordinary version and
-then switches to the parallel version of the MDA.
-",0,0,1,1,0,0
-19394,Bearing fault diagnosis under varying working condition based on domain adaptation,"  Traditional intelligent fault diagnosis of rolling bearings work well only
-under a common assumption that the labeled training data (source domain) and
-unlabeled testing data (target domain) are drawn from the same distribution.
-When the distribution changes, most fault diagnosis models need to be rebuilt
-from scratch using newly recollected labeled training data. However, it is
-expensive or impossible to annotate huge amount of training data to rebuild
-such new model. Meanwhile, large amounts of labeled training data have not been
-fully utilized yet, which is apparently a waste of resources. As one of the
-important research directions of transfer learning, domain adaptation (DA)
-typically aims at minimizing the differences between distributions of different
-domains in order to minimize the cross-domain prediction error by taking full
-advantage of information coming from both source and target domains. In this
-paper, we present one of the first studies on unsupervised DA in the field of
-fault diagnosis of rolling bearings under varying working conditions and a
-novel diagnosis strategy based on unsupervised DA using subspace alignment (SA)
-is proposed. After processed by unsupervised DA with SA, the distributions of
-training data and testing data become close and the classifier trained on
-training data can be used to classify the testing data. Experimental results on
-the 60 domain adaptation diagnosis problems under varying working condition in
-Case Western Reserve benchmark data and 12 domain adaptation diagnosis problems
-under varying working conditions in our new data are given to demonstrate the
-effectiveness of the proposed method. The proposed methods can effectively
-distinguish not only bearing faults categories but also fault severities.
-",1,0,0,0,0,0
-19395,"Dialectical Rough Sets, Parthood and Figures of Opposition-1","  In one perspective, the main theme of this research revolves around the
-inverse problem in the context of general rough sets that concerns the
-existence of rough basis for given approximations in a context. Granular
-operator spaces and variants were recently introduced by the present author as
-an optimal framework for anti-chain based algebraic semantics of general rough
-sets and the inverse problem. In the framework, various sub-types of crisp and
-non-crisp objects are identifiable that may be missed in more restrictive
-formalism. This is also because in the latter cases concepts of complementation
-and negation are taken for granted - while in reality they have a complicated
-dialectical basis. This motivates a general approach to dialectical rough sets
-building on previous work of the present author and figures of opposition. In
-this paper dialectical rough logics are invented from a semantic perspective, a
-concept of dialectical predicates is formalised, connection with dialetheias
-and glutty negation are established, parthood analyzed and studied from the
-viewpoint of classical and dialectical figures of opposition by the present
-author. Her methods become more geometrical and encompass parthood as a primary
-relation (as opposed to roughly equivalent objects) for algebraic semantics.
-",1,0,1,0,0,0
-19396,On quasi-hereditary algebras,"  In this paper we introduce an easily verifiable sufficient condition to
-determine whether an algebra is quasi-hereditary. In the case of monomial
-algebras, we give conditions that are both necessary and sufficient to show
-whether an algebra is quasi-hereditary.
-",0,0,1,0,0,0
-19397,Clustering Analysis on Locally Asymptotically Self-similar Processes with Known Number of Clusters,"  We study the problems of clustering locally asymptotically self-similar
-stochastic processes, when the true number of clusters is priorly known. A new
-covariance-based dissimilarity measure is introduced, from which the so-called
-approximately asymptotically consistent clustering algorithms are obtained. In
-a simulation study, clustering data sampled from multifractional Brownian
-motions is performed to illustrate the approximated asymptotic consistency of
-the proposed algorithms.
-",0,0,0,1,0,0
-19398,The OSIRIS-REx Visible and InfraRed Spectrometer (OVIRS): Spectral Maps of the Asteroid Bennu,"  The OSIRIS-REx Visible and Infrared Spectrometer (OVIRS) is a point
-spectrometer covering the spectral range of 0.4 to 4.3 microns (25,000-2300
-cm-1). Its primary purpose is to map the surface composition of the asteroid
-Bennu, the target asteroid of the OSIRIS-REx asteroid sample return mission.
-The information it returns will help guide the selection of the sample site. It
-will also provide global context for the sample and high spatial resolution
-spectra that can be related to spatially unresolved terrestrial observations of
-asteroids. It is a compact, low-mass (17.8 kg), power efficient (8.8 W
-average), and robust instrument with the sensitivity needed to detect a 5%
-spectral absorption feature on a very dark surface (3% reflectance) in the
-inner solar system (0.89-1.35 AU). It, in combination with the other
-instruments on the OSIRIS-REx Mission, will provide an unprecedented view of an
-asteroid's surface.
-",0,1,0,0,0,0
-19399,The effect of the smoothness of fractional type operators over their commutators with Lipschitz symbols on weighted spaces,"  We prove boundedness results for integral operators of fractional type and
-their higher order commutators between weighted spaces, including $L^p$-$L^q$,
-$L^p$-$BMO$ and $L^p$-Lipschitz estimates. The kernels of such operators
-satisfy certain size condition and a Lipschitz type regularity, and the symbol
-of the commutator belongs to a Lipschitz class. We also deal with commutators
-of fractional type operators with less regular kernels satisfying a
-Hörmander's type inequality. As far as we know, these last results are new
-even in the unweighted case. Moreover, we give a characterization result
-involving symbols of the commutators and continuity results for extreme values
-of $p$.
-",0,0,1,0,0,0
-19400,Fast mean-reversion asymptotics for large portfolios of stochastic volatility models,"  We consider a large portfolio limit where the asset prices evolve according
-certain stochastic volatility models with default upon hitting a lower barrier.
-When the asset prices and the volatilities are correlated via systemic Brownian
-Motions, that limit exist and it is described by a SPDE on the positive
-half-space with Dirichlet boundary conditions which has been studied in
-\cite{HK17}. We study the convergence of the total mass of a solution to this
-stochastic initial-boundary value problem when the mean-reversion coefficients
-of the volatilities are multiples of a parameter that tends to infinity. When
-the volatilities of the volatilities are multiples of the square root of the
-same parameter, the convergence is extremely weak. On the other hand, when the
-volatilities of the volatilities are independent of this exploding parameter,
-the volatilities converge to their means and we can have much better
-approximations. Our aim is to use such approximations to improve the accuracy
-of certain risk-management methods in markets where fast volatility
-mean-reversion is observed.
-",0,0,0,0,0,1
-19401,Poincaré profiles of groups and spaces,"  We introduce a spectrum of monotone coarse invariants for metric measure
-spaces called Poincaré profiles. The two extremes of this spectrum
-determine the growth of the space, and the separation profile as defined by
-Benjamini--Schramm--Timár. In this paper we focus on properties of the
-Poincaré profiles of groups with polynomial growth, and of hyperbolic
-spaces, where we deduce a striking connection between these profiles and
-conformal dimension. One application of our results is that there is a
-collection of hyperbolic Coxeter groups, indexed by a countable dense subset of
-$(1,\infty)$, such that $G_s$ does not coarsely embed into $G_t$ whenever
-$s<t$.
-",0,0,1,0,0,0
-19402,NMR Study of the New Magnetic Superconductor CaK(Fe$0.951Ni0.049)4As4: Microscopic Coexistence of Hedgehog Spin-vortex Crystal and Superconductivity,"  Coexistence of a new-type antiferromagnetic (AFM) state, the so-called
-hedgehog spin-vortex crystal (SVC), and superconductivity (SC) is evidenced by
-$^{75}$As nuclear magnetic resonance study on single-crystalline
-CaK(Fe$_{0.951}$Ni$_{0.049}$)$_4$As$_4$. The hedgehog SVC order is clearly
-demonstrated by the direct observation of the internal magnetic induction along
-the $c$ axis at the As1 site (close to K) and a zero net internal magnetic
-induction at the As2 site (close to Ca) below an AFM ordering temperature
-$T_{\rm N}$ $\sim$ 52 K. The nuclear spin-lattice relaxation rate 1/$T_1$ shows
-a distinct decrease below $T_{\rm c}$ $\sim$ 10 K, providing also unambiguous
-evidence for the microscopic coexistence. Furthermore, based on the analysis of
-the 1/$T_1$ data, the hedgehog SVC-type spin correlations are found to be
-enhanced below $T$ $\sim$ 150 K in the paramagnetic state. These results
-indicate the hedgehog SVC-type spin correlations play an important role for the
-appearance of SC in the new magnetic superconductor.
-",0,1,0,0,0,0
-19403,Light spanners for bounded treewidth graphs imply light spanners for $H$-minor-free graphs,"  Grigni and Hung~\cite{GH12} conjectured that H-minor-free graphs have
-$(1+\epsilon)$-spanners that are light, that is, of weight $g(|H|,\epsilon)$
-times the weight of the minimum spanning tree for some function $g$. This
-conjecture implies the {\em efficient} polynomial-time approximation scheme
-(PTAS) of the traveling salesperson problem in $H$-minor free graphs; that is,
-a PTAS whose running time is of the form $2^{f(\epsilon)}n^{O(1)}$ for some
-function $f$. The state of the art PTAS for TSP in H-minor-free-graphs has
-running time $n^{1/\epsilon^c}$. We take a further step toward proving this
-conjecture by showing that if the bounded treewidth graphs have light greedy
-spanners, then the conjecture is true. We also prove that the greedy spanner of
-a bounded pathwidth graph is light and discuss the possibility of extending our
-proof to bounded treewidth graphs.
-",1,0,0,0,0,0
-19404,Modeling Daily Seasonality of Mexico City Ozone using Nonseparable Covariance Models on Circles Cross Time,"  Mexico City tracks ground-level ozone levels to assess compliance with
-national ambient air quality standards and to prevent environmental health
-emergencies. Ozone levels show distinct daily patterns, within the city, and
-over the course of the year. To model these data, we use covariance models over
-space, circular time, and linear time. We review existing models and develop
-new classes of nonseparable covariance models of this type, models appropriate
-for quasi-periodic data collected at many locations. With these covariance
-models, we use nearest-neighbor Gaussian processes to predict hourly ozone
-levels at unobserved locations in April and May, the peak ozone season, to
-infer compliance to Mexican air quality standards and to estimate respiratory
-health risk associated with ozone. Predicted compliance with air quality
-standards and estimated respiratory health risk vary greatly over space and
-time. In some regions, we predict exceedance of national standards for more
-than a third of the hours in April and May. On many days, we predict that
-nearly all of Mexico City exceeds nationally legislated ozone thresholds at
-least once. In peak regions, we estimate respiratory risk for ozone to be 55%
-higher on average than the annual average risk and as much at 170% higher on
-some days.
-",0,0,0,1,0,0
-19405,Huygens-Fresnel Picture for Electron-Molecule Elastic Scattering,"  The elastic scattering cross sections for a slow electron by C2 and H2
-molecules have been calculated within the framework of the non-overlapping
-atomic potential model. For the amplitudes of the multiple electron scattering
-by a target the wave function of the molecular continuum is represented as a
-combination of a plane wave and two spherical waves generated by the centers of
-atomic spheres. This wave function obeys the Huygens-Fresnel principle
-according to which the electron wave scattering by a system of two centers is
-accompanied by generation of two spherical waves; their interaction creates a
-diffraction pattern far from the target. Each of the Huygens waves, in turn, is
-a superposition of the partial spherical waves with different orbital angular
-momenta l and their projections m. The amplitudes of these partial waves are
-defined by the corresponding phases of electron elastic scattering by an
-isolated atomic potential. In numerical calculations the s- and p-phase shifts
-are taken into account. So the number of interfering electron waves is equal to
-eight: two of which are the s-type waves and the remaining six waves are of the
-p-type with different m values. The calculation of the scattering amplitudes in
-closed form (rather than in the form of S-matrix expansion) is reduced to
-solving a system of eight inhomogeneous algebraic equations. The differential
-and total cross sections of electron scattering by fixed-in-space molecules and
-randomly oriented ones have been calculated as well. We conclude by discussing
-the special features of the S-matrix method for the case of arbitrary
-non-spherical potentials.
-",0,1,0,0,0,0
-19406,When Point Process Meets RNNs: Predicting Fine-Grained User Interests with Mutual Behavioral Infectivity,"  Predicting fine-grained interests of users with temporal behavior is
-important to personalization and information filtering applications. However,
-existing interest prediction methods are incapable of capturing the subtle
-degreed user interests towards particular items, and the internal time-varying
-drifting attention of individuals is not studied yet. Moreover, the prediction
-process can also be affected by inter-personal influence, known as behavioral
-mutual infectivity. Inspired by point process in modeling temporal point
-process, in this paper we present a deep prediction method based on two
-recurrent neural networks (RNNs) to jointly model each user's continuous
-browsing history and asynchronous event sequences in the context of inter-user
-behavioral mutual infectivity. Our model is able to predict the fine-grained
-interest from a user regarding a particular item and corresponding timestamps
-when an occurrence of event takes place. The proposed approach is more flexible
-to capture the dynamic characteristic of event sequences by using the temporal
-point process to model event data and timely update its intensity function by
-RNNs. Furthermore, to improve the interpretability of the model, the attention
-mechanism is introduced to emphasize both intra-personal and inter-personal
-behavior influence over time. Experiments on real datasets demonstrate that our
-model outperforms the state-of-the-art methods in fine-grained user interest
-prediction.
-",1,0,0,1,0,0
-19407,Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer,"  Quantitative extraction of high-dimensional mineable data from medical images
-is a process known as radiomics. Radiomics is foreseen as an essential
-prognostic tool for cancer risk assessment and the quantification of
-intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying
-tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET
-and CT images of 300 patients from four different cohorts were analyzed for the
-risk assessment of locoregional recurrences (LR) and distant metastases (DM) in
-head-and-neck cancer. Prediction models combining radiomic and clinical
-variables were constructed via random forests and imbalance-adjustment
-strategies using two of the four cohorts. Independent validation of the
-prediction and prognostic performance of the models was carried out on the
-other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88).
-Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the
-potential of radiomics for assessing the risk of specific tumour outcomes using
-multiple stratification groups. This could have important clinical impact,
-notably by allowing for a better personalization of chemo-radiation treatments
-for head-and-neck cancer patients from different risk groups.
-",1,0,0,0,0,0
-19408,Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data,"  Recently, two new indicators (Equalized Mean-based Normalized Proportion
-Cited, EMNPC; Mean-based Normalized Proportion Cited, MNPC) were proposed which
-are intended for sparse scientometrics data. The indicators compare the
-proportion of mentioned papers (e.g. on Facebook) of a unit (e.g., a researcher
-or institution) with the proportion of mentioned papers in the corresponding
-fields and publication years (the expected values). In this study, we propose a
-third indicator (Mantel-Haenszel quotient, MHq) belonging to the same indicator
-family. The MHq is based on the MH analysis - an established method in
-statistics for the comparison of proportions. We test (using citations and
-assessments by peers, i.e. F1000Prime recommendations) if the three indicators
-can distinguish between different quality levels as defined on the basis of the
-assessments by peers. Thus, we test their convergent validity. We find that the
-indicator MHq is able to distinguish between the quality levels in most cases
-while MNPC and EMNPC are not. Since the MHq is shown in this study to be a
-valid indicator, we apply it to six types of zero-inflated altmetrics data and
-test whether different altmetrics sources are related to quality. The results
-for the various altmetrics demonstrate that the relationship between altmetrics
-(Wikipedia, Facebook, blogs, and news data) and assessments by peers is not as
-strong as the relationship between citations and assessments by peers.
-Actually, the relationship between citations and peer assessments is about two
-to three times stronger than the association between altmetrics and assessments
-by peers.
-",1,0,0,0,0,0
-19409,Gravity Formality,"  We show that Willwacher's cyclic formality theorem can be extended to
-preserve natural Gravity operations on cyclic multivector fields and cyclic
-multidifferential operators. We express this in terms of a homotopy Gravity
-quasi-isomorphism with explicit local formulas. For this, we develop operadic
-tools related to mixed complexes and cyclic homology and prove that the operad
-$\mathsf M_\circlearrowleft$ of natural operations on cyclic operators is
-formal and hence quasi-isomorphic to the Gravity operad.
-",0,0,1,0,0,0
-19410,On the Feasibility of Distinguishing Between Process Disturbances and Intrusions in Process Control Systems Using Multivariate Statistical Process Control,"  Process Control Systems (PCSs) are the operating core of Critical
-Infrastructures (CIs). As such, anomaly detection has been an active research
-field to ensure CI normal operation. Previous approaches have leveraged network
-level data for anomaly detection, or have disregarded the existence of process
-disturbances, thus opening the possibility of mislabelling disturbances as
-attacks and vice versa. In this paper we present an anomaly detection and
-diagnostic system based on Multivariate Statistical Process Control (MSPC),
-that aims to distinguish between attacks and disturbances. For this end, we
-expand traditional MSPC to monitor process level and controller level data. We
-evaluate our approach using the Tennessee-Eastman process. Results show that
-our approach can be used to distinguish disturbances from intrusions to a
-certain extent and we conclude that the proposed approach can be extended with
-other sources of data for improving results.
-",1,0,0,0,0,0
-19411,Quantum hydrodynamic approximations to the finite temperature trapped Bose gases,"  For the quantum kinetic system modelling the Bose-Einstein Condensate that
-accounts for interactions between condensate and excited atoms, we use the
-Chapman-Enskog expansion to derive its hydrodynamic approximations, include
-both Euler and Navier-Stokes approximations. The hydrodynamic approximations
-describe not only the macroscopic behavior of the BEC but also its coupling
-with the non-condensates, which agrees with Landau's two fluid theory.
-",0,1,0,0,0,0
-19412,Game-theoretic dynamic investment model with incomplete information: futures contracts,"  Over the past few years, the futures market has been successfully developing
-in the North-West region. Futures markets are one of the most effective and
-liquid-visible trading mechanisms. A large number of buyers are forced to
-compete with each other and raise their prices. A large number of sellers make
-them reduce prices. Thus, the gap between the prices of offers of buyers and
-sellers is reduced due to high competition, and this is a good criterion for
-the liquidity of the market. This high degree of liquidity contributed to the
-fact that futures trading took such an important role in commerce and finance.
-A multi-step, non-cooperative n persons game is formalized and studied
-",0,0,0,0,0,1
-19413,Deep Collaborative Learning for Visual Recognition,"  Deep neural networks are playing an important role in state-of-the-art visual
-recognition. To represent high-level visual concepts, modern networks are
-equipped with large convolutional layers, which use a large number of filters
-and contribute significantly to model complexity. For example, more than half
-of the weights of AlexNet are stored in the first fully-connected layer (4,096
-filters).
-We formulate the function of a convolutional layer as learning a large visual
-vocabulary, and propose an alternative way, namely Deep Collaborative Learning
-(DCL), to reduce the computational complexity. We replace a convolutional layer
-with a two-stage DCL module, in which we first construct a couple of smaller
-convolutional layers individually, and then fuse them at each spatial position
-to consider feature co-occurrence. In mathematics, DCL can be explained as an
-efficient way of learning compositional visual concepts, in which the
-vocabulary size increases exponentially while the model complexity only
-increases linearly. We evaluate DCL on a wide range of visual recognition
-tasks, including a series of multi-digit number classification datasets, and
-some generic image classification datasets such as SVHN, CIFAR and ILSVRC2012.
-We apply DCL to several state-of-the-art network structures, improving the
-recognition accuracy meanwhile reducing the number of parameters (16.82% fewer
-in AlexNet).
-",1,0,0,0,0,0
-19414,Co-location Epidemic Tracking on London Public Transports Using Low Power Mobile Magnetometer,"  The public transports provide an ideal means to enable contagious diseases
-transmission. This paper introduces a novel idea to detect co-location of
-people in such environment using just the ubiquitous geomagnetic field sensor
-on the smart phone. Essentially, given that all passengers must share the same
-journey between at least two consecutive stations, we have a long window to
-match the user trajectory. Our idea was assessed over a painstakingly survey of
-over 150 kilometres of travelling distance, covering different parts of London,
-using the overground trains, the underground tubes and the buses.
-",1,0,0,0,0,0
-19415,"Waring-Goldbach Problem: One Square, Four Cubes and Higher Powers","  Let $\mathcal{P}_r$ denote an almost-prime with at most $r$ prime factors,
-counted according to multiplicity. In this paper, it is proved that, for
-$12\leqslant b\leqslant 35$ and for every sufficiently large odd integer $N$,
-the equation \begin{equation*}
-N=x^2+p_1^3+p_2^3+p_3^3+p_4^3+p_5^4+p_6^b \end{equation*} is solvable with
-$x$ being an almost-prime $\mathcal{P}_{r(b)}$ and the other variables primes,
-where $r(b)$ is defined in the Theorem. This result constitutes an improvement
-upon that of Lü and Mu.
-",0,0,1,0,0,0
-19416,Realizability of tropical canonical divisors,"  We use recent results by Bainbridge-Chen-Gendron-Grushevsky-Moeller on
-compactifications of strata of abelian differentials to give a comprehensive
-solution to the realizability problem for effective tropical canonical divisors
-in equicharacteristic zero. Given a pair $(\Gamma, D)$ consisting of a stable
-tropical curve $\Gamma$ and a divisor $D$ in the canonical linear system on
-$\Gamma$, we give a purely combinatorial condition to decide whether there is a
-smooth curve $X$ over a non-Archimedean field whose stable reduction has
-$\Gamma$ as its dual tropical curve together with a effective canonical divisor
-$K_X$ that specializes to $D$. Along the way, we develop a moduli-theoretic
-framework to understand Baker's specialization of divisors from algebraic to
-tropical curves as a natural toroidal tropicalization map in the sense of
-Abramovich-Caporaso-Payne.
-",0,0,1,0,0,0
-19417,Causal inference for interfering units with cluster and population level treatment allocation programs,"  Interference arises when an individual's potential outcome depends on the
-individual treatment level, but also on the treatment level of others. A common
-assumption in the causal inference literature in the presence of interference
-is partial interference, implying that the population can be partitioned in
-clusters of individuals whose potential outcomes only depend on the treatment
-of units within the same cluster. Previous literature has defined average
-potential outcomes under counterfactual scenarios where treatments are randomly
-allocated to units within a cluster. However, within clusters there may be
-units that are more or less likely to receive treatment based on covariates or
-neighbors' treatment. We define new estimands that describe average potential
-outcomes for realistic counterfactual treatment allocation programs, extending
-existing estimands to take into consideration the units' covariates and
-dependence between units' treatment assignment. We further propose entirely new
-estimands for population-level interventions over the collection of clusters,
-which correspond in the motivating setting to regulations at the federal (vs.
-cluster or regional) level. We discuss these estimands, propose unbiased
-estimators and derive asymptotic results as the number of clusters grows.
-Finally, we estimate effects in a comparative effectiveness study of power
-plant emission reduction technologies on ambient ozone pollution.
-",0,0,1,1,0,0
-19418,"A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation","  We present a unifying framework to solve several computer vision problems
-with event cameras: motion, depth and optical flow estimation. The main idea of
-our framework is to find the point trajectories on the image plane that are
-best aligned with the event data by maximizing an objective function: the
-contrast of an image of warped events. Our method implicitly handles data
-association between the events, and therefore, does not rely on additional
-appearance information about the scene. In addition to accurately recovering
-the motion parameters of the problem, our framework produces motion-corrected
-edge-like images with high dynamic range that can be used for further scene
-analysis. The proposed method is not only simple, but more importantly, it is,
-to the best of our knowledge, the first method that can be successfully applied
-to such a diverse set of important vision tasks with event cameras.
-",1,0,0,0,0,0
-19419,TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing,"  Machine learning models are notoriously difficult to interpret and debug.
-This is particularly true of neural networks. In this work, we introduce
-automated software testing techniques for neural networks that are well-suited
-to discovering errors which occur only for rare inputs. Specifically, we
-develop coverage-guided fuzzing (CGF) methods for neural networks. In CGF,
-random mutations of inputs to a neural network are guided by a coverage metric
-toward the goal of satisfying user-specified constraints. We describe how fast
-approximate nearest neighbor algorithms can provide this coverage metric. We
-then discuss the application of CGF to the following goals: finding numerical
-errors in trained neural networks, generating disagreements between neural
-networks and quantized versions of those networks, and surfacing undesirable
-behavior in character level language models. Finally, we release an open source
-library called TensorFuzz that implements the described techniques.
-",0,0,0,1,0,0
-19420,Inferring directed climatic interactions with renormalized partial directed coherence and directed partial correlation,"  Inferring interactions between processes promises deeper insight into
-mechanisms underlying network phenomena. Renormalised partial directed
-coherence (rPDC) is a frequency-domain representation of the concept of Granger
-causality while directed partial correlation (DPC) is an alternative approach
-for quantifying Granger causality in the time domain. Both methodologies have
-been successfully applied to neurophysiological signals for detecting directed
-relationships. This paper introduces their application to climatological time
-series. We first discuss the application to ENSO -- Monsoon interaction, and
-then apply the methodologies to the more challenging air-sea interaction in the
-South Atlantic Convergence Zone (SACZ). While in the first case the results
-obtained are fully consistent with present knowledge in climate modeling, in
-the second case the results are, as expected, less clear, and to fully
-elucidate the SACZ air-sea interaction, further investigations on the
-specificity and sensitivity of these methodologies are needed.
-",0,1,0,0,0,0
-19421,Conditions for the invertibility of dual energy data,"  The Alvarez-Macovski method [Alvarez, R. E and Macovski, A.,
-""Energy-selective reconstructions in X-ray computerized tomography"", Phys. Med.
-Biol. (1976), 733--44] requires the inversion of the transformation from the
-line integrals of the basis set coefficients to measurements with multiple
-x-ray spectra. Analytical formulas for invertibility of the transformation from
-two measurements to two line integrals are derived. It is found that
-non-invertible systems have near zero Jacobian determinants on a nearly
-straight line in the line integrals plane. Formulas are derived for the points
-where the line crosses the axes, thus determining the line. Additional formulas
-are derived for the values of the terms of the Jacobian determinant at the
-endpoints of the line of non-invertibility. The formulas are applied to a set
-of spectra including one suggested by Levine that is not invertible as well as
-similar spectra that are invertible and voltage switched x-ray tube spectra
-that are also invertible. An iterative inverse transformation algorithm
-exhibits large errors with non-invertible spectra.
-",0,1,0,0,0,0
-19422,"Examples of lattice-polarized K3 surfaces with automorphic discriminant, and Lorentzian Kac--Moody algebras","  Using our results about Lorentzian Kac--Moody algebras and arithmetic mirror
-symmetry, we give six series of examples of lattice-polarized K3 surfaces with
-automorphic discriminant.
-",0,0,1,0,0,0
-19423,Quantum ferrofluid turbulence,"  We study the elementary characteristics of turbulence in a quantum ferrofluid
-through the context of a dipolar Bose gas condensing from a highly
-non-equilibrium thermal state. Our simulations reveal that the dipolar
-interactions drive the emergence of polarized turbulence and density
-corrugations. The superfluid vortex lines and density fluctuations adopt a
-columnar or stratified configuration, depending on the sign of the dipolar
-interactions, with the vortices tending to form in the low density regions to
-minimize kinetic energy. When the interactions are dominantly dipolar, the
-decay of vortex line length is enhanced, closely following a $t^{-3/2}$
-behaviour. This system poses exciting prospects for realizing stratified
-quantum turbulence and new levels of generating and controlling turbulence
-using magnetic fields.
-",0,1,0,0,0,0
-19424,Estimation of the discontinuous leverage effect: Evidence from the NASDAQ order book,"  An extensive empirical literature documents a generally negative correlation,
-named the ""leverage effect,"" between asset returns and changes of volatility.
-It is more challenging to establish such a return-volatility relationship for
-jumps in high-frequency data. We propose new nonparametric methods to assess
-and test for a discontinuous leverage effect --- i.e. a relation between
-contemporaneous jumps in prices and volatility. The methods are robust to
-market microstructure noise and build on a newly developed price-jump
-localization and estimation procedure. Our empirical investigation of six years
-of transaction data from 320 NASDAQ firms displays no unconditional negative
-correlation between price and volatility cojumps. We show, however, that there
-is a strong relation between price-volatility cojumps if one conditions on the
-sign of price jumps and whether the price jumps are market-wide or
-idiosyncratic. Firms' volatility levels strongly explain the cross-section of
-discontinuous leverage while debt-to-equity ratios have no significant
-explanatory power.
-",0,0,1,1,0,0
-19425,Group representations that resist worst-case sampling,"  Motivated by expansion in Cayley graphs, we show that there exist infinitely
-many groups $G$ with a nontrivial irreducible unitary representation whose
-average over every set of $o(\log\log|G|)$ elements of $G$ has operator norm $1
-- o(1)$. This answers a question of Lovett, Moore, and Russell, and strengthens
-their negative answer to a question of Wigderson.
-The construction is the affine group of $\mathbb{F}_p$ and uses the fact that
-for every $A \subset \mathbb{F}_p\setminus\{0\}$, there is a set of size
-$\exp(\exp(O(|A|)))$ that is almost invariant under both additive and
-multiplicatpive translations by elements of $A$.
-",0,0,1,0,0,0
-19426,On the nearly smooth complex spaces,"  We introduce a class of normal complex spaces having only mild sin-gularities
-(close to quotient singularities) for which we generalize the notion of a
-(analytic) fundamental class for an analytic cycle and also the notion of a
-relative fundamental class for an analytic family of cycles. We also generalize
-to these spaces the geometric intersection theory for analytic cycles with
-rational positive coefficients and show that it behaves well with respect to
-analytic families of cycles. We prove that this intersection theory has most of
-the usual properties of the standard geometric intersection theory on complex
-manifolds, but with the exception that the intersection cycle of two cycles
-with positive integral coefficients that intersect properly may have rational
-coefficients. AMS classification. 32 C 20-32 C 25-32 C 36.
-",0,0,1,0,0,0
-19427,Semi-supervised learning of hierarchical representations of molecules using neural message passing,"  With the rapid increase of compound databases available in medicinal and
-material science, there is a growing need for learning representations of
-molecules in a semi-supervised manner. In this paper, we propose an
-unsupervised hierarchical feature extraction algorithm for molecules (or more
-generally, graph-structured objects with fixed number of types of nodes and
-edges), which is applicable to both unsupervised and semi-supervised tasks. Our
-method extends recently proposed Paragraph Vector algorithm and incorporates
-neural message passing to obtain hierarchical representations of subgraphs. We
-applied our method to an unsupervised task and demonstrated that it outperforms
-existing proposed methods in several benchmark datasets. We also experimentally
-showed that semi-supervised tasks enhanced predictive performance compared with
-supervised ones with labeled molecules only.
-",1,0,0,1,0,0
-19428,Tracy-Widom at each edge of real covariance and MANOVA estimators,"  We study the sample covariance matrix for real-valued data with general
-population covariance, as well as MANOVA-type covariance estimators in variance
-components models under null hypotheses of global sphericity. In the limit as
-matrix dimensions increase proportionally, the asymptotic spectra of such
-estimators may have multiple disjoint intervals of support, possibly
-intersecting the negative half line. We show that the distribution of the
-extremal eigenvalue at each regular edge of the support has a GOE Tracy-Widom
-limit. Our proof extends a comparison argument of Ji Oon Lee and Kevin
-Schnelli, replacing a continuous Green function flow by a discrete Lindeberg
-swapping scheme.
-",0,0,1,1,0,0
-19429,Multi-stage Neural Networks with Single-sided Classifiers for False Positive Reduction and its Evaluation using Lung X-ray CT Images,"  Lung nodule classification is a class imbalanced problem because nodules are
-found with much lower frequency than non-nodules. In the class imbalanced
-problem, conventional classifiers tend to be overwhelmed by the majority class
-and ignore the minority class. We therefore propose cascaded convolutional
-neural networks to cope with the class imbalanced problem. In the proposed
-approach, multi-stage convolutional neural networks that perform as
-single-sided classifiers filter out obvious non-nodules. Successively, a
-convolutional neural network trained with a balanced data set calculates nodule
-probabilities. The proposed method achieved the sensitivity of 92.4\% and 94.5%
-at 4 and 8 false positives per scan in Free Receiver Operating Characteristics
-(FROC) curve analysis, respectively.
-",1,0,0,0,0,0
-19430,Monochromatic knots and other unusual electromagnetic disturbances: light localised in 3D,"  We introduce and examine a collection of unusual electromagnetic
-disturbances. Each of these is an exact, monochromatic solution of Maxwell's
-equations in free space with looped electric and magnetic field lines of finite
-extent and a localised appearance in all three spatial dimensions. Included are
-the first explicit examples of monochromatic electromagnetic knots. We also
-consider the generation of our unusual electromagnetic disturbances in the
-laboratory, at both low and high frequencies, and highlight possible directions
-for future research, including the use of unusual electromagnetic disturbances
-as the basis of a new form of three-dimensional display.
-",0,1,0,0,0,0
-19431,Zero-cycles of degree one on Skorobogatov's bielliptic surface,"  Skorobogatov constructed a bielliptic surface which is a counterexample to
-the Hasse principle not explained by the Brauer-Manin obstruction. We show that
-this surface has a $0$-cycle of degree 1, as predicted by a conjecture of
-Colliot-Thélène.
-",0,0,1,0,0,0
-19432,Locally Nameless Permutation Types,"  We define ""Locally Nameless Permutation Types"", which fuse permutation types
-as used in Nominal Isabelle with the locally nameless representation. We show
-that this combination is particularly useful when formalizing programming
-languages where bound names may become free during execution (""extrusion""),
-common in process calculi. It inherits the generic definition of permutations
-and support, and associated lemmas, from the Nominal approach, and the ability
-to stay close to pencil-and-paper proofs from the locally nameless approach. We
-explain how to use cofinite quantification in this setting, show why reasoning
-about renaming is more important here than in languages without extrusion, and
-provide results about infinite support, necessary when reasoning about
-countable choice.
-",1,0,0,0,0,0
-19433,On dimension-free variational inequalities for averaging operators in $\mathbb R^d$,"  We study dimension-free $L^p$ inequalities for $r$-variations of the
-Hardy--Littlewood averaging operators defined over symmetric convex bodies in
-$\mathbb R^d$.
-",0,0,1,0,0,0
-19434,One-particle density matrix of trapped one-dimensional impenetrable bosons from conformal invariance,"  The one-particle density matrix of the one-dimensional Tonks-Girardeau gas
-with inhomogeneous density profile is calculated, thanks to a recent
-observation that relates this system to a two-dimensional conformal field
-theory in curved space. The result is asymptotically exact in the limit of
-large particle density and small density variation, and holds for arbitrary
-trapping potentials. In the particular case of a harmonic trap, we recover a
-formula obtained by Forrester et al. [Phys. Rev. A 67, 043607 (2003)] from a
-different method.
-",0,1,1,0,0,0
-19435,Image Analysis Using a Dual-Tree $M$-Band Wavelet Transform,"  We propose a 2D generalization to the $M$-band case of the dual-tree
-decomposition structure (initially proposed by N. Kingsbury and further
-investigated by I. Selesnick) based on a Hilbert pair of wavelets. We
-particularly address (\textit{i}) the construction of the dual basis and
-(\textit{ii}) the resulting directional analysis. We also revisit the necessary
-pre-processing stage in the $M$-band case. While several reconstructions are
-possible because of the redundancy of the representation, we propose a new
-optimal signal reconstruction technique, which minimizes potential estimation
-errors. The effectiveness of the proposed $M$-band decomposition is
-demonstrated via denoising comparisons on several image types (natural,
-texture, seismics), with various $M$-band wavelets and thresholding strategies.
-Significant improvements in terms of both overall noise reduction and direction
-preservation are observed.
-",1,1,1,0,0,0
-19436,Nonlinear Cauchy-Riemann Equations and Liouville Equation For Conformal Metrics,"  We introduce the Nonlinear Cauchy-Riemann equations as Bäcklund
-transformations for several nonlinear and linear partial differential
-equations. From these equations we treat in details the Laplace and the
-Liouville equations by deriving general solution for the nonlinear Liouville
-equation. By Möbius transformation we relate solutions for the Poincare
-model of hyperbolic geometry, the Klein model in half-plane and the
-pseudo-sphere. Conformal form of the constant curvature metrics in these
-geometries, stereographic projections and special solutions are discussed. Then
-we introduce the hyperbolic analog of the Riemann sphere, which we call the
-Riemann pseudosphere. We identify point at infinity on this pseudosphere and
-show that it can be used in complex analysis as an alternative to usual Riemann
-sphere to extend the complex plane. Interpretation of symmetric and antipodal
-points on both, the Riemann sphere and the Riemann pseudo-sphere, are given. By
-Möbius transformation and homogenous coordinates, the most general solution
-of Liouville equation as discussed by Crowdy is derived.
-",0,1,1,0,0,0
-19437,SmartPaste: Learning to Adapt Source Code,"  Deep Neural Networks have been shown to succeed at a range of natural
-language tasks such as machine translation and text summarization. While tasks
-on source code (ie, formal languages) have been considered recently, most work
-in this area does not attempt to capitalize on the unique opportunities offered
-by its known syntax and structure. In this work, we introduce SmartPaste, a
-first task that requires to use such information. The task is a variant of the
-program repair problem that requires to adapt a given (pasted) snippet of code
-to surrounding, existing source code. As first solutions, we design a set of
-deep neural models that learn to represent the context of each variable
-location and variable usage in a data flow-sensitive way. Our evaluation
-suggests that our models can learn to solve the SmartPaste task in many cases,
-achieving 58.6% accuracy, while learning meaningful representation of variable
-usages.
-",1,0,0,0,0,0
-19438,An Outcome Model Approach to Translating a Randomized Controlled Trial Results to a Target Population,"  Participants enrolled into randomized controlled trials (RCTs) often do not
-reflect real-world populations. Previous research in how best to translate RCT
-results to target populations has focused on weighting RCT data to look like
-the target data. Simulation work, however, has suggested that an outcome model
-approach may be preferable. Here we describe such an approach using source data
-from the 2x2 factorial NAVIGATOR trial which evaluated the impact of valsartan
-and nateglinide on cardiovascular outcomes and new-onset diabetes in a
-pre-diabetic population. Our target data consisted of people with pre-diabetes
-serviced at our institution. We used Random Survival Forests to develop
-separate outcome models for each of the 4 treatments, estimating the 5-year
-risk difference for progression to diabetes and estimated the treatment effect
-in our local patient populations, as well as sub-populations, and the results
-compared to the traditional weighting approach. Our models suggested that the
-treatment effect for valsartan in our patient population was the same as in the
-trial, whereas for nateglinide treatment effect was stronger than observed in
-the original trial. Our effect estimates were more efficient than the weighting
-approach.
-",0,0,0,1,0,0
-19439,Influence of parameterized small-scale gravity waves on the migrating diurnal tide in Earth's thermosphere,"  Effects of subgrid-scale gravity waves (GWs) on the diurnal migrating tides
-are investigated from the mesosphere to the upper thermosphere for September
-equinox conditions, using a general circulation model coupled with the extended
-spectral nonlinear GW parameterization of Yiğit et al (2008). Simulations
-with GW effects cut-off above the turbopause and included in the entire
-thermosphere have been conducted. GWs appreciably impact the mean circulation
-and cool the thermosphere down by up to 12-18%. GWs significantly affect the
-winds modulated by the diurnal migrating tide, in particular in the
-low-latitude mesosphere and lower thermosphere and in the high-latitude
-thermosphere. These effects depend on the mutual correlation of the diurnal
-phases of the GW forcing and tides: GWs can either enhance or reduce the tidal
-amplitude. In the low-latitude MLT, the correlation between the direction of
-the deposited GW momentum and the tidal phase is positive due to propagation of
-a broad spectrum of GW harmonics through the alternating winds. In the Northern
-Hemisphere high-latitude thermosphere, GWs act against the tide due to an
-anti-correlation of tidal wind and GW momentum, while in the Southern
-high-latitudes they weakly enhance the tidal amplitude via a combination of a
-partial correlation of phases and GW-induced changes of the circulation. The
-variable nature of GW effects on the thermal tide can be captured in GCMs
-provided that a GW parameterization (1) considers a broad spectrum of
-harmonics, (2) properly describes their propagation, and (3) correctly accounts
-for the physics of wave breaking/saturation.
-",0,1,0,0,0,0
-19440,A Statistical Model for Ideal Team Selection for A National Cricket Squad,"  Cricket is a game played between two teams which consists of eleven players
-each. Nowadays cricket game is becoming more and more popular in Bangladesh and
-other South Asian Countries. Before a match people are very enthusiastic about
-team squads and ""Which players are playing today?"", ""How well will MR. X
-perform today?"" are the million dollar questions before a big match. This
-article will propose a method using statistical data analysis for recommending
-a national team squad. Recent match scorecards for domestic and international
-matches played by a specific team in recent years are used to recommend the
-ideal squad. Impact point or rating points of all players in different
-conditions are calculated and the best ones from different categories are
-chosen to form optimal line-ups. To evaluate the efficiency of impact point
-system, it will be tested with real time match data to see how much accuracy it
-gives.
-",0,0,0,1,0,0
-19441,The geometry of some generalized affine Springer fibers,"  We study basic geometric properties of some group analogue of affine Springer
-fibers and compare with the classical Lie algebra affine Springer fibers. The
-main purpose is to formulate a conjecture that relates the number of
-irreducible components of such varieties for a reductive group $G$ to certain
-weight multiplicities defined by the Langlands dual group $\hat{G}$. We prove
-our conjecture in the case of unramified conjugacy class.
-",0,0,1,0,0,0
-19442,Modelling of a Permanent Magnet Synchronous Machine Using Isogeometric Analysis,"  Isogeometric analysis (IGA) is used to simulate a permanent magnet
-synchronous machine. IGA uses non-uniform rational B-splines to parametrise the
-domain and to approximate the solution space, thus allowing for the exact
-description of the geometries even on the coarsest level of mesh refinement.
-Given the properties of the isogeometric basis functions, this choice
-guarantees a higher accuracy than the classical finite element method.
-For dealing with the different stator and rotor topologies, the domain is
-split into two non-overlapping parts on which Maxwell's equations are solved
-independently in the context of a classical Dirichlet-to-Neumann domain
-decomposition scheme. The results show good agreement with the ones obtained by
-the classical finite element approach.
-",1,0,0,0,0,0
-19443,Learning and Trust in Auction Markets,"  In this paper, we study behavior of bidders in an experimental launch of a
-new advertising auction platform by Zillow, as Zillow switched from negotiated
-contracts to using auctions in several geographically isolated markets. A
-unique feature of this experiment is that the bidders in this market are real
-estate agents that bid on their own behalf, not using third-party
-intermediaries. To help bidders, Zillow also provided a recommendation tool
-that suggested a bid for each bidder.
-Our main focus in this paper is on the decisions of bidders whether or not to
-adopt the platform-provided bid recommendation. We observe that a significant
-proportion of bidders do not use the recommended bid. Using the bid history of
-the agents we infer their value, and compare the agents' regret with their
-actual bidding history with results they would have obtained following the
-recommendation. We find that for half of the agents not following the
-recommendation, the increased effort of experimenting with alternate bids
-results in increased regret, i.e., they get decreased net value out of the
-system. The proportion of agents not following the recommendation slowly
-declines as markets mature, but it remains large in most markets that we
-observe. We argue that the main reason for this phenomenon is the lack of trust
-in the platform-provided tool.
-Our work provides an empirical insight into possible design choices for
-auction-based online advertising platforms. While search advertising platforms
-(such as Google or Bing) allow bidders to submit bids on their own, many
-display advertising platforms (such as Facebook) optimize bids on bidders'
-behalf and eliminate the need for bids. Our empirical analysis shows that the
-latter approach is preferred for markets where bidders are individuals, who
-don't have access to third party tools, and who may question the fairness of
-platform-provided suggestions.
-",1,0,0,0,0,0
-19444,"Like trainer, like bot? Inheritance of bias in algorithmic content moderation","  The internet has become a central medium through which `networked publics'
-express their opinions and engage in debate. Offensive comments and personal
-attacks can inhibit participation in these spaces. Automated content moderation
-aims to overcome this problem using machine learning classifiers trained on
-large corpora of texts manually annotated for offence. While such systems could
-help encourage more civil debate, they must navigate inherently normatively
-contestable boundaries, and are subject to the idiosyncratic norms of the human
-raters who provide the training data. An important objective for platforms
-implementing such measures might be to ensure that they are not unduly biased
-towards or against particular norms of offence. This paper provides some
-exploratory methods by which the normative biases of algorithmic content
-moderation systems can be measured, by way of a case study using an existing
-dataset of comments labelled for offence. We train classifiers on comments
-labelled by different demographic subsets (men and women) to understand how
-differences in conceptions of offence between these groups might affect the
-performance of the resulting models on various test sets. We conclude by
-discussing some of the ethical choices facing the implementers of algorithmic
-moderation systems, given various desired levels of diversity of viewpoints
-amongst discussion participants.
-",1,0,0,0,0,0
-19445,Holographic Neural Architectures,"  Representation learning is at the heart of what makes deep learning
-effective. In this work, we introduce a new framework for representation
-learning that we call ""Holographic Neural Architectures"" (HNAs). In the same
-way that an observer can experience the 3D structure of a holographed object by
-looking at its hologram from several angles, HNAs derive Holographic
-Representations from the training set. These representations can then be
-explored by moving along a continuous bounded single dimension. We show that
-HNAs can be used to make generative networks, state-of-the-art regression
-models and that they are inherently highly resistant to noise. Finally, we
-argue that because of their denoising abilities and their capacity to
-generalize well from very few examples, models based upon HNAs are particularly
-well suited for biological applications where training examples are rare or
-noisy.
-",0,0,0,1,1,0
-19446,An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists,"  Topological Data Analysis (tda) is a recent and fast growing eld providing a
-set of new topological and geometric tools to infer relevant features for
-possibly complex data. This paper is a brief introduction, through a few
-selected topics, to basic fundamental and practical aspects of tda for non
-experts. 1 Introduction and motivation Topological Data Analysis (tda) is a
-recent eld that emerged from various works in applied (algebraic) topology and
-computational geometry during the rst decade of the century. Although one can
-trace back geometric approaches for data analysis quite far in the past, tda
-really started as a eld with the pioneering works of Edelsbrunner et al. (2002)
-and Zomorodian and Carlsson (2005) in persistent homology and was popularized
-in a landmark paper in 2009 Carlsson (2009). tda is mainly motivated by the
-idea that topology and geometry provide a powerful approach to infer robust
-qualitative, and sometimes quantitative, information about the structure of
-data-see, e.g. Chazal (2017). tda aims at providing well-founded mathematical,
-statistical and algorithmic methods to infer, analyze and exploit the complex
-topological and geometric structures underlying data that are often represented
-as point clouds in Euclidean or more general metric spaces. During the last few
-years, a considerable eort has been made to provide robust and ecient data
-structures and algorithms for tda that are now implemented and available and
-easy to use through standard libraries such as the Gudhi library (C++ and
-Python) Maria et al. (2014) and its R software interface Fasy et al. (2014a).
-Although it is still rapidly evolving, tda now provides a set of mature and
-ecient tools that can be used in combination or complementary to other data
-sciences tools. The tdapipeline. tda has recently known developments in various
-directions and application elds. There now exist a large variety of methods
-inspired by topological and geometric approaches. Providing a complete overview
-of all these existing approaches is beyond the scope of this introductory
-survey. However, most of them rely on the following basic and standard pipeline
-that will serve as the backbone of this paper: 1. The input is assumed to be a
-nite set of points coming with a notion of distance-or similarity between them.
-This distance can be induced by the metric in the ambient space (e.g. the
-Euclidean metric when the data are embedded in R d) or come as an intrinsic
-metric dened by a pairwise distance matrix. The denition of the metric on the
-data is usually given as an input or guided by the application. It is however
-important to notice that the choice of the metric may be critical to reveal
-interesting topological and geometric features of the data.
-",1,0,1,1,0,0
-19447,Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures,"  The resemblance between the methods used in quantum-many body physics and in
-machine learning has drawn considerable attention. In particular, tensor
-networks (TNs) and deep learning architectures bear striking similarities to
-the extent that TNs can be used for machine learning. Previous results used
-one-dimensional TNs in image recognition, showing limited scalability and
-flexibilities. In this work, we train two-dimensional hierarchical TNs to solve
-image recognition problems, using a training algorithm derived from the
-multipartite entanglement renormalization ansatz. This approach introduces
-novel mathematical connections among quantum many-body physics, quantum
-information theory, and machine learning. While keeping the TN unitary in the
-training phase, TN states are defined, which optimally encode classes of images
-into quantum many-body states. We study the quantum features of the TN states,
-including quantum entanglement and fidelity. We find these quantities could be
-novel properties that characterize the image classes, as well as the machine
-learning tasks. Our work could contribute to the research on
-identifying/modeling quantum artificial intelligences.
-",0,1,0,1,0,0
-19448,Hierarchical Imitation and Reinforcement Learning,"  We study how to effectively leverage expert feedback to learn sequential
-decision-making policies. We focus on problems with sparse rewards and long
-time horizons, which typically pose significant challenges in reinforcement
-learning. We propose an algorithmic framework, called hierarchical guidance,
-that leverages the hierarchical structure of the underlying problem to
-integrate different modes of expert interaction. Our framework can incorporate
-different combinations of imitation learning (IL) and reinforcement learning
-(RL) at different levels, leading to dramatic reductions in both expert effort
-and cost of exploration. Using long-horizon benchmarks, including Montezuma's
-Revenge, we demonstrate that our approach can learn significantly faster than
-hierarchical RL, and be significantly more label-efficient than standard IL. We
-also theoretically analyze labeling cost for certain instantiations of our
-framework.
-",0,0,0,1,0,0
-19449,Social learning in a simple task allocation game,"  We investigate the effects of social interactions in task al- location using
-Evolutionary Game Theory (EGT). We propose a simple task-allocation game and
-study how different learning mechanisms can give rise to specialised and non-
-specialised colonies under different ecological conditions. By combining
-agent-based simulations and adaptive dynamics we show that social learning can
-result in colonies of generalists or specialists, depending on ecological
-parameters. Agent-based simulations further show that learning dynamics play a
-crucial role in task allocation. In particular, introspective individual
-learning readily favours the emergence of specialists, while a process
-resembling task recruitment favours the emergence of generalists.
-",1,0,0,0,0,0
-19450,Guided projections for analysing the structure of high-dimensional data,"  A powerful data transformation method named guided projections is proposed
-creating new possibilities to reveal the group structure of high-dimensional
-data in the presence of noise variables. Utilising projections onto a space
-spanned by a selection of a small number of observations allows measuring the
-similarity of other observations to the selection based on orthogonal and score
-distances. Observations are iteratively exchanged from the selection creating a
-non-random sequence of projections which we call guided projections. In
-contrast to conventional projection pursuit methods, which typically identify a
-low-dimensional projection revealing some interesting features contained in the
-data, guided projections generate a series of projections that serve as a basis
-not just for diagnostic plots but to directly investigate the group structure
-in data. Based on simulated data we identify the strengths and limitations of
-guided projections in comparison to commonly employed data transformation
-methods. We further show the relevance of the transformation by applying it to
-real-world data sets.
-",0,0,0,1,0,0
-19451,Limits of Predictability of Cascading Overload Failures in Spatially-Embedded Networks with Distributed Flows,"  Cascading failures are a critical vulnerability of complex information or
-infrastructure networks. Here we investigate the properties of load-based
-cascading failures in real and synthetic spatially-embedded network structures,
-and propose mitigation strategies to reduce the severity of damages caused by
-such failures. We introduce a stochastic method for optimal heterogeneous
-distribution of resources (node capacities) subject to a fixed total cost.
-Additionally, we design and compare the performance of networks with N-stable
-and (N-1)-stable network-capacity allocations by triggering cascades using
-various real-world node-attack and node-failure scenarios. We show that failure
-mitigation through increased node protection can be effectively achieved
-against single node failures. However, mitigating against multiple node
-failures is much more difficult due to the combinatorial increase in possible
-failures. We analyze the robustness of the system with increasing protection,
-and find that a critical tolerance exists at which the system undergoes a phase
-transition, and above which the network almost completely survives an attack.
-Moreover, we show that cascade-size distributions measured in this region
-exhibit a power-law decay. Finally, we find a strong correlation between
-cascade sizes induced by individual nodes and sets of nodes. We also show that
-network topology alone is a weak factor in determining the progression of
-cascading failures.
-",1,1,0,0,0,0
-19452,Strong electron-hole symmetric Rashba spin-orbit coupling in graphene/monolayer transition metal dichalcogenide heterostructures,"  Despite its extremely weak intrinsic spin-orbit coupling (SOC), graphene has
-been shown to acquire considerable SOC by proximity coupling with exfoliated
-transition metal dichalcogenides (TMDs). Here we demonstrate strong induced
-Rashba SOC in graphene that is proximity coupled to a monolayer TMD film, MoS2
-or WSe2, grown by chemical vapor deposition with drastically different Fermi
-level positions. Graphene/TMD heterostructures are fabricated with a
-pickup-transfer technique utilizing hexagonal boron nitride, which serves as a
-flat template to promote intimate contact and therefore a strong interfacial
-interaction between TMD and graphene as evidenced by quenching of the TMD
-photoluminescence. We observe strong induced graphene SOC that manifests itself
-in a pronounced weak anti-localization (WAL) effect in the graphene
-magnetoconductance. The spin relaxation rate extracted from the WAL analysis
-varies linearly with the momentum scattering time and is independent of the
-carrier type. This indicates a dominantly Dyakonov-Perel spin relaxation
-mechanism caused by the induced Rashba SOC. Our analysis yields a Rashba SOC
-energy of ~1.5 meV in graphene/WSe2 and ~0.9 meV in graphene/MoS2,
-respectively. The nearly electron-hole symmetric nature of the induced Rashba
-SOC provides a clue to possible underlying SOC mechanisms.
-",0,1,0,0,0,0
-19453,Robust Statistics for Image Deconvolution,"  We present a blind multiframe image-deconvolution method based on robust
-statistics. The usual shortcomings of iterative optimization of the likelihood
-function are alleviated by minimizing the M-scale of the residuals, which
-achieves more uniform convergence across the image. We focus on the
-deconvolution of astronomical images, which are among the most challenging due
-to their huge dynamic ranges and the frequent presence of large noise-dominated
-regions in the images. We show that high-quality image reconstruction is
-possible even in super-resolution and without the use of traditional
-regularization terms. Using a robust \r{ho}-function is straightforward to
-implement in a streaming setting and, hence our method is applicable to the
-large volumes of astronomy images. The power of our method is demonstrated on
-observations from the Sloan Digital Sky Survey (Stripe 82) and we briefly
-discuss the feasibility of a pipeline based on Graphical Processing Units for
-the next generation of telescope surveys.
-",1,1,0,0,0,0
-19454,Nash and Wardrop equilibria in aggregative games with coupling constraints,"  We consider the framework of aggregative games, in which the cost function of
-each agent depends on his own strategy and on the average population strategy.
-As first contribution, we investigate the relations between the concepts of
-Nash and Wardrop equilibrium. By exploiting a characterization of the two
-equilibria as solutions of variational inequalities, we bound their distance
-with a decreasing function of the population size. As second contribution, we
-propose two decentralized algorithms that converge to such equilibria and are
-capable of coping with constraints coupling the strategies of different agents.
-Finally, we study the applications of charging of electric vehicles and of
-route choice on a road network.
-",1,0,1,0,0,0
-19455,Magnetoresistance in the superconducting state at the (111) LaAlO$_3$/SrTiO$_3$ interface,"  Condensed matter systems that simultaneously exhibit superconductivity and
-ferromagnetism are rare due the antagonistic relationship between conventional
-spin-singlet superconductivity and ferromagnetic order. In materials in which
-superconductivity and magnetic order is known to coexist (such as some
-heavy-fermion materials), the superconductivity is thought to be of an
-unconventional nature. Recently, the conducting gas that lives at the interface
-between the perovskite band insulators LaAlO$_3$ (LAO) and SrTiO$_3$ (STO) has
-also been shown to host both superconductivity and magnetism. Most previous
-research has focused on LAO/STO samples in which the interface is in the (001)
-crystal plane. Relatively little work has focused on the (111) crystal
-orientation, which has hexagonal symmetry at the interface, and has been
-predicted to have potentially interesting topological properties, including
-unconventional superconducting pairing states. Here we report measurements of
-the magnetoresistance of (111) LAO/STO heterostructures at temperatures at
-which they are also superconducting. As with the (001) structures, the
-magnetoresistance is hysteretic, indicating the coexistence of magnetism and
-superconductivity, but in addition, we find that this magnetoresistance is
-anisotropic. Such an anisotropic response is completely unexpected in the
-superconducting state, and suggests that (111) LAO/STO heterostructures may
-support unconventional superconductivity.
-",0,1,0,0,0,0
-19456,Variational autoencoders for tissue heterogeneity exploration from (almost) no preprocessed mass spectrometry imaging data,"  The paper presents the application of Variational Autoencoders (VAE) for data
-dimensionality reduction and explorative analysis of mass spectrometry imaging
-data (MSI). The results confirm that VAEs are capable of detecting the patterns
-associated with the different tissue sub-types with performance than standard
-approaches.
-",1,0,0,1,0,0
-19457,Learning General Latent-Variable Graphical Models with Predictive Belief Propagation and Hilbert Space Embeddings,"  In this paper, we propose a new algorithm for learning general
-latent-variable probabilistic graphical models using the techniques of
-predictive state representation, instrumental variable regression, and
-reproducing-kernel Hilbert space embeddings of distributions. Under this new
-learning framework, we first convert latent-variable graphical models into
-corresponding latent-variable junction trees, and then reduce the hard
-parameter learning problem into a pipeline of supervised learning problems,
-whose results will then be used to perform predictive belief propagation over
-the latent junction tree during the actual inference procedure. We then give
-proofs of our algorithm's correctness, and demonstrate its good performance in
-experiments on one synthetic dataset and two real-world tasks from
-computational biology and computer vision - classifying DNA splice junctions
-and recognizing human actions in videos.
-",1,0,0,1,0,0
-19458,Can interacting dark energy solve the $H_0$ tension?,"  The answer is Yes! We indeed find that interacting dark energy can alleviate
-the current tension on the value of the Hubble constant $H_0$ between the
-Cosmic Microwave Background anisotropies constraints obtained from the Planck
-satellite and the recent direct measurements reported by Riess et al. 2016. The
-combination of these two datasets points towards an evidence for a non-zero
-dark matter-dark energy coupling $\xi$ at more than two standard deviations,
-with $\xi=-0.26_{-0.12}^{+0.16}$ at $95\%$ CL. However the $H_0$ tension is
-better solved when the equation of state of the interacting dark energy
-component is allowed to freely vary, with a phantom-like equation of state
-$w=-1.184\pm0.064$ (at $68 \%$ CL), ruling out the pure cosmological constant
-case, $w=-1$, again at more than two standard deviations. When Planck data are
-combined with external datasets, as BAO, JLA Supernovae Ia luminosity
-distances, cosmic shear or lensing data, we find good consistency with the
-cosmological constant scenario and no compelling evidence for a dark
-matter-dark energy coupling.
-",0,1,0,0,0,0
-19459,Fast Switching Dual Fabry-Perot Cavity Optical Refractometry - Methodologies for Accurate Assessment of Gas Density,"  Dual Fabry-Perot cavity based optical refractometry (DFPC-OR) has a high
-potential for assessments of gas density. However, drifts of the FP cavity
-often limit its performance. We show that by the use of two narrow-linewidth
-fiber lasers locked to two high finesse cavities and Allan-Werle plots that
-drift-free DFPC-OR can be obtained for short measurement times (for which the
-drifts of the cavity can be disregarded). Based on this, a novel strategy,
-termed fast switching DFPC-OR (FS-DFPC-OR), is presented. A set of novel
-methodologies for assessment of both gas density and flow rates (in particular
-from small leaks) that are not restricted by the conventional limitations
-imposed by the drifts of the cavity are presented. The methodologies deal with
-assessments in both open and closed (finite-sized) compartments. They
-circumvent the problem with volumetric expansion, i.e. that the gas density in
-a measurement cavity is not the same as that in the closed external compartment
-that should be assessed, by performing a pair of measurements in rapid
-succession; the first one serves the purpose of assessing the density of the
-gas that has been transferred into the measurement cavity by the gas
-equilibration process, while the 2nd is used to automatically calibrate the
-system with respect to the relative volumes of the measurement cavity and the
-external compartment. The methodologies for assessments of leak rates comprise
-triple cavity evacuation assessments, comprising two measurements performed in
-rapid succession, supplemented by a 3rd measurement a certain time thereafter.
-A clear explanation of why the technique has such a small temperature
-dependence is given. It is concluded that FS-DFPC-OR constitutes a novel
-strategy that can be used for precise and accurate assessment of gas number
-density and gas flows under a variety of conditions, in particular
-non-temperature stabilized ones.
-",0,1,0,0,0,0
-19460,A Large Self-Annotated Corpus for Sarcasm,"  We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for
-sarcasm research and for training and evaluating systems for sarcasm detection.
-The corpus has 1.3 million sarcastic statements -- 10 times more than any
-previous dataset -- and many times more instances of non-sarcastic statements,
-allowing for learning in both balanced and unbalanced label regimes. Each
-statement is furthermore self-annotated -- sarcasm is labeled by the author,
-not an independent annotator -- and provided with user, topic, and conversation
-context. We evaluate the corpus for accuracy, construct benchmarks for sarcasm
-detection, and evaluate baseline methods.
-",1,0,0,0,0,0
-19461,Aerosol properties in the atmospheres of extrasolar giant planets,"  We use a model of aerosol microphysics to investigate the impact of
-high-altitude photochemical aerosols on the transmission spectra and
-atmospheric properties of close-in exoplanets, such as HD209458b and HD189733b.
-The results depend strongly on the temperature profiles in the middle and upper
-atmosphere that are poorly understood. Nevertheless, our model of HD189733b,
-based on the most recently inferred temperature profiles, produces an aerosol
-distribution that matches the observed transmission spectrum. We argue that the
-hotter temperature of HD209458b inhibits the production of high-altitude
-aerosols and leads to the appearance of a more clear atmosphere than on
-HD189733b. The aerosol distribution also depends on the particle composition,
-the photochemical production, and the atmospheric mixing. Due to degeneracies
-among these inputs, current data cannot constrain the aerosol properties in
-detail. Instead, our work highlights the role of different factors in
-controlling the aerosol distribution that will prove useful in understanding
-different observations, including those from future missions. For the
-atmospheric mixing efficiency suggested by general circulation models (GCMs) we
-find that aerosol particles are small ($\sim$nm) and probably spherical. We
-further conclude that composition based on complex hydrocarbons (soots) is the
-most likely candidate to survive the high temperatures in hot Jupiter
-atmospheres. Such particles would have a significant impact on the energy
-balance of HD189733b's atmosphere and should be incorporated in future studies
-of atmospheric structure. We also evaluate the contribution of external sources
-in the photochemical aerosol formation and find that their spectral signature
-is not consistent with observations.
-",0,1,0,0,0,0
-19462,Influence of random opinion change in complex networks,"  Opinion formation in the population has attracted extensive research
-interest. Various models have been introduced and studied, including the ones
-with individuals' free will allowing them to change their opinions. Such
-models, however, have not taken into account the fact that individuals with
-different opinions may have different levels of loyalty, and consequently,
-different probabilities of changing their opinions. In this work, we study on
-how the non-uniform distribution of the opinion changing probability may affect
-the final state of opinion distribution. By simulating a few different cases
-with different symmetric and asymmetric non-uniform patterns of opinion
-changing probabilities, we demonstrate the significant effects that the
-different loyalty levels of different opinions have on the final state of the
-opinion distribution.
-",1,1,0,0,0,0
-19463,We Are Not Your Real Parents: Telling Causal from Confounded using MDL,"  Given data over variables $(X_1,...,X_m, Y)$ we consider the problem of
-finding out whether $X$ jointly causes $Y$ or whether they are all confounded
-by an unobserved latent variable $Z$. To do so, we take an
-information-theoretic approach based on Kolmogorov complexity. In a nutshell,
-we follow the postulate that first encoding the true cause, and then the
-effects given that cause, results in a shorter description than any other
-encoding of the observed variables.
-The ideal score is not computable, and hence we have to approximate it. We
-propose to do so using the Minimum Description Length (MDL) principle. We
-compare the MDL scores under the models where $X$ causes $Y$ and where there
-exists a latent variables $Z$ confounding both $X$ and $Y$ and show our scores
-are consistent. To find potential confounders we propose using latent factor
-modeling, in particular, probabilistic PCA (PPCA).
-Empirical evaluation on both synthetic and real-world data shows that our
-method, CoCa, performs very well -- even when the true generating process of
-the data is far from the assumptions made by the models we use. Moreover, it is
-robust as its accuracy goes hand in hand with its confidence.
-",1,0,0,1,0,0
-19464,Evolution of eccentricity and inclination of hot protoplanets embedded in radiative discs,"  We study the evolution of the eccentricity and inclination of protoplanetary
-embryos and low-mass protoplanets (from a fraction of an Earth mass to a few
-Earth masses) embedded in a protoplanetary disc, by means of three dimensional
-hydrodynamics calculations with radiative transfer in the diffusion limit. When
-the protoplanets radiate in the surrounding disc the energy released by the
-accretion of solids, their eccentricity and inclination experience a growth
-toward values which depend on the luminosity to mass ratio of the planet, which
-are comparable to the disc's aspect ratio and which are reached over timescales
-of a few thousand years. This growth is triggered by the appearance of a hot,
-under-dense region in the vicinity of the planet. The growth rate of the
-eccentricity is typically three times larger than that of the inclination. In
-long term calculations, we find that the excitation of eccentricity and the
-excitation of inclination are not independent. In the particular case in which
-a planet has initially a very small eccentricity and inclination, the
-eccentricity largely overruns the inclination. When the eccentricity reaches
-its asymptotic value, the growth of inclination is quenched, yielding an
-eccentric orbit with a very low inclination. As a side result, we find that the
-eccentricity and inclination of non-luminous planets are damped more vigorously
-in radiative discs than in isothermal discs.
-",0,1,0,0,0,0
-19465,Definitions in mathematics,"  We discuss various forms of definitions in mathematics and describe rules
-governing them.
-",0,0,1,0,0,0
-19466,Excellence in prime characteristic,"  Fix any field $K$ of characteristic $p$ such that $[K:K^p]$ is finite. We
-discuss excellence for Noetherian domains whose fraction field is $K$, showing
-for example, that $R$ is excellent if and only if the Frobenius map is finite
-on $R$. Furthermore, we show $R$ is excellent if and only if it admits some
-non-zero $p^{-e}$-linear map for $R$ or equivalently, that $R$ is a solid
-$R$-algebra under Frobenius. In particular, this means that Frobenius split
-Noetherian domains that are generically $F$-finite are always excellent. We
-also show that non-excellent rings are abundant and easy to construct in prime
-characteristic, even within the world of regular local rings of dimension one
-in function fields. This paper is mostly expository in nature.
-",0,0,1,0,0,0
-19467,Asymptotically optimal private estimation under mean square loss,"  We consider the minimax estimation problem of a discrete distribution with
-support size $k$ under locally differential privacy constraints. A
-privatization scheme is applied to each raw sample independently, and we need
-to estimate the distribution of the raw samples from the privatized samples. A
-positive number $\epsilon$ measures the privacy level of a privatization
-scheme.
-In our previous work (arXiv:1702.00610), we proposed a family of new
-privatization schemes and the corresponding estimator. We also proved that our
-scheme and estimator are order optimal in the regime $e^{\epsilon} \ll k$ under
-both $\ell_2^2$ and $\ell_1$ loss. In other words, for a large number of
-samples the worst-case estimation loss of our scheme was shown to differ from
-the optimal value by at most a constant factor. In this paper, we eliminate
-this gap by showing asymptotic optimality of the proposed scheme and estimator
-under the $\ell_2^2$ (mean square) loss. More precisely, we show that for any
-$k$ and $\epsilon,$ the ratio between the worst-case estimation loss of our
-scheme and the optimal value approaches $1$ as the number of samples tends to
-infinity.
-",1,0,1,1,0,0
-19468,Coulomb repulsion of holes and competition between d_{x^2-y^2}-wave and s-wave parings in cuprate superconductors,"  The effect of the Coulomb repulsion of holes on the Cooper instability in an
-ensemble of spin-polaron quasiparticles has been analyzed, taking into account
-the peculiarities of the crystallographic structure of the CuO$_2$ plane, which
-are associated with the presence of two oxygen ions and one copper ion in the
-unit cell, as well as the strong spin-fermion coupling. The investigation of
-the possibility of implementation superconducting phases with d-wave and s-wave
-pairing of the order parameter symmetry has shown that in the entire doping
-region only the d-wave pairing satisfies the self-consistency equations, while
-there is no solution for the s-wave pairing. This result completely corresponds
-to the experimental data on cuprate HTSC. It has been demonstrated analytically
-that the intersite Coulomb interaction does not affect the superconducting
-d-wave pairing, because its Fourier transform $V_q$ does not appear in the
-kernel of the corresponding integral equation.
-",0,1,0,0,0,0
-19469,Modeling Game Avatar Synergy and Opposition through Embedding in Multiplayer Online Battle Arena Games,"  Multiplayer Online Battle Arena (MOBA) games have received increasing
-worldwide popularity recently. In such games, players compete in teams against
-each other by controlling selected game avatars, each of which is designed with
-different strengths and weaknesses. Intuitively, putting together game avatars
-that complement each other (synergy) and suppress those of opponents
-(opposition) would result in a stronger team. In-depth understanding of synergy
-and opposition relationships among game avatars benefits player in making
-decisions in game avatar drafting and gaining better prediction of match
-events. However, due to intricate design and complex interactions between game
-avatars, thorough understanding of their relationships is not a trivial task.
-In this paper, we propose a latent variable model, namely Game Avatar
-Embedding (GAE), to learn avatars' numerical representations which encode
-synergy and opposition relationships between pairs of avatars. The merits of
-our model are twofold: (1) the captured synergy and opposition relationships
-are sensible to experienced human players' perception; (2) the learned
-numerical representations of game avatars allow many important downstream
-tasks, such as similar avatar search, match outcome prediction, and avatar pick
-recommender. To our best knowledge, no previous model is able to simultaneously
-support both features. Our quantitative and qualitative evaluations on real
-match data from three commercial MOBA games illustrate the benefits of our
-model.
-",1,0,0,0,0,0
-19470,Design and Development of Effective Transmission Mechanisms on a Tendon Driven Hand Orthosis for Stroke Patients,"  Tendon-driven hand orthoses have advantages over exoskeletons with respect to
-wearability and safety because of their low-profile design and ability to fit a
-range of patients without requiring custom joint alignment. However, no
-existing study on a wearable tendon-driven hand orthosis for stroke patients
-presents evidence that such devices can overcome spasticity given repeated use
-and fatigue, or discusses transmission efficiency. In this study, we propose
-two designs that provide effective force transmission by increasing moment arms
-around finger joints. We evaluate the designs with geometric models and
-experiment using a 3D-printed artificial finger to find force and joint angle
-characteristics of the suggested structures. We also perform clinical tests
-with stroke patients to demonstrate the feasibility of the designs. The testing
-supports the hypothesis that the proposed designs efficiently elicit extension
-of the digits in patients with spasticity as compared to existing baselines.
-",1,0,0,0,0,0
-19471,"eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys","  For years security machine learning research has promised to obviate the need
-for signature based detection by automatically learning to detect indicators of
-attack. Unfortunately, this vision hasn't come to fruition: in fact, developing
-and maintaining today's security machine learning systems can require
-engineering resources that are comparable to that of signature-based detection
-systems, due in part to the need to develop and continuously tune the
-""features"" these machine learning systems look at as attacks evolve. Deep
-learning, a subfield of machine learning, promises to change this by operating
-on raw input signals and automating the process of feature design and
-extraction. In this paper we propose the eXpose neural network, which uses a
-deep learning approach we have developed to take generic, raw short character
-strings as input (a common case for security inputs, which include artifacts
-like potentially malicious URLs, file paths, named pipes, named mutexes, and
-registry keys), and learns to simultaneously extract features and classify
-using character-level embeddings and convolutional neural network. In addition
-to completely automating the feature design and extraction process, eXpose
-outperforms manual feature extraction based baselines on all of the intrusion
-detection problems we tested it on, yielding a 5%-10% detection rate gain at
-0.1% false positive rate compared to these baselines.
-",1,0,0,0,0,0
-19472,"A Software Reuse Approach and Its Effect On Software Quality, An Empirical Study for The Software Industry","  Software reusability has become much interesting because of increased quality
-and reduce cost. A good process of software reuse leads to enhance the
-reliability, productivity, quality and the reduction of time and cost. Current
-reuse techniques focuses on the reuse of software artifact which grounded on
-anticipated functionality whereas, the non-functional (quality) aspect are also
-important. So, Software reusability used here to expand quality and
-productivity of software. It improves overall quality of software in minimum
-energy and time. Main objective of this study was to present a reuse approach
-that discovered that how software reuse improves the quality in Software
-Industry. The V&V technique used for this purpose which is part of software
-quality management process, it checks the quality and correctness during the
-software life cycle. A survey study conducted as QUESTIONAIR to find the impact
-of reuse approach on quality attributes which are requirement specification and
-design specification. Other quality enhancement techniques like ad hoc, CBSE,
-MBSE, Product line, COTS reuse checked on existing software industry. Results
-analyzed with the help of MATLAB tool as it provides effective data management,
-wide range of options, better output organization, to check weather quality
-enhancement technique is affected due to reusability and how quality will
-improve.
-",1,0,0,0,0,0
-19473,Portfolio Optimization with Nondominated Priors and Unbounded Parameters,"  We consider classical Merton problem of terminal wealth maximization in
-finite horizon. We assume that the drift of the stock is following
-Ornstein-Uhlenbeck process and the volatility of it is following GARCH(1)
-process. In particular, both mean and volatility are unbounded. We assume that
-there is Knightian uncertainty on the parameters of both mean and volatility.
-We take that the investor has logarithmic utility function, and solve the
-corresponding utility maximization problem explicitly. To the best of our
-knowledge, this is the first work on utility maximization with unbounded mean
-and volatility in Knightian uncertainty under nondominated priors.
-",0,0,0,0,0,1
-19474,Measuring heterogeneity in urban expansion via spatial entropy,"  The lack of efficiency in urban diffusion is a debated issue, important for
-biologists, urban specialists, planners and statisticians, both in developed
-and new developing countries. Many approaches have been considered to measure
-urban sprawl, i.e. chaotic urban expansion; such idea of chaos is here linked
-to the concept of entropy. Entropy, firstly introduced in information theory,
-rapidly became a standard tool in ecology, biology and geography to measure the
-degree of heterogeneity among observations; in these contexts, entropy measures
-should include spatial information. The aim of this paper is to employ a
-rigorous spatial entropy based approach to measure urban sprawl associated to
-the diffusion of metropolitan cities. In order to assess the performance of the
-considered measures, a comparative study is run over alternative urban
-scenarios; afterwards, measures are used to quantify the degree of disorder in
-the urban expansion of three cities in Europe. Results are easily interpretable
-and can be used both as an absolute measure of urban sprawl and for comparison
-over space and time.
-",0,0,0,1,0,0
-19475,Bayesian Cluster Enumeration Criterion for Unsupervised Learning,"  We derive a new Bayesian Information Criterion (BIC) by formulating the
-problem of estimating the number of clusters in an observed data set as
-maximization of the posterior probability of the candidate models. Given that
-some mild assumptions are satisfied, we provide a general BIC expression for a
-broad class of data distributions. This serves as a starting point when
-deriving the BIC for specific distributions. Along this line, we provide a
-closed-form BIC expression for multivariate Gaussian distributed variables. We
-show that incorporating the data structure of the clustering problem into the
-derivation of the BIC results in an expression whose penalty term is different
-from that of the original BIC. We propose a two-step cluster enumeration
-algorithm. First, a model-based unsupervised learning algorithm partitions the
-data according to a given set of candidate models. Subsequently, the number of
-clusters is determined as the one associated with the model for which the
-proposed BIC is maximal. The performance of the proposed two-step algorithm is
-tested using synthetic and real data sets.
-",1,0,1,1,0,0
-19476,Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation,"  Combinatorial interaction testing is an important software testing technique
-that has seen lots of recent interest. It can reduce the number of test cases
-needed by considering interactions between combinations of input parameters.
-Empirical evidence shows that it effectively detects faults, in particular, for
-highly configurable software systems. In real-world software testing, the input
-variables may vary in how strongly they interact, variable strength
-combinatorial interaction testing (VS-CIT) can exploit this for higher
-effectiveness. The generation of variable strength test suites is a
-non-deterministic polynomial-time (NP) hard computational problem
-\cite{BestounKamalFuzzy2017}. Research has shown that stochastic
-population-based algorithms such as particle swarm optimization (PSO) can be
-efficient compared to alternatives for VS-CIT problems. Nevertheless, they
-require detailed control for the exploitation and exploration trade-off to
-avoid premature convergence (i.e. being trapped in local optima) as well as to
-enhance the solution diversity. Here, we present a new variant of PSO based on
-Mamdani fuzzy inference system
-\cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive
-selection of its global and local search operations. We detail the design of
-this combined algorithm and evaluate it through experiments on multiple
-synthetic and benchmark problems. We conclude that fuzzy adaptive selection of
-global and local search operations is, at least, feasible as it performs only
-second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the
-best mean test suite size, the fuzzy adaptation even outperforms DPSO
-occasionally. We discuss the reasons behind this performance and outline
-relevant areas of future work.
-",1,0,0,0,0,0
-19477,Is Life Most Likely Around Sun-like Stars?,"  We consider the habitability of Earth-analogs around stars of different
-masses, which is regulated by the stellar lifetime, stellar wind-induced
-atmospheric erosion, and biologically active ultraviolet (UV) irradiance. By
-estimating the timescales associated with each of these processes, we show that
-they collectively impose limits on the habitability of Earth-analogs. We
-conclude that planets orbiting most M-dwarfs are not likely to host life, and
-that the highest probability of complex biospheres is for planets around K- and
-G-type stars. Our analysis suggests that the current existence of life near the
-Sun is slightly unusual, but not significantly anomalous.
-",0,1,0,0,0,0
-19478,Maximum Entropy Generators for Energy-Based Models,"  Unsupervised learning is about capturing dependencies between variables and
-is driven by the contrast between the probable vs. improbable configurations of
-these variables, often either via a generative model that only samples probable
-ones or with an energy function (unnormalized log-density) that is low for
-probable ones and high for improbable ones. Here, we consider learning both an
-energy function and an efficient approximate sampling mechanism. Whereas the
-discriminator in generative adversarial networks (GANs) learns to separate data
-and generator samples, introducing an entropy maximization regularizer on the
-generator can turn the interpretation of the critic into an energy function,
-which separates the training distribution from everything else, and thus can be
-used for tasks like anomaly or novelty detection. Then, we show how Markov
-Chain Monte Carlo can be done in the generator latent space whose samples can
-be mapped to data space, producing better samples. These samples are used for
-the negative phase gradient required to estimate the log-likelihood gradient of
-the data space energy function. To maximize entropy at the output of the
-generator, we take advantage of recently introduced neural estimators of mutual
-information. We find that in addition to producing a useful scoring function
-for anomaly detection, the resulting approach produces sharp samples while
-covering the modes well, leading to high Inception and Frechet scores.
-",1,0,0,1,0,0
-19479,A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols,"  Convolutional neural networks (CNNs) have shown promising results on several
-segmentation tasks in magnetic resonance (MR) images. However, the accuracy of
-CNNs may degrade severely when segmenting images acquired with different
-scanners and/or protocols as compared to the training data, thus limiting their
-practical utility. We address this shortcoming in a lifelong multi-domain
-learning setting by treating images acquired with different scanners or
-protocols as samples from different, but related domains. Our solution is a
-single CNN with shared convolutional filters and domain-specific batch
-normalization layers, which can be tuned to new domains with only a few
-($\approx$ 4) labelled images. Importantly, this is achieved while retaining
-performance on the older domains whose training data may no longer be
-available. We evaluate the method for brain structure segmentation in MR
-images. Results demonstrate that the proposed method largely closes the gap to
-the benchmark, which is training a dedicated CNN for each scanner.
-",0,0,0,1,0,0
-19480,Hybrid Kinematic Control for Rigid Body Pose Stabilization using Dual Quaternions,"  In this paper, we address the rigid body pose stabilization problem using
-dual quaternion formalism. We propose a hybrid control strategy to design a
-switching control law with hysteresis in such a way that the global asymptotic
-stability of the closed-loop system is guaranteed and such that the global
-attractivity of the stabilization pose does not exhibit chattering, a problem
-that is present in all discontinuous-based feedback controllers. Using
-numerical simulations, we illustrate the problems that arise from existing
-results in the literature -- as unwinding and chattering -- and verify the
-effectiveness of the proposed controller to solve the robust global pose
-stability problem.
-",1,0,1,0,0,0
-19481,On 2d-4d motivic wall-crossing formulas,"  In this paper we propose definitions and examples of categorical enhancements
-of the data involved in the $2d$-$4d$ wall-crossing formulas which generalize
-both Cecotti-Vafa and Kontsevich-Soibelman motivic wall-crossing formulas.
-",0,0,1,0,0,0
-19482,On Quitting: Performance and Practice in Online Game Play,"  We study the relationship between performance and practice by analyzing the
-activity of many players of a casual online game. We find significant
-heterogeneity in the improvement of player performance, given by score, and
-address this by dividing players into similar skill levels and segmenting each
-player's activity into sessions, i.e., sequence of game rounds without an
-extended break. After disaggregating data, we find that performance improves
-with practice across all skill levels. More interestingly, players are more
-likely to end their session after an especially large improvement, leading to a
-peak score in their very last game of a session. In addition, success is
-strongly correlated with a lower quitting rate when the score drops, and only
-weakly correlated with skill, in line with psychological findings about the
-value of persistence and ""grit"": successful players are those who persist in
-their practice despite lower scores. Finally, we train an epsilon-machine, a
-type of hidden Markov model, and find a plausible mechanism of game play that
-can predict player performance and quitting the game. Our work raises the
-possibility of real-time assessment and behavior prediction that can be used to
-optimize human performance.
-",1,0,0,0,0,0
-19483,Video Labeling for Automatic Video Surveillance in Security Domains,"  Beyond traditional security methods, unmanned aerial vehicles (UAVs) have
-become an important surveillance tool used in security domains to collect the
-required annotated data. However, collecting annotated data from videos taken
-by UAVs efficiently, and using these data to build datasets that can be used
-for learning payoffs or adversary behaviors in game-theoretic approaches and
-security applications, is an under-explored research question. This paper
-presents VIOLA, a novel labeling application that includes (i) a workload
-distribution framework to efficiently gather human labels from videos in a
-secured manner; (ii) a software interface with features designed for labeling
-videos taken by UAVs in the domain of wildlife security. We also present the
-evolution of VIOLA and analyze how the changes made in the development process
-relate to the efficiency of labeling, including when seemingly obvious
-improvements did not lead to increased efficiency. VIOLA enables collecting
-massive amounts of data with detailed information from challenging security
-videos such as those collected aboard UAVs for wildlife security. VIOLA will
-lead to the development of new approaches that integrate deep learning for
-real-time detection and response.
-",1,0,0,0,0,0
-19484,On density of subgraphs of Cartesian products,"  In this paper, we extend two classical results about the density of subgraphs
-of hypercubes to subgraphs $G$ of Cartesian products $G_1\times\cdots\times
-G_m$ of arbitrary connected graphs. Namely, we show that
-$\frac{|E(G)|}{|V(G)|}\le \lceil 2\max\{
-\text{dens}(G_1),\ldots,\text{dens}(G_m)\} \rceil\log|V(G)|$, where
-$\text{dens}(H)$ is the maximum ratio $\frac{|E(H')|}{|V(H')|}$ over all
-subgraphs $H'$ of $H$. We introduce the notions of VC-dimension
-$\text{VC-dim}(G)$ and VC-density $\text{VC-dens}(G)$ of a subgraph $G$ of a
-Cartesian product $G_1\times\cdots\times G_m$, generalizing the classical
-Vapnik-Chervonenkis dimension of set-families (viewed as subgraphs of
-hypercubes). We prove that if $G_1,\ldots,G_m$ belong to the class ${\mathcal
-G}(H)$ of all finite connected graphs not containing a given graph $H$ as a
-minor, then for any subgraph $G$ of $G_1\times\cdots\times G_m$ a sharper
-inequality $\frac{|E(G)|}{|V(G)|}\le \text{VC-dim}(G)\alpha(H)$ holds, where
-$\alpha(H)$ is the density of the graphs from ${\mathcal G}(H)$. We refine and
-sharpen those two results to several specific graph classes. We also derive
-upper bounds (some of them polylogarithmic) for the size of adjacency labeling
-schemes of subgraphs of Cartesian products.
-",1,0,0,0,0,0
-19485,Replica Analysis for Maximization of Net Present Value,"  In this paper, we use replica analysis to determine the investment strategy
-that can maximize the net present value for portfolios containing multiple
-development projects. Replica analysis was developed in statistical mechanical
-informatics and econophysics to evaluate disordered systems, and here we use it
-to formulate the maximization of the net present value as an optimization
-problem under budget and investment concentration constraints. Furthermore, we
-confirm that a common approach from operations research underestimates the true
-maximal net present value as the maximal expected net present value by
-comparing our results with the maximal expected net present value as derived in
-operations research. Moreover, it is shown that the conventional method for
-estimating the net present value does not consider variance in the cash flow.
-",0,0,0,0,0,1
-19486,On some properties of weak solutions to elliptic equations with divergence-free drifts,"  We discuss the local properties of weak solutions to the equation $-\Delta u
-+ b\cdot\nabla u=0$. The corresponding theory is well-known in the case $b\in
-L_n$, where $n$ is the dimension of the space. Our main interest is focused on
-the case $b\in L_2$. In this case the structure assumption $\operatorname{div}
-b=0$ turns out to be crucial.
-",0,0,1,0,0,0
-19487,The heptagon-wheel cocycle in the Kontsevich graph complex,"  The real vector space of non-oriented graphs is known to carry a differential
-graded Lie algebra structure. Cocycles in the Kontsevich graph complex,
-expressed using formal sums of graphs on $n$ vertices and $2n-2$ edges, induce
--- under the orientation mapping -- infinitesimal symmetries of classical
-Poisson structures on arbitrary finite-dimensional affine real manifolds.
-Willwacher has stated the existence of a nontrivial cocycle that contains the
-$(2\ell+1)$-wheel graph with a nonzero coefficient at every
-$\ell\in\mathbb{N}$. We present detailed calculations of the differential of
-graphs; for the tetrahedron and pentagon-wheel cocycles, consisting at $\ell =
-1$ and $\ell = 2$ of one and two graphs respectively, the cocycle condition
-$d(\gamma) = 0$ is verified by hand. For the next, heptagon-wheel cocycle
-(known to exist at $\ell = 3$), we provide an explicit representative: it
-consists of 46 graphs on 8 vertices and 14 edges.
-",0,0,1,0,0,0
-19488,Maneuver Regulation for Accelerating Bodies in Atmospheric Environments,"  In order to address the need for an affordable reduced gravity test platform,
-this work focuses on the analysis and implementation of atmospheric
-acceleration tracking with an autonomous aerial vehicle. As proof of concept,
-the vehicle is designed with the objective of flying accurate reduced-gravity
-parabolas. Suggestions from both academia and industry were taken into account,
-as well as requirements imposed by a regulatory agency. The novelty of this
-work is the Proportional Integral Ramp Quadratic PIRQ controller, which is
-employed to counteract the aerodynamic forces impeding the vehicles constant
-acceleration during the maneuver. The stability of the free-fall maneuver under
-this controller is studied in detail via the formation of the transverse
-dynamics and the application of the circle criterion. The implementation of
-such a controller is then outlined, and the PIRQ controller is validated
-through a flight test, where the vehicle successfully tracks Martian gravity
-0.378 G's with a standard deviation of 0.0426.
-",1,0,0,0,0,0
-19489,A Hybrid Deep Learning Approach for Texture Analysis,"  Texture classification is a problem that has various applications such as
-remote sensing and forest species recognition. Solutions tend to be custom fit
-to the dataset used but fails to generalize. The Convolutional Neural Network
-(CNN) in combination with Support Vector Machine (SVM) form a robust selection
-between powerful invariant feature extractor and accurate classifier. The
-fusion of experts provides stability in classification rates among different
-datasets.
-",1,0,0,0,0,0
-19490,Accounting Noise and the Pricing of CoCos,"  Contingent Convertible bonds (CoCos) are debt instruments that convert into
-equity or are written down in times of distress. Existing pricing models assume
-conversion triggers based on market prices and on the assumption that markets
-can always observe all relevant firm information. But all Cocos issued so far
-have triggers based on accounting ratios and/or regulatory intervention. We
-incorporate that markets receive information through noisy accounting reports
-issued at discrete time instants, which allows us to distinguish between market
-and accounting values, and between automatic triggers and regulator-mandated
-conversions. Our second contribution is to incorporate that coupon payments are
-contingent too: their payment is conditional on the Maximum Distributable
-Amount not being exceeded. We examine the impact of CoCo design parameters,
-asset volatility and accounting noise on the price of a CoCo; and investigate
-the interaction between CoCo design features, the capital structure of the
-issuing bank and their implications for risk taking and investment incentives.
-Finally, we use our model to explain the crash in CoCo prices after Deutsche
-Bank's profit warning in February 2016.
-",0,0,0,0,0,1
-19491,An Improved Algorithm for E-Generalization,"  E-generalization computes common generalizations of given ground terms w.r.t.
-a given equational background theory E. In 2005 [arXiv:1403.8118], we had
-presented a computation approach based on standard regular tree grammar
-algorithms, and a Prolog prototype implementation. In this report, we present
-algorithmic improvements, prove them correct and complete, and give some
-details of an efficiency-oriented implementation in C that allows us to handle
-problems larger by several orders of magnitude.
-",1,0,0,0,0,0
-19492,On quartic double fivefolds and the matrix factorizations of exceptional quaternionic representations,"  We study quartic double fivefolds from the perspective of Fano manifolds of
-Calabi-Yau type and that of exceptional quaternionic representations. We first
-prove that the generic quartic double fivefold can be represented, in a finite
-number of ways, as a double cover of P^5 ramified along a linear section of the
-Sp 12-invariant quartic in P^31. Then, using the geometry of the Vinberg's type
-II decomposition of some exceptional quaternionic representations, and backed
-by some cohomological computations performed by Macaulay2, we prove the
-existence of a spherical rank 6 vector bundle on such a generic quartic double
-fivefold. We finally use the existence this vector bundle to prove that the
-homological unit of the CY-3 category associated by Kuznetsov to the derived
-category of a generic quartic double fivefold is C $\oplus$ C[3].
-",0,0,1,0,0,0
-19493,Atmospheric thermal tides and planetary spin I. The complex interplay between stratification and rotation,"  Thermal atmospheric tides can torque telluric planets away from spin-orbit
-synchronous rotation, as observed in the case of Venus. They thus participate
-to determine the possible climates and general circulations of the atmospheres
-of these planets. In this work, we write the equations governing the dynamics
-of thermal tides in a local vertically-stratified section of a rotating
-planetary atmosphere by taking into account the effects of the complete
-Coriolis acceleration on tidal waves. This allows us to derive analytically the
-tidal torque and the tidally dissipated energy, which we use to discuss the
-possible regimes of tidal dissipation and examine the key role played by
-stratification.
-In agreement with early studies, we find that the frequency dependence of the
-thermal atmospheric tidal torque in the vicinity of synchronization can be
-approximated by a Maxwell model. This behaviour corresponds to weakly stably
-stratified or convective fluid layers, as observed in ADLM2016a. A strong
-stable stratification allows gravity waves to propagate, which makes the tidal
-torque become negligible. The transition is continuous between these two
-regimes. The traditional approximation appears to be valid in thin atmospheres
-and in regimes where the rotation frequency is dominated by the forcing or the
-buoyancy frequencies.
-Depending on the stability of their atmospheres with respect to convection,
-observed exoplanets can be tidally driven toward synchronous or asynchronous
-final rotation rates. The domain of applicability of the traditional
-approximation is rigorously constrained by calculations.
-",0,1,0,0,0,0
-19494,Inferring Generative Model Structure with Static Analysis,"  Obtaining enough labeled data to robustly train complex discriminative models
-is a major bottleneck in the machine learning pipeline. A popular solution is
-combining multiple sources of weak supervision using generative models. The
-structure of these models affects training label quality, but is difficult to
-learn without any ground truth labels. We instead rely on these weak
-supervision sources having some structure by virtue of being encoded
-programmatically. We present Coral, a paradigm that infers generative model
-structure by statically analyzing the code for these heuristics, thus reducing
-the data required to learn structure significantly. We prove that Coral's
-sample complexity scales quasilinearly with the number of heuristics and number
-of relations found, improving over the standard sample complexity, which is
-exponential in $n$ for identifying $n^{\textrm{th}}$ degree relations.
-Experimentally, Coral matches or outperforms traditional structure learning
-approaches by up to 3.81 F1 points. Using Coral to model dependencies instead
-of assuming independence results in better performance than a fully supervised
-model by 3.07 accuracy points when heuristics are used to label radiology data
-without ground truth labels.
-",1,0,0,1,0,0
-19495,"Maslov, Chern-Weil and Mean Curvature","  We provide an integral formula for the Maslov index of a pair $(E,F)$ over a
-surface $\Sigma$, where $E\rightarrow\Sigma$ is a complex vector bundle and
-$F\subset E_{|\partial\Sigma}$ is a totally real subbundle. As in Chern-Weil
-theory, this formula is written in terms of the curvature of $E$ plus a
-boundary contribution.
-When $(E,F)$ is obtained via an immersion of $(\Sigma,\partial\Sigma)$ into a
-pair $(M,L)$ where $M$ is Kähler and $L$ is totally real, the formula allows
-us to control the Maslov index in terms of the geometry of $(M,L)$. We exhibit
-natural conditions on $(M,L)$ which lead to bounds and monotonicity results.
-",0,0,1,0,0,0
-19496,Tuning Goodness-of-Fit Tests,"  As modern precision cosmological measurements continue to show agreement with
-the broad features of the standard $\Lambda$-Cold Dark Matter ($\Lambda$CDM)
-cosmological model, we are increasingly motivated to look for small departures
-from the standard model's predictions which might not be detected with standard
-approaches. While searches for extensions and modifications of $\Lambda$CDM
-have to date turned up no convincing evidence of beyond-the-standard-model
-cosmology, the list of models compared against $\Lambda$CDM is by no means
-complete and is often governed by readily-coded modifications to standard
-Boltzmann codes. Also, standard goodness-of-fit methods such as a naive
-$\chi^2$ test fail to put strong pressure on the null $\Lambda$CDM hypothesis,
-since modern datasets have orders of magnitudes more degrees of freedom than
-$\Lambda$CDM. Here we present a method of tuning goodness-of-fit tests to
-detect potential sub-dominant extra-$\Lambda$CDM signals present in the data
-through compressing observations in a way that maximizes extra-$\Lambda$CDM
-signal variation over noise and $\Lambda$CDM variation. This method, based on a
-Karhunen-Loève transformation of the data, is tuned to be maximally
-sensitive to particular types of variations characteristic of the tuning model;
-but, unlike direct model comparison, the test is also sensitive to features
-that only partially mimic the tuning model. As an example of its use, we apply
-this method in the context of a nonstandard primordial power spectrum compared
-against the $2015$ $Planck$ CMB temperature and polarization power spectrum. We
-find weak evidence of extra-$\Lambda$CDM physics, conceivably due to known
-systematics in the 2015 Planck polarization release.
-",0,1,0,0,0,0
-19497,The Lubin-Tate stack and Gross-Hopkins duality,"  Morava $E$-theory $E$ is an $E_\infty$-ring with an action of the Morava
-stabilizer group $\Gamma$. We study the derived stack $\operatorname{Spf}
-E/\Gamma$. Descent-theoretic techniques allow us to deduce a theorem of
-Hopkins-Mahowald-Sadofsky on the $K(n)$-local Picard group, as well as a recent
-result of Barthel-Beaudry-Stojanoska on the Anderson duals of higher real
-$K$-theories.
-",0,0,1,0,0,0
-19498,Generalized Probabilistic Bisection for Stochastic Root-Finding,"  We consider numerical schemes for root finding of noisy responses through
-generalizing the Probabilistic Bisection Algorithm (PBA) to the more practical
-context where the sampling distribution is unknown and location-dependent. As
-in standard PBA, we rely on a knowledge state for the approximate posterior of
-the root location. To implement the corresponding Bayesian updating, we also
-carry out inference of oracle accuracy, namely learning the probability of
-correct response. To this end we utilize batched querying in combination with a
-variety of frequentist and Bayesian estimators based on majority vote, as well
-as the underlying functional responses, if available. For guiding sampling
-selection we investigate both Information Directed sampling, as well as
-Quantile sampling. Our numerical experiments show that these strategies perform
-quite differently; in particular we demonstrate the efficiency of randomized
-quantile sampling which is reminiscent of Thompson sampling. Our work is
-motivated by the root-finding sub-routine in pricing of Bermudan financial
-derivatives, illustrated in the last section of the paper.
-",1,0,0,1,0,0
-19499,Towards Proof Synthesis Guided by Neural Machine Translation for Intuitionistic Propositional Logic,"  Inspired by the recent evolution of deep neural networks (DNNs) in machine
-learning, we explore their application to PL-related topics. This paper is the
-first step towards this goal; we propose a proof-synthesis method for the
-negation-free propositional logic in which we use a DNN to obtain a guide of
-proof search. The idea is to view the proof-synthesis problem as a translation
-from a proposition to its proof. We train seq2seq, which is a popular network
-in neural machine translation, so that it generates a proof encoded as a
-$\lambda$-term of a given proposition. We implement the whole framework and
-empirically observe that a generated proof term is close to a correct proof in
-terms of the tree edit distance of AST. This observation justifies using the
-output from a trained seq2seq model as a guide for proof search.
-",1,0,0,0,0,0
-19500,Fibonacci words in hyperbolic Pascal triangles,"  The hyperbolic Pascal triangle ${\cal HPT}_{4,q}$ $(q\ge5)$ is a new
-mathematical construction, which is a geometrical generalization of Pascal's
-arithmetical triangle. In the present study we show that a natural pattern of
-rows of ${\cal HPT}_{4,5}$ is almost the same as the sequence consisting of
-every second term of the well-known Fibonacci words. Further, we give a
-generalization of the Fibonacci words using the hyperbolic Pascal triangles.
-The geometrical properties of a ${\cal HPT}_{4,q}$ imply a graph structure
-between the finite Fibonacci words.
-",1,0,0,0,0,0
-19501,A Review on Quantile Regression for Stochastic Computer Experiments,"  We report on an empirical study of the main strategies for conditional
-quantile estimation in the context of stochastic computer experiments. To
-ensure adequate diversity, six metamodels are presented, divided into three
-categories based on order statistics, functional approaches, and those of
-Bayesian inspiration. The metamodels are tested on several problems
-characterized by the size of the training set, the input dimension, the
-quantile order and the value of the probability density function in the
-neighborhood of the quantile. The metamodels studied reveal good contrasts in
-our set of 480 experiments, enabling several patterns to be extracted. Based on
-our results, guidelines are proposed to allow users to select the best method
-for a given problem.
-",1,0,0,1,0,0
-19502,Scalability of Voltage-Controlled Filamentary and Nanometallic Resistance Memories,"  Much effort has been devoted to device and materials engineering to realize
-nanoscale resistance random access memory (RRAM) for practical applications,
-but there still lacks a rational physical basis to be relied on to design
-scalable devices spanning many length scales. In particular, the critical
-switching criterion is not clear for RRAM devices in which resistance changes
-are limited to localized nanoscale filaments that experience concentrated heat,
-electric current and field. Here, we demonstrate voltage-controlled resistance
-switching for macro and nano devices in both filamentary RRAM and nanometallic
-RRAM, the latter switches uniformly and does not require forming. As a result,
-using a constant current density as the compliance, we have achieved
-area-scalability for the low resistance state of the filamentary RRAM, and for
-both the low and high resistance states of the nanometallic RRAM. This finding
-will help design area-scalable RRAM at the nanoscale.
-",0,1,0,0,0,0
-19503,Functional geometry of protein-protein interaction networks,"  Motivation: Protein-protein interactions (PPIs) are usually modelled as
-networks. These networks have extensively been studied using graphlets, small
-induced subgraphs capturing the local wiring patterns around nodes in networks.
-They revealed that proteins involved in similar functions tend to be similarly
-wired. However, such simple models can only represent pairwise relationships
-and cannot fully capture the higher-order organization of protein interactions,
-including protein complexes. Results: To model the multi-sale organization of
-these complex biological systems, we utilize simplicial complexes from
-computational geometry. The question is how to mine these new representations
-of PPI networks to reveal additional biological information. To address this,
-we define simplets, a generalization of graphlets to simplicial complexes. By
-using simplets, we define a sensitive measure of similarity between simplicial
-complex network representations that allows for clustering them according to
-their data types better than clustering them by using other state-of-the-art
-measures, e.g., spectral distance, or facet distribution distance. We model
-human and baker's yeast PPI networks as simplicial complexes that capture PPIs
-and protein complexes as simplices. On these models, we show that our newly
-introduced simplet-based methods cluster proteins by function better than the
-clustering methods that use the standard PPI networks, uncovering the new
-underlying functional organization of the cell. We demonstrate the existence of
-the functional geometry in the PPI data and the superiority of our
-simplet-based methods to effectively mine for new biological information hidden
-in the complexity of the higher order organization of PPI networks.
-",0,0,0,0,1,0
-19504,A Two-Layer Component-Based Allocation for Embedded Systems with GPUs,"  Component-based development is a software engineering paradigm that can
-facilitate the construction of embedded systems and tackle its complexities.
-The modern embedded systems have more and more demanding requirements. One way
-to cope with such versatile and growing set of requirements is to employ
-heterogeneous processing power, i.e., CPU-GPU architectures. The new CPU-GPU
-embedded boards deliver an increased performance but also introduce additional
-complexity and challenges. In this work, we address the component-to-hardware
-allocation for CPU-GPU embedded systems. The allocation for such systems is
-much complex due to the increased amount of GPU-related information. For
-example, while in traditional embedded systems the allocation mechanism may
-consider only the CPU memory usage of components to find an appropriate
-allocation scheme, in heterogeneous systems, the GPU memory usage needs also to
-be taken into account in the allocation process. This paper aims at decreasing
-the component-to-hardware allocation complexity by introducing a 2-layer
-component-based architecture for heterogeneous embedded systems. The detailed
-CPU-GPU information of the system is abstracted at a high-layer by compacting
-connected components into single units that behave as regular components. The
-allocator, based on the compacted information received from the high-level
-layer, computes, with a decreased complexity, feasible allocation schemes. In
-the last part of the paper, the 2-layer allocation method is evaluated using an
-existing embedded system demonstrator; namely, an underwater robot.
-",1,0,0,0,0,0
-19505,Sales Forecast in E-commerce using Convolutional Neural Network,"  Sales forecast is an essential task in E-commerce and has a crucial impact on
-making informed business decisions. It can help us to manage the workforce,
-cash flow and resources such as optimizing the supply chain of manufacturers
-etc. Sales forecast is a challenging problem in that sales is affected by many
-factors including promotion activities, price changes, and user preferences
-etc. Traditional sales forecast techniques mainly rely on historical sales data
-to predict future sales and their accuracies are limited. Some more recent
-learning-based methods capture more information in the model to improve the
-forecast accuracy. However, these methods require case-by-case manual feature
-engineering for specific commercial scenarios, which is usually a difficult,
-time-consuming task and requires expert knowledge. To overcome the limitations
-of existing methods, we propose a novel approach in this paper to learn
-effective features automatically from the structured data using the
-Convolutional Neural Network (CNN). When fed with raw log data, our approach
-can automatically extract effective features from that and then forecast sales
-using those extracted features. We test our method on a large real-world
-dataset from CaiNiao.com and the experimental results validate the
-effectiveness of our method.
-",1,0,0,0,0,0
-19506,On normalization of inconsistency indicators in pairwise comparisons,"  In this study, we provide mathematical and practice-driven justification for
-using $[0,1]$ normalization of inconsistency indicators in pairwise
-comparisons. The need for normalization, as well as problems with the lack of
-normalization, are presented. A new type of paradox of infinity is described.
-",1,0,0,0,0,0
-19507,Gamma factors of intertwining periods and distinction for inner forms of $\GL(n)$,"  Let $F$ be a $p$-adic fied, $E$ be a quadratic extension of $F$, and $D$ be
-an $F$-division algebra of odd index. Set $H=\mathrm{GL}m,D)$ and
-$G=\mathrm{GL}(m,D\otimes_F E)$, we carry out a fine study of local
-intertwining open periods attached to $H$-distinguished induced representations
-of inner forms of $G$. These objects have been studied globally in \cite{JLR}
-and \cite{LR}, and locally in \cite{BD08}. Here we give sufficient conditions
-for the local intertwining periods to have singularities. By a local/global
-method, we also compute in terms of Asai gamma factors the proportionality
-constants involved in their functional equations with respect to certain
-intertwining operators. As a consequence, we classify distinguished unitary and
-ladder representations of $G$, extending respectively the results of \cite{M14}
-and \cite{G15} for $D=F$, which both relied at some crucial step on the theory
-of Bernstein-Zelevinsky derivatives. We make use of one of the main results of
-\cite{BP17} in our setting, which in the case of the group $G$, asserts that
-the Jacquet-Langlands correspondence preserves distinction. Such a result is
-for discrete series representations, but our method in fact allows us to use it
-only for cuspidal representations of $G$.
-",0,0,1,0,0,0
-19508,Spin-flip scattering selection in a controlled molecular junction,"  A simple double-decker molecule with magnetic anisotropy, nickelocene, is
-attached to the metallic tip of a low-temperature scanning tunneling
-microscope. In the presence of a Cu(100) surface, the conductance around the
-Fermi energy is governed by spin-flip scattering, the nature of which is
-determined by the tunneling barrier thickness. The molecular tip exhibits
-inelastic spin-flip scattering in the tunneling regime, while in the contact
-regime a Kondo ground state is stabilized causing an order of magnitude change
-in the zero-bias conductance. First principle calculations show that
-nickelocene reversibly switches from a spin 1 to 1/2 between the two transport
-regimes.
-",0,1,0,0,0,0
-19509,Mean-Field Sparse Jurdjevic--Quinn Control,"  We consider nonlinear transport equations with non-local velocity, describing
-the time-evolution of a measure, which in practice may represent the density of
-a crowd. Such equations often appear by taking the mean-field limit of
-finite-dimensional systems modelling collective dynamics. We first give a sense
-to dissipativity of these mean-field equations in terms of Lie derivatives of a
-Lyapunov function depending on the measure. Then, we address the problem of
-controlling such equations by means of a time-varying bounded control action
-localized on a time-varying control subset with bounded Lebesgue measure
-(sparsity space constraint). Finite-dimensional versions are given by
-control-affine systems, which can be stabilized by the well known
-Jurdjevic--Quinn procedure. In this paper, assuming that the uncontrolled
-dynamics are dissipative, we develop an approach in the spirit of the classical
-Jurdjevic--Quinn theorem, showing how to steer the system to an invariant
-sublevel of the Lyapunov function. The control function and the control domain
-are designed in terms of the Lie derivatives of the Lyapunov function, and
-enjoy sparsity properties in the sense that the control support is small.
-Finally, we show that our result applies to a large class of kinetic equations
-modelling multi-agent dynamics.
-",0,0,1,0,0,0
-19510,Sampling from Social Networks with Attributes,"  Sampling from large networks represents a fundamental challenge for social
-network research. In this paper, we explore the sensitivity of different
-sampling techniques (node sampling, edge sampling, random walk sampling, and
-snowball sampling) on social networks with attributes. We consider the special
-case of networks (i) where we have one attribute with two values (e.g., male
-and female in the case of gender), (ii) where the size of the two groups is
-unequal (e.g., a male majority and a female minority), and (iii) where nodes
-with the same or different attribute value attract or repel each other (i.e.,
-homophilic or heterophilic behavior). We evaluate the different sampling
-techniques with respect to conserving the position of nodes and the visibility
-of groups in such networks. Experiments are conducted both on synthetic and
-empirical social networks. Our results provide evidence that different network
-sampling techniques are highly sensitive with regard to capturing the expected
-centrality of nodes, and that their accuracy depends on relative group size
-differences and on the level of homophily that can be observed in the network.
-We conclude that uninformed sampling from social networks with attributes thus
-can significantly impair the ability of researchers to draw valid conclusions
-about the centrality of nodes and the visibility or invisibility of groups in
-social networks.
-",1,1,0,0,0,0
-19511,Exploiting Investors Social Network for Stock Prediction in China's Market,"  Recent works have shown that social media platforms are able to influence the
-trends of stock price movements. However, existing works have majorly focused
-on the U.S. stock market and lacked attention to certain emerging countries
-such as China, where retail investors dominate the market. In this regard, as
-retail investors are prone to be influenced by news or other social media,
-psychological and behavioral features extracted from social media platforms are
-thought to well predict stock price movements in the China's market. Recent
-advances in the investor social network in China enables the extraction of such
-features from web-scale data. In this paper, on the basis of tweets from
-Xueqiu, a popular Chinese Twitter-like social platform specialized for
-investors, we analyze features with regard to collective sentiment and
-perception on stock relatedness and predict stock price movements by employing
-nonlinear models. The features of interest prove to be effective in our
-experiments.
-",0,0,0,0,0,1
-19512,Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks,"  Can we make a famous rap singer like Eminem sing whatever our favorite song?
-Singing style transfer attempts to make this possible, by replacing the vocal
-of a song from the source singer to the target singer. This paper presents a
-method that learns from unpaired data for singing style transfer using
-generative adversarial networks.
-",1,0,0,0,0,0
-19513,A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots,"  We present a robust deep learning based 6 degrees-of-freedom (DoF)
-localization system for endoscopic capsule robots. Our system mainly focuses on
-localization of endoscopic capsule robots inside the GI tract using only visual
-information captured by a mono camera integrated to the robot. The proposed
-system is a 23-layer deep convolutional neural network (CNN) that is capable to
-estimate the pose of the robot in real time using a standard CPU. The dataset
-for the evaluation of the system was recorded inside a surgical human stomach
-model with realistic surface texture, softness, and surface liquid properties
-so that the pre-trained CNN architecture can be transferred confidently into a
-real endoscopic scenario. An average error of 7:1% and 3:4% for translation and
-rotation has been obtained, respectively. The results accomplished from the
-experiments demonstrate that a CNN pre-trained with raw 2D endoscopic images
-performs accurately inside the GI tract and is robust to various challenges
-posed by reflection distortions, lens imperfections, vignetting, noise, motion
-blur, low resolution, and lack of unique landmarks to track.
-",1,0,0,0,0,0
-19514,On a cross-diffusion system arising in image denosing,"  We study a generalization of a cross-diffusion problem deduced from a
-nonlinear complex-variable diffusion model for signal and image denoising.
-We prove the existence of weak solutions of the time-independent problem with
-fidelity terms under mild conditions on the data problem. Then, we show that
-this translates on the well-posedness of a quasi-steady state approximation of
-the evolution problem, and also prove the existence of weak solutions of the
-latter under more restrictive hypothesis.
-We finally perform some numerical simulations for image denoising, comparing
-the performance of the cross-diffusion model and its corresponding scalar
-Perona-Malik equation.
-",0,0,1,0,0,0
-19515,"Locally Repairable Codes with Multiple $(r_{i}, δ_{i})$-Localities","  In distributed storage systems, locally repairable codes (LRCs) are
-introduced to realize low disk I/O and repair cost. In order to tolerate
-multiple node failures, the LRCs with \emph{$(r, \delta)$-locality} are further
-proposed. Since hot data is not uncommon in a distributed storage system, both
-Zeh \emph{et al.} and Kadhe \emph{et al.} focus on the LRCs with \emph{multiple
-localities or unequal localities} (ML-LRCs) recently, which said that the
-localities among the code symbols can be different. ML-LRCs are attractive and
-useful in reducing repair cost for hot data. In this paper, we generalize the
-ML-LRCs to the $(r,\delta)$-locality case of multiple node failures, and define
-an LRC with multiple $(r_{i}, \delta_{i})_{i\in [s]}$ localities ($s\ge 2$),
-where $r_{1}\leq r_{2}\leq\dots\leq r_{s}$ and
-$\delta_{1}\geq\delta_{2}\geq\dots\geq\delta_{s}\geq2$. Such codes ensure that
-some hot data could be repaired more quickly and have better failure-tolerance
-in certain cases because of relatively smaller $r_{i}$ and larger $\delta_{i}$.
-Then, we derive a Singleton-like upper bound on the minimum distance for the
-proposed LRCs by employing the regenerating-set technique. Finally, we obtain a
-class of explicit and structured constructions of optimal ML-LRCs, and further
-extend them to the cases of multiple $(r_{i}, \delta)_{i\in [s]}$ localities.
-",1,0,0,0,0,0
-19516,Reconciling Enumerative and Symbolic Search in Syntax-Guided Synthesis,"  Syntax-guided synthesis aims to find a program satisfying semantic
-specification as well as user-provided structural hypothesis. For syntax-guided
-synthesis there are two main search strategies: concrete search, which
-systematically or stochastically enumerates all possible solutions, and
-symbolic search, which interacts with a constraint solver to solve the
-synthesis problem. In this paper, we propose a concolic synthesis framework
-which combines the best of the two worlds. Based on a decision tree
-representation, our framework works by enumerating tree heights from the
-smallest possible one to larger ones. For each fixed height, the framework
-symbolically searches a solution through the counterexample-guided inductive
-synthesis approach. To compensate the exponential blow-up problem with the
-concolic synthesis framework, we identify two fragments of synthesis problems
-and develop purely symbolic and more efficient procedures. The two fragments
-are decidable as these procedures are terminating and complete. We implemented
-our synthesis procedures and compared with state-of-the-art synthesizers on a
-range of benchmarks. Experiments show that our algorithms are promising.
-",1,0,0,0,0,0
-19517,Periodic solutions of semilinear Duffing equations with impulsive effects,"  In this paper we are concerned with the existence of periodic solutions for
-semilinear Duffing equations with impulsive effects. Firstly for the autonomous
-one, basing on Poincaré-Birkhoff twist theorem, we prove the existence of
-infinitely many periodic solutions. Secondly, as for the nonautonomous case,
-the impulse brings us great challenges for the study, and there are only
-finitely many periodic solutions, which is quite different from the
-corresponding equation without impulses. Here, taking the autonomous one as an
-auxiliary equation, we find the relation between these two equations and then
-obtain the result also by Poincaré-Birkhoff twist theorem.
-",0,1,1,0,0,0
-19518,Spectral and scattering theory for perturbed block Toeplitz operators,"  We analyse spectral properties of a class of compact perturbations of block
-Toeplitz operators associated with analytic symbols. In particular, a limiting
-absorption principle and the absence of singular continuous spectrum are shown.
-The existence and the completeness of wave operators are also obtained. Our
-study is based on the construction of a conjugate operator in Mourre sense for
-the corresponding Laurent operators.
-",0,0,1,0,0,0
-19519,Robust adaptive droop control for DC microgrids,"  There are tradeoffs between current sharing among distributed resources and
-DC bus voltage stability when conventional droop control is used in DC
-microgrids. As current sharing approaches the setpoint, bus voltage deviation
-increases. Previous studies have suggested using secondary control utilizing
-linear controllers to overcome drawbacks of droop control. However, linear
-control design depends on an accurate model of the system. The derivation of
-such a model is challenging because the noise and disturbances caused by the
-coupling between sources, loads, and switches in microgrids are
-under-represented. This under-representation makes linear modeling and control
-insufficient. Hence, in this paper, we propose a robust adaptive control to
-adjust droop characteristics to satisfy both current sharing and bus voltage
-stability. First, the time-varying models of DC microgrids are derived. Second,
-the improvements for the adaptive control method are presented. Third, the
-application of the enhanced adaptive method to DC microgrids is presented to
-satisfy the system objective. Fourth, simulation and experimental results on a
-microgrid show that the adaptive method precisely shares current between two
-distributed resources and maintains the nominal bus voltage. Last, the
-comparative study validates the effectiveness of the proposed method over the
-conventional method.
-",0,0,1,0,0,0
-19520,Off-diagonal asymptotic properties of Bergman kernels associated to analytic Kähler potentials,"  We prove a new off-diagonal asymptotic of the Bergman kernels associated to
-tensor powers of a positive line bundle on a compact Kähler manifold. We show
-that if the Kähler potential is real analytic, then the Bergman kernel
-accepts a complete asymptotic expansion in a neighborhood of the diagonal of
-shrinking size $k^{-\frac14}$. These improve the earlier results in the subject
-for smooth potentials, where an expansion exists in a $k^{-\frac12}$
-neighborhood of the diagonal. We obtain our results by finding upper bounds of
-the form $C^m m!^{2}$ for the Bergman coefficients $b_m(x, \bar y)$, which is
-an interesting problem on its own. We find such upper bounds using the method
-of Berman-Berndtsson-Sjöstrand. We also show that sharpening these upper
-bounds would improve the rate of shrinking neighborhoods of the diagonal $x=y$
-in our results. In the special case of metrics with local constant holomorphic
-sectional curvatures, we obtain off-diagonal asymptotic in a fixed (as $k \to
-\infty$) neighborhood of the diagonal, which recovers a result of Berman [Ber]
-(see Remark 3.5 of [Ber] for higher dimensions). In this case, we also find an
-explicit formula for the Bergman kernel mod $O(e^{-k \delta} )$.
-",0,0,1,0,0,0
-19521,Network Backboning with Noisy Data,"  Networks are powerful instruments to study complex phenomena, but they become
-hard to analyze in data that contain noise. Network backbones provide a tool to
-extract the latent structure from noisy networks by pruning non-salient edges.
-We describe a new approach to extract such backbones. We assume that edge
-weights are drawn from a binomial distribution, and estimate the error-variance
-in edge weights using a Bayesian framework. Our approach uses a more realistic
-null model for the edge weight creation process than prior work. In particular,
-it simultaneously considers the propensity of nodes to send and receive
-connections, whereas previous approaches only considered nodes as emitters of
-edges. We test our model with real world networks of different types (flows,
-stocks, co-occurrences, directed, undirected) and show that our Noise-Corrected
-approach returns backbones that outperform other approaches on a number of
-criteria. Our approach is scalable, able to deal with networks with millions of
-edges.
-",1,1,0,0,0,0
-19522,The 2017 DAVIS Challenge on Video Object Segmentation,"  We present the 2017 DAVIS Challenge on Video Object Segmentation, a public
-dataset, benchmark, and competition specifically designed for the task of video
-object segmentation. Following the footsteps of other successful initiatives,
-such as ILSVRC and PASCAL VOC, which established the avenue of research in the
-fields of scene classification and semantic segmentation, the DAVIS Challenge
-comprises a dataset, an evaluation methodology, and a public competition with a
-dedicated workshop co-located with CVPR 2017. The DAVIS Challenge follows up on
-the recent publication of DAVIS (Densely-Annotated VIdeo Segmentation), which
-has fostered the development of several novel state-of-the-art video object
-segmentation techniques. In this paper we describe the scope of the benchmark,
-highlight the main characteristics of the dataset, define the evaluation
-metrics of the competition, and present a detailed analysis of the results of
-the participants to the challenge.
-",1,0,0,0,0,0
-19523,How to Ask for Technical Help? Evidence-based Guidelines for Writing Questions on Stack Overflow,"  Context: The success of Stack Overflow and other community-based
-question-and-answer (Q&A) sites depends mainly on the will of their members to
-answer others' questions. In fact, when formulating requests on Q&A sites, we
-are not simply seeking for information. Instead, we are also asking for other
-people's help and feedback. Understanding the dynamics of the participation in
-Q&A communities is essential to improve the value of crowdsourced knowledge.
-Objective: In this paper, we investigate how information seekers can increase
-the chance of eliciting a successful answer to their questions on Stack
-Overflow by focusing on the following actionable factors: affect, presentation
-quality, and time.
-Method: We develop a conceptual framework of factors potentially influencing
-the success of questions in Stack Overflow. We quantitatively analyze a set of
-over 87K questions from the official Stack Overflow dump to assess the impact
-of actionable factors on the success of technical requests. The information
-seeker reputation is included as a control factor. Furthermore, to understand
-the role played by affective states in the success of questions, we
-qualitatively analyze questions containing positive and negative emotions.
-Finally, a survey is conducted to understand how Stack Overflow users perceive
-the guideline suggestions for writing questions.
-Results: We found that regardless of user reputation, successful questions
-are short, contain code snippets, and do not abuse with uppercase characters.
-As regards affect, successful questions adopt a neutral emotional style.
-Conclusion: We provide evidence-based guidelines for writing effective
-questions on Stack Overflow that software engineers can follow to increase the
-chance of getting technical help. As for the role of affect, we empirically
-confirmed community guidelines that suggest avoiding rudeness in question
-writing.
-",1,0,0,0,0,0
-19524,Advanced Quantizer Designs for FDD-Based FD-MIMO Systems Using Uniform Planar Arrays,"  Massive multiple-input multiple-output (MIMO) systems, which utilize a large
-number of antennas at the base station, are expected to enhance network
-throughput by enabling improved multiuser MIMO techniques. To deploy many
-antennas in reasonable form factors, base stations are expected to employ
-antenna arrays in both horizontal and vertical dimensions, which is known as
-full-dimension (FD) MIMO. The most popular two-dimensional array is the uniform
-planar array (UPA), where antennas are placed in a grid pattern. To exploit the
-full benefit of massive MIMO in frequency division duplexing (FDD), the
-downlink channel state information (CSI) should be estimated, quantized, and
-fed back from the receiver to the transmitter. However, it is difficult to
-accurately quantize the channel in a computationally efficient manner due to
-the high dimensionality of the massive MIMO channel. In this paper, we develop
-both narrowband and wideband CSI quantizers for FD-MIMO taking the properties
-of realistic channels and the UPA into consideration. To improve quantization
-quality, we focus on not only quantizing dominant radio paths in the channel,
-but also combining the quantized beams. We also develop a hierarchical beam
-search approach, which scans both vertical and horizontal domains jointly with
-moderate computational complexity. Numerical simulations verify that the
-performance of the proposed quantizers is better than that of previous CSI
-quantization techniques.
-",1,0,0,0,0,0
-19525,A Novel Stretch Energy Minimization Algorithm for Equiareal Parameterizations,"  Surface parameterizations have been widely applied to computer graphics and
-digital geometry processing. In this paper, we propose a novel stretch energy
-minimization (SEM) algorithm for the computation of equiareal parameterizations
-of simply connected open surfaces with a very small area distortion and a
-highly improved computational efficiency. In addition, the existence of
-nontrivial limit points of the SEM algorithm is guaranteed under some mild
-assumptions of the mesh quality. Numerical experiments indicate that the
-efficiency, accuracy, and robustness of the proposed SEM algorithm outperform
-other state-of-the-art algorithms. Applications of the SEM on surface remeshing
-and surface registration for simply connected open surfaces are demonstrated
-thereafter. Thanks to the SEM algorithm, the computations for these
-applications can be carried out efficiently and robustly.
-",1,0,0,0,0,0
-19526,Time Complexity Analysis of a Distributed Stochastic Optimization in a Non-Stationary Environment,"  In this paper, we consider a distributed stochastic optimization problem
-where the goal is to minimize the time average of a cost function subject to a
-set of constraints on the time averages of related stochastic processes called
-penalties. We assume that the state of the system is evolving in an independent
-and non-stationary fashion and the ""common information"" available at each node
-is distributed and delayed. Such stochastic optimization is an integral part of
-many important problems in wireless networks such as scheduling, routing,
-resource allocation and crowd sensing. We propose an approximate distributed
-Drift- Plus-Penalty (DPP) algorithm, and show that it achieves a time average
-cost (and penalties) that is within epsilon > 0 of the optimal cost (and
-constraints) with high probability. Also, we provide a condition on the
-convergence time t for this result to hold. In particular, for any delay D >= 0
-in the common information, we use a coupling argument to prove that the
-proposed algorithm converges almost surely to the optimal solution. We use an
-application from wireless sensor network to corroborate our theoretical
-findings through simulation results.
-",1,0,1,0,0,0
-19527,Robust Counterfactual Inferences using Feature Learning and their Applications,"  In a wide variety of applications, including personalization, we want to
-measure the difference in outcome due to an intervention and thus have to deal
-with counterfactual inference. The feedback from a customer in any of these
-situations is only 'bandit feedback' - that is, a partial feedback based on
-whether we chose to intervene or not. Typically randomized experiments are
-carried out to understand whether an intervention is overall better than no
-intervention. Here we present a feature learning algorithm to learn from a
-randomized experiment where the intervention in consideration is most effective
-and where it is least effective rather than only focusing on the overall
-impact, thus adding a context to our learning mechanism and extract more
-information. From the randomized experiment, we learn the feature
-representations which divide the population into subpopulations where we
-observe statistically significant difference in average customer feedback
-between those who were subjected to the intervention and those who were not,
-with a level of significance l, where l is a configurable parameter in our
-model. We use this information to derive the value of the intervention in
-consideration for each instance in the population. With experiments, we show
-that using this additional learning, in future interventions, the context for
-each instance could be leveraged to decide whether to intervene or not.
-",0,0,0,1,0,0
-19528,Regularity of symbolic powers and Arboricity of matroids,"  Let $\Delta$ be a simplicial complex of a matroid $M$. In this paper, we
-explicitly compute the regularity of all the symbolic powers of a
-Stanley-Reisner ideal $I_\Delta$ in terms of combinatorial data of the matroid
-$M$. In order to do that, we provide a sharp bound between the arboricity of
-$M$ and the circumference of its dual $M^*$.
-",0,0,1,0,0,0
-19529,Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting,"  We propose a max-pooling based loss function for training Long Short-Term
-Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low
-CPU, memory, and latency requirements. The max-pooling loss training can be
-further guided by initializing with a cross-entropy loss trained network. A
-posterior smoothing based evaluation approach is employed to measure keyword
-spotting performance. Our experimental results show that LSTM models trained
-using cross-entropy loss or max-pooling loss outperform a cross-entropy loss
-trained baseline feed-forward Deep Neural Network (DNN). In addition,
-max-pooling loss trained LSTM with randomly initialized network performs better
-compared to cross-entropy loss trained LSTM. Finally, the max-pooling loss
-trained LSTM initialized with a cross-entropy pre-trained network shows the
-best performance, which yields $67.6\%$ relative reduction compared to baseline
-feed-forward DNN in Area Under the Curve (AUC) measure.
-",1,0,0,1,0,0
-19530,On Packet Scheduling with Adversarial Jamming and Speedup,"  In Packet Scheduling with Adversarial Jamming packets of arbitrary sizes
-arrive over time to be transmitted over a channel in which instantaneous
-jamming errors occur at times chosen by the adversary and not known to the
-algorithm. The transmission taking place at the time of jamming is corrupt, and
-the algorithm learns this fact immediately. An online algorithm maximizes the
-total size of packets it successfully transmits and the goal is to develop an
-algorithm with the lowest possible asymptotic competitive ratio, where the
-additive constant may depend on packet sizes.
-Our main contribution is a universal algorithm that works for any speedup and
-packet sizes and, unlike previous algorithms for the problem, it does not need
-to know these properties in advance. We show that this algorithm guarantees
-1-competitiveness with speedup 4, making it the first known algorithm to
-maintain 1-competitiveness with a moderate speedup in the general setting of
-arbitrary packet sizes. We also prove a lower bound of $\phi+1\approx 2.618$ on
-the speedup of any 1-competitive deterministic algorithm, showing that our
-algorithm is close to the optimum.
-Additionally, we formulate a general framework for analyzing our algorithm
-locally and use it to show upper bounds on its competitive ratio for speedups
-in $[1,4)$ and for several special cases, recovering some previously known
-results, each of which had a dedicated proof. In particular, our algorithm is
-3-competitive without speedup, matching both the (worst-case) performance of
-the algorithm by Jurdzinski et al. and the lower bound by Anta et al.
-",1,0,0,0,0,0
-19531,Coarse-Grid Computational Fluid Dynamic (CG-CFD) Error Prediction using Machine Learning,"  Despite the progress in high performance computing, Computational Fluid
-Dynamics (CFD) simulations are still computationally expensive for many
-practical engineering applications such as simulating large computational
-domains and highly turbulent flows. One of the major reasons of the high
-expense of CFD is the need for a fine grid to resolve phenomena at the relevant
-scale, and obtain a grid-independent solution. The fine grid requirements often
-drive the computational time step size down, which makes long transient
-problems prohibitively expensive. In the research presented, the feasibility of
-a Coarse Grid CFD (CG-CFD) approach is investigated by utilizing Machine
-Learning (ML) algorithms. Relying on coarse grids increases the discretization
-error. Hence, a method is suggested to produce a surrogate model that predicts
-the CG-CFD local errors to correct the variables of interest. Given
-high-fidelity data, a surrogate model is trained to predict the CG-CFD local
-errors as a function of the coarse grid local features. ML regression
-algorithms are utilized to construct a surrogate model that relates the local
-error and the coarse grid features. This method is applied to a
-three-dimensional flow in a lid driven cubic cavity domain. The performance of
-the method was assessed by training the surrogate model on the flow full field
-spatial data and tested on new data (from flows of different Reynolds number
-and/or computed by different grid sizes). The proposed method maximizes the
-benefit of the available data and shows potential for a good predictive
-capability.
-",0,1,0,0,0,0
-19532,EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection,"  In this paper, we propose a deep learning based approach for facial action
-unit detection by enhancing and cropping the regions of interest. The approach
-is implemented by adding two novel nets (layers): the enhancing layers and the
-cropping layers, to a pretrained CNN model. For the enhancing layers, we
-designed an attention map based on facial landmark features and applied it to a
-pretrained neural network to conduct enhanced learning (The E-Net). For the
-cropping layers, we crop facial regions around the detected landmarks and
-design convolutional layers to learn deeper features for each facial region
-(C-Net). We then fuse the E-Net and the C-Net to obtain our Enhancing and
-Cropping (EAC) Net, which can learn both feature enhancing and region cropping
-functions. Our approach shows significant improvement in performance compared
-to the state-of-the-art methods applied to BP4D and DISFA AU datasets.
-",1,0,0,0,0,0
-19533,Influence of surface and bulk water ice on the reactivity of a water-forming reaction,"  On the surface of icy dust grains in the dense regions of the interstellar
-medium a rich chemistry can take place. Due to the low temperature, reactions
-that proceed via a barrier can only take place through tunneling. The reaction
-H + H$_2$O$_2$ $\rightarrow$ H$_2$O + OH is such a case with a gas-phase
-barrier of $\sim$26.5 kJ/mol. Still the reaction is known to be involved in
-water formation on interstellar grains. Here, we investigate the influence of a
-water ice surface and of bulk ice on the reaction rate constant. Rate constants
-are calculated using instanton theory down to 74 K. The ice is taken into
-account via multiscale modeling, describing the reactants and the direct
-surrounding at the quantum mechanical level with density functional theory
-(DFT), while the rest of the ice is modeled on the molecular mechanical level
-with a force field. We find that H$_2$O$_2$ binding energies cannot be captured
-by a single value, but rather depend on the number of hydrogen bonds with
-surface molecules. In highly amorphous surroundings the binding site can block
-the routes of attack and impede the reaction. Furthermore, the activation
-energies do not correlate with the binding energies of the same sites. The
-unimolecular rate constants related to the Langmuir-Hinshelwood mechanism
-increase as the activation energy decreases. Thus, we provide a lower limit for
-the rate constant and argue that rate constants can have values up to two order
-of magnitude larger than this limit.
-",0,1,0,0,0,0
-19534,Impact of energy dissipation on interface shapes and on rates for dewetting from liquid substrates,"  We revisit the fundamental problem of liquid-liquid dewetting and perform a
-detailed comparison of theoretical predictions based on thin-film models with
-experimental measurements obtained by atomic force microscopy (AFM).
-Specifically, we consider the dewetting of a liquid polystyrene (PS) layer from
-a liquid polymethyl methacrylate (PMMA) layer, where the thicknesses and the
-viscosities of PS and PMMA layers are similar. The excellent agreement of
-experiment and theory reveals that dewetting rates for such systems follow no
-universal power law, in contrast to dewetting scenarios on solid substrates.
-Our new energetic approach allows to assess the physical importance of
-different contributions to the energy-dissipation mechanism, for which we
-analyze the local flow fields and the local dissipation rates.
-",0,1,0,0,0,0
-19535,Resonance control of graphene drum resonator in nonlinear regime by standing wave of light,"  We demonstrate the control of resonance characteristics of a drum type
-graphene mechanical resonator in nonlinear oscillation regime by the
-photothermal effect, which is induced by a standing wave of light between a
-graphene and a substrate. Unlike the conventional Duffing type nonlinearity,
-the resonance characteristics in nonlinear oscillation regime is modulated by
-the standing wave of light despite a small variation amplitude. From numerical
-calculations with a combination of equations of heat and motion with Duffing
-type nonlinearity, this can be explained that the photothermal effect causes
-delayed modulation of stress or tension of the graphene.
-",0,1,0,0,0,0
-19536,Uncertainty quantification of coal seam gas production prediction using Polynomial Chaos,"  A surrogate model approximates a computationally expensive solver. Polynomial
-Chaos is a method to construct surrogate models by summing combinations of
-carefully chosen polynomials. The polynomials are chosen to respect the
-probability distributions of the uncertain input variables (parameters); this
-allows for both uncertainty quantification and global sensitivity analysis.
-In this paper we apply these techniques to a commercial solver for the
-estimation of peak gas rate and cumulative gas extraction from a coal seam gas
-well. The polynomial expansion is shown to honour the underlying geophysics
-with low error when compared to a much more complex and computationally slower
-commercial solver. We make use of advanced numerical integration techniques to
-achieve this accuracy using relatively small amounts of training data.
-",0,0,1,0,0,0
-19537,Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards,"  The most data-efficient algorithms for reinforcement learning in robotics are
-model-based policy search algorithms, which alternate between learning a
-dynamical model of the robot and optimizing a policy to maximize the expected
-return given the model and its uncertainties. However, the current algorithms
-lack an effective exploration strategy to deal with sparse or misleading reward
-scenarios: if they do not experience any state with a positive reward during
-the initial random exploration, it is very unlikely to solve the problem. Here,
-we propose a novel model-based policy search algorithm, Multi-DEX, that
-leverages a learned dynamical model to efficiently explore the task space and
-solve tasks with sparse rewards in a few episodes. To achieve this, we frame
-the policy search problem as a multi-objective, model-based policy optimization
-problem with three objectives: (1) generate maximally novel state trajectories,
-(2) maximize the expected return and (3) keep the system in state-space regions
-for which the model is as accurate as possible. We then optimize these
-objectives using a Pareto-based multi-objective optimization algorithm. The
-experiments show that Multi-DEX is able to solve sparse reward scenarios (with
-a simulated robotic arm) in much lower interaction time than VIME, TRPO,
-GEP-PG, CMA-ES and Black-DROPS.
-",1,0,0,1,0,0
-19538,Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification,"  This work explores the trade-off between the number of samples required to
-accurately build models of dynamical systems and the degradation of performance
-in various control objectives due to a coarse approximation. In particular, we
-show that simple models can be easily fit from input/output data and are
-sufficient for achieving various control objectives. We derive bounds on the
-number of noisy input/output samples from a stable linear time-invariant system
-that are sufficient to guarantee that the corresponding finite impulse response
-approximation is close to the true system in the $\mathcal{H}_\infty$-norm. We
-demonstrate that these demands are lower than those derived in prior art which
-aimed to accurately identify dynamical models. We also explore how different
-physical input constraints, such as power constraints, affect the sample
-complexity. Finally, we show how our analysis fits within the established
-framework of robust control, by demonstrating how a controller designed for an
-approximate system provably meets performance objectives on the true system.
-",1,0,1,0,0,0
-19539,"Construction,sensitivity index, and synchronization speed of optimal networks","  The stability (or instability) of synchronization is important in a number of
-real world systems, including the power grid, the human brain and biological
-cells. For identical synchronization, the synchronizability of a network, which
-can be measured by the range of coupling strength that admits stable
-synchronization, can be optimized for a given number of nodes and links.
-Depending on the geometric degeneracy of the Laplacian eigenvectors, optimal
-networks can be classified into different sensitivity levels, which we define
-as a network's sensitivity index. We introduce an efficient and explicit way to
-construct optimal networks of arbitrary size over a wide range of sensitivity
-and link densities. Using coupled chaotic oscillators, we study synchronization
-dynamics on optimal networks, showing that cospectral optimal networks can have
-drastically different speed of synchronization. Such difference in dynamical
-stability is found to be closely related to the different structural
-sensitivity of these networks: generally, networks with high sensitivity index
-are slower to synchronize, and, surprisingly, may not synchronize at all,
-despite being theoretically stable under linear stability analysis.
-",0,1,1,0,0,0
-19540,A Vorticity-Preserving Hydrodynamical Scheme for Modeling Accretion Disk Flows,"  Vortices, turbulence, and unsteady non-laminar flows are likely both
-prominent and dynamically important features of astrophysical disks. Such
-strongly nonlinear phenomena are often difficult, however, to simulate
-accurately, and are generally amenable to analytic treatment only in idealized
-form. In this paper, we explore the evolution of compressible two-dimensional
-flows using an implicit dual-time hydrodynamical scheme that strictly conserves
-vorticity (if applied to simulate inviscid flows for which Kelvin's Circulation
-Theorem is applicable). The algorithm is based on the work of Lerat, Falissard
-& Side (2007), who proposed it in the context of terrestrial applications such
-as the blade-vortex interactions generated by helicopter rotors. We present
-several tests of Lerat et al.'s vorticity-preserving approach, which we have
-implemented to second-order accuracy, providing side-by-side comparisons with
-other algorithms that are frequently used in protostellar disk simulations. The
-comparison codes include one based on explicit, second-order van-Leer
-advection, one based on spectral methods, and another that implements a
-higher-order Godunov solver. Our results suggest that Lerat et al's algorithm
-will be useful for simulations of astrophysical environments in which vortices
-play a dynamical role, and where strong shocks are not expected.
-",0,1,0,0,0,0
-19541,Direct simulation of liquid-gas-solid flow with a free surface lattice Boltzmann method,"  Direct numerical simulation of liquid-gas-solid flows is uncommon due to the
-considerable computational cost. As the grid spacing is determined by the
-smallest involved length scale, large grid sizes become necessary -- in
-particular if the bubble-particle aspect ratio is on the order of 10 or larger.
-Hence, it arises the question of both feasibility and reasonability. In this
-paper, we present a fully parallel, scalable method for direct numerical
-simulation of bubble-particle interaction at a size ratio of 1-2 orders of
-magnitude that makes simulations feasible on currently available
-super-computing resources. With the presented approach, simulations of bubbles
-in suspension columns consisting of more than $100\,000$ fully resolved
-particles become possible. Furthermore, we demonstrate the significance of
-particle-resolved simulations by comparison to previous unresolved solutions.
-The results indicate that fully-resolved direct numerical simulation is indeed
-necessary to predict the flow structure of bubble-particle interaction problems
-correctly.
-",0,1,0,0,0,0
-19542,TALL: Temporal Activity Localization via Language Query,"  This paper focuses on temporal localization of actions in untrimmed videos.
-Existing methods typically train classifiers for a pre-defined list of actions
-and apply them in a sliding window fashion. However, activities in the wild
-consist of a wide combination of actors, actions and objects; it is difficult
-to design a proper activity list that meets users' needs. We propose to
-localize activities by natural language queries. Temporal Activity Localization
-via Language (TALL) is challenging as it requires: (1) suitable design of text
-and video representations to allow cross-modal matching of actions and language
-queries; (2) ability to locate actions accurately given features from sliding
-windows of limited granularity. We propose a novel Cross-modal Temporal
-Regression Localizer (CTRL) to jointly model text query and video clips, output
-alignment scores and action boundary regression results for candidate clips.
-For evaluation, we adopt TaCoS dataset, and build a new dataset for this task
-on top of Charades by adding sentence temporal annotations, called
-Charades-STA. We also build complex sentence queries in Charades-STA for test.
-Experimental results show that CTRL outperforms previous methods significantly
-on both datasets.
-",1,0,0,0,0,0
-19543,2D granular flows with the $μ(I)$ rheology and side walls friction: a well balanced multilayer discretization,"  We present here numerical modelling of granular flows with the $\mu(I)$
-rheology in confined channels. The contribution is twofold: (i) a model to
-approximate the Navier-Stokes equations with the $\mu(I)$ rheology through an
-asymptotic analysis. Under the hypothesis of a one-dimensional flow, this model
-takes into account side walls friction; (ii) a multilayer discretization
-following Fernández-Nieto et al. (J. Fluid Mech., vol. 798, 2016, pp.
-643-681). In this new numerical scheme, we propose an appropriate treatment of
-the rheological terms through a hydrostatic reconstruction which allows this
-scheme to be well-balanced and therefore to deal with dry areas. Based on
-academic tests, we first evaluate the influence of the width of the channel on
-the normal profiles of the downslope velocity thanks to the multilayer approach
-that is intrinsically able to describe changes from Bagnold to S-shaped (and
-vice versa) velocity profiles. We also check the well balance property of the
-proposed numerical scheme. We show that approximating side walls friction using
-single-layer models may lead to strong errors. Secondly, we compare the
-numerical results with experimental data on granular collapses. We show that
-the proposed scheme allows us to qualitatively reproduce the deposit in the
-case of a rigid bed (i. e. dry area) and that the error made by replacing the
-dry area by a small layer of material may be large if this layer is not thin
-enough. The proposed model is also able to reproduce the time evolution of the
-free surface and of the flow/no-flow interface. In addition, it reproduces the
-effect of erosion for granular flows over initially static material lying on
-the bed. This is possible when using a variable friction coefficient $\mu(I)$
-but not with a constant friction coefficient.
-",0,1,0,0,0,0
-19544,A scientists' view of scientometrics: Not everything that counts can be counted,"  Like it or not, attempts to evaluate and monitor the quality of academic
-research have become increasingly prevalent worldwide. Performance reviews
-range from at the level of individuals, through research groups and
-departments, to entire universities. Many of these are informed by, or
-functions of, simple scientometric indicators and the results of such exercises
-impact onto careers, funding and prestige. However, there is sometimes a
-failure to appreciate that scientometrics are, at best, very blunt instruments
-and their incorrect usage can be misleading. Rather than accepting the rise and
-fall of individuals and institutions on the basis of such imprecise measures,
-calls have been made for indicators be regularly scrutinised and for
-improvements to the evidence base in this area. It is thus incumbent upon the
-scientific community, especially the physics, complexity-science and
-scientometrics communities, to scrutinise metric indicators. Here, we review
-recent attempts to do this and show that some metrics in widespread use cannot
-be used as reliable indicators research quality.
-",1,1,0,0,0,0
-19545,On analyzing and evaluating privacy measures for social networks under active attack,"  Widespread usage of complex interconnected social networks such as Facebook,
-Twitter and LinkedIn in modern internet era has also unfortunately opened the
-door for privacy violation of users of such networks by malicious entities. In
-this article we investigate, both theoretically and empirically, privacy
-violation measures of large networks under active attacks that was recently
-introduced in (Information Sciences, 328, 403-417, 2016). Our theoretical
-result indicates that the network manager responsible for prevention of privacy
-violation must be very careful in designing the network if its topology does
-not contain a cycle. Our empirical results shed light on privacy violation
-properties of eight real social networks as well as a large number of synthetic
-networks generated by both the classical Erdos-Renyi model and the scale-free
-random networks generated by the Barabasi-Albert preferential-attachment model.
-",1,0,0,0,0,0
-19546,Mitigation of Policy Manipulation Attacks on Deep Q-Networks with Parameter-Space Noise,"  Recent developments have established the vulnerability of deep reinforcement
-learning to policy manipulation attacks via intentionally perturbed inputs,
-known as adversarial examples. In this work, we propose a technique for
-mitigation of such attacks based on addition of noise to the parameter space of
-deep reinforcement learners during training. We experimentally verify the
-effect of parameter-space noise in reducing the transferability of adversarial
-examples, and demonstrate the promising performance of this technique in
-mitigating the impact of whitebox and blackbox attacks at both test and
-training times.
-",0,0,0,1,0,0
-19547,Multi-Observation Elicitation,"  We study loss functions that measure the accuracy of a prediction based on
-multiple data points simultaneously. To our knowledge, such loss functions have
-not been studied before in the area of property elicitation or in machine
-learning more broadly. As compared to traditional loss functions that take only
-a single data point, these multi-observation loss functions can in some cases
-drastically reduce the dimensionality of the hypothesis required. In
-elicitation, this corresponds to requiring many fewer reports; in empirical
-risk minimization, it corresponds to algorithms on a hypothesis space of much
-smaller dimension. We explore some examples of the tradeoff between
-dimensionality and number of observations, give some geometric
-characterizations and intuition for relating loss functions and the properties
-that they elicit, and discuss some implications for both elicitation and
-machine-learning contexts.
-",1,0,0,0,0,0
-19548,"Next Stop ""NoOps"": Enabling Cross-System Diagnostics Through Graph-based Composition of Logs and Metrics","  Performing diagnostics in IT systems is an increasingly complicated task, and
-it is not doable in satisfactory time by even the most skillful operators.
-Systems and their architecture change very rapidly in response to business and
-user demand. Many organizations see value in the maintenance and management
-model of NoOps that stands for No Operations. One of the implementations of
-this model is a system that is maintained automatically without any human
-intervention. The path to NoOps involves not only precise and fast diagnostics
-but also reusing as much knowledge as possible after the system is reconfigured
-or changed. The biggest challenge is to leverage knowledge on one IT system and
-reuse this knowledge for diagnostics of another, different system. We propose a
-framework of weighted graphs which can transfer knowledge, and perform
-high-quality diagnostics of IT systems. We encode all possible data in a graph
-representation of a system state and automatically calculate weights of these
-graphs. Then, thanks to the evaluation of similarity between graphs, we
-transfer knowledge about failures from one system to another and use it for
-diagnostics. We successfully evaluate the proposed approach on Spark, Hadoop,
-Kafka and Cassandra systems.
-",1,0,0,0,0,0
-19549,Davenport-Heilbronn Theorems for Quotients of Class Groups,"  We prove a generalization of the Davenport-Heilbronn theorem to quotients of
-ideal class groups of quadratic fields by the primes lying above a fixed set of
-rational primes $S$. Additionally, we obtain average sizes for the relaxed
-Selmer group $\mathrm{Sel}_3^S(K)$ and for
-$\mathcal{O}_{K,S}^\times/(\mathcal{O}_{K,S}^\times)^3$ as $K$ varies among
-quadratic fields with a fixed signature ordered by discriminant.
-",0,0,1,0,0,0
-19550,Learning Universal Adversarial Perturbations with Generative Models,"  Neural networks are known to be vulnerable to adversarial examples, inputs
-that have been intentionally perturbed to remain visually similar to the source
-input, but cause a misclassification. It was recently shown that given a
-dataset and classifier, there exists so called universal adversarial
-perturbations, a single perturbation that causes a misclassification when
-applied to any input. In this work, we introduce universal adversarial
-networks, a generative network that is capable of fooling a target classifier
-when it's generated output is added to a clean sample from a dataset. We show
-that this technique improves on known universal adversarial attacks.
-",1,0,0,1,0,0
-19551,Large-Scale Classification using Multinomial Regression and ADMM,"  We present a novel method for learning the weights in multinomial logistic
-regression based on the alternating direction method of multipliers (ADMM). In
-each iteration, our algorithm decomposes the training into three steps; a
-linear least-squares problem for the weights, a global variable update
-involving a separable cross-entropy loss function, and a trivial dual variable
-update The least-squares problem can be factorized in the off-line phase, and
-the separability in the global variable update allows for efficient
-parallelization, leading to faster convergence. We compare our method with
-stochastic gradient descent for linear classification as well as for transfer
-learning and show that the proposed ADMM-Softmax leads to improved
-generalization and convergence.
-",1,0,0,1,0,0
-19552,First order dipolar phase transition in the Dicke model with infinitely coordinated frustrating interaction,"  We found analytically a first order quantum phase transition in the Cooper
-pair box array of $N$ low-capacitance Josephson junctions capacitively coupled
-to a resonant photon in a microwave cavity. The Hamiltonian of the system maps
-on the extended Dicke Hamiltonian of $N$ spins one-half with infinitely
-coordinated antiferromagnetic (frustrating) interaction. This interaction
-arises from the gauge-invariant coupling of the Josephson junctions phases to
-the vector potential of the resonant photon field. In $N \gg 1$ semiclassical
-limit, we found a critical coupling at which ground state of the system
-switches to the one with a net collective electric dipole moment of the Cooper
-pair boxes coupled to superradiant equilibrium photonic condensate. This phase
-transition changes from the first to second order if the frustrating
-interaction is switched off. A self-consistently `rotating' Holstein-Primakoff
-representation for the Cartesian components of the total superspin is proposed,
-that enables to trace both the first and the second order quantum phase
-transitions in the extended and standard Dicke models respectively.
-",0,1,0,0,0,0
-19553,Parsimonious Bayesian deep networks,"  Combining Bayesian nonparametrics and a forward model selection strategy, we
-construct parsimonious Bayesian deep networks (PBDNs) that infer
-capacity-regularized network architectures from the data and require neither
-cross-validation nor fine-tuning when training the model. One of the two
-essential components of a PBDN is the development of a special infinite-wide
-single-hidden-layer neural network, whose number of active hidden units can be
-inferred from the data. The other one is the construction of a greedy
-layer-wise learning algorithm that uses a forward model selection criterion to
-determine when to stop adding another hidden layer. We develop both Gibbs
-sampling and stochastic gradient descent based maximum a posteriori inference
-for PBDNs, providing state-of-the-art classification accuracy and interpretable
-data subtypes near the decision boundaries, while maintaining low computational
-complexity for out-of-sample prediction.
-",0,0,0,1,0,0
-19554,Learning to Play Othello with Deep Neural Networks,"  Achieving superhuman playing level by AlphaGo corroborated the capabilities
-of convolutional neural architectures (CNNs) for capturing complex spatial
-patterns. This result was to a great extent due to several analogies between Go
-board states and 2D images CNNs have been designed for, in particular
-translational invariance and a relatively large board. In this paper, we verify
-whether CNN-based move predictors prove effective for Othello, a game with
-significantly different characteristics, including a much smaller board size
-and complete lack of translational invariance. We compare several CNN
-architectures and board encodings, augment them with state-of-the-art
-extensions, train on an extensive database of experts' moves, and examine them
-with respect to move prediction accuracy and playing strength. The empirical
-evaluation confirms high capabilities of neural move predictors and suggests a
-strong correlation between prediction accuracy and playing strength. The best
-CNNs not only surpass all other 1-ply Othello players proposed to date but
-defeat (2-ply) Edax, the best open-source Othello player.
-",1,0,0,1,0,0
-19555,A causal modelling framework for reference-based imputation and tipping point analysis,"  We consider estimating the ""de facto"" or effectiveness estimand in a
-randomised placebo-controlled or standard-of-care-controlled drug trial with
-quantitative outcome, where participants who discontinue an investigational
-treatment are not followed up thereafter. Carpenter et al (2013) proposed
-reference-based imputation methods which use a reference arm to inform the
-distribution of post-discontinuation outcomes and hence to inform an imputation
-model. However, the reference-based imputation methods were not formally
-justified. We present a causal model which makes an explicit assumption in a
-potential outcomes framework about the maintained causal effect of treatment
-after discontinuation. We show that the ""jump to reference"", ""copy reference""
-and ""copy increments in reference"" reference-based imputation methods, with the
-control arm as the reference arm, are special cases of the causal model with
-specific assumptions about the causal treatment effect. Results from simulation
-studies are presented. We also show that the causal model provides a flexible
-and transparent framework for a tipping point sensitivity analysis in which we
-vary the assumptions made about the causal effect of discontinued treatment. We
-illustrate the approach with data from two longitudinal clinical trials.
-",0,0,0,1,0,0
-19556,"The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race","  Recent studies in social media spam and automation provide anecdotal
-argumentation of the rise of a new generation of spambots, so-called social
-spambots. Here, for the first time, we extensively study this novel phenomenon
-on Twitter and we provide quantitative evidence that a paradigm-shift exists in
-spambot design. First, we measure current Twitter's capabilities of detecting
-the new social spambots. Later, we assess the human performance in
-discriminating between genuine accounts, social spambots, and traditional
-spambots. Then, we benchmark several state-of-the-art techniques proposed by
-the academic literature. Results show that neither Twitter, nor humans, nor
-cutting-edge applications are currently capable of accurately detecting the new
-social spambots. Our results call for new approaches capable of turning the
-tide in the fight against this raising phenomenon. We conclude by reviewing the
-latest literature on spambots detection and we highlight an emerging common
-research trend based on the analysis of collective behaviors. Insights derived
-from both our extensive experimental campaign and survey shed light on the most
-promising directions of research and lay the foundations for the arms race
-against the novel social spambots. Finally, to foster research on this novel
-phenomenon, we make publicly available to the scientific community all the
-datasets used in this study.
-",1,0,0,0,0,0
-19557,Weakly-Supervised Spatial Context Networks,"  We explore the power of spatial context as a self-supervisory signal for
-learning visual representations. In particular, we propose spatial context
-networks that learn to predict a representation of one image patch from another
-image patch, within the same image, conditioned on their real-valued relative
-spatial offset. Unlike auto-encoders, that aim to encode and reconstruct
-original image patches, our network aims to encode and reconstruct intermediate
-representations of the spatially offset patches. As such, the network learns a
-spatially conditioned contextual representation. By testing performance with
-various patch selection mechanisms we show that focusing on object-centric
-patches is important, and that using object proposal as a patch selection
-mechanism leads to the highest improvement in performance. Further, unlike
-auto-encoders, context encoders [21], or other forms of unsupervised feature
-learning, we illustrate that contextual supervision (with pre-trained model
-initialization) can improve on existing pre-trained model performance. We build
-our spatial context networks on top of standard VGG_19 and CNN_M architectures
-and, among other things, show that we can achieve improvements (with no
-additional explicit supervision) over the original ImageNet pre-trained VGG_19
-and CNN_M models in object categorization and detection on VOC2007.
-",1,0,0,0,0,0
-19558,Variation of ionizing continuum: the main driver of Broad Absorption Line Variability,"  We present a statistical analysis of the variability of broad absorption
-lines (BALs) in quasars using the large multi-epoch spectroscopic dataset of
-the Sloan Digital Sky Survey Data Release 12 (SDSS DR12). We divide the sample
-into two groups according to the pattern of the variation of C iv BAL with
-respect to that of continuum: the equivalent widths (EW) of the BAL decreases
-(increases) when the continuum brightens (dims) as group T1; and the variation
-of EW and continuum in the opposite relation as group T2. We find that T2 has
-significantly (P_T<10-6 , Students T Test) higher EW ratios (R) of Si iv to C
-iv BAL than T1. Our result agrees with the prediction of photoionization models
-that C +3 column density increases (decreases) if there is a (or no) C +3
-ionization front while R decreases with the incident continuum. We show that
-BAL variabilities in at least 80% quasars are driven by the variation of
-ionizing continuum while other models that predict uncorrelated BAL and
-continuum variability contribute less than 20%. Considering large uncertainty
-in the continuum flux calibration, the latter fraction may be much smaller.
-When the sample is binned into different time interval between the two
-observations, we find significant difference in the distribution of R between
-T1 and T2 in all time-bins down to a deltaT < 6 days, suggesting that BAL
-outflow in a fraction of quasars has a recombination time scale of only a few
-days.
-",0,1,0,0,0,0
-19559,Low Dimensional Atomic Norm Representations in Line Spectral Estimation,"  The line spectral estimation problem consists in recovering the frequencies
-of a complex valued time signal that is assumed to be sparse in the spectral
-domain from its discrete observations. Unlike the gridding required by the
-classical compressed sensing framework, line spectral estimation reconstructs
-signals whose spectral supports lie continuously in the Fourier domain. If
-recent advances have shown that atomic norm relaxation produces highly robust
-estimates in this context, the computational cost of this approach remains,
-however, the major flaw for its application to practical systems.
-In this work, we aim to bridge the complexity issue by studying the atomic
-norm minimization problem from low dimensional projection of the signal
-samples. We derive conditions on the sub-sampling matrix under which the
-partial atomic norm can be expressed by a low-dimensional semidefinite program.
-Moreover, we illustrate the tightness of this relaxation by showing that it is
-possible to recover the original signal in poly-logarithmic time for two
-specific sub-sampling patterns.
-",1,0,0,0,0,0
-19560,The Trio Identity for Quasi-Monte Carlo Error,"  Monte Carlo methods approximate integrals by sample averages of integrand
-values. The error of Monte Carlo methods may be expressed as a trio identity:
-the product of the variation of the integrand, the discrepancy of the sampling
-measure, and the confounding. The trio identity has different versions,
-depending on whether the integrand is deterministic or Bayesian and whether the
-sampling measure is deterministic or random. Although the variation and the
-discrepancy are common in the literature, the confounding is relatively unknown
-and under-appreciated. Theory and examples are used to show how the cubature
-error may be reduced by employing the low discrepancy sampling that defines
-quasi-Monte Carlo methods. The error may also be reduced by rewriting the
-integral in terms of a different integrand. Finally, the confounding explains
-why the cubature error might decay at a rate different from that of the
-discrepancy.
-",0,0,1,0,0,0
-19561,Weak type operator Lipschitz and commutator estimates for commuting tuples,"  Let $f: \mathbb{R}^d \to\mathbb{R}$ be a Lipschitz function. If $B$ is a
-bounded self-adjoint operator and if $\{A_k\}_{k=1}^d$ are commuting bounded
-self-adjoint operators such that $[A_k,B]\in L_1(H),$ then
-$$\|[f(A_1,\cdots,A_d),B]\|_{1,\infty}\leq
-c(d)\|\nabla(f)\|_{\infty}\max_{1\leq k\leq d}\|[A_k,B]\|_1,$$ where $c(d)$ is
-a constant independent of $f$, $\mathcal{M}$ and $A,B$ and
-$\|\cdot\|_{1,\infty}$ denotes the weak $L_1$-norm. If $\{X_k\}_{k=1}^d$
-(respectively, $\{Y_k\}_{k=1}^d$) are commuting bounded self-adjoint operators
-such that $X_k-Y_k\in L_1(H),$ then
-$$\|f(X_1,\cdots,X_d)-f(Y_1,\cdots,Y_d)\|_{1,\infty}\leq
-c(d)\|\nabla(f)\|_{\infty}\max_{1\leq k\leq d}\|X_k-Y_k\|_1.$$
-",0,0,1,0,0,0
-19562,Fast Spectral Ranking for Similarity Search,"  Despite the success of deep learning on representing images for particular
-object retrieval, recent studies show that the learned representations still
-lie on manifolds in a high dimensional space. This makes the Euclidean nearest
-neighbor search biased for this task. Exploring the manifolds online remains
-expensive even if a nearest neighbor graph has been computed offline. This work
-introduces an explicit embedding reducing manifold search to Euclidean search
-followed by dot product similarity search. This is equivalent to linear graph
-filtering of a sparse signal in the frequency domain. To speed up online
-search, we compute an approximate Fourier basis of the graph offline. We
-improve the state of art on particular object retrieval datasets including the
-challenging Instre dataset containing small objects. At a scale of 10^5 images,
-the offline cost is only a few hours, while query time is comparable to
-standard similarity search.
-",1,0,0,0,0,0
-19563,Transition of multi-diffusive states in a biased periodic potential,"  We study a frequency-dependent damping model of hyper-diffusion within the
-generalized Langevin equation. The model allows for the colored noise defined
-by its spectral density, assumed to be proportional to $\omega^{\delta-1}$ at
-low frequencies with $0<\delta<1$ (sub-Ohmic damping) or $1<\delta<2$
-(super-Ohmic damping), where the frequency-dependent damping is deduced from
-the noise by means of the fluctuation-dissipation theorem. It is shown that for
-super-Ohmic damping and certain parameters, the diffusive process of the
-particle in a titled periodic potential undergos sequentially four
-time-regimes: thermalization, hyper-diffusion, collapse and asymptotical
-restoration. For analysing transition phenomenon of multi-diffusive states, we
-demonstrate that the first exist time of the particle escaping from the locked
-state into the running state abides by an exponential distribution. The concept
-of equivalent velocity trap is introduced in the present model, moreover,
-reformation of ballistic diffusive system is also considered as a marginal
-situation, however there does not exhibit the collapsed state of diffusion.
-",0,1,0,0,0,0
-19564,Deformable Classifiers,"  Geometric variations of objects, which do not modify the object class, pose a
-major challenge for object recognition. These variations could be rigid as well
-as non-rigid transformations. In this paper, we design a framework for training
-deformable classifiers, where latent transformation variables are introduced,
-and a transformation of the object image to a reference instantiation is
-computed in terms of the classifier output, separately for each class. The
-classifier outputs for each class, after transformation, are compared to yield
-the final decision. As a by-product of the classification this yields a
-transformation of the input object to a reference pose, which can be used for
-downstream tasks such as the computation of object support. We apply a two-step
-training mechanism for our framework, which alternates between optimizing over
-the latent transformation variables and the classifier parameters to minimize
-the loss function. We show that multilayer perceptrons, also known as deep
-networks, are well suited for this approach and achieve state of the art
-results on the rotated MNIST and the Google Earth dataset, and produce
-competitive results on MNIST and CIFAR-10 when training on smaller subsets of
-training data.
-",0,0,0,1,0,0
-19565,Energy Dissipation in Hamiltonian Chains of Rotators,"  We discuss, in the context of energy flow in high-dimensional systems and
-Kolmogorov-Arnol'd-Moser (KAM) theory, the behavior of a chain of rotators
-(rotors) which is purely Hamiltonian, apart from dissipation at just one end.
-We derive bounds on the dissipation rate which become arbitrarily small in
-certain physical regimes, and we present numerical evidence that these bounds
-are sharp. We relate this to the decoupling of non-resonant terms as is known
-in KAM problems.
-",0,1,1,0,0,0
-19566,"Computational complexity, torsion-freeness of homoclinic Floer homology, and homoclinic Morse inequalities","  Floer theory was originally devised to estimate the number of 1-periodic
-orbits of Hamiltonian systems. In earlier works, we constructed Floer homology
-for homoclinic orbits on two dimensional manifolds using combinatorial
-techniques. In the present paper, we study theoretic aspects of computational
-complexity of homoclinic Floer homology. More precisely, for finding the
-homoclinic points and immersions that generate the homology and its boundary
-operator, we establish sharp upper bounds in terms of iterations of the
-underlying symplectomorphism. This prepares the ground for future numerical
-works.
-Although originally aimed at numerics, the above bounds provide also purely
-algebraic applications, namely
-1) Torsion-freeness of primary homoclinic Floer homology.
-2) Morse type inequalities for primary homoclinic orbits.
-",0,0,1,0,0,0
-19567,Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models,"  Bayesian inference for stochastic volatility models using MCMC methods highly
-depends on actual parameter values in terms of sampling efficiency. While draws
-from the posterior utilizing the standard centered parameterization break down
-when the volatility of volatility parameter in the latent state equation is
-small, non-centered versions of the model show deficiencies for highly
-persistent latent variable series. The novel approach of
-ancillarity-sufficiency interweaving has recently been shown to aid in
-overcoming these issues for a broad class of multilevel models. In this paper,
-we demonstrate how such an interweaving strategy can be applied to stochastic
-volatility models in order to greatly improve sampling efficiency for all
-parameters and throughout the entire parameter range. Moreover, this method of
-""combining best of different worlds"" allows for inference for parameter
-constellations that have previously been infeasible to estimate without the
-need to select a particular parameterization beforehand.
-",0,0,0,1,0,0
-19568,Simple root flows for Hitchin representations,"  We study simple root flows and Liouville currents for Hitchin
-representations. We show that the Liouville current is associated to the
-measure of maximal entropy for a simple root flow, derive a Liouville volume
-rigidity result, and construct a Liouville pressure metric on the Hitchin
-component.
-",0,0,1,0,0,0
-19569,On the Origin of Deep Learning,"  This paper is a review of the evolutionary history of deep learning models.
-It covers from the genesis of neural networks when associationism modeling of
-the brain is studied, to the models that dominate the last decade of research
-in deep learning like convolutional neural networks, deep belief networks, and
-recurrent neural networks. In addition to a review of these models, this paper
-primarily focuses on the precedents of the models above, examining how the
-initial ideas are assembled to construct the early models and how these
-preliminary models are developed into their current forms. Many of these
-evolutionary paths last more than half a century and have a diversity of
-directions. For example, CNN is built on prior knowledge of biological vision
-system; DBN is evolved from a trade-off of modeling power and computation
-complexity of graphical models and many nowadays models are neural counterparts
-of ancient linear models. This paper reviews these evolutionary paths and
-offers a concise thought flow of how these models are developed, and aims to
-provide a thorough background for deep learning. More importantly, along with
-the path, this paper summarizes the gist behind these milestones and proposes
-many directions to guide the future research of deep learning.
-",1,0,0,1,0,0
-19570,Control of Asynchronous Imitation Dynamics on Networks,"  Imitation is widely observed in populations of decision-making agents. Using
-our recent convergence results for asynchronous imitation dynamics on networks,
-we consider how such networks can be efficiently driven to a desired
-equilibrium state by offering payoff incentives for using a certain strategy,
-either uniformly or targeted to individuals. In particular, if for each
-available strategy, agents playing that strategy receive maximum payoff when
-their neighbors play that same strategy, we show that providing incentives to
-agents in a network that is at equilibrium will result in convergence to a
-unique new equilibrium. For the case when a uniform incentive can be offered to
-all agents, this result allows the computation of the optimal incentive using a
-binary search algorithm. When incentives can be targeted to individual agents,
-we propose an algorithm to select which agents should be chosen based on
-iteratively maximizing a ratio of the number of agents who adopt the desired
-strategy to the payoff incentive required to get those agents to do so.
-Simulations demonstrate that the proposed algorithm computes near-optimal
-targeted payoff incentives for a range of networks and payoff distributions in
-coordination games.
-",1,1,0,0,0,0
-19571,Finsler structures on holomorphic Lie algebroids,"  Complex Finsler vector bundles have been studied mainly by T. Aikou, who
-defined complex Finsler structures on holomorphic vector bundles. In this
-paper, we consider the more general case of a holomorphic Lie algebroid E and
-we introduce Finsler structures, partial and Chern-Finsler connections on it.
-First, we recall some basic notions on holomorphic Lie algebroids. Then, using
-an idea from E. Martinez, we introduce the concept of complexified prolongation
-of such an algebroid. Also, we study nonlinear and linear connections on the
-tangent bundle of E and on the prolongation of E and we investigate the
-relation between their coefficients. The analogue of the classical
-Chern-Finsler connection is defined and studied in the paper for the case of
-the holomorphic Lie algebroid.
-",0,0,1,0,0,0
-19572,The role of complex analysis in modeling economic growth,"  Development and growth are complex and tumultuous processes. Modern economic
-growth theories identify some key determinants of economic growth. However, the
-relative importance of the determinants remains unknown, and additional
-variables may help clarify the directions and dimensions of the interactions.
-The novel stream of literature on economic complexity goes beyond aggregate
-measures of productive inputs, and considers instead a more granular and
-structural view of the productive possibilities of countries, i.e. their
-capabilities. Different endowments of capabilities are crucial ingredients in
-explaining differences in economic performances. In this paper we employ
-economic fitness, a measure of productive capabilities obtained through complex
-network techniques. Focusing on the combined roles of fitness and some more
-traditional drivers of growth, we build a bridge between economic growth
-theories and the economic complexity literature. Our findings, in agreement
-with other recent empirical studies, show that fitness plays a crucial role in
-fostering economic growth and, when it is included in the analysis, can be
-either complementary to traditional drivers of growth or can completely
-overshadow them.
-",0,0,0,0,0,1
-19573,Inputs from Hell: Generating Uncommon Inputs from Common Samples,"  Generating structured input files to test programs can be performed by
-techniques that produce them from a grammar that serves as the specification
-for syntactically correct input files. Two interesting scenarios then arise for
-effective testing. In the first scenario, software engineers would like to
-generate inputs that are as similar as possible to the inputs in common usage
-of the program, to test the reliability of the program. More interesting is the
-second scenario where inputs should be as dissimilar as possible from normal
-usage. This is useful for robustness testing and exploring yet uncovered
-behavior. To provide test cases for both scenarios, we leverage a context-free
-grammar to parse a set of sample input files that represent the program's
-common usage, and determine probabilities for individual grammar production as
-they occur during parsing the inputs. Replicating these probabilities during
-grammar-based test input generation, we obtain inputs that are close to the
-samples. Inverting these probabilities yields inputs that are strongly
-dissimilar to common inputs, yet still valid with respect to the grammar. Our
-evaluation on three common input formats (JSON, JavaScript, CSS) shows the
-effectiveness of these approaches in obtaining instances from both sets of
-inputs.
-",1,0,0,0,0,0
-19574,On equivariant formal deformation theory,"  Using the set-up of deformation categories of Talpo and Vistoli, we
-re-interpret and generalize, in the context of cartesian morphisms in abstract
-categories, some results of Rim concerning obstructions against extensions of
-group actions in infinitesimal deformations. Furthermore, we observe that
-finite étale coverings can be infinitesimally extended and the resulting
-formal scheme is algebraizable. Finally, we show that pre-Tango structures
-survive under pullbacks with respect to finite, generically étale surjections
-$\pi:X\rightarrow Y$, and record some consequences regarding Kodaira vanishing
-in degree one.
-",0,0,1,0,0,0
-19575,A self-consistent cloud model for brown dwarfs and young giant exoplanets: comparison with photometric and spectroscopic observations,"  We developed a simple, physical and self-consistent cloud model for brown
-dwarfs and young giant exoplanets. We compared different parametrisations for
-the cloud particle size, by either fixing particle radii, or fixing the mixing
-efficiency (parameter fsed) or estimating particle radii from simple
-microphysics. The cloud scheme with simple microphysics appears as the best
-parametrisation by successfully reproducing the observed photometry and spectra
-of brown dwarfs and young giant exoplanets. In particular, it reproduces the
-L-T transition, due to the condensation of silicate and iron clouds below the
-visible/near-IR photosphere. It also reproduces the reddening observed for
-low-gravity objects, due to an increase of cloud optical depth for low gravity.
-In addition, we found that the cloud greenhouse effect shifts chemical
-equilibriums, increasing the abundances of species stable at high temperature.
-This effect should significantly contribute to the strong variation of methane
-abundance at the L-T transition and to the methane depletion observed on young
-exoplanets. Finally, we predict the existence of a continuum of brown dwarfs
-and exoplanets for absolute J magnitude=15-18 and J-K color=0-3, due to the
-evolution of the L-T transition with gravity. This self-consistent model
-therefore provides a general framework to understand the effects of clouds and
-appears well-suited for atmospheric retrievals.
-",0,1,0,0,0,0
-19576,Consistency Between the Luminosity Function of Resolved Millisecond Pulsars and the Galactic Center Excess,"  Fermi Large Area Telescope data reveal an excess of GeV gamma rays from the
-direction of the Galactic Center and bulge. Several explanations have been
-proposed for this excess including an unresolved population of millisecond
-pulsars (MSPs) and self-annihilating dark matter. It has been claimed that a
-key discriminant for or against the MSP explanation can be extracted from the
-properties of the luminosity function describing this source population.
-Specifically, is the luminosity function of the putative MSPs in the Galactic
-Center consistent with that characterizing the resolved MSPs in the Galactic
-disk? To investigate this we have used a Bayesian Markov Chain Monte Carlo to
-evaluate the posterior distribution of the parameters of the MSP luminosity
-function describing both resolved MSPs and the Galactic Center excess. At
-variance with some other claims, our analysis reveals that, within current
-uncertainties, both data sets can be well fit with the same luminosity
-function.
-",0,1,0,0,0,0
-19577,First functionality tests of a 64 x 64 pixel DSSC sensor module connected to the complete ladder readout,"  The European X-ray Free Electron Laser (XFEL.EU) will provide every 0.1 s a
-train of 2700 spatially coherent ultrashort X-ray pulses at 4.5 MHz repetition
-rate. The Small Quantum Systems (SQS) instrument and the Spectroscopy and
-Coherent Scattering instrument (SCS) operate with soft X-rays between 0.5 keV -
-6keV. The DEPFET Sensor with Signal Compression (DSSC) detector is being
-developed to meet the requirements set by these two XFEL.EU instruments. The
-DSSC imager is a 1 mega-pixel camera able to store up to 800 single-pulse
-images per train. The so-called ladder is the basic unit of the DSSC detector.
-It is the single unit out of sixteen identical-units composing the
-DSSC-megapixel camera, containing all representative electronic components of
-the full-size system and allows testing the full electronic chain. Each DSSC
-ladder has a focal plane sensor with 128 x 512 pixels. The read-out ASIC
-provides full-parallel readout of the sensor pixels. Every read-out channel
-contains an amplifier and an analog filter, an up-to 9 bit ADC and the digital
-memory. The ASIC amplifier have a double front-end to allow one to use either
-DEPFET sensors or Mini-SDD sensors. In the first case, the signal compression
-is a characteristic intrinsic of the sensor; in the second case, the
-compression is implemented at the first amplification stage. The goal of signal
-compression is to meet the requirement of single-photon detection capability
-and wide dynamic range. We present the first results of measurements obtained
-using a 64 x 64 pixel DEPFET sensor attached to the full final electronic and
-data-acquisition chain.
-",0,1,0,0,0,0
-19578,Label Propagation on K-partite Graphs with Heterophily,"  In this paper, for the first time, we study label propagation in
-heterogeneous graphs under heterophily assumption. Homophily label propagation
-(i.e., two connected nodes share similar labels) in homogeneous graph (with
-same types of vertices and relations) has been extensively studied before.
-Unfortunately, real-life networks are heterogeneous, they contain different
-types of vertices (e.g., users, images, texts) and relations (e.g.,
-friendships, co-tagging) and allow for each node to propagate both the same and
-opposite copy of labels to its neighbors. We propose a $\mathcal{K}$-partite
-label propagation model to handle the mystifying combination of heterogeneous
-nodes/relations and heterophily propagation. With this model, we develop a
-novel label inference algorithm framework with update rules in near-linear time
-complexity. Since real networks change over time, we devise an incremental
-approach, which supports fast updates for both new data and evidence (e.g.,
-ground truth labels) with guaranteed efficiency. We further provide a utility
-function to automatically determine whether an incremental or a re-modeling
-approach is favored. Extensive experiments on real datasets have verified the
-effectiveness and efficiency of our approach, and its superiority over the
-state-of-the-art label propagation methods.
-",1,0,0,0,0,0
-19579,On LoRaWAN Scalability: Empirical Evaluation of Susceptibility to Inter-Network Interference,"  Appearing on the stage quite recently, the Low Power Wide Area Networks
-(LPWANs) are currently getting much of attention. In the current paper we study
-the susceptibility of one LPWAN technology, namely LoRaWAN, to the
-inter-network interferences. By means of excessive empirical measurements
-employing the certified commercial transceivers, we characterize the effect of
-modulation coding schemes (known for LoRaWAN as data rates (DRs)) of a
-transmitter and an interferer on probability of successful packet delivery
-while operating in EU 868 MHz band. We show that in reality the transmissions
-with different DRs in the same frequency channel can negatively affect each
-other and that the high DRs are influenced by interferences more severely than
-the low ones. Also, we show that the LoRa-modulated DRs are affected by the
-interferences much less than the FSK-modulated one. Importantly, the presented
-results provide insight into the network-level operation of the LoRa LPWAN
-technology in general, and its scalability potential in particular. The results
-can also be used as a reference for simulations and analyses or for defining
-the communication parameters for real-life applications.
-",1,0,0,0,0,0
-19580,DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution,"  The impacts of climate change are felt by most critical systems, such as
-infrastructure, ecological systems, and power-plants. However, contemporary
-Earth System Models (ESM) are run at spatial resolutions too coarse for
-assessing effects this localized. Local scale projections can be obtained using
-statistical downscaling, a technique which uses historical climate observations
-to learn a low-resolution to high-resolution mapping. Depending on statistical
-modeling choices, downscaled projections have been shown to vary significantly
-terms of accuracy and reliability. The spatio-temporal nature of the climate
-system motivates the adaptation of super-resolution image processing techniques
-to statistical downscaling. In our work, we present DeepSD, a generalized
-stacked super resolution convolutional neural network (SRCNN) framework for
-statistical downscaling of climate variables. DeepSD augments SRCNN with
-multi-scale input channels to maximize predictability in statistical
-downscaling. We provide a comparison with Bias Correction Spatial
-Disaggregation as well as three Automated-Statistical Downscaling approaches in
-downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km)
-over the Continental United States. Furthermore, a framework using the NASA
-Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM
-models with multiple emission scenarios.
-",1,0,0,0,0,0
-19581,Statistical analysis of the ambiguities in the asteroid period determinations,"  Among asteroids there exist ambiguities in their rotation period
-determinations. They are due to incomplete coverage of the rotation, noise
-and/or aliases resulting from gaps between separate lightcurves. To help to
-remove such uncertainties, basic characteristic of the lightcurves resulting
-from constraints imposed by the asteroid shapes and geometries of observations
-should be identified. We simulated light variations of asteroids which shapes
-were modelled as Gaussian random spheres, with random orientations of spin
-vectors and phase angles changed every $5^\circ$ from $0^\circ$ to $65^\circ$.
-This produced 1.4 mln lightcurves. For each simulated lightcurve Fourier
-analysis has been made and the harmonic of the highest amplitude was recorded.
-From the statistical point of view, all lightcurves observed at phase angles
-$\alpha < 30^\circ$, with peak-to-peak amplitudes $A>0.2$ mag are bimodal.
-Second most frequently dominating harmonic is the first one, with the 3rd
-harmonic following right after. For 1% of lightcurves with amplitudes $A < 0.1$
-mag and phase angles $\alpha < 40^\circ$ 4th harmonic dominates.
-",0,1,0,0,0,0
-19582,The Assistive Multi-Armed Bandit,"  Learning preferences implicit in the choices humans make is a well studied
-problem in both economics and computer science. However, most work makes the
-assumption that humans are acting (noisily) optimally with respect to their
-preferences. Such approaches can fail when people are themselves learning about
-what they want. In this work, we introduce the assistive multi-armed bandit,
-where a robot assists a human playing a bandit task to maximize cumulative
-reward. In this problem, the human does not know the reward function but can
-learn it through the rewards received from arm pulls; the robot only observes
-which arms the human pulls but not the reward associated with each pull. We
-offer sufficient and necessary conditions for successfully assisting the human
-in this framework. Surprisingly, better human performance in isolation does not
-necessarily lead to better performance when assisted by the robot: a human
-policy can do better by effectively communicating its observed rewards to the
-robot. We conduct proof-of-concept experiments that support these results. We
-see this work as contributing towards a theory behind algorithms for
-human-robot interaction.
-",1,0,0,1,0,0
-19583,Localized Manifold Harmonics for Spectral Shape Analysis,"  The use of Laplacian eigenfunctions is ubiquitous in a wide range of computer
-graphics and geometry processing applications. In particular, Laplacian
-eigenbases allow generalizing the classical Fourier analysis to manifolds. A
-key drawback of such bases is their inherently global nature, as the Laplacian
-eigenfunctions carry geometric and topological structure of the entire
-manifold. In this paper, we introduce a new framework for local spectral shape
-analysis. We show how to efficiently construct localized orthogonal bases by
-solving an optimization problem that in turn can be posed as the
-eigendecomposition of a new operator obtained by a modification of the standard
-Laplacian. We study the theoretical and computational aspects of the proposed
-framework and showcase our new construction on the classical problems of shape
-approximation and correspondence. We obtain significant improvement compared to
-classical Laplacian eigenbases as well as other alternatives for constructing
-localized bases.
-",1,0,0,0,0,0
-19584,How to Search the Internet Archive Without Indexing It,"  Significant parts of cultural heritage are produced on the web during the
-last decades. While easy accessibility to the current web is a good baseline,
-optimal access to the past web faces several challenges. This includes dealing
-with large-scale web archive collections and lacking of usage logs that contain
-implicit human feedback most relevant for today's web search. In this paper, we
-propose an entity-oriented search system to support retrieval and analytics on
-the Internet Archive. We use Bing to retrieve a ranked list of results from the
-current web. In addition, we link retrieved results to the WayBack Machine;
-thus allowing keyword search on the Internet Archive without processing and
-indexing its raw archived content. Our search system complements existing web
-archive search tools through a user-friendly interface, which comes close to
-the functionalities of modern web search engines (e.g., keyword search, query
-auto-completion and related query suggestion), and provides a great benefit of
-taking user feedback on the current web into account also for web archive
-search. Through extensive experiments, we conduct quantitative and qualitative
-analyses in order to provide insights that enable further research on and
-practical applications of web archives.
-",1,0,0,0,0,0
-19585,Quantum Dot at a Luttinger liquid edge - Exact solution via Bethe Ansatz,"  We study a system consisting of a Luttinger liquid coupled to a quantum dot
-on the boundary. The Luttinger liquid is expressed in terms of fermions
-interacting via density-density coupling and the dot is modeled as an
-interacting resonant level on to which the bulk fermions can tunnel. We solve
-the Hamiltonian exactly and construct all eigenstates. We study both the zero
-and finite temperature properties of the system, in particular we compute the
-exact dot occupation as a function of the dot energy in all parameter regimes.
-The system is seen to flow from weak to to strong coupling for all values of
-the bulk interaction, with the flow characterized by a non-perturbative Kondo
-scale. We identify the critical exponents at the weak and strong coupling
-regimes.
-",0,1,0,0,0,0
-19586,Optimal Jittered Sampling for two Points in the Unit Square,"  Jittered Sampling is a refinement of the classical Monte Carlo sampling
-method. Instead of picking $n$ points randomly from $[0,1]^2$, one partitions
-the unit square into $n$ regions of equal measure and then chooses a point
-randomly from each partition. Currently, no good rules for how to partition the
-space are available. In this paper, we present a solution for the special case
-of subdividing the unit square by a decreasing function into two regions so as
-to minimize the expected squared $\mathcal{L}_2-$discrepancy. The optimal
-partitions are given by a \textit{highly} nonlinear integral equation for which
-we determine an approximate solution. In particular, there is a break of
-symmetry and the optimal partition is not into two sets of equal measure. We
-hope this stimulates further interest in the construction of good partitions.
-",0,0,1,0,0,0
-19587,Brain structural connectivity atrophy in Alzheimer's disease,"  Analysis and quantification of brain structural changes, using Magnetic
-resonance imaging (MRI), are increasingly used to define novel biomarkers of
-brain pathologies, such as Alzheimer's disease (AD). Network-based models of
-the brain have shown that both local and global topological properties can
-reveal patterns of disease propagation. On the other hand, intra-subject
-descriptions cannot exploit the whole information context, accessible through
-inter-subject comparisons. To address this, we developed a novel approach,
-which models brain structural connectivity atrophy with a multiplex network and
-summarizes it within a classification score. On an independent dataset
-multiplex networks were able to correctly segregate, from normal controls (NC),
-AD patients and subjects with mild cognitive impairment that will convert to AD
-(cMCI) with an accuracy of, respectively, $0.86 \pm 0.01$ and $0.84 \pm 0.01$.
-The model also shows that illness effects are maximally detected by parceling
-the brain in equal volumes of $3000$ $mm^3$ (""patches""), without any $a$
-$priori$ segmentation based on anatomical features. A direct comparison to
-standard voxel-based morphometry on the same dataset showed that the multiplex
-network approach had higher sensitivity. This method is general and can have
-twofold potential applications: providing a reliable tool for clinical trials
-and a disease signature of neurodegenerative pathologies.
-",0,1,0,0,0,0
-19588,Probabilistic Combination of Noisy Points and Planes for RGB-D Odometry,"  This work proposes a visual odometry method that combines points and plane
-primitives, extracted from a noisy depth camera. Depth measurement uncertainty
-is modelled and propagated through the extraction of geometric primitives to
-the frame-to-frame motion estimation, where pose is optimized by weighting the
-residuals of 3D point and planes matches, according to their uncertainties.
-Results on an RGB-D dataset show that the combination of points and planes,
-through the proposed method, is able to perform well in poorly textured
-environments, where point-based odometry is bound to fail.
-",1,0,0,0,0,0
-19589,Autoignition of Butanol Isomers at Low to Intermediate Temperature and Elevated Pressure,"  Autoignition delay experiments for the isomers of butanol, including n-,
-sec-, tert-, and iso-butanol, have been performed using a heated rapid
-compression machine. For a compressed pressure of 15 bar, the compressed
-temperatures have been varied in the range of 725-855 K for all the
-stoichiometric fuel/oxidizer mixtures. Over the conditions investigated in this
-study, the ignition delay decreases monotonically as temperature increases and
-exhibits single-stage characteristics. Experimental ignition delays are also
-compared to simulations computed using three kinetic mechanisms available in
-the literature. Reasonable agreement is found for three isomers (tert-, iso-,
-and n-butanol).
-",0,1,0,0,0,0
-19590,On the $k$-abelian complexity of the Cantor sequence,"  In this paper, we prove that for every integer $k \geq 1$, the $k$-abelian
-complexity function of the Cantor sequence $\mathbf{c} = 101000101\cdots$ is a
-$3$-regular sequence.
-",1,0,1,0,0,0
-19591,Sharp rates of convergence for accumulated spectrograms,"  We investigate an inverse problem in time-frequency localization: the
-approximation of the symbol of a time-frequency localization operator from
-partial spectral information by the method of accumulated spectrograms (the sum
-of the spectrograms corresponding to large eigenvalues). We derive a sharp
-bound for the rate of convergence of the accumulated spectrogram, improving on
-recent results.
-",0,0,1,0,0,0
-19592,Putting a Face to the Voice: Fusing Audio and Visual Signals Across a Video to Determine Speakers,"  In this paper, we present a system that associates faces with voices in a
-video by fusing information from the audio and visual signals. The thesis
-underlying our work is that an extremely simple approach to generating (weak)
-speech clusters can be combined with visual signals to effectively associate
-faces and voices by aggregating statistics across a video. This approach does
-not need any training data specific to this task and leverages the natural
-coherence of information in the audio and visual streams. It is particularly
-applicable to tracking speakers in videos on the web where a priori information
-about the environment (e.g., number of speakers, spatial signals for
-beamforming) is not available. We performed experiments on a real-world dataset
-using this analysis framework to determine the speaker in a video. Given a
-ground truth labeling determined by human rater consensus, our approach had
-~71% accuracy.
-",1,0,0,0,0,0
-19593,Topological Interplay between Knots and Entangled Vortex-Membranes,"  In this paper, the Kelvin wave and knot dynamics are studied on three
-dimensional smoothly deformed entangled vortex-membranes in five dimensional
-space. Owing to the existence of local Lorentz invariance and diffeomorphism
-invariance, in continuum limit gravity becomes an emergent phenomenon on 3+1
-dimensional zero-lattice (a lattice of projected zeroes): On the one hand, the
-deformed zero-lattice can be denoted by curved space-time for knots; on the
-other hand, the knots as topological defect of 3+1 dimensional zero-lattice
-indicates matter may curve space-time. This work would help researchers to
-understand the mystery in gravity.
-",0,1,0,0,0,0
-19594,The first and second fundamental theorems of invariant theory for the quantum general linear supergroup,"  We develop the non-commutative polynomial version of the invariant theory for
-the quantum general linear supergroup ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$. A
-non-commutative ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$-module superalgebra
-$\mathcal{P}^{k|l}_{\,r|s}$ is constructed, which is the quantum analogue of
-the supersymmetric algebra over $\mathbb{C}^{k|l}\otimes \mathbb{C}^{m|n}\oplus
-\mathbb{C}^{r|s}\otimes (\mathbb{C}^{m|n})^{\ast}$. We analyse the structure of
-the subalgebra of ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$-invariants in
-$\mathcal{P}^{k|l}_{\,r|s}$ by using the quantum super analogue of Howe
-duality.
-The subalgebra of ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$-invariants in
-$\mathcal{P}^{k|l}_{\,r|s}$ is shown to be finitely generated. We determine its
-generators and establish a surjective superalgebra homomorphism from a braided
-supersymmetric algebra onto it. This establishes the first fundamental theorem
-of invariant theory for ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$.
-We show that the above mentioned superalgebra homomorphism is an isomorphism
-if and only if $m\geq \min\{k,r\}$ and $n\geq \min\{l,s\}$, and obtain a
-monomial basis for the subalgebra of invariants in this case. When the
-homomorphism is not injective, we give a representation theoretical description
-of the generating elements of the kernel associated to the partition
-$((m+1)^{n+1})$, producing the second fundamental theorem of invariant theory
-for ${\rm{ U}}_q(\mathfrak{gl}_{m|n})$.
-We consider two applications of our results. A complete treatment of the
-non-commutative polynomial version of invariant theory for ${\rm{
-U}}_q(\mathfrak{gl}_{m})$ is obtained as the special case with $n=0$, where an
-explicit SFT is proved, which we believe to be new. The FFT and SFT of the
-invariant theory for the general linear superalgebra are recovered from the
-classical (i.e., $q\to 1$) limit of our results.
-",0,0,1,0,0,0
-19595,Poly-Spline Finite Element Method,"  We introduce an integrated meshing and finite element method pipeline
-enabling black-box solution of partial differential equations in the volume
-enclosed by a boundary representation. We construct a hybrid
-hexahedral-dominant mesh, which contains a small number of star-shaped
-polyhedra, and build a set of high-order basis on its elements, combining
-triquadratic B-splines, triquadratic hexahedra (27 degrees of freedom), and
-harmonic elements. We demonstrate that our approach converges cubically under
-refinement, while requiring around 50% of the degrees of freedom than a
-similarly dense hexahedral mesh composed of triquadratic hexahedra. We validate
-our approach solving Poisson's equation on a large collection of models, which
-are automatically processed by our algorithm, only requiring the user to
-provide boundary conditions on their surface.
-",1,0,0,0,0,0
-19596,"Turing Completeness of Finite, Epistemic Programs","  In this note, we show the class of finite, epistemic programs to be Turing
-complete. Epistemic programs is a widely used update mechanism used in
-epistemic logic, where it such are a special type of action models: One which
-does not contain postconditions.
-",1,0,0,0,0,0
-19597,Spontaneous domain formation in disordered copolymers as a mechanism for chromosome structuring,"  Motivated by the problem of domain formation in chromosomes, we studied a
-co--polymer model where only a subset of the monomers feel attractive
-interactions. These monomers are displaced randomly from a regularly-spaced
-pattern, thus introducing some quenched disorder in the system. Previous work
-has shown that in the case of regularly-spaced interacting monomers this chain
-can fold into structures characterized by multiple distinct domains of
-consecutive segments. In each domain, attractive interactions are balanced by
-the entropy cost of forming loops. We show by advanced replica-exchange
-simulations that adding disorder in the position of the interacting monomers
-further stabilizes these domains. The model suggests that the partitioning of
-the chain into well-defined domains of consecutive monomers is a spontaneous
-property of heteropolymers. In the case of chromosomes, evolution could have
-acted on the spacing of interacting monomers to modulate in a simple way the
-underlying domains for functional reasons.
-",0,0,0,0,1,0
-19598,Reconstruction of Correlated Sources with Energy Harvesting Constraints in Delay-constrained and Delay-tolerant Communication Scenarios,"  In this paper, we investigate the reconstruction of time-correlated sources
-in a point-to-point communications scenario comprising an energy-harvesting
-sensor and a Fusion Center (FC). Our goal is to minimize the average distortion
-in the reconstructed observations by using data from previously encoded sources
-as side information. First, we analyze a delay-constrained scenario, where the
-sources must be reconstructed before the next time slot. We formulate the
-problem in a convex optimization framework and derive the optimal transmission
-(i.e., power and rate allocation) policy. To solve this problem, we propose an
-iterative algorithm based on the subgradient method. Interestingly, the
-solution to the problem consists of a coupling between a two-dimensional
-directional water-filling algorithm (for power allocation) and a reverse
-water-filling algorithm (for rate allocation). Then we find a more general
-solution to this problem in a delay-tolerant scenario where the time horizon
-for source reconstruction is extended to multiple time slots. Finally, we
-provide some numerical results that illustrate the impact of delay and
-correlation in the power and rate allocation policies, and in the resulting
-reconstruction distortion. We also discuss the performance gap exhibited by a
-heuristic online policy derived from the optimal (offline) one.
-",1,0,0,0,0,0
-19599,Future of Flexible Robotic Endoscopy Systems,"  Robotics enables a variety of unconventional actuation strategies to be used
-for endoscopes, resulting in reduced trauma to the GI tract. For transmission
-of force to distally mounted endoscopic instruments, robotically actuated
-tendon sheath mechanisms are the current state of the art. Robotics in surgical
-endoscopy enables an ergonomic mapping of the surgeon movements to remotely
-controlled slave arms, facilitating tissue manipulation. The learning curve for
-difficult procedures such as endoscopic submucosal dissection and
-full-thickness resection can be significantly reduced. Improved surgical
-outcomes are also observed from clinical and pre-clinical trials. The
-technology behind master-slave surgical robotics will continue to mature, with
-the addition of position and force sensors enabling better control and tactile
-feedback. More robotic assisted GI luminal and NOTES surgeries are expected to
-be conducted in future, and gastroenterologists will have a key collaborative
-role to play.
-",1,1,0,0,0,0
-19600,Shear-driven parametric instability in a precessing sphere,"  The present numerical study aims at shedding light on the mechanism
-underlying the precessional instability in a sphere. Precessional instabilities
-in the form of parametric resonance due to topographic coupling have been
-reported in a spheroidal geometry both analytically and numerically. We show
-that such parametric resonances can also develop in spherical geometry due to
-the conical shear layers driven by the Ekman pumping singularities at the
-critical latitudes. Scaling considerations lead to a stability criterion of the
-form, $|P_o|>O(E^{4/5})$, where $P_o$ represents the Poincaré number and $E$
-the Ekman number. The predicted threshold is consistent with our numerical
-simulations as well as previous experimental results. When the precessional
-forcing is supercriticial, our simulations show evidence of an inverse cascade,
-i.e. small scale flows merging into large scale cyclones with a retrograde
-drift. Finally, it is shown that this instability mechanism may be relevant to
-precessing celestial bodies such as the Earth and Earth's moon.
-",0,1,0,0,0,0
-19601,"Linear-Size Hopsets with Small Hopbound, and Distributed Routing with Low Memory","  For a positive parameter $\beta$, the $\beta$-bounded distance between a pair
-of vertices $u,v$ in a weighted undirected graph $G = (V,E,\omega)$ is the
-length of the shortest $u-v$ path in $G$ with at most $\beta$ edges, aka {\em
-hops}. For $\beta$ as above and $\epsilon>0$, a {\em $(\beta,\epsilon)$-hopset}
-of $G = (V,E,\omega)$ is a graph $G' =(V,H,\omega_H)$ on the same vertex set,
-such that all distances in $G$ are $(1+\epsilon)$-approximated by
-$\beta$-bounded distances in $G\cup G'$.
-Hopsets are a fundamental graph-theoretic and graph-algorithmic construct,
-and they are widely used for distance-related problems in a variety of
-computational settings. Currently existing constructions of hopsets produce
-hopsets either with $\Omega(n \log n)$ edges, or with a hopbound
-$n^{\Omega(1)}$. In this paper we devise a construction of {\em linear-size}
-hopsets with hopbound $(\log n)^{\log^{(3)}n+O(1)}$. This improves the previous
-bound almost exponentially.
-We also devise efficient implementations of our construction in PRAM and
-distributed settings. The only existing PRAM algorithm \cite{EN16} for
-computing hopsets with a constant (i.e., independent of $n$) hopbound requires
-$n^{\Omega(1)}$ time. We devise a PRAM algorithm with polylogarithmic running
-time for computing hopsets with a constant hopbound, i.e., our running time is
-exponentially better than the previous one. Moreover, these hopsets are also
-significantly sparser than their counterparts from \cite{EN16}.
-We use our hopsets to devise a distributed routing scheme that exhibits
-near-optimal tradeoff between individual memory requirement
-$\tilde{O}(n^{1/k})$ of vertices throughout preprocessing and routing phases of
-the algorithm, and stretch $O(k)$, along with a near-optimal construction time
-$\approx D + n^{1/2 + 1/k}$, where $D$ is the hop-diameter of the input graph.
-",1,0,0,0,0,0
-19602,The Openpipeflow Navier--Stokes Solver,"  Pipelines are used in a huge range of industrial processes involving fluids,
-and the ability to accurately predict properties of the flow through a pipe is
-of fundamental engineering importance. Armed with parallel MPI, Arnoldi and
-Newton--Krylov solvers, the Openpipeflow code can be used in a range of
-settings, from large-scale simulation of highly turbulent flow, to the detailed
-analysis of nonlinear invariant solutions (equilibria and periodic orbits) and
-their influence on the dynamics of the flow.
-",0,1,0,0,0,0
-19603,Logical and Inequality Implications for Reducing the Size and Complexity of Quadratic Unconstrained Binary Optimization Problems,"  The quadratic unconstrained binary optimization (QUBO) problem arises in
-diverse optimization applications ranging from Ising spin problems to classical
-problems in graph theory and binary discrete optimization. The use of
-preprocessing to transform the graph representing the QUBO problem into a
-smaller equivalent graph is important for improving solution quality and time
-for both exact and metaheuristic algorithms and is a step towards mapping large
-scale QUBO to hardware graphs used in quantum annealing computers. In an
-earlier paper (Lewis and Glover, 2016) a set of rules was introduced that
-achieved significant QUBO reductions as verified through computational testing.
-Here this work is extended with additional rules that provide further
-reductions that succeed in exactly solving 10% of the benchmark QUBO problems.
-An algorithm and associated data structures to efficiently implement the entire
-set of rules is detailed and computational experiments are reported that
-demonstrate their efficacy.
-",1,0,0,0,0,0
-19604,Optimal Decentralized Economical-sharing Criterion and Scheme for Microgrid,"  In order to address the economical dispatch problem in islanded microgrid,
-this letter proposes an optimal criterion and two decentralized
-economical-sharing schemes. The criterion is to judge whether global optimal
-economical-sharing can be realized via a decentralized manner. On the one hand,
-if the system cost functions meet this criterion, the corresponding
-decentralized droop method is proposed to achieve the global optimal dispatch.
-Otherwise, if the system does not meet this criterion, a modified method to
-achieve suboptimal dispatch is presented. The advantages of these methods are
-convenient,effective and communication-less.
-",1,0,0,0,0,0
-19605,Unbiased inference for discretely observed hidden Markov model diffusions,"  We develop an importance sampling (IS) type estimator for Bayesian joint
-inference on the model parameters and latent states of a class of hidden Markov
-models. The hidden state dynamics is a diffusion process and noisy observations
-are obtained at discrete points in time. We suppose that the diffusion dynamics
-can not be simulated exactly and hence one must time-discretise the diffusion.
-Our approach is based on particle marginal Metropolis--Hastings, particle
-filters, and multilevel Monte Carlo. The resulting IS type estimator leads to
-inference without a bias from the time-discretisation. We give convergence
-results and recommend allocations for algorithm inputs. In contrast to existing
-unbiased methods requiring strong conditions on the diffusion and tailored
-solutions, our method relies on standard Euler approximations of the diffusion.
-Our method is parallelisable, and can be computationally efficient. The
-user-friendly approach is illustrated with two examples.
-",0,0,0,1,0,0
-19606,Electric propulsion reliability: statistical analysis of on-orbit anomalies and comparative analysis of electric versus chemical propulsion failure rates,"  With a few hundred spacecraft launched to date with electric propulsion (EP),
-it is possible to conduct an epidemiological study of EP on orbit reliability.
-The first objective of the present work was to undertake such a study and
-analyze EP track record of on orbit anomalies and failures by different
-covariates. The second objective was to provide a comparative analysis of EP
-failure rates with those of chemical propulsion. After a thorough data
-collection, 162 EP-equipped satellites launched between January 1997 and
-December 2015 were included in our dataset for analysis. Several statistical
-analyses were conducted, at the aggregate level and then with the data
-stratified by severity of the anomaly, by orbit type, and by EP technology.
-Mean Time To Anomaly (MTTA) and the distribution of the time to anomaly were
-investigated, as well as anomaly rates. The important findings in this work
-include the following: (1) Post-2005, EP reliability has outperformed that of
-chemical propulsion; (2) Hall thrusters have robustly outperformed chemical
-propulsion, and they maintain a small but shrinking reliability advantage over
-gridded ion engines. Other results were also provided, for example the
-differentials in MTTA of minor and major anomalies for gridded ion engines and
-Hall thrusters. It was shown that: (3) Hall thrusters exhibit minor anomalies
-very early on orbit, which might be indicative of infant anomalies, and thus
-would benefit from better ground testing and acceptance procedures; (4) Strong
-evidence exists that EP anomalies (onset and likelihood) and orbit type are
-dependent, a dependence likely mediated by either the space environment or
-differences in thrusters duty cycles; (5) Gridded ion thrusters exhibit both
-infant and wear-out failures, and thus would benefit from a reliability growth
-program that addresses both these types of problems.
-",0,1,0,1,0,0
-19607,A Bayesian Framework for Cosmic String Searches in CMB Maps,"  There exists various proposals to detect cosmic strings from Cosmic Microwave
-Background (CMB) or 21 cm temperature maps. Current proposals do not aim to
-find the location of strings on sky maps, all of these approaches can be
-thought of as a statistic on a sky map. We propose a Bayesian interpretation of
-cosmic string detection and within that framework, we derive a connection
-between estimates of cosmic string locations and cosmic string tension $G\mu$.
-We use this Bayesian framework to develop a machine learning framework for
-detecting strings from sky maps and outline how to implement this framework
-with neural networks. The neural network we trained was able to detect and
-locate cosmic strings on noiseless CMB temperature map down to a string tension
-of $G\mu=5 \times10^{-9}$ and when analyzing a CMB temperature map that does
-not contain strings, the neural network gives a 0.95 probability that
-$G\mu\leq2.3\times10^{-9}$.
-",0,1,0,0,0,0
-19608,Constraining the giant planets' initial configuration from their evolution: implications for the timing of the planetary instability,"  Recent works on planetary migration show that the orbital structure of the
-Kuiper belt can be very well reproduced if before the onset of the planetary
-instability Neptune underwent a long-range planetesimal-driven migration up to
-$\sim$28 au. However, considering that all giant planets should have been
-captured in mean motion resonances among themselves during the gas-disk phase,
-it is not clear whether such a very specific evolution for Neptune is possible,
-nor whether the instability could have happened at late times. Here, we first
-investigate which initial resonant configuration of the giant planets can be
-compatible with Neptune being extracted from the resonant chain and migrating
-to $\sim$28 au before that the planetary instability happened. We address the
-late instability issue by investigating the conditions where the planets can
-stay in resonance for about 400 My. Our results indicate that this can happen
-only in the case where the planetesimal disk is beyond a specific minimum
-distance $\delta_{stab}$ from Neptune. Then, if there is a sufficient amount of
-dust produced in the planetesimal disk, that drifts inwards, Neptune can enter
-in a slow dust-driven migration phase for hundreds of Mys until it reaches a
-critical distance $\delta_{mig}$ from the disk. From that point, faster
-planetesimal-driven migration takes over and Neptune continues migrating
-outward until the instability happens. We conclude that, although an early
-instability reproduces more easily the evolution of Neptune required to explain
-the structure of the Kuiper belt, such evolution is also compatible with a late
-instability.
-",0,1,0,0,0,0
-19609,General AI Challenge - Round One: Gradual Learning,"  The General AI Challenge is an initiative to encourage the wider artificial
-intelligence community to focus on important problems in building intelligent
-machines with more general scope than is currently possible. The challenge
-comprises of multiple rounds, with the first round focusing on gradual
-learning, i.e. the ability to re-use already learned knowledge for efficiently
-learning to solve subsequent problems. In this article, we will present details
-of the first round of the challenge, its inspiration and aims. We also outline
-a more formal description of the challenge and present a preliminary analysis
-of its curriculum, based on ideas from computational mechanics. We believe,
-that such formalism will allow for a more principled approach towards
-investigating tasks in the challenge, building new curricula and for
-potentially improving consequent challenge rounds.
-",1,0,0,0,0,0
-19610,DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks,"  A key enabler for optimizing business processes is accurately estimating the
-probability distribution of a time series future given its past. Such
-probabilistic forecasts are crucial for example for reducing excess inventory
-in supply chains. In this paper we propose DeepAR, a novel methodology for
-producing accurate probabilistic forecasts, based on training an
-auto-regressive recurrent network model on a large number of related time
-series. We show through extensive empirical evaluation on several real-world
-forecasting data sets that our methodology is more accurate than
-state-of-the-art models, while requiring minimal feature engineering.
-",1,0,0,1,0,0
-19611,Algebraic and logistic investigations on free lattices,"  Lorenzen's ""Algebraische und logistische Untersuchungen über freie
-Verbände"" appeared in 1951 in The journal of symbolic logic. These
-""Investigations"" have immediately been recognised as a landmark in the history
-of infinitary proof theory, but their approach and method of proof have not
-been incorporated into the corpus of proof theory. More precisely, Lorenzen
-proves the admissibility of cut by double induction, on the cut formula and on
-the complexity of the derivations, without using any ordinal assignment,
-contrary to the presentation of cut elimination in most standard texts on proof
-theory. This translation has the intent of giving a new impetus to their
-reception.
-The ""Investigations"" are best known for providing a constructive proof of
-consistency for ramified type theory without axiom of reducibility. They do so
-by showing that it is a part of a trivially consistent ""inductive calculus""
-that describes our knowledge of arithmetic without detour. The proof resorts
-only to the inductive definition of formulas and theorems.
-They propose furthermore a definition of a semilattice, of a distributive
-lattice, of a pseudocomplemented semilattice, and of a countably complete
-boolean lattice as deductive calculuses, and show how to present them for
-constructing the respective free object over a given preordered set.
-This translation is published with the kind permission of Lorenzen's
-daughter, Jutta Reinhardt.
-",0,0,1,0,0,0
-19612,Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms,"  Online interactive recommender systems strive to promptly suggest to
-consumers appropriate items (e.g., movies, news articles) according to the
-current context including both the consumer and item content information.
-However, such context information is often unavailable in practice for the
-recommendation, where only the users' interaction data on items can be
-utilized. Moreover, the lack of interaction records, especially for new users
-and items, worsens the performance of recommendation further. To address these
-issues, collaborative filtering (CF), one of the recommendation techniques
-relying on the interaction data only, as well as the online multi-armed bandit
-mechanisms, capable of achieving the balance between exploitation and
-exploration, are adopted in the online interactive recommendation settings, by
-assuming independent items (i.e., arms). Nonetheless, the assumption rarely
-holds in reality, since the real-world items tend to be correlated with each
-other (e.g., two articles with similar topics). In this paper, we study online
-interactive collaborative filtering problems by considering the dependencies
-among items. We explicitly formulate the item dependencies as the clusters on
-arms, where the arms within a single cluster share the similar latent topics.
-In light of the topic modeling techniques, we come up with a generative model
-to generate the items from their underlying topics. Furthermore, an efficient
-online algorithm based on particle learning is developed for inferring both
-latent parameters and states of our model. Additionally, our inferred model can
-be naturally integrated with existing multi-armed selection strategies in the
-online interactive collaborating setting. Empirical studies on two real-world
-applications, online recommendations of movies and news, demonstrate both the
-effectiveness and efficiency of the proposed approach.
-",1,0,0,0,0,0
-19613,Learning Latent Features with Pairwise Penalties in Matrix Completion,"  Low-rank matrix completion (MC) has achieved great success in many real-world
-data applications. A latent feature model formulation is usually employed and,
-to improve prediction performance, the similarities between latent variables
-can be exploited by pairwise learning, e.g., the graph regularized matrix
-factorization (GRMF) method. However, existing GRMF approaches often use a
-squared L2 norm to measure the pairwise difference, which may be overly
-influenced by dissimilar pairs and lead to inferior prediction. To fully
-empower pairwise learning for matrix completion, we propose a general
-optimization framework that allows a rich class of (non-)convex pairwise
-penalty functions. A new and efficient algorithm is further developed to
-uniformly solve the optimization problem, with a theoretical convergence
-guarantee. In an important situation where the latent variables form a small
-number of subgroups, its statistical guarantee is also fully characterized. In
-particular, we theoretically characterize the complexity-regularized maximum
-likelihood estimator, as a special case of our framework. It has a better error
-bound when compared to the standard trace-norm regularized matrix completion.
-We conduct extensive experiments on both synthetic and real datasets to
-demonstrate the superior performance of this general framework.
-",0,0,0,1,0,0
-19614,Renewal theorems and mixing for non Markov flows with infinite measure,"  We obtain results on mixing for a large class of (not necessarily Markov)
-infinite measure semiflows and flows. Erickson proved, amongst other things, a
-strong renewal theorem in the corresponding i.i.d. setting. Using operator
-renewal theory, we extend Erickson's methods to the deterministic (i.e.
-non-i.i.d.) continuous time setting and obtain results on mixing as a
-consequence.
-Our results apply to intermittent semiflows and flows of Pomeau-Manneville
-type (both Markov and nonMarkov), and to semiflows and flows over
-Collet-Eckmann maps with nonintegrable roof function.
-",0,0,1,0,0,0
-19615,Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods,"  Many cognitive, sensory and motor processes have correlates in oscillatory
-neural sources, which are embedded as a subspace into the recorded brain
-signals. Decoding such processes from noisy
-magnetoencephalogram/electroencephalogram (M/EEG) signals usually requires the
-use of data-driven analysis methods. The objective evaluation of such decoding
-algorithms on experimental raw signals, however, is a challenge: the amount of
-available M/EEG data typically is limited, labels can be unreliable, and raw
-signals often are contaminated with artifacts. The latter is specifically
-problematic, if the artifacts stem from behavioral confounds of the oscillatory
-neural processes of interest.
-To overcome some of these problems, simulation frameworks have been
-introduced for benchmarking decoding methods. Generating artificial brain
-signals, however, most simulation frameworks make strong and partially
-unrealistic assumptions about brain activity, which limits the generalization
-of obtained results to real-world conditions.
-In the present contribution, we thrive to remove many shortcomings of current
-simulation frameworks and propose a versatile alternative, that allows for
-objective evaluation and benchmarking of novel data-driven decoding methods for
-neural signals. Its central idea is to utilize post-hoc labelings of arbitrary
-M/EEG recordings. This strategy makes it paradigm-agnostic and allows to
-generate comparatively large datasets with noiseless labels. Source code and
-data of the novel simulation approach are made available for facilitating its
-adoption.
-",1,0,0,1,0,0
-19616,"Sums of two cubes as twisted perfect powers, revisited","  In this paper, we sharpen earlier work of the first author, Luca and
-Mulholland, showing that the Diophantine equation $$ A^3+B^3 = q^\alpha C^p, \,
-\, ABC \neq 0, \, \, \gcd (A,B) =1, $$ has, for ""most"" primes $q$ and suitably
-large prime exponents $p$, no solutions. We handle a number of (presumably
-infinite) families where no such conclusion was hitherto known. Through further
-application of certain {\it symplectic criteria}, we are able to make some
-conditional statements about still more values of $q$, a sample such result is
-that, for all but $O(\sqrt{x}/\log x)$ primes $q$ up to $x$, the equation $$
-A^3 + B^3 = q C^p. $$ has no solutions in coprime, nonzero integers $A, B$
-and $C$, for a positive proportion of prime exponents $p$.
-",0,0,1,0,0,0
-19617,A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications,"  Bilevel optimization is defined as a mathematical program, where an
-optimization problem contains another optimization problem as a constraint.
-These problems have received significant attention from the mathematical
-programming community. Only limited work exists on bilevel problems using
-evolutionary computation techniques; however, recently there has been an
-increasing interest due to the proliferation of practical applications and the
-potential of evolutionary algorithms in tackling these problems. This paper
-provides a comprehensive review on bilevel optimization from the basic
-principles to solution strategies; both classical and evolutionary. A number of
-potential application problems are also discussed. To offer the readers
-insights on the prominent developments in the field of bilevel optimization, we
-have performed an automated text-analysis of an extended list of papers
-published on bilevel optimization to date. This paper should motivate
-evolutionary computation researchers to pay more attention to this practical
-yet challenging area.
-",1,0,1,0,0,0
-19618,"Speaker Recognition with Cough, Laugh and ""Wei""","  This paper proposes a speaker recognition (SRE) task with trivial speech
-events, such as cough and laugh. These trivial events are ubiquitous in
-conversations and less subjected to intentional change, therefore offering
-valuable particularities to discover the genuine speaker from disguised speech.
-However, trivial events are often short and idiocratic in spectral patterns,
-making SRE extremely difficult. Fortunately, we found a very powerful deep
-feature learning structure that can extract highly speaker-sensitive features.
-By employing this tool, we studied the SRE performance on three types of
-trivial events: cough, laugh and ""Wei"" (a short Chinese ""Hello""). The results
-show that there is rich speaker information within these trivial events, even
-for cough that is intuitively less speaker distinguishable. With the deep
-feature approach, the EER can reach 10%-14% with the three trivial events,
-despite their extremely short durations (0.2-1.0 seconds).
-",1,0,0,0,0,0
-19619,Explicit equations for two-dimensional water waves with constant vorticity,"  Governing equations for two-dimensional inviscid free-surface flows with
-constant vorticity over arbitrary non-uniform bottom profile are presented in
-exact and compact form using conformal variables. An efficient and very
-accurate numerical method for this problem is developed.
-",0,1,0,0,0,0
-19620,On the Global Limiting Absorption Principle for Massless Dirac Operators,"  We prove a global limiting absorption principle on the entire real line for
-free, massless Dirac operators $H_0 = \alpha \cdot (-i \nabla)$ for all space
-dimensions $n \in \mathbb{N}$, $n \geq 2$. This is a new result for all
-dimensions other than three, in particular, it applies to the two-dimensional
-case which is known to be of some relevance in applications to graphene.
-We also prove an essential self-adjointness result for first-order
-matrix-valued differential operators with Lipschitz coefficients.
-",0,0,1,0,0,0
-19621,Rotation Averaging and Strong Duality,"  In this paper we explore the role of duality principles within the problem of
-rotation averaging, a fundamental task in a wide range of computer vision
-applications. In its conventional form, rotation averaging is stated as a
-minimization over multiple rotation constraints. As these constraints are
-non-convex, this problem is generally considered challenging to solve globally.
-We show how to circumvent this difficulty through the use of Lagrangian
-duality. While such an approach is well-known it is normally not guaranteed to
-provide a tight relaxation. Based on spectral graph theory, we analytically
-prove that in many cases there is no duality gap unless the noise levels are
-severe. This allows us to obtain certifiably global solutions to a class of
-important non-convex problems in polynomial time.
-We also propose an efficient, scalable algorithm that out-performs general
-purpose numerical solvers and is able to handle the large problem instances
-commonly occurring in structure from motion settings. The potential of this
-proposed method is demonstrated on a number of different problems, consisting
-of both synthetic and real-world data.
-",1,0,0,0,0,0
-19622,Active Learning with Gaussian Processes for High Throughput Phenotyping,"  A looming question that must be solved before robotic plant phenotyping
-capabilities can have significant impact to crop improvement programs is
-scalability. High Throughput Phenotyping (HTP) uses robotic technologies to
-analyze crops in order to determine species with favorable traits, however, the
-current practices rely on exhaustive coverage and data collection from the
-entire crop field being monitored under the breeding experiment. This works
-well in relatively small agricultural fields but can not be scaled to the
-larger ones, thus limiting the progress of genetics research. In this work, we
-propose an active learning algorithm to enable an autonomous system to collect
-the most informative samples in order to accurately learn the distribution of
-phenotypes in the field with the help of a Gaussian Process model. We
-demonstrate the superior performance of our proposed algorithm compared to the
-current practices on sorghum phenotype data collection.
-",1,0,0,1,0,0
-19623,Comparative Autoignition Trends in the Butanol Isomers at Elevated Pressure,"  Autoignition experiments of stoichiometric mixtures of s-, t-, and i-butanol
-in air have been performed using a heated rapid compression machine (RCM). At
-compressed pressures of 15 and 30 bar and for compressed temperatures in the
-range of 715-910 K, no evidence of a negative temperature coefficient region in
-terms of ignition delay response is found. The present experimental results are
-also compared with previously reported RCM data of n-butanol in air. The order
-of reactivity of the butanols is
-n-butanol>s-butanol$\approx$i-butanol>t-butanol at the lower pressure, but
-changes to n-butanol>t-butanol>s-butanol>i-butanol at higher pressure. In
-addition, t-butanol shows pre-ignition heat release behavior, which is
-especially evident at higher pressures. To help identify the controlling
-chemistry leading to this pre-ignition heat release, off-stoichiometric
-experiments are further performed at 30 bar compressed pressure, for t-butanol
-at $\phi$ = 0.5 and $\phi$ = 2.0 in air. For these experiments, higher fuel
-loading (i.e. $\phi$ = 2.0) causes greater pre-ignition heat release (as
-indicated by greater pressure rise) than the stoichiometric or $\phi$ = 0.5
-cases. Comparison of the experimental ignition delays with the simulated
-results using two literature kinetic mechanisms shows generally good agreement,
-and one mechanism is further used to explore and compare the fuel decomposition
-pathways of the butanol isomers. Using this mechanism, the importance of peroxy
-chemistry in the autoignition of the butanol isomers is highlighted and
-discussed.
-",0,1,0,0,0,0
-19624,A cross-vendor and cross-state analysis of the GPS-probe data latency,"  Crowdsourced GPS probe data has become a major source of real-time traffic
-information applications. In addition to traditional traveler advisory systems
-such as dynamic message signs (DMS) and 511 systems, probe data is being used
-for automatic incident detection, Integrated Corridor Management (ICM), end of
-queue warning systems, and mobility-related smartphone applications. Several
-private sector vendors offer minute by minute network-wide travel time and
-speed probe data. The quality of such data in terms of deviation of the
-reported travel time and speeds from ground-truth has been extensively studied
-in recent years, and as a result concerns over the accuracy of probe data has
-mostly faded away. However, the latency of probe data, defined as the lag
-between the time that disturbance in traffic speed is reported in the
-outsourced data feed, and the time that the traffic is perturbed, has become a
-subject of interest. The extent of latency of probe data for real-time
-applications is critical, so it is important to have a good understanding of
-the amount of latency and its influencing factors. This paper uses high-quality
-independent Bluetooth/Wi-Fi re-identification data collected on multiple
-freeway segments in three different states, to measure the latency of the
-vehicle probe data provided by three major vendors. The statistical
-distribution of the latency and its sensitivity to speed slowdown and recovery
-periods are discussed.
-",0,0,0,1,0,0
-19625,Determining Phonon Coherence Using Photon Sideband Detection,"  Generating and detection coherent high-frequency heat-carrying phonons has
-been a great topic of interest in recent years. While there have been
-successful attempts in generating and observing coherent phonons, rigorous
-techniques to characterize and detect these phonon coherence in a crystalline
-material have been lagging compared to what has been achieved for photons. One
-main challenge is a lack of detailed understanding of how detection signals for
-phonons can be related to coherence. The quantum theory of photoelectric
-detection has greatly advanced the ability to characterize photon coherence in
-the last century and a similar theory for phonon detection is necessary. Here,
-we re-examine the optical sideband fluorescence technique that has been used
-detect high frequency phonons in materials with optically active defects. We
-apply the quantum theory of photodetection to the sideband technique and
-propose signatures in sideband photon-counting statistics and second-order
-correlation measurement of sideband signals that indicates the degree of phonon
-coherence. Our theory can be implemented in recently performed experiments to
-bridge the gap of determining phonon coherence to be on par with that of
-photons.
-",0,1,0,0,0,0
-19626,Landscape of Configurational Density of States for Discrete Large Systems,"  For classical many-body systems, our recent study reveals that expectation
-value of internal energy, structure, and free energy can be well characterized
-by a single specially-selected microscopic structure. This finding relies on
-the fact that configurational density of states (CDOS) for typical classical
-system before applying interatomic interaction can be well characterized by
-multidimensional gaussian distribution. Although gaussian distribution is an
-well-known and widely-used function in diverse fields, it is quantitatively
-unclear why the CDOS takes gaussian when system size gets large, even for
-projected CDOS onto a single chosen coordination. Here we demonstrate that for
-equiatomic binary system, one-dimensional CDOS along coordination of pair
-correlation can be reasonably described by gaussian distribution under an
-appropriate condition, whose deviation from real CDOS mainly reflects the
-existence of triplet closed link consisting of the pair figure considered. The
-present result thus significantly makes advance in analytic determination of
-the special microscopic states to characterized macroscopic physical property
-in equilibrium state.
-",0,1,0,0,0,0
-19627,On the domain of Dirac and Laplace type operators on stratified spaces,"  We consider a generalized Dirac operator on a compact stratified space with
-an iterated cone-edge metric. Assuming a spectral Witt condition, we prove its
-essential self-adjointness and identify its domain and the domain of its square
-with weighted edge Sobolev spaces. This sharpens previous results where the
-minimal domain is shown only to be a subset of an intersection of weighted edge
-Sobolev spaces. Our argument does not rely on microlocal techniques and is very
-explicit. The novelty of our approach is the use of an abstract functional
-analytic notion of interpolation scales. Our results hold for the Gauss-Bonnet
-and spin Dirac operators satisfying a spectral Witt condition.
-",0,0,1,0,0,0
-19628,Modeling Influence with Semantics in Social Networks: a Survey,"  The discovery of influential entities in all kinds of networks (e.g. social,
-digital, or computer) has always been an important field of study. In recent
-years, Online Social Networks (OSNs) have been established as a basic means of
-communication and often influencers and opinion makers promote politics,
-events, brands or products through viral content. In this work, we present a
-systematic review across i) online social influence metrics, properties, and
-applications and ii) the role of semantic in modeling OSNs information. We end
-up with the conclusion that both areas can jointly provide useful insights
-towards the qualitative assessment of viral user-generated content, as well as
-for modeling the dynamic properties of influential content and its flow
-dynamics.
-",1,0,0,0,0,0
-19629,Differentiable Submodular Maximization,"  We consider learning of submodular functions from data. These functions are
-important in machine learning and have a wide range of applications, e.g. data
-summarization, feature selection and active learning. Despite their
-combinatorial nature, submodular functions can be maximized approximately with
-strong theoretical guarantees in polynomial time. Typically, learning the
-submodular function and optimization of that function are treated separately,
-i.e. the function is first learned using a proxy objective and subsequently
-maximized. In contrast, we show how to perform learning and optimization
-jointly. By interpreting the output of greedy maximization algorithms as
-distributions over sequences of items and smoothening these distributions, we
-obtain a differentiable objective. In this way, we can differentiate through
-the maximization algorithms and optimize the model to work well with the
-optimization algorithm. We theoretically characterize the error made by our
-approach, yielding insights into the tradeoff of smoothness and accuracy. We
-demonstrate the effectiveness of our approach for jointly learning and
-optimizing on synthetic maximum cut data, and on real world applications such
-as product recommendation and image collection summarization.
-",0,0,0,1,0,0
-19630,On the Support of Weight Modules for Affine Kac-Moody-Algebras,"  An irreducible weight module of an affine Kac-Moody algebra $\mathfrak{g}$ is
-called dense if its support is equal to a coset in $\mathfrak{h}^{*}/Q$.
-Following a conjecture of V. Futorny about affine Kac-Moody algebras
-$\mathfrak{g}$, an irreducible weight $\mathfrak{g}$-module is dense if and
-only if it is cuspidal (i.e. not a quotient of an induced module). The
-conjecture is confirmed for $\mathfrak{g}=A_{2}^{\left(1\right)}$,
-$A_{3}^{\left(1\right)}$ and$A_{4}^{\left(1\right)}$ and a classification of
-the supports of the irreducible weight $\mathfrak{g}$-modules obtained. For all
-$A_{n}^{\left(1\right)}$ the problem is reduced to finding primitive elements
-for only finitely many cases, all lying below a certain bound. For the
-left-over finitely many cases an algorithm is proposed, which leads to the
-solution of Futorny's conjecture for the cases $A_{2}^{\left(1\right)}$ and
-$A_{3}^{\left(1\right)}$. Yet, the solution of the case
-$A_{4}^{\left(1\right)}$ required additional combinatorics.
-For the proofs, a new category of hypoabelian Lie subalgebras,
-pre-prosolvable subalgebras, and a subclass thereof, quasicone subalgebras, is
-introduced and its tropical matrix algebra structure outlined.
-",0,0,1,0,0,0
-19631,The middle-scale asymptotics of Wishart matrices,"  We study the behavior of a real $p$-dimensional Wishart random matrix with
-$n$ degrees of freedom when $n,p\rightarrow\infty$ but $p/n\rightarrow 0$. We
-establish the existence of phase transitions when $p$ grows at the order
-$n^{(K+1)/(K+3)}$ for every $k\in\mathbb{N}$, and derive expressions for
-approximating densities between every two phase transitions. To do this, we
-make use of a novel tool we call the G-transform of a distribution, which is
-closely related to the characteristic function. We also derive an extension of
-the $t$-distribution to the real symmetric matrices, which naturally appears as
-the conjugate distribution to the Wishart under a G-transformation, and show
-its empirical spectral distribution obeys a semicircle law when $p/n\rightarrow
-0$. Finally, we discuss how the phase transitions of the Wishart distribution
-might originate from changes in rates of convergence of symmetric $t$
-statistics.
-",0,0,1,1,0,0
-19632,Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping,"  In this paper, we propose an online learning algorithm based on a
-Rao-Blackwellized particle filter for spatial concept acquisition and mapping.
-We have proposed a nonparametric Bayesian spatial concept acquisition model
-(SpCoA). We propose a novel method (SpCoSLAM) integrating SpCoA and FastSLAM in
-the theoretical framework of the Bayesian generative model. The proposed method
-can simultaneously learn place categories and lexicons while incrementally
-generating an environmental map. Furthermore, the proposed method has scene
-image features and a language model added to SpCoA. In the experiments, we
-tested online learning of spatial concepts and environmental maps in a novel
-environment of which the robot did not have a map. Then, we evaluated the
-results of online learning of spatial concepts and lexical acquisition. The
-experimental results demonstrated that the robot was able to more accurately
-learn the relationships between words and the place in the environmental map
-incrementally by using the proposed method.
-",1,0,0,0,0,0
-19633,The Method of Arbitrarily Large Moments to Calculate Single Scale Processes in Quantum Field Theory,"  We device a new method to calculate a large number of Mellin moments of
-single scale quantities using the systems of differential and/or difference
-equations obtained by integration-by-parts identities between the corresponding
-Feynman integrals of loop corrections to physical quantities. These scalar
-quantities have a much simpler mathematical structure than the complete
-quantity. A sufficiently large set of moments may even allow the analytic
-reconstruction of the whole quantity considered, holding in case of first order
-factorizing systems. In any case, one may derive highly precise numerical
-representations in general using this method, which is otherwise completely
-analytic.
-",1,0,1,0,0,0
-19634,Unraveling the escape dynamics and the nature of the normally hyperbolic invariant manifolds in tidally limited star clusters,"  The escape mechanism of orbits in a star cluster rotating around its parent
-galaxy in a circular orbit is investigated. A three degrees of freedom model is
-used for describing the dynamical properties of the Hamiltonian system. The
-gravitational field of the star cluster is represented by a smooth and
-spherically symmetric Plummer potential. We distinguish between ordered and
-chaotic orbits as well as between trapped and escaping orbits, considering only
-unbounded motion for several energy levels. The Smaller Alignment Index (SALI)
-method is used for determining the regular or chaotic nature of the orbits. The
-basins of escape are located and they are also correlated with the
-corresponding escape time of the orbits. Areas of bounded regular or chaotic
-motion and basins of escape were found to coexist in the $(x,z)$ plane. The
-properties of the normally hyperbolic invariant manifolds (NHIMs), located in
-the vicinity of the index-1 Lagrange points $L_1$ and $L_2$, are also explored.
-These manifolds are of paramount importance as they control the flow of stars
-over the saddle points, while they also trigger the formation of tidal tails
-observed in star clusters. Bifurcation diagrams of the Lyapunov periodic orbits
-as well as restrictions of the Poincaré map to the NHIMs are deployed for
-elucidating the dynamics in the neighbourhood of the saddle points. The
-extended tidal tails, or tidal arms, formed by stars with low velocity which
-escape through the Lagrange points are monitored. The numerical results of this
-work are also compared with previous related work.
-",0,1,0,0,0,0
-19635,The ALMA Early Science View of FUor/EXor objects. III. The Slow and Wide Outflow of V883 Ori,"  We present Atacama Large Millimeter/ sub-millimeter Array (ALMA) observations
-of V883 Ori, an FU Ori object. We describe the molecular outflow and envelope
-of the system based on the $^{12}$CO and $^{13}$CO emissions, which together
-trace a bipolar molecular outflow. The C$^{18}$O emission traces the rotational
-motion of the circumstellar disk. From the $^{12}$CO blue-shifted emission, we
-estimate a wide opening angle of $\sim$ 150$^{^{\circ}}$ for the outflow
-cavities. Also, we find that the outflow is very slow (characteristic velocity
-of only 0.65 km~s$^{-1}$), which is unique for an FU Ori object. We calculate
-the kinematic properties of the outflow in the standard manner using the
-$^{12}$CO and $^{13}$CO emissions. In addition, we present a P Cygni profile
-observed in the high-resolution optical spectrum, evidence of a wind driven by
-the accretion and being the cause for the particular morphology of the
-outflows. We discuss the implications of our findings and the rise of these
-slow outflows during and/or after the formation of a rotationally supported
-disk.
-",0,1,0,0,0,0
-19636,Hydrodynamical models of cometary HII regions,"  We have modelled the evolution of cometary HII regions produced by zero-age
-main-sequence stars of O and B spectral types, which are driving strong winds
-and are born off-centre from spherically symmetric cores with power-law
-($\alpha = 2$) density slopes. A model parameter grid was produced that spans
-stellar mass, age and core density. Exploring this parameter space we
-investigated limb-brightening, a feature commonly seen in cometary HII regions.
-We found that stars with mass $M_\star \geq 12\, \mathrm{M}_\odot$ produce this
-feature. Our models have a cavity bounded by a contact discontinuity separating
-hot shocked wind and ionised ambient gas that is similar in size to the
-surrounding HII region. Due to early pressure confinement we did not see shocks
-outside of the contact discontinuity for stars with $M_\star \leq 40\,
-\mathrm{M}_\odot$, but the cavities were found to continue to grow. The cavity
-size in each model plateaus as the HII region stagnates. The spectral energy
-distributions of our models are similar to those from identical stars evolving
-in uniform density fields. The turn-over frequency is slightly lower in our
-power-law models due to a higher proportion of low density gas covered by the
-HII regions.
-",0,1,0,0,0,0
-19637,Feature Selection based on the Local Lift Dependence Scale,"  This paper uses a classical approach to feature selection: minimization of a
-cost function applied on estimated joint distributions. However, the search
-space in which such minimization is performed is extended. In the original
-formulation, the search space is the Boolean lattice of features sets (BLFS),
-while, in the present formulation, it is a collection of Boolean lattices of
-ordered pairs (features, associated value) (CBLOP), indexed by the elements of
-the BLFS. In this approach, we may not only select the features that are most
-related to a variable Y, but also select the values of the features that most
-influence the variable or that are most prone to have a specific value of Y. A
-local formulation of Shanon's mutual information is applied on a CBLOP to
-select features, namely, the Local Lift Dependence Scale, an scale for
-measuring variable dependence in multiple resolutions. The main contribution of
-this paper is to define and apply this local measure, which permits to analyse
-local properties of joint distributions that are neglected by the classical
-Shanon's global measure. The proposed approach is applied to a dataset
-consisting of student performances on a university entrance exam, as well as on
-undergraduate courses. The approach is also applied to two datasets of the UCI
-Machine Learning Repository.
-",1,0,0,1,0,0
-19638,Weyl Rings and enhanced susceptibilities in Pyrochlore Iridates: $k\cdot p$ Analysis of Cluster Dynamical Mean-Field Theory Results,"  We match analytic results to numerical calculations to provide a detailed
-picture of the metal-insulator and topological transitions found in density
-functional plus cluster dynamical mean-field calculations of pyrochlore
-iridates. We discuss the transition from Weyl metal to Weyl semimetal regimes,
-and then analyse in detail the properties of the Weyl semimetal phase and its
-evolution into the topologically trivial insulator. The energy scales in the
-Weyl semimetal phase are found to be very small, as are the anisotropy
-parameters. The electronic structure can to a good approximation be described
-as `Weyl rings' and one of the two branches that contributes to the Weyl bands
-is essentially flat, leading to enhanced susceptibilities. The optical
-longitudinal and Hall conductivities are determined; the frequency dependence
-includes pronounced features that reveal the basic energy scales of the Weyl
-semimetal phase.
-",0,1,0,0,0,0
-19639,Inconsistency of Template Estimation by Minimizing of the Variance/Pre-Variance in the Quotient Space,"  We tackle the problem of template estimation when data have been randomly
-deformed under a group action in the presence of noise. In order to estimate
-the template, one often minimizes the variance when the influence of the
-transformations have been removed (computation of the Fr{é}chet mean in the
-quotient space). The consistency bias is defined as the distance (possibly
-zero) between the orbit of the template and the orbit of one element which
-minimizes the variance. In the first part, we restrict ourselves to isometric
-group action, in this case the Hilbertian distance is invariant under the group
-action. We establish an asymptotic behavior of the consistency bias which is
-linear with respect to the noise level. As a result the inconsistency is
-unavoidable as soon as the noise is enough. In practice, template estimation
-with a finite sample is often done with an algorithm called ""max-max"". In the
-second part, also in the case of isometric group finite, we show the
-convergence of this algorithm to an empirical Karcher mean. Our numerical
-experiments show that the bias observed in practice can not be attributed to
-the small sample size or to a convergence problem but is indeed due to the
-previously studied inconsistency. In a third part, we also present some
-insights of the case of a non invariant distance with respect to the group
-action. We will see that the inconsistency still holds as soon as the noise
-level is large enough. Moreover we prove the inconsistency even when a
-regularization term is added.
-",0,0,1,1,0,0
-19640,Surrogate-Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Erro,"  Bayesian inverse modeling is important for a better understanding of
-hydrological processes. However, this approach can be computationally demanding
-as it usually requires a large number of model evaluations. To address this
-issue, one can take advantage of surrogate modeling techniques. Nevertheless,
-when approximation error of the surrogate model is neglected in inverse
-modeling, the inversion result will be biased. In this paper, we develop a
-surrogate-based Bayesian inversion framework that explicitly quantifies and
-gradually reduces the approximation error of the surrogate. Specifically, two
-strategies are proposed and compared. The first strategy works by obtaining an
-ensemble of sparse polynomial chaos expansion (PCE) surrogates with Markov
-chain Monte Carlo sampling, while the second one uses Gaussian process (GP) to
-simulate the approximation error of a single sparse PCE surrogate. The two
-strategies can also be applied with other surrogates, thus they have general
-applicability. By adaptively refining the surrogate over the posterior
-distribution, we can gradually reduce the surrogate approximation error to a
-small level. Demonstrated with three case studies involving
-high-dimensionality, multi-modality and a real-world application, respectively,
-it is found that both strategies can reduce the bias introduced by surrogate
-modeling, while the second strategy has a better performance as it integrates
-two methods (i.e., sparse PCE and GP) that complement each other.
-",0,0,0,1,0,0
-19641,On The Construction of Extreme Learning Machine for Online and Offline One-Class Classification - An Expanded Toolbox,"  One-Class Classification (OCC) has been prime concern for researchers and
-effectively employed in various disciplines. But, traditional methods based
-one-class classifiers are very time consuming due to its iterative process and
-various parameters tuning. In this paper, we present six OCC methods based on
-extreme learning machine (ELM) and Online Sequential ELM (OSELM). Our proposed
-classifiers mainly lie in two categories: reconstruction based and boundary
-based, which supports both types of learning viz., online and offline learning.
-Out of various proposed methods, four are offline and remaining two are online
-methods. Out of four offline methods, two methods perform random feature
-mapping and two methods perform kernel feature mapping. Kernel feature mapping
-based approaches have been tested with RBF kernel and online version of
-one-class classifiers are tested with both types of nodes viz., additive and
-RBF. It is well known fact that threshold decision is a crucial factor in case
-of OCC, so, three different threshold deciding criteria have been employed so
-far and analyses the effectiveness of one threshold deciding criteria over
-another. Further, these methods are tested on two artificial datasets to check
-there boundary construction capability and on eight benchmark datasets from
-different discipline to evaluate the performance of the classifiers. Our
-proposed classifiers exhibit better performance compared to ten traditional
-one-class classifiers and ELM based two one-class classifiers. Through proposed
-one-class classifiers, we intend to expand the functionality of the most used
-toolbox for OCC i.e. DD toolbox. All of our methods are totally compatible with
-all the present features of the toolbox.
-",1,0,0,1,0,0
-19642,2nd order PDEs: geometric and functional considerations,"  INTRODUCTION
-This papers deals with partial differential equations of second order,
-linear, with constant and not constant coefficients, in two variables, which
-admit real characteristics. I face the study of PDEs with the mentality of the
-applied physicist, but with a weakness for formalization: look inside the black
-box of the formulas, try to compact them (for example, proceeding from an
-inverse transformation of coordinates) and make them smart (in the context,
-reformulating the theory by means of differential operators and related
-invariants), applying them with awareness and then connecting them to geometry
-or to spatial categories, which are in mathematics what is closest to the
-sensible reality. Finally, proposing examples that are exercise and
-corroborating for theory.
-TOPICS
-The geometric meaning of invariant to a differential operator. Operator
-Principal Part and its factorization: commutativity and product with and
-without residues(first order terms). Related conditions by operators and
-invariants derivatives. Coordinate transformation by invariants and expression
-of the hyperbolic and parabolic operators in the new coordinates. Properties of
-the Jacobian Matrix and relations between invariants derivatives and inverse
-coordinates transformation or the initial variables derivatives. Commutativity
-conditions and product without residues in terms of inverse coordinate
-transformations that allow to build commutative differential operators or whose
-product is without residues (or both). Diffeomorphisms and plane
-transformations: new operators and invariants in the new coordinate space which
-lead to the chain rule in compact form. Conclusive considerations and examples
-who compares different methods of solution.
-",0,0,1,0,0,0
-19643,Adversarial Perturbation Intensity Achieving Chosen Intra-Technique Transferability Level for Logistic Regression,"  Machine Learning models have been shown to be vulnerable to adversarial
-examples, ie. the manipulation of data by a attacker to defeat a defender's
-classifier at test time. We present a novel probabilistic definition of
-adversarial examples in perfect or limited knowledge setting using prior
-probability distributions on the defender's classifier. Using the asymptotic
-properties of the logistic regression, we derive a closed-form expression of
-the intensity of any adversarial perturbation, in order to achieve a given
-expected misclassification rate. This technique is relevant in a threat model
-of known model specifications and unknown training data. To our knowledge, this
-is the first method that allows an attacker to directly choose the probability
-of attack success. We evaluate our approach on two real-world datasets.
-",0,0,0,1,0,0
-19644,Data Aggregation and Packet Bundling of Uplink Small Packets for Monitoring Applications in LTE,"  In cellular massive Machine-Type Communications (MTC), a device can transmit
-directly to the base station (BS) or through an aggregator (intermediate node).
-While direct device-BS communication has recently been in the focus of 5G/3GPP
-research and standardization efforts, the use of aggregators remains a less
-explored topic. In this paper we analyze the deployment scenarios in which
-aggregators can perform cellular access on behalf of multiple MTC devices. We
-study the effect of packet bundling at the aggregator, which alleviates
-overhead and resource waste when sending small packets. The aggregators give
-rise to a tradeoff between access congestion and resource starvation and we
-show that packet bundling can minimize resource starvation, especially for
-smaller numbers of aggregators. Under the limitations of the considered model,
-we investigate the optimal settings of the network parameters, in terms of
-number of aggregators and packet-bundle size. Our results show that, in
-general, data aggregation can benefit the uplink massive MTC in LTE, by
-reducing the signalling overhead.
-",1,0,0,0,0,0
-19645,The GTC exoplanet transit spectroscopy survey. VII. Detection of sodium in WASP-52b's cloudy atmosphere,"  We report the first detection of sodium absorption in the atmosphere of the
-hot Jupiter WASP-52b. We observed one transit of WASP-52b with the
-low-resolution Optical System for Imaging and low-Intermediate-Resolution
-Integrated Spectroscopy (OSIRIS) at the 10.4 m Gran Telescopio Canarias (GTC).
-The resulting transmission spectrum, covering the wavelength range from 522 nm
-to 903 nm, is flat and featureless, except for the significant narrow
-absorption signature at the sodium doublet, which can be explained by an
-atmosphere in solar composition with clouds at 1 mbar. A cloud-free atmosphere
-is stringently ruled out. By assessing the absorption depths of sodium in
-various bin widths, we find that temperature increases towards lower
-atmospheric pressure levels, with a positive temperature gradient of 0.88 +/-
-0.65 K/km, possibly indicative of upper atmospheric heating and a temperature
-inversion.
-",0,1,0,0,0,0
-19646,That's Enough: Asynchrony with Standard Choreography Primitives,"  Choreographies are widely used for the specification of concurrent and
-distributed software architectures. Since asynchronous communications are
-ubiquitous in real-world systems, previous works have proposed different
-approaches for the formal modelling of asynchrony in choreographies. Such
-approaches typically rely on ad-hoc syntactic terms or semantics for capturing
-the concept of messages in transit, yielding different formalisms that have to
-be studied separately.
-In this work, we take a different approach, and show that such extensions are
-not needed to reason about asynchronous communications in choreographies.
-Rather, we demonstrate how a standard choreography calculus already has all the
-needed expressive power to encode messages in transit (and thus asynchronous
-communications) through the primitives of process spawning and name mobility.
-The practical consequence of our results is that we can reason about real-world
-systems within a choreography formalism that is simpler than those hitherto
-proposed.
-",1,0,0,0,0,0
-19647,Doing Things Twice (Or Differently): Strategies to Identify Studies for Targeted Validation,"  The ""reproducibility crisis"" has been a highly visible source of scientific
-controversy and dispute. Here, I propose and review several avenues for
-identifying and prioritizing research studies for the purpose of targeted
-validation. Of the various proposals discussed, I identify scientific data
-science as being a strategy that merits greater attention among those
-interested in reproducibility. I argue that the tremendous potential of
-scientific data science for uncovering high-value research studies is a
-significant and rarely discussed benefit of the transition to a fully
-open-access publishing model.
-",1,1,0,0,0,0
-19648,SciSports: Learning football kinematics through two-dimensional tracking data,"  SciSports is a Dutch startup company specializing in football analytics. This
-paper describes a joint research effort with SciSports, during the Study Group
-Mathematics with Industry 2018 at Eindhoven, the Netherlands. The main
-challenge that we addressed was to automatically process empirical football
-players' trajectories, in order to extract useful information from them. The
-data provided to us was two-dimensional positional data during entire matches.
-We developed methods based on Newtonian mechanics and the Kalman filter,
-Generative Adversarial Nets and Variational Autoencoders. In addition, we
-trained a discriminator network to recognize and discern different movement
-patterns of players. The Kalman-filter approach yields an interpretable model,
-in which a small number of player-dependent parameters can be fit; in theory
-this could be used to distinguish among players. The
-Generative-Adversarial-Nets approach appears promising in theory, and some
-initial tests showed an improvement with respect to the baseline, but the
-limits in time and computational power meant that we could not fully explore
-it. We also trained a Discriminator network to distinguish between two players
-based on their trajectories; after training, the network managed to distinguish
-between some pairs of players, but not between others. After training, the
-Variational Autoencoders generated trajectories that are difficult to
-distinguish, visually, from the data. These experiments provide an indication
-that deep generative models can learn the underlying structure and statistics
-of football players' trajectories. This can serve as a starting point for
-determining player qualities based on such trajectory data.
-",0,0,0,1,0,0
-19649,A Model-based Projection Technique for Segmenting Customers,"  We consider the problem of segmenting a large population of customers into
-non-overlapping groups with similar preferences, using diverse preference
-observations such as purchases, ratings, clicks, etc. over subsets of items. We
-focus on the setting where the universe of items is large (ranging from
-thousands to millions) and unstructured (lacking well-defined attributes) and
-each customer provides observations for only a few items. These data
-characteristics limit the applicability of existing techniques in marketing and
-machine learning. To overcome these limitations, we propose a model-based
-projection technique, which transforms the diverse set of observations into a
-more comparable scale and deals with missing data by projecting the transformed
-data onto a low-dimensional space. We then cluster the projected data to obtain
-the customer segments. Theoretically, we derive precise necessary and
-sufficient conditions that guarantee asymptotic recovery of the true customer
-segments. Empirically, we demonstrate the speed and performance of our method
-in two real-world case studies: (a) 84% improvement in the accuracy of new
-movie recommendations on the MovieLens data set and (b) 6% improvement in the
-performance of similar item recommendations algorithm on an offline dataset at
-eBay. We show that our method outperforms standard latent-class and
-demographic-based techniques.
-",1,0,0,1,0,0
-19650,Distinct Effects of Cr Bulk Doping and Surface Deposition on the Chemical Environment and Electronic Structure of the Topological Insulator Bi2Se3,"  In this report, it is shown that Cr doped into the bulk and Cr deposited on
-the surface of Bi2Se3 films produced by molecular beam epitaxy (MBE) have
-strikingly different effects on both the electronic structure and chemical
-environment.
-",0,1,0,0,0,0
-19651,High-Performance Code Generation though Fusion and Vectorization,"  We present a technique for automatically transforming kernel-based
-computations in disparate, nested loops into a fused, vectorized form that can
-reduce intermediate storage needs and lead to improved performance on
-contemporary hardware.
-We introduce representations for the abstract relationships and data
-dependencies of kernels in loop nests and algorithms for manipulating them into
-more efficient form; we similarly introduce techniques for determining data
-access patterns for stencil-like array accesses and show how this can be used
-to elide storage and improve vectorization.
-We discuss our prototype implementation of these ideas---named HFAV---and its
-use of a declarative, inference-based front-end to drive transformations, and
-we present results for some prominent codes in HPC.
-",1,0,0,0,0,0
-19652,Language Model Pre-training for Hierarchical Document Representations,"  Hierarchical neural architectures are often used to capture long-distance
-dependencies and have been applied to many document-level tasks such as
-summarization, document segmentation, and sentiment analysis. However,
-effective usage of such a large context can be difficult to learn, especially
-in the case where there is limited labeled data available. Building on the
-recent success of language model pretraining methods for learning flat
-representations of text, we propose algorithms for pre-training hierarchical
-document representations from unlabeled data. Unlike prior work, which has
-focused on pre-training contextual token representations or context-independent
-{sentence/paragraph} representations, our hierarchical document representations
-include fixed-length sentence/paragraph representations which integrate
-contextual information from the entire documents. Experiments on document
-segmentation, document-level question answering, and extractive document
-summarization demonstrate the effectiveness of the proposed pre-training
-algorithms.
-",1,0,0,0,0,0
-19653,Interpretation of Semantic Tweet Representations,"  Research in analysis of microblogging platforms is experiencing a renewed
-surge with a large number of works applying representation learning models for
-applications like sentiment analysis, semantic textual similarity computation,
-hashtag prediction, etc. Although the performance of the representation
-learning models has been better than the traditional baselines for such tasks,
-little is known about the elementary properties of a tweet encoded within these
-representations, or why particular representations work better for certain
-tasks. Our work presented here constitutes the first step in opening the
-black-box of vector embeddings for tweets. Traditional feature engineering
-methods for high-level applications have exploited various elementary
-properties of tweets. We believe that a tweet representation is effective for
-an application because it meticulously encodes the application-specific
-elementary properties of tweets. To understand the elementary properties
-encoded in a tweet representation, we evaluate the representations on the
-accuracy to which they can model each of those properties such as tweet length,
-presence of particular words, hashtags, mentions, capitalization, etc. Our
-systematic extensive study of nine supervised and four unsupervised tweet
-representations against most popular eight textual and five social elementary
-properties reveal that Bi-directional LSTMs (BLSTMs) and Skip-Thought Vectors
-(STV) best encode the textual and social properties of tweets respectively.
-FastText is the best model for low resource settings, providing very little
-degradation with reduction in embedding size. Finally, we draw interesting
-insights by correlating the model performance obtained for elementary property
-prediction tasks with the highlevel downstream applications.
-",1,0,0,0,0,0
-19654,LoIDE: a web-based IDE for Logic Programming - Preliminary Technical Report,"  Logic-based paradigms are nowadays widely used in many different fields, also
-thank to the availability of robust tools and systems that allow the
-development of real-world and industrial applications.
-In this work we present LoIDE, an advanced and modular web-editor for
-logic-based languages that also integrates with state-of-the-art solvers.
-",1,0,0,0,0,0
-19655,Introduction to finite mixtures,"  Mixture models have been around for over 150 years, as an intuitively simple
-and practical tool for enriching the collection of probability distributions
-available for modelling data. In this chapter we describe the basic ideas of
-the subject, present several alternative representations and perspectives on
-these models, and discuss some of the elements of inference about the unknowns
-in the models. Our focus is on the simplest set-up, of finite mixture models,
-but we discuss also how various simplifying assumptions can be relaxed to
-generate the rich landscape of modelling and inference ideas traversed in the
-rest of this book.
-",0,0,0,1,0,0
-19656,On the complexity of the projective splitting and Spingarn's methods for the sum of two maximal monotone operators,"  In this work we study the pointwise and ergodic iteration-complexity of a
-family of projective splitting methods proposed by Eckstein and Svaiter, for
-finding a zero of the sum of two maximal monotone operators. As a consequence
-of the complexity analysis of the projective splitting methods, we obtain
-complexity bounds for the two-operator case of Spingarn's partial inverse
-method. We also present inexact variants of two specific instances of this
-family of algorithms, and derive corresponding convergence rate results.
-",0,0,1,0,0,0
-19657,Demystifying Relational Latent Representations,"  Latent features learned by deep learning approaches have proven to be a
-powerful tool for machine learning. They serve as a data abstraction that makes
-learning easier by capturing regularities in data explicitly. Their benefits
-motivated their adaptation to relational learning context. In our previous
-work, we introduce an approach that learns relational latent features by means
-of clustering instances and their relations. The major drawback of latent
-representations is that they are often black-box and difficult to interpret.
-This work addresses these issues and shows that (1) latent features created by
-clustering are interpretable and capture interesting properties of data; (2)
-they identify local regions of instances that match well with the label, which
-partially explains their benefit; and (3) although the number of latent
-features generated by this approach is large, often many of them are highly
-redundant and can be removed without hurting performance much.
-",1,0,0,1,0,0
-19658,A direct method to compute the galaxy count angular correlation function including redshift-space distortions,"  In the near future, cosmology will enter the wide and deep galaxy survey area
-allowing high-precision studies of the large scale structure of the universe in
-three dimensions. To test cosmological models and determine their parameters
-accurately, it is natural to confront data with exact theoretical expectations
-expressed in the observational parameter space (angles and redshift). The
-data-driven galaxy number count fluctuations on redshift shells, can be used to
-build correlation functions $C(\theta; z_1, z_2)$ on and between shells which
-can probe the baryonic acoustic oscillations, the distance-redshift distortions
-as well as gravitational lensing and other relativistic effects. Transforming
-the model to the data space usually requires the computation of the angular
-power spectrum $C_\ell(z_1, z_2)$ but this appears as an artificial and
-inefficient step plagued by apodization issues. In this article we show that it
-is not necessary and present a compact expression for $C(\theta; z_1, z_2)$
-that includes directly the leading density and redshift space distortions terms
-from the full linear theory. It can be evaluated using a fast integration
-method based on Clenshaw-Curtis quadrature and Chebyshev polynomial series.
-This new method to compute the correlation functions without any Limber
-approximation, allows us to produce and discuss maps of the correlation
-function directly in the observable space and is a significant step towards
-disentangling the data from the tested models.
-",0,1,0,0,0,0
-19659,Contributors profile modelization in crowdsourcing platforms,"  The crowdsourcing consists in the externalisation of tasks to a crowd of
-people remunerated to execute this ones. The crowd, usually diversified, can
-include users without qualification and/or motivation for the tasks. In this
-paper we will introduce a new method of user expertise modelization in the
-crowdsourcing platforms based on the theory of belief functions in order to
-identify serious and qualificated users.
-",1,0,0,0,0,0
-19660,Discrete versions of the Li-Yau gradient estimate,"  We study positive solutions to the heat equation on graphs. We prove variants
-of the Li-Yau gradient estimate and the differential Harnack inequality. For
-some graphs, we can show the estimates to be sharp. We establish new
-computation rules for differential operators on discrete spaces and introduce a
-relaxation function that governs the time dependency in the differential
-Harnack estimate.
-",0,0,1,0,0,0
-19661,Efficient Modelling & Forecasting with range based volatility models and application,"  This paper considers an alternative method for fitting CARR models using
-combined estimating functions (CEF) by showing its usefulness in applications
-in economics and quantitative finance. The associated information matrix for
-corresponding new estimates is derived to calculate the standard errors. A
-simulation study is carried out to demonstrate its superiority relative to
-other two competitors: linear estimating functions (LEF) and the maximum
-likelihood (ML). Results show that CEF estimates are more efficient than LEF
-and ML estimates when the error distribution is mis-specified. Taking a real
-data set from financial economics, we illustrate the usefulness and
-applicability of the CEF method in practice and report reliable forecast values
-to minimize the risk in the decision making process.
-",0,0,1,1,0,0
-19662,A Constrained Shortest Path Scheme for Virtual Network Service Management,"  Virtual network services that span multiple data centers are important to
-support emerging data-intensive applications in fields such as bioinformatics
-and retail analytics. Successful virtual network service composition and
-maintenance requires flexible and scalable 'constrained shortest path
-management' both in the management plane for virtual network embedding (VNE) or
-network function virtualization service chaining (NFV-SC), as well as in the
-data plane for traffic engineering (TE). In this paper, we show analytically
-and empirically that leveraging constrained shortest paths within recent VNE,
-NFV-SC and TE algorithms can lead to network utilization gains (of up to 50%)
-and higher energy efficiency. The management of complex VNE, NFV-SC and TE
-algorithms can be, however, intractable for large scale substrate networks due
-to the NP-hardness of the constrained shortest path problem. To address such
-scalability challenges, we propose a novel, exact constrained shortest path
-algorithm viz., 'Neighborhoods Method' (NM). Our NM uses novel search space
-reduction techniques and has a theoretical quadratic speed-up making it
-practically faster (by an order of magnitude) than recent branch-and-bound
-exhaustive search solutions. Finally, we detail our NM-based SDN controller
-implementation in a real-world testbed to further validate practical NM
-benefits for virtual network services.
-",1,0,0,0,0,0
-19663,Tunneling of Glashow-Weinberg-Salam model particles from Black Hole Solutions in Rastall Theory,"  Using the semiclassical WKB approximation and Hamilton-Jacobi method, we
-solve an equation of motion for the Glashow-Weinberg-Salam model, which is
-important for understanding the unified gauge-theory of weak and
-electromagnetic interactions. We calculate the tunneling rate of the massive
-charged W-bosons in a background of electromagnetic field to investigate the
-Hawking temperature of black holes surrounded by perfect fluid in Rastall
-theory. Then, we study the quantum gravity effects on the generalized Proca
-equation with generalized uncertainty principle (GUP) on this background. We
-show that quantum gravity effects leave the remnants on the Hawking temperature
-and the Hawking radiation becomes nonthermal.
-",0,1,0,0,0,0
-19664,"Symbolic, Distributed and Distributional Representations for Natural Language Processing in the Era of Deep Learning: a Survey","  Natural language and symbols are intimately correlated. Recent advances in
-machine learning (ML) and in natural language processing (NLP) seem to
-contradict the above intuition: symbols are fading away, erased by vectors or
-tensors called distributed and distributional representations. However, there
-is a strict link between distributed/distributional representations and
-symbols, being the first an approximation of the second. A clearer
-understanding of the strict link between distributed/distributional
-representations and symbols will certainly lead to radically new deep learning
-networks. In this paper we make a survey that aims to draw the link between
-symbolic representations and distributed/distributional representations. This
-is the right time to revitalize the area of interpreting how symbols are
-represented inside neural networks.
-",1,0,0,0,0,0
-19665,Joint Embedding of Graphs,"  Feature extraction and dimension reduction for networks is critical in a wide
-variety of domains. Efficiently and accurately learning features for multiple
-graphs has important applications in statistical inference on graphs. We
-propose a method to jointly embed multiple undirected graphs. Given a set of
-graphs, the joint embedding method identifies a linear subspace spanned by rank
-one symmetric matrices and projects adjacency matrices of graphs into this
-subspace. The projection coefficients can be treated as features of the graphs.
-We also propose a random graph model which generalizes classical random graph
-model and can be used to model multiple graphs. We show through theory and
-numerical experiments that under the model, the joint embedding method produces
-estimates of parameters with small errors. Via simulation experiments, we
-demonstrate that the joint embedding method produces features which lead to
-state of the art performance in classifying graphs. Applying the joint
-embedding method to human brain graphs, we find it extract interpretable
-features that can be used to predict individual composite creativity index.
-",1,0,0,1,0,0
-19666,Tuples of polynomials over finite fields with pairwise coprimality conditions,"  Let $q$ be a prime power. We estimate the number of tuples of degree bounded
-monic polynomials $(Q_1,\ldots,Q_v) \in (\mathbb{F}_q[z])^v$ that satisfy given
-pairwise coprimality conditions. We show how this generalises from monic
-polynomials in finite fields to Dedekind domains with finite norms.
-",0,0,1,0,0,0
-19667,Pythagorean theorem of Sharpe ratio,"  In the present paper, using a replica analysis, we examine the portfolio
-optimization problem handled in previous work and discuss the minimization of
-investment risk under constraints of budget and expected return for the case
-that the distribution of the hyperparameters of the mean and variance of the
-return rate of each asset are not limited to a specific probability family.
-Findings derived using our proposed method are compared with those in previous
-work to verify the effectiveness of our proposed method. Further, we derive a
-Pythagorean theorem of the Sharpe ratio and macroscopic relations of
-opportunity loss. Using numerical experiments, the effectiveness of our
-proposed method is demonstrated for a specific situation.
-",0,1,1,0,0,0
-19668,Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models,"  Researchers often summarize their work in the form of scientific posters.
-Posters provide a coherent and efficient way to convey core ideas expressed in
-scientific papers. Generating a good scientific poster, however, is a complex
-and time consuming cognitive task, since such posters need to be readable,
-informative, and visually aesthetic. In this paper, for the first time, we
-study the challenging problem of learning to generate posters from scientific
-papers. To this end, a data-driven framework, that utilizes graphical models,
-is proposed. Specifically, given content to display, the key elements of a good
-poster, including attributes of each panel and arrangements of graphical
-elements are learned and inferred from data. During the inference stage, an MAP
-inference framework is employed to incorporate some design principles. In order
-to bridge the gap between panel attributes and the composition within each
-panel, we also propose a recursive page splitting algorithm to generate the
-panel layout for a poster. To learn and validate our model, we collect and
-release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which
-consists of scientific papers and corresponding posters with exhaustively
-labelled panels and attributes. Qualitative and quantitative results indicate
-the effectiveness of our approach.
-",1,0,0,0,0,0
-19669,Transformable Biomimetic Liquid Metal Chameleon,"  Liquid metal (LM) is of current core interest for a wide variety of newly
-emerging areas. However, the functional materials thus made so far by LM only
-could display a single silver-white appearance. Here in this study, the new
-conceptual colorful LM marbles working like transformable biomimetic chameleons
-were proposed and fabricated from LM droplets through encasing them with
-fluorescent nano-particles. We demonstrated that this unique LM marble can be
-manipulated into various stable magnificent appearances as one desires. And it
-can also splitt and merge among different colors. Such multifunctional LM
-chameleon is capable of responding to the outside electric-stimulus and
-realizing shape transformation and discoloration behaviors as well. Further
-more, the electric-stimuli has been disclosed to be an easy going way to
-trigger the release of nano/micro-particles from the LM. The present
-fluorescent biomimetic liquid metal chameleon is expected to offer important
-opportunities for diverse unconventional applications, especially in a wide
-variety of functional smart material and color changeable soft robot areas.
-",0,1,0,0,0,0
-19670,Local Private Hypothesis Testing: Chi-Square Tests,"  The local model for differential privacy is emerging as the reference model
-for practical applications collecting and sharing sensitive information while
-satisfying strong privacy guarantees. In the local model, there is no trusted
-entity which is allowed to have each individual's raw data as is assumed in the
-traditional curator model for differential privacy. So, individuals' data are
-usually perturbed before sharing them.
-We explore the design of private hypothesis tests in the local model, where
-each data entry is perturbed to ensure the privacy of each participant.
-Specifically, we analyze locally private chi-square tests for goodness of fit
-and independence testing, which have been studied in the traditional, curator
-model for differential privacy.
-",1,0,1,1,0,0
-19671,Rapid point-of-care Hemoglobin measurement through low-cost optics and Convolutional Neural Network based validation,"  A low-cost, robust, and simple mechanism to measure hemoglobin would play a
-critical role in the modern health infrastructure. Consistent sample
-acquisition has been a long-standing technical hurdle for photometer-based
-portable hemoglobin detectors which rely on micro cuvettes and dry chemistry.
-Any particulates (e.g. intact red blood cells (RBCs), microbubbles, etc.) in a
-cuvette's sensing area drastically impact optical absorption profile, and
-commercial hemoglobinometers lack the ability to automatically detect faulty
-samples. We present the ground-up development of a portable, low-cost and open
-platform with equivalent accuracy to medical-grade devices, with the addition
-of CNN-based image processing for rapid sample viability prechecks. The
-developed platform has demonstrated precision to the nearest $0.18[g/dL]$ of
-hemoglobin, an R^2 = 0.945 correlation to hemoglobin absorption curves reported
-in literature, and a 97% detection accuracy of poorly-prepared samples. We see
-the developed hemoglobin device/ML platform having massive implications in
-rural medicine, and consider it an excellent springboard for robust deep
-learning optical spectroscopy: a currently untapped source of data for
-detection of countless analytes.
-",0,1,0,1,0,0
-19672,Families of sets with no matchings of sizes 3 and 4,"  In this paper, we study the following classical question of extremal set
-theory: what is the maximum size of a family of subsets of $[n]$ such that no
-$s$ sets from the family are pairwise disjoint? This problem was first posed by
-Erd\H os and resolved for $n\equiv 0, -1\ (\mathrm{mod }\ s)$ by Kleitman in
-the 60s. Very little progress was made on the problem until recently. The only
-result was a very lengthy resolution of the case $s=3,\ n\equiv 1\ (\mathrm{mod
-}\ 3)$ by Quinn, which was written in his PhD thesis and never published in a
-refereed journal. In this paper, we give another, much shorter proof of Quinn's
-result, as well as resolve the case $s=4,\ n\equiv 2\ (\mathrm{mod }\ 4)$. This
-complements the results in our recent paper, where, in particular, we answered
-the question in the case $n\equiv -2\ (\mathrm{mod }\ s)$ for $s\ge 5$.
-",1,0,1,0,0,0
-19673,Light fields in complex media: mesoscopic scattering meets wave control,"  The newly emerging field of wave front shaping in complex media has recently
-seen enormous progress. The driving force behind these advances has been the
-experimental accessibility of the information stored in the scattering matrix
-of a disordered medium, which can nowadays routinely be exploited to focus
-light as well as to image or to transmit information even across highly turbid
-scattering samples. We will provide an overview of these new techniques, of
-their experimental implementations as well as of the underlying theoretical
-concepts following from mesoscopic scattering theory. In particular, we will
-highlight the intimate connections between quantum transport phenomena and the
-scattering of light fields in disordered media, which can both be described by
-the same theoretical concepts. We also put particular emphasis on how the above
-topics relate to application-oriented research fields such as optical imaging,
-sensing and communication.
-",0,1,0,0,0,0
-19674,Tailoring Product Ownership in Large-Scale Agile,"  In large-scale agile projects, product owners undertake a range of
-challenging and varied activities beyond those conventionally associated with
-that role. Using in-depth research interviews from 93 practitioners working in
-cross-border teams, from 21 organisations, our rich empirical data offers a
-unique international perspective into product owner activities. We found that
-the leaders of large-scale agile projects create product owner teams. Product
-owner team members undertake sponsor, intermediary and release plan master
-activities to manage scale. They undertake communicator and traveller
-activities to manage distance and technical architect, governor and risk
-assessor activities to manage governance. Based on our findings, we describe
-product owner behaviors that are valued by experienced product owners and their
-line managers.
-",1,0,0,0,0,0
-19675,"Gravitational wave, collider and dark matter signals from a scalar singlet electroweak baryogenesis","  We analyse a simple extension of the SM with just an additional scalar
-singlet coupled to the Higgs boson. We discuss the possible probes for
-electroweak baryogenesis in this model including collider searches,
-gravitational wave and direct dark matter detection signals. We show that a
-large portion of the model parameter space exists where the observation of
-gravitational waves would allow detection while the indirect collider searches
-would not.
-",0,1,0,0,0,0
-19676,Canonical correlation coefficients of high-dimensional Gaussian vectors: finite rank case,"  Consider a Gaussian vector $\mathbf{z}=(\mathbf{x}',\mathbf{y}')'$,
-consisting of two sub-vectors $\mathbf{x}$ and $\mathbf{y}$ with dimensions $p$
-and $q$ respectively, where both $p$ and $q$ are proportional to the sample
-size $n$. Denote by $\Sigma_{\mathbf{u}\mathbf{v}}$ the population
-cross-covariance matrix of random vectors $\mathbf{u}$ and $\mathbf{v}$, and
-denote by $S_{\mathbf{u}\mathbf{v}}$ the sample counterpart. The canonical
-correlation coefficients between $\mathbf{x}$ and $\mathbf{y}$ are known as the
-square roots of the nonzero eigenvalues of the canonical correlation matrix
-$\Sigma_{\mathbf{x}\mathbf{x}}^{-1}\Sigma_{\mathbf{x}\mathbf{y}}\Sigma_{\mathbf{y}\mathbf{y}}^{-1}\Sigma_{\mathbf{y}\mathbf{x}}$.
-In this paper, we focus on the case that $\Sigma_{\mathbf{x}\mathbf{y}}$ is of
-finite rank $k$, i.e. there are $k$ nonzero canonical correlation coefficients,
-whose squares are denoted by $r_1\geq\cdots\geq r_k>0$. We study the sample
-counterparts of $r_i,i=1,\ldots,k$, i.e. the largest $k$ eigenvalues of the
-sample canonical correlation matrix
-$§_{\mathbf{x}\mathbf{x}}^{-1}§_{\mathbf{x}\mathbf{y}}§_{\mathbf{y}\mathbf{y}}^{-1}§_{\mathbf{y}\mathbf{x}}$,
-denoted by $\lambda_1\geq\cdots\geq \lambda_k$. We show that there exists a
-threshold $r_c\in(0,1)$, such that for each $i\in\{1,\ldots,k\}$, when $r_i\leq
-r_c$, $\lambda_i$ converges almost surely to the right edge of the limiting
-spectral distribution of the sample canonical correlation matrix, denoted by
-$d_{+}$. When $r_i>r_c$, $\lambda_i$ possesses an almost sure limit in
-$(d_{+},1]$. We also obtain the limiting distribution of $\lambda_i$'s under
-appropriate normalization. Specifically, $\lambda_i$ possesses Gaussian type
-fluctuation if $r_i>r_c$, and follows Tracy-Widom distribution if $r_i<r_c$.
-Some applications of our results are also discussed.
-",0,0,1,1,0,0
-19677,Synthesizing Normalized Faces from Facial Identity Features,"  We present a method for synthesizing a frontal, neutral-expression image of a
-person's face given an input face photograph. This is achieved by learning to
-generate facial landmarks and textures from features extracted from a
-facial-recognition network. Unlike previous approaches, our encoding feature
-vector is largely invariant to lighting, pose, and facial expression.
-Exploiting this invariance, we train our decoder network using only frontal,
-neutral-expression photographs. Since these photographs are well aligned, we
-can decompose them into a sparse set of landmark points and aligned texture
-maps. The decoder then predicts landmarks and textures independently and
-combines them using a differentiable image warping operation. The resulting
-images can be used for a number of applications, such as analyzing facial
-attributes, exposure and white balance adjustment, or creating a 3-D avatar.
-",1,0,0,1,0,0
-19678,One-Shot Coresets: The Case of k-Clustering,"  Scaling clustering algorithms to massive data sets is a challenging task.
-Recently, several successful approaches based on data summarization methods,
-such as coresets and sketches, were proposed. While these techniques provide
-provably good and small summaries, they are inherently problem dependent - the
-practitioner has to commit to a fixed clustering objective before even
-exploring the data. However, can one construct small data summaries for a wide
-range of clustering problems simultaneously? In this work, we affirmatively
-answer this question by proposing an efficient algorithm that constructs such
-one-shot summaries for k-clustering problems while retaining strong theoretical
-guarantees.
-",1,0,0,1,0,0
-19679,Multiple nodal solutions of nonlinear Choquard equations,"  In this paper, we consider the existence of multiple nodal solutions of the
-nonlinear Choquard equation \begin{equation*} \ \ \ \ (P)\ \ \ \ \begin{cases}
--\Delta u+u=(|x|^{-1}\ast|u|^p)|u|^{p-2}u \ \ \ \text{in}\ \mathbb{R}^3, \ \ \
-\ \\ u\in H^1(\mathbb{R}^3),\\ \end{cases} \end{equation*} where $p\in
-(\frac{5}{2},5)$. We show that for any positive integer $k$, problem $(P)$ has
-at least a radially symmetrical solution changing sign exactly $k$-times.
-",0,0,1,0,0,0
-19680,"Computational Sufficiency, Reflection Groups, and Generalized Lasso Penalties","  We study estimators with generalized lasso penalties within the computational
-sufficiency framework introduced by Vu (2018, arXiv:1807.05985). By
-representing these penalties as support functions of zonotopes and more
-generally Minkowski sums of line segments and rays, we show that there is a
-natural reflection group associated with the underlying optimization problem. A
-consequence of this point of view is that for large classes of estimators
-sharing the same penalty, the penalized least squares estimator is
-computationally minimal sufficient. This means that all such estimators can be
-computed by refining the output of any algorithm for the least squares case. An
-interesting technical component is our analysis of coordinate descent on the
-dual problem. A key insight is that the iterates are obtained by reflecting and
-averaging, so they converge to an element of the dual feasible set that is
-minimal with respect to a ordering induced by the group associated with the
-penalty. Our main application is fused lasso/total variation denoising and
-isotonic regression on arbitrary graphs. In those cases the associated group is
-a permutation group.
-",0,0,0,1,0,0
-19681,Driving Simulator Platform for Development and Evaluation of Safety and Emergency Systems,"  According to data from the United Nations, more than 3000 people have died
-each day in the world due to road traffic collision. Considering recent
-researches, the human error may be considered as the main responsible for these
-fatalities. Because of this, researchers seek alternatives to transfer the
-vehicle control from people to autonomous systems. However, providing this
-technological innovation for the people may demand complex challenges in the
-legal, economic and technological areas. Consequently, carmakers and
-researchers have divided the driving automation in safety and emergency systems
-that improve the driver perception on the road. This may reduce the human
-error. Therefore, the main contribution of this study is to propose a driving
-simulator platform to develop and evaluate safety and emergency systems, in the
-first design stage. This driving simulator platform has an advantage: a
-flexible software structure.This allows in the simulation one adaptation for
-development or evaluation of a system. The proposed driving simulator platform
-was tested in two applications: cooperative vehicle system development and the
-influence evaluation of a Driving Assistance System (\textit{DAS}) on a driver.
-In the cooperative vehicle system development, the results obtained show that
-the increment of the time delay in the communication among vehicles ($V2V$) is
-determinant for the system performance. On the other hand, in the influence
-evaluation of a \textit{DAS} in a driver, it was possible to conclude that the
-\textit{DAS'} model does not have the level of influence necessary in a driver
-to avoid an accident.
-",1,0,0,0,0,0
-19682,Electrical transient laws in neuronal microdomains based on electro-diffusion,"  The current-voltage (I-V) conversion characterizes the physiology of cellular
-microdomains and reflects cellular communication, excitability, and electrical
-transduction. Yet deriving such I-V laws remains a major challenge in most
-cellular microdomains due to their small sizes and the difficulty of accessing
-voltage with a high nanometer precision. We present here novel analytical
-relations derived for different numbers of ionic species inside a neuronal
-micro/nano-domains, such as dendritic spines. When a steady-state current is
-injected, we find a large deviation from the classical Ohm's law, showing that
-the spine neck resistance is insuficent to characterize electrical properties.
-For a constricted spine neck, modeled by a hyperboloid, we obtain a new I-V law
-that illustrates the consequences of narrow passages on electrical conduction.
-Finally, during a fast current transient, the local voltage is modulated by the
-distance between activated voltage-gated channels. To conclude,
-electro-diffusion laws can now be used to interpret voltage distribution in
-neuronal microdomains.
-",0,0,0,0,1,0
-19683,Characterization of a Deuterium-Deuterium Plasma Fusion Neutron Generator,"  We characterize the neutron output of a deuterium-deuterium plasma fusion
-neutron generator, model 35-DD-W-S, manufactured by NSD/Gradel-Fusion. The
-measured energy spectrum is found to be dominated by neutron peaks at 2.2 MeV
-and 2.7 MeV. A detailed GEANT4 simulation accurately reproduces the measured
-energy spectrum and confirms our understanding of the fusion process in this
-generator. Additionally, a contribution of 14.1 MeV neutrons from
-deuterium-tritium fusion is found at a level of~$3.5\%$, from tritium produced
-in previous deuterium-deuterium reactions. We have measured both the absolute
-neutron flux as well as its relative variation on the operational parameters of
-the generator. We find the flux to be proportional to voltage $V^{3.32 \pm
-0.14}$ and current $I^{0.97 \pm 0.01}$. Further, we have measured the angular
-dependence of the neutron emission with respect to the polar angle. We conclude
-that it is well described by isotropic production of neutrons within the
-cathode field cage.
-",0,1,0,0,0,0
-19684,Turbulent Mass Inhomogeneities induced by a point-source,"  We describe how turbulence distributes tracers away from a localized source
-of injection, and analyse how the spatial inhomogeneities of the concentration
-field depend on the amount of randomness in the injection mechanism. For that
-purpose, we contrast the mass correlations induced by purely random injections
-with those induced by continuous injections in the environment. Using the
-Kraichnan model of turbulent advection, whereby the underlying velocity field
-is assumed to be shortly correlated in time, we explicitly identify scaling
-regions for the statistics of the mass contained within a shell of radius $r$
-and located at a distance $\rho$ away from the source. The two key parameters
-are found to be (i) the ratio $s^2$ between the absolute and the relative
-timescales of dispersion and (ii) the ratio $\Lambda$ between the size of the
-cloud and its distance away from the source. When the injection is random, only
-the former is relevant, as previously shown by Celani, Martins-Afonso $\&$
-Mazzino, $J. Fluid. Mech$, 2007 in the case of an incompressible fluid. It is
-argued that the space partition in terms of $s^2$ and $\Lambda$ is a robust
-feature of the injection mechanism itself, which should remain relevant beyond
-the Kraichnan model. This is for instance the case in a generalised version of
-the model, where the absolute dispersion is prescribed to be ballistic rather
-than diffusive.
-",0,1,0,0,0,0
-19685,Berezin-toeplitz quantization and complex weyl quantization of the torus t${}^2$,"  In this paper, we give a correspondence between the Berezin-Toeplitz and the
-complex Weyl quantizations of the torus $ \mathbb{T}^2$. To achieve this, we
-use the correspondence between the Berezin-Toeplitz and the complex Weyl
-quantizations of the complex plane and a relation between the Berezin-Toeplitz
-quantization of a periodic symbol on the real phase space $\mathbb{R}^2$ and
-the Berezin-Toeplitz quantization of a symbol on the torus $ \mathbb{T}^2 $.
-",0,0,1,0,0,0
-19686,Conformal predictive distributions with kernels,"  This paper reviews the checkered history of predictive distributions in
-statistics and discusses two developments, one from recent literature and the
-other new. The first development is bringing predictive distributions into
-machine learning, whose early development was so deeply influenced by two
-remarkable groups at the Institute of Automation and Remote Control. The second
-development is combining predictive distributions with kernel methods, which
-were originated by one of those groups, including Emmanuel Braverman.
-",1,0,0,1,0,0
-19687,Degree bound for toric envelope of a linear algebraic group,"  Algorithms working with linear algebraic groups often represent them via
-defining polynomial equations. One can always choose defining equations for an
-algebraic group to be of the degree at most the degree of the group as an
-algebraic variety. However, the degree of a linear algebraic group $G \subset
-\mathrm{GL}_n(C)$ can be arbitrarily large even for $n = 1$. One of the key
-ingredients of Hrushovski's algorithm for computing the Galois group of a
-linear differential equation was an idea to `approximate' every algebraic
-subgroup of $\mathrm{GL}_n(C)$ by a `similar' group so that the degree of the
-latter is bounded uniformly in $n$. Making this uniform bound computationally
-feasible is crucial for making the algorithm practical.
-In this paper, we derive a single-exponential degree bound for such an
-approximation (we call it toric envelope), which is qualitatively optimal. As
-an application, we improve the quintuply exponential bound for the first step
-of the Hrushovski's algorithm due to Feng to a single-exponential bound. For
-the cases $n = 2, 3$ often arising in practice, we further refine our general
-bound.
-",1,0,0,0,0,0
-19688,The Modified Lommel functions: monotonic pattern and inequalities,"  This article studies the monotonicity, log-convexity of the modified Lommel
-functions by using its power series and infinite product representation. Same
-properties for the ratio of the modified Lommel functions with the Lommel
-function, $\sinh$ and $\cosh$ are also discussed. As a consequence, some
-Turán type and reverse Turán type inequalities are given. A Rayleigh type
-function for the Lommel functions are derived and as an application, we obtain
-the Redheffer-type inequality.
-",0,0,1,0,0,0
-19689,Anticipating epileptic seizures through the analysis of EEG synchronization as a data classification problem,"  Epilepsy is a neurological disorder arising from anomalies of the electrical
-activity in the brain, affecting about 0.5--0.8\% of the world population.
-Several studies investigated the relationship between seizures and brainwave
-synchronization patterns, pursuing the possibility of identifying interictal,
-preictal, ictal and postictal states. In this work, we introduce a graph-based
-model of the brain interactions developed to study synchronization patterns in
-the electroencephalogram (EEG) signals. The aim is to develop a
-patient-specific approach, also for a real-time use, for the prediction of
-epileptic seizures' occurrences. Different synchronization measures of the EEG
-signals and easily computable functions able to capture in real-time the
-variations of EEG synchronization have been considered. Both standard and
-ad-hoc classification algorithms have been developed and used. Results on scalp
-EEG signals show that this simple and computationally viable processing is able
-to highlight the changes in the synchronization corresponding to the preictal
-state.
-",0,0,0,0,1,0
-19690,On distances in lattices from algebraic number fields,"  In this paper, we study a classical construction of lattices from number
-fields and obtain a series of new results about their minimum distance and
-other characteristics by introducing a new measure of algebraic numbers. In
-particular, we show that when the number fields have few complex embeddings,
-the minimum distances of these lattices can be computed exactly.
-",0,0,1,0,0,0
-19691,"Significance of distinct electron correlation effects in determining the P,T-odd electric dipole moment of $^{171}$Yb","  Parity and time-reversal violating electric dipole moment (EDM) of $^{171}$Yb
-is calculated accounting for the electron correlation effects over the
-Dirac-Hartree-Fock (DHF) method in the relativistic Rayleigh-Schrödinger
-many-body perturbation theory, with the second (MBPT(2) method) and third order
-(MBPT(3) method) approximations, and two variants of all-order relativistic
-many-body approaches, in the random phase approximation (RPA) and
-coupled-cluster (CC) method with singles and doubles (CCSD method) framework.
-We consider electron-nucleus tensor-pseudotensor (T-PT) and nuclear Schiff
-moment (NSM) interactions as the predominant sources that induce EDM in a
-diamagnetic atomic system. Our results from the CCSD method to EDM ($d_a$) of
-$^{171}$Yb due to the T-PT and NSM interactions are found to be $d_a = 4.85(6)
-\times 10^{-20} \langle \sigma \rangle C_T \ |e| \ cm$ and $d_a=2.89(4) \times
-10^{-17} {S/(|e|\ fm^3)}$, respectively, where $C_T$ is the T-PT coupling
-constant and $S$ is the NSM. These values differ significantly from the earlier
-calculations. The reason for the same has been attributed to large correlation
-effects arising through non-RPA type of interactions among the electrons in
-this atom that are observed by analyzing the differences in the RPA and CCSD
-results. This has been further scrutinized from the MBPT(2) and MBPT(3) results
-and their roles have been demonstrated explicitly.
-",0,1,0,0,0,0
-19692,Facilitating information system development with Panoramic view on data,"  The increasing amount of information and the absence of an effective tool for
-assisting users with minimal technical knowledge lead us to use associative
-thinking paradigm for implementation of a software solution - Panorama. In this
-study, we present object recognition process, based on context + focus
-information visualization techniques, as a foundation for realization of
-Panorama. We show that user can easily define data vocabulary of selected
-domain that is furthermore used as the application framework. The purpose of
-Panorama approach is to facilitate software development of certain problem
-domains by shortening the Software Development Life Cycle with minimizing the
-impact of implementation, review and maintenance phase. Our approach is focused
-on using and updating data vocabulary by users without extensive programming
-skills. Panorama therefore facilitates traversing through data by following
-associations where user does not need to be familiar with the query language,
-the data structure and does not need to know the problem domain fully. Our
-approach has been verified by detailed comparison to existing approaches and in
-an experiment by implementing selected use cases. The results confirmed that
-Panorama fits problem domains with emphasis on data oriented rather than ones
-with process oriented aspects. In such cases the development of selected
-problem domains is shortened up to 25%, where emphasis is mainly on analysis,
-logical design and testing, while omitting physical design and programming,
-which is performed automatically by Panorama tool.
-",1,0,0,0,0,0
-19693,Neutron Diffraction and $μ$SR Studies of Two Polymorphs of Nickel Niobate (NiNb$_2$O$_6$),"  Neutron diffraction and muon spin relaxation ($\mu$SR) studies are presented
-for the newly characterized polymorph of NiNb$_2$O$_6$ ($\beta$-NiNb$_2$O$_6$)
-with space group P4$_2$/n and $\mu$SR data only for the previously known
-columbite structure polymorph with space group Pbcn. The magnetic structure of
-the P4$_2$/n form was determined from neutron diffraction using both powder and
-single crystal data. Powder neutron diffraction determined an ordering wave
-vector $\vec{k}$ = ($\frac{1}{2},\frac{1}{2},\frac{1}{2}$). Single crystal data
-confirmed the same $\vec{k}$-vector and showed that the correct magnetic
-structure consists of antiferromagnetically-coupled chains running along the a
-or b-axes in adjacent Ni$^{2+}$ layers perpendicular to the c-axis, which is
-consistent with the expected exchange interaction hierarchy in this system. The
-refined magnetic structure is compared with the known magnetic structures of
-the closely related tri-rutile phases, NiSb$_2$O$_6$ and NiTa$_2$O$_6$. $\mu$SR
-data finds a transition temperature of $T_N \sim$ 15 K for this system, while
-the columbite polymorph exhibits a lower $T_N =$ 5.7(3) K. Our $\mu$SR
-measurements also allowed us to estimate the critical exponent of the order
-parameter $\beta$ for each polymorph. We found $\beta =$ 0.25(3) and 0.16(2)
-for the $\beta$ and columbite polymorphs respectively. The single crystal
-neutron scattering data gives a value for the critical exponent $\beta
-=$~0.28(3) for $\beta$-NiNb$_2$O$_6$, in agreement with the $\mu$SR value.
-While both systems have $\beta$ values less than 0.3, which is indicative of
-reduced dimensionality, this effect appears to be much stronger for the
-columbite system. In other words, although both systems appear to
-well-described by $S = 1$ spin chains, the interchain interactions in the
-$\beta$-polymorph are likely much larger.
-",0,1,0,0,0,0
-19694,Semiclassical Propagation: Hilbert Space vs. Wigner Representation,"  A unified viewpoint on the van Vleck and Herman-Kluk propagators in Hilbert
-space and their recently developed counterparts in Wigner representation is
-presented. It is shown that the numerical protocol for the Herman-Kluk
-propagator, which contains the van Vleck one as a particular case, coincides in
-both representations. The flexibility of the Wigner version in choosing the
-Gaussians' width for the underlying coherent states, being not bound to minimal
-uncertainty, is investigated numerically on prototypical potentials. Exploiting
-this flexibility provides neither qualitative nor quantitative improvements.
-Thus, the well-established Herman-Kluk propagator in Hilbert space remains the
-best choice to date given the large number of semiclassical developments and
-applications based on it.
-",0,1,0,0,0,0
-19695,Dynamic analysis and control PID path of a model type gantry crane,"  This paper presents an alternate form for the dynamic modelling of a
-mechanical system that simulates in real life a gantry crane type, using
-Euler's classical mechanics and Lagrange formalism, which allows find the
-equations of motion that our model describe. Moreover, it has a basic model
-design system using the SolidWorks software, based on the material and
-dimensions of the model provides some physical variables necessary for
-modelling. In order to verify the theoretical results obtained, a contrast was
-made between solutions obtained by simulation in SimMechanics-Matlab and
-Euler-Lagrange equations system, has been solved through Matlab libraries for
-solving equation's systems of the type and order obtained. The force is
-determined, but not as exerted by the spring, as this will be the control
-variable. The objective to bring the mass of the pendulum from one point to
-another with a specified distance without the oscillation from it, so that, the
-answer is overdamped. This article includes an analysis of PID control in which
-the equations of motion of Euler-Lagrange are rewritten in the state space,
-once there, they were implemented in Simulink to get the natural response of
-the system to a step input in F and then draw the desired trajectories.
-",1,1,0,0,0,0
-19696,Dynamic Minimum Spanning Forest with Subpolynomial Worst-case Update Time,"  We present a Las Vegas algorithm for dynamically maintaining a minimum
-spanning forest of an $n$-node graph undergoing edge insertions and deletions.
-Our algorithm guarantees an $O(n^{o(1)})$ worst-case update time with high
-probability. This significantly improves the two recent Las Vegas algorithms by
-Wulff-Nilsen [STOC'17] with update time $O(n^{0.5-\epsilon})$ for some constant
-$\epsilon>0$ and, independently, by Nanongkai and Saranurak [STOC'17] with
-update time $O(n^{0.494})$ (the latter works only for maintaining a spanning
-forest).
-Our result is obtained by identifying the common framework that both two
-previous algorithms rely on, and then improve and combine the ideas from both
-works. There are two main algorithmic components of the framework that are
-newly improved and critical for obtaining our result. First, we improve the
-update time from $O(n^{0.5-\epsilon})$ in Wulff-Nilsen [STOC'17] to
-$O(n^{o(1)})$ for decrementally removing all low-conductance cuts in an
-expander undergoing edge deletions. Second, by revisiting the ""contraction
-technique"" by Henzinger and King [1997] and Holm et al. [STOC'98], we show a
-new approach for maintaining a minimum spanning forest in connected graphs with
-very few (at most $(1+o(1))n$) edges. This significantly improves the previous
-approach in [Wulff-Nilsen STOC'17] and [Nanongkai and Saranurak STOC'17] which
-is based on Frederickson's 2-dimensional topology tree and illustrates a new
-application to this old technique.
-",1,0,0,0,0,0
-19697,Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference,"  The rising popularity of intelligent mobile devices and the daunting
-computational cost of deep learning-based models call for efficient and
-accurate on-device inference schemes. We propose a quantization scheme that
-allows inference to be carried out using integer-only arithmetic, which can be
-implemented more efficiently than floating point inference on commonly
-available integer-only hardware. We also co-design a training procedure to
-preserve end-to-end model accuracy post quantization. As a result, the proposed
-quantization scheme improves the tradeoff between accuracy and on-device
-latency. The improvements are significant even on MobileNets, a model family
-known for run-time efficiency, and are demonstrated in ImageNet classification
-and COCO detection on popular CPUs.
-",1,0,0,1,0,0
-19698,airpred: A Flexible R Package Implementing Methods for Predicting Air Pollution,"  Fine particulate matter (PM$_{2.5}$) is one of the criteria air pollutants
-regulated by the Environmental Protection Agency in the United States. There is
-strong evidence that ambient exposure to (PM$_{2.5}$) increases risk of
-mortality and hospitalization. Large scale epidemiological studies on the
-health effects of PM$_{2.5}$ provide the necessary evidence base for lowering
-the safety standards and inform regulatory policy. However, ambient monitors of
-PM$_{2.5}$ (as well as monitors for other pollutants) are sparsely located
-across the U.S., and therefore studies based only on the levels of PM$_{2.5}$
-measured from the monitors would inevitably exclude large amounts of the
-population. One approach to resolving this issue has been developing models to
-predict local PM$_{2.5}$, NO$_2$, and ozone based on satellite, meteorological,
-and land use data. This process typically relies developing a prediction model
-that relies on large amounts of input data and is highly computationally
-intensive to predict levels of air pollution in unmonitored areas. We have
-developed a flexible R package that allows for environmental health researchers
-to design and train spatio-temporal models capable of predicting multiple
-pollutants, including PM$_{2.5}$. We utilize H2O, an open source big data
-platform, to achieve both performance and scalability when used in conjunction
-with cloud or cluster computing systems.
-",0,0,0,1,0,0
-19699,Accurate and Efficient Profile Matching in Knowledge Bases,"  A profile describes a set of properties, e.g. a set of skills a person may
-have, a set of skills required for a particular job, or a set of abilities a
-football player may have with respect to a particular team strategy. Profile
-matching aims to determine how well a given profile fits to a requested
-profile. The approach taken in this article is grounded in a matching theory
-that uses filters in lattices to represent profiles, and matching values in the
-interval [0,1]: the higher the matching value the better is the fit. Such
-lattices can be derived from knowledge bases exploiting description logics to
-represent the knowledge about profiles. An interesting first question is, how
-human expertise concerning the matching can be exploited to obtain most
-accurate matchings. It will be shown that if a set of filters together with
-matching values by some human expert is given, then under some mild
-plausibility assumptions a matching measure can be determined such that the
-computed matching values preserve the rankings given by the expert. A second
-question concerns the efficient querying of databases of profile instances. For
-matching queries that result in a ranked list of profile instances matching a
-given one it will be shown how corresponding top-k queries can be evaluated on
-grounds of pre-computed matching values, which in turn allows the maintenance
-of the knowledge base to be decoupled from the maintenance of profile
-instances. In addition, it will be shown how the matching queries can be
-exploited for gap queries that determine how profile instances need to be
-extended in order to improve in the rankings. Finally, the theory of matching
-will be extended beyond the filters, which lead to a matching theory that
-exploits fuzzy sets or probabilistic logic with maximum entropy semantics. It
-will be shown that added fuzzy links can be captured by extending the
-underlying lattice.
-",1,0,0,0,0,0
-19700,Boötes-HiZELS: an optical to near-infrared survey of emission-line galaxies at $\bf z=0.4-4.7$,"  We present a sample of $\sim 1000$ emission line galaxies at $z=0.4-4.7$ from
-the $\sim0.7$deg$^2$ High-$z$ Emission Line Survey (HiZELS) in the Boötes
-field identified with a suite of six narrow-band filters at $\approx 0.4-2.1$
-$\mu$m. These galaxies have been selected on their Ly$\alpha$ (73), {\sc [Oii]}
-(285), H$\beta$/{\sc [Oiii]} (387) or H$\alpha$ (362) emission-line, and have
-been classified with optical to near-infrared colours. A subsample of 98
-sources have reliable redshifts from multiple narrow-band (e.g. [O{\sc
-ii}]-H$\alpha$) detections and/or spectroscopy. In this survey paper, we
-present the observations, selection and catalogs of emitters. We measure number
-densities of Ly$\alpha$, [O{\sc ii}], H$\beta$/{\sc [Oiii]} and H$\alpha$ and
-confirm strong luminosity evolution in star-forming galaxies from $z\sim0.4$ to
-$\sim 5$, in agreement with previous results. To demonstrate the usefulness of
-dual-line emitters, we use the sample of dual [O{\sc ii}]-H$\alpha$ emitters to
-measure the observed [O{\sc ii}]/H$\alpha$ ratio at $z=1.47$. The observed
-[O{\sc ii}]/H$\alpha$ ratio increases significantly from 0.40$\pm0.01$ at
-$z=0.1$ to 0.52$\pm0.05$ at $z=1.47$, which we attribute to either decreasing
-dust attenuation with redshift, or due to a bias in the (typically)
-fiber-measurements in the local Universe which only measure the central kpc
-regions. At the bright end, we find that both the H$\alpha$ and Ly$\alpha$
-number densities at $z\approx2.2$ deviate significantly from a Schechter form,
-following a power-law. We show that this is driven entirely by an increasing
-X-ray/AGN fraction with line-luminosity, which reaches $\approx 100$ \% at
-line-luminosities $L\gtrsim3\times10^{44}$ erg s$^{-1}$.
-",0,1,0,0,0,0
-19701,Efficient Constrained Tensor Factorization by Alternating Optimization with Primal-Dual Splitting,"  Tensor factorization with hard and/or soft constraints has played an
-important role in signal processing and data analysis. However, existing
-algorithms for constrained tensor factorization have two drawbacks: (i) they
-require matrix-inversion; and (ii) they cannot (or at least is very difficult
-to) handle structured regularizations. We propose a new tensor factorization
-algorithm that circumvents these drawbacks. The proposed method is built upon
-alternating optimization, and each subproblem is solved by a primal-dual
-splitting algorithm, yielding an efficient and flexible algorithmic framework
-to constrained tensor factorization. The advantages of the proposed method over
-a state-of-the-art constrained tensor factorization algorithm, called AO-ADMM,
-are demonstrated on regularized nonnegative tensor factorization.
-",1,0,0,0,0,0
-19702,Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes,"  In this paper, we propose a replay attack spoofing detection system for
-automatic speaker verification using multitask learning of noise classes. We
-define the noise that is caused by the replay attack as replay noise. We
-explore the effectiveness of training a deep neural network simultaneously for
-replay attack spoofing detection and replay noise classification. The
-multi-task learning includes classifying the noise of playback devices,
-recording environments, and recording devices as well as the spoofing
-detection. Each of the three types of the noise classes also includes a genuine
-class. The experiment results on the ASVspoof2017 datasets demonstrate that the
-performance of our proposed system is improved by 30% relatively on the
-evaluation set.
-",1,0,0,1,0,0
-19703,On One Property of Tikhonov Regularization Algorithm,"  For linear inverse problem with Gaussian random noise we show that Tikhonov
-regularization algorithm is minimax in the class of linear estimators and is
-asymptotically minimax in the sense of sharp asymptotic in the class of all
-estimators. The results are valid if some a priori information on a Fourier
-coefficients of solution is provided. For trigonometric basis this a priori
-information implies that the solution belongs to a ball in Besov space
-$B^r_{2\infty}$.
-",0,0,1,1,0,0
-19704,Observation of Extra Photon Recoil in a Distorted Optical Field,"  Light carries momentum which induces on atoms a recoil for each photon
-absorbed. In vacuum, for a monochromatic beam of frequency $\nu$, the global
-momentum per photon is bounded by general principles and is smaller than $h
-\nu/c$ leading to a limit on the recoil. However, locally this limit can be
-broken. In this paper, we give a general formula to calculate the recoil in
-vacuum. We show that in a laser beam with a distorted optical field, there are
-regions where the recoil can be higher than this limit. Using atoms placed in
-those regions we are able to measure directly the extra recoil.
-",0,1,0,0,0,0
-19705,Weak Poincaré Inequalities for Convergence Rate of Degenerate Diffusion Processes,"  For a contraction $C_0$-semigroup on a separable Hilbert space, the decay
-rate is estimated by using the weak Poincaré inequalities for the symmetric
-and anti-symmetric part of the generator. As applications, non-exponential
-convergence rate is characterized for a class of degenerate diffusion
-processes, so that the study of hypocoercivity is extended. Concrete examples
-are presented.
-",0,0,1,0,0,0
-19706,rTraceroute: Réunion Traceroute Visualisation,"  Traceroute is the main tools to explore Internet path. It provides limited
-information about each node along the path. However, Traceroute cannot go
-further in statistics analysis, or \emph{Man-Machine Interface (MMI)}.
-Indeed, there are no graphical tool that is able to draw all paths used by IP
-routes. We present a new tool that can handle more than 1,000 Traceroute
-results, map them, identify graphically MPLS links, get information of usage of
-all routes (in percent) to improve the knowledge between countries' links.
-rTraceroute want to go deeper in usage of atomic traces. In this paper, we will
-discuss the concept of rTraceroute and present some example of usage.
-",1,0,0,0,0,0
-19707,Effects of heterogeneity in site-site couplings for tight-binding models on scale-invariant structures,"  We studied the thermodynamic behaviors of non-interacting bosons and fermions
-trapped by a scale-invariant branching structure of adjustable degree of
-heterogeneity. The full energy spectrum in tight-binding approximation was
-analytically solved . We found that the log-periodic oscillation of the
-specific heat for Fermi gas depended on the heterogeneity of hopping. Also, low
-dimensional Bose-Einstein condensation occurred only for non-homogeneous setup.
-",0,1,0,0,0,0
-19708,Are generative deep models for novelty detection truly better?,"  Many deep models have been recently proposed for anomaly detection. This
-paper presents comparison of selected generative deep models and classical
-anomaly detection methods on an extensive number of non--image benchmark
-datasets. We provide statistical comparison of the selected models, in many
-configurations, architectures and hyperparamaters. We arrive to conclusion that
-performance of the generative models is determined by the process of selection
-of their hyperparameters. Specifically, performance of the deep generative
-models deteriorates with decreasing amount of anomalous samples used in
-hyperparameter selection. In practical scenarios of anomaly detection, none of
-the deep generative models systematically outperforms the kNN.
-",0,0,0,1,0,0
-19709,Minimizing the waiting time for a one-way shuttle service,"  Consider a terminal in which users arrive continuously over a finite period
-of time at a variable rate known in advance. A fleet of shuttles has to carry
-the users over a fixed trip. What is the shuttle schedule that minimizes their
-waiting time? This is the question addressed in the present paper. We propose
-efficient algorithms for several variations of this question with proven
-performance guarantees. The techniques used are of various types (convex
-optimization, shortest paths,...). The paper ends with numerical experiments
-showing that most of our algorithms behave also well in practice.
-",0,0,1,0,0,0
-19710,Two weight bump conditions for matrix weights,"  In this paper we extend the theory of two weight, $A_p$ bump conditions to
-the setting of matrix weights. We prove two matrix weight inequalities for
-fractional maximal operators, fractional and singular integrals, sparse
-operators and averaging operators. As applications we prove quantitative, one
-weight estimates, in terms of the matrix $A_p$ constant, for singular
-integrals, and prove a Poincaré inequality related to those that appear in
-the study of degenerate elliptic PDEs.
-",0,0,1,0,0,0
-19711,Legendrian ribbons and strongly quasipositive links in an open book,"  We show that a link in an open book can be realized as a strongly
-quasipositive braid if and only if it bounds a Legendrian ribbon with respect
-to the associated contact structure. This generalizes a result due to Baader
-and Ishikawa for links in the three-sphere. We highlight some related
-techniques for determining whether or not a link is strongly quasipositive,
-emphasizing applications to fibered links and satellites.
-",0,0,1,0,0,0
-19712,Δ-cumulants in terms of moments,"  The \Delta-convolution of real probability measures, introduced by Bożejko,
-generalizes both free and boolean convolutions. It is linearized by the
-\Delta-cumulants, and Yoshida gave a combinatorial formula for moments in terms
-of \Delta-cumulants, that implicitly defines the latter. It relies on the
-definition of an appropriate weight on noncrossing partitions. We give here two
-different expressions for the \Delta-cumulants: the first one is a simple
-variant of Lagrange inversion formula, and the second one is a combinatorial
-inversion of Yoshida's formula involving Schröder trees.
-",0,0,1,0,0,0
-19713,A Fully Convolutional Network for Semantic Labeling of 3D Point Clouds,"  When classifying point clouds, a large amount of time is devoted to the
-process of engineering a reliable set of features which are then passed to a
-classifier of choice. Generally, such features - usually derived from the
-3D-covariance matrix - are computed using the surrounding neighborhood of
-points. While these features capture local information, the process is usually
-time-consuming, and requires the application at multiple scales combined with
-contextual methods in order to adequately describe the diversity of objects
-within a scene. In this paper we present a 1D-fully convolutional network that
-consumes terrain-normalized points directly with the corresponding spectral
-data,if available, to generate point-wise labeling while implicitly learning
-contextual features in an end-to-end fashion. Our method uses only the
-3D-coordinates and three corresponding spectral features for each point.
-Spectral features may either be extracted from 2D-georeferenced images, as
-shown here for Light Detection and Ranging (LiDAR) point clouds, or extracted
-directly for passive-derived point clouds,i.e. from muliple-view imagery. We
-train our network by splitting the data into square regions, and use a pooling
-layer that respects the permutation-invariance of the input points. Evaluated
-using the ISPRS 3D Semantic Labeling Contest, our method scored second place
-with an overall accuracy of 81.6%. We ranked third place with a mean F1-score
-of 63.32%, surpassing the F1-score of the method with highest accuracy by
-1.69%. In addition to labeling 3D-point clouds, we also show that our method
-can be easily extended to 2D-semantic segmentation tasks, with promising
-initial results.
-",1,0,0,1,0,0
-19714,Boundedness properties in function spaces,"  Some boundedness properties of function spaces (considered as topological
-groups) are studied.
-",0,0,1,0,0,0
-19715,Linear-size CDAWG: new repetition-aware indexing and grammar compression,"  In this paper, we propose a novel approach to combine \emph{compact directed
-acyclic word graphs} (CDAWGs) and grammar-based compression. This leads us to
-an efficient self-index, called Linear-size CDAWGs (L-CDAWGs), which can be
-represented with $O(\tilde e_T \log n)$ bits of space allowing for $O(\log
-n)$-time random and $O(1)$-time sequential accesses to edge labels, and $O(m
-\log \sigma + occ)$-time pattern matching. Here, $\tilde e_T$ is the number of
-all extensions of maximal repeats in $T$, $n$ and $m$ are respectively the
-lengths of the text $T$ and a given pattern, $\sigma$ is the alphabet size, and
-$occ$ is the number of occurrences of the pattern in $T$. The repetitiveness
-measure $\tilde e_T$ is known to be much smaller than the text length $n$ for
-highly repetitive text. For constant alphabets, our L-CDAWGs achieve $O(m +
-occ)$ pattern matching time with $O(e_T^r \log n)$ bits of space, which
-improves the pattern matching time of Belazzougui et al.'s run-length
-BWT-CDAWGs by a factor of $\log \log n$, with the same space complexity. Here,
-$e_T^r$ is the number of right extensions of maximal repeats in $T$. As a
-byproduct, our result gives a way of constructing an SLP of size $O(\tilde
-e_T)$ for a given text $T$ in $O(n + \tilde e_T \log \sigma)$ time.
-",1,0,0,0,0,0
-19716,Topic Lifecycle on Social Networks: Analyzing the Effects of Semantic Continuity and Social Communities,"  Topic lifecycle analysis on Twitter, a branch of study that investigates
-Twitter topics from their birth through lifecycle to death, has gained immense
-mainstream research popularity. In the literature, topics are often treated as
-one of (a) hashtags (independent from other hashtags), (b) a burst of keywords
-in a short time span or (c) a latent concept space captured by advanced text
-analysis methodologies, such as Latent Dirichlet Allocation (LDA). The first
-two approaches are not capable of recognizing topics where different users use
-different hashtags to express the same concept (semantically related), while
-the third approach misses out the user's explicit intent expressed via
-hashtags. In our work, we use a word embedding based approach to cluster
-different hashtags together, and the temporal concurrency of the hashtag
-usages, thus forming topics (a semantically and temporally related group of
-hashtags).We present a novel analysis of topic lifecycles with respect to
-communities. We characterize the participation of social communities in the
-topic clusters, and analyze the lifecycle of topic clusters with respect to
-such participation. We derive first-of-its-kind novel insights with respect to
-the complex evolution of topics over communities and time: temporal morphing of
-topics over hashtags within communities, how the hashtags die in some
-communities but morph into some other hashtags in some other communities (that,
-it is a community-level phenomenon), and how specific communities adopt to
-specific hashtags. Our work is fundamental in the space of topic lifecycle
-modeling and understanding in communities: it redefines our understanding of
-topic lifecycles and shows that the social boundaries of topic lifecycles are
-deeply ingrained with community behavior.
-",1,0,0,0,0,0
-19717,Unstable Footwear as a Speed-Dependent Noise-Based Training Gear to Exercise Inverted Pendulum Motion During Walking,"  Previous research on unstable footwear has suggested that it may induce
-plantar mechanical noise during walking. The purpose of this study was to
-explore whether unstable footwear could be considered as a noise-based training
-gear to exercise body center of mass (CoM) motion during walking or not. Ground
-reaction forces were collected among 24 healthy young women walking at speeds
-between 3 and 6 km h-1 with control running shoes and unstable rocker-bottom
-shoes. The external mechanical work, the recovery of mechanical energy of the
-CoM during and within the step cycles, and the phase shift between potential
-and kinetic energy curves of the CoM were computed. Our findings support the
-idea that unstable rocker-bottom footwear could serve as a speed-dependent
-noise- based training gear to exercise CoM motion during walking. At slow
-speed, it acts as a stochastic resonance or facilitator, whereas at brisk speed
-it acts as a constraint.
-",0,1,0,0,0,0
-19718,Gaussian autoregressive process with dependent innovations. Some asymptotic results,"  In this paper we introduce a modified version of a gaussian standard
-first-order autoregressive process where we allow for a dependence structure
-between the state variable $Y_{t-1}$ and the next innovation $\xi_t$. We call
-this model dependent innovations gaussian AR(1) process (DIG-AR(1)). We analyze
-the moment and temporal dependence properties of the new model. After proving
-that the OLS estimator does not consistently estimate the autoregressive
-parameter, we introduce an infeasible estimator and we provide its
-$\sqrt{T}$-asymptotic normality.
-",0,0,1,1,0,0
-19719,Structured Peer Learning Program - An Innovative Approach to Computer Science Education,"  Structured Peer Learning (SPL) is a form of peer-based supplemental
-instruction that focuses on mentoring, guidance, and development of technical,
-communication, and social skills in both the students receiving assistance and
-the students in teaching roles. This paper explores the methodology, efficacy,
-and reasoning behind the practical realization of a SPL program designed to
-increase student knowledge and success in undergraduate Computer Science
-courses. Students expressed an increased level of comfort when asking for help
-from student teachers versus traditional educational resources, historically
-showed an increased average grade in lower-level courses, and felt that the
-program positively impacted their desire to continue in or switch to a Computer
-major. Additionally, results indicated that advances in programming, analytical
-thinking, and abstract analysis skills were evident in not only the students
-but also the student teachers, suggesting a strong bidirectional flow of
-knowledge.
-",1,0,0,0,0,0
-19720,Analysis of the possibility for time-optimal control of the scanning system of the GREEN-WAKE's project lidar,"  The monograph represents analysis of the possibility for time-optimal control
-of a prototype of an aircraft-mounted scanning-imaging system of a Light
-Detection and Ranging based wake vortex detection system. The study is a part
-of the research project ""Demonstration of Light Detection and Ranging based
-wake vortex detection system incorporating an atmospheric hazard map"" or
-GREEN-WAKE (Project ID 213254) of the European Union Seventh Framework Program
-for Research and Technological Development. The scanning system comprises two
-light mirror actuators. The study is decomposed into several group of problems.
-The first and second groups consider the mathematical models of the scanning
-system and the mirror actuators. The third group of problems deals with the
-design of closed loop tracking control systems of both the large and small
-mirror actuators. The control of each one system is synthesized as a near
-time-optimal control of the precise linear model of the mirror actuator. The
-control algorithms realize the state of the art method for synthesis of
-time-optimal control of any order for a class of linear time-optimal control
-problems developed in the author's dissertation. The last discovers and
-theoretically proves new properties of a class of linear time-optimal control
-problems. From the point of view of the control synthesis algorithms the main
-advantage is that the time-optimal control is produced by a multistage
-procedure within the class of problems but without the need of describing the
-hyper-surfaces of switching over. The fourth group of problems considers
-modeling the real scan picture but with inclusion also of the Coulomb's
-friction model. The fifth group of problems investigates the ways of
-improvement of the real scan picture. As a result an excellent repetition and
-clearness of the scanning alongside with symmetry and matching the scan pattern
-are seen.
-",1,0,0,0,0,0
-19721,Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples,"  Recently, researchers have discovered that the state-of-the-art object
-classifiers can be fooled easily by small perturbations in the input
-unnoticeable to human eyes. It is also known that an attacker can generate
-strong adversarial examples if she knows the classifier parameters. Conversely,
-a defender can robustify the classifier by retraining if she has access to the
-adversarial examples. We explain and formulate this adversarial example problem
-as a two-player continuous zero-sum game, and demonstrate the fallacy of
-evaluating a defense or an attack as a static problem. To find the best
-worst-case defense against whitebox attacks, we propose a continuous minimax
-optimization algorithm. We demonstrate the minimax defense with two types of
-attack classes -- gradient-based and neural network-based attacks. Experiments
-with the MNIST and the CIFAR-10 datasets demonstrate that the defense found by
-numerical minimax optimization is indeed more robust than non-minimax defenses.
-We discuss directions for improving the result toward achieving robustness
-against multiple types of attack classes.
-",1,0,0,1,0,0
-19722,From Attention to Participation: Reviewing and Modelling Engagement with Computers,"  Over the last decades, the Internet and mobile technology have consolidated
-the digital as a public sphere of life. Designers are asked to create engaging
-digital experiences. However, in some cases engagement is seen as a
-psychological state, while in others it emphasizes a participative vein. In
-this paper, I review and discuss both and propose a new definition to clarify
-the concept engagement with computers. Thus, engagement is a quality of an
-active connection between a user and a computing product, either a website or a
-mobile phone app. Studying it requires understanding a set of aspects like the
-user's affect, motivation and attention, as well as the product's design,
-content and composition. Finally, I propose explaining these concepts aligned
-with engagement and integrate them into a preliminary model to measure the
-manifestations.
-",1,0,0,0,0,0
-19723,"Circularizing Planet Nine through dynamical friction with an extended, cold planetesimal belt","  Unexpected clustering in the orbital elements of minor bodies beyond the
-Kuiper belt has led to speculations that our solar system actually hosts nine
-planets, the eight established plus a hypothetical ""Planet Nine"". Several
-recent studies have shown that a planet with a mass of about 10 Earth masses on
-a distant eccentric orbit with perihelion far beyond the Kuiper belt could
-create and maintain this clustering. The evolutionary path resulting in an
-orbit such as the one suggested for Planet Nine is nevertheless not easily
-explained. Here we investigate whether a planet scattered away from the
-giant-planet region could be lifted to an orbit similar to the one suggested
-for Planet Nine through dynamical friction with a cold, distant planetesimal
-belt. Recent simulations of planetesimal formation via the streaming
-instability suggest that planetesimals can readily form beyond 100au. We
-explore this circularisation by dynamical friction with a set of numerical
-simulations. We find that a planet that is scattered from the region close to
-Neptune onto an eccentric orbit has a 20-30% chance of obtaining an orbit
-similar to that of Planet Nine after 4.6Gyr. Our simulations also result in
-strong or partial clustering of the planetesimals; however, whether or not this
-clustering is observable depends on the location of the inner edge of the
-planetesimal belt. If the inner edge is located at 200au the degree of
-clustering amongst observable objects is significant.
-",0,1,0,0,0,0
-19724,Tactics of Adversarial Attack on Deep Reinforcement Learning Agents,"  We introduce two tactics to attack agents trained by deep reinforcement
-learning algorithms using adversarial examples, namely the strategically-timed
-attack and the enchanting attack. In the strategically-timed attack, the
-adversary aims at minimizing the agent's reward by only attacking the agent at
-a small subset of time steps in an episode. Limiting the attack activity to
-this subset helps prevent detection of the attack by the agent. We propose a
-novel method to determine when an adversarial example should be crafted and
-applied. In the enchanting attack, the adversary aims at luring the agent to a
-designated target state. This is achieved by combining a generative model and a
-planning algorithm: while the generative model predicts the future states, the
-planning algorithm generates a preferred sequence of actions for luring the
-agent. A sequence of adversarial examples is then crafted to lure the agent to
-take the preferred sequence of actions. We apply the two tactics to the agents
-trained by the state-of-the-art deep reinforcement learning algorithm including
-DQN and A3C. In 5 Atari games, our strategically timed attack reduces as much
-reward as the uniform attack (i.e., attacking at every time step) does by
-attacking the agent 4 times less often. Our enchanting attack lures the agent
-toward designated target states with a more than 70% success rate. Videos are
-available at this http URL
-",1,0,0,1,0,0
-19725,Differentially Private Distributed Learning for Language Modeling Tasks,"  One of the big challenges in machine learning applications is that training
-data can be different from the real-world data faced by the algorithm. In
-language modeling, users' language (e.g. in private messaging) could change in
-a year and be completely different from what we observe in publicly available
-data. At the same time, public data can be used for obtaining general knowledge
-(i.e. general model of English). We study approaches to distributed fine-tuning
-of a general model on user private data with the additional requirements of
-maintaining the quality on the general data and minimization of communication
-costs. We propose a novel technique that significantly improves prediction
-quality on users' language compared to a general model and outperforms gradient
-compression methods in terms of communication efficiency. The proposed
-procedure is fast and leads to an almost 70% perplexity reduction and 8.7
-percentage point improvement in keystroke saving rate on informal English
-texts. We also show that the range of tasks our approach is applicable to is
-not limited by language modeling only. Finally, we propose an experimental
-framework for evaluating differential privacy of distributed training of
-language models and show that our approach has good privacy guarantees.
-",1,0,0,0,0,0
-19726,Tensor Train Neighborhood Preserving Embedding,"  In this paper, we propose a Tensor Train Neighborhood Preserving Embedding
-(TTNPE) to embed multi-dimensional tensor data into low dimensional tensor
-subspace. Novel approaches to solve the optimization problem in TTNPE are
-proposed. For this embedding, we evaluate novel trade-off gain among
-classification, computation, and dimensionality reduction (storage) for
-supervised learning. It is shown that compared to the state-of-the-arts tensor
-embedding methods, TTNPE achieves superior trade-off in classification,
-computation, and dimensionality reduction in MNIST handwritten digits and
-Weizmann face datasets.
-",1,0,0,1,0,0
-19727,Explicit three dimensional topology optimization via Moving Morphable Void (MMV) approach,"  Three dimensional (3D) topology optimization problems always involve huge
-numbers of Degrees of Freedom (DOFs) in finite element analysis (FEA) and
-design variables in numerical optimization, respectively. This will inevitably
-lead to large computational efforts in the solution process. In the present
-paper, an efficient and explicit topology optimization approach which can
-reduce not only the number of design variables but also the number of degrees
-of freedom in FEA is proposed based on the Moving Morphable Voids (MMVs)
-solution framework. This is achieved by introducing a set of geometry
-parameters (e.g., control points of B-spline surfaces) to describe the boundary
-of a structure explicitly and removing the unnecessary DOFs from the FE model
-at every step of numerical optimization. Numerical examples demonstrate that
-the proposed approach does can overcome the bottleneck problems associated with
-a 3D topology optimization problem in a straightforward way and enhance the
-solution efficiency significantly.
-",0,1,0,0,0,0
-19728,The index of compact simple Lie groups,"  Let M be an irreducible Riemannian symmetric space. The index i(M) of M is
-the minimal codimension of a (non-trivial) totally geodesic submanifold of M.
-The purpose of this note is to determine the index i(M) for all irreducible
-Riemannian symmetric spaces M of type (II) and (IV).
-",0,0,1,0,0,0
-19729,Cell Tracking via Proposal Generation and Selection,"  Microscopy imaging plays a vital role in understanding many biological
-processes in development and disease. The recent advances in automation of
-microscopes and development of methods and markers for live cell imaging has
-led to rapid growth in the amount of image data being captured. To efficiently
-and reliably extract useful insights from these captured sequences, automated
-cell tracking is essential. This is a challenging problem due to large
-variation in the appearance and shapes of cells depending on many factors
-including imaging methodology, biological characteristics of cells, cell matrix
-composition, labeling methodology, etc. Often cell tracking methods require a
-sequence-specific segmentation method and manual tuning of many tracking
-parameters, which limits their applicability to sequences other than those they
-are designed for. In this paper, we propose 1) a deep learning based cell
-proposal method, which proposes candidates for cells along with their scores,
-and 2) a cell tracking method, which links proposals in adjacent frames in a
-graphical model using edges representing different cellular events and poses
-joint cell detection and tracking as the selection of a subset of cell and edge
-proposals. Our method is completely automated and given enough training data
-can be applied to a wide variety of microscopy sequences. We evaluate our
-method on multiple fluorescence and phase contrast microscopy sequences
-containing cells of various shapes and appearances from ISBI cell tracking
-challenge, and show that our method outperforms existing cell tracking methods.
-Code is available at: this https URL
-",1,0,0,0,0,0
-19730,Geometric Properties of Isostables and Basins of Attraction of Monotone Systems,"  In this paper, we study geometric properties of basins of attraction of
-monotone systems. Our results are based on a combination of monotone systems
-theory and spectral operator theory. We exploit the framework of the Koopman
-operator, which provides a linear infinite-dimensional description of nonlinear
-dynamical systems and spectral operator-theoretic notions such as eigenvalues
-and eigenfunctions. The sublevel sets of the dominant eigenfunction form a
-family of nested forward-invariant sets and the basin of attraction is the
-largest of these sets. The boundaries of these sets, called isostables, allow
-studying temporal properties of the system. Our first observation is that the
-dominant eigenfunction is increasing in every variable in the case of monotone
-systems. This is a strong geometric property which simplifies the computation
-of isostables. We also show how variations in basins of attraction can be
-bounded under parametric uncertainty in the vector field of monotone systems.
-Finally, we study the properties of the parameter set for which a monotone
-system is multistable. Our results are illustrated on several systems of two to
-four dimensions.
-",1,0,1,0,0,0
-19731,Elongation and shape changes in organisms with cell walls: a dialogue between experiments and models,"  The generation of anisotropic shapes occurs during morphogenesis of almost
-all organisms. With the recent renewal of the interest in mechanical aspects of
-morphogenesis, it has become clear that mechanics contributes to anisotropic
-forms in a subtle interaction with various molecular actors. Here, we consider
-plants, fungi, oomycetes, and bacteria, and we review the mechanisms by which
-elongated shapes are generated and maintained. We focus on theoretical models
-of the interplay between growth and mechanics, in relation with experimental
-data, and discuss how models may help us improve our understanding of the
-underlying biological mechanisms.
-",0,0,0,0,1,0
-19732,Localic completion of uniform spaces,"  We extend the notion of localic completion of generalised metric spaces by
-Steven Vickers to the setting of generalised uniform spaces. A generalised
-uniform space (gus) is a set X equipped with a family of generalised metrics on
-X, where a generalised metric on X is a map from the product of X to the upper
-reals satisfying zero self-distance law and triangle inequality.
-For a symmetric generalised uniform space, the localic completion lifts its
-generalised uniform structure to a point-free generalised uniform structure.
-This point-free structure induces a complete generalised uniform structure on
-the set of formal points of the localic completion that gives the standard
-completion of the original gus with Cauchy filters.
-We extend the localic completion to a full and faithful functor from the
-category of locally compact uniform spaces into that of overt locally compact
-completely regular formal topologies. Moreover, we give an elementary
-characterisation of the cover of the localic completion of a locally compact
-uniform space that simplifies the existing characterisation for metric spaces.
-These results generalise the corresponding results for metric spaces by Erik
-Palmgren.
-Furthermore, we show that the localic completion of a symmetric gus is
-equivalent to the point-free completion of the uniform formal topology
-associated with the gus.
-We work in Aczel's constructive set theory CZF with the Regular Extension
-Axiom. Some of our results also require Countable Choice.
-",1,0,1,0,0,0
-19733,Kinetic inhibition of MHD-shocks in the vicinity of a parallel magnetic field,"  According to magnetohydrodynamics (MHD), the encounter of two collisional
-magnetized plasmas at high velocity gives rise to shock waves. Investigations
-conducted so far have found that the same conclusion still holds in the case of
-collisionless plasmas. For the case of a flow-aligned field, MHD stipulates
-that the field and the fluid are disconnected, so that the shock produced is
-independent of the field. We present a violation of this MHD prediction when
-considering the encounter of two cold pair plasmas along a flow-aligned
-magnetic field. As the guiding magnetic field grows, isotropization is
-progressively suppressed, resulting in a strong influence of the field on the
-resulting structure. A micro-physics analysis allows to understand the
-mechanisms at work. Particle-in-cell simulations also support our conclusions
-and show that the results are not restricted to a strictly parallel field.
-",0,1,0,0,0,0
-19734,Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things,"  In this paper, we propose Squeezed Convolutional Variational AutoEncoder
-(SCVAE) for anomaly detection in time series data for Edge Computing in
-Industrial Internet of Things (IIoT). The proposed model is applied to labeled
-time series data from UCI datasets for exact performance evaluation, and
-applied to real world data for indirect model performance comparison. In
-addition, by comparing the models before and after applying Fire Modules from
-SqueezeNet, we show that model size and inference times are reduced while
-similar levels of performance is maintained.
-",1,0,0,0,0,0
-19735,Global Weisfeiler-Lehman Graph Kernels,"  Most state-of-the-art graph kernels only take local graph properties into
-account, i.e., the kernel is computed with regard to properties of the
-neighborhood of vertices or other small substructures. On the other hand,
-kernels that do take global graph propertiesinto account may not scale well to
-large graph databases. Here we propose to start exploring the space between
-local and global graph kernels, striking the balance between both worlds.
-Specifically, we introduce a novel graph kernel based on the $k$-dimensional
-Weisfeiler-Lehman algorithm. Unfortunately, the $k$-dimensional
-Weisfeiler-Lehman algorithm scales exponentially in $k$. Consequently, we
-devise a stochastic version of the kernel with provable approximation
-guarantees using conditional Rademacher averages. On bounded-degree graphs, it
-can even be computed in constant time. We support our theoretical results with
-experiments on several graph classification benchmarks, showing that our
-kernels often outperform the state-of-the-art in terms of classification
-accuracies.
-",1,0,0,1,0,0
-19736,An analytical Model which Determines the Apparent T1 for Modified Look-Locker Inversion Recovery (MOLLI) -- Analysis of the Longitudinal Relaxation under the Influence of Discontinuous Balanced and Spoiled Gradient Echo Readouts,"  Quantitative nuclear magnetic resonance imaging (MRI) shifts more and more
-into the focus of clinical research. Especially determination of relaxation
-times without/and with contrast agents becomes the foundation of tissue
-characterization, e.g. in cardiac MRI for myocardial fibrosis. Techniques which
-assess longitudinal relaxation times rely on repetitive application of readout
-modules, which are interrupted by free relaxation periods, e.g. the Modified
-Look-Locker Inversion Recovery = MOLLI sequence. These discontinuous sequences
-reveal an apparent relaxation time, and, by techniques extrapolated from
-continuous readout sequences, the real T1 is determined. What is missing is a
-rigorous analysis of the dependence of the apparent relaxation time on its real
-partner, readout sequence parameters and biological parameters as heart rate.
-This is provided in this paper for the discontinuous balanced steady state free
-precession (bSSFP) and spoiled gradient echo readouts. It turns out that the
-apparente longitudinal relaxation rate is the time average of the relaxation
-rates during the readout module, and free relaxation period. Knowing the heart
-rate our results vice versa allow to determine the real T1 from its measured
-apparent partner.
-",0,1,0,0,0,0
-19737,Updated Constraints on the Dark Matter Interpretation of CDMS-II-Si Data,"  We present an updated halo-dependent and halo-independent analysis of viable
-light WIMP dark matter candidates which could account for the excess observed
-in CDMS-II-Si. We include recent constraints from LUX, PandaX-II, and PICO-60,
-as well as projected sensitivities for XENON1T, SuperCDMS SNOLAB, LZ, DARWIN,
-DarkSide-20k, and PICO-250, on candidates with spin-independent isospin
-conserving and isospin-violating interactions, and either elastic or exothermic
-scattering. We show that there exist dark matter candidates which can explain
-the CDMS-II-Si data and remain very marginally consistent with the null results
-of all current experiments, however such models are highly tuned, making a dark
-matter interpretation of CDMS-II-Si very unlikely. We find that these models
-can only be ruled out in the future by an experiment comparable to LZ or
-PICO-250.
-",0,1,0,0,0,0
-19738,"Emulating Simulations of Cosmic Dawn for 21cm Power Spectrum Constraints on Cosmology, Reionization, and X-ray Heating","  Current and upcoming radio interferometric experiments are aiming to make a
-statistical characterization of the high-redshift 21cm fluctuation signal
-spanning the hydrogen reionization and X-ray heating epochs of the universe.
-However, connecting 21cm statistics to underlying physical parameters is
-complicated by the theoretical challenge of modeling the relevant physics at
-computational speeds quick enough to enable exploration of the high dimensional
-and weakly constrained parameter space. In this work, we use machine learning
-algorithms to build a fast emulator that mimics expensive simulations of the
-21cm signal across a wide parameter space to high precision. We embed our
-emulator within a Markov-Chain Monte Carlo framework, enabling it to explore
-the posterior distribution over a large number of model parameters, including
-those that govern the Epoch of Reionization, the Epoch of X-ray Heating, and
-cosmology. As a worked example, we use our emulator to present an updated
-parameter constraint forecast for the Hydrogen Epoch of Reionization Array
-experiment, showing that its characterization of a fiducial 21cm power spectrum
-will considerably narrow the allowed parameter space of reionization and
-heating parameters, and could help strengthen Planck's constraints on
-$\sigma_8$. We provide both our generalized emulator code and its
-implementation specifically for 21cm parameter constraints as publicly
-available software.
-",0,1,0,0,0,0
-19739,Fluctuations and Noise Signatures of Driven Magnetic Skyrmions,"  Magnetic skyrmions are particle-like objects with topologically-protected
-stability which can be set into motion with an applied current. Using a
-particle-based model we simulate current-driven magnetic skyrmions interacting
-with random quenched disorder and examine the skyrmion velocity fluctuations
-parallel and perpendicular to the direction of motion as a function of
-increasing drive. We show that the Magnus force contribution to skyrmion
-dynamics combined with the random pinning produces an isotropic effective
-shaking temperature. As a result, the skyrmions form a moving crystal at large
-drives instead of the moving smectic state observed in systems with a
-negligible Magnus force where the effective shaking temperature is anisotropic.
-We demonstrate that spectral analysis of the velocity noise fluctuations can be
-used to identify dynamical phase transitions and to extract information about
-the different dynamic phases, and show how the velocity noise fluctuations are
-correlated with changes in the skyrmion Hall angle, transport features, and
-skyrmion lattice structure.
-",0,1,0,0,0,0
-19740,Metallic maps between metallic Riemannian manifolds and constancy of certain maps,"  In this paper, we introduce metallic maps between metallic Riemannian
-manifolds, provide an example and obtain certain conditions for such maps to be
-totally geodesic. We also give a sufficient condition for a map between
-metallic Riemannian manifolds to be harmonic map. Then we investigate the
-constancy of certain maps between metallic Riemannian manifolds and various
-manifolds by imposing the holomorphic-like condition. Moreover, we check the
-reverse case and show that some such maps are constant if there is a condition
-for this.
-",0,0,1,0,0,0
-19741,Hashing as Tie-Aware Learning to Rank,"  Hashing, or learning binary embeddings of data, is frequently used in nearest
-neighbor retrieval. In this paper, we develop learning to rank formulations for
-hashing, aimed at directly optimizing ranking-based evaluation metrics such as
-Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We
-first observe that the integer-valued Hamming distance often leads to tied
-rankings, and propose to use tie-aware versions of AP and NDCG to evaluate
-hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive
-their continuous relaxations, and perform gradient-based optimization with deep
-neural networks. Our results establish the new state-of-the-art for image
-retrieval by Hamming ranking in common benchmarks.
-",1,0,0,1,0,0
-19742,Models of compact stars on paraboloidal spacetime satisfying Karmarkar condition,"  A new exact solution of Einstein's field equations on the background of
-paraboloidal spacetime using Karmarkar condition is reported. The physical
-acceptability conditions of the model are investigated and found that the model
-is compatible with a number of compact star candidates like Her X-1, LMC X-4,
-EXO 1785-248, PSR J1903+327, Vela X-1 and PSR J1614-2230. A noteworthy feature
-of the model is that it is geometrically significant and simple in form.
-",0,1,0,0,0,0
-19743,Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification,"  Implicit discourse relation classification is of great challenge due to the
-lack of connectives as strong linguistic cues, which motivates the use of
-annotated implicit connectives to improve the recognition. We propose a feature
-imitation framework in which an implicit relation network is driven to learn
-from another neural network with access to connectives, and thus encouraged to
-extract similarly salient features for accurate classification. We develop an
-adversarial model to enable an adaptive imitation scheme through competition
-between the implicit network and a rival feature discriminator. Our method
-effectively transfers discriminability of connectives to the implicit features,
-and achieves state-of-the-art performance on the PDTB benchmark.
-",1,0,0,1,0,0
-19744,An Input Reconstruction Approach for Command Following in Linear MIMO Systems,"  The idea of posing a command following or tracking control problem as an
-input reconstruction problem is explored in the paper. For a class of square
-MIMO systems with known dynamics, by pretending that reference commands are
-actual outputs of the system, input reconstruction methods can be used to
-determine control action that will result in a system following desired
-reference commands. A feedback controller which is a combination of an unbiased
-state estimator and an input reconstructor that ensures unbiased tracking of
-reference commands is proposed. Simulations and real-time implementation are
-presented to demonstrate utility of the proposed idea. Conditions under which
-proposed controller may be used for non-square systems are also discussed.
-",1,0,0,0,0,0
-19745,Simulation Framework for Cooperative Adaptive Cruise Control with Empirical DSRC Module,"  Wireless communication plays a vital role in the promising performance of
-connected and automated vehicle (CAV) technology. This paper proposes a
-Vissim-based microscopic traffic simulation framework with an analytical
-dedicated short-range communication (DSRC) module for packet reception. Being
-derived from ns-2, a packet-level network simulator, the DSRC probability
-module takes into account the imperfect wireless communication that occurs in
-real-world deployment. Four managed lane deployment strategies are evaluated
-using the proposed framework. While the average packet reception rate is above
-93\% among all tested scenarios, the results reveal that the reliability of the
-vehicle-to-vehicle (V2V) communication can be influenced by the deployment
-strategies. Additionally, the proposed framework exhibits desirable scalability
-for traffic simulation and it is able to evaluate transportation-network-level
-deployment strategies in the near future for CAV technologies.
-",1,0,0,0,0,0
-19746,Synchronization of Kuramoto oscillators in a bidirectional frequency-dependent tree network,"  This paper studies the synchronization of a finite number of Kuramoto
-oscillators in a frequency-dependent bidirectional tree network. We assume that
-the coupling strength of each link in each direction is equal to the product of
-a common coefficient and the exogenous frequency of its corresponding head
-oscillator. We derive a sufficient condition for the common coupling strength
-in order to guarantee frequency synchronization in tree networks. Moreover, we
-discuss the dependency of the obtained bound on both the graph structure and
-the way that exogenous frequencies are distributed. Further, we present an
-application of the obtained result by means of an event-triggered algorithm for
-achieving frequency synchronization in a star network assuming that the common
-coupling coefficient is given.
-",1,0,0,0,0,0
-19747,On Exchangeability in Network Models,"  We derive representation theorems for exchangeable distributions on finite
-and infinite graphs using elementary arguments based on geometric and
-graph-theoretic concepts. Our results elucidate some of the key differences,
-and their implications, between statistical network models that are finitely
-exchangeable and models that define a consistent sequence of probability
-distributions on graphs of increasing size.
-",0,0,1,1,0,0
-19748,Replicator equation on networks with degree regular communities,"  The replicator equation is one of the fundamental tools to study evolutionary
-dynamics in well-mixed populations. This paper contributes to the literature on
-evolutionary graph theory, providing a version of the replicator equation for a
-family of connected networks with communities, where nodes in the same
-community have the same degree. This replicator equation is applied to the
-study of different classes of games, exploring the impact of the graph
-structure on the equilibria of the evolutionary dynamics.
-",0,0,0,0,1,0
-19749,Go game formal revealing by Ising model,"  Go gaming is a struggle for territory control between rival, black and white,
-stones on a board. We model the Go dynamics in a game by means of the Ising
-model whose interaction coefficients reflect essential rules and tactics
-employed in Go to build long-term strategies. At any step of the game, the
-energy functional of the model provides the control degree (strength) of a
-player over the board. A close fit between predictions of the model with actual
-games is obtained.
-",1,0,0,0,0,0
-19750,High resolution structural characterisation of laser-induced defect clusters inside diamond,"  Laser writing with ultrashort pulses provides a potential route for the
-manufacture of three-dimensional wires, waveguides and defects within diamond.
-We present a transmission electron microscopy (TEM) study of the intrinsic
-structure of the laser modifications and reveal a complex distribution of
-defects. Electron energy loss spectroscopy (EELS) indicates that the majority
-of the irradiated region remains as $sp^3$ bonded diamond.
-Electrically-conductive paths are attributed to the formation of multiple
-nano-scale, $sp^2$-bonded graphitic wires and a network of strain-relieving
-micro-cracks.
-",0,1,0,0,0,0
-19751,Neural networks for post-processing ensemble weather forecasts,"  Ensemble weather predictions require statistical post-processing of
-systematic errors to obtain reliable and accurate probabilistic forecasts.
-Traditionally, this is accomplished with distributional regression models in
-which the parameters of a predictive distribution are estimated from a training
-period. We propose a flexible alternative based on neural networks that can
-incorporate nonlinear relationships between arbitrary predictor variables and
-forecast distribution parameters that are automatically learned in a
-data-driven way rather than requiring pre-specified link functions. In a case
-study of 2-meter temperature forecasts at surface stations in Germany, the
-neural network approach significantly outperforms benchmark post-processing
-methods while being computationally more affordable. Key components to this
-improvement are the use of auxiliary predictor variables and station-specific
-information with the help of embeddings. Furthermore, the trained neural
-network can be used to gain insight into the importance of meteorological
-variables thereby challenging the notion of neural networks as uninterpretable
-black boxes. Our approach can easily be extended to other statistical
-post-processing and forecasting problems. We anticipate that recent advances in
-deep learning combined with the ever-increasing amounts of model and
-observation data will transform the post-processing of numerical weather
-forecasts in the coming decade.
-",0,0,0,1,0,0
-19752,Power maps in finite groups,"  In recent work, Pomerance and Shparlinski have obtained results on the number
-of cycles in the functional graph of the map $x \mapsto x^a$ in
-$\mathbb{F}_p^*$. We prove similar results for other families of finite groups.
-In particular, we obtain estimates for the number of cycles for cyclic groups,
-symmetric groups, dihedral groups and $SL_2(\mathbb{F}_q)$. We also show that
-the cyclic group of order $n$ minimizes the number of cycles among all
-nilpotent groups of order $n$ for a fixed exponent. Finally, we pose several
-problems.
-",0,0,1,0,0,0
-19753,"Permutation polynomials, fractional polynomials, and algebraic curves","  In this note we prove a conjecture by Li, Qu, Li, and Fu on permutation
-trinomials over $\mathbb{F}_3^{2k}$. In addition, new examples and
-generalizations of some families of permutation polynomials of
-$\mathbb{F}_{3^k}$ and $\mathbb{F}_{5^k}$ are given. We also study permutation
-quadrinomials of type $Ax^{q(q-1)+1} + Bx^{2(q-1)+1} + Cx^{q} + x$. Our method
-is based on the investigation of an algebraic curve associated with a
-{fractional polynomial} over a finite field.
-",0,0,1,0,0,0
-19754,Recursive Whitening Transformation for Speaker Recognition on Language Mismatched Condition,"  Recently in speaker recognition, performance degradation due to the channel
-domain mismatched condition has been actively addressed. However, the
-mismatches arising from language is yet to be sufficiently addressed. This
-paper proposes an approach which employs recursive whitening transformation to
-mitigate the language mismatched condition. The proposed method is based on the
-multiple whitening transformation, which is intended to remove un-whitened
-residual components in the dataset associated with i-vector length
-normalization. The experiments were conducted on the Speaker Recognition
-Evaluation 2016 trials of which the task is non-English speaker recognition
-using development dataset consist of both a large scale out-of-domain (English)
-dataset and an extremely low-quantity in-domain (non-English) dataset. For
-performance comparison, we develop a state-of- the-art system using deep neural
-network and bottleneck feature, which is based on a phonetically aware model.
-From the experimental results, along with other prior studies, effectiveness of
-the proposed method on language mismatched condition is validated.
-",1,0,0,0,0,0
-19755,Learning Representations for Soft Skill Matching,"  Employers actively look for talents having not only specific hard skills but
-also various soft skills. To analyze the soft skill demands on the job market,
-it is important to be able to detect soft skill phrases from job advertisements
-automatically. However, a naive matching of soft skill phrases can lead to
-false positive matches when a soft skill phrase, such as friendly, is used to
-describe a company, a team, or another entity, rather than a desired candidate.
-In this paper, we propose a phrase-matching-based approach which
-differentiates between soft skill phrases referring to a candidate vs.
-something else. The disambiguation is formulated as a binary text
-classification problem where the prediction is made for the potential soft
-skill based on the context where it occurs. To inform the model about the soft
-skill for which the prediction is made, we develop several approaches,
-including soft skill masking and soft skill tagging.
-We compare several neural network based approaches, including CNN, LSTM and
-Hierarchical Attention Model. The proposed tagging-based input representation
-using LSTM achieved the highest recall of 83.92% on the job dataset when fixing
-a precision to 95%.
-",0,0,0,1,0,0
-19756,Time-reversal and spatial reflection symmetry localization anomalies in (2+1)D topological phases of matter,"  We study a class of anomalies associated with time-reversal and spatial
-reflection symmetry in (2+1)D topological phases of matter. In these systems,
-the topological quantum numbers of the quasiparticles, such as the fusion rules
-and braiding statistics, possess a $\mathbb{Z}_2$ symmetry which can be
-associated with either time-reversal (denoted $\mathbb{Z}_2^{\bf T})$ or
-spatial reflections. Under this symmetry, correlation functions of all Wilson
-loop operators in the low energy topological quantum field theory (TQFT) are
-invariant. However, the theories that we study possess a severe anomaly
-associated with the failure to consistently localize the symmetry action to the
-quasiparticles, precluding even defining a notion of symmetry
-fractionalization. We present simple sufficient conditions which determine when
-$\mathbb{Z}_2^{\bf T}$ symmetry localization anomalies exist. We present an
-infinite series of TQFTs with such anomalies, some examples of which include
-USp$(4)_2$ and SO$(4)_4$ Chern-Simons (CS) theory. The theories that we find
-with these $\mathbb{Z}_2^{\bf T}$ anomalies can be obtained by gauging the
-unitary $\mathbb{Z}_2$ subgroup of a different TQFT with a $\mathbb{Z}_4^{\bf
-T}$ symmetry. We show that the anomaly can be resolved in several ways: (1) the
-true symmetry of the theory is $\mathbb{Z}_4^{\bf T}$, or (2) the theory can be
-considered to be a theory of fermions, with ${\bf T}^2 = (-1)^{N_f}$
-corresponding to fermion parity. Finally, we demonstrate that theories with the
-$\mathbb{Z}_2^{\bf T}$ localization anomaly can be compatible with
-$\mathbb{Z}_2^{\bf T}$ if they are ""pseudo-realized"" at the surface of a (3+1)D
-symmetry-enriched topological phase. The ""pseudo-realization"" refers to the
-fact that the bulk (3+1)D system is described by a dynamical $\mathbb{Z}_2$
-gauge theory and thus only a subset of the quasiparticles are confined to the
-surface.
-",0,1,0,0,0,0
-19757,Extending the modeling of the anisotropic galaxy power spectrum to $k = 0.4 \ h\mathrm{Mpc}^{-1}$,"  We present a new model for the redshift-space power spectrum of galaxies and
-demonstrate its accuracy in modeling the monopole, quadrupole, and hexadecapole
-of the galaxy density field down to scales of $k = 0.4 \ h\mathrm{Mpc}^{-1}$.
-The model describes the clustering of galaxies in the context of a halo model
-and the clustering of the underlying halos in redshift space using a
-combination of Eulerian perturbation theory and $N$-body simulations. The
-modeling of redshift-space distortions is done using the so-called distribution
-function approach. The final model has 13 free parameters, and each parameter
-is physically motivated rather than a nuisance parameter, which allows the use
-of well-motivated priors. We account for the Finger-of-God effect from centrals
-and both isolated and non-isolated satellites rather than using a single
-velocity dispersion to describe the combined effect. We test and validate the
-accuracy of the model on several sets of high-fidelity $N$-body simulations, as
-well as realistic mock catalogs designed to simulate the BOSS DR12 CMASS data
-set. The suite of simulations covers a range of cosmologies and galaxy bias
-models, providing a rigorous test of the level of theoretical systematics
-present in the model. The level of bias in the recovered values of $f \sigma_8$
-is found to be small. When including scales to $k = 0.4 \ h\mathrm{Mpc}^{-1}$,
-we find 15-30\% gains in the statistical precision of $f \sigma_8$ relative to
-$k = 0.2 \ h\mathrm{Mpc}^{-1}$ and a roughly 10-15\% improvement for the
-perpendicular Alcock-Paczynski parameter $\alpha_\perp$. Using the BOSS DR12
-CMASS mocks as a benchmark for comparison, we estimate an uncertainty on $f
-\sigma_8$ that is $\sim$10-20\% larger than other similar Fourier-space RSD
-models in the literature that use $k \leq 0.2 \ h\mathrm{Mpc}^{-1}$, suggesting
-that these models likely have a too-limited parametrization.
-",0,1,0,0,0,0
-19758,Finite rank and pseudofinite groups,"  It is proven that an infinite finitely generated group cannot be elementarily
-equivalent to an ultraproduct of finite groups of a given Prüfer rank.
-Furthermore, it is shown that an infinite finitely generated group of finite
-Prüfer rank is not pseudofinite.
-",0,0,1,0,0,0
-19759,Thermographic measurements of the spin Peltier effect in metal/yttrium-iron-garnet junction systems,"  The spin Peltier effect (SPE), heat-current generation due to spin-current
-injection, in various metal (Pt, W, and Au single layers and Pt/Cu
-bilayer)/ferrimagnetic insulator (yttrium iron garnet: YIG) junction systems
-has been investigated by means of a lock-in thermography (LIT) method. The SPE
-is excited by a spin current across the metal/YIG interface, which is generated
-by applying a charge current to the metallic layer via the spin Hall effect.
-The LIT method enables the thermal imaging of the SPE free from the
-Joule-heating contribution. Importantly, we observed spin-current-induced
-temperature modulation not only in the Pt/YIG and W/YIG systems but also in the
-Au/YIG and Pt/Cu/YIG systems, excluding the possible contamination by anomalous
-Ettingshausen effects due to proximity-induced ferromagnetism near the
-metal/YIG interface. As demonstrated in our previous study, the SPE signals are
-confined only in the vicinity of the metal/YIG interface; we buttress this
-conclusion by reducing a spatial blur due to thermal diffusion in an infrared
-emission layer on the sample surface used for the LIT measurements. We also
-found that the YIG-thickness dependence of the SPE is similar to that of the
-spin Seebeck effect measured in the same Pt/YIG sample, implying the reciprocal
-relation between them.
-",0,1,0,0,0,0
-19760,"Number-theoretic aspects of 1D localization: ""popcorn function"" with Lifshitz tails and its continuous approximation by the Dedekind $η$","  We discuss the number-theoretic properties of distributions appearing in
-physical systems when an observable is a quotient of two independent
-exponentially weighted integers. The spectral density of ensemble of linear
-polymer chains distributed with the law $\sim f^L$ ($0<f<1$), where $L$ is the
-chain length, serves as a particular example. At $f\to 1$, the spectral density
-can be expressed through the discontinuous at all rational points, Thomae
-(""popcorn"") function. We suggest a continuous approximation of the popcorn
-function, based on the Dedekind $\eta$-function near the real axis. Moreover,
-we provide simple arguments, based on the ""Euclid orchard"" construction, that
-demonstrate the presence of Lifshitz tails, typical for the 1D Anderson
-localization, at the spectral edges. We emphasize that the ultrametric
-structure of the spectral density is ultimately connected with number-theoretic
-relations on asymptotic modular functions. We also pay attention to connection
-of the Dedekind $\eta$-function near the real axis to invariant measures of
-some continued fractions studied by Borwein and Borwein in 1993.
-",0,1,1,0,0,0
-19761,Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates,"  In this paper, we describe a phenomenon, which we named ""super-convergence"",
-where neural networks can be trained an order of magnitude faster than with
-standard training methods. The existence of super-convergence is relevant to
-understanding why deep networks generalize well. One of the key elements of
-super-convergence is training with one learning rate cycle and a large maximum
-learning rate. A primary insight that allows super-convergence training is that
-large learning rates regularize the training, hence requiring a reduction of
-all other forms of regularization in order to preserve an optimal
-regularization balance. We also derive a simplification of the Hessian Free
-optimization method to compute an estimate of the optimal learning rate.
-Experiments demonstrate super-convergence for Cifar-10/100, MNIST and Imagenet
-datasets, and resnet, wide-resnet, densenet, and inception architectures. In
-addition, we show that super-convergence provides a greater boost in
-performance relative to standard training when the amount of labeled training
-data is limited. The architectures and code to replicate the figures in this
-paper are available at github.com/lnsmith54/super-convergence. See
-this http URL for an application of
-super-convergence to win the DAWNBench challenge (see
-this https URL).
-",1,0,0,1,0,0
-19762,Space of initial conditions for a cubic Hamiltonian system,"  In this paper we perform the analysis that leads to the space of initial
-conditions for the Hamiltonian system $q' = p^2 + zq + \alpha$, $p' = -q^2 - zp
-- \beta$, studied by the author in an earlier article. By compactifying the
-phase space of the system from $\mathbb{C}^2$ to $\mathbb{CP}^2$ three base
-points arise in the standard coordinate charts covering the complex projective
-space. Each of these is removed by a sequence of three blow-ups, a construction
-to regularise the system at these points. The resulting space, where the
-exceptional curves introduced after the first and second blow-up are removed,
-is the so-called Okamoto's space of initial conditions for this system which,
-at every point, defines a regular initial value problem in some coordinate
-chart of the space.
-",0,1,1,0,0,0
-19763,Electronic properties of WS$_2$ on epitaxial graphene on SiC(0001),"  This work reports an electronic and micro-structural study of an appealing
-system for optoelectronics: tungsten disulphide WS$_2$ on epitaxial graphene
-(EG) on SiC(0001). The WS$_2$ is grown via chemical vapor deposition (CVD) onto
-the EG. Low-energy electron diffraction (LEED) measurements assign the
-zero-degree orientation as the preferential azimuthal alignment for WS$_2$/EG.
-The valence-band (VB) structure emerging from this alignment is investigated by
-means of photoelectron spectroscopy measurements, with both high space and
-energy resolution. We find that the spin-orbit splitting of monolayer WS$_2$ on
-graphene is of 462 meV, larger than what is reported to date for other
-substrates. We determine the value of the work function for the WS$_2$/EG to be
-4.5$\pm$0.1 eV. A large shift of the WS$_2$ VB maximum is observed as well ,
-due to the lowering of the WS$_2$ work function caused by the donor-like
-interfacial states of EG. Density functional theory (DFT) calculations carried
-out on a coincidence supercell confirm the experimental band structure to an
-excellent degree. X-ray photoemission electron microscopy (XPEEM) measurements
-performed on single WS$_2$ crystals confirm the van der Waals nature of the
-interface coupling between the two layers. In virtue of its band alignment and
-large spin-orbit splitting, this system gains strong appeal for optical
-spin-injection experiments and opto-spintronic applications in general.
-",0,1,0,0,0,0
-19764,Electrical transport and optical band gap of NiFe$_\textrm{2}$O$_\textrm{x}$ thin films,"  We fabricated NiFe$_\textrm{2}$O$_\textrm{x}$ thin films on
-MgAl$_2$O$_4$(001) substrates by reactive dc magnetron co-sputtering varying
-the oxygen partial pressure during deposition. The fabrication of a variable
-material with oxygen deficiency leads to controllable electrical and optical
-properties which would be beneficial for the investigations of the transport
-phenomena and would, therefore, promote the use of such materials in spintronic
-and spin caloritronic applications. We used several characterization techniques
-in order to investigate the film properties, focusing on their structural,
-magnetic, electrical, and optical properties. From the electrical resistivity
-measurements we obtained the conduction mechanisms that govern the systems in
-high and low temperature regimes, extracting low thermal activation energies
-which unveil extrinsic transport mechanisms. The thermal activation energy
-decreases in the less oxidized samples revealing the pronounced contribution of
-a large amount of electronic states localized in the band gap to the electrical
-conductivity. Hall effect measurements showed the mixed-type semiconducting
-character of our films. The optical band gaps were determined via
-ultraviolet-visible spectroscopy. They follow a similar trend as the thermal
-activation energy, with lower band gap values in the less oxidized samples.
-",0,1,0,0,0,0
-19765,Towards parallelizable sampling-based Nonlinear Model Predictive Control,"  This paper proposes a new sampling-based nonlinear model predictive control
-(MPC) algorithm, with a bound on complexity quadratic in the prediction horizon
-N and linear in the number of samples. The idea of the proposed algorithm is to
-use the sequence of predicted inputs from the previous time step as a warm
-start, and to iteratively update this sequence by changing its elements one by
-one, starting from the last predicted input and ending with the first predicted
-input. This strategy, which resembles the dynamic programming principle, allows
-for parallelization up to a certain level and yields a suboptimal nonlinear MPC
-algorithm with guaranteed recursive feasibility, stability and improved cost
-function at every iteration, which is suitable for real-time implementation.
-The complexity of the algorithm per each time step in the prediction horizon
-depends only on the horizon, the number of samples and parallel threads, and it
-is independent of the measured system state. Comparisons with the fmincon
-nonlinear optimization solver on benchmark examples indicate that as the
-simulation time progresses, the proposed algorithm converges rapidly to the
-""optimal"" solution, even when using a small number of samples.
-",1,0,0,0,0,0
-19766,Quantum decoherence from entanglement during inflation,"  We study the primary entanglement effect on the decoherence of fields reduced
-density matrix which are in interaction with another fields or independent mode
-functions. We show that the primary entanglement has a significant role in
-decoherence of the system quantum state. We find that the existence of
-entanglement could couple dynamical equations coming from Schrödinger
-equation. We show if one wants to see no effect of the entanglement parameter
-in decoherence then interaction terms in Hamiltonian can not be independent
-from each other. Generally, including the primary entanglement destroys the
-independence of the interaction terms. Our results could be generalized to
-every scalar quantum field theory with a well defined quantization in a given
-curved space time.
-",0,1,0,0,0,0
-19767,Breast density classification with deep convolutional neural networks,"  Breast density classification is an essential part of breast cancer
-screening. Although a lot of prior work considered this problem as a task for
-learning algorithms, to our knowledge, all of them used small and not
-clinically realistic data both for training and evaluation of their models. In
-this work, we explore the limits of this task with a data set coming from over
-200,000 breast cancer screening exams. We use this data to train and evaluate a
-strong convolutional neural network classifier. In a reader study, we find that
-our model can perform this task comparably to a human expert.
-",1,0,0,1,0,0
-19768,Spatial Integration by a Dielectric Slab Waveguide and its Planar Graphene-based Counterpart,"  Motivated by the recent progress in analog computing [Science 343, 160
-(2014)], a new approach to perform spatial integration is presented using a
-dielectric slab waveguide. Our approach is indeed based on the fact that the
-transmission coefficient of a simple dielectric slab waveguide at its mode
-excitation angle matches the Green's function of first order integration.
-Inspired by the mentioned dielectric-based integrator, we further demonstrate
-its graphene-based counterpart. The latter is not only reconfigurable but also
-highly miniaturized in contrast to the previously reported designs [Opt.
-Commun. 338, 457 (2015)]. Such integrators have the potential to be used in
-ultrafast analog computation and signal processing.
-",0,1,0,0,0,0
-19769,Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders,"  A crucial challenge in image-based modeling of biomedical data is to identify
-trends and features that separate normality and pathology. In many cases, the
-morphology of the imaged object exhibits continuous change as it deviates from
-normality, and thus a generative model can be trained to model this
-morphological continuum. Moreover, given side information that correlates to
-certain trend in morphological change, a latent variable model can be
-regularized such that its latent representation reflects this side information.
-In this work, we use the Wasserstein Auto-encoder to model this pathology
-continuum, and apply the Hilbert-Schmitt Independence Criterion (HSIC) to
-enforce dependency between certain latent features and the provided side
-information. We experimentally show that the model can provide disentangled and
-interpretable latent representations and also generate a continuum of
-morphological changes that corresponds to change in the side information.
-",1,0,0,1,0,0
-19770,Control Problems with Vanishing Lie Bracket Arising from Complete Odd Circulant Evolutionary Games,"  We study an optimal control problem arising from a generalization of
-rock-paper-scissors in which the number of strategies may be selected from any
-positive odd number greater than 1 and in which the payoff to the winner is
-controlled by a control variable $\gamma$. Using the replicator dynamics as the
-equations of motion, we show that a quasi-linearization of the problem admits a
-special optimal control form in which explicit dynamics for the controller can
-be identified. We show that all optimal controls must satisfy a specific second
-order differential equation parameterized by the number of strategies in the
-game. We show that as the number of strategies increases, a limiting case
-admits a closed form for the open-loop optimal control. In performing our
-analysis we show necessary conditions on an optimal control problem that allow
-this analytic approach to function.
-",1,0,0,0,0,0
-19771,Teaching DevOps in Corporate Environments: An experience report,"  This paper describes our experience of training a team of developers of an
-East-European phone service provider. The training experience was structured in
-two sessions of two days each conducted in different weeks with a gap of about
-fifteen days. The first session was dedicated to the Continuous Integration
-Delivery Pipeline, and the second on Agile methods. We summarize the activity,
-its preparation and delivery and draw some conclusions out of it on our
-mistakes and how future session should be addressed.
-",1,0,0,0,0,0
-19772,WKB solutions of difference equations and reconstruction by the topological recursion,"  The purpose of this article is to analyze the connection between
-Eynard-Orantin topological recursion and formal WKB solutions of a
-$\hbar$-difference equation: $\Psi(x+\hbar)=\left(e^{\hbar\frac{d}{dx}}\right)
-\Psi(x)=L(x;\hbar)\Psi(x)$ with $L(x;\hbar)\in GL_2( (\mathbb{C}(x))[\hbar])$.
-In particular, we extend the notion of determinantal formulas and topological
-type property proposed for formal WKB solutions of $\hbar$-differential systems
-to this setting. We apply our results to a specific $\hbar$-difference system
-associated to the quantum curve of the Gromov-Witten invariants of
-$\mathbb{P}^1$ for which we are able to prove that the correlation functions
-are reconstructed from the Eynard-Orantin differentials computed from the
-topological recursion applied to the spectral curve $y=\cosh^{-1}\frac{x}{2}$.
-Finally, identifying the large $x$ expansion of the correlation functions,
-proves a recent conjecture made by B. Dubrovin and D. Yang regarding a new
-generating series for Gromov-Witten invariants of $\mathbb{P}^1$.
-",0,1,1,0,0,0
-19773,Diagrammatic Exciton Basis Theory of the Photophysics of Pentacene Dimers,"  Covalently linked acene dimers are of interest as candidates for
-intramolecular singlet fission. We report many-electron calculations of the
-energies and wavefunctions of the optical singlets, the lowest triplet exciton
-and the triplet-triplet biexciton, as well as the final states of excited state
-absorptions from these states in a family of phenyl-linked pentacene dimers.
-While it is difficult to distinguish between the triplet and the
-triplet-triplet from their transient absorptions in the 500-600 nm region, by
-comparing theoretical transient absorption spectra against published and
-unpublished experimental transient absorptions in the near and mid infrared we
-conclude that the end product of photoexcitation in these particular
-bipentacenes is the bound triplet-triplet and not free triplets. We predict
-additional transient absorptions at even longer wavelengths, beyond 1500 nm, to
-the equivalent of the classic 2$^1$A$_g^-$ in linear polyenes.
-",0,1,0,0,0,0
-19774,Developing and evaluating an interactive tutorial on degenerate perturbation theory,"  We discuss an investigation of student difficulties with degenerate
-perturbation theory (DPT) carried out in advanced quantum mechanics courses by
-administering free-response and multiple-choice questions and conducting
-individual interviews with students. We find that students share many common
-difficulties related to this topic. We used the difficulties found via research
-as resources to develop and evaluate a Quantum Interactive Learning Tutorial
-(QuILT) which strives to help students develop a functional understanding of
-DPT. We discuss the development of the DPT QuILT and its preliminary evaluation
-in the undergraduate and graduate courses.
-",0,1,0,0,0,0
-19775,Time optimal sampled-data controls for heat equations,"  In this paper, we first design a time optimal control problem for the heat
-equation with sampled-data controls, and then use it to approximate a time
-optimal control problem for the heat equation with distributed controls. Our
-design is reasonable from perspective of sampled-data controls. And it might
-provide a right way for the numerical approach of a time optimal distributed
-control problem, via the corresponding semi-discretized (in time variable) time
-optimal control problem.
-The study of such a time optimal sampled-data control problem is not easy,
-because it may have infinitely many optimal controls. We find connections among
-this problem, a minimal norm sampled-data control problem and a minimization
-problem. And obtain some properties on these problems. Based on these, we not
-only build up error estimates for optimal time and optimal controls between the
-time optimal sampled-data control problem and the time optimal distributed
-control problem, in terms of the sampling period, but also prove that such
-estimates are optimal in some sense.
-",0,0,1,0,0,0
-19776,Malware distributions and graph structure of the Web,"  Knowledge about the graph structure of the Web is important for understanding
-this complex socio-technical system and for devising proper policies supporting
-its future development. Knowledge about the differences between clean and
-malicious parts of the Web is important for understanding potential treats to
-its users and for devising protection mechanisms. In this study, we conduct
-data science methods on a large crawl of surface and deep Web pages with the
-aim to increase such knowledge. To accomplish this, we answer the following
-questions. Which theoretical distributions explain important local
-characteristics and network properties of websites? How are these
-characteristics and properties different between clean and malicious
-(malware-affected) websites? What is the prediction power of local
-characteristics and network properties to classify malware websites? To the
-best of our knowledge, this is the first large-scale study describing the
-differences in global properties between malicious and clean parts of the Web.
-In other words, our work is building on and bridging the gap between
-\textit{Web science} that tackles large-scale graph representations and
-\textit{Web cyber security} that is concerned with malicious activities on the
-Web. The results presented herein can also help antivirus vendors in devising
-approaches to improve their detection algorithms.
-",1,0,0,0,0,0
-19777,Depth creates no more spurious local minima,"  We show that for any convex differentiable loss function, a deep linear
-network has no spurious local minima as long as it is true for the two layer
-case. When applied to the quadratic loss, our result immediately implies the
-powerful result in [Kawaguchi 2016] that there is no spurious local minima in
-deep linear networks. Further, with the recent work [Zhou and Liang 2018], we
-can remove all the assumptions in [Kawaguchi 2016]. Our proof is short and
-elementary. It builds on the recent work of [Laurent and von Brecht 2018] and
-uses a new rank one perturbation argument.
-",1,0,0,1,0,0
-19778,On the Application of ISO 26262 in Control Design for Automated Vehicles,"  Research on automated vehicles has experienced an explosive growth over the
-past decade. A main obstacle to their practical realization, however, is a
-convincing safety concept. This question becomes ever more important as more
-sophisticated algorithms are used and the vehicle automation level increases.
-The field of functional safety offers a systematic approach to identify
-possible sources of risk and to improve the safety of a vehicle. It is based on
-practical experience across the aerospace, process and other industries over
-multiple decades. This experience is compiled in the functional safety standard
-for the automotive domain, ISO 26262, which is widely adopted throughout the
-automotive industry. However, its applicability and relevance for highly
-automated vehicles is subject to a controversial debate. This paper takes a
-critical look at the discussion and summarizes the main steps of ISO 26262 for
-a safe control design for automated vehicles.
-",1,0,0,0,0,0
-19779,Semi-wavefront solutions in models of collective movements with density-dependent diffusivity,"  This paper deals with a nonhomogeneous scalar parabolic equation with
-possibly degenerate diffusion term; the process has only one stationary state.
-The equation can be interpreted as modeling collective movements (crowd
-dynamics, for instance). We first prove the existence of semi-wavefront
-solutions for every wave speed; their properties are investigated. Then, a
-family of travelling wave solutions is constructed by a suitable combination of
-the previous semi-wavefront solutions. Proofs exploit comparison-type
-techniques and are carried out in the case of one spatial variable; the
-extension to the general case is straightforward.
-",0,0,1,0,0,0
-19780,Self-Regulated Transport in Photonic Crystals with Phase-Changing Defects,"  Phase changing materials (PCM) are widely used for optical data recording,
-sensing, all-optical switching, and optical limiting. Our focus here is on the
-case when the change in the transmission characteristics of the optical
-material is caused by the input light itself. Specifically, the light-induced
-heating triggers the phase transition in the PCM. In this paper, using a
-numerical example, we demonstrate that incorporating the PCM in a photonic
-structure can lead to a dramatic modification of the effects of light-induced
-phase transition, as compared to a stand-alone sample of the same PCM. Our
-focus is on short pulses. We discuss some possible applications of such
-phase-changing photonic structures for optical sensing and limiting.
-",0,1,0,0,0,0
-19781,Assessing randomness in case assignment: the case study of the Brazilian Supreme Court,"  Sortition, i.e., random appointment for public duty, has been employed by
-societies throughout the years, especially for duties related to the judicial
-system, as a firewall designated to prevent illegitimate interference between
-parties in a legal case and agents of the legal system. In judicial systems of
-modern western countries, random procedures are mainly employed to select the
-jury, the court and/or the judge in charge of judging a legal case, so that
-they have a significant role in the course of a case. Therefore, these random
-procedures must comply with some principles, as statistical soundness; complete
-auditability; open-source programming; and procedural, cryptographical and
-computational security. Nevertheless, some of these principles are neglected by
-some random procedures in judicial systems, that are, in some cases, performed
-in secrecy and are not auditable by the involved parts. The assignment of cases
-in the Brazilian Supreme Court (Supremo Tribunal Federal) is an example of such
-procedures, for it is performed by a closed-source algorithm, unknown to the
-public and to the parts involved in the judicial cases, that allegedly assign
-the cases randomly to the justice chairs based on their caseload.
-In this context, this article presents a review of how sortition has been
-employed historically by societies, and discusses how Mathematical Statistics
-may be applied to random procedures of the judicial system, as it has been
-applied for almost a century on clinical trials, for example. Based on this
-discussion, a statistical model for assessing randomness in case assignment is
-proposed and applied to the Brazilian Supreme Court in order to shed light on
-how this assignment process is performed by the closed-source algorithm.
-Guidelines for random procedures are outlined and topics for further researches
-presented.
-",0,0,0,1,0,0
-19782,Particle picture representation of the non-symmetric Rosenblatt process and Hermite processes of any order,"  We provide a particle picture representation for the non-symmetric Rosenblatt
-process and for Hermite processes of any order, extending the result of
-Bojdecki, Gorostiza and Talarczyk in~\cite{FILT}. We show that these processes
-can be obtained as limits in the sense of finite-dimensional distributions of
-certain functionals of a system of particles evolving according to symmetric
-stable Lévy motions. In the case of $k$-Hermite processes the corresponding
-functional involves $k$-intersection local time of symmetric stable Lévy
-processes
-",0,0,1,0,0,0
-19783,Houdini: Fooling Deep Structured Prediction Models,"  Generating adversarial examples is a critical step for evaluating and
-improving the robustness of learning machines. So far, most existing methods
-only work for classification and are not designed to alter the true performance
-measure of the problem at hand. We introduce a novel flexible approach named
-Houdini for generating adversarial examples specifically tailored for the final
-performance measure of the task considered, be it combinatorial and
-non-decomposable. We successfully apply Houdini to a range of applications such
-as speech recognition, pose estimation and semantic segmentation. In all cases,
-the attacks based on Houdini achieve higher success rate than those based on
-the traditional surrogates used to train the models while using a less
-perceptible adversarial perturbation.
-",1,0,0,1,0,0
-19784,Don't relax: early stopping for convex regularization,"  We consider the problem of designing efficient regularization algorithms when
-regularization is encoded by a (strongly) convex functional. Unlike classical
-penalization methods based on a relaxation approach, we propose an iterative
-method where regularization is achieved via early stopping. Our results show
-that the proposed procedure achieves the same recovery accuracy as penalization
-methods, while naturally integrating computational considerations. An empirical
-analysis on a number of problems provides promising results with respect to the
-state of the art.
-",1,0,1,0,0,0
-19785,Interplay between social influence and competitive strategical games in multiplex networks,"  We present a model that takes into account the coupling between evolutionary
-game dynamics and social influence. Importantly, social influence and game
-dynamics take place in different domains, which we model as different layers of
-a multiplex network. We show that the coupling between these dynamical
-processes can lead to cooperation in scenarios where the pure game dynamics
-predicts defection. In addition, we show that the structure of the network
-layers and the relation between them can further increase cooperation.
-Remarkably, if the layers are related in a certain way, the system can reach a
-polarized metastable state.These findings could explain the prevalence of
-polarization observed in many social dilemmas.
-",1,1,0,0,0,0
-19786,Data Augmentation for Low-Resource Neural Machine Translation,"  The quality of a Neural Machine Translation system depends substantially on
-the availability of sizable parallel corpora. For low-resource language pairs
-this is not the case, resulting in poor translation quality. Inspired by work
-in computer vision, we propose a novel data augmentation approach that targets
-low-frequency words by generating new sentence pairs containing rare words in
-new, synthetically created contexts. Experimental results on simulated
-low-resource settings show that our method improves translation quality by up
-to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation.
-",1,0,0,0,0,0
-19787,A Lagrangian approach to modeling heat flux driven close-contact melting,"  Close-contact melting refers to the process of a heat source melting its way
-into a phase-change material. Of special interest is the close-contact melting
-velocity, or more specifically the relative velocity between the heat source
-and the phase-change material. In this work, we present a novel numerical
-approach to simulate quasi-steady, heat flux driven close-contact melting. It
-extends existing approaches found in the literature, and, for the first time,
-allows to study the impact of a spatially varying heat flux distribution. We
-will start by deriving the governing equations in a Lagrangian reference frame
-fixed to the heat source. Exploiting the narrowness of the melt film enables us
-to reduce the momentum balance to the Reynolds equation, which is coupled to
-the energy balance via the velocity field. We particularize our derivation for
-two simple, yet technically relevant geometries, namely a 3d circular disc and
-a 2d planar heat source. An iterative solution procedure for the coupled system
-is described in detail and discussed on the basis of a convergence study.
-Furthermore, we present an extension to allow for rotational melting modes.
-Various test cases demonstrate the proficiency of our method. In particular, we
-will utilize the method to assess the efficiency of the close-contact melting
-process and to quantify the model error introduced if convective losses are
-neglected. Finally, we will draw conclusions and present an outlook to future
-work.
-",0,1,0,0,0,0
-19788,Spectral Graph Analysis: A Unified Explanation and Modern Perspectives,"  Complex networks or graphs are ubiquitous in sciences and engineering:
-biological networks, brain networks, transportation networks, social networks,
-and the World Wide Web, to name a few. Spectral graph theory provides a set of
-useful techniques and models for understanding `patterns of interconnectedness'
-in a graph. Our prime focus in this paper is on the following question: Is
-there a unified explanation and description of the fundamental spectral graph
-methods? There are at least two reasons to be interested in this question.
-Firstly, to gain a much deeper and refined understanding of the basic
-foundational principles, and secondly, to derive rich consequences with
-practical significance for algorithm design. However, despite half a century of
-research, this question remains one of the most formidable open issues, if not
-the core problem in modern network science. The achievement of this paper is to
-take a step towards answering this question by discovering a simple, yet
-universal statistical logic of spectral graph analysis. The prescribed
-viewpoint appears to be good enough to accommodate almost all existing spectral
-graph techniques as a consequence of just one single formalism and algorithm.
-",0,0,1,1,0,0
-19789,ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting,"  Accurate demand forecasts can help on-line retail organizations better plan
-their supply-chain processes. The challenge, however, is the large number of
-associative factors that result in large, non-stationary shifts in demand,
-which traditional time series and regression approaches fail to model. In this
-paper, we propose a Neural Network architecture called AR-MDN, that
-simultaneously models associative factors, time-series trends and the variance
-in the demand. We first identify several causal features and use a combination
-of feature embeddings, MLP and LSTM to represent them. We then model the output
-density as a learned mixture of Gaussian distributions. The AR-MDN can be
-trained end-to-end without the need for additional supervision. We experiment
-on a dataset of an year's worth of data over tens-of-thousands of products from
-Flipkart. The proposed architecture yields a significant improvement in
-forecasting accuracy when compared with existing alternatives.
-",0,0,0,1,0,0
-19790,Extreme Dimension Reduction for Handling Covariate Shift,"  In the covariate shift learning scenario, the training and test covariate
-distributions differ, so that a predictor's average loss over the training and
-test distributions also differ. In this work, we explore the potential of
-extreme dimension reduction, i.e. to very low dimensions, in improving the
-performance of importance weighting methods for handling covariate shift, which
-fail in high dimensions due to potentially high train/test covariate divergence
-and the inability to accurately estimate the requisite density ratios. We first
-formulate and solve a problem optimizing over linear subspaces a combination of
-their predictive utility and train/test divergence within. Applying it to
-simulated and real data, we show extreme dimension reduction helps sometimes
-but not always, due to a bias introduced by dimension reduction.
-",1,0,0,1,0,0
-19791,Landau-Zener transitions for Majorana fermions,"  One-dimensional systems obtained as low-energy limits of hybrid
-superconductor-topological insulator devices provide means of production,
-transport, and destruction of Majorana bound states (MBSs) by variations of the
-magnetic flux. When two or more pairs of MBSs are present in the intermediate
-state, there is a possibility of a Landau-Zener transition, wherein even a slow
-variation of the flux leads to production of a quasiparticle pair. We study
-numerically a version of this process, with four MBSs produced and subsequently
-destroyed, and find that, quite universally, the probability of quasiparticle
-production in it is 50%. This implies that the effect may be a limiting factor
-in applications requiring a high degree of quantum coherence.
-",0,1,0,0,0,0
-19792,Deep Hyperspherical Learning,"  Convolution as inner product has been the founding basis of convolutional
-neural networks (CNNs) and the key to end-to-end visual representation
-learning. Benefiting from deeper architectures, recent CNNs have demonstrated
-increasingly strong representation abilities. Despite such improvement, the
-increased depth and larger parameter space have also led to challenges in
-properly training a network. In light of such challenges, we propose
-hyperspherical convolution (SphereConv), a novel learning framework that gives
-angular representations on hyperspheres. We introduce SphereNet, deep
-hyperspherical convolution networks that are distinct from conventional inner
-product based convolutional networks. In particular, SphereNet adopts
-SphereConv as its basic convolution operator and is supervised by generalized
-angular softmax loss - a natural loss formulation under SphereConv. We show
-that SphereNet can effectively encode discriminative representation and
-alleviate training difficulty, leading to easier optimization, faster
-convergence and comparable (even better) classification accuracy over
-convolutional counterparts. We also provide some theoretical insights for the
-advantages of learning on hyperspheres. In addition, we introduce the learnable
-SphereConv, i.e., a natural improvement over prefixed SphereConv, and
-SphereNorm, i.e., hyperspherical learning as a normalization method.
-Experiments have verified our conclusions.
-",1,0,0,1,0,0
-19793,Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching,"  This paper presents a robotic pick-and-place system that is capable of
-grasping and recognizing both known and novel objects in cluttered
-environments. The key new feature of the system is that it handles a wide range
-of object categories without needing any task-specific training data for novel
-objects. To achieve this, it first uses a category-agnostic affordance
-prediction algorithm to select and execute among four different grasping
-primitive behaviors. It then recognizes picked objects with a cross-domain
-image classification framework that matches observed images to product images.
-Since product images are readily available for a wide range of objects (e.g.,
-from the web), the system works out-of-the-box for novel objects without
-requiring any additional training data. Exhaustive experimental results
-demonstrate that our multi-affordance grasping achieves high success rates for
-a wide variety of objects in clutter, and our recognition algorithm achieves
-high accuracy for both known and novel grasped objects. The approach was part
-of the MIT-Princeton Team system that took 1st place in the stowing task at the
-2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are
-available online at this http URL
-",1,0,0,0,0,0
-19794,Toward an enumerative geometry with quadratic forms,"  We develop various aspects of classical enumerative geometry, including Euler
-characteristics and formulas for counting degenerate fibres in a pencil, with
-the classical numerical formulas being replaced by identitites in the
-Grothendieck-Witt group of quadratic forms with coefficients in the base-field.
-",0,0,1,0,0,0
-19795,Robust reputation-based ranking on multipartite rating networks,"  The spread of online reviews, ratings and opinions and its growing influence
-on people's behavior and decisions boosted the interest to extract meaningful
-information from this data deluge. Hence, crowdsourced ratings of products and
-services gained a critical role in business, governments, and others. We
-propose a new reputation-based ranking system utilizing multipartite rating
-subnetworks, that clusters users by their similarities, using Kolmogorov
-complexity. Our system is novel in that it reflects a diversity of
-opinions/preferences by assigning possibly distinct rankings, for the same
-item, for different groups of users. We prove the convergence and efficiency of
-the system and show that it copes better with spamming/spurious users, and it
-is more robust to attacks than state-of-the-art approaches.
-",1,0,0,0,0,0
-19796,Duality and upper bounds in optimal stochastic control governed by partial differential equations,"  A dual control problem is presented for the optimal stochastic control of a
-system governed by partial differential equations. Relationships between the
-optimal values of the original and the dual problems are investigated and two
-duality theorems are proved. The dual problem serves to provide upper bounds
-for the optimal and maximum value of the original one or even to give the
-optimal value.
-",0,0,1,0,0,0
-19797,On Sampling from Massive Graph Streams,"  We propose Graph Priority Sampling (GPS), a new paradigm for order-based
-reservoir sampling from massive streams of graph edges. GPS provides a general
-way to weight edge sampling according to auxiliary and/or size variables so as
-to accomplish various estimation goals of graph properties. In the context of
-subgraph counting, we show how edge sampling weights can be chosen so as to
-minimize the estimation variance of counts of specified sets of subgraphs. In
-distinction with many prior graph sampling schemes, GPS separates the functions
-of edge sampling and subgraph estimation. We propose two estimation frameworks:
-(1) Post-Stream estimation, to allow GPS to construct a reference sample of
-edges to support retrospective graph queries, and (2) In-Stream estimation, to
-allow GPS to obtain lower variance estimates by incrementally updating the
-subgraph count estimates during stream processing. Unbiasedness of subgraph
-estimators is established through a new Martingale formulation of graph stream
-order sampling, which shows that subgraph estimators, written as a product of
-constituent edge estimators are unbiased, even when computed at different
-points in the stream. The separation of estimation and sampling enables
-significant resource savings relative to previous work. We illustrate our
-framework with applications to triangle and wedge counting. We perform a
-large-scale experimental study on real-world graphs from various domains and
-types. GPS achieves high accuracy with less than 1% error for triangle and
-wedge counting, while storing a small fraction of the graph with average update
-times of a few microseconds per edge. Notably, for a large Twitter graph with
-more than 260M edges, GPS accurately estimates triangle counts with less than
-1% error, while storing only 40K edges.
-",1,0,1,1,0,0
-19798,"Optimal Investment, Demand and Arbitrage under Price Impact","  This paper studies the optimal investment problem with random endowment in an
-inventory-based price impact model with competitive market makers. Our goal is
-to analyze how price impact affects optimal policies, as well as both pricing
-rules and demand schedules for contingent claims. For exponential market makers
-preferences, we establish two effects due to price impact: constrained trading,
-and non-linear hedging costs. To the former, wealth processes in the impact
-model are identified with those in a model without impact, but with constrained
-trading, where the (random) constraint set is generically neither closed nor
-convex. Regarding hedging, non-linear hedging costs motivate the study of
-arbitrage free prices for the claim. We provide three such notions, which
-coincide in the frictionless case, but which dramatically differ in the
-presence of price impact. Additionally, we show arbitrage opportunities, should
-they arise from claim prices, can be exploited only for limited position sizes,
-and may be ignored if outweighed by hedging considerations. We also show that
-arbitrage inducing prices may arise endogenously in equilibrium, and that
-equilibrium positions are inversely proportional to the market makers'
-representative risk aversion. Therefore, large positions endogenously arise in
-the limit of either market maker risk neutrality, or a large number of market
-makers.
-",0,0,0,0,0,1
-19799,Amplitude Mediated Chimera States with Active and Inactive Oscillators,"  The emergence and nature of amplitude mediated chimera states,
-spatio-temporal patterns of co-existing coherent and incoherent regions, are
-investigated for a globally coupled system of active and inactive
-Ginzburg-Landau oscillators. The existence domain of such states is found to
-shrink and shift in parametric space as the fraction of inactive oscillators is
-increased. The role of inactive oscillators is found to be two fold - they get
-activated to form a separate region of coherent oscillations and in addition
-decrease the common collective frequency of the coherent regions by their
-presence. The dynamical origin of these effects is delineated through a
-detailed bifurcation analysis of a reduced model equation that is based on a
-mean field approximation. Our results may have practical implications for the
-robustness of such states in biological or physical systems where age related
-deterioration in the functionality of components can occur.
-",0,1,0,0,0,0
-19800,Astronomical random numbers for quantum foundations experiments,"  Photons from distant astronomical sources can be used as a classical source
-of randomness to improve fundamental tests of quantum nonlocality,
-wave-particle duality, and local realism through Bell's inequality and
-delayed-choice quantum eraser tests inspired by Wheeler's cosmic-scale
-Mach-Zehnder interferometer gedankenexperiment. Such sources of random numbers
-may also be useful for information-theoretic applications such as key
-distribution for quantum cryptography. Building on the design of an
-""astronomical random-number generator"" developed for the recent ""cosmic Bell""
-experiment [Handsteiner et al., Phys. Rev. Lett. 118, 060401 (2017)], in this
-paper we report on the design and characterization of a device that, with
-20-nanosecond latency, outputs a bit based on whether the wavelength of an
-incoming photon is greater than or less than 700 nm. Using the one-meter
-telescope at the Jet Propulsion Laboratory (JPL) Table Mountain Observatory, we
-generated random bits from astronomical photons in both color channels from 50
-stars of varying color and magnitude, and from 12 quasars with redshifts up to
-$z = 3.9$. With stars, we achieved bit rates of $\sim 1 \times 10^6$ Hz /
-m$^2$, limited by saturation for our single-photon detectors, and with quasars
-of magnitudes between 12.9 and 16, we achieved rates between $\sim 10^2$ and $2
-\times 10^3$ Hz /m$^2$. For bright quasars, the resulting bitstreams exhibit
-sufficiently low amounts of statistical predictability as quantified by the
-mutual information. In addition, a sufficiently high fraction of bits generated
-are of true astronomical origin in order to address both the locality and
-freedom-of-choice loopholes when used to set the measurement settings in a test
-of the Bell-CHSH inequality.
-",0,1,0,0,0,0
-19801,Application of generative autoencoder in de novo molecular design,"  A major challenge in computational chemistry is the generation of novel
-molecular structures with desirable pharmacological and physiochemical
-properties. In this work, we investigate the potential use of autoencoder, a
-deep learning methodology, for de novo molecular design. Various generative
-autoencoders were used to map molecule structures into a continuous latent
-space and vice versa and their performance as structure generator was assessed.
-Our results show that the latent space preserves chemical similarity principle
-and thus can be used for the generation of analogue structures. Furthermore,
-the latent space created by autoencoders were searched systematically to
-generate novel compounds with predicted activity against dopamine receptor type
-2 and compounds similar to known active compounds not included in the training
-set were identified.
-",1,0,0,1,0,0
-19802,Prospects for Measuring Cosmic Microwave Background Spectral Distortions in the Presence of Foregrounds,"  Measurements of cosmic microwave background spectral distortions have
-profound implications for our understanding of physical processes taking place
-over a vast window in cosmological history. Foreground contamination is
-unavoidable in such measurements and detailed signal-foreground separation will
-be necessary to extract cosmological science. We present MCMC-based spectral
-distortion detection forecasts in the presence of Galactic and extragalactic
-foregrounds for a range of possible experimental configurations, focusing on
-the Primordial Inflation Explorer (PIXIE) as a fiducial concept. We consider
-modifications to the baseline PIXIE mission (operating 12 months in distortion
-mode), searching for optimal configurations using a Fisher approach. Using only
-spectral information, we forecast an extended PIXIE mission to detect the
-expected average non-relativistic and relativistic thermal Sunyaev-Zeldovich
-distortions at high significance (194$\sigma$ and 11$\sigma$, respectively),
-even in the presence of foregrounds. The $\Lambda$CDM Silk damping $\mu$-type
-distortion is not detected without additional modifications of the instrument
-or external data. Galactic synchrotron radiation is the most problematic source
-of contamination in this respect, an issue that could be mitigated by combining
-PIXIE data with future ground-based observations at low frequencies ($\nu <
-15-30$GHz). Assuming moderate external information on the synchrotron spectrum,
-we project an upper limit of $|\mu| < 3.6\times 10^{-7}$ (95\% c.l.), slightly
-more than one order of magnitude above the fiducial $\Lambda$CDM signal from
-the damping of small-scale primordial fluctuations, but a factor of $\simeq
-250$ improvement over the current upper limit from COBE/FIRAS. This limit could
-be further reduced to $|\mu| < 9.4\times 10^{-8}$ (95\% c.l.) with more
-optimistic assumptions about low-frequency information. (Abridged)
-",0,1,0,0,0,0
-19803,The Fog of War: A Machine Learning Approach to Forecasting Weather on Mars,"  For over a decade, scientists at NASA's Jet Propulsion Laboratory (JPL) have
-been recording measurements from the Martian surface as a part of the Mars
-Exploration Rovers mission. One quantity of interest has been the opacity of
-Mars's atmosphere for its importance in day-to-day estimations of the amount of
-power available to the rover from its solar arrays. This paper proposes the use
-of neural networks as a method for forecasting Martian atmospheric opacity that
-is more effective than the current empirical model. The more accurate
-prediction provided by these networks would allow operators at JPL to make more
-accurate predictions of the amount of energy available to the rover when they
-plan activities for coming sols.
-",1,1,0,0,0,0
-19804,Substrate inhibition imposes fitness penalty at high protein stability,"  Proteins are only moderately stable. It has long been debated whether this
-narrow range of stabilities is solely a result of neutral drift towards lower
-stability or purifying selection against excess stability is also at work - for
-which no experimental evidence was found so far. Here we show that mutations
-outside the active site in the essential E. coli enzyme adenylate kinase result
-in stability-dependent increase in substrate inhibition by AMP, thereby
-impairing overall enzyme activity at high stability. Such inhibition caused
-substantial fitness defects not only in the presence of excess substrate but
-also under physiological conditions. In the latter case, substrate inhibition
-caused differential accumulation of AMP in the stationary phase for the
-inhibition prone mutants. Further, we show that changes in flux through Adk
-could accurately describe the variation in fitness effects. Taken together,
-these data suggest that selection against substrate inhibition and hence excess
-stability may have resulted in a narrow range of optimal stability observed for
-modern proteins.
-",0,0,0,0,1,0
-19805,Exact relativistic Toda chain eigenfunctions from Separation of Variables and gauge theory,"  We provide a proposal, motivated by Separation of Variables and gauge theory
-arguments, for constructing exact solutions to the quantum Baxter equation
-associated to the $N$-particle relativistic Toda chain and test our proposal
-against numerical results. Quantum Mechanical non-perturbative corrections,
-essential in order to obtain a sensible solution, are taken into account in our
-gauge theory approach by considering codimension two defects on curved
-backgrounds (squashed $S^5$ and degenerate limits) rather than flat space; this
-setting also naturally incorporates exact quantization conditions and energy
-spectrum of the relativistic Toda chain as well as its modular dual structure.
-",0,1,0,0,0,0
-19806,Simple Compact Monotone Tree Drawings,"  A monotone drawing of a graph G is a straight-line drawing of G such that
-every pair of vertices is connected by a path that is monotone with respect to
-some direction.
-Trees, as a special class of graphs, have been the focus of several papers
-and, recently, He and He~\cite{mt:4} showed how to produce a monotone drawing
-of an arbitrary $n$-vertex tree that is contained in a $12n \times 12n$ grid.
-All monotone tree drawing algorithms that have appeared in the literature
-consider rooted ordered trees and they draw them so that (i) the root of the
-tree is drawn at the origin of the drawing, (ii) the drawing is confined in the
-first quadrant, and (iii) the ordering/embedding of the tree is respected. In
-this paper, we provide a simple algorithm that has the exact same
-characteristics and, given an $n$-vertex rooted tree $T$, it outputs a monotone
-drawing of $T$ that fits on a $n \times n$ grid.
-For unrooted ordered trees, we present an algorithms that produces monotone
-drawings that respect the ordering and fit in an $(n+1) \times (\frac{n}{2}
-+1)$ grid, while, for unrooted non-ordered trees we produce monotone drawings
-of good aspect ratio which fit on a grid of size at most $\left\lfloor
-\frac{3}{4} \left(n+2\right)\right\rfloor \times \left\lfloor \frac{3}{4}
-\left(n+2\right)\right\rfloor$.
-",1,0,0,0,0,0
-19807,Context-Free Path Querying by Matrix Multiplication,"  Graph data models are widely used in many areas, for example, bioinformatics,
-graph databases. In these areas, it is often required to process queries for
-large graphs. Some of the most common graph queries are navigational queries.
-The result of query evaluation is a set of implicit relations between nodes of
-the graph, i.e. paths in the graph. A natural way to specify these relations is
-by specifying paths using formal grammars over the alphabet of edge labels. An
-answer to a context-free path query in this approach is usually a set of
-triples (A, m, n) such that there is a path from the node m to the node n,
-whose labeling is derived from a non-terminal A of the given context-free
-grammar. This type of queries is evaluated using the relational query
-semantics. Another example of path query semantics is the single-path query
-semantics which requires presenting a single path from the node m to the node
-n, whose labeling is derived from a non-terminal A for all triples (A, m, n)
-evaluated using the relational query semantics. There is a number of algorithms
-for query evaluation which use these semantics but all of them perform poorly
-on large graphs. One of the most common technique for efficient big data
-processing is the use of a graphics processing unit (GPU) to perform
-computations, but these algorithms do not allow to use this technique
-efficiently. In this paper, we show how the context-free path query evaluation
-using these query semantics can be reduced to the calculation of the matrix
-transitive closure. Also, we propose an algorithm for context-free path query
-evaluation which uses relational query semantics and is based on matrix
-operations that make it possible to speed up computations by using a GPU.
-",1,0,0,0,0,0
-19808,Equivalence of sparse and Carleson coefficients for general sets,"  We remark that sparse and Carleson coefficients are equivalent for every
-countable collection of Borel sets and hence, in particular, for dyadic
-rectangles, the case relevant to the theory of bi-parameter singular integrals.
-The key observation is that a dual refomulation by I. E. Verbitsky for
-Carleson coefficients over dyadic cubes holds also for Carleson coefficients
-over general sets. We give a simple proof for this reformulation.
-",0,0,1,0,0,0
-19809,Discovering Business Rules from Business Process Models,"  Discovering business rules from business process models are of advantage to
-ensure the compliance of business processes with business rules. Furthermore it
-provides the agility of business processes in case of business rules evolution.
-Current approaches are limited on types of rules that can be discovered. This
-paper analyses the expression power of some popular business process modelling
-languages in embedding business rules in its presentation and provides
-indicators to extract various types of business rules from business process
-models.
-",1,0,0,0,0,0
-19810,Fast Learning and Prediction for Object Detection using Whitened CNN Features,"  We combine features extracted from pre-trained convolutional neural networks
-(CNNs) with the fast, linear Exemplar-LDA classifier to get the advantages of
-both: the high detection performance of CNNs, automatic feature engineering,
-fast model learning from few training samples and efficient sliding-window
-detection. The Adaptive Real-Time Object Detection System (ARTOS) has been
-refactored broadly to be used in combination with Caffe for the experimental
-studies reported in this work.
-",1,0,0,0,0,0
-19811,N-body simulations of planet formation via pebble accretion I: First Results,"  Context. Planet formation with pebbles has been proposed to solve a couple of
-long-standing issues in the classical formation model. Some sophisticated
-simulations have been done to confirm the efficiency of pebble accretion.
-However, there has not been any global N-body simulations that compare the
-outcomes of planet formation via pebble accretion with observed extrasolar
-planetary systems. Aims. In this paper, we study the effects of a range of
-initial parameters of planet formation via pebble accretion, and present the
-first results of our simulations. Methods. We incorporate the pebble accretion
-model by Ida et al. (2016) in the N-body code SyMBA (Duncan et al. 1998), along
-with the effects of gas accretion, eccentricity and inclination damping and
-planet migration in the disc. Results. We confirm that pebble accretion leads
-to a variety of planetary systems, but have difficulty in reproducing observed
-properties of exoplanetary systems, such as planetary mass, semimajor axis, and
-eccentricity distributions. The main reason behind this is a too-efficient type
-I migration, which sensitively depends on the disc model. However, our
-simulations also lead to a few interesting predictions. First, we find that
-formation efficiencies of planets depend on the stellar metallicities, not only
-for giant planets, but also for Earths (Es) and Super-Earths (SEs). The
-dependency for Es/SEs is subtle. Although higher metallicity environments lead
-to faster formation of a larger number of Es/SEs, they also tend to be lost
-later via dynamical instability. Second, our results indicate that a wide range
-of bulk densities observed for Es and SEs is a natural consequence of dynamical
-evolution of planetary systems. Third, the ejection trend of our simulations
-suggest that one free-floating E/SE may be expected for two smaller-mass
-planets.
-",0,1,0,0,0,0
-19812,Local character of Kim-independence,"  We show that NSOP$_{1}$ theories are exactly the theories in which
-Kim-independence satisfies a form of local character. In particular, we show
-that if $T$ is NSOP$_{1}$, $M\models T$, and $p$ is a type over $M$, then the
-collection of elementary substructures of size $\left|T\right|$ over which $p$
-does not Kim-fork is a club of $\left[M\right]^{\left|T\right|}$ and that this
-characterizes NSOP$_{1}$.
-We also present a new phenomenon we call dual local-character for
-Kim-independence in NSOP$_{1}$-theories.
-",0,0,1,0,0,0
-19813,A Further Analysis of The Role of Heterogeneity in Coevolutionary Spatial Games,"  Heterogeneity has been studied as one of the most common explanations of the
-puzzle of cooperation in social dilemmas. A large number of papers have been
-published discussing the effects of increasing heterogeneity in structured
-populations of agents, where it has been established that heterogeneity may
-favour cooperative behaviour if it supports agents to locally coordinate their
-strategies. In this paper, assuming an existing model of a heterogeneous
-weighted network, we aim to further this analysis by exploring the relationship
-(if any) between heterogeneity and cooperation. We adopt a weighted network
-which is fully populated by agents playing both the Prisoner's Dilemma or the
-Optional Prisoner's Dilemma games with coevolutionary rules, i.e., not only the
-strategies but also the link weights evolve over time. Surprisingly, results
-show that the heterogeneity of link weights (states) on their own does not
-always promote cooperation; rather cooperation is actually favoured by the
-increase in the number of overlapping states and not by the heterogeneity
-itself. We believe that these results can guide further research towards a more
-accurate analysis of the role of heterogeneity in social dilemmas.
-",1,0,0,0,0,0
-19814,Transport properties of the Azimuthal Magnetorotational Instability,"  The magnetorotational instability (MRI) is thought to be a powerful source of
-turbulence in Keplerian accretion disks. Motivated by recent laboratory
-experiments, we study the MRI driven by an azimuthal magnetic field in an
-electrically conducting fluid sheared between two concentric rotating
-cylinders. By adjusting the rotation rates of the cylinders, we approximate
-angular velocity profiles $\omega \propto r^{q}$. We perform direct numerical
-simulations of a steep profile close to the Rayleigh line $q \gtrsim -2 $ and a
-quasi-Keplerian profile $q \approx -3/2$ and cover wide ranges of Reynolds
-($Re\le 4\cdot10^4$) and magnetic Prandtl numbers ($0\le Pm \le 1$). In the
-quasi-Keplerian case, the onset of instability depends on the magnetic Reynolds
-number, with $Rm_c \approx 50$, and angular momentum transport scales as
-$\sqrt{Pm} Re^2$ in the turbulent regime. The ratio of Maxwell to Reynolds
-stresses is set by $Rm$. At the onset of instability both stresses have similar
-magnitude, whereas the Reynolds stress vanishes or becomes even negative as
-$Rm$ increases. For the profile close to the Rayleigh line, the instability
-shares these properties as long as $Pm\gtrsim0.1$, but exhibits a markedly
-different character if $Pm\rightarrow 0$, where the onset of instability is
-governed by the Reynolds number, with $Re_c \approx 1250$, transport is via
-Reynolds stresses and scales as $Re^2$. At intermediate $Pm=0.01$ we observe a
-continuous transition from one regime to the other, with a crossover at
-$Rm=\mathcal{O}(100)$. Our results give a comprehensive picture of angular
-momentum transport of the MRI with an imposed azimuthal field.
-",0,1,0,0,0,0
-19815,New bounds for the Probability of Causation in Mediation Analysis,"  An individual has been subjected to some exposure and has developed some
-outcome. Using data on similar individuals, we wish to evaluate, for this case,
-the probability that the outcome was in fact caused by the exposure. Even with
-the best possible experimental data on exposure and outcome, we typically can
-not identify this ""probability of causation"" exactly, but we can provide
-information in the form of bounds for it. Under appropriate assumptions, these
-bounds can be tightened if we can make other observations (e.g., on
-non-experimental cases), measure additional variables (e.g., covariates) or
-measure complete mediators. In this work we propose new bounds for the case
-that a third variable mediates partially the effect of the exposure on the
-outcome.
-",0,0,1,1,0,0
-19816,Searching for a cosmological preferred direction with 147 rotationally supported galaxies,"  It is well known that the Milgrom's MOND (modified Newtonian dynamics)
-explains well the mass discrepancy problem in galaxy rotation curves. The MOND
-predicts a universal acceleration scale below which the Newtonian dynamics is
-invalid yet. The universal acceleration scale we got from the SPARC dataset is
-$g_†=1.02\times10^{-10} \rm m~s^{-2}$. Milgrom suggested that the
-acceleration scale may be a fingerprint of cosmology on local dynamics and
-related with the Hubble constant $g_†\sim cH_0$. In this paper, we use the
-hemisphere comparison method with the SPARC dataset to investigate the spatial
-anisotropy on the acceleration scale. We find that the hemisphere of the
-maximum acceleration scale is in the direction $(l,b) =
-({175.5^\circ}^{+6^\circ}_{-10^\circ}, {-6.5^\circ}^{+8^\circ}_{-3^\circ})$
-with $g_{†,max}=1.10\times10^{-10} \rm m~s^{-2}$, while the hemisphere of
-the minimum acceleration scale is in the opposite direction $(l,b) =
-({355.5^\circ}^{+6^\circ}_{-10^\circ}, {6.5^\circ}^{+3^\circ}_{-8^\circ})$ with
-$g_{†,min}=0.76\times10^{-10} \rm m~s^{-2}$. The maximum anisotropy level
-reaches up to $0.37\pm0.04$. Robust tests present that such a level of
-anisotropy can't be reproduced by a statistically isotropic data. In addition,
-we show that the spatial anisotropy on the acceleration scale has little
-correlation with the non-uniform distribution of the SPARC data points in sky.
-We also find that the maximum anisotropy direction is close with other
-cosmological preferred directions, especially the direction of the ""Australia
-dipole"" for the fine structure constant.
-",0,1,0,0,0,0
-19817,MultiFIT: A Multivariate Multiscale Framework for Independence Tests,"  We present a framework for testing independence between two random vectors
-that is scalable to massive data. We break down the multivariate test into
-univariate tests of independence on a collection of $2\times 2$ contingency
-tables, constructed by sequentially discretizing the sample space. This
-transforms a complex problem that traditionally requires quadratic
-computational complexity with respect to the sample size into one that scales
-almost linearly with the sample size. We further consider the scenario when the
-dimensionality of the random vectors grows large, in which case the curse of
-dimensionality arises in the proposed framework through an explosion in the
-number of univariate tests to be completed. To overcome this difficulty we
-propose a data-adaptive version of our method that completes a fraction of the
-univariate tests judged to be more likely to contain evidence for dependency.
-We demonstrate the tremendous computational advantage of the algorithm in
-comparison to existing approaches while achieving desirable statistical power
-through an extensive simulation study. In addition, we illustrate how our
-method can be used for learning the nature of the underlying dependency. We
-demonstrate the use of our method through analyzing a data set from flow
-cytometry.
-",0,0,0,1,0,0
-19818,Stability of algebraic varieties and Kahler geometry,"  This is a survey article, based on the author's lectures in the 2015 AMS
-Summer Research Institute in Algebraic Geometry, and to appear in the
-Proceedings.
-",0,0,1,0,0,0
-19819,Scratch iridescence: Wave-optical rendering of diffractive surface structure,"  The surface of metal, glass and plastic objects is often characterized by
-microscopic scratches caused by manufacturing and/or wear. A closer look onto
-such scratches reveals iridescent colors with a complex dependency on viewing
-and lighting conditions. The physics behind this phenomenon is well understood;
-it is caused by diffraction of the incident light by surface features on the
-order of the optical wavelength. Existing analytic models are able to reproduce
-spatially unresolved microstructure such as the iridescent appearance of
-compact disks and similar materials. Spatially resolved scratches, on the other
-hand, have proven elusive due to the highly complex wave-optical light
-transport simulations needed to account for their appearance. In this paper, we
-propose a wave-optical shading model based on non-paraxial scalar diffraction
-theory to render this class of effects. Our model expresses surface roughness
-as a collection of line segments. To shade a point on the surface, the
-individual diffraction patterns for contributing scratch segments are computed
-analytically and superimposed coherently. This provides natural transitions
-from localized glint-like iridescence to smooth BRDFs representing the
-superposition of many reflections at large viewing distances. We demonstrate
-that our model is capable of recreating the overall appearance as well as
-characteristic detail effects observed on real-world examples.
-",1,0,0,0,0,0
-19820,An oscillation criterion for delay differential equations with several non-monotone arguments,"  The oscillatory behavior of the solutions to a differential equation with
-several non-monotone delay arguments and non-negative coefficients is studied.
-A new sufficient oscillation condition, involving lim sup, is obtained. An
-example illustrating the significance of the result is also given.
-",0,0,1,0,0,0
-19821,Item Recommendation with Variational Autoencoders and Heterogenous Priors,"  In recent years, Variational Autoencoders (VAEs) have been shown to be highly
-effective in both standard collaborative filtering applications and extensions
-such as incorporation of implicit feedback. We extend VAEs to collaborative
-filtering with side information, for instance when ratings are combined with
-explicit text feedback from the user. Instead of using a user-agnostic standard
-Gaussian prior, we incorporate user-dependent priors in the latent VAE space to
-encode users' preferences as functions of the review text. Taking into account
-both the rating and the text information to represent users in this multimodal
-latent space is promising to improve recommendation quality. Our proposed model
-is shown to outperform the existing VAE models for collaborative filtering (up
-to 29.41% relative improvement in ranking metric) along with other baselines
-that incorporate both user ratings and text for item recommendation.
-",0,0,0,1,0,0
-19822,Boltzmann Exploration Done Right,"  Boltzmann exploration is a classic strategy for sequential decision-making
-under uncertainty, and is one of the most standard tools in Reinforcement
-Learning (RL). Despite its widespread use, there is virtually no theoretical
-understanding about the limitations or the actual benefits of this exploration
-scheme. Does it drive exploration in a meaningful way? Is it prone to
-misidentifying the optimal actions or spending too much time exploring the
-suboptimal ones? What is the right tuning for the learning rate? In this paper,
-we address several of these questions in the classic setup of stochastic
-multi-armed bandits. One of our main results is showing that the Boltzmann
-exploration strategy with any monotone learning-rate sequence will induce
-suboptimal behavior. As a remedy, we offer a simple non-monotone schedule that
-guarantees near-optimal performance, albeit only when given prior access to key
-problem parameters that are typically not available in practical situations
-(like the time horizon $T$ and the suboptimality gap $\Delta$). More
-importantly, we propose a novel variant that uses different learning rates for
-different arms, and achieves a distribution-dependent regret bound of order
-$\frac{K\log^2 T}{\Delta}$ and a distribution-independent bound of order
-$\sqrt{KT}\log K$ without requiring such prior knowledge. To demonstrate the
-flexibility of our technique, we also propose a variant that guarantees the
-same performance bounds even if the rewards are heavy-tailed.
-",1,0,0,1,0,0
-19823,Deep reinforcement learning from human preferences,"  For sophisticated reinforcement learning (RL) systems to interact usefully
-with real-world environments, we need to communicate complex goals to these
-systems. In this work, we explore goals defined in terms of (non-expert) human
-preferences between pairs of trajectory segments. We show that this approach
-can effectively solve complex RL tasks without access to the reward function,
-including Atari games and simulated robot locomotion, while providing feedback
-on less than one percent of our agent's interactions with the environment. This
-reduces the cost of human oversight far enough that it can be practically
-applied to state-of-the-art RL systems. To demonstrate the flexibility of our
-approach, we show that we can successfully train complex novel behaviors with
-about an hour of human time. These behaviors and environments are considerably
-more complex than any that have been previously learned from human feedback.
-",1,0,0,1,0,0
-19824,Entanglement spectroscopy on a quantum computer,"  We present a quantum algorithm to compute the entanglement spectrum of
-arbitrary quantum states. The interesting universal part of the entanglement
-spectrum is typically contained in the largest eigenvalues of the density
-matrix which can be obtained from the lower Renyi entropies through the
-Newton-Girard method. Obtaining the $p$ largest eigenvalues
-($\lambda_1>\lambda_2\ldots>\lambda_p$) requires a parallel circuit depth of
-$\mathcal{O}(p(\lambda_1/\lambda_p)^p)$ and $\mathcal{O}(p\log(N))$ qubits
-where up to $p$ copies of the quantum state defined on a Hilbert space of size
-$N$ are needed as the input. We validate this procedure for the entanglement
-spectrum of the topologically-ordered Laughlin wave function corresponding to
-the quantum Hall state at filling factor $\nu=1/3$. Our scaling analysis
-exposes the tradeoffs between time and number of qubits for obtaining the
-entanglement spectrum in the thermodynamic limit using finite-size digital
-quantum computers. We also illustrate the utility of the second Renyi entropy
-in predicting a topological phase transition and in extracting the localization
-length in a many-body localized system.
-",0,1,0,0,0,0
-19825,Light Attenuation Length of High Quality Linear Alkyl Benzene as Liquid Scintillator Solvent for the JUNO Experiment,"  The Jiangmen Underground Neutrino Observatory (JUNO) is a multipurpose
-neutrino experiment with a 20 kt liquid scintillator detector designed to
-determine the neutrino mass hierarchy, and measure the neutrino oscillation
-parameters. Linear alkyl benzene (LAB) will be used as the solvent for the
-liquid scintillation system in the central detector of JUNO. For this purpose,
-we have prepared LAB samples, and have measured their light attenuation
-lengths, with one achieving a length of 25.8 m, comparable to the diameter of
-the JUNO detector.
-",0,1,0,0,0,0
-19826,OIL: Observational Imitation Learning,"  Recent work has explored the problem of autonomous navigation by imitating a
-teacher and learning an end-to-end policy, which directly predicts controls
-from raw images. However, these approaches tend to be sensitive to mistakes by
-the teacher and do not scale well to other environments or vehicles. To this
-end, we propose Observational Imitation Learning (OIL), a novel imitation
-learning variant that supports online training and automatic selection of
-optimal behavior by observing multiple imperfect teachers. We apply our
-proposed methodology to the challenging problems of autonomous driving and UAV
-racing. For both tasks, we utilize the Sim4CV simulator that enables the
-generation of large amounts of synthetic training data and also allows for
-online learning and evaluation. We train a perception network to predict
-waypoints from raw image data and use OIL to train another network to predict
-controls from these waypoints. Extensive experiments demonstrate that our
-trained network outperforms its teachers, conventional imitation learning (IL)
-and reinforcement learning (RL) baselines and even humans in simulation. The
-project website is available at this https URL
-",1,0,0,0,0,0
-19827,A Multi-Scan Labeled Random Finite Set Model for Multi-object State Estimation,"  State space models in which the system state is a finite set--called the
-multi-object state--have generated considerable interest in recent years.
-Smoothing for state space models provides better estimation performance than
-filtering by using the full posterior rather than the filtering density. In
-multi-object state estimation, the Bayes multi-object filtering recursion
-admits an analytic solution known as the Generalized Labeled Multi-Bernoulli
-(GLMB) filter. In this work, we extend the analytic GLMB recursion to propagate
-the multi-object posterior. We also propose an implementation of this so-called
-multi-scan GLMB posterior recursion using a similar approach to the GLMB filter
-implementation.
-",0,0,0,1,0,0
-19828,On the Complexity of Sampling Nodes Uniformly from a Graph,"  We study a number of graph exploration problems in the following natural
-scenario: an algorithm starts exploring an undirected graph from some seed
-node; the algorithm, for an arbitrary node $v$ that it is aware of, can ask an
-oracle to return the set of the neighbors of $v$. (In social network analysis,
-a call to this oracle corresponds to downloading the profile page of user $v$
-in a social network.) The goal of the algorithm is to either learn something
-(e.g., average degree) about the graph, or to return some random function of
-the graph (e.g., a uniform-at-random node), while accessing/downloading as few
-nodes of the graph as possible. Motivated by practical applications, we study
-the complexities of a variety of problems in terms of the graph's mixing time
-and average degree -- two measures that are believed to be quite small in
-real-world social networks, and that have often been used in the applied
-literature to bound the performance of online exploration algorithms. Our main
-result is that the algorithm has to access $\Omega\left(t_{\rm mix} d_{\rm avg}
-\epsilon^{-2} \ln \delta^{-1}\right)$ nodes to obtain, with probability at
-least $1-\delta$, an $\epsilon$-additive approximation of the average of a
-bounded function on the nodes of a graph -- this lower bound matches the
-performance of an algorithm that was proposed in the literature. We also give
-tight bounds for the problem of returning a close-to-uniform-at-random node
-from the graph. Finally, we give lower bounds for the problems of estimating
-the average degree of the graph, and the number of nodes of the graph.
-",1,0,0,0,0,0
-19829,A structure theorem for almost low-degree functions on the slice,"  The Fourier-Walsh expansion of a Boolean function $f \colon \{0,1\}^n
-\rightarrow \{0,1\}$ is its unique representation as a multilinear polynomial.
-The Kindler-Safra theorem (2002) asserts that if in the expansion of $f$, the
-total weight on coefficients beyond degree $k$ is very small, then $f$ can be
-approximated by a Boolean-valued function depending on at most $O(2^k)$
-variables.
-In this paper we prove a similar theorem for Boolean functions whose domain
-is the `slice' ${{[n]}\choose{pn}} = \{x \in \{0,1\}^n\colon \sum_i x_i =
-pn\}$, where $0 \ll p \ll 1$, with respect to their unique representation as
-harmonic multilinear polynomials. We show that if in the representation of
-$f\colon {{[n]}\choose{pn}} \rightarrow \{0,1\}$, the total weight beyond
-degree $k$ is at most $\epsilon$, where $\epsilon = \min(p, 1-p)^{O(k)}$, then
-$f$ can be $O(\epsilon)$-approximated by a degree-$k$ Boolean function on the
-slice, which in turn depends on $O(2^{k})$ coordinates. This proves a
-conjecture of Filmus, Kindler, Mossel, and Wimmer (2015). Our proof relies on
-hypercontractivity, along with a novel kind of a shifting procedure.
-In addition, we show that the approximation rate in the Kindler-Safra theorem
-can be improved from $\epsilon + \exp(O(k)) \epsilon^{1/4}$ to
-$\epsilon+\epsilon^2 (2\ln(1/\epsilon))^k/k!$, which is tight in terms of the
-dependence on $\epsilon$ and misses at most a factor of $2^{O(k)}$ in the
-lower-order term.
-",1,0,0,0,0,0
-19830,Oxygen-vacancy driven electron localization and itinerancy in rutile-based TiO$_2$,"  Oxygen-deficient TiO$_2$ in the rutile structure as well as the Ti$_3$O$_5$
-Magn{é}li phase is investigated within the charge self-consistent combination
-of density functional theory (DFT) with dynamical mean-field theory (DMFT). It
-is shown that an isolated oxygen vacancy (V$_{\rm O}$) in titanium dioxide is
-not sufficient to metallize the system at low temperatures. In a semiconducting
-phase, an in-gap state is identified at $\varepsilon_{\rm IG}^{\hfill}\sim
--0.75\,$eV\, in excellent agreement with experimental data. Band-like impurity
-levels, resulting from a threefold V$_{\rm O}$-Ti coordination as well as
-entangled $(t_{2g},e_g)$ states, become localized due to site-dependent
-electronic correlations. Charge localization and strong orbital polarization
-occur in the V$_{\rm O}$-near Ti ions, which details can be modified by a
-variation of the correlated subspace. At higher oxygen vacancy concentration, a
-correlated metal is stabilized in the Magn{é}li phase. A V$_{\rm O}$-defect
-rutile structure of identical stoichiometry shows key differences in the
-orbital-resolved character and the spectral properties. Charge
-disproportionation is vital in the oxygen-deficient compounds, but obvious
-metal-insulator transitions driven or sustained by charge order are not
-identified.
-",0,1,0,0,0,0
-19831,3D printable multimaterial cellular auxetics with tunable stiffness,"  Auxetic materials are a novel class of mechanical metamaterials which exhibit
-an interesting property of negative Poisson ratio by virtue of their
-architecture rather than composition. It has been well established that a wide
-range of negative Poisson ratio can be obtained by varying the geometry and
-architecture of the cellular materials. However, the limited range of stiffness
-values obtained from a given geometry restricts their applications. Research
-trials have revealed that multi-material cellular designs have the capability
-to generate range of stiffness values as per the requirement of application.
-With the advancements in 3D printing, multi-material cellular designs can be
-realized in practice. In this work, multi-material cellular designs are
-investigated using finite element method. It was observed that introduction of
-material gradient/distribution in the cell provides a means to tune cellular
-stiffness as per the specific requirement. These results will aid in the design
-of wearable auxetic impact protection devices which rely on stiffness gradients
-and variable auxeticity.
-",0,1,0,0,0,0
-19832,MACS J0416.1-2403: Impact of line-of-sight structures on strong gravitational lensing modelling of galaxy clusters,"  Exploiting the powerful tool of strong gravitational lensing by galaxy
-clusters to study the highest-redshift Universe and cluster mass distributions
-relies on precise lens mass modelling. In this work, we present the first
-attempt at modelling line-of-sight mass distribution in addition to that of the
-cluster, extending previous modelling techniques that assume mass distributions
-to be on a single lens plane. We focus on the Hubble Frontier Field cluster
-MACS J0416.1-2403, and our multi-plane model reproduces the observed image
-positions with a rms offset of ~0.53"". Starting from this best-fitting model,
-we simulate a mock cluster that resembles MACS J0416.1-2403 in order to explore
-the effects of line-of-sight structures on cluster mass modelling. By
-systematically analysing the mock cluster under different model assumptions, we
-find that neglecting the lensing environment has a significant impact on the
-reconstruction of image positions (rms ~0.3""); accounting for line-of-sight
-galaxies as if they were at the cluster redshift can partially reduce this
-offset. Moreover, foreground galaxies are more important to include into the
-model than the background ones. While the magnification factors of the lensed
-multiple images are recovered within ~10% for ~95% of them, those ~5% that lie
-near critical curves can be significantly affected by the exclusion of the
-lensing environment in the models (up to a factor of ~200). In addition,
-line-of-sight galaxies cannot explain the apparent discrepancy in the
-properties of massive subhalos between MACS J0416.1-2403 and N-body simulated
-clusters. Since our model of MACS J0416.1-2403 with line-of-sight galaxies only
-reduced modestly the rms offset in the image positions, we conclude that
-additional complexities, such as more flexible halo shapes, would be needed in
-future models of MACS J0416.1-2403.
-",0,1,0,0,0,0
-19833,What can you do with a rock? Affordance extraction via word embeddings,"  Autonomous agents must often detect affordances: the set of behaviors enabled
-by a situation. Affordance detection is particularly helpful in domains with
-large action spaces, allowing the agent to prune its search space by avoiding
-futile behaviors. This paper presents a method for affordance extraction via
-word embeddings trained on a Wikipedia corpus. The resulting word vectors are
-treated as a common knowledge database which can be queried using linear
-algebra. We apply this method to a reinforcement learning agent in a text-only
-environment and show that affordance-based action selection improves
-performance most of the time. Our method increases the computational complexity
-of each learning step but significantly reduces the total number of steps
-needed. In addition, the agent's action selections begin to resemble those a
-human would choose.
-",1,0,0,0,0,0
-19834,On Generalized Gibbs Ensembles with an infinite set of conserved charges,"  We revisit the question of whether and how the steady states arising after
-non-equilibrium time evolution in integrable models (and in particular in the
-XXZ spin chain) can be described by the so-called Generalized Gibbs Ensemble
-(GGE). It is known that the micro-canonical ensemble built on a complete set of
-charges correctly describes the long-time limit of local observables, and
-recently a canonical ensemble was built by Ilievski et. al. using particle
-occupation number operators. Here we provide an alternative construction by
-considering truncated GGE's (tGGE's) that only include a finite number of well
-localized conserved operators. It is shown that the tGGE's can approximate the
-steady states with arbitrary precision, i.e. all physical observables are
-exactly reproduced in the infinite truncation limit. In addition, we show that
-a complete canonical ensemble can in fact be built in terms of a new (discrete)
-set of charges built as linear combinations of the standard ones.
-Our general arguments are applied to concrete quench situations in the XXZ
-chain, where the initial states are simple two-site or four-site product
-states. Depending on the quench we find that numerical results for the local
-correlators can be obtained with remarkable precision using truncated GGE's
-with only 10-100 charges.
-",0,1,0,0,0,0
-19835,High resolution ion trap time-of-flight mass spectrometer for cold trapped ion experiments,"  Trapping molecular ions that have been sympathetically cooled with
-laser-cooled atomic ions is a useful platform for exploring cold ion chemistry.
-We designed and characterized a new experimental apparatus for probing chemical
-reaction dynamics between molecular cations and neutral radicals at
-temperatures below 1 K. The ions are trapped in a linear quadrupole
-radio-frequency trap and sympathetically cooled by co-trapped, laser-cooled,
-atomic ions. The ion trap is coupled to a time-of-flight mass spectrometer to
-readily identify product ion species, as well as to accurately determine
-trapped ion numbers. We discuss, and present in detail, the design of this ion
-trap time-of-flight mass spectrometer, as well as the electronics required for
-driving the trap and mass spectrometer. Furthermore, we measure the performance
-of this system, which yields mass resolutions of $m/\Delta{}m \geq 1100$ over a
-wide mass range, and discuss its relevance for future measurements in chemical
-reaction kinetics and dynamics.
-",0,1,0,0,0,0
-19836,The role of local-geometrical-orders on the growth of dynamic-length-scales in glass-forming liquids,"  The precise nature of complex structural relaxation as well as an explanation
-for the precipitous growth of relaxation time in cooling glass-forming liquids
-are essential to the understanding of vitrification of liquids. The dramatic
-increase of relaxation time is believed to be caused by the growth of one or
-more correlation lengths, which has received much attention recently. Here, we
-report a direct link between the growth of a specific local-geometrical-order
-and an increase of dynamic-length-scale as the atomic dynamics in metallic
-glass-forming liquids slow down. Although several types of local
-geometrical-orders are present in these metallic liquids, the growth of
-icosahedral ordering is found to be directly related to the increase of the
-dynamic-length-scale. This finding suggests an intriguing scenario that the
-transient icosahedral ordering could be the origin of the dynamic-length-scale
-in metallic glass-forming liquids.
-",0,1,0,0,0,0
-19837,Snake: a Stochastic Proximal Gradient Algorithm for Regularized Problems over Large Graphs,"  A regularized optimization problem over a large unstructured graph is
-studied, where the regularization term is tied to the graph geometry. Typical
-regularization examples include the total variation and the Laplacian
-regularizations over the graph. When applying the proximal gradient algorithm
-to solve this problem, there exist quite affordable methods to implement the
-proximity operator (backward step) in the special case where the graph is a
-simple path without loops. In this paper, an algorithm, referred to as ""Snake"",
-is proposed to solve such regularized problems over general graphs, by taking
-benefit of these fast methods. The algorithm consists in properly selecting
-random simple paths in the graph and performing the proximal gradient algorithm
-over these simple paths. This algorithm is an instance of a new general
-stochastic proximal gradient algorithm, whose convergence is proven.
-Applications to trend filtering and graph inpainting are provided among others.
-Numerical experiments are conducted over large graphs.
-",1,0,0,1,0,0
-19838,Stability of the coexistent superconducting-nematic phase under the presence of intersite interactions,"  We analyze the effect of intersite-interaction terms on the stability of the
-coexisting superconucting-nematic phase (SC+N) within the extended Hubbard and
-$t$-$J$-$U$ models on the square lattice. In order to take into account the
-correlation effects with a proper precision, we use the approach based on the
-\textit{diagrammatic expansion of the Gutzwiller wave function} (DE-GWF), which
-goes beyond the renormalized mean field theory (RMFT) in a systematic manner.
-As a starting point of our analysis we discuss the stability region of the SC+N
-phase on the intrasite Coulomb repulsion-hole doping plane for the case of the
-Hubbard model. Next, we show that the exchange interaction term enhances
-superconductivity while suppresses the nematicity, whereas the intersite
-Coulomb repulsion term acts in the opposite manner. The competing character of
-the SC and N phases interplay is clearly visible throughout the analysis. A
-universal conclusion is that the nematic phase does not survive within the
-$t$-$J$-$U$ model with the value of $J$ integral typical for the high-T$_C$
-cuprates ($J\approx 0.1$eV). For the sake of completeness, the effect of the
-correlated hopping term is also analyzed. Thus the present discussion contains
-all relevant two-site interaction terms which appear in the parametrized
-one-band model within the second quantization scheme. At the end, the influence
-of the higher-order terms of the diagrammatic expansion on the rotational
-symmetry breaking is also shown by comparing the DE-GWF results with those
-corresponding to the RMFT.
-",0,1,0,0,0,0
-19839,Multi-Advisor Reinforcement Learning,"  We consider tackling a single-agent RL problem by distributing it to $n$
-learners. These learners, called advisors, endeavour to solve the problem from
-a different focus. Their advice, taking the form of action values, is then
-communicated to an aggregator, which is in control of the system. We show that
-the local planning method for the advisors is critical and that none of the
-ones found in the literature is flawless: the egocentric planning overestimates
-values of states where the other advisors disagree, and the agnostic planning
-is inefficient around danger zones. We introduce a novel approach called
-empathic and discuss its theoretical aspects. We empirically examine and
-validate our theoretical findings on a fruit collection task.
-",1,0,0,1,0,0
-19840,Using CMB spectral distortions to distinguish between dark matter solutions to the small-scale crisis,"  The dissipation of small-scale perturbations in the early universe produces a
-distortion in the blackbody spectrum of cosmic microwave background photons. In
-this work, we propose to use these distortions as a probe of the microphysics
-of dark matter on scales $1\,\textrm{Mpc}^{-1}\lesssim k \lesssim
-10^{4}\,\textrm{Mpc}^{-1}$. We consider in particular models in which the dark
-matter is kinetically coupled to either neutrinos or photons until shortly
-before recombination, and compute the photon heating rate and the resultant
-$\mu$-distortion in both cases. We show that the $\mu$-parameter is generally
-enhanced relative to $\Lambda$CDM for interactions with neutrinos, and may be
-either enhanced or suppressed in the case of interactions with photons. The
-deviations from the $\Lambda$CDM signal are potentially within the sensitivity
-reach of a PRISM-like experiment if $\sigma_{\textrm{DM}-\gamma} \gtrsim
-1.1\times10^{-30} \left(m_{\textrm{DM}}/\textrm{GeV}\right) \textrm{cm}^{2}$
-and $\sigma_{\textrm{DM}-\nu} \gtrsim 4.8\times 10^{-32}
-\left(m_{\textrm{DM}}/\textrm{GeV}\right) \textrm{cm}^{2}$ for time-independent
-cross sections, and $\sigma^{0}_{\textrm{DM}-\gamma} \gtrsim 1.8 \times
-10^{-40} \left(m_{\textrm{DM}}/\textrm{GeV}\right) \textrm{cm}^{2}$ and
-$\sigma^{0}_{\textrm{DM}-\nu} \gtrsim 2.5 \times 10^{-47}
-\left(m_{\textrm{DM}}/\textrm{GeV}\right) \textrm{cm}^{2}$ for cross sections
-scaling as temperature squared, coinciding with the parameter regions in which
-late kinetic decoupling may serve as a solution to the small-scale crisis.
-Furthermore, these $\mu$-distortion signals differ from those of warm dark
-matter (no deviation from $\Lambda$CDM) and a suppressed primordial power
-spectrum (strongly suppressed or a negative $\mu$-parameter), demonstrating
-that CMB spectral distortion can potentially be used to distinguish between
-solutions to the small-scale crisis.
-",0,1,0,0,0,0
-19841,Gradient Hyperalignment for multi-subject fMRI data alignment,"  Multi-subject fMRI data analysis is an interesting and challenging problem in
-human brain decoding studies. The inherent anatomical and functional
-variability across subjects make it necessary to do both anatomical and
-functional alignment before classification analysis. Besides, when it comes to
-big data, time complexity becomes a problem that cannot be ignored. This paper
-proposes Gradient Hyperalignment (Gradient-HA) as a gradient-based functional
-alignment method that is suitable for multi-subject fMRI datasets with large
-amounts of samples and voxels. The advantage of Gradient-HA is that it can
-solve independence and high dimension problems by using Independent Component
-Analysis (ICA) and Stochastic Gradient Ascent (SGA). Validation using
-multi-classification tasks on big data demonstrates that Gradient-HA method has
-less time complexity and better or comparable performance compared with other
-state-of-the-art functional alignment methods.
-",0,0,0,1,1,0
-19842,Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer,"  Knowledge transfer between tasks can improve the performance of learned
-models, but requires an accurate estimate of the inter-task relationships to
-identify the relevant knowledge to transfer. These inter-task relationships are
-typically estimated based on training data for each task, which is inefficient
-in lifelong learning settings where the goal is to learn each consecutive task
-rapidly from as little data as possible. To reduce this burden, we develop a
-lifelong learning method based on coupled dictionary learning that utilizes
-high-level task descriptions to model the inter-task relationships. We show
-that using task descriptors improves the performance of the learned task
-policies, providing both theoretical justification for the benefit and
-empirical demonstration of the improvement across a variety of learning
-problems. Given only the descriptor for a new task, the lifelong learner is
-also able to accurately predict a model for the new task through zero-shot
-learning using the coupled dictionary, eliminating the need to gather training
-data before addressing the task.
-",1,0,0,1,0,0
-19843,Giant Unification Theory of the Grand Unification and Gravitation Theories,"  Because the grand unification theory of gauge theories of strong, weak and
-electromagnetic interactions is based on principal bundle theory, and
-gravitational theory is based on the tangent vector bundle theory, so people
-cannot unify the these four basic interactions in principal bundle theory. This
-Letter discovers and gives giant unification theory of the grand unification
-theory and gravitation theory, i.e., the giant unification theory of strong,
-weak, electromagnetic and gravitational interactions according to the general
-fiber bundle theory, symmetry, quantitative causal principle (QCP) and so on.
-Consequently, the research of this Letter is based on the rigorous scientific
-bases of mathematics and physics. The Lagrangians of the well-known fundamental
-physics interactions are unifiedly deduced from QCP and satisfy the gauge
-invariant principle of general gauge fields interacting with Fermion and/or
-boson fields. The geometry and physics meanings of gauge invariant property of
-different physical systems are revealed, and it is discovered that all the
-Lagrangians of the well-known fundamental physics interactions are composed of
-the invariant quantities in corresponding spacetime structures. The
-difficulties that fundamental physics interactions and Noether theorem are not
-able to be unifiedly given and investigated are overcome, the unified
-description and origin of the fundamental physics interactions and Noether
-theorem are shown by QCP, their two-order general Euler-Lagrange Equations and
-corresponding Noether conservation currents are derived in general curved
-spacetime. Therefore, using the new unification theory, a lot of research works
-about different branches of physics etc can be renewedly done and expressed
-simpler with clear quantitative causal physical meanings.
-",0,1,0,0,0,0
-19844,Generalized Rich-Club Ordering in Networks,"  Rich-club ordering refers to the tendency of nodes with a high degree to be
-more interconnected than expected. In this paper we consider the concept of
-rich-club ordering when generalized to structural measures that differ from the
-node degree and to non-structural measures (i.e. to node metadata). The
-differences in considering rich-club ordering (RCO) with respect to both
-structural and non-structural measures is then discussed in terms of employed
-coefficients and of appropriate null models (link rewiring vs metadata
-reshuffling). Once a framework for the evaluation of generalized rich-club
-ordering (GRCO) is defined, we investigate such a phenomenon in real networks
-provided with node metadata. By considering different notions of node richness,
-we compare structural and non-structural rich-club ordering, observing how
-external information about the network nodes is able to validate the presence
-of rich-clubs in networked systems.
-",1,0,0,0,0,0
-19845,Integrating Proactive Mode Changes in Mixed Criticality Systems,"  In this work, we propose to integrate prediction algorithms to the scheduling
-of mode changes under the Earliest-Deadline-First and Fixed-priority scheduling
-in mixed-criticality real-time systems. The method proactively schedules a mode
-change in the system based on state variables such as laxity, to the percentage
-difference in the temporal distance between the completion time of the instance
-of a task and its respective deadline, by the deadline (D) stipulated for the
-task, in order to minimize deadline misses. The simulation model was validated
-against an analytical model prior to the logical integration of the
-Kalman-based prediction algorithm. Two study cases were presented, one covering
-earliest-deadline first and the other the fixed-priority scheduling approach.
-The results showed the gains in the adoption of the prediction approach for
-both scheduling paradigms by presenting a significant reduction of the number
-of missed deadlines for low-criticality tasks.
-",1,0,0,0,0,0
-19846,Leveraging Continuous Material Averaging for Inverse Electromagnetic Design,"  Inverse electromagnetic design has emerged as a way of efficiently designing
-active and passive electromagnetic devices. This maturing strategy involves
-optimizing the shape or topology of a device in order to improve a figure of
-merit--a process which is typically performed using some form of steepest
-descent algorithm. Naturally, this requires that we compute the gradient of a
-figure of merit which describes device performance, potentially with respect to
-many design variables. In this paper, we introduce a new strategy based on
-smoothing abrupt material interfaces which enables us to efficiently compute
-these gradients with high accuracy irrespective of the resolution of the
-underlying simulation. This has advantages over previous approaches to shape
-and topology optimization in nanophotonics which are either prone to gradient
-errors or place important constraints on the shape of the device. As a
-demonstration of this new strategy, we optimize a non-adiabatic waveguide taper
-between a narrow and wide waveguide. This optimization leads to a non-intuitive
-design with a very low insertion loss of only 0.041 dB at 1550 nm.
-",0,1,0,0,0,0
-19847,Robust Speech Recognition Using Generative Adversarial Networks,"  This paper describes a general, scalable, end-to-end framework that uses the
-generative adversarial network (GAN) objective to enable robust speech
-recognition. Encoders trained with the proposed approach enjoy improved
-invariance by learning to map noisy audio to the same embedding space as that
-of clean audio. Unlike previous methods, the new framework does not rely on
-domain expertise or simplifying assumptions as are often needed in signal
-processing, and directly encourages robustness in a data-driven way. We show
-the new approach improves simulated far-field speech recognition of vanilla
-sequence-to-sequence models without specialized front-ends or preprocessing.
-",1,0,0,0,0,0
-19848,Minimal inequalities for an infinite relaxation of integer programs,"  We show that maximal $S$-free convex sets are polyhedra when $S$ is the set
-of integral points in some rational polyhedron of $\mathbb{R}^n$. This result
-extends a theorem of Lovász characterizing maximal lattice-free convex sets.
-Our theorem has implications in integer programming. In particular, we show
-that maximal $S$-free convex sets are in one-to-one correspondence with minimal
-inequalities.
-",0,0,1,0,0,0
-19849,The population of SNe/SNRs in the starburst galaxy Arp 220. A self-consistent analysis of 20 years of VLBI monitoring,"  The nearby ultra-luminous infrared galaxy (ULIRG) Arp 220 is an excellent
-laboratory for studies of extreme astrophysical environments. For 20 years,
-Very Long Baseline Interferometry (VLBI) has been used to monitor a population
-of compact sources thought to be supernovae (SNe), supernova remnants (SNRs)
-and possibly active galactic nuclei (AGNs). Using new and archival VLBI data
-spanning 20 years, we obtain 23 high-resolution radio images of Arp 220 at
-wavelengths from 18 cm to 2 cm. From model-fitting to the images we obtain
-estimates of flux densities and sizes of all detected sources. We detect radio
-continuum emission from 97 compact sources and present flux densities and sizes
-for all analysed observation epochs. We find evidence for a LD-relation within
-Arp 220, with larger sources being less luminous. We find a compact source LF
-$n(L)\propto L^\beta$ with $\beta=-2.02\pm0.11$, similar to SNRs in normal
-galaxies. Based on simulations we argue that there are many relatively large
-and weak sources below our detection threshold. The rapidly declining object
-0.2227+0.482 is proposed as a possible AGN candidate. The observations can be
-explained by a mixed population of SNe and SNRs, where the former expand in a
-dense circumstellar medium (CSM) and the latter interact with the surrounding
-interstellar medium (ISM). Several sources (9 of the 94 with fitted sizes) are
-likely luminous, type IIn SNe. This number of luminous SNe correspond to few
-percent of the total number of SNe in Arp 220 which is consistent with a total
-SN-rate of 4 yr$^{-1}$ as inferred from the total radio emission given a normal
-stellar initial mass function (IMF). Based on the fitted luminosity function,
-we argue that emission from all compact sources, also below our detection
-threshold, make up at most 20\% of the total radio emission at GHz frequencies.
-",0,1,0,0,0,0
-19850,Dirac and Chiral Quantum Spin Liquids on the Honeycomb Lattice in a Magnetic Field,"  Motivated by recent experimental observations in $\alpha$-RuCl$_3$, we study
-the $K$-$\Gamma$ model on the honeycomb lattice in an external magnetic field.
-By a slave-particle representation and Variational Monte Carlo calculations, we
-reproduce the phase transition from zigzag magnetic order to a field-induced
-disordered phase. The nature of this state depends crucially on the field
-orientation. For particular field directions in the honeycomb plane, we find a
-gapless Dirac spin liquid, in agreement with recent experiments on
-$\alpha$-RuCl$_3$. For a range of out-of-plane fields, we predict the existence
-of a Kalmeyer-Laughlin-type chiral spin liquid, which would show an
-integer-quantized thermal Hall effect.
-",0,1,0,0,0,0
-19851,Secular Dynamics of an Exterior Test Particle: The Inverse Kozai and Other Eccentricity-Inclination Resonances,"  The behavior of an interior test particle in the secular 3-body problem has
-been studied extensively. A well-known feature is the Lidov-Kozai resonance in
-which the test particle's argument of periapse librates about $\pm 90^\circ$
-and large oscillations in eccentricity and inclination are possible. Less
-explored is the inverse problem: the dynamics of an exterior test particle and
-an interior perturber. We survey numerically the inverse secular problem,
-expanding the potential to hexadecapolar order and correcting an error in the
-published expansion. Four secular resonances are uncovered that persist in full
-$N$-body treatments (in what follows, $\varpi$ and $\Omega$ are the longitudes
-of periapse and of ascending node, $\omega$ is the argument of periapse, and
-subscripts 1 and 2 refer to the inner perturber and outer test particle): (i)
-an orbit-flipping quadrupole resonance requiring a non-zero perturber
-eccentricity $e_1$, in which $\Omega_2-\varpi_1$ librates about $\pm 90^\circ$;
-(ii) a hexadecapolar resonance (the ""inverse Kozai"" resonance) for perturbers
-that are circular or nearly so and inclined by $I \simeq 63^\circ/117^\circ$,
-in which $\omega_2$ librates about $\pm 90^\circ$ and which can vary the
-particle eccentricity by $\Delta e_2 \simeq 0.2$ and lead to orbit crossing;
-(iii) an octopole ""apse-aligned"" resonance at $I \simeq 46^\circ/107^\circ$
-wherein $\varpi_2 - \varpi_1$ librates about $0^\circ$ and $\Delta e_2$ grows
-with $e_1$; and (iv) an octopole resonance at $I \simeq 73^\circ/134^\circ$
-wherein $\varpi_2 + \varpi_1 - 2 \Omega_2$ librates about $0^\circ$ and $\Delta
-e_2$ can be as large as 0.3 for small $e_1 \neq 0$. The more eccentric the
-perturber, the more the particle's eccentricity and inclination vary; also,
-more polar orbits are more chaotic. Our inverse solutions may be applied to the
-Kuiper belt and debris disks, circumbinary planets, and stellar systems.
-",0,1,0,0,0,0
-19852,"A pulsed, mono-energetic and angular-selective UV photo-electron source for the commissioning of the KATRIN experiment","  The KATRIN experiment aims to determine the neutrino mass scale with a
-sensitivity of 200 meV/c^2 (90% C.L.) by a precision measurement of the shape
-of the tritium $\beta$-spectrum in the endpoint region. The energy analysis of
-the decay electrons is achieved by a MAC-E filter spectrometer. To determine
-the transmission properties of the KATRIN main spectrometer, a mono-energetic
-and angular-selective electron source has been developed. In preparation for
-the second commissioning phase of the main spectrometer, a measurement phase
-was carried out at the KATRIN monitor spectrometer where the device was
-operated in a MAC-E filter setup for testing. The results of these measurements
-are compared with simulations using the particle-tracking software
-""Kassiopeia"", which was developed in the KATRIN collaboration over recent
-years.
-",0,1,0,0,0,0
-19853,Carrier frequency modulation of an acousto-optic modulator for laser stabilization,"  The stabilization of lasers to absolute frequency references is a fundamental
-requirement in several areas of atomic, molecular and optical physics. A range
-of techniques are available to produce a suitable reference onto which one can
-'lock' the laser, many of which depend on the specific internal structure of
-the reference or are sensitive to laser intensity noise. We present a novel
-method using the frequency modulation of an acousto-optic modulator's carrier
-(drive) signal to generate two spatially separated beams, with a frequency
-difference of only a few MHz. These beams are used to probe a narrow absorption
-feature and the difference in their detected signals leads to a dispersion-like
-feature suitable for wavelength stabilization of a diode laser. This simple and
-versatile method only requires a narrow absorption line and is therefore
-suitable for both atomic and cavity based stabilization schemes. To demonstrate
-the suitability of this method we lock an external cavity diode laser near the
-$^{85}\mathrm{Rb}\,5S_{1/2}\rightarrow5P_{3/2}, F=3\rightarrow F^{\prime}=4$
-using sub-Doppler pump probe spectroscopy and also demonstrate excellent
-agreement between the measured signal and a theoretical model.
-",0,1,0,0,0,0
-19854,Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Method,"  In this paper, a new type of 3D bin packing problem (BPP) is proposed, in
-which a number of cuboid-shaped items must be put into a bin one by one
-orthogonally. The objective is to find a way to place these items that can
-minimize the surface area of the bin. This problem is based on the fact that
-there is no fixed-sized bin in many real business scenarios and the cost of a
-bin is proportional to its surface area. Our research shows that this problem
-is NP-hard. Based on previous research on 3D BPP, the surface area is
-determined by the sequence, spatial locations and orientations of items. Among
-these factors, the sequence of items plays a key role in minimizing the surface
-area. Inspired by recent achievements of deep reinforcement learning (DRL)
-techniques, especially Pointer Network, on combinatorial optimization problems
-such as TSP, a DRL-based method is applied to optimize the sequence of items to
-be packed into the bin. Numerical results show that the method proposed in this
-paper achieve about 5% improvement than heuristic method.
-",1,0,0,0,0,0
-19855,Entendendo o Pensamento Computacional,"  The goal of this article is to clarify the meaning of Computational Thinking.
-We differentiate logical from computational reasoning and discuss the
-importance of Computational Thinking in solving problems. The three pillars of
-Computational Thinking - Abstraction, Automation and Analysis - are outlined,
-highlighting the role of each one in developing the skills needed for the
-problem-solving process.
------
-O objetivo deste artigo é esclarecer o significado de Pensamento
-Computacional. Diferencia-se o raciocínio lógico do computacional e
-discute-se a importância do Pensamento Computacional na resolução de
-problemas. Os três pilares do Pensamento Computacional - Abstração,
-Automação e Análise - são delineados, destacando-se o papel de cada
-um deles no desenvolvimento das habilidades necessárias para o processo de
-solução de problemas.
-",1,0,0,0,0,0
-19856,Estimation of samples relevance by their histograms,"  The problem of the estimation of relevance to a set of histograms generated
-by samples of a discrete time process is discussed on the base of the
-variational principles proposed in the previous paper [1]. Some conditions for
-dimension reduction of corresponding linear programming problems are presented
-also.
-",0,0,1,0,0,0
-19857,On the synthesis of acoustic sources with controllable near fields,"  In this paper we present a strategy for the the synthesis of acoustic sources
-with controllable near fields in free space and finite depth homogeneous ocean
-environments. We first present the theoretical results at the basis of our
-discussion and then, to illustrate our findings we focus on the following three
-particular examples:
-1. acoustic source approximating a prescribed field pattern in a given
-bounded sub- region of its near field. 2. acoustic source approximating
-different prescribed field patterns in given disjoint bounded near field
-sub-regions. 3. acoustic source approximating a prescribed back-propagating
-field in a given bounded near field sub-region while maintaining a very low far
-field signature.
-For each of these three examples, we discuss the optimization scheme used to
-approx- imate their solutions and support our claims through relevant numerical
-simulations.
-",0,0,1,0,0,0
-19858,Inverse Protein Folding Problem via Quadratic Programming,"  This paper presents a method of reconstruction a primary structure of a
-protein that folds into a given geometrical shape. This method predicts the
-primary structure of a protein and restores its linear sequence of amino acids
-in the polypeptide chain using the tertiary structure of a molecule. Unknown
-amino acids are determined according to the principle of energy minimization.
-This study represents inverse folding problem as a quadratic optimization
-problem and uses different relaxation techniques to reduce it to the problem of
-convex optimizations. Computational experiment compares the quality of these
-approaches on real protein structures.
-",0,0,1,0,0,0
-19859,Spoken Language Understanding on the Edge,"  We consider the problem of performing Spoken Language Understanding (SLU) on
-small devices typical of IoT applications. Our contributions are twofold.
-First, we outline the design of an embedded, private-by-design SLU system and
-show that it has performance on par with cloud-based commercial solutions.
-Second, we release the datasets used in our experiments in the interest of
-reproducibility and in the hope that they can prove useful to the SLU
-community.
-",1,0,0,0,0,0
-19860,On the link between column density distribution and density scaling relation in star formation regions,"  We present a method to derive the density scaling relation $\langle n\rangle
-\propto L^{-\alpha}$ in regions of star formation or in their turbulent
-vicinities from straightforward binning of the column-density distribution
-($N$-pdf). The outcome of the method is studied for three types of $N$-pdf:
-power law ($7/5\le\alpha\le5/3$), lognormal ($0.7\lesssim\alpha\lesssim1.4$)
-and combination of lognormals. In the last case, the method of Stanchev et al.
-(2015) was also applied for comparison and a very weak (or close to zero)
-correlation was found. We conclude that the considered `binning approach'
-reflects rather the local morphology of the $N$-pdf with no reference to the
-physical conditions in a considered region. The rough consistency of the
-derived slopes with the widely adopted Larson's (1981) value $\alpha\sim1.1$ is
-suggested to support claims that the density-size relation in molecular clouds
-is indeed an artifact of the observed $N$-pdf.
-",0,1,0,0,0,0
-19861,Measuring Effectiveness of Video Advertisements,"  Advertisements are unavoidable in modern society. Times Square is notorious
-for its incessant display of advertisements. Its popularity is worldwide and
-smaller cities possess miniature versions of the display, such as Pittsburgh
-and its digital works in Oakland on Forbes Avenue. Tokyo's Ginza district
-recently rose to popularity due to its upscale shops and constant onslaught of
-advertisements to pedestrians. Advertisements arise in other mediums as well.
-For example, they help popular streaming services, such as Spotify, Hulu, and
-Youtube TV gather significant streams of revenue to reduce the cost of monthly
-subscriptions for consumers. Ads provide an additional source of money for
-companies and entire industries to allocate resources toward alternative
-business motives. They are attractive to companies and nearly unavoidable for
-consumers. One challenge for advertisers is examining a advertisement's
-effectiveness or usefulness in conveying a message to their targeted
-demographics. Rather than constructing a single, static image of content, a
-video advertisement possesses hundreds of frames of data with varying scenes,
-actors, objects, and complexity. Therefore, measuring effectiveness of video
-advertisements is important to impacting a billion-dollar industry. This paper
-explores the combination of human-annotated features and common video
-processing techniques to predict effectiveness ratings of advertisements
-collected from Youtube. This task is seen as a binary (effective vs.
-non-effective), four-way, and five-way machine learning classification task.
-The first findings in terms of accuracy and inference on this dataset, as well
-as some of the first ad research, on a small dataset are presented. Accuracies
-of 84\%, 65\%, and 55\% are reached on the binary, four-way, and five-way tasks
-respectively.
-",1,0,0,0,0,0
-19862,Multilinear compressive sensing and an application to convolutional linear networks,"  We study a deep linear network endowed with a structure. It takes the form of
-a matrix $X$ obtained by multiplying $K$ matrices (called factors and
-corresponding to the action of the layers). The action of each layer (i.e. a
-factor) is obtained by applying a fixed linear operator to a vector of
-parameters satisfying a constraint. The number of layers is not limited.
-Assuming that $X$ is given and factors have been estimated, the error between
-the product of the estimated factors and $X$ (i.e. the reconstruction error) is
-either the statistical or the empirical risk. In this paper, we provide
-necessary and sufficient conditions on the network topology under which a
-stability property holds. The stability property requires that the error on the
-parameters defining the factors (i.e. the stability of the recovered
-parameters) scales linearly with the reconstruction error (i.e. the risk).
-Therefore, under these conditions on the network topology, any successful
-learning task leads to stably defined features and therefore interpretable
-layers/network.In order to do so, we first evaluate how the Segre embedding and
-its inverse distort distances. Then, we show that any deep structured linear
-network can be cast as a generic multilinear problem (that uses the Segre
-embedding). This is the {\em tensorial lifting}. Using the tensorial lifting,
-we provide necessary and sufficient conditions for the identifiability of the
-factors (up to a scale rearrangement). We finally provide the necessary and
-sufficient condition called \NSPlong~(because of the analogy with the usual
-Null Space Property in the compressed sensing framework) which guarantees that
-the stability property holds. We illustrate the theory with a practical example
-where the deep structured linear network is a convolutional linear network. As
-expected, the conditions are rather strong but not empty. A simple test on the
-network topology can be implemented to test if the condition holds.
-",0,0,1,1,0,0
-19863,Normalization of closed Ekedahl-Oort strata,"  We apply our theory of partial flag spaces developed in previous articles to
-study a group-theoretical generalization of the canonical filtration of a
-truncated Barsotti-Tate group of level 1. As an application, we determine
-explicitly the normalization of the Zariski closures of Ekedahl-Oort strata of
-Shimura varieties of Hodge-type as certain closed coarse strata in the
-associated partial flag spaces.
-",0,0,1,0,0,0
-19864,Robustness of functional networks at criticality against structural defects,"  The robustness of dynamical properties of neuronal networks against
-structural damages is a central problem in computational and experimental
-neuroscience. Research has shown that the cortical network of a healthy brain
-works near a critical state, and moreover, that functional neuronal networks
-often have scale-free and small-world properties. In this work, we study how
-the robustness of simple functional networks at criticality is affected by
-structural defects. In particular, we consider a 2D Ising model at the critical
-temperature and investigate how its functional network changes with the
-increasing degree of structural defects. We show that the scale-free and
-small-world properties of the functional network at criticality are robust
-against large degrees of structural lesions while the system remains below the
-percolation limit. Although the Ising model is only a conceptual description of
-a two-state neuron, our research reveals fundamental robustness properties of
-functional networks derived from classical statistical mechanics models.
-",0,0,0,0,1,0
-19865,Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning,"  Maximizing the area under the receiver operating characteristic curve (AUC)
-is a standard approach to imbalanced classification. So far, various supervised
-AUC optimization methods have been developed and they are also extended to
-semi-supervised scenarios to cope with small sample problems. However, existing
-semi-supervised AUC optimization methods rely on strong distributional
-assumptions, which are rarely satisfied in real-world problems. In this paper,
-we propose a novel semi-supervised AUC optimization method that does not
-require such restrictive assumptions. We first develop an AUC optimization
-method based only on positive and unlabeled data (PU-AUC) and then extend it to
-semi-supervised learning by combining it with a supervised AUC optimization
-method. We theoretically prove that, without the restrictive distributional
-assumptions, unlabeled data contribute to improving the generalization
-performance in PU and semi-supervised AUC optimization methods. Finally, we
-demonstrate the practical usefulness of the proposed methods through
-experiments.
-",1,0,0,1,0,0
-19866,Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering,"  Motivated by problems in data clustering, we establish general conditions
-under which families of nonparametric mixture models are identifiable, by
-introducing a novel framework involving clustering overfitted \emph{parametric}
-(i.e. misspecified) mixture models. These identifiability conditions generalize
-existing conditions in the literature, and are flexible enough to include for
-example mixtures of Gaussian mixtures. In contrast to the recent literature on
-estimating nonparametric mixtures, we allow for general nonparametric mixture
-components, and instead impose regularity assumptions on the underlying mixing
-measure. As our primary application, we apply these results to partition-based
-clustering, generalizing the notion of a Bayes optimal partition from classical
-parametric model-based clustering to nonparametric settings. Furthermore, this
-framework is constructive so that it yields a practical algorithm for learning
-identified mixtures, which is illustrated through several examples on real
-data. The key conceptual device in the analysis is the convex, metric geometry
-of probability measures on metric spaces and its connection to the Wasserstein
-convergence of mixing measures. The result is a flexible framework for
-nonparametric clustering with formal consistency guarantees.
-",0,0,0,1,0,0
-19867,The geometric classification of Leibniz algebras,"  We describe all rigid algebras and all irreducible components in the variety
-of four dimensional Leibniz algebras $\mathfrak{Leib}_4$ over $\mathbb{C}.$ In
-particular, we prove that the Grunewald--O'Halloran conjecture is not valid and
-the Vergne conjecture is valid for $\mathfrak{Leib}_4.$
-",0,0,1,0,0,0
-19868,Discrete fundamental groups of Warped Cones and expanders,"  In this paper we compute the discrete fundamental groups of warped cones. As
-an immediate consequence, this allows us to show that there exist coarsely
-simply-connected expanders and superexpanders. This also provides a strong
-coarse invariant of warped cones and implies that many warped cones cannot be
-coarsely equivalent to any box space.
-",0,0,1,0,0,0
-19869,Simulated Tempering Method in the Infinite Switch Limit with Adaptive Weight Learning,"  We investigate the theoretical foundations of the simulated tempering method
-and use our findings to design efficient algorithms. Employing a large
-deviation argument first used for replica exchange molecular dynamics [Plattner
-et al., J. Chem. Phys. 135:134111 (2011)], we demonstrate that the most
-efficient approach to simulated tempering is to vary the temperature infinitely
-rapidly. In this limit, we can replace the equations of motion for the
-temperature and physical variables by averaged equations for the latter alone,
-with the forces rescaled according to a position-dependent function defined in
-terms of temperature weights. The averaged equations are similar to those used
-in Gao's integrated-over-temperature method, except that we show that it is
-better to use a continuous rather than a discrete set of temperatures. We give
-a theoretical argument for the choice of the temperature weights as the
-reciprocal partition function, thereby relating simulated tempering to
-Wang-Landau sampling. Finally, we describe a self-consistent algorithm for
-simultaneously sampling the canonical ensemble and learning the weights during
-simulation. This algorithm is tested on a system of harmonic oscillators as
-well as a continuous variant of the Curie-Weiss model, where it is shown to
-perform well and to accurately capture the second-order phase transition
-observed in this model.
-",0,0,0,1,0,0
-19870,Analysis and Modeling of 3D Indoor Scenes,"  We live in a 3D world, performing activities and interacting with objects in
-the indoor environments everyday. Indoor scenes are the most familiar and
-essential environments in everyone's life. In the virtual world, 3D indoor
-scenes are also ubiquitous in 3D games and interior design. With the fast
-development of VR/AR devices and the emerging applications, the demand of
-realistic 3D indoor scenes keeps growing rapidly. Currently, designing detailed
-3D indoor scenes requires proficient 3D designing and modeling skills and is
-often time-consuming. For novice users, creating realistic and complex 3D
-indoor scenes is even more difficult and challenging.
-Many efforts have been made in different research communities, e.g. computer
-graphics, vision and robotics, to capture, analyze and generate the 3D indoor
-data. This report mainly focuses on the recent research progress in graphics on
-geometry, structure and semantic analysis of 3D indoor data and different
-modeling techniques for creating plausible and realistic indoor scenes. We
-first review works on understanding and semantic modeling of scenes from
-captured 3D data of the real world. Then, we focus on the virtual scenes
-composed of 3D CAD models and study methods for 3D scene analysis and
-processing. After that, we survey various modeling paradigms for creating 3D
-indoor scenes and investigate human-centric scene analysis and modeling, which
-bridge indoor scene studies of graphics, vision and robotics. At last, we
-discuss open problems in indoor scene processing that might bring interests to
-graphics and all related communities.
-",1,0,0,0,0,0
-19871,Introduction to the Special Issue on Digital Signal Processing in Radio Astronomy,"  Advances in astronomy are intimately linked to advances in digital signal
-processing (DSP). This special issue is focused upon advances in DSP within
-radio astronomy. The trend within that community is to use off-the-shelf
-digital hardware where possible and leverage advances in high performance
-computing. In particular, graphics processing units (GPUs) and field
-programmable gate arrays (FPGAs) are being used in place of
-application-specific circuits (ASICs); high-speed Ethernet and Infiniband are
-being used for interconnect in place of custom backplanes. Further, to lower
-hurdles in digital engineering, communities have designed and released
-general-purpose FPGA-based DSP systems, such as the CASPER ROACH board, ASTRON
-Uniboard and CSIRO Redback board. In this introductory article, we give a brief
-historical overview, a summary of recent trends, and provide an outlook on
-future directions.
-",0,1,0,0,0,0
-19872,HPDedup: A Hybrid Prioritized Data Deduplication Mechanism for Primary Storage in the Cloud,"  Eliminating duplicate data in primary storage of clouds increases the
-cost-efficiency of cloud service providers as well as reduces the cost of users
-for using cloud services. Existing primary deduplication techniques either use
-inline caching to exploit locality in primary workloads or use post-processing
-deduplication running in system idle time to avoid the negative impact on I/O
-performance. However, neither of them works well in the cloud servers running
-multiple services or applications for the following two reasons: Firstly, the
-temporal locality of duplicate data writes may not exist in some primary
-storage workloads thus inline caching often fails to achieve good deduplication
-ratio. Secondly, the post-processing deduplication allows duplicate data to be
-written into disks, therefore does not provide the benefit of I/O deduplication
-and requires high peak storage capacity. This paper presents HPDedup, a Hybrid
-Prioritized data Deduplication mechanism to deal with the storage system shared
-by applications running in co-located virtual machines or containers by fusing
-an inline and a post-processing process for exact deduplication. In the inline
-deduplication phase, HPDedup gives a fingerprint caching mechanism that
-estimates the temporal locality of duplicates in data streams from different
-VMs or applications and prioritizes the cache allocation for these streams
-based on the estimation. HPDedup also allows different deduplication threshold
-for streams based on their spatial locality to reduce the disk fragmentation.
-The post-processing phase removes duplicates whose fingerprints are not able to
-be cached due to the weak temporal locality from disks. Our experimental
-results show that HPDedup clearly outperforms the state-of-the-art primary
-storage deduplication techniques in terms of inline cache efficiency and
-primary deduplication efficiency.
-",1,0,0,0,0,0
-19873,Two-domain and three-domain limit cycles in a typical aeroelastic system with freeplay in pitch,"  Freeplay is a significant source of nonlinearity in aeroelastic systems and
-is strictly regulated by airworthiness authorities. It splits the phase plane
-of such systems into three piecewise linear subdomains. Depending on the
-location of the freeplay, limit cycle oscillations can result that span either
-two or three of these subdomains. The purpose of this work is to demonstrate
-the existence of two-domain cycles both theoretically and experimentally. A
-simple aeroelastic system with pitch, plunge and control deflection degrees of
-freedom is investigated in the presence of freeplay in pitch. It is shown that
-two-domain and three-domain cycles can result from a grazing bifurcation and
-propagate in the decreasing airspeed direction. Close to the bifurcation, the
-two limit cycle branches interact with each other and aperiodic oscillations
-ensue. Equivalent linearization is used to derive the conditions of existence
-of each type of limit cycle and to predict their amplitudes and frequencies.
-Comparisons with measurements from wind tunnel experiments demonstrate that the
-theory describes these phenomena with accuracy.
-",0,1,0,0,0,0
-19874,Preference Modeling by Exploiting Latent Components of Ratings,"  Understanding user preference is essential to the optimization of recommender
-systems. As a feedback of user's taste, rating scores can directly reflect the
-preference of a given user to a given product. Uncovering the latent components
-of user ratings is thus of significant importance for learning user interests.
-In this paper, a new recommendation approach, called LCR, was proposed by
-investigating the latent components of user ratings. The basic idea is to
-decompose an existing rating into several components via a cost-sensitive
-learning strategy. Specifically, each rating is assigned to several latent
-factor models and each model is updated according to its predictive errors.
-Afterwards, these accumulated predictive errors of models are utilized to
-decompose a rating into several components, each of which is treated as an
-independent part to retrain the latent factor models. Finally, all latent
-factor models are combined linearly to estimate predictive ratings for users.
-In contrast to existing methods, LCR provides an intuitive preference modeling
-strategy via multiple component analysis at an individual perspective.
-Meanwhile, it is verified by the experimental results on several benchmark
-datasets that the proposed method is superior to the state-of-art methods in
-terms of recommendation accuracy.
-",1,0,0,0,0,0
-19875,Inductive Representation Learning on Large Graphs,"  Low-dimensional embeddings of nodes in large graphs have proved extremely
-useful in a variety of prediction tasks, from content recommendation to
-identifying protein functions. However, most existing approaches require that
-all nodes in the graph are present during training of the embeddings; these
-previous approaches are inherently transductive and do not naturally generalize
-to unseen nodes. Here we present GraphSAGE, a general, inductive framework that
-leverages node feature information (e.g., text attributes) to efficiently
-generate node embeddings for previously unseen data. Instead of training
-individual embeddings for each node, we learn a function that generates
-embeddings by sampling and aggregating features from a node's local
-neighborhood. Our algorithm outperforms strong baselines on three inductive
-node-classification benchmarks: we classify the category of unseen nodes in
-evolving information graphs based on citation and Reddit post data, and we show
-that our algorithm generalizes to completely unseen graphs using a multi-graph
-dataset of protein-protein interactions.
-",1,0,0,1,0,0
-19876,Milnor and Tjurina numbers for a hypersurface germ with isolated singularity,"  Assume that $f:(\mathbb{C}^n,0) \to (\mathbb{C},0)$ is an analytic function
-germ at the origin with only isolated singularity. Let $\mu$ and $\tau$ be the
-corresponding Milnor and Tjurina numbers. We show that $\dfrac{\mu}{\tau} \leq
-n$. As an application, we give a lower bound for the Tjurina number in terms of
-$n$ and the multiplicity of $f$ at the origin.
-",0,0,1,0,0,0
-19877,Analysis of the Gibbs Sampler for Gaussian hierarchical models via multigrid decomposition,"  We study the convergence properties of the Gibbs Sampler in the context of
-posterior distributions arising from Bayesian analysis of Gaussian hierarchical
-models. We consider centred and non-centred parameterizations as well as their
-hybrids including the full family of partially non-centred parameterizations.
-We develop a novel methodology based on multi-grid decompositions to derive
-analytic expressions for the convergence rates of the algorithm for an
-arbitrary number of layers in the hierarchy, while previous work was typically
-limited to the two-level case. Our work gives a complete understanding for the
-three-level symmetric case and this gives rise to approximations for the
-non-symmetric case. We also give analogous, if less explicit, results for
-models of arbitrary level. This theory gives rise to simple and
-easy-to-implement guidelines for the practical implementation of Gibbs samplers
-on conditionally Gaussian hierarchical models.
-",0,0,0,1,0,0
-19878,A Cosmic Selection Rule for Glueball Dark Matter Relic Density,"  We point out a unique mechanism to produce the relic abundance for glueball
-dark matter from a gauged $SU(N)_d$ hidden sector which is bridged to the
-standard model sector through heavy vectorlike quarks colored under gauge
-interactions from both sides. A necessary ingredient of our assumption is that
-the vectorlike quarks, produced either thermally or non-thermally, are abundant
-enough to dominate the universe for some time in the early universe. They later
-undergo dark color confinement and form unstable vectorlike-quarkonium states
-which annihilate decay and reheat the visible and dark sectors. The ratio of
-entropy dumped into two sectors and the final energy budget in the dark
-glueballs is only determined by low energy parameters, including the intrinsic
-scale of the dark $SU(N)_d$, $\Lambda_d$, and number of dark colors, $N_d$, but
-depend weakly on parameters in the ultraviolet such as the vectorlike quark
-mass or the initial condition. We call this a cosmic selection rule for the
-glueball dark matter relic density.
-",0,1,0,0,0,0
-19879,Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network,"  In this paper, we propose a method using a three dimensional convolutional
-neural network (3-D-CNN) to fuse together multispectral (MS) and hyperspectral
-(HS) images to obtain a high resolution hyperspectral image. Dimensionality
-reduction of the hyperspectral image is performed prior to fusion in order to
-significantly reduce the computational time and make the method more robust to
-noise. Experiments are performed on a data set simulated using a real
-hyperspectral image. The results obtained show that the proposed approach is
-very promising when compared to conventional methods. This is especially true
-when the hyperspectral image is corrupted by additive noise.
-",1,0,0,1,0,0
-19880,The fundamental Lepage form in variational theory for submanifolds,"  A setting for global variational geometry on Grassmann fibrations is
-presented. The integral variational functionals for finite dimensional immersed
-submanifolds are studied by means of the fundamental Lepage equivalent of a
-homogeneous Lagrangian, which can be regarded as a generalization of the
-well-known Hilbert form in the classical mechanics. Prolongations of
-immersions, diffeomorphisms and vector fields to the Grassmann fibrations are
-introduced as geometric tools for the variations of immersions. The first
-infinitesimal variation formula together with its consequences, the
-Euler-Lagrange equations for extremal submanifolds and the Noether theorem for
-invariant variational functionals are proved. The theory is illustrated on the
-variational functional for minimal submanifolds.
-",0,0,1,0,0,0
-19881,Adding educational funcionalities to classic board games,"  In this paper we revisit some classic board games like Pachisi or the Game of
-Gosse. The main contribution of the paper is to design and add some
-functionalities to the games in order to transform them in serious games, that
-is, in games with learning and educational purposes. To do that, at the
-beginning of the game, players choose one or several topics and during the
-game, players have to anwers questions on these topics in order to move their
-markers. We choose classic board games because a lot of people are familiar
-with them so it is very easy to start to play without wasting time learning
-game rules and, we think that this is an important element to make the game
-more attractive to people. To enlarge the number of potential users we have
-implement the games just using html and javascript and the games can be used in
-any web browser, in any computer (including tablets) , in any computer
-arquitecture (Windows, Mac, Linux) and no internet/server conexion is required.
-Associated software is distributed under Creative Commons
-Attribution-NonCommercial-ShareAlike 3.0 licence and can be obtained at
-this http URL
-",1,0,0,0,0,0
-19882,Conceptual Frameworks for Building Online Citizen Science Projects,"  In recent years, citizen science has grown in popularity due to a number of
-reasons, including the emphasis on informal learning and creativity potential
-associated with these initiatives. Citizen science projects address research
-questions from various domains, ranging from Ecology to Astronomy. Due to the
-advancement of communication technologies, which makes outreach and engagement
-of wider communities easier, scientists are keen to turn their own research
-into citizen science projects. However, the development, deployment and
-management of these projects remains challenging. One of the most important
-challenges is building the project itself. There is no single tool or
-framework, which guides the step-by-step development of the project, since
-every project has specific characteristics, such as geographical constraints or
-volunteers' mode of participation. Therefore, in this article, we present a
-series of conceptual frameworks for categorisation, decision and deployment,
-which guide a citizen science project creator in every step of creating a new
-project starting from the research question to project deployment. The
-frameworks are designed with consideration to the properties of already
-existing citizen science projects and could be easily extended to include other
-dimensions, which are not currently perceived.
-",1,0,0,0,0,0
-19883,SDN Architecture and Southbound APIs for IPv6 Segment Routing Enabled Wide Area Networks,"  The SRv6 architecture (Segment Routing based on IPv6 data plane) is a
-promising solution to support services like Traffic Engineering, Service
-Function Chaining and Virtual Private Networks in IPv6 backbones and
-datacenters. The SRv6 architecture has interesting scalability properties as it
-reduces the amount of state information that needs to be configured in the
-nodes to support the network services. In this paper, we describe the
-advantages of complementing the SRv6 technology with an SDN based approach in
-backbone networks. We discuss the architecture of a SRv6 enabled network based
-on Linux nodes. In addition, we present the design and implementation of the
-Southbound API between the SDN controller and the SRv6 device. We have defined
-a data-model and four different implementations of the API, respectively based
-on gRPC, REST, NETCONF and remote Command Line Interface (CLI). Since it is
-important to support both the development and testing aspects we have realized
-an Intent based emulation system to build realistic and reproducible
-experiments. This collection of tools automate most of the configuration
-aspects relieving the experimenter from a significant effort. Finally, we have
-realized an evaluation of some performance aspects of our architecture and of
-the different variants of the Southbound APIs and we have analyzed the effects
-of the configuration updates in the SRv6 enabled nodes.
-",1,0,0,0,0,0
-19884,Topology-optimized Dual-Polarization Dirac Cones,"  We apply a large-scale computational technique, known as topology
-optimization, to the inverse design of photonic Dirac cones. In particular, we
-report on a variety of photonic crystal geometries, realizable in simple
-isotropic dielectric materials, which exhibit dual-polarization and
-dual-wavelength Dirac cones. We demonstrate the flexibility of this technique
-by designing photonic crystals of different symmetry types, such as ones with
-four-fold and six-fold rotational symmetry, which possess Dirac cones at
-different points within the Brillouin zone. The demonstrated and related
-optimization techniques could open new avenues to band-structure engineering
-and manipulating the propagation of light in periodic media, with possible
-applications in exotic optical phenomena such as effective zero-index media and
-topological photonics.
-",0,1,0,0,0,0
-19885,Reconstruction of Word Embeddings from Sub-Word Parameters,"  Pre-trained word embeddings improve the performance of a neural model at the
-cost of increasing the model size. We propose to benefit from this resource
-without paying the cost by operating strictly at the sub-lexical level. Our
-approach is quite simple: before task-specific training, we first optimize
-sub-word parameters to reconstruct pre-trained word embeddings using various
-distance measures. We report interesting results on a variety of tasks: word
-similarity, word analogy, and part-of-speech tagging.
-",1,0,0,0,0,0
-19886,Deep Submodular Functions,"  We start with an overview of a class of submodular functions called SCMMs
-(sums of concave composed with non-negative modular functions plus a final
-arbitrary modular). We then define a new class of submodular functions we call
-{\em deep submodular functions} or DSFs. We show that DSFs are a flexible
-parametric family of submodular functions that share many of the properties and
-advantages of deep neural networks (DNNs). DSFs can be motivated by considering
-a hierarchy of descriptive concepts over ground elements and where one wishes
-to allow submodular interaction throughout this hierarchy. Results in this
-paper show that DSFs constitute a strictly larger class of submodular functions
-than SCMMs. We show that, for any integer $k>0$, there are $k$-layer DSFs that
-cannot be represented by a $k'$-layer DSF for any $k'<k$. This implies that,
-like DNNs, there is a utility to depth, but unlike DNNs, the family of DSFs
-strictly increase with depth. Despite this, we show (using a ""backpropagation""
-like method) that DSFs, even with arbitrarily large $k$, do not comprise all
-submodular functions. In offering the above results, we also define the notion
-of an antitone superdifferential of a concave function and show how this
-relates to submodular functions (in general), DSFs (in particular), negative
-second-order partial derivatives, continuous submodularity, and concave
-extensions. To further motivate our analysis, we provide various special case
-results from matroid theory, comparing DSFs with forms of matroid rank, in
-particular the laminar matroid. Lastly, we discuss strategies to learn DSFs,
-and define the classes of deep supermodular functions, deep difference of
-submodular functions, and deep multivariate submodular functions, and discuss
-where these can be useful in applications.
-",1,0,0,0,0,0
-19887,Inverse problems for the wave equation with under-determined data,"  We consider the inverse problems of determining the potential or the damping
-coefficient appearing in the wave equation. We will prove the unique
-determination of these coefficients from the one point measurement. Since our
-problem is under-determined, so some extra assumption on the coefficients is
-required to prove the uniqueness.
-",0,0,1,0,0,0
-19888,Spatial interactions and oscillatory tragedies of the commons,"  A tragedy of the commons (TOC) occurs when individuals acting in their own
-self-interest deplete commonly-held resources, leading to a worse outcome than
-had they cooperated. Over time, the depletion of resources can change
-incentives for subsequent actions. Here, we investigate long-term feedback
-between game and environment across a continuum of incentives in an
-individual-based framework. We identify payoff-dependent transition rules that
-lead to oscillatory TOC-s in stochastic simulations and the mean field limit.
-Further extending the stochastic model, we find that spatially explicit
-interactions can lead to emergent, localized dynamics, including the
-propagation of cooperative wave fronts and cluster formation of both social
-context and resources. These dynamics suggest new mechanisms underlying how
-TOCs arise and how they might be averted.
-",0,0,0,0,1,0
-19889,An upper bound on transport,"  The linear growth of operators in local quantum systems leads to an effective
-lightcone even if the system is non-relativistic. We show that consistency of
-diffusive transport with this lightcone places an upper bound on the
-diffusivity: $D \lesssim v^2 \tau_\text{eq}$. The operator growth velocity $v$
-defines the lightcone and $\tau_\text{eq}$ is the local equilibration
-timescale, beyond which the dynamics of conserved densities is diffusive. We
-verify that the bound is obeyed in various weakly and strongly interacting
-theories. In holographic models this bound establishes a relation between the
-hydrodynamic and leading non-hydrodynamic quasinormal modes of planar black
-holes. Our bound relates transport data --- including the electrical
-resistivity and the shear viscosity --- to the local equilibration time, even
-in the absence of a quasiparticle description. In this way, the bound sheds
-light on the observed $T$-linear resistivity of many unconventional metals, the
-shear viscosity of the quark-gluon plasma and the spin transport of unitary
-fermions.
-",0,1,0,0,0,0
-19890,Taylor expansion in linear logic is invertible,"  Each Multiplicative Exponential Linear Logic (MELL) proof-net can be expanded
-into a differential net, which is its Taylor expansion. We prove that two
-different MELL proof-nets have two different Taylor expansions. As a corollary,
-we prove a completeness result for MELL: We show that the relational model is
-injective for MELL proof-nets, i.e. the equality between MELL proof-nets in the
-relational model is exactly axiomatized by cut-elimination.
-",1,0,0,0,0,0
-19891,Collision-Free Multi Robot Trajectory Optimization in Unknown Environments using Decentralized Trajectory Planning,"  Multi robot systems have the potential to be utilized in a variety of
-applications. In most of the previous works, the trajectory generation for
-multi robot systems is implemented in known environments. To overcome that we
-present an online trajectory optimization algorithm that utilizes communication
-of robots' current states to account to the other robots while using local
-object based maps for identifying obstacles. Based upon this data, we predict
-the trajectory expected to be traversed by the robots and utilize that to avoid
-collisions by formulating regions of free space that the robot can be without
-colliding with other robots and obstacles. A trajectory is optimized
-constraining the robot to remain within this region.The proposed method is
-tested in simulations on Gazebo using ROS.
-",1,0,0,0,0,0
-19892,On the bottom of spectra under coverings,"  For a Riemannian covering $M_1\to M_0$ of complete Riemannian manifolds with
-boundary (possibly empty) and respective fundamental groups
-$\Gamma_1\subseteq\Gamma_0$, we show that the bottoms of the spectra of $M_0$
-and $M_1$ coincide if the right action of $\Gamma_0$ on
-$\Gamma_1\backslash\Gamma_0$ is amenable.
-",0,0,1,0,0,0
-19893,Theory of ground states for classical Heisenberg spin systems IV,"  We extend the theory of ground states of classical Heisenberg spin systems
-previously published to the case where the interaction with an external
-magnetic field is described by a Zeeman term. The ground state problem for the
-Heisenberg-Zeeman Hamiltonian can be reduced first to the relative ground state
-problem, and, in a second step, to the absolute ground state problem for pure
-Heisenberg Hamiltonians depending on an additional Lagrange parameter. We
-distinguish between continuous and discontinuous reduction. Moreover, there are
-various general statements about Heisenberg-Zeeman systems that will be proven
-under most general assumptions. One topic is the connection between the minimal
-energy functions $E_{min}$ for the Heisenberg energy and $H_{min}$ for the
-Heisenberg-Zeeman energy which turn out to be essentially mutual
-Legendre-Fenchel transforms. This generalization of the traditional Legendre
-transform is especially suited to cope with situations where the function
-$E_{min}$ is not convex and consequently there is a magnetization jump at a
-critical field. Another topic is magnetization and the occurrence of threshold
-fields $B_{thr}$ and saturation fields $B_{sat}$, where we provide a general
-formula for the latter. We suggest a distinction between ferromagnetic and
-anti-ferromagnetic systems based on the vanishing of $B_{sat}$ for the former
-ones. Parabolic systems are defined in such a way that $E_{min}$ and $H_{min}$
-have a particularly simple form and studied in detail. For a large class of
-parabolic systems the relative ground states can be constructed from the
-absolute ground state by means of a so-called umbrella family. Finally we
-provide a counter-example of a parabolic system where this construction is not
-possible.
-",0,1,0,0,0,0
-19894,Aggressive Deep Driving: Model Predictive Control with a CNN Cost Model,"  We present a framework for vision-based model predictive control (MPC) for
-the task of aggressive, high-speed autonomous driving. Our approach uses deep
-convolutional neural networks to predict cost functions from input video which
-are directly suitable for online trajectory optimization with MPC. We
-demonstrate the method in a high speed autonomous driving scenario, where we
-use a single monocular camera and a deep convolutional neural network to
-predict a cost map of the track in front of the vehicle. Results are
-demonstrated on a 1:5 scale autonomous vehicle given the task of high speed,
-aggressive driving.
-",1,0,0,0,0,0
-19895,K-theory of line bundles and smooth varieties,"  We give a $K$-theoretic criterion for a quasi-projective variety to be
-smooth. If $\mathbb{L}$ is a line bundle corresponding to an ample invertible
-sheaf on $X$, it suffices that $K_q(X) = K_q(\mathbb{L})$ for all
-$q\le\dim(X)+1$.
-",0,0,1,0,0,0
-19896,Local Two-Sample Testing: A New Tool for Analysing High-Dimensional Astronomical Data,"  Modern surveys have provided the astronomical community with a flood of
-high-dimensional data, but analyses of these data often occur after their
-projection to lower-dimensional spaces. In this work, we introduce a local
-two-sample hypothesis test framework that an analyst may directly apply to data
-in their native space. In this framework, the analyst defines two classes based
-on a response variable of interest (e.g. higher-mass galaxies versus lower-mass
-galaxies) and determines at arbitrary points in predictor space whether the
-local proportions of objects that belong to the two classes significantly
-differs from the global proportion.
-Our framework has a potential myriad of uses throughout astronomy; here, we
-demonstrate its efficacy by applying it to a sample of 2487 i-band-selected
-galaxies observed by the HST ACS in four of the CANDELS program fields. For
-each galaxy, we have seven morphological summary statistics along with an
-estimated stellar mass and star-formation rate. We perform two studies: one in
-which we determine regions of the seven-dimensional space of morphological
-statistics where high-mass galaxies are significantly more numerous than
-low-mass galaxies, and vice-versa, and another study where we use SFR in place
-of mass. We find that we are able to identify such regions, and show how
-high-mass/low-SFR regions are associated with concentrated and undisturbed
-galaxies while galaxies in low-mass/high-SFR regions appear more extended
-and/or disturbed than their high-mass/low-SFR counterparts.
-",0,1,0,0,0,0
-19897,Are You Tampering With My Data?,"  We propose a novel approach towards adversarial attacks on neural networks
-(NN), focusing on tampering the data used for training instead of generating
-attacks on trained models. Our network-agnostic method creates a backdoor
-during training which can be exploited at test time to force a neural network
-to exhibit abnormal behaviour. We demonstrate on two widely used datasets
-(CIFAR-10 and SVHN) that a universal modification of just one pixel per image
-for all the images of a class in the training set is enough to corrupt the
-training procedure of several state-of-the-art deep neural networks causing the
-networks to misclassify any images to which the modification is applied. Our
-aim is to bring to the attention of the machine learning community, the
-possibility that even learning-based methods that are personally trained on
-public datasets can be subject to attacks by a skillful adversary.
-",0,0,0,1,0,0
-19898,Answering Spatial Multiple-Set Intersection Queries Using 2-3 Cuckoo Hash-Filters,"  We show how to answer spatial multiple-set intersection queries in O(n(log
-w)/w + kt) expected time, where n is the total size of the t sets involved in
-the query, w is the number of bits in a memory word, k is the output size, and
-c is any fixed constant. This improves the asymptotic performance over previous
-solutions and is based on an interesting data structure, known as 2-3 cuckoo
-hash-filters. Our results apply in the word-RAM model (or practical RAM model),
-which allows for constant-time bit-parallel operations, such as bitwise AND,
-OR, NOT, and MSB (most-significant 1-bit), as exist in modern CPUs and GPUs.
-Our solutions apply to any multiple-set intersection queries in spatial data
-sets that can be reduced to one-dimensional range queries, such as spatial join
-queries for one-dimensional points or sets of points stored along space-filling
-curves, which are used in GIS applications.
-",1,0,0,0,0,0
-19899,A framework for cascade size calculations on random networks,"  We present a framework to calculate the cascade size evolution for a large
-class of cascade models on random network ensembles in the limit of infinite
-network size. Our method is exact and applies to network ensembles with almost
-arbitrary degree distribution, degree-degree correlations and, in case of
-threshold models, with arbitrary threshold distribution. With our approach, we
-shift the perspective from the known branching process approximations to the
-iterative update of suitable probability distributions. Such distributions are
-key to capture cascade dynamics that involve possibly continuous quantities and
-that depend on the cascade history, e.g. if load is accumulated over time.
-These distributions respect the Markovian nature of the studied random
-processes. Random variables capture the impact of nodes that have failed at any
-point in the past on their neighborhood. As a proof of concept, we provide two
-examples: (a) Constant load models that cover many of the analytically
-tractable cascade models, and, as a highlight, (b) a fiber bundle model that
-was not tractable by branching process approximations before. Our derivations
-cover the whole cascade dynamics, not only their steady state. This allows to
-include interventions in time or further model complexity in the analysis.
-",1,1,0,0,0,0
-19900,A Gaussian Process Regression Model for Distribution Inputs,"  Monge-Kantorovich distances, otherwise known as Wasserstein distances, have
-received a growing attention in statistics and machine learning as a powerful
-discrepancy measure for probability distributions. In this paper, we focus on
-forecasting a Gaussian process indexed by probability distributions. For this,
-we provide a family of positive definite kernels built using transportation
-based distances. We provide a probabilistic understanding of these kernels and
-characterize the corresponding stochastic processes. We prove that the Gaussian
-processes indexed by distributions corresponding to these kernels can be
-efficiently forecast, opening new perspectives in Gaussian process modeling.
-",0,0,1,1,0,0
-19901,The intrinsic stable normal cone,"  We construct an analog of the intrinsic normal cone of Behrend-Fantechi in
-the equivariant motivic stable homotopy category over a base-scheme B and
-construct a fundament class in E-cohomology for any cohomology theory E in
-SH(B). For affine B, a perfect obstruction theory gives rise to a virtual
-fundamental class in a twisted Borel-Moore E-homology for arbitrary E. This
-includes motivic cohomology (homotopy invariant) K-theory algebraic cobordism
-and the oriented Chow groups of Barge-Morel and Fasel. In the case of motivic
-cohomology, we recover the constructions of Behrend-Fantechi, with values in
-the Chow group.
-",0,0,1,0,0,0
-19902,Globular cluster formation with multiple stellar populations from hierarchical star cluster complexes,"  Most old globular clusters (GCs) in the Galaxy are observed to have internal
-chemical abundance spreads in light elements. We discuss a new GC formation
-scenario based on hierarchical star formation within fractal molecular clouds.
-In the new scenario, a cluster of bound and unbound star clusters (`star
-cluster complex', SCC) that have a power-law cluster mass function with a slope
-(beta) of 2 is first formed from a massive gas clump developed in a dwarf
-galaxy. Such cluster complexes and beta=2 are observed and expected from
-hierarchical star formation. The most massive star cluster (`main cluster'),
-which is the progenitor of a GC, can accrete gas ejected from asymptotic giant
-branch (AGB) stars initially in the cluster and other low-mass clusters before
-the clusters are tidally stripped or destroyed to become field stars in the
-dwarf. The SCC is initially embedded in a giant gas hole created by numerous
-supernovae of the SCC so that cold gas outside the hole can be accreted onto
-the main cluster later. New stars formed from the accreted gas have chemical
-abundances that are different from those of the original SCC. Using
-hydrodynamical simulations of GC formation based on this scenario, we show that
-the main cluster with the initial mass as large as [2-5]x10^5 Msun can accrete
-more than 10^5 Msun gas from AGB stars of the SCC. We suggest that merging of
-hierarchical star cluster complexes can play key roles in stellar halo
-formation around GCs and self-enrichment processes of GCs.
-",0,1,0,0,0,0
-19903,Multiple Source Domain Adaptation with Adversarial Training of Neural Networks,"  While domain adaptation has been actively researched in recent years, most
-theoretical results and algorithms focus on the single-source-single-target
-adaptation setting. Naive application of such algorithms on multiple source
-domain adaptation problem may lead to suboptimal solutions. As a step toward
-bridging the gap, we propose a new generalization bound for domain adaptation
-when there are multiple source domains with labeled instances and one target
-domain with unlabeled instances. Compared with existing bounds, the new bound
-does not require expert knowledge about the target distribution, nor the
-optimal combination rule for multisource domains. Interestingly, our theory
-also leads to an efficient learning strategy using adversarial neural networks:
-we show how to interpret it as learning feature representations that are
-invariant to the multiple domain shifts while still being discriminative for
-the learning task. To this end, we propose two models, both of which we call
-multisource domain adversarial networks (MDANs): the first model optimizes
-directly our bound, while the second model is a smoothed approximation of the
-first one, leading to a more data-efficient and task-adaptive model. The
-optimization tasks of both models are minimax saddle point problems that can be
-optimized by adversarial training. To demonstrate the effectiveness of MDANs,
-we conduct extensive experiments showing superior adaptation performance on
-three real-world datasets: sentiment analysis, digit classification, and
-vehicle counting.
-",1,0,0,1,0,0
-19904,OSSOS: V. Diffusion in the orbit of a high-perihelion distant Solar System object,"  We report the discovery of the minor planet 2013 SY$_{99}$, on an
-exceptionally distant, highly eccentric orbit. With a perihelion of 50.0 au,
-2013 SY$_{99}$'s orbit has a semi-major axis of $730 \pm 40$ au, the largest
-known for a high-perihelion trans-Neptunian object (TNO), well beyond those of
-(90377) Sedna and 2012 VP$_{113}$. Yet, with an aphelion of $1420 \pm 90$ au,
-2013 SY$_{99}$'s orbit is interior to the region influenced by Galactic tides.
-Such TNOs are not thought to be produced in the current known planetary
-architecture of the Solar System, and they have informed the recent debate on
-the existence of a distant giant planet. Photometry from the
-Canada-France-Hawaii Telescope, Gemini North and Subaru indicate 2013 SY$_{99}$
-is $\sim 250$ km in diameter and moderately red in colour, similar to other
-dynamically excited TNOs. Our dynamical simulations show that Neptune's weak
-influence during 2013 SY$_{99}$'s perihelia encounters drives diffusion in its
-semi-major axis of hundreds of astronomical units over 4 Gyr. The overall
-symmetry of random walks in semi-major axis allow diffusion to populate 2013
-SY$_{99}$'s orbital parameter space from the 1000-2000 au inner fringe of the
-Oort cloud. Diffusion affects other known TNOs on orbits with perihelia of 45
-to 49 au and semi-major axes beyond 250 au, providing a formation mechanism
-that implies an extended population, gently cycling into and returning from the
-inner fringe of the Oort cloud.
-",0,1,0,0,0,0
-19905,Solution of the Lindblad equation for spin helix states,"  Using Lindblad dynamics we study quantum spin systems with dissipative
-boundary dynamics that generate a stationary nonequilibrium state with a
-non-vanishing spin current that is locally conserved except at the boundaries.
-We demonstrate that with suitably chosen boundary target states one can solve
-the many-body Lindblad equation exactly in any dimension. As solution we obtain
-pure states at any finite value of the dissipation strength and any system
-size. They are characterized by a helical stationary magnetization profile and
-a superdiffusive ballistic current of order one, independent of system size
-even when the quantum spin system is not integrable. These results are derived
-in explicit form for the one-dimensional spin-1/2 Heisenberg chain and its
-higher-spin generalizations (which include for spin-1 the integrable
-Zamolodchikov-Fateev model and the bi-quadratic Heisenberg chain). The
-extension of the results to higher dimensions is straightforward.
-",0,1,0,0,0,0
-19906,Global bifurcation map of the homogeneus states in the Gray-Scott model,"  We study the spatially homogeneous time dependent solutions and their
-bifurcations of the Gray-Scott model. We find the global map of bifurcations by
-a combination of rigorous verification of the existence of Takens Bogdanov and
-a Bautin bifurcations, in the space of two parameters k and F. With the aid of
-numerical continuation of local bifurcation curves we give a global description
-of all the possible bifurcations
-",0,0,1,0,0,0
-19907,Stochastic Composite Least-Squares Regression with convergence rate O(1/n),"  We consider the minimization of composite objective functions composed of the
-expectation of quadratic functions and an arbitrary convex function. We study
-the stochastic dual averaging algorithm with a constant step-size, showing that
-it leads to a convergence rate of O(1/n) without strong convexity assumptions.
-This thus extends earlier results on least-squares regression with the
-Euclidean geometry to (a) all convex regularizers and constraints, and (b) all
-geome-tries represented by a Bregman divergence. This is achieved by a new
-proof technique that relates stochastic and deterministic recursions.
-",0,0,1,1,0,0
-19908,Fisher GAN,"  Generative Adversarial Networks (GANs) are powerful models for learning
-complex distributions. Stable training of GANs has been addressed in many
-recent works which explore different metrics between distributions. In this
-paper we introduce Fisher GAN which fits within the Integral Probability
-Metrics (IPM) framework for training GANs. Fisher GAN defines a critic with a
-data dependent constraint on its second order moments. We show in this paper
-that Fisher GAN allows for stable and time efficient training that does not
-compromise the capacity of the critic, and does not need data independent
-constraints such as weight clipping. We analyze our Fisher IPM theoretically
-and provide an algorithm based on Augmented Lagrangian for Fisher GAN. We
-validate our claims on both image sample generation and semi-supervised
-classification using Fisher GAN.
-",1,0,0,1,0,0
-19909,Language as a matrix product state,"  We propose a statistical model for natural language that begins by
-considering language as a monoid, then representing it in complex matrices with
-a compatible translation invariant probability measure. We interpret the
-probability measure as arising via the Born rule from a translation invariant
-matrix product state.
-",1,1,0,1,0,0
-19910,On the application of Mattis-Bardeen theory in strongly disordered superconductors,"  The low energy optical conductivity of conventional superconductors is
-usually well described by Mattis-Bardeen (MB) theory which predicts the onset
-of absorption above an energy corresponding to twice the superconducing (SC)
-gap parameter Delta. Recent experiments on strongly disordered superconductors
-have challenged the application of the MB formulas due to the occurrence of
-additional spectral weight at low energies below 2Delta. Here we identify three
-crucial items which have to be included in the analysis of optical-conductivity
-data for these systems: (a) the correct identification of the optical threshold
-in the Mattis-Bardeen theory, and its relation with the gap value extracted
-from the measured density of states, (b) the gauge-invariant evaluation of the
-current-current response function, needed to account for the optical absorption
-by SC collective modes, and (c) the inclusion into the MB formula of the energy
-dependence of the density of states present already above Tc. By computing the
-optical conductvity in the disordered attractive Hubbard model we analyze the
-relevance of all these items, and we provide a compelling scheme for the
-analysis and interpretation of the optical data in real materials.
-",0,1,0,0,0,0
-19911,Nanoscale Magnetic Imaging using Circularly Polarized High-Harmonic Radiation,"  This work demonstrates nanoscale magnetic imaging using bright circularly
-polarized high-harmonic radiation. We utilize the magneto-optical contrast of
-worm-like magnetic domains in a Co/Pd multilayer structure, obtaining
-quantitative amplitude and phase maps by lensless imaging. A
-diffraction-limited spatial resolution of 49 nm is achieved with iterative
-phase reconstruction enhanced by a holographic mask. Harnessing the unique
-coherence of high harmonics, this approach will facilitate quantitative,
-element-specific and spatially-resolved studies of ultrafast magnetization
-dynamics, advancing both fundamental and applied aspects of nanoscale
-magnetism.
-",0,1,0,0,0,0
-19912,Towards integrated superconducting detectors on lithium niobate waveguides,"  Superconducting detectors are now well-established tools for low-light
-optics, and in particular quantum optics, boasting high-efficiency, fast
-response and low noise. Similarly, lithium niobate is an important platform for
-integrated optics given its high second-order nonlinearity, used for high-speed
-electro-optic modulation and polarization conversion, as well as frequency
-conversion and sources of quantum light. Combining these technologies addresses
-the requirements for a single platform capable of generating, manipulating and
-measuring quantum light in many degrees of freedom, in a compact and
-potentially scalable manner. We will report on progress integrating tungsten
-transition-edge sensors (TESs) and amorphous tungsten silicide superconducting
-nanowire single-photon detectors (SNSPDs) on titanium in-diffused lithium
-niobate waveguides. The travelling-wave design couples the evanescent field
-from the waveguides into the superconducting absorber. We will report on
-simulations and measurements of the absorption, which we can characterize at
-room temperature prior to cooling down the devices. Independently, we show how
-the detectors respond to flood illumination, normally incident on the devices,
-demonstrating their functionality.
-",0,1,0,0,0,0
-19913,The Hilbert scheme of 11 points in A^3 is irreducible,"  We prove that the Hilbert scheme of 11 points on a smooth threefold is
-irreducible. In the course of the proof, we present several known and new
-techniques for producing curves on the Hilbert scheme.
-",0,0,1,0,0,0
-19914,Dynamically reconfigurable metal-semiconductor Yagi-Uda nanoantenna,"  We propose a novel type of tunable Yagi-Uda nanoantenna composed of
-metal-dielectric (Ag-Ge) core-shell nanoparticles. We show that, due to the
-combination of two types of resonances in each nanoparticle, such hybrid
-Yagi-Uda nanoantenna can operate in two different regimes. Besides the
-conventional nonresonant operation regime at low frequencies, characterized by
-highly directive emission in the forward direction, there is another one at
-higher frequencies caused by hybrid magneto-electric response of the core-shell
-nanoparticles. This regime is based on the excitation of the van Hove
-singularity, and emission in this regime is accompanied by high values of
-directivity and Purcell factor within the same narrow frequency range. Our
-analysis reveals the possibility of flexible dynamical tuning of the hybrid
-nanoantenna emission pattern via electron-hole plasma excitation by 100
-femtosecond pump pulse with relatively low peak intensities $\sim$200
-MW/cm$^2$.
-",0,1,0,0,0,0
-19915,Synthesis and In Situ Modification of Hierarchical SAPO-34 by PEG with Different Molecular Weights; Application in MTO Process,"  Modified structures of SAPO-34 were prepared using polyethylene glycol as the
-mesopores generating agent. The synthesized catalysts were applied in
-methanol-to-olefins (MTO) process. All modified synthesized catalysts were
-characterized via XRD, XRF, FESEM, FTIR, N2 adsorption-desorption techniques,
-and temperature-programmed NH3 desorption and they were compared with
-conventional microporous SAPO-34. Introduction of non-ionic PEG capping agent
-affected the degree of homogeneity and integrity of the synthesis media and
-thus reduced the number of nuclei and order of coordination structures
-resulting in larger and less crystalline particles compared with the
-conventional sample. During the calcination process, decomposition of absorbed
-PEG moieties among the piled up SAPO patches formed a great portion of tuned
-mesopores into the microporous matrix. These tailored mesopores were served as
-auxiliary diffusion pathways in MTO reaction. The effects of molecular weight
-of PEG and PEG/Al molar ratio on the properties of the synthesized materials
-were investigated in order to optimize their MTO reaction performance. It was
-revealed that both of these two parameters can significantly change the
-structural composition and physicochemical properties of resultant products.
-Using PEG with MW of 6000 has led to the formation of RHO and CHA structural
-frameworks i.e. DNL-6 and SAPO-34, simultaneously, while addition of PEG with
-MW of 4000 resulted the formation of pure SAPO-34 phase. Altering the PEG/Al
-molar ratio in the precursor significantly influenced the porosity and acidity
-of the synthesized silicoaluminophosphate products. SAPO-34 impregnated with
-PEG molecular weight of 4000 and PEG/Al molar ratio of 0.0125 showed superior
-catalytic stability in MTO reaction because of the tuned bi-modal porosity and
-tailored acidity pattern.
-",0,1,0,0,0,0
-19916,Support Feature Machines,"  Support Vector Machines (SVMs) with various kernels have played dominant role
-in machine learning for many years, finding numerous applications. Although
-they have many attractive features interpretation of their solutions is quite
-difficult, the use of a single kernel type may not be appropriate in all areas
-of the input space, convergence problems for some kernels are not uncommon, the
-standard quadratic programming solution has $O(m^3)$ time and $O(m^2)$ space
-complexity for $m$ training patterns. Kernel methods work because they
-implicitly provide new, useful features. Such features, derived from various
-kernels and other vector transformations, may be used directly in any machine
-learning algorithm, facilitating multiresolution, heterogeneous models of data.
-Therefore Support Feature Machines (SFM) based on linear models in the extended
-feature spaces, enabling control over selection of support features, give at
-least as good results as any kernel-based SVMs, removing all problems related
-to interpretation, scaling and convergence. This is demonstrated for a number
-of benchmark datasets analyzed with linear discrimination, SVM, decision trees
-and nearest neighbor methods.
-",1,0,0,1,0,0
-19917,Voltage Control Using Eigen Value Decomposition of Fast Decoupled Load Flow Jacobian,"  Voltage deviations occur frequently in power systems. If the violation at
-some buses falls outside the prescribed range, it will be necessary to correct
-the problem by controlling reactive power resources. In this paper, an optimal
-algorithm is proposed to solve this problem by identifying the voltage buses,
-that will have a maximum effect on the affected buses, and setting their new
-set-points. This algorithm is based on the Eigen-Value Decomposition of the
-fast decoupled load flow Jacobian matrix. Different Case studies including IEEE
-9, 14, 30 and 57 bus systems have been used to verify the method.
-",1,0,0,0,0,0
-19918,Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer,"  Convolutional Neural Networks (CNNs) are a popular deep learning architecture
-widely applied in different domains, in particular in classifying over images,
-for which the concept of convolution with a filter comes naturally.
-Unfortunately, the requirement of a distance (or, at least, of a neighbourhood
-function) in the input feature space has so far prevented its direct use on
-data types such as omics data. However, a number of omics data are metrizable,
-i.e., they can be endowed with a metric structure, enabling to adopt a
-convolutional based deep learning framework, e.g., for prediction. We propose a
-generalized solution for CNNs on omics data, implemented through a dedicated
-Keras layer. In particular, for metagenomics data, a metric can be derived from
-the patristic distance on the phylogenetic tree. For transcriptomics data, we
-combine Gene Ontology semantic similarity and gene co-expression to define a
-distance; the function is defined through a multilayer network where 3 layers
-are defined by the GO mutual semantic similarity while the fourth one by gene
-co-expression. As a general tool, feature distance on omics data is enabled by
-OmicsConv, a novel Keras layer, obtaining OmicsCNN, a dedicated deep learning
-framework. Here we demonstrate OmicsCNN on gut microbiota sequencing data, for
-Inflammatory Bowel Disease (IBD) 16S data, first on synthetic data and then a
-metagenomics collection of gut microbiota of 222 IBD patients.
-",0,0,0,1,0,0
-19919,Neural Network Multitask Learning for Traffic Flow Forecasting,"  Traditional neural network approaches for traffic flow forecasting are
-usually single task learning (STL) models, which do not take advantage of the
-information provided by related tasks. In contrast to STL, multitask learning
-(MTL) has the potential to improve generalization by transferring information
-in training signals of extra tasks. In this paper, MTL based neural networks
-are used for traffic flow forecasting. For neural network MTL, a
-backpropagation (BP) network is constructed by incorporating traffic flows at
-several contiguous time instants into an output layer. Nodes in the output
-layer can be seen as outputs of different but closely related STL tasks.
-Comprehensive experiments on urban vehicular traffic flow data and comparisons
-with STL show that MTL in BP neural networks is a promising and effective
-approach for traffic flow forecasting.
-",1,0,0,0,0,0
-19920,A note on a new paradox in superluminal signalling,"  The Tolman paradox is well known as a base for demonstrating the causality
-violation by faster-than-light signals within special relativity. It is
-constructed using a two-way exchange of faster-than-light signals between two
-inertial observers who are in a relative motion receding one from another.
-Recently a one-way superluminal signalling arrangement was suggested as a
-possible construction of a causal paradox. In this note we show that this
-suggestion is not correct, and no causality principle violation can occur in
-any one-way signalling by the use of faster-than-light particles and signals.
-",0,1,0,0,0,0
-19921,The Compressed Overlap Index,"  For analysing text algorithms, for computing superstrings, or for testing
-random number generators, one needs to compute all overlaps between any pairs
-of words in a given set. The positions of overlaps of a word onto itself, or of
-two words, are needed to compute the absence probability of a word in a random
-text, or the numbers of common words shared by two random texts. In all these
-contexts, one needs to compute or to query overlaps between pairs of words in a
-given set. For this sake, we designed COvI, a compressed overlap index that
-supports multiple queries on overlaps: like computing the correlation of two
-words, or listing pairs of words whose longest overlap is maximal among all
-possible pairs. COvI stores overlaps in a hierarchical and non-redundant
-manner. We propose an implementation that can handle datasets of millions of
-words and still answer queries efficiently. Comparison with a baseline solution
-- called FullAC - relying on the Aho-Corasick automaton shows that COvI
-provides significant advantages. For similar construction times, COvI requires
-half the memory FullAC, and still solves complex queries much faster.
-",1,0,0,0,0,0
-19922,An intuitive approach to the unified theory of spin-relaxation,"  Spin-relaxation is conventionally discussed using two different approaches
-for materials with and without inversion symmetry. The former is known as the
-Elliott-Yafet (EY) theory and for the latter the D'yakonov-Perel' (DP) theory
-applies, respectively. We discuss herein a simple and intuitive approach to
-demonstrate that the two seemingly disparate mechanisms are closely related. A
-compelling analogy between the respective Hamiltonian is presented and that the
-usual derivation of spin-relaxation times, in the respective frameworks of the
-two theories, can be performed. The result also allows to obtain the less
-canonical spin-relaxation regimes; the generalization of the EY when the
-material has a large quasiparticle broadening and the DP mechanism in ultrapure
-semiconductors. The method also allows a practical and intuitive numerical
-implementation of the spin-relaxation calculation, which is demonstrated for
-MgB$_2$ that has anomalous spin-relaxation properties.
-",0,1,0,0,0,0
-19923,Weighted Random Walk Sampling for Multi-Relational Recommendation,"  In the information overloaded web, personalized recommender systems are
-essential tools to help users find most relevant information. The most
-heavily-used recommendation frameworks assume user interactions that are
-characterized by a single relation. However, for many tasks, such as
-recommendation in social networks, user-item interactions must be modeled as a
-complex network of multiple relations, not only a single relation. Recently
-research on multi-relational factorization and hybrid recommender models has
-shown that using extended meta-paths to capture additional information about
-both users and items in the network can enhance the accuracy of recommendations
-in such networks. Most of this work is focused on unweighted heterogeneous
-networks, and to apply these techniques, weighted relations must be simplified
-into binary ones. However, information associated with weighted edges, such as
-user ratings, which may be crucial for recommendation, are lost in such
-binarization. In this paper, we explore a random walk sampling method in which
-the frequency of edge sampling is a function of edge weight, and apply this
-generate extended meta-paths in weighted heterogeneous networks. With this
-sampling technique, we demonstrate improved performance on multiple data sets
-both in terms of recommendation accuracy and model generation efficiency.
-",1,0,0,0,0,0
-19924,Understanding the Twitter Usage of Humanities and Social Sciences Academic Journals,"  Scholarly communication has the scope to transcend the limitations of the
-physical world through social media extended coverage and shortened information
-paths. Accordingly, publishers have created profiles for their journals in
-Twitter to promote their publications and to initiate discussions with public.
-This paper investigates the Twitter presence of humanities and social sciences
-(HSS) journal titles obtained from mainstream citation indices, by analysing
-the interaction and communication patterns. This study utilizes webometric data
-collection, descriptive analysis, and social network analysis. Findings
-indicate that the presence of HSS journals in Twitter across disciplines is not
-yet substantial. Sharing of general websites appears to be the key activity
-performed by HSS journals in Twitter. Among them, web content from news portals
-and magazines are highly disseminated. Sharing of research articles and
-retweeting was not majorly observed. Inter-journal communication is apparent
-within the same citation index, but it is very minimal with journals from the
-other index. However, there seems to be an effort to broaden communication
-beyond the research community, reaching out to connect with the public.
-",1,0,0,0,0,0
-19925,Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting,"  Machine learning algorithms, when applied to sensitive data, pose a distinct
-threat to privacy. A growing body of prior work demonstrates that models
-produced by these algorithms may leak specific private information in the
-training data to an attacker, either through the models' structure or their
-observable behavior. However, the underlying cause of this privacy risk is not
-well understood beyond a handful of anecdotal accounts that suggest overfitting
-and influence might play a role.
-This paper examines the effect that overfitting and influence have on the
-ability of an attacker to learn information about the training data from
-machine learning models, either through training set membership inference or
-attribute inference attacks. Using both formal and empirical analyses, we
-illustrate a clear relationship between these factors and the privacy risk that
-arises in several popular machine learning algorithms. We find that overfitting
-is sufficient to allow an attacker to perform membership inference and, when
-the target attribute meets certain conditions about its influence, attribute
-inference attacks. Interestingly, our formal analysis also shows that
-overfitting is not necessary for these attacks and begins to shed light on what
-other factors may be in play. Finally, we explore the connection between
-membership inference and attribute inference, showing that there are deep
-connections between the two that lead to effective new attacks.
-",1,0,0,1,0,0
-19926,Nanoplatelets as material system between strong confinement and weak confinement,"  Recently, the fabrication of CdSe nanoplatelets became an important research
-topic. Nanoplatelets are often described as having a similar electronic
-structure as 2D dimensional quantum wells and are promoted as colloidal quantum
-wells with monolayer precision width. In this paper, we show, that
-nanoplatelets are not ideal quantum wells, but cover depending on the size: the
-strong confinement regime, an intermediate regime and a Coulomb dominated
-regime. Thus, nanoplatelets are an ideal platform to study the physics in these
-regimes. Therefore, the exciton states of the nanoplatelets are numerically
-calculated by solving the full four dimensional Schrödinger equation. We
-compare the results with approximate solutions from semiconductor quantum well
-and quantum dot theory. The paper can also act as review of these concepts for
-the colloidal nanoparticle community.
-",0,1,0,0,0,0
-19927,Combined Thermal Control and GNC: An Enabling Technology for CubeSat Surface Probes and Small Robots,"  Advances in GNC, particularly from miniaturized control electronics,
-reaction-wheels and attitude determination sensors make it possible to design
-surface probes and small robots to perform surface exploration and science on
-low-gravity environments. These robots would use their reaction wheels to roll,
-hop and tumble over rugged surfaces. These robots could provide 'Google
-Streetview' quality images of off-world surfaces and perform some unique
-science using penetrometers. These systems can be powered by high-efficiency
-fuel cells that operate at 60-65 % and utilize hydrogen and oxygen electrolyzed
-from water. However, one of the major challenges that prevent these probes and
-robots from performing long duration surface exploration and science is thermal
-design and control. In the inner solar system, during the day time, there is
-often enough solar-insolation to keep these robots warm and power these
-devices, but during eclipse the temperatures falls well below storage
-temperature. We have developed a thermal control system that utilizes chemicals
-to store and dispense heat when needed. The system takes waste products, such
-as water from these robots and transfers them to a thermochemical storage
-system. These thermochemical storage systems when mixed with water (a waste
-product from a PEM fuel cell) releases heat. Under eclipse, the heat from the
-thermochemical storage system is released to keep the probe warm enough to
-survive. In sunlight, solar photovoltaics are used to electrolyze the water and
-reheat the thermochemical storage system to release the water. Our research has
-showed thermochemical storage systems are a feasible solution for use on
-surface probes and robots for applications on the Moon, Mars and asteroids.
-",1,1,0,0,0,0
-19928,Denoising Adversarial Autoencoders,"  Unsupervised learning is of growing interest because it unlocks the potential
-held in vast amounts of unlabelled data to learn useful representations for
-inference. Autoencoders, a form of generative model, may be trained by learning
-to reconstruct unlabelled input data from a latent representation space. More
-robust representations may be produced by an autoencoder if it learns to
-recover clean input samples from corrupted ones. Representations may be further
-improved by introducing regularisation during training to shape the
-distribution of the encoded data in latent space. We suggest denoising
-adversarial autoencoders, which combine denoising and regularisation, shaping
-the distribution of latent space using adversarial training. We introduce a
-novel analysis that shows how denoising may be incorporated into the training
-and sampling of adversarial autoencoders. Experiments are performed to assess
-the contributions that denoising makes to the learning of representations for
-classification and sample synthesis. Our results suggest that autoencoders
-trained using a denoising criterion achieve higher classification performance,
-and can synthesise samples that are more consistent with the input data than
-those trained without a corruption process.
-",1,0,0,1,0,0
-19929,Monodromy map for tropical Dolbeault cohomology,"  We define monodromy maps for tropical Dolbeault cohomology of algebraic
-varieties over non-Archimedean fields. We propose a conjecture of Hodge
-isomorphisms via monodromy maps, and provide some evidence.
-",0,0,1,0,0,0
-19930,Randomness Evaluation with the Discrete Fourier Transform Test Based on Exact Analysis of the Reference Distribution,"  In this paper, we study the problems in the discrete Fourier transform (DFT)
-test included in NIST SP 800-22 released by the National Institute of Standards
-and Technology (NIST), which is a collection of tests for evaluating both
-physical and pseudo-random number generators for cryptographic applications.
-The most crucial problem in the DFT test is that its reference distribution of
-the test statistic is not derived mathematically but rather numerically
-estimated, the DFT test for randomness is based on a pseudo-random number
-generator (PRNG). Therefore, the present DFT test should not be used unless the
-reference distribution is mathematically derived. Here, we prove that a power
-spectrum, which is a component of the test statistic, follows a chi-squared
-distribution with 2 degrees of freedom. Based on this fact, we propose a test
-whose reference distribution of the test statistic is mathematically derived.
-Furthermore, the results of testing non-random sequences and several PRNGs
-showed that the proposed test is more reliable and definitely more sensitive
-than the present DFT test.
-",1,0,0,1,0,0
-19931,Analogies Explained: Towards Understanding Word Embeddings,"  Word embeddings generated by neural network methods such as word2vec (W2V)
-are well known to exhibit seemingly linear behaviour, e.g. the embeddings of
-analogy ""woman is to queen as man is to king"" approximately describe a
-parallelogram. This property is particularly intriguing since the embeddings
-are not trained to achieve it. Several explanations have been proposed, but
-each introduces assumptions that do not hold in practice. We derive a
-probabilistically grounded definition of paraphrasing and show it can be
-re-interpreted as word transformation, a mathematical description of ""$w_x$ is
-to $w_y$"". From these concepts we prove existence of the linear relationship
-between W2V-type embeddings that underlies the analogical phenomenon, and
-identify explicit error terms in the relationship.
-",1,0,0,1,0,0
-19932,Zero sum partition into sets of the same order and its applications,"  We will say that an Abelian group $\Gamma$ of order $n$ has the
-$m$-\emph{zero-sum-partition property} ($m$-\textit{ZSP-property}) if $m$
-divides $n$, $m\geq 2$ and there is a partition of $\Gamma$ into pairwise
-disjoint subsets $A_1, A_2,\ldots , A_t$, such that $|A_i| = m$ and $\sum_{a\in
-A_i}a = g_0$ for $1 \leq i \leq t$, where $g_0$ is the identity element of
-$\Gamma$.
-In this paper we study the $m$-ZSP property of $\Gamma$. We show that
-$\Gamma$ has $m$-ZSP if and only if $|\Gamma|$ is odd or $m\geq 3$ and $\Gamma$
-has more than one involution. We will apply the results to the study of group
-distance magic graphs as well as to generalized Kotzig arrays.
-",0,0,1,0,0,0
-19933,Strongly Coupled Dark Energy with Warm dark matter vs. LCDM,"  Cosmologies including strongly Coupled (SC) Dark Energy (DE) and Warm dark
-matter (SCDEW) are based on a conformally invariant (CI) attractor solution
-modifying the early radiative expansion. Then, aside of radiation, a kinetic
-field $\Phi$ and a DM component account for a stationary fraction, $\sim 1\,
-\%$, of the total energy. Most SCDEW predictions are hardly distinguishable
-from LCDM, while SCDEW alleviates quite a few LCDM conceptual problems, as well
-as its difficulties to meet data below the average galaxy scale. The CI
-expansion begins at the inflation end, when $\Phi$ (future DE) possibly plays a
-role in reheating, and ends at the Higgs' scale. Afterwards, a number of viable
-options is open, allowing for the transition from the CI expansion to the
-present Universe. In this paper: (i) We show how the attractor is recovered
-when the spin degrees of freedom decreases. (ii) We perform a detailed
-comparison of CMB anisotropy and polarization spectra for SCDEW and LCDM,
-including tensor components, finding negligible discrepancies. (iii) Linear
-spectra exhibit a greater parameter dependence at large $k$'s, but are still
-consistent with data for suitable parameter choices. (iv) We also compare
-previous simulation results with fresh data on galaxy concentration. Finally,
-(v) we outline numerical difficulties at high $k$. This motivates a second
-related paper, where such problems are treated in a quantitative way.
-",0,1,0,0,0,0
-19934,Clausal Analysis of First-order Proof Schemata,"  Proof schemata are a variant of LK-proofs able to simulate various induction
-schemes in first-order logic by adding so called proof links to the standard
-first-order LK-calculus. Proof links allow proofs to reference proofs thus
-giving proof schemata a recursive structure. Unfortunately, applying reductive
-cut- elimination is non-trivial in the presence of proof links. Borrowing the
-concept of lazy instantiation from functional programming, we evaluate proof
-links locally allowing reductive cut-elimination to proceed past them. Though,
-this method cannot be used to obtain cut-free proof schemata, we nonetheless
-obtain important results concerning the schematic CERES method, that is a
-method of cut-elimination for proof schemata based on resolution. In ""Towards a
-clausal analysis of cut-elimination"", it was shown that reductive
-cut-elimination transforms a given LK-proof in such a way that a subsumption
-relation holds between the pre- and post-transformation characteristic clause
-sets, i.e. the clause set representing the cut-structure of an LK-proof. Let
-CL(A') be the characteristic clause set of a normal form A' of an LK-proof A
-that is reached by performing reductive cut-elimination on A without atomic cut
-elimination. Then CL(A') is subsumed by all characteristic clause sets
-extractable from any application of reductive cut-elimination to A. Such a
-normal form is referred to as an ACNF top and plays an essential role in
-methods of cut-elimination by resolution. These results can be extended to
-proof schemata through our ""lazy instantiation"" of proof links, and provides an
-essential step toward a complete cut-elimination method for proof schemata.
-",1,0,1,0,0,0
-19935,Emotion Intensities in Tweets,"  This paper examines the task of detecting intensity of emotion from text. We
-create the first datasets of tweets annotated for anger, fear, joy, and sadness
-intensities. We use a technique called best--worst scaling (BWS) that improves
-annotation consistency and obtains reliable fine-grained scores. We show that
-emotion-word hashtags often impact emotion intensity, usually conveying a more
-intense emotion. Finally, we create a benchmark regression system and conduct
-experiments to determine: which features are useful for detecting emotion
-intensity, and, the extent to which two emotions are similar in terms of how
-they manifest in language.
-",1,0,0,0,0,0
-19936,Modelling the descent of nitric oxide during the elevated stratopause event of January 2013,"  Using simulations with a whole-atmosphere chemistry-climate model nudged by
-meteorological analyses, global satellite observations of nitrogen oxide (NO)
-and water vapour by the Sub-Millimetre Radiometer instrument (SMR), of
-temperature by the Microwave Limb Sounder (MLS), as well as local radar
-observations, this study examines the recent major stratospheric sudden warming
-accompanied by an elevated stratopause event (ESE) that occurred in January
-2013. We examine dynamical processes during the ESE, including the role of
-planetary wave, gravity wave and tidal forcing on the initiation of the descent
-in the mesosphere-lower thermosphere (MLT) and its continuation throughout the
-mesosphere and stratosphere, as well as the impact of model eddy diffusion. We
-analyse the transport of NO and find the model underestimates the large descent
-of NO compared to SMR observations. We demonstrate that the discrepancy arises
-abruptly in the MLT region at a time when the resolved wave forcing and the
-planetary wave activity increase, just before the elevated stratopause reforms.
-The discrepancy persists despite doubling the model eddy diffusion. While the
-simulations reproduce an enhancement of the semi-diurnal tide following the
-onset of the 2013 SSW, corroborating new meteor radar observations at high
-northern latitudes over Trondheim (63.4$^{\circ}$N), the modelled tidal
-contribution to the forcing of the mean meridional circulation and to the
-descent is a small portion of the resolved wave forcing, and lags it by about
-ten days.
-",0,1,0,0,0,0
-19937,Optimal Evidence Accumulation on Social Networks,"  A fundamental question in biology is how organisms integrate sensory and
-social evidence to make decisions. However, few models describe how both these
-streams of information can be combined to optimize choices. Here we develop a
-normative model for collective decision making in a network of agents
-performing a two-alternative forced choice task. We assume that rational
-(Bayesian) agents in this network make private measurements, and observe the
-decisions of their neighbors until they accumulate sufficient evidence to make
-an irreversible choice. As each agent communicates its decision to those
-observing it, the flow of social information is described by a directed graph.
-The decision-making process in this setting is intuitive, but can be complex.
-We describe when and how the absence of a decision of a neighboring agent
-communicates social information, and how an agent must marginalize over all
-unobserved decisions. We also show how decision thresholds and network
-connectivity affect group evidence accumulation, and describe the dynamics of
-decision making in social cliques. Our model provides a bridge between the
-abstractions used in the economics literature and the evidence accumulator
-models used widely in neuroscience and psychology.
-",1,0,0,0,1,0
-19938,Efficient Data-Driven Geologic Feature Detection from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm,"  Conventional seismic techniques for detecting the subsurface geologic
-features are challenged by limited data coverage, computational inefficiency,
-and subjective human factors. We developed a novel data-driven geological
-feature detection approach based on pre-stack seismic measurements. Our
-detection method employs an efficient and accurate machine-learning detection
-approach to extract useful subsurface geologic features automatically.
-Specifically, our method is based on kernel ridge regression model. The
-conventional kernel ridge regression can be computationally prohibited because
-of the large volume of seismic measurements. We employ a data reduction
-technique in combination with the conventional kernel ridge regression method
-to improve the computational efficiency and reduce memory usage. In particular,
-we utilize a randomized numerical linear algebra technique, named Nyström
-method, to effectively reduce the dimensionality of the feature space without
-compromising the information content required for accurate detection. We
-provide thorough computational cost analysis to show efficiency of our new
-geological feature detection methods. We further validate the performance of
-our new subsurface geologic feature detection method using synthetic surface
-seismic data for 2D acoustic and elastic velocity models. Our numerical
-examples demonstrate that our new detection method significantly improves the
-computational efficiency while maintaining comparable accuracy. Interestingly,
-we show that our method yields a speed-up ratio on the order of $\sim10^2$ to
-$\sim 10^3$ in a multi-core computational environment.
-",1,0,0,1,0,0
-19939,Metastable Modular Metastructures for On-Demand Reconfiguration of Band Structures and Non-Reciprocal Wave Propagation,"  We present a novel approach to achieve adaptable band structures and
-non-reciprocal wave propagation by exploring and exploiting the concept of
-metastable modular metastructures. Through studying the dynamics of wave
-propagation in a chain composed of finite metastable modules, we provide
-experimental and analysis results on non-reciprocal wave propagation and unveil
-the underlying mechanisms in accomplishing such unidirectional energy
-transmission. Utilizing the property adaptation feature afforded via
-transitioning amongst metastable states, we uncovered an unprecedented bandgap
-reconfiguration characteristic, which enables the adaptivity of wave
-propagation within the metastructure. Overall, this investigation elucidates
-the rich dynamics attainable by periodicity, nonlinearity, asymmetry, and
-metastability, and creates a new class of adaptive structural and material
-systems capable of realizing tunable bandgaps and non-reciprocal wave
-transmissions.
-",0,1,0,0,0,0
-19940,Learning to Plan Chemical Syntheses,"  From medicines to materials, small organic molecules are indispensable for
-human well-being. To plan their syntheses, chemists employ a problem solving
-technique called retrosynthesis. In retrosynthesis, target molecules are
-recursively transformed into increasingly simpler precursor compounds until a
-set of readily available starting materials is obtained. Computer-aided
-retrosynthesis would be a highly valuable tool, however, past approaches were
-slow and provided results of unsatisfactory quality. Here, we employ Monte
-Carlo Tree Search (MCTS) to efficiently discover retrosynthetic routes. MCTS
-was combined with an expansion policy network that guides the search, and an
-""in-scope"" filter network to pre-select the most promising retrosynthetic
-steps. These deep neural networks were trained on 12 million reactions, which
-represents essentially all reactions ever published in organic chemistry. Our
-system solves almost twice as many molecules and is 30 times faster in
-comparison to the traditional search method based on extracted rules and
-hand-coded heuristics. Finally after a 60 year history of computer-aided
-synthesis planning, chemists can no longer distinguish between routes generated
-by a computer system and real routes taken from the scientific literature. We
-anticipate that our method will accelerate drug and materials discovery by
-assisting chemists to plan better syntheses faster, and by enabling fully
-automated robot synthesis.
-",1,1,0,0,0,0
-19941,Projective embedding of pairs and logarithmic K-stability,"  Let $\hat{L}$ be the projective completion of an ample line bundle $L$ over
-$D$, a smooth projective manifold. Hwang-Singer \cite{HwangS} have constructed
-complete CSCK metric on $\hat{L}\backslash D$. When the corresponding \kahler
-form is in the cohomology class of a rational divisor $A$ and when $L$ has
-negative CSCK metric on $D$, we show that the Kodaira embedding induced by
-orthonormal basis of the Bergman space of $kA$ is almost balanced. As a
-corollary, $(\hat{L},D,cA,0)$ is K-semistable.
-",0,0,1,0,0,0
-19942,Motion Switching with Sensory and Instruction Signals by designing Dynamical Systems using Deep Neural Network,"  To ensure that a robot is able to accomplish an extensive range of tasks, it
-is necessary to achieve a flexible combination of multiple behaviors. This is
-because the design of task motions suited to each situation would become
-increasingly difficult as the number of situations and the types of tasks
-performed by them increase. To handle the switching and combination of multiple
-behaviors, we propose a method to design dynamical systems based on point
-attractors that accept (i) ""instruction signals"" for instruction-driven
-switching. We incorporate the (ii) ""instruction phase"" to form a point
-attractor and divide the target task into multiple subtasks. By forming an
-instruction phase that consists of point attractors, the model embeds a subtask
-in the form of trajectory dynamics that can be manipulated using sensory and
-instruction signals. Our model comprises two deep neural networks: a
-convolutional autoencoder and a multiple time-scale recurrent neural network.
-In this study, we apply the proposed method to manipulate soft materials. To
-evaluate our model, we design a cloth-folding task that consists of four
-subtasks and three patterns of instruction signals, which indicate the
-direction of motion. The results depict that the robot can perform the required
-task by combining subtasks based on sensory and instruction signals. And, our
-model determined the relations among these signals using its internal dynamics.
-",1,0,0,0,0,0
-19943,The evolution of magnetic fields in hot stars,"  Over the last decade, tremendous strides have been achieved in our
-understanding of magnetism in main sequence hot stars. In particular, the
-statistical occurrence of their surface magnetism has been established (~10%)
-and the field origin is now understood to be fossil. However, fundamental
-questions remain: how do these fossil fields evolve during the post-main
-sequence phases, and how do they influence the evolution of hot stars from the
-main sequence to their ultimate demise? Filling the void of known magnetic
-evolved hot (OBA) stars, studying the evolution of their fossil magnetic fields
-along stellar evolution, and understanding the impact of these fields on the
-angular momentum, rotation, mass loss, and evolution of the star itself, is
-crucial to answering these questions, with far reaching consequences, in
-particular for the properties of the precursors of supernovae explosions and
-stellar remnants. In the framework of the BRITE spectropolarimetric survey and
-LIFE project, we have discovered the first few magnetic hot supergiants. Their
-longitudinal surface magnetic field is very weak but their configuration
-resembles those of main sequence hot stars. We present these first
-observational results and propose to interpret them at first order in the
-context of magnetic flux conservation as the radius of the star expands with
-evolution. We then also consider the possible impact of stellar structure
-changes along evolution.
-",0,1,0,0,0,0
-19944,Polynomial-Time Methods to Solve Unimodular Quadratic Programs With Performance Guarantees,"  We develop polynomial-time heuristic methods to solve unimodular quadratic
-programs (UQPs) approximately, which are known to be NP-hard. In the UQP
-framework, we maximize a quadratic function of a vector of complex variables
-with unit modulus. Several problems in active sensing and wireless
-communication applications boil down to UQP. With this motivation, we present
-three new heuristic methods with polynomial-time complexity to solve the UQP
-approximately. The first method is called dominant-eigenvector-matching; here
-the solution is picked that matches the complex arguments of the dominant
-eigenvector of the Hermitian matrix in the UQP formulation. We also provide a
-performance guarantee for this method. The second method, a greedy strategy, is
-shown to provide a performance guarantee of (1-1/e) with respect to the optimal
-objective value given that the objective function possesses a property called
-string submodularity. The third heuristic method is called row-swap greedy
-strategy, which is an extension to the greedy strategy and utilizes certain
-properties of the UQP to provide a better performance than the greedy strategy
-at the expense of an increase in computational complexity. We present numerical
-results to demonstrate the performance of these heuristic methods, and also
-compare the performance of these methods against a standard heuristic method
-called semidefinite relaxation.
-",1,0,1,0,0,0
-19945,Can the Wild Bootstrap be Tamed into a General Analysis of Covariance Model?,"  It is well known that the F test is severly affected by heteroskedasticity in
-unbalanced analysis of covariance (ANCOVA) models. Currently available remedies
-for such a scenario are either based on heteroskedasticity-consistent
-covariance matrix estimation (HCCME) or bootstrap techniques. However, the
-HCCME approach tends to be liberal in small samples. Therefore, we propose a
-combination of HCCME and a wild bootstrap technique. We prove the theoretical
-validity of our approach and investigate its performance in an extensive
-simulation study in comparison to existing procedures. The results indicate
-that our proposed test remedies all problems of the ANCOVA F test and its
-heteroskedasticityconsistent alternatives. Our test only requires very general
-conditions, thus being applicable in a broad range of real-life settings.
-",0,0,0,1,0,0
-19946,Efficient K-Shot Learning with Regularized Deep Networks,"  Feature representations from pre-trained deep neural networks have been known
-to exhibit excellent generalization and utility across a variety of related
-tasks. Fine-tuning is by far the simplest and most widely used approach that
-seeks to exploit and adapt these feature representations to novel tasks with
-limited data. Despite the effectiveness of fine-tuning, itis often sub-optimal
-and requires very careful optimization to prevent severe over-fitting to small
-datasets. The problem of sub-optimality and over-fitting, is due in part to the
-large number of parameters used in a typical deep convolutional neural network.
-To address these problems, we propose a simple yet effective regularization
-method for fine-tuning pre-trained deep networks for the task of k-shot
-learning. To prevent overfitting, our key strategy is to cluster the model
-parameters while ensuring intra-cluster similarity and inter-cluster diversity
-of the parameters, effectively regularizing the dimensionality of the parameter
-search space. In particular, we identify groups of neurons within each layer of
-a deep network that shares similar activation patterns. When the network is to
-be fine-tuned for a classification task using only k examples, we propagate a
-single gradient to all of the neuron parameters that belong to the same group.
-The grouping of neurons is non-trivial as neuron activations depend on the
-distribution of the input data. To efficiently search for optimal groupings
-conditioned on the input data, we propose a reinforcement learning search
-strategy using recurrent networks to learn the optimal group assignments for
-each network layer. Experimental results show that our method can be easily
-applied to several popular convolutional neural networks and improve upon other
-state-of-the-art fine-tuning based k-shot learning strategies by more than10%
-",1,0,0,1,0,0
-19947,The cohomology ring of some Hopf algebras,"  Let p be a prime, and k be a field of characteristic p. We investigate the
-algebra structure and the structure of the cohomology ring for the connected
-Hopf algebras of dimension p^3, which appear in the classification obtained in
-[V.C. Nguyen, L.-H. Wang and X.-T. Wang, Classification of connected Hopf
-algebras of dimension p^3, J. Algebra 424 (2015), 473-505]. The list consists
-of 23 algebras together with two infinite families. We identify the Morita type
-of the algebra, and in almost all cases this is sufficient to clarify the
-structure of the cohomology ring.
-",0,0,1,0,0,0
-19948,Eckart ro-vibrational Hamiltonians via the gateway Hamilton operator: theory and practice,"  Recently, a general expression for Eckart-frame Hamilton operators has been
-obtained by the gateway Hamiltonian method ({\it J. Chem. Phys.} {\bf 142},
-174107 (2015); {\it ibid.} {\bf 143}, 064104 (2015)). The kinetic energy
-operator in this general Hamiltonian is nearly identical with that of the
-Eckart-Watson operator even when curvilinear vibrational coordinates are
-employed. Its different realizations correspond to different methods of
-calculating Eckart displacements. There are at least two different methods for
-calculating such displacements: rotation and projection. In this communication
-the application of Eckart Hamiltonian operators constructed by rotation and
-projection, respectively, is numerically demonstrated in calculating
-vibrational energy levels. The numerical examples confirm that there is no need
-for rotation to construct an Eckart ro-vibrational Hamiltonian. The application
-of the gateway method is advantageous even when rotation is used, since it
-obviates the need for differentiation of the matrix rotating into the Eckart
-frame. Simple geometrical arguments explain that there are infinitely many
-different methods for calculating Eckart displacements. The geometrical picture
-also suggests that a unique Eckart displacement vector may be defined as the
-shortest (mass-weighted) Eckart displacement vector among Eckart displacement
-vectors corresponding to configurations related by rotation. Its length, as
-shown analytically and demonstrated by way of numerical examples, is equal to
-or less than that of the Eckart displacement vector one can obtain by rotation
-to the Eckart frame.
-",0,1,0,0,0,0
-19949,Optimal Timing to Trade Along a Randomized Brownian Bridge,"  This paper studies an optimal trading problem that incorporates the trader's
-market view on the terminal asset price distribution and uninformative noise
-embedded in the asset price dynamics. We model the underlying asset price
-evolution by an exponential randomized Brownian bridge (rBb) and consider
-various prior distributions for the random endpoint. We solve for the optimal
-strategies to sell a stock, call, or put, and analyze the associated delayed
-liquidation premia. We solve for the optimal trading strategies numerically and
-compare them across different prior beliefs. Among our results, we find that
-disconnected continuation/exercise regions arise when the trader prescribe a
-two-point discrete distribution and double exponential distribution.
-",0,0,0,0,0,1
-19950,Inflationary Features and Shifts in Cosmological Parameters from Planck 2015 Data,"  We explore the relationship between features in the Planck 2015 temperature
-and polarization data, shifts in the cosmological parameters, and features from
-inflation. Residuals in the temperature data at low multipole $\ell$, which are
-responsible for the high $H_0\approx 70$ km s$^{-1}$Mpc$^{-1}$ and low
-$\sigma_8\Omega_m^{1/2}$ values from $\ell<1000$ in power-law $\Lambda$CDM
-models, are better fit to inflationary features with a $1.9\sigma$ preference
-for running of the running of the tilt or a stronger $99\%$ CL local
-significance preference for a sharp drop in power around $k=0.004$ Mpc$^{-1}$
-in generalized slow roll and a lower $H_0\approx 67$ km s$^{-1}$Mpc$^{-1}$. The
-same in-phase acoustic residuals at $\ell>1000$ that drive the global $H_0$
-constraints and appear as a lensing anomaly also favor running parameters which
-allow even lower $H_0$, but not once lensing reconstruction is considered.
-Polarization spectra are intrinsically highly sensitive to these parameter
-shifts, and even more so in the Planck 2015 TE data due to an outlier at $\ell
-\approx 165$, which disfavors the best fit $H_0$ $\Lambda$CDM solution by more
-than $2\sigma$, and high $H_0$ value at almost $3\sigma$. Current polarization
-data also slightly enhance the significance of a sharp suppression of
-large-scale power but leave room for large improvements in the future with
-cosmic variance limited $E$-mode measurements.
-",0,1,0,0,0,0
-19951,Tree based weighted learning for estimating individualized treatment rules with censored data,"  Estimating individualized treatment rules is a central task for personalized
-medicine. [zhao2012estimating] and [zhang2012robust] proposed outcome weighted
-learning to estimate individualized treatment rules directly through maximizing
-the expected outcome without modeling the response directly. In this paper, we
-extend the outcome weighted learning to right censored survival data without
-requiring either an inverse probability of censoring weighting or a
-semiparametric modeling of the censoring and failure times as done in
-[zhao2015doubly]. To accomplish this, we take advantage of the tree based
-approach proposed in [zhu2012recursively] to nonparametrically impute the
-survival time in two different ways. The first approach replaces the reward of
-each individual by the expected survival time, while in the second approach
-only the censored observations are imputed by their conditional expected
-failure times. We establish consistency and convergence rates for both
-estimators. In simulation studies, our estimators demonstrate improved
-performance compared to existing methods. We also illustrate the proposed
-method on a phase III clinical trial of non-small cell lung cancer.
-",0,0,1,1,0,0
-19952,Fine-Tuning in the Context of Bayesian Theory Testing,"  Fine-tuning in physics and cosmology is often used as evidence that a theory
-is incomplete. For example, the parameters of the standard model of particle
-physics are ""unnaturally"" small (in various technical senses), which has driven
-much of the search for physics beyond the standard model. Of particular
-interest is the fine-tuning of the universe for life, which suggests that our
-universe's ability to create physical life forms is improbable and in need of
-explanation, perhaps by a multiverse. This claim has been challenged on the
-grounds that the relevant probability measure cannot be justified because it
-cannot be normalized, and so small probabilities cannot be inferred. We show
-how fine-tuning can be formulated within the context of Bayesian theory testing
-(or \emph{model selection}) in the physical sciences. The normalizability
-problem is seen to be a general problem for testing any theory with free
-parameters, and not a unique problem for fine-tuning. Physical theories in fact
-avoid such problems in one of two ways. Dimensional parameters are bounded by
-the Planck scale, avoiding troublesome infinities, and we are not compelled to
-assume that dimensionless parameters are distributed uniformly, which avoids
-non-normalizability.
-",0,1,0,0,0,0
-19953,Nuclear physics insights for new-physics searches using nuclei: Neutrinoless $ββ$ decay and dark matter direct detection,"  Experiments using nuclei to probe new physics beyond the Standard Model, such
-as neutrinoless $\beta\beta$ decay searches testing whether neutrinos are their
-own antiparticle, and direct detection experiments aiming to identify the
-nature of dark matter, require accurate nuclear physics input for optimizing
-their discovery potential and for a correct interpretation of their results.
-This demands a detailed knowledge of the nuclear structure relevant for these
-processes. For instance, neutrinoless $\beta\beta$ decay nuclear matrix
-elements are very sensitive to the nuclear correlations in the initial and
-final nuclei, and the spin-dependent nuclear structure factors of dark matter
-scattering depend on the subtle distribution of the nuclear spin among all
-nucleons. In addition, nucleons are composite and strongly interacting, which
-implies that many-nucleon processes are necessary for a correct description of
-nuclei and their interactions. It is thus crucial that theoretical studies and
-experimental analyses consider $\beta$ decays and dark matter interactions with
-a coupling to two nucleons, called two-nucleon currents.
-",0,1,0,0,0,0
-19954,On the (Statistical) Detection of Adversarial Examples,"  Machine Learning (ML) models are applied in a variety of tasks such as
-network intrusion detection or Malware classification. Yet, these models are
-vulnerable to a class of malicious inputs known as adversarial examples. These
-are slightly perturbed inputs that are classified incorrectly by the ML model.
-The mitigation of these adversarial inputs remains an open problem. As a step
-towards understanding adversarial examples, we show that they are not drawn
-from the same distribution than the original data, and can thus be detected
-using statistical tests. Using thus knowledge, we introduce a complimentary
-approach to identify specific inputs that are adversarial. Specifically, we
-augment our ML model with an additional output, in which the model is trained
-to classify all adversarial inputs. We evaluate our approach on multiple
-adversarial example crafting methods (including the fast gradient sign and
-saliency map methods) with several datasets. The statistical test flags sample
-sets containing adversarial inputs confidently at sample sizes between 10 and
-100 data points. Furthermore, our augmented model either detects adversarial
-examples as outliers with high accuracy (> 80%) or increases the adversary's
-cost - the perturbation added - by more than 150%. In this way, we show that
-statistical properties of adversarial examples are essential to their
-detection.
-",1,0,0,1,0,0
-19955,Search Rank Fraud De-Anonymization in Online Systems,"  We introduce the fraud de-anonymization problem, that goes beyond fraud
-detection, to unmask the human masterminds responsible for posting search rank
-fraud in online systems. We collect and study search rank fraud data from
-Upwork, and survey the capabilities and behaviors of 58 search rank fraudsters
-recruited from 6 crowdsourcing sites. We propose Dolos, a fraud
-de-anonymization system that leverages traits and behaviors extracted from
-these studies, to attribute detected fraud to crowdsourcing site fraudsters,
-thus to real identities and bank accounts. We introduce MCDense, a min-cut
-dense component detection algorithm to uncover groups of user accounts
-controlled by different fraudsters, and leverage stylometry and deep learning
-to attribute them to crowdsourcing site profiles. Dolos correctly identified
-the owners of 95% of fraudster-controlled communities, and uncovered fraudsters
-who promoted as many as 97.5% of fraud apps we collected from Google Play. When
-evaluated on 13,087 apps (820,760 reviews), which we monitored over more than 6
-months, Dolos identified 1,056 apps with suspicious reviewer groups. We report
-orthogonal evidence of their fraud, including fraud duplicates and fraud
-re-posts.
-",1,0,0,0,0,0
-19956,Instabilities in Interacting Binary Stars,"  The types of instability in the interacting binary stars are reviewed. The
-project ""Inter-Longitude Astronomy"" is a series of smaller projects on concrete
-stars or groups of stars. It has no special funds, and is supported from
-resources and grants of participating organizations, when informal working
-groups are created. Totally we studied 1900+ variable stars of different types.
-The characteristic timescale is from seconds to decades and (extrapolating)
-even more. The monitoring of the first star of our sample AM Her was initiated
-by Prof. V.P. Tsesevich (1907-1983). Since more than 358 ADS papers were
-published. Some highlights of our photometric and photo-polarimetric monitoring
-and mathematical modelling of interacting binary stars of different types are
-presented: classical, asynchronous, intermediate polars and magnetic dwarf
-novae (DO Dra) with 25 timescales corresponding to different physical
-mechanisms and their combinations (part ""Polar""); negative and positive
-superhumpers in nova-like and many dwarf novae stars (""Superhumper""); eclipsing
-""non-magnetic"" cataclysmic variables; symbiotic systems (""Symbiosis"");
-super-soft sources (SSS, QR And); spotted (and not spotted) eclipsing variables
-with (and without) evidence for a current mass transfer (""Eclipser"") with a
-special emphasis on systems with a direct impact of the stream into the gainer
-star's atmosphere, or V361 Lyr-type stars. Other parts of the ILA project are
-""Stellar Bell"" (interesting pulsating variables of different types and periods
-- M, SR, RV Tau, RR Lyr, Delta Sct) and ""Novice""(=""New Variable"") discoveries
-and classification with a subsequent monitoring for searching and studying
-possible multiple components of variability. Special mathematical methods have
-been developed to create a set of complementary software for statistically
-optimal modelling of variable stars of different types.
-",0,1,0,0,0,0
-19957,Interaction blockade for bosons in an asymmetric double well,"  The interaction blockade phenomenon isolates the motion of a single quantum
-particle within a multi-particle system, in particular for coherent
-oscillations in and out of a region affected by the blockade mechanism. For
-identical quantum particles with Bose statistics, the presence of the other
-particles is still felt by a bosonic stimulation factor $\sqrt{N}$ that speeds
-up the coherent oscillations, where $N$ is the number of bosons. Here we
-propose an experiment to observe this enhancement factor with a small number of
-bosonic atoms. The proposed protocol realises an asymmetric double well
-potential with multiple optical tweezer laser beams. The ability to adjust bias
-independently of the coherent coupling between the wells allows the potential
-to be loaded with different particle numbers while maintaining the resonance
-condition needed for coherent oscillations. Numerical simulations with up to
-three bosons in a realistic potential generated by three optical tweezers
-predict that the relevant avoided level crossing can be probed and the expected
-bosonic enhancement factor observed.
-",0,1,0,0,0,0
-19958,SPIDER: CMB polarimetry from the edge of space,"  SPIDER is a balloon-borne instrument designed to map the polarization of the
-millimeter-wave sky at large angular scales. SPIDER targets the B-mode
-signature of primordial gravitational waves in the cosmic microwave background
-(CMB), with a focus on mapping a large sky area with high fidelity at multiple
-frequencies. SPIDER's first longduration balloon (LDB) flight in January 2015
-deployed a total of 2400 antenna-coupled Transition Edge Sensors (TESs) at 90
-GHz and 150 GHz. In this work we review the design and in-flight performance of
-the SPIDER instrument, with a particular focus on the measured performance of
-the detectors and instrument in a space-like loading and radiation environment.
-SPIDER's second flight in December 2018 will incorporate payload upgrades and
-new receivers to map the sky at 285 GHz, providing valuable information for
-cleaning polarized dust emission from CMB maps.
-",0,1,0,0,0,0
-19959,Wind Shear and Turbulence on Titan : Huygens Analysis,"  Wind shear measured by Doppler tracking of the Huygens probe is evaluated,
-and found to be within the range anticipated by pre-flight assessments (namely
-less than two times the Brunt-Vaisala frequency). The strongest large-scale
-shear encountered was ~5 m/s/km, a level associated with 'Light' turbulence in
-terrestrial aviation. Near-surface winds (below 4km) have small-scale
-fluctuations of ~0.2 m/s , indicated both by probe tilt and Doppler tracking,
-and the characteristics of the fluctuation, of interest for future missions to
-Titan, can be reproduced with a simple autoregressive (AR(1)) model. The
-turbulent dissipation rate at an altitude of ~500m is found to be 16 cm2/sec3,
-which may be a useful benchmark for atmospheric circulation models.
-",0,1,0,0,0,0
-19960,Composable security in relativistic quantum cryptography,"  Relativistic protocols have been proposed to overcome some impossibility
-results in classical and quantum cryptography. In such a setting, one takes the
-location of honest players into account, and uses the fact that information
-cannot travel faster than the speed of light to limit the abilities of
-dishonest agents. For example, various relativistic bit commitment protocols
-have been proposed. Although it has been shown that bit commitment is
-sufficient to construct oblivious transfer and thus multiparty computation,
-composing specific relativistic protocols in this way is known to be insecure.
-A composable framework is required to perform such a modular security analysis
-of construction schemes, but no known frameworks can handle models of
-computation in Minkowski space.
-By instantiating the systems model from the Abstract Cryptography framework
-with Causal Boxes, we obtain such a composable framework, in which messages are
-assigned a location in Minkowski space (or superpositions thereof). This allows
-us to analyse relativistic protocols and to derive novel possibility and
-impossibility results. We show that (1) coin flipping can be constructed from
-the primitive channel with delay, (2) biased coin flipping, bit commitment and
-channel with delay are all impossible without further assumptions, and (3) it
-is impossible to improve a channel with delay. Note that the impossibility
-results also hold in the computational and bounded storage settings. This
-implies in particular non-composability of all proposed relativistic bit
-commitment protocols, of bit commitment in the bounded storage model, and of
-biased coin flipping.
-",1,0,0,0,0,0
-19961,Tensor networks demonstrate the robustness of localization and symmetry protected topological phases,"  We prove that all eigenstates of many-body localized symmetry protected
-topological systems with time reversal symmetry have four-fold degenerate
-entanglement spectra in the thermodynamic limit. To that end, we employ unitary
-quantum circuits where the number of sites the gates act on grows linearly with
-the system size. We find that the corresponding matrix product operator
-representation has similar local symmetries as matrix product ground states of
-symmetry protected topological phases. Those local symmetries give rise to a
-$\mathbb{Z}_2$ topological index, which is robust against arbitrary
-perturbations so long as they do not break time reversal symmetry or drive the
-system out of the fully many-body localized phase.
-",0,1,0,0,0,0
-19962,Formation of Intermediate-Mass Black Holes through Runaway Collisions in the First Star Clusters,"  We study the formation of massive black holes in the first star clusters. We
-first locate star-forming gas clouds in proto-galactic haloes of $\gtrsim
-\!10^7\,{\rm M}_{\odot}$ in cosmological hydrodynamics simulations and use them
-to generate the initial conditions for star clusters with masses of $\sim
-\!10^5\,{\rm M}_{\odot}$. We then perform a series of direct-tree hybrid
-$N$-body simulations to follow runaway stellar collisions in the dense star
-clusters. In all the cluster models except one, runaway collisions occur within
-a few million years, and the mass of the central, most massive star reaches
-$\sim \!400-1900\,{\rm M}_{\odot}$. Such very massive stars collapse to leave
-intermediate-mass black holes (IMBHs). The diversity of the final masses may be
-attributed to the differences in a few basic properties of the host haloes such
-as mass, central gas velocity dispersion, and mean gas density of the central
-core. Finally, we derive the IMBH mass to cluster mass ratios, and compare them
-with the observed black hole to bulge mass ratios in the present-day Universe.
-",0,1,0,0,0,0
-19963,Mean Reverting Portfolios via Penalized OU-Likelihood Estimation,"  We study an optimization-based approach to con- struct a mean-reverting
-portfolio of assets. Our objectives are threefold: (1) design a portfolio that
-is well-represented by an Ornstein-Uhlenbeck process with parameters estimated
-by maximum likelihood, (2) select portfolios with desirable characteristics of
-high mean reversion and low variance, and (3) select a parsimonious portfolio,
-i.e. find a small subset of a larger universe of assets that can be used for
-long and short positions. We present the full problem formulation, a
-specialized algorithm that exploits partial minimization, and numerical
-examples using both simulated and empirical price data.
-",0,0,0,1,0,1
-19964,When the cookie meets the blockchain: Privacy risks of web payments via cryptocurrencies,"  We show how third-party web trackers can deanonymize users of
-cryptocurrencies. We present two distinct but complementary attacks. On most
-shopping websites, third party trackers receive information about user
-purchases for purposes of advertising and analytics. We show that, if the user
-pays using a cryptocurrency, trackers typically possess enough information
-about the purchase to uniquely identify the transaction on the blockchain, link
-it to the user's cookie, and further to the user's real identity. Our second
-attack shows that if the tracker is able to link two purchases of the same user
-to the blockchain in this manner, it can identify the user's entire cluster of
-addresses and transactions on the blockchain, even if the user employs
-blockchain anonymity techniques such as CoinJoin. The attacks are passive and
-hence can be retroactively applied to past purchases. We discuss several
-mitigations, but none are perfect.
-",1,0,0,0,0,0
-19965,Performance Evaluation of Container-based Virtualization for High Performance Computing Environments,"  Virtualization technologies have evolved along with the development of
-computational environments since virtualization offered needed features at that
-time such as isolation, accountability, resource allocation, resource fair
-sharing and so on. Novel processor technologies bring to commodity computers
-the possibility to emulate diverse environments where a wide range of
-computational scenarios can be run. Along with processors evolution, system
-developers have created different virtualization mechanisms where each new
-development enhanced the performance of previous virtualized environments.
-Recently, operating system-based virtualization technologies captured the
-attention of communities abroad (from industry to academy and research) because
-their important improvements on performance area.
-In this paper, the features of three container-based operating systems
-virtualization tools (LXC, Docker and Singularity) are presented. LXC, Docker,
-Singularity and bare metal are put under test through a customized single node
-HPL-Benchmark and a MPI-based application for the multi node testbed. Also the
-disk I/O performance, Memory (RAM) performance, Network bandwidth and GPU
-performance are tested for the COS technologies vs bare metal. Preliminary
-results and conclusions around them are presented and discussed.
-",1,0,0,0,0,0
-19966,An integral formula for the $Q$-prime curvature in 3-dimensional CR geometry,"  We give an integral formula for the total $Q^\prime$-curvature of a
-three-dimensional CR manifold with positive CR Yamabe constant and nonnegative
-Paneitz operator. Our derivation includes a relationship between the Green's
-functions of the CR Laplacian and the $P^\prime$-operator.
-",0,0,1,0,0,0
-19967,Intelligent Device Discovery in the Internet of Things - Enabling the Robot Society,"  The Internet of Things (IoT) is continuously growing to connect billions of
-smart devices anywhere and anytime in an Internet-like structure, which enables
-a variety of applications, services and interactions between human and objects.
-In the future, the smart devices are supposed to be able to autonomously
-discover a target device with desired features and generate a set of entirely
-new services and applications that are not supervised or even imagined by human
-beings. The pervasiveness of smart devices, as well as the heterogeneity of
-their design and functionalities, raise a major concern: How can a smart device
-efficiently discover a desired target device? In this paper, we propose a
-Social-Aware and Distributed (SAND) scheme that achieves a fast, scalable and
-efficient device discovery in the IoT. The proposed SAND scheme adopts a novel
-device ranking criteria that measures the device's degree, social relationship
-diversity, clustering coefficient and betweenness. Based on the device ranking
-criteria, the discovery request can be guided to travel through critical
-devices that stand at the major intersections of the network, and thus quickly
-reach the desired target device by contacting only a limited number of
-intermediate devices. With the help of such an intelligent device discovery as
-SAND, the IoT devices, as well as other computing facilities, software and data
-on the Internet, can autonomously establish new social connections with each
-other as human being do. They can formulate self-organized computing groups to
-perform required computing tasks, facilitate a fusion of a variety of computing
-service, network service and data to generate novel applications and services,
-evolve from the individual aritificial intelligence to the collaborative
-intelligence, and eventually enable the birth of a robot society.
-",1,0,0,0,0,0
-19968,Super-blockers and the effect of network structure on information cascades,"  Modelling information cascades over online social networks is important in
-fields from marketing to civil unrest prediction, however the underlying
-network structure strongly affects the probability and nature of such cascades.
-Even with simple cascade dynamics the probability of large cascades are almost
-entirely dictated by network properties, with well-known networks such as
-Erdos-Renyi and Barabasi-Albert producing wildly different cascades from the
-same model. Indeed, the notion of 'superspreaders' has arisen to describe
-highly influential nodes promoting global cascades in a social network. Here we
-use a simple model of global cascades to show that the presence of locality in
-the network increases the probability of a global cascade due to the increased
-vulnerability of connecting nodes. Rather than 'super-spreaders', we find that
-the presence of these highly connected 'super-blockers' in heavy-tailed
-networks in fact reduces the probability of global cascades, while promoting
-information spread when targeted as the initial spreader.
-",1,0,0,0,0,0
-19969,"Super-Gaussian, super-diffusive transport of multi-mode active matter","  Living cells exhibit multi-mode transport that switches between an active,
-self-propelled motion and a seemingly passive, random motion. Cellular
-decision-making over transport mode switching is a stochastic process that
-depends on the dynamics of the intracellular chemical network regulating the
-cell migration process. Here, we propose a theory and an exactly solvable model
-of multi-mode active matter. Our exact model study shows that the reversible
-transition between a passive mode and an active mode is the origin of the
-anomalous, super-Gaussian transport dynamics, which has been observed in
-various experiments for multi-mode active matter. We also present the
-generalization of our model to encompass complex multi-mode matter with
-arbitrary internal state chemical dynamics and internal state dependent
-transport dynamics.
-",0,1,0,0,0,0
-19970,Height functions for motives,"  We define various height functions for motives over number fields. We compare
-these height functions with classical height functions on algebraic varieties,
-and also with analogous height functions for variations of Hodge structures on
-curves over C. These comparisons provide new questions on motives over number
-fields.
-",0,0,1,0,0,0
-19971,On the dimension effect of regularized linear discriminant analysis,"  This paper studies the dimension effect of the linear discriminant analysis
-(LDA) and the regularized linear discriminant analysis (RLDA) classifiers for
-large dimensional data where the observation dimension $p$ is of the same order
-as the sample size $n$. More specifically, built on properties of the Wishart
-distribution and recent results in random matrix theory, we derive explicit
-expressions for the asymptotic misclassification errors of LDA and RLDA
-respectively, from which we gain insights of how dimension affects the
-performance of classification and in what sense. Motivated by these results, we
-propose adjusted classifiers by correcting the bias brought by the unequal
-sample sizes. The bias-corrected LDA and RLDA classifiers are shown to have
-smaller misclassification rates than LDA and RLDA respectively. Several
-interesting examples are discussed in detail and the theoretical results on
-dimension effect are illustrated via extensive simulation studies.
-",0,0,1,1,0,0
-19972,Plasma Wake Accelerators: Introduction and Historical Overview,"  Fundamental questions on the nature of matter and energy have found answers
-thanks to the use of particle accelerators. Societal applications, such as
-cancer treatment or cancer imaging, illustrate the impact of accelerators in
-our current life. Today, accelerators use metallic cavities that sustain
-electricfields with values limited to about 100 MV/m. Because of their ability
-to support extreme accelerating gradients, the plasma medium has recently been
-proposed for future cavity-like accelerating structures. This contribution
-highlights the tremendous evolution of plasma accelerators driven by either
-laser or particle beams that allow the production of high quality particle
-beams with a degree of tunability and a set of parameters that make them very
-pertinent for many applications.
-",0,1,0,0,0,0
-19973,$R$-triviality of some exceptional groups,"  The main aim of this paper is to prove $R$-triviality for simple, simply
-connected algebraic groups with Tits index $E_{8,2}^{78}$ or $E_{7,1}^{78}$,
-defined over a field $k$ of arbitrary characteristic. Let $G$ be such a group.
-We prove that there exists a quadratic extension $K$ of $k$ such that $G$ is
-$R$-trivial over $K$, i.e., for any extension $F$ of $K$, $G(F)/R=\{1\}$, where
-$G(F)/R$ denotes the group of $R$-equivalence classes in $G(F)$, in the sense
-of Manin (see \cite{M}). As a consequence, it follows that the variety $G$ is
-retract $K$-rational and that the Kneser-Tits conjecture holds for these groups
-over $K$. Moreover, $G(L)$ is projectively simple as an abstract group for any
-field extension $L$ of $K$. In their monograph (\cite{TW}) J. Tits and Richard
-Weiss conjectured that for an Albert division algebra $A$ over a field $k$, its
-structure group $Str(A)$ is generated by scalar homotheties and its
-$U$-operators. This is known to be equivalent to the Kneser-Tits conjecture for
-groups with Tits index $E_{8,2}^{78}$. We settle this conjecture for Albert
-division algebras which are first constructions, in affirmative. These results
-are obtained as corollaries to the main result, which shows that if $A$ is an
-Albert division algebra which is a first construction and $\Gamma$ its
-structure group, i.e., the algebraic group of the norm similarities of $A$,
-then $\Gamma(F)/R=\{1\}$ for any field extension $F$ of $k$, i.e., $\Gamma$ is
-$R$-trivial.
-",0,0,1,0,0,0
-19974,Aperture synthesis imaging of the carbon AGB star R Sculptoris: Detection of a complex structure and a dominating spot on the stellar disk,"  We present near-infrared interferometry of the carbon-rich asymptotic giant
-branch (AGB) star R Sculptoris.
-The visibility data indicate a broadly circular resolved stellar disk with a
-complex substructure. The observed AMBER squared visibility values show drops
-at the positions of CO and CN bands, indicating that these lines form in
-extended layers above the photosphere. The AMBER visibility values are best fit
-by a model without a wind. The PIONIER data are consistent with the same model.
-We obtain a Rosseland angular diameter of 8.9+-0.3 mas, corresponding to a
-Rosseland radius of 355+-55 Rsun, an effective temperature of 2640+-80 K, and a
-luminosity of log L/Lsun=3.74+-0.18. These parameters match evolutionary tracks
-of initial mass 1.5+-0.5 Msun and current mass 1.3+-0.7 Msun. The reconstructed
-PIONIER images exhibit a complex structure within the stellar disk including a
-dominant bright spot located at the western part of the stellar disk. The spot
-has an H-band peak intensity of 40% to 60% above the average intensity of the
-limb-darkening-corrected stellar disk. The contrast between the minimum and
-maximum intensity on the stellar disk is about 1:2.5.
-Our observations are broadly consistent with predictions by dynamic
-atmosphere and wind models, although models with wind appear to have a
-circumstellar envelope that is too extended compared to our observations. The
-detected complex structure within the stellar disk is most likely caused by
-giant convection cells, resulting in large-scale shock fronts, and their
-effects on clumpy molecule and dust formation seen against the photosphere at
-distances of 2-3 stellar radii.
-",0,1,0,0,0,0
-19975,Intrinsically Sparse Long Short-Term Memory Networks,"  Long Short-Term Memory (LSTM) has achieved state-of-the-art performances on a
-wide range of tasks. Its outstanding performance is guaranteed by the long-term
-memory ability which matches the sequential data perfectly and the gating
-structure controlling the information flow. However, LSTMs are prone to be
-memory-bandwidth limited in realistic applications and need an unbearable
-period of training and inference time as the model size is ever-increasing. To
-tackle this problem, various efficient model compression methods have been
-proposed. Most of them need a big and expensive pre-trained model which is a
-nightmare for resource-limited devices where the memory budget is strictly
-limited. To remedy this situation, in this paper, we incorporate the Sparse
-Evolutionary Training (SET) procedure into LSTM, proposing a novel model dubbed
-SET-LSTM. Rather than starting with a fully-connected architecture, SET-LSTM
-has a sparse topology and dramatically fewer parameters in both phases,
-training and inference. Considering the specific architecture of LSTMs, we
-replace the LSTM cells and embedding layers with sparse structures and further
-on, use an evolutionary strategy to adapt the sparse connectivity to the data.
-Additionally, we find that SET-LSTM can provide many different good
-combinations of sparse connectivity to substitute the overparameterized
-optimization problem of dense neural networks. Evaluated on four sentiment
-analysis classification datasets, the results demonstrate that our proposed
-model is able to achieve usually better performance than its fully connected
-counterpart while having less than 4\% of its parameters.
-",1,0,0,0,0,0
-19976,Inferencing into the void: problems with implicit populations Comments on `Empirical software engineering experts on the use of students and professionals in experiments',"  I welcome the contribution from Falessi et al. [1] hereafter referred to as
-F++ , and the ensuing debate. Experimentation is an important tool within
-empirical software engineering, so how we select participants is clearly a
-relevant question. Moreover as F++ point out, the question is considerably more
-nuanced than the simple dichotomy it might appear to be at first sight.
-This commentary is structured as follows. In Section 2 I briefly summarise
-the arguments of F++ and comment on their approach. Next, in Section 3, I take
-a step back to consider the nature of representativeness in inferential
-arguments and the need for careful definition. Then I give three examples of
-using different types of participant to consider impact. I conclude by arguing,
-largely in agreement with F++, that the question of whether student
-participants are representative or not depends on the target population.
-However, we need to give careful consideration to defining that population and,
-in particular, not to overlook the representativeness of tasks and environment.
-This is facilitated by explicit description of the target populations.
-",1,0,0,0,0,0
-19977,NoScope: Optimizing Neural Network Queries over Video at Scale,"  Recent advances in computer vision-in the form of deep neural networks-have
-made it possible to query increasing volumes of video data with high accuracy.
-However, neural network inference is computationally expensive at scale:
-applying a state-of-the-art object detector in real time (i.e., 30+ frames per
-second) to a single video requires a $4000 GPU. In response, we present
-NoScope, a system for querying videos that can reduce the cost of neural
-network video analysis by up to three orders of magnitude via
-inference-optimized model search. Given a target video, object to detect, and
-reference neural network, NoScope automatically searches for and trains a
-sequence, or cascade, of models that preserves the accuracy of the reference
-network but is specialized to the target video and are therefore far less
-computationally expensive. NoScope cascades two types of models: specialized
-models that forego the full generality of the reference model but faithfully
-mimic its behavior for the target video and object; and difference detectors
-that highlight temporal differences across frames. We show that the optimal
-cascade architecture differs across videos and objects, so NoScope uses an
-efficient cost-based optimizer to search across models and cascades. With this
-approach, NoScope achieves two to three order of magnitude speed-ups
-(265-15,500x real-time) on binary classification tasks over fixed-angle webcam
-and surveillance video while maintaining accuracy within 1-5% of
-state-of-the-art neural networks.
-",1,0,0,0,0,0
-19978,TensorQuant - A Simulation Toolbox for Deep Neural Network Quantization,"  Recent research implies that training and inference of deep neural networks
-(DNN) can be computed with low precision numerical representations of the
-training/test data, weights and gradients without a general loss in accuracy.
-The benefit of such compact representations is twofold: they allow a
-significant reduction of the communication bottleneck in distributed DNN
-training and faster neural network implementations on hardware accelerators
-like FPGAs. Several quantization methods have been proposed to map the original
-32-bit floating point problem to low-bit representations. While most related
-publications validate the proposed approach on a single DNN topology, it
-appears to be evident, that the optimal choice of the quantization method and
-number of coding bits is topology dependent. To this end, there is no general
-theory available, which would allow users to derive the optimal quantization
-during the design of a DNN topology. In this paper, we present a quantization
-tool box for the TensorFlow framework. TensorQuant allows a transparent
-quantization simulation of existing DNN topologies during training and
-inference. TensorQuant supports generic quantization methods and allows
-experimental evaluation of the impact of the quantization on single layers as
-well as on the full topology. In a first series of experiments with
-TensorQuant, we show an analysis of fix-point quantizations of popular CNN
-topologies.
-",1,0,0,1,0,0
-19979,Information-geometrical characterization of statistical models which are statistically equivalent to probability simplexes,"  The probability simplex is the set of all probability distributions on a
-finite set and is the most fundamental object in the finite probability theory.
-In this paper we give a characterization of statistical models on finite sets
-which are statistically equivalent to probability simplexes in terms of
-$\alpha$-families including exponential families and mixture families. The
-subject has a close relation to some fundamental aspects of information
-geometry such as $\alpha$-connections and autoparallelity.
-",1,0,1,1,0,0
-19980,The Network Nullspace Property for Compressed Sensing of Big Data over Networks,"  We present a novel condition, which we term the net- work nullspace property,
-which ensures accurate recovery of graph signals representing massive
-network-structured datasets from few signal values. The network nullspace
-property couples the cluster structure of the underlying network-structure with
-the geometry of the sampling set. Our results can be used to design efficient
-sampling strategies based on the network topology.
-",1,0,0,1,0,0
-19981,On physically redundant and irrelevant features when applying Lie-group symmetry analysis to hydrodynamic stability analysis,"  Every linear system of partial differential equations (PDEs) admits a scaling
-symmetry in its dependent variables. In conjunction with other admitted
-symmetries of linear type, the associated invariant solution condition poses a
-linear eigenvalue problem. If this problem is structured such that the spectral
-theorem applies, then the general solution of the considered linear PDE system
-is obtained by summing or integrating the invariant eigenfunctions (modes) over
-all eigenvalues, depending on whether the spectrum of the operator is discrete
-or continuous. By first studying the 1-D diffusion equation as a demonstrating
-example, this method is then applied to a relevant 2-D problem from
-hydrodynamic stability analysis. The aim of this study is to draw attention to
-the following two independent facts that need to be addressed in future studies
-when constructing solutions for linear PDEs with the method of Lie-symmetries:
-(i) Although each new symmetry leads to a mathematically different spectral
-decomposition, they may all be physically redundant to standard ones and do not
-reveal a new physical mechanism behind the overall considered dynamical
-process, as incorrectly asserted, for example, in the recent studies by the
-group of Oberlack et al. Hence, with regard to linear stability analysis, no
-physically ""new"" or more ""general"" modes are generated by this method than the
-ones already established. (ii) Next to the eigenvalue parameters, each single
-mode can also acquire non-system parameters, depending on the choice of its
-underlying symmetry. These symmetry-induced parameters, however, are all
-physically irrelevant, since their effect on a single mode will cancel when
-considering all modes collectively. In particular, the collective action of all
-single modes is identical for all symmetry-based decompositions and thus
-indistinguishable when considering the full physical fields.
-",0,1,0,0,0,0
-19982,Transforming Musical Signals through a Genre Classifying Convolutional Neural Network,"  Convolutional neural networks (CNNs) have been successfully applied on both
-discriminative and generative modeling for music-related tasks. For a
-particular task, the trained CNN contains information representing the decision
-making or the abstracting process. One can hope to manipulate existing music
-based on this 'informed' network and create music with new features
-corresponding to the knowledge obtained by the network. In this paper, we
-propose a method to utilize the stored information from a CNN trained on
-musical genre classification task. The network was composed of three
-convolutional layers, and was trained to classify five-second song clips into
-five different genres. After training, randomly selected clips were modified by
-maximizing the sum of outputs from the network layers. In addition to the
-potential of such CNNs to produce interesting audio transformation, more
-information about the network and the original music could be obtained from the
-analysis of the generated features since these features indicate how the
-network 'understands' the music.
-",1,0,0,0,0,0
-19983,AMTnet: Action-Micro-Tube Regression by End-to-end Trainable Deep Architecture,"  Dominant approaches to action detection can only provide sub-optimal
-solutions to the problem, as they rely on seeking frame-level detections, to
-later compose them into ""action tubes"" in a post-processing step. With this
-paper we radically depart from current practice, and take a first step towards
-the design and implementation of a deep network architecture able to classify
-and regress whole video subsets, so providing a truly optimal solution of the
-action detection problem. In this work, in particular, we propose a novel deep
-net framework able to regress and classify 3D region proposals spanning two
-successive video frames, whose core is an evolution of classical region
-proposal networks (RPNs). As such, our 3D-RPN net is able to effectively encode
-the temporal aspect of actions by purely exploiting appearance, as opposed to
-methods which heavily rely on expensive flow maps. The proposed model is
-end-to-end trainable and can be jointly optimised for action localisation and
-classification in a single step. At test time the network predicts
-""micro-tubes"" encompassing two successive frames, which are linked up into
-complete action tubes via a new algorithm which exploits the temporal encoding
-learned by the network and cuts computation time by 50%. Promising results on
-the J-HMDB-21 and UCF-101 action detection datasets show that our model does
-outperform the state-of-the-art when relying purely on appearance.
-",1,0,0,0,0,0
-19984,Adaptive Estimation in Structured Factor Models with Applications to Overlapping Clustering,"  This work introduces a novel estimation method, called LOVE, of the entries
-and structure of a loading matrix A in a sparse latent factor model X = AZ + E,
-for an observable random vector X in Rp, with correlated unobservable factors Z
-\in RK, with K unknown, and independent noise E. Each row of A is scaled and
-sparse. In order to identify the loading matrix A, we require the existence of
-pure variables, which are components of X that are associated, via A, with one
-and only one latent factor. Despite the fact that the number of factors K, the
-number of the pure variables, and their location are all unknown, we only
-require a mild condition on the covariance matrix of Z, and a minimum of only
-two pure variables per latent factor to show that A is uniquely defined, up to
-signed permutations. Our proofs for model identifiability are constructive, and
-lead to our novel estimation method of the number of factors and of the set of
-pure variables, from a sample of size n of observations on X. This is the first
-step of our LOVE algorithm, which is optimization-free, and has low
-computational complexity of order p2. The second step of LOVE is an easily
-implementable linear program that estimates A. We prove that the resulting
-estimator is minimax rate optimal up to logarithmic factors in p. The model
-structure is motivated by the problem of overlapping variable clustering,
-ubiquitous in data science. We define the population level clusters as groups
-of those components of X that are associated, via the sparse matrix A, with the
-same unobservable latent factor, and multi-factor association is allowed.
-Clusters are respectively anchored by the pure variables, and form overlapping
-sub-groups of the p-dimensional random vector X. The Latent model approach to
-OVErlapping clustering is reflected in the name of our algorithm, LOVE.
-",0,0,1,1,0,0
-19985,Asymptotic profile of solutions for some wave equations with very strong structural damping,"  We consider the Cauchy problem in R^n for some types of damped wave
-equations. We derive asymptotic profiles of solutions with weighted
-L^{1,1}(R^n) initial data by employing a simple method introduced by the first
-author. The obtained results will include regularity loss type estimates, which
-are essentially new in this kind of equations.
-",0,0,1,0,0,0
-19986,Spontaneous generation of fractional vortex-antivortex pairs at single edges of high-Tc superconductors,"  Unconventional d-wave superconductors with pair-breaking edges are predicted
-to have ground states with spontaneously broken time-reversal and translational
-symmetries. We use the quasiclassical theory of superconductivity to
-demonstrate that such phases can exist at any single pair-breaking facet. This
-implies that a greater variety of systems, not necessarily mesoscopic in size,
-should be unstable to such symmetry breaking. The density of states averaged
-over the facet displays a broad peak centered at zero energy, which is
-consistent with experimental findings of a broad zero-bias conductance peak
-with a temperature-independent width at low temperatures.
-",0,1,0,0,0,0
-19987,A Survey of Neuromorphic Computing and Neural Networks in Hardware,"  Neuromorphic computing has come to refer to a variety of brain-inspired
-computers, devices, and models that contrast the pervasive von Neumann computer
-architecture. This biologically inspired approach has created highly connected
-synthetic neurons and synapses that can be used to model neuroscience theories
-as well as solve challenging machine learning problems. The promise of the
-technology is to create a brain-like ability to learn and adapt, but the
-technical challenges are significant, starting with an accurate neuroscience
-model of how the brain works, to finding materials and engineering
-breakthroughs to build devices to support these models, to creating a
-programming framework so the systems can learn, to creating applications with
-brain-like capabilities. In this work, we provide a comprehensive survey of the
-research and motivations for neuromorphic computing over its history. We begin
-with a 35-year review of the motivations and drivers of neuromorphic computing,
-then look at the major research areas of the field, which we define as
-neuro-inspired models, algorithms and learning approaches, hardware and
-devices, supporting systems, and finally applications. We conclude with a broad
-discussion on the major research topics that need to be addressed in the coming
-years to see the promise of neuromorphic computing fulfilled. The goals of this
-work are to provide an exhaustive review of the research conducted in
-neuromorphic computing since the inception of the term, and to motivate further
-work by illuminating gaps in the field where new research is needed.
-",1,0,0,0,0,0
-19988,Junctions of refined Wilson lines and one-parameter deformation of quantum groups,"  We study junctions of Wilson lines in refined SU(N) Chern-Simons theory and
-their local relations. We focus on junctions of Wilson lines in antisymmetric
-and symmetric powers of the fundamental representation and propose a set of
-local relations which realize one-parameter deformations of quantum groups
-$\dot{U}_{q}(\mathfrak{sl}_{m})$ and $\dot{U}_{q}(\mathfrak{sl}_{n|m})$.
-",0,0,1,0,0,0
-19989,Online estimation of the asymptotic variance for averaged stochastic gradient algorithms,"  Stochastic gradient algorithms are more and more studied since they can deal
-efficiently and online with large samples in high dimensional spaces. In this
-paper, we first establish a Central Limit Theorem for these estimates as well
-as for their averaged version in general Hilbert spaces. Moreover, since having
-the asymptotic normality of estimates is often unusable without an estimation
-of the asymptotic variance, we introduce a new recursive algorithm for
-estimating this last one, and we establish its almost sure rate of convergence
-as well as its rate of convergence in quadratic mean. Finally, two examples
-consisting in estimating the parameters of the logistic regression and
-estimating geometric quantiles are given.
-",0,0,1,1,0,0
-19990,The response of the terrestrial bow shock and magnetopause of the long term decline in solar polar fields,"  The location of the terrestrial magnetopause (MP) and it's subsolar stand-off
-distance depends not only on the solar wind dynamic pressure and the
-interplanetary magnetic field (IMF), both of which play a crucial role in
-determining it's shape, but also on the nature of the processes involved in the
-interaction between the solar wind and the magnetosphere. The stand-off
-distance of the earth's MP and bow shock (BS) also define the extent of
-terrestrial magnetic fields into near-earth space on the sunward side and have
-important consequences for space weather. However, asymmetries due to the
-direction of the IMF are hard to account for, making it nearly impossible to
-favour any specific model over the other in estimating the extent of the MP or
-BS. Thus, both numerical and empirical models have been used and compared to
-estimate the BS and MP stand-off distances as well as the MP shape, in the
-period Jan. 1975-Dec. 2016, covering solar cycles 21-24. The computed MP and BS
-stand-off distances have been found to be increasing steadily over the past two
-decades, since ~1995, spanning solar cycles 23 and 24. The increasing trend is
-consistent with earlier reported studies of a long term and steady decline in
-solar polar magnetic fields and solar wind micro-turbulence levels. The present
-study, thus, highlights the response of the terrestrial magnetosphere to the
-long term global changes in both solar and solar wind activity, through a
-detailed study of the extent and shape of the terrestrial MP and BS over the
-past four solar cycles, a period spanning the last four decades.
-",0,1,0,0,0,0
-19991,Superconductivity of barium-VI synthesized via compression at low temperatures,"  Using a membrane-driven diamond anvil cell and both ac magnetic
-susceptibility and electrical resistivity measurements, we have characterized
-the superconducting phase diagram of elemental barium to pressures as high as
-65 GPa. We have determined the superconducting properties of the recently
-discovered Ba-VI crystal structure, which can only be accessed via the
-application of pressure at low temperature. We find that Ba-VI exhibits a
-maximum Tc near 8 K, which is substantially higher than the maximum Tc found
-when pressure is applied at room temperature.
-",0,1,0,0,0,0
-19992,Self-Supervised Damage-Avoiding Manipulation Strategy Optimization via Mental Simulation,"  Everyday robotics are challenged to deal with autonomous product handling in
-applications like logistics or retail, possibly causing damage on the items
-during manipulation. Traditionally, most approaches try to minimize physical
-interaction with goods. However, we propose to take into account any unintended
-motion of objects in the scene and to learn manipulation strategies in a
-self-supervised way which minimize the potential damage. The presented approach
-consists of a planning method that determines the optimal sequence to
-manipulate a number of objects in a scene with respect to possible damage by
-simulating interaction and hence anticipating scene dynamics. The planned
-manipulation sequences are taken as input to a machine learning process which
-generalizes to new, unseen scenes in the same application scenario. This
-learned manipulation strategy is continuously refined in a self-supervised
-optimization cycle dur- ing load-free times of the system. Such a
-simulation-in-the-loop setup is commonly known as mental simulation and allows
-for efficient, fully automatic generation of training data as opposed to
-classical supervised learning approaches. In parallel, the generated
-manipulation strategies can be deployed in near-real time in an anytime
-fashion. We evaluate our approach on one industrial scenario (autonomous
-container unloading) and one retail scenario (autonomous shelf replenishment).
-",1,0,0,0,0,0
-19993,Adversarial Deep Learning for Robust Detection of Binary Encoded Malware,"  Malware is constantly adapting in order to avoid detection. Model based
-malware detectors, such as SVM and neural networks, are vulnerable to so-called
-adversarial examples which are modest changes to detectable malware that allows
-the resulting malware to evade detection. Continuous-valued methods that are
-robust to adversarial examples of images have been developed using saddle-point
-optimization formulations. We are inspired by them to develop similar methods
-for the discrete, e.g. binary, domain which characterizes the features of
-malware. A specific extra challenge of malware is that the adversarial examples
-must be generated in a way that preserves their malicious functionality. We
-introduce methods capable of generating functionally preserved adversarial
-malware examples in the binary domain. Using the saddle-point formulation, we
-incorporate the adversarial examples into the training of models that are
-robust to them. We evaluate the effectiveness of the methods and others in the
-literature on a set of Portable Execution~(PE) files. Comparison prompts our
-introduction of an online measure computed during training to assess general
-expectation of robustness.
-",0,0,0,1,0,0
-19994,"Simple closed curves, finite covers of surfaces, and power subgroups of Out(F_n)","  We construct examples of finite covers of punctured surfaces where the first
-rational homology is not spanned by lifts of simple closed curves. More
-generally, for any set $\mathcal{O} \subset F_n$ which is contained in the
-union of finitely many $Aut(F_n)$-orbits, we construct finite-index normal
-subgroups of $F_n$ whose first rational homology is not spanned by powers of
-elements of $\mathcal{O}$. These examples answer questions of Farb-Hensel,
-Looijenga, and Marche. We also show that the quotient of $Out(F_n)$ by the
-subgroup generated by kth powers of transvections often contains infinite order
-elements, strengthening a result of Bridson-Vogtmann saying that it is often
-infinite. Finally, for any set $\mathcal{O} \subset F_n$ which is contained in
-the union of finitely many $Aut(F_n)$-orbits, we construct integral linear
-representations of free groups that have infinite image and map all elements of
-$\mathcal{O}$ to torsion elements.
-",0,0,1,0,0,0
-19995,Hard Mixtures of Experts for Large Scale Weakly Supervised Vision,"  Training convolutional networks (CNN's) that fit on a single GPU with
-minibatch stochastic gradient descent has become effective in practice.
-However, there is still no effective method for training large CNN's that do
-not fit in the memory of a few GPU cards, or for parallelizing CNN training. In
-this work we show that a simple hard mixture of experts model can be
-efficiently trained to good effect on large scale hashtag (multilabel)
-prediction tasks. Mixture of experts models are not new (Jacobs et. al. 1991,
-Collobert et. al. 2003), but in the past, researchers have had to devise
-sophisticated methods to deal with data fragmentation. We show empirically that
-modern weakly supervised data sets are large enough to support naive
-partitioning schemes where each data point is assigned to a single expert.
-Because the experts are independent, training them in parallel is easy, and
-evaluation is cheap for the size of the model. Furthermore, we show that we can
-use a single decoding layer for all the experts, allowing a unified feature
-embedding space. We demonstrate that it is feasible (and in fact relatively
-painless) to train far larger models than could be practically trained with
-standard CNN architectures, and that the extra capacity can be well used on
-current datasets.
-",1,0,0,1,0,0
-19996,Single-hole GPR reflection imaging of solute transport in a granitic aquifer,"  Identifying transport pathways in fractured rock is extremely challenging as
-flow is often organized in a few fractures that occupy a very small portion of
-the rock volume. We demonstrate that saline tracer experiments combined with
-single-hole ground penetrating radar (GPR) reflection imaging can be used to
-monitor saline tracer movement within mm-aperture fractures. A dipole tracer
-test was performed in a granitic aquifer by injecting a saline solution in a
-known fracture, while repeatedly acquiring single-hole GPR sections in the
-pumping borehole located 6 m away. The final depth-migrated difference sections
-make it possible to identify consistent temporal changes over a 30 m depth
-interval at locations corresponding to fractures previously imaged in GPR
-sections acquired under natural flow and tracer-free conditions. The experiment
-allows determining the dominant flow paths of the injected tracer and the
-velocity (0.4-0.7 m/min) of the tracer front.
-",0,1,0,0,0,0
-19997,Episodic Torque-Luminosity Correlations and Anticorrelations of GX 1+4,"  We analyse archival CGRO-BATSE X-ray flux and spin frequency measurements of
-GX 1+4 over a time span of 3000 days. We systematically search for time
-dependent variations of torque luminosity correlation. Our preliminary results
-indicate that the correlation shifts from being positive to negative on time
-scales of few 100 days.
-",0,1,0,0,0,0
-19998,Applications of Fractional Calculus to Newtonian Mechanics,"  We investigate some basic applications of Fractional Calculus (FC) to
-Newtonian mechanics. After a brief review of FC, we consider a possible
-generalization of Newton's second law of motion and apply it to the case of a
-body subject to a constant force. In our second application of FC to Newtonian
-gravity, we consider a generalized fractional gravitational potential and
-derive the related circular orbital velocities. This analysis might be used as
-a tool to model galactic rotation curves, in view of the dark matter problem.
-Both applications have a pedagogical value in connecting fractional calculus to
-standard mechanics and can be used as a starting point for a more advanced
-treatment of fractional mechanics.
-",0,1,0,0,0,0
-19999,Radial Surface Density Profiles of Gas and Dust in the Debris Disk around 49 Ceti,"  We present ~0.4 resolution images of CO(3-2) and associated continuum
-emission from the gas-bearing debris disk around the nearby A star 49 Ceti,
-observed with the Atacama Large Millimeter/Submillimeter Array (ALMA). We
-analyze the ALMA visibilities in tandem with the broad-band spectral energy
-distribution to measure the radial surface density profiles of dust and gas
-emission from the system. The dust surface density decreases with radius
-between ~100 and 310 au, with a marginally significant enhancement of surface
-density at a radius of ~110 au. The SED requires an inner disk of small grains
-in addition to the outer disk of larger grains resolved by ALMA. The gas disk
-exhibits a surface density profile that increases with radius, contrary to most
-previous spatially resolved observations of circumstellar gas disks. While ~80%
-of the CO flux is well described by an axisymmetric power-law disk in Keplerian
-rotation about the central star, residuals at ~20% of the peak flux exhibit a
-departure from axisymmetry suggestive of spiral arms or a warp in the gas disk.
-The radial extent of the gas disk (~220 au) is smaller than that of the dust
-disk (~300 au), consistent with recent observations of other gas-bearing debris
-disks. While there are so far only three broad debris disks with well
-characterized radial dust profiles at millimeter wavelengths, 49 Ceti's disk
-shows a markedly different structure from two radially resolved gas-poor debris
-disks, implying that the physical processes generating and sculpting the gas
-and dust are fundamentally different.
-",0,1,0,0,0,0
-20000,Spontaneously broken translational symmetry at edges of high-temperature superconductors: thermodynamics in magnetic field,"  We investigate equilibrium properties, including structure of the order
-parameter, superflow patterns, and thermodynamics of low-temperature surface
-phases of layered d_{x^2-y^2}-wave superconductors in magnetic field. At zero
-external magnetic field, time-reversal symmetry and continuous translational
-symmetry along the edge are broken spontaneously in a second order phase
-transition at a temperature $T^*\approx 0.18 T_c$, where $T_c$ is the
-superconducting transition temperature. At the phase transition there is a jump
-in the specific heat that scales with the ratio between the edge length $D$ and
-layer area ${\cal A}$ as $(D\xi_0/{\cal A})\Delta C_d$, where $\Delta C_d$ is
-the jump in the specific heat at the d-wave superconducting transition and
-$\xi_0$ is the superconducting coherence length. The phase with broken symmetry
-is characterized by a gauge invariant superfluid momentum ${\bf p}_s$ that
-forms a non-trivial planar vector field with a chain of sources and sinks along
-the edges with a period of approximately $12\xi_0$, and saddle point
-disclinations in the interior. To find out the relative importance of
-time-reversal and translational symmetry breaking we apply an external field
-that breaks time-reversal symmetry explicitly. We find that the phase
-transition into the state with the non-trivial ${\bf p}_s$ vector field keeps
-its main signatures, and is still of second order. In the external field, the
-saddle point disclinations are pushed towards the edges, and thereby a chain of
-edge motifs are formed, where each motif contains a source, a sink, and a
-saddle point. Due to a competing paramagnetic response at the edges, the phase
-transition temperature $T^*$ is slowly suppressed with increasing magnetic
-field strength, but the phase with broken symmetry survives into the mixed
-state.
-",0,1,0,0,0,0
-20001,Asymmetric Spin-wave Dispersion on Fe(110): Direct Evidence of Dzyaloshinskii--Moriya Interaction,"  The influence of the Dzyaloshinskii-Moriya interaction on the spin-wave
-dispersion in an Fe double layer grown on W(110) is measured for the first
-time. It is demonstrated that the Dzyaloshinskii-Moriya interaction breaks the
-degeneracy of spin waves and leads to an asymmetric spin-wave dispersion
-relation. An extended Heisenberg spin Hamiltonian is employed to obtain the
-longitudinal component of the Dzyaloshinskii-Moriya vectors from the
-experimentally measured energy asymmetry.
-",0,1,0,0,0,0
-20002,Consumption smoothing in the working-class households of interwar Japan,"  I analyze Osaka factory worker households in the early 1920s, whether
-idiosyncratic income shocks were shared efficiently, and which consumption
-categories were robust to shocks. While the null hypothesis of full
-risk-sharing of total expenditures was rejected, factory workers maintained
-their households, in that they paid for essential expenditures (rent,
-utilities, and commutation) during economic hardship. Additionally, children's
-education expenditures were possibly robust to idiosyncratic income shocks. The
-results suggest that temporary income is statistically significantly increased
-if disposable income drops due to idiosyncratic shocks. Historical documents
-suggest microfinancial lending and saving institutions helped mitigate
-risk-based vulnerabilities.
-",0,0,0,0,0,1
-20003,Observations and Modelling of the Pre-Flare Period of the 29 March 2014 X1 Flare,"  On the 29 March 2014 NOAA active region (AR) 12017 produced an X1 flare which
-was simultaneously observed by an unprecedented number of observatories. We
-have investigated the pre-flare period of this flare from 14:00 UT until 19:00
-UT using joint observations made by the Interface Region Imaging Spectrometer
-(IRIS) and the Hinode Extreme Ultraviolet Imaging Spectrometer (EIS). Spectral
-lines providing coverage of the solar atmosphere from chromosphere to the
-corona were analysed to investigate pre-flare activity within the AR. The
-results of the investigation have revealed evidence of strongly blue-shifted
-plasma flows, with velocities up to 200 km/s, being observed 40 minutes prior
-to flaring. These flows are located along the filament present in the active
-region and are both spatially discrete and transient. In order to constrain the
-possible explanations for this activity, we undertake non-potential magnetic
-field modelling of the active region. This modelling indicates the existence of
-a weakly twisted flux rope along the polarity inversion line in the region
-where a filament and the strong pre-flare flows are observed. We then discuss
-how these observations relate to the current models of flare triggering. We
-conclude that the most likely drivers of the observed activity are internal
-reconnection in the flux rope, early onset of the flare reconnection, or tether
-cutting reconnection along the filament.
-",0,1,0,0,0,0
-20004,"Wild ramification and K(pi, 1) spaces","  We prove that every connected affine scheme of positive characteristic is a
-K(pi, 1) space for the etale topology. The main ingredient is the special case
-of the affine space over a field k. This is dealt with by induction on n, using
-a key ""Bertini-type""' statement regarding the wild ramification of l-adic local
-systems on affine spaces, which might be of independent interest. Its proof
-uses in an essential way recent advances in higher ramification theory due to
-T. Saito. We also give rigid analytic and mixed characteristic versions of the
-main result.
-",0,0,1,0,0,0
-20005,A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading,"  Deep neural network models have been proven to be very successful in image
-classification tasks, also for medical diagnosis, but their main concern is its
-lack of interpretability. They use to work as intuition machines with high
-statistical confidence but unable to give interpretable explanations about the
-reported results. The vast amount of parameters of these models make difficult
-to infer a rationale interpretation from them. In this paper we present a
-diabetic retinopathy interpretable classifier able to classify retine images
-into the different levels of disease severity and of explaining its results by
-assigning a score for every point in the hidden and input space, evaluating its
-contribution to the final classification in a linear way. The generated visual
-maps can be interpreted by an expert in order to compare its own knowledge with
-the interpretation given by the model.
-",1,0,0,1,0,0
-20006,Arabic Multi-Dialect Segmentation: bi-LSTM-CRF vs. SVM,"  Arabic word segmentation is essential for a variety of NLP applications such
-as machine translation and information retrieval. Segmentation entails breaking
-words into their constituent stems, affixes and clitics. In this paper, we
-compare two approaches for segmenting four major Arabic dialects using only
-several thousand training examples for each dialect. The two approaches involve
-posing the problem as a ranking problem, where an SVM ranker picks the best
-segmentation, and as a sequence labeling problem, where a bi-LSTM RNN coupled
-with CRF determines where best to segment words. We are able to achieve solid
-segmentation results for all dialects using rather limited training data. We
-also show that employing Modern Standard Arabic data for domain adaptation and
-assuming context independence improve overall results.
-",1,0,0,0,0,0
-20007,Iterative bidding in electricity markets: rationality and robustness,"  This paper studies an electricity market consisting of an independent system
-operator (ISO) and a group of generators. The goal is to solve the DC optimal
-power flow (DC-OPF) problem: have the generators collectively meet the power
-demand while minimizing the aggregate generation cost and respecting line flow
-limits in the network. The ISO by itself cannot solve the DC-OPF problem as
-generators are strategic and do not share their cost functions. Instead, each
-generator submits to the ISO a bid, consisting of the price per unit of
-electricity at which it is willing to provide power. Based on the bids, the ISO
-decides how much production to allocate to each generator to minimize the total
-payment while meeting the load and satisfying the line limits. We provide a
-provably correct, decentralized iterative scheme, termed BID ADJUSTMENT
-ALGORITHM, for the resulting Bertrand competition game. Regarding convergence,
-we show that the algorithm takes the generators' bids to any desired
-neighborhood of the efficient Nash equilibrium at a linear convergence rate. As
-a consequence, the optimal production of the generators converges to the
-optimizer of the DC-OPF problem. Regarding robustness, we show that the
-algorithm is robust to affine perturbations in the bid adjustment scheme and
-that there is no incentive for any individual generator to deviate from the
-algorithm by using an alternative bid update scheme. We also establish the
-algorithm robustness to collusion, i.e., we show that, as long as each bus with
-generation has a generator following the strategy, there is no incentive for
-any group of generators to share information with the intent of tricking the
-system to obtain a higher payoff. Simulations illustrate our results.
-",1,0,1,0,0,0
-20008,Short-baseline electron antineutrino disappearance study by using neutrino sources from $^{13}$C + $^{9}$Be reaction,"  To investigate the existence of sterile neutrino, we propose a new neutrino
-production method using $^{13}$C beams and a $^{9}$Be target for short-baseline
-electron antineutrino (${\bar{\nu}}_{e}$) disappearance study. The production
-of secondary unstable isotopes which can emit neutrinos from the $^{13}$C +
-$^{9}$Be reaction is calculated with three different nucleus-nucleus (AA)
-reaction models. Different isotope yields are obtained using these models, but
-the results of the neutrino flux are found to have unanimous similarities. This
-feature gives an opportunity to study neutrino oscillation through shape
-analysis. In this work, expected neutrino flux and event rates are discussed in
-detail through intensive simulation of the light ion collision reaction and the
-neutrino flux from the beta decay of unstable isotopes followed by this
-collision. Together with the reactor and accelerator anomalies, the present
-proposed ${\bar{\nu}}_{e}$ source is shown to be a practically alternative test
-of the existence of the $\Delta m^{2}$ $\sim$ 1 eV$^{2}$ scale sterile
-neutrino.
-",0,1,0,0,0,0
-20009,Temperature-dependent non-covalent protein-protein interactions explain normal and inverted solubility in a mutant of human gamma D-crystallin,"  Protein crystal production is a major bottleneck for the structural
-characterisation of proteins. To advance beyond large-scale screening, rational
-strategies for protein crystallization are crucial. Understanding how chemical
-anisotropy (or patchiness) of the protein surface due to the variety of amino
-acid side chains in contact with solvent, contributes to protein protein
-contact formation in the crystal lattice is a major obstacle to predicting and
-optimising crystallization. The relative scarcity of sophisticated theoretical
-models that include sufficient detail to link collective behaviour, captured in
-protein phase diagrams, and molecular level details, determined from
-high-resolution structural information is a further barrier. Here we present
-two crystals structures for the P23TR36S mutant of gamma D-crystallin, each
-with opposite solubility behaviour, one melts when heated, the other when
-cooled. When combined with the protein phase diagram and a tailored patchy
-particle model we show that a single temperature dependent interaction is
-sufficient to stabilise the inverted solubility crystal. This contact, at the
-P23T substitution site, relates to a genetic cataract and reveals at a
-molecular level, the origin of the lowered and retrograde solubility of the
-protein. Our results show that the approach employed here may present an
-alternative strategy for the rationalization of protein crystallization.
-",0,0,0,0,1,0
-20010,Planetary Candidates Observed by Kepler. VIII. A Fully Automated Catalog With Measured Completeness and Reliability Based on Data Release 25,"  We present the Kepler Object of Interest (KOI) catalog of transiting
-exoplanets based on searching four years of Kepler time series photometry (Data
-Release 25, Q1-Q17). The catalog contains 8054 KOIs of which 4034 are planet
-candidates with periods between 0.25 and 632 days. Of these candidates, 219 are
-new and include two in multi-planet systems (KOI-82.06 and KOI-2926.05), and
-ten high-reliability, terrestrial-size, habitable zone candidates. This catalog
-was created using a tool called the Robovetter which automatically vets the
-DR25 Threshold Crossing Events (TCEs, Twicken et al. 2016). The Robovetter also
-vetted simulated data sets and measured how well it was able to separate TCEs
-caused by noise from those caused by low signal-to-noise transits. We discusses
-the Robovetter and the metrics it uses to sort TCEs. For orbital periods less
-than 100 days the Robovetter completeness (the fraction of simulated transits
-that are determined to be planet candidates) across all observed stars is
-greater than 85%. For the same period range, the catalog reliability (the
-fraction of candidates that are not due to instrumental or stellar noise) is
-greater than 98%. However, for low signal-to-noise candidates between 200 and
-500 days around FGK dwarf stars, the Robovetter is 76.7% complete and the
-catalog is 50.5% reliable. The KOI catalog, the transit fits and all of the
-simulated data used to characterize this catalog are available at the NASA
-Exoplanet Archive.
-",0,1,0,0,0,0
-20011,Dark Photons from Captured Inelastic Dark Matter Annihilation: Charged Particle Signatures,"  The dark sector may contain a dark photon that kinetically mixes with the
-Standard Model photon, allowing dark matter to interact weakly with normal
-matter. In previous work we analyzed the implications of this scenario for dark
-matter capture by the Sun. Dark matter will gather in the core of the Sun and
-annihilate to dark photons. These dark photons travel outwards from the center
-of the Sun and may decay to produce positrons that can be detected by the Alpha
-Magnetic Spectrometer (AMS-02) on the ISS. We found that the dark photon
-parameter space accessible to this analysis is largely constrained by strong
-limits on the spin-independent WIMP-nucleon cross section from direct detection
-experiments. In this paper we build upon previous work by considering the case
-where the dark sector contains two species of Dirac fermion that are nearly
-degenerate in mass and couple inelastically to the dark photon. We find that
-for small values of the mass splitting $\Delta \sim 100 ~\text{keV}$, the
-predicted positron signal at AMS-02 remains largely unchanged from the
-previously considered elastic case while constraints from direct detection are
-relaxed, leaving a region of parameter space with dark matter mass $100
-~\text{GeV} \lesssim m_X \lesssim 10 ~\text{TeV}$, dark photon mass $1
-~\text{MeV} \lesssim m_{A'} \lesssim 100 ~\text{MeV}$, and kinetic mixing
-parameter $10^{-9} \lesssim \varepsilon \lesssim 10^{-8}$ that is untouched by
-supernova observations and fixed target experiments but where an inelastic dark
-sector may still be discovered using existing AMS-02 data.
-",0,1,0,0,0,0
-20012,A Polya Contagion Model for Networks,"  A network epidemics model based on the classical Polya urn scheme is
-investigated. Temporal contagion processes are generated on the network nodes
-using a modified Polya sampling scheme that accounts for spatial infection
-among neighbouring nodes. The stochastic properties and the asymptotic
-behaviour of the resulting network contagion process are analyzed. Unlike the
-classical Polya process, the network process is noted to be non-stationary in
-general, although it is shown to be time-invariant in its first and some of its
-second-order statistics and to satisfy martingale convergence properties under
-certain conditions. Three classical Polya processes, one computational and two
-analytical, are proposed to statistically approximate the contagion process of
-each node, showing a good fit for a range of system parameters. Finally,
-empirical results compare and contrast our model with the well-known discrete
-time SIS model.
-",1,1,1,0,0,0
-20013,Compressive Statistical Learning with Random Feature Moments,"  We describe a general framework --compressive statistical learning-- for
-resource-efficient large-scale learning: the training collection is compressed
-in one pass into a low-dimensional sketch (a vector of random empirical
-generalized moments) that captures the information relevant to the considered
-learning task. A near-minimizer of the risk is computed from the sketch through
-the solution of a nonlinear least squares problem. We investigate sufficient
-sketch sizes to control the generalization error of this procedure. The
-framework is illustrated on compressive clustering, compressive Gaussian
-mixture Modeling with fixed known variance, and compressive PCA.
-",1,0,1,1,0,0
-20014,Bandit Structured Prediction for Neural Sequence-to-Sequence Learning,"  Bandit structured prediction describes a stochastic optimization framework
-where learning is performed from partial feedback. This feedback is received in
-the form of a task loss evaluation to a predicted output structure, without
-having access to gold standard structures. We advance this framework by lifting
-linear bandit learning to neural sequence-to-sequence learning problems using
-attention-based recurrent neural networks. Furthermore, we show how to
-incorporate control variates into our learning algorithms for variance
-reduction and improved generalization. We present an evaluation on a neural
-machine translation task that shows improvements of up to 5.89 BLEU points for
-domain adaptation from simulated bandit feedback.
-",1,0,0,1,0,0
-20015,Dynamic Input Structure and Network Assembly for Few-Shot Learning,"  The ability to learn from a small number of examples has been a difficult
-problem in machine learning since its inception. While methods have succeeded
-with large amounts of training data, research has been underway in how to
-accomplish similar performance with fewer examples, known as one-shot or more
-generally few-shot learning. This technique has been shown to have promising
-performance, but in practice requires fixed-size inputs making it impractical
-for production systems where class sizes can vary. This impedes training and
-the final utility of few-shot learning systems. This paper describes an
-approach to constructing and training a network that can handle arbitrary
-example sizes dynamically as the system is used.
-",1,0,0,1,0,0
-20016,Face R-CNN,"  Faster R-CNN is one of the most representative and successful methods for
-object detection, and has been becoming increasingly popular in various
-objection detection applications. In this report, we propose a robust deep face
-detection approach based on Faster R-CNN. In our approach, we exploit several
-new techniques including new multi-task loss function design, online hard
-example mining, and multi-scale training strategy to improve Faster R-CNN in
-multiple aspects. The proposed approach is well suited for face detection, so
-we call it Face R-CNN. Extensive experiments are conducted on two most popular
-and challenging face detection benchmarks, FDDB and WIDER FACE, to demonstrate
-the superiority of the proposed approach over state-of-the-arts.
-",1,0,0,0,0,0
-20017,Multi-Sensor Data Pattern Recognition for Multi-Target Localization: A Machine Learning Approach,"  Data-target pairing is an important step towards multi-target localization
-for the intelligent operation of unmanned systems. Target localization plays a
-crucial role in numerous applications, such as search, and rescue missions,
-traffic management and surveillance. The objective of this paper is to present
-an innovative target location learning approach, where numerous machine
-learning approaches, including K-means clustering and supported vector machines
-(SVM), are used to learn the data pattern across a list of spatially
-distributed sensors. To enable the accurate data association from different
-sensors for accurate target localization, appropriate data pre-processing is
-essential, which is then followed by the application of different machine
-learning algorithms to appropriately group data from different sensors for the
-accurate localization of multiple targets. Through simulation examples, the
-performance of these machine learning algorithms is quantified and compared.
-",1,0,0,1,0,0
-20018,Calipso: Physics-based Image and Video Editing through CAD Model Proxies,"  We present Calipso, an interactive method for editing images and videos in a
-physically-coherent manner. Our main idea is to realize physics-based
-manipulations by running a full physics simulation on proxy geometries given by
-non-rigidly aligned CAD models. Running these simulations allows us to apply
-new, unseen forces to move or deform selected objects, change physical
-parameters such as mass or elasticity, or even add entire new objects that
-interact with the rest of the underlying scene. In Calipso, the user makes
-edits directly in 3D; these edits are processed by the simulation and then
-transfered to the target 2D content using shape-to-image correspondences in a
-photo-realistic rendering process. To align the CAD models, we introduce an
-efficient CAD-to-image alignment procedure that jointly minimizes for rigid and
-non-rigid alignment while preserving the high-level structure of the input
-shape. Moreover, the user can choose to exploit image flow to estimate scene
-motion, producing coherent physical behavior with ambient dynamics. We
-demonstrate Calipso's physics-based editing on a wide range of examples
-producing myriad physical behavior while preserving geometric and visual
-consistency.
-",1,0,0,0,0,0
-20019,"Maximal (120,8)-arcs in projective planes of order 16 and related designs","  The resolutions and maximal sets of compatible resolutions of all 2-(120,8,1)
-designs arising frommaximal (120,8)-arcs in the known projective planes of
-order 16 are computed. It is shown that each of these designs is embeddable in
-a unique way in a projective plane of order 16.
-",0,0,1,0,0,0
-20020,First order sentences about random graphs: small number of alternations,"  Spectrum of a first order sentence is the set of all $\alpha$ such that $G(n,
-n^{-\alpha})$ does not obey zero-one law w.r.t. this sentence. We have proved
-that the minimal number of quantifier alternations of a first order sentence
-with an infinite spectrum equals 3. We have also proved that the spectrum of a
-first order sentence with a quantifier depth 4 has no limit points except
-possibly the points 1/2 and 3/5.
-",0,0,1,0,0,0
-20021,New Methods for Metadata Extraction from Scientific Literature,"  Within the past few decades we have witnessed digital revolution, which moved
-scholarly communication to electronic media and also resulted in a substantial
-increase in its volume. Nowadays keeping track with the latest scientific
-achievements poses a major challenge for the researchers. Scientific
-information overload is a severe problem that slows down scholarly
-communication and knowledge propagation across the academia. Modern research
-infrastructures facilitate studying scientific literature by providing
-intelligent search tools, proposing similar and related documents, visualizing
-citation and author networks, assessing the quality and impact of the articles,
-and so on. In order to provide such high quality services the system requires
-the access not only to the text content of stored documents, but also to their
-machine-readable metadata. Since in practice good quality metadata is not
-always available, there is a strong demand for a reliable automatic method of
-extracting machine-readable metadata directly from source documents. This
-research addresses these problems by proposing an automatic, accurate and
-flexible algorithm for extracting wide range of metadata directly from
-scientific articles in born-digital form. Extracted information includes basic
-document metadata, structured full text and bibliography section. Designed as a
-universal solution, proposed algorithm is able to handle a vast variety of
-publication layouts with high precision and thus is well-suited for analyzing
-heterogeneous document collections. This was achieved by employing supervised
-and unsupervised machine-learning algorithms trained on large, diverse
-datasets. The evaluation we conducted showed good performance of proposed
-metadata extraction algorithm. The comparison with other similar solutions also
-proved our algorithm performs better than competition for most metadata types.
-",1,0,0,0,0,0
-20022,Unexpected biases in prime factorizations and Liouville functions for arithmetic progressions,"  We introduce a refinement of the classical Liouville function to primes in
-arithmetic progressions. Using this, we discover new biases in the appearances
-of primes in a given arithmetic progression in the prime factorizations of
-integers. For example, we observe that the primes of the form $4k+1$ tend to
-appear an even number of times in the prime factorization of a given integer,
-more so than for primes of the form $4k+3$. We are led to consider variants of
-Pólya's conjecture, supported by extensive numerical evidence, and its
-relation to other conjectures.
-",0,0,1,0,0,0
-20023,Nonconvection and uniqueness in Navier-Stokes equation,"  In the presence of a certain class of functions we show that there exists a
-smooth solution to Navier-Stokes equation. This solution entertains the
-property of being nonconvective. We introduce a definition for any possible
-solution to the problem with minimum assumptions on the existence and the
-regularity of such solution. Then we prove that the proposed class of functions
-represents the unique solution to the problem and consequently we conclude that
-there exists no convective solutions to the problem in the sense of the given
-definition.
-",0,0,1,0,0,0
-20024,An alternative approach for compatibility of two discrete conditional distributions,"  Conditional specification of distributions is a developing area with
-increasing applications. In the finite discrete case, a variety of compatible
-conditions can be derived. In this paper, we propose an alternative approach to
-study the compatibility of two conditional probability distributions under the
-finite discrete setup. A technique based on rank-based criterion is shown to be
-particularly convenient for identifying compatible distributions corresponding
-to complete conditional specification including the case with zeros.The
-proposed methods are illustrated with several examples.
-",0,0,1,1,0,0
-20025,System of unbiased representatives for a collection of bicolorings,"  Let $\mathcal{B}$ denote a set of bicolorings of $[n]$, where each bicoloring
-is a mapping of the points in $[n]$ to $\{-1,+1\}$.
-For each $B \in \mathcal{B}$, let $Y_B=(B(1),\ldots,B(n))$.
-For each $A \subseteq [n]$, let $X_A \in \{0,1\}^n$ denote the incidence
-vector of $A$.
-A non-empty set $A$ is said to be an `unbiased representative' for a
-bicoloring $B \in \mathcal{B}$ if $\left\langle X_A,Y_B\right\rangle =0$.
-Given a set $\mathcal{B}$ of bicolorings, we study the minimum cardinality of
-a family $\mathcal{A}$ consisting of subsets of $[n]$ such that every
-bicoloring in $\mathcal{B}$ has an unbiased representative in $\mathcal{A}$.
-",1,0,0,0,0,0
-20026,Stability and instability in saddle point dynamics Part II: The subgradient method,"  In part I we considered the problem of convergence to a saddle point of a
-concave-convex function via gradient dynamics and an exact characterization was
-given to their asymptotic behaviour. In part II we consider a general class of
-subgradient dynamics that provide a restriction in an arbitrary convex domain.
-We show that despite the nonlinear and non-smooth character of these dynamics
-their $\omega$-limit set is comprised of solutions to only linear ODEs. In
-particular, we show that the latter are solutions to subgradient dynamics on
-affine subspaces which is a smooth class of dynamics the asymptotic properties
-of which have been exactly characterized in part I. Various convergence
-criteria are formulated using these results and several examples and
-applications are also discussed throughout the manuscript.
-",1,0,1,0,0,0
-20027,"Refactoring Legacy JavaScript Code to Use Classes: The Good, The Bad and The Ugly","  JavaScript systems are becoming increasingly complex and large. To tackle the
-challenges involved in implementing these systems, the language is evolving to
-include several constructions for programming- in-the-large. For example,
-although the language is prototype-based, the latest JavaScript standard, named
-ECMAScript 6 (ES6), provides native support for implementing classes. Even
-though most modern web browsers support ES6, only a very few applications use
-the class syntax. In this paper, we analyze the process of migrating structures
-that emulate classes in legacy JavaScript code to adopt the new syntax for
-classes introduced by ES6. We apply a set of migration rules on eight legacy
-JavaScript systems. In our study, we document: (a) cases that are
-straightforward to migrate (the good parts); (b) cases that require manual and
-ad-hoc migration (the bad parts); and (c) cases that cannot be migrated due to
-limitations and restrictions of ES6 (the ugly parts). Six out of eight systems
-(75%) contain instances of bad and/or ugly cases. We also collect the
-perceptions of JavaScript developers about migrating their code to use the new
-syntax for classes.
-",1,0,0,0,0,0
-20028,Parylene-C microfibrous thin films as phononic crystals,"  Phononic bandgaps of Parylene-C microfibrous thin films (muFTFs) were
-computationally determined by treating them as phononic crystals comprising
-identical microfibers arranged either on a square or a hexagonal lattice. The
-microfibers could be columnar,chevronic, or helical in shape, and the host
-medium could be either water or air. All bandgaps were observed to lie in the
-0.01-to-162.9-MHz regime, for microfibers of realistically chosen dimensions.
-The upper limit of the frequency of bandgaps was the highest for the columnar
-muFTF and the lowest for the chiral muFTF. More bandgaps exist when the host
-medium is water than air. Complete bandgaps were observed for the columnar
-muFTF with microfibers arranged on a hexagonal lattice in air, the chevronic
-muFTF with microfibers arranged on a square lattice in water, and the chiral
-muFTF with microfibers arranged on a hexagonal lattice in either air or water.
-The softness of the Parylene-C muFTFs makes them mechanically tunable, and
-their bandgaps can be exploited in multiband ultrasonic filters.
-",0,1,0,0,0,0
-20029,A General Theory for Training Learning Machine,"  Though the deep learning is pushing the machine learning to a new stage,
-basic theories of machine learning are still limited. The principle of
-learning, the role of the a prior knowledge, the role of neuron bias, and the
-basis for choosing neural transfer function and cost function, etc., are still
-far from clear. In this paper, we present a general theoretical framework for
-machine learning. We classify the prior knowledge into common and
-problem-dependent parts, and consider that the aim of learning is to maximally
-incorporate them. The principle we suggested for maximizing the former is the
-design risk minimization principle, while the neural transfer function, the
-cost function, as well as pretreatment of samples, are endowed with the role
-for maximizing the latter. The role of the neuron bias is explained from a
-different angle. We develop a Monte Carlo algorithm to establish the
-input-output responses, and we control the input-output sensitivity of a
-learning machine by controlling that of individual neurons. Applications of
-function approaching and smoothing, pattern recognition and classification, are
-provided to illustrate how to train general learning machines based on our
-theory and algorithm. Our method may in addition induce new applications, such
-as the transductive inference.
-",1,0,0,1,0,0
-20030,Semiparametric spectral modeling of the Drosophila connectome,"  We present semiparametric spectral modeling of the complete larval Drosophila
-mushroom body connectome. Motivated by a thorough exploratory data analysis of
-the network via Gaussian mixture modeling (GMM) in the adjacency spectral
-embedding (ASE) representation space, we introduce the latent structure model
-(LSM) for network modeling and inference. LSM is a generalization of the
-stochastic block model (SBM) and a special case of the random dot product graph
-(RDPG) latent position model, and is amenable to semiparametric GMM in the ASE
-representation space. The resulting connectome code derived via semiparametric
-GMM composed with ASE captures latent connectome structure and elucidates
-biologically relevant neuronal properties.
-",0,0,0,1,0,0
-20031,A Large Term Rewrite System Modelling a Pioneering Cryptographic Algorithm,"  We present a term rewrite system that formally models the Message
-Authenticator Algorithm (MAA), which was one of the first cryptographic
-functions for computing a Message Authentication Code and was adopted, between
-1987 and 2001, in international standards (ISO 8730 and ISO 8731-2) to ensure
-the authenticity and integrity of banking transactions. Our term rewrite system
-is large (13 sorts, 18 constructors, 644 non-constructors, and 684 rewrite
-rules), confluent, and terminating. Implementations in thirteen different
-languages have been automatically derived from this model and used to validate
-200 official test vectors for the MAA.
-",1,0,0,0,0,0
-20032,Malleability of complex networks,"  Most complex networks are not static, but evolve along time. Given a specific
-configuration of one such changing network, it becomes a particularly
-interesting issue to quantify the diversity of possible unfoldings of its
-topology. In this work, we suggest the concept of malleability of a network,
-which is defined as the exponential of the entropy of the probabilities of each
-possible unfolding with respect to a given configuration. We calculate the
-malleability with respect to specific measurements of the involved topologies.
-More specifically, we identify the possible topologies derivable from a given
-configuration and calculate some topological measurement of them (e.g.
-clustering coefficient, shortest path length, assortativity, etc.), leading to
-respective probabilities being associated to each possible measurement value.
-Though this approach implies some level of degeneracy in the mapping from
-topology to measurement space, it still paves the way to inferring the
-malleability of specific network types with respect to given topological
-measurements. We report that the malleability, in general, depends on each
-specific measurement, with the average shortest path length and degree
-assortativity typically leading to large malleability values. The maximum
-malleability was observed for the Wikipedia network and the minimum for the
-Watts-Strogatz model.
-",1,0,0,0,0,0
-20033,Identifying Critical Risks of Cascading Failures in Power Systems,"  Potential critical risks of cascading failures in power systems can be
-identified by exposing those critical electrical elements on which certain
-initial disturbances may cause maximum disruption to power transmission
-networks. In this work, we investigate cascading failures in power systems
-described by the direct current (DC) power flow equations, while initial
-disturbances take the form of altering admittance of elements. The disruption
-is quantified with the remaining transmission power at the end of cascading
-process. In particular, identifying the critical elements and the corresponding
-initial disturbances causing the worst-case cascading blackout is formulated as
-a dynamic optimization problem (DOP) in the framework of optimal control
-theory, where the entire propagation process of cascading failures is put under
-consideration. An Identifying Critical Risk Algorithm (ICRA) based on the
-maximum principle is proposed to solve the DOP. Simulation results on the IEEE
-9-Bus and the IEEE 14-Bus test systems are presented to demonstrate the
-effectiveness of the algorithm.
-",1,0,0,0,0,0
-20034,Radiation Hardness Test of Eljen EJ-500 Optical Cement,"  We present a comprehensive account of the proton radiation hardness of Eljen
-Technology's EJ-500 optical cement used in the construction of experiment
-detectors. The cement was embedded into five plastic scintillator tiles which
-were each exposed to one of five different levels of radiation by a 50 MeV
-proton beam produced at the 88-Inch Cyclotron at Lawrence Berkeley National
-Laboratory. A cosmic ray telescope setup was used to measure signal amplitudes
-before and after irradiation. Another post-radiation measurement was taken four
-months after the experiment to investigate whether the radiation damage to the
-cement recovers after a short amount of time. We verified that the radiation
-damage to the tiles increased with increasing dose but showed significant
-improvement after the four months time interval.
-",0,1,0,0,0,0
-20035,Triviality of the ground-state metastate in long-range Ising spin glasses in one dimension,"  We consider the one-dimensional model of a spin glass with independent
-Gaussian-distributed random interactions, that have mean zero and variance
-$1/|i-j|^{2\sigma}$, between the spins at sites $i$ and $j$ for all $i\neq j$.
-It is known that, for $\sigma>1$, there is no phase transition at any non-zero
-temperature in this model. We prove rigorously that, for $\sigma>3/2$, any
-Newman-Stein metastate for the ground states (i.e.\ the frequencies with which
-distinct ground states are observed in finite size samples in the limit of
-infinite size, for given disorder) is trivial and unique. In other words, for
-given disorder and asymptotically at large sizes, the same ground state, or its
-global spin flip, is obtained (almost) always. The proof consists of two parts:
-one is a theorem (based on one by Newman and Stein for short-range
-two-dimensional models), valid for all $\sigma>1$, that establishes triviality
-under a convergence hypothesis on something similar to the energies of domain
-walls, and the other (based on older results for the one-dimensional model)
-establishes that the hypothesis is true for $\sigma>3/2$. In addition, we
-derive heuristic scaling arguments and rigorous exponent inequalities which
-tend to support the validity of the hypothesis under broader conditions. The
-constructions of various metastates are extended to all values $\sigma>1/2$.
-Triviality of the metastate in bond-diluted power-law models for $\sigma>1$ is
-proved directly.
-",0,1,0,0,0,0
-20036,Thermodynamics of BTZ Black Holes in Gravity's Rainbow,"  In this paper, we deform the thermodynamics of a BTZ black hole from rainbow
-functions in gravity's rainbow. The rainbow functions will be motivated from
-results in loop quantum gravity and Noncommutative geometry. It will be
-observed that the thermodynamics gets deformed due to these rainbow functions,
-indicating the existence of a remnant. However, the Gibbs free energy does not
-get deformed due to these rainbow functions, and so the critical behaviour from
-Gibbs does not change by this deformation.This is because the deformation in
-the entropy cancel's out the temperature deformation.
-",0,1,0,0,0,0
-20037,Nondegeneracy and the Jacobi fields of rotationally symmetric solutions to the Cahn-Hillard equation,"  In this paper we study rotationally symmetric solutions of the Cahn-Hilliard
-equation in $\mathbb R^3$ constructed by the authors. These solutions form a
-one parameter family analog to the family of Delaunay surfaces and in fact the
-zero level sets of their blowdowns approach these surfaces. Presently we go a
-step further and show that their stability properties are inherited from the
-stability properties of the Delaunay surfaces. Our main result states that the
-rotationally symmetric solutions are non degenerate and that they have exactly
-$6$ Jacobi fields of temperate growth coming from the natural invariances of
-the problem (3 translations and 2 rotations) and the variation of the Delaunay
-parameter.
-",0,0,1,0,0,0
-20038,Quasiflats in hierarchically hyperbolic spaces,"  The rank of a hierarchically hyperbolic space is the maximal number of
-unbounded factors of standard product regions; this coincides with the maximal
-dimension of a quasiflat for hierarchically hyperbolic groups. Noteworthy
-examples where the rank coincides with familiar quantities include: the
-dimension of maximal Dehn twist flats for mapping class groups, the maximal
-rank of a free abelian subgroup for right-angled Coxeter groups and
-right-angled Artin groups (in the latter this coincides with the clique number
-of the defining graph), and, for the Weil-Petersson metric the rank is half the
-complex dimension of Teichmuller space.
-We prove that in a HHS, any quasiflat of dimension equal to the rank lies
-within finite distance of a union of standard orthants (under a very mild
-condition satisfied by all natural examples). This resolves outstanding
-conjectures when applied to a number of different groups and spaces. The
-mapping class group case resolves a conjecture of Farb, in Teichmuller space
-this resolves a question of Brock, and in the context of CAT(0) cubical groups
-it strengthens previous results (so as to handle, for example, the right-angled
-Coxeter case).
-An important ingredient, is our proof that the hull of any finite set in an
-HHS is quasi-isometric to a cube complex of dimension equal to the rank.
-We deduce a number of applications; for instance we show that any
-quasi-isometry between HHS induces a quasi-isometry between certain simpler
-HHS. This allows one, for example, to distinguish quasi-isometry classes of
-right-angled Artin/Coxeter groups.
-Another application is that our tools, in many cases, allow one to reduce the
-problem of quasi-isometric rigidity for a given HHG to a combinatorial problem.
-As a template, we give a new proof of quasi-isometric rigidity of mapping class
-groups, using simpler combinatorial arguments than in previous proofs.
-",0,0,1,0,0,0
-20039,Learning to Skim Text,"  Recurrent Neural Networks are showing much promise in many sub-areas of
-natural language processing, ranging from document classification to machine
-translation to automatic question answering. Despite their promise, many
-recurrent models have to read the whole text word by word, making it slow to
-handle long documents. For example, it is difficult to use a recurrent network
-to read a book and answer questions about it. In this paper, we present an
-approach of reading text while skipping irrelevant information if needed. The
-underlying model is a recurrent network that learns how far to jump after
-reading a few words of the input text. We employ a standard policy gradient
-method to train the model to make discrete jumping decisions. In our benchmarks
-on four different tasks, including number prediction, sentiment analysis, news
-article classification and automatic Q\&A, our proposed model, a modified LSTM
-with jumping, is up to 6 times faster than the standard sequential LSTM, while
-maintaining the same or even better accuracy.
-",1,0,0,0,0,0
-20040,Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications,"  Statistical analysis (SA) is a complex process to deduce population
-properties from analysis of data. It usually takes a well-trained analyst to
-successfully perform SA, and it becomes extremely challenging to apply SA to
-big data applications. We propose to use deep neural networks to automate the
-SA process. In particular, we propose to construct convolutional neural
-networks (CNNs) to perform automatic model selection and parameter estimation,
-two most important SA tasks. We refer to the resulting CNNs as the neural model
-selector and the neural model estimator, respectively, which can be properly
-trained using labeled data systematically generated from candidate models.
-Simulation study shows that both the selector and estimator demonstrate
-excellent performances. The idea and proposed framework can be further extended
-to automate the entire SA process and have the potential to revolutionize how
-SA is performed in big data analytics.
-",1,0,0,1,0,0
-20041,Duty to Delete on Non-Volatile Memory,"  We firstly suggest new cache policy applying the duty to delete invalid cache
-data on Non-volatile Memory (NVM). This cache policy includes generating random
-data and overwriting the random data into invalid cache data. Proposed cache
-policy is more economical and effective regarding perfect deletion of data. It
-is ensure that the invalid cache data in NVM is secure against malicious
-hackers.
-",1,0,0,0,0,0
-20042,Geometry of Log-Concave Density Estimation,"  Shape-constrained density estimation is an important topic in mathematical
-statistics. We focus on densities on $\mathbb{R}^d$ that are log-concave, and
-we study geometric properties of the maximum likelihood estimator (MLE) for
-weighted samples. Cule, Samworth, and Stewart showed that the logarithm of the
-optimal log-concave density is piecewise linear and supported on a regular
-subdivision of the samples. This defines a map from the space of weights to the
-set of regular subdivisions of the samples, i.e. the face poset of their
-secondary polytope. We prove that this map is surjective. In fact, every
-regular subdivision arises in the MLE for some set of weights with positive
-probability, but coarser subdivisions appear to be more likely to arise than
-finer ones. To quantify these results, we introduce a continuous version of the
-secondary polytope, whose dual we name the Samworth body. This article
-establishes a new link between geometric combinatorics and nonparametric
-statistics, and it suggests numerous open problems.
-",0,0,0,1,0,0
-20043,Antenna Arrays for Line-of-Sight Massive MIMO: Half Wavelength is not Enough,"  The aim of this paper is to analyze the array synthesis for 5 G massive MIMO
-systems in the line-of-sight working condition. The main result of the
-numerical investigation performed is that non-uniform arrays are the natural
-choice in this kind of application. In particular, by using non-equispaced
-arrays, we show that it is possible to achieve a better average condition
-number of the channel matrix and a significantly higher spectral efficiency.
-Furthermore, we verify that increasing the array size is beneficial also for
-circular arrays, and we provide some useful rules-of-thumb for antenna array
-design for massive MIMO applications. These results are in contrast to the
-widely-accepted idea in the 5 G massive MIMO literature, in which the
-half-wavelength linear uniform array is universally adopted.
-",1,0,0,0,0,0
-20044,Gravitational octree code performance evaluation on Volta GPU,"  In this study, the gravitational octree code originally optimized for the
-Fermi, Kepler, and Maxwell GPU architectures is adapted to the Volta
-architecture. The Volta architecture introduces independent thread scheduling
-requiring either the insertion of the explicit synchronizations at appropriate
-locations or the enforcement of the same implicit synchronizations as do the
-Pascal or earlier architectures by specifying \texttt{-gencode
-arch=compute\_60,code=sm\_70}. The performance measurements on Tesla V100, the
-current flagship GPU by NVIDIA, revealed that the $N$-body simulations of the
-Andromeda galaxy model with $2^{23} = 8388608$ particles took $3.8 \times
-10^{-2}$~s or $3.3 \times 10^{-2}$~s per step for each case. Tesla V100
-achieves a 1.4 to 2.2-fold acceleration in comparison with Tesla P100, the
-flagship GPU in the previous generation. The observed speed-up of 2.2 is
-greater than 1.5, which is the ratio of the theoretical peak performance of the
-two GPUs. The independence of the units for integer operations from those for
-floating-point number operations enables the overlapped execution of integer
-and floating-point number operations. It hides the execution time of the
-integer operations leading to the speed-up rate above the theoretical peak
-performance ratio. Tesla V100 can execute $N$-body simulation with up to $25
-\times 2^{20} = 26214400$ particles, and it took $2.0 \times 10^{-1}$~s per
-step. It corresponds to $3.5$~TFlop/s, which is 22\% of the single-precision
-theoretical peak performance.
-",1,0,0,0,0,0
-20045,"Instanton bundles on the flag variety F(0,1,2)","  Instanton bundles on $\mathbb{P}^3$ have been at the core of the research in
-Algebraic Geometry during the last thirty years. Motivated by the recent
-extension of their definition to other Fano threefolds of Picard number one, we
-develop the theory of instanton bundles on the complete flag variety
-$F:=F(0,1,2)$ of point-lines on $\mathbb{P}^2$. After giving for them two
-different monadic presentations, we use it to show that the moduli space
-$MI_F(k)$ of instanton bundles of charge $k$ is a geometric GIT quotient with a
-generically smooth component of dim $8k-3$. Finally we study their locus of
-jumping conics.
-",0,0,1,0,0,0
-20046,Non-linear motor control by local learning in spiking neural networks,"  Learning weights in a spiking neural network with hidden neurons, using
-local, stable and online rules, to control non-linear body dynamics is an open
-problem. Here, we employ a supervised scheme, Feedback-based Online Local
-Learning Of Weights (FOLLOW), to train a network of heterogeneous spiking
-neurons with hidden layers, to control a two-link arm so as to reproduce a
-desired state trajectory. The network first learns an inverse model of the
-non-linear dynamics, i.e. from state trajectory as input to the network, it
-learns to infer the continuous-time command that produced the trajectory.
-Connection weights are adjusted via a local plasticity rule that involves
-pre-synaptic firing and post-synaptic feedback of the error in the inferred
-command. We choose a network architecture, termed differential feedforward,
-that gives the lowest test error from different feedforward and recurrent
-architectures. The learned inverse model is then used to generate a
-continuous-time motor command to control the arm, given a desired trajectory.
-",1,0,0,1,0,0
-20047,Training Deep Neural Networks via Optimization Over Graphs,"  In this work, we propose to train a deep neural network by distributed
-optimization over a graph. Two nonlinear functions are considered: the
-rectified linear unit (ReLU) and a linear unit with both lower and upper
-cutoffs (DCutLU). The problem reformulation over a graph is realized by
-explicitly representing ReLU or DCutLU using a set of slack variables. We then
-apply the alternating direction method of multipliers (ADMM) to update the
-weights of the network layerwise by solving subproblems of the reformulated
-problem. Empirical results suggest that the ADMM-based method is less sensitive
-to overfitting than the stochastic gradient descent (SGD) and Adam methods.
-",1,0,0,0,0,0
-20048,Calculation of Effective Interaction Potential During Positron Channeling in Ionic Crystals,"  An analytical expression is received for the effective interaction potential
-of a fast charged particle with the ionic crystal CsCl near the direction of
-<100> axis as a function of the temperature of the medium. By numerical
-analysis it is shown that the effective potential of axial channeling of
-positrons along the axis <100> of negatively charged ions practically does not
-depend on temperature of the media
-",0,1,0,0,0,0
-20049,The Follower Count Fallacy: Detecting Twitter Users with Manipulated Follower Count,"  Online Social Networks (OSN) are increasingly being used as platform for an
-effective communication, to engage with other users, and to create a social
-worth via number of likes, followers and shares. Such metrics and crowd-sourced
-ratings give the OSN user a sense of social reputation which she tries to
-maintain and boost to be more influential. Users artificially bolster their
-social reputation via black-market web services. In this work, we identify
-users which manipulate their projected follower count using an unsupervised
-local neighborhood detection method. We identify a neighborhood of the user
-based on a robust set of features which reflect user similarity in terms of the
-expected follower count. We show that follower count estimation using our
-method has 84.2% accuracy with a low error rate. In addition, we estimate the
-follower count of the user under suspicion by finding its neighborhood drawn
-from a large random sample of Twitter. We show that our method is highly
-tolerant to synthetic manipulation of followers. Using the deviation of
-predicted follower count from the displayed count, we are also able to detect
-customers with a high precision of 98.62%
-",1,0,0,0,0,0
-20050,Bergman kernel estimates and Toeplitz operators on holomorphic line bundles,"  We characterize operator-theoretic properties (boundedness, compactness, and
-Schatten class membership) of Toeplitz operators with positive measure symbols
-on Bergman spaces of holomorphic hermitian line bundles over Kähler
-Cartan-Hadamard manifolds in terms of geometric or operator-theoretic
-properties of measures.
-",0,0,1,0,0,0
-20051,HST PanCET program: A Cloudy Atmosphere for the promising JWST target WASP-101b,"  We present results from the first observations of the Hubble Space Telescope
-(HST) Panchromatic Comparative Exoplanet Treasury (PanCET) program for
-WASP-101b, a highly inflated hot Jupiter and one of the community targets
-proposed for the James Webb Space Telescope (JWST) Early Release Science (ERS)
-program. From a single HST Wide Field Camera 3 (WFC3) observation, we find that
-the near-infrared transmission spectrum of WASP-101b contains no significant
-H$_2$O absorption features and we rule out a clear atmosphere at 13{\sigma}.
-Therefore, WASP-101b is not an optimum target for a JWST ERS program aimed at
-observing strong molecular transmission features. We compare WASP-101b to the
-well studied and nearly identical hot Jupiter WASP-31b. These twin planets show
-similar temperature-pressure profiles and atmospheric features in the
-near-infrared. We suggest exoplanets in the same parameter space as WASP-101b
-and WASP-31b will also exhibit cloudy transmission spectral features. For
-future HST exoplanet studies, our analysis also suggests that a lower count
-limit needs to be exceeded per pixel on the detector in order to avoid unwanted
-instrumental systematics.
-",0,1,0,0,0,0
-20052,Quantum gauge symmetry of reducible gauge theory,"  We derive the gaugeon formalism of the Kalb-Ramond field theory, a reducible
-gauge theory, which discusses the quantum gauge freedom. In gaugeon formalism,
-theory admits quantum gauge symmetry which leaves the action form-invariant.
-The BRST symmetric gaugeon formalism is also studied which introduces the
-gaugeon ghost fields and gaugeon ghosts of ghosts fields. To replace the
-Yokoyama subsidiary conditions by a single Kugo-Ojima type condition the virtue
-of BRST symmetry is utilized. Under generalized BRST transformations, we show
-that the gaugeon fields appear naturally in the reducible gauge theory.
-",0,1,0,0,0,0
-20053,The Tian Pseudo-Atom Method,"  In this work, the authors give a new method for phase determination, the Tian
-pseudo atom method (TPAM) or pseudo atom method (PAM) for short. In this new
-method, the figure of merit function, Rtian, replaces Rcf in the charge
-flipping algorithm. The key difference between Rcf and Rtian is the oberved
-structure factor was replaced by the pseudo structure factor. The test results
-show that Rtian is more powerful and robust than Rcf to estimate the correct
-structure especially with low resolution data. Therefore, the pseudo atom
-method could overcome the charge flipping method's defeat to some extent. In
-theory, the pseudo atom method could deal with quite low resolution data but it
-needs a further test.
-",0,1,0,0,0,0
-20054,"The Phenotypes of Fluctuating Flow: Development of Distribution Networks in Biology and the Trade-off between Efficiency, Cost, and Resilience","  Complex distribution networks are pervasive in biology. Examples include
-nutrient transport in the slime mold $Physarum$ $polycephalum$ as well as
-mammalian and plant venation. Adaptive rules are believed to guide development
-of these networks and lead to a reticulate, hierarchically nested topology that
-is both efficient and resilient against perturbations. However, as of yet no
-mechanism is known that can generate such networks on all scales. We show how
-hierarchically organized reticulation can be generated and maintained through
-spatially collective load fluctuations on a particular length scale. We
-demonstrate that the resulting network topologies represent a trade-off between
-optimizing power dissipation, construction cost, and damage robustness and
-identify the Pareto-efficient front that evolution is expected to favor and
-select for. We show that the typical fluctuation length scale controls the
-position of the networks on the Pareto front and thus on the spectrum of
-venation phenotypes. We compare the Pareto archetypes predicted by our model
-with examples of real leaf networks.
-",0,1,0,0,0,0
-20055,Recent Trends in Deep Learning Based Natural Language Processing,"  Deep learning methods employ multiple processing layers to learn hierarchical
-representations of data and have produced state-of-the-art results in many
-domains. Recently, a variety of model designs and methods have blossomed in the
-context of natural language processing (NLP). In this paper, we review
-significant deep learning related models and methods that have been employed
-for numerous NLP tasks and provide a walk-through of their evolution. We also
-summarize, compare and contrast the various models and put forward a detailed
-understanding of the past, present and future of deep learning in NLP.
-",1,0,0,0,0,0
-20056,Volatile memory forensics for the Robot Operating System,"  The increasing impact of robotics on industry and on society will unavoidably
-lead to the involvement of robots in incidents and mishaps. In such cases,
-forensic analyses are key techniques to provide useful evidence on what
-happened, and try to prevent future incidents. This article discusses volatile
-memory forensics for the Robot Operating System (ROS). The authors start by
-providing a general overview of forensic techniques in robotics and then
-present a robotics-specific Volatility plugin named linux_rosnode, packaged
-within the ros_volatility project and aimed to extract evidence from robot's
-volatile memory. They demonstrate how this plugin can be used to detect a
-specific attack pattern on ROS, where a publisher node is unregistered
-externally, leading to denial of service and disruption of robotic behaviors.
-Step-by-step, common practices are introduced for performing forensic analysis
-and several techniques to capture memory are described. The authors finalize by
-introducing some future remarks while providing references to reproduce their
-work.
-",1,0,0,0,0,0
-20057,HYDRA: HYbrid Design for Remote Attestation (Using a Formally Verified Microkernel),"  Remote Attestation (RA) allows a trusted entity (verifier) to securely
-measure internal state of a remote untrusted hardware platform (prover). RA can
-be used to establish a static or dynamic root of trust in embedded and
-cyber-physical systems. It can also be used as a building block for other
-security services and primitives, such as software updates and patches,
-verifiable deletion and memory resetting. There are three major classes of RA
-designs: hardware-based, software-based, and hybrid, each with its own set of
-benefits and drawbacks. This paper presents the first hybrid RA design, called
-HYDRA, that builds upon formally verified software components that ensure
-memory isolation and protection, as well as enforce access control to memory
-and other resources. HYDRA obtains these properties by using the formally
-verified seL4 microkernel. (Until now, this was only attainable with purely
-hardware-based designs.) Using seL4 requires fewer hardware modifications to
-the underlying microprocessor. Building upon a formally verified software
-component increases confidence in security of the overall design of HYDRA and
-its implementation. We instantiate HYDRA on two commodity hardware platforms
-and assess the performance and overhead of performing RA on such platforms via
-experimentation; we show that HYDRA can attest 10MB of memory in less than
-500msec when using a Speck-based message authentication code (MAC) to compute a
-cryptographic checksum over the memory to be attested.
-",1,0,0,0,0,0
-20058,Social Robots for People with Developmental Disabilities: A User Study on Design Features of a Graphical User Interface,"  Social robots, also known as service or assistant robots, have been developed
-to improve the quality of human life in recent years. The design of socially
-capable and intelligent robots can vary, depending on the target user groups.
-In this work, we assess the effect of social robots' roles, functions, and
-communication approaches in the context of a social agent providing service or
-entertainment to users with developmental disabilities. In this paper, we
-describe an exploratory study of interface design for a social robot that
-assists people suffering from developmental disabilities. We developed series
-of prototypes and tested one in a user study that included three residents with
-various function levels. This entire study had been recorded for the following
-qualitative data analysis. Results show that each design factor played a
-different role in delivering information and in increasing engagement. We also
-note that some of the fundamental design principles that would work for
-ordinary users did not apply to our target user group. We conclude that social
-robots could benefit our target users, and acknowledge that these robots were
-not suitable for certain scenarios based on the feedback from our users.
-",1,0,0,0,0,0
-20059,Inverse problem for multi-species mean field models in the low temperature phase,"  In this paper we solve the inverse problem for a class of mean field models
-(Curie-Weiss model and its multi-species version) when multiple thermodynamic
-states are present, as in the low temperature phase where the phase space is
-clustered. The inverse problem consists in reconstructing the model parameters
-starting from configuration data generated according to the distribution of the
-model. We show that the application of the inversion procedure without taking
-into account the presence of many states produces very poor inference results.
-This problem is overcomed using the clustering algorithm. When the system has
-two symmetric states of positive and negative magnetization, the parameter
-reconstruction can be also obtained with smaller computational effort simply by
-flipping the sign of the magnetizations from positive to negative (or
-viceversa). The parameter reconstruction fails when the system is critical: in
-this case we give the correct inversion formulas for the Curie-Weiss model and
-we show that they can be used to measuring how much the system is close to
-criticality.
-",0,1,0,0,0,0
-20060,Inertial Odometry on Handheld Smartphones,"  Building a complete inertial navigation system using the limited quality data
-provided by current smartphones has been regarded challenging, if not
-impossible. This paper shows that by careful crafting and accounting for the
-weak information in the sensor samples, smartphones are capable of pure
-inertial navigation. We present a probabilistic approach for orientation and
-use-case free inertial odometry, which is based on double-integrating rotated
-accelerations. The strength of the model is in learning additive and
-multiplicative IMU biases online. We are able to track the phone position,
-velocity, and pose in real-time and in a computationally lightweight fashion by
-solving the inference with an extended Kalman filter. The information fusion is
-completed with zero-velocity updates (if the phone remains stationary),
-altitude correction from barometric pressure readings (if available), and
-pseudo-updates constraining the momentary speed. We demonstrate our approach
-using an iPad and iPhone in several indoor dead-reckoning applications and in a
-measurement tool setup.
-",1,0,0,1,0,0
-20061,Asymptotic network models of subwavelength metamaterials formed by closely packed photonic and phononic crystals,"  We demonstrate that photonic and phononic crystals consisting of closely
-spaced inclusions constitute a versatile class of subwavelength metamaterials.
-Intuitively, the voids and narrow gaps that characterise the crystal form an
-interconnected network of Helmholtz-like resonators. We use this intuition to
-argue that these continuous photonic (phononic) crystals are in fact
-asymptotically equivalent, at low frequencies, to discrete capacitor-inductor
-(mass-spring) networks whose lumped parameters we derive explicitly. The
-crystals are tantamount to metamaterials as their entire acoustic branch, or
-branches when the discrete analogue is polyatomic, is squeezed into a
-subwavelength regime where the ratio of wavelength to period scales like the
-ratio of period to gap width raised to the power 1/4; at yet larger wavelengths
-we accordingly find a comparably large effective refractive index. The fully
-analytical dispersion relations predicted by the discrete models yield
-dispersion curves that agree with those from finite-element simulations of the
-continuous crystals. The insight gained from the network approach is used to
-show that, surprisingly, the continuum created by a closely packed hexagonal
-lattice of cylinders is represented by a discrete honeycomb lattice. The
-analogy is utilised to show that the hexagonal continuum lattice has a
-Dirac-point degeneracy that is lifted in a controlled manner by specifying the
-area of a symmetry-breaking defect.
-",0,1,0,0,0,0
-20062,The Hydrogen Epoch of Reionization Array Dish III: Measuring Chromaticity of Prototype Element with Reflectometry,"  The experimental efforts to detect the redshifted 21 cm signal from the Epoch
-of Reionization (EoR) are limited predominantly by the chromatic instrumental
-systematic effect. The delay spectrum methodology for 21 cm power spectrum
-measurements brought new attention to the critical impact of an antenna's
-chromaticity on the viability of making this measurement. This methodology
-established a straightforward relationship between time-domain response of an
-instrument and the power spectrum modes accessible to a 21 cm EoR experiment.
-We examine the performance of a prototype of the Hydrogen Epoch of Reionization
-Array (HERA) array element that is currently observing in Karoo desert, South
-Africa. We present a mathematical framework to derive the beam integrated
-frequency response of a HERA prototype element in reception from the return
-loss measurements between 100-200 MHz and determined the extent of additional
-foreground contamination in the delay space. The measurement reveals excess
-spectral structures in comparison to the simulation studies of the HERA
-element. Combined with the HERA data analysis pipeline that incorporates
-inverse covariance weighting in optimal quadratic estimation of power spectrum,
-we find that in spite of its departure from the simulated response, HERA
-prototype element satisfies the necessary criteria posed by the foreground
-attenuation limits and potentially can measure the power spectrum at spatial
-modes as low as $k_{\parallel} > 0.1h$~Mpc$^{-1}$. The work highlights a
-straightforward method for directly measuring an instrument response and
-assessing its impact on 21 cm EoR power spectrum measurements for future
-experiments that will use reflector-type antenna.
-",0,1,0,0,0,0
-20063,Unifying the micro and macro properties of AGN feeding and feedback,"  We unify the feeding and feedback of supermassive black holes with the global
-properties of galaxies, groups, and clusters, by linking for the first time the
-physical mechanical efficiency at the horizon and Mpc scale. The macro hot halo
-is tightly constrained by the absence of overheating and overcooling as probed
-by X-ray data and hydrodynamic simulations ($\varepsilon_{\rm BH} \simeq$
-10$^{-3}\, T_{\rm x,7.4}$). The micro flow is shaped by general relativistic
-effects tracked by state-of-the-art GR-RMHD simulations ($\varepsilon_\bullet
-\simeq$ 0.03). The SMBH properties are tied to the X-ray halo temperature
-$T_{\rm x}$, or related cosmic scaling relation (as $L_{\rm x}$). The model is
-minimally based on first principles, as conservation of energy and mass
-recycling. The inflow occurs via chaotic cold accretion (CCA), the rain of cold
-clouds condensing out of the quenched cooling flow and recurrently funneled via
-inelastic collisions. Within 100s gravitational radii, the accretion energy is
-transformed into ultrafast 10$^4$ km s$^{-1}$ outflows (UFOs) ejecting most of
-the inflowing mass. At larger radii the energy-driven outflow entrains
-progressively more mass: at roughly kpc scale, the velocities of the
-hot/warm/cold outflows are a few 10$^3$, 1000, 500 km s$^{-1}$, with median
-mass rates ~10, 100, several 100 M$_\odot$ yr$^{-1}$, respectively. The unified
-CCA model is consistent with the observations of nuclear UFOs, and ionized,
-neutral, and molecular macro outflows. We provide step-by-step implementation
-for subgrid simulations, (semi)analytic works, or observational interpretations
-which require self-regulated AGN feedback at coarse scales, avoiding the
-a-posteriori fine-tuning of efficiencies.
-",0,1,0,0,0,0
-20064,Model-based Iterative Restoration for Binary Document Image Compression with Dictionary Learning,"  The inherent noise in the observed (e.g., scanned) binary document image
-degrades the image quality and harms the compression ratio through breaking the
-pattern repentance and adding entropy to the document images. In this paper, we
-design a cost function in Bayesian framework with dictionary learning.
-Minimizing our cost function produces a restored image which has better quality
-than that of the observed noisy image, and a dictionary for representing and
-encoding the image. After the restoration, we use this dictionary (from the
-same cost function) to encode the restored image following the
-symbol-dictionary framework by JBIG2 standard with the lossless mode.
-Experimental results with a variety of document images demonstrate that our
-method improves the image quality compared with the observed image, and
-simultaneously improves the compression ratio. For the test images with
-synthetic noise, our method reduces the number of flipped pixels by 48.2% and
-improves the compression ratio by 36.36% as compared with the best encoding
-methods. For the test images with real noise, our method visually improves the
-image quality, and outperforms the cutting-edge method by 28.27% in terms of
-the compression ratio.
-",1,0,0,0,0,0
-20065,A First Look at Ad Blocking Apps on Google Play,"  Online advertisers and analytics services (or trackers), are constantly
-tracking users activities as they access web services either through browsers
-or a mobile apps. Numerous tools such as browser plugins and specialized mobile
-apps have been proposed to limit intrusive advertisements and prevent tracking
-on desktop computing and mobile phones. For desktop computing, browser plugins
-are heavily studied for their usability and efficiency issues, however, tools
-that block ads and prevent trackers in mobile platforms, have received the
-least or no attention.
-In this paper, we present a first look at 97 Android adblocking apps (or
-adblockers), extracted from more than 1.5 million apps from Google Play, that
-promise to block advertisements and analytics services. With our data
-collection and analysis pipeline of the Android adblockers, we reveal the
-presences of third-party tracking libraries and sensitive permissions for
-critical resources on user mobile devices as well as have malware in the source
-codes. We analyze users' reviews for the in-effectiveness of adblockers in
-terms of not blocking ads and trackers. We found that a significant fraction of
-adblockers are not fulfilling their advertised functionality.
-",1,0,0,0,0,0
-20066,Nondegeneracy of the traveling lump solution to the $2+1$ Toda lattice,"  We consider the $2+1$ Toda system \[ \frac{1}{4}\Delta
-q_{n}=e^{q_{n-1}-q_{n}}-e^{q_{n}-q_{n+1}}\text{ in }\mathbb{R}^{2},\
-n\in\mathbb{Z}. \] It has a traveling wave type solution $\left\{ Q_{n}\right\}
-$ satisfying $Q_{n+1}(x,y)=Q_{n}(x+\frac{1}{2\sqrt{2}},y)$, and is explicitly
-given by \[ Q_{n}\left( x,y\right) =\ln\frac{\frac{1}{4}+\left(
-n-1+2\sqrt{2}x\right) ^{2}+4y^{2}}{\frac{1}{4}+\left( n+2\sqrt{2}x\right)
-^{2}+4y^{2}}. \] In this paper we prove that \{$Q_{n}$\} is nondegenerate.
-",0,0,1,0,0,0
-20067,One side continuity of meromorphic mappings between real analytic hypersurfaces,"  We prove that a meromorphic mapping, which sends a peace of a real analytic
-strictly pseudoconvex hypersurface in $\cc^2$ to a compact subset of $\cc^N$
-which doesn't contain germs of non-constant complex curves is continuous from
-the concave side of the hypersurface. This implies the analytic continuability
-along CR-paths of germs of holomorphic mappings from real analytic
-hypersurfaces with non-vanishing Levi form to the locally spherical ones in all
-dimensions.
-",0,0,1,0,0,0
-20068,Generic Camera Attribute Control using Bayesian Optimization,"  Cameras are the most widely exploited sensor in both robotics and computer
-vision communities. Despite their popularity, two dominant attributes (i.e.,
-gain and exposure time) have been determined empirically and images are
-captured in very passive manner. In this paper, we present an active and
-generic camera attribute control scheme using Bayesian optimization. We extend
-from our previous work [1] in two aspects. First, we propose a method that
-jointly controls camera gain and exposure time. Secondly, to speed up the
-Bayesian optimization process, we introduce image synthesis using the camera
-response function (CRF). These synthesized images allowed us to diminish the
-image acquisition time during the Bayesian optimization phase, substantially
-improving overall control performance. The proposed method is validated both in
-an indoor and an outdoor environment where light condition rapidly changes.
-Supplementary material is available at this https URL .
-",1,0,0,0,0,0
-20069,Far-from-equilibrium energy flow and entanglement entropy,"  The time evolution of the energy transport triggered in a strongly coupled
-system by a temperature gradient is holographically related to the evolution of
-an asymptotically AdS black brane. We study the far-from-equilibrium properties
-of such a system by using the AdS/CFT correspondence. In particular, we
-describe the appearance of a steady state, and study the information flow by
-computing the time evolution of the holographic entanglement entropy. Some
-universal properties of the quenching process are presented.
-",0,1,0,0,0,0
-20070,Algebraic Aspects of Conditional Independence and Graphical Models,"  This chapter of the forthcoming Handbook of Graphical Models contains an
-overview of basic theorems and techniques from algebraic geometry and how they
-can be applied to the study of conditional independence and graphical models.
-It also introduces binomial ideals and some ideas from real algebraic geometry.
-When random variables are discrete or Gaussian, tools from computational
-algebraic geometry can be used to understand implications between conditional
-independence statements. This is accomplished by computing primary
-decompositions of conditional independence ideals. As examples the chapter
-presents in detail the graphical model of a four cycle and the intersection
-axiom, a certain implication of conditional independence statements. Another
-important problem in the area is to determine all constraints on a graphical
-model, for example, equations determined by trek separation. The full set of
-equality constraints can be determined by computing the model's vanishing
-ideal. The chapter illustrates these techniques and ideas with examples from
-the literature and provides references for further reading.
-",0,0,1,1,0,0
-20071,A Survey of Riccati Equation Results in Negative Imaginary Systems Theory and Quantum Control Theory,"  This paper presents a survey of some new applications of algebraic Riccati
-equations. In particular, the paper surveys some recent results on the use of
-algebraic Riccati equations in testing whether a system is negative imaginary
-and in synthesizing state feedback controllers which make the closed loop
-system negative imaginary. The paper also surveys the use of Riccati equation
-methods in the control of quantum linear systems including coherent $H^\infty$
-control.
-",1,0,1,0,0,0
-20072,"The circumstellar disk HD$\,$169142: gas, dust and planets acting in concert?","  HD$\,$169142 is an excellent target to investigate signs of planet-disk
-interaction due to the previous evidence of gap structures. We performed J-band
-(~1.2{\mu}m) polarized intensity imaging of HD169142 with VLT/SPHERE. We
-observe polarized scattered light down to 0.16"" (~19 au) and find an inner gap
-with a significantly reduced scattered light flux. We confirm the previously
-detected double ring structure peaking at 0.18"" (~21 au) and 0.56"" (~66 au),
-and marginally detect a faint third gap at 0.70""-0.73"" (~82-85 au). We explore
-dust evolution models in a disk perturbed by two giant planets, as well as
-models with a parameterized dust size distribution. The dust evolution model is
-able to reproduce the ring locations and gap widths in polarized intensity, but
-fails to reproduce their depths. It, however, gives a good match with the ALMA
-dust continuum image at 1.3 mm. Models with a parameterized dust size
-distribution better reproduce the gap depth in scattered light, suggesting that
-dust filtration at the outer edges of the gaps is less effective. The pile-up
-of millimeter grains in a dust trap and the continuous distribution of small
-grains throughout the gap likely require a more efficient dust fragmentation
-and dust diffusion in the dust trap. Alternatively, turbulence or charging
-effects might lead to a reservoir of small grains at the surface layer that is
-not affected by the dust growth and fragmentation cycle dominating the dense
-disk midplane. The exploration of models shows that extracting planet
-properties such as mass from observed gap profiles is highly degenerate.
-",0,1,0,0,0,0
-20073,Quantitative analysis of the influence of keV He ion bombardment on exchange bias layer systems,"  The mechanism of ion bombardment induced magnetic patterning of exchange bias
-layer systems for creating engineered magnetic stray field landscapes is still
-unclear. We compare results from vectorial magneto-optic Kerr effect
-measurements to a recently proposed model with time dependent rotatable
-magnetic anisotropy. Results show massive reduction of rotational magnetic
-anisotropy compared to all other magnetic anisotropies. We disprove the
-assumption of comparable weakening of all magnetic anisotropies and show that
-ion bombardment mainly influences smaller grains in the antiferromagnet.
-",0,1,0,0,0,0
-20074,Automatic classification of automorphisms of lower-dimensional Lie algebras,"  We implement two algorithms in MATHEMATICA for classifying automorphisms of
-lower-dimensional non-commutative Lie algebras. The first algorithm is a
-brute-force approach whereas the second is an evolutionary strategy. These
-algorithms are delivered as the MATHEMATICA package cwsAutoClass. In order to
-facilitate the application of this package to symmetry Lie algebras of
-differential equations, we also provide a package, cwsLieSymTools, for
-manipulating finite-dimensional Lie algebras of vector fields. In particular,
-this package allows the computations of Lie brackets, structure constants, and
-the visualization of commutator tables. Several examples are provided to
-illustrate the pertinence of our approach.
-",0,0,1,0,0,0
-20075,Information-Theoretic Understanding of Population Risk Improvement with Model Compression,"  We show that model compression can improve the population risk of a
-pre-trained model, by studying the tradeoff between the decrease in the
-generalization error and the increase in the empirical risk with model
-compression. We first prove that model compression reduces an
-information-theoretic bound on the generalization error; this allows for an
-interpretation of model compression as a regularization technique to avoid
-overfitting. We then characterize the increase in empirical risk with model
-compression using rate distortion theory. These results imply that the
-population risk could be improved by model compression if the decrease in
-generalization error exceeds the increase in empirical risk. We show through a
-linear regression example that such a decrease in population risk due to model
-compression is indeed possible. Our theoretical results further suggest that
-the Hessian-weighted $K$-means clustering compression approach can be improved
-by regularizing the distance between the clustering centers. We provide
-experiments with neural networks to support our theoretical assertions.
-",1,0,0,1,0,0
-20076,Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries,"  This paper presents a procedure to retrieve subsets of relevant documents
-from large text collections for Content Analysis, e.g. in social sciences.
-Document retrieval for this purpose needs to take account of the fact that
-analysts often cannot describe their research objective with a small set of key
-terms, especially when dealing with theoretical or rather abstract research
-interests. Instead, it is much easier to define a set of paradigmatic documents
-which reflect topics of interest as well as targeted manner of speech. Thus, in
-contrast to classic information retrieval tasks we employ manually compiled
-collections of reference documents to compose large queries of several hundred
-key terms, called dictionaries. We extract dictionaries via Topic Models and
-also use co-occurrence data from reference collections. Evaluations show that
-the procedure improves retrieval results for this purpose compared to
-alternative methods of key term extraction as well as neglecting co-occurrence
-data.
-",1,0,0,0,0,0
-20077,Cepheids with the eyes of photometric space telescopes,"  Space photometric missions have been steadily accumulating observations of
-Cepheids in recent years, leading to a flow of new discoveries. In this short
-review we summarize the findings provided by the early missions such as WIRE,
-MOST, and CoRoT, and the recent results of the Kepler and K2 missions. The
-surprising and fascinating results from the high-precision, quasi-continuous
-data include the detection of the amplitude increase of Polaris, and exquisite
-details about V1154 Cyg within the original Kepler field of view. We also
-briefly discuss the current opportunities with the K2 mission, and the
-prospects of the TESS space telescope regarding Cepheids.
-",0,1,0,0,0,0
-20078,Explicitly correlated formalism for second-order single-particle Green's function,"  We present an explicitly correlated formalism for the second-order
-single-particle Green's function method (GF2-F12) that does not assume the
-popular diagonal approximation, and describes the energy dependence of the
-explicitly correlated terms. For small and medium organic molecules the basis
-set errors of ionization potentials of GF2-F12 are radically improved relative
-to GF2: the performance of GF2-F12/aug- cc-pVDZ is better than that of
-GF2/aug-cc-pVQZ, at a significantly lower cost.
-",0,1,0,0,0,0
-20079,Relative stability associated to quantised extremal Kähler metrics,"  We study algebro-geometric consequences of the quantised extremal Kähler
-metrics, introduced in the previous work of the author. We prove that the
-existence of quantised extremal metrics implies weak relative Chow
-polystability. As a consequence, we obtain asymptotic weak relative Chow
-polystability and $K$-semistability of extremal manifolds by using quantised
-extremal metrics; this gives an alternative proof of the results of Mabuchi and
-Stoppa--Székelyhidi. In proving them, we further provide an explicit local
-density formula for the equivariant Riemann--Roch theorem.
-",0,0,1,0,0,0
-20080,Beyond the EULA: Improving consent for data mining,"  Companies and academic researchers may collect, process, and distribute large
-quantities of personal data without the explicit knowledge or consent of the
-individuals to whom the data pertains. Existing forms of consent often fail to
-be appropriately readable and ethical oversight of data mining may not be
-sufficient. This raises the question of whether existing consent instruments
-are sufficient, logistically feasible, or even necessary, for data mining. In
-this chapter, we review the data collection and mining landscape, including
-commercial and academic activities, and the relevant data protection concerns,
-to determine the types of consent instruments used. Using three case studies,
-we use the new paradigm of human-data interaction to examine whether these
-existing approaches are appropriate. We then introduce an approach to consent
-that has been empirically demonstrated to improve on the state of the art and
-deliver meaningful consent. Finally, we propose some best practices for data
-collectors to ensure their data mining activities do not violate the
-expectations of the people to whom the data relate.
-",1,0,0,0,0,0
-20081,Estimating a Separably-Markov Random Field (SMuRF) from Binary Observations,"  A fundamental problem in neuroscience is to characterize the dynamics of
-spiking from the neurons in a circuit that is involved in learning about a
-stimulus or a contingency. A key limitation of current methods to analyze
-neural spiking data is the need to collapse neural activity over time or
-trials, which may cause the loss of information pertinent to understanding the
-function of a neuron or circuit. We introduce a new method that can determine
-not only the trial-to-trial dynamics that accompany the learning of a
-contingency by a neuron, but also the latency of this learning with respect to
-the onset of a conditioned stimulus. The backbone of the method is a separable
-two-dimensional (2D) random field (RF) model of neural spike rasters, in which
-the joint conditional intensity function of a neuron over time and trials
-depends on two latent Markovian state sequences that evolve separately but in
-parallel. Classical tools to estimate state-space models cannot be applied
-readily to our 2D separable RF model. We develop efficient statistical and
-computational tools to estimate the parameters of the separable 2D RF model. We
-apply these to data collected from neurons in the pre-frontal cortex (PFC) in
-an experiment designed to characterize the neural underpinnings of the
-associative learning of fear in mice. Overall, the separable 2D RF model
-provides a detailed, interpretable, characterization of the dynamics of neural
-spiking that accompany the learning of a contingency.
-",1,0,0,1,0,0
-20082,Using Deep Reinforcement Learning for the Continuous Control of Robotic Arms,"  Deep reinforcement learning enables algorithms to learn complex behavior,
-deal with continuous action spaces and find good strategies in environments
-with high dimensional state spaces. With deep reinforcement learning being an
-active area of research and many concurrent inventions, we decided to focus on
-a relatively simple robotic task to evaluate a set of ideas that might help to
-solve recent reinforcement learning problems. We test a newly created
-combination of two commonly used reinforcement learning methods, whether it is
-able to learn more effectively than a baseline. We also compare different ideas
-to preprocess information before it is fed to the reinforcement learning
-algorithm. The goal of this strategy is to reduce training time and eventually
-help the algorithm to converge. The concluding evaluation proves the general
-applicability of the described concepts by testing them using a simulated
-environment. These concepts might be reused for future experiments.
-",1,0,0,0,0,0
-20083,The First Detection of Gravitational Waves,"  This article deals with the first detection of gravitational waves by the
-advanced Laser Interferometer Gravitational Wave Observatory (LIGO) detectors
-on 14 September 2015, where the signal was generated by two stellar mass black
-holes with masses 36 $ M_{\odot}$ and 29 $ M_{\odot}$ that merged to form a 62
-$ M_{\odot}$ black hole, releasing 3 $M_{\odot}$ energy in gravitational waves,
-almost 1.3 billion years ago. We begin by providing a brief overview of
-gravitational waves, their sources and the gravitational wave detectors. We
-then describe in detail the first detection of gravitational waves from a
-binary black hole merger. We then comment on the electromagnetic follow up of
-the detection event with various telescopes. Finally, we conclude with the
-discussion on the tests of gravity and fundamental physics with the first
-gravitational wave detection event.
-",0,1,0,0,0,0
-20084,Remarkably strong chemisorption of nitric oxide on insulating oxide films promoted by hybrid structure,"  The remarkably strong chemical adsorption behaviors of nitric oxide on
-magnesia (001) film deposited on metal substrate have been investigated by
-employing periodic density functional calculations with Van der Waals
-corrections. The molybdenum supported magnesia (001) show significantly
-enhanced adsorption properties and the nitric oxide is chemisorbed strongly and
-preferably trapped in flat adsorption configuration on metal supported oxide
-film, due to the substantially large adsorption energies and transformation
-barriers. The analysis of Bader charges, projected density of states,
-differential charge densities, electron localization function, highest occupied
-orbital and particular orbital with largest Mg-NO-Mg bonding coefficients, are
-applied to reveal the electronic adsorption properties and characteristics of
-bonding between nitric oxide and surface as well as the bonding within the
-hybrid structure. The strong chemical binding of nitric oxide on magnesia
-deposited on molybdenum slab offers new opportunities for toxic gas detection
-and treatment. We anticipate that hybrid structure promoted remarkable chemical
-adsorption of nitric oxide on magnesia in this study will provide versatile
-strategy for enhancing chemical reactivity and properties of insulating oxide.
-",0,1,0,0,0,0
-20085,Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes,"  In this work we propose approaches to effectively transfer knowledge from
-weakly labeled web audio data. We first describe a convolutional neural network
-(CNN) based framework for sound event detection and classification using weakly
-labeled audio data. Our model trains efficiently from audios of variable
-lengths; hence, it is well suited for transfer learning. We then propose
-methods to learn representations using this model which can be effectively used
-for solving the target task. We study both transductive and inductive transfer
-learning tasks, showing the effectiveness of our methods for both domain and
-task adaptation. We show that the learned representations using the proposed
-CNN model generalizes well enough to reach human level accuracy on ESC-50 sound
-events dataset and set state of art results on this dataset. We further use
-them for acoustic scene classification task and once again show that our
-proposed approaches suit well for this task as well. We also show that our
-methods are helpful in capturing semantic meanings and relations as well.
-Moreover, in this process we also set state-of-art results on Audioset dataset,
-relying on balanced training set.
-",1,0,0,0,0,0
-20086,A priori estimates for the free-boundary Euler equations with surface tension in three dimensions,"  We derive a priori estimates for the incompressible free-boundary Euler
-equations with surface tension in three spatial dimensions. Working in
-Lagrangian coordinates, we provide a priori estimates for the local existence
-when the initial velocity, which is rotational, belongs to $H^3$ and the trace
-of initial velocity on the free boundary to $H^{3.5}$, thus lowering the
-requirement on the regularity of initial data in the Lagrangian setting. Our
-methods are direct and involve three key elements: estimates for the pressure,
-the boundary regularity provided by the mean curvature, and the Cauchy
-invariance.
-",0,0,1,0,0,0
-20087,Koszul duality via suspending Lefschetz fibrations,"  Let $M$ be a Liouville 6-manifold which is the smooth fiber of a Lefschetz
-fibration on $\mathbb{C}^4$ constructed by suspending a Lefschetz fibration on
-$\mathbb{C}^3$. We prove that for many examples including stabilizations of
-Milnor fibers of hypersurface cusp singularities, the compact Fukaya category
-$\mathcal{F}(M)$ and the wrapped Fukaya category $\mathcal{W}(M)$ are related
-through $A_\infty$-Koszul duality, by identifying them with cyclic and
-Calabi-Yau completions of the same quiver algebra. This implies the
-split-generation of the compact Fukaya category $\mathcal{F}(M)$ by vanishing
-cycles. Moreover, new examples of Liouville manifolds which admit
-quasi-dilations in the sense of Seidel-Solomon are obtained.
-",0,0,1,0,0,0
-20088,Attractor of Cantor Type with Positive Measure,"  We construct an iterated function system consisting of strictly increasing
-contractions $f,g\colon [0,1]\to [0,1]$ with $f([0,1])\cap g([0,1])=\emptyset$
-and such that its attractor has positive Lebesgue measure.
-",0,0,1,0,0,0
-20089,Generating online social networks based on socio-demographic attributes,"  Recent years have seen tremendous growth of many online social networks such
-as Facebook, LinkedIn and MySpace. People connect to each other through these
-networks forming large social communities providing researchers rich datasets
-to understand, model and predict social interactions and behaviors. New
-contacts in these networks can be formed due to an individual's demographic
-attributes such as age group, gender, geographic location, or due to a
-network's structural dynamics such as triadic closure and preferential
-attachment, or a combination of both demographic and structural
-characteristics.
-A number of network generation models have been proposed in the last decade
-to explain the structure, evolution and processes taking place in different
-types of networks, and notably social networks. Network generation models
-studied in the literature primarily consider structural properties, and in some
-cases an individual's demographic profile in the formation of new social
-contacts. These models do not present a mechanism to combine both structural
-and demographic characteristics for the formation of new links. In this paper,
-we propose a new network generation algorithm which incorporates both these
-characteristics to model network formation. We use different publicly available
-Facebook datasets as benchmarks to demonstrate the correctness of the proposed
-network generation model. The proposed model is flexible and thus can generate
-networks with varying demographic and structural properties.
-",1,1,0,0,0,0
-20090,Optimal control of diffuser shapes for confined turbulent shear flows,"  A model for the development of turbulent shear flows, created by non-uniform
-parallel flows in a confining channel, is used to identify the diffuser shape
-that maximises pressure recovery when the inflow is non-uniform. Wide diffuser
-angles tend to accentuate the non- uniform flow, causing poor pressure
-recovery. On the other hand, shallow diffuser angles create longer regions with
-large wall drag, which is also detrimental to pressure recovery. Thus, optimal
-diffuser shapes strike a balance between the two effects. We use a simple model
-which describes the evolution of an approximate flow profile and pressure in
-the diffuser. The model equations form the dynamics of an optimal control
-problem where the control is the diffuser channel shape. A numerical
-optimisation approach is used to solve the optimal control problem and we use
-analytical results to interpret the numerics in some limiting cases. The
-results of the optimisation are compared to calculations from computational
-fluid dynamics.
-",0,1,0,0,0,0
-20091,Criteria for Solar Car Optimized Route Estimation,"  This paper gives a thorough overview of Solar Car Optimized Route Estimation
-(SCORE), novel route optimization scheme for solar vehicles based on solar
-irradiance and target distance. In order to conduct the optimization, both data
-collection and the optimization algorithm itself have to be performed using
-appropriate hardware. Here we give an insight to both stages, hardware and
-software used and present some results of the SCORE system together with
-certain improvements of its fusion and optimization criteria. Results and the
-limited applicability of SCORE are discussed together with an overview of
-future research plans and comparison with state-of-the-art solar vehicle
-optimization solutions.
-",1,0,1,0,0,0
-20092,Dykes for filtering ocean waves using c-shaped vertical cylinders,"  The present study investigates a way to design dykes which can filter the
-wavelengths of ocean surface waves. This offers the possibility to achieve a
-structure that can attenuate waves associated with storm swell, without
-affecting coastline in other conditions. Our approach is based on low frequency
-resonances in metamaterials combined with Bragg frequencies for which waves
-cannot propagate in periodic lattices.
-",0,1,0,0,0,0
-20093,Origin of Non-axisymmetric Features of Virgo Cluster Early-type Dwarf Galaxies. I. Bar Formation and Recurrent Buckling,"  A fraction of early-type dwarf galaxies in the Virgo cluster have a disk
-component and even possess disk features such as bar, lens, and spiral arms. In
-this study, we construct 15 galaxy models that resemble VCC856, considered to
-be an infalling progenitor of disk dwarf galaxies, within observational error
-ranges, and use $N$-body simulations to study their long-term dynamical
-evolution in isolation as well as the formation of bar in them. We find that
-dwarf disk galaxies readily form bars unless they have an excessively
-concentrated halo or a hot disk. This suggests that infalling dwarf disk
-galaxies are intrinsically unstable to bar formation, even without any external
-perturbation, accounting for a population of barred dwarf galaxies in the
-outskirts of the Virgo cluster. The bars form earlier and stronger in galaxies
-with a lower fraction of counter-streaming motions, lower halo concentration,
-lower velocity anisotropy, and thinner disk. Similarly to normal disk galaxies,
-dwarf disk galaxies also undergo recurrent buckling instabilities. The first
-buckling instability tends to shorten the bar and to thicken the disk, and
-drives a dynamical transition in the bar pattern speed as well as mass inflow
-rate. In nine models, the bars regrow after the mild first buckling instability
-due to the efficient transfer of disk angular momentum to the halo, and are
-subject to recurrent buckling instabilities to turn into X-shaped bulges.
-",0,1,0,0,0,0
-20094,First results from the DEAP-3600 dark matter search with argon at SNOLAB,"  This paper reports the first results of a direct dark matter search with the
-DEAP-3600 single-phase liquid argon (LAr) detector. The experiment was
-performed 2 km underground at SNOLAB (Sudbury, Canada) utilizing a large target
-mass, with the LAr target contained in a spherical acrylic vessel of 3600 kg
-capacity. The LAr is viewed by an array of PMTs, which would register
-scintillation light produced by rare nuclear recoil signals induced by dark
-matter particle scattering. An analysis of 4.44 live days (fiducial exposure of
-9.87 tonne-days) of data taken with the nearly full detector during the initial
-filling phase demonstrates the detector performance and the best electronic
-recoil rejection using pulse-shape discrimination in argon, with leakage
-$<1.2\times 10^{-7}$ (90% C.L.) between 16 and 33 keV$_{ee}$. No candidate
-signal events are observed, which results in the leading limit on WIMP-nucleon
-spin-independent cross section on argon, $<1.2\times 10^{-44}$ cm$^2$ for a 100
-GeV/c$^2$ WIMP mass (90% C.L.).
-",0,1,0,0,0,0
-20095,Camera Calibration by Global Constraints on the Motion of Silhouettes,"  We address the problem of epipolar geometry using the motion of silhouettes.
-Such methods match epipolar lines or frontier points across views, which are
-then used as the set of putative correspondences. We introduce an approach that
-improves by two orders of magnitude the performance over state-of-the-art
-methods, by significantly reducing the number of outliers in the putative
-matching. We model the frontier points' correspondence problem as constrained
-flow optimization, requiring small differences between their coordinates over
-consecutive frames. Our approach is formulated as a Linear Integer Program and
-we show that due to the nature of our problem, it can be solved efficiently in
-an iterative manner. Our method was validated on four standard datasets
-providing accurate calibrations across very different viewpoints.
-",1,0,0,0,0,0
-20096,Ann: A domain-specific language for the effective design and validation of Java annotations,"  This paper describes a new modelling language for the effective design and
-validation of Java annotations. Since their inclusion in the 5th edition of
-Java, annotations have grown from a useful tool for the addition of meta-data
-to play a central role in many popular software projects. Usually they are not
-conceived in isolation, but in groups, with dependency and integrity
-constraints between them. However, the native support provided by Java for
-expressing this design is very limited.
-To overcome its deficiencies and make explicit the rich conceptual model
-which lies behind a set of annotations, we propose a domain-specific modelling
-language. The proposal has been implemented as an Eclipse plug-in, including an
-editor and an integrated code generator that synthesises annotation processors.
-The environment also integrates a model finder, able to detect unsatisfiable
-constraints between different annotations, and to provide examples of correct
-annotation usages for validation. The language has been tested using a real set
-of annotations from the Java Persistence API (JPA). Within this subset we have
-found enough rich semantics expressible with Ann and omitted nowadays by the
-Java language, which shows the benefits of Ann in a relevant field of
-application.
-",1,0,0,0,0,0
-20097,Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery,"  The new wave of successful generative models in machine learning has
-increased the interest in deep learning driven de novo drug design. However,
-assessing the performance of such generative models is notoriously difficult.
-Metrics that are typically used to assess the performance of such generative
-models are the percentage of chemically valid molecules or the similarity to
-real molecules in terms of particular descriptors, such as the partition
-coefficient (logP) or druglikeness. However, method comparison is difficult
-because of the inconsistent use of evaluation metrics, the necessity for
-multiple metrics, and the fact that some of these measures can easily be
-tricked by simple rule-based systems. We propose a novel distance measure
-between two sets of molecules, called Fréchet ChemNet distance (FCD), that
-can be used as an evaluation metric for generative models. The FCD is similar
-to a recently established performance metric for comparing image generation
-methods, the Fréchet Inception Distance (FID). Whereas the FID uses one of
-the hidden layers of InceptionNet, the FCD utilizes the penultimate layer of a
-deep neural network called ChemNet, which was trained to predict drug
-activities. Thus, the FCD metric takes into account chemically and biologically
-relevant information about molecules, and also measures the diversity of the
-set via the distribution of generated molecules. The FCD's advantage over
-previous metrics is that it can detect if generated molecules are a) diverse
-and have similar b) chemical and c) biological properties as real molecules. We
-further provide an easy-to-use implementation that only requires the SMILES
-representation of the generated molecules as input to calculate the FCD.
-Implementations are available at: this https URL
-",0,0,0,1,1,0
-20098,RANSAC Algorithms for Subspace Recovery and Subspace Clustering,"  We consider the RANSAC algorithm in the context of subspace recovery and
-subspace clustering. We derive some theory and perform some numerical
-experiments. We also draw some correspondences with the methods of Hardt and
-Moitra (2013) and Chen and Lerman (2009b).
-",0,0,1,1,0,0
-20099,Convex Formulations for Fair Principal Component Analysis,"  Though there is a growing body of literature on fairness for supervised
-learning, the problem of incorporating fairness into unsupervised learning has
-been less well-studied. This paper studies fairness in the context of principal
-component analysis (PCA). We first present a definition of fairness for
-dimensionality reduction, and our definition can be interpreted as saying that
-a reduction is fair if information about a protected class (e.g., race or
-gender) cannot be inferred from the dimensionality-reduced data points. Next,
-we develop convex optimization formulations that can improve the fairness (with
-respect to our definition) of PCA and kernel PCA. These formulations are
-semidefinite programs (SDP's), and we demonstrate the effectiveness of our
-formulations using several datasets. We conclude by showing how our approach
-can be used to perform a fair (with respect to age) clustering of health data
-that may be used to set health insurance rates.
-",0,0,0,1,0,0
-20100,Report: Dynamic Eye Movement Matching and Visualization Tool in Neuro Gesture,"  In the research of the impact of gestures using by a lecturer, one
-challenging task is to infer the attention of a group of audiences. Two
-important measurements that can help infer the level of attention are eye
-movement data and Electroencephalography (EEG) data. Under the fundamental
-assumption that a group of people would look at the same place if they all pay
-attention at the same time, we apply a method, ""Time Warp Edit Distance"", to
-calculate the similarity of their eye movement trajectories. Moreover, we also
-cluster eye movement pattern of audiences based on these pair-wised similarity
-metrics. Besides, since we don't have a direct metric for the ""attention""
-ground truth, a visual assessment would be beneficial to evaluate the
-gesture-attention relationship. Thus we also implement a visualization tool.
-",1,0,0,0,0,0
-20101,Infinite symmetric ergodic index and related examples in infinite measure,"  We define an infinite measure-preserving transformation to have infinite
-symmetric ergodic index if all finite Cartesian products of the transformation
-and its inverse are ergodic, and show that infinite symmetric ergodic index
-does not imply that all products of powers are conservative, so does not imply
-power weak mixing. We provide a sufficient condition for $k$-fold and infinite
-symmetric ergodic index and use it to answer a question on the relationship
-between product conservativity and product ergodicity. We also show that a
-class of rank-one transformations that have infinite symmetric ergodic index
-are not power weakly mixing, and precisely characterize a class of power weak
-transformations that generalizes existing examples.
-",0,0,1,0,0,0
-20102,Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature,"  An exciting branch of machine learning research focuses on methods for
-learning, optimizing, and integrating unknown functions that are difficult or
-costly to evaluate. A popular Bayesian approach to this problem uses a Gaussian
-process (GP) to construct a posterior distribution over the function of
-interest given a set of observed measurements, and selects new points to
-evaluate using the statistics of this posterior. Here we extend these methods
-to exploit derivative information from the unknown function. We describe
-methods for Bayesian optimization (BO) and Bayesian quadrature (BQ) in settings
-where first and second derivatives may be evaluated along with the function
-itself. We perform sampling-based inference in order to incorporate uncertainty
-over hyperparameters, and show that both hyperparameter and function
-uncertainty decrease much more rapidly when using derivative information.
-Moreover, we introduce techniques for overcoming ill-conditioning issues that
-have plagued earlier methods for gradient-enhanced Gaussian processes and
-kriging. We illustrate the efficacy of these methods using applications to real
-and simulated Bayesian optimization and quadrature problems, and show that
-exploting derivatives can provide substantial gains over standard methods.
-",0,0,0,1,0,0
-20103,Permanency of the age-structured population model on several temporally variable patches,"  We consider a system of nonlinear partial differential equations that
-describes an age-structured population inhabiting several temporally varying
-patches. We prove existence and uniqueness of solution and analyze its
-large-time behavior in cases when the environment is constant and when it
-changes periodically. A pivotal assumption is that individuals can disperse and
-that each patch can be reached from every other patch, directly or through
-several intermediary patches. We introduce the net reproductive operator and
-characteristic equations for time-independent and periodical models and prove
-that permanency is defined by the net reproductive rate for the whole system.
-If the net reproductive rate is less or equal to one, extinction on all patches
-is imminent. Otherwise, permanency on all patches is guaranteed. The proof is
-based on a new approach to analysis of large-time stability.
-",0,1,1,0,0,0
-20104,Rapid laser-induced photochemical conversion of sol-gel precursors to In2O3 layers and their application in thin-film transistors,"  We report the development of indium oxide (In2O3) transistors via a single
-step laser-induced photochemical conversion process of a sol-gel metal oxide
-precursor. Through careful optimization of the laser annealing conditions we
-demonstrated successful conversion of the precursor to In2O3 and its subsequent
-implementation in n-channel transistors with electron mobility up to 13 cm2/Vs.
-Importantly, the process does not require thermal annealing making it
-compatible with temperature sensitive materials such as plastic. On the other
-hand, the spatial conversion/densification of the sol-gel layer eliminates
-additional process steps associated with semiconductor patterning and hence
-significantly reduces fabrication complexity and cost. Our work demonstrates
-unambiguously that laser-induced photochemical conversion of sol-gel metal
-oxide precursors can be rapid and compatible with large-area electronics
-manufacturing.
-",0,1,0,0,0,0
-20105,Testing for Change in Stochastic Volatility with Long Range Dependence,"  In this paper, change-point problems for long memory stochastic volatility
-models are considered. A general testing problem which includes various
-alternative hypotheses is discussed. Under the hypothesis of stationarity the
-limiting behavior of CUSUM- and Wilcoxon-type test statistics is derived. In
-this context, a limit theorem for the two-parameter empirical process of long
-memory stochastic volatility time series is proved. In particular, it is shown
-that the asymptotic distribution of CUSUM test statistics may not be affected
-by long memory, unlike Wilcoxon test statistics which are typically influenced
-by long range dependence. To avoid the estimation of nuisance parameters in
-applications, the usage of self-normalized test statistics is proposed. The
-theoretical results are accompanied by simulation studies which characterize
-the finite sample behavior of the considered testing procedures when testing
-for changes in mean, in variance, and in the tail index.
-",0,0,1,1,0,0
-20106,Kinematics effects of atmospheric friction in spacecraft flybys,"  Gravity assist manoeuvres are one of the most succesful techniques in
-astrodynamics. In these trajectories the spacecraft comes very close to the
-surface of the Earth, or other Solar system planets or moons, and, as a
-consequence, it experiences the effect of atmospheric friction by the outer
-layers of the Earth's atmosphere or ionosphere.
-In this paper we analyze a standard atmospheric model to estimate the density
-profile during the two Galileo flybys, the NEAR and the Juno flyby. We show
-that, even allowing for a margin of uncertainty in the spacecraft cross-section
-and the drag coefficient, the observed -8 mm/sec anomalous velocity decrease
-during the second Galileo flyby of December, 8th, 1992 cannot be attributed
-only to atmospheric friction. On the other hand, for perigees on the border
-between the termosphere and the exosphere the friction only accounts for a
-fraction of a millimeter per second in the final asymptotic velocity.
-",0,1,0,0,0,0
-20107,Interval vs. Point Temporal Logic Model Checking: an Expressiveness Comparison,"  In the last years, model checking with interval temporal logics is emerging
-as a viable alternative to model checking with standard point-based temporal
-logics, such as LTL, CTL, CTL*, and the like. The behavior of the system is
-modeled by means of (finite) Kripke structures, as usual. However, while
-temporal logics which are interpreted ""point-wise"" describe how the system
-evolves state-by-state, and predicate properties of system states, those which
-are interpreted ""interval-wise"" express properties of computation stretches,
-spanning a sequence of states. A proposition letter is assumed to hold over a
-computation stretch (interval) if and only if it holds over each component
-state (homogeneity assumption). A natural question arises: is there any
-advantage in replacing points by intervals as the primary temporal entities, or
-is it just a matter of taste?
-In this paper, we study the expressiveness of Halpern and Shoham's interval
-temporal logic (HS) in model checking, in comparison with those of LTL, CTL,
-and CTL*. To this end, we consider three semantic variants of HS: the
-state-based one, introduced by Montanari et al., that allows time to branch
-both in the past and in the future, the computation-tree-based one, that allows
-time to branch in the future only, and the trace-based variant, that disallows
-time to branch. These variants are compared among themselves and to the
-aforementioned standard logics, getting a complete picture. In particular, we
-show that HS with trace-based semantics is equivalent to LTL (but at least
-exponentially more succinct), HS with computation-tree-based semantics is
-equivalent to finitary CTL*, and HS with state-based semantics is incomparable
-with all of them (LTL, CTL, and CTL*).
-",1,0,0,0,0,0
-20108,Calculations for electron-impact ionization of magnesium and calcium atoms in the method of interacting configurations in the complex number representation,"  Next investigations in our program of transition from the He atom to the
-complex atoms description have been presented. The method of interacting
-configurations in the complex number representation is under consideration. The
-spectroscopic characteristics of the Mg and Ca atoms in the problem of the
-electron-impact ionization of these atoms are investigated. The energies and
-the widths of the lowest autoionizing states of Mg and Ca atoms are calculated.
-Few results in the photoionization problem on the autoionizing states above the
-n=2 threshold of helium-like Be ion are presented.
-",0,1,0,0,0,0
-20109,An Information Theoretic Approach to Sample Acquisition and Perception in Planetary Robotics,"  An important and emerging component of planetary exploration is sample
-retrieval and return to Earth. Obtaining and analyzing rock samples can provide
-unprecedented insight into the geology, geo-history and prospects for finding
-past life and water. Current methods of exploration rely on mission scientists
-to identify objects of interests and this presents major operational
-challenges. Finding objects of interests will require systematic and efficient
-methods to quickly and correctly evaluate the importance of hundreds if not
-thousands of samples so that the most interesting are saved for further
-analysis by the mission scientists. In this paper, we propose an automated
-information theoretic approach to identify shapes of interests using a library
-of predefined interesting shapes. These predefined shapes maybe human input or
-samples that are then extrapolated by the shape matching system using the
-Superformula to judge the importance of newly obtained objects. Shape samples
-are matched to a library of shapes using the eigenfaces approach enabling
-categorization and prioritization of the sample. The approach shows robustness
-to simulated sensor noise of up to 20%. The effect of shape parameters and
-rotational angle on shape matching accuracy has been analyzed. The approach
-shows significant promise and efforts are underway in testing the algorithm
-with real rock samples.
-",1,1,0,0,0,0
-20110,Global solvability of the Navier-Stokes equations with a free surface in the maximal $L_p\text{-}L_q$ regularity class,"  We consider the motion of incompressible viscous fluids bounded above by a
-free surface and below by a solid surface in the $N$-dimensional Euclidean
-space for $N\geq 2$ when the gravity is not taken into account. The aim of this
-paper is to show the global solvability of the Naiver-Stokes equations with a
-free surface, describing the above-mentioned motion, in the maximal
-$L_p\text{-}L_q$ regularity class. Our approach is based on the maximal
-$L_p\text{-}L_q$ regularity with exponential stability for the linearized
-equations, and solutions to the original nonlinear problem are also
-exponentially stable.
-",0,0,1,0,0,0
-20111,The fundamental group of the complement of the singular locus of Lauricella's $F_C$,"  We study the fundamental group of the complement of the singular locus of
-Lauricella's hypergeometric function $F_C$ of $n$ variables. The singular locus
-consists of $n$ hyperplanes and a hypersurface of degree $2^{n-1}$ in the
-complex $n$-space. We derive some relations that holds for general $n\geq 3$.
-We give an explicit presentation of the fundamental groupin the
-three-dimensional case. We also consider a presentation of the fundamental
-group of $2^3$-covering of this space.
-In the version 2, we omit some of the calculations. For all the calculations,
-refer to the version 1 (arXiv:1710.09594v1) of this article.
-",0,0,1,0,0,0
-20112,Nonconvex Sparse Spectral Clustering by Alternating Direction Method of Multipliers and Its Convergence Analysis,"  Spectral Clustering (SC) is a widely used data clustering method which first
-learns a low-dimensional embedding $U$ of data by computing the eigenvectors of
-the normalized Laplacian matrix, and then performs k-means on $U^\top$ to get
-the final clustering result. The Sparse Spectral Clustering (SSC) method
-extends SC with a sparse regularization on $UU^\top$ by using the block
-diagonal structure prior of $UU^\top$ in the ideal case. However, encouraging
-$UU^\top$ to be sparse leads to a heavily nonconvex problem which is
-challenging to solve and the work (Lu, Yan, and Lin 2016) proposes a convex
-relaxation in the pursuit of this aim indirectly. However, the convex
-relaxation generally leads to a loose approximation and the quality of the
-solution is not clear. This work instead considers to solve the nonconvex
-formulation of SSC which directly encourages $UU^\top$ to be sparse. We propose
-an efficient Alternating Direction Method of Multipliers (ADMM) to solve the
-nonconvex SSC and provide the convergence guarantee. In particular, we prove
-that the sequences generated by ADMM always exist a limit point and any limit
-point is a stationary point. Our analysis does not impose any assumptions on
-the iterates and thus is practical. Our proposed ADMM for nonconvex problems
-allows the stepsize to be increasing but upper bounded, and this makes it very
-efficient in practice. Experimental analysis on several real data sets verifies
-the effectiveness of our method.
-",1,0,0,0,0,0
-20113,Stable Geodesic Update on Hyperbolic Space and its Application to Poincare Embeddings,"  A hyperbolic space has been shown to be more capable of modeling complex
-networks than a Euclidean space. This paper proposes an explicit update rule
-along geodesics in a hyperbolic space. The convergence of our algorithm is
-theoretically guaranteed, and the convergence rate is better than the
-conventional Euclidean gradient descent algorithm. Moreover, our algorithm
-avoids the ""bias"" problem of existing methods using the Riemannian gradient.
-Experimental results demonstrate the good performance of our algorithm in the
-\Poincare embeddings of knowledge base data.
-",0,0,0,1,0,0
-20114,A Parameterized Approach to Personalized Variable Length Summarization of Soccer Matches,"  We present a parameterized approach to produce personalized variable length
-summaries of soccer matches. Our approach is based on temporally segmenting the
-soccer video into 'plays', associating a user-specifiable 'utility' for each
-type of play and using 'bin-packing' to select a subset of the plays that add
-up to the desired length while maximizing the overall utility (volume in
-bin-packing terms). Our approach systematically allows a user to override the
-default weights assigned to each type of play with individual preferences and
-thus see a highly personalized variable length summarization of soccer matches.
-We demonstrate our approach based on the output of an end-to-end pipeline that
-we are building to produce such summaries. Though aspects of the overall
-end-to-end pipeline are human assisted at present, the results clearly show
-that the proposed approach is capable of producing semantically meaningful and
-compelling summaries. Besides the obvious use of producing summaries of
-superior league matches for news broadcasts, we anticipate our work to promote
-greater awareness of the local matches and junior leagues by producing
-consumable summaries of them.
-",1,0,0,0,0,0
-20115,Towards Robust Neural Networks via Random Self-ensemble,"  Recent studies have revealed the vulnerability of deep neural networks: A
-small adversarial perturbation that is imperceptible to human can easily make a
-well-trained deep neural network misclassify. This makes it unsafe to apply
-neural networks in security-critical applications. In this paper, we propose a
-new defense algorithm called Random Self-Ensemble (RSE) by combining two
-important concepts: {\bf randomness} and {\bf ensemble}. To protect a targeted
-model, RSE adds random noise layers to the neural network to prevent the strong
-gradient-based attacks, and ensembles the prediction over random noises to
-stabilize the performance. We show that our algorithm is equivalent to ensemble
-an infinite number of noisy models $f_\epsilon$ without any additional memory
-overhead, and the proposed training procedure based on noisy stochastic
-gradient descent can ensure the ensemble model has a good predictive
-capability. Our algorithm significantly outperforms previous defense techniques
-on real data sets. For instance, on CIFAR-10 with VGG network (which has 92\%
-accuracy without any attack), under the strong C\&W attack within a certain
-distortion tolerance, the accuracy of unprotected model drops to less than
-10\%, the best previous defense technique has $48\%$ accuracy, while our method
-still has $86\%$ prediction accuracy under the same level of attack. Finally,
-our method is simple and easy to integrate into any neural network.
-",1,0,0,1,0,0
-20116,Prospects for detection of intermediate-mass black holes in globular clusters using integrated-light spectroscopy,"  The detection of intermediate mass black holes (IMBHs) in Galactic globular
-clusters (GCs) has so far been controversial. In order to characterize the
-effectiveness of integrated-light spectroscopy through integral field units, we
-analyze realistic mock data generated from state-of-the-art Monte Carlo
-simulations of GCs with a central IMBH, considering different setups and
-conditions varying IMBH mass, cluster distance, and accuracy in determination
-of the center. The mock observations are modeled with isotropic Jeans models to
-assess the success rate in identifying the IMBH presence, which we find to be
-primarily dependent on IMBH mass. However, even for a IMBH of considerable mass
-(3% of the total GC mass), the analysis does not yield conclusive results in 1
-out of 5 cases, because of shot noise due to bright stars close to the IMBH
-line-of-sight. This stochastic variability in the modeling outcome grows with
-decreasing BH mass, with approximately 3 failures out of 4 for IMBHs with 0.1%
-of total GC mass. Finally, we find that our analysis is generally unable to
-exclude at 68% confidence an IMBH with mass of $10^3~M_\odot$ in snapshots
-without a central BH. Interestingly, our results are not sensitive to GC
-distance within 5-20 kpc, nor to mis-identification of the GC center by less
-than 2'' (<20% of the core radius). These findings highlight the value of
-ground-based integral field spectroscopy for large GC surveys, where systematic
-failures can be accounted for, but stress the importance of discrete kinematic
-measurements that are less affected by stochasticity induced by bright stars.
-",0,1,0,0,0,0
-20117,A Multi-frequency analysis of possible Dark Matter Contributions to M31 Gamma-Ray Emissions,"  We examine the possibility of a dark matter (DM) contribution to the recently
-observed gamma-ray spectrum seen in the M31 galaxy. In particular, we apply
-limits on Weakly Interacting Massive Particle DM annihilation cross-sections
-derived from the Coma galaxy cluster and the Reticulum II dwarf galaxy to
-determine the maximal flux contribution by DM annihilation to both the M31
-gamma-ray spectrum and that of the Milky-Way galactic centre. We limit the
-energy range between 1 and 12 GeV in M31 and galactic centre spectra due to the
-limited range of former's data, as well as to encompass the high-energy
-gamma-ray excess observed in the latter target. In so doing, we will make use
-of Fermi-LAT data for all mentioned targets, as well as diffuse radio data for
-the Coma cluster. The multi-target strategy using both Coma and Reticulum II to
-derive cross-section limits, as well as multi-frequency data, ensures that our
-results are robust against the various uncertainties inherent in modelling of
-indirect DM emissions.
-Our results indicate that, when a Navarro-Frenk-White (or shallower) radial
-density profile is assumed, severe constraints can be imposed upon the fraction
-of the M31 and galactic centre spectra that can be accounted for by DM, with
-the best limits arising from cross-section constraints from Coma radio data and
-Reticulum II gamma-ray limits. These particular limits force all the studied
-annihilation channels to contribute 1% or less to the total integrated
-gamma-ray flux within both M31 and galactic centre targets. In contrast,
-considerably more, 10-100%, of the flux can be attributed to DM when a
-contracted Navarro-Frenk-White profile is assumed. This demonstrates how
-sensitive DM contributions to gamma-ray emissions are to the possibility of
-cored profiles in galaxies.
-",0,1,0,0,0,0
-20118,Tensor Decompositions for Modeling Inverse Dynamics,"  Modeling inverse dynamics is crucial for accurate feedforward robot control.
-The model computes the necessary joint torques, to perform a desired movement.
-The highly non-linear inverse function of the dynamical system can be
-approximated using regression techniques. We propose as regression method a
-tensor decomposition model that exploits the inherent three-way interaction of
-positions x velocities x accelerations. Most work in tensor factorization has
-addressed the decomposition of dense tensors. In this paper, we build upon the
-decomposition of sparse tensors, with only small amounts of nonzero entries.
-The decomposition of sparse tensors has successfully been used in relational
-learning, e.g., the modeling of large knowledge graphs. Recently, the approach
-has been extended to multi-class classification with discrete input variables.
-Representing the data in high dimensional sparse tensors enables the
-approximation of complex highly non-linear functions. In this paper we show how
-the decomposition of sparse tensors can be applied to regression problems.
-Furthermore, we extend the method to continuous inputs, by learning a mapping
-from the continuous inputs to the latent representations of the tensor
-decomposition, using basis functions. We evaluate our proposed model on a
-dataset with trajectories from a seven degrees of freedom SARCOS robot arm. Our
-experimental results show superior performance of the proposed functional
-tensor model, compared to challenging state-of-the art methods.
-",1,0,0,1,0,0
-20119,Synthesis and Hydrogen Sorption Characteristics of Mechanically Alloyed Mg(NixMn1-x)2 Intermetallics,"  New ternary Mg-Ni-Mn intermetallics have been successfully synthesized by
-High Energy Ball Milling (HEBM) and have been studied as possible materials for
-efficient hydrogen storage applications. The microstructures of the as-cast and
-milled alloys were characterized by means of X-ray Powder Diffraction (XRD) and
-Scanning Electron Microscopy (SEM) both prior and after the hydrogenation
-process, while the hydrogen storage characteristics (P-c-T) and the kinetics
-were measured by using a commercial and automatically controlled Sievert-type
-apparatus. The hydrogenation and dehydrogenation measurements were performed at
-four different temperatures 150-200-250-300oC and the results showed that the
-kinetics for both the hydrogenation and dehydrogenation process are very fast
-for operation temperatures 250 and 300oC, but for temperatures below 200oC the
-hydrogenation process becomes very slow and the dehydrogenation process cannot
-be achieved.
-",0,1,0,0,0,0
-20120,Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective,"  Recent advances in the field of network embedding have shown the
-low-dimensional network representation is playing a critical role in network
-analysis. However, most of the existing principles of network embedding do not
-incorporate auxiliary information such as content and labels of nodes flexibly.
-In this paper, we take a matrix factorization perspective of network embedding,
-and incorporate structure, content and label information of the network
-simultaneously. For structure, we validate that the matrix we construct
-preserves high-order proximities of the network. Label information can be
-further integrated into the matrix via the process of random walk sampling to
-enhance the quality of embedding in an unsupervised manner, i.e., without
-leveraging downstream classifiers. In addition, we generalize the Skip-Gram
-Negative Sampling model to integrate the content of the network in a matrix
-factorization framework. As a consequence, network embedding can be learned in
-a unified framework integrating network structure and node content as well as
-label information simultaneously. We demonstrate the efficacy of the proposed
-model with the tasks of semi-supervised node classification and link prediction
-on a variety of real-world benchmark network datasets.
-",1,0,0,1,0,0
-20121,Incremental Principal Component Analysis Exact implementation and continuity corrections,"  This paper describes some applications of an incremental implementation of
-the principal component analysis (PCA). The algorithm updates the
-transformation coefficients matrix on-line for each new sample, without the
-need to keep all the samples in memory. The algorithm is formally equivalent to
-the usual batch version, in the sense that given a sample set the
-transformation coefficients at the end of the process are the same. The
-implications of applying the PCA in real time are discussed with the help of
-data analysis examples. In particular we focus on the problem of the continuity
-of the PCs during an on-line analysis.
-",1,0,0,1,0,0
-20122,More investment in Research and Development for better Education in the future?,"  The question in this paper is whether R&D efforts affect education
-performance in small classes. Merging two datasets collected from the PISA
-studies and the World Development Indicators and using Learning Bayesian
-Networks, we prove the existence of a statistical causal relationship between
-investment in R&D of a country and its education performance (PISA scores). We
-also prove that the effect of R\&D on Education is long term as a country has
-to invest at least 10 years before beginning to improve the level of young
-pupils.
-",0,0,0,1,0,0
-20123,Discriminative Modeling of Social Influence for Prediction and Explanation in Event Cascades,"  The global dynamics of event cascades are often governed by the local
-dynamics of peer influence. However, detecting social influence from
-observational data is challenging, due to confounds like homophily and
-practical issues like missing data. In this work, we propose a novel
-discriminative method to detect influence from observational data. The core of
-the approach is to train a ranking algorithm to predict the source of the next
-event in a cascade, and compare its out-of-sample accuracy against a
-competitive baseline which lacks access to features corresponding to social
-influence. Using synthetically generated data, we provide empirical evidence
-that this method correctly identifies influence in the presence of confounds,
-and is robust to both missing data and misspecification --- unlike popular
-alternatives. We also apply the method to two real-world datasets: (1) cascades
-of co-sponsorship of legislation in the U.S. House of Representatives, on a
-social network of shared campaign donors; (2) rumors about the Higgs boson
-discovery, on a follower network of $10^5$ Twitter accounts. Our model
-identifies the role of peer influence in these scenarios, and uses it to make
-more accurate predictions about the future trajectory of cascades.
-",1,0,0,0,0,0
-20124,Online codes for analog signals,"  We revisit a classical scenario in communication theory: a source is
-generating a waveform which we sample at regular intervals; we wish to
-transform the signal in such a way as to minimize distortion in its
-reconstruction, despite noise. The transformation must be online (also called
-causal), in order to enable real-time signaling. The noise model we consider is
-adversarial $\ell_1$-bounded; this is the ""atomic norm"" convex relaxation of
-the standard adversary model in discrete-alphabet communications, namely
-sparsity (low Hamming weight). We require that our encoding not increase the
-power of the original signal.
-In the ""block coding"" setting such encoding is possible due to the existence
-of large almost-Euclidean sections in $\ell_1$ spaces (established in the work
-of Dvoretzky, Milman, Kašin, and Figiel, Lindenstrauss and Milman).
-Our main result is that an analogous result is achievable even online.
-Equivalently, we show a ""lower triangular"" version of $\ell_1$ Dvoretzky
-theorems. In terms of communication, the result has the following form: If the
-signal is a stream of reals $x_1,\ldots$, one per unit time, which we encode
-causally into $\rho$ (a constant) reals per unit time (forming altogether an
-output stream $\mathcal{E}(x)$), and if the adversarial noise added to this
-encoded stream up to time $s$ is a vector $\vec{y}$, then at time $s$ the
-decoder's reconstruction of the input prefix $x_{[s]}$ is accurate in a
-time-weighted $\ell_2$ norm, to within $s^{-1/2+\delta}$ (any $\delta>0$) times
-the adversary's noise as measured in a time-weighted $\ell_1$ norm. The
-time-weighted decoding norm forces increasingly accurate reconstruction of the
-distant past, while the time-weighted noise norm permits only vanishing effect
-from noise in the distant past.
-Encoding is linear, and decoding is performed by an LP analogous to those
-used in compressed sensing.
-",1,0,0,0,0,0
-20125,Statistical test for fractional Brownian motion based on detrending moving average algorithm,"  Motivated by contemporary and rich applications of anomalous diffusion
-processes we propose a new statistical test for fractional Brownian motion,
-which is one of the most popular models for anomalous diffusion systems. The
-test is based on detrending moving average statistic and its probability
-distribution. Using the theory of Gaussian quadratic forms we determined it as
-a generalized chi-squared distribution. The proposed test could be generalized
-for statistical testing of any centered non-degenerate Gaussian process.
-Finally, we examine the test via Monte Carlo simulations for two exemplary
-scenarios of subdiffusive and superdiffusive dynamics.
-",0,0,0,1,0,0
-20126,Fast Radio Map Construction and Position Estimation via Direct Mapping for WLAN Indoor Localization System,"  The main limitation that constrains the fast and comprehensive application of
-Wireless Local Area Network (WLAN) based indoor localization systems with
-Received Signal Strength (RSS) positioning algorithms is the building of the
-fingerprinting radio map, which is time-consuming especially when the indoor
-environment is large and/or with high frequent changes. Different approaches
-have been proposed to reduce workload, including fingerprinting deployment and
-update efforts, but the performance degrades greatly when the workload is
-reduced below a certain level. In this paper, we propose an indoor localization
-scenario that applies metric learning and manifold alignment to realize direct
-mapping localization (DML) using a low resolution radio map with single sample
-of RSS that reduces the fingerprinting workload by up to 87\%. Compared to
-previous work. The proposed two localization approaches, DML and $k$ nearest
-neighbors based on reconstructed radio map (reKNN), were shown to achieve less
-than 4.3\ m and 3.7\ m mean localization error respectively in a typical office
-environment with an area of approximately 170\ m$^2$, while the unsupervised
-localization with perturbation algorithm was shown to achieve 4.7\ m mean
-localization error with 8 times more workload than the proposed methods. As for
-the room level localization application, both DML and reKNN can meet the
-requirement with at most 9\ m of localization error which is enough to tell
-apart different rooms with over 99\% accuracy.
-",1,0,0,1,0,0
-20127,Lagrangian solutions to the Vlasov-Poisson system with a point charge,"  We consider the Cauchy problem for the repulsive Vlasov-Poisson system in the
-three dimensional space, where the initial datum is the sum of a diffuse
-density, assumed to be bounded and integrable, and a point charge. Under some
-decay assumptions for the diffuse density close to the point charge, under
-bounds on the total energy, and assuming that the initial total diffuse charge
-is strictly less than one, we prove existence of global Lagrangian solutions.
-Our result extends the Eulerian theory of [16], proving that solutions are
-transported by the flow trajectories. The proof is based on the ODE theory
-developed in [8] in the setting of vector fields with anisotropic regularity,
-where some components of the gradient of the vector field is a singular
-integral of a measure.
-",0,0,1,0,0,0
-20128,MITHRIL: Mining Sporadic Associations for Cache Prefetching,"  The growing pressure on cloud application scalability has accentuated storage
-performance as a critical bottle- neck. Although cache replacement algorithms
-have been extensively studied, cache prefetching - reducing latency by
-retrieving items before they are actually requested remains an underexplored
-area. Existing approaches to history-based prefetching, in particular, provide
-too few benefits for real systems for the resources they cost. We propose
-MITHRIL, a prefetching layer that efficiently exploits historical patterns in
-cache request associations. MITHRIL is inspired by sporadic association rule
-mining and only relies on the timestamps of requests. Through evaluation of 135
-block-storage traces, we show that MITHRIL is effective, giving an average of a
-55% hit ratio increase over LRU and PROBABILITY GRAPH, a 36% hit ratio gain
-over AMP at reasonable cost. We further show that MITHRIL can supplement any
-cache replacement algorithm and be readily integrated into existing systems.
-Furthermore, we demonstrate the improvement comes from MITHRIL being able to
-capture mid-frequency blocks.
-",1,0,0,0,0,0
-20129,On 2-level polytopes arising in combinatorial settings,"  2-level polytopes naturally appear in several areas of pure and applied
-mathematics, including combinatorial optimization, polyhedral combinatorics,
-communication complexity, and statistics. In this paper, we present a study of
-some 2-level polytopes arising in combinatorial settings. Our first
-contribution is proving that v(P)*f(P) is upper bounded by d*2^(d+1), for a
-large collection of families of such polytopes P. Here v(P) (resp. f(P)) is the
-number of vertices (resp. facets) of P, and d is its dimension. Whether this
-holds for all 2-level polytopes was asked in [Bohn et al., ESA 2015], and
-experimental results from [Fiorini et al., ISCO 2016] showed it true up to
-dimension 7. The key to most of our proofs is a deeper understanding of the
-relations among those polytopes and their underlying combinatorial structures.
-This leads to a number of results that we believe to be of independent
-interest: a trade-off formula for the number of cliques and stable sets in a
-graph; a description of stable matching polytopes as affine projections of
-certain order polytopes; and a linear-size description of the base polytope of
-matroids that are 2-level in terms of cuts of an associated tree.
-",1,0,1,0,0,0
-20130,Learned Optimizers that Scale and Generalize,"  Learning to learn has emerged as an important direction for achieving
-artificial intelligence. Two of the primary barriers to its adoption are an
-inability to scale to larger problems and a limited ability to generalize to
-new tasks. We introduce a learned gradient descent optimizer that generalizes
-well to new tasks, and which has significantly reduced memory and computation
-overhead. We achieve this by introducing a novel hierarchical RNN architecture,
-with minimal per-parameter overhead, augmented with additional architectural
-features that mirror the known structure of optimization tasks. We also develop
-a meta-training ensemble of small, diverse optimization tasks capturing common
-properties of loss landscapes. The optimizer learns to outperform RMSProp/ADAM
-on problems in this corpus. More importantly, it performs comparably or better
-when applied to small convolutional neural networks, despite seeing no neural
-networks in its meta-training set. Finally, it generalizes to train Inception
-V3 and ResNet V2 architectures on the ImageNet dataset for thousands of steps,
-optimization problems that are of a vastly different scale than those it was
-trained on. We release an open source implementation of the meta-training
-algorithm.
-",1,0,0,1,0,0
-20131,Toward Optimal Run Racing: Application to Deep Learning Calibration,"  This paper aims at one-shot learning of deep neural nets, where a highly
-parallel setting is considered to address the algorithm calibration problem -
-selecting the best neural architecture and learning hyper-parameter values
-depending on the dataset at hand. The notoriously expensive calibration problem
-is optimally reduced by detecting and early stopping non-optimal runs. The
-theoretical contribution regards the optimality guarantees within the multiple
-hypothesis testing framework. Experimentations on the Cifar10, PTB and Wiki
-benchmarks demonstrate the relevance of the approach with a principled and
-consistent improvement on the state of the art with no extra hyper-parameter.
-",1,0,0,0,0,0
-20132,Bernstein - von Mises theorems for statistical inverse problems I: Schrödinger equation,"  The inverse problem of determining the unknown potential $f>0$ in the partial
-differential equation $$\frac{\Delta}{2} u - fu =0 \text{ on } \mathcal O
-~~\text{s.t. } u = g \text { on } \partial \mathcal O,$$ where $\mathcal O$ is
-a bounded $C^\infty$-domain in $\mathbb R^d$ and $g>0$ is a given function
-prescribing boundary values, is considered. The data consist of the solution
-$u$ corrupted by additive Gaussian noise. A nonparametric Bayesian prior for
-the function $f$ is devised and a Bernstein - von Mises theorem is proved which
-entails that the posterior distribution given the observations is approximated
-in a suitable function space by an infinite-dimensional Gaussian measure that
-has a `minimal' covariance structure in an information-theoretic sense. As a
-consequence the posterior distribution performs valid and optimal frequentist
-statistical inference on $f$ in the small noise limit.
-",0,0,1,1,0,0
-20133,Tailoring Architecture Centric Design Method with Rapid Prototyping,"  Many engineering processes exist in the industry, text books and
-international standards. However, in practice rarely any of the processes are
-followed consistently and literally. It is observed across industries the
-processes are altered based on the requirements of the projects. Two features
-commonly lacking from many engineering processes are, 1) the formal capacity to
-rapidly develop prototypes in the rudimentary stage of the project, 2)
-transitioning of requirements into architectural designs, when and how to
-evaluate designs and how to use the throw away prototypes throughout the system
-lifecycle. Prototypes are useful for eliciting requirements, generating
-customer feedback and identifying, examining or mitigating risks in a project
-where the product concept is at a cutting edge or not fully perceived. Apart
-from the work that the product is intended to do, systemic properties like
-availability, performance and modifiability matter as much as functionality.
-Architects must even these concerns with the method they select to promote
-these systemic properties and at the same time equip the stakeholders with the
-desired functionality. Architectural design and prototyping is one of the key
-ways to build the right product embedded with the desired systemic properties.
-Once the product is built it can be almost impossible to retrofit the system
-with the desired attributes. This paper customizes the architecture centric
-development method with rapid prototyping to achieve the above-mentioned goals
-and reducing the number of iterations across the stages of ACDM.
-",1,0,0,0,0,0
-20134,Easy High-Dimensional Likelihood-Free Inference,"  We introduce a framework using Generative Adversarial Networks (GANs) for
-likelihood--free inference (LFI) and Approximate Bayesian Computation (ABC)
-where we replace the black-box simulator model with an approximator network and
-generate a rich set of summary features in a data driven fashion. On benchmark
-data sets, our approach improves on others with respect to scalability, ability
-to handle high dimensional data and complex probability distributions.
-",1,0,0,1,0,0
-20135,Narrow-line Laser Cooling by Adiabatic Transfer,"  We propose and demonstrate a novel laser cooling mechanism applicable to
-particles with narrow-linewidth optical transitions. By sweeping the frequency
-of counter-propagating laser beams in a sawtooth manner, we cause adiabatic
-transfer back and forth between the ground state and a long-lived optically
-excited state. The time-ordering of these adiabatic transfers is determined by
-Doppler shifts, which ensures that the associated photon recoils are in the
-opposite direction to the particle's motion. This ultimately leads to a robust
-cooling mechanism capable of exerting large forces via a weak transition and
-with reduced reliance on spontaneous emission. We present a simple intuitive
-model for the resulting frictional force, and directly demonstrate its efficacy
-for increasing the total phase-space density of an atomic ensemble. We rely on
-both simulation and experimental studies using the 7.5~kHz linewidth $^1$S$_0$
-to $^3$P$_1$ transition in $^{88}$Sr. The reduced reliance on spontaneous
-emission may allow this adiabatic sweep method to be a useful tool for cooling
-particles that lack closed cycling transitions, such as molecules.
-",0,1,0,0,0,0
-20136,Assessing the Effect of Stellar Companions from High-Resolution Imaging of Kepler Objects of Interest,"  We report on 176 close (<2"") stellar companions detected with high-resolution
-imaging near 170 hosts of Kepler Objects of Interest. These Kepler targets were
-prioritized for imaging follow-up based on the presence of small planets, so
-most of the KOIs in these systems (176 out of 204) have nominal radii <6 R_E .
-Each KOI in our sample was observed in at least 2 filters with adaptive optics,
-speckle imaging, lucky imaging, or HST. Multi-filter photometry provides color
-information on the companions, allowing us to constrain their stellar
-properties and assess the probability that the companions are physically bound.
-We find that 60 -- 80% of companions within 1"" are bound, and the bound
-fraction is >90% for companions within 0.5""; the bound fraction decreases with
-increasing angular separation. This picture is consistent with simulations of
-the binary and background stellar populations in the Kepler field. We also
-reassess the planet radii in these systems, converting the observed
-differential magnitudes to a contamination in the Kepler bandpass and
-calculating the planet radius correction factor, $X_R = R_p (true) / R_p
-(single)$. Under the assumption that planets in bound binaries are equally
-likely to orbit the primary or secondary, we find a mean radius correction
-factor for planets in stellar multiples of $X_R = 1.65$. If stellar
-multiplicity in the Kepler field is similar to the solar neighborhood, then
-nearly half of all Kepler planets may have radii underestimated by an average
-of 65%, unless vetted using high resolution imaging or spectroscopy.
-",0,1,0,0,0,0
-20137,Semi-Supervised Learning via New Deep Network Inversion,"  We exploit a recently derived inversion scheme for arbitrary deep neural
-networks to develop a new semi-supervised learning framework that applies to a
-wide range of systems and problems. The approach outperforms current
-state-of-the-art methods on MNIST reaching $99.14\%$ of test set accuracy while
-using $5$ labeled examples per class. Experiments with one-dimensional signals
-highlight the generality of the method. Importantly, our approach is simple,
-efficient, and requires no change in the deep network architecture.
-",1,0,0,1,0,0
-20138,Using English as Pivot to Extract Persian-Italian Parallel Sentences from Non-Parallel Corpora,"  The effectiveness of a statistical machine translation system (SMT) is very
-dependent upon the amount of parallel corpus used in the training phase. For
-low-resource language pairs there are not enough parallel corpora to build an
-accurate SMT. In this paper, a novel approach is presented to extract bilingual
-Persian-Italian parallel sentences from a non-parallel (comparable) corpus. In
-this study, English is used as the pivot language to compute the matching
-scores between source and target sentences and candidate selection phase.
-Additionally, a new monolingual sentence similarity metric, Normalized Google
-Distance (NGD) is proposed to improve the matching process. Moreover, some
-extensions of the baseline system are applied to improve the quality of
-extracted sentences measured with BLEU. Experimental results show that using
-the new pivot based extraction can increase the quality of bilingual corpus
-significantly and consequently improves the performance of the Persian-Italian
-SMT system.
-",1,0,0,0,0,0
-20139,Intrinsic geometry and analysis of Finsler structures,"  In this short note, we prove that if $F$ is a weak upper semicontinuous
-admissible Finsler structure on a domain in $\mathbb{R}^n$, $n\geq 2$, then the
-intrinsic distance and differential structures coincide.
-",0,0,1,0,0,0
-20140,Combining Prediction of Human Decisions with ISMCTS in Imperfect Information Games,"  Monte Carlo Tree Search (MCTS) has been extended to many imperfect
-information games. However, due to the added complexity that uncertainty
-introduces, these adaptations have not reached the same level of practical
-success as their perfect information counterparts. In this paper we consider
-the development of agents that perform well against humans in imperfect
-information games with partially observable actions. We introduce the
-Semi-Determinized-MCTS (SDMCTS), a variant of the Information Set MCTS
-algorithm (ISMCTS). More specifically, SDMCTS generates a predictive model of
-the unobservable portion of the opponent's actions from historical behavioral
-data. Next, SDMCTS performs simulations on an instance of the game where the
-unobservable portion of the opponent's actions are determined. Thereby, it
-facilitates the use of the predictive model in order to decrease uncertainty.
-We present an implementation of the SDMCTS applied to the Cheat Game, a
-well-known card game, with partially observable (and often deceptive) actions.
-Results from experiments with 120 subjects playing a head-to-head Cheat Game
-against our SDMCTS agents suggest that SDMCTS performs well against humans, and
-its performance improves as the predictive model's accuracy increases.
-",1,0,0,0,0,0
-20141,The KLASH Proposal,"  We propose a search of galactic axions with mass about 0.2 microeV using a
-large volume resonant cavity, about 50 m^3, cooled down to 4 K and immersed in
-a moderate axial magnetic field of about 0.6 T generated inside the
-superconducting magnet of the KLOE experiment located at the National
-Laboratory of Frascati of INFN. This experiment, called KLASH (KLoe magnet for
-Axion SearcH) in the following, has a potential sensitivity on the
-axion-to-photon coupling, g_agg, of about 6x10^-17 GeV-1, reaching the region
-predicted by KSVZ and DFSZ models of QCD axions.
-",0,1,0,0,0,0
-20142,Dimensionality-Driven Learning with Noisy Labels,"  Datasets with significant proportions of noisy (incorrect) class labels
-present challenges for training accurate Deep Neural Networks (DNNs). We
-propose a new perspective for understanding DNN generalization for such
-datasets, by investigating the dimensionality of the deep representation
-subspace of training samples. We show that from a dimensionality perspective,
-DNNs exhibit quite distinctive learning styles when trained with clean labels
-versus when trained with a proportion of noisy labels. Based on this finding,
-we develop a new dimensionality-driven learning strategy, which monitors the
-dimensionality of subspaces during training and adapts the loss function
-accordingly. We empirically demonstrate that our approach is highly tolerant to
-significant proportions of noisy labels, and can effectively learn
-low-dimensional local subspaces that capture the data distribution.
-",0,0,0,1,0,0
-20143,Delving into adversarial attacks on deep policies,"  Adversarial examples have been shown to exist for a variety of deep learning
-architectures. Deep reinforcement learning has shown promising results on
-training agent policies directly on raw inputs such as image pixels. In this
-paper we present a novel study into adversarial attacks on deep reinforcement
-learning polices. We compare the effectiveness of the attacks using adversarial
-examples vs. random noise. We present a novel method for reducing the number of
-times adversarial examples need to be injected for a successful attack, based
-on the value function. We further explore how re-training on random noise and
-FGSM perturbations affects the resilience against adversarial examples.
-",1,0,0,1,0,0
-20144,On two functions arising in the study of the Euler and Carmichael quotients,"  We investigate two arithmetic functions naturally occurring in the study of
-the Euler and Carmichael quotients. The functions are related to the frequency
-of vanishing of the Euler and Carmichael quotients. We obtain several results
-concerning the relations between these functions as well as their typical and
-extreme values.
-",0,0,1,0,0,0
-20145,Lurking Variable Detection via Dimensional Analysis,"  Lurking variables represent hidden information, and preclude a full
-understanding of phenomena of interest. Detection is usually based on
-serendipity -- visual detection of unexplained, systematic variation. However,
-these approaches are doomed to fail if the lurking variables do not vary. In
-this article, we address these challenges by introducing formal hypothesis
-tests for the presence of lurking variables, based on Dimensional Analysis.
-These procedures utilize a modified form of the Buckingham Pi theorem to
-provide structure for a suitable null hypothesis. We present analytic tools for
-reasoning about lurking variables in physical phenomena, construct procedures
-to handle cases of increasing complexity, and present examples of their
-application to engineering problems. The results of this work enable
-algorithm-driven lurking variable detection, complementing a traditionally
-inspection-based approach.
-",0,0,0,1,0,0
-20146,System calibration method for Fourier ptychographic microscopy,"  Fourier ptychographic microscopy (FPM) is a recently proposed quantitative
-phase imaging technique with high resolution and wide field-of-view (FOV). In
-current FPM imaging platforms, systematic error sources come from the
-aberrations, LED intensity fluctuation, parameter imperfections and noise,
-which will severely corrupt the reconstruction results with artifacts. Although
-these problems have been researched and some special methods have been proposed
-respectively, there is no method to solve all of them. However, the systematic
-error is a mixture of various sources in the real situation. It is difficult to
-distinguish a kind of error source from another due to the similar artifacts.
-To this end, we report a system calibration procedure, termed SC-FPM, based on
-the simulated annealing (SA) algorithm, LED intensity correction and adaptive
-step-size strategy, which involves the evaluation of an error matric at each
-iteration step, followed by the re-estimation of accurate parameters. The great
-performance has been achieved both in simulation and experiments. The reported
-system calibration scheme improves the robustness of FPM and relaxes the
-experiment conditions, which makes the FPM more pragmatic.
-",0,1,0,0,0,0
-20147,Categories for Dynamic Epistemic Logic,"  The primary goal of this paper is to recast the semantics of modal logic, and
-dynamic epistemic logic (DEL) in particular, in category-theoretic terms. We
-first review the category of relations and categories of Kripke frames, with
-particular emphasis on the duality between relations and adjoint homomorphisms.
-Using these categories, we then reformulate the semantics of DEL in a more
-categorical and algebraic form. Several virtues of the new formulation will be
-demonstrated: The DEL idea of updating a model into another is captured
-naturally by the categorical perspective -- which emphasizes a family of
-objects and structural relationships among them, as opposed to a single object
-and structure on it. Also, the categorical semantics of DEL can be merged
-straightforwardly with a standard categorical semantics for first-order logic,
-providing a semantics for first-order DEL.
-",1,0,1,0,0,0
-20148,Accurate Pouring with an Autonomous Robot Using an RGB-D Camera,"  Robotic assistants in a home environment are expected to perform various
-complex tasks for their users. One particularly challenging task is pouring
-drinks into cups, which for successful completion, requires the detection and
-tracking of the liquid level during a pour to determine when to stop. In this
-paper, we present a novel approach to autonomous pouring that tracks the liquid
-level using an RGB-D camera and adapts the rate of pouring based on the liquid
-level feedback. We thoroughly evaluate our system on various types of liquids
-and under different conditions, conducting over 250 pours with a PR2 robot. The
-results demonstrate that our approach is able to pour liquids to a target
-height with an accuracy of a few millimeters.
-",1,0,0,0,0,0
-20149,Restriction of Odd Degree Characters of $\mathfrak{S}_n$,"  Let $n$ and $k$ be natural numbers such that $2^k < n$. We study the
-restriction to $\mathfrak{S}_{n-2^k}$ of odd-degree irreducible characters of
-the symmetric group $\mathfrak{S}_n$. This analysis completes the study begun
-in [Ayyer A., Prasad A., Spallone S., Sem. Lothar. Combin. 75 (2015), Art.
-B75g, 13 pages] and recently developed in [Isaacs I.M., Navarro G., Olsson
-J.B., Tiep P.H., J. Algebra 478 (2017), 271-282].
-",0,0,1,0,0,0
-20150,CSGNet: Neural Shape Parser for Constructive Solid Geometry,"  We present a neural architecture that takes as input a 2D or 3D shape and
-outputs a program that generates the shape. The instructions in our program are
-based on constructive solid geometry principles, i.e., a set of boolean
-operations on shape primitives defined recursively. Bottom-up techniques for
-this shape parsing task rely on primitive detection and are inherently slow
-since the search space over possible primitive combinations is large. In
-contrast, our model uses a recurrent neural network that parses the input shape
-in a top-down manner, which is significantly faster and yields a compact and
-easy-to-interpret sequence of modeling instructions. Our model is also more
-effective as a shape detector compared to existing state-of-the-art detection
-techniques. We finally demonstrate that our network can be trained on novel
-datasets without ground-truth program annotations through policy gradient
-techniques.
-",1,0,0,0,0,0
-20151,Composite Weyl nodes stabilized by screw symmetry with and without time reversal,"  We classify the band degeneracies in 3D crystals with screw symmetry $n_m$
-and broken $\mathcal P*\mathcal T$ symmetry, where $\mathcal P$ stands for
-spatial inversion and $\mathcal T$ for time reversal. The generic degeneracies
-along symmetry lines are Weyl nodes: Chiral contact points between pairs of
-bands. They can be single nodes with a chiral charge of magnitude $|\chi|=1$ or
-composite nodes with $|\chi|=2$ or $3$, and the possible $\chi$ values only
-depend on the order $n$ of the axis, not on the pitch $m/n$ of the screw.
-Double Weyl nodes require $n=4$ or 6, and triple nodes require $n=6$. In all
-cases the bands split linearly along the axis, and for composite nodes the
-splitting is quadratic on the orthogonal plane. This is true for triple as well
-as double nodes, due to the presence in the effective two-band Hamiltonian of a
-nonchiral quadratic term that masks the chiral cubic dispersion. If $\mathcal
-T$ symmetry is present and $\mathcal P$ is broken there may exist on some
-symmetry lines Weyl nodes pinned to $\mathcal T$-invariant momenta, which in
-some cases are unavoidable. In the absence of other symmetries their
-classification depends on $n$, $m$, and the type of $\mathcal T$ symmetry. With
-spinless $\mathcal T$ such $\mathcal T$-invariant Weyl nodes are always double
-nodes, while with spinful $\mathcal T$ they can be single or triple nodes.
-$\mathcal T$-invariant triples nodes can occur not only on 6-fold axes but also
-on 3-fold ones, and their in-plane band splitting is cubic, not quadratic as in
-the case of generic triple nodes. These rules are illustrated by means of
-first-principles calculations for hcp cobalt, a $\mathcal T$-broken, $\mathcal
-P$-invariant crystal with $6_3$ symmetry, and for trigonal tellurium and
-hexagonal NbSi$_2$, which are $\mathcal T$-invariant, $\mathcal P$-broken
-crystals with 3-fold and 6-fold screw symmetry respectively.
-",0,1,0,0,0,0
-20152,Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study,"  Deep Neural Networks (DNNs) are very popular these days, and are the subject
-of a very intense investigation. A DNN is made by layers of internal units (or
-neurons), each of which computes an affine combination of the output of the
-units in the previous layer, applies a nonlinear operator, and outputs the
-corresponding value (also known as activation). A commonly-used nonlinear
-operator is the so-called rectified linear unit (ReLU), whose output is just
-the maximum between its input value and zero. In this (and other similar cases
-like max pooling, where the max operation involves more than one input value),
-one can model the DNN as a 0-1 Mixed Integer Linear Program (0-1 MILP) where
-the continuous variables correspond to the output values of each unit, and a
-binary variable is associated with each ReLU to model its yes/no nature. In
-this paper we discuss the peculiarity of this kind of 0-1 MILP models, and
-describe an effective bound-tightening technique intended to ease its solution.
-We also present possible applications of the 0-1 MILP model arising in feature
-visualization and in the construction of adversarial examples. Preliminary
-computational results are reported, aimed at investigating (on small DNNs) the
-computational performance of a state-of-the-art MILP solver when applied to a
-known test case, namely, hand-written digit recognition.
-",1,0,0,0,0,0
-20153,Directionality Fields generated by a Local Hilbert Transform,"  We propose a new approach based on a local Hilbert transform to design
-non-Hermitian potentials generating arbitrary vector fields of directionality,
-p(r), with desired shapes and topologies. We derive a local Hilbert transform
-to systematically build such potentials, by modifying background potentials
-(being either regular or random, extended or localized). In particular, we
-explore particular directionality fields, for instance in the form of a focus
-to create sinks for probe fields (which could help to increase absorption at
-the sink), or to generate vortices in the probe fields. Physically, the
-proposed directionality fields provide a flexible new mechanism for dynamically
-shaping and precise control over probe fields leading to novel effects in wave
-dynamics.
-",0,1,0,0,0,0
-20154,Bulk Eigenvalue Correlation Statistics of Random Biregular Bipartite Graphs,"  This paper is the second chapter of three of the author's undergraduate
-thesis. In this paper, we consider the random matrix ensemble given by $(d_b,
-d_w)$-regular graphs on $M$ black vertices and $N$ white vertices, where $d_b
-\in [N^{\gamma}, N^{2/3 - \gamma}]$ for any $\gamma > 0$. We simultaneously
-prove that the bulk eigenvalue correlation statistics for both normalized
-adjacency matrices and their corresponding covariance matrices are stable for
-short times. Combined with an ergodicity analysis of the Dyson Brownian motion
-in another paper, this proves universality of bulk eigenvalue correlation
-statistics, matching normalized adjacency matrices with the GOE and the
-corresponding covariance matrices with the Gaussian Wishart Ensemble.
-",0,0,1,1,0,0
-20155,A local weighted Axler-Zheng theorem in $\mathbb{C}^n$,"  The well-known Axler-Zheng theorem characterizes compactness of finite sums
-of finite products of Toeplitz operators on the unit disk in terms of the
-Berezin transform of these operators. Subsequently this theorem was generalized
-to other domains and appeared in different forms, including domains in
-$\mathbb{C}^n$ on which the $\overline{\partial}$-Neumann operator $N$ is
-compact. In this work we remove the assumption on $N$, and we study weighted
-Bergman spaces on smooth bounded pseudoconvex domains. We prove a local version
-of the Axler-Zheng theorem characterizing compactness of Toeplitz operators in
-the algebra generated by symbols continuous up to the boundary in terms of the
-behavior of the Berezin transform at strongly pseudoconvex points. We employ a
-Forelli-Rudin type inflation method to handle the weights.
-",0,0,1,0,0,0
-20156,Spatial modulation of Joule losses to increase the normal zone propagation velocity in (RE)BaCuO tapes,"  This paper presents a simple approach to increase the normal zone propagation
-velocity in (RE)BaCuO thin films grown on a flexible metallic substrate, also
-called superconducting tapes. The key idea behind this approach is to use a
-specific geometry of the silver thermal stabilizer that surrounds the
-superconducting tape. More specifically, a very thin layer of silver stabilizer
-is deposited on top of the superconductor layer, typically less than 100 nm,
-while the remaining stabilizer (still silver) is deposited on the substrate
-side. Normal zone propagation velocities up to 170 cm/s have been measured
-experimentally, corresponding to a stabilizer thickness of 20 nm on top of the
-superconductor layer. This is one order of magnitude faster than the speed
-measured on actual commercial tapes. Our results clearly demonstrate that a
-very thin stabilizer on top of the superconductor layer leads to high normal
-zone propagation velocities. The experimental values are in good agreement with
-predictions realized by finite element simulations. Furthermore, the
-propagation of the normal zone during the quench was recorded in situ and in
-real time using a high-speed camera. Due to high Joule losses generated on both
-edges of the tape sample, a ""U-shaped"" profile could be observed at the
-boundaries between the superconducting and the normal zones, which matches very
-closely the profile predicted by the simulations.
-",0,1,0,0,0,0
-20157,Vortex Thermometry for Turbulent Two-Dimensional Fluids,"  We introduce a new method of statistical analysis to characterise the
-dynamics of turbulent fluids in two dimensions. We establish that, in
-equilibrium, the vortex distributions can be uniquely connected to the
-temperature of the vortex gas, and apply this vortex thermometry to
-characterise simulations of decaying superfluid turbulence. We confirm the
-hypothesis of vortex evaporative heating leading to Onsager vortices proposed
-in Phys. Rev. Lett. 113, 165302 (2014), and find previously unidentified vortex
-power-law distributions that emerge from the dynamics.
-",0,1,0,0,0,0
-20158,The Hurwitz-type theorem for the regular Coulomb wave function via Hankel determinants,"  We derive a closed formula for the determinant of the Hankel matrix whose
-entries are given by sums of negative powers of the zeros of the regular
-Coulomb wave function. This new identity applied together with results of
-Grommer and Chebotarev allows us to prove a Hurwitz-type theorem about the
-zeros of the regular Coulomb wave function. As a particular case, we obtain a
-new proof of the classical Hurwitz's theorem from the theory of Bessel
-functions that is based on algebraic arguments. In addition, several Hankel
-determinants with entries given by the Rayleigh function and Bernoulli numbers
-are also evaluated.
-",0,0,1,0,0,0
-20159,An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients,"  In this technical report, we consider an approach that combines the PPO
-objective and K-FAC natural gradient optimization, for which we call PPOKFAC.
-We perform a range of empirical analysis on various aspects of the algorithm,
-such as sample complexity, training speed, and sensitivity to batch size and
-training epochs. We observe that PPOKFAC is able to outperform PPO in terms of
-sample complexity and speed in a range of MuJoCo environments, while being
-scalable in terms of batch size. In spite of this, it seems that adding more
-epochs is not necessarily helpful for sample efficiency, and PPOKFAC seems to
-be worse than its A2C counterpart, ACKTR.
-",0,0,0,1,0,0
-20160,On the density of sets avoiding parallelohedron distance 1,"  The maximal density of a measurable subset of R^n avoiding Euclidean
-distance1 is unknown except in the trivial case of dimension 1. In this paper,
-we consider thecase of a distance associated to a polytope that tiles space,
-where it is likely that the setsavoiding distance 1 are of maximal density
-2^-n, as conjectured by Bachoc and Robins. We prove that this is true for n =
-2, and for the Voronoï regions of the lattices An, n >= 2.
-",0,0,1,0,0,0
-20161,Adversarial Deep Structured Nets for Mass Segmentation from Mammograms,"  Mass segmentation provides effective morphological features which are
-important for mass diagnosis. In this work, we propose a novel end-to-end
-network for mammographic mass segmentation which employs a fully convolutional
-network (FCN) to model a potential function, followed by a CRF to perform
-structured learning. Because the mass distribution varies greatly with pixel
-position, the FCN is combined with a position priori. Further, we employ
-adversarial training to eliminate over-fitting due to the small sizes of
-mammogram datasets. Multi-scale FCN is employed to improve the segmentation
-performance. Experimental results on two public datasets, INbreast and
-DDSM-BCRP, demonstrate that our end-to-end network achieves better performance
-than state-of-the-art approaches.
-\footnote{this https URL}
-",1,0,0,0,0,0
-20162,The downward directed grounds hypothesis and very large cardinals,"  A transitive model $M$ of ZFC is called a ground if the universe $V$ is a set
-forcing extension of $M$. We show that the grounds of $V$ are downward
-set-directed. Consequently, we establish some fundamental theorems on the
-forcing method and the set-theoretic geology. For instance, (1) the mantle, the
-intersection of all grounds, must be a model of ZFC. (2) $V$ has only set many
-grounds if and only if the mantle is a ground. We also show that if the
-universe has some very large cardinal, then the mantle must be a ground.
-",0,0,1,0,0,0
-20163,A Distributed Control Framework of Multiple Unmanned Aerial Vehicles for Dynamic Wildfire Tracking,"  Wild-land fire fighting is a hazardous job. A key task for firefighters is to
-observe the ""fire front"" to chart the progress of the fire and areas that will
-likely spread next. Lack of information of the fire front causes many
-accidents. Using Unmanned Aerial Vehicles (UAVs) to cover wildfire is promising
-because it can replace humans in hazardous fire tracking and significantly
-reduce operation costs. In this paper we propose a distributed control
-framework designed for a team of UAVs that can closely monitor a wildfire in
-open space, and precisely track its development. The UAV team, designed for
-flexible deployment, can effectively avoid in-flight collisions and cooperate
-well with neighbors. They can maintain a certain height level to the ground for
-safe flight above fire. Experimental results are conducted to demonstrate the
-capabilities of the UAV team in covering a spreading wildfire.
-",1,0,0,0,0,0
-20164,Rayleigh-Brillouin light scattering spectroscopy of nitrous oxide (N$_2$O),"  High signal-to-noise and high-resolution light scattering spectra are
-measured for nitrous oxide (N$_2$O) gas at an incident wavelength of 403.00 nm,
-at 90$^\circ$ scattering, at room temperature and at gas pressures in the range
-$0.5-4$ bar. The resulting Rayleigh-Brillouin light scattering spectra are
-compared to a number of models describing in an approximate manner the
-collisional dynamics and energy transfer in this gaseous medium of this
-polyatomic molecular species. The Tenti-S6 model, based on macroscopic gas
-transport coefficients, reproduces the scattering profiles in the entire
-pressure range at less than 2\% deviation at a similar level as does the
-alternative kinetic Grad's 6-moment model, which is based on the internal
-collisional relaxation as a decisive parameter. A hydrodynamic model fails to
-reproduce experimental spectra for the low pressures of 0.5-1 bar, but yields
-very good agreement ($< 1$\%) in the pressure range $2-4$ bar. While these
-three models have a different physical basis the internal molecular relaxation
-derived can for all three be described in terms of a bulk viscosity of $\eta_b
-\sim (6 \pm 2) \times 10^{-5}$ Pa$\cdot$s. A 'rough-sphere' model, previously
-shown to be effective to describe light scattering in SF$_6$ gas, is not found
-to be suitable, likely in view of the non-sphericity and asymmetry of the N-N-O
-structured linear polyatomic molecule.
-",0,1,0,0,0,0
-20165,Competition between Chaotic and Non-Chaotic Phases in a Quadratically Coupled Sachdev-Ye-Kitaev Model,"  The Sachdev-Ye-Kitaev (SYK) model is a concrete solvable model to study
-non-Fermi liquid properties, holographic duality and maximally chaotic
-behavior. In this work, we consider a generalization of the SYK model that
-contains two SYK models with different number of Majorana modes coupled by
-quadratic terms. This model is also solvable, and the solution shows a
-zero-temperature quantum phase transition between two non-Fermi liquid chaotic
-phases. This phase transition is driven by tuning the ratio of two mode
-numbers, and a Fermi liquid non-chaotic phase sits at the critical point with
-equal mode number. At finite temperature, the Fermi liquid phase expands to a
-finite regime. More intriguingly, a different non-Fermi liquid phase emerges at
-finite temperature. We characterize the phase diagram in term of the spectral
-function, the Lyapunov exponent and the entropy. Our results illustrate a
-concrete example of quantum phase transition and critical regime between two
-non-Fermi liquid phases.
-",0,1,0,0,0,0
-20166,Symmetry Realization via a Dynamical Inverse Higgs Mechanism,"  The Ward identities associated with spontaneously broken symmetries can be
-saturated by Goldstone bosons. However, when space-time symmetries are broken,
-the number of Goldstone bosons necessary to non-linearly realize the symmetry
-can be less than the number of broken generators. The loss of Goldstones may be
-due to a redundancy or the generation of a gap. This phenomena is called an
-Inverse Higgs Mechanism (IHM). However, there are cases when a Goldstone boson
-associated with a broken generator does not appear in the low energy theory
-despite the lack of the existence of an associated IHM. In this paper we will
-show that in such cases the relevant broken symmetry can be realized, without
-the aid of an associated Goldstone, if there exists a proper set of operator
-constraints, which we call a Dynamical Inverse Higgs Mechanism (DIHM). We
-consider the spontaneous breaking of boosts, rotations and conformal
-transformations in the context of Fermi liquids, finding three possible paths
-to symmetry realization: pure Goldstones, no Goldstones and DIHM, or some
-mixture thereof. We show that in the two dimensional degenerate electron system
-the DIHM route is the only consistent way to realize spontaneously broken
-boosts and dilatations, while in three dimensions these symmetries could just
-as well be realized via the inclusion of non-derivatively coupled Goldstone
-bosons. We have present the action, including the leading order
-non-linearities, for the rotational Goldstone (angulon), and discuss the
-constraint associated with the possible DIHM that would need to be imposed to
-remove it from the spectrum. Finally we discuss the conditions under which
-Goldstone bosons are non-derivatively coupled, a necessary condition for the
-existence of a Dynamical Inverse Higgs Constraint (DIHC), generalizaing the
-results for Vishwanath and Wantanabe.
-",0,1,0,0,0,0
-20167,Growth of strontium ruthenate films by hybrid molecular beam epitaxy,"  We report on the growth of epitaxial Sr2RuO4 films using a hybrid molecular
-beam epitaxy approach in which a volatile precursor containing RuO4 is used to
-supply ruthenium and oxygen. The use of the precursor overcomes a number of
-issues encountered in traditional MBE that uses elemental metal sources.
-Phase-pure, epitaxial thin films of Sr2RuO4 are obtained. At high substrate
-temperatures, growth proceeds in a layer-by-layer mode with intensity
-oscillations observed in reflection high-energy electron diffraction. Films are
-of high structural quality, as documented by x-ray diffraction, atomic force
-microscopy, and transmission electron microscopy. The method should be suitable
-for the growth of other complex oxides containing ruthenium, opening up
-opportunities to investigate thin films that host rich exotic ground states.
-",0,1,0,0,0,0
-20168,Possible Evidence for the Stochastic Acceleration of Secondary Antiprotons by Supernova Remnants,"  The antiproton-to-proton ratio in the cosmic-ray spectrum is a sensitive
-probe of new physics. Using recent measurements of the cosmic-ray antiproton
-and proton fluxes in the energy range of 1-1000 GeV, we study the contribution
-to the $\bar{p}/p$ ratio from secondary antiprotons that are produced and
-subsequently accelerated within individual supernova remnants. We consider
-several well-motivated models for cosmic-ray propagation in the interstellar
-medium and marginalize our results over the uncertainties related to the
-antiproton production cross section and the time-, charge-, and
-energy-dependent effects of solar modulation. We find that the increase in the
-$\bar{p}/p$ ratio observed at rigidities above $\sim$ 100 GV cannot be
-accounted for within the context of conventional cosmic-ray propagation models,
-but is consistent with scenarios in which cosmic-ray antiprotons are produced
-and subsequently accelerated by shocks within a given supernova remnant. In
-light of this, the acceleration of secondary cosmic rays in supernova remnants
-is predicted to substantially contribute to the cosmic-ray positron spectrum,
-accounting for a significant fraction of the observed positron excess.
-",0,1,0,0,0,0
-20169,The Host Galaxy and Redshift of the Repeating Fast Radio Burst FRB 121102,"  The precise localization of the repeating fast radio burst (FRB 121102) has
-provided the first unambiguous association (chance coincidence probability
-$p\lesssim3\times10^{-4}$) of an FRB with an optical and persistent radio
-counterpart. We report on optical imaging and spectroscopy of the counterpart
-and find that it is an extended ($0.6^{\prime\prime}-0.8^{\prime\prime}$)
-object displaying prominent Balmer and [OIII] emission lines. Based on the
-spectrum and emission line ratios, we classify the counterpart as a
-low-metallicity, star-forming, $m_{r^\prime} = 25.1$ AB mag dwarf galaxy at a
-redshift of $z=0.19273(8)$, corresponding to a luminosity distance of 972 Mpc.
-From the angular size, the redshift, and luminosity, we estimate the host
-galaxy to have a diameter $\lesssim4$ kpc and a stellar mass of
-$M_*\sim4-7\times 10^{7}\,M_\odot$, assuming a mass-to-light ratio between 2 to
-3$\,M_\odot\,L_\odot^{-1}$. Based on the H$\alpha$ flux, we estimate the star
-formation rate of the host to be $0.4\,M_\odot\,\mathrm{yr^{-1}}$ and a
-substantial host dispersion measure depth $\lesssim 324\,\mathrm{pc\,cm^{-3}}$.
-The net dispersion measure contribution of the host galaxy to FRB 121102 is
-likely to be lower than this value depending on geometrical factors. We show
-that the persistent radio source at FRB 121102's location reported by Marcote
-et al (2017) is offset from the galaxy's center of light by $\sim$200 mas and
-the host galaxy does not show optical signatures for AGN activity. If FRB
-121102 is typical of the wider FRB population and if future interferometric
-localizations preferentially find them in dwarf galaxies with low metallicities
-and prominent emission lines, they would share such a preference with long
-gamma ray bursts and superluminous supernovae.
-",0,1,0,0,0,0
-20170,Thicket Density,"  Thicket density is a new measure of the complexity of a set system, having
-the same relationship to stable formulas that VC density has to NIP formulas.
-It satisfies a Sauer-Shelah type dichotomy that has applications in both model
-theory and the theory of algorithms
-",0,0,1,0,0,0
-20171,"Uniformizations of stable $(γ,n)$-gonal Riemann surfaces","  A $(\gamma,n)$-gonal pair is a pair $(S,f)$, where $S$ is a closed Riemann
-surface and $f:S \to R$ is a degree $n$ holomorphic map onto a closed Riemann
-surface $R$ of genus $\gamma$. If the signature of $(S,f)$ is of hyperbolic
-type, then there is pair $(\Gamma,G)$, called an uniformization of $(S,f)$,
-where $G$ is a Fuchsian group acting on the unit disc ${\mathbb D}$ containing
-$\Gamma$ as an index $n$ subgroup, so that $f$ is induced by the inclusion of
-$\Gamma <G$. The uniformization is uniquely determined by $(S,f)$, up to
-conjugation by holomorphic automorphisms of ${\mathbb D}$, and it permits to
-provide natural complex orbifold structures on the Hurwitz spaces parametrizing
-(twisted) isomorphic classes of pairs topologically equivalent to $(S,f)$. In
-order to produce certain compactifications of these Hurwitz spaces, one needs
-to consider the so called stable $(\gamma,n)$-gonal pairs, which are natural
-geometrical deformations of $(\gamma,n)$-gonal pairs. Due to the above, it
-seems interesting to search for uniformizations of stable $(\gamma,n)$-gonal
-pairs, in terms of certain class of Kleinian groups. In this paper we review
-such uniformizations by using noded Fuchsian groups, which are (geometric)
-limits of quasiconformal deformations of Fuchsian groups, and which provide
-uniformizations of stable Riemann orbifolds. These uniformizations permit to
-obtain a compactification of the Hurwitz spaces with a complex orbifold
-structure, these being quotients of the augmented Teichmüller space of $G$ by
-a suitable finite index subgroup of its modular group.
-",0,0,1,0,0,0
-20172,"If you are not paying for it, you are the product: How much do advertisers pay to reach you?","  Online advertising is progressively moving towards a programmatic model in
-which ads are matched to actual interests of individuals collected as they
-browse the web. Letting the huge debate around privacy aside, a very important
-question in this area, for which little is known, is: How much do advertisers
-pay to reach an individual? In this study, we develop a first of its kind
-methodology for computing exactly that -- the price paid for a web user by the
-ad ecosystem -- and we do that in real time. Our approach is based on tapping
-on the Real Time Bidding (RTB) protocol to collect cleartext and encrypted
-prices for winning bids paid by advertisers in order to place targeted ads. Our
-main technical contribution is a method for tallying winning bids even when
-they are encrypted. We achieve this by training a model using as ground truth
-prices obtained by running our own ""probe"" ad-campaigns. We design our
-methodology through a browser extension and a back-end server that provides it
-with fresh models for encrypted bids. We validate our methodology using a one
-year long trace of 1600 mobile users and demonstrate that it can estimate a
-user's advertising worth with more than 82% accuracy.
-",1,0,0,0,0,0
-20173,Circularly polarized vacuum field in three-dimensional chiral photonic crystals probed by quantum dot emission,"  The quantum nature of light-matter interactions in a circularly polarized
-vacuum field was probed by spontaneous emission from quantum dots in
-three-dimensional chiral photonic crystals. Due to the circularly polarized
-eigenmodes along the helical axis in the GaAs-based mirror-asymmetric
-structures we studied, we observed highly circularly polarized emission from
-the quantum dots. Both spectroscopic and time-resolved measurements confirmed
-that the obtained circularly polarized light was influenced by a large
-difference in the photonic density of states between the orthogonal components
-of the circular polarization in the vacuum field.
-",0,1,0,0,0,0
-20174,Critical behavior of quasi-two-dimensional semiconducting ferromagnet CrGeTe$_3$,"  The critical properties of the single-crystalline semiconducting ferromagnet
-CrGeTe$_3$ were investigated by bulk dc magnetization around the paramagnetic
-to ferromagnetic phase transition. Critical exponents $\beta = 0.200\pm0.003$
-with critical temperature $T_c = 62.65\pm0.07$ K and $\gamma = 1.28\pm0.03$
-with $T_c = 62.75\pm0.06$ K are obtained by the Kouvel-Fisher method whereas
-$\delta = 7.96\pm0.01$ is obtained by the critical isotherm analysis at $T_c =
-62.7$ K. These critical exponents obey the Widom scaling relation $\delta =
-1+\gamma/\beta$, indicating self-consistency of the obtained values. With these
-critical exponents the isotherm $M(H)$ curves below and above the critical
-temperatures collapse into two independent universal branches, obeying the
-single scaling equation $m = f_\pm(h)$, where $m$ and $h$ are renormalized
-magnetization and field, respectively. The determined exponents match well with
-those calculated from the results of renormalization group approach for a
-two-dimensional Ising system coupled with long-range interaction between spins
-decaying as $J(r)\approx r^{-(d+\sigma)}$ with $\sigma=1.52$.
-",0,1,0,0,0,0
-20175,Scale-free Monte Carlo method for calculating the critical exponent $γ$ of self-avoiding walks,"  We implement a scale-free version of the pivot algorithm and use it to sample
-pairs of three-dimensional self-avoiding walks, for the purpose of efficiently
-calculating an observable that corresponds to the probability that pairs of
-self-avoiding walks remain self-avoiding when they are concatenated. We study
-the properties of this Markov chain, and then use it to find the critical
-exponent $\gamma$ for self-avoiding walks to unprecedented accuracy. Our final
-estimate for $\gamma$ is $1.15695300(95)$.
-",0,1,1,0,0,0
-20176,The quadratic M-convexity testing problem,"  M-convex functions, which are a generalization of valuated matroids, play a
-central role in discrete convex analysis. Quadratic M-convex functions
-constitute a basic and important subclass of M-convex functions, which has a
-close relationship with phylogenetics as well as valued constraint satisfaction
-problems. In this paper, we consider the quadratic M-convexity testing problem
-(QMCTP), which is the problem of deciding whether a given quadratic function on
-$\{0,1\}^n$ is M-convex. We show that QMCTP is co-NP-complete in general, but
-is polynomial-time solvable under a natural assumption. Furthermore, we propose
-an $O(n^2)$-time algorithm for solving QMCTP in the polynomial-time solvable
-case.
-",0,0,1,0,0,0
-20177,Distributed Bayesian Matrix Factorization with Limited Communication,"  Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank
-representations of matrices and for predicting missing values and providing
-confidence intervals. Scaling up the posterior inference for massive-scale
-matrices is challenging and requires distributing both data and computation
-over many workers, making communication the main computational bottleneck.
-Embarrassingly parallel inference would remove the communication needed, by
-using completely independent computations on different data subsets, but it
-suffers from the inherent unidentifiability of BMF solutions. We introduce a
-hierarchical decomposition of the joint posterior distribution, which couples
-the subset inferences, allowing for embarrassingly parallel computations in a
-sequence of at most three stages. Using an efficient approximate
-implementation, we show improvements empirically on both real and simulated
-data. Our distributed approach is able to achieve a speed-up of almost an order
-of magnitude over the full posterior, with a negligible effect on predictive
-accuracy. Our method outperforms state-of-the-art embarrassingly parallel MCMC
-methods in accuracy, and achieves results competitive to other available
-distributed and parallel implementations of BMF.
-",1,0,0,1,0,0
-20178,Optimized Certificate Revocation List Distribution for Secure V2X Communications,"  The successful deployment of safe and trustworthy Connected and Autonomous
-Vehicles (CAVs) will highly depend on the ability to devise robust and
-effective security solutions to resist sophisticated cyber attacks and patch up
-critical vulnerabilities. Pseudonym Public Key Infrastructure (PPKI) is a
-promising approach to secure vehicular networks as well as ensure data and
-location privacy, concealing the vehicles' real identities. Nevertheless,
-pseudonym distribution and management affect PPKI scalability due to the
-significant number of digital certificates required by a single vehicle. In
-this paper, we focus on the certificate revocation process and propose a
-versatile and low-complexity framework to facilitate the distribution of the
-Certificate Revocation Lists (CRL) issued by the Certification Authority (CA).
-CRL compression is achieved through optimized Bloom filters, which guarantee a
-considerable overhead reduction with a configurable rate of false positives.
-Our results show that the distribution of compressed CRLs can significantly
-enhance the system scalability without increasing the complexity of the
-revocation process.
-",1,0,0,0,0,0
-20179,Optical response of highly reflective film used in the water Cherenkov muon veto of the XENON1T dark matter experiment,"  The XENON1T experiment is the most recent stage of the XENON Dark Matter
-Search, aiming for the direct detection of Weakly Interacting Massive Particles
-(WIMPs). To reach its projected sensitivity, the background has to be reduced
-by two orders of magnitude compared to its predecessor XENON100. This requires
-a water Cherenkov muon veto surrounding the XENON1T TPC, both to shield
-external backgrounds and to tag muon-induced energetic neutrons through
-detection of a passing muon or the secondary shower induced by a muon
-interacting in the surrounding rock. The muon veto is instrumented with $84$
-$8""$ PMTs with high quantum efficiency (QE) in the Cherenkov regime and the
-walls of the watertank are clad with the highly reflective DF2000MA foil by 3M.
-Here, we present a study of the reflective properties of this foil, as well as
-the measurement of its wavelength shifting (WLS) properties. Further, we
-present the impact of reflectance and WLS on the detection efficiency of the
-muon veto, using a Monte Carlo simulation carried out with Geant4. The
-measurements yield a specular reflectance of $\approx100\%$ for wavelengths
-larger than $400\,$nm, while $\approx90\%$ of the incoming light below
-$370\,$nm is absorbed by the foil. Approximately $3-7.5\%$ of the light hitting
-the foil within the wavelength range $250\,$nm $\leq \lambda \leq 390\,$nm is
-used for the WLS process. The intensity of the emission spectrum of the WLS
-light is slightly dependent on the absorbed wavelength and shows the shape of a
-rotational-vibrational fluorescence spectrum, peaking at around $\lambda
-\approx 420\,$nm. Adjusting the reflectance values to the measured ones in the
-Monte Carlo simulation originally used for the muon veto design, the veto
-detection efficiency remains unchanged. Including the wavelength shifting in
-the Monte Carlo simulation leads to an increase of the efficiency of
-approximately $0.5\%$.
-",0,1,0,0,0,0
-20180,On spectral partitioning of signed graphs,"  We argue that the standard graph Laplacian is preferable for spectral
-partitioning of signed graphs compared to the signed Laplacian. Simple examples
-demonstrate that partitioning based on signs of components of the leading
-eigenvectors of the signed Laplacian may be meaningless, in contrast to
-partitioning based on the Fiedler vector of the standard graph Laplacian for
-signed graphs. We observe that negative eigenvalues are beneficial for spectral
-partitioning of signed graphs, making the Fiedler vector easier to compute.
-",1,0,1,1,0,0
-20181,Sensor Fusion for Public Space Utilization Monitoring in a Smart City,"  Public space utilization is crucial for urban developers to understand how
-efficient a place is being occupied in order to improve existing or future
-infrastructures. In a smart cities approach, implementing public space
-monitoring with Internet-of-Things (IoT) sensors appear to be a viable
-solution. However, choice of sensors often is a challenging problem and often
-linked with scalability, coverage, energy consumption, accuracy, and privacy.
-To get the most from low cost sensor with aforementioned design in mind, we
-proposed data processing modules for capturing public space utilization with
-Renewable Wireless Sensor Network (RWSN) platform using pyroelectric infrared
-(PIR) and analog sound sensor. We first proposed a calibration process to
-remove false alarm of PIR sensor due to the impact of weather and environment.
-We then demonstrate how the sounds sensor can be processed to provide various
-insight of a public space. Lastly, we fused both sensors and study a particular
-public space utilization based on one month data to unveil its usage.
-",1,0,0,1,0,0
-20182,Symplectic integrators for second-order linear non-autonomous equations,"  Two families of symplectic methods specially designed for second-order
-time-dependent linear systems are presented. Both are obtained from the Magnus
-expansion of the corresponding first-order equation, but otherwise they differ
-in significant aspects. The first family is addressed to problems with low to
-moderate dimension, whereas the second is more appropriate when the dimension
-is large, in particular when the system corresponds to a linear wave equation
-previously discretised in space. Several numerical experiments illustrate the
-main features of the new schemes.
-",0,0,1,0,0,0
-20183,"Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations","  The recent breakthroughs of deep reinforcement learning (DRL) technique in
-Alpha Go and playing Atari have set a good example in handling large state and
-actions spaces of complicated control problems. The DRL technique is comprised
-of (i) an offline deep neural network (DNN) construction phase, which derives
-the correlation between each state-action pair of the system and its value
-function, and (ii) an online deep Q-learning phase, which adaptively derives
-the optimal action and updates value estimates. In this paper, we first present
-the general DRL framework, which can be widely utilized in many applications
-with different optimization objectives. This is followed by the introduction of
-three specific applications: the cloud computing resource allocation problem,
-the residential smart grid task scheduling problem, and building HVAC system
-optimal control problem. The effectiveness of the DRL technique in these three
-cyber-physical applications have been validated. Finally, this paper
-investigates the stochastic computing-based hardware implementations of the DRL
-framework, which consumes a significant improvement in area efficiency and
-power consumption compared with binary-based implementation counterparts.
-",1,0,0,0,0,0
-20184,Experimental observation of node-line-like surface states in LaBi,"  In a Dirac nodal line semimetal, the bulk conduction and valence bands touch
-at extended lines in the Brillouin zone. To date, most of the theoretically
-predicted and experimentally discovered nodal lines derive from the bulk bands
-of two- and three-dimensional materials. Here, based on combined angle-resolved
-photoemission spectroscopy measurements and first-principles calculations, we
-report the discovery of node-line-like surface states on the (001) surface of
-LaBi. These bands derive from the topological surface states of LaBi and bridge
-the band gap opened by spin-orbit coupling and band inversion. Our
-first-principles calculations reveal that these ""nodal lines"" have a tiny gap,
-which is beyond typical experimental resolution. These results may provide
-important information to understand the extraordinary physical properties of
-LaBi, such as the extremely large magnetoresistance and resistivity plateau.
-",0,1,0,0,0,0
-20185,Manin's conjecture for a class of singular cubic hypersurfaces,"  Let $n$ be a positive multiple of $4$. We establish an asymptotic formula for
-the number of rational points of bounded height on singular cubic hypersurfaces
-$S_n$ defined by $$ x^3=(y_1^2 + \cdots + y_n^2)z . $$ This result is new in
-two aspects: first, it can be viewed as a modest start on the study of density
-of rational points on those singular cubic hypersurfaces which are not covered
-by the classical theorems of Davenport or Heath-Brown; second, it proves
-Manin's conjecture for singular cubic hypersurfaces $S_n$ defined above.
-",0,0,1,0,0,0
-20186,A Flexible Approach to Automated RNN Architecture Generation,"  The process of designing neural architectures requires expert knowledge and
-extensive trial and error. While automated architecture search may simplify
-these requirements, the recurrent neural network (RNN) architectures generated
-by existing methods are limited in both flexibility and components. We propose
-a domain-specific language (DSL) for use in automated architecture search which
-can produce novel RNNs of arbitrary depth and width. The DSL is flexible enough
-to define standard architectures such as the Gated Recurrent Unit and Long
-Short Term Memory and allows the introduction of non-standard RNN components
-such as trigonometric curves and layer normalization. Using two different
-candidate generation techniques, random search with a ranking function and
-reinforcement learning, we explore the novel architectures produced by the RNN
-DSL for language modeling and machine translation domains. The resulting
-architectures do not follow human intuition yet perform well on their targeted
-tasks, suggesting the space of usable RNN architectures is far larger than
-previously assumed.
-",1,0,0,1,0,0
-20187,Convexification of Neural Graph,"  Traditionally, most complex intelligence architectures are extremely
-non-convex, which could not be well performed by convex optimization. However,
-this paper decomposes complex structures into three types of nodes: operators,
-algorithms and functions. Iteratively, propagating from node to node along
-edge, we prove that ""regarding the tree-structured neural graph, it is nearly
-convex in each variable, when the other variables are fixed."" In fact, the
-non-convex properties stem from circles and functions, which could be
-transformed to be convex with our proposed \textit{\textbf{scale mechanism}}.
-Experimentally, we justify our theoretical analysis by two practical
-applications.
-",0,0,0,1,0,0
-20188,Pseudogaps in strongly interacting Fermi gases,"  A central challenge in modern condensed matter physics is developing the
-tools for understanding nontrivial yet unordered states of matter. One
-important idea to emerge in this context is that of a ""pseudogap"": the fact
-that under appropriate circumstances the normal state displays a suppression of
-the single particle spectral density near the Fermi level, reminiscent of the
-gaps seen in ordered states of matter. While these concepts arose in a solid
-state context, it is now being explored in cold gases. This article reviews the
-current experimental and theoretical understanding of the normal state of
-strongly interacting Fermi gases, with particular focus on the phenomonology
-which is traditionally associated with the pseudogap.
-",0,1,0,0,0,0
-20189,Resolution-Exact Planner for Thick Non-Crossing 2-Link Robots,"  We consider the path planning problem for a 2-link robot amidst polygonal
-obstacles. Our robot is parametrizable by the lengths $\ell_1, \ell_2>0$ of its
-two links, the thickness $\tau \ge 0$ of the links, and an angle $\kappa$ that
-constrains the angle between the 2 links to be strictly greater than $\kappa$.
-The case $\tau>0$ and $\kappa \ge 0$ corresponds to ""thick non-crossing""
-robots. This results in a novel 4DOF configuration space ${\mathbb R}^2\times
-({\mathbb T}\setminus\Delta(\kappa))$ where ${\mathbb T}$ is the torus and
-$\Delta(\kappa)$ the diagonal band of width $\kappa$. We design a
-resolution-exact planner for this robot using the framework of Soft Subdivision
-Search (SSS). First, we provide an analysis of the space of forbidden angles,
-leading to a soft predicate for classifying configuration boxes. We further
-exploit the T/R splitting technique which was previously introduced for
-self-crossing thin 2-link robots. Our open-source implementation in Core
-Library achieves real-time performance for a suite of combinatorially
-non-trivial obstacle sets. Experimentally, our algorithm is significantly
-better than any of the state-of-art sampling algorithms we looked at, in timing
-and in success rate.
-",1,0,0,0,0,0
-20190,Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks,"  Heterogeneous information networks (HINs) are ubiquitous in real-world
-applications. In the meantime, network embedding has emerged as a convenient
-tool to mine and learn from networked data. As a result, it is of interest to
-develop HIN embedding methods. However, the heterogeneity in HINs introduces
-not only rich information but also potentially incompatible semantics, which
-poses special challenges to embedding learning in HINs. With the intention to
-preserve the rich yet potentially incompatible information in HIN embedding, we
-propose to study the problem of comprehensive transcription of heterogeneous
-information networks. The comprehensive transcription of HINs also provides an
-easy-to-use approach to unleash the power of HINs, since it requires no
-additional supervision, expertise, or feature engineering. To cope with the
-challenges in the comprehensive transcription of HINs, we propose the HEER
-algorithm, which embeds HINs via edge representations that are further coupled
-with properly-learned heterogeneous metrics. To corroborate the efficacy of
-HEER, we conducted experiments on two large-scale real-words datasets with an
-edge reconstruction task and multiple case studies. Experiment results
-demonstrate the effectiveness of the proposed HEER model and the utility of
-edge representations and heterogeneous metrics. The code and data are available
-at this https URL.
-",1,0,0,0,0,0
-20191,Generic Dynamical Phase Transition in One-Dimensional Bulk-Driven Lattice Gases with Exclusion,"  Dynamical phase transitions are crucial features of the fluctuations of
-statistical systems, corresponding to boundaries between qualitatively
-different mechanisms of maintaining unlikely values of dynamical observables
-over long periods of time. They manifest themselves in the form of
-non-analyticities in the large deviation function of those observables. In this
-paper, we look at bulk-driven exclusion processes with open boundaries. It is
-known that the standard asymmetric simple exclusion process exhibits a
-dynamical phase transition in the large deviations of the current of particles
-flowing through it. That phase transition has been described thanks to specific
-calculation methods relying on the model being exactly solvable, but more
-general methods have also been used to describe the extreme large deviations of
-that current, far from the phase transition. We extend those methods to a large
-class of models based on the ASEP, where we add arbitrary spatial
-inhomogeneities in the rates and short-range potentials between the particles.
-We show that, as for the regular ASEP, the large deviation function of the
-current scales differently with the size of the system if one considers very
-high or very low currents, pointing to the existence of a dynamical phase
-transition between those two regimes: high current large deviations are
-extensive in the system size, and the typical states associated to them are
-Coulomb gases, which are correlated ; low current large deviations do not
-depend on the system size, and the typical states associated to them are
-anti-shocks, consistently with a hydrodynamic behaviour. Finally, we illustrate
-our results numerically on a simple example, and we interpret the transition in
-terms of the current pushing beyond its maximal hydrodynamic value, as well as
-relate it to the appearance of Tracy-Widom distributions in the relaxation
-statistics of such models.
-",0,1,0,0,0,0
-20192,Semianalytical calculation of the zonal-flow oscillation frequency in stellarators,"  Due to their capability to reduce turbulent transport in magnetized plasmas,
-understanding the dynamics of zonal flows is an important problem in the fusion
-programme. Since the pioneering work by Rosenbluth and Hinton in axisymmetric
-tokamaks, it is known that studying the linear and collisionless relaxation of
-zonal flow perturbations gives valuable information and physical insight.
-Recently, the problem has been investigated in stellarators and it has been
-found that in these devices the relaxation process exhibits a characteristic
-feature: a damped oscillation. The frequency of this oscillation might be a
-relevant parameter in the regulation of turbulent transport, and therefore its
-efficient and accurate calculation is important. Although an analytical
-expression can be derived for the frequency, its numerical evaluation is not
-simple and has not been exploited systematically so far. Here, a numerical
-method for its evaluation is considered, and the results are compared with
-those obtained by calculating the frequency from gyrokinetic simulations. This
-""semianalytical"" approach for the determination of the zonal-flow frequency
-reveals accurate and faster than the one based on gyrokinetic simulations.
-",0,1,0,0,0,0
-20193,Geometric phase of a moving dipole under a magnetic field at a distance,"  We predict a geometric quantum phase shift of a moving electric dipole in the
-presence of an external magnetic field at a distance. On the basis of the
-Lorentz-covariant field interaction approach, we show that a geometric phase
-appears under the condition that the dipole is moving in the field-free region,
-which is distinct from the topological He-McKellar-Wilkens phase generated by a
-direct overlap of the dipole and the field. We discuss the experimental
-feasibility of detecting this phase with atomic interferometry and argue that
-detection of this phase would result in a deeper understanding of the locality
-in quantum electromagnetic interaction.
-",0,1,0,0,0,0
-20194,A refined count of Coxeter element factorizations,"  For well-generated complex reflection groups, Chapuy and Stump gave a simple
-product for a generating function counting reflection factorizations of a
-Coxeter element by their length. This is refined here to record the number of
-reflections used from each orbit of hyperplanes. The proof is case-by-case via
-the classification of well-generated groups. It implies a new expression for
-the Coxeter number, expressed via data coming from a hyperplane orbit; a
-case-free proof of this due to J. Michel is included.
-",0,0,1,0,0,0
-20195,Tuning the magnetism of the top-layer FeAs on BaFe$_{2}$As$_{2}$(001): First-principles study,"  The magnetic properties of BaFe$_{2}$As$_{2}$(001) surface have been studied
-by using first-principles electronic structure calculations. We find that for
-As-terminated surface the magnetic ground state of the top-layer FeAs is in the
-staggered dimer antiferromagnetic (AFM) order, while for Ba-terminated surface
-the collinear (single stripe) AFM order is the most stable. When a certain
-coverage of Ba or K atoms are deposited onto the As-terminated surface, the
-calculated energy differences among different AFM orders for the top-layer FeAs
-on BaFe$_{2}$As$_{2}$(001) can be much reduced, indicating enhanced spin
-fluctuations. To identify the novel staggered dimer AFM order for the As
-termination, we have simulated the scanning tunneling microscopy (STM) image
-for this state, which shows a different $\sqrt{2}\times\sqrt{2}$ pattern from
-the case of half Ba coverage. Our results suggest: i) the magnetic properties
-of the top-layer FeAs on BaFe$_{2}$As$_{2}$(001) can be tuned effectively by
-surface doping; ii) both the surface termination and the AFM order in the
-top-layer FeAs can affect the STM image of BaFe$_{2}$As$_{2}$(001).
-",0,1,0,0,0,0
-20196,Cubature methods to solve BSDEs: Error expansion and complexity control,"  We obtain an explicit error expansion for the solution of Backward Stochastic
-Differential Equations (BSDEs) using the cubature on Wiener spaces method. The
-result is proved under a mild strengthening of the assumptions needed for the
-application of the cubature method. The explicit expansion can then be used to
-construct implementable higher order approximations via Richardson-Romberg
-extrapolation. To allow for an effective efficiency improvement of the
-interpolated algorithm, we introduce an additional projection on sparse grids,
-and study the resulting complexity reduction. Numerical examples are provided
-to illustrate our results.
-",0,0,1,0,0,0
-20197,An Earth-mass Planet in a 1-AU Orbit around an Ultracool Dwarf,"  We combine $Spitzer$ and ground-based KMTNet microlensing observations to
-identify and precisely measure an Earth-mass ($1.43^{+0.45}_{-0.32} M_\oplus$)
-planet OGLE-2016-BLG-1195Lb at $1.16^{+0.16}_{-0.13}$ AU orbiting a
-$0.078^{+0.016}_{-0.012} M_\odot$ ultracool dwarf. This is the lowest-mass
-microlensing planet to date. At $3.91^{+0.42}_{-0.46}$ kpc, it is the third
-consecutive case among the $Spitzer$ ""Galactic distribution"" planets toward the
-Galactic bulge that lies in the Galactic disk as opposed to the bulge itself,
-hinting at a skewed distribution of planets. Together with previous
-microlensing discoveries, the seven Earth-size planets orbiting the ultracool
-dwarf TRAPPIST-1, and the detection of disks around young brown dwarfs,
-OGLE-2016-BLG-1195Lb suggests that such planets might be common around
-ultracool dwarfs. It therefore sheds light on the formation of both ultracool
-dwarfs and planetary systems at the limit of low-mass protoplanetary disks.
-",0,1,0,0,0,0
-20198,Distributed Statistical Estimation and Rates of Convergence in Normal Approximation,"  This paper presents a class of new algorithms for distributed statistical
-estimation that exploit divide-and-conquer approach. We show that one of the
-key benefits of the divide-and-conquer strategy is robustness, an important
-characteristic for large distributed systems. We establish connections between
-performance of these distributed algorithms and the rates of convergence in
-normal approximation, and prove non-asymptotic deviations guarantees, as well
-as limit theorems, for the resulting estimators. Our techniques are illustrated
-through several examples: in particular, we obtain new results for the
-median-of-means estimator, as well as provide performance guarantees for
-distributed maximum likelihood estimation.
-",0,0,1,1,0,0
-20199,Connecting HL Tau to the Observed Exoplanet Sample,"  The Atacama Large Millimeter/submilimeter Array (ALMA) recently revealed a
-set of nearly concentric gaps in the protoplanetary disk surrounding the young
-star HL Tau. If these are carved by forming gas giants, this provides the first
-set of orbital initial conditions for planets as they emerge from their birth
-disks. Using N-body integrations, we have followed the evolution of the system
-for 5 Gyr to explore the possible outcomes. We find that HL Tau initial
-conditions scaled down to the size of typically observed exoplanet orbits
-naturally produce several populations in the observed exoplanet sample. First,
-for a plausible range of planetary masses, we can match the observed
-eccentricity distribution of dynamically excited radial velocity giant planets
-with eccentricities $>$ 0.2. Second, we roughly obtain the observed rate of hot
-Jupiters around FGK stars. Finally, we obtain a large efficiency of planetary
-ejections of $\approx 2$ per HL Tau-like system, but the small fraction of
-stars observed to host giant planets makes it hard to match the rate of
-free-floating planets inferred from microlensing observations. In view of
-upcoming GAIA results, we also provide predictions for the expected mutual
-inclination distribution, which is significantly broader than the absolute
-inclination distributions typically considered by previous studies.
-",0,1,0,0,0,0
-20200,How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics,"  Endowing robots with the capability of assessing risk and making risk-aware
-decisions is widely considered a key step toward ensuring safety for robots
-operating under uncertainty. But, how should a robot quantify risk? A natural
-and common approach is to consider the framework whereby costs are assigned to
-stochastic outcomes - an assignment captured by a cost random variable.
-Quantifying risk then corresponds to evaluating a risk metric, i.e., a mapping
-from the cost random variable to a real number. Yet, the question of what
-constitutes a ""good"" risk metric has received little attention within the
-robotics community. The goal of this paper is to explore and partially address
-this question by advocating axioms that risk metrics in robotics applications
-should satisfy in order to be employed as rational assessments of risk. We
-discuss general representation theorems that precisely characterize the class
-of metrics that satisfy these axioms (referred to as distortion risk metrics),
-and provide instantiations that can be used in applications. We further discuss
-pitfalls of commonly used risk metrics in robotics, and discuss additional
-properties that one must consider in sequential decision making tasks. Our hope
-is that the ideas presented here will lead to a foundational framework for
-quantifying risk (and hence safety) in robotics applications.
-",1,0,0,0,0,0
-20201,Pitfalls and Best Practices in Algorithm Configuration,"  Good parameter settings are crucial to achieve high performance in many areas
-of artificial intelligence (AI), such as propositional satisfiability solving,
-AI planning, scheduling, and machine learning (in particular deep learning).
-Automated algorithm configuration methods have recently received much attention
-in the AI community since they replace tedious, irreproducible and error-prone
-manual parameter tuning and can lead to new state-of-the-art performance.
-However, practical applications of algorithm configuration are prone to several
-(often subtle) pitfalls in the experimental design that can render the
-procedure ineffective. We identify several common issues and propose best
-practices for avoiding them. As one possibility for automatically handling as
-many of these as possible, we also propose a tool called GenericWrapper4AC.
-",1,0,0,0,0,0
-20202,Critical behaviors in contagion dynamics,"  We study the critical behavior of a general contagion model where nodes are
-either active (e.g. with opinion A, or functioning) or inactive (e.g. with
-opinion B, or damaged). The transitions between these two states are determined
-by (i) spontaneous transitions independent of the neighborhood, (ii)
-transitions induced by neighboring nodes and (iii) spontaneous reverse
-transitions. The resulting dynamics is extremely rich including limit cycles
-and random phase switching. We derive a unifying mean-field theory.
-Specifically, we analytically show that the critical behavior of systems whose
-dynamics is governed by processes (i-iii) can only exhibit three distinct
-regimes: (a) uncorrelated spontaneous transition dynamics (b) contact process
-dynamics and (c) cusp catastrophes. This ends a long-standing debate on the
-universality classes of complex contagion dynamics in mean-field and
-substantially deepens its mathematical understanding.
-",0,1,1,0,0,0
-20203,Machine Learning in Appearance-based Robot Self-localization,"  An appearance-based robot self-localization problem is considered in the
-machine learning framework. The appearance space is composed of all possible
-images, which can be captured by a robot's visual system under all robot
-localizations. Using recent manifold learning and deep learning techniques, we
-propose a new geometrically motivated solution based on training data
-consisting of a finite set of images captured in known locations of the robot.
-The solution includes estimation of the robot localization mapping from the
-appearance space to the robot localization space, as well as estimation of the
-inverse mapping for modeling visual image features. The latter allows solving
-the robot localization problem as the Kalman filtering problem.
-",1,0,0,1,0,0
-20204,Towards Communication-Aware Robust Topologies,"  We currently witness the emergence of interesting new network topologies
-optimized towards the traffic matrices they serve, such as demand-aware
-datacenter interconnects (e.g., ProjecToR) and demand-aware overlay networks
-(e.g., SplayNets). This paper introduces a formal framework and approach to
-reason about and design such topologies. We leverage a connection between the
-communication frequency of two nodes and the path length between them in the
-network, which depends on the entropy of the communication matrix. Our main
-contribution is a novel robust, yet sparse, family of network topologies which
-guarantee an expected path length that is proportional to the entropy of the
-communication patterns.
-",1,0,0,0,0,0
-20205,Zero-shot Domain Adaptation without Domain Semantic Descriptors,"  We propose a method to infer domain-specific models such as classifiers for
-unseen domains, from which no data are given in the training phase, without
-domain semantic descriptors. When training and test distributions are
-different, standard supervised learning methods perform poorly. Zero-shot
-domain adaptation attempts to alleviate this problem by inferring models that
-generalize well to unseen domains by using training data in multiple source
-domains. Existing methods use observed semantic descriptors characterizing
-domains such as time information to infer the domain-specific models for the
-unseen domains. However, it cannot always be assumed that such metadata can be
-used in real-world applications. The proposed method can infer appropriate
-domain-specific models without any semantic descriptors by introducing the
-concept of latent domain vectors, which are latent representations for the
-domains and are used for inferring the models. The latent domain vector for the
-unseen domain is inferred from the set of the feature vectors in the
-corresponding domain, which is given in the testing phase. The domain-specific
-models consist of two components: the first is for extracting a representation
-of a feature vector to be predicted, and the second is for inferring model
-parameters given the latent domain vector. The posterior distributions of the
-latent domain vectors and the domain-specific models are parametrized by neural
-networks, and are optimized by maximizing the variational lower bound using
-stochastic gradient descent. The effectiveness of the proposed method was
-demonstrated through experiments using one regression and two classification
-tasks.
-",0,0,0,1,0,0
-20206,Network modelling of topological domains using Hi-C data,"  Genome-wide chromosome conformation capture techniques such as Hi-C enable
-the generation of 3D genome contact maps and offer new pathways toward
-understanding the spatial organization of genome. One specific feature of the
-3D organization is known as topologically associating domains (TADs), which are
-densely interacting, contiguous chromatin regions playing important roles in
-regulating gene expression. A few algorithms have been proposed to detect TADs.
-In particular, the structure of Hi-C data naturally inspires application of
-community detection methods. However, one of the drawbacks of community
-detection is that most methods take exchangeability of the nodes in the network
-for granted; whereas the nodes in this case, i.e. the positions on the
-chromosomes, are not exchangeable. We propose a network model for detecting
-TADs using Hi-C data that takes into account this non-exchangeability. In
-addition, our model explicitly makes use of cell-type specific CTCF binding
-sites as biological covariates and can be used to identify conserved TADs
-across multiple cell types. The model leads to a likelihood objective that can
-be efficiently optimized via relaxation. We also prove that when suitably
-initialized, this model finds the underlying TAD structure with high
-probability. Using simulated data, we show the advantages of our method and the
-caveats of popular community detection methods, such as spectral clustering, in
-this application. Applying our method to real Hi-C data, we demonstrate the
-domains identified have desirable epigenetic features and compare them across
-different cell types. The code is available upon request.
-",0,0,0,1,0,0
-20207,On a Possible Giant Impact Origin for the Colorado Plateau,"  It is proposed and substantiated that an extraterrestrial object of the
-approximate size and mass of Planet Mars, impacting the Earth in an oblique
-angle along an approximately NE-SW route (with respect to the current
-orientation of the North America continent) around 750 million years ago (750
-Ma), is likely to be the direct cause of a chain of events which led to the
-rifting of the Rodinia supercontinent and the severing of the foundation of the
-Colorado Plateau from its surrounding craton.
-It is further argued that the impactor most likely originated as a rouge
-exoplanet produced during one of the past crossings of our Solar System through
-the Galactic spiral arms in its orbital motion around the center of the Milky
-Way Galaxy. Recent work has shown that the sites of galactic spiral arms are
-locations of density-wave collisionless shocks. The perturbations from such
-shock are known lead to the formation of massive stars, which evolve quickly
-and die as supernovae. The blastwaves from supernova explosions, in addition to
-the collisionless shocks at the spiral arms, can perturb the orbits of the
-streaming disk matter, occasionally producing rogue exoplanets that can reach
-the inner confines of our Solar System. The similarity between the period of
-spiral-arm crossings of our Solar System to the period of major extinction
-events in the Phanerozoic Eon of the Earth's history, as well as to the period
-of the supercontinent cycle (the so-called Wilson Cycle), indicates that the
-global environment of the Milky Way Galaxy may have played a major role in
-initiating Earth's past tectonic activities.
-",0,1,0,0,0,0
-20208,Reversing Parallel Programs with Blocks and Procedures,"  We show how to reverse a while language extended with blocks, local
-variables, procedures and the interleaving parallel composition. Annotation is
-defined along with a set of operational semantics capable of storing necessary
-reversal information, and identifiers are introduced to capture the
-interleaving order of an execution. Inversion is defined with a set of
-operational semantics that use saved information to undo an execution. We prove
-that annotation does not alter the behaviour of the original program, and that
-inversion correctly restores the initial program state.
-",1,0,0,0,0,0
-20209,Detection of the Stellar Intracluster Medium in Perseus (Abell 426),"  Hubble Space Telescope photometry from the ACS/WFC and WFPC2 cameras is used
-to detect and measure globular clusters (GCs) in the central region of the rich
-Perseus cluster of galaxies. A detectable population of Intragalactic GCs is
-found extending out to at least 500 kpc from the cluster center. These objects
-display luminosity and color (metallicity) distributions that are entirely
-normal for GC populations. Extrapolating from the limited spatial coverage of
-the HST fields, we estimate very roughly that the entire Perseus cluster should
-contain ~50000 or more IGCs, but a targetted wide-field survey will be needed
-for a more definitive answer. Separate brief results are presented for the rich
-GC systems in NGC 1272 and NGC 1275, the two largest Perseus ellipticals. For
-NGC 1272 we find a specific frequency S_N = 8, while for the central giant NGC
-1275, S_N ~ 12. In both these giant galaxies, the GC colors are well matched by
-bimodal distributions, with the majority in the blue (metal-poor) component.
-This preliminary study suggests that Perseus is a prime target for a more
-comprehensive deep imaging survey of Intragalactic GCs.
-",0,1,0,0,0,0
-20210,Binets: fundamental building blocks for phylogenetic networks,"  Phylogenetic networks are a generalization of evolutionary trees that are
-used by biologists to represent the evolution of organisms which have undergone
-reticulate evolution. Essentially, a phylogenetic network is a directed acyclic
-graph having a unique root in which the leaves are labelled by a given set of
-species. Recently, some approaches have been developed to construct
-phylogenetic networks from collections of networks on 2- and 3-leaved networks,
-which are known as binets and trinets, respectively. Here we study in more
-depth properties of collections of binets, one of the simplest possible types
-of networks into which a phylogenetic network can be decomposed. More
-specifically, we show that if a collection of level-1 binets is compatible with
-some binary network, then it is also compatible with a binary level-1 network.
-Our proofs are based on useful structural results concerning lowest stable
-ancestors in networks. In addition, we show that, although the binets do not
-determine the topology of the network, they do determine the number of
-reticulations in the network, which is one of its most important parameters. We
-also consider algorithmic questions concerning binets. We show that deciding
-whether an arbitrary set of binets is compatible with some network is at least
-as hard as the well-known Graph Isomorphism problem. However, if we restrict to
-level-1 binets, it is possible to decide in polynomial time whether there
-exists a binary network that displays all the binets. We also show that to find
-a network that displays a maximum number of the binets is NP-hard, but that
-there exists a simple polynomial-time 1/3-approximation algorithm for this
-problem. It is hoped that these results will eventually assist in the
-development of new methods for constructing phylogenetic networks from
-collections of smaller networks.
-",1,0,1,0,0,0
-20211,G-Deformations of maps into projective space,"  $G$-deformability of maps into projective space is characterised by the
-existence of certain Lie algebra valued 1-forms. This characterisation gives a
-unified way to obtain well known results regarding deformability in different
-geometries.
-",0,0,1,0,0,0
-20212,Cloudless atmospheres for young low-gravity substellar objects,"  Atmospheric modeling of low-gravity (VL-G) young brown dwarfs remains a
-challenge. The presence of very thick clouds has been suggested because of
-their extremely red near-infrared (NIR) spectra, but no cloud models provide a
-good fit to the data with a radius compatible with evolutionary models for
-these objects. We show that cloudless atmospheres assuming a temperature
-gradient reduction caused by fingering convection provides a very good model to
-match the observed VL-G NIR spectra. The sequence of extremely red colors in
-the NIR for atmospheres with effective temperature from ~2000 K down to ~1200 K
-is very well reproduced with predicted radii typical of young low-gravity
-objects. Future observations with NIRSPEC and MIRI on the James Webb Space
-Telescope (JWST) will provide more constrains in the mid-infrared, helping to
-confirm/refute whether or not the NIR reddening is caused by fingering
-convection. We suggest that the presence/absence of clouds will be directly
-determined by the silicate absorption features that can be observed with MIRI.
-JWST will therefore be able to better characterize the atmosphere of these hot
-young brown dwarfs and their low-gravity exoplanet analogues.
-",0,1,0,0,0,0
-20213,On the State of the Art of Evaluation in Neural Language Models,"  Ongoing innovations in recurrent neural network architectures have provided a
-steady influx of apparently state-of-the-art results on language modelling
-benchmarks. However, these have been evaluated using differing code bases and
-limited computational resources, which represent uncontrolled sources of
-experimental variation. We reevaluate several popular architectures and
-regularisation methods with large-scale automatic black-box hyperparameter
-tuning and arrive at the somewhat surprising conclusion that standard LSTM
-architectures, when properly regularised, outperform more recent models. We
-establish a new state of the art on the Penn Treebank and Wikitext-2 corpora,
-as well as strong baselines on the Hutter Prize dataset.
-",1,0,0,0,0,0
-20214,Weakly Supervised Audio Source Separation via Spectrum Energy Preserved Wasserstein Learning,"  Separating audio mixtures into individual instrument tracks has been a long
-standing challenging task. We introduce a novel weakly supervised audio source
-separation approach based on deep adversarial learning. Specifically, our loss
-function adopts the Wasserstein distance which directly measures the
-distribution distance between the separated sources and the real sources for
-each individual source. Moreover, a global regularization term is added to
-fulfill the spectrum energy preservation property regardless separation. Unlike
-state-of-the-art weakly supervised models which often involve deliberately
-devised constraints or careful model selection, our approach need little prior
-model specification on the data, and can be straightforwardly learned in an
-end-to-end fashion. We show that the proposed method performs competitively on
-public benchmark against state-of-the-art weakly supervised methods.
-",1,0,0,0,0,0
-20215,Proof of a conjecture of Kløve on permutation codes under the Chebychev distance,"  Let $d$ be a positive integer and $x$ a real number. Let $A_{d, x}$ be a
-$d\times 2d$ matrix with its entries $$ a_{i,j}=\left\{ \begin{array}{ll} x\ \
-& \mbox{for} \ 1\leqslant j\leqslant d+1-i, 1\ \ & \mbox{for} \ d+2-i\leqslant
-j\leqslant d+i, 0\ \ & \mbox{for} \ d+1+i\leqslant j\leqslant 2d. \end{array}
-\right. $$ Further, let $R_d$ be a set of sequences of integers as follows:
-$$R_d=\{(\rho_1, \rho_2,\ldots, \rho_d)|1\leqslant \rho_i\leqslant d+i,
-1\leqslant i \leqslant d,\ \mbox{and}\ \rho_r\neq \rho_s\ \mbox{for}\ r\neq
-s\}.$$ and define $$\Omega_d(x)=\sum_{\rho\in R_d}a_{1,\rho_1}a_{2,
-\rho_2}\ldots a_{d,\rho_d}.$$ In order to give a better bound on the size of
-spheres of permutation codes under the Chebychev distance, Kl{\o}ve introduced
-the above function and conjectured that $$\Omega_d(x)=\sum_{m=0}^d{d\choose
-m}(m+1)^d(x-1)^{d-m}.$$ In this paper, we settle down this conjecture
-positively.
-",1,0,0,0,0,0
-20216,Herschel observations of the Galactic HII region RCW 79,"  Triggered star formation around HII regions could be an important process.
-The Galactic HII region RCW 79 is a prototypical object for triggered high-mass
-star formation. We take advantage of Herschel data from the surveys HOBYS,
-""Evolution of Interstellar Dust"", and Hi-Gal to extract compact sources in this
-region, complemented with archival 2MASS, Spitzer, and WISE data to determine
-the physical parameters of the sources (e.g., envelope mass, dust temperature,
-and luminosity) by fitting the spectral energy distribution. We obtained a
-sample of 50 compact sources, 96% of which are situated in the
-ionization-compressed layer of cold and dense gas that is characterized by the
-column density PDF with a double-peaked lognormal distribution. The 50 sources
-have sizes of 0.1-0.4 pc with a typical value of 0.2 pc, temperatures of 11-26
-K, envelope masses of 6-760 $M_\odot$, densities of 0.1-44 $\times$ $10^5$
-cm$^{-3}$, and luminosities of 19-12712 $L_\odot$. The sources are classified
-into 16 class 0, 19 intermediate, and 15 class I objects. Their distribution
-follows the evolutionary tracks in the diagram of bolometric luminosity versus
-envelope mass (Lbol-Menv) well. A mass threshold of 140 $M_\odot$, determined
-from the Lbol-Menv diagram, yields 12 candidate massive dense cores that may
-form high-mass stars. The core formation efficiency (CFE) for the 8 massive
-condensations shows an increasing trend of the CFE with density. This suggests
-that the denser the condensation, the higher the fraction of its mass
-transformation into dense cores, as previously observed in other high-mass
-star-forming regions.
-",0,1,0,0,0,0
-20217,Software-Defined Robotics -- Idea & Approach,"  The methodology of Software-Defined Robotics hierarchical-based and
-stand-alone framework can be designed and implemented to program and control
-different sets of robots, regardless of their manufacturers' parameters and
-specifications, with unified commands and communications. This framework
-approach will increase the capability of (re)programming a specific group of
-robots during the runtime without affecting the others as desired in the
-critical missions and industrial operations, expand the shared bandwidth,
-enhance the reusability of code, leverage the computational processing power,
-decrease the unnecessary analyses of vast supplemental electrical components
-for each robot, as well as get advantages of the most state-of-the-art
-industrial trends in the cloud-based computing, Virtual Machines (VM), and
-Robot-as-a-Service (RaaS) technologies.
-",1,0,0,0,0,0
-20218,Stability interchanges in a curved Sitnikov problem,"  We consider a curved Sitnikov problem, in which an infinitesimal particle
-moves on a circle under the gravitational influence of two equal masses in
-Keplerian motion within a plane perpendicular to that circle. There are two
-equilibrium points, whose stability we are studying. We show that one of the
-equilibrium points undergoes stability interchanges as the semi-major axis of
-the Keplerian ellipses approaches the diameter of that circle. To derive this
-result, we first formulate and prove a general theorem on stability
-interchanges, and then we apply it to our model. The motivation for our model
-resides with the $n$-body problem in spaces of constant curvature.
-",0,1,1,0,0,0
-20219,Hardy Spaces over Half-strip Domains,"  We define Hardy spaces $H^p(\Omega_\pm)$ on half-strip domain~$\Omega_+$ and
-$\Omega_-= \mathbb{C}\setminus\overline{\Omega_+}$, where $0<p<\infty$, and
-prove that functions in $H^p(\Omega_\pm)$ has non-tangential boundary limit
-a.e. on $\Gamma$, the common boundary of $\Omega_\pm$. We then prove that
-Cauchy integral of functions in $L^p(\Gamma)$ are in $H^p(\Omega_\pm)$, where
-$1<p<\infty$, that is, Cauchy transform is bounded. Besides, if $1\leqslant
-p<\infty$, then $H^p(\Omega_\pm)$ functions are the Cauchy integral of their
-non-tangential boundary limits. We also establish an isomorphism between
-$H^p(\Omega_\pm)$ and $H^p(\mathbb{C}_\pm)$, the classical Hardy spaces over
-upper and lower half complex planes.
-",0,0,1,0,0,0
-20220,An Optimization Framework with Flexible Inexact Inner Iterations for Nonconvex and Nonsmooth Programming,"  In recent years, numerous vision and learning tasks have been (re)formulated
-as nonconvex and nonsmooth programmings(NNPs). Although some algorithms have
-been proposed for particular problems, designing fast and flexible optimization
-schemes with theoretical guarantee is a challenging task for general NNPs. It
-has been investigated that performing inexact inner iterations often benefit to
-special applications case by case, but their convergence behaviors are still
-unclear. Motivated by these practical experiences, this paper designs a novel
-algorithmic framework, named inexact proximal alternating direction method
-(IPAD) for solving general NNPs. We demonstrate that any numerical algorithms
-can be incorporated into IPAD for solving subproblems and the convergence of
-the resulting hybrid schemes can be consistently guaranteed by a series of
-simple error conditions. Beyond the guarantee in theory, numerical experiments
-on both synthesized and real-world data further demonstrate the superiority and
-flexibility of our IPAD framework for practical use.
-",1,0,1,0,0,0
-20221,Attitude and angular velocity tracking for a rigid body using geometric methods on the two-sphere,"  The control task of tracking a reference pointing direction (the attitude
-about the pointing direction is irrelevant) while obtaining a desired angular
-velocity (PDAV) around the pointing direction using geometric techniques is
-addressed here. Existing geometric controllers developed on the two-sphere only
-address the tracking of a reference pointing direction while driving the
-angular velocity about the pointing direction to zero. In this paper a tracking
-controller on the two-sphere, able to address the PDAV control task, is
-developed globally in a geometric frame work, to avoid problems related to
-other attitude representations such as unwinding (quaternions) or singularities
-(Euler angles). An attitude error function is constructed resulting in a
-control system with desired tracking performance for rotational maneuvers with
-large initial attitude/angular velocity errors and the ability to negotiate
-bounded modeling inaccuracies. The tracking ability of the developed control
-system is evaluated by comparing its performance with an existing geometric
-controller on the two-sphere and by numerical simulations, showing improved
-performance for large initial attitude errors, smooth transitions between
-desired angular velocities and the ability to negotiate bounded modeling
-inaccuracies.
-",1,0,1,0,0,0
-20222,Miscomputation in software: Learning to live with errors,"  Computer programs do not always work as expected. In fact, ominous warnings
-about the desperate state of the software industry continue to be released with
-almost ritualistic regularity. In this paper, we look at the 60 years history
-of programming and at the different practical methods that software community
-developed to live with programming errors. We do so by observing a class of
-students discussing different approaches to programming errors. While learning
-about the different methods for dealing with errors, we uncover basic
-assumptions that proponents of different paradigms follow. We learn about the
-mathematical attempt to eliminate errors through formal methods, scientific
-method based on testing, a way of building reliable systems through engineering
-methods, as well as an artistic approach to live coding that accepts errors as
-a creative inspiration. This way, we can explore the differences and
-similarities among the different paradigms. By inviting proponents of different
-methods into a single discussion, we hope to open potential for new thinking
-about errors. When should we use which of the approaches? And what can software
-development learn from mathematics, science, engineering and art? When
-programming or studying programming, we are often enclosed in small communities
-and we take our basic assumptions for granted. Through the discussion in this
-paper, we attempt to map the large and rich space of programming ideas and
-provide reference points for exploring, perhaps foreign, ideas that can
-challenge some of our assumptions.
-",1,0,0,0,0,0
-20223,The Combinatorics of Weighted Vector Compositions,"  A vector composition of a vector $\mathbf{\ell}$ is a matrix $\mathbf{A}$
-whose rows sum to $\mathbf{\ell}$. We define a weighted vector composition as a
-vector composition in which the column values of $\mathbf{A}$ may appear in
-different colors. We study vector compositions from different viewpoints: (1)
-We show how they are related to sums of random vectors and (2) how they allow
-to derive formulas for partial derivatives of composite functions. (3) We study
-congruence properties of the number of weighted vector compositions, for fixed
-and arbitrary number of parts, many of which are analogous to those of ordinary
-binomial coefficients and related quantities. Via the Central Limit Theorem and
-their multivariate generating functions, (4) we also investigate the asymptotic
-behavior of several special cases of numbers of weighted vector compositions.
-Finally, (5) we conjecture an extension of a primality criterion due to Mann
-and Shanks in the context of weighted vector compositions.
-",1,0,1,0,0,0
-20224,Fast Linear Model for Knowledge Graph Embeddings,"  This paper shows that a simple baseline based on a Bag-of-Words (BoW)
-representation learns surprisingly good knowledge graph embeddings. By casting
-knowledge base completion and question answering as supervised classification
-problems, we observe that modeling co-occurences of entities and relations
-leads to state-of-the-art performance with a training time of a few minutes
-using the open sourced library fastText.
-",1,0,0,1,0,0
-20225,Deep learning for plasma tomography using the bolometer system at JET,"  Deep learning is having a profound impact in many fields, especially those
-that involve some form of image processing. Deep neural networks excel in
-turning an input image into a set of high-level features. On the other hand,
-tomography deals with the inverse problem of recreating an image from a number
-of projections. In plasma diagnostics, tomography aims at reconstructing the
-cross-section of the plasma from radiation measurements. This reconstruction
-can be computed with neural networks. However, previous attempts have focused
-on learning a parametric model of the plasma profile. In this work, we use a
-deep neural network to produce a full, pixel-by-pixel reconstruction of the
-plasma profile. For this purpose, we use the overview bolometer system at JET,
-and we introduce an up-convolutional network that has been trained and tested
-on a large set of sample tomograms. We show that this network is able to
-reproduce existing reconstructions with a high level of accuracy, as measured
-by several metrics.
-",0,1,0,1,0,0
-20226,Assembly Bias and Splashback in Galaxy Clusters,"  We use publicly available data for the Millennium Simulation to explore the
-implications of the recent detection of assembly bias and splashback signatures
-in a large sample of galaxy clusters. These were identified in the SDSS/DR8
-photometric data by the redMaPPer algorithm and split into high- and
-low-concentration subsamples based on the projected positions of cluster
-members. We use simplified versions of these procedures to build cluster
-samples of similar size from the simulation data. These match the observed
-samples quite well and show similar assembly bias and splashback signals.
-Previous theoretical work has found the logarithmic slope of halo density
-profiles to have a well-defined minimum whose depth decreases and whose radius
-increases with halo concentration. Projected profiles for the observed and
-simulated cluster samples show trends with concentration which are opposite to
-these predictions. In addition, for high-concentration clusters the minimum
-slope occurs at significantly smaller radius than predicted. We show that these
-discrepancies all reflect confusion between splashback features and features
-imposed on the profiles by the cluster identification and concentration
-estimation procedures. The strong apparent assembly bias is not reflected in
-the three-dimensional distribution of matter around clusters. Rather it is a
-consequence of the preferential contamination of low-concentration clusters by
-foreground or background groups.
-",0,1,0,0,0,0
-20227,Accelerating Kernel Classifiers Through Borders Mapping,"  Support vector machines (SVM) and other kernel techniques represent a family
-of powerful statistical classification methods with high accuracy and broad
-applicability. Because they use all or a significant portion of the training
-data, however, they can be slow, especially for large problems. Piecewise
-linear classifiers are similarly versatile, yet have the additional advantages
-of simplicity, ease of interpretation and, if the number of component linear
-classifiers is not too large, speed. Here we show how a simple, piecewise
-linear classifier can be trained from a kernel-based classifier in order to
-improve the classification speed. The method works by finding the root of the
-difference in conditional probabilities between pairs of opposite classes to
-build up a representation of the decision boundary. When tested on 17 different
-datasets, it succeeded in improving the classification speed of a SVM for 9 of
-them by factors as high as 88 times or more. The method is best suited to
-problems with continuum features data and smooth probability functions. Because
-the component linear classifiers are built up individually from an existing
-classifier, rather than through a simultaneous optimization procedure, the
-classifier is also fast to train.
-",1,0,0,1,0,0
-20228,Information Criterion for Minimum Cross-Entropy Model Selection,"  This paper considers the problem of approximating a density when it can be
-evaluated up to a normalizing constant at a finite number of points. This
-density approximation problem is ubiquitous in machine learning, such as
-approximating a posterior density for Bayesian inference and estimating an
-optimal density for importance sampling. Approximating the density with a
-parametric model can be cast as a model selection problem. This problem cannot
-be addressed with traditional approaches that maximize the (marginal)
-likelihood of a model, for example, using the Akaike information criterion
-(AIC) or Bayesian information criterion (BIC). We instead aim to minimize the
-cross-entropy that gauges the deviation of a parametric model from the target
-density. We propose a novel information criterion called the cross-entropy
-information criterion (CIC) and prove that the CIC is an asymptotically
-unbiased estimator of the cross-entropy (up to a multiplicative constant) under
-some regularity conditions. We propose an iterative method to approximate the
-target density by minimizing the CIC. We demonstrate that the proposed method
-selects a parametric model that well approximates the target density.
-",0,0,0,1,0,0
-20229,Scaling relations in large-Prandtl-number natural thermal convection,"  In this study we follow Grossmann and Lohse, Phys. Rev. Lett. 86 (2001), who
-derived various scalings regimes for the dependence of the Nusselt number $Nu$
-and the Reynolds number $Re$ on the Rayleigh number $Ra$ and the Prandtl number
-$Pr$. We focus on theoretical arguments as well as on numerical simulations for
-the case of large-$Pr$ natural thermal convection. Based on an analysis of
-self-similarity of the boundary layer equations, we derive that in this case
-the limiting large-$Pr$ boundary-layer dominated regime is I$_\infty^<$,
-introduced and defined in [1], with the scaling relations $Nu\sim
-Pr^0\,Ra^{1/3}$ and $Re\sim Pr^{-1}\,Ra^{2/3}$. Our direct numerical
-simulations for $Ra$ from $10^4$ to $10^9$ and $Pr$ from 0.1 to 200 show that
-the regime I$_\infty^<$ is almost indistinguishable from the regime
-III$_\infty$, where the kinetic dissipation is bulk-dominated. With increasing
-$Ra$, the scaling relations undergo a transition to those in IV$_u$ of
-reference [1], where the thermal dissipation is determined by its bulk
-contribution.
-",0,1,0,0,0,0
-20230,"The renormalization method from continuous to discrete dynamical systems: asymptotic solutions, reductions and invariant manifolds","  The renormalization method based on the Taylor expansion for asymptotic
-analysis of differential equations is generalized to difference equations. The
-proposed renormalization method is based on the Newton-Maclaurin expansion.
-Several basic theorems on the renormalization method are proven. Some
-interesting applications are given, including asymptotic solutions of quantum
-anharmonic oscillator and discrete boundary layer, the reductions and invariant
-manifolds of some discrete dynamics systems. Furthermore, the homotopy
-renormalization method based on the Newton-Maclaurin expansion is proposed and
-applied to those difference equations including no a small parameter.
-",0,0,1,0,0,0
-20231,A Stress/Displacement Virtual Element Method for Plane Elasticity Problems,"  The numerical approximation of 2D elasticity problems is considered, in the
-framework of the small strain theory and in connection with the mixed
-Hellinger-Reissner variational formulation. A low-order Virtual Element Method
-(VEM) with a-priori symmetric stresses is proposed. Several numerical tests are
-provided, along with a rigorous stability and convergence analysis.
-",0,0,1,0,0,0
-20232,Estimation of the lead-lag parameter between two stochastic processes driven by fractional Brownian motions,"  In this paper, we consider the problem of estimating the lead-lag parameter
-between two stochastic processes driven by fractional Brownian motions (fBMs)
-of the Hurst parameter greater than 1/2. First we propose a lead-lag model
-between two stochastic processes involving fBMs, and then construct a
-consistent estimator of the lead-lag parameter with possible convergence rate.
-Our estimator has the following two features. Firstly, we can construct the
-lead-lag estimator without using the Hurst parameters of the underlying fBMs.
-Secondly, our estimator can deal with some non-synchronous and irregular
-observations. We explicitly calculate possible convergence rate when the
-observation times are (1) synchronous and equidistant, and (2) given by the
-Poisson sampling scheme. We also present numerical simulations of our results
-using the R package YUIMA.
-",0,0,1,1,0,0
-20233,Multi-resolution polymer Brownian dynamics with hydrodynamic interactions,"  A polymer model given in terms of beads, interacting through Hookean springs
-and hydrodynamic forces, is studied. Brownian dynamics description of this
-bead-spring polymer model is extended to multiple resolutions. Using this
-multiscale approach, a modeller can efficiently look at different regions of
-the polymer in different spatial and temporal resolutions with scalings given
-for the number of beads, statistical segment length and bead radius in order to
-maintain macro-scale properties of the polymer filament. The Boltzmann
-distribution of a Gaussian chain for differing statistical segment lengths
-gives a Langevin equation for the multi-resolution model with a mobility tensor
-for different bead sizes. Using the pre-averaging approximation, the
-translational diffusion coefficient is obtained as a function of the inverse of
-a matrix and then in closed form in the long-chain limit. This is then
-confirmed with numerical experiments.
-",0,1,0,0,0,0
-20234,Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network,"  Distributed learning is an effective way to analyze big data. In distributed
-regression, a typical approach is to divide the big data into multiple blocks,
-apply a base regression algorithm on each of them, and then simply average the
-output functions learnt from these blocks. Since the average process will
-decrease the variance, not the bias, bias correction is expected to improve the
-learning performance if the base regression algorithm is a biased one.
-Regularization kernel network is an effective and widely used method for
-nonlinear regression analysis. In this paper we will investigate a bias
-corrected version of regularization kernel network. We derive the error bounds
-when it is applied to a single data set and when it is applied as a base
-algorithm in distributed regression. We show that, under certain appropriate
-conditions, the optimal learning rates can be reached in both situations.
-",1,0,0,1,0,0
-20235,Cluster-based Kriging Approximation Algorithms for Complexity Reduction,"  Kriging or Gaussian Process Regression is applied in many fields as a
-non-linear regression model as well as a surrogate model in the field of
-evolutionary computation. However, the computational and space complexity of
-Kriging, that is cubic and quadratic in the number of data points respectively,
-becomes a major bottleneck with more and more data available nowadays. In this
-paper, we propose a general methodology for the complexity reduction, called
-cluster Kriging, where the whole data set is partitioned into smaller clusters
-and multiple Kriging models are built on top of them. In addition, four Kriging
-approximation algorithms are proposed as candidate algorithms within the new
-framework. Each of these algorithms can be applied to much larger data sets
-while maintaining the advantages and power of Kriging. The proposed algorithms
-are explained in detail and compared empirically against a broad set of
-existing state-of-the-art Kriging approximation methods on a well-defined
-testing framework. According to the empirical study, the proposed algorithms
-consistently outperform the existing algorithms. Moreover, some practical
-suggestions are provided for using the proposed algorithms.
-",1,0,0,1,0,0
-20236,How to model fake news,"  Over the past three years it has become evident that fake news is a danger to
-democracy. However, until now there has been no clear understanding of how to
-define fake news, much less how to model it. This paper addresses both these
-issues. A definition of fake news is given, and two approaches for the
-modelling of fake news and its impact in elections and referendums are
-introduced. The first approach, based on the idea of a representative voter, is
-shown to be suitable to obtain a qualitative understanding of phenomena
-associated with fake news at a macroscopic level. The second approach, based on
-the idea of an election microstructure, describes the collective behaviour of
-the electorate by modelling the preferences of individual voters. It is shown
-through a simulation study that the mere knowledge that pieces of fake news may
-be in circulation goes a long way towards mitigating the impact of fake news.
-",1,0,0,0,0,1
-20237,Analysis of Dropout in Online Learning,"  Deep learning is the state-of-the-art in fields such as visual object
-recognition and speech recognition. This learning uses a large number of layers
-and a huge number of units and connections. Therefore, overfitting is a serious
-problem with it, and the dropout which is a kind of regularization tool is
-used. However, in online learning, the effect of dropout is not well known.
-This paper presents our investigation on the effect of dropout in online
-learning. We analyzed the effect of dropout on convergence speed near the
-singular point. Our results indicated that dropout is effective in online
-learning. Dropout tends to avoid the singular point for convergence speed near
-that point.
-",1,0,0,1,0,0
-20238,TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's Disease,"  The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE)
-Challenge compares the performance of algorithms at predicting future evolution
-of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants
-train their models and algorithms on historical data from the Alzheimer's
-Disease Neuroimaging Initiative (ADNI) study or any other datasets to which
-they have access. Participants are then required to make monthly forecasts over
-a period of 5 years from January 2018, of three key outcomes for ADNI-3
-rollover participants: clinical diagnosis, Alzheimer's Disease Assessment Scale
-Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. These
-individual forecasts are later compared with the corresponding future
-measurements in ADNI-3 (obtained after the TADPOLE submission deadline). The
-first submission phase of TADPOLE was open for prize-eligible submissions
-between 15 June and 15 November 2017. The submission system remains open via
-the website: this https URL, although since 15 November
-2017 submissions are not eligible for the first round of prizes. This paper
-describes the design of the TADPOLE Challenge.
-",0,0,0,1,1,0
-20239,A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction,"  The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
-Inspired by recent advances in deep learning, we propose a framework for
-reconstructing MR images from undersampled data using a deep cascade of
-convolutional neural networks to accelerate the data acquisition process. We
-show that for Cartesian undersampling of 2D cardiac MR images, the proposed
-method outperforms the state-of-the-art compressed sensing approaches, such as
-dictionary learning-based MRI (DLMRI) reconstruction, in terms of
-reconstruction error, perceptual quality and reconstruction speed for both
-3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the
-method proposed is approximately twice as small, allowing to preserve
-anatomical structures more faithfully. Using our method, each image can be
-reconstructed in 23 ms, which is fast enough to enable real-time applications.
-",1,0,0,0,0,0
-20240,Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM,"  We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR)
-model. We learn to listen and write characters with a joint Connectionist
-Temporal Classification (CTC) and attention-based encoder-decoder network. The
-encoder is a deep Convolutional Neural Network (CNN) based on the VGG network.
-The CTC network sits on top of the encoder and is jointly trained with the
-attention-based decoder. During the beam search process, we combine the CTC
-predictions, the attention-based decoder predictions and a separately trained
-LSTM language model. We achieve a 5-10\% error reduction compared to prior
-systems on spontaneous Japanese and Chinese speech, and our end-to-end model
-beats out traditional hybrid ASR systems.
-",1,0,0,0,0,0
-20241,The Social and Work Structure of an Afterschool Math Club,"  This study focuses on the social structure and interpersonal dynamics of an
-afterschool math club for middle schoolers. Using social network analysis, two
-networks were formed and analyzed: The network of friendship relationships and
-the network of working relationships. The interconnections and correlations
-between friendship relationships, working relationships, and student opinion
-surveys are studied. A core working group of talented students emerged from the
-network of working relations. This group acted a central go-between for other
-members in the club. This core working group expanded into largest friendship
-group in the friendship network. Although there were working isolates, they
-were not found to be socially isolated. Students who were less popular tended
-to report a greater favorable impact from club participation. Implications for
-the study of the social structure of afterschool STEM clubs and classrooms are
-discussed.
-",0,0,1,0,0,0
-20242,Fourier Transform of Schwartz Algebras on Groups in the Harish-Chandra class,"  It is well-known that the Harish-Chandra transform, $f\mapsto\mathcal{H}f,$
-is a topological isomorphism of the spherical (Schwartz) convolution algebra
-$\mathcal{C}^{p}(G//K)$ (where $K$ is a maximal compact subgroup of any
-arbitrarily chosen group $G$ in the Harish-Chandra class and $0<p\leq2$) onto
-the (Schwartz) multiplication algebra
-$\bar{\mathcal{Z}}({\mathfrak{F}}^{\epsilon})$ (of $\mathfrak{w}-$invariant
-members of $\mathcal{Z}({\mathfrak{F}}^{\epsilon}),$ with $\epsilon=(2/p)-1$).
-The same cannot however be said of the full Schwartz convolution algebra
-$\mathcal{C}^{p}(G),$ except for few specific examples of groups (notably
-$G=SL(2,\mathbb{R})$) and for some notable values of $p$ (with restrictions on
-$G$ and/or on $\mathcal{C}^{p}(G)$). Nevertheless the full Harish-Chandra
-Plancherel formula on $G$ is known for all of
-$\mathcal{C}^{2}(G)=:\mathcal{C}(G).$ In order to then understand the structure
-of Harish-Chandra transform more clearly and to compute the image of
-$\mathcal{C}^{p}(G)$ under it (without any restriction) we derive an absolutely
-convergent series expansion (in terms of known functions) for the
-Harish-Chandra transform by an application of the full Plancherel formula on
-$G.$ This leads to a computation of the image of $\mathcal{C}(G)$ under the
-Harish-Chandra transform which may be seen as a concrete realization of
-Arthur's result and be easily extended to all of $\mathcal{C}^{p}(G)$ in much
-the same way as it is known in the work of Trombi and Varadarajan.
-",0,0,1,0,0,0
-20243,Mutual Information and Optimality of Approximate Message-Passing in Random Linear Estimation,"  We consider the estimation of a signal from the knowledge of its noisy linear
-random Gaussian projections. A few examples where this problem is relevant are
-compressed sensing, sparse superposition codes, and code division multiple
-access. There has been a number of works considering the mutual information for
-this problem using the replica method from statistical physics. Here we put
-these considerations on a firm rigorous basis. First, we show, using a
-Guerra-Toninelli type interpolation, that the replica formula yields an upper
-bound to the exact mutual information. Secondly, for many relevant practical
-cases, we present a converse lower bound via a method that uses spatial
-coupling, state evolution analysis and the I-MMSE theorem. This yields a single
-letter formula for the mutual information and the minimal-mean-square error for
-random Gaussian linear estimation of all discrete bounded signals. In addition,
-we prove that the low complexity approximate message-passing algorithm is
-optimal outside of the so-called hard phase, in the sense that it
-asymptotically reaches the minimal-mean-square error. In this work spatial
-coupling is used primarily as a proof technique. However our results also prove
-two important features of spatially coupled noisy linear random Gaussian
-estimation. First there is no algorithmically hard phase. This means that for
-such systems approximate message-passing always reaches the minimal-mean-square
-error. Secondly, in a proper limit the mutual information associated to such
-systems is the same as the one of uncoupled linear random Gaussian estimation.
-",1,1,1,0,0,0
-20244,A Memristor-Based Optimization Framework for AI Applications,"  Memristors have recently received significant attention as ubiquitous
-device-level components for building a novel generation of computing systems.
-These devices have many promising features, such as non-volatility, low power
-consumption, high density, and excellent scalability. The ability to control
-and modify biasing voltages at the two terminals of memristors make them
-promising candidates to perform matrix-vector multiplications and solve systems
-of linear equations. In this article, we discuss how networks of memristors
-arranged in crossbar arrays can be used for efficiently solving optimization
-and machine learning problems. We introduce a new memristor-based optimization
-framework that combines the computational merit of memristor crossbars with the
-advantages of an operator splitting method, alternating direction method of
-multipliers (ADMM). Here, ADMM helps in splitting a complex optimization
-problem into subproblems that involve the solution of systems of linear
-equations. The capability of this framework is shown by applying it to linear
-programming, quadratic programming, and sparse optimization. In addition to
-ADMM, implementation of a customized power iteration (PI) method for
-eigenvalue/eigenvector computation using memristor crossbars is discussed. The
-memristor-based PI method can further be applied to principal component
-analysis (PCA). The use of memristor crossbars yields a significant speed-up in
-computation, and thus, we believe, has the potential to advance optimization
-and machine learning research in artificial intelligence (AI).
-",1,0,0,1,0,0
-20245,Estimating parameters of a directed weighted graph model with beta-distributed edge-weights,"  We introduce a directed, weighted random graph model, where the edge-weights
-are independent and beta-distributed with parameters depending on their
-endpoints. We will show that the row- and column-sums of the transformed
-edge-weight matrix are sufficient statistics for the parameters, and use the
-theory of exponential families to prove that the ML estimate of the parameters
-exists and is unique. Then an algorithm to find this estimate is introduced
-together with convergence proof that uses properties of the digamma function.
-Simulation results and applications are also presented.
-",0,0,1,1,0,0
-20246,On the Performance of Reduced-Complexity Transmit/Receive Diversity Systems over MIMO-V2V Channel Model,"  In this letter, we investigate the performance of multiple-input
-multiple-output techniques in a vehicle-to-vehicle communication system. We
-consider both transmit antenna selection with maximal-ratio combining and
-transmit antenna selection with selection combining. The channel propagation
-model between two vehicles is represented as n*Rayleigh distribution, which has
-been shown to be a realistic model for vehicle-to-vehicle communication
-scenarios. We derive tight analytical expressions for the outage probability
-and amount of fading of the post-processing signal-to-noise ratio.
-",1,0,0,0,0,0
-20247,Internal migration and education: A cross-national comparison,"  Migration the main process shaping patterns of human settlement within and
-between countries. It is widely acknowledged to be integral to the process of
-human development as it plays a significant role in enhancing educational
-outcomes. At regional and national levels, internal migration underpins the
-efficient functioning of the economy by bringing knowledge and skills to the
-locations where they are needed. It is the multi-dimensional nature of
-migration that underlines its significance in the process of human development.
-Human mobility extends in the spatial domain from local travel to international
-migration, and in the temporal dimension from short-term stays to permanent
-relocations. Classification and measurement of such phenomena is inevitably
-complex, which has severely hindered progress in comparative research, with
-very few large-scale cross-national comparisons of migration. The linkages
-between migration and education have been explored in a separate line of
-inquiry that has predominantly focused on country-specific analyses as to the
-ways in which migration affects educational outcomes and how educational
-attainment affects migration behaviour. A recurrent theme has been the
-educational selectivity of migrants, which in turn leads to an increase of
-human capital in some regions, primarily cities, at the expense of others.
-Questions have long been raised as to the links between education and migration
-in response to educational expansion, but have not yet been fully answered
-because of the absence, until recently, of adequate data for comparative
-analysis of migration. In this paper, we bring these two separate strands of
-research together to systematically explore links between internal migration
-and education across a global sample of 57 countries at various stages of
-development, using data drawn from the IPUMS database.
-",0,0,0,0,0,1
-20248,Free differential Lie Rota-Baxter algebras and Gröbner-Shirshov bases,"  We establish the Gröbner-Shirshov bases theory for differential Lie
-$\Omega$-algebras. As an application, we give a linear basis of a free
-differential Lie Rota-Baxter algebra on a set.
-",0,0,1,0,0,0
-20249,Conversion Rate Optimization through Evolutionary Computation,"  Conversion optimization means designing a web interface so that as many users
-as possible take a desired action on it, such as register or purchase. Such
-design is usually done by hand, testing one change at a time through A/B
-testing, or a limited number of combinations through multivariate testing,
-making it possible to evaluate only a small fraction of designs in a vast
-design space. This paper describes Sentient Ascend, an automatic conversion
-optimization system that uses evolutionary optimization to create effective web
-interface designs. Ascend makes it possible to discover and utilize
-interactions between the design elements that are difficult to identify
-otherwise. Moreover, evaluation of design candidates is done in parallel
-online, i.e. with a large number of real users interacting with the system. A
-case study on an existing media site shows that significant improvements (i.e.
-over 43%) are possible beyond human design. Ascend can therefore be seen as an
-approach to massively multivariate conversion optimization, based on a
-massively parallel interactive evolution.
-",1,0,0,0,0,0
-20250,Emergence of spatial curvature,"  This paper investigates the phenomenon of emergence of spatial curvature.
-This phenomenon is absent in the Standard Cosmological Model, which has a flat
-and fixed spatial curvature (small perturbations are considered in the Standard
-Cosmological Model but their global average vanishes, leading to spatial
-flatness at all times). This paper shows that with the nonlinear growth of
-cosmic structures the global average deviates from zero. The analysis is based
-on the {\em silent universes} (a wide class of inhomogeneous cosmological
-solutions of the Einstein equations). The initial conditions are set in the
-early universe as perturbations around the $\Lambda$CDM model with $\Omega_m =
-0.31$, $\Omega_\Lambda = 0.69$, and $H_0 = 67.8$ km s$^{-1}$ Mpc$^{-1}$. As the
-growth of structures becomes nonlinear, the model deviates from the
-$\Lambda$CDM model, and at the present instant if averaged over a domain ${\cal
-D}$ with volume $V = (2150\,{\rm Mpc})^3$ (at these scales the cosmic variance
-is negligibly small) gives: $\Omega_m^{\cal D} = 0.22$, $\Omega_\Lambda^{\cal
-D} = 0.61$, $\Omega_{\cal R}^{\cal D} = 0.15$ (in the FLRW limit $\Omega_{\cal
-R}^{\cal D} \to \Omega_k$), and $\langle H \rangle_{\cal D} = 72.2$ km s$^{-1}$
-Mpc$^{-1}$. Given the fact that low-redshift observations favor higher values
-of the Hubble constant and lower values of matter density, compared to the CMB
-constraints, the emergence of the spatial curvature in the low-redshift
-universe could be a possible solution to these discrepancies.
-",0,1,0,0,0,0
-20251,Asymptotic generalized bivariate extreme with random index,"  In many biological, agricultural, military activity problems and in some
-quality control problems, it is almost impossible to have a fixed sample size,
-because some observations are always lost for various reasons. Therefore, the
-sample size itself is considered frequently to be a random variable (rv). The
-class of limit distribution functions (df's) of the random bivariate extreme
-generalized order statistics (GOS) from independent and identically distributed
-RV's are fully characterized. When the random sample size is assumed to be
-independent of the basic variables and its df is assumed to converge weakly to
-a non-degenerate limit, the necessary and sufficient conditions for the weak
-convergence of the random bivariate extreme GOS are obtained. Furthermore, when
-the interrelation of the random size and the basic rv's is not restricted,
-sufficient conditions for the convergence and the forms of the limit df's are
-deduced. Illustrative examples are given which lend further support to our
-theoretical results.
-",0,0,1,1,0,0
-20252,On problems in the calculus of variations in increasingly elongated domains,"  We consider minimization problems in the calculus of variations set in a
-sequence of domains the size of which tends to infinity in certain directions
-and such that the data only depend on the coordinates in the directions that
-remain constant. We study the asymptotic behavior of minimizers in various
-situations and show that they converge in an appropriate sense toward
-minimizers of a related energy functional in the constant directions.
-",0,0,1,0,0,0
-20253,Inequalities related to Symmetrized Harmonic Convex Functions,"  In this paper, we extend the Hermite-Hadamard type $\dot{I}$scan inequality
-to the class of symmetrized harmonic convex functions. The corresponding
-version for harmonic h-convex functions is also investigated. Furthermore, we
-establish Hermite-Hadamard type inequalites for the product of a harmonic
-convex function with a symmetrized harmonic convex function.
-",0,0,1,0,0,0
-20254,Parameter estimation for fractional Ornstein-Uhlenbeck processes of general Hurst parameter,"  This paper provides several statistical estimators for the drift and
-volatility parameters of an Ornstein-Uhlenbeck process driven by fractional
-Brownian motion, whose observations can be made either continuously or at
-discrete time instants. First and higher order power variations are used to
-estimate the volatility parameter. The almost sure convergence of the
-estimators and the corresponding central limit theorems are obtained for all
-the Hurst parameter range $H\in (0, 1)$. The least squares estimator is used
-for the drift parameter. A central limit theorem is proved when the Hurst
-parameter $H \in (0, 1/2)$ and a noncentral limit theorem is proved for
-$H\in[3/4, 1)$. Thus, the open problem left in the paper by Hu and Nualart
-(2010) is completely solved, where a central limit theorem for least squares
-estimator is proved for $H\in [1/2, 3/4)$.
-",0,0,1,1,0,0
-20255,"E-polynomials of $PGL(2,\mathbb{C})$-character varieties of surface groups","  In this paper, we compute the E-polynomials of the
-$PGL(2,\mathbb{C})$-character varieties associated to surfaces of genus $g$
-with one puncture, for any holonomy around it, and compare it with its
-Langlands dual case, $SL(2,\mathbb{C})$. The study is based on the
-stratification of the space of representations and on the analysis of the
-behaviour of the E-polynomial under fibrations.
-",0,0,1,0,0,0
-20256,Security Analysis of Cache Replacement Policies,"  Modern computer architectures share physical resources between different
-programs in order to increase area-, energy-, and cost-efficiency.
-Unfortunately, sharing often gives rise to side channels that can be exploited
-for extracting or transmitting sensitive information. We currently lack
-techniques for systematic reasoning about this interplay between security and
-efficiency. In particular, there is no established way for quantifying security
-properties of shared caches.
-In this paper, we propose a novel model that enables us to characterize
-important security properties of caches. Our model encompasses two aspects: (1)
-The amount of information that can be absorbed by a cache, and (2) the amount
-of information that can effectively be extracted from the cache by an
-adversary. We use our model to compute both quantities for common cache
-replacement policies (FIFO, LRU, and PLRU) and to compare their isolation
-properties. We further show how our model for information extraction leads to
-an algorithm that can be used to improve the bounds delivered by the CacheAudit
-static analyzer.
-",1,0,0,0,0,0
-20257,Supercongruences related to ${}_3F_2(1)$ involving harmonic numbers,"  We show various supercongruences for truncated series which involve central
-binomial coefficients and harmonic numbers. The corresponding infinite series
-are also evaluated.
-",0,0,1,0,0,0
-20258,Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition,"  Layer normalization is a recently introduced technique for normalizing the
-activities of neurons in deep neural networks to improve the training speed and
-stability. In this paper, we introduce a new layer normalization technique
-called Dynamic Layer Normalization (DLN) for adaptive neural acoustic modeling
-in speech recognition. By dynamically generating the scaling and shifting
-parameters in layer normalization, DLN adapts neural acoustic models to the
-acoustic variability arising from various factors such as speakers, channel
-noises, and environments. Unlike other adaptive acoustic models, our proposed
-approach does not require additional adaptation data or speaker information
-such as i-vectors. Moreover, the model size is fixed as it dynamically
-generates adaptation parameters. We apply our proposed DLN to deep
-bidirectional LSTM acoustic models and evaluate them on two benchmark datasets
-for large vocabulary ASR experiments: WSJ and TED-LIUM release 2. The
-experimental results show that our DLN improves neural acoustic models in terms
-of transcription accuracy by dynamically adapting to various speakers and
-environments.
-",1,0,0,0,0,0
-20259,First-Order vs. Second-Order Encodings for LTLf-to-Automata Translation,"  Translating formulas of Linear Temporal Logic (LTL) over finite traces, or
-LTLf, to symbolic Deterministic Finite Automata (DFA) plays an important role
-not only in LTLf synthesis, but also in synthesis for Safety LTL formulas. The
-translation is enabled by using MONA, a powerful tool for symbolic, BDD-based,
-DFA construction from logic specifications. Recent works used a first-order
-encoding of LTLf formulas to translate LTLf to First Order Logic (FOL), which
-is then fed to MONA to get the symbolic DFA. This encoding was shown to perform
-well, but other encodings have not been studied. Specifically, the natural
-question of whether second-order encoding, which has significantly simpler
-quantificational structure, can outperform first-order encoding remained open.
-In this paper we address this challenge and study second-order encodings for
-LTLf formulas. We first introduce a specific MSO encoding that captures the
-semantics of LTLf in a natural way and prove its correctness. We then explore
-is a Compact MSO encoding, which benefits from automata-theoretic minimization,
-thus suggesting a possible practical advantage. To that end, we propose a
-formalization of symbolic DFA in second-order logic, thus developing a novel
-connection between BDDs and MSO. We then show by empirical evaluations that the
-first-order encoding does perform better than both second-order encodings. The
-conclusion is that first-order encoding is a better choice than second-order
-encoding in LTLf-to-Automata translation.
-",1,0,0,0,0,0
-20260,Regularity results and parametrices of semi-linear boundary problems of product type,"  This short note describes the benefit one obtains from a specific
-construction of a family of parametrices for a class of elliptic boundary value
-problems perturbed by non-linear terms of product type. The construction is
-based on the Boutet de Monvel calculus of pseudo-differential boundary
-operators for the linear elliptic parts, and on paradifferential operators for
-the product terms.
-",0,0,1,0,0,0
-20261,"$\left( β, \varpi \right)$-stability for cross-validation and the choice of the number of folds","  In this paper, we introduce a new concept of stability for cross-validation,
-called the $\left( \beta, \varpi \right)$-stability, and use it as a new
-perspective to build the general theory for cross-validation. The $\left(
-\beta, \varpi \right)$-stability mathematically connects the generalization
-ability and the stability of the cross-validated model via the Rademacher
-complexity. Our result reveals mathematically the effect of cross-validation
-from two sides: on one hand, cross-validation picks the model with the best
-empirical generalization ability by validating all the alternatives on test
-sets; on the other hand, cross-validation may compromise the stability of the
-model selection by causing subsampling error. Moreover, the difference between
-training and test errors in q\textsuperscript{th} round, sometimes referred to
-as the generalization error, might be autocorrelated on q. Guided by the ideas
-above, the $\left( \beta, \varpi \right)$-stability help us derivd a new class
-of Rademacher bounds, referred to as the one-round/convoluted Rademacher
-bounds, for the stability of cross-validation in both the i.i.d.\ and
-non-i.i.d.\ cases. For both light-tail and heavy-tail losses, the new bounds
-quantify the stability of the one-round/average test error of the
-cross-validated model in terms of its one-round/average training error, the
-sample sizes $n$, number of folds $K$, the tail property of the loss (encoded
-as Orlicz-$\Psi_\nu$ norms) and the Rademacher complexity of the model class
-$\Lambda$. The new class of bounds not only quantitatively reveals the
-stability of the generalization ability of the cross-validated model, it also
-shows empirically the optimal choice for number of folds $K$, at which the
-upper bound of the one-round/average test error is lowest, or, to put it in
-another way, where the test error is most stable.
-",1,0,1,1,0,0
-20262,Directional convexity of harmonic mappings,"  The convolution properties are discussed for the complex-valued harmonic
-functions in the unit disk $\mathbb{D}$ constructed from the harmonic shearing
-of the analytic function $\phi(z):=\int_0^z
-(1/(1-2\xi\textit{e}^{\textit{i}\mu}\cos\nu+\xi^2\textit{e}^{2\textit{i}\mu}))\textit{d}\xi$,
-where $\mu$ and $\nu$ are real numbers. For any real number $\alpha$ and
-harmonic function $f=h+\overline{g}$, define an analytic function
-$f_{\alpha}:=h+\textit{e}^{-2\textit{i}\alpha}g$. Let $\mu_1$ and $\mu_2$
-$(\mu_1+\mu_2=\mu)$ be real numbers, and $f=h+\overline{g}$ and
-$F=H+\overline{G}$ be locally-univalent and sense-preserving harmonic functions
-such that $f_{\mu_1}*F_{\mu_2}=\phi$. It is shown that the convolution $f*F$ is
-univalent and convex in the direction of $-\mu$, provided it is locally
-univalent and sense-preserving. Also, local-univalence of the above convolution
-$f*F$ is shown for some specific analytic dilatations of $f$ and $F$.
-Furthermore, if $g\equiv0$ and both the analytic functions $f_{\mu_1}$ and
-$F_{\mu_2}$ are convex, then the convolution $f*F$ is shown to be convex. These
-results extends the work done by Dorff \textit{et al.} to a larger class of
-functions.
-",0,0,1,0,0,0
-20263,Optimal Non-uniform Deployments in Ultra-Dense Finite-Area Cellular Networks,"  Network densification and heterogenisation through the deployment of small
-cellular access points (picocells and femtocells) are seen as key mechanisms in
-handling the exponential increase in cellular data traffic. Modelling such
-networks by leveraging tools from Stochastic Geometry has proven particularly
-useful in understanding the fundamental limits imposed on network coverage and
-capacity by co-channel interference. Most of these works however assume
-infinite sized and uniformly distributed networks on the Euclidean plane. In
-contrast, we study finite sized non-uniformly distributed networks, and find
-the optimal non-uniform distribution of access points which maximises network
-coverage for a given non-uniform distribution of mobile users, and vice versa.
-",1,0,0,0,0,0
-20264,"Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder","  We investigate the integration of a planning mechanism into an
-encoder-decoder architecture with an explicit alignment for character-level
-machine translation. We develop a model that plans ahead when it computes
-alignments between the source and target sequences, constructing a matrix of
-proposed future alignments and a commitment vector that governs whether to
-follow or recompute the plan. This mechanism is inspired by the strategic
-attentive reader and writer (STRAW) model. Our proposed model is end-to-end
-trainable with fully differentiable operations. We show that it outperforms a
-strong baseline on three character-level decoder neural machine translation on
-WMT'15 corpus. Our analysis demonstrates that our model can compute
-qualitatively intuitive alignments and achieves superior performance with fewer
-parameters.
-",1,0,0,0,0,0
-20265,Deep Recurrent NMF for Speech Separation by Unfolding Iterative Thresholding,"  In this paper, we propose a novel recurrent neural network architecture for
-speech separation. This architecture is constructed by unfolding the iterations
-of a sequential iterative soft-thresholding algorithm (ISTA) that solves the
-optimization problem for sparse nonnegative matrix factorization (NMF) of
-spectrograms. We name this network architecture deep recurrent NMF (DR-NMF).
-The proposed DR-NMF network has three distinct advantages. First, DR-NMF
-provides better interpretability than other deep architectures, since the
-weights correspond to NMF model parameters, even after training. This
-interpretability also provides principled initializations that enable faster
-training and convergence to better solutions compared to conventional random
-initialization. Second, like many deep networks, DR-NMF is an order of
-magnitude faster at test time than NMF, since computation of the network output
-only requires evaluating a few layers at each time step. Third, when a limited
-amount of training data is available, DR-NMF exhibits stronger generalization
-and separation performance compared to sparse NMF and state-of-the-art
-long-short term memory (LSTM) networks. When a large amount of training data is
-available, DR-NMF achieves lower yet competitive separation performance
-compared to LSTM networks.
-",1,0,0,1,0,0
-20266,Birth of isolated nested cylinders and limit cycles in 3D piecewise smooth vector fields with symmetry,"  Our start point is a 3D piecewise smooth vector field defined in two zones
-and presenting a shared fold curve for the two smooth vector fields considered.
-Moreover, these smooth vector fields are symmetric relative to the fold curve,
-giving raise to a continuum of nested topological cylinders such that each
-orthogonal section of these cylinders is filled by centers. First we prove that
-the normal form considered represents a whole class of piecewise smooth vector
-fields. After we perturb the initial model in order to obtain exactly
-$\mathcal{L}$ invariant planes containing centers. A second perturbation of the
-initial model also is considered in order to obtain exactly $k$ isolated
-cylinders filled by periodic orbits. Finally, joining the two previous
-bifurcations we are able to exhibit a model, preserving the symmetry relative
-to the fold curve, and having exactly $k.\mathcal{L}$ limit cycles.
-",0,0,1,0,0,0
-20267,The difficulty of folding self-folding origami,"  Why is it difficult to refold a previously folded sheet of paper? We show
-that even crease patterns with only one designed folding motion inevitably
-contain an exponential number of `distractor' folding branches accessible from
-a bifurcation at the flat state. Consequently, refolding a sheet requires
-finding the ground state in a glassy energy landscape with an exponential
-number of other attractors of higher energy, much like in models of protein
-folding (Levinthal's paradox) and other NP-hard satisfiability (SAT) problems.
-As in these problems, we find that refolding a sheet requires actuation at
-multiple carefully chosen creases. We show that seeding successful folding in
-this way can be understood in terms of sub-patterns that fold when cut out
-(`folding islands'). Besides providing guidelines for the placement of active
-hinges in origami applications, our results point to fundamental limits on the
-programmability of energy landscapes in sheets.
-",0,1,0,0,0,0
-20268,Integrable Trotterization: Local Conservation Laws and Boundary Driving,"  We discuss a general procedure to construct an integrable real-time
-trotterization of interacting lattice models. As an illustrative example we
-consider a spin-$1/2$ chain, with continuous time dynamics described by the
-isotropic ($XXX$) Heisenberg Hamiltonian. For periodic boundary conditions
-local conservation laws are derived from an inhomogeneous transfer matrix and a
-boost operator is constructed. In the continuous time limit these local charges
-reduce to the known integrals of motion of the Heisenberg chain. In a simple
-Kraus representation we also examine the nonequilibrium setting, where our
-integrable cellular automaton is driven by stochastic processes at the
-boundaries. We show explicitly, how an exact nonequilibrium steady state
-density matrix can be written in terms of a staggered matrix product ansatz.
-This simple trotterization scheme, in particular in the open system framework,
-could prove to be a useful tool for experimental simulations of the lattice
-models in terms of trapped ion and atom optics setups.
-",0,1,0,0,0,0
-20269,The Multivariate Hawkes Process in High Dimensions: Beyond Mutual Excitation,"  The Hawkes process is a class of point processes whose future depends on its
-own history. Previous theoretical work on the Hawkes process is limited to the
-case of a mutually-exciting process, in which a past event can only increase
-the occurrence of future events. However, in neuronal networks and other
-real-world applications, inhibitory relationships may be present. In this
-paper, we develop a new approach for establishing the properties of the Hawkes
-process without the restriction to mutual excitation. To this end, we employ a
-thinning process representation and a coupling construction to bound the
-dependence coefficient of the Hawkes process. Using recent developments on
-weakly dependent sequences, we establish a concentration inequality for
-second-order statistics of the Hawkes process. We apply this concentration
-inequality in order to establish theoretical results for penalized regression
-and clustering analysis in the high-dimensional regime. Our theoretical results
-are corroborated by simulation studies and an application to a neuronal spike
-train data set.
-",0,0,0,1,0,0
-20270,Aerial-Ground collaborative sensing: Third-Person view for teleoperation,"  Rapid deployment and operation are key requirements in time critical
-application, such as Search and Rescue (SaR). Efficiently teleoperated ground
-robots can support first-responders in such situations. However, first-person
-view teleoperation is sub-optimal in difficult terrains, while a third-person
-perspective can drastically increase teleoperation performance. Here, we
-propose a Micro Aerial Vehicle (MAV)-based system that can autonomously provide
-third-person perspective to ground robots. While our approach is based on local
-visual servoing, it further leverages the global localization of several ground
-robots to seamlessly transfer between these ground robots in GPS-denied
-environments. Therewith one MAV can support multiple ground robots on a demand
-basis. Furthermore, our system enables different visual detection regimes, and
-enhanced operability, and return-home functionality. We evaluate our system in
-real-world SaR scenarios.
-",1,0,0,0,0,0
-20271,A universal thin film model for Ginzburg-Landau energy with dipolar interaction,"  We present an analytical treatment of a three-dimensional variational model
-of a system that exhibits a second-order phase transition in the presence of
-dipolar interactions. Within the framework of Ginzburg-Landau theory, we
-concentrate on the case in which the domain occupied by the sample has the
-shape of a flat thin film and obtain a reduced two-dimensional, non-local
-variational model that describes the energetics of the system in terms of the
-order parameter averages across the film thickness. Namely, we show that the
-reduced two-dimensional model is in a certain sense asymptotically equivalent
-to the original three-dimensional model for small film thicknesses. Using this
-asymptotic equivalence, we analyze two different thin film limits for the full
-three-dimensional model via the methods of $\Gamma$-convergence applied to the
-reduced two-dimensional model. In the first regime, in which the film thickness
-vanishes while all other parameters remain fixed, we recover the local
-two-dimensional Ginzburg-Landau model. On the other hand, when the film
-thickness vanishes while the sample's lateral dimensions diverge at the right
-rate, we show that the system exhibits a transition from homogeneous to
-spatially modulated global energy minimizers. We identify a sharp threshold for
-this transition.
-",0,1,1,0,0,0
-20272,LOCATA challenge: speaker localization with a planar array,"  This document describes our submission to the 2018 LOCalization And TrAcking
-(LOCATA) challenge (Tasks 1, 3, 5). We estimate the 3D position of a speaker
-using the Global Coherence Field (GCF) computed from multiple microphone pairs
-of a DICIT planar array. One of the main challenges when using such an array
-with omnidirectional microphones is the front-back ambiguity, which is
-particularly evident in Task 5. We address this challenge by post-processing
-the peaks of the GCF and exploiting the attenuation introduced by the frame of
-the array. Moreover, the intermittent nature of speech and the changing
-orientation of the speaker make localization difficult. For Tasks 3 and 5, we
-also employ a Particle Filter (PF) that favors the spatio-temporal continuity
-of the localization results.
-",1,0,0,0,0,0
-20273,Less Is More: A Comprehensive Framework for the Number of Components of Ensemble Classifiers,"  The number of component classifiers chosen for an ensemble greatly impacts
-the prediction ability. In this paper, we use a geometric framework for a
-priori determining the ensemble size, which is applicable to most of existing
-batch and online ensemble classifiers. There are only a limited number of
-studies on the ensemble size examining Majority Voting (MV) and Weighted
-Majority Voting (WMV). Almost all of them are designed for batch-mode, hardly
-addressing online environments. Big data dimensions and resource limitations,
-in terms of time and memory, make determination of ensemble size crucial,
-especially for online environments. For the MV aggregation rule, our framework
-proves that the more strong components we add to the ensemble, the more
-accurate predictions we can achieve. For the WMV aggregation rule, our
-framework proves the existence of an ideal number of components, which is equal
-to the number of class labels, with the premise that components are completely
-independent of each other and strong enough. While giving the exact definition
-for a strong and independent classifier in the context of an ensemble is a
-challenging task, our proposed geometric framework provides a theoretical
-explanation of diversity and its impact on the accuracy of predictions. We
-conduct a series of experimental evaluations to show the practical value of our
-theorems and existing challenges.
-",1,0,0,1,0,0
-20274,Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers,"  Low-rank modeling has many important applications in computer vision and
-machine learning. While the matrix rank is often approximated by the convex
-nuclear norm, the use of nonconvex low-rank regularizers has demonstrated
-better empirical performance. However, the resulting optimization problem is
-much more challenging. Recent state-of-the-art requires an expensive full SVD
-in each iteration. In this paper, we show that for many commonly-used nonconvex
-low-rank regularizers, a cutoff can be derived to automatically threshold the
-singular values obtained from the proximal operator. This allows such operator
-being efficiently approximated by power method. Based on it, we develop a
-proximal gradient algorithm (and its accelerated variant) with inexact proximal
-splitting and prove that a convergence rate of O(1/T) where T is the number of
-iterations is guaranteed. Furthermore, we show the proposed algorithm can be
-well parallelized, which achieves nearly linear speedup w.r.t the number of
-threads. Extensive experiments are performed on matrix completion and robust
-principal component analysis, which shows a significant speedup over the
-state-of-the-art. Moreover, the matrix solution obtained is more accurate and
-has a lower rank than that of the nuclear norm regularizer.
-",1,0,0,1,0,0
-20275,Computational Eco-Systems for Handwritten Digits Recognition,"  Inspired by the importance of diversity in biological system, we built an
-heterogeneous system that could achieve this goal. Our architecture could be
-summarized in two basic steps. First, we generate a diverse set of
-classification hypothesis using both Convolutional Neural Networks, currently
-the state-of-the-art technique for this task, among with other traditional and
-innovative machine learning techniques. Then, we optimally combine them through
-Meta-Nets, a family of recently developed and performing ensemble methods.
-",0,0,0,1,0,0
-20276,Compressing networks with super nodes,"  Community detection is a commonly used technique for identifying groups in a
-network based on similarities in connectivity patterns. To facilitate community
-detection in large networks, we recast the network to be partitioned into a
-smaller network of 'super nodes', each super node comprising one or more nodes
-in the original network. To define the seeds of our super nodes, we apply the
-'CoreHD' ranking from dismantling and decycling. We test our approach through
-the analysis of two common methods for community detection: modularity
-maximization with the Louvain algorithm and maximum likelihood optimization for
-fitting a stochastic block model. Our results highlight that applying community
-detection to the compressed network of super nodes is significantly faster
-while successfully producing partitions that are more aligned with the local
-network connectivity, more stable across multiple (stochastic) runs within and
-between community detection algorithms, and overlap well with the results
-obtained using the full network.
-",1,1,0,0,0,0
-20277,Mode specific electronic friction in dissociative chemisorption on metal surfaces: H$_2$ on Ag(111),"  Electronic friction and the ensuing nonadiabatic energy loss play an
-important role in chemical reaction dynamics at metal surfaces. Using molecular
-dynamics with electronic friction evaluated on-the-fly from Density Functional
-Theory, we find strong mode dependence and a dominance of nonadiabatic energy
-loss along the bond stretch coordinate for scattering and dissociative
-chemisorption of H$_2$ on the Ag(111) surface. Exemplary trajectories with
-varying initial conditions indicate that this mode-specificity translates into
-modulated energy loss during a dissociative chemisorption event. Despite minor
-nonadiabatic energy loss of about 5\%, the directionality of friction forces
-induces dynamical steering that affects individual reaction outcomes,
-specifically for low-incidence energies and vibrationally excited molecules.
-Mode-specific friction induces enhanced loss of rovibrational rather than
-translational energy and will be most visible in its effect on final energy
-distributions in molecular scattering experiments.
-",0,1,0,0,0,0
-20278,The quantum auxiliary linear problem & quantum Darboux-Backlund transformations,"  We explore the notion of the quantum auxiliary linear problem and the
-associated problem of quantum Backlund transformations (BT). In this context we
-systematically construct the analogue of the classical formula that provides
-the whole hierarchy of the time components of Lax pairs at the quantum level
-for both closed and open integrable lattice models. The generic time evolution
-operator formula is particularly interesting and novel at the quantum level
-when dealing with systems with open boundary conditions. In the same frame we
-show that the reflection K-matrix can also be viewed as a particular type of
-BT, fixed at the boundaries of the system. The q-oscillator (q-boson) model, a
-variant of the Ablowitz-Ladik model, is then employed as a paradigm to
-illustrate the method. Particular emphasis is given to the time part of the
-quantum BT as possible connections and applications to the problem of quantum
-quenches as well as the time evolution of local quantum impurities are evident.
-A discussion on the use of Bethe states as well as coherent states for the
-study of the time evolution is also presented.
-",0,1,0,0,0,0
-20279,Simplified Minimal Gated Unit Variations for Recurrent Neural Networks,"  Recurrent neural networks with various types of hidden units have been used
-to solve a diverse range of problems involving sequence data. Two of the most
-recent proposals, gated recurrent units (GRU) and minimal gated units (MGU),
-have shown comparable promising results on example public datasets. In this
-paper, we introduce three model variants of the minimal gated unit (MGU) which
-further simplify that design by reducing the number of parameters in the
-forget-gate dynamic equation. These three model variants, referred to simply as
-MGU1, MGU2, and MGU3, were tested on sequences generated from the MNIST dataset
-and from the Reuters Newswire Topics (RNT) dataset. The new models have shown
-similar accuracy to the MGU model while using fewer parameters and thus
-lowering training expense. One model variant, namely MGU2, performed better
-than MGU on the datasets considered, and thus may be used as an alternate to
-MGU or GRU in recurrent neural networks.
-",1,0,0,1,0,0
-20280,Parallel G-duplex and C-duplex DNA with Uninterrupted Spines of AgI-Mediated Base Pairs,"  Hydrogen bonding between nucleobases produces diverse DNA structural motifs,
-including canonical duplexes, guanine (G) quadruplexes and cytosine (C)
-i-motifs. Incorporating metal-mediated base pairs into nucleic acid structures
-can introduce new functionalities and enhanced stabilities. Here we
-demonstrate, using mass spectrometry (MS), ion mobility spectrometry (IMS) and
-fluorescence resonance energy transfer (FRET), that parallel-stranded
-structures consisting of up to 20 G-Ag(I)-G contiguous base pairs are formed
-when natural DNA sequences are mixed with silver cations in aqueous solution.
-FRET indicates that duplexes formed by poly(cytosine) strands with 20
-contiguous C-Ag(I)-C base pairs are also parallel. Silver-mediated G-duplexes
-form preferentially over G-quadruplexes, and the ability of Ag+ to convert
-G-quadruplexes into silver-paired duplexes may provide a new route to
-manipulating these biologically relevant structures. IMS indicates that
-G-duplexes are linear and more rigid than B-DNA. DFT calculations were used to
-propose structures compatible with the IMS experiments. Such inexpensive,
-defect-free and soluble DNA-based nanowires open new directions in the design
-of novel metal-mediated DNA nanotechnology.
-",0,0,0,0,1,0
-20281,Constraints on Vacuum Energy from Structure Formation and Nucleosynthesis,"  This paper derives an upper limit on the density $\rho_{\scriptstyle\Lambda}$
-of dark energy based on the requirement that cosmological structure forms
-before being frozen out by the eventual acceleration of the universe. By
-allowing for variations in both the cosmological parameters and the strength of
-gravity, the resulting constraint is a generalization of previous limits. The
-specific parameters under consideration include the amplitude $Q$ of the
-primordial density fluctuations, the Planck mass $M_{\rm pl}$, the
-baryon-to-photon ratio $\eta$, and the density ratio $\Omega_M/\Omega_b$. In
-addition to structure formation, we use considerations from stellar structure
-and Big Bang Nucleosynthesis (BBN) to constrain these quantities. The resulting
-upper limit on the dimensionless density of dark energy becomes
-$\rho_{\scriptstyle\Lambda}/M_{\rm pl}^4<10^{-90}$, which is $\sim30$ orders of
-magnitude larger than the value in our universe
-$\rho_{\scriptstyle\Lambda}/M_{\rm pl}^4\sim10^{-120}$. This new limit is much
-less restrictive than previous constraints because additional parameters are
-allowed to vary. With these generalizations, a much wider range of universes
-can develop cosmic structure and support observers. To constrain the
-constituent parameters, new BBN calculations are carried out in the regime
-where $\eta$ and $G=M_{\rm pl}^{-2}$ are much larger than in our universe. If
-the BBN epoch were to process all of the protons into heavier elements, no
-hydrogen would be left behind to make water, and the universe would not be
-viable. However, our results show that some hydrogen is always left over, even
-under conditions of extremely large $\eta$ and $G$, so that a wide range of
-alternate universes are potentially habitable.
-",0,1,0,0,0,0
-20282,Dynamic Mobile Edge Caching with Location Differentiation,"  Mobile edge caching enables content delivery directly within the radio access
-network, which effectively alleviates the backhaul burden and reduces
-round-trip latency. To fully exploit the edge resources, the most popular
-contents should be identified and cached. Observing that content popularity
-varies greatly at different locations, to maximize local hit rate, this paper
-proposes an online learning algorithm that dynamically predicts content hit
-rate, and makes location-differentiated caching decisions. Specifically, a
-linear model is used to estimate the future hit rate. Considering the
-variations in user demand, a perturbation is added to the estimation to account
-for uncertainty. The proposed learning algorithm requires no training phase,
-and hence is adaptive to the time-varying content popularity profile.
-Theoretical analysis indicates that the proposed algorithm asymptotically
-approaches the optimal policy in the long term. Extensive simulations based on
-real world traces show that, the proposed algorithm achieves higher hit rate
-and better adaptiveness to content popularity fluctuation, compared with other
-schemes.
-",1,0,0,0,0,0
-20283,Online Learning for Distribution-Free Prediction,"  We develop an online learning method for prediction, which is important in
-problems with large and/or streaming data sets. We formulate the learning
-approach using a covariance-fitting methodology, and show that the resulting
-predictor has desirable computational and distribution-free properties: It is
-implemented online with a runtime that scales linearly in the number of
-samples; has a constant memory requirement; avoids local minima problems; and
-prunes away redundant feature dimensions without relying on restrictive
-assumptions on the data distribution. In conjunction with the split conformal
-approach, it also produces distribution-free prediction confidence intervals in
-a computationally efficient manner. The method is demonstrated on both real and
-synthetic datasets.
-",1,0,0,1,0,0
-20284,"Thermal transitions, pseudogap behavior and BCS-BEC crossover in Fermi-Fermi mixtures","  We study the mass imbalanced Fermi-Fermi mixture within the framework of a
-two-dimensional lattice fermion model. Based on the thermodynamic and species
-dependent quasiparticle behavior we map out the finite temperature phase
-diagram of this system and show that unlike the balanced Fermi superfluid there
-are now two different pseudogap regimes as PG-I and PG-II. While within the
-PG-I regime both the fermionic species are pseudogapped, PG-II corresponds to
-the regime where pseudogap feature survives only in the light species. We
-believe that the single particle spectral features that we discuss in this
-paper are observable through the species resolved radio frequency spectroscopy
-and momentum resolved photo emission spectroscopy measurements on systems such
-as, 6$_{Li}$-40$_{K}$ mixture. We further investigate the interplay between the
-population and mass imbalances and report that at a fixed population imbalance
-the BCS-BEC crossover in a Fermi-Fermi mixture would require a critical
-interaction (U$_{c}$), for the realization of the uniform superfluid state. The
-effect of imbalance in mass on the exotic Fulde-Ferrell-Larkin-Ovchinnikov
-(FFLO) superfluid phase has been probed in detail in terms of the thermodynamic
-and quasiparticle behavior of this phase. It has been observed that in spite of
-the s-wave symmetry of the pairing field a nodal superfluid gap is realized in
-the LO regime. Our results on the various thermal scales and regimes are
-expected to serve as benchmarks for the experimental observations on
-6$_{Li}$-40$_{K}$ mixture.
-",0,1,0,0,0,0
-20285,Spectral Approximation for Ergodic CMV Operators with an Application to Quantum Walks,"  We establish concrete criteria for fully supported absolutely continuous
-spectrum for ergodic CMV matrices and purely absolutely continuous spectrum for
-limit-periodic CMV matrices. We proceed by proving several variational
-estimates on the measure of the spectrum and the vanishing set of the Lyapunov
-exponent for CMV matrices, which represent CMV analogues of results obtained
-for Schrödinger operators due to Y.\ Last in the early 1990s. Having done so,
-we combine those estimates with results from inverse spectral theory to obtain
-purely absolutely continuous spectrum.
-",0,0,1,0,0,0
-20286,Quantitative Photoacoustic Imaging in the Acoustic Regime using SPIM,"  While in standard photoacoustic imaging the propagation of sound waves is
-modeled by the standard wave equation, our approach is based on a generalized
-wave equation with variable sound speed and material density, respectively. In
-this paper we present an approach for photoacoustic imaging, which in addition
-to recovering of the absorption density parameter, the imaging parameter of
-standard photoacoustics, also allows to reconstruct the spatially varying sound
-speed and density, respectively, of the medium. We provide analytical
-reconstruction formulas for all three parameters based in a linearized model
-based on single plane illumination microscopy (SPIM) techniques.
-",0,0,1,0,0,0
-20287,Integrated Deep and Shallow Networks for Salient Object Detection,"  Deep convolutional neural network (CNN) based salient object detection
-methods have achieved state-of-the-art performance and outperform those
-unsupervised methods with a wide margin. In this paper, we propose to integrate
-deep and unsupervised saliency for salient object detection under a unified
-framework. Specifically, our method takes results of unsupervised saliency
-(Robust Background Detection, RBD) and normalized color images as inputs, and
-directly learns an end-to-end mapping between inputs and the corresponding
-saliency maps. The color images are fed into a Fully Convolutional Neural
-Networks (FCNN) adapted from semantic segmentation to exploit high-level
-semantic cues for salient object detection. Then the results from deep FCNN and
-RBD are concatenated to feed into a shallow network to map the concatenated
-feature maps to saliency maps. Finally, to obtain a spatially consistent
-saliency map with sharp object boundaries, we fuse superpixel level saliency
-map at multi-scale. Extensive experimental results on 8 benchmark datasets
-demonstrate that the proposed method outperforms the state-of-the-art
-approaches with a margin.
-",1,0,0,0,0,0
-20288,Feedback Capacity over Networks,"  In this paper, we investigate the fundamental limitations of feedback
-mechanism in dealing with uncertainties for network systems. The study of
-maximum capability of feedback control was pioneered in Xie and Guo (2000) for
-scalar systems with nonparametric nonlinear uncertainty. In a network setting,
-nodes with unknown and nonlinear dynamics are interconnected through a directed
-interaction graph. Nodes can design feedback controls based on all available
-information, where the objective is to stabilize the network state. Using
-information structure and decision pattern as criteria, we specify three
-categories of network feedback laws, namely the
-global-knowledge/global-decision, network-flow/local-decision, and
-local-flow/local-decision feedback. We establish a series of network capacity
-characterizations for these three fundamental types of network control laws.
-First of all, we prove that for global-knowledge/global-decision and
-network-flow/local-decision control where nodes know the information flow
-across the entire network, there exists a critical number
-$\big(3/2+\sqrt{2}\big)/\|A_{\mathrm{G}}\|_\infty$, where $3/2+\sqrt{2}$ is as
-known as the Xie-Guo constant and $A_{\mathrm{G}}$ is the network adjacency
-matrix, defining exactly how much uncertainty in the node dynamics can be
-overcome by feedback. Interestingly enough, the same feedback capacity can be
-achieved under max-consensus enhanced local flows where nodes only observe
-information flows from neighbors as well as extreme (max and min) states in the
-network. Next, for local-flow/local-decision control, we prove that there
-exists a structure-determined value being a lower bound of the network feedback
-capacity. These results reveal the important connection between network
-structure and fundamental capabilities of in-network feedback control.
-",1,0,0,0,0,0
-20289,Flexural phonons in supported graphene: from pinning to localization,"  We identify graphene layer on a disordered substrate as a possible system
-where Anderson localization of phonons can be observed. Generally, observation
-of localization for scattering waves is not simple, because the Rayleigh
-scattering is inversely proportional to a high power of wavelength. The
-situation is radically different for the out of plane vibrations, so-called
-flexural phonons, scattered by pinning centers induced by a substrate. In this
-case, the scattering time for vanishing wave vector tends to a finite limit.
-One may, therefore, expect that physics of the flexural phonons exhibits
-features characteristic for electron localization in two dimensions, albeit
-without complications caused by the electron-electron interactions. We confirm
-this idea by calculating statistical properties of the Anderson localization of
-flexural phonons for a model of elastic sheet in the presence of the pinning
-centers. Finally, we discuss possible manifestations of the flexural phonons,
-including the localized ones, in the electronic thermal conductance.
-",0,1,0,0,0,0
-20290,"Test them all, is it worth it? Assessing configuration sampling on the JHipster Web development stack","  Many approaches for testing configurable software systems start from the same
-assumption: it is impossible to test all configurations. This motivated the
-definition of variability-aware abstractions and sampling techniques to cope
-with large configuration spaces. Yet, there is no theoretical barrier that
-prevents the exhaustive testing of all configurations by simply enumerating
-them, if the effort required to do so remains acceptable. Not only this: we
-believe there is lots to be learned by systematically and exhaustively testing
-a configurable system. In this case study, we report on the first ever
-endeavour to test all possible configurations of an industry-strength, open
-source configurable software system, JHipster, a popular code generator for web
-applications. We built a testing scaffold for the 26,000+ configurations of
-JHipster using a cluster of 80 machines during 4 nights for a total of 4,376
-hours (182 days) CPU time. We find that 35.70% configurations fail and we
-identify the feature interactions that cause the errors. We show that sampling
-strategies (like dissimilarity and 2-wise): (1) are more effective to find
-faults than the 12 default configurations used in the JHipster continuous
-integration; (2) can be too costly and exceed the available testing budget. We
-cross this quantitative analysis with the qualitative assessment of JHipster's
-lead developers.
-",1,0,0,0,0,0
-20291,Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells,"  We propose a reinforcement learning (RL) based closed loop power control
-algorithm for the downlink of the voice over LTE (VoLTE) radio bearer for an
-indoor environment served by small cells. The main contributions of our paper
-are to 1) use RL to solve performance tuning problems in an indoor cellular
-network for voice bearers and 2) show that our derived lower bound loss in
-effective signal to interference plus noise ratio due to neighboring cell
-failure is sufficient for VoLTE power control purposes in practical cellular
-networks. In our simulation, the proposed RL-based power control algorithm
-significantly improves both voice retainability and mean opinion score compared
-to current industry standards. The improvement is due to maintaining an
-effective downlink signal to interference plus noise ratio against adverse
-network operational issues and faults.
-",1,0,0,1,0,0
-20292,Ancient shrinking spherical interfaces in the Allen-Cahn flow,"  We consider the parabolic Allen-Cahn equation in $\mathbb{R}^n$, $n\ge 2$,
-$$u_t= \Delta u + (1-u^2)u \quad \hbox{ in } \mathbb{R}^n \times (-\infty,
-0].$$ We construct an ancient radially symmetric solution $u(x,t)$ with any
-given number $k$ of transition layers between $-1$ and $+1$. At main order they
-consist of $k$ time-traveling copies of $w$ with spherical interfaces distant
-$O(\log |t| )$ one to each other as $t\to -\infty$. These interfaces are
-resemble at main order copies of the {\em shrinking sphere} ancient solution to
-mean the flow by mean curvature of surfaces: $|x| = \sqrt{- 2(n-1)t}$. More
-precisely, if $w(s)$ denotes the heteroclinic 1-dimensional solution of $w'' +
-(1-w^2)w=0$ $w(\pm \infty)= \pm 1$ given by $w(s) = \tanh \left(\frac
-s{\sqrt{2}} \right) $ we have $$ u(x,t) \approx \sum_{j=1}^k
-(-1)^{j-1}w(|x|-\rho_j(t)) - \frac 12 (1+ (-1)^{k}) \quad \hbox{ as } t\to
--\infty $$ where
-$$\rho_j(t)=\sqrt{-2(n-1)t}+\frac{1}{\sqrt{2}}\left(j-\frac{k+1}{2}\right)\log\left(\frac
-{|t|}{\log |t| }\right)+ O(1),\quad j=1,\ldots ,k.$$
-",0,0,1,0,0,0
-20293,Measuring the Eccentricity of Items,"  The long-tail phenomenon tells us that there are many items in the tail.
-However, not all tail items are the same. Each item acquires different kinds of
-users. Some items are loved by the general public, while some items are
-consumed by eccentric fans. In this paper, we propose a novel metric, item
-eccentricity, to incorporate this difference between consumers of the items.
-Eccentric items are defined as items that are consumed by eccentric users. We
-used this metric to analyze two real-world datasets of music and movies and
-observed the characteristics of items in terms of eccentricity. The results
-showed that our defined eccentricity of an item does not change much over time,
-and classified eccentric and noneccentric items present significantly distinct
-characteristics. The proposed metric effectively separates the eccentric and
-noneccentric items mixed in the tail, which could not be done with the previous
-measures, which only consider the popularity of items.
-",1,0,0,0,0,0
-20294,Dataset: Rare Event Classification in Multivariate Time Series,"  A real-world dataset is provided from a pulp-and-paper manufacturing
-industry. The dataset comes from a multivariate time series process. The data
-contains a rare event of paper break that commonly occurs in the industry. The
-data contains sensor readings at regular time-intervals (x's) and the event
-label (y). The primary purpose of the data is thought to be building a
-classification model for early prediction of the rare event. However, it can
-also be used for multivariate time series data exploration and building other
-supervised and unsupervised models.
-",0,0,0,1,0,0
-20295,Experimental verification of stopping-power prediction from single- and dual-energy computed tomography in biological tissues,"  An experimental setup for consecutive measurement of ion and x-ray absorption
-in tissue or other materials is introduced. With this setup using a 3D-printed
-sample container, the reference stopping-power ratio (SPR) of materials can be
-measured with an uncertainty of below 0.1%. A total of 65 porcine and bovine
-tissue samples were prepared for measurement, comprising five samples each of
-13 tissue types representing about 80% of the total body mass (three different
-muscle and fatty tissues, liver, kidney, brain, heart, blood, lung and bone).
-Using a standard stoichiometric calibration for single-energy CT (SECT) as well
-as a state-of-the-art dual-energy CT (DECT) approach, SPR was predicted for all
-tissues and then compared to the measured reference. With the SECT approach,
-the SPRs of all tissues were predicted with a mean error of (-0.84 $\pm$ 0.12)%
-and a mean absolute error of (1.27 $\pm$ 0.12)%. In contrast, the DECT-based
-SPR predictions were overall consistent with the measured reference with a mean
-error of (-0.02 $\pm$ 0.15)% and a mean absolute error of (0.10 $\pm$ 0.15)%.
-Thus, in this study, the potential of DECT to decrease range uncertainty could
-be confirmed in biological tissue.
-",0,1,0,0,0,0
-20296,How to Beat Science and Influence People: Policy Makers and Propaganda in Epistemic Networks,"  In their recent book Merchants of Doubt [New York:Bloomsbury 2010], Naomi
-Oreskes and Erik Conway describe the ""tobacco strategy"", which was used by the
-tobacco industry to influence policy makers regarding the health risks of
-tobacco products. The strategy involved two parts, consisting of (1) promoting
-and sharing independent research supporting the industry's preferred position
-and (2) funding additional research, but selectively publishing the results. We
-introduce a model of the Tobacco Strategy, and use it to argue that both prongs
-of the strategy can be extremely effective--even when policy makers rationally
-update on all evidence available to them. As we elaborate, this model helps
-illustrate the conditions under which the Tobacco Strategy is particularly
-successful. In addition, we show how journalists engaged in ""fair"" reporting
-can inadvertently mimic the effects of industry on public belief.
-",1,0,0,0,0,0
-20297,A complete and partial integrability technique of the Lorenz system,"  In this paper we deal with the well-known nonlinear Lorenz system that
-describes the deterministic chaos phenomenon. We consider an interesting
-problem with time-varying phenomena in quantum optics. Then we establish from
-the motion equations the passage to the Lorenz system. Furthermore, we show
-that the reduction to the third order non linear equation can be performed.
-Therefore, the obtained differential equation can be analytically solved in
-some special cases and transformed to Abel, Dufing, Painlevé and
-generalized Emden-Fowler equations. So, a motivating technique that permitted a
-complete and partial integrability of the Lorenz system is presented.
-",0,1,0,0,0,0
-20298,Comparision of the definitions of generalized solution of the Cauchy problem for quasi-linear equation,"  In preprint we consider and compare different definitions of generalized
-solution of the Cauchy problem for 1d-scalar quasilinear equation (conservation
-law). We start from the classical approaches goes back to I.M. Gelfand, O.A.
-Oleinik, S.N. Kruzhkov and move to the modern finite-difference approximations
-approaches belongs to A.A. Shananin and G.M. Henkin. We discuss the conditions
-that provide definitions to be equivalent.
-",0,0,1,0,0,0
-20299,On The Complexity of Sparse Label Propagation,"  This paper investigates the computational complexity of sparse label
-propagation which has been proposed recently for processing network structured
-data. Sparse label propagation amounts to a convex optimization problem and
-might be considered as an extension of basis pursuit from sparse vectors to
-network structured datasets. Using a standard first-order oracle model, we
-characterize the number of iterations for sparse label propagation to achieve a
-prescribed accuracy. In particular, we derive an upper bound on the number of
-iterations required to achieve a certain accuracy and show that this upper
-bound is sharp for datasets having a chain structure (e.g., time series).
-",0,0,0,1,0,0
-20300,Detecting Qualia in Natural and Artificial Agents,"  The Hard Problem of consciousness has been dismissed as an illusion. By
-showing that computers are capable of experiencing, we show that they are at
-least rudimentarily conscious with potential to eventually reach
-superconsciousness. The main contribution of the paper is a test for confirming
-certain subjective experiences in a tested agent. We follow with analysis of
-benefits and problems with conscious machines and implications of such
-capability on future of computing, machine rights and artificial intelligence
-safety.
-",1,0,0,0,0,0
-20301,The interplay between Steinberg algebras and partial skew rings,"  We study the interplay between Steinberg algebras and partial skew rings: For
-a partial action of a group in a Hausdorff, locally compact, totally
-disconnected topological space, we realize the associated partial skew group
-ring as a Steinberg algebra (over the transformation groupoid attached to the
-partial action). We then apply this realization to characterize diagonal
-preserving isomorphisms of partial skew group rings, over commutative algebras,
-in terms of continuous orbit equivalence of the associated partial actions.
-Finally, we show that any Steinberg algebra, associated to a Hausdorff ample
-groupoid, can be seen as a partial skew inverse semigroup ring.
-",0,0,1,0,0,0
-20302,Type-II Dirac Photons,"  The Dirac equation for relativistic electron waves is the parent model for
-Weyl and Majorana fermions as well as topological insulators. Simulation of
-Dirac physics in three-dimensional photonic crystals, though fundamentally
-important for topological phenomena at optical frequencies, encounters the
-challenge of synthesis of both Kramers double degeneracy and parity inversion.
-Here we show how type-II Dirac points---exotic Dirac relativistic waves yet to
-be discovered---are robustly realized through the nonsymmorphic screw symmetry.
-The emergent type-II Dirac points carry nontrivial topology and are the mother
-states of type-II Weyl points. The proposed all-dielectric architecture enables
-robust cavity states at photonic-crystal---air interfaces and anomalous
-refraction, with very low energy dissipation.
-",0,1,0,0,0,0
-20303,Improving Network Robustness against Adversarial Attacks with Compact Convolution,"  Though Convolutional Neural Networks (CNNs) have surpassed human-level
-performance on tasks such as object classification and face verification, they
-can easily be fooled by adversarial attacks. These attacks add a small
-perturbation to the input image that causes the network to misclassify the
-sample. In this paper, we focus on neutralizing adversarial attacks by compact
-feature learning. In particular, we show that learning features in a closed and
-bounded space improves the robustness of the network. We explore the effect of
-L2-Softmax Loss, that enforces compactness in the learned features, thus
-resulting in enhanced robustness to adversarial perturbations. Additionally, we
-propose compact convolution, a novel method of convolution that when
-incorporated in conventional CNNs improves their robustness. Compact
-convolution ensures feature compactness at every layer such that they are
-bounded and close to each other. Extensive experiments show that Compact
-Convolutional Networks (CCNs) neutralize multiple types of attacks, and perform
-better than existing methods in defending adversarial attacks, without
-incurring any additional training overhead compared to CNNs.
-",1,0,0,1,0,0
-20304,From sudden quench to adiabatic dynamics in the attractive Hubbard model,"  We study the crossover between the sudden quench limit and the adiabatic
-dynamics of superconducting states in the attractive Hubbard model. We focus on
-the dynamics induced by the change of the attractive interaction during a
-finite ramp time which is varied in order to track the evolution of the
-dynamical phase diagram from the sudden quench to the equilibrium limit. Two
-different dynamical regimes are realized for quenches towards weak and strong
-coupling interactions. At weak coupling the dynamics depends only on the energy
-injected into the system, whereas a dynamics retaining memory of the initial
-state takes place at strong coupling. We show that this is related to a sharp
-transition between a weak and a strong coupling quench dynamical regime, which
-defines the boundaries beyond which a dynamics independent from the initial
-state is recovered. Comparing the dynamics in the superconducting and
-non-superconducting phases we argue that this is due to the lack of an
-adiabatic connection to the equilibrium ground state for non-equilibrium
-superconducting states in the strong coupling quench regime.
-",0,1,0,0,0,0
-20305,Strongly exchange-coupled and surface-state-modulated magnetization dynamics in Bi2Se3/YIG heterostructures,"  We report strong interfacial exchange coupling in Bi2Se3/yttrium iron garnet
-(YIG) bilayers manifested as large in-plane interfacial magnetic anisotropy
-(IMA) and enhancement of damping probed by ferromagnetic resonance (FMR). The
-IMA and spin mixing conductance reached a maximum when Bi2Se3 was around 6
-quintuple-layer (QL) thick. The unconventional Bi2Se3 thickness dependence of
-the IMA and spin mixing conductance are correlated with the evolution of
-surface band structure of Bi2Se3, indicating that topological surface states
-play an important role in the magnetization dynamics of YIG.
-Temperature-dependent FMR of Bi2Se3/YIG revealed signatures of magnetic
-proximity effect of $T_c$ as high as 180 K, and an effective field parallel to
-the YIG magnetization direction at low temperature. Our study sheds light on
-the effects of topological insulators on magnetization dynamics, essential for
-development of TI-based spintronic devices.
-",0,1,0,0,0,0
-20306,Survivable Probability of SDN-enabled Cloud Networking with Random Physical Link Failure,"  Software-driven cloud networking is a new paradigm in orchestrating physical
-resources (CPU, network bandwidth, energy, storage) allocated to network
-functions, services, and applications, which is commonly modeled as a
-cross-layer network. This model carries a physical network representing the
-physical infrastructure, a logical network showing demands, and
-logical-to-physical node/link mappings. In such networks, a single failure in
-the physical network may trigger cascading failures in the logical network and
-disable network services and connectivity. In this paper, we propose an
-evaluation metric, survivable probability, to evaluate the reliability of such
-networks under random physical link failure(s). We propose the concept of base
-protecting spanning tree and prove the necessary and sufficient conditions for
-its existence and relation to survivability. We then develop mathematical
-programming formulations for reliable cross-layer network routing design with
-the maximal reliable probability. Computation results demonstrate the viability
-of our approach.
-",1,0,0,0,0,0
-20307,Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers,"  PID control architectures are widely used in industrial applications. Despite
-their low number of open parameters, tuning multiple, coupled PID controllers
-can become tedious in practice. In this paper, we extend PILCO, a model-based
-policy search framework, to automatically tune multivariate PID controllers
-purely based on data observed on an otherwise unknown system. The system's
-state is extended appropriately to frame the PID policy as a static state
-feedback policy. This renders PID tuning possible as the solution of a finite
-horizon optimal control problem without further a priori knowledge. The
-framework is applied to the task of balancing an inverted pendulum on a seven
-degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast
-and data-efficient policy learning, even on complex real world problems.
-",1,0,0,1,0,0
-20308,Engineering a Simplified 0-Bit Consistent Weighted Sampling,"  The Min-Hashing approach to sketching has become an important tool in data
-analysis, information retrial, and classification. To apply it to real-valued
-datasets, the ICWS algorithm has become a seminal approach that is widely used,
-and provides state-of-the-art performance for this problem space. However, ICWS
-suffers a computational burden as the sketch size K increases. We develop a new
-Simplified approach to the ICWS algorithm, that enables us to obtain over 20x
-speedups compared to the standard algorithm. The veracity of our approach is
-demonstrated empirically on multiple datasets and scenarios, showing that our
-new Simplified CWS obtains the same quality of results while being an order of
-magnitude faster.
-",0,0,0,1,0,0
-20309,Quantifying macroeconomic expectations in stock markets using Google Trends,"  Among other macroeconomic indicators, the monthly release of U.S.
-unemployment rate figures in the Employment Situation report by the U.S. Bureau
-of Labour Statistics gets a lot of media attention and strongly affects the
-stock markets. I investigate whether a profitable investment strategy can be
-constructed by predicting the likely changes in U.S. unemployment before the
-official news release using Google query volumes for related search terms. I
-find that massive new data sources of human interaction with the Internet not
-only improves U.S. unemployment rate predictability, but can also enhance
-market timing of trading strategies when considered jointly with macroeconomic
-data. My results illustrate the potential of combining extensive behavioural
-data sets with economic data to anticipate investor expectations and stock
-market moves.
-",0,0,0,0,0,1
-20310,Effects of Incomplete Ionization on Beta - Ga2O3 Power Devices: Unintentional Donor with Energy 110 meV,"  Understanding the origin of unintentional doping in Ga2O3 is key to
-increasing breakdown voltages of Ga2O3 based power devices. Therefore,
-transport and capacitance spectroscopy studies have been performed to better
-understand the origin of unintentional doping in Ga2O3. Previously unobserved
-unintentional donors in commercially available (-201) Ga2O3 substrates have
-been electrically characterized via temperature dependent Hall effect
-measurements up to 1000 K and found to have a donor energy of 110 meV. The
-existence of the unintentional donor is confirmed by temperature dependent
-admittance spectroscopy, with an activation energy of 131 meV determined via
-that technique, in agreement with Hall effect measurements. With the
-concentration of this donor determined to be in the mid to high 10^16 cm^-3
-range, elimination of this donor from the drift layer of Ga2O3 power
-electronics devices will be key to pushing the limits of device performance.
-Indeed, analytical assessment of the specific on-resistance (Ronsp) and
-breakdown voltage of Schottky diodes containing the 110 meV donor indicates
-that incomplete ionization increases Ronsp and decreases breakdown voltage as
-compared to Ga2O3 Schottky diodes containing only the shallow donor. The
-reduced performance due to incomplete ionization occurs in addition to the
-usual tradeoff between Ronsp and breakdown voltage. To achieve 10 kV operation
-in Ga2O3 Schottky diode devices, analysis indicates that the concentration of
-110 meV donors must be reduced below 5x10^14 cm^-3 to limit the increase in
-Ronsp to one percent.
-",0,1,0,0,0,0
-20311,A single coordinate framework for optic flow and binocular disparity,"  Optic flow is two dimensional, but no special qualities are attached to one
-or other of these dimensions. For binocular disparity, on the other hand, the
-terms 'horizontal' and 'vertical' disparities are commonly used. This is odd,
-since binocular disparity and optic flow describe essentially the same thing.
-The difference is that, generally, people tend to fixate relatively close to
-the direction of heading as they move, meaning that fixation is close to the
-optic flow epipole, whereas, for binocular vision, fixation is close to the
-head-centric midline, i.e. approximately 90 degrees from the binocular epipole.
-For fixating animals, some separations of flow may lead to simple algorithms
-for the judgement of surface structure and the control of action. We consider
-the following canonical flow patterns that sum to produce overall flow: (i)
-'towards' flow, the component of translational flow produced by approaching (or
-retreating from) the fixated object, which produces pure radial flow on the
-retina; (ii) 'sideways' flow, the remaining component of translational flow,
-which is produced by translation of the optic centre orthogonal to the
-cyclopean line of sight and (iii) 'vergence' flow, rotational flow produced by
-a counter-rotation of the eye in order to maintain fixation. A general flow
-pattern could also include (iv) 'cyclovergence' flow, produced by rotation of
-one eye relative to the other about the line of sight. We consider some
-practical advantages of dividing up flow in this way when an observer fixates
-as they move. As in some previous treatments, we suggest that there are certain
-tasks for which it is sensible to consider 'towards' flow as one component and
-'sideways' + 'vergence' flow as another.
-",0,0,0,0,1,0
-20312,Gain control with A-type potassium current: IA as a switch between divisive and subtractive inhibition,"  Neurons process information by transforming barrages of synaptic inputs into
-spiking activity. Synaptic inhibition suppresses the output firing activity of
-a neuron, and is commonly classified as having a subtractive or divisive effect
-on a neuron's output firing activity. Subtractive inhibition can narrow the
-range of inputs that evoke spiking activity by eliminating responses to
-non-preferred inputs. Divisive inhibition is a form of gain control: it
-modifies firing rates while preserving the range of inputs that evoke firing
-activity. Since these two ""modes"" of inhibition have distinct impacts on neural
-coding, it is important to understand the biophysical mechanisms that
-distinguish these response profiles.
-We use simulations and mathematical analysis of a neuron model to find the
-specific conditions for which inhibitory inputs have subtractive or divisive
-effects. We identify a novel role for the A-type Potassium current (IA). In our
-model, this fast-activating, slowly- inactivating outward current acts as a
-switch between subtractive and divisive inhibition. If IA is strong (large
-maximal conductance) and fast (activates on a time-scale similar to spike
-initiation), then inhibition has a subtractive effect on neural firing. In
-contrast, if IA is weak or insufficiently fast-activating, then inhibition has
-a divisive effect on neural firing. We explain these findings using dynamical
-systems methods to define how a spike threshold condition depends on synaptic
-inputs and IA.
-Our findings suggest that neurons can ""self-regulate"" the gain control
-effects of inhibition via combinations of synaptic plasticity and/or modulation
-of the conductance and kinetics of A-type Potassium channels. This novel role
-for IA would add flexibility to neurons and networks, and may relate to recent
-observations of divisive inhibitory effects on neurons in the nucleus of the
-solitary tract.
-",0,0,0,0,1,0
-20313,Cyclic Datatypes modulo Bisimulation based on Second-Order Algebraic Theories,"  Cyclic data structures, such as cyclic lists, in functional programming are
-tricky to handle because of their cyclicity. This paper presents an
-investigation of categorical, algebraic, and computational foundations of
-cyclic datatypes. Our framework of cyclic datatypes is based on second-order
-algebraic theories of Fiore et al., which give a uniform setting for syntax,
-types, and computation rules for describing and reasoning about cyclic
-datatypes. We extract the ""fold"" computation rules from the categorical
-semantics based on iteration categories of Bloom and Esik. Thereby, the rules
-are correct by construction. We prove strong normalisation using the General
-Schema criterion for second-order computation rules. Rather than the fixed
-point law, we particularly choose Bekic law for computation, which is a key to
-obtaining strong normalisation. We also prove the property of ""Church-Rosser
-modulo bisimulation"" for the computation rules. Combining these results, we
-have a remarkable decidability result of the equational theory of cyclic data
-and fold.
-",1,0,0,0,0,0
-20314,High-order harmonic generation from highly-excited states in acetylene,"  High-order harmonic generation (HHG) from aligned acetylene molecules
-interacting with mid infra-red (IR), linearly polarized laser pulses is studied
-theoretically using a mixed quantum-classical approach in which the electrons
-are described using time-dependent density functional theory while the ions are
-treated classically. We find that for molecules aligned perpendicular to the
-laser polarization axis, HHG arises from the highest-occupied molecular orbital
-(HOMO) while for molecules aligned along the laser polarization axis, HHG is
-dominated by the HOMO-1. In the parallel orientation we observe a double
-plateau with an inner plateau that is produced by ionization from and
-recombination back to an autoionizing state. Two pieces of evidence support
-this idea. Firstly, by choosing a suitably tuned vacuum ultraviolet pump pulse
-that directly excites the autoionizing state we observe a dramatic enhancement
-of all harmonics in the inner plateau. Secondly, in certain circumstances, the
-position of the inner plateau cut-off does not agree with the classical
-three-step model. We show that this discrepancy can be understood in terms of a
-minimum in the dipole recombination matrix element from the continuum to the
-autoionizing state. As far as we are aware, this represents the first
-observation of harmonic enhancement over a wide range of frequencies arising
-from autoionizing states in molecules.
-",0,1,0,0,0,0
-20315,Cellulyzer - Automated analysis and interactive visualization/simulation of select cellular processes,"  Here we report on a set of programs developed at the ZMBH Bio-Imaging
-Facility for tracking real-life images of cellular processes. These programs
-perform 1) automated tracking; 2) quantitative and comparative track analyses
-of different images in different groups; 3) different interactive visualization
-schemes; and 4) interactive realistic simulation of different cellular
-processes for validation and optimal problem-specific adjustment of image
-acquisition parameters (tradeoff between speed, resolution, and quality with
-feedback from the very final results). The collection of programs is primarily
-developed for the common bio-image analysis software ImageJ (as a single Java
-Plugin). Some programs are also available in other languages (C++ and
-Javascript) and may be run simply with a web-browser; even on a low-end Tablet
-or Smartphone. The programs are available at
-this https URL
-",1,1,0,0,0,0
-20316,Vprop: Variational Inference using RMSprop,"  Many computationally-efficient methods for Bayesian deep learning rely on
-continuous optimization algorithms, but the implementation of these methods
-requires significant changes to existing code-bases. In this paper, we propose
-Vprop, a method for Gaussian variational inference that can be implemented with
-two minor changes to the off-the-shelf RMSprop optimizer. Vprop also reduces
-the memory requirements of Black-Box Variational Inference by half. We derive
-Vprop using the conjugate-computation variational inference method, and
-establish its connections to Newton's method, natural-gradient methods, and
-extended Kalman filters. Overall, this paper presents Vprop as a principled,
-computationally-efficient, and easy-to-implement method for Bayesian deep
-learning.
-",1,0,0,1,0,0
-20317,Hidden symmetries in $N$-layer dielectric stacks,"  The optical properties of a multilayer system of dielectric media with
-arbitrary $N$ layers is investigated. Each layer is one of two dielectric
-media, with thickness one-quarter the wavelength of light in that medium,
-corresponding to a central frequency. Using the transfer matrix method, the
-transmittance $T$ is calculated for all possible $2^N$ sequences for small $N$.
-Unexpectedly, it is found that instead of $2^N$ different values of $T$ at the
-central frequency ($T_0$), there are either $(N/2+1)$ or $(N+1)$ discrete
-values of $T_0$ for even or odd $N$, respectively. We explain the high
-degeneracy in the $T_0$ values by defining new symmetry operations that do not
-change $T_0$. Analytical formulae were derived for the $T_0$ values and their
-degeneracy as functions of $N$ and an integer parameter for each sequence we
-call ""charge"". Additionally, the bandwidth of the transmission spectra at $f_0$
-is investigated, revealing some asymptotic behavior at large $N$.
-",0,1,0,0,0,0
-20318,Solvability regions of affinely parameterized quadratic equations,"  Quadratic systems of equations appear in several applications. The results in
-this paper are motivated by quadratic systems of equations that describe
-equilibrium behavior of physical infrastructure networks like the power and gas
-grids. The quadratic systems in infrastructure networks are parameterized- the
-parameters can represent uncertainty (estimation error in resistance/inductance
-of a power transmission line, for example)or controllable decision variables
-(power outputs of generators,for example). It is then of interest to understand
-conditions on the parameters under which the quadratic system is guaranteed to
-have a solution within a specified set (for example, bounds on voltages and
-flows in a power grid). Given nominal values of the parameters at which the
-quadratic system has a solution and the Jacobian of the quadratic system at the
-solution i snon-singular, we develop a general framework to construct convex
-regions around the nominal value such that the system is guaranteed to have a
-solution within a given distance of the nominal solution. We show that several
-results from recen tliterature can be recovered as special cases of our
-framework,and demonstrate our approach on several benchmark power systems.
-",1,0,1,0,0,0
-20319,Mid-price estimation for European corporate bonds: a particle filtering approach,"  In most illiquid markets, there is no obvious proxy for the market price of
-an asset. The European corporate bond market is an archetypal example of such
-an illiquid market where mid-prices can only be estimated with a statistical
-model. In this OTC market, dealers / market makers only have access, indeed, to
-partial information about the market. In real-time, they know the price
-associated with their trades on the dealer-to-dealer (D2D) and dealer-to-client
-(D2C) markets, they know the result of the requests for quotes (RFQ) they
-answered, and they have access to composite prices (e.g., Bloomberg CBBT). This
-paper presents a Bayesian method for estimating the mid-price of corporate
-bonds by using the real-time information available to a dealer. This method
-relies on recent ideas coming from the particle filtering (PF) / sequential
-Monte-Carlo (SMC) literature.
-",0,0,0,0,0,1
-20320,Matching RGB Images to CAD Models for Object Pose Estimation,"  We propose a novel method for 3D object pose estimation in RGB images, which
-does not require pose annotations of objects in images in the training stage.
-We tackle the pose estimation problem by learning how to establish
-correspondences between RGB images and rendered depth images of CAD models.
-During training, our approach only requires textureless CAD models and aligned
-RGB-D frames of a subset of object instances, without explicitly requiring pose
-annotations for the RGB images. We employ a deep quadruplet convolutional
-neural network for joint learning of suitable keypoints and their associated
-descriptors in pairs of rendered depth images which can be matched across
-modalities with aligned RGB-D views. During testing, keypoints are extracted
-from a query RGB image and matched to keypoints extracted from rendered depth
-images, followed by establishing 2D-3D correspondences. The object's pose is
-then estimated using the RANSAC and PnP algorithms. We conduct experiments on
-the recently introduced Pix3D dataset and demonstrate the efficacy of our
-proposed approach in object pose estimation as well as generalization to object
-instances not seen during training.
-",1,0,0,0,0,0
-20321,A Model for Paired-Multinomial Data and Its Application to Analysis of Data on a Taxonomic Tree,"  In human microbiome studies, sequencing reads data are often summarized as
-counts of bacterial taxa at various taxonomic levels specified by a taxonomic
-tree. This paper considers the problem of analyzing two repeated measurements
-of microbiome data from the same subjects. Such data are often collected to
-assess the change of microbial composition after certain treatment, or the
-difference in microbial compositions across body sites. Existing models for
-such count data are limited in modeling the covariance structure of the counts
-and in handling paired multinomial count data. A new probability distribution
-is proposed for paired-multinomial count data, which allows flexible covariance
-structure and can be used to model repeatedly measured multivariate count data.
-Based on this distribution, a test statistic is developed for testing the
-difference in compositions based on paired multinomial count data. The proposed
-test can be applied to the count data observed on a taxonomic tree in order to
-test difference in microbiome compositions and to identify the subtrees with
-different subcompositions. Simulation results indicate that proposed test has
-correct type 1 errors and increased power compared to some commonly used
-methods. An analysis of an upper respiratory tract microbiome data set is used
-to illustrate the proposed methods.
-",0,0,0,1,0,0
-20322,Adhesion-induced Discontinuous Transitions and Classifying Social Networks,"  Transition points mark qualitative changes in the macroscopic properties of
-large complex systems. Explosive transitions, exhibiting properties of both
-continuous and discontinuous phase transitions, have recently been uncovered in
-network growth processes. Real networks not only grow but often also
-restructure, yet common network restructuring processes, such as small world
-rewiring, do not exhibit phase transitions. Here, we uncover a class of
-intrinsically discontinuous transitions emerging in network restructuring
-processes controlled by \emph{adhesion} -- the preference of a chosen link to
-remain connected to its end node. Deriving a master equation for the temporal
-network evolution and working out an analytic solution, we identify genuinely
-discontinuous transitions in non-growing networks, separating qualitatively
-distinct phases with monotonic and with peaked degree distributions.
-Intriguingly, our analysis of heuristic data indicates a separation between the
-same two forms of degree distributions distinguishing abstract from
-face-to-face social networks.
-",1,0,0,0,0,0
-20323,Numerical simulation of polynomial-speed convergence phenomenon,"  We provide a hybrid method that captures the polynomial speed of convergence
-and polynomial speed of mixing for Markov processes. The hybrid method that we
-introduce is based on the coupling technique and renewal theory. We propose to
-replace some estimates in classical results about the ergodicity of Markov
-processes by numerical simulations when the corresponding analytical proof is
-difficult. After that, all remaining conclusions can be derived from rigorous
-analysis. Then we apply our results to two 1D microscopic heat conduction
-models. The mixing rate of these two models are expected to be polynomial but
-very difficult to prove. In both examples, our numerical results match the
-expected polynomial mixing rate well.
-",0,0,1,0,0,0
-20324,Efficient determination of optimised multi-arm multi-stage experimental designs with control of generalised error-rates,"  Primarily motivated by the drug development process, several publications
-have now presented methodology for the design of multi-arm multi-stage
-experiments with normally distributed outcome variables of known variance.
-Here, we extend these past considerations to allow the design of what we refer
-to as an abcd multi-arm multi-stage experiment. We provide a proof of how
-strong control of the a-generalised type-I familywise error-rate can be
-ensured. We then describe how to attain the power to reject at least b out of c
-false hypotheses, which is related to controlling the b-generalised type-II
-familywise error-rate. Following this, we detail how a design can be optimised
-for a scenario in which rejection of any d null hypotheses brings about
-termination of the experiment. We achieve this by proposing a highly
-computationally efficient approach for evaluating the performance of a
-candidate design. Finally, using a real clinical trial as a motivating example,
-we explore the effect of the design's control parameters on the statistical
-operating characteristics.
-",0,0,0,1,0,0
-20325,Application of the Waveform Relaxation Technique to the Co-Simulation of Power Converter Controller and Electrical Circuit Models,"  In this paper we present the co-simulation of a PID class power converter
-controller and an electrical circuit by means of the waveform relaxation
-technique. The simulation of the controller model is characterized by a
-fixed-time stepping scheme reflecting its digital implementation, whereas a
-circuit simulation usually employs an adaptive time stepping scheme in order to
-account for a wide range of time constants within the circuit model. In order
-to maintain the characteristic of both models as well as to facilitate model
-replacement, we treat them separately by means of input/output relations and
-propose an application of a waveform relaxation algorithm. Furthermore, the
-maximum and minimum number of iterations of the proposed algorithm are
-mathematically analyzed. The concept of controller/circuit coupling is
-illustrated by an example of the co-simulation of a PI power converter
-controller and a model of the main dipole circuit of the Large Hadron Collider.
-",1,1,0,0,0,0
-20326,Some Investigations about the Properties of Maximum Likelihood Estimations Based on Lower Record Values for a Sub-Family of the Exponential Family,"  Here, in this paper it has been considered a sub family of exponential
-family. Maximum likelihood estimations (MLE) for the parameter of this family,
-probability density function, and cumulative density function based on a sample
-and based on lower record values have been obtained. It has been considered
-Mean Square Error (MSE) as a criterion for determining which is better in
-different situations. Additionally, it has been proved some theories about the
-relations between MLE based on lower record values and based on a random
-sample. Also, some interesting asymptotically properties for these estimations
-have been shown during some theories.
-",0,0,1,1,0,0
-20327,Some Insights on Synthesizing Optimal Linear Quadratic Controller Using Krotov's Sufficiency Conditions,"  This paper revisits the problem of optimal control law design for linear
-systems using the global optimal control framework introduced by Vadim Krotov.
-Krotov's approach is based on the idea of total decomposition of the original
-optimal control problem (OCP) with respect to time, by an $ad$ $hoc$ choice of
-the so-called Krotov's function or solving function, thereby providing
-sufficient conditions for the existence of global solution based on another
-optimization problem, which is completely equivalent to the original OCP. It is
-well known that the solution of this equivalent optimization problem is
-obtained using an iterative method. In this paper, we propose suitable Krotov's
-functions for linear quadratic OCP and subsequently, show that by imposing
-convexity condition on this equivalent optimization problem, there is no need
-to compute an iterative solution. We also give some key insights into the
-solution procedure of the linear quadratic OCP using the proposed methodology
-in contrast to the celebrated Calculus of Variations (CoV) and
-Hamilton-Jacobi-Bellman (HJB) equation based approach.
-",1,0,0,0,0,0
-20328,Schumann resonance transients and the search for gravitational waves,"  Schumann resonance transients which propagate around the globe can
-potentially generate a correlated background in widely separated gravitational
-wave detectors. We show that due to the distribution of lightning hotspots
-around the globe these transients have characteristic time lags, and this
-feature can be useful to further suppress such a background, especially in
-searches of the stochastic gravitational-wave background. A brief review of the
-corresponding literature on Schumann resonances and lightnings is also given.
-",0,1,0,0,0,0
-20329,Learning to Fly by Crashing,"  How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid
-obstacles? One approach is to use a small dataset collected by human experts:
-however, high capacity learning algorithms tend to overfit when trained with
-little data. An alternative is to use simulation. But the gap between
-simulation and real world remains large especially for perception problems. The
-reason most research avoids using large-scale real data is the fear of crashes!
-In this paper, we propose to bite the bullet and collect a dataset of crashes
-itself! We build a drone whose sole purpose is to crash into objects: it
-samples naive trajectories and crashes into random objects. We crash our drone
-11,500 times to create one of the biggest UAV crash dataset. This dataset
-captures the different ways in which a UAV can crash. We use all this negative
-flying data in conjunction with positive data sampled from the same
-trajectories to learn a simple yet powerful policy for UAV navigation. We show
-that this simple self-supervised model is quite effective in navigating the UAV
-even in extremely cluttered environments with dynamic obstacles including
-humans. For supplementary video see: this https URL
-",1,0,0,0,0,0
-20330,Estimating Graphlet Statistics via Lifting,"  Exploratory analysis over network data is often limited by our ability to
-efficiently calculate graph statistics, which can provide a model-free
-understanding of macroscopic properties of a network. This work introduces a
-framework for estimating the graphlet count - the number of occurrences of a
-small subgraph motif (e.g. a wedge or a triangle) in the network. For massive
-graphs, where accessing the whole graph is not possible, the only viable
-algorithms are those which act locally by making a limited number of vertex
-neighborhood queries.
-We introduce a Monte Carlo sampling technique for graphlet counts, called
-lifting, which can simultaneously sample all graphlets of size up to $k$
-vertices. We outline three variants of lifted graphlet counts: the ordered,
-unordered, and shotgun estimators. We prove that our graphlet count updates are
-unbiased for the true graphlet count, have low correlation between samples, and
-have a controlled variance. We compare the experimental performance of lifted
-graphlet counts to the state-of-the art graphlet sampling procedures: Waddling
-and the pairwise subgraph random walk.
-",0,0,0,1,0,0
-20331,Calibration for the (Computationally-Identifiable) Masses,"  As algorithms increasingly inform and influence decisions made about
-individuals, it becomes increasingly important to address concerns that these
-algorithms might be discriminatory. The output of an algorithm can be
-discriminatory for many reasons, most notably: (1) the data used to train the
-algorithm might be biased (in various ways) to favor certain populations over
-others; (2) the analysis of this training data might inadvertently or
-maliciously introduce biases that are not borne out in the data. This work
-focuses on the latter concern.
-We develop and study multicalbration -- a new measure of algorithmic fairness
-that aims to mitigate concerns about discrimination that is introduced in the
-process of learning a predictor from data. Multicalibration guarantees accurate
-(calibrated) predictions for every subpopulation that can be identified within
-a specified class of computations. We think of the class as being quite rich;
-in particular, it can contain many overlapping subgroups of a protected group.
-We show that in many settings this strong notion of protection from
-discrimination is both attainable and aligned with the goal of obtaining
-accurate predictions. Along the way, we present new algorithms for learning a
-multicalibrated predictor, study the computational complexity of this task, and
-draw new connections to computational learning models such as agnostic
-learning.
-",1,0,0,1,0,0
-20332,The distribution of old stars around the Milky Way's central black hole I: Star counts,"  (abridged) In this paper we revisit the problem of inferring the innermost
-structure of the Milky Way's nuclear star cluster via star counts, to clarify
-whether it displays a core or a cusp around the central black hole. Through
-image stacking and improved PSF fitting we push the completeness limit about
-one magnitude deeper than in previous, comparable work. Contrary to previous
-work, we analyse the stellar density in well-defined magnitude ranges in order
-to be able to constrain stellar masses and ages. The RC and brighter giant
-stars display a core-like surface density profile within a projected radius
-R<0.3 pc of the central black hole, in agreement with previous studies, but
-show a cusp-like surface density distribution at larger R. The surface density
-of the fainter stars can be described well by a single power-law at R<2 pc. The
-cusp-like profile of the faint stars persists even if we take into account the
-possible contamination of stars in this brightness range by young pre-main
-sequence stars. The data are inconsistent with a core-profile for the faint
-stars.Finally, we show that a 3D Nuker law provides a very good description of
-the cluster structure. We conclude that the observed stellar density at the
-Galactic Centre, as it can be inferred with current instruments, is consistent
-with the existence of a stellar cusp around the Milky Way's central black hole,
-Sgr A*. This cusp is well developed inside the influence radius of about 3 pc
-of Sgr A* and can be described by a single three-dimensional power-law with an
-exponent gamma=1.23+-0.05. The apparent lack of RC stars and brighter giants at
-projected distances of R < 0.3 pc (R<8"") of the massive black hole may indicate
-that some mechanism has altered their distribution or intrinsic luminosity.
-",0,1,0,0,0,0
-20333,On infinite order differential operators in fractional viscoelasticity,"  In this paper we discuss some general properties of viscoelastic models
-defined in terms of constitutive equations involving infinitely many
-derivatives (of integer and fractional order). In particular, we consider as a
-working example the recently developed Bessel models of linear viscoelasticiy
-that, for short times, behave like fractional Maxwell bodies of order $1/2$.
-",0,1,1,0,0,0
-20334,Gap structure of FeSe determined by field-angle-resolved specific heat measurements,"  Quasiparticle excitations in FeSe were studied by means of specific heat
-($C$) measurements on a high-quality single crystal under rotating magnetic
-fields. The field dependence of $C$ shows three-stage behavior with different
-slopes, indicating the existence of three gaps ($\Delta_1$, $\Delta_2$, and
-$\Delta_3$). In the low-temperature and low-field region, the azimuthal-angle
-($\phi$) dependence of $C$ shows a four-fold symmetric oscillation with sign
-change. On the other hand, the polar-angle ($\theta$) dependence manifests as
-an anisotropy-inverted two-fold symmetry with unusual shoulder behavior.
-Combining the angle-resolved results and the theoretical calculation, the
-smaller gap $\Delta_1$ is proved to have two vertical-line nodes or gap minima
-along the $k_z$ direction, and is determined to reside on the electron-type
-$\varepsilon$ band. $\Delta_2$ is found to be related to the electron-type
-$\delta$ band, and is isotropic in the $ab$-plane but largely anisotropic out
-of the plane. $\Delta_3$ residing on the hole-type $\alpha$ band shows a small
-out-of-plane anisotropy with a strong Pauli-paramagnetic effect.
-",0,1,0,0,0,0
-20335,Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets,"  This paper presents a model based on Deep Learning algorithms of LSTM and GRU
-for facilitating an anomaly detection in Large Hadron Collider superconducting
-magnets. We used high resolution data available in Post Mortem database to
-train a set of models and chose the best possible set of their
-hyper-parameters. Using Deep Learning approach allowed to examine a vast body
-of data and extract the fragments which require further experts examination and
-are regarded as anomalies. The presented method does not require tedious manual
-threshold setting and operator attention at the stage of the system setup.
-Instead, the automatic approach is proposed, which achieves according to our
-experiments accuracy of 99%. This is reached for the largest dataset of 302 MB
-and the following architecture of the network: single layer LSTM, 128 cells, 20
-epochs of training, look_back=16, look_ahead=128, grid=100 and optimizer Adam.
-All the experiments were run on GPU Nvidia Tesla K80
-",0,1,0,0,0,0
-20336,Electro-Oxidation of Ni42 Steel: A highly Active Bifunctional Electrocatalyst,"  Janus type Water-Splitting Catalysts have attracted highest attention as a
-tool of choice for solar to fuel conversion. AISI Ni 42 steel was upon harsh
-anodization converted in a bifunctional electrocatalyst. Oxygen evolution
-reaction- (OER) and hydrogen evolution reaction (HER) are highly efficiently
-and steadfast catalyzed at pH 7, 13, 14, 14.6 (OER) respectively at pH 0, 1,
-13, 14, 14.6 (HER). The current density taken from long-term OER measurements
-in pH 7 buffer solution upon the electro activated steel at 491 mV
-overpotential was around 4 times higher (4 mA/cm2) in comparison with recently
-developed OER electrocatalysts. The very strong voltage-current behavior of the
-catalyst shown in OER polarization experiments at both pH 7 and at pH 13 were
-even superior to those known for IrO2-RuO2. No degradation of the catalyst was
-detected even when conditions close to standard industrial operations were
-applied to the catalyst. A stable Ni-, Fe- oxide based passivating layer
-sufficiently protected the bare metal for further oxidation. Quantitative
-charge to oxygen- (OER) and charge to hydrogen (HER) conversion was confirmed.
-High resolution XPS spectra showed that most likely gamma-NiO(OH) and FeO(OH)
-are the catalytic active OER and NiO is the catalytic active HER species.
-",0,1,0,0,0,0
-20337,Shape-constrained partial identification of a population mean under unknown probabilities of sample selection,"  A prevailing challenge in the biomedical and social sciences is to estimate a
-population mean from a sample obtained with unknown selection probabilities.
-Using a well-known ratio estimator, Aronow and Lee (2013) proposed a method for
-partial identification of the mean by allowing the unknown selection
-probabilities to vary arbitrarily between two fixed extreme values. In this
-paper, we show how to leverage auxiliary shape constraints on the population
-outcome distribution, such as symmetry or log-concavity, to obtain tighter
-bounds on the population mean. We use this method to estimate the performance
-of Aymara students---an ethnic minority in the north of Chile---in a national
-educational standardized test. We implement this method in the new statistical
-software package scbounds for R.
-",0,0,1,1,0,0
-20338,Warped metrics for location-scale models,"  This paper argues that a class of Riemannian metrics, called warped metrics,
-plays a fundamental role in statistical problems involving location-scale
-models. The paper reports three new results : i) the Rao-Fisher metric of any
-location-scale model is a warped metric, provided that this model satisfies a
-natural invariance condition, ii) the analytic expression of the sectional
-curvature of this metric, iii) the exact analytic solution of the geodesic
-equation of this metric. The paper applies these new results to several
-examples of interest, where it shows that warped metrics turn location-scale
-models into complete Riemannian manifolds of negative sectional curvature. This
-is a very suitable situation for developing algorithms which solve problems of
-classification and on-line estimation. Thus, by revealing the connection
-between warped metrics and location-scale models, the present paper paves the
-way to the introduction of new efficient statistical algorithms.
-",0,0,1,1,0,0
-20339,Detecting Changes in Time Series Data using Volatility Filters,"  This work develops techniques for the sequential detection and location
-estimation of transient changes in the volatility (standard deviation) of time
-series data. In particular, we introduce a class of change detection algorithms
-based on the windowed volatility filter. The first method detects changes by
-employing a convex combination of two such filters with differing window sizes,
-such that the adaptively updated convex weight parameter is then used as an
-indicator for the detection of instantaneous power changes. Moreover, the
-proposed adaptive filtering based method is readily extended to the
-multivariate case by using recent advances in distributed adaptive filters,
-thereby using cooperation between the data channels for more effective
-detection of change points. Furthermore, this work also develops a novel change
-point location estimator based on the differenced output of the volatility
-filter. Finally, the performance of the proposed methods were evaluated on both
-synthetic and real world data.
-",1,0,0,0,0,0
-20340,A minimax and asymptotically optimal algorithm for stochastic bandits,"  We propose the kl-UCB ++ algorithm for regret minimization in stochastic
-bandit models with exponential families of distributions. We prove that it is
-simultaneously asymptotically optimal (in the sense of Lai and Robbins' lower
-bound) and minimax optimal. This is the first algorithm proved to enjoy these
-two properties at the same time. This work thus merges two different lines of
-research with simple and clear proofs.
-",0,0,1,1,0,0
-20341,Eigenfunctions of Periodic Differential Operators Analytic in a Strip,"  Ordinary differential operators with periodic coefficients analytic in a
-strip act on a Hardy-Hilbert space of analytic functions with inner product
-defined by integration over a period on the boundary of the strip. Simple
-examples show that eigenfunctions may form a complete set for a narrow strip,
-but completeness may be lost for a wide strip. Completeness of the
-eigenfunctions in the Hardy-Hilbert space is established for regular second
-order operators with matrix-valued coefficients when the leading coefficient
-satisfies a positive real part condition throughout the strip.
-",0,0,1,0,0,0
-20342,Infinite ergodic index of the ehrenfest wind-tree model,"  The set of all possible configurations of the Ehrenfest wind-tree model
-endowed with the Hausdorff topology is a compact metric space. For a typical
-configuration we show that the wind-tree dynamics has infinite ergodic index in
-almost every direction. In particular some ergodic theorems can be applied to
-show that if we start with a large number of initially parallel particles their
-directions decorrelate as the dynamics evolve answering the question posed by
-the Ehrenfests.
-",0,0,1,0,0,0
-20343,Arrays of strongly-coupled atoms in a one-dimensional waveguide,"  We study the cooperative optical coupling between regularly spaced atoms in a
-one-dimensional waveguide using decompositions to subradiant and superradiant
-collective excitation eigenmodes, direct numerical solutions, and analytical
-transfer-matrix methods. We illustrate how the spectrum of transmitted light
-through the waveguide including the emergence of narrow Fano resonances can be
-understood by the resonance features of the eigenmodes. We describe a method
-based on superradiant and subradiant modes to engineer the optical response of
-the waveguide and to store light. The stopping of light is obtained by
-transferring an atomic excitation to a subradiant collective mode with the zero
-radiative resonance linewidth by controlling the level shift of an atom in the
-waveguide. Moreover, we obtain an exact analytic solution for the transmitted
-light through the waveguide for the case of a regular lattice of atoms and
-provide a simple description how the light transmission may present large
-resonance shifts when the lattice spacing is close, but not exactly equal, to
-half of the wavelength of the light. Experimental imperfections such as
-fluctuations of the positions of the atoms and loss of light from the waveguide
-are easily quantified in the numerical simulations, which produce the natural
-result that the optical response of the atomic array tends toward the response
-of a gas with random atomic positions.
-",0,1,0,0,0,0
-20344,"MEG-Derived Functional Tractography, Results for Normal and Concussed Cohorts","  Measures of neuroelectric activity from each of 18 automatically identified
-white matter tracts were extracted from resting MEG recordings from a
-normative, n=588, and a chronic TBI, traumatic brain injury, n=63, cohort, 60
-of whose TBIs were mild. Activity in the TBI cohort was significantly reduced
-compared with the norms for ten of the tracts, p < 10-6 for each. Significantly
-reduced activity (p < 10-3) was seen in more than one tract in seven mTBI
-individuals and one member of the normative cohort.
-",0,0,0,0,1,0
-20345,Coincidence of magnetic and valence quantum critical points in CeRhIn5 under pressure,"  We present accurate electrical resistivity measurements along the two
-principle crystallographic axes of the pressure-induced heavy-fermion
-superconductor CeRhIn5 up to 5.63 GPa. For both directions, a valence crossover
-line is identified in the p-T plane and the extrapolation of this line to zero
-temperature coincides with the collapse of the magnetic ordering temperature.
-Furthermore, it is found that the p-T phase diagram of CeRhIn5 in the valence
-crossover region is very similar to that of CeCu2Si2. These results point to
-the essential role of Ce-4f electron delocalization in both destroying magnetic
-order and realizing superconductivity in CeRhIn5 under pressure.
-",0,1,0,0,0,0
-20346,Sequential Inverse Approximation of a Regularized Sample Covariance Matrix,"  One of the goals in scaling sequential machine learning methods pertains to
-dealing with high-dimensional data spaces. A key related challenge is that many
-methods heavily depend on obtaining the inverse covariance matrix of the data.
-It is well known that covariance matrix estimation is problematic when the
-number of observations is relatively small compared to the number of variables.
-A common way to tackle this problem is through the use of a shrinkage estimator
-that offers a compromise between the sample covariance matrix and a
-well-conditioned matrix, with the aim of minimizing the mean-squared error. We
-derived sequential update rules to approximate the inverse shrinkage estimator
-of the covariance matrix. The approach paves the way for improved large-scale
-machine learning methods that involve sequential updates.
-",0,0,0,1,0,0
-20347,"Sesqui-arrays, a generalisation of triple arrays","  A triple array is a rectangular array containing letters, each letter
-occurring equally often with no repeats in rows or columns, such that the
-number of letters common to two rows, two columns, or a row and a column are
-(possibly different) non-zero constants. Deleting the condition on the letters
-common to a row and a column gives a double array. We propose the term
-\emph{sesqui-array} for such an array when only the condition on pairs of
-columns is deleted. Thus all triple arrays are sesqui-arrays.
-In this paper we give three constructions for sesqui-arrays. The first gives
-$(n+1)\times n^2$ arrays on $n(n+1)$ letters for $n\geq 2$. (Such an array for
-$n=2$ was found by Bagchi.) This construction uses Latin squares. The second
-uses the \emph{Sylvester graph}, a subgraph of the Hoffman--Singleton graph, to
-build a good block design for $36$ treatments in $42$ blocks of size~$6$, and
-then uses this in a $7\times 36$ sesqui-array for $42$ letters.
-We also give a construction for $K\times(K-1)(K-2)/2$ sesqui-arrays on
-$K(K-1)/2$ letters. This construction uses biplanes. It starts with a block of
-a biplane and produces an array which satisfies the requirements for a
-sesqui-array except possibly that of having no repeated letters in a row or
-column. We show that this condition holds if and only if the \emph{Hussain
-chains} for the selected block contain no $4$-cycles. A sufficient condition
-for the construction to give a triple array is that each Hussain chain is a
-union of $3$-cycles; but this condition is not necessary, and we give a few
-further examples.
-We also discuss the question of which of these arrays provide good designs
-for experiments.
-",0,0,1,1,0,0
-20348,Investigating how well contextual features are captured by bi-directional recurrent neural network models,"  Learning algorithms for natural language processing (NLP) tasks traditionally
-rely on manually defined relevant contextual features. On the other hand,
-neural network models using an only distributional representation of words have
-been successfully applied for several NLP tasks. Such models learn features
-automatically and avoid explicit feature engineering. Across several domains,
-neural models become a natural choice specifically when limited characteristics
-of data are known. However, this flexibility comes at the cost of
-interpretability. In this paper, we define three different methods to
-investigate ability of bi-directional recurrent neural networks (RNNs) in
-capturing contextual features. In particular, we analyze RNNs for sequence
-tagging tasks. We perform a comprehensive analysis on general as well as
-biomedical domain datasets. Our experiments focus on important contextual words
-as features, which can easily be extended to analyze various other feature
-types. We also investigate positional effects of context words and show how the
-developed methods can be used for error analysis.
-",1,0,0,0,0,0
-20349,A Hybridizable Discontinuous Galerkin solver for the Grad-Shafranov equation,"  In axisymmetric fusion reactors, the equilibrium magnetic configuration can
-be expressed in terms of the solution to a semi-linear elliptic equation known
-as the Grad-Shafranov equation, the solution of which determines the poloidal
-component of the magnetic field. When the geometry of the confinement region is
-known, the problem becomes an interior Dirichlet boundary value problem. We
-propose a high order solver based on the Hybridizable Discontinuous Galerkin
-method. The resulting algorithm (1) provides high order of convergence for the
-flux function and its gradient, (2) incorporates a novel method for handling
-piecewise smooth geometries by extension from polygonal meshes, (3) can handle
-geometries with non-smooth boundaries and x-points, and (4) deals with the
-semi-linearity through an accelerated two-grid fixed-point iteration. The
-effectiveness of the algorithm is verified with computations for cases where
-analytic solutions are known on configurations similar to those of actual
-devices (ITER with single null and double null divertor, NSTX, ASDEX upgrade,
-and Field Reversed Configurations).
-",0,1,0,0,0,0
-20350,Sketching Linear Classifiers over Data Streams,"  We introduce a new sub-linear space sketch---the Weight-Median Sketch---for
-learning compressed linear classifiers over data streams while supporting the
-efficient recovery of large-magnitude weights in the model. This enables
-memory-limited execution of several statistical analyses over streams,
-including online feature selection, streaming data explanation, relative
-deltoid detection, and streaming estimation of pointwise mutual information.
-Unlike related sketches that capture the most frequently-occurring features (or
-items) in a data stream, the Weight-Median Sketch captures the features that
-are most discriminative of one stream (or class) compared to another. The
-Weight-Median Sketch adopts the core data structure used in the Count-Sketch,
-but, instead of sketching counts, it captures sketched gradient updates to the
-model parameters. We provide a theoretical analysis that establishes recovery
-guarantees for batch and online learning, and demonstrate empirical
-improvements in memory-accuracy trade-offs over alternative memory-budgeted
-methods, including count-based sketches and feature hashing.
-",1,0,0,1,0,0
-20351,Tamed to compatible when b^(2+) = 1 and b^1 = 2,"  Weiyi Zhang noticed recently a gap in the proof of the main theorem of the
-authors article ""Tamed to compatible: Symplectic forms via moduli space
-integration"" [T] for the case when the symplectic 4-manifold in question has
-first Betti number 2 (and necessarily self-dual second Betti number 1). This
-note explains how to fill this gap.
-",0,0,1,0,0,0
-20352,The Mass Transference Principle: Ten Years On,"  In this article we discuss the Mass Transference Principle due to Beresnevich
-and Velani and survey several generalisations and variants, both deterministic
-and random. Using a Hausdorff measure analogue of the inhomogeneous
-Khintchine-Groshev Theorem, proved recently via an extension of the Mass
-Transference Principle to systems of linear forms, we give an alternative proof
-of a general inhomogeneous Jarn\'{\i}k-Besicovitch Theorem which was originally
-proved by Levesley. We additionally show that without monotonicity Levesley's
-theorem no longer holds in general. Thereafter, we discuss recent advances by
-Wang, Wu and Xu towards mass transference principles where one transitions from
-$\limsup$ sets defined by balls to $\limsup$ sets defined by rectangles (rather
-than from ""balls to balls"" as is the case in the original Mass Transference
-Principle). Furthermore, we consider mass transference principles for
-transitioning from rectangles to rectangles and extend known results using a
-slicing technique. We end this article with a brief survey of random analogues
-of the Mass Transference Principle.
-",0,0,1,0,0,0
-20353,Ground-state properties of Ca$_2$ from narrow line two-color photoassociation,"  By two-color photoassociation of $^{40}$Ca four weakly bound vibrational
-levels in the Ca$_2$ \Xpot ground state potential were measured, using highly
-spin-forbidden transitions to intermediate states of the coupled system
-$^3\Pi_{u}$ and $^3\Sigma^+ _{u}$ near the ${^3P_1}$+${^1S_0}$ asymptote. From
-the observed binding energies, including the least bound state, the long range
-dispersion coefficients $\mathrm{C}_6, \mathrm{C}_8,\mathrm{C}_{10}$ and a
-precise value for the s-wave scattering length of 308.5(50)~$a_0$ were derived.
-From mass scaling we also calculated the corresponding scattering length for
-other natural isotopes. From the Autler-Townes splitting of the spectra, the
-molecular Rabi frequency has been determined as function of the laser intensity
-for one bound-bound transition. The observed value for the Rabi-frequency is in
-good agreement with calculated transition moments based on the derived
-potentials, assuming a dipole moment being independent of internuclear
-separation for the atomic pair model.
-",0,1,0,0,0,0
-20354,On the semisimplicity of the cyclotomic quiver Hecke algebra of type C,"  We provide criteria for the cyclotomic quiver Hecke algebras of type C to be
-semisimple. In the semisimple case, we construct the irreducible modules.
-",0,0,1,0,0,0
-20355,"On the origin of the shallow and ""replica"" bands in FeSe monolayer superconductors","  We compare electronic structures of single FeSe layer films on SrTiO$_3$
-substrate (FeSe/STO) and K$_x$Fe$_{2-y}$Se$_{2}$ superconductors obtained from
-extensive LDA and LDA+DMFT calculations with the results of ARPES experiments.
-It is demonstrated that correlation effects on Fe-3d states are sufficient in
-principle to explain the formation of the shallow electron -- like bands at the
-M(X)-point. However, in FeSe/STO these effects alone are apparently
-insufficient for the simultaneous elimination of the hole -- like Fermi surface
-around the $\Gamma$-point which is not observed in ARPES experiments. Detailed
-comparison of ARPES detected and calculated quasiparticle bands shows
-reasonable agreement between theory and experiment. Analysis of the bands with
-respect to their origin and orbital composition shows, that for FeSe/STO system
-the experimentally observed ""replica"" quasiparticle band at the M-point
-(usually attributed to forward scattering interactions with optical phonons in
-SrTiO$_3$ substrate) can be reasonably understood just as the LDA calculated
-Fe-3d$_{xy}$ band, renormalized by electronic correlations. The only
-manifestation of the substrate reduces to lifting the degeneracy between
-Fe-3d$_{xz}$ and Fe-3d$_{yz}$ bands in the vicinity of M-point. For the case of
-K$_x$Fe$_{2-y}$Se$_{2}$ most bands observed in ARPES can also be understood as
-correlation renormalized Fe-3d LDA calculated bands, with overall semi --
-quantitative agreement with LDA+DMFT calculations.
-",0,1,0,0,0,0
-20356,A Matrix Factorization Approach for Learning Semidefinite-Representable Regularizers,"  Regularization techniques are widely employed in optimization-based
-approaches for solving ill-posed inverse problems in data analysis and
-scientific computing. These methods are based on augmenting the objective with
-a penalty function, which is specified based on prior domain-specific expertise
-to induce a desired structure in the solution. We consider the problem of
-learning suitable regularization functions from data in settings in which
-precise domain knowledge is not directly available. Previous work under the
-title of `dictionary learning' or `sparse coding' may be viewed as learning a
-regularization function that can be computed via linear programming. We
-describe generalizations of these methods to learn regularizers that can be
-computed and optimized via semidefinite programming. Our framework for learning
-such semidefinite regularizers is based on obtaining structured factorizations
-of data matrices, and our algorithmic approach for computing these
-factorizations combines recent techniques for rank minimization problems along
-with an operator analog of Sinkhorn scaling. Under suitable conditions on the
-input data, our algorithm provides a locally linearly convergent method for
-identifying the correct regularizer that promotes the type of structure
-contained in the data. Our analysis is based on the stability properties of
-Operator Sinkhorn scaling and their relation to geometric aspects of
-determinantal varieties (in particular tangent spaces with respect to these
-varieties). The regularizers obtained using our framework can be employed
-effectively in semidefinite programming relaxations for solving inverse
-problems.
-",1,0,1,1,0,0
-20357,A Bootstrap Method for Goodness of Fit and Model Selection with a Single Observed Network,"  Network models are applied in numerous domains where data can be represented
-as a system of interactions among pairs of actors. While both statistical and
-mechanistic network models are increasingly capable of capturing various
-dependencies amongst these actors, these dependencies imply the lack of
-independence. This poses statistical challenges for analyzing such data,
-especially when there is only a single observed network, and often leads to
-intractable likelihoods regardless of the modeling paradigm, which limit the
-application of existing statistical methods for networks. We explore a
-subsampling bootstrap procedure to serve as the basis for goodness of fit and
-model selection with a single observed network that circumvents the
-intractability of such likelihoods. Our approach is based on flexible
-resampling distributions formed from the single observed network, allowing for
-finer and higher dimensional comparisons than simply point estimates of
-quantities of interest. We include worked examples for model selection, with
-simulation, and assessment of goodness of fit, with duplication-divergence
-model fits for yeast (S.cerevisiae) protein-protein interaction data from the
-literature. The proposed procedure produces a flexible resampling distribution
-that can be based on any statistics of one's choosing and can be employed
-regardless of choice of model.
-",0,0,0,1,0,0
-20358,Nonlinear Acceleration of Stochastic Algorithms,"  Extrapolation methods use the last few iterates of an optimization algorithm
-to produce a better estimate of the optimum. They were shown to achieve optimal
-convergence rates in a deterministic setting using simple gradient iterates.
-Here, we study extrapolation methods in a stochastic setting, where the
-iterates are produced by either a simple or an accelerated stochastic gradient
-algorithm. We first derive convergence bounds for arbitrary, potentially biased
-perturbations, then produce asymptotic bounds using the ratio between the
-variance of the noise and the accuracy of the current point. Finally, we apply
-this acceleration technique to stochastic algorithms such as SGD, SAGA, SVRG
-and Katyusha in different settings, and show significant performance gains.
-",0,0,1,0,0,0
-20359,Motion of Massive Particles in Rindler Space and the Problem of Fall at the Centre,"  The motion of a massive particle in Rindler space has been studied and
-obtained the geodesics of motion. The orbits in Rindler space are found to be
-quite different from that of Schwarzschild case. The paths are not like the
-Perihelion Precession type. Further we have set up the non-relativistic
-Schrodinger equation for the particle in the quantum mechanical scenario in
-presence of background constant gravitational field and investigated the
-problem of fall of the particle at the center. This problem is also treated
-classically. Unlike the conventional scenario, here the fall occurs at the
-surface of a sphere of unit radius.
-",0,1,0,0,0,0
-20360,Measuring Sample Quality with Kernels,"  Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid
-sampling at the cost of more biased inference. Since standard MCMC diagnostics
-fail to detect these biases, researchers have developed computable Stein
-discrepancy measures that provably determine the convergence of a sample to its
-target distribution. This approach was recently combined with the theory of
-reproducing kernels to define a closed-form kernel Stein discrepancy (KSD)
-computable by summing kernel evaluations across pairs of sample points. We
-develop a theory of weak convergence for KSDs based on Stein's method,
-demonstrate that commonly used KSDs fail to detect non-convergence even for
-Gaussian targets, and show that kernels with slowly decaying tails provably
-determine convergence for a large class of target distributions. The resulting
-convergence-determining KSDs are suitable for comparing biased, exact, and
-deterministic sample sequences and simpler to compute and parallelize than
-alternative Stein discrepancies. We use our tools to compare biased samplers,
-select sampler hyperparameters, and improve upon existing KSD approaches to
-one-sample hypothesis testing and sample quality improvement.
-",1,0,0,1,0,0
-20361,Diversity of preferences can increase collective welfare in sequential exploration problems,"  In search engines, online marketplaces and other human-computer interfaces
-large collectives of individuals sequentially interact with numerous
-alternatives of varying quality. In these contexts, trial and error
-(exploration) is crucial for uncovering novel high-quality items or solutions,
-but entails a high cost for individual users. Self-interested decision makers,
-are often better off imitating the choices of individuals who have already
-incurred the costs of exploration. Although imitation makes sense at the
-individual level, it deprives the group of additional information that could
-have been gleaned by individual explorers. In this paper we show that in such
-problems, preference diversity can function as a welfare enhancing mechanism.
-It leads to a consistent increase in the quality of the consumed alternatives
-that outweighs the increased cost of search for the users.
-",1,0,0,0,0,0
-20362,Epidemic spread in interconnected directed networks,"  In the real world, many complex systems interact with other systems. In
-addition, the intra- or inter-systems for the spread of information about
-infectious diseases and the transmission of infectious diseases are often not
-random, but with direction. Hence, in this paper, we build epidemic model based
-on an interconnected directed network, which can be considered as the
-generalization of undirected networks and bipartite networks. By using the
-mean-field approach, we establish the Susceptible-Infectious-Susceptible model
-on this network. We theoretically analyze the model, and obtain the basic
-reproduction number, which is also the generalization of the critical number
-corresponding to undirected or bipartite networks. And we prove the global
-stability of disease-free and endemic equilibria via the basic reproduction
-number as a forward bifurcation parameter. We also give a condition for
-epidemic prevalence only on a single subnetwork. Furthermore, we carry out
-numerical simulations, and find that the independence between each node's in-
-and out-degrees greatly reduce the impact of the network's topological
-structure on disease spread.
-",0,1,0,0,0,0
-20363,Controllability and maximum matchings of complex networks,"  Previously, the controllability problem of a linear time-invariant dynamical
-system was mapped to the maximum matching (MM) problem on the bipartite
-representation of the underlying directed graph, and the sizes of MMs on random
-bipartite graphs were calculated analytically with the cavity method at zero
-temperature limit. Here we present an alternative theory to estimate MM sizes
-based on the core percolation theory and the perfect matching of cores. Our
-theory is much more simplified and easily interpreted, and can estimate MM
-sizes on random graphs with or without symmetry between out- and in-degree
-distributions. Our result helps to illuminate the fundamental connection
-between the controllability problem and the underlying structure of complex
-systems.
-",1,0,0,0,0,0
-20364,"Dimers, crystals and quantum Kostka numbers","  We relate the counting of honeycomb dimer configurations on the cylinder to
-the counting of certain vertices in Kirillov-Reshetikhin crystal graphs. We
-show that these dimer configurations yield the quantum Kostka numbers of the
-small quantum cohomology ring of the Grassmannian, i.e. the expansion
-coefficients when multiplying a Schubert class repeatedly with different Chern
-classes. This allows one to derive sum rules for Gromov-Witten invariants.
-",0,0,1,0,0,0
-20365,Generalized Task-Parameterized Skill Learning,"  Programming by demonstration has recently gained much attention due to its
-user-friendly and natural way to transfer human skills to robots. In order to
-facilitate the learning of multiple demonstrations and meanwhile generalize to
-new situations, a task-parameterized Gaussian mixture model (TP-GMM) has been
-recently developed. This model has achieved reliable performance in areas such
-as human-robot collaboration and dual-arm manipulation. However, the crucial
-task frames and associated parameters in this learning framework are often set
-by the human teacher, which renders three problems that have not been addressed
-yet: (i) task frames are treated equally, without considering their individual
-importance, (ii) task parameters are defined without taking into account
-additional task constraints, such as robot joint limits and motion smoothness,
-and (iii) a fixed number of task frames are pre-defined regardless of whether
-some of them may be redundant or even irrelevant for the task at hand. In this
-paper, we generalize the task-parameterized learning by addressing the
-aforementioned problems. Moreover, we provide a novel learning perspective
-which allows the robot to refine and adapt previously learned skills in a low
-dimensional space. Several examples are studied in both simulated and real
-robotic systems, showing the applicability of our approach.
-",1,0,0,0,0,0
-20366,Nonparametric Bayesian volatility learning under microstructure noise,"  Aiming at financial applications, we study the problem of learning the
-volatility under market microstructure noise. Specifically, we consider noisy
-discrete time observations from a stochastic differential equation and develop
-a novel computational method to learn the diffusion coefficient of the
-equation. We take a nonparametric Bayesian approach, where we model the
-volatility function a priori as piecewise constant. Its prior is specified via
-the inverse Gamma Markov chain. Sampling from the posterior is accomplished by
-incorporating the Forward Filtering Backward Simulation algorithm in the Gibbs
-sampler. Good performance of the method is demonstrated on two representative
-synthetic data examples. Finally, we apply the method on the EUR/USD exchange
-rate dataset.
-",0,0,0,0,0,1
-20367,Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals,"  Supervised learning based methods for source localization, being data driven,
-can be adapted to different acoustic conditions via training and have been
-shown to be robust to adverse acoustic environments. In this paper, a
-convolutional neural network (CNN) based supervised learning method for
-estimating the direction-of-arrival (DOA) of multiple speakers is proposed.
-Multi-speaker DOA estimation is formulated as a multi-class multi-label
-classification problem, where the assignment of each DOA label to the input
-feature is treated as a separate binary classification problem. The phase
-component of the short-time Fourier transform (STFT) coefficients of the
-received microphone signals are directly fed into the CNN, and the features for
-DOA estimation are learnt during training. Utilizing the assumption of disjoint
-speaker activity in the STFT domain, a novel method is proposed to train the
-CNN with synthesized noise signals. Through experimental evaluation with both
-simulated and measured acoustic impulse responses, the ability of the proposed
-DOA estimation approach to adapt to unseen acoustic conditions and its
-robustness to unseen noise type is demonstrated. Through additional empirical
-investigation, it is also shown that with an array of M microphones our
-proposed framework yields the best localization performance with M-1
-convolution layers. The ability of the proposed method to accurately localize
-speakers in a dynamic acoustic scenario with varying number of sources is also
-shown.
-",1,0,0,0,0,0
-20368,Follow-up of eROSITA and Euclid Galaxy Clusters with XMM-Newton,"  A revolution in galaxy cluster science is only a few years away. The survey
-machines eROSITA and Euclid will provide cluster samples of never-before-seen
-statistical quality. XMM-Newton will be the key instrument to exploit these
-rich datasets in terms of detailed follow-up of the cluster hot gas content,
-systematically characterizing sub-samples as well as exotic new objects.
-",0,1,0,0,0,0
-20369,Effect of viscosity ratio on the self-sustained instabilities in planar immiscible jets,"  Previous studies have shown that intermediate surface tension has a
-counterintuitive destabilizing effect on 2-phase planar jets. Here, the
-transition process in confined 2D jets of two fluids with varying viscosity
-ratio is investigated using DNS. Neutral curves for persistent oscillations are
-found by recording the norm of the velocity residuals in DNS for over 1000
-nondimensional time units, or until the signal has reached a constant level in
-a logarithmic scale - either a converged steady state, or a ""statistically
-steady"" oscillatory state. Oscillatory final states are found for all viscosity
-ratios (0.1-10). For uniform viscosity (m=1), the first bifurcation is through
-a surface tension-driven global instability. For low viscosity of the outer
-fluid, there is a mode competition between a steady asymmetric Coanda-type
-attachment mode and the surface tension-induced mode. At moderate surface
-tension, the Coanda-type attachment dominates and eventually triggers
-time-dependent convective bursts. At high surface tension, the surface
-tension-dominated mode dominates. For high viscosity of the outer fluid,
-persistent oscillations appear due to a strong convective instability. Finally,
-the m=1 jet remains unstable far from the inlet when the shear profile is
-nearly constant. Comparing this to a parallel Couette flow (without inflection
-points), we show that in both flows, a hidden interfacial mode brought out by
-surface tension becomes temporally and absolutely unstable in an intermediate
-Weber and Reynolds regime. An energy analysis of the Couette setup shows that
-surface tension, although dissipative, induces a velocity field near the
-interface which extracts energy from the flow through a viscous mechanism. This
-study highlights the rich dynamics of immiscible planar uniform-density jets,
-where several self-sustained and convective mechanisms compete depending on the
-exact parameters.
-",0,1,0,0,0,0
-20370,On the Effects of Batch and Weight Normalization in Generative Adversarial Networks,"  Generative adversarial networks (GANs) are highly effective unsupervised
-learning frameworks that can generate very sharp data, even for data such as
-images with complex, highly multimodal distributions. However GANs are known to
-be very hard to train, suffering from problems such as mode collapse and
-disturbing visual artifacts. Batch normalization (BN) techniques have been
-introduced to address the training. Though BN accelerates the training in the
-beginning, our experiments show that the use of BN can be unstable and
-negatively impact the quality of the trained model. The evaluation of BN and
-numerous other recent schemes for improving GAN training is hindered by the
-lack of an effective objective quality measure for GAN models. To address these
-issues, we first introduce a weight normalization (WN) approach for GAN
-training that significantly improves the stability, efficiency and the quality
-of the generated samples. To allow a methodical evaluation, we introduce
-squared Euclidean reconstruction error on a test set as a new objective
-measure, to assess training performance in terms of speed, stability, and
-quality of generated samples. Our experiments with a standard DCGAN
-architecture on commonly used datasets (CelebA, LSUN bedroom, and CIFAR-10)
-indicate that training using WN is generally superior to BN for GANs, achieving
-10% lower mean squared loss for reconstruction and significantly better
-qualitative results than BN. We further demonstrate the stability of WN on a
-21-layer ResNet trained with the CelebA data set. The code for this paper is
-available at this https URL
-",1,0,0,1,0,0
-20371,A noise-immune cavity-assisted non-destructive detection for an optical lattice clock in the quantum regime,"  We present and implement a non-destructive detection scheme for the
-transition probability readout of an optical lattice clock. The scheme relies
-on a differential heterodyne measurement of the dispersive properties of
-lattice-trapped atoms enhanced by a high finesse cavity. By design, this scheme
-offers a 1st order rejection of the technical noise sources, an enhanced
-signal-to-noise ratio, and an homogeneous atom-cavity coupling. We
-theoretically show that this scheme is optimal with respect to the photon shot
-noise limit. We experimentally realize this detection scheme in an operational
-strontium optical lattice clock. The resolution is on the order of a few atoms
-with a photon scattering rate low enough to keep the atoms trapped after
-detection. This scheme opens the door to various different interrogations
-protocols, which reduce the frequency instability, including atom recycling,
-zero-dead time clocks with a fast repetition rate, and sub quantum projection
-noise frequency stability.
-",0,1,0,0,0,0
-20372,Remarks on planar edge-chromatic critical graphs,"  The only open case of Vizing's conjecture that every planar graph with
-$\Delta\geq 6$ is a class 1 graph is $\Delta = 6$. We give a short proof of the
-following statement: there is no 6-critical plane graph $G$, such that every
-vertex of $G$ is incident to at most three 3-faces. A stronger statement
-without restriction to critical graphs is stated in \cite{Wang_Xu_2013}.
-However, the proof given there works only for critical graphs. Furthermore, we
-show that every 5-critical plane graph has a 3-face which is adjacent to a
-$k$-face $(k\in \{3,4\})$.
-For $\Delta = 5$ our result gives insights into the structure of planar
-$5$-critical graphs, and the result for $\Delta=6$ gives support for the truth
-of Vizing's planar graph conjecture.
-",0,0,1,0,0,0
-20373,The LOFAR window on star-forming galaxies and AGN - curved radio SEDs and IR-radio correlation at $0 < z < 2.5$,"  We present a study of the low-frequency radio properties of star forming (SF)
-galaxies and active galactic nuclei (AGN) up to redshift $z=2.5$. The new
-spectral window probed by the Low Frequency Array (LOFAR) allows us to
-reconstruct the radio continuum emission from 150 MHz to 1.4 GHz to an
-unprecedented depth for a radio-selected sample of $1542$ galaxies in $\sim 7~
-\rm{deg}^2$ of the LOFAR Boötes field. Using the extensive multi-wavelength
-dataset available in Boötes and detailed modelling of the FIR to UV spectral
-energy distribution (SED), we are able to separate the star-formation (N=758)
-and the AGN (N=784) dominated populations. We study the shape of the radio SEDs
-and their evolution across cosmic time and find significant differences in the
-spectral curvature between the SF galaxy and AGN populations. While the radio
-spectra of SF galaxies exhibit a weak but statistically significant flattening,
-AGN SEDs show a clear trend to become steeper towards lower frequencies. No
-evolution of the spectral curvature as a function of redshift is found for SF
-galaxies or AGN. We investigate the redshift evolution of the infrared-radio
-correlation (IRC) for SF galaxies and find that the ratio of total infrared to
-1.4 GHz radio luminosities decreases with increasing redshift: $ q_{\rm 1.4GHz}
-= (2.45 \pm 0.04) \times (1+z)^{-0.15 \pm 0.03} $. Similarly, $q_{\rm 150MHz}$
-shows a redshift evolution following $ q_{\rm 150GHz} = (1.72 \pm 0.04) \times
-(1+z)^{-0.22 \pm 0.05}$. Calibration of the 150 MHz radio luminosity as a star
-formation rate tracer suggests that a single power-law extrapolation from
-$q_{\rm 1.4GHz}$ is not an accurate approximation at all redshifts.
-",0,1,0,0,0,0
-20374,Interplay between the Inverse Scattering Method and Fokas's Unified Transform with an Application,"  It is known that the initial-boundary value problem for certain integrable
-partial differential equations (PDEs) on the half-line with integrable boundary
-conditions can be mapped to a special case of the Inverse Scattering Method
-(ISM) on the full-line. This can also be established within the so-called
-Unified Transform (UT) for initial-boundary value problems with linearizable
-boundary conditions. In this paper, we show a converse to this statement within
-the Ablowitz-Kaup-Newell-Segur (AKNS) scheme: the ISM on the full-line can be
-mapped to an initial-boundary value problem with linearizable boundary
-conditions. To achieve this, we need a matrix version of the UT that was
-introduced by the author to study integrable PDEs on star-graphs. As an
-application of the result, we show that the new, nonlocal reduction of the AKNS
-scheme introduced by Ablowitz and Musslimani to obtain the nonlocal Nonlinear
-Schrödinger (NLS) equation can be recast as an old, local reduction, thus
-putting the nonlocal NLS and the NLS equations on equal footing from the point
-of view of the reduction group theory of Mikhailov.
-",0,1,1,0,0,0
-20375,GNC of the SphereX Robot for Extreme Environment Exploration on Mars,"  Wheeled ground robots are limited from exploring extreme environments such as
-caves, lava tubes and skylights. Small robots that can utilize unconventional
-mobility through hopping, flying or rolling can overcome these limitations.
-Mul-tiple robots operating as a team offer significant benefits over a single
-large ro-bot, as they are not prone to single-point failure, enable distributed
-command and control and enable execution of tasks in parallel. These robots can
-complement large rovers and landers, helping to explore inaccessible sites,
-obtaining samples and for planning future exploration missions. Our robots, the
-SphereX, are 3-kg in mass, spherical and contain computers equivalent to
-current smartphones. They contain an array of guidance, navigation and control
-sensors and electronics. SphereX contains room for a 1-kg science payload,
-including for sample return. Our work in this field has recognized the need for
-miniaturized chemical mobility systems that provide power and propulsion. Our
-research explored the use of miniature rockets, including solid rockets,
-bi-propellants including RP1/hydrogen-peroxide and
-polyurethane/ammonium-perchlorate. These propulsion options provide maximum
-flight times of 10 minutes on Mars. Flying, especially hovering consumes
-significant fuel; hence, we have been developing our robots to perform
-ballistic hops that enable the robots to travel efficiently over long
-distances. Techniques are being developed to enable mid-course correction
-during a ballistic hop. Using multiple cameras, it is possible to reconstitute
-an image scene from motion blur. Hence our approach is to enable photo mapping
-as the robots travel on a ballistic hop. The same images would also be used for
-navigation and path planning. Using our proposed design approach, we are
-developing low-cost methods for surface exploration of planetary bodies using a
-network of small robots.
-",1,1,0,0,0,0
-20376,Stable Signatures for Dynamic Graphs and Dynamic Metric Spaces via Zigzag Persistence,"  When studying flocking/swarming behaviors in animals one is interested in
-quantifying and comparing the dynamics of the clustering induced by the
-coalescence and disbanding of animals in different groups. In a similar vein,
-studying the dynamics of social networks leads to the problem of characterizing
-groups/communities as they form and disperse throughout time.
-Motivated by this, we study the problem of obtaining persistent homology
-based summaries of time-dependent data. Given a finite dynamic graph (DG), we
-first construct a zigzag persistence module arising from linearizing the
-dynamic transitive graph naturally induced from the input DG. Based on standard
-results, we then obtain a persistence diagram or barcode from this zigzag
-persistence module. We prove that these barcodes are stable under perturbations
-in the input DG under a suitable distance between DGs that we identify.
-More precisely, our stability theorem can be interpreted as providing a lower
-bound for the distance between DGs. Since it relies on barcodes, and their
-bottleneck distance, this lower bound can be computed in polynomial time from
-the DG inputs.
-Since DGs can be given rise by applying the Rips functor (with a fixed
-threshold) to dynamic metric spaces, we are also able to derive related stable
-invariants for these richer class of dynamic objects.
-Along the way, we propose a summarization of dynamic graphs that captures
-their time-dependent clustering features which we call formigrams. These
-set-valued functions generalize the notion of dendrogram, a prevalent tool for
-hierarchical clustering. In order to elucidate the relationship between our
-distance between two DGs and the bottleneck distance between their associated
-barcodes, we exploit recent advances in the stability of zigzag persistence due
-to Botnan and Lesnick, and to Bjerkevik.
-",0,0,1,0,0,0
-20377,A Data-driven Approach Towards Human-robot Collaborative Problem Solving in a Shared Space,"  We are developing a system for human-robot communication that enables people
-to communicate with robots in a natural way and is focused on solving problems
-in a shared space. Our strategy for developing this system is fundamentally
-data-driven: we use data from multiple input sources and train key components
-with various machine learning techniques. We developed a web application that
-is collecting data on how two humans communicate to accomplish a task, as well
-as a mobile laboratory that is instrumented to collect data on how two humans
-communicate to accomplish a task in a physically shared space. The data from
-these systems will be used to train and fine-tune the second stage of our
-system, in which the robot will be simulated through software. A physical robot
-will be used in the final stage of our project. We describe these instruments,
-a test-suite and performance metrics designed to evaluate and automate the data
-gathering process as well as evaluate an initial data set.
-",1,0,0,0,0,0
-20378,Discursive Landscapes and Unsupervised Topic Modeling in IR: A Validation of Text-As-Data Approaches through a New Corpus of UN Security Council Speeches on Afghanistan,"  The recent turn towards quantitative text-as-data approaches in IR brought
-new ways to study the discursive landscape of world politics. Here seen as
-complementary to qualitative approaches, quantitative assessments have the
-advantage of being able to order and make comprehensible vast amounts of text.
-However, the validity of unsupervised methods applied to the types of text
-available in large quantities needs to be established before they can speak to
-other studies relying on text and discourse as data. In this paper, we
-introduce a new text corpus of United Nations Security Council (UNSC) speeches
-on Afghanistan between 2001 and 2017; we study this corpus through unsupervised
-topic modeling (LDA) with the central aim to validate the topic categories that
-the LDA identifies; and we discuss the added value, and complementarity, of
-quantitative text-as-data approaches. We set-up two tests using mixed- method
-approaches. Firstly, we evaluate the identified topics by assessing whether
-they conform with previous qualitative work on the development of the situation
-in Afghanistan. Secondly, we use network analysis to study the underlying
-social structures of what we will call 'speaker-topic relations' to see whether
-they correspondent to know divisions and coalitions in the UNSC. In both cases
-we find that the unsupervised LDA indeed provides valid and valuable outputs.
-In addition, the mixed-method approaches themselves reveal interesting patterns
-deserving future qualitative research. Amongst these are the coalition and
-dynamics around the 'women and human rights' topic as part of the UNSC debates
-on Afghanistan.
-",1,0,0,0,0,0
-20379,Lattice Gas with Molecular Dynamics Collision Operator,"  We introduce a lattice gas implementation that is based on coarse-graining a
-Molecular Dynamics (MD) simulation. Such a lattice gas is similar to standard
-lattice gases, but its collision operator is informed by an underlying MD
-simulation. This can be considered an optimal lattice gas implementation
-because it allows for the representation of any system that can be simulated
-with MD. We show here that equilibrium behavior of the popular lattice
-Boltzmann algorithm is consistent with this optimal lattice gas. This
-comparison allows us to make a more accurate identification of the expressions
-for temperature and pressure in lattice Boltzmann simulations which turn out to
-be related not only to the physical temperature and pressure but also to the
-lattice discretization. We show that for any spatial discretization we need to
-choose a particular temporal discretization to recover the lattice Boltzmann
-equilibrium.
-",0,1,0,0,0,0
-20380,From dynamical systems with time-varying delay to circle maps and Koopmanism,"  In the present paper we investigate the influence of the retarded access by a
-time-varying delay on the dynamics of delay systems. We show that there are two
-universality classes of delays, which lead to fundamental differences in
-dynamical quantities such as the Lyapunov spectrum. Therefore we introduce an
-operator theoretic framework, where the solution operator of the delay system
-is decomposed into the Koopman operator describing the delay access and an
-operator similar to the solution operator known from systems with constant
-delay. The Koopman operator corresponds to an iterated map, called access map,
-which is defined by the iteration of the delayed argument of the delay
-equation. The dynamics of this one-dimensional iterated map determines the
-universality classes of the infinite-dimensional state dynamics governed by the
-delay differential equation. In this way, we connect the theory of time-delay
-systems with the theory of circle maps and the framework of the Koopman
-operator. In the present paper we extend our previous work [Otto, Müller, and
-Radons, Phys. Rev. Lett. 118, 044104 (2017)], by elaborating the mathematical
-details and presenting further results also on the Lyapunov vectors.
-",0,1,1,0,0,0
-20381,Engineering Frequency-dependent Superfluidity in Bose-Fermi Mixtures,"  Unconventional superconductivity or superfluidity are among the most exciting
-and fascinating quantum states in condensed matter physics. Usually these
-states are characterized by non-trivial spatial symmetry of the pairing order
-parameter, such as in $^{3}He$ and high-$T_{c}$ cuprates. Besides spatial
-dependence the order parameter could have unconventional frequency dependence,
-which is also allowed by Fermi-Dirac statistics. For instance, odd-frequency
-pairing is an exciting paradigm when discussing exotic superfluidity or
-superconductivity and is yet to be realized in the experiments. In this paper
-we propose a symmetry-based method of controlling frequency dependence of the
-pairing order parameter via manipulating the inversion symmetry of the system.
-First, a toy model is introduced to illustrate that frequency dependence of the
-order parameter can be adjusted by controlling the inversion symmetry of the
-system. Second, taking advantage of the recent rapid developments of shaken
-optical lattices in ultracold gases, we propose a Bose-Fermi mixture to realize
-such frequency dependent superfluids. The key idea is introducing the
-frequency-dependent attraction between Fermions mediated by Bogoliubov phonons
-with asymmetric dispersion. Our proposal should pave an alternative way for
-exploring frequency-dependent superconductors or superfluids with cold atoms.
-",0,1,0,0,0,0
-20382,In-Silico Proportional-Integral Moment Control of Stochastic Reaction Networks with Applications to Gene Expression (with Dimerization),"  The problem of controlling the mean and the variance of a species of interest
-in a simple gene expression is addressed. It is shown that the protein mean
-level can be globally and robustly tracked to any desired value using a simple
-PI controller that satisfies certain sufficient conditions. Controlling both
-the mean and variance however requires an additional control input, e.g. the
-mRNA degradation rate, and local robust tracking of mean and variance is proved
-to be achievable using multivariable PI control, provided that the reference
-point satisfies necessary conditions imposed by the system. Even more
-importantly, it is shown that there exist PI controllers that locally, robustly
-and simultaneously stabilize all the equilibrium points inside the admissible
-region. The results are then extended to the mean control of a gene expression
-with protein dimerization. It is shown that the moment closure problem can be
-circumvented without invoking any moment closure technique. Local stabilization
-and convergence of the average dimer population to any desired reference value
-is ensured using a pure integral control law. Explicit bounds on the controller
-gain are provided and shown to be valid for any reference value. As a
-byproduct, an explicit upper-bound of the variance of the monomer species,
-acting on the system as unknown input due to the moment openness, is obtained.
-The results are illustrated by simulation.
-",1,0,0,0,1,0
-20383,Big enterprise registration data imputation: Supporting spatiotemporal analysis of industries in China,"  Big, fine-grained enterprise registration data that includes time and
-location information enables us to quantitatively analyze, visualize, and
-understand the patterns of industries at multiple scales across time and space.
-However, data quality issues like incompleteness and ambiguity, hinder such
-analysis and application. These issues become more challenging when the volume
-of data is immense and constantly growing. High Performance Computing (HPC)
-frameworks can tackle big data computational issues, but few studies have
-systematically investigated imputation methods for enterprise registration data
-in this type of computing environment. In this paper, we propose a big data
-imputation workflow based on Apache Spark as well as a bare-metal computing
-cluster, to impute enterprise registration data. We integrated external data
-sources, employed Natural Language Processing (NLP), and compared several
-machine-learning methods to address incompleteness and ambiguity problems found
-in enterprise registration data. Experimental results illustrate the
-feasibility, efficiency, and scalability of the proposed HPC-based imputation
-framework, which also provides a reference for other big georeferenced text
-data processing. Using these imputation results, we visualize and briefly
-discuss the spatiotemporal distribution of industries in China, demonstrating
-the potential applications of such data when quality issues are resolved.
-",1,0,0,0,0,0
-20384,Fast Simulation of Vehicles with Non-deformable Tracks,"  This paper presents a novel technique that allows for both computationally
-fast and sufficiently plausible simulation of vehicles with non-deformable
-tracks. The method is based on an effect we have called Contact Surface Motion.
-A comparison with several other methods for simulation of tracked vehicle
-dynamics is presented with the aim to evaluate methods that are available
-off-the-shelf or with minimum effort in general-purpose robotics simulators.
-The proposed method is implemented as a plugin for the open-source
-physics-based simulator Gazebo using the Open Dynamics Engine.
-",1,0,0,0,0,0
-20385,Persistent Hidden States and Nonlinear Transformation for Long Short-Term Memory,"  Recurrent neural networks (RNNs) have been drawing much attention with great
-success in many applications like speech recognition and neural machine
-translation. Long short-term memory (LSTM) is one of the most popular RNN units
-in deep learning applications. LSTM transforms the input and the previous
-hidden states to the next states with the affine transformation, multiplication
-operations and a nonlinear activation function, which makes a good data
-representation for a given task. The affine transformation includes rotation
-and reflection, which change the semantic or syntactic information of
-dimensions in the hidden states. However, considering that a model interprets
-the output sequence of LSTM over the whole input sequence, the dimensions of
-the states need to keep the same type of semantic or syntactic information
-regardless of the location in the sequence. In this paper, we propose a simple
-variant of the LSTM unit, persistent recurrent unit (PRU), where each dimension
-of hidden states keeps persistent information across time, so that the space
-keeps the same meaning over the whole sequence. In addition, to improve the
-nonlinear transformation power, we add a feedforward layer in the PRU
-structure. In the experiment, we evaluate our proposed methods with three
-different tasks, and the results confirm that our methods have better
-performance than the conventional LSTM.
-",0,0,0,1,0,0
-20386,Satellite Image-based Localization via Learned Embeddings,"  We propose a vision-based method that localizes a ground vehicle using
-publicly available satellite imagery as the only prior knowledge of the
-environment. Our approach takes as input a sequence of ground-level images
-acquired by the vehicle as it navigates, and outputs an estimate of the
-vehicle's pose relative to a georeferenced satellite image. We overcome the
-significant viewpoint and appearance variations between the images through a
-neural multi-view model that learns location-discriminative embeddings in which
-ground-level images are matched with their corresponding satellite view of the
-scene. We use this learned function as an observation model in a filtering
-framework to maintain a distribution over the vehicle's pose. We evaluate our
-method on different benchmark datasets and demonstrate its ability localize
-ground-level images in environments novel relative to training, despite the
-challenges of significant viewpoint and appearance variations.
-",1,0,0,0,0,0
-20387,Optimality of codes with respect to error probability in Gaussian noise,"  We consider geometrical optimization problems related to optimizing the error
-probability in the presence of a Gaussian noise. One famous questions in the
-field is the ""weak simplex conjecture"". We discuss possible approaches to it,
-and state related conjectures about the Gaussian measure, in particular, the
-conjecture about minimizing of the Gaussian measure of a simplex. We also
-consider antipodal codes, apply the Šidák inequality and establish some
-theoretical and some numerical results about their optimality.
-",1,0,1,0,0,0
-20388,Improved TDNNs using Deep Kernels and Frequency Dependent Grid-RNNs,"  Time delay neural networks (TDNNs) are an effective acoustic model for large
-vocabulary speech recognition. The strength of the model can be attributed to
-its ability to effectively model long temporal contexts. However, current TDNN
-models are relatively shallow, which limits the modelling capability. This
-paper proposes a method of increasing the network depth by deepening the kernel
-used in the TDNN temporal convolutions. The best performing kernel consists of
-three fully connected layers with a residual (ResNet) connection from the
-output of the first to the output of the third. The addition of
-spectro-temporal processing as the input to the TDNN in the form of a
-convolutional neural network (CNN) and a newly designed Grid-RNN was
-investigated. The Grid-RNN strongly outperforms a CNN if different sets of
-parameters for different frequency bands are used and can be further enhanced
-by using a bi-directional Grid-RNN. Experiments using the multi-genre broadcast
-(MGB3) English data (275h) show that deep kernel TDNNs reduces the word error
-rate (WER) by 6% relative and when combined with the frequency dependent
-Grid-RNN gives a relative WER reduction of 9%.
-",0,0,0,1,0,0
-20389,A Structured Self-attentive Sentence Embedding,"  This paper proposes a new model for extracting an interpretable sentence
-embedding by introducing self-attention. Instead of using a vector, we use a
-2-D matrix to represent the embedding, with each row of the matrix attending on
-a different part of the sentence. We also propose a self-attention mechanism
-and a special regularization term for the model. As a side effect, the
-embedding comes with an easy way of visualizing what specific parts of the
-sentence are encoded into the embedding. We evaluate our model on 3 different
-tasks: author profiling, sentiment classification, and textual entailment.
-Results show that our model yields a significant performance gain compared to
-other sentence embedding methods in all of the 3 tasks.
-",1,0,0,0,0,0
-20390,When Neurons Fail,"  We view a neural network as a distributed system of which neurons can fail
-independently, and we evaluate its robustness in the absence of any (recovery)
-learning phase. We give tight bounds on the number of neurons that can fail
-without harming the result of a computation. To determine our bounds, we
-leverage the fact that neural activation functions are Lipschitz-continuous.
-Our bound is on a quantity, we call the \textit{Forward Error Propagation},
-capturing how much error is propagated by a neural network when a given number
-of components is failing, computing this quantity only requires looking at the
-topology of the network, while experimentally assessing the robustness of a
-network requires the costly experiment of looking at all the possible inputs
-and testing all the possible configurations of the network corresponding to
-different failure situations, facing a discouraging combinatorial explosion.
-We distinguish the case of neurons that can fail and stop their activity
-(crashed neurons) from the case of neurons that can fail by transmitting
-arbitrary values (Byzantine neurons). Interestingly, as we show in the paper,
-our bound can easily be extended to the case where synapses can fail.
-We show how our bound can be leveraged to quantify the effect of memory cost
-reduction on the accuracy of a neural network, to estimate the amount of
-information any neuron needs from its preceding layer, enabling thereby a
-boosting scheme that prevents neurons from waiting for unnecessary signals. We
-finally discuss the trade-off between neural networks robustness and learning
-cost.
-",0,0,0,1,0,0
-20391,Heavy Traffic Limit for a Tandem Queue with Identical Service Times,"  We consider a two-node tandem queueing network in which the upstream queue is
-M/G/1 and each job reuses its upstream service requirement when moving to the
-downstream queue. Both servers employ the first-in-first-out policy. We
-investigate the amount of work in the second queue at certain embedded arrival
-time points, namely when the upstream queue has just emptied. We focus on the
-case of infinite-variance service times and obtain a heavy traffic process
-limit for the embedded Markov chain.
-",0,0,1,0,0,0
-20392,Autonomous Sweet Pepper Harvesting for Protected Cropping Systems,"  In this letter, we present a new robotic harvester (Harvey) that can
-autonomously harvest sweet pepper in protected cropping environments. Our
-approach combines effective vision algorithms with a novel end-effector design
-to enable successful harvesting of sweet peppers. Initial field trials in
-protected cropping environments, with two cultivar, demonstrate the efficacy of
-this approach achieving a 46% success rate for unmodified crop, and 58% for
-modified crop. Furthermore, for the more favourable cultivar we were also able
-to detach 90% of sweet peppers, indicating that improvements in the grasping
-success rate would result in greatly improved harvesting performance.
-",1,0,0,0,0,0
-20393,AMI SZ observation of galaxy-cluster merger CIZA J2242+5301: perpendicular flows of gas and dark matter,"  AMI observations towards CIZA J2242+5301, in comparison with observations of
-weak gravitational lensing and X-ray emission from the literature, are used to
-investigate the behaviour of non-baryonic dark matter (NBDM) and gas during the
-merger. Analysis of the Sunyaev-Zel'dovich (SZ) signal indicates the presence
-of high pressure gas elongated perpendicularly to the X-ray and weak-lensing
-morphologies which, given the merger-axis constraints in the literature,
-implies that high pressure gas is pushed out into a linear structure during
-core passing. Simulations in the literature closely matching the inferred
-merger scenario show the formation of gas density and temperature structures
-perpendicular to the merger axis. These SZ observations are challenging for
-modified gravity theories in which NBDM is not the dominant contributor to
-galaxy-cluster gravity.
-",0,1,0,0,0,0
-20394,Development of a computer-aided design software for dental splint in orthognathic surgery,"  In the orthognathic surgery, dental splints are important and necessary to
-help the surgeon reposition the maxilla or mandible. However, the traditional
-methods of manual design of dental splints are difficult and time-consuming.
-The research on computer-aided design software for dental splints is rarely
-reported. Our purpose is to develop a novel special software named EasySplint
-to design the dental splints conveniently and efficiently. The design can be
-divided into two steps, which are the generation of initial splint base and the
-Boolean operation between it and the maxilla-mandibular model. The initial
-splint base is formed by ruled surfaces reconstructed using the manually picked
-points. Then, a method to accomplish Boolean operation based on the distance
-filed of two meshes is proposed. The interference elimination can be conducted
-on the basis of marching cubes algorithm and Boolean operation. The accuracy of
-the dental splint can be guaranteed since the original mesh is utilized to form
-the result surface. Using EasySplint, the dental splints can be designed in
-about 10 minutes and saved as a stereo lithography (STL) file for 3D printing
-in clinical applications. Three phantom experiments were conducted and the
-efficiency of our method was demonstrated.
-",1,1,0,0,0,0
-20395,"Energy-transport systems for optical lattices: derivation, analysis, simulation","  Energy-transport equations for the transport of fermions in optical lattices
-are formally derived from a Boltzmann transport equation with a periodic
-lattice potential in the diffusive limit. The limit model possesses a formal
-gradient-flow structure like in the case of the energy-transport equations for
-semiconductors. At the zeroth-order high temperature limit, the
-energy-transport equations reduce to the whole-space logarithmic diffusion
-equation which has some unphysical properties. Therefore, the first-order
-expansion is derived and analyzed. The existence of weak solutions to the
-time-discretized system for the particle and energy densities with periodic
-boundary conditions is proved. The difficulties are the nonstandard degeneracy
-and the quadratic gradient term. The main tool of the proof is a result on the
-strong convergence of the gradients of the approximate solutions. Numerical
-simulations in one space dimension show that the particle density converges to
-a constant steady state if the initial energy density is sufficiently large,
-otherwise the particle density converges to a nonconstant steady state.
-",0,0,1,0,0,0
-20396,"First principles study of structural, magnetic and electronic properties of CrAs","  We report ab initio density functional calculations of the structural and
-magnetic properties, and the electronic structure of CrAs. To simulate the
-observed pressure-driven experimental results, we perform our analysis for
-different volumes of the unit cell, showing that the structural, magnetic and
-electronic properties strongly depend on the size of the cell. We find that the
-calculated quantities are in good agreement with the experimental data, and we
-review our results in terms of the observed superconductivity.
-",0,1,0,0,0,0
-20397,Subspace Robust Wasserstein distances,"  Making sense of Wasserstein distances between discrete measures in
-high-dimensional settings remains a challenge. Recent work has advocated a
-two-step approach to improve robustness and facilitate the computation of
-optimal transport, using for instance projections on random real lines, or a
-preliminary quantization to reduce the number of points. We propose in this
-work a new robust variant of the Wasserstein distance. This quantity captures
-the maximal possible distance that can be realized between these two measures,
-after they have been projected orthogonally on a lower k dimensional subspace.
-We show that this distance inherits several favorably properties of OT, and
-that computing it can be cast as a convex problem involving the top k
-eigenvalues of the second order moment matrix of the displacements induced by a
-transport plan. We provide algorithms to approximate the computation of this
-saddle point using entropic regularization, and illustrate the interest of this
-approach empirically.
-",1,0,0,1,0,0
-20398,Wind accretion onto compact objects,"  X-ray emission associated to accretion onto compact objects displays
-important levels of photometric and spectroscopic time-variability. When the
-accretor orbits a Supergiant star, it captures a fraction of the supersonic
-radiatively-driven wind which forms shocks in its vicinity. The amplitude and
-stability of this gravitational beaming of the flow conditions the mass
-accretion rate responsible, in fine, for the X-ray luminosity of those
-Supergiant X-ray Binaries. The capacity of this low angular momentum inflow to
-form a disc-like structure susceptible to be the stage of well-known
-instabilities remains at stake. Using state-of-the-art numerical setups, we
-characterized the structure of a Bondi-Hoyle-Lyttleton flow onto a compact
-object, from the shock down to the vicinity of the accretor, typically five
-orders of magnitude smaller. The evolution of the mass accretion rate and of
-the bow shock which forms around the accretor (transverse structure, opening
-angle, stability, temperature profile) with the Mach number of the incoming
-flow is described in detail. The robustness of those simulations based on the
-High Performance Computing MPI-AMRVAC code is supported by the topology of the
-inner sonic surface, in agreement with theoretical expectations. We developed a
-synthetic model of mass transfer in Supergiant X-ray Binaries which couples the
-launching of the wind accordingly to the stellar parameters, the orbital
-evolution of the streamlines in a modified Roche potential and the accretion
-process. We show that the shape of the permanent flow is entirely determined by
-the mass ratio, the filling factor, the Eddington factor and the alpha-force
-multiplier. Provided scales such as the orbital period are known, we can trace
-back the observables to evaluate the mass accretion rates, the accretion
-mechanism (stream or wind-dominated) and the shearing of the inflow.
-",0,1,0,0,0,0
-20399,Riemannian Stein Variational Gradient Descent for Bayesian Inference,"  We develop Riemannian Stein Variational Gradient Descent (RSVGD), a Bayesian
-inference method that generalizes Stein Variational Gradient Descent (SVGD) to
-Riemann manifold. The benefits are two-folds: (i) for inference tasks in
-Euclidean spaces, RSVGD has the advantage over SVGD of utilizing information
-geometry, and (ii) for inference tasks on Riemann manifolds, RSVGD brings the
-unique advantages of SVGD to the Riemannian world. To appropriately transfer to
-Riemann manifolds, we conceive novel and non-trivial techniques for RSVGD,
-which are required by the intrinsically different characteristics of general
-Riemann manifolds from Euclidean spaces. We also discover Riemannian Stein's
-Identity and Riemannian Kernelized Stein Discrepancy. Experimental results show
-the advantages over SVGD of exploring distribution geometry and the advantages
-of particle-efficiency, iteration-effectiveness and approximation flexibility
-over other inference methods on Riemann manifolds.
-",0,0,0,1,0,0
-20400,Suspension-thermal noise in spring-antispring systems for future gravitational-wave detectors,"  Spring-antispring systems have been investigated as possible low-frequency
-seismic isolation in high-precision optical experiments. These systems provide
-the possibility to tune the fundamental resonance frequency to, in principle,
-arbitrarily low values, and at the same time maintain a compact design of the
-isolation system. It was argued though that thermal noise in spring-antispring
-systems would not be as small as one may naively expect from lowering the
-fundamental resonance frequency. In this paper, we present a detailed
-calculation of the suspension thermal noise for a specific spring-antispring
-system, namely the Roberts linkage. We find a concise expression of the
-suspension thermal noise spectrum, which assumes a form very similar to the
-well-known expression for a simple pendulum. It is found that while the Roberts
-linkage can provide strong seismic isolation due to a very low fundamental
-resonance frequency, its thermal noise is rather determined by the dimension of
-the system. We argue that this is true for all horizontal mechanical isolation
-systems with spring-antispring dynamics. This imposes strict requirements on
-mechanical spring-antispring systems for the seismic isolation in potential
-future low-frequency gravitational-wave detectors as we discuss for the four
-main concepts: atom-interferometric, superconducting, torsion-bars, and
-conventional laser interferometer.
-",0,1,0,0,0,0
-20401,Fooling Sets and the Spanning Tree Polytope,"  In the study of extensions of polytopes of combinatorial optimization
-problems, a notorious open question is that for the size of the smallest
-extended formulation of the Minimum Spanning Tree problem on a complete graph
-with $n$ nodes. The best known lower bound is $\Omega(n^2)$, the best known
-upper bound is $O(n^3)$.
-In this note we show that the venerable fooling set method cannot be used to
-improve the lower bound: every fooling set for the Spanning Tree polytope has
-size $O(n^2)$.
-",1,0,1,0,0,0
-20402,From Azéma supermartingales of finite honest times to optional semimartingales of class-($Σ$),"  Given a finite honest time, we derive representations for the additive and
-multiplicative decomposition of it's Azéma supermartingale in terms of
-optional supermartingales and its running supremum. We then extend the notion
-of semimartingales of class-$(\Sigma)$ to optional semimartingales with jumps
-in its finite variation part, allowing one to establish formulas similar to the
-Madan-Roynette-Yor option pricing formulas for larger class of processes.
-Finally, we introduce the optional multiplicative systems associated with
-positive submartingales and apply them to construct random times with given
-Azéma supermartingale.
-",0,0,0,0,0,1
-20403,Single versus Double Blind Reviewing at WSDM 2017,"  In this paper we study the implications for conference program committees of
-using single-blind reviewing, in which committee members are aware of the names
-and affiliations of paper authors, versus double-blind reviewing, in which this
-information is not visible to committee members. WSDM 2017, the 10th ACM
-International ACM Conference on Web Search and Data Mining, performed a
-controlled experiment in which each paper was reviewed by four committee
-members. Two of these four reviewers were chosen from a pool of committee
-members who had access to author information; the other two were chosen from a
-disjoint pool who did not have access to this information. This information
-asymmetry persisted through the process of bidding for papers, reviewing
-papers, and entering scores. Reviewers in the single-blind condition typically
-bid for 22% fewer papers, and preferentially bid for papers from top
-institutions. Once papers were allocated to reviewers, single-blind reviewers
-were significantly more likely than their double-blind counterparts to
-recommend for acceptance papers from famous authors and top institutions. The
-estimated odds multipliers are 1.63 for famous authors and 1.58 and 2.10 for
-top universities and companies respectively, so the result is tangible. For
-female authors, the associated odds multiplier of 0.78 is not statistically
-significant in our study. However, a meta-analysis places this value in line
-with that of other experiments, and in the context of this larger aggregate the
-gender effect is also statistically significant.
-",1,0,0,0,0,0
-20404,Scale-variant Topological Information for Characterizing Complex Networks,"  Real-world networks are difficult to characterize because of the variation of
-topological scales, the non-dyadic complex interactions, and the fluctuations.
-Here, we propose a general framework to address these problems via a
-methodology grounded on topology data analysis. By observing the diffusion
-process in a network at a single specified timescale, we can map the network
-nodes to a point cloud, which contains the topological information of the
-network at a single scale. We then calculate the point clouds constructed over
-variable timescales, which provide scale-variant topological information and
-enable a deep understanding of the network structure and functionality.
-Experiments on synthetic and real-world data demonstrate the effectiveness of
-our framework in identifying network models, classifying real-world networks
-and detecting transition points in time-evolving networks. Our work presents a
-unified analysis that is potentially applicable to more complicated network
-structures such as multilayer and multiplex networks.
-",1,0,0,0,0,0
-20405,Topological orbital superfluid with chiral d-wave order in a rotating optical lattice,"  Topological superfluid is an exotic state of quantum matter that possesses a
-nodeless superfluid gap in the bulk and Andreev edge modes at the boundary of a
-finite system. Here, we study a multi-orbital superfluid driven by attractive
-s-wave interaction in a rotating optical lattice. Interestingly, we find that
-the rotation induces the inter- orbital hybridization and drives the system
-into topological orbital superfluid in accordance with intrinsically chiral
-d-wave pairing characteristics. Thanks to the conservation of spin, the
-topological orbital superfluid supports four rather than two chiral Andreev
-edge modes at the boundary of the lattice. Moreover, we find that the intrinsic
-harmonic confining potential forms a circular spatial barrier which accumulates
-atoms and supports a mass current under injection of small angular momentum as
-external driving force. This feature provides an experimentally detectable
-phenomenon to verify the topological orbital superfluid with chiral d-wave
-order in a rotating optical lattice.
-",0,1,0,0,0,0
-20406,Optimal client recommendation for market makers in illiquid financial products,"  The process of liquidity provision in financial markets can result in
-prolonged exposure to illiquid instruments for market makers. In this case,
-where a proprietary position is not desired, pro-actively targeting the right
-client who is likely to be interested can be an effective means to offset this
-position, rather than relying on commensurate interest arising through natural
-demand. In this paper, we consider the inference of a client profile for the
-purpose of corporate bond recommendation, based on typical recorded information
-available to the market maker. Given a historical record of corporate bond
-transactions and bond meta-data, we use a topic-modelling analogy to develop a
-probabilistic technique for compiling a curated list of client recommendations
-for a particular bond that needs to be traded, ranked by probability of
-interest. We show that a model based on Latent Dirichlet Allocation offers
-promising performance to deliver relevant recommendations for sales traders.
-",0,0,0,1,0,0
-20407,Design of an Audio Interface for Patmos,"  This paper describes the design and implementation of an audio interface for
-the Patmos processor, which runs on an Altera DE2-115 FPGA board. This board
-has an audio codec included, the WM8731. The interface described in this work
-allows to receive and send audio from and to the WM8731, and to synthesize,
-store or manipulate audio signals writing C programs for Patmos. The audio
-interface described in this paper is intended to be used with the Patmos
-processor. Patmos is an open source RISC ISAs with a load-store architecture,
-that is optimized for Real-Time Systems. Patmos is part of a project founded by
-the European Union called T-CREST (Time-predictable Multi-Core Architecture for
-Embedded Systems).[5] The structure of this project is integrated with the
-Patmos project: new hardware modules have been added as IOs, which allow the
-communication between the processor and the audio codec. These modules include
-a clock generator for the audio chip, ADC and DAC modules for the audio
-conversion from analog to digital and vice versa, and an I2C module which
-allows setting configuration parameters on the audio codec. Moreover, a top
-module has been created, which connects all the modules previously mentioned
-between them, to Patmos and to the WM8731, using the external pins of the FPGA.
-",1,0,0,0,0,0
-20408,A Classifying Variational Autoencoder with Application to Polyphonic Music Generation,"  The variational autoencoder (VAE) is a popular probabilistic generative
-model. However, one shortcoming of VAEs is that the latent variables cannot be
-discrete, which makes it difficult to generate data from different modes of a
-distribution. Here, we propose an extension of the VAE framework that
-incorporates a classifier to infer the discrete class of the modeled data. To
-model sequential data, we can combine our Classifying VAE with a recurrent
-neural network such as an LSTM. We apply this model to algorithmic music
-generation, where our model learns to generate musical sequences in different
-keys. Most previous work in this area avoids modeling key by transposing data
-into only one or two keys, as opposed to the 10+ different keys in the original
-music. We show that our Classifying VAE and Classifying VAE+LSTM models
-outperform the corresponding non-classifying models in generating musical
-samples that stay in key. This benefit is especially apparent when trained on
-untransposed music data in the original keys.
-",1,0,0,1,0,0
-20409,On Estimation of Isotonic Piecewise Constant Signals,"  Consider a sequence of real data points $X_1,\ldots, X_n$ with underlying
-means $\theta^*_1,\dots,\theta^*_n$. This paper starts from studying the
-setting that $\theta^*_i$ is both piecewise constant and monotone as a function
-of the index $i$. For this, we establish the exact minimax rate of estimating
-such monotone functions, and thus give a non-trivial answer to an open problem
-in the shape-constrained analysis literature. The minimax rate involves an
-interesting iterated logarithmic dependence on the dimension, a phenomenon that
-is revealed through characterizing the interplay between the isotonic shape
-constraint and model selection complexity. We then develop a penalized
-least-squares procedure for estimating the vector
-$\theta^*=(\theta^*_1,\dots,\theta^*_n)^T$. This estimator is shown to achieve
-the derived minimax rate adaptively. For the proposed estimator, we further
-allow the model to be misspecified and derive oracle inequalities with the
-optimal rates, and show there exists a computationally efficient algorithm to
-compute the exact solution.
-",0,0,1,1,0,0
-20410,Deep Stacked Stochastic Configuration Networks for Non-Stationary Data Streams,"  The concept of stochastic configuration networks (SCNs) others a solid
-framework for fast implementation of feedforward neural networks through
-randomized learning. Unlike conventional randomized approaches, SCNs provide an
-avenue to select appropriate scope of random parameters to ensure the universal
-approximation property. In this paper, a deep version of stochastic
-configuration networks, namely deep stacked stochastic configuration network
-(DSSCN), is proposed for modeling non-stationary data streams. As an extension
-of evolving stochastic connfiguration networks (eSCNs), this work contributes a
-way to grow and shrink the structure of deep stochastic configuration networks
-autonomously from data streams. The performance of DSSCN is evaluated by six
-benchmark datasets. Simulation results, compared with prominent data stream
-algorithms, show that the proposed method is capable of achieving comparable
-accuracy and evolving compact and parsimonious deep stacked network
-architecture.
-",0,0,0,1,0,0
-20411,Extracting Syntactic Patterns from Databases,"  Many database columns contain string or numerical data that conforms to a
-pattern, such as phone numbers, dates, addresses, product identifiers, and
-employee ids. These patterns are useful in a number of data processing
-applications, including understanding what a specific field represents when
-field names are ambiguous, identifying outlier values, and finding similar
-fields across data sets. One way to express such patterns would be to learn
-regular expressions for each field in the database. Unfortunately, exist- ing
-techniques on regular expression learning are slow, taking hundreds of seconds
-for columns of just a few thousand values. In contrast, we develop XSystem, an
-efficient method to learn patterns over database columns in significantly less
-time. We show that these patterns can not only be built quickly, but are
-expressive enough to capture a number of key applications, including detecting
-outliers, measuring column similarity, and assigning semantic labels to columns
-(based on a library of regular expressions). We evaluate these applications
-with datasets that range from chemical databases (based on a collaboration with
-a pharmaceutical company), our university data warehouse, and open data from
-MassData.gov.
-",1,0,0,0,0,0
-20412,Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification,"  We address the problem of multi-class classification in the case where the
-number of classes is very large. We propose a double sampling strategy on top
-of a multi-class to binary reduction strategy, which transforms the original
-multi-class problem into a binary classification problem over pairs of
-examples. The aim of the sampling strategy is to overcome the curse of
-long-tailed class distributions exhibited in majority of large-scale
-multi-class classification problems and to reduce the number of pairs of
-examples in the expanded data. We show that this strategy does not alter the
-consistency of the empirical risk minimization principle defined over the
-double sample reduction. Experiments are carried out on DMOZ and Wikipedia
-collections with 10,000 to 100,000 classes where we show the efficiency of the
-proposed approach in terms of training and prediction time, memory consumption,
-and predictive performance with respect to state-of-the-art approaches.
-",1,0,0,1,0,0
-20413,"Understanding and predicting travel time with spatio-temporal features of network traffic flow, weather and incidents","  Travel time on a route varies substantially by time of day and from day to
-day. It is critical to understand to what extent this variation is correlated
-with various factors, such as weather, incidents, events or travel demand level
-in the context of dynamic networks. This helps a better decision making for
-infrastructure planning and real-time traffic operation. We propose a
-data-driven approach to understand and predict highway travel time using
-spatio-temporal features of those factors, all of which are acquired from
-multiple data sources. The prediction model holistically selects the most
-related features from a high-dimensional feature space by correlation analysis,
-principle component analysis and LASSO. We test and compare the performance of
-several regression models in predicting travel time 30 min in advance via two
-case studies: (1) a 6-mile highway corridor of I-270N in D.C. region, and (2) a
-2.3-mile corridor of I-376E in Pittsburgh region. We found that some
-bottlenecks scattered in the network can imply congestion on those corridors at
-least 30 minutes in advance, including those on the alternative route to the
-corridors of study. In addition, real-time travel time is statistically related
-to incidents on some specific locations, morning/afternoon travel demand,
-visibility, precipitation, wind speed/gust and the weather type. All those
-spatio-temporal information together help improve prediction accuracy,
-comparing to using only speed data. In both case studies, random forest shows
-the most promise, reaching a root-mean-squared error of 16.6\% and 17.0\%
-respectively in afternoon peak hours for the entire year of 2014.
-",0,0,0,1,0,0
-20414,The power of sum-of-squares for detecting hidden structures,"  We study planted problems---finding hidden structures in random noisy
-inputs---through the lens of the sum-of-squares semidefinite programming
-hierarchy (SoS). This family of powerful semidefinite programs has recently
-yielded many new algorithms for planted problems, often achieving the best
-known polynomial-time guarantees in terms of accuracy of recovered solutions
-and robustness to noise. One theme in recent work is the design of spectral
-algorithms which match the guarantees of SoS algorithms for planted problems.
-Classical spectral algorithms are often unable to accomplish this: the twist in
-these new spectral algorithms is the use of spectral structure of matrices
-whose entries are low-degree polynomials of the input variables. We prove that
-for a wide class of planted problems, including refuting random constraint
-satisfaction problems, tensor and sparse PCA, densest-k-subgraph, community
-detection in stochastic block models, planted clique, and others, eigenvalues
-of degree-d matrix polynomials are as powerful as SoS semidefinite programs of
-roughly degree d. For such problems it is therefore always possible to match
-the guarantees of SoS without solving a large semidefinite program. Using
-related ideas on SoS algorithms and low-degree matrix polynomials (and inspired
-by recent work on SoS and the planted clique problem by Barak et al.), we prove
-new nearly-tight SoS lower bounds for the tensor and sparse principal component
-analysis problems. Our lower bounds for sparse principal component analysis are
-the first to suggest that going beyond existing algorithms for this problem may
-require sub-exponential time.
-",1,0,0,0,0,0
-20415,An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis,"  Polynomial chaos expansions (PCE) have seen widespread use in the context of
-uncertainty quantification. However, their application to structural
-reliability problems has been hindered by the limited performance of PCE in the
-tails of the model response and due to the lack of local metamodel error
-estimates. We propose a new method to provide local metamodel error estimates
-based on bootstrap resampling and sparse PCE. An initial experimental design is
-iteratively updated based on the current estimation of the limit-state surface
-in an active learning algorithm. The greedy algorithm uses the bootstrap-based
-local error estimates for the polynomial chaos predictor to identify the best
-candidate set of points to enrich the experimental design. We demonstrate the
-effectiveness of this approach on a well-known analytical benchmark
-representing a series system, on a truss structure and on a complex realistic
-frame structure problem.
-",0,0,0,1,0,0
-20416,Experimental Tests of Spirituality,"  We currently harness technologies that could shed new light on old
-philosophical questions, such as whether our mind entails anything beyond our
-body or whether our moral values reflect universal truth.
-",0,0,0,0,1,0
-20417,Chern classes and Gromov--Witten theory of projective bundles,"  We prove that the Gromov--Witten theory (GWT) of a projective bundle can be
-determined by the Chern classes and the GWT of the base. It completely answers
-a question raised in a previous paper (arXiv:1607.00740). Its consequences
-include that the GWT of the blow-up of X at a smooth subvariety Z is uniquely
-determined by GWT of X, Z plus some topological data.
-",0,0,1,0,0,0
-20418,Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations,"  We introduce the concept of numerical Gaussian processes, which we define as
-Gaussian processes with covariance functions resulting from temporal
-discretization of time-dependent partial differential equations. Numerical
-Gaussian processes, by construction, are designed to deal with cases where: (1)
-all we observe are noisy data on black-box initial conditions, and (2) we are
-interested in quantifying the uncertainty associated with such noisy data in
-our solutions to time-dependent partial differential equations. Our method
-circumvents the need for spatial discretization of the differential operators
-by proper placement of Gaussian process priors. This is an attempt to construct
-structured and data-efficient learning machines, which are explicitly informed
-by the underlying physics that possibly generated the observed data. The
-effectiveness of the proposed approach is demonstrated through several
-benchmark problems involving linear and nonlinear time-dependent operators. In
-all examples, we are able to recover accurate approximations of the latent
-solutions, and consistently propagate uncertainty, even in cases involving very
-long time integration.
-",1,0,1,1,0,0
-20419,Learning from Experience: A Dynamic Closed-Loop QoE Optimization for Video Adaptation and Delivery,"  The quality of experience (QoE) is known to be subjective and
-context-dependent. Identifying and calculating the factors that affect QoE is
-indeed a difficult task. Recently, a lot of effort has been devoted to estimate
-the users QoE in order to improve video delivery. In the literature, most of
-the QoE-driven optimization schemes that realize trade-offs among different
-quality metrics have been addressed under the assumption of homogenous
-populations. Nevertheless, people perceptions on a given video quality may not
-be the same, which makes the QoE optimization harder. This paper aims at taking
-a step further in order to address this limitation and meet users profiles. To
-do so, we propose a closed-loop control framework based on the
-users(subjective) feedbacks to learn the QoE function and optimize it at the
-same time. Our simulation results show that our system converges to a steady
-state, where the resulting QoE function noticeably improves the users
-feedbacks.
-",1,0,0,0,0,0
-20420,Deep CNN based feature extractor for text-prompted speaker recognition,"  Deep learning is still not a very common tool in speaker verification field.
-We study deep convolutional neural network performance in the text-prompted
-speaker verification task. The prompted passphrase is segmented into word
-states - i.e. digits -to test each digit utterance separately. We train a
-single high-level feature extractor for all states and use cosine similarity
-metric for scoring. The key feature of our network is the Max-Feature-Map
-activation function, which acts as an embedded feature selector. By using
-multitask learning scheme to train the high-level feature extractor we were
-able to surpass the classic baseline systems in terms of quality and achieved
-impressive results for such a novice approach, getting 2.85% EER on the RSR2015
-evaluation set. Fusion of the proposed and the baseline systems improves this
-result.
-",0,0,0,1,0,0
-20421,Data-Injection Attacks in Stochastic Control Systems: Detectability and Performance Tradeoffs,"  Consider a stochastic process being controlled across a communication
-channel. The control signal that is transmitted across the control channel can
-be replaced by a malicious attacker. The controller is allowed to implement any
-arbitrary detection algorithm to detect if an attacker is present. This work
-characterizes some fundamental limitations of when such an attack can be
-detected, and quantifies the performance degradation that an attacker that
-seeks to be undetected or stealthy can introduce.
-",1,0,1,0,0,0
-20422,Efficient Version-Space Reduction for Visual Tracking,"  Discrminative trackers, employ a classification approach to separate the
-target from its background. To cope with variations of the target shape and
-appearance, the classifier is updated online with different samples of the
-target and the background. Sample selection, labeling and updating the
-classifier is prone to various sources of errors that drift the tracker. We
-introduce the use of an efficient version space shrinking strategy to reduce
-the labeling errors and enhance its sampling strategy by measuring the
-uncertainty of the tracker about the samples. The proposed tracker, utilize an
-ensemble of classifiers that represents different hypotheses about the target,
-diversify them using boosting to provide a larger and more consistent coverage
-of the version-space and tune the classifiers' weights in voting. The proposed
-system adjusts the model update rate by promoting the co-training of the
-short-memory ensemble with a long-memory oracle. The proposed tracker
-outperformed state-of-the-art trackers on different sequences bearing various
-tracking challenges.
-",1,0,0,0,0,0
-20423,Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts,"  Most of the JavaScript code deployed in the wild has been minified, a process
-in which identifier names are replaced with short, arbitrary and meaningless
-names. Minified code occupies less space, but also makes the code extremely
-difficult to manually inspect and understand. This paper presents Context2Name,
-a deep learningbased technique that partially reverses the effect of
-minification by predicting natural identifier names for minified names. The
-core idea is to predict from the usage context of a variable a name that
-captures the meaning of the variable. The approach combines a lightweight,
-token-based static analysis with an auto-encoder neural network that summarizes
-usage contexts and a recurrent neural network that predict natural names for a
-given usage context. We evaluate Context2Name with a large corpus of real-world
-JavaScript code and show that it successfully predicts 47.5% of all minified
-identifiers while taking only 2.9 milliseconds on average to predict a name. A
-comparison with the state-of-the-art tools JSNice and JSNaughty shows that our
-approach performs comparably in terms of accuracy while improving in terms of
-efficiency. Moreover, Context2Name complements the state-of-the-art by
-predicting 5.3% additional identifiers that are missed by both existing tools.
-",1,0,0,1,0,0
-20424,Reconstructing fluid dynamics with micro-finite element,"  In the theory of the Navier-Stokes equations, the viscous fluid in
-incompressible flow is modelled as a homogeneous and dense assemblage of
-constituent ""fluid particles"" with viscous stress proportional to rate of
-strain. The crucial concept of fluid flow is the velocity of the particle that
-is accelerated by the pressure and viscous interaction around it. In this
-paper, by virtue of the alternative constituent ""micro-finite element"", we
-introduce a set of new intrinsic quantities, called the vortex fields, to
-characterise the relative orientation between elements and the feature of
-micro-eddies in the element, while the description of viscous interaction in
-fluid returns to the initial intuition that the interlayer friction is
-proportional to the slip strength. Such a framework enables us to reconstruct
-the dynamics theory of viscous fluid, in which the flowing fluid can be
-modelled as a finite covering of elements and consequently indicated by a
-space-time differential manifold that admits complex topological evolution.
-",0,1,0,0,0,0
-20425,Towards Black-box Iterative Machine Teaching,"  In this paper, we make an important step towards the black-box machine
-teaching by considering the cross-space machine teaching, where the teacher and
-the learner use different feature representations and the teacher can not fully
-observe the learner's model. In such scenario, we study how the teacher is
-still able to teach the learner to achieve faster convergence rate than the
-traditional passive learning. We propose an active teacher model that can
-actively query the learner (i.e., make the learner take exams) for estimating
-the learner's status and provably guide the learner to achieve faster
-convergence. The sample complexities for both teaching and query are provided.
-In the experiments, we compare the proposed active teacher with the omniscient
-teacher and verify the effectiveness of the active teacher model.
-",1,0,0,1,0,0
-20426,On Generalization and Regularization in Deep Learning,"  Why do large neural network generalize so well on complex tasks such as image
-classification or speech recognition? What exactly is the role regularization
-for them? These are arguably among the most important open questions in machine
-learning today. In a recent and thought provoking paper [C. Zhang et al.]
-several authors performed a number of numerical experiments that hint at the
-need for novel theoretical concepts to account for this phenomenon. The paper
-stirred quit a lot of excitement among the machine learning community but at
-the same time it created some confusion as discussions on OpenReview.net
-testifies. The aim of this pedagogical paper is to make this debate accessible
-to a wider audience of data scientists without advanced theoretical knowledge
-in statistical learning. The focus here is on explicit mathematical definitions
-and on a discussion of relevant concepts, not on proofs for which we provide
-references.
-",0,0,1,1,0,0
-20427,Nonparametric Inference for Auto-Encoding Variational Bayes,"  We would like to learn latent representations that are low-dimensional and
-highly interpretable. A model that has these characteristics is the Gaussian
-Process Latent Variable Model. The benefits and negative of the GP-LVM are
-complementary to the Variational Autoencoder, the former provides interpretable
-low-dimensional latent representations while the latter is able to handle large
-amounts of data and can use non-Gaussian likelihoods. Our inspiration for this
-paper is to marry these two approaches and reap the benefits of both. In order
-to do so we will introduce a novel approximate inference scheme inspired by the
-GP-LVM and the VAE. We show experimentally that the approximation allows the
-capacity of the generative bottle-neck (Z) of the VAE to be arbitrarily large
-without losing a highly interpretable representation, allowing reconstruction
-quality to be unlimited by Z at the same time as a low-dimensional space can be
-used to perform ancestral sampling from as well as a means to reason about the
-embedded data.
-",1,0,0,1,0,0
-20428,Imbalanced Malware Images Classification: a CNN based Approach,"  Deep convolutional neural networks (CNNs) can be applied to malware binary
-detection through images classification. The performance, however, is degraded
-due to the imbalance of malware families (classes). To mitigate this issue, we
-propose a simple yet effective weighted softmax loss which can be employed as
-the final layer of deep CNNs. The original softmax loss is weighted, and the
-weight value can be determined according to class size. A scaling parameter is
-also included in computing the weight. Proper selection of this parameter has
-been studied and an empirical option is given. The weighted loss aims at
-alleviating the impact of data imbalance in an end-to-end learning fashion. To
-validate the efficacy, we deploy the proposed weighted loss in a pre-trained
-deep CNN model and fine-tune it to achieve promising results on malware images
-classification. Extensive experiments also indicate that the new loss function
-can fit other typical CNNs with an improved classification performance.
-",1,0,0,1,0,0
-20429,Integrated optical force sensors using focusing photonic crystal arrays,"  Mechanical oscillators are at the heart of many sensor applications. Recently
-several groups have developed oscillators that are probed optically, fabricated
-from high-stress silicon nitride films. They exhibit outstanding force
-sensitivities of a few aN/Hz$^{1/2}$ and can also be made highly reflective,
-for efficient detection. The optical read-out usually requires complex
-experimental setups, including positioning stages and bulky cavities, making
-them impractical for real applications. In this paper we propose a novel way of
-building fully integrated all-optical force sensors based on low-loss silicon
-nitride mechanical resonators with a photonic crystal reflector. We can
-circumvent previous limitations in stability and complexity by simulating a
-suspended focusing photonic crystal, purely made of silicon nitride. Our design
-allows for an all integrated sensor, built out of a single block that
-integrates a full Fabry-Pérot cavity, without the need for assembly or
-alignment. The presented simulations will allow for a radical simplification of
-sensors based on high-Q silicon nitride membranes. Our results comprise, to the
-best of our knowledge, the first simulations of a focusing mirror made from a
-mechanically suspended flat membrane with subwavelength thickness. Cavity
-lengths between a few hundred $\mu$m and mm should be directly realizable.
-",0,1,0,0,0,0
-20430,Comparison of Signaling and Media Approaches to Detect VoIP SPIT Attack,"  IP networks became the most dominant type of information networks nowadays.
-It provides a number of services and makes it easy for users to be connected.
-IP networks provide an efficient way with a large number of services compared
-to other ways of voice communication. This leads to the migration to make voice
-calls via IP networks. Despite the wide range of IP networks services,
-availability, and its capabilities, there still a large number of security
-threats that affect IP networks and for sure affecting other services based on
-it and voice is one of them. This paper discusses reasons of migration from
-making voice calls via IP networks and leaving legacy networks, requirements to
-be available in IP networks to support voice transport, and concentrating on
-SPIT attack and its detection methods. Experiments took place to compare the
-different approaches used to detect spam over VoIP networks.
-",1,0,0,0,0,0
-20431,Coupling functions: Universal insights into dynamical interaction mechanisms,"  The dynamical systems found in Nature are rarely isolated. Instead they
-interact and influence each other. The coupling functions that connect them
-contain detailed information about the functional mechanisms underlying the
-interactions and prescribe the physical rule specifying how an interaction
-occurs. Here, we aim to present a coherent and comprehensive review
-encompassing the rapid progress made recently in the analysis, understanding
-and applications of coupling functions. The basic concepts and characteristics
-of coupling functions are presented through demonstrative examples of different
-domains, revealing the mechanisms and emphasizing their multivariate nature.
-The theory of coupling functions is discussed through gradually increasing
-complexity from strong and weak interactions to globally-coupled systems and
-networks. A variety of methods that have been developed for the detection and
-reconstruction of coupling functions from measured data is described. These
-methods are based on different statistical techniques for dynamical inference.
-Stemming from physics, such methods are being applied in diverse areas of
-science and technology, including chemistry, biology, physiology, neuroscience,
-social sciences, mechanics and secure communications. This breadth of
-application illustrates the universality of coupling functions for studying the
-interaction mechanisms of coupled dynamical systems.
-",0,1,0,0,0,0
-20432,Uncertainty quantification for radio interferometric imaging: II. MAP estimation,"  Uncertainty quantification is a critical missing component in radio
-interferometric imaging that will only become increasingly important as the
-big-data era of radio interferometry emerges. Statistical sampling approaches
-to perform Bayesian inference, like Markov Chain Monte Carlo (MCMC) sampling,
-can in principle recover the full posterior distribution of the image, from
-which uncertainties can then be quantified. However, for massive data sizes,
-like those anticipated from the Square Kilometre Array (SKA), it will be
-difficult if not impossible to apply any MCMC technique due to its inherent
-computational cost. We formulate Bayesian inference problems with
-sparsity-promoting priors (motivated by compressive sensing), for which we
-recover maximum a posteriori (MAP) point estimators of radio interferometric
-images by convex optimisation. Exploiting recent developments in the theory of
-probability concentration, we quantify uncertainties by post-processing the
-recovered MAP estimate. Three strategies to quantify uncertainties are
-developed: (i) highest posterior density credible regions; (ii) local credible
-intervals (cf. error bars) for individual pixels and superpixels; and (iii)
-hypothesis testing of image structure. These forms of uncertainty
-quantification provide rich information for analysing radio interferometric
-observations in a statistically robust manner. Our MAP-based methods are
-approximately $10^5$ times faster computationally than state-of-the-art MCMC
-methods and, in addition, support highly distributed and parallelised
-algorithmic structures. For the first time, our MAP-based techniques provide a
-means of quantifying uncertainties for radio interferometric imaging for
-realistic data volumes and practical use, and scale to the emerging big-data
-era of radio astronomy.
-",0,1,0,1,0,0
-20433,Semialgebraic Invariant Synthesis for the Kannan-Lipton Orbit Problem,"  The \emph{Orbit Problem} consists of determining, given a linear
-transformation $A$ on $\mathbb{Q}^d$, together with vectors $x$ and $y$,
-whether the orbit of $x$ under repeated applications of $A$ can ever reach $y$.
-This problem was famously shown to be decidable by Kannan and Lipton in the
-1980s.
-In this paper, we are concerned with the problem of synthesising suitable
-\emph{invariants} $\mathcal{P} \subseteq \mathbb{R}^d$, \emph{i.e.}, sets that
-are stable under $A$ and contain $x$ and not $y$, thereby providing compact and
-versatile certificates of non-reachability. We show that whether a given
-instance of the Orbit Problem admits a semialgebraic invariant is decidable,
-and moreover in positive instances we provide an algorithm to synthesise
-suitable invariants of polynomial size.
-It is worth noting that the existence of \emph{semilinear} invariants, on the
-other hand, is (to the best of our knowledge) not known to be decidable.
-",1,0,1,0,0,0
-20434,Design and characterization of the Large-Aperture Experiment to Detect the Dark Age (LEDA) radiometer systems,"  The Large-Aperture Experiment to Detect the Dark Age (LEDA) was designed to
-detect the predicted O(100)mK sky-averaged absorption of the Cosmic Microwave
-Background by Hydrogen in the neutral pre- and intergalactic medium just after
-the cosmological Dark Age. The spectral signature would be associated with
-emergence of a diffuse Ly$\alpha$ background from starlight during 'Cosmic
-Dawn'. Recently, Bowman et al. (2018) have reported detection of this predicted
-absorption feature, with an unexpectedly large amplitude of 530 mK, centered at
-78 MHz. Verification of this result by an independent experiment, such as LEDA,
-is pressing. In this paper, we detail design and characterization of the LEDA
-radiometer systems, and a first-generation pipeline that instantiates a signal
-path model. Sited at the Owens Valley Radio Observatory Long Wavelength Array,
-LEDA systems include the station correlator, five well-separated redundant dual
-polarization radiometers and backend electronics. The radiometers deliver a
-30-85MHz band (16<z<34) and operate as part of the larger interferometric
-array, for purposes ultimately of in situ calibration. Here, we report on the
-LEDA system design, calibration approach, and progress in characterization as
-of January 2016. The LEDA systems are currently being modified to improve
-performance near 78 MHz in order to verify the purported absorption feature.
-",0,1,0,0,0,0
-20435,Automatic Bayesian Density Analysis,"  Making sense of a dataset in an automatic and unsupervised fashion is a
-challenging problem in statistics and AI. Classical approaches for density
-estimation are usually not flexible enough to deal with the uncertainty
-inherent to real-world data: they are often restricted to fixed latent
-interaction models and homogeneous likelihoods; they are sensitive to missing,
-corrupt and anomalous data; moreover, their expressiveness generally comes at
-the price of intractable inference. As a result, supervision from statisticians
-is usually needed to find the right model for the data. However, as domain
-experts do not necessarily have to be experts in statistics, we propose
-Automatic Bayesian Density Analysis (ABDA) to make density estimation
-accessible at large. ABDA automates the selection of adequate likelihood models
-from arbitrarily rich dictionaries while modeling their interactions via a deep
-latent structure adaptively learned from data as a sum-product network. ABDA
-casts uncertainty estimation at these local and global levels into a joint
-Bayesian inference problem, providing robust and yet tractable inference.
-Extensive empirical evidence shows that ABDA is a suitable tool for automatic
-exploratory analysis of heterogeneous tabular data, allowing for missing value
-estimation, statistical data type and likelihood discovery, anomaly detection
-and dependency structure mining, on top of providing accurate density
-estimation.
-",0,0,0,1,0,0
-20436,MLCapsule: Guarded Offline Deployment of Machine Learning as a Service,"  With the widespread use of machine learning (ML) techniques, ML as a service
-has become increasingly popular. In this setting, an ML model resides on a
-server and users can query the model with their data via an API. However, if
-the user's input is sensitive, sending it to the server is not an option.
-Equally, the service provider does not want to share the model by sending it to
-the client for protecting its intellectual property and pay-per-query business
-model. In this paper, we propose MLCapsule, a guarded offline deployment of
-machine learning as a service. MLCapsule executes the machine learning model
-locally on the user's client and therefore the data never leaves the client.
-Meanwhile, MLCapsule offers the service provider the same level of control and
-security of its model as the commonly used server-side execution. In addition,
-MLCapsule is applicable to offline applications that require local execution.
-Beyond protecting against direct model access, we demonstrate that MLCapsule
-allows for implementing defenses against advanced attacks on machine learning
-models such as model stealing/reverse engineering and membership inference.
-",0,0,0,1,0,0
-20437,Brownian forgery of statistical dependences,"  The balance held by Brownian motion between temporal regularity and
-randomness is embodied in a remarkable way by Levy's forgery of continuous
-functions. Here we describe how this property can be extended to forge
-arbitrary dependences between two statistical systems, and then establish a new
-Brownian independence test based on fluctuating random paths. We also argue
-that this result allows revisiting the theory of Brownian covariance from a
-physical perspective and opens the possibility of engineering nonlinear
-correlation measures from more general functional integrals.
-",0,0,1,1,0,0
-20438,Internal sizes in $μ$-abstract elementary classes,"  Working in the context of $\mu$-abstract elementary classes ($\mu$-AECs) -
-or, equivalently, accessible categories with all morphisms monomorphisms - we
-examine the two natural notions of size that occur, namely cardinality of
-underlying sets and internal size. The latter, purely category-theoretic,
-notion generalizes e.g. density character in complete metric spaces and
-cardinality of orthogonal bases in Hilbert spaces. We consider the relationship
-between these notions under mild set-theoretic hypotheses, including weakenings
-of the singular cardinal hypothesis. We also establish preliminary results on
-the existence and categoricity spectra of $\mu$-AECs, including specific
-examples showing dramatic failures of the eventual categoricity conjecture
-(with categoricity defined using cardinality) in $\mu$-AECs.
-",0,0,1,0,0,0
-20439,Seasonal forecasts of the summer 2016 Yangtze River basin rainfall,"  The Yangtze River has been subject to heavy flooding throughout history, and
-in recent times severe floods such as those in 1998 have resulted in heavy loss
-of life and livelihoods. Dams along the river help to manage flood waters, and
-are important sources of electricity for the region. Being able to forecast
-high-impact events at long lead times therefore has enormous potential benefit.
-Recent improvements in seasonal forecasting mean that dynamical climate models
-can start to be used directly for operational services. The teleconnection from
-El Niño to Yangtze River basin rainfall meant that the strong El Niño in
-winter 2015/2016 provided a valuable opportunity to test the application of a
-dynamical forecast system.
-This paper therefore presents a case study of a real time seasonal forecast
-for the Yangtze River basin, building on previous work demonstrating the
-retrospective skill of such a forecast. A simple forecasting methodology is
-presented, in which the forecast probabilities are derived from the historical
-relationship between hindcast and observations. Its performance for 2016 is
-discussed. The heavy rainfall in the May-June-July period was correctly
-forecast well in advance. August saw anomalously low rainfall, and the
-forecasts for the June-July-August period correctly showed closer to average
-levels. The forecasts contributed to the confidence of decision-makers across
-the Yangtze River basin. Trials of climate services such as this help to
-promote appropriate use of seasonal forecasts, and highlight areas for future
-improvements.
-",0,1,0,0,0,0
-20440,Modeling Interference Via Symmetric Treatment Decomposition,"  Classical causal inference assumes a treatment meant for a given unit does
-not have an effect on other units. When this ""no interference"" assumption is
-violated, new types of spillover causal effects arise, and causal inference
-becomes much more difficult. In addition, interference introduces a unique
-complication where outcomes may transmit treatment influences to each other,
-which is a relationship that has some features of a causal one, but is
-symmetric. In settings where detailed temporal information on outcomes is not
-available, addressing this complication using statistical inference methods
-based on Directed Acyclic Graphs (DAGs) (Ogburn & VanderWeele, 2014) leads to
-conceptual difficulties.
-In this paper, we develop a new approach to decomposing the spillover effect
-into direct (also known as the contagion effect) and indirect (also known as
-the infectiousness effect) components that extends the DAG based treatment
-decomposition approach to mediation found in (Robins & Richardson, 2010) to
-causal chain graph models (Lauritzen & Richardson, 2002). We show that when
-these components of the spillover effect are identified in these models, they
-have an identifying functional, which we call the symmetric mediation formula,
-that generalizes the mediation formula in DAGs (Pearl, 2011). We further show
-that, unlike assumptions in classical mediation analysis, an assumption
-permitting identification in our setting leads to restrictions on the observed
-data law, making the assumption empirically falsifiable. Finally, we discuss
-statistical inference for the components of the spillover effect in the special
-case of two interacting outcomes, and discuss a maximum likelihood estimator,
-and a doubly robust estimator.
-",0,0,0,1,0,0
-20441,A Bayesian algorithm for distributed network localization using distance and direction data,"  A reliable, accurate, and affordable positioning service is highly required
-in wireless networks. In this paper, the novel Message Passing Hybrid
-Localization (MPHL) algorithm is proposed to solve the problem of cooperative
-distributed localization using distance and direction estimates. This hybrid
-approach combines two sensing modalities to reduce the uncertainty in
-localizing the network nodes. A statistical model is formulated for the
-problem, and approximate minimum mean square error (MMSE) estimates of the node
-locations are computed. The proposed MPHL is a distributed algorithm based on
-belief propagation (BP) and Markov chain Monte Carlo (MCMC) sampling. It
-improves the identifiability of the localization problem and reduces its
-sensitivity to the anchor node geometry, compared to distance-only or
-direction-only localization techniques. For example, the unknown location of a
-node can be found if it has only a single neighbor; and a whole network can be
-localized using only a single anchor node. Numerical results are presented
-showing that the average localization error is significantly reduced in almost
-every simulation scenario, about 50% in most cases, compared to the competing
-algorithms.
-",1,0,0,1,0,0
-20442,Bounds for multivariate residues and for the polynomials in the elimination theorem,"  We present several upper bounds for the height of global residues of rational
-forms on an affine variety. As a consequence, we deduce upper bounds for the
-height of the coefficients in the Bergman-Weil trace formula.
-We also present upper bounds for the degree and the height of the polynomials
-in the elimination theorem on an affine variety. This is an arithmetic analogue
-of Jelonek's effective elimination theorem, that plays a crucial role in the
-proof of our bounds for the height of global residues.
-",0,0,1,0,0,0
-20443,An adsorbed gas estimation model for shale gas reservoirs via statistical learning,"  Shale gas plays an important role in reducing pollution and adjusting the
-structure of world energy. Gas content estimation is particularly significant
-in shale gas resource evaluation. There exist various estimation methods, such
-as first principle methods and empirical models. However, resource evaluation
-presents many challenges, especially the insufficient accuracy of existing
-models and the high cost resulting from time-consuming adsorption experiments.
-In this research, a low-cost and high-accuracy model based on geological
-parameters is constructed through statistical learning methods to estimate
-adsorbed shale gas content
-",0,1,0,1,0,0
-20444,A Semantic Loss Function for Deep Learning with Symbolic Knowledge,"  This paper develops a novel methodology for using symbolic knowledge in deep
-learning. From first principles, we derive a semantic loss function that
-bridges between neural output vectors and logical constraints. This loss
-function captures how close the neural network is to satisfying the constraints
-on its output. An experimental evaluation shows that it effectively guides the
-learner to achieve (near-)state-of-the-art results on semi-supervised
-multi-class classification. Moreover, it significantly increases the ability of
-the neural network to predict structured objects, such as rankings and paths.
-These discrete concepts are tremendously difficult to learn, and benefit from a
-tight integration of deep learning and symbolic reasoning methods.
-",1,0,0,1,0,0
-20445,Chaotic properties of a turbulent isotropic fluid,"  By tracking the divergence of two initially close trajectories in phase space
-in an Eulerian approach to forced turbulence, the relation between the maximal
-Lyapunov exponent $\lambda$, and the Reynolds number $Re$ is measured using
-direct numerical simulations, performed on up to $2048^3$ collocation points.
-The Lyapunov exponent is found to solely depend on the Reynolds number with
-$\lambda \propto Re^{0.53}$ and that after a transient period the divergence of
-trajectories grows at the same rate at all scales. Finally a linear divergence
-is seen that is dependent on the energy forcing rate. Links are made with other
-chaotic systems.
-",0,1,0,0,0,0
-20446,White light emission from silicon nanoparticles,"  As one of the most important semiconductors, silicon (Si) has been used to
-fabricate electronic devices, waveguides, detectors, and solar cells etc.
-However, its indirect bandgap hinders the use of Si for making good emitters1.
-For integrated photonic circuits, Si-based emitters with sizes in the range of
-100-300 nm are highly desirable. Here, we show that efficient white light
-emission can be realized in spherical and cylindrical Si nanoparticles with
-feature sizes of ~200 nm. The up-converted luminescence appears at the magnetic
-and electric multipole resonances when the nanoparticles are resonantly excited
-at their magnetic and electric dipole resonances by using femtosecond (fs)
-laser pulses with ultralow low energy of ~40 pJ. The lifetime of the white
-light is as short as ~52 ps, almost three orders of magnitude smaller than the
-state-of-the-art results reported so far for Si (~10 ns). Our finding paves the
-way for realizing efficient Si-based emitters compatible with current
-semiconductor fabrication technology, which can be integrated to photonic
-circuits.
-",0,1,0,0,0,0
-20447,The collapse of ecosystem engineer populations,"  Humans are the ultimate ecosystem engineers who have profoundly transformed
-the world's landscapes in order to enhance their survival. Somewhat
-paradoxically, however, sometimes the unforeseen effect of this ecosystem
-engineering is the very collapse of the population it intended to protect. Here
-we use a spatial version of a standard population dynamics model of ecosystem
-engineers to study the colonization of unexplored virgin territories by a small
-settlement of engineers. We find that during the expansion phase the population
-density reaches values much higher than those the environment can support in
-the equilibrium situation. When the colonization front reaches the boundary of
-the available space, the population density plunges sharply and attains its
-equilibrium value. The collapse takes place without warning and happens just
-after the population reaches its peak number. We conclude that overpopulation
-and the consequent collapse of an expanding population of ecosystem engineers
-is a natural consequence of the nonlinear feedback between the population and
-environment variables.
-",0,1,0,0,0,0
-20448,Highly Viscous Microjet Generator,"  This paper describes a simple yet novel system for generating a highly
-viscous microjet. The jet is produced inside a wettable thin tube partially
-submerged in a liquid. The gas-liquid interface inside the tube, which is
-initially concave, is kept much deeper than that outside the tube. An impulsive
-force applied at the bottom of a liquid container leads to significant
-acceleration of the liquid inside the tube followed by flow-focusing due to the
-concave interface. The jet generation process can be divided into two parts
-that occur in different time scales, i.e. the Impact time (impact duration $\le
-O(10^{-4})$ s) and Focusing time (focusing duration $\gg O(10^{-4})$ s). In
-Impact time, the liquid accelerates suddenly due to the impact. In Focusing
-time, the microjet emerges due to flow-focusing. In order to explain the sudden
-acceleration inside the tube in Impact time, we develop a physical model based
-on a pressure impulse approach. Numerical simulations confirm the proposed
-model, indicating that the basic mechanism of the acceleration of the liquid
-due to the impulsive force is elucidated. Remarkably, the viscous effect is
-negligible in Impact time. In contrast, in Focusing time, the viscosity plays
-an important role in the microjet generation. We experimentally and numerically
-investigate the velocity of microjets with various viscosities. We find that
-higher viscosities lead to reduction of the jet velocity, which can be
-described by using Reynolds number (the ratio between the inertia force and the
-viscous force). This novel device may be a starting point for next-generation
-technologies, such as high-viscosity inkjet printers including bioprinters and
-needle-free injection devices for minimally invasive medical treatments.
-",0,1,0,0,0,0
-20449,Section problems for configuration spaces of surfaces,"  In this paper we give a close-to-sharp answer to the basic questions: When is
-there a continuous way to add a point to a configuration of $n$ ordered points
-on a surface $S$ of finite type so that all the points are still distinct? When
-this is possible, what are all the ways to do it? More precisely, let
-PConf$_n(S)$ be the space of ordered $n$-tuples of distinct points in $S$. Let
-$f_n(S): \text{PConf}_{n+1}(S) \to \text{PConf}_n(S)$ be the map given by
-$f_n(x_0,x_1,\ldots,x_n):=(x_1,\ldots,x_n)$. We will classify all continuous
-sections of $f_n$ by proving:
-1. If $S=\mathbb{R}^2$ and $n>3$, any section of $f_{n}(S)$ is either ""adding
-a point at infinity"" or ""adding a point near $x_k$"". (We define these two terms
-in Section 2.1, whether we can define ""adding a point near $x_k$"" or ""adding a
-point at infinity"" depends in a delicate way on properties of $S$.)
-2. If $S=S^2$ a $2$-sphere and $n>4$, any section of $f_{n}(S)$ is ""adding a
-point near $x_k$"", if $S=S^2$ and $n=2$, the bundle $f_n(S)$ does not have a
-section. (We define this term in Section 3.2)
-3. If $S=S_g$ a surface of genus $g>1$ and for $n>1$, the bundle $f_{n}(S)$
-does not have a section.
-",0,0,1,0,0,0
-20450,Odd-triplet superconductivity in single-level quantum dots,"  We study the interplay of spin and charge coherence in a single-level quantum
-dot. A tunnel coupling to a superconducting lead induces superconducting
-correlations in the dot. With full spin symmetry retained, only even-singlet
-superconducting correlations are generated. An applied magnetic field or
-attached ferromagnetic leads partially or fully reduce the spin symmetry, and
-odd-triplet superconducting correlations are generated as well. For
-single-level quantum dots, no other superconducting correlations are possible.
-We analyze, with the help of a diagrammatic real-time technique, the interplay
-of spin symmetry and superconductivity and its signatures in electronic
-transport, in particular current and shot noise.
-",0,1,0,0,0,0
-20451,From Neuronal Models to Neuronal Dynamics and Image Processing,"  This paper is an introduction to the membrane potential equation for neurons.
-Its properties are described, as well as sample applications. Networks of these
-equations can be used for modeling neuronal systems, which also process images
-and video sequences, respectively. Specifically, (i) a dynamic retina is
-proposed (based on a reaction-diffusion system), which predicts afterimages and
-simple visual illusions, (ii) a system for texture segregation (texture
-elements are understood as even-symmetric contrast features), and (iii) a
-network for detecting object approaches (inspired by the locust visual system).
-",0,0,0,0,1,0
-20452,A compilation of LEGO Technic parts to support learning experiments on linkages,"  We present a compilation of LEGO Technic parts to provide easy-to-build
-constructions of basic planar linkages. Some technical issues and their
-possible solutions are discussed. To solve questions on fine details---like
-deciding whether the motion is an exactly straight line or not---we refer to
-the dynamic mathematics software tool GeoGebra.
-",0,0,1,0,0,0
-20453,On the Casas-Alvero conjecture,"  The conjecture is formulated in an affine structure and linked with
-dimension=1 of the defined CA sets. Then some known results are proved in this
-context. The short intended proof relies on a direct yet unclear statement
-about homogeneous dependence of algebraic equations. This might not be a
-complete proof or even one on the right track, but it may provoke more thoughts
-in this respect as expected.
-",0,0,1,0,0,0
-20454,Effect of compressibility and aspect ratio on performance of long elastic seals,"  Recent experiments show no statistical impact of seal length on the
-performance of long elastomeric seals in relatively smooth test fixtures.
-Motivated by these results, we analytically and computationally investigate the
-combined effects of seal length and compressibility on the maximum differential
-pressure a seal can support. We present a Saint-Venant type analytic shear lag
-solution for slightly compressible seals with large aspect ratios, which
-compares well with nonlinear finite element simulations in regions far from the
-ends of the seal. However, at the high- and low-pressure ends, where fracture
-is observed experimentally, the analytic solution is in poor agreement with
-detailed finite element calculations. Nevertheless, we show that the analytic
-solution provides far-field stress measures that correlate, over a range of
-aspect ratios and bulk moduli, the calculated energy release rates for the
-growth of small cracks at the two ends of the seal. Thus a single finite
-element simulation coupled with the analytic solution can be used to determine
-tendencies for fracture at the two ends of the seal over a wide range of
-geometry and compressibility. Finally, using a hypothetical critical energy
-release rate, predictions for whether a crack on the high-pressure end will
-begin to grow before or after a crack on the low-pressure end begins to grow
-are made using the analytic solution and compared with finite element
-simulations for finite deformation, hyperelastic seals.
-",0,1,0,0,0,0
-20455,Integral models of reductive groups and integral Mumford-Tate groups,"  Let $G$ be a reductive algebraic group over a $p$-adic field or number field
-$K$, and let $V$ be a $K$-linear faithful representation of $G$. A lattice
-$\Lambda$ in the vector space $V$ defines a model $\hat{G}_{\Lambda}$ of $G$
-over $\mathscr{O}_K$. One may wonder to what extent $\Lambda$ is determined by
-the group scheme $\hat{G}_{\Lambda}$. In this paper we prove that up to a
-natural equivalence relation on the set of lattices there are only finitely
-many $\Lambda$ corresponding to one model $\hat{G}_{\Lambda}$. Furthermore, we
-relate this fact to moduli spaces of abelian varieties as follows: let
-$\mathscr{A}_{g,n}$ be the moduli space of principally polarised abelian
-varieties of dimension $g$ with level $n$ structure. We prove that there are at
-most finitely many special subvarieties of $\mathscr{A}_{g,n}$ with a given
-integral generic Mumford-Tate group.
-",0,0,1,0,0,0
-20456,A proof of Boca's Theorem,"  We give a general method of extending unital completely positive maps to
-amalgamated free products of C*-algebras. As an application we give a dilation
-theoretic proof of Boca's Theorem.
-",0,0,1,0,0,0
-20457,Vico-Greengard-Ferrando quadratures in the tensor solver for integral equations,"  Convolution with Green's function of a differential operator appears in a lot
-of applications e.g. Lippmann-Schwinger integral equation. Algorithms for
-computing such are usually non-trivial and require non-uniform mesh. However,
-recently Vico, Greengard and Ferrando developed method for computing
-convolution with smooth functions with compact support with spectral accuracy,
-requiring nothing more than Fast Fourier Transform (FFT). Their approach is
-very suitable for the low-rank tensor implementation which we develop using
-Quantized Tensor Train (QTT) decomposition.
-",1,0,0,0,0,0
-20458,"Mott insulators of hardcore bosons in 1D: many-body orders, entanglement, edge modes","  Many-body phenomena were always an integral part of physics comprising of
-collective behaviors through self-organization, in systems consisting of many
-components and degrees of freedom. We investigate the collective behaviors of
-strongly interacting particles confined in one dimension. We show that
-many-body orders with topological characteristics can be found at the Mott
-insulator limit for hardcore bosons, at different fillings, without considering
-the spin degree of freedom or long-range microscopic interactions. These orders
-have unique properties like weak or strong quantum correlations (entanglement),
-quantified by the entanglement entropy, edge excitations/modes and gapped
-energy spectrum with highly degenerate ground state, bearing resemblance to
-topologically ordered phases of matter.
-",0,1,0,0,0,0
-20459,Fast Monte Carlo Markov chains for Bayesian shrinkage models with random effects,"  When performing Bayesian data analysis using a general linear mixed model,
-the resulting posterior density is almost always analytically intractable.
-However, if proper conditionally conjugate priors are used, there is a simple
-two-block Gibbs sampler that is geometrically ergodic in nearly all practical
-settings, including situations where $p > n$ (Abrahamsen and Hobert, 2017).
-Unfortunately, the (conditionally conjugate) multivariate normal prior on
-$\beta$ does not perform well in the high-dimensional setting where $p \gg n$.
-In this paper, we consider an alternative model in which the multivariate
-normal prior is replaced by the normal-gamma shrinkage prior developed by
-Griffin and Brown (2010). This change leads to a much more complex posterior
-density, and we develop a simple MCMC algorithm for exploring it. This
-algorithm, which has both deterministic and random scan components, is easier
-to analyze than the more obvious three-step Gibbs sampler. Indeed, we prove
-that the new algorithm is geometrically ergodic in most practical settings.
-",0,0,1,1,0,0
-20460,Distributed Optimal Vehicle Grid Integration Strategy with User Behavior Prediction,"  With the increasing of electric vehicle (EV) adoption in recent years, the
-impact of EV charging activities to the power grid becomes more and more
-significant. In this article, an optimal scheduling algorithm which combines
-smart EV charging and V2G gird service is developed to integrate EVs into power
-grid as distributed energy resources, with improved system cost performance.
-Specifically, an optimization problem is formulated and solved at each EV
-charging station according to control signal from aggregated control center and
-user charging behavior prediction by mean estimation and linear regression. The
-control center collects distributed optimization results and updates the
-control signal, periodically. The iteration continues until it converges to
-optimal scheduling. Experimental result shows this algorithm helps fill the
-valley and shave the peak in electric load profiles within a microgrid, while
-the energy demand of individual driver can be satisfied.
-",1,0,1,0,0,0
-20461,Horcrux: A Password Manager for Paranoids,"  Vulnerabilities in password managers are unremitting because current designs
-provide large attack surfaces, both at the client and server. We describe and
-evaluate Horcrux, a password manager that is designed holistically to minimize
-and decentralize trust, while retaining the usability of a traditional password
-manager. The prototype Horcrux client, implemented as a Firefox add-on, is
-split into two components, with code that has access to the user's master's
-password and any key material isolated into a small auditable component,
-separate from the complexity of managing the user interface. Instead of
-exposing actual credentials to the DOM, a dummy username and password are
-autofilled by the untrusted component. The trusted component intercepts and
-modifies POST requests before they are encrypted and sent over the network. To
-avoid trusting a centralized store, stored credentials are secret-shared over
-multiple servers. To provide domain and username privacy, while maintaining
-resilience to off-line attacks on a compromised password store, we incorporate
-cuckoo hashing in a way that ensures an attacker cannot determine if a guessed
-master password is correct. Our approach only works for websites that do not
-manipulate entered credentials in the browser client, so we conducted a
-large-scale experiment that found the technique appears to be compatible with
-over 98% of tested login forms.
-",1,0,0,0,0,0
-20462,Cross-validation,"  This text is a survey on cross-validation. We define all classical
-cross-validation procedures, and we study their properties for two different
-goals: estimating the risk of a given estimator, and selecting the best
-estimator among a given family. For the risk estimation problem, we compute the
-bias (which can also be corrected) and the variance of cross-validation
-methods. For estimator selection, we first provide a first-order analysis
-(based on expectations). Then, we explain how to take into account second-order
-terms (from variance computations, and by taking into account the usefulness of
-overpenalization). This allows, in the end, to provide some guidelines for
-choosing the best cross-validation method for a given learning problem.
-",0,0,1,1,0,0
-20463,Learning from Label Proportions in Brain-Computer Interfaces: Online Unsupervised Learning with Guarantees,"  Objective: Using traditional approaches, a Brain-Computer Interface (BCI)
-requires the collection of calibration data for new subjects prior to online
-use. Calibration time can be reduced or eliminated e.g.~by transfer of a
-pre-trained classifier or unsupervised adaptive classification methods which
-learn from scratch and adapt over time. While such heuristics work well in
-practice, none of them can provide theoretical guarantees. Our objective is to
-modify an event-related potential (ERP) paradigm to work in unison with the
-machine learning decoder to achieve a reliable calibration-less decoding with a
-guarantee to recover the true class means.
-Method: We introduce learning from label proportions (LLP) to the BCI
-community as a new unsupervised, and easy-to-implement classification approach
-for ERP-based BCIs. The LLP estimates the mean target and non-target responses
-based on known proportions of these two classes in different groups of the
-data. We modified a visual ERP speller to meet the requirements of the LLP. For
-evaluation, we ran simulations on artificially created data sets and conducted
-an online BCI study with N=13 subjects performing a copy-spelling task.
-Results: Theoretical considerations show that LLP is guaranteed to minimize
-the loss function similarly to a corresponding supervised classifier. It
-performed well in simulations and in the online application, where 84.5% of
-characters were spelled correctly on average without prior calibration.
-Significance: The continuously adapting LLP classifier is the first
-unsupervised decoder for ERP BCIs guaranteed to find the true class means. This
-makes it an ideal solution to avoid a tedious calibration and to tackle
-non-stationarities in the data. Additionally, LLP works on complementary
-principles compared to existing unsupervised methods, allowing for their
-further enhancement when combined with LLP.
-",1,0,0,1,0,0
-20464,Generalized Index Coding Problem and Discrete Polymatroids,"  The index coding problem has been generalized recently to accommodate
-receivers which demand functions of messages and which possess functions of
-messages. The connections between index coding and matroid theory have been
-well studied in the recent past. Index coding solutions were first connected to
-multi linear representation of matroids. For vector linear index codes discrete
-polymatroids which can be viewed as a generalization of the matroids was used.
-It was shown that a vector linear solution to an index coding problem exists if
-and only if there exists a representable discrete polymatroid satisfying
-certain conditions. In this work we explore the connections between generalized
-index coding and discrete polymatroids. The conditions that needs to be
-satisfied by a representable discrete polymatroid for a generalized index
-coding problem to have a vector linear solution is established. From a discrete
-polymatroid we construct an index coding problem with coded side information
-and shows that if the index coding problem has a certain optimal length
-solution then the discrete polymatroid satisfies certain properties. From a
-matroid we construct a similar generalized index coding problem and shows that
-the index coding problem has a binary scalar linear solution of optimal length
-if and only if the matroid is binary representable.
-",1,0,1,0,0,0
-20465,Reconstruction via the intrinsic geometric structures of interior transmission eigenfunctions,"  We are concerned with the inverse scattering problem of extracting the
-geometric structures of an unknown/inaccessible inhomogeneous medium by using
-the corresponding acoustic far-field measurement. Using the intrinsic geometric
-properties of the so-called interior transmission eigenfunctions, we develop a
-novel inverse scattering scheme. The proposed method can efficiently capture
-the cusp singularities of the support of the inhomogeneous medium. If further a
-priori information is available on the support of the medium, say, it is a
-convex polyhedron, then one can actually recover its shape. Both theoretical
-analysis and numerical experiments are provided. Our reconstruction method is
-new to the literature and opens up a new direction in the study of inverse
-scattering problems.
-",0,0,1,0,0,0
-20466,Thermoelectric phase diagram of the SrTiO3-SrNbO3 solid solution system,"  Thermoelectric energy conversion - the exploitation of the Seebeck effect to
-convert waste heat into electricity - has attracted an increasing amount of
-research attention for energy harvesting technology. Niobium-doped strontium
-titanate (SrTi1-xNbxO3) is one of the most promising thermoelectric material
-candidates, particularly as it poses a much lesser environmental risk in
-comparison to materials based on heavy metal elements. Two-dimensional electron
-confinement, e.g. through the formation of superlattices or two-dimensional
-electron gases, is recognized as an effective strategy to improve the
-thermoelectric performance of SrTi1-xNbxO3. Although electron confinement is
-closely related to the electronic structure, the fundamental electronic phase
-behavior of the SrTi1-xNbxO3 solid solution system has yet to be
-comprehensively investigated. Here, we present a thermoelectric phase diagram
-for the SrTi1-xNbxO3 (0.05 =< x =< 1) solid solution system, which we derived
-from the characterization of epitaxial films. We observed two thermoelectric
-phase boundaries in the system, which originate from the step-like decrease in
-carrier effective mass at x ~ 0.3, and from a local minimum in carrier
-relaxation time at x ~ 0.5. The origins of these phase boundaries are
-considered to be related to isovalent/heterovalent B-site substitution:
-parabolic Ti 3d orbitals dominate electron conduction for compositions with x <
-0.3, whereas the Nb 4d orbital dominates when x > 0.3. At x ~ 0.5, a tetragonal
-distortion of the lattice, in which the B-site is composed of Ti4+ and Nb4+
-ions, leads to the formation of tail-like impurity bands, which maximizes the
-electron scattering. These results provide a foundation for further research
-into improving the thermoelectric performance of SrTi1-xNbxO3.
-",0,1,0,0,0,0
-20467,"AMPA, NMDA and GABAA receptor mediated network burst dynamics in cortical cultures in vitro","  In this work we study the excitatory AMPA, and NMDA, and inhibitory GABAA
-receptor mediated dynamical changes in neuronal networks of neonatal rat cortex
-in vitro. Extracellular network-wide activity was recorded with 59 planar
-electrodes simultaneously under different pharmacological conditions. We
-analyzed the changes of overall network activity and network-wide burst
-frequency between baseline and AMPA receptor (AMPA-R) or NMDA receptor (NMDA-R)
-driven activity, as well as between the latter states and disinhibited
-activity. Additionally, spatiotemporal structures of pharmacologically modified
-bursts and recruitment of electrodes during the network bursts were studied.
-Our results show that AMPA-R and NMDA-R receptors have clearly distinct roles
-in network dynamics. AMPA-Rs are in greater charge to initiate network wide
-bursts. Therefore NMDA-Rs maintain the already initiated activity. GABAA
-receptors (GABAA-Rs) inhibit AMPA-R driven network activity more strongly than
-NMDA-R driven activity during the bursts.
-",0,0,0,0,1,0
-20468,Coarse fundamental groups and box spaces,"  We use a coarse version of the fundamental group first introduced by Barcelo,
-Kramer, Laubenbacher and Weaver to show that box spaces of finitely presented
-groups detect the normal subgroups used to construct the box space, up to
-isomorphism. As a consequence we have that two finitely presented groups admit
-coarsely equivalent box spaces if and only if they are commensurable via normal
-subgroups. We also provide an example of two filtrations $(N_i)$ and $(M_i)$ of
-a free group $F$ such that $M_i>N_i$ for all $i$ with $[M_i:N_i]$ uniformly
-bounded, but with $\Box_{(N_i)}F$ not coarsely equivalent to $\Box_{(M_i)}F$.
-Finally, we give some applications of the main theorem for rank gradient and
-the first $\ell^2$ Betti number, and show that the main theorem can be used to
-construct infinitely many coarse equivalence classes of box spaces with various
-properties.
-",0,0,1,0,0,0
-20469,Algebraic entropy of (integrable) lattice equations and their reductions,"  We study the growth of degrees in many autonomous and non-autonomous lattice
-equations defined by quad rules with corner boundary values, some of which are
-known to be integrable by other characterisations. Subject to an enabling
-conjecture, we prove polynomial growth for a large class of equations which
-includes the Adler-Bobenko-Suris equations and Viallet's $Q_V$ and its
-non-autonomous generalization. Our technique is to determine the ambient degree
-growth of the projective version of the lattice equations and to conjecture the
-growth of their common factors at each lattice vertex, allowing the true degree
-growth to be found. The resulting degrees satisfy a linear partial difference
-equation which is universal, i.e. the same for all the integrable lattice
-equations considered. When we take periodic reductions of these equations,
-which includes staircase initial conditions, we obtain from this linear partial
-difference equation an ordinary difference equation for degrees that implies
-quadratic or linear degree growth. We also study growth of degree of several
-non-integrable lattice equations. Exponential growth of degrees of these
-equations, and their mapping reductions, is also proved subject to a
-conjecture.
-",0,1,0,0,0,0
-20470,Measurement of mirror birefringence with laser heterodyne polarimetry,"  A laser heterodyne polarimeter (LHP) designed for the measurement of the
-birefringence of dielectric super-mirrors is described and initial results are
-reported. The LHP does not require an optical resonator and so promises
-unprecedented accuracy in the measurement of the birefringence of individual
-mirrors. The working principle of the LHP can be applied to the measurement of
-vacuum birefringence and potentially ALPS (Any Light Particle Search).
-",0,1,0,0,0,0
-20471,Individual position diversity in dependence socioeconomic networks increases economic output,"  The availability of big data recorded from massively multiplayer online
-role-playing games (MMORPGs) allows us to gain a deeper understanding of the
-potential connection between individuals' network positions and their economic
-outputs. We use a statistical filtering method to construct dependence networks
-from weighted friendship networks of individuals. We investigate the 30
-distinct motif positions in the 13 directed triadic motifs which represent
-microscopic dependences among individuals. Based on the structural similarity
-of motif positions, we further classify individuals into different groups. The
-node position diversity of individuals is found to be positively correlated
-with their economic outputs. We also find that the economic outputs of leaf
-nodes are significantly lower than that of the other nodes in the same motif.
-Our findings shed light on understanding the influence of network structure on
-economic activities and outputs in socioeconomic system.
-",1,1,0,0,0,0
-20472,A formula for the nonsymmetric Opdam's hypergeometric function of type $A_2$,"  The aim of this paper is to give an explicit formula for the nonsymmetric
-Heckman-Opdam's hypergeometric function of type $A_2$. This is obtained by
-differentiating the corresponding symmetric hypergeometric function.
-",0,0,1,0,0,0
-20473,A new algorithm for irreducible decomposition of representations of finite groups,"  An algorithm for irreducible decomposition of representations of finite
-groups over fields of characteristic zero is described. The algorithm uses the
-fact that the decomposition induces a partition of the invariant inner product
-into a complete set of mutually orthogonal projectors. By expressing the
-projectors through the basis elements of the centralizer ring of the
-representation, the problem is reduced to solving systems of quadratic
-equations. The current implementation of the algorithm is able to split
-representations of dimensions up to hundreds of thousands. Examples of
-calculations are given.
-",1,0,0,0,0,0
-20474,Stability and Grothendieck,"  This note is a commentary on the model-theoretic interpretation of
-Grothendieck's double limit characterization of weak relative compactness.
-",0,0,1,0,0,0
-20475,Visual Analogies between Atari Games for Studying Transfer Learning in RL,"  In this work, we ask the following question: Can visual analogies, learned in
-an unsupervised way, be used in order to transfer knowledge between pairs of
-games and even play one game using an agent trained for another game? We
-attempt to answer this research question by creating visual analogies between a
-pair of games: a source game and a target game. For example, given a video
-frame in the target game, we map it to an analogous state in the source game
-and then attempt to play using a trained policy learned for the source game. We
-demonstrate convincing visual mapping between four pairs of games (eight
-mappings), which are used to evaluate three transfer learning approaches.
-",0,0,0,1,0,0
-20476,Learning in the Repeated Secretary Problem,"  In the classical secretary problem, one attempts to find the maximum of an
-unknown and unlearnable distribution through sequential search. In many
-real-world searches, however, distributions are not entirely unknown and can be
-learned through experience. To investigate learning in such a repeated
-secretary problem we conduct a large-scale behavioral experiment in which
-people search repeatedly from fixed distributions. In contrast to prior
-investigations that find no evidence for learning in the classical scenario, in
-the repeated setting we observe substantial learning resulting in near-optimal
-stopping behavior. We conduct a Bayesian comparison of multiple behavioral
-models which shows that participants' behavior is best described by a class of
-threshold-based models that contains the theoretically optimal strategy.
-Fitting such a threshold-based model to data reveals players' estimated
-thresholds to be surprisingly close to the optimal thresholds after only a
-small number of games.
-",1,0,0,0,0,0
-20477,Flexible Mixture Modeling on Constrained Spaces,"  This paper addresses challenges in flexibly modeling multimodal data that lie
-on constrained spaces. Applications include climate or crime measurements in a
-geographical area, or flow-cytometry experiments, where unsuitable recordings
-are discarded. A simple approach to modeling such data is through the use of
-mixture models, with each component following an appropriate truncated
-distribution. Problems arise when the truncation involves complicated
-constraints, leading to difficulties in specifying the component distributions,
-and in evaluating their normalization constants. Bayesian inference over the
-parameters of these models results in posterior distributions that are
-doubly-intractable. We address this problem via an algorithm based on rejection
-sampling and data augmentation. We view samples from a truncated distribution
-as outcomes of a rejection sampling scheme, where proposals are made from a
-simple mixture model, and are rejected if they violate the constraints. Our
-scheme proceeds by imputing the rejected samples given mixture parameters, and
-then resampling parameters given all samples. We study two modeling approaches:
-mixtures of truncated components and truncated mixtures of components. In both
-situations, we describe exact Markov chain Monte Carlo sampling algorithms, as
-well as approximations that bound the number of rejected samples, achieving
-computational efficiency and lower variance at the cost of asymptotic bias.
-Overall, our methodology only requires practitioners to provide an indicator
-function for the set of interest. We present results on simulated data and
-apply our algorithm to two problems, one involving flow-cytometry data, and the
-other, crime recorded in the city of Chicago.
-",0,0,0,1,0,0
-20478,Universal equilibrium scaling functions at short times after a quench,"  By analyzing spin-spin correlation functions at relatively short distances,
-we show that equilibrium near-critical properties can be extracted at short
-times after quenches into the vicinity of a quantum critical point. The time
-scales after which equilibrium properties can be extracted are sufficiently
-short so that the proposed scheme should be viable for quantum simulators of
-spin models based on ultracold atoms or trapped ions. Our results, analytic as
-well as numeric, are for one-dimensional spin models, either integrable or
-nonintegrable, but we expect our conclusions to be valid in higher dimensions
-as well.
-",0,1,0,0,0,0
-20479,First Results from CUORE: A Search for Lepton Number Violation via $0νββ$ Decay of $^{130}$Te,"  The CUORE experiment, a ton-scale cryogenic bolometer array, recently began
-operation at the Laboratori Nazionali del Gran Sasso in Italy. The array
-represents a significant advancement in this technology, and in this work we
-apply it for the first time to a high-sensitivity search for a
-lepton-number--violating process: $^{130}$Te neutrinoless double-beta decay.
-Examining a total TeO$_2$ exposure of 86.3 kg$\cdot$yr, characterized by an
-effective energy resolution of (7.7 $\pm$ 0.5) keV FWHM and a background in the
-region of interest of (0.014 $\pm$ 0.002) counts/(keV$\cdot$kg$\cdot$yr), we
-find no evidence for neutrinoless double-beta decay. The median statistical
-sensitivity of this search is $7.0\times10^{24}$ yr. Including systematic
-uncertainties, we place a lower limit on the decay half-life of
-$T^{0\nu}_{1/2}$($^{130}$Te) > $1.3\times 10^{25}$ yr (90% C.L.). Combining
-this result with those of two earlier experiments, Cuoricino and CUORE-0, we
-find $T^{0\nu}_{1/2}$($^{130}$Te) > $1.5\times 10^{25}$ yr (90% C.L.), which is
-the most stringent limit to date on this decay. Interpreting this result as a
-limit on the effective Majorana neutrino mass, we find $m_{\beta\beta}<(110 -
-520)$ meV, where the range reflects the nuclear matrix element estimates
-employed.
-",0,1,0,0,0,0
-20480,Bernoulli-Carlitz and Cauchy-Carlitz numbers with Stirling-Carlitz numbers,"  Recently, the Cauchy-Carlitz number was defined as the counterpart of the
-Bernoulli-Carlitz number. Both numbers can be expressed explicitly in terms of
-so-called Stirling-Carlitz numbers. In this paper, we study the second analogue
-of Stirling-Carlitz numbers and give some general formulae, including Bernoulli
-and Cauchy numbers in formal power series with complex coefficients, and
-Bernoulli-Carlitz and Cauchy-Carlitz numbers in function fields. We also give
-some applications of Hasse-Teichmüller derivative to hypergeometric Bernoulli
-and Cauchy numbers in terms of associated Stirling numbers.
-",0,0,1,0,0,0
-20481,Irreducible network backbones: unbiased graph filtering via maximum entropy,"  Networks provide an informative, yet non-redundant description of complex
-systems only if links represent truly dyadic relationships that cannot be
-directly traced back to node-specific properties such as size, importance, or
-coordinates in some embedding space. In any real-world network, some links may
-be reducible, and others irreducible, to such local properties. This dichotomy
-persists despite the steady increase in data availability and resolution, which
-actually determines an even stronger need for filtering techniques aimed at
-discerning essential links from non-essential ones. Here we introduce a
-rigorous method that, for any desired level of statistical significance,
-outputs the network backbone that is irreducible to the local properties of
-nodes, i.e. their degrees and strengths. Unlike previous approaches, our method
-employs an exact maximum-entropy formulation guaranteeing that the filtered
-network encodes only the links that cannot be inferred from local information.
-Extensive empirical analysis confirms that this approach uncovers essential
-backbones that are otherwise hidden amidst many redundant relationships and
-inaccessible to other methods. For instance, we retrieve the hub-and-spoke
-skeleton of the US airport network and many specialised patterns of
-international trade. Being irreducible to local transportation and economic
-constraints of supply and demand, these backbones single out genuinely
-higher-order wiring principles.
-",1,1,0,0,0,0
-20482,Synkhronos: a Multi-GPU Theano Extension for Data Parallelism,"  We present Synkhronos, an extension to Theano for multi-GPU computations
-leveraging data parallelism. Our framework provides automated execution and
-synchronization across devices, allowing users to continue to write serial
-programs without risk of race conditions. The NVIDIA Collective Communication
-Library is used for high-bandwidth inter-GPU communication. Further
-enhancements to the Theano function interface include input slicing (with
-aggregation) and input indexing, which perform common data-parallel computation
-patterns efficiently. One example use case is synchronous SGD, which has
-recently been shown to scale well for a growing set of deep learning problems.
-When training ResNet-50, we achieve a near-linear speedup of 7.5x on an NVIDIA
-DGX-1 using 8 GPUs, relative to Theano-only code running a single GPU in
-isolation. Yet Synkhronos remains general to any data-parallel computation
-programmable in Theano. By implementing parallelism at the level of individual
-Theano functions, our framework uniquely addresses a niche between manual
-multi-device programming and prescribed multi-GPU training routines.
-",1,0,0,0,0,0
-20483,The PomXYZ Proteins Self-Organize on the Bacterial Nucleoid to Stimulate Cell Division,"  Cell division site positioning is precisely regulated to generate correctly
-sized and shaped daughters. We uncover a novel strategy to position the FtsZ
-cytokinetic ring at midcell in the social bacterium Myxococcus xanthus. PomX,
-PomY and the nucleoid-binding ParA/MinD ATPase PomZ self-assemble forming a
-large nucleoid-associated complex that localizes at the division site before
-FtsZ to directly guide and stimulate division. PomXYZ localization is generated
-through self-organized biased random motion on the nucleoid towards midcell and
-constrained motion at midcell. Experiments and theory show that PomXYZ motion
-is produced by diffusive PomZ fluxes on the nucleoid into the complex. Flux
-differences scale with the intracellular asymmetry of the complex and are
-converted into a local PomZ concentration gradient across the complex with
-translocation towards the higher PomZ concentration. At midcell, fluxes
-equalize resulting in constrained motion. Flux-based mechanisms may represent a
-general paradigm for positioning of macromolecular structures in bacteria.
-",0,0,0,0,1,0
-20484,Quantitative Results on Diophantine Equations in Many Variables,"  We consider a system of polynomials $f_1,\ldots, f_R\in
-\mathbb{Z}[x_1,\ldots, x_n]$ of the same degree with non-singular local zeros
-and in many variables. Generalising the work of Birch (1962) we find
-quantitative asymptotics (in terms of the maximum of the absolute value of the
-coefficients of these polynomials) for the number of integer zeros of this
-system within a growing box. Using a quantitative version of the
-Nullstellensatz, we obtain a quantitative strong approximation result, i.e. an
-upper bound on the smallest integer zero provided the system of polynomials is
-non-singular.
-",0,0,1,0,0,0
-20485,Generalized Springer correspondence for symmetric spaces associated to orthogonal groups,"  Let $G = GL_N$ over an algebraically closed field of odd characteristic, and
-$\theta$ an involutive automorphism on $G$ such that $H = (G^{\theta})^0$ is
-isomorphic to $SO_N$. Then $G^{\iota\theta} = \{ g \in G \mid \theta(g) =
-g^{-1} \}$ is regarded as a symmetric space $G/G^{\theta}$. Let
-$G^{\iota\theta}_{uni}$ be the set of unipotent elements in $G^{\iota\theta}$.
-$H$ acts on $G^{\iota\theta}_{uni}$ by the conjugation. As an analogue of the
-generalized Springer correspondence in the case of reductive groups, we
-establish in this paper the generalized Springer correspondence between
-$H$-orbits in $G^{\iota\theta}_{uni}$ and irreducible representations of
-various symmetric groups.
-",0,0,1,0,0,0
-20486,An Ensemble Boosting Model for Predicting Transfer to the Pediatric Intensive Care Unit,"  Our work focuses on the problem of predicting the transfer of pediatric
-patients from the general ward of a hospital to the pediatric intensive care
-unit. Using data collected over 5.5 years from the electronic health records of
-two medical facilities, we develop classifiers based on adaptive boosting and
-gradient tree boosting. We further combine these learned classifiers into an
-ensemble model and compare its performance to a modified pediatric early
-warning score (PEWS) baseline that relies on expert defined guidelines. To
-gauge model generalizability, we perform an inter-facility evaluation where we
-train our algorithm on data from one facility and perform evaluation on a
-hidden test dataset from a separate facility. We show that improvements are
-witnessed over the PEWS baseline in accuracy (0.77 vs. 0.69), sensitivity (0.80
-vs. 0.68), specificity (0.74 vs. 0.70) and AUROC (0.85 vs. 0.73).
-",1,0,0,1,0,0
-20487,Effects of Network Structure on the Performance of a Modeled Traffic Network under Drivers' Bounded Rationality,"  We propose a minority route choice game to investigate the effect of the
-network structure on traffic network performance under the assumption of
-drivers' bounded rationality. We investigate ring-and-hub topologies to capture
-the nature of traffic networks in cities, and employ a minority game-based
-inductive learning process to model the characteristic behavior under the route
-choice scenario. Through numerical experiments, we find that topological
-changes in traffic networks induce a phase transition from an uncongested phase
-to a congested phase. Understanding this phase transition is helpful in
-planning new traffic networks.
-",1,1,0,0,0,0
-20488,Sneak into Devil's Colony- A study of Fake Profiles in Online Social Networks and the Cyber Law,"  Massive content about user's social, personal and professional life stored on
-Online Social Networks (OSNs) has attracted not only the attention of
-researchers and social analysts but also the cyber criminals. These cyber
-criminals penetrate illegally into an OSN by establishing fake profiles or by
-designing bots and exploit the vulnerabilities of an OSN to carry out illegal
-activities. With the growth of technology cyber crimes have been increasing
-manifold. Daily reports of the security and privacy threats in the OSNs demand
-not only the intelligent automated detection systems that can identify and
-alleviate fake profiles in real time but also the reinforcement of the security
-and privacy laws to curtail the cyber crime. In this paper, we have studied
-various categories of fake profiles like compromised profiles, cloned profiles
-and online bots (spam-bots, social-bots, like-bots and influential-bots) on
-different OSN sites along with existing cyber laws to mitigate their threats.
-In order to design fake profile detection systems, we have highlighted
-different category of fake profile features which are capable to distinguish
-different kinds of fake entities from real ones. Another major challenges faced
-by researchers while building the fake profile detection systems is the
-unavailability of data specific to fake users. The paper addresses this
-challenge by providing extremely obliging data collection techniques along with
-some existing data sources. Furthermore, an attempt is made to present several
-machine learning techniques employed to design different fake profile detection
-systems.
-",1,0,0,0,0,0
-20489,Preserving Data-Privacy with Added Noises: Optimal Estimation and Privacy Analysis,"  Networked system often relies on distributed algorithms to achieve a global
-computation goal with iterative local information exchanges between neighbor
-nodes. To preserve data privacy, a node may add a random noise to its original
-data for information exchange at each iteration. Nevertheless, a neighbor node
-can estimate other's original data based on the information it received. The
-estimation accuracy and data privacy can be measured in terms of $(\epsilon,
-\delta)$-data-privacy, defined as the probability of $\epsilon$-accurate
-estimation (the difference of an estimation and the original data is within
-$\epsilon$) is no larger than $\delta$ (the disclosure probability). How to
-optimize the estimation and analyze data privacy is a critical and open issue.
-In this paper, a theoretical framework is developed to investigate how to
-optimize the estimation of neighbor's original data using the local information
-received, named optimal distributed estimation. Then, we study the disclosure
-probability under the optimal estimation for data privacy analysis. We further
-apply the developed framework to analyze the data privacy of the
-privacy-preserving average consensus algorithm and identify the optimal noises
-for the algorithm.
-",1,0,0,0,0,0
-20490,Geometric tuning of self-propulsion for Janus catalytic particles,"  Catalytic swimmers have attracted much attention as alternatives to
-biological systems for examining collective microscopic dynamics and the
-response to physico-chemical signals. Yet, understanding and predicting even
-the most fundamental characteristics of their individual propulsion still
-raises important challenges. While chemical asymmetry is widely recognized as
-the cornerstone of catalytic propulsion, different experimental studies have
-reported that particles with identical chemical properties may propel in
-opposite directions. Here, we show that, beyond its chemical properties, the
-detailed shape of a catalytic swimmer plays an essential role in determining
-its direction of motion, demonstrating the compatibility of the classical
-theoretical framework with experimental observations.
-",0,1,0,0,0,0
-20491,On the Bogolubov-de Gennes Equations,"  We consider the Bogolubov-de Gennes equations giving an equivalent
-formulation of the BCS theory of superconductivity. We are interested in the
-case when the magnetic field is present. We (a) discuss their general features,
-(b) isolate key physical classes of solutions (normal, vortex and vortex
-lattice states) and (c) prove existence of the normal, vortex and vortex
-lattice states and stability/instability of the normal states for large/small
-temperature or/and magnetic fields.
-",0,0,1,0,0,0
-20492,"High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking","  Penalized likelihood methods are widely used for high-dimensional regression.
-Although many methods have been proposed and the associated theory is now
-well-developed, the relative efficacy of different methods in finite-sample
-settings, as encountered in practice, remains incompletely understood. There is
-therefore a need for empirical investigations in this area that can offer
-practical insight and guidance to users of these methods. In this paper we
-present a large-scale comparison of penalized regression methods. We
-distinguish between three related goals: prediction, variable selection and
-variable ranking. Our results span more than 1,800 data-generating scenarios,
-allowing us to systematically consider the influence of various factors (sample
-size, dimensionality, sparsity, signal strength and multicollinearity). We
-consider several widely-used methods (Lasso, Elastic Net, Ridge Regression,
-SCAD, the Dantzig Selector as well as Stability Selection). We find
-considerable variation in performance between methods, with results dependent
-on details of the data-generating scenario and the specific goal. Our results
-support a `no panacea' view, with no unambiguous winner across all scenarios,
-even in this restricted setting where all data align well with the assumptions
-underlying the methods. Lasso is well-behaved, performing competitively in many
-scenarios, while SCAD is highly variable. Substantial benefits from a
-Ridge-penalty are only seen in the most challenging scenarios with strong
-multi-collinearity. The results are supported by semi-synthetic analyzes using
-gene expression data from cancer samples. Our empirical results complement
-existing theory and provide a resource to compare methods across a range of
-scenarios and metrics.
-",0,0,0,1,0,0
-20493,Using Human Brain Activity to Guide Machine Learning,"  Machine learning is a field of computer science that builds algorithms that
-learn. In many cases, machine learning algorithms are used to recreate a human
-ability like adding a caption to a photo, driving a car, or playing a game.
-While the human brain has long served as a source of inspiration for machine
-learning, little effort has been made to directly use data collected from
-working brains as a guide for machine learning algorithms. Here we demonstrate
-a new paradigm of ""neurally-weighted"" machine learning, which takes fMRI
-measurements of human brain activity from subjects viewing images, and infuses
-these data into the training process of an object recognition learning
-algorithm to make it more consistent with the human brain. After training,
-these neurally-weighted classifiers are able to classify images without
-requiring any additional neural data. We show that our neural-weighting
-approach can lead to large performance gains when used with traditional machine
-vision features, as well as to significant improvements with already
-high-performing convolutional neural network features. The effectiveness of
-this approach points to a path forward for a new class of hybrid machine
-learning algorithms which take both inspiration and direct constraints from
-neuronal data.
-",1,0,0,0,0,0
-20494,Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning,"  We use a deep learning model trained only on a patient's blood oxygenation
-data (measurable with an inexpensive fingertip sensor) to predict impending
-hypoxemia (low blood oxygen) more accurately than trained anesthesiologists
-with access to all the data recorded in a modern operating room. We also
-provide a simple way to visualize the reason why a patient's risk is low or
-high by assigning weight to the patient's past blood oxygen values. This work
-has the potential to provide cutting-edge clinical decision support in
-low-resource settings, where rates of surgical complication and death are
-substantially greater than in high-resource areas.
-",1,0,0,1,0,0
-20495,Merging fragments of classical logic,"  We investigate the possibility of extending the non-functionally complete
-logic of a collection of Boolean connectives by the addition of further Boolean
-connectives that make the resulting set of connectives functionally complete.
-More precisely, we will be interested in checking whether an axiomatization for
-Classical Propositional Logic may be produced by merging Hilbert-style calculi
-for two disjoint incomplete fragments of it. We will prove that the answer to
-that problem is a negative one, unless one of the components includes only
-top-like connectives.
-",1,0,1,0,0,0
-20496,Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series,"  We propose a new variational Bayes estimator for high-dimensional copulas
-with discrete, or a combination of discrete and continuous, margins. The method
-is based on a variational approximation to a tractable augmented posterior, and
-is faster than previous likelihood-based approaches. We use it to estimate
-drawable vine copulas for univariate and multivariate Markov ordinal and mixed
-time series. These have dimension $rT$, where $T$ is the number of observations
-and $r$ is the number of series, and are difficult to estimate using previous
-methods. The vine pair-copulas are carefully selected to allow for
-heteroskedasticity, which is a feature of most ordinal time series data. When
-combined with flexible margins, the resulting time series models also allow for
-other common features of ordinal data, such as zero inflation, multiple modes
-and under- or over-dispersion. Using six example series, we illustrate both the
-flexibility of the time series copula models, and the efficacy of the
-variational Bayes estimator for copulas of up to 792 dimensions and 60
-parameters. This far exceeds the size and complexity of copula models for
-discrete data that can be estimated using previous methods.
-",0,0,0,1,0,0
-20497,COSMO: Contextualized Scene Modeling with Boltzmann Machines,"  Scene modeling is very crucial for robots that need to perceive, reason about
-and manipulate the objects in their environments. In this paper, we adapt and
-extend Boltzmann Machines (BMs) for contextualized scene modeling. Although
-there are many models on the subject, ours is the first to bring together
-objects, relations, and affordances in a highly-capable generative model. For
-this end, we introduce a hybrid version of BMs where relations and affordances
-are introduced with shared, tri-way connections into the model. Moreover, we
-contribute a dataset for relation estimation and modeling studies. We evaluate
-our method in comparison with several baselines on object estimation,
-out-of-context object detection, relation estimation, and affordance estimation
-tasks. Moreover, to illustrate the generative capability of the model, we show
-several example scenes that the model is able to generate.
-",1,0,0,0,0,0
-20498,Learning Large-Scale Topological Maps Using Sum-Product Networks,"  In order to perform complex actions in human environments, an autonomous
-robot needs the ability to understand the environment, that is, to gather and
-maintain spatial knowledge. Topological map is commonly used for representing
-large scale, global maps such as floor plans. Although much work has been done
-in topological map extraction, we have found little previous work on the
-problem of learning the topological map using a probabilistic model. Learning a
-topological map means learning the structure of the large-scale space and
-dependency between places, for example, how the evidence of a group of places
-influence the attributes of other places. This is an important step towards
-planning complex actions in the environment. In this thesis, we consider the
-problem of using probabilistic deep learning model to learn the topological
-map, which is essentially a sparse undirected graph where nodes represent
-places annotated with their semantic attributes (e.g. place category). We
-propose to use a novel probabilistic deep model, Sum-Product Networks (SPNs),
-due to their unique properties. We present two methods for learning topological
-maps using SPNs: the place grid method and the template-based method. We
-contribute an algorithm that builds SPNs for graphs using template models. Our
-experiments evaluate the ability of our models to enable robots to infer
-semantic attributes and detect maps with novel semantic attribute arrangements.
-Our results demonstrate their understanding of the topological map structure
-and spatial relations between places.
-",1,0,0,0,0,0
-20499,Entanglement scaling and spatial correlations of the transverse field Ising model with perturbations,"  We study numerically the entanglement entropy and spatial correlations of the
-one dimensional transverse field Ising model with three different
-perturbations. First, we focus on the out of equilibrium, steady state with an
-energy current passing through the system. By employing a variety of
-matrix-product state based methods, we confirm the phase diagram and compute
-the entanglement entropy. Second, we consider a small perturbation that takes
-the system away from integrability and calculate the correlations, the central
-charge and the entanglement entropy. Third, we consider periodically weakened
-bonds, exploring the phase diagram and entanglement properties first in the
-situation when the weak and strong bonds alternate (period two-bonds) and then
-the general situation of a period of n bonds. In the latter case we find a
-critical weak bond that scales with the transverse field as $J'_c/J$ =
-$(h/J)^n$, where $J$ is the strength of the strong bond, $J'$ of the weak bond
-and $h$ the transverse field. We explicitly show that the energy current is not
-a conserved quantity in this case.
-",0,1,0,0,0,0
-20500,Anyonic excitations of hardcore anyons,"  Strongly interacting many-body systems consisting of fermions or bosons can
-host exotic quasiparticles with anyonic statistics. Here, we demonstrate that
-many-body systems of anyons can also form anyonic quasi-particles. The charge
-and statistics of the emergent anyons can be different from those of the
-original anyons.
-",0,1,0,0,0,0
-20501,Chaotic behavior in Casimir oscillators: A case study for phase change materials,"  Casimir forces between material surfaces at close proximity of less than 200
-nm can lead to increased chaotic behavior of actuating devices depending on the
-strength of the Casimir interaction. We investigate these phenomena for phase
-change materials in torsional oscillators, where the amorphous to crystalline
-phase transitions lead to transitions between high and low Casimir force and
-torque states respectively, without material compositions. For a conservative
-system bifurcation curve and Poincare maps analysis show the absence of chaotic
-behavior but with the crystalline phase (high force/torque state) favoring more
-unstable behavior and stiction. However, for a non-conservative system chaotic
-behavior can occur introducing significant risk for stiction, which is again
-more pronounced for the crystalline phase. The latter illustrates the more
-general scenario that stronger Casimir forces and torques increase the
-possibility for chaotic behavior. The latter is making impossible to predict
-whether stiction or stable actuation will occur on a long term basis, and it is
-setting limitations in the design of micro/nano devices operating at short
-range nanoscale separations.
-",0,1,0,0,0,0
-20502,Confluence in Probabilistic Rewriting,"  Driven by the interest of reasoning about probabilistic programming
-languages, we set out to study a notion of unicity of normal forms for them. To
-provide a tractable proof method for it, we define a property of distribution
-confluence which is shown to imply the desired uniqueness (even for infinite
-sequences of reduction) and further properties. We then carry over several
-criteria from the classical case, such as Newman's lemma, to simplify proving
-confluence in concrete languages. Using these criteria, we obtain simple proofs
-of confluence for $\lambda_1$, an affine probabilistic $\lambda$-calculus, and
-for Q$^*$, a quantum programming language for which a related property has
-already been proven in the literature.
-",1,0,0,0,0,0
-20503,Structure preserving schemes for mean-field equations of collective behavior,"  In this paper we consider the development of numerical schemes for mean-field
-equations describing the collective behavior of a large group of interacting
-agents. The schemes are based on a generalization of the classical Chang-Cooper
-approach and are capable to preserve the main structural properties of the
-systems, namely nonnegativity of the solution, physical conservation laws,
-entropy dissipation and stationary solutions. In particular, the methods here
-derived are second order accurate in transient regimes whereas they can reach
-arbitrary accuracy asymptotically for large times. Several examples are
-reported to show the generality of the approach.
-",0,1,0,0,0,0
-20504,Localization optoacoustic tomography,"  Localization-based imaging has revolutionized fluorescence optical microscopy
-and has also enabled unprecedented ultrasound images of microvascular
-structures in deep tissues. Herein, we introduce a new concept of localization
-optoacoustic tomography (LOAT) that employs rapid sequential acquisition of
-three-dimensional optoacoustic images from flowing absorbing particles. We show
-that the new method enables breaking through the spatial resolution barrier of
-acoustic diffraction while further enhancing the visibility of structures under
-limited-view tomographic conditions. Given the intrinsic sensitivity of
-optoacoustics to multiple hemodynamic and oxygenation parameters, LOAT may
-enable new level of performance in studying functional and anatomical
-alterations of microcirculation.
-",0,1,0,0,0,0
-20505,Reduced-basis approach to many-body localization,"  Within the standard model of many-body localization, i.e., the disordered
-chain of spinless fermions, we investigate how the interaction affects the
-many-body states in the basis of noninteracting localized Anderson states. From
-this starting point we follow the approach that uses a reduced basis of
-many-body states. Together with an extrapolation to the full basis, it proves
-to be efficient for the evaluation of the stiffnesses of local observables,
-which remain finite within the non-ergodic regime and represent the hallmark of
-the many-body localization (MBL). The method enables a larger span of system
-sizes and, within the MBL regime, allows for a more careful analysis of the
-size scaling of calculated quantities. On the other hand, the survival
-stiffness as the representative of non--local quantities, reveals limitations
-of the reduced-basis approach.
-",0,1,0,0,0,0
-20506,Electron-correlation study of Y III-Tc VII ions using a relativistic coupled-cluster theory,"  Spectroscopic properties, useful for plasma diagnostics and astrophysics, of
-a few rubidium-like ions are studied here. We choose one of the simplest, but
-correlationally challenging series where $d-$ and $f-$ orbitals are present in
-the core and/or valence shells with $4d$ $^2D_{3/2}$ as the ground state. We
-study different correlation characteristics of this series and make precise
-calculations of electronic structure and rates of electromagnetic transitions.
-Our calculated lifetimes and transition rates are compared with other available
-experimental and theoretical values. Radiative rates of vacuum ultra-violet
-electromagnetic transitions of the long lived Tc$^{6+}$ ion, useful in several
-areas of physics and chemistry, are estimated. To the best of our knowledge,
-there is no literature for most of these transitions.
-",0,1,0,0,0,0
-20507,Align and Copy: UZH at SIGMORPHON 2017 Shared Task for Morphological Reinflection,"  This paper presents the submissions by the University of Zurich to the
-SIGMORPHON 2017 shared task on morphological reinflection. The task is to
-predict the inflected form given a lemma and a set of morpho-syntactic
-features. We focus on neural network approaches that can tackle the task in a
-limited-resource setting. As the transduction of the lemma into the inflected
-form is dominated by copying over lemma characters, we propose two recurrent
-neural network architectures with hard monotonic attention that are strong at
-copying and, yet, substantially different in how they achieve this. The first
-approach is an encoder-decoder model with a copy mechanism. The second approach
-is a neural state-transition system over a set of explicit edit actions,
-including a designated COPY action. We experiment with character alignment and
-find that naive, greedy alignment consistently produces strong results for some
-languages. Our best system combination is the overall winner of the SIGMORPHON
-2017 Shared Task 1 without external resources. At a setting with 100 training
-samples, both our approaches, as ensembles of models, outperform the next best
-competitor.
-",1,0,0,0,0,0
-20508,Krylov methods for low-rank commuting generalized Sylvester equations,"  We consider generalizations of the Sylvester matrix equation, consisting of
-the sum of a Sylvester operator and a linear operator $\Pi$ with a particular
-structure. More precisely, the commutator of the matrix coefficients of the
-operator $\Pi$ and the Sylvester operator coefficients are assumed to be
-matrices with low rank. We show (under certain additional conditions) low-rank
-approximability of this problem, i.e., the solution to this matrix equation can
-be approximated with a low-rank matrix. Projection methods have successfully
-been used to solve other matrix equations with low-rank approximability. We
-propose a new projection method for this class of matrix equations. The choice
-of subspace is a crucial ingredient for any projection method for matrix
-equations. Our method is based on an adaption and extension of the extended
-Krylov subspace method for Sylvester equations. A constructive choice of the
-starting vector/block is derived from the low-rank commutators. We illustrate
-the effectiveness of our method by solving large-scale matrix equations arising
-from applications in control theory and the discretization of PDEs. The
-advantages of our approach in comparison to other methods are also illustrated.
-",0,0,1,0,0,0
-20509,Atomic-scale origin of dynamic viscoelastic response and creep in disordered solids,"  Viscoelasticity has been described since the time of Maxwell as an
-interpolation of purely viscous and purely elastic response, but its
-microscopic atomic-level mechanism in solids has remained elusive. We studied
-three model disordered solids: a random lattice, the bond-depleted fcc lattice,
-and the fcc lattice with vacancies. Within the harmonic approximation for
-central-force lattices, we applied sum-rules for viscoelastic response derived
-on the basis of non-affine atomic motions. The latter motions are a direct
-result of local structural disorder, and in particular, of the lack of
-inversion-symmetry in disordered lattices. By defining a suitable quantitative
-and general atomic-level measure of nonaffinity and inversion-symmetry, we show
-that the viscoelastic responses of all three systems collapse onto a master
-curve upon normalizing by the overall strength of inversion-symmetry breaking
-in each system. Close to the isostatic point for central-force lattices,
-power-law creep $G(t)\sim t^{-1/2}$ emerges as a consequence of the interplay
-between soft vibrational modes and non-affine dynamics, and various analytical
-scalings, supported by numerical calculations, are predicted by the theory.
-",0,1,0,0,0,0
-20510,On-sky closed loop correction of atmospheric dispersion for high-contrast coronagraphy and astrometry,"  Adaptive optic (AO) systems delivering high levels of wavefront correction
-are now common at observatories. One of the main limitations to image quality
-after wavefront correction comes from atmospheric refraction. An Atmospheric
-dispersion compensator (ADC) is employed to correct for atmospheric refraction.
-The correction is applied based on a look-up table consisting of dispersion
-values as a function of telescope elevation angle. The look-up table based
-correction of atmospheric dispersion results in imperfect compensation leading
-to the presence of residual dispersion in the point-spread function (PSF) and
-is insufficient when sub-milliarcsecond precision is required. The presence of
-residual dispersion can limit the achievable contrast while employing
-high-performance coronagraphs or can compromise high-precision astrometric
-measurements. In this paper, we present the first on-sky closed-loop correction
-of atmospheric dispersion by directly using science path images. The concept
-behind the measurement of dispersion utilizes the chromatic scaling of focal
-plane speckles. An adaptive speckle grid generated with a deformable mirror
-(DM) that has a sufficiently large number of actuators is used to accurately
-measure the residual dispersion and subsequently correct it by driving the ADC.
-We have demonstrated with the Subaru Coronagraphic Extreme AO (SCExAO) system
-on-sky closed-loop correction of residual dispersion to < 1 mas across H-band.
-This work will aid in the direct detection of habitable exoplanets with
-upcoming extremely large telescopes (ELTs) and also provide a diagnostic tool
-to test the performance of instruments which require sub-milliarcsecond
-correction.
-",0,1,0,0,0,0
-20511,Experimental observations and modelling of intrinsic rotation reversals in tokamaks,"  The progress made in understanding spontaneous toroidal rotation reversals in
-tokamaks is reviewed and current ideas to solve this ten-year-old puzzle are
-explored. The paper includes a summarial synthesis of the experimental
-observations in AUG, C-Mod, KSTAR, MAST and TCV tokamaks, reasons why turbulent
-momentum transport is thought to be responsible for the reversals, a review of
-the theory of turbulent momentum transport and suggestions for future
-investigations.
-",0,1,0,0,0,0
-20512,"Where, When, and How mmWave is Used in 5G and Beyon","  Wireless engineers and business planners commonly raise the question on
-where, when, and how millimeter-wave (mmWave) will be used in 5G and beyond.
-Since the next generation network is not just a new radio access standard, but
-instead an integration of networks for vertical markets with diverse
-applications, answers to the question depend on scenarios and use cases to be
-deployed. This paper gives four 5G mmWave deployment examples and describes in
-chronological order the scenarios and use cases of their probable deployment,
-including expected system architectures and hardware prototypes. The paper
-starts with 28 GHz outdoor backhauling for fixed wireless access and moving
-hotspots, which will be demonstrated at the PyeongChang winter Olympic games in
-2018. The second deployment example is a 60 GHz unlicensed indoor access system
-at the Tokyo-Narita airport, which is combined with Mobile Edge Computing (MEC)
-to enable ultra-high speed content download with low latency. The third example
-is mmWave mesh network to be used as a micro Radio Access Network ({\mu}-RAN),
-for cost-effective backhauling of small-cell Base Stations (BSs) in dense urban
-scenarios. The last example is mmWave based Vehicular-to-Vehicular (V2V) and
-Vehicular-to-Everything (V2X) communications system, which enables automated
-driving by exchanging High Definition (HD) dynamic map information between cars
-and Roadside Units (RSUs). For 5G and beyond, mmWave and MEC will play
-important roles for a diverse set of applications that require both ultra-high
-data rate and low latency communications.
-",1,0,0,0,0,0
-20513,"A simple script language for choreography of multiple, synchronizing non-anthropomorphic robots","  The scripting language described in this document is (in the first place)
-intended to be used on robots developed by Anja M{\o}lle Lindelof and Henning
-Christiansen as part of a research project about robots performing on stage.
-The target robots are expected to appear as familiar domestic objects that
-take their own life, so to speak, and perhaps perform together with human
-players, creating at illusion of a communication between them. In the current
-version, these robots' common behaviour is determined uniquely by a script
-written in the language described here -- the only possible autonomy for the
-robots is action to correct dynamically for inaccuracies that arise during a
-performance.
-The present work is preliminary and has not been compared to properly to
-other research work in this area, and the testing is still limited. A first
-implementation on small Lego Mindstorms based robots is under development by
-Mads Saustrup Fox as part of his master thesis work.
-",1,0,0,0,0,0
-20514,Stability results for abstract evolution equations with intermittent time-delay feedback,"  We consider abstract evolution equations with on-off time delay feedback.
-Without the time delay term, the model is described by an exponentially stable
-semigroup. We show that, under appropriate conditions involving the delay term,
-the system remains asymptotically stable. Under additional assumptions
-exponential stability results are also obtained. Concrete examples illustrating
-the abstract results are finally given.
-",0,0,1,0,0,0
-20515,Solar wind turbulent cascade from MHD to sub-ion scales: large-size 3D hybrid particle-in-cell simulations,"  Spectral properties of the turbulent cascade from fluid to kinetic scales in
-collisionless plasmas are investigated by means of large-size three-dimensional
-(3D) hybrid (fluid electrons, kinetic protons) particle-in-cell simulations.
-Initially isotropic Alfvènic fluctuations rapidly develop a strongly
-anisotropic turbulent cascade, mainly in the direction perpendicular to the
-ambient magnetic field. The omnidirectional magnetic field spectrum shows a
-double power-law behavior over almost two decades in wavenumber, with a
-Kolmogorov-like index at large scales, a spectral break around ion scales, and
-a steepening at sub-ion scales. Power laws are also observed in the spectra of
-the ion bulk velocity, density, and electric field, both at magnetohydrodynamic
-(MHD) and at kinetic scales. Despite the complex structure, the omnidirectional
-spectra of all fields at ion and sub-ion scales are in remarkable quantitative
-agreement with those of a two-dimensional (2D) simulation with similar physical
-parameters. This provides a partial, a-posteriori validation of the 2D
-approximation at kinetic scales. Conversely, at MHD scales, the spectra of the
-density and of the velocity (and, consequently, of the electric field) exhibit
-differences between the 2D and 3D cases. Although they can be partly ascribed
-to the lower spatial resolution, the main reason is likely the larger
-importance of compressible effects in a full geometry. Our findings are also in
-remarkable quantitative agreement with solar wind observations.
-",0,1,0,0,0,0
-20516,Blind source separation of tensor-valued time series,"  The blind source separation model for multivariate time series generally
-assumes that the observed series is a linear transformation of an unobserved
-series with temporally uncorrelated or independent components. Given the
-observations, the objective is to find a linear transformation that recovers
-the latent series. Several methods for accomplishing this exist and three
-particular ones are the classic SOBI and the recently proposed generalized FOBI
-(gFOBI) and generalized JADE (gJADE), each based on the use of joint lagged
-moments. In this paper we generalize the methodologies behind these algorithms
-for tensor-valued time series. We assume that our data consists of a tensor
-observed at each time point and that the observations are linear
-transformations of latent tensors we wish to estimate. The tensorial
-generalizations are shown to have particularly elegant forms and we show that
-each of them is Fisher consistent and orthogonal equivariant. Comparing the new
-methods with the original ones in various settings shows that the tensorial
-extensions are superior to both their vector-valued counterparts and to two
-existing tensorial dimension reduction methods for i.i.d. data. Finally,
-applications to fMRI-data and video processing show that the methods are
-capable of extracting relevant information from noisy high-dimensional data.
-",0,0,1,1,0,0
-20517,Mixed Cages,"  We introduce the notion of a $[z, r; g]$-mixed cage. A $[z, r; g]$-mixed cage
-is a mixed graph $G$, $z$-regular by arcs, $r$-regular by edges, with girth $g$
-and minimum order. In this paper we prove the existence of $[z, r ;g]$-mixed
-cages and exhibit families of mixed cages for some specific values. We also
-give lower and upper bounds for some choices of $z, r$ and $g$. In particular
-we present the first results on $[z,r;g]$- mixed cages for $z=1$ and any $r\geq
-1$ and $g\geq 3$, and for any $z\geq 1$, $r=1$ and $g=4$.
-",0,0,1,0,0,0
-20518,Single-beam dielectric-microsphere trapping with optical heterodyne detection,"  A technique to levitate and measure the three-dimensional position of
-micrometer-sized dielectric spheres with heterodyne detection is presented. The
-two radial degrees of freedom are measured by interfering light transmitted
-through the microsphere with a reference wavefront, while the axial degree of
-freedom is measured from the phase of the light reflected from the surface of
-the microsphere. This method pairs the simplicity and accessibility of single
-beam optical traps to a measurement of displacement that is intrinsically
-calibrated by the wavelength of the trapping light and has exceptional immunity
-to stray light. A theoretical shot noise limit of
-$1.3\times10^{-13}\,\text{m}/\sqrt{\text{Hz}}$ for the radial degrees of
-freedom, and $3.0\times10^{-15} \, \text{m}/\sqrt{\text{Hz}}$ for the axial
-degree of freedom can be obtained in the system described. The measured
-acceleration noise in the radial direction is $7.5\times10^{-5} \,
-(\text{m/s}^2)/\sqrt{\text{Hz}}$.
-",0,1,0,0,0,0
-20519,An Empirical Analysis of Traceability in the Monero Blockchain,"  Monero is a privacy-centric cryptocurrency that allows users to obscure their
-transactions by including chaff coins, called ""mixins,"" along with the actual
-coins they spend. In this paper, we empirically evaluate two weaknesses in
-Monero's mixin sampling strategy. First, about 62% of transaction inputs with
-one or more mixins are vulnerable to ""chain-reaction"" analysis -- that is, the
-real input can be deduced by elimination. Second, Monero mixins are sampled in
-such a way that they can be easily distinguished from the real coins by their
-age distribution; in short, the real input is usually the ""newest"" input. We
-estimate that this heuristic can be used to guess the real input with 80%
-accuracy over all transactions with 1 or more mixins. Next, we turn to the
-Monero ecosystem and study the importance of mining pools and the former
-anonymous marketplace AlphaBay on the transaction volume. We find that after
-removing mining pool activity, there remains a large amount of potentially
-privacy-sensitive transactions that are affected by these weaknesses. We
-propose and evaluate two countermeasures that can improve the privacy of future
-transactions.
-",1,0,0,0,0,0
-20520,Automatic Face Image Quality Prediction,"  Face image quality can be defined as a measure of the utility of a face image
-to automatic face recognition. In this work, we propose (and compare) two
-methods for automatic face image quality based on target face quality values
-from (i) human assessments of face image quality (matcher-independent), and
-(ii) quality values computed from similarity scores (matcher-dependent). A
-support vector regression model trained on face features extracted using a deep
-convolutional neural network (ConvNet) is used to predict the quality of a face
-image. The proposed methods are evaluated on two unconstrained face image
-databases, LFW and IJB-A, which both contain facial variations with multiple
-quality factors. Evaluation of the proposed automatic face image quality
-measures shows we are able to reduce the FNMR at 1% FMR by at least 13% for two
-face matchers (a COTS matcher and a ConvNet matcher) by using the proposed face
-quality to select subsets of face images and video frames for matching
-templates (i.e., multiple faces per subject) in the IJB-A protocol. To our
-knowledge, this is the first work to utilize human assessments of face image
-quality in designing a predictor of unconstrained face quality that is shown to
-be effective in cross-database evaluation.
-",1,0,0,0,0,0
-20521,Stochastic Block Models are a Discrete Surface Tension,"  Networks, which represent agents and interactions between them, arise in
-myriad applications throughout the sciences, engineering, and even the
-humanities. To understand large-scale structure in a network, a common task is
-to cluster a network's nodes into sets called ""communities"" such that there are
-dense connections within communities but sparse connections between them. A
-popular and statistically principled method to perform such clustering is to
-use a family of generative models known as stochastic block models (SBMs). In
-this paper, we show that maximum likelihood estimation in an SBM is a network
-analog of a well-known continuum surface-tension problem that arises from an
-application in metallurgy. To illustrate the utility of this bridge, we
-implement network analogs of three surface-tension algorithms, with which we
-successfully recover planted community structure in synthetic networks and
-which yield fascinating insights on empirical networks from the field of
-hyperspectral video segmentation.
-",1,0,0,1,0,0
-20522,Robust d-wave pairing symmetry in multi-orbital cobalt high temperature superconductors,"  The pairing symmetry of the newly proposed cobalt high temperature
-(high-$T_c$) superconductors formed by vertex shared cation-anion tetrahedral
-complexes is studied by the methods of mean field, random phase approximation
-(RPA) and functional renormalization group (FRG) analysis. The results of all
-these methods show that the $d_{x^2-y^2}$ pairing symmetry is robustly favored
-near half filling. The RPA and FRG methods, which are valid in weak interaction
-regions, predict that the superconducting state is also strongly orbital
-selective, namely the $d_{x^2-y^2}$ orbital that has the largest density near
-half filling among the three $t_{2g}$ orbitals dominates superconducting
-pairing. These results suggest that the new materials, if synthesized, can
-provide indisputable test to high-$T_c$ pairing mechanism and the validity of
-different theoretical methods.
-",0,1,0,0,0,0
-20523,Thermodynamic properties of Ba$_2$CoSi$_2$O$_6$Cl$_2$ in strong magnetic field: Realization of flat-band physics in a highly frustrated quantum magnet,"  The search for flat-band solid-state realizations is a crucial issue to
-verify or to challenge theoretical predictions for quantum many-body flat-band
-systems. For frustrated quantum magnets flat bands lead to various
-unconventional properties related to the existence of localized many-magnon
-states. The recently synthesized magnetic compound Ba$_2$CoSi$_2$O$_6$Cl$_2$
-seems to be an almost perfect candidate to observe these features in
-experiments. We develop a theory for Ba$_2$CoSi$_2$O$_6$Cl$_2$ by adapting the
-localized-magnon concept to this compound. We first show that our theory
-describes the known experimental facts and then we propose new experimental
-studies to detect a field-driven phase transition related to a
-Wigner-crystal-like ordering of localized magnons at low temperatures.
-",0,1,0,0,0,0
-20524,Fisher Information and Natural Gradient Learning of Random Deep Networks,"  A deep neural network is a hierarchical nonlinear model transforming input
-signals to output signals. Its input-output relation is considered to be
-stochastic, being described for a given input by a parameterized conditional
-probability distribution of outputs. The space of parameters consisting of
-weights and biases is a Riemannian manifold, where the metric is defined by the
-Fisher information matrix. The natural gradient method uses the steepest
-descent direction in a Riemannian manifold, so it is effective in learning,
-avoiding plateaus. It requires inversion of the Fisher information matrix,
-however, which is practically impossible when the matrix has a huge number of
-dimensions. Many methods for approximating the natural gradient have therefore
-been introduced. The present paper uses statistical neurodynamical method to
-reveal the properties of the Fisher information matrix in a net of random
-connections under the mean field approximation. We prove that the Fisher
-information matrix is unit-wise block diagonal supplemented by small order
-terms of off-block-diagonal elements, which provides a justification for the
-quasi-diagonal natural gradient method by Y. Ollivier. A unitwise
-block-diagonal Fisher metrix reduces to the tensor product of the Fisher
-information matrices of single units. We further prove that the Fisher
-information matrix of a single unit has a simple reduced form, a sum of a
-diagonal matrix and a rank 2 matrix of weight-bias correlations. We obtain the
-inverse of Fisher information explicitly. We then have an explicit form of the
-natural gradient, without relying on the numerical matrix inversion, which
-drastically speeds up stochastic gradient learning.
-",0,0,0,1,0,0
-20525,Dynamic of plumes and scaling during the melting of a Phase Change Material heated from below,"  We identify and describe the main dynamic regimes occurring during the
-melting of the PCM n-octadecane in horizontal layers of several sizes heated
-from below. This configuration allows to cover a wide range of effective
-Rayleigh numbers on the liquid PCM phase, up to $\sim 10^9$, without changing
-any external parameter control. We identify four different regimes as time
-evolves: (i) the conductive regime, (ii) linear regime, (iii) coarsening regime
-and (iv) turbulent regime. The first two regimes appear at all domain sizes.
-However the third and fourth regime require a minimum advance of the
-solid/liquid interface to develop, and we observe them only for large enough
-domains.
-The transition to turbulence takes places after a secondary instability that
-forces the coarsening of the thermal plumes. Each one of the melting regimes
-creates a distinct solid/liquid front that characterizes the internal state of
-the melting process. We observe that most of the magnitudes of the melting
-process are ruled by power laws, although not all of them. Thus the number of
-plumes, some regimes of the Rayleigh number as a function of time, the number
-of plumes after the primary and secondary instability, the thermal and kinetic
-boundary layers follow simple power laws. In particular, we find that the
-Nusselt number scales with the Rayleigh number as $Nu \sim Ra^{0.29}$ in the
-turbulent regime, consistent with theories and experiments on Rayleigh-Bénard
-convection that show an exponent $2/7$.
-",0,1,0,0,0,0
-20526,Parallel mean curvature surfaces in four-dimensional homogeneous spaces,"  We survey different classification results for surfaces with parallel mean
-curvature immersed into some Riemannian homogeneous four-manifolds, including
-real and complex space forms, and product spaces. We provide a common framework
-for this problem, with special attention to the existence of holomorphic
-quadratic differentials on such surfaces. The case of spheres with parallel
-mean curvature is also explained in detail, as well as the state-of-the-art
-advances in the general problem.
-",0,0,1,0,0,0
-20527,"Recent implementations, applications, and extensions of the Locally Optimal Block Preconditioned Conjugate Gradient method (LOBPCG)","  Since introduction [A. Knyazev, Toward the optimal preconditioned
-eigensolver: Locally optimal block preconditioned conjugate gradient method,
-SISC (2001) DOI:10.1137/S1064827500366124] and efficient parallel
-implementation [A. Knyazev et al., Block locally optimal preconditioned
-eigenvalue xolvers (BLOPEX) in HYPRE and PETSc, SISC (2007)
-DOI:10.1137/060661624], LOBPCG has been used is a wide range of applications in
-mechanics, material sciences, and data sciences. We review its recent
-implementations and applications, as well as extensions of the local optimality
-idea beyond standard eigenvalue problems.
-",1,0,0,1,0,0
-20528,An evaluation of cosmological models from expansion and growth of structure measurements,"  We compare a large suite of theoretical cosmological models to observational
-data from the cosmic microwave background, baryon acoustic oscillation
-measurements of expansion, Type Ia SNe measurements of expansion, redshift
-space distortion measurements of the growth of structure, and the local Hubble
-constant. Our theoretical models include parametrizations of dark energy as
-well as physical models of dark energy and modified gravity. We determine the
-constraints on the model parameters, incorporating the redshift space
-distortion data directly in the analysis. To determine whether models can be
-ruled out, we evaluate the $p$ value (the probability under the model of
-obtaining data as bad or worse than the observed data). In our comparison, we
-find the well known tension of H$_0$ with the other data; no model resolves
-this tension successfully. Among the models we consider, the large scale growth
-of structure data does not affect the modified gravity models as a category
-particularly differently than dark energy models; it matters for some modified
-gravity models but not others, and the same is true for dark energy models. We
-compute predicted observables for each model under current observational
-constraints, and identify models for which future observational constraints
-will be particularly informative.
-",0,1,0,0,0,0
-20529,The variety of $2$-dimensional algebras over an algebraically closed field,"  The work is devoted to the variety of $2$-dimensional algebras over an
-algebraically closed field. Firstly, we classify such algebras modulo
-isomorphism. Then we describe the degenerations and the closures of principal
-algebra series in the variety under consideration. Finally, we apply our
-results to obtain analogous descriptions for the subvarieties of flexible, and
-bicommutative algebras. In particular, we describe rigid algebras and
-irreducible components for these subvarieties.
-",0,0,1,0,0,0
-20530,A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM,"  We present a Bayesian object observation model for complete probabilistic
-semantic SLAM. Recent studies on object detection and feature extraction have
-become important for scene understanding and 3D mapping. However, 3D shape of
-the object is too complex to formulate the probabilistic observation model;
-therefore, performing the Bayesian inference of the object-oriented features as
-well as their pose is less considered. Besides, when the robot equipped with an
-RGB mono camera only observes the projected single view of an object, a
-significant amount of the 3D shape information is abandoned. Due to these
-limitations, semantic SLAM and viewpoint-independent loop closure using
-volumetric 3D object shape is challenging. In order to enable the complete
-formulation of probabilistic semantic SLAM, we approximate the observation
-model of a 3D object with a tractable distribution. We also estimate the
-variational likelihood from the 2D image of the object to exploit its observed
-single view. In order to evaluate the proposed method, we perform pose and
-feature estimation, and demonstrate that the automatic loop closure works
-seamlessly without additional loop detector in various environments.
-",1,0,0,0,0,0
-20531,Generation of optical frequency combs via four-wave mixing processes for low- and medium-resolution astronomy,"  We investigate the generation of optical frequency combs through a cascade of
-four-wave mixing processes in nonlinear fibres with optimised parameters. The
-initial optical field consists of two continuous-wave lasers with frequency
-separation larger than 40 GHz (312.7 pm at 1531 nm). It propagates through
-three nonlinear fibres. The first fibre serves to pulse shape the initial
-sinusoidal-square pulse, while a strong pulse compression down to sub-100 fs
-takes place in the second fibre which is an amplifying erbium-doped fibre. The
-last stage is a low-dispersion highly nonlinear fibre where the frequency comb
-bandwidth is increased and the line intensity is equalised. We model this
-system using the generalised nonlinear Schrödinger equation and investigate
-it in terms of fibre lengths, fibre dispersion, laser frequency separation and
-input powers with the aim to minimise the frequency comb noise. With the
-support of the numerical results, a frequency comb is experimentally generated,
-first in the near infra-red and then it is frequency-doubled into the visible
-spectral range. Using a MUSE-type spectrograph, we evaluate the comb
-performance for astronomical wavelength calibration in terms of equidistancy of
-the comb lines and their stability.
-",0,1,0,0,0,0
-20532,Cross-referencing Social Media and Public Surveillance Camera Data for Disaster Response,"  Physical media (like surveillance cameras) and social media (like Instagram
-and Twitter) may both be useful in attaining on-the-ground information during
-an emergency or disaster situation. However, the intersection and reliability
-of both surveillance cameras and social media during a natural disaster are not
-fully understood. To address this gap, we tested whether social media is of
-utility when physical surveillance cameras went off-line during Hurricane Irma
-in 2017. Specifically, we collected and compared geo-tagged Instagram and
-Twitter posts in the state of Florida during times and in areas where public
-surveillance cameras went off-line. We report social media content and
-frequency and content to determine the utility for emergency managers or first
-responders during a natural disaster.
-",1,0,0,0,0,0
-20533,Nonlinear Dynamics of a Viscous Bubbly Fluid,"  A physical model of a three-dimensional flow of a viscous bubbly fluid in an
-intermediate regime between bubble formation and breakage is presented. The
-model is based on mechanics and thermodynamics of a single bubble coupled to
-the dynamics of a viscous fluid as a whole, and takes into account multiple
-physical effects, including gravity, viscosity, and surface tension.
-Dimensionless versions of the resulting nonlinear model are obtained, and
-values of dimensionless parameters are estimated for typical magma flows in
-horizontal subaerial lava fields and vertical volcanic conduits.
-Exact solutions of the resulting system of nonlinear equations corresponding
-to equilibrium flows and traveling waves are analyzed in the one-dimensional
-setting. Generalized Su-Gardner-type perturbation analysis is employed to study
-approximate solutions of the model in the long-wave ansatz. Simplified
-nonlinear partial differential equations (PDE) satisfied by the leading terms
-of the perturbation solutions are systematically derived. It is shown that for
-specific classes of perturbations, approximate solutions of the bubbly fluid
-model arise from solutions of the classical diffusion, Burgers,
-variable-coefficient Burgers, and Korteweg-de Vries equations.
-",0,1,0,0,0,0
-20534,ConvNet-Based Localization of Anatomical Structures in 3D Medical Images,"  Localization of anatomical structures is a prerequisite for many tasks in
-medical image analysis. We propose a method for automatic localization of one
-or more anatomical structures in 3D medical images through detection of their
-presence in 2D image slices using a convolutional neural network (ConvNet).
-A single ConvNet is trained to detect presence of the anatomical structure of
-interest in axial, coronal, and sagittal slices extracted from a 3D image. To
-allow the ConvNet to analyze slices of different sizes, spatial pyramid pooling
-is applied. After detection, 3D bounding boxes are created by combining the
-output of the ConvNet in all slices.
-In the experiments 200 chest CT, 100 cardiac CT angiography (CTA), and 100
-abdomen CT scans were used. The heart, ascending aorta, aortic arch, and
-descending aorta were localized in chest CT scans, the left cardiac ventricle
-in cardiac CTA scans, and the liver in abdomen CT scans. Localization was
-evaluated using the distances between automatically and manually defined
-reference bounding box centroids and walls.
-The best results were achieved in localization of structures with clearly
-defined boundaries (e.g. aortic arch) and the worst when the structure boundary
-was not clearly visible (e.g. liver). The method was more robust and accurate
-in localization multiple structures.
-",1,0,0,0,0,0
-20535,Gradient estimates for heat kernels and harmonic functions,"  Let $(X,d,\mu)$ be a doubling metric measure space endowed with a Dirichlet
-form $\E$ deriving from a ""carré du champ"". Assume that $(X,d,\mu,\E)$
-supports a scale-invariant $L^2$-Poincaré inequality. In this article, we
-study the following properties of harmonic functions, heat kernels and Riesz
-transforms for $p\in (2,\infty]$:
-(i) $(G_p)$: $L^p$-estimate for the gradient of the associated heat
-semigroup;
-(ii) $(RH_p)$: $L^p$-reverse Hölder inequality for the gradients of
-harmonic functions;
-(iii) $(R_p)$: $L^p$-boundedness of the Riesz transform ($p<\infty$);
-(iv) $(GBE)$: a generalised Bakry-Émery condition.
-We show that, for $p\in (2,\infty)$, (i), (ii) (iii) are equivalent, while
-for $p=\infty$, (i), (ii), (iv) are equivalent.
-Moreover, some of these equivalences still hold under weaker conditions than
-the $L^2$-Poincaré inequality.
-Our result gives a characterisation of Li-Yau's gradient estimate of heat
-kernels for $p=\infty$, while for $p\in (2,\infty)$ it is a substantial
-improvement as well as a generalisation of earlier results by
-Auscher-Coulhon-Duong-Hofmann [7] and Auscher-Coulhon [6]. Applications to
-isoperimetric inequalities and Sobolev inequalities are given. Our results
-apply to Riemannian and sub-Riemannian manifolds as well as to non-smooth
-spaces, and to degenerate elliptic/parabolic equations in these settings.
-",0,0,1,0,0,0
-20536,The Deep Underground Neutrino Experiment -- DUNE: the precision era of neutrino physics,"  The last decade was remarkable for neutrino physics. In particular, the
-phenomenon of neutrino flavor oscillations has been firmly established by a
-series of independent measurements. All parameters of the neutrino mixing are
-now known and we have elements to plan a judicious exploration of new scenarios
-that are opened by these recent advances. With precise measurements, we can
-test the 3-neutrino paradigm, neutrino mass hierarchy and CP asymmetry in the
-lepton sector. The future long-baseline experiments are considered to be a
-fundamental tool to deepen our knowledge of electroweak interactions. The Deep
-Underground Neutrino Experiment -- DUNE will detect a broad-band neutrino beam
-from Fermilab in an underground massive Liquid Argon Time-Projection Chamber at
-an L/E of about $10^3$ km / GeV to reach good sensitivity for CP-phase
-measurements and the determination of the mass hierarchy. The dimensions and
-the depth of the Far Detector also create an excellent opportunity to look for
-rare signals like proton decay to study violation of baryonic number, as well
-as supernova neutrino bursts, broadening the scope of the experiment to
-astrophysics and associated impacts in cosmology. In this presentation, we will
-discuss the physics motivations and the main experimental features of the DUNE
-project required to reach its scientific goals.
-",0,1,0,0,0,0
-20537,Variations of $q$-Garnier system,"  We study several variants of q-Garnier system corresponding to various
-directions of discrete time evolutions. We also investigate a relation between
-the $q$-Garnier system and Suzuki's higher order $q$-Painlev/'e system by using
-a duality of the $q$-KP system.
-",0,1,1,0,0,0
-20538,Quantum Quench dynamics in Non-local Luttinger Model: Rigorous Results,"  We investigate, in the Luttinger model with fixed box potential, the time
-evolution of an inhomogeneous state prepared as a localized fermion added to
-the noninteracting ground state. We proved that, if the state is evolved with
-the interacting Hamiltonian, the averaged density has two peaks moving in
-opposite directions, with a constant but renormalized velocity. We also proved
-that a dynamical `Landau quasi-particle weight' appears in the oscillating part
-of the averaged density, asymptotically vanishing with large time. The results
-are proved with the Mattis-Lieb diagonalization method. A simpler proof with
-the exact Bosonization formulas is also provided.
-",0,0,1,0,0,0
-20539,Dynamics of Relaxed Inflation,"  The cosmological relaxation of the electroweak scale has been proposed as a
-mechanism to address the hierarchy problem of the Standard Model. A field, the
-relaxion, rolls down its potential and, in doing so, scans the squared mass
-parameter of the Higgs, relaxing it to a parametrically small value. In this
-work, we promote the relaxion to an inflaton. We couple it to Abelian gauge
-bosons, thereby introducing the necessary dissipation mechanism which slows
-down the field in the last stages. We describe a novel reheating mechanism,
-which relies on the gauge-boson production leading to strong electromagnetic
-fields, and proceeds via the vacuum production of electron-positron pairs
-through the Schwinger effect. We refer to this mechanism as Schwinger
-reheating. We discuss the cosmological dynamics of the model and the
-phenomenological constraints from CMB and other experiments. We find that a
-cutoff close to the Planck scale may be achieved. In its minimal form, the
-model does not generate sufficient curvature perturbations and additional
-ingredients, such as a curvaton field, are needed.
-",0,1,0,0,0,0
-20540,Clustering is semidefinitely not that hard: Nonnegative SDP for manifold disentangling,"  In solving hard computational problems, semidefinite program (SDP)
-relaxations often play an important role because they come with a guarantee of
-optimality. Here, we focus on a popular semidefinite relaxation of K-means
-clustering which yields the same solution as the non-convex original
-formulation for well segregated datasets. We report an unexpected finding: when
-data contains (greater than zero-dimensional) manifolds, the SDP solution
-captures such geometrical structures. Unlike traditional manifold embedding
-techniques, our approach does not rely on manually defining a kernel but rather
-enforces locality via a nonnegativity constraint. We thus call our approach
-NOnnegative MAnifold Disentangling, or NOMAD. To build an intuitive
-understanding of its manifold learning capabilities, we develop a theoretical
-analysis of NOMAD on idealized datasets. While NOMAD is convex and the globally
-optimal solution can be found by generic SDP solvers with polynomial time
-complexity, they are too slow for modern datasets. To address this problem, we
-analyze a non-convex heuristic and present a new, convex and yet efficient,
-algorithm, based on the conditional gradient method. Our results render NOMAD a
-versatile, understandable, and powerful tool for manifold learning.
-",1,0,0,0,0,0
-20541,Dispersive optical detection of magnetic Feshbach resonances in ultracold gases,"  Magnetically tunable Feshbach resonances in ultracold atomic systems are
-chiefly identified and characterized through time consuming atom loss
-spectroscopy. We describe an off-resonant dispersive optical probing technique
-to rapidly locate Feshbach resonances and demonstrate the method by locating
-four resonances of $^{87}$Rb, between the $|\rm{F} = 1, \rm{m_F}=1 \rangle$ and
-$|\rm{F} = 2, \rm{m_F}=0 \rangle$ states. Despite the loss features being
-$\lesssim0.1$ G wide, we require only 21 experimental runs to explore a
-magnetic field range >18 G, where $1~\rm{G}=10^{-4}$ T. The resonances consist
-of two known s-wave features in the vicinity of 9 G and 18 G and two previously
-unobserved p-wave features near 5 G and 10 G. We further utilize the dispersive
-approach to directly characterize the two-body loss dynamics for each Feshbach
-resonance.
-",0,1,0,0,0,0
-20542,Unveiling Bias Compensation in Turbo-Based Algorithms for (Discrete) Compressed Sensing,"  In Compressed Sensing, a real-valued sparse vector has to be recovered from
-an underdetermined system of linear equations. In many applications, however,
-the elements of the sparse vector are drawn from a finite set. Adapted
-algorithms incorporating this additional knowledge are required for the
-discrete-valued setup. In this paper, turbo-based algorithms for both cases are
-elucidated and analyzed from a communications engineering perspective, leading
-to a deeper understanding of the algorithm. In particular, we gain the
-intriguing insight that the calculation of extrinsic values is equal to the
-unbiasing of a biased estimate and present an improved algorithm.
-",1,0,0,0,0,0
-20543,Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances,"  Momentum methods such as Polyak's heavy ball (HB) method, Nesterov's
-accelerated gradient (AG) as well as accelerated projected gradient (APG)
-method have been commonly used in machine learning practice, but their
-performance is quite sensitive to noise in the gradients. We study these
-methods under a first-order stochastic oracle model where noisy estimates of
-the gradients are available. For strongly convex problems, we show that the
-distribution of the iterates of AG converges with the accelerated
-$O(\sqrt{\kappa}\log(1/\varepsilon))$ linear rate to a ball of radius
-$\varepsilon$ centered at a unique invariant distribution in the 1-Wasserstein
-metric where $\kappa$ is the condition number as long as the noise variance is
-smaller than an explicit upper bound we can provide. Our analysis also
-certifies linear convergence rates as a function of the stepsize, momentum
-parameter and the noise variance; recovering the accelerated rates in the
-noiseless case and quantifying the level of noise that can be tolerated to
-achieve a given performance. In the special case of strongly convex quadratic
-objectives, we can show accelerated linear rates in the $p$-Wasserstein metric
-for any $p\geq 1$ with improved sensitivity to noise for both AG and HB through
-a non-asymptotic analysis under some additional assumptions on the noise
-structure. Our analysis for HB and AG also leads to improved non-asymptotic
-convergence bounds in suboptimality for both deterministic and stochastic
-settings which is of independent interest. To the best of our knowledge, these
-are the first linear convergence results for stochastic momentum methods under
-the stochastic oracle model. We also extend our results to the APG method and
-weakly convex functions showing accelerated rates when the noise magnitude is
-sufficiently small.
-",1,0,0,1,0,0
-20544,Compressed sensing with sparse corruptions: Fault-tolerant sparse collocation approximations,"  The recovery of approximately sparse or compressible coefficients in a
-Polynomial Chaos Expansion is a common goal in modern parametric uncertainty
-quantification (UQ). However, relatively little effort in UQ has been directed
-toward theoretical and computational strategies for addressing the sparse
-corruptions problem, where a small number of measurements are highly corrupted.
-Such a situation has become pertinent today since modern computational
-frameworks are sufficiently complex with many interdependent components that
-may introduce hardware and software failures, some of which can be difficult to
-detect and result in a highly polluted simulation result.
-In this paper we present a novel compressive sampling-based theoretical
-analysis for a regularized $\ell^1$ minimization algorithm that aims to recover
-sparse expansion coefficients in the presence of measurement corruptions. Our
-recovery results are uniform, and prescribe algorithmic regularization
-parameters in terms of a user-defined a priori estimate on the ratio of
-measurements that are believed to be corrupted. We also propose an iteratively
-reweighted optimization algorithm that automatically refines the value of the
-regularization parameter, and empirically produces superior results. Our
-numerical results test our framework on several medium-to-high dimensional
-examples of solutions to parameterized differential equations, and demonstrate
-the effectiveness of our approach.
-",0,0,1,0,0,0
-20545,Learning to Remember Rare Events,"  Despite recent advances, memory-augmented deep neural networks are still
-limited when it comes to life-long and one-shot learning, especially in
-remembering rare events. We present a large-scale life-long memory module for
-use in deep learning. The module exploits fast nearest-neighbor algorithms for
-efficiency and thus scales to large memory sizes. Except for the
-nearest-neighbor query, the module is fully differentiable and trained
-end-to-end with no extra supervision. It operates in a life-long manner, i.e.,
-without the need to reset it during training.
-Our memory module can be easily added to any part of a supervised neural
-network. To show its versatility we add it to a number of networks, from simple
-convolutional ones tested on image classification to deep sequence-to-sequence
-and recurrent-convolutional models. In all cases, the enhanced network gains
-the ability to remember and do life-long one-shot learning. Our module
-remembers training examples shown many thousands of steps in the past and it
-can successfully generalize from them. We set new state-of-the-art for one-shot
-learning on the Omniglot dataset and demonstrate, for the first time, life-long
-one-shot learning in recurrent neural networks on a large-scale machine
-translation task.
-",1,0,0,0,0,0
-20546,Stacking-dependent electronic structure of trilayer graphene resolved by nanospot angle-resolved photoemission spectroscopy,"  The crystallographic stacking order in multilayer graphene plays an important
-role in determining its electronic structure. In trilayer graphene,
-rhombohedral stacking (ABC) is particularly intriguing, exhibiting a flat band
-with an electric-field tunable band gap. Such electronic structure is distinct
-from simple hexagonal stacking (AAA) or typical Bernal stacking (ABA), and is
-promising for nanoscale electronics, optoelectronics applications. So far clean
-experimental electronic spectra on the first two stackings are missing because
-the samples are usually too small in size (um or nm scale) to be resolved by
-conventional angle-resolved photoemission spectroscopy (ARPES). Here by using
-ARPES with nanospot beam size (NanoARPES), we provide direct experimental
-evidence for the coexistence of three different stackings of trilayer graphene
-and reveal their distinctive electronic structures directly. By fitting the
-experimental data, we provide important experimental band parameters for
-describing the electronic structure of trilayer graphene with different
-stackings.
-",0,1,0,0,0,0
-20547,Interaction energy between vortices of vector fields on Riemannian surfaces,"  We study a variational Ginzburg-Landau type model depending on a small
-parameter $\epsilon>0$ for (tangent) vector fields on a $2$-dimensional
-Riemannian surface. As $\epsilon\to 0$, the vector fields tend to be of unit
-length and will have singular points of a (non-zero) index, called vortices.
-Our main result determines the interaction energy between these vortices as a
-$\Gamma$-limit (at the second order) as $\epsilon\to 0$.
-",0,0,1,0,0,0
-20548,Multimodal Nonlinear Microscope based on a Compact Fiber-format Laser Source,"  We present a multimodal non-linear optical (NLO) laser-scanning microscope,
-based on a compact fiber-format excitation laser and integrating coherent
-anti-Stokes Raman scattering (CARS), stimulated Raman scattering (SRS) and
-two-photon-excitation fluorescence (TPEF) on a single platform. We demonstrate
-its capabilities in simultaneously acquiring CARS and SRS images of a blend of
-6-{\mu}m poly(methyl methacrylate) beads and 3-{\mu}m polystyrene beads. We
-then apply it to visualize cell walls and chloroplast of an unprocessed fresh
-leaf of Elodea aquatic plant via SRS and TPEF modalities, respectively. The
-presented NLO microscope, developed in house using off-the-shelf components,
-offers full accessibility to the optical path and ensures its easy
-re-configurability and flexibility.
-",0,1,0,0,0,0
-20549,The Diffuse Light of the Universe - On the microwave background before and after its discovery: open questions,"  In 1965, the discovery of a new type of uniform radiation, located between
-radiowaves and infrared light, was accidental. Known today as Cosmic Microwave
-background (CMB), this diffuse radiation is commonly interpreted as a fossil
-light released in an early hot and dense universe and constitutes today the
-main 'pilar' of the big bang cosmology. Considerable efforts have been devoted
-to derive fundamental cosmological parameters from the characteristics of this
-radiation that led to a surprising universe that is shaped by at least three
-major unknown components: inflation, dark matter and dark energy. This is an
-important weakness of the present consensus cosmological model that justifies
-raising several questions on the CMB interpretation. Can we consider its
-cosmological nature as undisputable? Do other possible interpretations exist in
-the context of other cosmological theories or simply as a result of other
-physical mechanisms that could account for it? In an effort to questioning the
-validity of scientific hypotheses and the under-determination of theories
-compared to observations, we examine here the difficulties that still exist on
-the interpretation of this diffuse radiation and explore other proposed tracks
-to explain its origin. We discuss previous historical concepts of diffuse
-radiation before and after the CMB discovery and underline the limit of our
-present understanding.
-",0,1,0,0,0,0
-20550,Classification of $5$-Dimensional Complex Nilpotent Leibniz Algebras,"  Leibniz algebras are certain generalization of Lie algebras. In this paper we
-give the classification of $5-$dimensional complex non-Lie nilpotent Leibniz
-algebras. We use the canonical forms for the congruence classes of matrices of
-bilinear forms to classify the case $\dim(A^2)=3$ and $\dim(Leib(A))=1$ which
-can be applied to higher dimensions. The remaining cases are classified via
-direct method.
-",0,0,1,0,0,0
-20551,Haptics of Screwing and Unscrewing for its Application in Smart Factories for Disassembly,"  Reconstruction of skilled humans sensation and control system often leads to
-a development of robust control for the robots. We are developing an unscrewing
-robot for the automated disassembly which requires a comprehensive control
-system, but unscrewing experiments with robots are often limited to several
-conditions. On the contrary, humans typically have a broad range of screwing
-experiences and sensations throughout their lives, and we conducted an
-experiment to find these haptic patterns. Results show that people apply axial
-force to the screws to avoid screwdriver slippage (cam-outs), which is one of
-the key problems during screwing and unscrewing, and this axial force is
-proportional to the torque which is required for screwing. We have found that
-type of the screw head influences the amount of axial force applied. Using this
-knowledge an unscrewing robot for the smart disassembly factory RecyBot is
-developed, and experiments confirm the optimality of the strategy, used by
-humans. Finally, a methodology for robust unscrewing algorithm design is
-presented as a generalization of the findings. It can seriously speed up the
-development of the screwing and unscrewing robots and tools.
-",1,0,0,0,0,0
-20552,$G$-invariant Szegö kernel asymptotics and CR reduction,"  Let $(X, T^{1,0}X)$ be a compact connected orientable CR manifold of
-dimension $2n+1$ with non-degenerate Levi curvature. Assume that $X$ admits a
-connected compact Lie group action $G$. Under certain natural assumptions about
-the group action $G$, we show that the $G$-invariant Szegö kernel for $(0,q)$
-forms is a complex Fourier integral operator, smoothing away $\mu^{-1}(0)$ and
-there is a precise description of the singularity near $\mu^{-1}(0)$, where
-$\mu$ denotes the CR moment map. We apply our result to the case when $X$
-admits a transversal CR $S^1$ action and deduce an asymptotic expansion for the
-$m$-th Fourier component of the $G$-invariant Szegö kernel for $(0,q)$ forms
-as $m \to+\infty$. As an application, we show that if $m$ large enough,
-quantization commutes with reduction.
-",0,0,1,0,0,0
-20553,Self-similar resistive circuits as fractal-like structures,"  In the present work we explore resistive circuits where the individual
-resistors are arranged in fractal-like patterns. These circuits have some of
-the characteristics typically found in geometric fractals, namely
-self-similarity and scale invariance. Considering resistive circuits as graphs,
-we propose a definition of self-similar circuits which mimics a self-similar
-fractal. General properties of the resistive circuits generated by this
-approach are investigated, and interesting examples are commented in detail.
-Specifically, we consider self-similar resistive series, tree-like resistive
-networks and Sierpinski's configurations with resistors.
-",0,1,0,0,0,0
-20554,DynaPhoPy: A code for extracting phonon quasiparticles from molecular dynamics simulations,"  We have developed a computational code, DynaPhoPy, that allow us to extract
-the microscopic anharmonic phonon properties from molecular dynamics (MD)
-simulations using the normal-mode-decomposition technique as presented by Sun
-et al. [T. Sun, D. Zhang, R. Wentzcovitch, 2014]. Using this code we calculated
-the quasiparticle phonon frequencies and linewidths of crystalline silicon at
-different temperatures using both of first-principles and the Tersoff empirical
-potential approaches. In this work we show the dependence of these properties
-on the temperature using both approaches and compare them with reported
-experimental data obtained by Raman spectroscopy [M. Balkanski, R. Wallis, E.
-Haro, 1983 and R. Tsu, J. G. Hernandez, 1982].
-",0,1,0,0,0,0
-20555,Lower Bounds for Two-Sample Structural Change Detection in Ising and Gaussian Models,"  The change detection problem is to determine if the Markov network structures
-of two Markov random fields differ from one another given two sets of samples
-drawn from the respective underlying distributions. We study the trade-off
-between the sample sizes and the reliability of change detection, measured as a
-minimax risk, for the important cases of the Ising models and the Gaussian
-Markov random fields restricted to the models which have network structures
-with $p$ nodes and degree at most $d$, and obtain information-theoretic lower
-bounds for reliable change detection over these models. We show that for the
-Ising model, $\Omega\left(\frac{d^2}{(\log d)^2}\log p\right)$ samples are
-required from each dataset to detect even the sparsest possible changes, and
-that for the Gaussian, $\Omega\left( \gamma^{-2} \log(p)\right)$ samples are
-required from each dataset to detect change, where $\gamma$ is the smallest
-ratio of off-diagonal to diagonal terms in the precision matrices of the
-distributions. These bounds are compared to the corresponding results in
-structure learning, and closely match them under mild conditions on the model
-parameters. Thus, our change detection bounds inherit partial tightness from
-the structure learning schemes in previous literature, demonstrating that in
-certain parameter regimes, the naive structure learning based approach to
-change detection is minimax optimal up to constant factors.
-",1,0,1,0,0,0
-20556,How Usable are Rust Cryptography APIs?,"  Context: Poor usability of cryptographic APIs is a severe source of
-vulnerabilities. Aim: We wanted to find out what kind of cryptographic
-libraries are present in Rust and how usable they are. Method: We explored
-Rust's cryptographic libraries through a systematic search, conducted an
-exploratory study on the major libraries and a controlled experiment on two of
-these libraries with 28 student participants. Results: Only half of the major
-libraries explicitly focus on usability and misuse resistance, which is
-reflected in their current APIs. We found that participants were more
-successful using rust-crypto which we considered less usable than ring before
-the experiment. Conclusion: We discuss API design insights and make
-recommendations for the design of crypto libraries in Rust regarding the detail
-and structure of the documentation, higher-level APIs as wrappers for the
-existing low-level libraries, and selected, good-quality example code to
-improve the emerging cryptographic libraries of Rust.
-",1,0,0,0,0,0
-20557,Augmenting End-to-End Dialog Systems with Commonsense Knowledge,"  Building dialog agents that can converse naturally with humans is a
-challenging yet intriguing problem of artificial intelligence. In open-domain
-human-computer conversation, where the conversational agent is expected to
-respond to human responses in an interesting and engaging way, commonsense
-knowledge has to be integrated into the model effectively. In this paper, we
-investigate the impact of providing commonsense knowledge about the concepts
-covered in the dialog. Our model represents the first attempt to integrating a
-large commonsense knowledge base into end-to-end conversational models. In the
-retrieval-based scenario, we propose the Tri-LSTM model to jointly take into
-account message and commonsense for selecting an appropriate response. Our
-experiments suggest that the knowledge-augmented models are superior to their
-knowledge-free counterparts in automatic evaluation.
-",1,0,0,0,0,0
-20558,A framework for Multi-A(rmed)/B(andit) testing with online FDR control,"  We propose an alternative framework to existing setups for controlling false
-alarms when multiple A/B tests are run over time. This setup arises in many
-practical applications, e.g. when pharmaceutical companies test new treatment
-options against control pills for different diseases, or when internet
-companies test their default webpages versus various alternatives over time.
-Our framework proposes to replace a sequence of A/B tests by a sequence of
-best-arm MAB instances, which can be continuously monitored by the data
-scientist. When interleaving the MAB tests with an an online false discovery
-rate (FDR) algorithm, we can obtain the best of both worlds: low sample
-complexity and any time online FDR control. Our main contributions are: (i) to
-propose reasonable definitions of a null hypothesis for MAB instances; (ii) to
-demonstrate how one can derive an always-valid sequential p-value that allows
-continuous monitoring of each MAB test; and (iii) to show that using rejection
-thresholds of online-FDR algorithms as the confidence levels for the MAB
-algorithms results in both sample-optimality, high power and low FDR at any
-point in time. We run extensive simulations to verify our claims, and also
-report results on real data collected from the New Yorker Cartoon Caption
-contest.
-",1,0,0,1,0,0
-20559,Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization,"  This paper presents a statistical method of single-channel speech enhancement
-that uses a variational autoencoder (VAE) as a prior distribution on clean
-speech. A standard approach to speech enhancement is to train a deep neural
-network (DNN) to take noisy speech as input and output clean speech. Although
-this supervised approach requires a very large amount of pair data for
-training, it is not robust against unknown environments. Another approach is to
-use non-negative matrix factorization (NMF) based on basis spectra trained on
-clean speech in advance and those adapted to noise on the fly. This
-semi-supervised approach, however, causes considerable signal distortion in
-enhanced speech due to the unrealistic assumption that speech spectrograms are
-linear combinations of the basis spectra. Replacing the poor linear generative
-model of clean speech in NMF with a VAE---a powerful nonlinear deep generative
-model---trained on clean speech, we formulate a unified probabilistic
-generative model of noisy speech. Given noisy speech as observed data, we can
-sample clean speech from its posterior distribution. The proposed method
-outperformed the conventional DNN-based method in unseen noisy environments.
-",1,0,0,1,0,0
-20560,BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning,"  Understanding the global optimality in deep learning (DL) has been attracting
-more and more attention recently. Conventional DL solvers, however, have not
-been developed intentionally to seek for such global optimality. In this paper
-we propose a novel approximation algorithm, BPGrad, towards optimizing deep
-models globally via branch and pruning. Our BPGrad algorithm is based on the
-assumption of Lipschitz continuity in DL, and as a result it can adaptively
-determine the step size for current gradient given the history of previous
-updates, wherein theoretically no smaller steps can achieve the global
-optimality. We prove that, by repeating such branch-and-pruning procedure, we
-can locate the global optimality within finite iterations. Empirically an
-efficient solver based on BPGrad for DL is proposed as well, and it outperforms
-conventional DL solvers such as Adagrad, Adadelta, RMSProp, and Adam in the
-tasks of object recognition, detection, and segmentation.
-",1,0,0,1,0,0
-20561,Lagrangian Flow Network approach to an open flow model,"  Concepts and tools from network theory, the so-called Lagrangian Flow Network
-framework, have been successfully used to obtain a coarse-grained description
-of transport by closed fluid flows. Here we explore the application of this
-methodology to open chaotic flows, and check it with numerical results for a
-model open flow, namely a jet with a localized wave perturbation. We find that
-network nodes with high values of out-degree and of finite-time entropy in the
-forward-in-time direction identify the location of the chaotic saddle and its
-stable manifold, whereas nodes with high in-degree and backwards finite-time
-entropy highlight the location of the saddle and its unstable manifold. The
-cyclic clustering coefficient, associated to the presence of periodic orbits,
-takes non-vanishing values at the location of the saddle itself.
-",0,1,0,0,0,0
-20562,Some new gradient estimates for two nonlinear parabolic equations under Ricci flow,"  In this paper, by maximum principle and cutoff function, we investigate
-gradient estimates for positive solutions to two nonlinear parabolic equations
-under Ricci flow. The related Harnack inequalities are deduced. An result about
-positive solutions on closed manifolds under Ricci flow is abtained. As
-applications, gradient estimates and Harnack inequalities for positive
-solutions to the heat equation under Ricci flow are derived. These results in
-the paper can be regard as generalizing the gradient estimates of Li-Yau, J. Y.
-Li, Hamilton and Li-Xu to the Ricci flow. Our results also improve the
-estimates of S. P. Liu and J. Sun to the nonlinear parabolic equation under
-Ricci flow.
-",0,0,1,0,0,0
-20563,On the pointwise iteration-complexity of a dynamic regularized ADMM with over-relaxation stepsize,"  In this paper, we extend the improved pointwise iteration-complexity result
-of a dynamic regularized alternating direction method of multipliers (ADMM) for
-a new stepsize domain. In this complexity analysis, the stepsize parameter can
-even be chosen in the interval $(0,2)$ instead of interval
-$(0,(1+\sqrt{5})/2)$. As usual, our analysis is established by interpreting
-this ADMM variant as an instance of a hybrid proximal extragradient framework
-applied to a specific monotone inclusion problem.
-",0,0,1,0,0,0
-20564,DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders,"  This paper presents a novel deep learning-based method for learning a
-functional representation of mammalian neural images. The method uses a deep
-convolutional denoising autoencoder (CDAE) for generating an invariant, compact
-representation of in situ hybridization (ISH) images. While most existing
-methods for bio-imaging analysis were not developed to handle images with
-highly complex anatomical structures, the results presented in this paper show
-that functional representation extracted by CDAE can help learn features of
-functional gene ontology categories for their classification in a highly
-accurate manner. Using this CDAE representation, our method outperforms the
-previous state-of-the-art classification rate, by improving the average AUC
-from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates
-on input images that were downsampled significantly with respect to the
-original ones to make it computationally feasible.
-",1,0,0,1,0,0
-20565,Nonlocal heat equations in the Heisenberg group,"  We study the following nonlocal diffusion equation in the Heisenberg group
-$\mathbb{H}_n$, \[ u_t(z,s,t)=J\ast u(z,s,t)-u(z,s,t), \] where $\ast$ denote
-convolution product and $J$ satisfies appropriated hypothesis. For the Cauchy
-problem we obtain that the asymptotic behavior of the solutions is the same
-form that the one for the heat equation in the Heisenberg group. To obtain this
-result we use the spherical transform related to the pair
-$(U(n),\mathbb{H}_n)$. Finally we prove that solutions of properly rescaled
-nonlocal Dirichlet problem converge uniformly to the solution of the
-corresponding Dirichlet problem for the classical heat equation in the
-Heisenberg group.
-",0,0,1,0,0,0
-20566,Motion Planning Networks,"  Fast and efficient motion planning algorithms are crucial for many
-state-of-the-art robotics applications such as self-driving cars. Existing
-motion planning methods such as RRT*, A*, and D*, become ineffective as their
-computational complexity increases exponentially with the dimensionality of the
-motion planning problem. To address this issue, we present a neural
-network-based novel planning algorithm which generates end-to-end
-collision-free paths irrespective of the obstacles' geometry. The proposed
-method, called MPNet (Motion Planning Network), comprises of a Contractive
-Autoencoder which encodes the given workspaces directly from a point cloud
-measurement, and a deep feedforward neural network which takes the workspace
-encoding, start and goal configuration, and generates end-to-end feasible
-motion trajectories for the robot to follow. We evaluate MPNet on multiple
-planning problems such as planning of a point-mass robot, rigid-body, and 7 DOF
-Baxter robot manipulators in various 2D and 3D environments. The results show
-that MPNet is not only consistently computationally efficient in all 2D and 3D
-environments but also show remarkable generalization to completely unseen
-environments. The results also show that computation time of MPNet consistently
-remains less than 1 second which is significantly lower than existing
-state-of-the-art motion planning algorithms. Furthermore, through transfer
-learning, the MPNet trained in one scenario (e.g., indoor living places) can
-also quickly adapt to new scenarios (e.g., factory floors) with a little amount
-of data.
-",1,0,0,1,0,0
-20567,Query Expansion Techniques for Information Retrieval: a Survey,"  With the ever increasing size of web, relevant information extraction on the
-Internet with a query formed by a few keywords has become a big challenge. To
-overcome this, query expansion (QE) plays a crucial role in improving the
-Internet searches, where the user's initial query is reformulated to a new
-query by adding new meaningful terms with similar significance. QE -- as part
-of information retrieval (IR) -- has long attracted researchers' attention. It
-has also become very influential in the field of personalized social document,
-Question Answering over Linked Data (QALD), and, Text Retrieval Conference
-(TREC) and REAL sets. This paper surveys QE techniques in IR from 1960 to 2017
-with respect to core techniques, data sources used, weighting and ranking
-methodologies, user participation and applications (of QE techniques) --
-bringing out similarities and differences.
-",1,0,0,0,0,0
-20568,Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis,"  This paper fills a gap in aspect-based sentiment analysis and aims to present
-a new method for preparing and analysing texts concerning opinion and
-generating user-friendly descriptive reports in natural language. We present a
-comprehensive set of techniques derived from Rhetorical Structure Theory and
-sentiment analysis to extract aspects from textual opinions and then build an
-abstractive summary of a set of opinions. Moreover, we propose aspect-aspect
-graphs to evaluate the importance of aspects and to filter out unimportant ones
-from the summary. Additionally, the paper presents a prototype solution of data
-flow with interesting and valuable results. The proposed method's results
-proved the high accuracy of aspect detection when applied to the gold standard
-dataset.
-",1,0,0,0,0,0
-20569,Commutativity of integral quasi-arithmetic means on measure spaces,"  Let $(X, \mathscr{L}, \lambda)$ and $(Y, \mathscr{M}, \mu)$ be finite measure
-spaces for which there exist $A \in \mathscr{L}$ and $B \in \mathscr{M}$ with
-$0 < \lambda(A) < \lambda(X)$ and $0 < \mu(B) < \mu(Y)$, and let $I\subseteq
-\mathbf{R}$ be a non-empty interval. We prove that, if $f$ and $g$ are
-continuous bijections $I \to \mathbf{R}^+$, then the equation $$
-f^{-1}\!\left(\int_X f\!\left(g^{-1}\!\left(\int_Y g \circ
-h\;d\mu\right)\right)d \lambda\right)\! = g^{-1}\!\left(\int_Y
-g\!\left(f^{-1}\!\left(\int_X f \circ h\;d\lambda\right)\right)d \mu\right)$$
-is satisfied by every $\mathscr{L} \otimes \mathscr{M}$-measurable simple
-function $h: X \times Y \to I$ if and only if $f=c g$ for some $c \in
-\mathbf{R}^+$ (it is easy to see that the equation is well posed). An
-analogous, but essentially different, result, with $f$ and $g$ replaced by
-continuous injections $I \to \mathbf R$ and $\lambda(X)=\mu(Y)=1$, was recently
-obtained in [Indag. Math. 27 (2016), 945-953].
-",0,0,1,0,0,0
-20570,Moderate Deviation for Random Elliptic PDEs with Small Noise,"  Partial differential equations with random inputs have become popular models
-to characterize physical systems with uncertainty coming from, e.g., imprecise
-measurement and intrinsic randomness. In this paper, we perform asymptotic rare
-event analysis for such elliptic PDEs with random inputs. In particular, we
-consider the asymptotic regime that the noise level converges to zero
-suggesting that the system uncertainty is low, but does exists. We develop
-sharp approximations of the probability of a large class of rare events.
-",0,0,1,0,0,0
-20571,Music Transformer,"  Music relies heavily on repetition to build structure and meaning.
-Self-reference occurs on multiple timescales, from motifs to phrases to reusing
-of entire sections of music, such as in pieces with ABA structure. The
-Transformer (Vaswani et al., 2017), a sequence model based on self-attention,
-has achieved compelling results in many generation tasks that require
-maintaining long-range coherence. This suggests that self-attention might also
-be well-suited to modeling music. In musical composition and performance,
-however, relative timing is critically important. Existing approaches for
-representing relative positional information in the Transformer modulate
-attention based on pairwise distance (Shaw et al., 2018). This is impractical
-for long sequences such as musical compositions since their memory complexity
-for intermediate relative information is quadratic in the sequence length. We
-propose an algorithm that reduces their intermediate memory requirement to
-linear in the sequence length. This enables us to demonstrate that a
-Transformer with our modified relative attention mechanism can generate
-minute-long compositions (thousands of steps, four times the length modeled in
-Oore et al., 2018) with compelling structure, generate continuations that
-coherently elaborate on a given motif, and in a seq2seq setup generate
-accompaniments conditioned on melodies. We evaluate the Transformer with our
-relative attention mechanism on two datasets, JSB Chorales and
-Piano-e-Competition, and obtain state-of-the-art results on the latter.
-",1,0,0,1,0,0
-20572,Residual-Based Detections and Unified Architecture for Massive MIMO Uplink,"  Massive multiple-input multiple-output (M-MIMO) technique brings better
-energy efficiency and coverage but higher computational complexity than
-small-scale MIMO. For linear detections such as minimum mean square error
-(MMSE), prohibitive complexity lies in solving large-scale linear equations.
-For a better trade-off between bit-error-rate (BER) performance and
-computational complexity, iterative linear algorithms like conjugate gradient
-(CG) have been applied and have shown their feasibility in recent years. In
-this paper, residual-based detection (RBD) algorithms are proposed for M-MIMO
-detection, including minimal residual (MINRES) algorithm, generalized minimal
-residual (GMRES) algorithm, and conjugate residual (CR) algorithm. RBD
-algorithms focus on the minimization of residual norm per iteration, whereas
-most existing algorithms focus on the approximation of exact signal. Numerical
-results have shown that, for $64$-QAM $128\times 8$ MIMO, RBD algorithms are
-only $0.13$ dB away from the exact matrix inversion method when BER$=10^{-4}$.
-Stability of RBD algorithms has also been verified in various correlation
-conditions. Complexity comparison has shown that, CR algorithm require $87\%$
-less complexity than the traditional method for $128\times 60$ MIMO. The
-unified hardware architecture is proposed with flexibility, which guarantees a
-low-complexity implementation for a family of RBD M-MIMO detectors.
-",1,0,0,0,0,0
-20573,Sparsity constrained split feasibility for dose-volume constraints in inverse planning of intensity-modulated photon or proton therapy,"  A split feasibility formulation for the inverse problem of
-intensity-modulated radiation therapy (IMRT) treatment planning with
-dose-volume constraints (DVCs) included in the planning algorithm is presented.
-It involves a new type of sparsity constraint that enables the inclusion of a
-percentage-violation constraint in the model problem and its handling by
-continuous (as opposed to integer) methods. We propose an iterative algorithmic
-framework for solving such a problem by applying the feasibility-seeking
-CQ-algorithm of Byrne combined with the automatic relaxation method (ARM) that
-uses cyclic projections. Detailed implementation instructions are furnished.
-Functionality of the algorithm was demonstrated through the creation of an
-intensity-modulated proton therapy plan for a simple 2D C-shaped geometry and
-also for a realistic base-of-skull chordoma treatment site. Monte Carlo
-simulations of proton pencil beams of varying energy were conducted to obtain
-dose distributions for the 2D test case. A research release of the Pinnacle3
-proton treatment planning system was used to extract pencil beam doses for a
-clinical base-of-skull chordoma case. In both cases the beamlet doses were
-calculated to satisfy dose-volume constraints according to our new algorithm.
-Examination of the dose-volume histograms following inverse planning with our
-algorithm demonstrated that it performed as intended. The application of our
-proposed algorithm to dose-volume constraint inverse planning was successfully
-demonstrated. Comparison with optimized dose distributions from the research
-release of the Pinnacle3 treatment planning system showed the algorithm could
-achieve equivalent or superior results.
-",0,1,1,0,0,0
-20574,On 2-Verma modules for quantum $\mathfrak{sl}_2$,"  In this paper we study the superalgebra $A_n$, introduced by the authors in
-previous work on categorification of Verma modules for quantum
-$\mathfrak{sl}_2$. The superalgebra $A_n$ is akin to the nilHecke algebra, and
-shares similar properties. In particular, we prove a uniqueness result about
-2-Verma modules on $\Bbbk$-linear 2-categories.
-",0,0,1,0,0,0
-20575,On the Number of Single-Peaked Narcissistic or Single-Crossing Narcissistic Preference Profiles,"  We investigate preference profiles for a set $\mathcal{V}$ of voters, where
-each voter $i$ has a preference order $\succ_i$ on a finite set $A$ of
-alternatives (that is, a linear order on $A$) such that for each two
-alternatives $a,b\in A$, voter $i$ prefers $a$ to $b$ if $a\succ_i b$. Such a
-profile is narcissistic if each alternative $a$ is preferred the most by at
-least one voter. It is single-peaked if there is a linear order
-$\triangleright^{\text{sp}}$ on the alternatives such that each voter's
-preferences on the alternatives along the order $\triangleright^{\text{sp}}$
-are either strictly increasing, or strictly decreasing, or first strictly
-increasing and then strictly decreasing. It is single-crossing if there is a
-linear order $\triangleright^{\text{sc}}$ on the voters such that each pair of
-alternatives divides the order $\triangleright^{\text{sc}}$ into at most two
-suborders, where in each suborder, all voters have the same linear order on
-this pair.
-We show that for $n$ voters and $n$ alternatives,the number of single-peaked
-narcissistic profiles is $\prod_{i=2}^{n-1} \binom{n-1}{i-1}$ while the number
-of single-crossing narcissistic profiles is $2^{\binom{n-1}{2}}$.
-",0,0,1,0,0,0
-20576,Planet formation and disk-planet interactions,"  This review is based on lectures given at the 45th Saas-Fee Advanced Course
-'From Protoplanetary Disks to Planet Formation' held in March 2015 in Les
-Diablerets, Switzerland. Starting with an overview of the main characterictics
-of the Solar System and extrasolar planets, we describe the planet formation
-process in terms of the sequential accretion scenario. First the growth
-processes of dust particles to planetesimals and subsequently to terrestrial
-planets or planetary cores are presented. This is followed by the formation
-process of the giant planets either by core accretion or gravitational
-instability. Finally, the dynamical evolution of the orbital elements as driven
-by disk-planet interaction and the overall evolution of multi-object systems is
-presented.
-",0,1,0,0,0,0
-20577,Cherednik algebras and Calogero-Moser cells,"  Using the representation theory of Cherednik algebras at $t=0$ and a Galois
-covering of the Calogero-Moser space, we define the notions of left, right and
-two-sided Calogero-Moser cells for any finite complex reflection group. To each
-Caloger-Moser two-sided cell is associated a Calogero-Moser family, while to
-each Calogero-Moser left cell is associated a Calogero-Moser cellular
-representation. We study properties of these objects and we conjecture that,
-whenever the reflection group is real (i.e. is a Coxeter group), these notions
-coincide with the one of Kazhdan-Lusztig left, right and two-sided cells,
-Kazhdan-Lusztig families and Kazhdan-Lusztig cellular representations.
-",0,0,1,0,0,0
-20578,Khovanov complexes of rational tangles,"  We show that the Khovanov complex of a rational tangle has a very simple
-representative whose backbone of non-zero morphisms forms a zig-zag.
-Furthermore, this minimal complex can be computed quickly by an inductive
-algorithm. (For example, we calculate $Kh(8_2)$ by hand.) We find that the
-bigradings of the subobjects in these minimal complexes can be described by
-matrix actions, which after a change of basis is the reduced Burau
-representation of $B_3$.
-",0,0,1,0,0,0
-20579,Asymmetric Connectedness of Fears in the U.S. Financial Sector,"  We study how shocks to the forward-looking expectations of investors buying
-call and put options transmit across the financial system. We introduce a new
-contagion measure, called asymmetric fear connectedness (AFC), which captures
-the information related to ""fear"" on the two sides of the options market and
-can be used as a forward-looking systemic risk monitoring tool. The decomposed
-connectedness measures provide timely predictive information for near-future
-macroeconomic conditions and uncertainty indicators, and they contain
-additional valuable information that is not included in the aggregate
-connectedness measure. The role of a positive/negative ""fear""
-transmitter/receiver emerges clearly when we focus more closely on
-idiosyncratic events for financial institutions. We identify banks that are
-predominantly positive/negative receivers of ""fear"", as well as banks that
-positively/negatively transmit ""fear"" in the financial system.
-",0,0,0,0,0,1
-20580,Neural Network Based Speaker Classification and Verification Systems with Enhanced Features,"  This work presents a novel framework based on feed-forward neural network for
-text-independent speaker classification and verification, two related systems
-of speaker recognition. With optimized features and model training, it achieves
-100% classification rate in classification and less than 6% Equal Error Rate
-(ERR), using merely about 1 second and 5 seconds of data respectively. Features
-with stricter Voice Active Detection (VAD) than the regular one for speech
-recognition ensure extracting stronger voiced portion for speaker recognition,
-speaker-level mean and variance normalization helps to eliminate the
-discrepancy between samples from the same speaker. Both are proven to improve
-the system performance. In building the neural network speaker classifier, the
-network structure parameters are optimized with grid search and dynamically
-reduced regularization parameters are used to avoid training terminated in
-local minimum. It enables the training goes further with lower cost. In speaker
-verification, performance is improved with prediction score normalization,
-which rewards the speaker identity indices with distinct peaks and penalizes
-the weak ones with high scores but more competitors, and speaker-specific
-thresholding, which significantly reduces ERR in the ROC curve. TIMIT corpus
-with 8K sampling rate is used here. First 200 male speakers are used to train
-and test the classification performance. The testing files of them are used as
-in-domain registered speakers, while data from the remaining 126 male speakers
-are used as out-of-domain speakers, i.e. imposters in speaker verification.
-",1,0,0,0,0,0
-20581,Slimness of graphs,"  Slimness of a graph measures the local deviation of its metric from a tree
-metric. In a graph $G=(V,E)$, a geodesic triangle $\bigtriangleup(x,y,z)$ with
-$x, y, z\in V$ is the union $P(x,y) \cup P(x,z) \cup P(y,z)$ of three shortest
-paths connecting these vertices. A geodesic triangle $\bigtriangleup(x,y,z)$ is
-called $\delta$-slim if for any vertex $u\in V$ on any side $P(x,y)$ the
-distance from $u$ to $P(x,z) \cup P(y,z)$ is at most $\delta$, i.e. each path
-is contained in the union of the $\delta$-neighborhoods of two others. A graph
-$G$ is called $\delta$-slim, if all geodesic triangles in $G$ are
-$\delta$-slim. The smallest value $\delta$ for which $G$ is $\delta$-slim is
-called the slimness of $G$. In this paper, using the layering partition
-technique, we obtain sharp bounds on slimness of such families of graphs as (1)
-graphs with cluster-diameter $\Delta(G)$ of a layering partition of $G$, (2)
-graphs with tree-length $\lambda$, (3) graphs with tree-breadth $\rho$, (4)
-$k$-chordal graphs, AT-free graphs and HHD-free graphs. Additionally, we show
-that the slimness of every 4-chordal graph is at most 2 and characterize those
-4-chordal graphs for which the slimness of every of its induced subgraph is at
-most 1.
-",1,0,0,0,0,0
-20582,SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems,"  Despite their immense popularity, deep learning-based acoustic systems are
-inherently vulnerable to adversarial attacks, wherein maliciously crafted
-audios trigger target systems to misbehave. In this paper, we present
-SirenAttack, a new class of attacks to generate adversarial audios. Compared
-with existing attacks, SirenAttack highlights with a set of significant
-features: (i) versatile -- it is able to deceive a range of end-to-end acoustic
-systems under both white-box and black-box settings; (ii) effective -- it is
-able to generate adversarial audios that can be recognized as specific phrases
-by target acoustic systems; and (iii) stealthy -- it is able to generate
-adversarial audios indistinguishable from their benign counterparts to human
-perception. We empirically evaluate SirenAttack on a set of state-of-the-art
-deep learning-based acoustic systems (including speech command recognition,
-speaker recognition and sound event classification), with results showing the
-versatility, effectiveness, and stealthiness of SirenAttack. For instance, it
-achieves 99.45% attack success rate on the IEMOCAP dataset against the ResNet18
-model, while the generated adversarial audios are also misinterpreted by
-multiple popular ASR platforms, including Google Cloud Speech, Microsoft Bing
-Voice, and IBM Speech-to-Text. We further evaluate three potential defense
-methods to mitigate such attacks, including adversarial training, audio
-downsampling, and moving average filtering, which leads to promising directions
-for further research.
-",1,0,0,0,0,0
-20583,k-server via multiscale entropic regularization,"  We present an $O((\log k)^2)$-competitive randomized algorithm for the
-$k$-server problem on hierarchically separated trees (HSTs). This is the first
-$o(k)$-competitive randomized algorithm for which the competitive ratio is
-independent of the size of the underlying HST. Our algorithm is designed in the
-framework of online mirror descent where the mirror map is a multiscale
-entropy. When combined with Bartal's static HST embedding reduction, this leads
-to an $O((\log k)^2 \log n)$-competitive algorithm on any $n$-point metric
-space. We give a new dynamic HST embedding that yields an $O((\log k)^3 \log
-\Delta)$-competitive algorithm on any metric space where the ratio of the
-largest to smallest non-zero distance is at most $\Delta$.
-",1,0,1,0,0,0
-20584,Some Large Sample Results for the Method of Regularized Estimators,"  We present a general framework for studying regularized estimators; i.e.,
-estimation problems wherein ""plug-in"" type estimators are either ill-defined or
-ill-behaved. We derive primitive conditions that imply consistency and
-asymptotic linear representation for regularized estimators, allowing for
-slower than $\sqrt{n}$ estimators as well as infinite dimensional parameters.
-We also provide data-driven methods for choosing tuning parameters that, under
-some conditions, achieve the aforementioned results. We illustrate the scope of
-our approach by studying a wide range of applications, revisiting known results
-and deriving new ones.
-",0,0,1,0,0,0
-20585,Coregionalised Locomotion Envelopes - A Qualitative Approach,"  'Sharing of statistical strength' is a phrase often employed in machine
-learning and signal processing. In sensor networks, for example, missing
-signals from certain sensors may be predicted by exploiting their correlation
-with observed signals acquired from other sensors. For humans, our hands move
-synchronously with our legs, and we can exploit these implicit correlations for
-predicting new poses and for generating new natural-looking walking sequences.
-We can also go much further and exploit this form of transfer learning, to
-develop new control schemas for robust control of rehabilitation robots. In
-this short paper we introduce coregionalised locomotion envelopes - a method
-for multi-dimensional manifold regression, on human locomotion variates. Herein
-we render a qualitative description of this method.
-",1,0,0,1,0,0
-20586,Optimal Invariant Tests in an Instrumental Variables Regression With Heteroskedastic and Autocorrelated Errors,"  This paper uses model symmetries in the instrumental variable (IV) regression
-to derive an invariant test for the causal structural parameter. Contrary to
-popular belief, we show there exist model symmetries when equation errors are
-heteroskedastic and autocorrelated (HAC). Our theory is consistent with
-existing results for the homoskedastic model (Andrews, Moreira and Stock(2006}
-and Chamberlain (2007}), but in general uses information on the structural
-parameter beyond the Anderson-Rubin, score, and rank statistics. This suggests
-that tests based only the Anderson-Rubin and score statistics discard
-information on the causal parameter of interest. We apply our theory to
-construct designs in which these tests indeed have power arbitrarily close to
-size. Other tests, including other adaptations to the CLR test, do not suffer
-the same deficiencies. Finally, we use the model symmetries to propose novel
-weighted-average power tests for the HAC-IV model.
-",0,0,1,1,0,0
-20587,Longitudinal electric field: from Maxwell equation to non-locality in time and space,"  In this paper we use the classical electrodynamics to show that the Lorenz
-gauge can be incompatible with some particular solutions of the d Alembert
-equations for electromagnetic potentials. In its turn, the d Alembert equations
-for the elec- tromagnetic potentials is the result of application of the Lorenz
-gauge to general equations for the potentials. The last ones is the
-straightforward consequence of Maxwell equations. Since the d Alembert
-equations and the electromagnetic poten- tials are necessary for quantum
-electrodynamics formulation, one should oblige to satisfy these equations also
-in classical case. The solution of d Alembert equations, which modifies
-longitudinal electric field is found. The requirement of this modifi- cation
-follows from the necessity to satisfy the physical condition of impossibility
-of instantaneous transferring of interaction in space.
-",0,1,0,0,0,0
-20588,Estimating linear functionals of a sparse family of Poisson means,"  Assume that we observe a sample of size n composed of p-dimensional signals,
-each signal having independent entries drawn from a scaled Poisson distribution
-with an unknown intensity. We are interested in estimating the sum of the n
-unknown intensity vectors, under the assumption that most of them coincide with
-a given 'background' signal. The number s of p-dimensional signals different
-from the background signal plays the role of sparsity and the goal is to
-leverage this sparsity assumption in order to improve the quality of estimation
-as compared to the naive estimator that computes the sum of the observed
-signals. We first introduce the group hard thresholding estimator and analyze
-its mean squared error measured by the squared Euclidean norm. We establish a
-nonasymptotic upper bound showing that the risk is at most of the order of
-{\sigma}^2(sp + s^2sqrt(p)) log^3/2(np). We then establish lower bounds on the
-minimax risk over a properly defined class of collections of s-sparse signals.
-These lower bounds match with the upper bound, up to logarithmic terms, when
-the dimension p is fixed or of larger order than s^2. In the case where the
-dimension p increases but remains of smaller order than s^2, our results show a
-gap between the lower and the upper bounds, which can be up to order sqrt(p).
-",0,0,1,1,0,0
-20589,Fabrication of porous microrings via laser printing and ion-beam post-etching,"  Pulsed-laser dry printing of noble-metal microrings with a tunable internal
-porous structure, which can be revealed via an ion-beam etching post-procedure,
-was demonstrated. Abundance and average size of the pores inside the microrings
-were shown to be tuned in a wide range by varying incident pulse energy and a
-nitrogen doping level controlled in the process of magnetron deposition of the
-gold film in the appropriate gaseous environment. The fabricated porous
-microrings were shown to provide many-fold near-field enhancement of incident
-electromagnetic fields, which was confirmed by mapping of the characteristic
-Raman band of a nanometer-thick covering layer of Rhodamine 6G dye molecules
-and supporting finite-difference time-domain calculations. The proposed laser
-printing/ion-beam etching approach is demonstrated to be a unique tool aimed at
-designing and fabricating multifunctional plasmonic structures and metasurfaces
-for spectroscopic bioidentification based on surface-enhanced infrared
-absorption, Raman scattering and photoluminescence detection schemes.
-",0,1,0,0,0,0
-20590,Performance Measurements of Supercomputing and Cloud Storage Solutions,"  Increasing amounts of data from varied sources, particularly in the fields of
-machine learning and graph analytics, are causing storage requirements to grow
-rapidly. A variety of technologies exist for storing and sharing these data,
-ranging from parallel file systems used by supercomputers to distributed block
-storage systems found in clouds. Relatively few comparative measurements exist
-to inform decisions about which storage systems are best suited for particular
-tasks. This work provides these measurements for two of the most popular
-storage technologies: Lustre and Amazon S3. Lustre is an open-source, high
-performance, parallel file system used by many of the largest supercomputers in
-the world. Amazon's Simple Storage Service, or S3, is part of the Amazon Web
-Services offering, and offers a scalable, distributed option to store and
-retrieve data from anywhere on the Internet. Parallel processing is essential
-for achieving high performance on modern storage systems. The performance tests
-used span the gamut of parallel I/O scenarios, ranging from single-client,
-single-node Amazon S3 and Lustre performance to a large-scale, multi-client
-test designed to demonstrate the capabilities of a modern storage appliance
-under heavy load. These results show that, when parallel I/O is used correctly
-(i.e., many simultaneous read or write processes), full network bandwidth
-performance is achievable and ranged from 10 gigabits/s over a 10 GigE S3
-connection to 0.35 terabits/s using Lustre on a 1200 port 10 GigE switch. These
-results demonstrate that S3 is well-suited to sharing vast quantities of data
-over the Internet, while Lustre is well-suited to processing large quantities
-of data locally.
-",1,1,0,0,0,0
-20591,Multi-Objective Event-triggered Consensus of Linear Multi-agent Systems,"  This paper proposes a distributed consensus algorithm for linear event-based
-heterogeneous multi-agent systems (MAS). The proposed scheme is event-triggered
-in the sense that an agent selectively transmits its information within its
-local neighbourhood based on a directed network topology under the fulfillment
-of certain conditions. Using the Lyapunov stability theorem, the system
-constraints and event-triggering condition are expressed in terms of several
-linear matrix inequalities (LMIs) to derive the consensus parameters. The
-objective is to design the transmission threshold and minimum-norm
-heterogeneous control gains which collectively ensure an exponential consensus
-convergence rate for the closed-loop systems. The LMI computed control gains
-are robust to uncertainty with some deviation from their nominal values
-allowed. The practicability of the proposed event-based framework is further
-studied by proving the Zeno behaviour exclusion. Numerical simulations quantify
-the advantages of our event-triggered consensus approach in second-order,
-linear and heterogeneous multi-agent systems.
-",1,0,0,0,0,0
-20592,Private and Secure Coordination of Match-Making for Heavy-Duty Vehicle Platooning,"  A secure and private framework for inter-agent communication and coordination
-is developed. This allows an agent, in our case a fleet owner, to ask questions
-or submit queries in an encrypted fashion using semi-homomorphic encryption.
-The submitted query can be about the interest of the other fleet owners for
-using a road at a specific time of the day, for instance, for the purpose of
-collaborative vehicle platooning. The other agents can then provide appropriate
-responses without knowing the content of the questions or the queries. Strong
-privacy and security guarantees are provided for the agent who is submitting
-the queries. It is also shown that the amount of the information that this
-agent can extract from the other agent is bounded. In fact, with submitting one
-query, a sophisticated agent can at most extract the answer to two queries.
-This secure communication platform is used subsequently to develop a
-distributed coordination mechanisms among fleet owners.
-",1,0,1,0,0,0
-20593,Kernel Robust Bias-Aware Prediction under Covariate Shift,"  Under covariate shift, training (source) data and testing (target) data
-differ in input space distribution, but share the same conditional label
-distribution. This poses a challenging machine learning task. Robust Bias-Aware
-(RBA) prediction provides the conditional label distribution that is robust to
-the worstcase logarithmic loss for the target distribution while matching
-feature expectation constraints from the source distribution. However,
-employing RBA with insufficient feature constraints may result in high
-certainty predictions for much of the source data, while leaving too much
-uncertainty for target data predictions. To overcome this issue, we extend the
-representer theorem to the RBA setting, enabling minimization of regularized
-expected target risk by a reweighted kernel expectation under the source
-distribution. By applying kernel methods, we establish consistency guarantees
-and demonstrate better performance of the RBA classifier than competing methods
-on synthetically biased UCI datasets as well as datasets that have natural
-covariate shift.
-",1,0,0,1,0,0
-20594,Degeneration in VAE: in the Light of Fisher Information Loss,"  While enormous progress has been made to Variational Autoencoder (VAE) in
-recent years, similar to other deep networks, VAE with deep networks suffers
-from the problem of degeneration, which seriously weakens the correlation
-between the input and the corresponding latent codes, deviating from the goal
-of the representation learning. To investigate how degeneration affects VAE
-from a theoretical perspective, we illustrate the information transmission in
-VAE and analyze the intermediate layers of the encoders/decoders. Specifically,
-we propose a Fisher Information measure for the layer-wise analysis. With such
-measure, we demonstrate that information loss is ineluctable in feed-forward
-networks and causes the degeneration in VAE. We show that skip connections in
-VAE enable the preservation of information without changing the model
-architecture. We call this class of VAE equipped with skip connections as SCVAE
-and perform a range of experiments to show its advantages in information
-preservation and degeneration mitigation.
-",0,0,0,1,0,0
-20595,Room-temperature spin transport in n-Ge probed by four-terminal nonlocal measurements,"  We demonsrtate electrical spin injection and detection in $n$-type Ge
-($n$-Ge) at room temperature using four-terminal nonlocal spin-valve and
-Hanle-effect measurements in lateral spin-valve (LSV) devices with
-Heusler-alloy Schottky tunnel contacts. The spin diffusion length
-($\lambda$$_{\rm Ge}$) of the Ge layer used ($n \sim$ 1 $\times$ 10$^{19}$
-cm$^{-3}$) at 296 K is estimated to be $\sim$ 0.44 $\pm$ 0.02 $\mu$m.
-Room-temperature spin signals can be observed reproducibly at the low bias
-voltage range ($\le$ 0.7 V) for LSVs with relatively low resistance-area
-product ($RA$) values ($\le$ 1 k$\Omega$$\mu$m$^{2}$). This means that the
-Schottky tunnel contacts used here are more suitable than ferromagnet/MgO
-tunnel contacts ($RA \ge$ 100 k$\Omega$$\mu$m$^{2}$) for developing Ge
-spintronic applications.
-",0,1,0,0,0,0
-20596,Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning,"  We present an algorithm for rapidly learning controllers for robotics
-systems. The algorithm follows the model-based reinforcement learning paradigm,
-and improves upon existing algorithms; namely Probabilistic learning in Control
-(PILCO) and a sample-based version of PILCO with neural network dynamics
-(Deep-PILCO). We propose training a neural network dynamics model using
-variational dropout with truncated Log-Normal noise. This allows us to obtain a
-dynamics model with calibrated uncertainty, which can be used to simulate
-controller executions via rollouts. We also describe set of techniques,
-inspired by viewing PILCO as a recurrent neural network model, that are crucial
-to improve the convergence of the method. We test our method on a variety of
-benchmark tasks, demonstrating data-efficiency that is competitive with PILCO,
-while being able to optimize complex neural network controllers. Finally, we
-assess the performance of the algorithm for learning motor controllers for a
-six legged autonomous underwater vehicle. This demonstrates the potential of
-the algorithm for scaling up the dimensionality and dataset sizes, in more
-complex control tasks.
-",1,0,0,0,0,0
-20597,Stealthy Deception Attacks Against SCADA Systems,"  SCADA protocols for Industrial Control Systems (ICS) are vulnerable to
-network attacks such as session hijacking. Hence, research focuses on network
-anomaly detection based on meta--data (message sizes, timing, command
-sequence), or on the state values of the physical process. In this work we
-present a class of semantic network-based attacks against SCADA systems that
-are undetectable by the above mentioned anomaly detection. After hijacking the
-communication channels between the Human Machine Interface (HMI) and
-Programmable Logic Controllers (PLCs), our attacks cause the HMI to present a
-fake view of the industrial process, deceiving the human operator into taking
-manual actions. Our most advanced attack also manipulates the messages
-generated by the operator's actions, reversing their semantic meaning while
-causing the HMI to present a view that is consistent with the attempted human
-actions. The attacks are totaly stealthy because the message sizes and timing,
-the command sequences, and the data values of the ICS's state all remain
-legitimate.
-We implemented and tested several attack scenarios in the test lab of our
-local electric company, against a real HMI and real PLCs, separated by a
-commercial-grade firewall. We developed a real-time security assessment tool,
-that can simultaneously manipulate the communication to multiple PLCs and cause
-the HMI to display a coherent system--wide fake view. Our tool is configured
-with message-manipulating rules written in an ICS Attack Markup Language (IAML)
-we designed, which may be of independent interest. Our semantic attacks all
-successfully fooled the operator and brought the system to states of blackout
-and possible equipment damage.
-",1,0,0,0,0,0
-20598,Beta-rhythm oscillations and synchronization transition in network models of Izhikevich neurons: effect of topology and synaptic type,"  Despite their significant functional roles, beta-band oscillations are least
-understood. Synchronization in neuronal networks have attracted much attention
-in recent years with the main focus on transition type. Whether one obtains
-explosive transition or a continuous transition is an important feature of the
-neuronal network which can depend on network structure as well as synaptic
-types. In this study we consider the effect of synaptic interaction (electrical
-and chemical) as well as structural connectivity on synchronization transition
-in network models of Izhikevich neurons which spike regularly with beta
-rhythms. We find a wide range of behavior including continuous transition,
-explosive transition, as well as lack of global order. The stronger electrical
-synapses are more conducive to synchronization and can even lead to explosive
-synchronization. The key network element which determines the order of
-transition is found to be the clustering coefficient and not the small world
-effect, or the existence of hubs in a network. These results are in contrast to
-previous results which use phase oscillator models such as the Kuramoto model.
-Furthermore, we show that the patterns of synchronization changes when one goes
-to the gamma band. We attribute such a change to the change in the refractory
-period of Izhikevich neurons which changes significantly with frequency.
-",0,0,0,0,1,0
-20599,Renyi Differential Privacy,"  We propose a natural relaxation of differential privacy based on the Renyi
-divergence. Closely related notions have appeared in several recent papers that
-analyzed composition of differentially private mechanisms. We argue that the
-useful analytical tool can be used as a privacy definition, compactly and
-accurately representing guarantees on the tails of the privacy loss.
-We demonstrate that the new definition shares many important properties with
-the standard definition of differential privacy, while additionally allowing
-tighter analysis of composite heterogeneous mechanisms.
-",1,0,0,0,0,0
-20600,Enhancing Quality for VVC Compressed Videos by Jointly Exploiting Spatial Details and Temporal Structure,"  In this paper, we propose a quality enhancement network for Versatile Video
-Coding (VVC) compressed videos by jointly exploiting spatial details and
-temporal structure (SDTS). The network consists of a temporal structure
-prediction subnet and a spatial detail enhancement subnet. The former subnet is
-used to estimate and compensate the temporal motion across frames, and the
-spatial detail subnet is used to reduce the compression artifacts and enhance
-the reconstruction quality of the VVC compressed video. Experimental results
-demonstrate the effectiveness of our SDTS-based approach. It offers over
-7.82$\%$ BD-rate saving on the common test video sequences and achieves the
-state-of-the-art performance.
-",1,0,0,0,0,0
-20601,Discrete CMC surfaces in R^3 and discrete minimal surfaces in S^3. A discrete Lawson correspondence,"  The main result of this paper is a discrete Lawson correspondence between
-discrete CMC surfaces in R^3 and discrete minimal surfaces in S^3. This is a
-correspondence between two discrete isothermic surfaces. We show that this
-correspondence is an isometry in the following sense: it preserves the metric
-coefficients introduced previously by Bobenko and Suris for isothermic nets.
-Exactly as in the smooth case, this is a correspondence between nets with the
-same Lax matrices, and the immersion formulas also coincide with the smooth
-case.
-",0,1,1,0,0,0
-20602,Network Model Selection Using Task-Focused Minimum Description Length,"  Networks are fundamental models for data used in practically every
-application domain. In most instances, several implicit or explicit choices
-about the network definition impact the translation of underlying data to a
-network representation, and the subsequent question(s) about the underlying
-system being represented. Users of downstream network data may not even be
-aware of these choices or their impacts. We propose a task-focused network
-model selection methodology which addresses several key challenges. Our
-approach constructs network models from underlying data and uses minimum
-description length (MDL) criteria for selection. Our methodology measures
-efficiency, a general and comparable measure of the network's performance of a
-local (i.e. node-level) predictive task of interest. Selection on efficiency
-favors parsimonious (e.g. sparse) models to avoid overfitting and can be
-applied across arbitrary tasks and representations. We show stability,
-sensitivity, and significance testing in our methodology.
-",1,0,0,0,0,0
-20603,Concentration of curvature and Lipschitz invariants of holomorphic functions of two variables,"  By combining analytic and geometric viewpoints on the concentration of the
-curvature of the Milnor fibre, we prove that Lipschitz homeomorphisms preserve
-the zones of multi-scale curvature concentration as well as the gradient canyon
-structure of holomorphic functions of two variables. This yields the first new
-Lipschitz invariants after those discovered by Henry and Parusinski in 2003.
-",0,0,1,0,0,0
-20604,Beam tuning and bunch length measurement in the bunch compression operation at the cERL,"  Realization of a short bunch beam by manipulating the longitudinal phase
-space distribution with a finite longitudinal dispersion following an off-crest
-accelera- tion is a widely used technique. The technique was applied in a
-compact test accelerator of an energy-recovery linac scheme for compressing the
-bunch length at the return loop. A diagnostic system utilizing coherent
-transition radiation was developed for the beam tuning and for estimating the
-bunch length. By scanning the beam parameters, we experimentally found the best
-condition for the bunch compression. The RMS bunch length of 250+-50 fs was
-obtained at a bunch charge of 2 pC. This result confirmed the design and the
-tuning pro- cedure of the bunch compression operation for the future
-energy-recovery linac (ERL).
-",0,1,0,0,0,0
-20605,Long-Lived Ultracold Molecules with Electric and Magnetic Dipole Moments,"  We create fermionic dipolar $^{23}$Na$^6$Li molecules in their triplet ground
-state from an ultracold mixture of $^{23}$Na and $^6$Li. Using
-magneto-association across a narrow Feshbach resonance followed by a two-photon
-STIRAP transfer to the triplet ground state, we produce $3\,{\times}\,10^4$
-ground state molecules in a spin-polarized state. We observe a lifetime of
-$4.6\,\text{s}$ in an isolated molecular sample, approaching the $p$-wave
-universal rate limit. Electron spin resonance spectroscopy of the triplet state
-was used to determine the hyperfine structure of this previously unobserved
-molecular state.
-",0,1,0,0,0,0
-20606,Bilinear generalized Radon transforms in the plane,"  Let $\sigma$ be arc-length measure on $S^1\subset \mathbb R^2$ and $\Theta$
-denote rotation by an angle $\theta \in (0, \pi]$. Define a model bilinear
-generalized Radon transform, $$B_{\theta}(f,g)(x)=\int_{S^1} f(x-y)g(x-\Theta
-y)\, d\sigma(y),$$ an analogue of the linear generalized Radon transforms of
-Guillemin and Sternberg \cite{GS} and Phong and Stein (e.g.,
-\cite{PhSt91,St93}). Operators such as $B_\theta$ are motivated by problems in
-geometric measure theory and combinatorics. For $\theta<\pi$, we show that
-$B_{\theta}: L^p({\Bbb R}^2) \times L^q({\Bbb R}^2) \to L^r({\Bbb R}^2)$ if
-$\left(\frac{1}{p},\frac{1}{q},\frac{1}{r}\right)\in Q$, the polyhedron with
-the vertices $(0,0,0)$, $(\frac{2}{3}, \frac{2}{3}, 1)$, $(0, \frac{2}{3},
-\frac{1}{3})$, $(\frac{2}{3},0,\frac{1}{3})$, $(1,0,1)$, $(0,1,1)$ and
-$(\frac{1}{2},\frac{1}{2},\frac{1}{2})$, except for $\left(
-\frac{1}{2},\frac{1}{2},\frac{1}{2} \right)$, where we obtain a restricted
-strong type estimate. For the degenerate case $\theta=\pi$, a more restrictive
-set of exponents holds. In the scale of normed spaces, $p,q,r \ge 1$, the type
-set $Q$ is sharp. Estimates for the same exponents are also proved for a class
-of bilinear generalized Radon transforms in $\mathbb R^2$ of the form $$
-B(f,g)(x)=\int \int \delta(\phi_1(x,y)-t_1)\delta(\phi_2(x,z)-t_2)
-\delta(\phi_3(y,z)-t_3) f(y)g(z) \psi(y,z) \, dy\, dz, $$ where $\delta$
-denotes the Dirac distribution, $t_1,t_2,t_3\in\mathbb R$, $\psi$ is a smooth
-cut-off and the defining functions $\phi_j$ satisfy some natural geometric
-assumptions.
-",0,0,1,0,0,0
-20607,Diffusion of new products with recovering consumers,"  We consider the diffusion of new products in the discrete Bass-SIR model, in
-which consumers who adopt the product can later ""recover"" and stop influencing
-their peers to adopt the product. To gain insight into the effect of the social
-network structure on the diffusion, we focus on two extreme cases. In the
-""most-connected"" configuration where all consumers are inter-connected
-(complete network), averaging over all consumers leads to an aggregate model,
-which combines the Bass model for diffusion of new products with the SIR model
-for epidemics. In the ""least-connected"" configuration where consumers are
-arranged on a circle and each consumer can only be influenced by his left
-neighbor (one-sided 1D network), averaging over all consumers leads to a
-different aggregate model which is linear, and can be solved explicitly. We
-conjecture that for any other network, the diffusion is bounded from below and
-from above by that on a one-sided 1D network and on a complete network,
-respectively. When consumers are arranged on a circle and each consumer can be
-influenced by his left and right neighbors (two-sided 1D network), the
-diffusion is strictly faster than on a one-sided 1D network. This is different
-from the case of non-recovering adopters, where the diffusion on one-sided and
-on two-sided 1D networks is identical. We also propose a nonlinear model for
-recoveries, and show that consumers' heterogeneity has a negligible effect on
-the aggregate diffusion.
-",1,1,0,0,0,0
-20608,Answering Complex Questions Using Open Information Extraction,"  While there has been substantial progress in factoid question-answering (QA),
-answering complex questions remains challenging, typically requiring both a
-large body of knowledge and inference techniques. Open Information Extraction
-(Open IE) provides a way to generate semi-structured knowledge for QA, but to
-date such knowledge has only been used to answer simple questions with
-retrieval-based methods. We overcome this limitation by presenting a method for
-reasoning with Open IE knowledge, allowing more complex questions to be
-handled. Using a recently proposed support graph optimization framework for QA,
-we develop a new inference model for Open IE, in particular one that can work
-effectively with multiple short facts, noise, and the relational structure of
-tuples. Our model significantly outperforms a state-of-the-art structured
-solver on complex questions of varying difficulty, while also removing the
-reliance on manually curated knowledge.
-",1,0,0,0,0,0
-20609,On Testing Quantum Programs,"  A quantum computer (QC) can solve many computational problems more
-efficiently than a classic one. The field of QCs is growing: companies (such as
-DWave, IBM, Google, and Microsoft) are building QC offerings. We position that
-software engineers should look into defining a set of software engineering
-practices that apply to QC's software. To start this process, we give examples
-of challenges associated with testing such software and sketch potential
-solutions to some of these challenges.
-",1,0,0,0,0,0
-20610,Degree weighted recurrence networks for the analysis of time series data,"  Recurrence networks are powerful tools used effectively in the nonlinear
-analysis of time series data. The analysis in this context is done mostly with
-unweighted and undirected complex networks constructed with specific criteria
-from the time series. In this work, we propose a novel method to construct
-""weighted recurrence network""(WRN) from a time series and show how it can
-reveal useful information regarding the structure of a chaotic attractor, which
-the usual unweighted recurrence network cannot provide. Especially, we find the
-node strength distribution of the WRN, from every chaotic attractor follows a
-power law (with exponential tail) with the index characteristic to the fractal
-structure of the attractor. This leads to a new class among complex networks,
-to which networks from all standard chaotic attractors are found to belong. In
-addition, we present generalized definitions for clustering coefficient and
-characteristic path length and show that these measures can effectively
-discriminate chaotic dynamics from white noise and $1/f$ colored noise. Our
-results indicate that the WRN and the associated measures can become
-potentially important tools for the analysis of short and noisy time series
-from the real world systems as they are clearly demarked from that of noisy or
-stochastic systems.
-",0,1,0,0,0,0
-20611,Parcels v0.9: prototyping a Lagrangian Ocean Analysis framework for the petascale age,"  As Ocean General Circulation Models (OGCMs) move into the petascale age,
-where the output from global high-resolution model runs can be of the order of
-hundreds of terabytes in size, tools to analyse the output of these models will
-need to scale up too. Lagrangian Ocean Analysis, where virtual particles are
-tracked through hydrodynamic fields, is an increasingly popular way to analyse
-OGCM output, by mapping pathways and connectivity of biotic and abiotic
-particulates. However, the current software stack of Lagrangian Ocean Analysis
-codes is not dynamic enough to cope with the increasing complexity, scale and
-need for customisation of use-cases. Furthermore, most community codes are
-developed for stand-alone use, making it a nontrivial task to integrate virtual
-particles at runtime of the OGCM. Here, we introduce the new Parcels code,
-which was designed from the ground up to be sufficiently scalable to cope with
-petascale computing. We highlight its API design that combines flexibility and
-customisation with the ability to optimise for HPC workflows, following the
-paradigm of domain-specific languages. Parcels is primarily written in Python,
-utilising the wide range of tools available in the scientific Python ecosystem,
-while generating low-level C-code and using Just-In-Time compilation for
-performance-critical computation. We show a worked-out example of its API, and
-validate the accuracy of the code against seven idealised test cases. This
-version~0.9 of Parcels is focussed on laying out the API, with future work
-concentrating on optimisation, efficiency and at-runtime coupling with OGCMs.
-",1,1,0,0,0,0
-20612,TFLMS: Large Model Support in TensorFlow by Graph Rewriting,"  While accelerators such as GPUs have limited memory, deep neural networks are
-becoming larger and will not fit with the memory limitation of accelerators for
-training. We propose an approach to tackle this problem by rewriting the
-computational graph of a neural network, in which swap-out and swap-in
-operations are inserted to temporarily store intermediate results on CPU
-memory. In particular, we first revise the concept of a computational graph by
-defining a concrete semantics for variables in a graph. We then formally show
-how to derive swap-out and swap-in operations from an existing graph and
-present rules to optimize the graph. To realize our approach, we developed a
-module in TensorFlow, named TFLMS. TFLMS is published as a pull request in the
-TensorFlow repository for contributing to the TensorFlow community. With TFLMS,
-we were able to train ResNet-50 and 3DUnet with 4.7x and 2x larger batch size,
-respectively. In particular, we were able to train 3DUNet using images of size
-of $192^3$ for image segmentation, which, without TFLMS, had been done only by
-dividing the images to smaller images, which affects the accuracy.
-",0,0,0,1,0,0
-20613,Machine Learning for Quantum Dynamics: Deep Learning of Excitation Energy Transfer Properties,"  Understanding the relationship between the structure of light-harvesting
-systems and their excitation energy transfer properties is of fundamental
-importance in many applications including the development of next generation
-photovoltaics. Natural light harvesting in photosynthesis shows remarkable
-excitation energy transfer properties, which suggests that pigment-protein
-complexes could serve as blueprints for the design of nature inspired devices.
-Mechanistic insights into energy transport dynamics can be gained by leveraging
-numerically involved propagation schemes such as the hierarchical equations of
-motion (HEOM). Solving these equations, however, is computationally costly due
-to the adverse scaling with the number of pigments. Therefore virtual
-high-throughput screening, which has become a powerful tool in material
-discovery, is less readily applicable for the search of novel excitonic
-devices. We propose the use of artificial neural networks to bypass the
-computational limitations of established techniques for exploring the
-structure-dynamics relation in excitonic systems. Once trained, our neural
-networks reduce computational costs by several orders of magnitudes. Our
-predicted transfer times and transfer efficiencies exhibit similar or even
-higher accuracies than frequently used approximate methods such as secular
-Redfield theory
-",0,1,0,1,0,0
-20614,Multi-Player Bandits: A Trekking Approach,"  We study stochastic multi-armed bandits with many players. The players do not
-know the number of players, cannot communicate with each other and if multiple
-players select a common arm they collide and none of them receive any reward.
-We consider the static scenario, where the number of players remains fixed, and
-the dynamic scenario, where the players enter and leave at any time. We provide
-algorithms based on a novel `trekking approach' that guarantees constant regret
-for the static case and sub-linear regret for the dynamic case with high
-probability. The trekking approach eliminates the need to estimate the number
-of players resulting in fewer collisions and improved regret performance
-compared to the state-of-the-art algorithms. We also develop an epoch-less
-algorithm that eliminates any requirement of time synchronization across the
-players provided each player can detect the presence of other players on an
-arm. We validate our theoretical guarantees using simulation based and real
-test-bed based experiments.
-",0,0,0,1,0,0
-20615,High-resolution investigation of spinal cord and spine,"  High-resolution non-invasive 3D study of intact spine and spinal cord
-morphology on the level of complex vascular and neuronal organization is a
-crucial issue for the development of treatments for the injuries and
-pathologies of central nervous system (CNS). X-ray phase contrast tomography
-enables high quality 3D visualization in ex-vivo mouse model of both vascular
-and neuronal network of the soft spinal cord tissue at the scale from
-millimeters to hundreds of nanometers without any contrast agents and
-sectioning. Until now, 3D high resolution visualization of spinal cord mostly
-has been limited by imaging of organ extracted from vertebral column because
-high absorbing boney tissue drastically reduces the morphological details of
-soft tissue in image. However, the extremely destructive procedure of bones
-removal leads to sample deterioration and, therefore, to the lack of
-considerable part of information about the object. In this work we present the
-data analysis procedure to get high resolution and high contrast 3D images of
-intact mice spinal cord surrounded by vertebras, preserving all richness of
-micro-details of the spinal cord inhabiting inside. Our results are the first
-step forward to the difficult way toward the high- resolution investigation of
-in-vivo model central nervous system.
-",0,1,0,0,0,0
-20616,Bohr--Rogosinski radius for analytic functions,"  There are a number of articles which deal with Bohr's phenomenon whereas only
-a few papers appeared in the literature on Rogosinski's radii for analytic
-functions defined on the unit disk $|z|<1$. In this article, we introduce and
-investigate Bohr-Rogosinski's radii for analytic functions defined for $|z|<1$.
-Also, we prove several different improved versions of the classical Bohr's
-inequality. Finally, we also discuss the Bohr-Rogosinski's radius for a class
-of subordinations. All the results are proved to be sharp.
-",0,0,1,0,0,0
-20617,Methods to locate Saddle Points in Complex Landscapes,"  We present a class of simple algorithms that allows to find the reaction path
-in systems with a complex potential energy landscape. The approach does not
-need any knowledge on the product state and does not require the calculation of
-any second derivatives. The underlying idea is to use two nearby points in
-configuration space to locate the path of slowest ascent. By introducing a weak
-noise term, the algorithm is able to find even low-lying saddle points that are
-not reachable by means of a slowest ascent path. Since the algorithm makes only
-use of the value of the potential and its gradient, the computational effort to
-find saddles is linear in the number of degrees of freedom, if the potential is
-short-ranged. We test the performance of the algorithm for two potential energy
-landscapes. For the Müller-Brown surface we find that the algorithm always
-finds the correct saddle point. For the modified Müller-Brown surface, which
-has a saddle point that is not reachable by means of a slowest ascent path, the
-algorithm is still able to find this saddle point with high probability.
-",0,1,0,0,0,0
-20618,On the Complexity of Opinions and Online Discussions,"  In an increasingly polarized world, demagogues who reduce complexity down to
-simple arguments based on emotion are gaining in popularity. Are opinions and
-online discussions falling into demagoguery? In this work, we aim to provide
-computational tools to investigate this question and, by doing so, explore the
-nature and complexity of online discussions and their space of opinions,
-uncovering where each participant lies.
-More specifically, we present a modeling framework to construct latent
-representations of opinions in online discussions which are consistent with
-human judgements, as measured by online voting. If two opinions are close in
-the resulting latent space of opinions, it is because humans think they are
-similar. Our modeling framework is theoretically grounded and establishes a
-surprising connection between opinions and voting models and the sign-rank of a
-matrix. Moreover, it also provides a set of practical algorithms to both
-estimate the dimension of the latent space of opinions and infer where opinions
-expressed by the participants of an online discussion lie in this space.
-Experiments on a large dataset from Yahoo! News, Yahoo! Finance, Yahoo! Sports,
-and the Newsroom app suggest that unidimensional opinion models may often be
-unable to accurately represent online discussions, provide insights into human
-judgements and opinions, and show that our framework is able to circumvent
-language nuances such as sarcasm or humor by relying on human judgements
-instead of textual analysis.
-",1,0,0,1,0,0
-20619,Free energy distribution of the stationary O'Connell-Yor directed random polymer model,"  We study the semi-discrete directed polymer model introduced by O'Connell-Yor
-in its stationary regime, based on our previous work on the stationary
-$q$-totally asymmetric simple exclusion process ($q$-TASEP) using a two-sided
-$q$-Whittaker process. We give a formula for the free energy distribution of
-the polymer model in terms of Fredholm determinant and show that the universal
-KPZ stationary distribution appears in the long time limit. We also consider
-the limit to the stationary KPZ equation and discuss the connections with
-previously found formulas.
-",0,1,1,0,0,0
-20620,Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13),"  In this paper, we introduce the use of a personalized Gaussian Process model
-(pGP) to predict per-patient changes in ADAS-Cog13 -- a significant predictor
-of Alzheimer's Disease (AD) in the cognitive domain -- using data from each
-patient's previous visits, and testing on future (held-out) data. We start by
-learning a population-level model using multi-modal data from previously seen
-patients using a base Gaussian Process (GP) regression. The personalized GP
-(pGP) is formed by adapting the base GP sequentially over time to a new
-(target) patient using domain adaptive GPs. We extend this personalized
-approach to predict the values of ADAS-Cog13 over the future 6, 12, 18, and 24
-months. We compare this approach to a GP model trained only on past data of the
-target patients (tGP), as well as to a new approach that combines pGP with tGP.
-We find that the new approach, combining pGP with tGP, leads to large
-improvements in accurately forecasting future ADAS-Cog13 scores.
-",0,0,0,1,0,0
-20621,Analysis and Control of a Non-Standard Hyperbolic PDE Traffic Flow Model,"  The paper provides results for a non-standard, hyperbolic, 1-D, nonlinear
-traffic flow model on a bounded domain. The model consists of two first-order
-PDEs with a dynamic boundary condition that involves the time derivative of the
-velocity. The proposed model has features that are important from a
-traffic-theoretic point of view: is completely anisotropic and information
-travels forward exactly at the same speed as traffic. It is shown that, for all
-physically meaningful initial conditions, the model admits a globally defined,
-unique, classical solution that remains positive and bounded for all times.
-Moreover, it is shown that global stabilization can be achieved for arbitrary
-equilibria by means of an explicit boundary feedback law. The stabilizing
-feedback law depends only on the inlet velocity and consequently, the
-measurement requirements for the implementation of the proposed boundary
-feedback law are minimal. The efficiency of the proposed boundary feedback law
-is demonstrated by means of a numerical example.
-",1,0,1,0,0,0
-20622,Learning Robust Representations for Computer Vision,"  Unsupervised learning techniques in computer vision often require learning
-latent representations, such as low-dimensional linear and non-linear
-subspaces. Noise and outliers in the data can frustrate these approaches by
-obscuring the latent spaces.
-Our main goal is deeper understanding and new development of robust
-approaches for representation learning. We provide a new interpretation for
-existing robust approaches and present two specific contributions: a new robust
-PCA approach, which can separate foreground features from dynamic background,
-and a novel robust spectral clustering method, that can cluster facial images
-with high accuracy. Both contributions show superior performance to standard
-methods on real-world test sets.
-",1,0,0,1,0,0
-20623,Detection Estimation and Grid matching of Multiple Targets with Single Snapshot Measurements,"  In this work, we explore the problems of detecting the number of narrow-band,
-far-field targets and estimating their corresponding directions from single
-snapshot measurements. The principles of sparse signal recovery (SSR) are used
-for the single snapshot detection and estimation of multiple targets. In the
-SSR framework, the DoA estimation problem is grid based and can be posed as the
-lasso optimization problem. However, the SSR framework for DoA estimation gives
-rise to the grid mismatch problem, when the unknown targets (sources) are not
-matched with the estimation grid chosen for the construction of the array
-steering matrix at the receiver. The block sparse recovery framework is known
-to mitigate the grid mismatch problem by jointly estimating the targets and
-their corresponding offsets from the estimation grid using the group lasso
-estimator. The corresponding detection problem reduces to estimating the
-optimal regularization parameter ($\tau$) of the lasso (in case of perfect
-grid-matching) or group-lasso estimation problem for achieving the required
-probability of correct detection ($P_c$). We propose asymptotic and finite
-sample test statistics for detecting the number of sources with the required
-$P_c$ at moderate to high signal to noise ratios. Once the number of sources
-are detected, or equivalently the optimal $\hat{\tau}$ is estimated, the
-corresponding estimation and grid matching of the DoAs can be performed by
-solving the lasso or group-lasso problem at $\hat{\tau}$
-",0,0,0,1,0,0
-20624,Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels,"  The latent feature relational model (LFRM) is a generative model for
-graph-structured data to learn a binary vector representation for each node in
-the graph. The binary vector denotes the node's membership in one or more
-communities. At its core, the LFRM miller2009nonparametric is an overlapping
-stochastic blockmodel, which defines the link probability between any pair of
-nodes as a bilinear function of their community membership vectors. Moreover,
-using a nonparametric Bayesian prior (Indian Buffet Process) enables learning
-the number of communities automatically from the data. However, despite its
-appealing properties, inference in LFRM remains a challenge and is typically
-done via MCMC methods. This can be slow and may take a long time to converge.
-In this work, we develop a small-variance asymptotics based framework for the
-non-parametric Bayesian LFRM. This leads to an objective function that retains
-the nonparametric Bayesian flavor of LFRM, while enabling us to design
-deterministic inference algorithms for this model, that are easy to implement
-(using generic or specialized optimization routines) and are fast in practice.
-Our results on several benchmark datasets demonstrate that our algorithm is
-competitive to methods such as MCMC, while being much faster.
-",0,0,0,1,0,0
-20625,Towards a Science of Mind,"  The ancient mind/body problem continues to be one of deepest mysteries of
-science and of the human spirit. Despite major advances in many fields, there
-is still no plausible link between subjective experience (qualia) and its
-realization in the body. This paper outlines some of the elements of a rigorous
-science of mind (SoM) - key ideas include scientific realism of mind, agnostic
-mysterianism, careful attention to language, and a focus on concrete
-(touchstone) questions and results.
-",1,0,0,0,1,0
-20626,Resistance distance criterion for optimal slack bus selection,"  We investigate the dependence of transmission losses on the choice of a slack
-bus in high voltage AC transmission networks. We formulate a transmission loss
-minimization problem in terms of slack variables representing the additional
-power injection that each generator provides to compensate the transmission
-losses. We show analytically that for transmission lines having small,
-homogeneous resistance over reactance ratios ${r/x\ll1}$, transmission losses
-are generically minimal in the case of a unique \textit{slack bus} instead of a
-distributed slack bus. For the unique slack bus scenario, to lowest order in
-${r/x}$, transmission losses depend linearly on a resistance distance based
-indicator measuring the separation of the slack bus candidate from the rest of
-the network. We confirm these results numerically for several IEEE and Pegase
-testcases, and show that our predictions qualitatively hold also in the case of
-lines having inhomogeneous ${r/x}$ ratios, with optimal slack bus choices
-reducing transmission losses by ${10}\%$ typically.
-",1,1,0,0,0,0
-20627,Interpolation in the Presence of Domain Inhomogeneity,"  Standard interpolation techniques are implicitly based on the assumption that
-the signal lies on a homogeneous domain. In this letter, the proposed
-interpolation method instead exploits prior information about domain
-inhomogeneity, characterized by different, potentially overlapping, subdomains.
-By introducing a domain-similarity metric for each sample, the interpolation
-process is then based on a domain-informed consistency principle. We illustrate
-and demonstrate the feasibility of domain-informed linear interpolation in 1D,
-and also, on a real fMRI image in 2D. The results show the benefit of
-incorporating domain knowledge so that, for example, sharp domain boundaries
-can be recovered by the interpolation, if such information is available.
-",0,0,1,0,0,0
-20628,Lectures on the mean values of functionals -- An elementary introduction to infinite-dimensional probability,"  This is an elementary introduction to infinite-dimensional probability. In
-the lectures, we compute the exact mean values of some functionals on C[0,1]
-and L[0,1] by considering these functionals as infinite-dimensional random
-variables. The results show that there exist the complete concentration of
-measure phenomenon for these mean values since the variances are all zeroes.
-",0,1,1,1,0,0
-20629,Synthesis of Optimal Resilient Control Strategies,"  Repair mechanisms are important within resilient systems to maintain the
-system in an operational state after an error occurred. Usually, constraints on
-the repair mechanisms are imposed, e.g., concerning the time or resources
-required (such as energy consumption or other kinds of costs). For systems
-modeled by Markov decision processes (MDPs), we introduce the concept of
-resilient schedulers, which represent control strategies guaranteeing that
-these constraints are always met within some given probability. Assigning
-rewards to the operational states of the system, we then aim towards resilient
-schedulers which maximize the long-run average reward, i.e., the expected mean
-payoff. We present a pseudo-polynomial algorithm that decides whether a
-resilient scheduler exists and if so, yields an optimal resilient scheduler. We
-show also that already the decision problem asking whether there exists a
-resilient scheduler is PSPACE-hard.
-",1,0,0,0,0,0
-20630,Limits of the Kucera-Gacs coding method,"  Every real is computable from a Martin-Loef random real. This well known
-result in algorithmic randomness was proved by Kucera and Gacs. In this survey
-article we discuss various approaches to the problem of coding an arbitrary
-real into a Martin-Loef random real,and also describe new results concerning
-optimal methods of coding. We start with a simple presentation of the original
-methods of Kucera and Gacs and then rigorously demonstrate their limitations in
-terms of the size of the redundancy in the codes that they produce. Armed with
-a deeper understanding of these methods, we then proceed to motivate and
-illustrate aspects of the new coding method that was recently introduced by
-Barmpalias and Lewis-Pye and which achieves optimal logarithmic redundancy, an
-exponential improvement over the original redundancy bounds.
-",0,0,1,0,0,0
-20631,Subspace Tracking Algorithms for Millimeter Wave MIMO Channel Estimation with Hybrid Beamforming,"  This paper proposes the use of subspace tracking algorithms for performing
-MIMO channel estimation at millimeter wave (mmWave) frequencies. Using a
-subspace approach, we develop a protocol enabling the estimation of the right
-(resp. left) singular vectors at the transmitter (resp. receiver) side; then,
-we adapt the projection approximation subspace tracking with deflation (PASTd)
-and the orthogonal Oja (OOJA) algorithms to our framework and obtain two
-channel estimation algorithms. The hybrid analog/digital nature of the
-beamformer is also explicitly taken into account at the algorithm design stage.
-Numerical results show that the proposed estimation algorithms are effective,
-and that they perform better than two relevant competing alternatives available
-in the open literature.
-",1,0,0,0,0,0
-20632,Thomas Precession for Dressed Particles,"  We consider a particle dressed with boundary gravitons in three-dimensional
-Minkowski space. The existence of BMS transformations implies that the
-particle's wavefunction picks up a Berry phase when subjected to changes of
-reference frames that trace a closed path in the asymptotic symmetry group. We
-evaluate this phase and show that, for BMS superrotations, it provides a
-gravitational generalization of Thomas precession. In principle, such phases
-are observable signatures of asymptotic symmetries.
-",0,1,1,0,0,0
-20633,Exponential random graphs behave like mixtures of stochastic block models,"  We study the behavior of exponential random graphs in both the sparse and the
-dense regime. We show that exponential random graphs are approximate mixtures
-of graphs with independent edges whose probability matrices are critical points
-of an associated functional, thereby satisfying a certain matrix equation. In
-the dense regime, every solution to this equation is close to a block matrix,
-concluding that the exponential random graph behaves roughly like a mixture of
-stochastic block models. We also show existence and uniqueness of solutions to
-this equation for several families of exponential random graphs, including the
-case where the subgraphs are counted with positive weights and the case where
-all weights are small in absolute value. In particular, this generalizes some
-of the results in a paper by Chatterjee and Diaconis from the dense regime to
-the sparse regime and strengthens their bounds from the cut-metric to the
-one-metric.
-",1,0,1,1,0,0
-20634,Certificate Enhanced Data-Flow Analysis,"  Proof-carrying-code was proposed as a solution to ensure a trust relationship
-between two parties: a (heavyweight) analyzer and a (lightweight) checker. The
-analyzer verifies the conformance of a given application to a specified
-property and generates a certificate attesting the validity of the analysis
-result. It suffices then for the checker just to test the consistency of the
-proof instead of constructing it. We set out to study the applicability of this
-technique in the context of data- flow analysis. In particular, we want to know
-if there is a significant performance difference between the analyzer and the
-checker. Therefore, we developed a tool, called DCert, implementing an
-inter-procedural context and flow-sensitive data-flow analyzer and checker for
-Android. Applying our tool to real-world large applications, we found out that
-checking can be up to 8 times faster than verification. This important gain in
-time suggests a potential for equipping applications on app stores with
-certificates that can be checked on mobile devices which are limited in
-computation and storage resources. We describe our implementation and report on
-experimental results.
-",1,0,0,0,0,0
-20635,Small cells in a Poisson hyperplane tessellation,"  Until now, little was known about properties of small cells in a Poisson
-hyperplane tessellation. The few existing results were either heuristic or
-applying only to the two dimensional case and for very specific size
-functionals and directional distributions. This paper fills this gap by
-providing a systematic study of small cells in a Poisson hyperplane
-tessellation of arbitrary dimension, arbitrary directional distribution
-$\varphi$ and with respect to an arbitrary size functional $\Sigma$. More
-precisely, we investigate the distribution of the typical cell $Z$, conditioned
-on the event $\{\Sigma(Z)<a\}$, where $a\to0$ and $\Sigma$ is a size
-functional, i.e. a functional on the set of convex bodies which is continuous,
-not identically zero, homogeneous of degree $k>0$, and increasing with respect
-to set inclusion. We focus on the number of facets and the shape of such small
-cells. We show in various general settings that small cells tend to minimize
-the number of facets and that they have a non degenerated limit shape
-distribution which depends on the size $\Sigma$ and the directional
-distribution. We also exhibit a class of directional distribution for which
-cells with small inradius do not tend to minimize the number of facets.
-",0,0,1,0,0,0
-20636,Privacy-Aware Guessing Efficiency,"  We investigate the problem of guessing a discrete random variable $Y$ under a
-privacy constraint dictated by another correlated discrete random variable $X$,
-where both guessing efficiency and privacy are assessed in terms of the
-probability of correct guessing. We define $h(P_{XY}, \epsilon)$ as the maximum
-probability of correctly guessing $Y$ given an auxiliary random variable $Z$,
-where the maximization is taken over all $P_{Z|Y}$ ensuring that the
-probability of correctly guessing $X$ given $Z$ does not exceed $\epsilon$. We
-show that the map $\epsilon\mapsto h(P_{XY}, \epsilon)$ is strictly increasing,
-concave, and piecewise linear, which allows us to derive a closed form
-expression for $h(P_{XY}, \epsilon)$ when $X$ and $Y$ are connected via a
-binary-input binary-output channel. For $(X^n, Y^n)$ being pairs of independent
-and identically distributed binary random vectors, we similarly define
-$\underline{h}_n(P_{X^nY^n}, \epsilon)$ under the assumption that $Z^n$ is also
-a binary vector. Then we obtain a closed form expression for
-$\underline{h}_n(P_{X^nY^n}, \epsilon)$ for sufficiently large, but nontrivial
-values of $\epsilon$.
-",1,0,1,1,0,0
-20637,Positioning services of a travel agency in social networks,"  In this paper the methods of forming a travel company customer base by means
-of social networks are observed. These methods are made to involve web-users of
-the social networks (VK.com and Facebook) for positioning of the service of the
-travel agency ""New Europe"" on the Internet. The methods of applying the
-maintenance activities and interests of web-users are also used. So, the main
-method of information exchanging in modern network society is on-line social
-networks. The rapid development and improvement of such information and
-communication technologies is a key factor in the positioning of the travel
-agency brand in the global information space. The absence of time and space
-restrictions and the speed of spreading of the information among an aim
-audience of social networks create all the conditions for effective
-popularization of the travel agency ""New Europe"" and its service in the
-Internet.
-",1,0,0,0,0,0
-20638,Inverse Mapping for Rainfall-Runoff Models using History Matching Approach,"  In this paper, we consider two rainfall-runoff computer models. The first
-model is Matlab-Simulink model which simulates runoff from windrow compost pad
-(located at the Bioconversion Center in Athens, GA) over a period of time based
-on rainfall events. The second model is Soil Water Assessment Tool (SWAT) which
-estimates surface runoff in the Middle Oconee River in Athens, GA. The input
-parameter spaces of both models are sensitive and high dimensional, the model
-output for every input combination is a time-series of runoff, and these two
-computer models generate a wide spectrum of outputs including some that are far
-from reality. In order to improve the prediction accuracy, in this paper we
-propose to apply a history matching approach for calibrating these hydrological
-models, which also gives better insights for improved management of these
-systems.
-",0,0,0,1,0,0
-20639,Geometric SMOTE: Effective oversampling for imbalanced learning through a geometric extension of SMOTE,"  Classification of imbalanced datasets is a challenging task for standard
-algorithms. Although many methods exist to address this problem in different
-ways, generating artificial data for the minority class is a more general
-approach compared to algorithmic modifications. SMOTE algorithm and its
-variations generate synthetic samples along a line segment that joins minority
-class instances. In this paper we propose Geometric SMOTE (G-SMOTE) as a
-generalization of the SMOTE data generation mechanism. G-SMOTE generates
-synthetic samples in a geometric region of the input space, around each
-selected minority instance. While in the basic configuration this region is a
-hyper-sphere, G-SMOTE allows its deformation to a hyper-spheroid and finally to
-a line segment, emulating, in the last case, the SMOTE mechanism. The
-performance of G-SMOTE is compared against multiple standard oversampling
-algorithms. We present empirical results that show a significant improvement in
-the quality of the generated data when G-SMOTE is used as an oversampling
-algorithm.
-",1,0,0,0,0,0
-20640,Liveness Verification and Synthesis: New Algorithms for Recursive Programs,"  We consider the problems of liveness verification and liveness synthesis for
-recursive programs. The liveness verification problem (LVP) is to decide
-whether a given omega-context-free language is contained in a given
-omega-regular language. The liveness synthesis problem (LSP) is to compute a
-strategy so that a given omega-context-free game, when played along the
-strategy, is guaranteed to derive a word in a given omega-regular language. The
-problems are known to be EXPTIME-complete and EXPTIME-complete, respectively.
-Our contributions are new algorithms with optimal time complexity. For LVP, we
-generalize recent lasso-finding algorithms (also known as Ramsey-based
-algorithms) from finite to recursive programs. For LSP, we generalize a recent
-summary-based algorithm from finite to infinite words. Lasso finding and
-summaries have proven to be efficient in a number of implementations for the
-finite state and finite word setting.
-",1,0,0,0,0,0
-20641,A semiparametric approach for bivariate extreme exceedances,"  Inference over tails is performed by applying only the results of extreme
-value theory. Whilst such theory is well defined and flexible enough in the
-univariate case, multivariate inferential methods often require the imposition
-of arbitrary constraints not fully justifed by the underlying theory. In
-contrast, our approach uses only the constraints imposed by theory. We build on
-previous, theoretically justified work for marginal exceedances over a high,
-unknown threshold, by combining it with flexible, semiparametric copulae
-specifications to investigate extreme dependence. Whilst giving probabilistic
-judgements about the extreme regime of all marginal variables, our approach
-formally uses the full dataset and allows for a variety of patterns of
-dependence, be them extremal or not. A new probabilistic criterion quantifying
-the possibility that the data exhibits asymptotic independence is introduced
-and its robustness empirically studied. Estimation of functions of interest in
-extreme value analyses is performed via MCMC algorithms. Attention is also
-devoted to the prediction of new extreme observations. Our approach is
-evaluated through a series of simulations, applied to real data sets and
-assessed against competing approaches. Evidence demonstrates that the bulk of
-the data does not bias and improves the inferential process for the extremal
-dependence.
-",0,0,0,1,0,0
-20642,Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network,"  The Soil Moisture Active Passive (SMAP) mission has delivered valuable
-sensing of surface soil moisture since 2015. However, it has a short time span
-and irregular revisit schedule. Utilizing a state-of-the-art time-series deep
-learning neural network, Long Short-Term Memory (LSTM), we created a system
-that predicts SMAP level-3 soil moisture data with atmospheric forcing,
-model-simulated moisture, and static physiographic attributes as inputs. The
-system removes most of the bias with model simulations and improves predicted
-moisture climatology, achieving small test root-mean-squared error (<0.035) and
-high correlation coefficient >0.87 for over 75\% of Continental United States,
-including the forested Southeast. As the first application of LSTM in
-hydrology, we show the proposed network avoids overfitting and is robust for
-both temporal and spatial extrapolation tests. LSTM generalizes well across
-regions with distinct climates and physiography. With high fidelity to SMAP,
-LSTM shows great potential for hindcasting, data assimilation, and weather
-forecasting.
-",0,0,0,1,0,0
-20643,Note on Green Function Formalism and Topological Invariants,"  It has been discovered previously that the topological order parameter could
-be identified from the topological data of the Green function, namely the
-(generalized) TKNN invariant in general dimensions, for both non-interacting
-and interacting systems. In this note, we show that this phenomena has a clear
-geometric derivation. This proposal could be regarded as an alternative proof
-for the identification of the corresponding topological invariant and
-topological order parameter.
-",0,0,1,0,0,0
-20644,Unsupervised Document Embedding With CNNs,"  We propose a new model for unsupervised document embedding. Leading existing
-approaches either require complex inference or use recurrent neural networks
-(RNN) that are difficult to parallelize. We take a different route and develop
-a convolutional neural network (CNN) embedding model. Our CNN architecture is
-fully parallelizable resulting in over 10x speedup in inference time over RNN
-models. Parallelizable architecture enables to train deeper models where each
-successive layer has increasingly larger receptive field and models longer
-range semantic structure within the document. We additionally propose a fully
-unsupervised learning algorithm to train this model based on stochastic forward
-prediction. Empirical results on two public benchmarks show that our approach
-produces comparable to state-of-the-art accuracy at a fraction of computational
-cost.
-",1,0,0,1,0,0
-20645,A metric model for the functional architecture of the visual cortex,"  The purpose of this work is to construct a model for the functional
-architecture of the primary visual cortex (V1), based on a structure of metric
-measure space induced by the underlying organization of receptive profiles
-(RPs) of visual cells. In order to account for the horizontal connectivity of
-V1 in such a context, a diffusion process compatible with the geometry of the
-space is defined following the classical approach of K.-T. Sturm. The
-construction of our distance function does neither require any group
-parameterization of the family of RPs, nor involve any differential structure.
-As such, it adapts to non-parameterized sets of RPs, possibly obtained through
-numerical procedures; it also allows to model the lateral connectivity arising
-from non-differential metrics such as the one induced on a pinwheel surface by
-a family of filters of vanishing scale. On the other hand, when applied to the
-classical framework of Gabor filters, this construction yields a distance
-approximating the sub-Riemannian structure proposed as a model for V1 by G.
-Citti and A. Sarti [J Math Imaging Vis 24: 307 (2006)], thus showing itself to
-be consistent with existing cortex models.
-",0,0,0,0,1,0
-20646,Bayesian parameter identification in Cahn-Hilliard models for biological growth,"  We consider the inverse problem of parameter estimation in a diffuse
-interface model for tumour growth. The model consists of a fourth-order
-Cahn--Hilliard system and contains three phenomenological parameters: the
-tumour proliferation rate, the nutrient consumption rate, and the chemotactic
-sensitivity. We study the inverse problem within the Bayesian framework and
-construct the likelihood and noise for two typical observation settings. One
-setting involves an infinite-dimensional data space where we observe the full
-tumour. In the second setting we observe only the tumour volume, hence the data
-space is finite-dimensional. We show the well-posedness of the posterior
-measure for both settings, building upon and improving the analytical results
-in [C. Kahle and K.F. Lam, Appl. Math. Optim. (2018)]. A numerical example
-involving synthetic data is presented in which the posterior measure is
-numerically approximated by the Sequential Monte Carlo approach with tempering.
-",0,0,0,1,0,0
-20647,Transport Phase Diagram and Anderson Localization in Hyperuniform Disordered Photonic Materials,"  Hyperuniform disordered photonic materials (HDPM) are spatially correlated
-dielectric structures with unconventional optical properties. They can be
-transparent to long-wavelength radiation while at the same time have isotropic
-band gaps in another frequency range. This phenomenon raises fundamental
-questions concerning photon transport through disordered media. While optical
-transparency is robust against recurrent multiple scattering, little is known
-about other transport regimes like diffusive multiple scattering or Anderson
-localization. Here we investigate band gaps, and we report Anderson
-localization in two-dimensional stealthy HDPM using numerical simulations of
-the density of states and optical transport statistics. To establish a unified
-view, we propose a transport phase diagram. Our results show that, depending
-only on the degree of correlation, a dielectric material can transition from
-localization behavior to a bandgap crossing an intermediate regime dominated by
-tunneling between weakly coupled states.
-",0,1,0,0,0,0
-20648,Nearly Maximally Predictive Features and Their Dimensions,"  Scientific explanation often requires inferring maximally predictive features
-from a given data set. Unfortunately, the collection of minimal maximally
-predictive features for most stochastic processes is uncountably infinite. In
-such cases, one compromises and instead seeks nearly maximally predictive
-features. Here, we derive upper-bounds on the rates at which the number and the
-coding cost of nearly maximally predictive features scales with desired
-predictive power. The rates are determined by the fractal dimensions of a
-process' mixed-state distribution. These results, in turn, show how widely-used
-finite-order Markov models can fail as predictors and that mixed-state
-predictive features offer a substantial improvement.
-",1,1,0,1,0,0
-20649,A Survey Of Cross-lingual Word Embedding Models,"  Cross-lingual representations of words enable us to reason about word meaning
-in multilingual contexts and are a key facilitator of cross-lingual transfer
-when developing natural language processing models for low-resource languages.
-In this survey, we provide a comprehensive typology of cross-lingual word
-embedding models. We compare their data requirements and objective functions.
-The recurring theme of the survey is that many of the models presented in the
-literature optimize for the same objectives, and that seemingly different
-models are often equivalent modulo optimization strategies, hyper-parameters,
-and such. We also discuss the different ways cross-lingual word embeddings are
-evaluated, as well as future challenges and research horizons.
-",1,0,0,0,0,0
-20650,Space-efficient classical and quantum algorithms for the shortest vector problem,"  A lattice is the integer span of some linearly independent vectors. Lattice
-problems have many significant applications in coding theory and cryptographic
-systems for their conjectured hardness. The Shortest Vector Problem (SVP),
-which is to find the shortest non-zero vector in a lattice, is one of the
-well-known problems that are believed to be hard to solve, even with a quantum
-computer. In this paper we propose space-efficient classical and quantum
-algorithms for solving SVP. Currently the best time-efficient algorithm for
-solving SVP takes $2^{n+o(n)}$ time and $2^{n+o(n)}$ space. Our classical
-algorithm takes $2^{2.05n+o(n)}$ time to solve SVP with only $2^{0.5n+o(n)}$
-space. We then modify our classical algorithm to a quantum version, which can
-solve SVP in time $2^{1.2553n+o(n)}$ with $2^{0.5n+o(n)}$ classical space and
-only poly(n) qubits.
-",1,0,0,0,0,0
-20651,Low Power SI Class E Power Amplifier and RF Switch For Health Care,"  This research was to design a 2.4 GHz class E Power Amplifier (PA) for health
-care, with 0.18um Semiconductor Manufacturing International Corporation CMOS
-technology by using Cadence software. And also RF switch was designed at
-cadence software with power Jazz 180nm SOI process. The ultimate goal for such
-application is to reach high performance and low cost, and between high
-performance and low power consumption design. This paper introduces the design
-of a 2.4GHz class E power amplifier and RF switch design. PA consists of
-cascade stage with negative capacitance. This power amplifier can transmit
-16dBm output power to a 50{\Omega} load. The performance of the power amplifier
-and switch meet the specification requirements of the desired.
-",1,0,0,0,0,0
-20652,Improved approximation algorithm for the Dense-3-Subhypergraph Problem,"  The study of Dense-$3$-Subhypergraph problem was initiated in Chlamt{á}c
-et al. [Approx'16]. The input is a universe $U$ and collection ${\cal S}$ of
-subsets of $U$, each of size $3$, and a number $k$. The goal is to choose a set
-$W$ of $k$ elements from the universe, and maximize the number of sets, $S\in
-{\cal S}$ so that $S\subseteq W$. The members in $U$ are called {\em vertices}
-and the sets of ${\cal S}$ are called the {\em hyperedges}. This is the
-simplest extension into hyperedges of the case of sets of size $2$ which is the
-well known Dense $k$-subgraph problem.
-The best known ratio for the Dense-$3$-Subhypergraph is $O(n^{0.69783..})$ by
-Chlamt{á}c et al. We improve this ratio to $n^{0.61802..}$. More
-importantly, we give a new algorithm that approximates Dense-$3$-Subhypergraph
-within a ratio of $\tilde O(n/k)$, which improves the ratio of $O(n^2/k^2)$ of
-Chlamt{á}c et al.
-We prove that under the {\em log density conjecture} (see Bhaskara et al.
-[STOC'10]) the ratio cannot be better than $\Omega(\sqrt{n})$ and demonstrate
-some cases in which this optimum can be attained.
-",1,0,0,0,0,0
-20653,A Survey of Security Assessment Ontologies,"  A literature survey on ontologies concerning the Security Assessment domain
-has been carried out to uncover initiatives that aim at formalizing concepts
-from the Security Assessment field of research. A preliminary analysis and a
-discussion on the selected works are presented. Our main contribution is an
-updated literature review, describing key characteristics, results, research
-issues, and application domains of the papers. We have also detected gaps in
-the Security Assessment literature that could be the subject of further studies
-in the field. This work is meant to be useful for security researchers who wish
-to adopt a formal approach in their methods.
-",1,0,0,0,0,0
-20654,Distributed Online Learning of Event Definitions,"  Logic-based event recognition systems infer occurrences of events in time
-using a set of event definitions in the form of first-order rules. The Event
-Calculus is a temporal logic that has been used as a basis in event recognition
-applications, providing among others, direct connections to machine learning,
-via Inductive Logic Programming (ILP). OLED is a recently proposed ILP system
-that learns event definitions in the form of Event Calculus theories, in a
-single pass over a data stream. In this work we present a version of OLED that
-allows for distributed, online learning. We evaluate our approach on a
-benchmark activity recognition dataset and show that we can significantly
-reduce training times, exchanging minimal information between processing nodes.
-",1,0,0,0,0,0
-20655,Solving Graph Isomorphism Problem for a Special case,"  Graph isomorphism is an important computer science problem. The problem for
-the general case is unknown to be in polynomial time. The base algorithm for
-the general case works in quasi-polynomial time. The solutions in polynomial
-time for some special type of classes are known. In this work, we have worked
-with a special type of graphs. We have proposed a method to represent these
-graphs and finding isomorphism between these graphs. The method uses a modified
-version of the degree list of a graph and neighbourhood degree list. These
-special type of graphs have a property that neighbourhood degree list of any
-two immediate neighbours is different for every vertex.The representation
-becomes invariant to the order in which the node was selected for giving the
-representation making the isomorphism problem trivial for this case. The
-algorithm works in $O(n^4)$ time, where n is the number of vertices present in
-the graph. The proposed algorithm runs faster than quasi-polynomial time for
-the graphs used in the study.
-",1,0,0,0,0,0
-20656,Selective inference for effect modification via the lasso,"  Effect modification occurs when the effect of the treatment on an outcome
-varies according to the level of other covariates and often has important
-implications in decision making. When there are tens or hundreds of covariates,
-it becomes necessary to use the observed data to select a simpler model for
-effect modification and then make valid statistical inference. We propose a two
-stage procedure to solve this problem. First, we use Robinson's transformation
-to decouple the nuisance parameters from the treatment effect of interest and
-use machine learning algorithms to estimate the nuisance parameters. Next,
-after plugging in the estimates of the nuisance parameters, we use the Lasso to
-choose a low-complexity model for effect modification. Compared to a full model
-consisting of all the covariates, the selected model is much more
-interpretable. Compared to the univariate subgroup analyses, the selected model
-greatly reduces the number of false discoveries. We show that the conditional
-selective inference for the selected model is asymptotically valid given the
-rate assumptions in classical semiparametric regression. Extensive simulation
-studies are conducted to verify the asymptotic results and an epidemiological
-application is used to demonstrate the method.
-",0,0,1,1,0,0
-20657,Is Climate Change Controversial? Modeling Controversy as Contention Within Populations,"  A growing body of research focuses on computationally detecting controversial
-topics and understanding the stances people hold on them. Yet gaps remain in
-our theoretical and practical understanding of how to define controversy, how
-it manifests, and how to measure it. In this paper, we introduce a novel
-measure we call ""contention"", defined with respect to a topic and a population.
-We model contention from a mathematical standpoint. We validate our model by
-examining a diverse set of sources: real-world polling data sets, actual voter
-data, and Twitter coverage on several topics. In our publicly-released Twitter
-data set of nearly 100M tweets, we examine several topics such as Brexit, the
-2016 U.S. Elections, and ""The Dress"", and cross-reference them with other
-sources. We demonstrate that the contention measure holds explanatory power for
-a wide variety of observed phenomena, such as controversies over climate change
-and other topics that are well within scientific consensus. Finally, we
-re-examine the notion of controversy, and present a theoretical framework that
-defines it in terms of population. We present preliminary evidence suggesting
-that contention is one dimension of controversy, along with others, such as
-""importance"". Our new contention measure, along with the hypothesized model of
-controversy, suggest several avenues for future work in this emerging
-interdisciplinary research area.
-",1,1,0,0,0,0
-20658,Defending Against Adversarial Attacks by Leveraging an Entire GAN,"  Recent work has shown that state-of-the-art models are highly vulnerable to
-adversarial perturbations of the input. We propose cowboy, an approach to
-detecting and defending against adversarial attacks by using both the
-discriminator and generator of a GAN trained on the same dataset. We show that
-the discriminator consistently scores the adversarial samples lower than the
-real samples across multiple attacks and datasets. We provide empirical
-evidence that adversarial samples lie outside of the data manifold learned by
-the GAN. Based on this, we propose a cleaning method which uses both the
-discriminator and generator of the GAN to project the samples back onto the
-data manifold. This cleaning procedure is independent of the classifier and
-type of attack and thus can be deployed in existing systems.
-",0,0,0,1,0,0
-20659,Spectral Graph Convolutions for Population-based Disease Prediction,"  Exploiting the wealth of imaging and non-imaging information for disease
-prediction tasks requires models capable of representing, at the same time,
-individual features as well as data associations between subjects from
-potentially large populations. Graphs provide a natural framework for such
-tasks, yet previous graph-based approaches focus on pairwise similarities
-without modelling the subjects' individual characteristics and features. On the
-other hand, relying solely on subject-specific imaging feature vectors fails to
-model the interaction and similarity between subjects, which can reduce
-performance. In this paper, we introduce the novel concept of Graph
-Convolutional Networks (GCN) for brain analysis in populations, combining
-imaging and non-imaging data. We represent populations as a sparse graph where
-its vertices are associated with image-based feature vectors and the edges
-encode phenotypic information. This structure was used to train a GCN model on
-partially labelled graphs, aiming to infer the classes of unlabelled nodes from
-the node features and pairwise associations between subjects. We demonstrate
-the potential of the method on the challenging ADNI and ABIDE databases, as a
-proof of concept of the benefit from integrating contextual information in
-classification tasks. This has a clear impact on the quality of the
-predictions, leading to 69.5% accuracy for ABIDE (outperforming the current
-state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion,
-significantly outperforming standard linear classifiers where only individual
-features are considered.
-",1,0,0,1,0,0
-20660,An independence system as knot invariant,"  An independence system (with respect to the unknotting number) is defined for
-a classical knot diagram. It is proved that the independence system is a knot
-invariant for alternating knots. The exchange property for minimal unknotting
-sets are also discussed. It is shown that there exists an infinite family of
-knot diagrams whose corresponding independence systems are matroids. In
-contrast, infinite families of knot diagrams exist whose independence systems
-are not matroids.
-",0,0,1,0,0,0
-20661,A cavity-induced artificial gauge field in a Bose-Hubbard ladder,"  We consider theoretically ultracold interacting bosonic atoms confined to
-quasi-one-dimensional ladder structures formed by optical lattices and coupled
-to the field of an optical cavity. The atoms can collect a spatial phase
-imprint during a cavity-assisted tunneling along a rung via Raman transitions
-employing a cavity mode and a transverse running wave pump beam. By adiabatic
-elimination of the cavity field we obtain an effective Hamiltonian for the
-bosonic atoms, with a self-consistency condition. Using the numerical density
-matrix renormalization group method, we obtain a rich steady state diagram of
-self-organized steady states. Transitions between superfluid to Mott-insulating
-states occur, on top of which we can have Meissner, vortex liquid, and vortex
-lattice phases. Also a state that explicitly breaks the symmetry between the
-two legs of the ladder, namely the biased-ladder phase is dynamically
-stabilized.
-",0,1,0,0,0,0
-20662,A Review of Laser-Plasma Ion Acceleration,"  An overview of research on laser-plasma based acceleration of ions is given.
-The experimental state of the art is summarized and recent progress is
-discussed. The basic acceleration processes are briefly reviewed with an
-outlook on hybrid mechanisms and novel concepts. Finally, we put focus on the
-development of engineered targets for enhanced acceleration and of all-optical
-methods for beam post-acceleration and control.
-",0,1,0,0,0,0
-20663,Minimax Optimal Estimators for Additive Scalar Functionals of Discrete Distributions,"  In this paper, we consider estimators for an additive functional of $\phi$,
-which is defined as $\theta(P;\phi)=\sum_{i=1}^k\phi(p_i)$, from $n$ i.i.d.
-random samples drawn from a discrete distribution $P=(p_1,...,p_k)$ with
-alphabet size $k$. We propose a minimax optimal estimator for the estimation
-problem of the additive functional. We reveal that the minimax optimal rate is
-characterized by the divergence speed of the fourth derivative of $\phi$ if the
-divergence speed is high. As a result, we show there is no consistent estimator
-if the divergence speed of the fourth derivative of $\phi$ is larger than
-$p^{-4}$. Furthermore, if the divergence speed of the fourth derivative of
-$\phi$ is $p^{4-\alpha}$ for $\alpha \in (0,1)$, the minimax optimal rate is
-obtained within a universal multiplicative constant as $\frac{k^2}{(n\ln
-n)^{2\alpha}} + \frac{k^{2-2\alpha}}{n}$.
-",1,0,1,1,0,0
-20664,Van der Waals Heterostructures Based on Allotropes of Phosphorene and MoSe2,"  The van der Waals heterostructures of allotropes of phosphorene (${\alpha}$-
-and $\beta-P$) with MoSe2 (H-, T-, ZT- and SO-MoSe2) are investigated in the
-framework of state-of-the-art density functional theory. The semiconducting
-heterostructures, $\beta$-P /H-MoSe2 and ${\alpha}$-P / H-MoSe2, forms
-anti-type structures with type I and type II band alignments, respectively,
-whose bands are tunable with external electric field. ${\alpha}$-P / ZT-MoSe2
-and ${\alpha}$-P / SO-MoSe2 form ohmic semiconductor-metal contacts while
-Schottky barrier in $\beta$-P / T-MoSe2 can be reduced to zero by external
-electric field to form ohmic contact which is useful to realize
-high-performance devices. Simulated STM images of given heterostructures reveal
-that ${\alpha}$-P can be used as a capping layer to differentiate between
-various allotropes of underlying MoSe2. The dielectric response of considered
-heterostructures is highly anisotropic in terms of lateral and vertical
-polarization. The tunable electronic and dielectric response of van der Waals
-phosphorene/MoSe2 heterostructure may find potentials applications in the
-fabrication of optoelectronic devices.
-",0,1,0,0,0,0
-20665,On separated solutions of logistic population equation with harvesting,"  We provide a surprising answer to a question raised in S. Ahmad and A.C.
-Lazer [2], and extend the results of that paper.
-",0,0,1,0,0,0
-20666,Nonreciprocal Electromagnetic Scattering from a Periodically Space-Time Modulated Slab and Application to a Quasisonic Isolator,"  Scattering of obliquely incident electromagnetic waves from periodically
-space-time modulated slabs is investigated. It is shown that such structures
-operate as nonreciprocal harmonic generators and spatial-frequency filters. For
-oblique incidences, low-frequency harmonics are filtered out in the form of
-surface waves, while high-frequency harmonics are transmitted as space waves.
-In the quasisonic regime, where the velocity of the space-time modulation is
-close to the velocity of the electromagnetic waves in the background medium,
-the incident wave is strongly coupled to space-time harmonics in the forward
-direction, while in the backward direction it exhibits low coupling to other
-harmonics. This nonreciprocity is leveraged for the realization of an
-electromagnetic isolator in the quasisonic regime and is experimentally
-demonstrated at microwave frequencies.
-",0,1,0,0,0,0
-20667,Story Cloze Ending Selection Baselines and Data Examination,"  This paper describes two supervised baseline systems for the Story Cloze Test
-Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using
-features based on word embeddings and semantic similarity computation. We
-further implement a neural LSTM system with different encoding strategies that
-try to model the relation between the story and the provided endings. Our
-experiments show that a model using representation features based on average
-word embedding vectors over the given story words and the candidate ending
-sentences words, joint with similarity features between the story and candidate
-ending representations performed better than the neural models. Our best model
-achieves an accuracy of 72.42, ranking 3rd in the official evaluation.
-",1,0,0,0,0,0
-20668,Linear Quadratic Optimal Control Problems with Fixed Terminal States and Integral Quadratic Constraints,"  This paper is concerned with a linear quadratic (LQ, for short) optimal
-control problem with fixed terminal states and integral quadratic constraints.
-A Riccati equation with infinite terminal value is introduced, which is
-uniquely solvable and whose solution can be approximated by the solution for a
-suitable unconstrained LQ problem with penalized terminal state. Using results
-from duality theory, the optimal control is explicitly derived by solving the
-Riccati equation together with an optimal parameter selection problem. It turns
-out that the optimal control is not only a feedback of the current state, but
-also a feedback of the target (terminal state). Some examples are presented to
-illustrate the theory developed.
-",0,0,1,0,0,0
-20669,A new sampling density condition for shift-invariant spaces,"  Let $X=\{x_i:i\in\mathbb{Z}\}$, $\dots<x_{i-1}<x_i<x_{i+1}<\dots$, be a
-sampling set which is separated by a constant $\gamma>0$. Under certain
-conditions on $\phi$, it is proved that if there exists a positive integer
-$\nu$ such that
-$$\delta_\nu:=\sup\limits_{i\in\mathbb{Z}}(x_{i+\nu}-x_i)<\dfrac{\nu}{2\pi}\left(\dfrac{c_{k}^2}{M_{2k}}\right)^{\frac{1}{4k}},$$
-then every function belonging to a shift-invariant space $V(\phi)$ can be
-reconstructed stably from its nonuniform sample values
-$\{f^{(j)}(x_i):j=0,1,\dots, k-1, i\in\mathbb{Z}\}$, where $c_k$ is a
-Wirtinger-Sobolev constant and $M_{2k}$ is a constant in Bernstein-type
-inequality of $V(\phi)$. Further, when $k=1$, the maximum gap $\delta_\nu<\nu$
-is sharp for certain shift-invariant spaces.
-",0,0,1,0,0,0
-20670,Experimental Design via Generalized Mean Objective Cost of Uncertainty,"  The mean objective cost of uncertainty (MOCU) quantifies the performance cost
-of using an operator that is optimal across an uncertainty class of systems as
-opposed to using an operator that is optimal for a particular system.
-MOCU-based experimental design selects an experiment to maximally reduce MOCU,
-thereby gaining the greatest reduction of uncertainty impacting the operational
-objective. The original formulation applied to finding optimal system
-operators, where optimality is with respect to a cost function, such as
-mean-square error; and the prior distribution governing the uncertainty class
-relates directly to the underlying physical system. Here we provide a
-generalized MOCU and the corresponding experimental design. We then demonstrate
-how this new formulation includes as special cases MOCU-based experimental
-design methods developed for materials science and genomic networks when there
-is experimental error. Most importantly, we show that the classical Knowledge
-Gradient and Efficient Global Optimization experimental design procedures are
-actually implementations of MOCU-based experimental design under their modeling
-assumptions.
-",0,0,0,1,0,0
-20671,Parallelizing Over Artificial Neural Network Training Runs with Multigrid,"  Artificial neural networks are a popular and effective machine learning
-technique. Great progress has been made parallelizing the expensive training
-phase of an individual network, leading to highly specialized pieces of
-hardware, many based on GPU-type architectures, and more concurrent algorithms
-such as synthetic gradients. However, the training phase continues to be a
-bottleneck, where the training data must be processed serially over thousands
-of individual training runs. This work considers a multigrid reduction in time
-(MGRIT) algorithm that is able to parallelize over the thousands of training
-runs and converge to the exact same solution as traditional training would
-provide. MGRIT was originally developed to provide parallelism for time
-evolution problems that serially step through a finite number of time-steps.
-This work recasts the training of a neural network similarly, treating neural
-network training as an evolution equation that evolves the network weights from
-one step to the next. Thus, this work concerns distributed computing approaches
-for neural networks, but is distinct from other approaches which seek to
-parallelize only over individual training runs. The work concludes with
-supporting numerical results for two model problems.
-",1,0,0,0,0,0
-20672,Learning the Structure of Generative Models without Labeled Data,"  Curating labeled training data has become the primary bottleneck in machine
-learning. Recent frameworks address this bottleneck with generative models to
-synthesize labels at scale from weak supervision sources. The generative
-model's dependency structure directly affects the quality of the estimated
-labels, but selecting a structure automatically without any labeled data is a
-distinct challenge. We propose a structure estimation method that maximizes the
-$\ell_1$-regularized marginal pseudolikelihood of the observed data. Our
-analysis shows that the amount of unlabeled data required to identify the true
-structure scales sublinearly in the number of possible dependencies for a broad
-class of models. Simulations show that our method is 100$\times$ faster than a
-maximum likelihood approach and selects $1/4$ as many extraneous dependencies.
-We also show that our method provides an average of 1.5 F1 points of
-improvement over existing, user-developed information extraction applications
-on real-world data such as PubMed journal abstracts.
-",1,0,0,1,0,0
-20673,Robust Loss Functions under Label Noise for Deep Neural Networks,"  In many applications of classifier learning, training data suffers from label
-noise. Deep networks are learned using huge training data where the problem of
-noisy labels is particularly relevant. The current techniques proposed for
-learning deep networks under label noise focus on modifying the network
-architecture and on algorithms for estimating true labels from noisy labels. An
-alternate approach would be to look for loss functions that are inherently
-noise-tolerant. For binary classification there exist theoretical results on
-loss functions that are robust to label noise. In this paper, we provide some
-sufficient conditions on a loss function so that risk minimization under that
-loss function would be inherently tolerant to label noise for multiclass
-classification problems. These results generalize the existing results on
-noise-tolerant loss functions for binary classification. We study some of the
-widely used loss functions in deep networks and show that the loss function
-based on mean absolute value of error is inherently robust to label noise. Thus
-standard back propagation is enough to learn the true classifier even under
-label noise. Through experiments, we illustrate the robustness of risk
-minimization with such loss functions for learning neural networks.
-",1,0,0,1,0,0
-20674,A quality model for evaluating and choosing a stream processing framework architecture,"  Today, we have to deal with many data (Big data) and we need to make
-decisions by choosing an architectural framework to analyze these data coming
-from different area. Due to this, it become problematic when we want to process
-these data, and even more, when it is continuous data. When you want to process
-some data, you have to first receive it, store it, and then query it. This is
-what we call Batch Processing. It works well when you process big amount of
-data, but it finds its limits when you want to get fast (or real-time)
-processing results, such as financial trades, sensors, user session activity,
-etc. The solution to this problem is stream processing. Stream processing
-approach consists of data arriving record by record and rather than storing it,
-the processing should be done directly. Therefore, direct results are needed
-with a latency that may vary in real-time.
-In this paper, we propose an assessment quality model to evaluate and choose
-stream processing frameworks. We describe briefly different architectural
-frameworks such as Kafka, Spark Streaming and Flink that address the stream
-processing. Using our quality model, we present a decision tree to support
-engineers to choose a framework following the quality aspects. Finally, we
-evaluate our model doing a case study to Twitter and Netflix streaming.
-",1,0,0,0,0,0
-20675,On the Hardness of Inventory Management with Censored Demand Data,"  We consider a repeated newsvendor problem where the inventory manager has no
-prior information about the demand, and can access only censored/sales data. In
-analogy to multi-armed bandit problems, the manager needs to simultaneously
-""explore"" and ""exploit"" with her inventory decisions, in order to minimize the
-cumulative cost. We make no probabilistic assumptions---importantly,
-independence or time stationarity---regarding the mechanism that creates the
-demand sequence. Our goal is to shed light on the hardness of the problem, and
-to develop policies that perform well with respect to the regret criterion,
-that is, the difference between the cumulative cost of a policy and that of the
-best fixed action/static inventory decision in hindsight, uniformly over all
-feasible demand sequences. We show that a simple randomized policy, termed the
-Exponentially Weighted Forecaster, combined with a carefully designed cost
-estimator, achieves optimal scaling of the expected regret (up to logarithmic
-factors) with respect to all three key primitives: the number of time periods,
-the number of inventory decisions available, and the demand support. Through
-this result, we derive an important insight: the benefit from ""information
-stalking"" as well as the cost of censoring are both negligible in this dynamic
-learning problem, at least with respect to the regret criterion. Furthermore,
-we modify the proposed policy in order to perform well in terms of the tracking
-regret, that is, using as benchmark the best sequence of inventory decisions
-that switches a limited number of times. Numerical experiments suggest that the
-proposed approach outperforms existing ones (that are tailored to, or
-facilitated by, time stationarity) on nonstationary demand models. Finally, we
-extend the proposed approach and its analysis to a ""combinatorial"" version of
-the repeated newsvendor problem.
-",1,0,0,1,0,0
-20676,Social Media Analysis For Organizations: Us Northeastern Public And State Libraries Case Study,"  Social networking sites such as Twitter have provided a great opportunity for
-organizations such as public libraries to disseminate information for public
-relations purposes. However, there is a need to analyze vast amounts of social
-media data. This study presents a computational approach to explore the content
-of tweets posted by nine public libraries in the northeastern United States of
-America. In December 2017, this study extracted more than 19,000 tweets from
-the Twitter accounts of seven state libraries and two urban public libraries.
-Computational methods were applied to collect the tweets and discover
-meaningful themes. This paper shows how the libraries have used Twitter to
-represent their services and provides a starting point for different
-organizations to evaluate the themes of their public tweets.
-",1,0,0,1,0,0
-20677,Weakly tripotent rings,"  We study the class of rings $R$ with the property that for $x\in R$ at least
-one of the elements $x$ and $1+x$ are tripotent.
-",0,0,1,0,0,0
-20678,ML for Flood Forecasting at Scale,"  Effective riverine flood forecasting at scale is hindered by a multitude of
-factors, most notably the need to rely on human calibration in current
-methodology, the limited amount of data for a specific location, and the
-computational difficulty of building continent/global level models that are
-sufficiently accurate. Machine learning (ML) is primed to be useful in this
-scenario: learned models often surpass human experts in complex
-high-dimensional scenarios, and the framework of transfer or multitask learning
-is an appealing solution for leveraging local signals to achieve improved
-global performance. We propose to build on these strengths and develop ML
-systems for timely and accurate riverine flood prediction.
-",1,0,0,1,0,0
-20679,Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis,"  We consider the frequency domain form of proper orthogonal decomposition
-(POD) called spectral proper orthogonal decomposition (SPOD). Spectral POD is
-derived from a space-time POD problem for statistically stationary flows and
-leads to modes that each oscillate at a single frequency. This form of POD goes
-back to the original work of Lumley (Stochastic tools in turbulence, Academic
-Press, 1970), but has been overshadowed by a space-only form of POD since the
-1990s. We clarify the relationship between these two forms of POD and show that
-SPOD modes represent structures that evolve coherently in space and time while
-space-only POD modes in general do not. We also establish a relationship
-between SPOD and dynamic mode decomposition (DMD); we show that SPOD modes are
-in fact optimally averaged DMD modes obtained from an ensemble DMD problem for
-stationary flows. Accordingly, SPOD modes represent structures that are dynamic
-in the same sense as DMD modes but also optimally account for the statistical
-variability of turbulent flows. Finally, we establish a connection between SPOD
-and resolvent analysis. The key observation is that the resolvent-mode
-expansion coefficients must be regarded as statistical quantities to ensure
-convergent approximations of the flow statistics. When the expansion
-coefficients are uncorrelated, we show that SPOD and resolvent modes are
-identical. Our theoretical results and the overall utility of SPOD are
-demonstrated using two example problems: the complex Ginzburg-Landau equation
-and a turbulent jet.
-",0,1,0,0,0,0
-20680,An optimal transportation approach for assessing almost stochastic order,"  When stochastic dominance $F\leq_{st}G$ does not hold, we can improve
-agreement to stochastic order by suitably trimming both distributions. In this
-work we consider the $L_2-$Wasserstein distance, $\mathcal W_2$, to stochastic
-order of these trimmed versions. Our characterization for that distance
-naturally leads to consider a $\mathcal W_2$-based index of disagreement with
-stochastic order, $\varepsilon_{\mathcal W_2}(F,G)$. We provide asymptotic
-results allowing to test $H_0: \varepsilon_{\mathcal W_2}(F,G)\geq
-\varepsilon_0$ vs $H_a: \varepsilon_{\mathcal W_2}(F,G)<\varepsilon_0$, that,
-under rejection, would give statistical guarantee of almost stochastic
-dominance. We include a simulation study showing a good performance of the
-index under the normal model.
-",0,0,0,1,0,0
-20681,The Effect of Electron Lens as Landau Damping Device on Single Particle Dynamics in HL-LHC,"  An electron lens can serve as an effective mechanism for suppressing coherent
-instabilities in high intensity storage rings through nonlinear amplitude
-dependent betatron tune shift. However, the addition of a strong localized
-nonlinear focusing element to the accelerator lattice may lead to undesired
-effects in particle dynamics. We evaluate the effect of a Gaussian electron
-lens on single particle motion in HL-LHC using numerical tracking simulations,
-and compare the results to the case when an equal tune spread is generated by
-conventional octupole magnets.
-",0,1,0,0,0,0
-20682,Search for sterile neutrinos in holographic dark energy cosmology: Reconciling Planck observation with the local measurement of the Hubble constant,"  We search for sterile neutrinos in the holographic dark energy cosmology by
-using the latest observational data. To perform the analysis, we employ the
-current cosmological observations, including the cosmic microwave background
-temperature power spectrum data from the Planck mission, the baryon acoustic
-oscillation measurements, the type Ia supernova data, the redshift space
-distortion measurements, the shear data of weak lensing observation, the Planck
-lensing measurement, and the latest direct measurement of $H_0$ as well. We
-show that, compared to the $\Lambda$CDM cosmology, the holographic dark energy
-cosmology with sterile neutrinos can relieve the tension between the Planck
-observation and the direct measurement of $H_0$ much better. Once we include
-the $H_0$ measurement in the global fit, we find that the hint of the existence
-of sterile neutrinos in the holographic dark energy cosmology can be given.
-Under the constraint of the all-data combination, we obtain $N_{\rm eff}=
-3.76\pm0.26$ and $m_{\nu,\rm sterile}^{\rm eff}< 0.215\,\rm eV$, indicating
-that the detection of $\Delta N_{\rm eff}>0$ in the holographic dark energy
-cosmology is at the $2.75\sigma$ level and the massless or very light sterile
-neutrino is favored by the current observations.
-",0,1,0,0,0,0
-20683,Simulation of high temperature superconductors and experimental validation,"  In this work, we present a parallel, fully-distributed finite element
-numerical framework to simulate the low-frequency electromagnetic response of
-superconducting devices, which allows to efficiently exploit HPC platforms. We
-select the so-called H-formulation, which uses the magnetic field as a state
-variable. Nédélec elements (of arbitrary order) are required for an
-accurate approximation of the H-formulation for modelling electromagnetic
-fields along interfaces between regions with high contrast medium properties.
-An h-adaptive mesh refinement technique customized for Nédélec elements
-leads to a structured fine mesh in areas of interest whereas a smart coarsening
-is obtained in other regions. The composition of a tailored, robust, parallel
-nonlinear solver completes the exposition of the developed tools to tackle the
-problem. First, a comparison against experimental data is performed to show the
-availability of the finite element approximation to model the physical
-phenomena. Then, a selected state-of-the-art 3D benchmark is reproduced,
-focusing on the parallel performance of the algorithms.
-",1,1,0,0,0,0
-20684,Hardware Translation Coherence for Virtualized Systems,"  To improve system performance, modern operating systems (OSes) often
-undertake activities that require modification of virtual-to-physical page
-translation mappings. For example, the OS may migrate data between physical
-frames to defragment memory and enable superpages. The OS may migrate pages of
-data between heterogeneous memory devices. We refer to all such activities as
-page remappings. Unfortunately, page remappings are expensive. We show that
-translation coherence is a major culprit and that systems employing
-virtualization are especially badly affected by their overheads. In response,
-we propose hardware translation invalidation and coherence or HATRIC, a readily
-implementable hardware mechanism to piggyback translation coherence atop
-existing cache coherence protocols. We perform detailed studies using KVM-based
-virtualization, showing that HATRIC achieves up to 30% performance and 10%
-energy benefits, for per-CPU area overheads of 2%. We also quantify HATRIC's
-benefits on systems running Xen and find up to 33% performance improvements.
-",1,0,0,0,0,0
-20685,MotifMark: Finding Regulatory Motifs in DNA Sequences,"  The interaction between proteins and DNA is a key driving force in a
-significant number of biological processes such as transcriptional regulation,
-repair, recombination, splicing, and DNA modification. The identification of
-DNA-binding sites and the specificity of target proteins in binding to these
-regions are two important steps in understanding the mechanisms of these
-biological activities. A number of high-throughput technologies have recently
-emerged that try to quantify the affinity between proteins and DNA motifs.
-Despite their success, these technologies have their own limitations and fall
-short in precise characterization of motifs, and as a result, require further
-downstream analysis to extract useful and interpretable information from a
-haystack of noisy and inaccurate data. Here we propose MotifMark, a new
-algorithm based on graph theory and machine learning, that can find binding
-sites on candidate probes and rank their specificity in regard to the
-underlying transcription factor. We developed a pipeline to analyze
-experimental data derived from compact universal protein binding microarrays
-and benchmarked it against two of the most accurate motif search methods. Our
-results indicate that MotifMark can be a viable alternative technique for
-prediction of motif from protein binding microarrays and possibly other related
-high-throughput techniques.
-",1,0,0,0,0,0
-20686,Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning,"  This paper presents KeypointNet, an end-to-end geometric reasoning framework
-to learn an optimal set of category-specific 3D keypoints, along with their
-detectors. Given a single image, KeypointNet extracts 3D keypoints that are
-optimized for a downstream task. We demonstrate this framework on 3D pose
-estimation by proposing a differentiable objective that seeks the optimal set
-of keypoints for recovering the relative pose between two views of an object.
-Our model discovers geometrically and semantically consistent keypoints across
-viewing angles and instances of an object category. Importantly, we find that
-our end-to-end framework using no ground-truth keypoint annotations outperforms
-a fully supervised baseline using the same neural network architecture on the
-task of pose estimation. The discovered 3D keypoints on the car, chair, and
-plane categories of ShapeNet are visualized at this http URL.
-",0,0,0,1,0,0
-20687,Multi-State Trajectory Approach to Non-Adiabatic Dynamics: General Formalism and the Active State Trajectory Approximation,"  A general theoretical framework is derived for the recently developed
-multi-state trajectory (MST) approach from the time dependent Schrödinger
-equation, resulting in equations of motion for coupled nuclear-electronic
-dynamics equivalent to Hamilton dynamics or Heisenberg equation based on a new
-multistate Meyer-Miller (MM) model. The derived MST formalism incorporates both
-diabatic and adiabatic representations as limiting cases, and reduces to
-Ehrenfest or Born-Oppenheimer dynamics in the mean field or the single state
-limits, respectively. By quantizing nuclear dynamics to a particular active
-state, the MST algorithm does not suffer from the instability caused by the
-negative instant electronic population variables unlike the standard MM
-dynamics. Furthermore the multistate representation for electron coupled
-nuclear dynamics with each state associated with one individual trajectory
-presumably captures single state dynamics better than the mean field
-description. The coupled electronic-nuclear coherence is incorporated
-consistently in the MST framework with no ad-hoc state switch and the
-associated momentum adjustment or parameters for artificial decoherence, unlike
-the original or modified surface hopping treatments. The implementation of the
-MST approach to benchmark problems shows reasonably good agreement with exact
-quantum calculations, and the results in both representations are similar in
-accuracy. The active state trajectory (AST) approximation of the MST approach
-provides a consistent interpretation to trajectory surface hopping, which
-predicts the transition probabilities reasonably well for multiple nonadiabatic
-transitions and conical intersection problems.
-",0,1,0,0,0,0
-20688,Combining Homotopy Methods and Numerical Optimal Control to Solve Motion Planning Problems,"  This paper presents a systematic approach for computing local solutions to
-motion planning problems in non-convex environments using numerical optimal
-control techniques. It extends the range of use of state-of-the-art numerical
-optimal control tools to problem classes where these tools have previously not
-been applicable. Today these problems are typically solved using motion
-planners based on randomized or graph search. The general principle is to
-define a homotopy that perturbs, or preferably relaxes, the original problem to
-an easily solved problem. By combining a Sequential Quadratic Programming (SQP)
-method with a homotopy approach that gradually transforms the problem from a
-relaxed one to the original one, practically relevant locally optimal solutions
-to the motion planning problem can be computed. The approach is demonstrated in
-motion planning problems in challenging 2D and 3D environments, where the
-presented method significantly outperforms a state-of-the-art open-source
-optimizing sampled-based planner commonly used as benchmark.
-",0,0,1,0,0,0
-20689,High quality atomically thin PtSe2 films grown by molecular beam epitaxy,"  Atomically thin PtSe2 films have attracted extensive research interests for
-potential applications in high-speed electronics, spintronics and
-photodetectors. Obtaining high quality, single crystalline thin films with
-large size is critical. Here we report the first successful layer-by-layer
-growth of high quality PtSe2 films by molecular beam epitaxy. Atomically thin
-films from 1 ML to 22 ML have been grown and characterized by low-energy
-electron diffraction, Raman spectroscopy and X-ray photoemission spectroscopy.
-Moreover, a systematic thickness dependent study of the electronic structure is
-revealed by angle-resolved photoemission spectroscopy (ARPES), and helical spin
-texture is revealed by spin-ARPES. Our work provides new opportunities for
-growing large size single crystalline films for investigating the physical
-properties and potential applications of PtSe2.
-",0,1,0,0,0,0
-20690,NAVREN-RL: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular images,"  We present NAVREN-RL, an approach to NAVigate an unmanned aerial vehicle in
-an indoor Real ENvironment via end-to-end reinforcement learning RL. A suitable
-reward function is designed keeping in mind the cost and weight constraints for
-micro drone with minimum number of sensing modalities. Collection of small
-number of expert data and knowledge based data aggregation is integrated into
-the RL process to aid convergence. Experimentation is carried out on a Parrot
-AR drone in different indoor arenas and the results are compared with other
-baseline technologies. We demonstrate how the drone successfully avoids
-obstacles and navigates across different arenas.
-",1,0,0,1,0,0
-20691,"Fast, Accurate and Fully Parallelizable Digital Image Correlation","  Digital image correlation (DIC) is a widely used optical metrology for
-surface deformation measurements. DIC relies on nonlinear optimization method.
-Thus an initial guess is quite important due to its influence on the converge
-characteristics of the algorithm. In order to obtain a reliable, accurate
-initial guess, a reliability-guided digital image correlation (RG-DIC) method,
-which is able to intelligently obtain a reliable initial guess without using
-time-consuming integer-pixel registration, was proposed. However, the RG-DIC
-and its improved methods are path-dependent and cannot be fully parallelized.
-Besides, it is highly possible that RG-DIC fails in the full-field analysis of
-deformation without manual intervention if the deformation fields contain large
-areas of discontinuous deformation. Feature-based initial guess is highly
-robust while it is relatively time-consuming. Recently, path-independent
-algorithm, fast Fourier transform-based cross correlation (FFT-CC) algorithm,
-was proposed to estimate the initial guess. Complete parallelizability is the
-major advantage of the FFT-CC algorithm, while it is sensitive to small
-deformation. Wu et al proposed an efficient integer-pixel search scheme, but
-the parameters of this algorithm are set by the users empirically. In this
-technical note, a fully parallelizable DIC method is proposed. Different from
-RG-DIC method, the proposed method divides DIC algorithm into two parts:
-full-field initial guess estimation and sub-pixel registration. The proposed
-method has the following benefits: 1) providing a pre-knowledge of deformation
-fields; 2) saving computational time; 3) reducing error propagation; 4)
-integratability with well-established DIC algorithms; 5) fully
-parallelizability.
-",0,1,0,0,0,0
-20692,Discovering the effect of nonlocal payoff calculation on the stabilty of ESS: Spatial patterns of Hawk-Dove game in metapopulations,"  The classical idea of evolutionarily stable strategy (ESS) modeling animal
-behavior does not involve any spatial dependence. We considered a spatial
-Hawk-Dove game played by animals in a patchy environment with wrap around
-boundaries. We posit that each site contains the same number of individuals. An
-evolution equation for analyzing the stability of the ESS is found as the mean
-dynamics of the classical frequency dependent Moran process coupled via
-migration and nonlocal payoff calculation in 1D and 2D habitats. The linear
-stability analysis of the model is performed and conditions to observe spatial
-patterns are investigated. For the nearest neighbor interactions (including von
-Neumann and Moore neighborhoods in 2D) we concluded that it is possible to
-destabilize the ESS of the game and observe pattern formation when the
-dispersal rate is small enough. We numerically investigate the spatial patterns
-arising from the replicator equations coupled via nearest neighbor payoff
-calculation and dispersal.
-",0,0,0,0,1,0
-20693,Analysis of the measurements of anisotropic a.c. vortex resistivity in tilted magnetic fields,"  Measurements of the high-frequency complex resistivity in superconductors are
-a tool often used to obtain the vortex parameters, such as the vortex
-viscosity, the pinning constant and the depinning frequency. In anisotropic
-superconductors, the extraction of these quantities from the measurements faces
-new difficulties due to the tensor nature of the electromagnetic problem. The
-problem is specifically intricate when the magnetic field is tilted with
-respect to the crystallographic axes. Partial solutions exist in the
-free-flux-flow (no pinning) and Campbell (pinning dominated) regimes. In this
-paper we develop a full tensor model for the vortex motion complex resistivity,
-including flux-flow, pinning, and creep. We give explicit expressions for the
-tensors involved. We obtain that, despite the complexity of the physics, some
-parameters remain scalar in nature. We show that under specific circumstances
-the directly measured quantities do not reflect the true vortex parameters, and
-we give procedures to derive the true vortex parameters from measurements taken
-with arbitrary field orientations. Finally, we discuss the applicability of the
-angular scaling properties to the measured and transformed vortex parameters
-and we exploit these properties as a tool to unveil the existence of
-directional pinning.
-",0,1,0,0,0,0
-20694,Deep Convolutional Neural Network to Detect J-UNIWARD,"  This paper presents an empirical study on applying convolutional neural
-networks (CNNs) to detecting J-UNIWARD, one of the most secure JPEG
-steganographic method. Experiments guiding the architectural design of the CNNs
-have been conducted on the JPEG compressed BOSSBase containing 10,000 covers of
-size 512x512. Results have verified that both the pooling method and the depth
-of the CNNs are critical for performance. Results have also proved that a
-20-layer CNN, in general, outperforms the most sophisticated feature-based
-methods, but its advantage gradually diminishes on hard-to-detect cases. To
-show that the performance generalizes to large-scale databases and to different
-cover sizes, one experiment has been conducted on the CLS-LOC dataset of
-ImageNet containing more than one million covers cropped to unified size of
-256x256. The proposed 20-layer CNN has cut the error achieved by a CNN recently
-proposed for large-scale JPEG steganalysis by 35%. Source code is available via
-GitHub: this https URL
-",1,0,0,0,0,0
-20695,Observing Power-Law Dynamics of Position-Velocity Correlation in Anomalous Diffusion,"  In this letter we present a measurement of the phase-space density
-distribution (PSDD) of ultra-cold \Rb atoms performing 1D anomalous diffusion.
-The PSDD is imaged using a direct tomographic method based on Raman velocity
-selection. It reveals that the position-velocity correlation function
-$C_{xv}(t)$ builds up on a timescale related to the initial conditions of the
-ensemble and then decays asymptotically as a power-law. We show that the decay
-follows a simple scaling theory involving the power-law asymptotic dynamics of
-position and velocity. The generality of this scaling theory is confirmed using
-Monte-Carlo simulations of two distinct models of anomalous diffusion.
-",0,1,0,0,0,0
-20696,"Modular curves, invariant theory and $E_8$","  The $E_8$ root lattice can be constructed from the modular curve $X(13)$ by
-the invariant theory for the simple group $\text{PSL}(2, 13)$. This gives a
-different construction of the $E_8$ root lattice. It also gives an explicit
-construction of the modular curve $X(13)$.
-",0,0,1,0,0,0
-20697,Analysis of Approximate Stochastic Gradient Using Quadratic Constraints and Sequential Semidefinite Programs,"  We present convergence rate analysis for the approximate stochastic gradient
-method, where individual gradient updates are corrupted by computation errors.
-We develop stochastic quadratic constraints to formulate a small linear matrix
-inequality (LMI) whose feasible set characterizes convergence properties of the
-approximate stochastic gradient. Based on this LMI condition, we develop a
-sequential minimization approach to analyze the intricate trade-offs that
-couple stepsize selection, convergence rate, optimization accuracy, and
-robustness to gradient inaccuracy. We also analytically solve this LMI
-condition and obtain theoretical formulas that quantify the convergence
-properties of the approximate stochastic gradient under various assumptions on
-the loss functions.
-",0,0,0,1,0,0
-20698,Invariant holomorphic discs in some non-convex domains,"  We give a description of complex geodesics and we study the structure of
-stationary discs in some non-convex domains for which complex geodesics are not
-unique.
-",0,0,1,0,0,0
-20699,MIMIX: a Bayesian Mixed-Effects Model for Microbiome Data from Designed Experiments,"  Recent advances in bioinformatics have made high-throughput microbiome data
-widely available, and new statistical tools are required to maximize the
-information gained from these data. For example, analysis of high-dimensional
-microbiome data from designed experiments remains an open area in microbiome
-research. Contemporary analyses work on metrics that summarize collective
-properties of the microbiome, but such reductions preclude inference on the
-fine-scale effects of environmental stimuli on individual microbial taxa. Other
-approaches model the proportions or counts of individual taxa as response
-variables in mixed models, but these methods fail to account for complex
-correlation patterns among microbial communities. In this paper, we propose a
-novel Bayesian mixed-effects model that exploits cross-taxa correlations within
-the microbiome, a model we call MIMIX (MIcrobiome MIXed model). MIMIX offers
-global tests for treatment effects, local tests and estimation of treatment
-effects on individual taxa, quantification of the relative contribution from
-heterogeneous sources to microbiome variability, and identification of latent
-ecological subcommunities in the microbiome. MIMIX is tailored to large
-microbiome experiments using a combination of Bayesian factor analysis to
-efficiently represent dependence between taxa and Bayesian variable selection
-methods to achieve sparsity. We demonstrate the model using a simulation
-experiment and on a 2x2 factorial experiment of the effects of nutrient
-supplement and herbivore exclusion on the foliar fungal microbiome of
-$\textit{Andropogon gerardii}$, a perennial bunchgrass, as part of the global
-Nutrient Network research initiative.
-",0,0,0,1,0,0
-20700,SpatEntropy: Spatial Entropy Measures in R,"  This article illustrates how to measure the heterogeneity of spatial data
-presenting a finite number of categories via computation of spatial entropy.
-The R package SpatEntropy contains functions for the computation of entropy and
-spatial entropy measures. The extension to spatial entropy measures is a unique
-feature of SpatEntropy. In addition to the traditional version of Shannon's
-entropy, the package includes Batty's spatial entropy, O'Neill's entropy, Li
-and Reynolds' contagion index, Karlstrom and Ceccato's entropy, Leibovici's
-entropy, Parresol and Edwards' entropy and Altieri's entropy. The package is
-able to work with both areal and point data. This paper is a general
-description of SpatEntropy, as well as its necessary theoretical background,
-and an introduction for new users.
-",0,0,0,1,0,0
-20701,Evidence for universality in the initial planetesimal mass function,"  Planetesimals may form from the gravitational collapse of dense particle
-clumps initiated by the streaming instability. We use simulations of
-aerodynamically coupled gas-particle mixtures to investigate whether the
-properties of planetesimals formed in this way depend upon the sizes of the
-particles that participate in the instability. Based on three high resolution
-simulations that span a range of dimensionless stopping time $6 \times 10^{-3}
-\leq \tau \leq 2$ no statistically significant differences in the initial
-planetesimal mass function are found. The mass functions are fit by a
-power-law, ${\rm d}N / {\rm d}M_p \propto M_p^{-p}$, with $p=1.5-1.7$ and
-errors of $\Delta p \approx 0.1$. Comparing the particle density fields prior
-to collapse, we find that the high wavenumber power spectra are similarly
-indistinguishable, though the large-scale geometry of structures induced via
-the streaming instability is significantly different between all three cases.
-We interpret the results as evidence for a near-universal slope to the mass
-function, arising from the small-scale structure of streaming-induced
-turbulence.
-",0,1,0,0,0,0
-20702,On covering systems of integers,"  A covering system of the integers is a finite collection of modular residue
-classes $\{a_m \bmod{m}\}_{m \in S}$ whose union is all integers. Given a
-finite set $S$ of moduli, it is often difficult to tell whether there is a
-choice of residues modulo elements of $S$ covering the integers. Hough has
-shown that if the smallest modulus in $S$ is at least $10^{16}$, then there is
-none. However, the question of whether there is a covering of the integers with
-all odd moduli remains open. We consider multiplicative restrictions on the set
-of moduli to generalize Hough's negative solution to the minimum modulus
-problem. In particular, we find that every covering system of the integers has
-a modulus divisible by a prime number less than or equal to $19$. Hough and
-Nielsen have shown that every covering system has a modulus divisible by either
-$2$ or $3$.
-",0,0,1,0,0,0
-20703,Asymptotically safe cosmology - a status report,"  Asymptotic Safety, based on a non-Gaussian fixed point of the gravitational
-renormalization group flow, provides an elegant mechanism for completing the
-gravitational force at sub-Planckian scales. At high energies the fixed point
-controls the scaling of couplings such that unphysical divergences are absent
-while the emergence of classical low-energy physics is linked to a crossover
-between two renormalization group fixed points. These features make Asymptotic
-Safety an attractive framework for cosmological model building. The resulting
-scenarios may naturally give rise to a quantum gravity driven inflationary
-phase in the very early universe and an almost scale-free fluctuation spectrum.
-Moreover, effective descriptions arising from an renormalization group
-improvement permit a direct comparison to cosmological observations as, e.g.
-Planck data.
-",0,1,0,0,0,0
-20704,Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction,"  Future predictions on sequence data (e.g., videos or audios) require the
-algorithms to capture non-Markovian and compositional properties of high-level
-semantics. Context-free grammars are natural choices to capture such
-properties, but traditional grammar parsers (e.g., Earley parser) only take
-symbolic sentences as inputs. In this paper, we generalize the Earley parser to
-parse sequence data which is neither segmented nor labeled. This generalized
-Earley parser integrates a grammar parser with a classifier to find the optimal
-segmentation and labels, and makes top-down future predictions. Experiments
-show that our method significantly outperforms other approaches for future
-human activity prediction.
-",0,0,0,1,0,0
-20705,COCrIP: Compliant OmniCrawler In-pipeline Robot,"  This paper presents a modular in-pipeline climbing robot with a novel
-compliant foldable OmniCrawler mechanism. The circular cross-section of the
-OmniCrawler module enables a holonomic motion to facilitate the alignment of
-the robot in the direction of bends. Additionally, the crawler mechanism
-provides a fair amount of traction, even on slippery surfaces. These advantages
-of crawler modules have been further supplemented by incorporating active
-compliance in the module itself which helps to negotiate sharp bends in small
-diameter pipes. The robot has a series of 3 such compliant foldable modules
-interconnected by the links via passive joints. For the desirable pipe diameter
-and curvature of the bends, the spring stiffness value for each passive joint
-is determined by formulating a constrained optimization problem using the
-quasi-static model of the robot. Moreover, a minimum friction coefficient value
-between the module-pipe surface which can be vertically climbed by the robot
-without slipping is estimated. The numerical simulation results have further
-been validated by experiments on real robot prototype.
-",1,0,0,0,0,0
-20706,An Inversion-Based Learning Approach for Improving Impromptu Trajectory Tracking of Robots with Non-Minimum Phase Dynamics,"  This paper presents a learning-based approach for impromptu trajectory
-tracking for non-minimum phase systems, i.e., systems with unstable inverse
-dynamics. Inversion-based feedforward approaches are commonly used for
-improving tracking performance; however, these approaches are not directly
-applicable to non-minimum phase systems due to their inherent instability. In
-order to resolve the instability issue, existing methods have assumed that the
-system model is known and used pre-actuation or inverse approximation
-techniques. In this work, we propose an approach for learning a stable,
-approximate inverse of a non-minimum phase baseline system directly from its
-input-output data. Through theoretical discussions, simulations, and
-experiments on two different platforms, we show the stability of our proposed
-approach and its effectiveness for high-accuracy, impromptu tracking. Our
-approach also shows that including more information in the training, as is
-commonly assumed to be useful, does not lead to better performance but may
-trigger instability and impact the effectiveness of the overall approach.
-",1,0,0,0,0,0
-20707,Decay Estimates for 1-D Parabolic PDEs with Boundary Disturbances,"  In this work decay estimates are derived for the solutions of 1-D linear
-parabolic PDEs with disturbances at both boundaries and distributed
-disturbances. The decay estimates are given in the L2 and H1 norms of the
-solution and discontinuous disturbances are allowed. Although an eigenfunction
-expansion for the solution is exploited for the proof of the decay estimates,
-the estimates do not require knowledge of the eigenvalues and the
-eigenfunctions of the corresponding Sturm-Liouville operator. Examples show
-that the obtained results can be applied for the stability analysis of
-parabolic PDEs with nonlocal terms.
-",1,0,1,0,0,0
-20708,GEANT4 Simulation of Nuclear Interaction Induced Soft Errors in Digital Nanoscale Electronics: Interrelation Between Proton and Heavy Ion Impacts,"  A simple and self-consistent approach has been proposed for simulation of the
-proton-induced soft error rate based on the heavy ion induced single event
-upset cross-section data and vice versa. The approach relies on the GEANT4
-assisted Monte Carlo simulation of the secondary particle LET spectra produced
-by nuclear interactions. The method has been validated with the relevant
-in-flight soft error rate data for space protons and heavy ions. An approximate
-analytical relation is proposed and validated for a fast recalculation between
-the two types of experimental data.
-",0,1,0,0,0,0
-20709,Analysis and Measurement of the Transfer Matrix of a 9-cell 1.3-GHz Superconducting Cavity,"  Superconducting linacs are capable of producing intense, stable, high-quality
-electron beams that have found widespread applications in science and industry.
-The 9-cell 1.3-GHz superconducting standing-wave accelerating RF cavity
-originally developed for $e^+/e^-$ linear-collider applications [B. Aunes, {\em
-et al.} Phys. Rev. ST Accel. Beams {\bf 3}, 092001 (2000)] has been broadly
-employed in various superconducting-linac designs. In this paper we discuss the
-transfer matrix of such a cavity and present its measurement performed at the
-Fermilab Accelerator Science and Technology (FAST) facility. The experimental
-results are found to be in agreement with analytical calculations and numerical
-simulations.
-",0,1,0,0,0,0
-20710,Size-aware Sharding For Improving Tail Latencies in In-memory Key-value Stores,"  This paper introduces the concept of size-aware sharding to improve tail
-latencies for in-memory key-value stores, and describes its implementation in
-the Minos key-value store. Tail latencies are crucial in distributed
-applications with high fan-out ratios, because overall response time is
-determined by the slowest response. Size-aware sharding distributes requests
-for keys to cores according to the size of the item associated with the key. In
-particular, requests for small and large items are sent to disjoint subsets of
-cores. Size-aware sharding improves tail latencies by avoiding head-of-line
-blocking, in which a request for a small item gets queued behind a request for
-a large item. Alternative size-unaware approaches to sharding, such as
-keyhash-based sharding, request dispatching and stealing do not avoid
-head-of-line blocking, and therefore exhibit worse tail latencies. The
-challenge in implementing size-aware sharding is to maintain high throughput by
-avoiding the cost of software dispatching and by achieving load balancing
-between different cores. Minos uses hardware dispatch for all requests for
-small items, which form the very large majority of all requests. It achieves
-load balancing by adapting the number of cores handling requests for small and
-large items to their relative presence in the workload. We compare Minos to
-three state-of-the-art designs of in-memory KV stores. Compared to its closest
-competitor, Minos achieves a 99th percentile latency that is up to two orders
-of magnitude lower. Put differently, for a given value for the 99th percentile
-latency equal to 10 times the mean service time, Minos achieves a throughput
-that is up to 7.4 times higher.
-",1,0,0,0,0,0
-20711,Abell 2744 may be a supercluster aligned along the sightline,"  To explain the unusual richness and compactness of the Abell 2744, we propose
-a hypothesis that it may be a rich supercluster aligned along the sightline,
-and present a supporting evidence obtained numerically from the MultiDark
-Planck 2 simulations with a linear box size of $1\,h^{-1}$Gpc. Applying the
-friends-of-friends (FoF) algorithm with a linkage length of $0.33$ to a sample
-of the cluster-size halos from the simulations, we identify the superclusters
-and investigate how many superclusters have filamentary branches that would
-appear to be similar to the Abell 2744 if the filamentary axis is aligned with
-the sightline. Generating randomly a unit vector as a sightline at the position
-of the core member of each supercluster and projecting the positions of the
-members onto the plane perpendicular to the direction of the sightline, we
-measure two dimensional distances ($R_{2d}$) of the member halos from the core
-for each supercluster. Defining a Abell 2744-like spuercluster as the one
-having a filamentary branch composed of eight or more members with $R_{2d}\le
-1\,$Mpc and masses comparable to those of the observed Abell 2744
-substructures, we find one Abell 2744-like supercluster at $z=0.3$ and two at
-$z=0$. Repeating the same analysis but with the data from the Big MultiDark
-Planck simulations performed on a larger box of linear size of
-$2.5\,h^{-1}$Mpc, we find that the number of the Abell 2744-like superclusters
-at $z=0$ increases up to eighteen, among which three are found more massive
-than $5\times 10^{15}\,M_{\odot}$.
-",0,1,0,0,0,0
-20712,Classification of Pressure Gradient of Human Common Carotid Artery and Ascending Aorta on the Basis of Age and Gender,"  The current work is done to see which artery has more chance of having
-cardiovascular diseases by measuring value of pressure gradient in the common
-carotid artery (CCA) and ascending aorta according to age and gender. Pressure
-gradient is determined in the CCA and ascending aorta of presumed healthy
-volunteers, having age between 10 and 60 years. A real 2D model of both aorta
-and common carotid artery is constructed for different age groups using
-computational fluid dynamics (CFD). Pressure gradient of both the arteries are
-calculated and compared for different age groups and gender. It is found that
-with increase in diameter of common carotid artery and ascending aorta with
-advancing age pressure gradient decreases. The value of pressure gradient of
-aorta is found less than common carotid artery in both cases of age and gender.
-",0,1,0,0,0,0
-20713,Second-order and local characteristics of network intensity functions,"  The last decade has witnessed an increase of interest in the spatial analysis
-of structured point patterns over networks whose analysis is challenging
-because of geometrical complexities and unique methodological problems. In this
-context, it is essential to incorporate the network specificity into the
-analysis as the locations of events are restricted to areas covered by line
-segments. Relying on concepts originating from graph theory, we extend the
-notions of first-order network intensity functions to second-order and local
-network intensity functions. We consider two types of local indicators of
-network association functions which can be understood as adaptations of the
-primary ideas of local analysis on the plane. We develop the node-wise and
-cross-hierarchical type of local functions. A real dataset on urban
-disturbances is also presented.
-",0,0,0,1,0,0
-20714,Modeling of hysteresis loop and its applications in ferroelectric materials,"  In order to understand the physical hysteresis loops clearly, we constructed
-a novel model, which is combined with the electric field, the temperature, and
-the stress as one synthetically parameter. This model revealed the shape of
-hysteresis loop was determined by few variables in ferroelectric materials: the
-saturation of polarization, the coercive field, the electric susceptibility and
-the equivalent field. Comparison with experimental results revealed the model
-can retrace polarization versus electric field and temperature. As a
-applications of this model, the calculate formula of energy storage efficiency,
-the electrocaloric effect, and the P(E,T) function have also been included in
-this article.
-",0,1,0,0,0,0
-20715,Online Factorization and Partition of Complex Networks From Random Walks,"  Finding the reduced-dimensional structure is critical to understanding
-complex networks. Existing approaches such as spectral clustering are
-applicable only when the full network is explicitly observed. In this paper, we
-focus on the online factorization and partition of implicit large-scale
-networks based on observations from an associated random walk. We formulate
-this into a nonconvex stochastic factorization problem and propose an efficient
-and scalable stochastic generalized Hebbian algorithm. The algorithm is able to
-process dependent state-transition data dynamically generated by the underlying
-network and learn a low-dimensional representation for each vertex. By applying
-a diffusion approximation analysis, we show that the continuous-time limiting
-process of the stochastic algorithm converges globally to the ""principal
-components"" of the Markov chain and achieves a nearly optimal sample
-complexity. Once given the learned low-dimensional representations, we further
-apply clustering techniques to recover the network partition. We show that when
-the associated Markov process is lumpable, one can recover the partition
-exactly with high probability. We apply the proposed approach to model the
-traffic flow of Manhattan as city-wide random walks. By using our algorithm to
-analyze the taxi trip data, we discover a latent partition of the Manhattan
-city that closely matches the traffic dynamics.
-",1,0,1,1,0,0
-20716,Powerful genome-wide design and robust statistical inference in two-sample summary-data Mendelian randomization,"  Two-sample summary-data Mendelian randomization (MR) has become a popular
-research design to estimate the causal effect of risk exposures. With the
-sample size of GWAS continuing to increase, it is now possible to utilize
-genetic instruments that are only weakly associated with the exposure. To
-maximize the statistical power of MR, we propose a genome-wide design where
-more than a thousand genetic instruments are used. For the statistical
-analysis, we use an empirical partially Bayes approach where instruments are
-weighted according to their strength, thus weak instruments bring less
-variation to the estimator. The estimator is highly efficient with many weak
-genetic instruments and is robust to balanced and/or sparse pleiotropy. We
-apply our method to estimate the causal effect of body mass index (BMI) and
-major blood lipids on cardiovascular disease outcomes and obtain substantially
-shorter confidence intervals. Some new and statistically significant findings
-are: the estimated causal odds ratio of BMI on ischemic stroke is 1.19 (95% CI:
-1.07--1.32, p-value < 0.001); the estimated causal odds ratio of high-density
-lipoprotein cholesterol (HDL-C) on coronary artery disease (CAD) is 0.78 (95%
-CI 0.73--0.84, p-value < 0.001). However, the estimated effect of HDL-C becomes
-substantially smaller and statistically non-significant when we only use the
-strong instruments. By employing a genome-wide design and robust statistical
-methods, the statistical power of MR studies can be greatly improved. Our
-empirical results suggest that, even though the relationship between HDL-C and
-CAD appears to be highly heterogeneous, it may be too soon to completely
-dismiss the HDL hypothesis.
-",0,0,0,1,0,0
-20717,A Benchmark on Reliability of Complex Discrete Systems: Emergency Power Supply of a Nuclear Power Plant,"  This paper contains two parts: the description of a real electrical system,
-with many redundancies, reconfigurations and repairs, then the description of a
-reliability model of this system, based on the BDMP (Boolean logic Driven
-Markov Processes) formalism and partial results of a reliability and
-availability calculation made from this model.
-",1,0,0,0,0,0
-20718,Riemannian Gaussian distributions on the space of positive-definite quaternion matrices,"  Recently, Riemannian Gaussian distributions were defined on spaces of
-positive-definite real and complex matrices. The present paper extends this
-definition to the space of positive-definite quaternion matrices. In order to
-do so, it develops the Riemannian geometry of the space of positive-definite
-quaternion matrices, which is shown to be a Riemannian symmetric space of
-non-positive curvature. The paper gives original formulae for the Riemannian
-metric of this space, its geodesics, and distance function. Then, it develops
-the theory of Riemannian Gaussian distributions, including the exact expression
-of their probability density, their sampling algorithm and statistical
-inference.
-",0,0,1,1,0,0
-20719,Analisis of the power flow in Low Voltage DC grids,"  Power flow in a low voltage direct current grid (LVDC) is a non-linear
-problem just as its counterpart ac. This paper demonstrates that, unlike in ac
-grids, convergence and uniqueness of the solution can be guaranteed in this
-type of grids. The result is not a linearization nor an approximation, but an
-analysis of the set of non-linear algebraic equations, which is valid for any
-LVDC grid regardless its size, topology or load condition. Computer simulation
-corroborate the theoretical analysis.
-",0,0,1,0,0,0
-20720,Multi-scale bilinear restriction estimates for general phases,"  We prove (adjoint) bilinear restriction estimates for general phases at
-different scales in the full non-endpoint mixed norm range, and give bounds
-with a sharp and explicit dependence on the phases. These estimates have
-applications to high-low frequency interactions for solutions to partial
-differential equations, as well as to the linear restriction problem for
-surfaces with degenerate curvature. As a consequence, we obtain new bilinear
-restriction estimates for elliptic phases and wave/Klein-Gordon interactions in
-the full bilinear range, and give a refined Strichartz inequality for the
-Klein-Gordon equation. In addition, we extend these bilinear estimates to hold
-in adapted function spaces by using a transference type principle which holds
-for vector valued waves.
-",0,0,1,0,0,0
-20721,Program algebra for Turing-machine programs,"  This note presents an algebraic theory of instruction sequences with
-instructions for Turing tapes as basic instructions, the behaviours produced by
-the instruction sequences concerned under execution, and the interaction
-between such behaviours and the Turing tapes provided by an execution
-environment. This theory provides a setting for investigating issues relating
-to computability and computational complexity that is more general than the
-closely related Turing-machine models of computation. The theory is essentially
-an instantiation of a parameterized algebraic theory which is the basis of a
-line of research in which issues relating to a wide variety of subjects from
-computer science have been rigorously investigated thinking in terms of
-instruction sequences.
-",1,0,0,0,0,0
-20722,"CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositions","  How can we find patterns and anomalies in a tensor, or multi-dimensional
-array, in an efficient and directly interpretable way? How can we do this in an
-online environment, where a new tensor arrives each time step? Finding patterns
-and anomalies in a tensor is a crucial problem with many applications,
-including building safety monitoring, patient health monitoring, cyber
-security, terrorist detection, and fake user detection in social networks.
-Standard PARAFAC and Tucker decomposition results are not directly
-interpretable. Although a few sampling-based methods have previously been
-proposed towards better interpretability, they need to be made faster, more
-memory efficient, and more accurate.
-In this paper, we propose CTD, a fast, accurate, and directly interpretable
-tensor decomposition method based on sampling. CTD-S, the static version of
-CTD, provably guarantees a high accuracy that is 17 ~ 83x more accurate than
-that of the state-of-the-art method. Also, CTD-S is made 5 ~ 86x faster, and 7
-~ 12x more memory-efficient than the state-of-the-art method by removing
-redundancy. CTD-D, the dynamic version of CTD, is the first interpretable
-dynamic tensor decomposition method ever proposed. Also, it is made 2 ~ 3x
-faster than already fast CTD-S by exploiting factors at previous time step and
-by reordering operations. With CTD, we demonstrate how the results can be
-effectively interpreted in the online distributed denial of service (DDoS)
-attack detection.
-",1,0,0,1,0,0
-20723,Memory Augmented Control Networks,"  Planning problems in partially observable environments cannot be solved
-directly with convolutional networks and require some form of memory. But, even
-memory networks with sophisticated addressing schemes are unable to learn
-intelligent reasoning satisfactorily due to the complexity of simultaneously
-learning to access memory and plan. To mitigate these challenges we introduce
-the Memory Augmented Control Network (MACN). The proposed network architecture
-consists of three main parts. The first part uses convolutions to extract
-features and the second part uses a neural network-based planning module to
-pre-plan in the environment. The third part uses a network controller that
-learns to store those specific instances of past information that are necessary
-for planning. The performance of the network is evaluated in discrete grid
-world environments for path planning in the presence of simple and complex
-obstacles. We show that our network learns to plan and can generalize to new
-environments.
-",1,0,0,0,0,0
-20724,Community Recovery in a Preferential Attachment Graph,"  A message passing algorithm is derived for recovering communities within a
-graph generated by a variation of the Barabási-Albert preferential
-attachment model. The estimator is assumed to know the arrival times, or order
-of attachment, of the vertices. The derivation of the algorithm is based on
-belief propagation under an independence assumption. Two precursors to the
-message passing algorithm are analyzed: the first is a degree thresholding (DT)
-algorithm and the second is an algorithm based on the arrival times of the
-children (C) of a given vertex, where the children of a given vertex are the
-vertices that attached to it. Comparison of the performance of the algorithms
-shows it is beneficial to know the arrival times, not just the number, of the
-children. The probability of correct classification of a vertex is
-asymptotically determined by the fraction of vertices arriving before it. Two
-extensions of Algorithm C are given: the first is based on joint likelihood of
-the children of a fixed set of vertices; it can sometimes be used to seed the
-message passing algorithm. The second is the message passing algorithm.
-Simulation results are given.
-",1,0,0,1,0,0
-20725,The descriptive look at the size of subsets of groups,"  We explore the Borel complexity of some basic families of subsets of a
-countable group (large, small, thin, sparse and other) defined by the size of
-their elements. Applying the obtained results to the Stone-Čech
-compactification $\beta G$ of $G$, we prove, in particular, that the closure of
-the minimal ideal of $\beta G$ is of type $F_{\sigma\delta}$.
-",0,0,1,0,0,0
-20726,The Impact of Antenna Height Difference on the Performance of Downlink Cellular Networks,"  Capable of significantly reducing cell size and enhancing spatial reuse,
-network densification is shown to be one of the most dominant approaches to
-expand network capacity. Due to the scarcity of available spectrum resources,
-nevertheless, the over-deployment of network infrastructures, e.g., cellular
-base stations (BSs), would strengthen the inter-cell interference as well, thus
-in turn deteriorating the system performance. On this account, we investigate
-the performance of downlink cellular networks in terms of user coverage
-probability (CP) and network spatial throughput (ST), aiming to shed light on
-the limitation of network densification. Notably, it is shown that both CP and
-ST would be degraded and even diminish to be zero when BS density is
-sufficiently large, provided that practical antenna height difference (AHD)
-between BSs and users is involved to characterize pathloss. Moreover, the
-results also reveal that the increase of network ST is at the expense of the
-degradation of CP. Therefore, to balance the tradeoff between user and network
-performance, we further study the critical density, under which ST could be
-maximized under the CP constraint. Through a special case study, it follows
-that the critical density is inversely proportional to the square of AHD. The
-results in this work could provide helpful guideline towards the application of
-network densification in the next-generation wireless networks.
-",1,0,0,0,0,0
-20727,Spin-wave propagation in cubic anisotropic materials,"  The information carrier of modern technologies is the electron charge whose
-transport inevitably generates Joule heating. Spin-waves, the collective
-precessional motion of electron spins, do not involve moving charges and thus
-avoid Joule heating. In this respect, magnonic devices in which the information
-is carried by spin-waves attract interest for low-power computing. However
-implementation of magnonic devices for practical use suffers from low spin-wave
-signal and on/off ratio. Here we demonstrate that cubic anisotropic materials
-can enhance spin-wave signals by improving spin-wave amplitude as well as group
-velocity and attenuation length. Furthermore, cubic anisotropic material shows
-an enhanced on/off ratio through a laterally localized edge mode, which closely
-mimics the gate-controlled conducting channel in traditional field-effect
-transistors. These attractive features of cubic anisotropic materials will
-invigorate magnonics research towards wave-based functional devices.
-",0,1,0,0,0,0
-20728,Relaxing Exclusive Control in Boolean Games,"  In the typical framework for boolean games (BG) each player can change the
-truth value of some propositional atoms, while attempting to make her goal
-true. In standard BG goals are propositional formulas, whereas in iterated BG
-goals are formulas of Linear Temporal Logic. Both notions of BG are
-characterised by the fact that agents have exclusive control over their set of
-atoms, meaning that no two agents can control the same atom. In the present
-contribution we drop the exclusivity assumption and explore structures where an
-atom can be controlled by multiple agents. We introduce Concurrent Game
-Structures with Shared Propositional Control (CGS-SPC) and show that they ac-
-count for several classes of repeated games, including iterated boolean games,
-influence games, and aggregation games. Our main result shows that, as far as
-verification is concerned, CGS-SPC can be reduced to concurrent game structures
-with exclusive control. This result provides a polynomial reduction for the
-model checking problem of specifications in Alternating-time Temporal Logic on
-CGS-SPC.
-",1,0,0,0,0,0
-20729,Representing de Rham cohomology classes on an open Riemann surface by holomorphic forms,"  Let $X$ be a connected open Riemann surface. Let $Y$ be an Oka domain in the
-smooth locus of an analytic subvariety of $\mathbb C^n$, $n\geq 1$, such that
-the convex hull of $Y$ is all of $\mathbb C^n$. Let $\mathscr O_*(X, Y)$ be the
-space of nondegenerate holomorphic maps $X\to Y$. Take a holomorphic $1$-form
-$\theta$ on $X$, not identically zero, and let $\pi:\mathscr O_*(X,Y) \to
-H^1(X,\mathbb C^n)$ send a map $g$ to the cohomology class of $g\theta$. Our
-main theorem states that $\pi$ is a Serre fibration. This result subsumes the
-1971 theorem of Kusunoki and Sainouchi that both the periods and the divisor of
-a holomorphic form on $X$ can be prescribed arbitrarily. It also subsumes two
-parametric h-principles in minimal surface theory proved by Forstneric and
-Larusson in 2016.
-",0,0,1,0,0,0
-20730,Unidirectional control of optically induced spin waves,"  Unidirectional control of optically induced spin waves in a rare-earth iron
-garnet crystal is demonstrated. We observed the interference of two spin-wave
-packets with different initial phases generated by circularly polarized light
-pulses. This interference results in unidirectional propagation if the
-spin-wave sources are spaced apart at 1/4 of the wavelength of the spin waves
-and the initial phase difference is set to pi/2. The propagating direction of
-the spin wave is switched by the polarization helicity of the light pulses.
-Moreover, in a numerical simulation, applying more than two spin-wave sources
-with a suitable polarization and spot shape, arbitrary manipulation of the spin
-wave by the phased array method was replicated.
-",0,1,0,0,0,0
-20731,Value Prediction Network,"  This paper proposes a novel deep reinforcement learning (RL) architecture,
-called Value Prediction Network (VPN), which integrates model-free and
-model-based RL methods into a single neural network. In contrast to typical
-model-based RL methods, VPN learns a dynamics model whose abstract states are
-trained to make option-conditional predictions of future values (discounted sum
-of rewards) rather than of future observations. Our experimental results show
-that VPN has several advantages over both model-free and model-based baselines
-in a stochastic environment where careful planning is required but building an
-accurate observation-prediction model is difficult. Furthermore, VPN
-outperforms Deep Q-Network (DQN) on several Atari games even with
-short-lookahead planning, demonstrating its potential as a new way of learning
-a good state representation.
-",1,0,0,0,0,0
-20732,Scalable Generalized Linear Bandits: Online Computation and Hashing,"  Generalized Linear Bandits (GLBs), a natural extension of the stochastic
-linear bandits, has been popular and successful in recent years. However,
-existing GLBs scale poorly with the number of rounds and the number of arms,
-limiting their utility in practice. This paper proposes new, scalable solutions
-to the GLB problem in two respects. First, unlike existing GLBs, whose
-per-time-step space and time complexity grow at least linearly with time $t$,
-we propose a new algorithm that performs online computations to enjoy a
-constant space and time complexity. At its heart is a novel Generalized Linear
-extension of the Online-to-confidence-set Conversion (GLOC method) that takes
-\emph{any} online learning algorithm and turns it into a GLB algorithm. As a
-special case, we apply GLOC to the online Newton step algorithm, which results
-in a low-regret GLB algorithm with much lower time and memory complexity than
-prior work. Second, for the case where the number $N$ of arms is very large, we
-propose new algorithms in which each next arm is selected via an inner product
-search. Such methods can be implemented via hashing algorithms (i.e.,
-""hash-amenable"") and result in a time complexity sublinear in $N$. While a
-Thompson sampling extension of GLOC is hash-amenable, its regret bound for
-$d$-dimensional arm sets scales with $d^{3/2}$, whereas GLOC's regret bound
-scales with $d$. Towards closing this gap, we propose a new hash-amenable
-algorithm whose regret bound scales with $d^{5/4}$. Finally, we propose a fast
-approximate hash-key computation (inner product) with a better accuracy than
-the state-of-the-art, which can be of independent interest. We conclude the
-paper with preliminary experimental results confirming the merits of our
-methods.
-",1,0,0,1,0,0
-20733,Dome of magnetic order inside the nematic phase of sulfur-substituted FeSe under pressure,"  The pressure dependence of the structural, magnetic and superconducting
-transitions and of the superconducting upper critical field were studied in
-sulfur-substituted Fe(Se$_{1-x}$S$_{x}$). Resistance measurements were
-performed on single crystals with three substitution levels ($x$=0.043, 0.096,
-0.12) under hydrostatic pressures up to 1.8 GPa and in magnetic fields up to 9
-T, and compared to data on pure FeSe. Our results illustrate the effects of
-chemical and physical pressure on Fe(Se$_{1-x}$S$_{x}$). On increasing sulfur
-content, magnetic order in the low-pressure range is strongly suppressed to a
-small dome-like region in the phase diagrams. However, $T_s$ is much less
-suppressed by sulfur substitution and $T_c$ of Fe(Se$_{1-x}$S$_{x}$) exhibits
-similar non-monotonic pressure dependence with a local maximum and a local
-minimum present in the low pressure range for all $x$. The local maximum in
-$T_c$ coincides with the emergence of the magnetic order above $T_c$. At this
-pressure the slope of the upper critical field decreases abruptly. The minimum
-of $T_c$ correlates with a broad maximum of the upper critical field slope
-normalized by $T_c$.
-",0,1,0,0,0,0
-20734,ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge,"  This paper describes Luminoso's participation in SemEval 2017 Task 2,
-""Multilingual and Cross-lingual Semantic Word Similarity"", with a system based
-on ConceptNet. ConceptNet is an open, multilingual knowledge graph that focuses
-on general knowledge that relates the meanings of words and phrases. Our
-submission to SemEval was an update of previous work that builds high-quality,
-multilingual word embeddings from a combination of ConceptNet and
-distributional semantics. Our system took first place in both subtasks. It
-ranked first in 4 out of 5 of the separate languages, and also ranked first in
-all 10 of the cross-lingual language pairs.
-",1,0,0,0,0,0
-20735,The symmetrized topological complexity of the circle,"  We determine the symmetrized topological complexity of the circle, using
-primarily just general topology.
-",0,0,1,0,0,0
-20736,Intertwining operators among twisted modules associated to not-necessarily-commuting automorphisms,"  We introduce intertwining operators among twisted modules or twisted
-intertwining operators associated to not-necessarily-commuting automorphisms of
-a vertex operator algebra. Let $V$ be a vertex operator algebra and let
-$g_{1}$, $g_{2}$ and $g_{3}$ be automorphisms of $V$. We prove that for
-$g_{1}$-, $g_{2}$- and $g_{3}$-twisted $V$-modules $W_{1}$, $W_{2}$ and
-$W_{3}$, respectively, such that the vertex operator map for $W_{3}$ is
-injective, if there exists a twisted intertwining operator of type
-${W_{3}\choose W_{1}W_{2}}$ such that the images of its component operators
-span $W_{3}$, then $g_{3}=g_{1}g_{2}$. We also construct what we call the
-skew-symmetry and contragredient isomorphisms between spaces of twisted
-intertwining operators among twisted modules of suitable types. The proofs of
-these results involve careful analysis of the analytic extensions corresponding
-to the actions of the not-necessarily-commuting automorphisms of the vertex
-operator algebra.
-",0,0,1,0,0,0
-20737,Enabling Visual Design Verification Analytics - From Prototype Visualizations to an Analytics Tool using the Unity Game Engine,"  The ever-increasing architectural complexity in contemporary ASIC projects
-turns Design Verification (DV) into a highly advanced endeavor. Pressing needs
-for short time-to-market has made automation a key solution in DV. However,
-recurring execution of large regression suites inevitably leads to challenging
-amounts of test results. Following the design science paradigm, we present an
-action research study to introduce visual analytics in a commercial ASIC
-project. We develop a cityscape visualization tool using the game engine Unity.
-Initial evaluations are promising, suggesting that the tool offers a novel
-approach to identify error-prone parts of the design, as well as coverage
-holes.
-",1,0,0,0,0,0
-20738,Self-regulation promotes cooperation in social networks,"  Cooperative behavior in real social dilemmas is often perceived as a
-phenomenon emerging from norms and punishment. To overcome this paradigm, we
-highlight the interplay between the influence of social networks on
-individuals, and the activation of spontaneous self-regulating mechanisms,
-which may lead them to behave cooperatively, while interacting with others and
-taking conflicting decisions over time. By extending Evolutionary game theory
-over networks, we prove that cooperation partially or fully emerges whether
-self-regulating mechanisms are sufficiently stronger than social pressure.
-Interestingly, even few cooperative individuals act as catalyzing agents for
-the cooperation of others, thus activating a recruiting mechanism, eventually
-driving the whole population to cooperate.
-",1,0,0,0,0,0
-20739,Magnetic properties of nanoparticles compacts with controlled broadening of the particle size distribution,"  Binary random compacts with different proportions of small (volume V) and
-large (volume 2V) bare maghemite nanoparticles (NPs) are used to investigate
-the effect of controllably broadening the particle size distribution on the
-magnetic properties of magnetic NP assemblies with strong dipolar interaction.
-A series of eight random mixtures of highly uniform 9.0 and 11.5 nm diameter
-maghemite particles prepared by thermal decomposition are studied. In spite of
-severely broadened size distributions in the mixed samples, well defined
-superspin glass transition temperatures are observed across the series, their
-values increasing linearly with the weight fraction of large particles.
-",0,1,0,0,0,0
-20740,A Generative Model for Exploring Structure Regularities in Attributed Networks,"  Many real-world networks known as attributed networks contain two types of
-information: topology information and node attributes. It is a challenging task
-on how to use these two types of information to explore structural
-regularities. In this paper, by characterizing potential relationship between
-link communities and node attributes, a principled statistical model named
-PSB_PG that generates link topology and node attributes is proposed. This model
-for generating links is based on the stochastic blockmodels following a Poisson
-distribution. Therefore, it is capable of detecting a wide range of network
-structures including community structures, bipartite structures and other
-mixture structures. The model for generating node attributes assumes that node
-attributes are high dimensional and sparse and also follow a Poisson
-distribution. This makes the model be uniform and the model parameters can be
-directly estimated by expectation-maximization (EM) algorithm. Experimental
-results on artificial networks and real networks containing various structures
-have shown that the proposed model PSB_PG is not only competitive with the
-state-of-the-art models, but also provides good semantic interpretation for
-each community via the learned relationship between the community and its
-related attributes.
-",1,0,0,0,0,0
-20741,On the maximal halfspace depth of permutation-invariant distributions on the simplex,"  We compute the maximal halfspace depth for a class of permutation-invariant
-distributions on the probability simplex. The derivations are based on
-stochastic ordering results that so far were only showed to be relevant for the
-Behrens-Fisher problem.
-",0,0,1,1,0,0
-20742,Theoretical Evaluation of Li et al.'s Approach for Improving a Binary Watermark-Based Scheme in Remote Sensing Data Communications,"  This letter is about a principal weakness of the published article by Li et
-al. in 2014. It seems that the mentioned work has a terrible conceptual mistake
-while presenting its theoretical approach. In fact, the work has tried to
-design a new attack and its effective solution for a basic watermarking
-algorithm by Zhu et al. published in 2013, however in practice, we show the Li
-et al.'s approach is not correct to obtain the aim. For disproof of the
-incorrect approach, we only apply a numerical example as the counterexample of
-the Li et al.'s approach.
-",1,0,0,0,0,0
-20743,Bioinformatics and Medicine in the Era of Deep Learning,"  Many of the current scientific advances in the life sciences have their
-origin in the intensive use of data for knowledge discovery. In no area this is
-so clear as in bioinformatics, led by technological breakthroughs in data
-acquisition technologies. It has been argued that bioinformatics could quickly
-become the field of research generating the largest data repositories, beating
-other data-intensive areas such as high-energy physics or astroinformatics.
-Over the last decade, deep learning has become a disruptive advance in machine
-learning, giving new live to the long-standing connectionist paradigm in
-artificial intelligence. Deep learning methods are ideally suited to
-large-scale data and, therefore, they should be ideally suited to knowledge
-discovery in bioinformatics and biomedicine at large. In this brief paper, we
-review key aspects of the application of deep learning in bioinformatics and
-medicine, drawing from the themes covered by the contributions to an ESANN 2018
-special session devoted to this topic.
-",0,0,0,1,1,0
-20744,A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously,"  Capabilities of detecting temporal relations between two events can benefit
-many applications. Most of existing temporal relation classifiers were trained
-in a supervised manner. Instead, we explore the observation that regular event
-pairs show a consistent temporal relation despite of their various contexts,
-and these rich contexts can be used to train a contextual temporal relation
-classifier, which can further recognize new temporal relation contexts and
-identify new regular event pairs. We focus on detecting after and before
-temporal relations and design a weakly supervised learning approach that
-extracts thousands of regular event pairs and learns a contextual temporal
-relation classifier simultaneously. Evaluation shows that the acquired regular
-event pairs are of high quality and contain rich commonsense knowledge and
-domain specific knowledge. In addition, the weakly supervised trained temporal
-relation classifier achieves comparable performance with the state-of-the-art
-supervised systems.
-",1,0,0,0,0,0
-20745,Privacy with Estimation Guarantees,"  We study the central problem in data privacy: how to share data with an
-analyst while providing both privacy and utility guarantees to the user that
-owns the data. In this setting, we present an estimation-theoretic analysis of
-the privacy-utility trade-off (PUT). Here, an analyst is allowed to reconstruct
-(in a mean-squared error sense) certain functions of the data (utility), while
-other private functions should not be reconstructed with distortion below a
-certain threshold (privacy). We demonstrate how $\chi^2$-information captures
-the fundamental PUT in this case and provide bounds for the best PUT. We
-propose a convex program to compute privacy-assuring mappings when the
-functions to be disclosed and hidden are known a priori and the data
-distribution is known. We derive lower bounds on the minimum mean-squared error
-of estimating a target function from the disclosed data and evaluate the
-robustness of our approach when an empirical distribution is used to compute
-the privacy-assuring mappings instead of the true data distribution. We
-illustrate the proposed approach through two numerical experiments.
-",1,0,0,0,0,0
-20746,A line of CFTs: from generalized free fields to SYK,"  We point out that there is a simple variant of the SYK model, which we call
-cSYK, that is $SL(2,R)$ invariant for all values of the coupling. The
-modification consists of replacing the UV part of the SYK action with a
-quadratic bilocal term. The corresponding bulk dual is a non-gravitational
-theory in a rigid AdS$_2$ background. At weak coupling cSYK is a generalized
-free field theory; at strong coupling, it approaches the infrared of SYK. The
-existence of this line of fixed points explains the previously found connection
-between the three-point function of bilinears in these two theories at large
-$q$.
-",0,1,0,0,0,0
-20747,Miura transformations for discrete Painlevé equations coming from the affine E$_8$ Weyl group,"  We derive integrable equations starting from autonomous mappings with a
-general form inspired by the additive systems associated to the affine Weyl
-group E$_8^{(1)}$. By deautonomisation we obtain two hitherto unknown systems,
-one of which turns out to be a linearisable one, and we show that both these
-systems arise from the deautonomisation of a non-QRT mapping. In order to
-unambiguously prove the integrability of these nonautonomous systems, we
-introduce a series of Miura transformations which allows us to prove that one
-of these systems is indeed a discrete Painlevé equation, related to the
-affine Weyl group E$_7^{(1)}$, and to cast it in canonical form. A similar
-sequence of Miura transformations allows us to effectively linearise the second
-system we obtain. An interesting off-shoot of our calculations is that the
-series of Miura transformations, when applied at the autonomous limit, allows
-one to transform a non-QRT invariant into a QRT one.
-",0,1,1,0,0,0
-20748,Virtual Molecular Dynamics,"  Molecular dynamics is based on solving Newton's equations for many-particle
-systems that evolve along complex, highly fluctuating trajectories. The orbital
-instability and short-time complexity of Newtonian orbits is in sharp contrast
-to the more coherent behavior of collective modes such as density profiles. The
-notion of virtual molecular dynamics is introduced here based on temporal
-coarse-graining via Pade approximants and the Ito formula for stochastic
-processes. It is demonstrated that this framework leads to significant
-efficiency over traditional molecular dynamics and avoids the need to introduce
-coarse-grained variables and phenomenological equations for their evolution. In
-this framework, an all-atom trajectory is represented by a Markov chain of
-virtual atomic states at a discrete sequence of timesteps, transitions between
-which are determined by an integration of conventional molecular dynamics with
-Pade approximants and a microstate energy annealing methodology. The latter is
-achieved by a conventional and an MD NVE energy minimization schemes. This
-multiscale framework is demonstrated for a pertussis toxin subunit undergoing a
-structural transition, a T=1 capsid-like structure of HPV16 L1 protein, and two
-coalescing argon droplets.
-",0,1,0,0,0,0
-20749,Regression with genuinely functional errors-in-covariates,"  Contamination of covariates by measurement error is a classical problem in
-multivariate regression, where it is well known that failing to account for
-this contamination can result in substantial bias in the parameter estimators.
-The nature and degree of this effect on statistical inference is also
-understood to crucially depend on the specific distributional properties of the
-measurement error in question. When dealing with functional covariates,
-measurement error has thus far been modelled as additive white noise over the
-observation grid. Such a setting implicitly assumes that the error arises
-purely at the discrete sampling stage, otherwise the model can only be viewed
-in a weak (stochastic differential equation) sense, white noise not being a
-second-order process. Departing from this simple distributional setting can
-have serious consequences for inference, similar to the multivariate case, and
-current methodology will break down. In this paper, we consider the case when
-the additive measurement error is allowed to be a valid stochastic process. We
-propose a novel estimator of the slope parameter in a functional linear model,
-for scalar as well as functional responses, in the presence of this general
-measurement error specification. The proposed estimator is inspired by the
-multivariate regression calibration approach, but hinges on recent advances on
-matrix completion methods for functional data in order to handle the nontrivial
-(and unknown) error covariance structure. The asymptotic properties of the
-proposed estimators are derived. We probe the performance of the proposed
-estimator of slope using simulations and observe that it substantially improves
-upon the spectral truncation estimator based on the erroneous observations,
-i.e., ignoring measurement error. We also investigate the behaviour of the
-estimators on a real dataset on hip and knee angle curves during a gait cycle.
-",0,0,0,1,0,0
-20750,The distance between a naive cumulative estimator and its least concave majorant,"  We consider the process $\widehat\Lambda_n-\Lambda_n$, where $\Lambda_n$ is a
-cadlag step estimator for the primitive $\Lambda$ of a nonincreasing function
-$\lambda$ on $[0,1]$, and $\widehat\Lambda_n$ is the least concave majorant of
-$\Lambda_n$. We extend the results in Kulikov and Lopuhaä (2006, 2008) to the
-general setting considered in Durot (2007). Under this setting we prove that a
-suitably scaled version of $\widehat\Lambda_n-\Lambda_n$ converges in
-distribution to the corresponding process for two-sided Brownian motion with
-parabolic drift and we establish a central limit theorem for the $L_p$-distance
-between $\widehat\Lambda_n$ and $\Lambda_n$.
-",0,0,1,1,0,0
-20751,Controllability of impulse controlled systems of heat equations coupled by constant matrices,"  This paper studies the approximate and null controllability for impulse
-controlled systems of heat equations coupled by a pair (A,B) of constant
-matrices. We present a necessary and sufficient condition for the approximate
-controllability, which is exactly Kalman's controllability rank condition of
-(A,B). We prove that when such a system is approximately controllable, the
-approximate controllability over an interval [0,T] can be realized by adding
-controls at arbitrary n different control instants
-0<\tau_1<\tau_2<\cdots<\tau_n<T, provided that \tau_n-\tau_1<d_A, where
-d_A=\min\{\pi/|Im \lambda| : \lambda\in \sigma(A)\}. We also show that in
-general, such systems are not null controllable.
-",0,0,1,0,0,0
-20752,Nearly Optimal Constructions of PIR and Batch Codes,"  In this work we study two families of codes with availability, namely private
-information retrieval (PIR) codes and batch codes. While the former requires
-that every information symbol has $k$ mutually disjoint recovering sets, the
-latter asks this property for every multiset request of $k$ information
-symbols. The main problem under this paradigm is to minimize the number of
-redundancy symbols. We denote this value by $r_P(n,k), r_B(n,k)$, for PIR,
-batch codes, respectively, where $n$ is the number of information symbols.
-Previous results showed that for any constant $k$, $r_P(n,k) =
-\Theta(\sqrt{n})$ and $r_B(n,k)=O(\sqrt{n}\log(n)$. In this work we study the
-asymptotic behavior of these codes for non-constant $k$ and specifically for
-$k=\Theta(n^\epsilon)$. We also study the largest value of $k$ such that the
-rate of the codes approaches 1, and show that for all $\epsilon<1$,
-$r_P(n,n^\epsilon) = o(n)$, while for batch codes, this property holds for all
-$\epsilon< 0.5$.
-",1,0,0,0,0,0
-20753,Submillimeter Array CO(2-1) Imaging of the NGC 6946 Giant Molecular Clouds,"  We present a CO(2-1) mosaic map of the spiral galaxy NGC 6946 by combining
-data from the Submillimeter Array and the IRAM 30 m telescope. We identify 390
-giant molecular clouds (GMCs) from the nucleus to 4.5 kpc in the disk. GMCs in
-the inner 1 kpc are generally more luminous and turbulent, some of which have
-luminosities >10^6 K km/s pc^2 and velocity dispersions >10 km/s. Large-scale
-bar-driven dynamics likely regulate GMC properties in the nuclear region.
-Similar to the Milky Way and other disk galaxies, GMC mass function of NGC 6946
-has a shallower slope (index>-2) in the inner region, and a steeper slope
-(index<-2) in the outer region. This difference in mass spectra may be
-indicative of different cloud formation pathways: gravitational instabilities
-might play a major role in the nuclear region, while cloud coalescence might be
-dominant in the outer disk. Finally, the NGC 6946 clouds are similar to those
-in M33 in terms of statistical properties, but they are generally less luminous
-and turbulent than the M51 clouds.
-",0,1,0,0,0,0
-20754,Optical and Near-Infrared Spectra of sigma Orionis Isolated Planetary-mass Objects,"  We have obtained low-resolution optical (0.7-0.98 micron) and near-infrared
-(1.11-1.34 micron and 0.8-2.5 micron) spectra of twelve isolated planetary-mass
-candidates (J = 18.2-19.9 mag) of the 3-Myr sigma Orionis star cluster with a
-view to determining the spectroscopic properties of very young, substellar
-dwarfs and assembling a complete cluster mass function. We have classified our
-targets by visual comparison with high- and low-gravity standards and by
-measuring newly defined spectroscopic indices. We derived L0-L4.5 and M9-L2.5
-using high- and low-gravity standards, respectively. Our targets reveal clear
-signposts of youth, thus corroborating their cluster membership and planetary
-masses (6-13 Mjup). These observations complete the sigma Orionis mass function
-by spectroscopically confirming the planetary-mass domain to a confidence level
-of $\sim$75 percent. The comparison of our spectra with BT-Settl solar
-metallicity model atmospheres yields a temperature scale of 2350-1800 K and a
-low surface gravity of log g ~ 4.0 [cm/s2], as would be expected for young
-planetary-mass objects. We discuss the properties of the cluster least-massive
-population as a function of spectral type. We have also obtained the first
-optical spectrum of S Ori 70, a T dwarf in the direction of sigma Orionis. Our
-data provide reference optical and near-infrared spectra of very young L dwarfs
-and a mass function that may be used as templates for future studies of
-low-mass substellar objects and exoplanets. The extrapolation of the sigma
-Orionis mass function to the solar neighborhood may indicate that isolated
-planetary-mass objects with temperatures of 200-300 K and masses in the
-interval 6-13-Mjup may be as numerous as very low-mass stars.
-",0,1,0,0,0,0
-20755,The Blackbird Dataset: A large-scale dataset for UAV perception in aggressive flight,"  The Blackbird unmanned aerial vehicle (UAV) dataset is a large-scale,
-aggressive indoor flight dataset collected using a custom-built quadrotor
-platform for use in evaluation of agile perception.Inspired by the potential of
-future high-speed fully-autonomous drone racing, the Blackbird dataset contains
-over 10 hours of flight data from 168 flights over 17 flight trajectories and 5
-environments at velocities up to $7.0ms^-1$. Each flight includes sensor data
-from 120Hz stereo and downward-facing photorealistic virtual cameras, 100Hz
-IMU, $\sim190Hz$ motor speed sensors, and 360Hz millimeter-accurate motion
-capture ground truth. Camera images for each flight were photorealistically
-rendered using FlightGoggles across a variety of environments to facilitate
-easy experimentation of high performance perception algorithms. The dataset is
-available for download at this http URL
-",1,0,0,0,0,0
-20756,An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification,"  Motivation: Cellular Electron CryoTomography (CECT) is an emerging 3D imaging
-technique that visualizes subcellular organization of single cells at
-submolecular resolution and in near-native state. CECT captures large numbers
-of macromolecular complexes of highly diverse structures and abundances.
-However, the structural complexity and imaging limits complicate the systematic
-de novo structural recovery and recognition of these macromolecular complexes.
-Efficient and accurate reference-free subtomogram averaging and classification
-represent the most critical tasks for such analysis. Existing subtomogram
-alignment based methods are prone to the missing wedge effects and low
-signal-to-noise ratio (SNR). Moreover, existing maximum-likelihood based
-methods rely on integration operations, which are in principle computationally
-infeasible for accurate calculation.
-Results: Built on existing works, we propose an integrated method, Fast
-Alignment Maximum Likelihood method (FAML), which uses fast subtomogram
-alignment to sample sub-optimal rigid transformations. The transformations are
-then used to approximate integrals for maximum-likelihood update of subtomogram
-averages through expectation-maximization algorithm. Our tests on simulated and
-experimental subtomograms showed that, compared to our previously developed
-fast alignment method (FA), FAML is significantly more robust to noise and
-missing wedge effects with moderate increases of computation cost.Besides, FAML
-performs well with significantly fewer input subtomograms when the FA method
-fails. Therefore, FAML can serve as a key component for improved construction
-of initial structural models from macromolecules captured by CECT.
-",0,0,0,1,1,0
-20757,Local and global existence of solutions to a strongly damped wave equation of the $p$-Laplacian type,"  This article focuses on a quasilinear wave equation of $p$-Laplacian type: $$
-u_{tt} - \Delta_p u - \Delta u_t=0$$ in a bounded domain
-$\Omega\subset\mathbb{R}^3$ with a sufficiently smooth boundary
-$\Gamma=\partial\Omega$ subject to a generalized Robin boundary condition
-featuring boundary damping and a nonlinear source term. The operator
-$\Delta_p$, $2 < p < 3$, denotes the classical $p$-Laplacian. The nonlinear
-boundary term $f (u)$ is a source feedback that is allowed to have a
-supercritical exponent, in the sense that the associated Nemytskii operator is
-not locally Lipschitz from $W^{1,p}(\Omega)$ into $L^2(\Gamma)$. Under suitable
-assumptions on the parameters we provide a rigorous proof of existence of a
-local weak solution which can be extended globally in time provided the source
-term satisfies an appropriate growth condition.
-",0,0,1,0,0,0
-20758,Quasi-flat representations of uniform groups and quantum groups,"  Given a discrete group $\Gamma=<g_1,\ldots,g_M>$ and a number $K\in\mathbb
-N$, a unitary representation $\rho:\Gamma\to U_K$ is called quasi-flat when the
-eigenvalues of each $\rho(g_i)\in U_K$ are uniformly distributed among the
-$K$-th roots of unity. The quasi-flat representations of $\Gamma$ form
-altogether a parametric matrix model $\pi:\Gamma\to C(X,U_K)$.
-We compute here the universal model space $X$ for various classes of discrete
-groups, notably with results in the case where $\Gamma$ is metabelian. We are
-particularly interested in the case where $X$ is a union of compact homogeneous
-spaces, and where the induced representation $\tilde{\pi}:C^*(\Gamma)\to
-C(X,U_K)$ is stationary in the sense that it commutes with the Haar
-functionals. We present several positive and negative results on this subject.
-We also discuss similar questions for the discrete quantum groups, proving a
-stationarity result for the discrete dual of the twisted orthogonal group
-$O_2^{-1}$.
-",0,0,1,0,0,0
-20759,Nonparametric Preference Completion,"  We consider the task of collaborative preference completion: given a pool of
-items, a pool of users and a partially observed item-user rating matrix, the
-goal is to recover the \emph{personalized ranking} of each user over all of the
-items. Our approach is nonparametric: we assume that each item $i$ and each
-user $u$ have unobserved features $x_i$ and $y_u$, and that the associated
-rating is given by $g_u(f(x_i,y_u))$ where $f$ is Lipschitz and $g_u$ is a
-monotonic transformation that depends on the user. We propose a $k$-nearest
-neighbors-like algorithm and prove that it is consistent. To the best of our
-knowledge, this is the first consistency result for the collaborative
-preference completion problem in a nonparametric setting. Finally, we
-demonstrate the performance of our algorithm with experiments on the Netflix
-and Movielens datasets.
-",1,0,0,1,0,0
-20760,Real-Time Reconstruction of Counting Process through Queues,"  For the emerging Internet of Things (IoT), one of the most critical problems
-is the real-time reconstruction of signals from a set of aged measurements.
-During the reconstruction, distortion occurs between the observed signal and
-the reconstructed signal due to sampling and transmission. In this paper, we
-focus on minimizing the average distortion defined as the 1-norm of the
-difference of the two signals under the scenario that a Poisson counting
-process is reconstructed in real-time on a remote monitor. Especially, we
-consider the reconstruction under uniform sampling policy and two non-uniform
-sampling policies, i.e., the threshold-based policy and the zero-wait policy.
-For each of the policy, we derive the closed-form expression of the average
-distortion by dividing the overall distortion area into polygons and analyzing
-their structures. It turns out that the polygons are built up by sub-polygons
-that account for distortions caused by sampling and transmission. The
-closed-form expressions of the average distortion help us find the optimal
-sampling parameters that achieve the minimum distortion. Simulation results are
-provided to validate our conclusion.
-",1,0,0,0,0,0
-20761,Discrepancy-Based Algorithms for Non-Stationary Rested Bandits,"  We study the multi-armed bandit problem where the rewards are realizations of
-general non-stationary stochastic processes, a setting that generalizes many
-existing lines of work and analyses. In particular, we present a theoretical
-analysis and derive regret guarantees for rested bandits in which the reward
-distribution of each arm changes only when we pull that arm. Remarkably, our
-regret bounds are logarithmic in the number of rounds under several natural
-conditions. We introduce a new algorithm based on classical UCB ideas combined
-with the notion of weighted discrepancy, a useful tool for measuring the
-non-stationarity of a stochastic process. We show that the notion of
-discrepancy can be used to design very general algorithms and a unified
-framework for the analysis of multi-armed rested bandit problems with
-non-stationary rewards. In particular, we show that we can recover the regret
-guarantees of many specific instances of bandit problems with non-stationary
-rewards that have been studied in the literature. We also provide experiments
-demonstrating that our algorithms can enjoy a significant improvement in
-practice compared to standard benchmarks.
-",1,0,0,0,0,0
-20762,Kondo lattice heavy fermion behavior in CeRh2Ga2,"  The physical properties of an intermetallic compound CeRh2Ga2 have been
-investigated by magnetic susceptibility \chi(T), isothermal magnetization M(H),
-heat capacity C_p(T), electrical resistivity \rho(T), thermal conductivity
-\kappa(T) and thermopower S(T) measurements. CeRh2Ga2 is found to crystallize
-with CaBe2Ge2-type primitive tetragonal structure (space group P4/nmm). No
-evidence of long range magnetic order is seen down to 1.8 K. The \chi(T) data
-show paramagnetic behavior with an effective moment \mu_eff ~ 2.5 \mu_B/Ce
-indicating Ce^3+ valence state of Ce ions. The \rho(T) data exhibit Kondo
-lattice behavior with a metallic ground state. The low-T C_p(T) data yield an
-enhanced Sommerfeld coefficient \gamma = 130(2) mJ/mol K^2 characterizing
-CeRh2Ga2 as a moderate heavy fermion system. The high-T C_p(T) and \rho(T) show
-an anomaly near 255 K, reflecting a phase transition. The \kappa(T) suggests
-phonon dominated thermal transport with considerably higher values of Lorenz
-number L(T) compared to the theoretical Sommerfeld value L_0.
-",0,1,0,0,0,0
-20763,Khovanov homology and periodic links,"  Based on the results of the second author, we define an equivariant version
-of Lee and Bar-Natan homology for periodic links and show that there exists an
-equivariant spectral sequence from the equivariant Khovanov homology to
-equivariant Lee homology. As a result we obtain new obstructions for a link to
-be periodic. These obstructions generalize previous results of Przytycki and of
-the second author.
-",0,0,1,0,0,0
-20764,A Note on Spectral Clustering and SVD of Graph Data,"  Spectral clustering and Singular Value Decomposition (SVD) are both widely
-used technique for analyzing graph data. In this note, I will present their
-connections using simple linear algebra, aiming to provide some in-depth
-understanding for future research.
-",1,0,0,0,0,0
-20765,An explicit analysis of the entropic penalty in linear programming,"  Solving linear programs by using entropic penalization has recently attracted
-new interest in the optimization community, since this strategy forms the basis
-for the fastest-known algorithms for the optimal transport problem, with many
-applications in modern large-scale machine learning. Crucial to these
-applications has been an analysis of how quickly solutions to the penalized
-program approach true optima to the original linear program. More than 20 years
-ago, Cominetti and San Martín showed that this convergence is exponentially
-fast; however, their proof is asymptotic and does not give any indication of
-how accurately the entropic program approximates the original program for any
-particular choice of the penalization parameter. We close this long-standing
-gap in the literature regarding entropic penalization by giving a new proof of
-the exponential convergence, valid for any linear program. Our proof is
-non-asymptotic, yields explicit constants, and has the virtue of being
-extremely simple. We provide matching lower bounds and show that the entropic
-approach does not lead to a near-linear time approximation scheme for the
-linear assignment problem.
-",0,0,0,1,0,0
-20766,Deconstructing the Tail at Scale Effect Across Network Protocols,"  Network latencies have become increasingly important for the performance of
-web servers and cloud computing platforms. Identifying network-related tail
-latencies and reasoning about their potential causes is especially important to
-gauge application run-time in online data-intensive applications, where the
-99th percentile latency of individual operations can significantly affect the
-the overall latency of requests.
-This paper deconstructs the ""tail at scale"" effect across TCP-IP, UDP-IP, and
-RDMA network protocols. Prior scholarly works have analyzed tail latencies
-caused by extrinsic network parameters like network congestion and flow
-fairness. Contrary to existing literature, we identify surprising rare tails in
-TCP-IP round-trip measurements that are as enormous as 110x higher than the
-median latency. Our experimental design eliminates network congestion as a
-tail-inducing factor. Moreover, we observe similar extreme tails in UDP-IP
-packet exchanges, ruling out additional TCP-IP protocol operations as the root
-cause of tail latency. However, we are unable to reproduce similar tail
-latencies in RDMA packet exchanges, which leads us to conclude that the TCP/UDP
-protocol stack within the operating system kernel is likely the primary source
-of extreme latency tails.
-",1,0,0,0,0,0
-20767,Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis,"  Accurate diagnosis of psychiatric disorders plays a critical role in
-improving quality of life for patients and potentially supports the development
-of new treatments. Many studies have been conducted on machine learning
-techniques that seek brain imaging data for specific biomarkers of disorders.
-These studies have encountered the following dilemma: An end-to-end
-classification overfits to a small number of high-dimensional samples but
-unsupervised feature-extraction has the risk of extracting a signal of no
-interest. In addition, such studies often provided only diagnoses for patients
-without presenting the reasons for these diagnoses. This study proposed a deep
-neural generative model of resting-state functional magnetic resonance imaging
-(fMRI) data. The proposed model is conditioned by the assumption of the
-subject's state and estimates the posterior probability of the subject's state
-given the imaging data, using Bayes' rule. This study applied the proposed
-model to diagnose schizophrenia and bipolar disorders. Diagnosis accuracy was
-improved by a large margin over competitive approaches, namely a support vector
-machine, logistic regression, and multilayer perceptron with or without
-unsupervised feature-extractors in addition to a Gaussian mixture model. The
-proposed model visualizes brain regions largely related to the disorders, thus
-motivating further biological investigation.
-",1,0,0,1,0,0
-20768,Deep Learning for Patient-Specific Kidney Graft Survival Analysis,"  An accurate model of patient-specific kidney graft survival distributions can
-help to improve shared-decision making in the treatment and care of patients.
-In this paper, we propose a deep learning method that directly models the
-survival function instead of estimating the hazard function to predict survival
-times for graft patients based on the principle of multi-task learning. By
-learning to jointly predict the time of the event, and its rank in the cox
-partial log likelihood framework, our deep learning approach outperforms, in
-terms of survival time prediction quality and concordance index, other common
-methods for survival analysis, including the Cox Proportional Hazards model and
-a network trained on the cox partial log-likelihood.
-",1,0,0,1,0,0
-20769,When is a Network a Network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks,"  We introduce a framework for the modeling of sequential data capturing
-pathways of varying lengths observed in a network. Such data are important,
-e.g., when studying click streams in information networks, travel patterns in
-transportation systems, information cascades in social networks, biological
-pathways or time-stamped social interactions. While it is common to apply graph
-analytics and network analysis to such data, recent works have shown that
-temporal correlations can invalidate the results of such methods. This raises a
-fundamental question: when is a network abstraction of sequential data
-justified? Addressing this open question, we propose a framework which combines
-Markov chains of multiple, higher orders into a multi-layer graphical model
-that captures temporal correlations in pathways at multiple length scales
-simultaneously. We develop a model selection technique to infer the optimal
-number of layers of such a model and show that it outperforms previously used
-Markov order detection techniques. An application to eight real-world data sets
-on pathways and temporal networks shows that it allows to infer graphical
-models which capture both topological and temporal characteristics of such
-data. Our work highlights fallacies of network abstractions and provides a
-principled answer to the open question when they are justified. Generalizing
-network representations to multi-order graphical models, it opens perspectives
-for new data mining and knowledge discovery algorithms.
-",1,1,0,0,0,0
-20770,Outlier-robust moment-estimation via sum-of-squares,"  We develop efficient algorithms for estimating low-degree moments of unknown
-distributions in the presence of adversarial outliers. The guarantees of our
-algorithms improve in many cases significantly over the best previous ones,
-obtained in recent works of Diakonikolas et al, Lai et al, and Charikar et al.
-We also show that the guarantees of our algorithms match information-theoretic
-lower-bounds for the class of distributions we consider. These improved
-guarantees allow us to give improved algorithms for independent component
-analysis and learning mixtures of Gaussians in the presence of outliers.
-Our algorithms are based on a standard sum-of-squares relaxation of the
-following conceptually-simple optimization problem: Among all distributions
-whose moments are bounded in the same way as for the unknown distribution, find
-the one that is closest in statistical distance to the empirical distribution
-of the adversarially-corrupted sample.
-",1,0,0,1,0,0
-20771,Clustering High Dimensional Dynamic Data Streams,"  We present data streaming algorithms for the $k$-median problem in
-high-dimensional dynamic geometric data streams, i.e. streams allowing both
-insertions and deletions of points from a discrete Euclidean space $\{1, 2,
-\ldots \Delta\}^d$. Our algorithms use $k \epsilon^{-2} poly(d \log \Delta)$
-space/time and maintain with high probability a small weighted set of points (a
-coreset) such that for every set of $k$ centers the cost of the coreset
-$(1+\epsilon)$-approximates the cost of the streamed point set. We also provide
-algorithms that guarantee only positive weights in the coreset with additional
-logarithmic factors in the space and time complexities. We can use this
-positively-weighted coreset to compute a $(1+\epsilon)$-approximation for the
-$k$-median problem by any efficient offline $k$-median algorithm. All previous
-algorithms for computing a $(1+\epsilon)$-approximation for the $k$-median
-problem over dynamic data streams required space and time exponential in $d$.
-Our algorithms can be generalized to metric spaces of bounded doubling
-dimension.
-",1,0,0,0,0,0
-20772,Edge contact angle and modified Kelvin equation for condensation in open pores,"  We consider capillary condensation transitions occurring in open slits of
-width $L$ and finite height $H$ immersed in a reservoir of vapour. In this case
-the pressure at which condensation occurs is closer to saturation compared to
-that occurring in an infinite slit ($H=\infty$) due to the presence of two
-menisci which are pinned near the open ends. Using macroscopic arguments we
-derive a modified Kelvin equation for the pressure, $p_{cc}(L;H)$, at which
-condensation occurs and show that the two menisci are characterised by an edge
-contact angle $\theta_e$ which is always larger than the equilibrium contact
-angle $\theta$, only equal to it in the limit of macroscopic $H$. For walls
-which are completely wet ($\theta=0$) the edge contact angle depends only on
-the aspect ratio of the capillary and is well described by $\theta_e\approx
-\sqrt{\pi L/2H}$ for large $H$. Similar results apply for condensation in
-cylindrical pores of finite length. We have tested these predictions against
-numerical results obtained using a microscopic density functional model where
-the presence of an edge contact angle characterising the shape of the menisci
-is clearly visible from the density profiles. Below the wetting temperature
-$T_w$ we find very good agreement for slit pores of widths of just a few tens
-of molecular diameters while above $T_w$ the modified Kelvin equation only
-becomes accurate for much larger systems.
-",0,1,0,0,0,0
-20773,Thresholds for hanger slackening and cable shortening in the Melan equation for suspension bridges,"  The Melan equation for suspension bridges is derived by assuming small
-displacements of the deck and inextensible hangers. We determine the thresholds
-for the validity of the Melan equation when the hangers slacken, thereby
-violating the inextensibility assumption. To this end, we preliminarily study
-the possible shortening of the cables: it turns out that there is a striking
-difference between even and odd vibrating modes since the former never shorten
-the cables. These problems are studied both on beams and plates.
-",0,1,1,0,0,0
-20774,Bendable Cuboid Robot Path Planning with Collision Avoidance using Generalized $L_p$ Norms,"  Optimal path planning problems for rigid and deformable (bendable) cuboid
-robots are considered by providing an analytic safety constraint using
-generalized $L_p$ norms. For regular cuboid robots, level sets of weighted
-$L_p$ norms generate implicit approximations of their surfaces. For bendable
-cuboid robots a weighted $L_p$ norm in polar coordinates implicitly
-approximates the surface boundary through a specified level set. Obstacle
-volumes, in the environment to navigate within, are presumed to be
-approximately described as sub-level sets of weighted $L_p$ norms. Using these
-approximate surface models, the optimal safe path planning problem is
-reformulated as a two stage optimization problem, where the safety constraint
-depends on a point on the robot which is closest to the obstacle in the
-obstacle's distance metric. A set of equality and inequality constraints are
-derived to replace the closest point problem, which is then defines additional
-analytic constraints on the original path planning problem. Combining all the
-analytic constraints with logical AND operations leads to a general optimal
-safe path planning problem. Numerically solving the problem involve conversion
-to a nonlinear programing problem. Simulations for rigid and bendable cuboid
-robot verify the proposed method.
-",1,0,0,0,0,0
-20775,"Quotients of triangulated categories and Equivalences of Buchweitz, Orlov and Amiot--Guo--Keller","  We give a sufficient condition for a Verdier quotient $\ct/\cs$ of a
-triangulated category $\ct$ by a thick subcategory $\cs$ to be realized inside
-of $\ct$ as an ideal quotient. As applications, we deduce three significant
-results by Buchweitz, Orlov and Amiot--Guo--Keller.
-",0,0,1,0,0,0
-20776,Sorting sums of binary decision summands,"  A sum where each of the $N$ summands can be independently chosen from two
-choices yields $2^N$ possible summation outcomes. There is an
-$\mathcal{O}(K^2)$-algorithm that finds the $K$ smallest/largest of these sums
-by evading the enumeration of all sums.
-",1,0,0,0,0,0
-20777,A Pursuit of Temporal Accuracy in General Activity Detection,"  Detecting activities in untrimmed videos is an important but challenging
-task. The performance of existing methods remains unsatisfactory, e.g., they
-often meet difficulties in locating the beginning and end of a long complex
-action. In this paper, we propose a generic framework that can accurately
-detect a wide variety of activities from untrimmed videos. Our first
-contribution is a novel proposal scheme that can efficiently generate
-candidates with accurate temporal boundaries. The other contribution is a
-cascaded classification pipeline that explicitly distinguishes between
-relevance and completeness of a candidate instance. On two challenging temporal
-activity detection datasets, THUMOS14 and ActivityNet, the proposed framework
-significantly outperforms the existing state-of-the-art methods, demonstrating
-superior accuracy and strong adaptivity in handling activities with various
-temporal structures.
-",1,0,0,0,0,0
-20778,Adjusting systematic bias in high dimensional principal component scores,"  Principal component analysis continues to be a powerful tool in dimension
-reduction of high dimensional data. We assume a variance-diverging model and
-use the high-dimension, low-sample-size asymptotics to show that even though
-the principal component directions are not consistent, the sample and
-prediction principal component scores can be useful in revealing the population
-structure. We further show that these scores are biased, and the bias is
-asymptotically decomposed into rotation and scaling parts. We propose methods
-of bias-adjustment that are shown to be consistent and work well in the finite
-but high dimensional situations with small sample sizes. The potential
-advantage of bias-adjustment is demonstrated in a classification setting.
-",0,0,1,1,0,0
-20779,Dynamical Exploration of Amplitude Bistability in Engineered Quantum Systems,"  Nonlinear systems, whose outputs are not directly proportional to their
-inputs, are well known to exhibit many interesting and important phenomena
-which have profoundly changed our technological landscape over the last 50
-years. Recently the ability to engineer quantum metamaterials through
-hybridisation has allowed to explore these nonlinear effects in systems with no
-natural analogue. Here we investigate amplitude bistability, which is one of
-the most fundamental nonlinear phenomena, in a hybrid system composed of a
-superconducting resonator inductively coupled to an ensemble of
-nitrogen-vacancy centres. One of the exciting properties of this spin system is
-its extremely long spin life-time, more than ten orders of magnitude longer
-than other relevant timescales of the hybrid system. This allows us to
-dynamically explore this nonlinear regime of cavity quantum electrodynamics
-(cQED) and demonstrate a critical slowing down of the cavity population on the
-order of several tens of thousands of seconds - a timescale much longer than
-observed so far for this effect. Our results provide the foundation for future
-quantum technologies based on nonlinear phenomena.
-",0,1,0,0,0,0
-20780,Map Memorization and Forgetting in the IARA Autonomous Car,"  In this work, we present a novel strategy for correcting imperfections in
-occupancy grid maps called map decay. The objective of map decay is to correct
-invalid occupancy probabilities of map cells that are unobservable by sensors.
-The strategy was inspired by an analogy between the memory architecture
-believed to exist in the human brain and the maps maintained by an autonomous
-vehicle. It consists in merging sensory information obtained during runtime
-(online) with a priori data from a high-precision map constructed offline. In
-map decay, cells observed by sensors are updated using traditional occupancy
-grid mapping techniques and unobserved cells are adjusted so that their
-occupancy probabilities tend to the values found in the offline map. This
-strategy is grounded in the idea that the most precise information available
-about an unobservable cell is the value found in the high-precision offline
-map. Map decay was successfully tested and is still in use in the IARA
-autonomous vehicle from Universidade Federal do Espírito Santo.
-",1,0,0,0,0,0
-20781,Effective Subgroup Separability of Finitely Generated Nilpotent Groups,"  This paper studies effective separability for subgroups of finitely generated
-nilpotent groups and more broadly effective subgroup separability of finitely
-generated nilpotent groups. We provide upper and lower bounds that are
-polynomial with respect to the logarithm of the word length for infinite index
-subgroups of nilpotent groups. In the case of normal subgroups, we provide an
-exact computation generalizing work of the second author. We introduce a
-function that quantifies subgroup separability, and we provide polynomial upper
-and lower bounds. We finish by demonstrating that our results extend to
-virtually nilpotent groups.
-",0,0,1,0,0,0
-20782,External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising,"  Most of existing image denoising methods learn image priors from either
-external data or the noisy image itself to remove noise. However, priors
-learned from external data may not be adaptive to the image to be denoised,
-while priors learned from the given noisy image may not be accurate due to the
-interference of corrupted noise. Meanwhile, the noise in real-world noisy
-images is very complex, which is hard to be described by simple distributions
-such as Gaussian distribution, making real-world noisy image denoising a very
-challenging problem. We propose to exploit the information in both external
-data and the given noisy image, and develop an external prior guided internal
-prior learning method for real-world noisy image denoising. We first learn
-external priors from an independent set of clean natural images. With the aid
-of learned external priors, we then learn internal priors from the given noisy
-image to refine the prior model. The external and internal priors are
-formulated as a set of orthogonal dictionaries to efficiently reconstruct the
-desired image. Extensive experiments are performed on several real-world noisy
-image datasets. The proposed method demonstrates highly competitive denoising
-performance, outperforming state-of-the-art denoising methods including those
-designed for real-world noisy images.
-",1,0,0,0,0,0
-20783,A Study of MAC Address Randomization in Mobile Devices and When it Fails,"  MAC address randomization is a privacy technique whereby mobile devices
-rotate through random hardware addresses in order to prevent observers from
-singling out their traffic or physical location from other nearby devices.
-Adoption of this technology, however, has been sporadic and varied across
-device manufacturers. In this paper, we present the first wide-scale study of
-MAC address randomization in the wild, including a detailed breakdown of
-different randomization techniques by operating system, manufacturer, and model
-of device.
-We then identify multiple flaws in these implementations which can be
-exploited to defeat randomization as performed by existing devices. First, we
-show that devices commonly make improper use of randomization by sending
-wireless frames with the true, global address when they should be using a
-randomized address. We move on to extend the passive identification techniques
-of Vanhoef et al. to effectively defeat randomization in ~96% of Android
-phones. Finally, we show a method that can be used to track 100% of devices
-using randomization, regardless of manufacturer, by exploiting a previously
-unknown flaw in the way existing wireless chipsets handle low-level control
-frames.
-",1,0,0,0,0,0
-20784,Completion of the integrable coupling systems,"  In this paper, we proposed an procedure to construct the completion of the
-integrable system by adding a perturbation to the generalized matrix problem,
-which can be used to continuous integrable couplings, discrete integrable
-couplings and super integrable couplings. As example, we construct the
-completion of the Kaup-Newell (KN) integrable coupling, the
-Wadati-Konno-Ichikawa (WKI) integrable couplingsis, vector
-Ablowitz-Kaup-Newell-Segur (vAKNS) integrable couplings, the Volterra
-integrable couplings, Dirac type integrable couplings and NLS-mKdV type
-integrable couplings.
-",0,1,0,0,0,0
-20785,On the wildness of cambrian lattices,"  In this note, we investigate the representation type of the cambrian lattices
-and some other related lattices. The result is expressed as a very simple
-trichotomy. When the rank of the underlined Coxeter group is at most 2, the
-lattices are of finite representation type. When the Coxeter group is a
-reducible group of type A 3 1 , the lattices are of tame representation type.
-In all the other cases they are of wild representation type.
-",0,0,1,0,0,0
-20786,Random Projections For Large-Scale Regression,"  Fitting linear regression models can be computationally very expensive in
-large-scale data analysis tasks if the sample size and the number of variables
-are very large. Random projections are extensively used as a dimension
-reduction tool in machine learning and statistics. We discuss the applications
-of random projections in linear regression problems, developed to decrease
-computational costs, and give an overview of the theoretical guarantees of the
-generalization error. It can be shown that the combination of random
-projections with least squares regression leads to similar recovery as ridge
-regression and principal component regression. We also discuss possible
-improvements when averaging over multiple random projections, an approach that
-lends itself easily to parallel implementation.
-",0,0,1,1,0,0
-20787,Properties of linear groups with restricted unipotent elements,"  We consider linear groups which do not contain unipotent elements of infinite
-order, which includes all linear groups in positive characteristic, and show
-that this class of groups has good properties which resemble those held by
-groups of non positive curvature and which do not hold for arbitrary
-characteristic zero linear groups. In particular if such a linear group is
-finitely generated then centralisers virtually split and all finitely generated
-abelian subgroups are undistorted. If further the group is virtually torsion
-free (which always holds in characteristic zero) then we have a strong property
-on small subgroups: any subgroup either contains a non abelian free group or is
-finitely generated and virtually abelian, hence also undistorted. We present
-applications, including that the mapping class group of a surface having genus
-at least 3 has no faithful linear representation which is complex unitary or
-over any field of positive characteristic.
-",0,0,1,0,0,0
-20788,Sparse Approximation of 3D Meshes using the Spectral Geometry of the Hamiltonian Operator,"  The discrete Laplace operator is ubiquitous in spectral shape analysis, since
-its eigenfunctions are provably optimal in representing smooth functions
-defined on the surface of the shape. Indeed, subspaces defined by its
-eigenfunctions have been utilized for shape compression, treating the
-coordinates as smooth functions defined on the given surface. However, surfaces
-of shapes in nature often contain geometric structures for which the general
-smoothness assumption may fail to hold. At the other end, some explicit mesh
-compression algorithms utilize the order by which vertices that represent the
-surface are traversed, a property which has been ignored in spectral
-approaches. Here, we incorporate the order of vertices into an operator that
-defines a novel spectral domain. We propose a method for representing 3D meshes
-using the spectral geometry of the Hamiltonian operator, integrated within a
-sparse approximation framework. We adapt the concept of a potential function
-from quantum physics and incorporate vertex ordering information into the
-potential, yielding a novel data-dependent operator. The potential function
-modifies the spectral geometry of the Laplacian to focus on regions with finer
-details of the given surface. By sparsely encoding the geometry of the shape
-using the proposed data-dependent basis, we improve compression performance
-compared to previous results that use the standard Laplacian basis and spectral
-graph wavelets.
-",1,0,0,0,0,0
-20789,Improved Convergence Rates for Distributed Resource Allocation,"  In this paper, we develop a class of decentralized algorithms for solving a
-convex resource allocation problem in a network of $n$ agents, where the agent
-objectives are decoupled while the resource constraints are coupled. The agents
-communicate over a connected undirected graph, and they want to collaboratively
-determine a solution to the overall network problem, while each agent only
-communicates with its neighbors. We first study the connection between the
-decentralized resource allocation problem and the decentralized consensus
-optimization problem. Then, using a class of algorithms for solving consensus
-optimization problems, we propose a novel class of decentralized schemes for
-solving resource allocation problems in a distributed manner. Specifically, we
-first propose an algorithm for solving the resource allocation problem with an
-$o(1/k)$ convergence rate guarantee when the agents' objective functions are
-generally convex (could be nondifferentiable) and per agent local convex
-constraints are allowed; We then propose a gradient-based algorithm for solving
-the resource allocation problem when per agent local constraints are absent and
-show that such scheme can achieve geometric rate when the objective functions
-are strongly convex and have Lipschitz continuous gradients. We have also
-provided scalability/network dependency analysis. Based on these two
-algorithms, we have further proposed a gradient projection-based algorithm
-which can handle smooth objective and simple constraints more efficiently.
-Numerical experiments demonstrates the viability and performance of all the
-proposed algorithms.
-",1,0,1,0,0,0
-20790,Sensory Metrics of Neuromechanical Trust,"  Today digital sources supply an unprecedented component of human sensorimotor
-data, the consumption of which is correlated with poorly understood maladies
-such as Internet Addiction Disorder and Internet Gaming Disorder. This paper
-offers a mathematical understanding of human sensorimotor processing as
-multiscale, continuous-time vibratory interaction. We quantify human
-informational needs using the signal processing metrics of entropy, noise,
-dimensionality, continuity, latency, and bandwidth. Using these metrics, we
-define the trust humans experience as a primitive statistical algorithm
-processing finely grained sensorimotor data from neuromechanical interaction.
-This definition of neuromechanical trust implies that artificial sensorimotor
-inputs and interactions that attract low-level attention through frequent
-discontinuities and enhanced coherence will decalibrate a brain's
-representation of its world over the long term by violating the implicit
-statistical contract for which self-calibration evolved. This approach allows
-us to model addiction in general as the result of homeostatic regulation gone
-awry in novel environments and digital dependency as a sub-case in which the
-decalibration caused by digital sensorimotor data spurs yet more consumption of
-them. We predict that institutions can use these sensorimotor metrics to
-quantify media richness to improve employee well-being; that dyads and
-family-size groups will bond and heal best through low-latency, high-resolution
-multisensory interaction such as shared meals and reciprocated touch; and that
-individuals can improve sensory and sociosensory resolution through deliberate
-sensory reintegration practices. We conclude that we humans are the victims of
-our own success, our hands so skilled they fill the world with captivating
-things, our eyes so innocent they follow eagerly.
-",0,1,0,0,0,0
-20791,Quantum algorithms for training Gaussian Processes,"  Gaussian processes (GPs) are important models in supervised machine learning.
-Training in Gaussian processes refers to selecting the covariance functions and
-the associated parameters in order to improve the outcome of predictions, the
-core of which amounts to evaluating the logarithm of the marginal likelihood
-(LML) of a given model. LML gives a concrete measure of the quality of
-prediction that a GP model is expected to achieve. The classical computation of
-LML typically carries a polynomial time overhead with respect to the input
-size. We propose a quantum algorithm that computes the logarithm of the
-determinant of a Hermitian matrix, which runs in logarithmic time for sparse
-matrices. This is applied in conjunction with a variant of the quantum linear
-system algorithm that allows for logarithmic time computation of the form
-$\mathbf{y}^TA^{-1}\mathbf{y}$, where $\mathbf{y}$ is a dense vector and $A$ is
-the covariance matrix. We hence show that quantum computing can be used to
-estimate the LML of a GP with exponentially improved efficiency under certain
-conditions.
-",0,0,0,1,0,0
-20792,Detecting Oriented Text in Natural Images by Linking Segments,"  Most state-of-the-art text detection methods are specific to horizontal Latin
-text and are not fast enough for real-time applications. We introduce Segment
-Linking (SegLink), an oriented text detection method. The main idea is to
-decompose text into two locally detectable elements, namely segments and links.
-A segment is an oriented box covering a part of a word or text line; A link
-connects two adjacent segments, indicating that they belong to the same word or
-text line. Both elements are detected densely at multiple scales by an
-end-to-end trained, fully-convolutional neural network. Final detections are
-produced by combining segments connected by links. Compared with previous
-methods, SegLink improves along the dimensions of accuracy, speed, and ease of
-training. It achieves an f-measure of 75.0% on the standard ICDAR 2015
-Incidental (Challenge 4) benchmark, outperforming the previous best by a large
-margin. It runs at over 20 FPS on 512x512 images. Moreover, without
-modification, SegLink is able to detect long lines of non-Latin text, such as
-Chinese.
-",1,0,0,0,0,0
-20793,Factorizable Module Algebras,"  The aim of this paper is to introduce and study a large class of
-$\mathfrak{g}$-module algebras which we call factorizable by generalizing the
-Gauss factorization of (square or rectangular) matrices. This class includes
-coordinate algebras of corresponding reductive groups $G$, their parabolic
-subgroups, basic affine spaces and many others. It turns out that tensor
-products of factorizable algebras are also factorizable and it is easy to
-create a factorizable algebra out of virtually any $\mathfrak{g}$-module
-algebra. We also have quantum versions of all these constructions in the
-category of $U_q(\mathfrak{g})$-module algebras. Quite surprisingly, our
-quantum factorizable algebras are naturally acted on by the quantized
-enveloping algebra $U_q(\mathfrak{g}^*)$ of the dual Lie bialgebra
-$\mathfrak{g}^*$ of $\mathfrak{g}$.
-",0,0,1,0,0,0
-20794,Simultaneous Feature and Body-Part Learning for Real-Time Robot Awareness of Human Behaviors,"  Robot awareness of human actions is an essential research problem in robotics
-with many important real-world applications, including human-robot
-collaboration and teaming. Over the past few years, depth sensors have become a
-standard device widely used by intelligent robots for 3D perception, which can
-also offer human skeletal data in 3D space. Several methods based on skeletal
-data were designed to enable robot awareness of human actions with satisfactory
-accuracy. However, previous methods treated all body parts and features equally
-important, without the capability to identify discriminative body parts and
-features. In this paper, we propose a novel simultaneous Feature And Body-part
-Learning (FABL) approach that simultaneously identifies discriminative body
-parts and features, and efficiently integrates all available information
-together to enable real-time robot awareness of human behaviors. We formulate
-FABL as a regression-like optimization problem with structured
-sparsity-inducing norms to model interrelationships of body parts and features.
-We also develop an optimization algorithm to solve the formulated problem,
-which possesses a theoretical guarantee to find the optimal solution. To
-evaluate FABL, three experiments were performed using public benchmark
-datasets, including the MSR Action3D and CAD-60 datasets, as well as a Baxter
-robot in practical assistive living applications. Experimental results show
-that our FABL approach obtains a high recognition accuracy with a processing
-speed of the order-of-magnitude of 10e4 Hz, which makes FABL a promising method
-to enable real-time robot awareness of human behaviors in practical robotics
-applications.
-",1,0,0,0,0,0
-20795,Off-diagonal estimates of some Bergman-type operators on tube domains over symmetric cones,"  We obtain some necessary and sufficient conditions for the boundedness of a
-family of positive operators defined on symmetric cones, we then deduce
-off-diagonal boundedness of associated Bergman-type operators in tube domains
-over symmetric cones.
-",0,0,1,0,0,0
-20796,Provenance and Pseudo-Provenance for Seeded Learning-Based Automated Test Generation,"  Many methods for automated software test generation, including some that
-explicitly use machine learning (and some that use ML more broadly conceived)
-derive new tests from existing tests (often referred to as seeds). Often, the
-seed tests from which new tests are derived are manually constructed, or at
-least simpler than the tests that are produced as the final outputs of such
-test generators. We propose annotation of generated tests with a provenance
-(trail) showing how individual generated tests of interest (especially failing
-tests) derive from seed tests, and how the population of generated tests
-relates to the original seed tests. In some cases, post-processing of generated
-tests can invalidate provenance information, in which case we also propose a
-method for attempting to construct ""pseudo-provenance"" describing how the tests
-could have been (partly) generated from seeds.
-",1,0,0,1,0,0
-20797,Learning Solving Procedure for Artificial Neural Network,"  It is expected that progress toward true artificial intelligence will be
-achieved through the emergence of a system that integrates representation
-learning and complex reasoning (LeCun et al. 2015). In response to this
-prediction, research has been conducted on implementing the symbolic reasoning
-of a von Neumann computer in an artificial neural network (Graves et al. 2016;
-Graves et al. 2014; Reed et al. 2015). However, these studies have many
-limitations in realizing neural-symbolic integration (Jaeger. 2016). Here, we
-present a new learning paradigm: a learning solving procedure (LSP) that learns
-the procedure for solving complex problems. This is not accomplished merely by
-learning input-output data, but by learning algorithms through a solving
-procedure that obtains the output as a sequence of tasks for a given input
-problem. The LSP neural network system not only learns simple problems of
-addition and multiplication, but also the algorithms of complicated problems,
-such as complex arithmetic expression, sorting, and Hanoi Tower. To realize
-this, the LSP neural network structure consists of a deep neural network and
-long short-term memory, which are recursively combined. Through
-experimentation, we demonstrate the efficiency and scalability of LSP and its
-validity as a mechanism of complex reasoning.
-",1,0,0,0,0,0
-20798,On primordial black holes from an inflection point,"  Recently, it has been claimed that inflationary models with an inflection
-point in the scalar potential can produce a large resonance in the power
-spectrum of curvature perturbation. In this paper however we show that the
-previous analyses are incorrect. The reason is twofold: firstly, the inflaton
-is over-shot from a stage of standard inflation and so deviates from the
-slow-roll attractor before reaching the inflection. Secondly, on the (or close
-to) the inflection point, the ultra-slow-roll trajectory supersede the
-slow-roll one and thus, the slow-roll approximations used in the literature
-cannot be used. We then reconsider the model and provide a recipe for how to
-produce nevertheless a large peak in the matter power spectrum via fine-tuning
-of parameters.
-",0,1,0,0,0,0
-20799,Dropout as a Low-Rank Regularizer for Matrix Factorization,"  Regularization for matrix factorization (MF) and approximation problems has
-been carried out in many different ways. Due to its popularity in deep
-learning, dropout has been applied also for this class of problems. Despite its
-solid empirical performance, the theoretical properties of dropout as a
-regularizer remain quite elusive for this class of problems. In this paper, we
-present a theoretical analysis of dropout for MF, where Bernoulli random
-variables are used to drop columns of the factors. We demonstrate the
-equivalence between dropout and a fully deterministic model for MF in which the
-factors are regularized by the sum of the product of squared Euclidean norms of
-the columns. Additionally, we inspect the case of a variable sized
-factorization and we prove that dropout achieves the global minimum of a convex
-approximation problem with (squared) nuclear norm regularization. As a result,
-we conclude that dropout can be used as a low-rank regularizer with data
-dependent singular-value thresholding.
-",1,0,0,1,0,0
-20800,Introduction to a Temporal Graph Benchmark,"  A temporal graph is a data structure, consisting of nodes and edges in which
-the edges are associated with time labels. To analyze the temporal graph, the
-first step is to find a proper graph dataset/benchmark. While many temporal
-graph datasets exist online, none could be found that used the interval labels
-in which each edge is associated with a starting and ending time. Therefore we
-create a temporal graph data based on Wikipedia reference graph for temporal
-analysis. This report aims to provide more details of this graph benchmark to
-those who are interested in using it.
-",1,1,0,0,0,0
-20801,Orbifold equivalence: structure and new examples,"  Orbifold equivalence is a notion of symmetry that does not rely on group
-actions. Among other applications, it leads to surprising connections between
-hitherto unrelated singularities. While the concept can be defined in a very
-general category-theoretic language, we focus on the most explicit setting in
-terms of matrix factorisations, where orbifold equivalences arise from defects
-with special properties. Examples are relatively difficult to construct, but we
-uncover some structural features that distinguish orbifold equivalences -- most
-notably a finite perturbation expansion. We use those properties to devise a
-search algorithm, then present some new examples including Arnold
-singularities.
-",0,0,1,0,0,0
-20802,Efficient Attention using a Fixed-Size Memory Representation,"  The standard content-based attention mechanism typically used in
-sequence-to-sequence models is computationally expensive as it requires the
-comparison of large encoder and decoder states at each time step. In this work,
-we propose an alternative attention mechanism based on a fixed size memory
-representation that is more efficient. Our technique predicts a compact set of
-K attention contexts during encoding and lets the decoder compute an efficient
-lookup that does not need to consult the memory. We show that our approach
-performs on-par with the standard attention mechanism while yielding inference
-speedups of 20% for real-world translation tasks and more for tasks with longer
-sequences. By visualizing attention scores we demonstrate that our models learn
-distinct, meaningful alignments.
-",1,0,0,0,0,0
-20803,Multiplicities of bifurcation sets of Pham singularities,"  The local multiplicities of the Maxwell sets in the spaces of versal
-deformations of Pham holomorphic function singularities are calculated. A
-similar calculation for some other bifurcation sets (generalized Stokes' sets)
-defined by more complicated relations between the critical values is given.
-Aplications to the complexity of algorithms enumerating topologically distinct
-morsifications of complicated real function singularities are discussed.
-",0,0,1,0,0,0
-20804,Fast and scalable Gaussian process modeling with applications to astronomical time series,"  The growing field of large-scale time domain astronomy requires methods for
-probabilistic data analysis that are computationally tractable, even with large
-datasets. Gaussian Processes are a popular class of models used for this
-purpose but, since the computational cost scales, in general, as the cube of
-the number of data points, their application has been limited to small
-datasets. In this paper, we present a novel method for Gaussian Process
-modeling in one-dimension where the computational requirements scale linearly
-with the size of the dataset. We demonstrate the method by applying it to
-simulated and real astronomical time series datasets. These demonstrations are
-examples of probabilistic inference of stellar rotation periods, asteroseismic
-oscillation spectra, and transiting planet parameters. The method exploits
-structure in the problem when the covariance function is expressed as a mixture
-of complex exponentials, without requiring evenly spaced observations or
-uniform noise. This form of covariance arises naturally when the process is a
-mixture of stochastically-driven damped harmonic oscillators -- providing a
-physical motivation for and interpretation of this choice -- but we also
-demonstrate that it can be a useful effective model in some other cases. We
-present a mathematical description of the method and compare it to existing
-scalable Gaussian Process methods. The method is fast and interpretable, with a
-range of potential applications within astronomical data analysis and beyond.
-We provide well-tested and documented open-source implementations of this
-method in C++, Python, and Julia.
-",0,1,0,1,0,0
-20805,Toward perfect reads: self-correction of short reads via mapping on de Bruijn graphs,"  Motivations Short-read accuracy is important for downstream analyses such as
-genome assembly and hybrid long-read correction. Despite much work on
-short-read correction, present-day correctors either do not scale well on large
-data sets or consider reads as mere suites of k-mers, without taking into
-account their full-length read information. Results We propose a new method to
-correct short reads using de Bruijn graphs, and implement it as a tool called
-Bcool. As a first st ep, Bcool constructs a compacted de Bruijn graph from the
-reads. This graph is filtered on the basis of k-mer abundance then of unitig
-abundance, thereby removing from most sequencing errors. The cleaned graph is
-then used as a reference on which the reads are mapped to correct them. We show
-that this approach yields more accurate reads than k-mer-spectrum correctors
-while being scalable to human-size genomic datasets and beyond. Availability
-and Implementation The implementation is open source and available at http:
-//github.com/Malfoy/BCOOL under the Affero GPL license. Contact Antoine
-Limasset antoine.limasset@gmail.com & Jean-François Flot jflot@ulb.ac.be &
-Pierre Peterlongo pierre.peterlongo@inria.fr
-",1,0,0,0,0,0
-20806,Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning,"  Patient pain can be detected highly reliably from facial expressions using a
-set of facial muscle-based action units (AUs) defined by the Facial Action
-Coding System (FACS). A key characteristic of facial expression of pain is the
-simultaneous occurrence of pain-related AU combinations, whose automated
-detection would be highly beneficial for efficient and practical pain
-monitoring. Existing general Automated Facial Expression Recognition (AFER)
-systems prove inadequate when applied specifically for detecting pain as they
-either focus on detecting individual pain-related AUs but not on combinations
-or they seek to bypass AU detection by training a binary pain classifier
-directly on pain intensity data but are limited by lack of enough labeled data
-for satisfactory training. In this paper, we propose a new approach that mimics
-the strategy of human coders of decoupling pain detection into two consecutive
-tasks: one performed at the individual video-frame level and the other at
-video-sequence level. Using state-of-the-art AFER tools to detect single AUs at
-the frame level, we propose two novel data structures to encode AU combinations
-from single AU scores. Two weakly supervised learning frameworks namely
-multiple instance learning (MIL) and multiple clustered instance learning
-(MCIL) are employed corresponding to each data structure to learn pain from
-video sequences. Experimental results show an 87% pain recognition accuracy
-with 0.94 AUC (Area Under Curve) on the UNBC-McMaster Shoulder Pain Expression
-dataset. Tests on long videos in a lung cancer patient video dataset
-demonstrates the potential value of the proposed system for pain monitoring in
-clinical settings.
-",1,0,0,1,0,0
-20807,A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects,"  Tracking humans that are interacting with the other subjects or environment
-remains unsolved in visual tracking, because the visibility of the human of
-interests in videos is unknown and might vary over time. In particular, it is
-still difficult for state-of-the-art human trackers to recover complete human
-trajectories in crowded scenes with frequent human interactions. In this work,
-we consider the visibility status of a subject as a fluent variable, whose
-change is mostly attributed to the subject's interaction with the surrounding,
-e.g., crossing behind another object, entering a building, or getting into a
-vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the
-causal-effect relations between an object's visibility fluent and its
-activities, and develop a probabilistic graph model to jointly reason the
-visibility fluent change (e.g., from visible to invisible) and track humans in
-videos. We formulate this joint task as an iterative search of a feasible
-causal graph structure that enables fast search algorithm, e.g., dynamic
-programming method. We apply the proposed method on challenging video sequences
-to evaluate its capabilities of estimating visibility fluent changes of
-subjects and tracking subjects of interests over time. Results with comparisons
-demonstrate that our method outperforms the alternative trackers and can
-recover complete trajectories of humans in complicated scenarios with frequent
-human interactions.
-",1,0,0,0,0,0
-20808,Exact Affine Counter Automata,"  We introduce an affine generalization of counter automata, and analyze their
-ability as well as affine finite automata. Our contributions are as follows. We
-show that there is a language that can be recognized by exact realtime affine
-counter automata but by neither 1-way deterministic pushdown automata nor
-realtime deterministic k-counter automata. We also show that a certain promise
-problem, which is conjectured not to be solved by two-way quantum finite
-automata in polynomial time, can be solved by Las Vegas affine finite automata.
-Lastly, we show that how a counter helps for affine finite automata by showing
-that the language MANYTWINS, which is conjectured not to be recognized by
-affine, quantum or classical finite state models in polynomial time, can be
-recognized by affine counter automata with one-sided bounded-error in realtime.
-",1,0,0,0,0,0
-20809,Preorder characterizations of lower separation axioms and their applications to foliations and flows,"  In this paper, we characterize several lower separation axioms $C_0, C_D$,
-$C_R$, $C_N$, $\lambda$-space, nested, $S_{YS}$, $S_{YY}$, $S_{YS}$, and
-$S_{\delta}$ using pre-order. To analyze topological properties of (resp.
-dynamical systems) foliations, we introduce notions of topology (resp.
-dynamical systems) for foliations. Then proper (resp. compact, minimal,
-recurrent) foliations are characterized by separation axioms. Conversely, lower
-separation axioms are interpreted into the condition for foliations and several
-relations of them are described. Moreover, we introduce some notions for
-topologies from dynamical systems and foliation theory.
-",0,0,1,0,0,0
-20810,Convergence of row sequences of simultaneous Padé-Faber approximants,"  We consider row sequences of vector valued Padé-Faber approximants
-(simultaneous Padé-Faber approximants) and prove a Montessus de Ballore
-type theorem.
-",0,0,1,0,0,0
-20811,A Deep Convolutional Neural Network for Background Subtraction,"  In this work, we present a novel background subtraction system that uses a
-deep Convolutional Neural Network (CNN) to perform the segmentation. With this
-approach, feature engineering and parameter tuning become unnecessary since the
-network parameters can be learned from data by training a single CNN that can
-handle various video scenes. Additionally, we propose a new approach to
-estimate background model from video. For the training of the CNN, we employed
-randomly 5 percent video frames and their ground truth segmentations taken from
-the Change Detection challenge 2014(CDnet 2014). We also utilized
-spatial-median filtering as the post-processing of the network outputs. Our
-method is evaluated with different data-sets, and the network outperforms the
-existing algorithms with respect to the average ranking over different
-evaluation metrics. Furthermore, due to the network architecture, our CNN is
-capable of real time processing.
-",1,0,0,0,0,0
-20812,A second main theorem for holomorphic curve intersecting hypersurfaces,"  In this paper, we establish a second main theorem for holomorphic curve
-intersecting hypersurfaces in general position in projective space with level
-of truncation. As an application, we reduce the number hypersurfaces in
-uniqueness problem for holomorphic curve of authors before.
-",0,0,1,0,0,0
-20813,Partially hyperbolic diffeomorphisms with one-dimensional neutral center on 3-manifolds,"  We prove that for any partially hyperbolic diffeomorphism with one
-dimensional neutral center on a 3-manifold, the center stable and center
-unstable foliations are complete; moreover, each leaf of center stable and
-center unstable foliations is a cylinder, a M$\ddot{o}$bius band or a plane.
-Further properties of the Bonatti-Parwani-Potrie type of partially hyperbolic
-diffeomorphisms are studied. Such examples are obtained by composing the time
-$m$-map (for $m>0$ large) of a non-transitive Anosov flow $\phi_t$ on an
-orientable 3-manifold with Dehn twists along some transverse tori, and the
-examples are partially hyperbolic with one-dimensional neutral center. We prove
-that the center foliation gives a topologically Anosov flow which is
-topologically equivalent to $\phi_t$. We also prove that for the precise
-example constructed by Bonatti-Parwani-Potrie, the center stable and center
-unstable foliations are robustly complete.
-",0,0,1,0,0,0
-20814,Audio-Visual Speech Enhancement based on Multimodal Deep Convolutional Neural Network,"  Speech enhancement (SE) aims to reduce noise in speech signals. Most SE
-techniques focus on addressing audio information only. In this work, inspired
-by multimodal learning, which utilizes data from different modalities, and the
-recent success of convolutional neural networks (CNNs) in SE, we propose an
-audio-visual deep CNN (AVDCNN) SE model, which incorporates audio and visual
-streams into a unified network model. In the proposed AVDCNN SE model, audio
-and visual data are first processed using individual CNNs, and then, fused into
-a joint network to generate enhanced speech at the output layer. The AVDCNN
-model is trained in an end-to-end manner, and parameters are jointly learned
-through back-propagation. We evaluate enhanced speech using five objective
-criteria. Results show that the AVDCNN yields notably better performance,
-compared with an audio-only CNN-based SE model and two conventional SE
-approaches, confirming the effectiveness of integrating visual information into
-the SE process.
-",1,0,0,1,0,0
-20815,"Finding structure in the dark: coupled dark energy, weak lensing, and the mildly nonlinear regime","  We reexamine interactions between the dark sectors of cosmology, with a focus
-on robust constraints that can be obtained using only mildly nonlinear scales.
-While it is well known that couplings between dark matter and dark energy can
-be constrained to the percent level when including the full range of scales
-probed by future optical surveys, calibrating matter power spectrum emulators
-to all possible choices of potentials and couplings requires many
-computationally expensive n-body simulations. Here we show that lensing and
-clustering of galaxies in combination with the Cosmic Microwave Background
-(CMB) is capable of probing the dark sector coupling to the few percent level
-for a given class of models, using only linear and quasi-linear Fourier modes.
-These scales can, in principle, be described by semi-analytical techniques such
-as the effective field theory of large-scale structure.
-",0,1,0,0,0,0
-20816,Semantic Autoencoder for Zero-Shot Learning,"  Existing zero-shot learning (ZSL) models typically learn a projection
-function from a feature space to a semantic embedding space (e.g.~attribute
-space). However, such a projection function is only concerned with predicting
-the training seen class semantic representation (e.g.~attribute prediction) or
-classification. When applied to test data, which in the context of ZSL contains
-different (unseen) classes without training data, a ZSL model typically suffers
-from the project domain shift problem. In this work, we present a novel
-solution to ZSL based on learning a Semantic AutoEncoder (SAE). Taking the
-encoder-decoder paradigm, an encoder aims to project a visual feature vector
-into the semantic space as in the existing ZSL models. However, the decoder
-exerts an additional constraint, that is, the projection/code must be able to
-reconstruct the original visual feature. We show that with this additional
-reconstruction constraint, the learned projection function from the seen
-classes is able to generalise better to the new unseen classes. Importantly,
-the encoder and decoder are linear and symmetric which enable us to develop an
-extremely efficient learning algorithm. Extensive experiments on six benchmark
-datasets demonstrate that the proposed SAE outperforms significantly the
-existing ZSL models with the additional benefit of lower computational cost.
-Furthermore, when the SAE is applied to supervised clustering problem, it also
-beats the state-of-the-art.
-",1,0,0,0,0,0
-20817,An Information-Theoretic Optimality Principle for Deep Reinforcement Learning,"  We methodologically address the problem of Q-value overestimation in deep
-reinforcement learning to handle high-dimensional state spaces efficiently. By
-adapting concepts from information theory, we introduce an intrinsic penalty
-signal encouraging reduced Q-value estimates. The resultant algorithm
-encompasses a wide range of learning outcomes containing deep Q-networks as a
-special case. Different learning outcomes can be demonstrated by tuning a
-Lagrange multiplier accordingly. We furthermore propose a novel scheduling
-scheme for this Lagrange multiplier to ensure efficient and robust learning. In
-experiments on Atari, our algorithm outperforms other algorithms (e.g. deep and
-double deep Q-networks) in terms of both game-play performance and sample
-complexity. These results remain valid under the recently proposed dueling
-architecture.
-",1,0,0,1,0,0
-20818,Noise-synchronizability of opinion dynamics,"  With the analysis of noise-induced synchronization of opinion dynamics with
-bounded confidence (BC), a natural and fundamental question is what opinion
-structures can be synchronized by noise. In the traditional Hegselmann-Krause
-(HK) model, each agent examines the opinion values of all the other ones and
-then choose neighbors to update its own opinion according to the BC scheme. In
-reality, people are more likely to interchange opinions with only some
-individuals, resulting in a predetermined local discourse relationship as
-introduced by the DeGroot model. In this paper, we consider an opinion dynamics
-that combines the schemes of BC and local discourse topology and investigate
-its synchronization induced by noise. The new model endows the heterogeneous HK
-model with a time-varying discourse topology. With the proposed definition of
-noise-synchronizability, it is shown that the compound noisy model is almost
-surely noise-synchronizable if and only if the time-varying discourse graph is
-uniformly jointly connected, taking the noise-induced synchronization of the
-classical heterogeneous HK model as a special case. As a natural implication,
-the result for the first time builds the equivalence between the connectivity
-of discourse graph and the beneficial effect of noise for opinion consensus.
-",1,0,0,0,0,0
-20819,Conformer-selection by matter-wave interference,"  We establish that matter-wave interference at near-resonant ultraviolet
-optical gratings can be used to spatially separate individual conformers of
-complex molecules. Our calculations show that the conformational purity of the
-prepared beam can be close to 100% and that all molecules remain in their
-electronic ground state. The proposed technique is independent of the dipole
-moment and the spin of the molecule and thus paves the way for
-structure-sensitive experiments with hydrocarbons and biomolecules, such as
-neurotransmitters and hormones, which evaded conformer-pure isolation so far
-",0,1,0,0,0,0
-20820,Building a Neural Machine Translation System Using Only Synthetic Parallel Data,"  Recent works have shown that synthetic parallel data automatically generated
-by translation models can be effective for various neural machine translation
-(NMT) issues. In this study, we build NMT systems using only synthetic parallel
-data. As an efficient alternative to real parallel data, we also present a new
-type of synthetic parallel corpus. The proposed pseudo parallel data are
-distinct from previous works in that ground truth and synthetic examples are
-mixed on both sides of sentence pairs. Experiments on Czech-German and
-French-German translations demonstrate the efficacy of the proposed pseudo
-parallel corpus, which shows not only enhanced results for bidirectional
-translation tasks but also substantial improvement with the aid of a ground
-truth real parallel corpus.
-",1,0,0,0,0,0
-20821,Learning to Predict Indoor Illumination from a Single Image,"  We propose an automatic method to infer high dynamic range illumination from
-a single, limited field-of-view, low dynamic range photograph of an indoor
-scene. In contrast to previous work that relies on specialized image capture,
-user input, and/or simple scene models, we train an end-to-end deep neural
-network that directly regresses a limited field-of-view photo to HDR
-illumination, without strong assumptions on scene geometry, material
-properties, or lighting. We show that this can be accomplished in a three step
-process: 1) we train a robust lighting classifier to automatically annotate the
-location of light sources in a large dataset of LDR environment maps, 2) we use
-these annotations to train a deep neural network that predicts the location of
-lights in a scene from a single limited field-of-view photo, and 3) we
-fine-tune this network using a small dataset of HDR environment maps to predict
-light intensities. This allows us to automatically recover high-quality HDR
-illumination estimates that significantly outperform previous state-of-the-art
-methods. Consequently, using our illumination estimates for applications like
-3D object insertion, we can achieve results that are photo-realistic, which is
-validated via a perceptual user study.
-",1,0,0,1,0,0
-20822,$H^\infty$-calculus for semigroup generators on BMO,"  We prove that the negative infinitesimal generator $L$ of a semigroup of
-positive contractions on $L^\infty$ has a bounded $H^\infty(S_\eta^0)$-calculus
-on BMO$(\sqrt L)$ for any angle $\eta>\pi/2$, provided the semigroup satisfies
-Bakry-Emry's $\Gamma_2 $ criterion. Our arguments only rely on the properties
-of the underlying semigroup and works well in the noncommutative setting. A key
-ingredient of our argument is a quasi monotone property for the subordinated
-semigroup $T_{t,\alpha}=e^{-tL^\alpha},0<\alpha<1$, that is proved in the first
-half of the article.
-",0,0,1,0,0,0
-20823,A Bayesian Game without epsilon equilibria,"  We present a three player Bayesian game for which there is no epsilon
-equilibria in Borel measurable strategies for small enough epsilon, however
-there are non-measurable equilibria.
-",1,0,1,0,0,0
-20824,Towards Robust Interpretability with Self-Explaining Neural Networks,"  Most recent work on interpretability of complex machine learning models has
-focused on estimating $\textit{a posteriori}$ explanations for previously
-trained models around specific predictions. $\textit{Self-explaining}$ models
-where interpretability plays a key role already during learning have received
-much less attention. We propose three desiderata for explanations in general --
-explicitness, faithfulness, and stability -- and show that existing methods do
-not satisfy them. In response, we design self-explaining models in stages,
-progressively generalizing linear classifiers to complex yet architecturally
-explicit models. Faithfulness and stability are enforced via regularization
-specifically tailored to such models. Experimental results across various
-benchmark datasets show that our framework offers a promising direction for
-reconciling model complexity and interpretability.
-",0,0,0,1,0,0
-20825,"Deep Learning Scaling is Predictable, Empirically","  Deep learning (DL) creates impactful advances following a virtuous recipe:
-model architecture search, creating large training data sets, and scaling
-computation. It is widely believed that growing training sets and models should
-improve accuracy and result in better products. As DL application domains grow,
-we would like a deeper understanding of the relationships between training set
-size, computational scale, and model accuracy improvements to advance the
-state-of-the-art.
-This paper presents a large scale empirical characterization of
-generalization error and model size growth as training sets grow. We introduce
-a methodology for this measurement and test four machine learning domains:
-machine translation, language modeling, image processing, and speech
-recognition. Our empirical results show power-law generalization error scaling
-across a breadth of factors, resulting in power-law exponents---the ""steepness""
-of the learning curve---yet to be explained by theoretical work. Further, model
-improvements only shift the error but do not appear to affect the power-law
-exponent. We also show that model size scales sublinearly with data size. These
-scaling relationships have significant implications on deep learning research,
-practice, and systems. They can assist model debugging, setting accuracy
-targets, and decisions about data set growth. They can also guide computing
-system design and underscore the importance of continued computational scaling.
-",1,0,0,1,0,0
-20826,Coupled Self-Organized Hydrodynamics and Stokes models for suspensions of active particles,"  We derive macroscopic dynamics for self-propelled particles in a fluid. The
-starting point is a coupled Vicsek-Stokes system. The Vicsek model describes
-self-propelled agents interacting through alignment. It provides a
-phenomenological description of hydrodynamic interactions between agents at
-high density. Stokes equations describe a low Reynolds number fluid. These two
-dynamics are coupled by the interaction between the agents and the fluid. The
-fluid contributes to rotating the particles through Jeffery's equation.
-Particle self-propulsion induces a force dipole on the fluid. After
-coarse-graining we obtain a coupled Self-Organised Hydrodynamics (SOH)-Stokes
-system. We perform a linear stability analysis for this system which shows that
-both pullers and pushers have unstable modes. We conclude by providing
-extensions of the Vicsek-Stokes model including short-distance repulsion,
-finite particle inertia and finite Reynolds number fluid regime.
-",0,1,1,0,0,0
-20827,Tunable Spin-Orbit Torques in Cu-Ta Binary Alloy Heterostructures,"  The spin Hall effect (SHE) is found to be strong in heavy transition metals
-(HM), such as Ta and W, in their amorphous and/or high resistivity form. In
-this work, we show that by employing a Cu-Ta binary alloy as buffer layer in an
-amorphous Cu$_{100-x}$Ta$_{x}$-based magnetic heterostructure with
-perpendicular magnetic anisotropy (PMA), the SHE-induced damping-like
-spin-orbit torque (DL-SOT) efficiency $|\xi_{DL}|$ can be linearly tuned by
-adjusting the buffer layer resistivity. Current-induced SOT switching can also
-be achieved in these Cu$_{100-x}$Ta$_{x}$-based magnetic heterostructures, and
-we find the switching behavior better explained by a SOT-assisted domain wall
-propagation picture. Through systematic studies on Cu$_{100-x}$Ta$_{x}$-based
-samples with various compositions, we determine the lower bound of spin Hall
-conductivity
-$|\sigma_{SH}|\approx2.02\times10^{4}[\hbar/2e]\Omega^{-1}\cdot\operatorname{m}^{-1}$
-in the Ta-rich regime. Based on the idea of resistivity tuning, we further
-demonstrate that $|\xi_{DL}|$ can be enhanced from 0.087 for pure Ta to 0.152
-by employing a resistive TaN buffer layer.
-",0,1,0,0,0,0
-20828,On Properties of Nests: Some Answers and Questions,"  By considering nests on a given space, we explore order-theoretical and
-topological properties that are closely related to the structure of a nest. In
-particular, we see how subbases given by two dual nests can be an indicator of
-how close or far are the properties of the space from the structure of a
-linearly ordered space. Having in mind that the term interlocking nest is a key
-tool to a general solution of the orderability problem, we give a
-characterization of interlocking nest via closed sets in the Alexandroff
-topology and via lower sets, respectively. We also characterize bounded subsets
-of a given set in terms of nests and, finally, we explore the possibility of
-characterizing topological groups via properties of nests. All sections are
-followed by a number of open questions, which may give new directions to the
-orderability problem.
-",0,0,1,0,0,0
-20829,Differential Characters of Drinfeld Modules and de Rham Cohomology,"  We introduce differential characters of Drinfeld modules. These are
-function-field analogues of Buium's p-adic differential characters of elliptic
-curves and of Manin's differential characters of elliptic curves in
-differential algebra, both of which have had notable Diophantine applications.
-We determine the structure of the group of differential characters. This shows
-the existence of a family of interesting differential modular functions on the
-moduli of Drinfeld modules. It also leads to a canonical $F$-crystal equipped
-with a map to the de Rham cohomology of the Drinfeld module. This $F$-crystal
-is of a differential-algebraic nature, and the relation to the classical
-cohomological realizations is presently not clear.
-",0,0,1,0,0,0
-20830,Strong submeasures and several applications,"  A strong submeasure on a compact metric space X is a sub-linear and bounded
-operator on the space of continuous functions on X. A strong submeasure is
-positive if it is non-decreasing. By Hahn-Banach theorem, a positive strong
-submeasure is the supremum of a non-empty collection of measures whose masses
-are uniformly bounded from above.
-We give several applications of strong submeasures in various diverse topics,
-thus illustrate the usefulness of this classical but largely overlooked notion.
-The applications include:
-- Pullback and pushforward of all measures by meromorphic selfmaps of compact
-complex varieties.
-- The existence of invariant positive strong submeasures for meromorphic maps
-between compact complex varieties, a notion of entropy for such submeasures
-(which coincide with the classical ones in good cases) and a version of the
-Variation Principle.
-- Intersection of every positive closed (1,1) currents on compact Kähler
-manifolds. Explicit calculations are given for self-intersection of the current
-of integration of some curves $C$ in a compact Kähler surface where the
-self-intersection in cohomology is negative.
-All of these points are new and have not been previously given in work by
-other authors. In addition, we will apply the same ideas to entropy of
-transcendental maps of $\mathbb{C}$ and $\mathbb{C}^2$.
-",0,0,1,0,0,0
-20831,Understanding Career Progression in Baseball Through Machine Learning,"  Professional baseball players are increasingly guaranteed expensive long-term
-contracts, with over 70 deals signed in excess of \$90 million, mostly in the
-last decade. These are substantial sums compared to a typical franchise
-valuation of \$1-2 billion. Hence, the players to whom a team chooses to give
-such a contract can have an enormous impact on both competitiveness and profit.
-Despite this, most published approaches examining career progression in
-baseball are fairly simplistic. We applied four machine learning algorithms to
-the problem and soundly improved upon existing approaches, particularly for
-batting data.
-",1,0,0,1,0,0
-20832,Testing the validity of the local and global GKLS master equations on an exactly solvable model,"  When deriving a master equation for a multipartite weakly-interacting open
-quantum systems, dissipation is often addressed \textit{locally} on each
-component, i.e. ignoring the coherent couplings, which are later added `by
-hand'. Although simple, the resulting local master equation (LME) is known to
-be thermodynamically inconsistent. Otherwise, one may always obtain a
-consistent \textit{global} master equation (GME) by working on the energy basis
-of the full interacting Hamiltonian. Here, we consider a two-node `quantum
-wire' connected to two heat baths. The stationary solution of the LME and GME
-are obtained and benchmarked against the exact result. Importantly, in our
-model, the validity of the GME is constrained by the underlying secular
-approximation. Whenever this breaks down (for resonant weakly-coupled nodes),
-we observe that the LME, in spite of being thermodynamically flawed: (a)
-predicts the correct steady state, (b) yields the exact asymptotic heat
-currents, and (c) reliably reflects the correlations between the nodes. In
-contrast, the GME fails at all three tasks. Nonetheless, as the inter-node
-coupling grows, the LME breaks down whilst the GME becomes correct. Hence, the
-global and local approach may be viewed as \textit{complementary} tools, best
-suited to different parameter regimes.
-",0,1,0,0,0,0
-20833,Unsupervised Contact Learning for Humanoid Estimation and Control,"  This work presents a method for contact state estimation using fuzzy
-clustering to learn contact probability for full, six-dimensional humanoid
-contacts. The data required for training is solely from proprioceptive sensors
-- endeffector contact wrench sensors and inertial measurement units (IMUs) -
-and the method is completely unsupervised. The resulting cluster means are used
-to efficiently compute the probability of contact in each of the six
-endeffector degrees of freedom (DoFs) independently. This clustering-based
-contact probability estimator is validated in a kinematics-based base state
-estimator in a simulation environment with realistic added sensor noise for
-locomotion over rough, low-friction terrain on which the robot is subject to
-foot slip and rotation. The proposed base state estimator which utilizes these
-six DoF contact probability estimates is shown to perform considerably better
-than that which determines kinematic contact constraints purely based on
-measured normal force.
-",1,0,0,0,0,0
-20834,Learning to Sequence Robot Behaviors for Visual Navigation,"  Recent literature in the robotics community has focused on learning robot
-behaviors that abstract out lower-level details of robot control. To fully
-leverage the efficacy of such behaviors, it is necessary to select and sequence
-them to achieve a given task. In this paper, we present an approach to both
-learn and sequence robot behaviors, applied to the problem of visual navigation
-of mobile robots. We construct a layered representation of control policies
-composed of low- level behaviors and a meta-level policy. The low-level
-behaviors enable the robot to locomote in a particular environment while
-avoiding obstacles, and the meta-level policy actively selects the low-level
-behavior most appropriate for the current situation based purely on visual
-feedback. We demonstrate the effectiveness of our method on three simulated
-robot navigation tasks: a legged hexapod robot which must successfully traverse
-varying terrain, a wheeled robot which must navigate a maze-like course while
-avoiding obstacles, and finally a wheeled robot navigating in the presence of
-dynamic obstacles. We show that by learning control policies in a layered
-manner, we gain the ability to successfully traverse new compound environments
-composed of distinct sub-environments, and outperform both the low-level
-behaviors in their respective sub-environments, as well as a hand-crafted
-selection of low-level policies on these compound environments.
-",1,0,0,0,0,0
-20835,"Evaluation complexity bounds for smooth constrained nonlinear optimisation using scaled KKT conditions, high-order models and the criticality measure $χ$","  Evaluation complexity for convexly constrained optimization is considered and
-it is shown first that the complexity bound of $O(\epsilon^{-3/2})$ proved by
-Cartis, Gould and Toint (IMAJNA 32(4) 2012, pp.1662-1695) for computing an
-$\epsilon$-approximate first-order critical point can be obtained under
-significantly weaker assumptions. Moreover, the result is generalized to the
-case where high-order derivatives are used, resulting in a bound of
-$O(\epsilon^{-(p+1)/p})$ evaluations whenever derivatives of order $p$ are
-available. It is also shown that the bound of
-$O(\epsilon_P^{-1/2}\epsilon_D^{-3/2})$ evaluations ($\epsilon_P$ and
-$\epsilon_D$ being primal and dual accuracy thresholds) suggested by Cartis,
-Gould and Toint (SINUM, 2015) for the general nonconvex case involving both
-equality and inequality constraints can be generalized to a bound of
-$O(\epsilon_P^{-1/p}\epsilon_D^{-(p+1)/p})$ evaluations under similarly
-weakened assumptions. This paper is variant of a companion report (NTR-11-2015,
-University of Namur, Belgium) which uses a different first-order criticality
-measure to obtain the same complexity bounds.
-",1,0,1,0,0,0
-20836,One level density of low-lying zeros of quadratic and quartic Hecke $L$-functions,"  In this paper, we prove some one level density results for the low-lying
-zeros of famliies of quadratic and quartic Hecke $L$-functions of the Gaussian
-field. As corollaries, we deduce that, respectively, at least $94.27 \%$ and
-$5\%$ of the members of the quadratic family and the quartic family do not
-vanish at the central point.
-",0,0,1,0,0,0
-20837,An Online Convex Optimization Approach to Dynamic Network Resource Allocation,"  Existing approaches to online convex optimization (OCO) make sequential
-one-slot-ahead decisions, which lead to (possibly adversarial) losses that
-drive subsequent decision iterates. Their performance is evaluated by the
-so-called regret that measures the difference of losses between the online
-solution and the best yet fixed overall solution in hindsight. The present
-paper deals with online convex optimization involving adversarial loss
-functions and adversarial constraints, where the constraints are revealed after
-making decisions, and can be tolerable to instantaneous violations but must be
-satisfied in the long term. Performance of an online algorithm in this setting
-is assessed by: i) the difference of its losses relative to the best dynamic
-solution with one-slot-ahead information of the loss function and the
-constraint (that is here termed dynamic regret); and, ii) the accumulated
-amount of constraint violations (that is here termed dynamic fit). In this
-context, a modified online saddle-point (MOSP) scheme is developed, and proved
-to simultaneously yield sub-linear dynamic regret and fit, provided that the
-accumulated variations of per-slot minimizers and constraints are sub-linearly
-growing with time. MOSP is also applied to the dynamic network resource
-allocation task, and it is compared with the well-known stochastic dual
-gradient method. Under various scenarios, numerical experiments demonstrate the
-performance gain of MOSP relative to the state-of-the-art.
-",1,0,1,1,0,0
-20838,Learning causal Bayes networks using interventional path queries in polynomial time and sample complexity,"  Causal discovery from empirical data is a fundamental problem in many
-scientific domains. Observational data allows for identifiability only up to
-Markov equivalence class. In this paper we first propose a polynomial time
-algorithm for learning the exact correctly-oriented structure of the transitive
-reduction of any causal Bayesian networks with high probability, by using
-interventional path queries. Each path query takes as input an origin node and
-a target node, and answers whether there is a directed path from the origin to
-the target. This is done by intervening the origin node and observing samples
-from the target node. We theoretically show the logarithmic sample complexity
-for the size of interventional data per path query, for continuous and discrete
-networks. We further extend our work to learn the transitive edges using
-logarithmic sample complexity (albeit in time exponential in the maximum number
-of parents for discrete networks). This allows us to learn the full network. We
-also provide an analysis of imperfect interventions.
-",1,0,0,1,0,0
-20839,The Fredholm alternative for the $p$-Laplacian in exterior domains,"  We investigate the Fredholm alternative for the $p$-Laplacian in an exterior
-domain which is the complement of the closed unit ball in $\mathbb{R}^N$
-($N\geq 2$). By employing techniques of Calculus of Variations we obtain the
-multiplicity of solutions. The striking difference between our case and the
-entire space case is also discussed.
-",0,0,1,0,0,0
-20840,Thermodynamic Stabilization of Precipitates through Interface Segregation: Chemical Effects,"  Precipitation hardening, which relies on a high density of intermetallic
-precipitates, is a commonly utilized technique for strengthening structural
-alloys. Structural alloys are commonly strengthened through a high density of
-small size intermetallic precipitates. At high temperatures, however, the
-precipitates coarsen to reduce the excess energy of the interface, resulting in
-a significant reduction in the strengthening provided by the precipitates. In
-certain ternary alloys, the secondary solute segregates to the interface and
-results in the formation of a high density of nanosize precipitates that
-provide enhanced strength and are resistant to coarsening. To understand the
-chemical effects involved, and to identify such systems, we develop a
-thermodynamic model using the framework of the regular nanocrystalline solution
-model. For various global compositions, temperatures and thermodynamic
-parameters, equilibrium configuration of Mg-Sn-Zn alloy is evaluated by
-minimizing the Gibbs free energy function with respect to the region-specific
-(bulk solid-solution, interface and precipitate) concentrations and sizes. The
-results show that Mg$_2$Sn precipitates can be stabilized to nanoscale sizes
-through Zn segregation to Mg/Mg$_2$Sn interface, and the precipitates can be
-stabilized against coarsening at high-temperatures by providing a larger Zn
-concentration in the system. Together with the inclusion of elastic strain
-energy effects and the input of computationally informed interface
-thermodynamic parameters in the future, the model is expected to provide a more
-realistic prediction of segregation and precipitate stabilization in ternary
-alloys of structural importance.
-",0,1,0,0,0,0
-20841,Wavelength Does Not Equal Pressure: Vertical Contribution Functions and their Implications for Mapping Hot Jupiters,"  Multi-band phase variations in principle allow us to infer the longitudinal
-temperature distributions of planets as a function of height in their
-atmospheres. For example, 3.6 micron emission originates from deeper layers of
-the atmosphere than 4.5 micron due to greater water vapor absorption at the
-longer wavelength. Since heat transport efficiency increases with pressure, we
-expect thermal phase curves at 3.6 micron to exhibit smaller amplitudes and
-greater phase offsets than at 4.5 micron; this trend is not observed. Of the
-seven hot Jupiters with full-orbit phase curves at 3.6 and 4.5 micron, all have
-greater phase amplitude at 3.6 micron than at 4.5 micron, while four of seven
-exhibit a greater phase offset at 3.6 micron. We use a 3D
-radiative-hydrodynamic model to calculate theoretical phase curves of HD
-189733b, assuming thermo-chemical equilibrium. The model exhibits temperature,
-pressure, and wavelength dependent opacity, primarily driven by carbon
-chemistry: CO is energetically favored on the dayside, while CH4 is favored on
-the cooler nightside. Infrared opacity therefore changes by orders of magnitude
-between day and night, producing dramatic vertical shifts in the
-wavelength-specific photospheres, which would complicate eclipse or phase
-mapping with spectral data. The model predicts greater relative phase amplitude
-and greater phase offset at 3.6 micron than at 4.5 micron, in agreement with
-the data. Our model qualitatively explains the observed phase curves, but is in
-tension with current thermo-chemical kinetics models that predict zonally
-uniform atmospheric composition due to transport of CO from the hot regions of
-the atmosphere.
-",0,1,0,0,0,0
-20842,Supervised Learning of Labeled Pointcloud Differences via Cover-Tree Entropy Reduction,"  We introduce a new algorithm, called CDER, for supervised machine learning
-that merges the multi-scale geometric properties of Cover Trees with the
-information-theoretic properties of entropy. CDER applies to a training set of
-labeled pointclouds embedded in a common Euclidean space. If typical
-pointclouds corresponding to distinct labels tend to differ at any scale in any
-sub-region, CDER can identify these differences in (typically) linear time,
-creating a set of distributional coordinates which act as a feature extraction
-mechanism for supervised learning. We describe theoretical properties and
-implementation details of CDER, and illustrate its benefits on several
-synthetic examples.
-",1,0,0,1,0,0
-20843,Design Considerations for Proposed Fermilab Integrable RCS,"  Integrable optics is an innovation in particle accelerator design that
-provides strong nonlinear focusing while avoiding parametric resonances. One
-promising application of integrable optics is to overcome the traditional
-limits on accelerator intensity imposed by betatron tune-spread and collective
-instabilities. The efficacy of high-intensity integrable accelerators will be
-undergo comprehensive testing over the next several years at the Fermilab
-Integrable Optics Test Accelerator (IOTA) and the University of Maryland
-Electron Ring (UMER). We propose an integrable Rapid-Cycling Synchrotron (iRCS)
-as a replacement for the Fermilab Booster to achieve multi-MW beam power for
-the Fermilab high-energy neutrino program. We provide a overview of the machine
-parameters and discuss an approach to lattice optimization. Integrable optics
-requires arcs with integer-pi phase advance followed by drifts with matched
-beta functions. We provide an example integrable lattice with features of a
-modern RCS - long dispersion-free drifts, low momentum compaction,
-superperiodicity, chromaticity correction, separate-function magnets, and
-bounded beta functions.
-",0,1,0,0,0,0
-20844,Consistent Estimation in General Sublinear Preferential Attachment Trees,"  We propose an empirical estimator of the preferential attachment function $f$
-in the setting of general preferential attachment trees. Using a supercritical
-continuous-time branching process framework, we prove the almost sure
-consistency of the proposed estimator. We perform simulations to study the
-empirical properties of our estimators.
-",0,0,1,1,0,0
-20845,Learned Watershed: End-to-End Learning of Seeded Segmentation,"  Learned boundary maps are known to outperform hand- crafted ones as a basis
-for the watershed algorithm. We show, for the first time, how to train
-watershed computation jointly with boundary map prediction. The estimator for
-the merging priorities is cast as a neural network that is con- volutional
-(over space) and recurrent (over iterations). The latter allows learning of
-complex shape priors. The method gives the best known seeded segmentation
-results on the CREMI segmentation challenge.
-",1,0,0,0,0,0
-20846,MAGIC Contributions to the 35th International Cosmic Ray Conference (ICRC2017),"  MAGIC (Major Atmospheric Gamma Imaging Cherenkov) is a system of two 17 m
-diameter, F/1.03 Imaging Atmospheric Cherenkov Telescopes (IACT). They are
-dedicated to the observation of gamma rays from galactic and extragalactic
-sources in the very high energy range (VHE, 30 GeV to 100 TeV). This submission
-contains links to the proceedings for the 35th International Cosmic Ray
-Conference (ICRC2017), held in Bexco, Busan, Korea from the 12th to the 17th of
-July, 2017.
-",0,1,0,0,0,0
-20847,Efficient Hidden Vector Encryptions and Its Applications,"  Predicate encryption is a new paradigm of public key encryption that enables
-searches on encrypted data. Using the predicate encryption, we can search
-keywords or attributes on encrypted data without decrypting the ciphertexts. In
-predicate encryption, a ciphertext is associated with attributes and a token
-corresponds to a predicate. The token that corresponds to a predicate $f$ can
-decrypt the ciphertext associated with attributes $x$ if and only if $f(x)=1$.
-Hidden vector encryption (HVE) is a special kind of predicate encryption. In
-this thesis, we consider the efficiency, the generality, and the security of
-HVE schemes. The results of this thesis are described as follows.
-The first results of this thesis are efficient HVE schemes where the token
-consists of just four group elements and the decryption only requires four
-bilinear map computations, independent of the number of attributes in the
-ciphertext. The construction uses composite order bilinear groups and is
-selectively secure under the well-known assumptions. The second results are
-efficient HVE schemes that are secure under any kind of pairing types. To
-achieve our goals, we proposed a general framework that converts HVE schemes
-from composite order bilinear groups to prime order bilinear groups. Using the
-framework, we convert the previous HVE schemes from composite order bilinear
-groups to prime order bilinear groups. The third results are fully secure HVE
-schemes with short tokens. Previous HVE schemes were proven to be secure only
-in the selective security model where the capabilities of the adversaries are
-severely restricted. Using the dual system encryption techniques, we construct
-fully secure HVE schemes with match revealing property in composite order
-groups.
-",1,0,0,0,0,0
-20848,The Galaxy Clustering Crisis in Abundance Matching,"  Galaxy clustering on small scales is significantly under-predicted by
-sub-halo abundance matching (SHAM) models that populate (sub-)haloes with
-galaxies based on peak halo mass, $M_{\rm peak}$. SHAM models based on the peak
-maximum circular velocity, $V_{\rm peak}$, have had much better success. The
-primary reason $M_{\rm peak}$ based models fail is the relatively low abundance
-of satellite galaxies produced in these models compared to those based on
-$V_{\rm peak}$. Despite success in predicting clustering, a simple $V_{\rm
-peak}$ based SHAM model results in predictions for galaxy growth that are at
-odds with observations. We evaluate three possible remedies that could ""save""
-mass-based SHAM: (1) SHAM models require a significant population of ""orphan""
-galaxies as a result of artificial disruption/merging of sub-haloes in modern
-high resolution dark matter simulations; (2) satellites must grow significantly
-after their accretion; and (3) stellar mass is significantly affected by halo
-assembly history. No solution is entirely satisfactory. However, regardless of
-the particulars, we show that popular SHAM models based on $M_{\rm peak}$
-cannot be complete physical models as presented. Either $V_{\rm peak}$ truly is
-a better predictor of stellar mass at $z\sim 0$ and it remains to be seen how
-the correlation between stellar mass and $V_{\rm peak}$ comes about, or SHAM
-models are missing vital component(s) that significantly affect galaxy
-clustering.
-",0,1,0,0,0,0
-20849,Supplying Dark Energy from Scalar Field Dark Matter,"  We consider the hypothesis that dark matter and dark energy consists of
-ultra-light self-interacting scalar particles. It is found that the
-Klein-Gordon equation with only two free parameters (mass and self-coupling) on
-a Schwarzschild background, at the galactic length-scales has the solution
-which corresponds to Bose-Einstein condensate, behaving as dark matter, while
-the constant solution at supra-galactic scales can explain dark energy.
-",0,1,0,0,0,0
-20850,GHz-Band Integrated Magnetic Inductors,"  The demand on mobile electronics to continue to shrink in size while increase
-in efficiency drives the demand on the internal passive components to do the
-same. Power amplifiers require inductors with small form factors, high quality
-factors, and high operating frequency in the single-digit GHz range. This work
-explores the use of magnetic materials to satisfy the needs of power amplifier
-inductor applications. This paper discusses the optimization choices regarding
-material selection, device design, and fabrication methodology. The inductors
-achieved here present the best performance to date for an integrated magnetic
-core inductor at high frequencies with a 1 nH inductance and peak quality
-factor of 4 at ~3 GHz. Such compact inductors show potential for efficiently
-meeting the need of mobile electronics in the future.
-",0,1,0,0,0,0
-20851,Acylindrical actions on projection complexes,"  We simplify the construction of projection complexes due to
-Bestvina-Bromberg-Fujiwara. To do so, we introduce a sharper version of the
-Behrstock inequality, and show that it can always be enforced. Furthermore, we
-use the new setup to prove acylindricity results for the action on the
-projection complexes. We also treat quasi-trees of metric spaces associated to
-projection complexes, and prove an acylindricity criterion in that context as
-well.
-",0,0,1,0,0,0
-20852,Introducing Geometric Algebra to Geometric Computing Software Developers: A Computational Thinking Approach,"  Designing software systems for Geometric Computing applications can be a
-challenging task. Software engineers typically use software abstractions to
-hide and manage the high complexity of such systems. Without the presence of a
-unifying algebraic system to describe geometric models, the use of software
-abstractions alone can result in many design and maintenance problems.
-Geometric Algebra (GA) can be a universal abstract algebraic language for
-software engineering geometric computing applications. Few sources, however,
-provide enough information about GA-based software implementations targeting
-the software engineering community. In particular, successfully introducing GA
-to software engineers requires quite different approaches from introducing GA
-to mathematicians or physicists. This article provides a high-level
-introduction to the abstract concepts and algebraic representations behind the
-elegant GA mathematical structure. The article focuses on the conceptual and
-representational abstraction levels behind GA mathematics with sufficient
-references for more details. In addition, the article strongly recommends
-applying the methods of Computational Thinking in both introducing GA to
-software engineers, and in using GA as a mathematical language for developing
-Geometric Computing software systems.
-",1,0,0,0,0,0
-20853,Recovering Nonuniform Planted Partitions via Iterated Projection,"  In the planted partition problem, the $n$ vertices of a random graph are
-partitioned into $k$ ""clusters,"" and edges between vertices in the same cluster
-and different clusters are included with constant probability $p$ and $q$,
-respectively (where $0 \le q < p \le 1$). We give an efficient spectral
-algorithm that recovers the clusters with high probability, provided that the
-sizes of any two clusters are either very close or separated by $\geq
-\Omega(\sqrt n)$. We also discuss a generalization of planted partition in
-which the algorithm's input is not a random graph, but a random real symmetric
-matrix with independent above-diagonal entries.
-Our algorithm is an adaptation of a previous algorithm for the uniform case,
-i.e., when all clusters are size $n / k \geq \Omega(\sqrt n)$. The original
-algorithm recovers the clusters one by one via iterated projection: it
-constructs the orthogonal projection operator onto the dominant $k$-dimensional
-eigenspace of the random graph's adjacency matrix, uses it to recover one of
-the clusters, then deletes it and recurses on the remaining vertices. We show
-herein that a similar algorithm works in the nonuniform case.
-",1,0,0,0,0,0
-20854,Data-driven Analytics for Business Architectures: Proposed Use of Graph Theory,"  Business Architecture (BA) plays a significant role in helping organizations
-understand enterprise structures and processes, and align them with strategic
-objectives. However, traditional BAs are represented in fixed structure with
-static model elements and fail to dynamically capture business insights based
-on internal and external data. To solve this problem, this paper introduces the
-graph theory into BAs with aim of building extensible data-driven analytics and
-automatically generating business insights. We use IBM's Component Business
-Model (CBM) as an example to illustrate various ways in which graph theory can
-be leveraged for data-driven analytics, including what and how business
-insights can be obtained. Future directions for applying graph theory to
-business architecture analytics are discussed.
-",0,0,0,1,0,0
-20855,Beyond Sparsity: Tree Regularization of Deep Models for Interpretability,"  The lack of interpretability remains a key barrier to the adoption of deep
-models in many applications. In this work, we explicitly regularize deep models
-so human users might step through the process behind their predictions in
-little time. Specifically, we train deep time-series models so their
-class-probability predictions have high accuracy while being closely modeled by
-decision trees with few nodes. Using intuitive toy examples as well as medical
-tasks for treating sepsis and HIV, we demonstrate that this new tree
-regularization yields models that are easier for humans to simulate than
-simpler L1 or L2 penalties without sacrificing predictive power.
-",1,0,0,1,0,0
-20856,Phase-type distributions in population genetics,"  Probability modelling for DNA sequence evolution is well established and
-provides a rich framework for understanding genetic variation between samples
-of individuals from one or more populations. We show that both classical and
-more recent models for coalescence (with or without recombination) can be
-described in terms of the so-called phase-type theory, where complicated and
-tedious calculations are circumvented by the use of matrices. The application
-of phase-type theory consists of describing the stochastic model as a Markov
-model by appropriately setting up a state space and calculating the
-corresponding intensity and reward matrices. Formulae of interest are then
-expressed in terms of these aforementioned matrices. We illustrate this by a
-few examples calculating the mean, variance and even higher order moments of
-the site frequency spectrum in the multiple merger coalescent models, and by
-analysing the mean and variance for the number of segregating sites for
-multiple samples in the two-locus ancestral recombination graph. We believe
-that phase-type theory has great potential as a tool for analysing probability
-models in population genetics. The compact matrix notation is useful for
-clarification of current models, in particular their formal manipulation
-(calculation), but also for further development or extensions.
-",0,0,0,1,1,0
-20857,Robust Wald-type test in GLM with random design based on minimum density power divergence estimators,"  We consider the problem of robust inference under the important generalized
-linear model (GLM) with stochastic covariates. We derive the properties of the
-minimum density power divergence estimator of the parameters in GLM with random
-design and used this estimator to propose a robust Wald-type test for testing
-any general composite null hypothesis about the GLM. The asymptotic and
-robustness properties of the proposed test are also examined for the GLM with
-random design. Application of the proposed robust inference procedures to the
-popular Poisson regression model for analyzing count data is discussed in
-detail both theoretically and numerically with some interesting real data
-examples.
-",0,0,0,1,0,0
-20858,Locally Smoothed Neural Networks,"  Convolutional Neural Networks (CNN) and the locally connected layer are
-limited in capturing the importance and relations of different local receptive
-fields, which are often crucial for tasks such as face verification, visual
-question answering, and word sequence prediction. To tackle the issue, we
-propose a novel locally smoothed neural network (LSNN) in this paper. The main
-idea is to represent the weight matrix of the locally connected layer as the
-product of the kernel and the smoother, where the kernel is shared over
-different local receptive fields, and the smoother is for determining the
-importance and relations of different local receptive fields. Specifically, a
-multi-variate Gaussian function is utilized to generate the smoother, for
-modeling the location relations among different local receptive fields.
-Furthermore, the content information can also be leveraged by setting the mean
-and precision of the Gaussian function according to the content. Experiments on
-some variant of MNIST clearly show our advantages over CNN and locally
-connected layer.
-",1,0,0,1,0,0
-20859,Asymptotic properties of a componentwise ARH(1) plug-in predictor,"  This paper presents new results on prediction of linear processes in function
-spaces. The autoregressive Hilbertian process framework of order one (ARH(1)
-process framework) is adopted. A componentwise estimator of the autocorrelation
-operator is formulated, from the moment-based estimation of its diagonal
-coefficients, with respect to the orthogonal eigenvectors of the
-auto-covariance operator, which are assumed to be known. Mean-square
-convergence to the theoretical autocorrelation operator, in the space of
-Hilbert-Schmidt operators, is proved. Consistency then follows in that space.
-For the associated ARH(1) plug-in predictor, mean absolute convergence to the
-corresponding conditional expectation, in the considered Hilbert space, is
-obtained. Hence, consistency in that space also holds. A simulation study is
-undertaken to illustrate the finite-large sample behavior of the formulated
-componentwise estimator and predictor. The performance of the presented
-approach is compared with alternative approaches in the previous and current
-ARH(1) framework literature, including the case of unknown eigenvectors.
-",0,0,1,1,0,0
-20860,The Effect of Population Control Policies on Societal Fragmentation,"  Population control policies are proposed and in some places employed as a
-means towards curbing population growth. This paper is concerned with a
-disturbing side-effect of such policies, namely, the potential risk of societal
-fragmentation due to changes in the distribution of family sizes. This effect
-is illustrated in some simple settings and demonstrated by simulation. In
-addition, the dependence of societal fragmentation on family size distribution
-is analyzed. In particular, it is shown that under the studied model, any
-population control policy that disallows families of 3 or more children incurs
-the possible risk of societal fragmentation.
-",1,1,0,0,0,0
-20861,Optimizing Beam Transport in Rapidly Compressing Beams on the Neutralized Drift Compression Experiment - II,"  The Neutralized Drift Compression Experiment-II (NDCX-II) is an induction
-linac that generates intense pulses of 1.2 MeV helium ions for heating matter
-to extreme conditions. Here, we present recent results on optimizing beam
-transport. The NDCX-II beamline includes a 1-meter-long drift section
-downstream of the last transport solenoid, which is filled with
-charge-neutralizing plasma that enables rapid longitudinal compression of an
-intense ion beam against space-charge forces. The transport section on NDCX-II
-consists of 28 solenoids. Finding optimal field settings for a group of
-solenoids requires knowledge of the envelope parameters of the beam. Imaging
-the beam on scintillator gives the radius of the beam, but the envelope angle
-dr/dz is not measured directly. We demonstrate how the parameters of the beam
-envelope (r, dr/dz, and emittance) can be reconstructed from a series of images
-taken at varying B-field strengths of a solenoid upstream of the scintillator.
-We use this technique to evaluate emittance at several points in the NDCX-II
-beamline and for optimizing the trajectory of the beam at the entry of the
-plasma-filled drift section.
-",0,1,0,0,0,0
-20862,Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations,"  Many structured prediction problems (particularly in vision and language
-domains) are ambiguous, with multiple outputs being correct for an input - e.g.
-there are many ways of describing an image, multiple ways of translating a
-sentence; however, exhaustively annotating the applicability of all possible
-outputs is intractable due to exponentially large output spaces (e.g. all
-English sentences). In practice, these problems are cast as multi-class
-prediction, with the likelihood of only a sparse set of annotations being
-maximized - unfortunately penalizing for placing beliefs on plausible but
-unannotated outputs. We make and test the following hypothesis - for a given
-input, the annotations of its neighbors may serve as an additional supervisory
-signal. Specifically, we propose an objective that transfers supervision from
-neighboring examples. We first study the properties of our developed method in
-a controlled toy setup before reporting results on multi-label classification
-and two image-grounded sequence modeling tasks - captioning and question
-generation. We evaluate using standard task-specific metrics and measures of
-output diversity, finding consistent improvements over standard maximum
-likelihood training and other baselines.
-",0,0,0,1,0,0
-20863,Direct and indirect seismic inversion: interpretation of certain mathematical theorems,"  Quantitative methods are more familiar to most geophysicists with direct
-inversion or indirect inversion. We will discuss seismic inversion in a high
-level sense without getting into the actual algorithms. We will stay with
-meta-equations and argue pros and cons based on certain mathematical theorems.
-",0,1,0,0,0,0
-20864,Kan's combinatorial spectra and their sheaves revisited,"  We define a right Cartan-Eilenberg structure on the category of Kan's
-combinatorial spectra, and the category of sheaves of such spectra, assuming
-some conditions. In both structures, we use the geometric concept of homotopy
-equivalence as the strong equivalence. In the case of sheaves, we use local
-equivalence as the weak equivalence. This paper is the first step in a
-larger-scale program of investigating sheaves of spectra from a geometric
-viewpoint.
-",0,0,1,0,0,0
-20865,Adiabatic Quantum Computing for Binary Clustering,"  Quantum computing for machine learning attracts increasing attention and
-recent technological developments suggest that especially adiabatic quantum
-computing may soon be of practical interest. In this paper, we therefore
-consider this paradigm and discuss how to adopt it to the problem of binary
-clustering. Numerical simulations demonstrate the feasibility of our approach
-and illustrate how systems of qubits adiabatically evolve towards a solution.
-",0,0,0,1,0,0
-20866,Similarity Search Over Graphs Using Localized Spectral Analysis,"  This paper provides a new similarity detection algorithm. Given an input set
-of multi-dimensional data points, where each data point is assumed to be
-multi-dimensional, and an additional reference data point for similarity
-finding, the algorithm uses kernel method that embeds the data points into a
-low dimensional manifold. Unlike other kernel methods, which consider the
-entire data for the embedding, our method selects a specific set of kernel
-eigenvectors. The eigenvectors are chosen to separate between the data points
-and the reference data point so that similar data points can be easily
-identified as being distinct from most of the members in the dataset.
-",1,0,0,0,0,0
-20867,"Reminiscences of Julian Schwinger: Late Harvard, Early UCLA Years (1968-1981)","  These are reminiscences of my interactions with Julian Schwinger from 1968
-through 1981 and beyond.
-",0,1,0,0,0,0
-20868,Site-resolved imaging of a bosonic Mott insulator using ytterbium atoms,"  We demonstrate site-resolved imaging of a strongly correlated quantum system
-without relying on laser-cooling techniques during fluorescence imaging. We
-observed the formation of Mott shells in the insulating regime and realized
-thermometry on the atomic cloud. This work proves the feasibility of the
-noncooled approach and opens the door to extending the detection technology to
-new atomic species.
-",0,1,0,0,0,0
-20869,"A unified continuum and variational multiscale formulation for fluids, solids, and fluid-structure interaction","  We develop a unified continuum modeling framework for viscous fluids and
-hyperelastic solids using the Gibbs free energy as the thermodynamic potential.
-This framework naturally leads to a pressure primitive variable formulation for
-the continuum body, which is well-behaved in both compressible and
-incompressible regimes. Our derivation also provides a rational justification
-of the isochoric-volumetric additive split of free energies in nonlinear
-continuum mechanics. The variational multiscale analysis is performed for the
-continuum model to construct a foundation for numerical discretization. We
-first consider the continuum body instantiated as a hyperelastic material and
-develop a variational multiscale formulation for the hyper-elastodynamic
-problem. The generalized-alpha method is applied for temporal discretization. A
-segregated algorithm for the nonlinear solver is designed and carefully
-analyzed. Second, we apply the new formulation to construct a novel unified
-formulation for fluid-solid coupled problems. The variational multiscale
-formulation is utilized for spatial discretization in both fluid and solid
-subdomains. The generalized-alpha method is applied for the whole continuum
-body, and optimal high-frequency dissipation is achieved in both fluid and
-solid subproblems. A new predictor multi-corrector algorithm is developed based
-on the segregated algorithm to attain a good balance between robustness and
-efficiency. The efficacy of the new formulations is examined in several
-benchmark problems. The results indicate that the proposed modeling and
-numerical methodologies constitute a promising technology for biomedical and
-engineering applications, particularly those necessitating incompressible
-models.
-",0,1,0,0,0,0
-20870,Time Assignment System and Its Performance aboard the Hitomi Satellite,"  Fast timing capability in X-ray observation of astrophysical objects is one
-of the key properties for the ASTRO-H (Hitomi) mission. Absolute timing
-accuracies of 350 micro second or 35 micro second are required to achieve
-nominal scientific goals or to study fast variabilities of specific sources.
-The satellite carries a GPS receiver to obtain accurate time information, which
-is distributed from the central onboard computer through the large and complex
-SpaceWire network. The details on the time system on the hardware and software
-design are described. In the distribution of the time information, the
-propagation delays and jitters affect the timing accuracy. Six other items
-identified within the timing system will also contribute to absolute time
-error. These error items have been measured and checked on ground to ensure the
-time error budgets meet the mission requirements. The overall timing
-performance in combination with hardware performance, software algorithm, and
-the orbital determination accuracies, etc, under nominal conditions satisfies
-the mission requirements of 35 micro second. This work demonstrates key points
-for space-use instruments in hardware and software designs and calibration
-measurements for fine timing accuracy on the order of microseconds for
-mid-sized satellites using the SpaceWire (IEEE1355) network.
-",0,1,0,0,0,0
-20871,Common fixed points via $λ$-sequences in $G$-metric spaces,"  In this article, we use $\lambda$-sequences to derive common fixed points for
-a family of self-mappings defined on a complete $G$-metric space. We imitate
-some existing techniques in our proofs and show that the tools emlyed can be
-used at a larger scale. These results generalise well known results in the
-literature.
-",0,0,1,0,0,0
-20872,Bidirectional Nested Weighted Automata,"  Nested weighted automata (NWA) present a robust and convenient
-automata-theoretic formalism for quantitative specifications. Previous works
-have considered NWA that processed input words only in the forward direction.
-It is natural to allow the automata to process input words backwards as well,
-for example, to measure the maximal or average time between a response and the
-preceding request. We therefore introduce and study bidirectional NWA that can
-process input words in both directions. First, we show that bidirectional NWA
-can express interesting quantitative properties that are not expressible by
-forward-only NWA. Second, for the fundamental decision problems of emptiness
-and universality, we establish decidability and complexity results for the new
-framework which match the best-known results for the special case of
-forward-only NWA. Thus, for NWA, the increased expressiveness of
-bidirectionality is achieved at no additional computational complexity. This is
-in stark contrast to the unweighted case, where bidirectional finite automata
-are no more expressive but exponentially more succinct than their forward-only
-counterparts.
-",1,0,0,0,0,0
-20873,Uniform Shapiro-Lopatinski conditions and boundary value problems on manifolds with bounded geometry,"  We study the regularity of the solutions of second order boundary value
-problems on manifolds with boundary and bounded geometry. We first show that
-the regularity property of a given boundary value problem $(P, C)$ is
-equivalent to the uniform regularity of the natural family $(P_x, C_x)$ of
-associated boundary value problems in local coordinates. We verify that this
-property is satisfied for the Dirichlet boundary conditions and strongly
-elliptic operators via a compactness argument. We then introduce a uniform
-Shapiro-Lopatinski regularity condition, which is a modification of the
-classical one, and we prove that it characterizes the boundary value problems
-that satisfy the usual regularity property. We also show that the natural Robin
-boundary conditions always satisfy the uniform Shapiro-Lopatinski regularity
-condition, provided that our operator satisfies the strong Legendre condition.
-This is achieved by proving that ""well-posedness implies regularity"" via a
-modification of the classical ""Nirenberg trick"". When combining our regularity
-results with the Poincaré inequality of (Ammann-Grosse-Nistor, preprint
-2015), one obtains the usual well-posedness results for the classical boundary
-value problems in the usual scale of Sobolev spaces, thus extending these
-important, well-known theorems from smooth, bounded domains, to manifolds with
-boundary and bounded geometry. As we show in several examples, these results do
-not hold true anymore if one drops the bounded geometry assumption. We also
-introduce a uniform Agmon condition and show that it is equivalent to the
-coerciveness. Consequently, we prove a well-posedness result for parabolic
-equations whose elliptic generator satisfies the uniform Agmon condition.
-",0,0,1,0,0,0
-20874,A Fourier-Chebyshev Spectral Method for Cavitation Computation in Nonlinear Elasticity,"  A Fourier-Chebyshev spectral method is proposed in this paper for solving the
-cavitation problem in nonlinear elasticity. The interpolation error for the
-cavitation solution is analyzed, the elastic energy error estimate for the
-discrete cavitation solution is obtained, and the convergence of the method is
-proved. An algorithm combined a gradient type method with a damped quasi-Newton
-method is applied to solve the discretized nonlinear equilibrium equations.
-Numerical experiments show that the Fourier-Chebyshev spectral method is
-efficient and capable of producing accurate numerical cavitation solutions.
-",0,0,1,0,0,0
-20875,Learning Criticality in an Embodied Boltzmann Machine,"  Many biological and cognitive systems do not operate deep into one or other
-regime of activity. Instead, they exploit critical surfaces poised at
-transitions in their parameter space. The pervasiveness of criticality in
-natural systems suggests that there may be general principles inducing this
-behaviour. However, there is a lack of conceptual models explaining how
-embodied agents propel themselves towards these critical points. In this paper,
-we present a learning model driving an embodied Boltzmann Machine towards
-critical behaviour by maximizing the heat capacity of the network. We test and
-corroborate the model implementing an embodied agent in the mountain car
-benchmark, controlled by a Boltzmann Machine that adjust its weights according
-to the model. We find that the neural controller reaches a point of
-criticality, which coincides with a transition point of the behaviour of the
-agent between two regimes of behaviour, maximizing the synergistic information
-between its sensors and the hidden and motor neurons. Finally, we discuss the
-potential of our learning model to study the contribution of criticality to the
-behaviour of embodied living systems in scenarios not necessarily constrained
-by biological restrictions of the examples of criticality we find in nature.
-",1,1,0,0,0,0
-20876,A contemporary look at Hermann Hankel's 1861 pioneering work on Lagrangian fluid dynamics,"  The present paper is a companion to the paper by Villone and Rampf (2017),
-titled ""Hermann Hankel's On the general theory of motion of fluids, an essay
-including an English translation of the complete Preisschrift from 1861""
-together with connected documents. Here we give a critical assessment of
-Hankel's work, which covers many important aspects of fluid dynamics considered
-from a Lagrangian-coordinates point of view: variational formulation in the
-spirit of Hamilton for elastic (barotropic) fluids, transport (we would now say
-Lie transport) of vorticity, the Lagrangian significance of Clebsch variables,
-etc. Hankel's work is also put in the perspective of previous and future work.
-Hence, the action spans about two centuries: from Lagrange's 1760-1761 Turin
-paper on variational approaches to mechanics and fluid mechanics problems to
-Arnold's 1966 founding paper on the geometrical/variational formulation of
-incompressible flow. The 22-year old Hankel - who was to die 12 years later -
-emerges as a highly innovative master of mathematical fluid dynamics, fully
-deserving Riemann's assessment that his Preisschrift contains ""all manner of
-good things.""
-",0,1,1,0,0,0
-20877,A Dynamic Edge Exchangeable Model for Sparse Temporal Networks,"  We propose a dynamic edge exchangeable network model that can capture sparse
-connections observed in real temporal networks, in contrast to existing models
-which are dense. The model achieved superior link prediction accuracy on
-multiple data sets when compared to a dynamic variant of the blockmodel, and is
-able to extract interpretable time-varying community structures from the data.
-In addition to sparsity, the model accounts for the effect of social influence
-on vertices' future behaviours. Compared to the dynamic blockmodels, our model
-has a smaller latent space. The compact latent space requires a smaller number
-of parameters to be estimated in variational inference and results in a
-computationally friendly inference algorithm.
-",0,0,0,1,0,0
-20878,Current-mode Memristor Crossbars for Neuromemristive Systems,"  Motivated by advantages of current-mode design, this brief contribution
-explores the implementation of weight matrices in neuromemristive systems via
-current-mode memristor crossbar circuits. After deriving theoretical results
-for the range and distribution of weights in the current-mode design, it is
-shown that any weight matrix based on voltage-mode crossbars can be mapped to a
-current-mode crossbar if the voltage-mode weights are carefully bounded. Then,
-a modified gradient descent rule is derived for the current-mode design that
-can be used to perform backpropagation training. Behavioral simulations on the
-MNIST dataset indicate that both voltage and current-mode designs are able to
-achieve similar accuracy and have similar defect tolerance. However, analysis
-of trained weight distributions reveals that current-mode and voltage-mode
-designs may use different feature representations.
-",1,0,0,1,0,0
-20879,Incomplete Gauss sums modulo primes,"  We obtain a new bound for incomplete Gauss sums modulo primes. Our argument
-falls under the framework of Vinogradov's method which we use to reduce the
-problem under consideration to bounding the number of solutions to two distinct
-systems of congruences. The first is related to Vinogradov's mean value
-theorem, although the second does not appear to have been considered before.
-Our bound improves on current results in the range $N\ge
-q^{2k^{-1/2}+O(k^{-3/2})}$.
-",0,0,1,0,0,0
-20880,FAST Adaptive Smoothing and Thresholding for Improved Activation Detection in Low-Signal fMRI,"  Functional Magnetic Resonance Imaging is a noninvasive tool used to study
-brain function. Detecting activation is challenged by many factors, and even
-more so in low-signal scenarios that arise in the performance of high-level
-cognitive tasks. We provide a fully automated and fast adaptive smoothing and
-thresholding (FAST) algorithm that uses smoothing and extreme value theory on
-correlated statistical parametric maps for thresholding. Performance on
-experiments spanning a range of low-signal settings is very encouraging. The
-methodology also performs well in a study to identify the cerebral regions that
-perceive only-auditory-reliable and only-visual-reliable speech stimuli as well
-as those that perceive one but not the other.
-",0,0,1,1,0,0
-20881,Improper multiferroicity and colossal dielectric constants in Bi$_{2}$CuO$_{4}$,"  The layered cuprate Bi$_{2}$CuO$_{4}$ is investigated using magnetic,
-dielectric and pyroelectric measurements. This system is observed to be an
-improper multiferroic, with a robust ferroelectric state being established near
-the magnetic transition. Magnetic and dielectric measurements indicate the
-presence of a region above the antiferromagnetic Neel temperature with
-concomitant polar and magnetic short range order. Bi$_{2}$CuO$_{4}$ is also
-seen to exhibit colossal dielectric constants at higher temperatures with
-clearly distinguishable grain and grain boundary contributions, both of which
-exhibit non-Debye relaxation.
-",0,1,0,0,0,0
-20882,Suppression of plasma echoes and Landau damping in Sobolev spaces by weak collisions in a Vlasov-Fokker-Planck equation,"  In this paper, we study Landau damping in the weakly collisional limit of a
-Vlasov-Fokker-Planck equation with nonlinear collisions in the phase-space
-$(x,v) \in \mathbb T_x^n \times \mathbb R^n_v$. The goal is four-fold: (A) to
-understand how collisions suppress plasma echoes and enable Landau damping in
-agreement with linearized theory in Sobolev spaces, (B) to understand how phase
-mixing accelerates collisional relaxation, (C) to understand better how the
-plasma returns to global equilibrium during Landau damping, and (D) to rule out
-that collision-driven nonlinear instabilities dominate. We give an estimate for
-the scaling law between Knudsen number and the maximal size of the perturbation
-necessary for linear theory to be accurate in Sobolev regularity. We conjecture
-this scaling to be sharp (up to logarithmic corrections) due to potential
-nonlinear echoes in the collisionless model.
-",0,0,1,0,0,0
-20883,Deep Learning for Semantic Segmentation on Minimal Hardware,"  Deep learning has revolutionised many fields, but it is still challenging to
-transfer its success to small mobile robots with minimal hardware.
-Specifically, some work has been done to this effect in the RoboCup humanoid
-football domain, but results that are performant and efficient and still
-generally applicable outside of this domain are lacking. We propose an approach
-conceptually different from those taken previously. It is based on semantic
-segmentation and does achieve these desired properties. In detail, it is being
-able to process full VGA images in real-time on a low-power mobile processor.
-It can further handle multiple image dimensions without retraining, it does not
-require specific domain knowledge for achieving a high frame rate and it is
-applicable on a minimal mobile hardware.
-",1,0,0,1,0,0
-20884,"Overpartition $M2$-rank differences, class number relations, and vector-valued mock Eisenstein series","  We prove that the generating function of overpartition $M2$-rank differences
-is, up to coefficient signs, a component of the vector-valued mock Eisenstein
-series attached to a certain quadratic form. We use this to compute analogs of
-the class number relations for $M2$-rank differences. As applications we split
-the Kronecker-Hurwitz relation into its ""even"" and ""odd"" parts and calculate
-sums over Hurwitz class numbers of the form $\sum_{r \in \mathbb{Z}} H(n -
-2r^2)$.
-",0,0,1,0,0,0
-20885,Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph,"  We study the decentralized machine learning scenario where many users
-collaborate to learn personalized models based on (i) their local datasets and
-(ii) a similarity graph over the users' learning tasks. Our approach trains
-nonlinear classifiers in a multi-task boosting manner without exchanging
-personal data and with low communication costs. When background knowledge about
-task similarities is not available, we propose to jointly learn the
-personalized models and a sparse collaboration graph through an alternating
-optimization procedure. We analyze the convergence rate, memory consumption and
-communication complexity of our decentralized algorithms, and demonstrate the
-benefits of our approach compared to competing techniques on synthetic and real
-datasets.
-",1,0,0,1,0,0
-20886,On the well-posedness of SPDEs with singular drift in divergence form,"  We prove existence and uniqueness of strong solutions for a class of
-second-order stochastic PDEs with multiplicative Wiener noise and drift of the
-form $\operatorname{div} \gamma(\nabla \cdot)$, where $\gamma$ is a maximal
-monotone graph in $\mathbb{R}^n \times \mathbb{R}^n$ obtained as the
-subdifferential of a convex function satisfying very mild assumptions on its
-behavior at infinity. The well-posedness result complements the corresponding
-one in our recent work arXiv:1612.08260 where, under the additional assumption
-that $\gamma$ is single-valued, a solution with better integrability and
-regularity properties is constructed. The proof given here, however, is
-self-contained.
-",0,0,1,0,0,0
-20887,Trace norm regularization and faster inference for embedded speech recognition RNNs,"  We propose and evaluate new techniques for compressing and speeding up dense
-matrix multiplications as found in the fully connected and recurrent layers of
-neural networks for embedded large vocabulary continuous speech recognition
-(LVCSR). For compression, we introduce and study a trace norm regularization
-technique for training low rank factored versions of matrix multiplications.
-Compared to standard low rank training, we show that our method leads to good
-accuracy versus number of parameter trade-offs and can be used to speed up
-training of large models. For speedup, we enable faster inference on ARM
-processors through new open sourced kernels optimized for small batch sizes,
-resulting in 3x to 7x speed ups over the widely used gemmlowp library. Beyond
-LVCSR, we expect our techniques and kernels to be more generally applicable to
-embedded neural networks with large fully connected or recurrent layers.
-",1,0,0,1,0,0
-20888,Scalable and Robust Sparse Subspace Clustering Using Randomized Clustering and Multilayer Graphs,"  Sparse subspace clustering (SSC) is one of the current state-of-the-art
-methods for partitioning data points into the union of subspaces, with strong
-theoretical guarantees. However, it is not practical for large data sets as it
-requires solving a LASSO problem for each data point, where the number of
-variables in each LASSO problem is the number of data points. To improve the
-scalability of SSC, we propose to select a few sets of anchor points using a
-randomized hierarchical clustering method, and, for each set of anchor points,
-solve the LASSO problems for each data point allowing only anchor points to
-have a non-zero weight (this reduces drastically the number of variables). This
-generates a multilayer graph where each layer corresponds to a different set of
-anchor points. Using the Grassmann manifold of orthogonal matrices, the shared
-connectivity among the layers is summarized within a single subspace. Finally,
-we use $k$-means clustering within that subspace to cluster the data points,
-similarly as done by spectral clustering in SSC. We show on both synthetic and
-real-world data sets that the proposed method not only allows SSC to scale to
-large-scale data sets, but that it is also much more robust as it performs
-significantly better on noisy data and on data with close susbspaces and
-outliers, while it is not prone to oversegmentation.
-",0,0,0,1,0,0
-20889,Deep Health Care Text Classification,"  Health related social media mining is a valuable apparatus for the early
-recognition of the diverse antagonistic medicinal conditions. Mostly, the
-existing methods are based on machine learning with knowledge-based learning.
-This working note presents the Recurrent neural network (RNN) and Long
-short-term memory (LSTM) based embedding for automatic health text
-classification in the social media mining. For each task, two systems are built
-and that classify the tweet at the tweet level. RNN and LSTM are used for
-extracting features and non-linear activation function at the last layer
-facilitates to distinguish the tweets of different categories. The experiments
-are conducted on 2nd Social Media Mining for Health Applications Shared Task at
-AMIA 2017. The experiment results are considerable; however the proposed method
-is appropriate for the health text classification. This is primarily due to the
-reason that, it doesn't rely on any feature engineering mechanisms.
-",1,0,0,0,0,0
-20890,"Study of cost functionals for ptychographic phase retrieval to improve the robustness against noise, and a proposal for another noise-robust ptychographic phase retrieval scheme","  Recently, efforts have been made to improve ptychography phase retrieval
-algorithms so that they are more robust against noise. Often the algorithm is
-adapted by changing the cost functional that needs to be minimized. In
-particular, it has been suggested that the cost functional should be obtained
-using a maximum-likelihood approach that takes the noise statistics into
-account. Here, we consider the different choices of cost functional, and to how
-they affect the reconstruction results. We find that seemingly the only
-consistently reliable way to improve reconstruction results in the presence of
-noise is to reduce the step size of the update function. In addition, a
-noise-robust ptychographic reconstruction method has been proposed that relies
-on adapting the intensity constraints
-",1,1,0,0,0,0
-20891,Predicting Native Language from Gaze,"  A fundamental question in language learning concerns the role of a speaker's
-first language in second language acquisition. We present a novel methodology
-for studying this question: analysis of eye-movement patterns in second
-language reading of free-form text. Using this methodology, we demonstrate for
-the first time that the native language of English learners can be predicted
-from their gaze fixations when reading English. We provide analysis of
-classifier uncertainty and learned features, which indicates that differences
-in English reading are likely to be rooted in linguistic divergences across
-native languages. The presented framework complements production studies and
-offers new ground for advancing research on multilingualism.
-",1,0,0,0,0,0
-20892,A form of Schwarz's lemma and a bound for the Kobayashi metric on convex domains,"  We present a form of Schwarz's lemma for holomorphic maps between convex
-domains $D_1$ and $D_2$. This result provides a lower bound on the distance
-between the images of relatively compact subsets of $D_1$ and the boundary of
-$D_2$. This is a natural improvement of an old estimate by Bernal-González
-that takes into account the geometry of $\partial{D_1}$. We also provide a new
-estimate for the Kobayashi metric on bounded convex domains.
-",0,0,1,0,0,0
-20893,The COM-negative binomial distribution: modeling overdispersion and ultrahigh zero-inflated count data,"  In this paper, we focus on the COM-type negative binomial distribution with
-three parameters, which belongs to COM-type $(a,b,0)$ class distributions and
-family of equilibrium distributions of arbitrary birth-death process. Besides,
-we show abundant distributional properties such as overdispersion and
-underdispersion, log-concavity, log-convexity (infinite divisibility), pseudo
-compound Poisson, stochastic ordering and asymptotic approximation. Some
-characterizations including sum of equicorrelated geometrically distributed
-random variables, conditional distribution, limit distribution of COM-negative
-hypergeometric distribution, and Stein's identity are given for theoretical
-properties. COM-negative binomial distribution was applied to overdispersion
-and ultrahigh zero-inflated data sets. With the aid of ratio regression, we
-employ maximum likelihood method to estimate the parameters and the
-goodness-of-fit are evaluated by the discrete Kolmogorov-Smirnov test.
-",0,0,1,1,0,0
-20894,Efficient Contextual Bandits in Non-stationary Worlds,"  Most contextual bandit algorithms minimize regret against the best fixed
-policy, a questionable benchmark for non-stationary environments that are
-ubiquitous in applications. In this work, we develop several efficient
-contextual bandit algorithms for non-stationary environments by equipping
-existing methods for i.i.d. problems with sophisticated statistical tests so as
-to dynamically adapt to a change in distribution.
-We analyze various standard notions of regret suited to non-stationary
-environments for these algorithms, including interval regret, switching regret,
-and dynamic regret. When competing with the best policy at each time, one of
-our algorithms achieves regret $\mathcal{O}(\sqrt{ST})$ if there are $T$ rounds
-with $S$ stationary periods, or more generally
-$\mathcal{O}(\Delta^{1/3}T^{2/3})$ where $\Delta$ is some non-stationarity
-measure. These results almost match the optimal guarantees achieved by an
-inefficient baseline that is a variant of the classic Exp4 algorithm. The
-dynamic regret result is also the first one for efficient and fully adversarial
-contextual bandit.
-Furthermore, while the results above require tuning a parameter based on the
-unknown quantity $S$ or $\Delta$, we also develop a parameter free algorithm
-achieving regret $\min\{S^{1/4}T^{3/4}, \Delta^{1/5}T^{4/5}\}$. This improves
-and generalizes the best existing result $\Delta^{0.18}T^{0.82}$ by Karnin and
-Anava (2016) which only holds for the two-armed bandit problem.
-",1,0,0,1,0,0
-20895,Search Engine Drives the Evolution of Social Networks,"  The search engine is tightly coupled with social networks and is primarily
-designed for users to acquire interested information. Specifically, the search
-engine assists the information dissemination for social networks, i.e.,
-enabling users to access interested contents with keywords-searching and
-promoting the process of contents-transferring from the source users directly
-to potential interested users. Accompanying such processes, the social network
-evolves as new links emerge between users with common interests. However, there
-is no clear understanding of such a ""chicken-and-egg"" problem, namely, new
-links encourage more social interactions, and vice versa. In this paper, we aim
-to quantitatively characterize the social network evolution phenomenon driven
-by a search engine. First, we propose a search network model for social network
-evolution. Second, we adopt two performance metrics, namely, degree
-distribution and network diameter. Theoretically, we prove that the degree
-distribution follows an intensified power-law, and the network diameter
-shrinks. Third, we quantitatively show that the search engine accelerates the
-rumor propagation in social networks. Finally, based on four real-world data
-sets (i.e., CDBLP, Facebook, Weibo Tweets, P2P), we verify our theoretical
-findings. Furthermore, we find that the search engine dramatically increases
-the speed of rumor propagation.
-",1,1,0,0,0,0
-20896,End-to-End Learning of Semantic Grasping,"  We consider the task of semantic robotic grasping, in which a robot picks up
-an object of a user-specified class using only monocular images. Inspired by
-the two-stream hypothesis of visual reasoning, we present a semantic grasping
-framework that learns object detection, classification, and grasp planning in
-an end-to-end fashion. A ""ventral stream"" recognizes object class while a
-""dorsal stream"" simultaneously interprets the geometric relationships necessary
-to execute successful grasps. We leverage the autonomous data collection
-capabilities of robots to obtain a large self-supervised dataset for training
-the dorsal stream, and use semi-supervised label propagation to train the
-ventral stream with only a modest amount of human supervision. We
-experimentally show that our approach improves upon grasping systems whose
-components are not learned end-to-end, including a baseline method that uses
-bounding box detection. Furthermore, we show that jointly training our model
-with auxiliary data consisting of non-semantic grasping data, as well as
-semantically labeled images without grasp actions, has the potential to
-substantially improve semantic grasping performance.
-",1,0,0,1,0,0
-20897,Von Neumann Regular Cellular Automata,"  For any group $G$ and any set $A$, a cellular automaton (CA) is a
-transformation of the configuration space $A^G$ defined via a finite memory set
-and a local function. Let $\text{CA}(G;A)$ be the monoid of all CA over $A^G$.
-In this paper, we investigate a generalisation of the inverse of a CA from the
-semigroup-theoretic perspective. An element $\tau \in \text{CA}(G;A)$ is von
-Neumann regular (or simply regular) if there exists $\sigma \in \text{CA}(G;A)$
-such that $\tau \circ \sigma \circ \tau = \tau$ and $\sigma \circ \tau \circ
-\sigma = \sigma$, where $\circ$ is the composition of functions. Such an
-element $\sigma$ is called a generalised inverse of $\tau$. The monoid
-$\text{CA}(G;A)$ itself is regular if all its elements are regular. We
-establish that $\text{CA}(G;A)$ is regular if and only if $\vert G \vert = 1$
-or $\vert A \vert = 1$, and we characterise all regular elements in
-$\text{CA}(G;A)$ when $G$ and $A$ are both finite. Furthermore, we study
-regular linear CA when $A= V$ is a vector space over a field $\mathbb{F}$; in
-particular, we show that every regular linear CA is invertible when $G$ is
-torsion-free elementary amenable (e.g. when $G=\mathbb{Z}^d, \ d \in
-\mathbb{N}$) and $V=\mathbb{F}$, and that every linear CA is regular when $V$
-is finite-dimensional and $G$ is locally finite with $\text{Char}(\mathbb{F})
-\nmid o(g)$ for all $g \in G$.
-",1,0,1,0,0,0
-20898,A Distance Between Filtered Spaces Via Tripods,"  We present a simplified treatment of stability of filtrations on finite
-spaces. Interestingly, we can lift the stability result for combinatorial
-filtrations from [CSEM06] to the case when two filtrations live on different
-spaces without directly invoking the concept of interleaving. We then prove
-that this distance is intrinsic by constructing explicit geodesics between any
-pair of filtered spaces. Finally we use this construction to obtain a
-strengthening of the stability result.
-",1,0,1,0,0,0
-20899,Robust Shape Estimation for 3D Deformable Object Manipulation,"  Existing shape estimation methods for deformable object manipulation suffer
-from the drawbacks of being off-line, model dependent, noise-sensitive or
-occlusion-sensitive, and thus are not appropriate for manipulation tasks
-requiring high precision. In this paper, we present a real-time shape
-estimation approach for autonomous robotic manipulation of 3D deformable
-objects. Our method fulfills all the requirements necessary for the
-high-quality deformable object manipulation in terms of being real-time,
-model-free and robust to noise and occlusion. These advantages are accomplished
-using a joint tracking and reconstruction framework, in which we track the
-object deformation by aligning a reference shape model with the stream input
-from the RGB-D camera, and simultaneously upgrade the reference shape model
-according to the newly captured RGB-D data. We have evaluated the quality and
-robustness of our real-time shape estimation pipeline on a set of deformable
-manipulation tasks implemented on physical robots. Videos are available at
-this https URL
-",1,0,0,0,0,0
-20900,Beyond Winning and Losing: Modeling Human Motivations and Behaviors Using Inverse Reinforcement Learning,"  In recent years, reinforcement learning (RL) methods have been applied to
-model gameplay with great success, achieving super-human performance in various
-environments, such as Atari, Go, and Poker. However, those studies mostly focus
-on winning the game and have largely ignored the rich and complex human
-motivations, which are essential for understanding different players' diverse
-behaviors. In this paper, we present a novel method called Multi-Motivation
-Behavior Modeling (MMBM) that takes the multifaceted human motivations into
-consideration and models the underlying value structure of the players using
-inverse RL. Our approach does not require the access to the dynamic of the
-system, making it feasible to model complex interactive environments such as
-massively multiplayer online games. MMBM is tested on the World of Warcraft
-Avatar History dataset, which recorded over 70,000 users' gameplay spanning
-three years period. Our model reveals the significant difference of value
-structures among different player groups. Using the results of motivation
-modeling, we also predict and explain their diverse gameplay behaviors and
-provide a quantitative assessment of how the redesign of the game environment
-impacts players' behaviors.
-",0,0,0,1,0,0
-20901,Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization,"  In this paper we demonstrate how genetic algorithms can be used to reverse
-engineer an evaluation function's parameters for computer chess. Our results
-show that using an appropriate mentor, we can evolve a program that is on par
-with top tournament-playing chess programs, outperforming a two-time World
-Computer Chess Champion. This performance gain is achieved by evolving a
-program with a smaller number of parameters in its evaluation function to mimic
-the behavior of a superior mentor which uses a more extensive evaluation
-function. In principle, our mentor-assisted approach could be used in a wide
-range of problems for which appropriate mentors are available.
-",1,0,0,1,0,0
-20902,Universal Constraints on the Location of Extrema of Eigenfunctions of Non-Local Schrödinger Operators,"  We derive a lower bound on the location of global extrema of eigenfunctions
-for a large class of non-local Schrödinger operators in convex domains under
-Dirichlet exterior conditions, featuring the symbol of the kinetic term, the
-strength of the potential, and the corresponding eigenvalue, and involving a
-new universal constant. We show a number of probabilistic and spectral
-geometric implications, and derive a Faber-Krahn type inequality for non-local
-operators. Our study also extends to potentials with compact support, and we
-establish bounds on the location of extrema relative to the boundary edge of
-the support or level sets around minima of the potential.
-",0,0,1,0,0,0
-20903,InScript: Narrative texts annotated with script information,"  This paper presents the InScript corpus (Narrative Texts Instantiating Script
-structure). InScript is a corpus of 1,000 stories centered around 10 different
-scenarios. Verbs and noun phrases are annotated with event and participant
-types, respectively. Additionally, the text is annotated with coreference
-information. The corpus shows rich lexical variation and will serve as a unique
-resource for the study of the role of script knowledge in natural language
-processing.
-",1,0,0,0,0,0
-20904,Semimetallic and charge-ordered $α$-(BEDT-TTF)$_2$I$_3$: on the role of disorder in dc transport and dielectric properties,"  $\alpha$-(BEDT-TTF)$_2$I$_3$ is a prominent example of charge ordering among
-organic conductors. In this work we explore the details of transport within the
-charge-ordered as well as semimetallic phase at ambient pressure. In the
-high-temperature semimetallic phase, the mobilities and concentrations of both
-electrons and holes conspire in such a way to create an almost
-temperature-independent conductivity as well as a low Hall effect. We explain
-these phenomena as a consequence of a predominantly inter-pocket scattering
-which equalizes mobilities of the two types of charge carriers. At low
-temperatures, within the insulating charge-ordered phase two channels of
-conduction can be discerned: a temperature-dependent activation which follows
-the mean-field behavior, and a nearest-neighbor hopping contribution. Together
-with negative magnetoresistance, the latter relies on the presence of disorder.
-The charge-ordered phase also features a prominent dielectric peak which bears
-a similarity to relaxor ferroelectrics. Its dispersion is determined by
-free-electron screening and pushed by disorder well below the transition
-temperature. The source of this disorder can be found in the anion layers which
-randomly perturb BEDT-TTF molecules through hydrogen bonds.
-",0,1,0,0,0,0
-20905,Robust Photometric Stereo Using Learned Image and Gradient Dictionaries,"  Photometric stereo is a method for estimating the normal vectors of an object
-from images of the object under varying lighting conditions. Motivated by
-several recent works that extend photometric stereo to more general objects and
-lighting conditions, we study a new robust approach to photometric stereo that
-utilizes dictionary learning. Specifically, we propose and analyze two
-approaches to adaptive dictionary regularization for the photometric stereo
-problem. First, we propose an image preprocessing step that utilizes an
-adaptive dictionary learning model to remove noise and other non-idealities
-from the image dataset before estimating the normal vectors. We also propose an
-alternative model where we directly apply the adaptive dictionary
-regularization to the normal vectors themselves during estimation. We study the
-practical performance of both methods through extensive simulations, which
-demonstrate the state-of-the-art performance of both methods in the presence of
-noise.
-",0,0,0,1,0,0
-20906,Toward Low-Flying Autonomous MAV Trail Navigation using Deep Neural Networks for Environmental Awareness,"  We present a micro aerial vehicle (MAV) system, built with inexpensive
-off-the-shelf hardware, for autonomously following trails in unstructured,
-outdoor environments such as forests. The system introduces a deep neural
-network (DNN) called TrailNet for estimating the view orientation and lateral
-offset of the MAV with respect to the trail center. The DNN-based controller
-achieves stable flight without oscillations by avoiding overconfident behavior
-through a loss function that includes both label smoothing and entropy reward.
-In addition to the TrailNet DNN, the system also utilizes vision modules for
-environmental awareness, including another DNN for object detection and a
-visual odometry component for estimating depth for the purpose of low-level
-obstacle detection. All vision systems run in real time on board the MAV via a
-Jetson TX1. We provide details on the hardware and software used, as well as
-implementation details. We present experiments showing the ability of our
-system to navigate forest trails more robustly than previous techniques,
-including autonomous flights of 1 km.
-",1,0,0,0,0,0
-20907,Origin of Charge Separation at Organic Photovoltaic Heterojunctions: A Mesoscale Quantum Mechanical View,"  The high efficiency of charge generation within organic photovoltaic blends
-apparently contrasts with the strong ""classical"" attraction between newly
-formed electron-hole pairs. Several factors have been identified as possible
-facilitators of charge dissociation, such as quantum mechanical coherence and
-delocalization, structural and energetic disorder, built-in electric fields,
-nanoscale intermixing of the donor and acceptor components of the blends. Our
-mesoscale quantum-chemical model allows an unbiased assessment of their
-relative importance, through excited-state calculations on systems containing
-thousands of donor and acceptor sites. The results on several model
-heterojunctions confirm that the classical model severely overestimates the
-binding energy of the electron-hole pairs, produced by vertical excitation from
-the electronic ground state. Using physically sensible parameters for the
-individual materials, we find that the quantum mechanical energy difference
-between the lowest interfacial charge transfer states and the fully separated
-electron and hole is of the order of the thermal energy.
-",0,1,0,0,0,0
-20908,Spectral Decimation for Families of Self-Similar Symmetric Laplacians on the Sierpinski Gasket,"  We construct a one-parameter family of Laplacians on the Sierpinski Gasket
-that are symmetric and self-similar for the 9-map iterated function system
-obtained by iterating the standard 3-map iterated function system. Our main
-result is the fact that all these Laplacians satisfy a version of spectral
-decimation that builds a precise catalog of eigenvalues and eigenfunctions for
-any choice of the parameter. We give a number of applications of this spectral
-decimation. We also prove analogous results for fractal Laplacians on the unit
-Interval, and this yields an analogue of the classical Sturm-Liouville theory
-for the eigenfunctions of these one-dimensional Laplacians.
-",0,0,1,0,0,0
-20909,Identification of multi-object dynamical systems: consistency and Fisher information,"  Learning the model parameters of a multi-object dynamical system from partial
-and perturbed observations is a challenging task. Despite recent numerical
-advancements in learning these parameters, theoretical guarantees are extremely
-scarce. In this article, we study the identifiability of these parameters and
-the consistency of the corresponding maximum likelihood estimate (MLE) under
-assumptions on the different components of the underlying multi-object system.
-In order to understand the impact of the various sources of observation noise
-on the ability to learn the model parameters, we study the asymptotic variance
-of the MLE through the associated Fisher information matrix. For example, we
-show that specific aspects of the multi-target tracking (MTT) problem such as
-detection failures and unknown data association lead to a loss of information
-which is quantified in special cases of interest.
-",0,0,1,1,0,0
-20910,Elliptic curves maximal over extensions of finite base fields,"  Given an elliptic curve $E$ over a finite field $\mathbb{F}_q$ we study the
-finite extensions $\mathbb{F}_{q^n}$ of $\mathbb{F}_q$ such that the number of
-$\mathbb{F}_{q^n}$-rational points on $E$ attains the Hasse upper bound. We
-obtain an upper bound on the degree $n$ for $E$ ordinary using an estimate for
-linear forms in logarithms, which allows us to compute the pairs of isogeny
-classes of such curves and degree $n$ for small $q$. Using a consequence of
-Schmidt's Subspace Theorem, we improve the upper bound to $n\leq 11$ for
-sufficiently large $q$. We also show that there are infinitely many isogeny
-classes of ordinary elliptic curves with $n=3$.
-",0,0,1,0,0,0
-20911,CURE: Curvature Regularization For Missing Data Recovery,"  Missing data recovery is an important and yet challenging problem in imaging
-and data science. Successful models often adopt certain carefully chosen
-regularization. Recently, the low dimension manifold model (LDMM) was
-introduced by S.Osher et al. and shown effective in image inpainting. They
-observed that enforcing low dimensionality on image patch manifold serves as a
-good image regularizer. In this paper, we observe that having only the low
-dimension manifold regularization is not enough sometimes, and we need
-smoothness as well. For that, we introduce a new regularization by combining
-the low dimension manifold regularization with a higher order Curvature
-Regularization, and we call this new regularization CURE for short. The key
-step of solving CURE is to solve a biharmonic equation on a manifold. We
-further introduce a weighted version of CURE, called WeCURE, in a similar
-manner as the weighted nonlocal Laplacian (WNLL) method. Numerical experiments
-for image inpainting and semi-supervised learning show that the proposed CURE
-and WeCURE significantly outperform LDMM and WNLL respectively.
-",1,0,0,0,0,0
-20912,Nonlinear parametric excitation effect induces stability transitions in swimming direction of flexible superparamagnetic microswimmers,"  Microscopic artificial swimmers have recently become highly attractive due to
-their promising potential for biomedical applications. The pioneering work of
-Dreyfus et al (2005) has demonstrated the motion of a microswimmer with an
-undulating chain of superparamagnetic beads, which is actuated by an
-oscillating external magnetic field. Interestingly, it has also been
-theoretically predicted that the swimming direction of this swimmer will
-undergo a $90^\circ$-transition when the magnetic field's oscillations
-amplitude is increased above a critical value of $\sqrt{2}$. In this work, we
-further investigate this transition both theoretically and experimentally by
-using numerical simulations and presenting a novel flexible microswimmer with a
-superparamagnetic head. We realize the $90^\circ$-transition in swimming
-direction, prove that this effect depends on both frequency and amplitude of
-the oscillating magnetic field, and demonstrate the existence of an optimal
-amplitude, under which, maximal swimming speed can be achieved. By
-asymptotically analyzing the dynamic motion of microswimmer with a minimal
-two-link model, we reveal that the stability transitions representing the
-changes in the swimming direction are induced by the effect of nonlinear
-parametric excitation.
-",0,1,0,0,0,0
-20913,$L^p$ Mapping Properties for the Cauchy-Riemann Equations on Lipschitz Domains Admitting Subelliptic Estimates,"  We show that on bounded Lipschitz pseudoconvex domains that admit good weight
-functions the $\overline{\partial}$-Neumann operators $N_q,
-\overline{\partial}^* N_{q}$, and $\overline{\partial} N_{q}$ are bounded on
-$L^p$ spaces for some values of $p$ greater than 2.
-",0,0,1,0,0,0
-20914,Fast dose optimization for rotating shield brachytherapy,"  Purpose: To provide a fast computational method, based on the proximal graph
-solver (POGS) - a convex optimization solver using the alternating direction
-method of multipliers (ADMM), for calculating an optimal treatment plan in
-rotating shield brachytherapy (RSBT). RSBT treatment planning has more degrees
-of freedom than conventional high-dose-rate brachytherapy (HDR-BT) due to the
-addition of emission direction, and this necessitates a fast optimization
-technique to enable clinical usage. // Methods: The multi-helix RSBT (H-RSBT)
-delivery technique was considered with five representative cervical cancer
-patients. Treatment plans were generated for all patients using the POGS method
-and the previously considered commercial solver IBM CPLEX. The rectum, bladder,
-sigmoid, high-risk clinical target volume (HR-CTV), and HR-CTV boundary were
-the structures considered in our optimization problem, called the asymmetric
-dose-volume optimization with smoothness control. Dose calculation resolution
-was 1x1x3 mm^3 for all cases. The H-RSBT applicator has 6 helices, with 33.3 mm
-of translation along the applicator per helical rotation and 1.7 mm spacing
-between dwell positions, yielding 17.5 degree emission angle spacing per 5 mm
-along the applicator.// Results: For each patient, HR-CTV D90, HR-CTV D100,
-rectum D2cc, sigmoid D2cc, and bladder D2cc matched within 1% for CPLEX and
-POGS. Also, we obtained similar EQD2 figures between CPLEX and POGS. POGS was
-around 18 times faster than CPLEX. Over all patients, total optimization times
-were 32.1-65.4 seconds for CPLEX and 2.1-3.9 seconds for POGS. // Conclusions:
-POGS substantially reduced treatment plan optimization time around 18 times for
-RSBT with similar HR-CTV D90, OAR D2cc values, and EQD2 figure relative to
-CPLEX, which is significant progress toward clinical translation of RSBT. POGS
-is also applicable to conventional HDR-BT.
-",0,1,1,0,0,0
-20915,3D Modeling of Electric Fields in the LUX Detector,"  This work details the development of a three-dimensional (3D) electric field
-model for the LUX detector. The detector took data during two periods of
-searching for weakly interacting massive particle (WIMP) searches. After the
-first period completed, a time-varying non-uniform negative charge developed in
-the polytetrafluoroethylene (PTFE) panels that define the radial boundary of
-the detector's active volume. This caused electric field variations in the
-detector in time, depth and azimuth, generating an electrostatic
-radially-inward force on electrons on their way upward to the liquid surface.
-To map this behavior, 3D electric field maps of the detector's active volume
-were built on a monthly basis. This was done by fitting a model built in COMSOL
-Multiphysics to the uniformly distributed calibration data that were collected
-on a regular basis. The modeled average PTFE charge density increased over the
-course of the exposure from -3.6 to $-5.5~\mu$C/m$^2$. From our studies, we
-deduce that the electric field magnitude varied while the mean value of the
-field of $\sim200$~V/cm remained constant throughout the exposure. As a result
-of this work the varying electric fields and their impact on event
-reconstruction and discrimination were successfully modeled.
-",0,1,0,0,0,0
-20916,Optimal Communication Strategies in Networked Cyber-Physical Systems with Adversarial Elements,"  This paper studies optimal communication and coordination strategies in
-cyber-physical systems for both defender and attacker within a game-theoretic
-framework. We model the communication network of a cyber-physical system as a
-sensor network which involves one single Gaussian source observed by many
-sensors, subject to additive independent Gaussian observation noises. The
-sensors communicate with the estimator over a coherent Gaussian multiple access
-channel. The aim of the receiver is to reconstruct the underlying source with
-minimum mean squared error. The scenario of interest here is one where some of
-the sensors are captured by the attacker and they act as the adversary
-(jammer): they strive to maximize distortion. The receiver (estimator) knows
-the captured sensors but still cannot simply ignore them due to the multiple
-access channel, i.e., the outputs of all sensors are summed to generate the
-estimator input. We show that the ability of transmitter sensors to secretly
-agree on a random event, that is ""coordination"", plays a key role in the
-analysis...
-",1,0,0,0,0,0
-20917,First- and Second-Order Models of Recursive Arithmetics,"  We study a quadruple of interrelated subexponential subsystems of arithmetic
-WKL$_0^-$, RCA$^-_0$, I$\Delta_0$, and $\Delta$RA$_1$, which complement the
-similarly related quadruple WKL$_0$, RCA$_0$, I$\Sigma_1$, and PRA studied by
-Simpson, and the quadruple WKL$_0^\ast$, RCA$_0^\ast$, I$\Delta_0$(exp), and
-EFA studied by Simpson and Smith. We then explore the space of subexponential
-arithmetic theories between I$\Delta_0$ and I$\Delta_0$(exp). We introduce and
-study first- and second-order theories of recursive arithmetic $A$RA$_1$ and
-$A$RA$_2$ capable of characterizing various computational complexity classes
-and based on function algebras $A$, studied by Clote and others.
-",1,0,0,0,0,0
-20918,Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks,"  Nowadays, modern earth observation programs produce huge volumes of satellite
-images time series (SITS) that can be useful to monitor geographical areas
-through time. How to efficiently analyze such kind of information is still an
-open question in the remote sensing field. Recently, deep learning methods
-proved suitable to deal with remote sensing data mainly for scene
-classification (i.e. Convolutional Neural Networks - CNNs - on single images)
-while only very few studies exist involving temporal deep learning approaches
-(i.e Recurrent Neural Networks - RNNs) to deal with remote sensing time series.
-In this letter we evaluate the ability of Recurrent Neural Networks, in
-particular the Long-Short Term Memory (LSTM) model, to perform land cover
-classification considering multi-temporal spatial data derived from a time
-series of satellite images. We carried out experiments on two different
-datasets considering both pixel-based and object-based classification. The
-obtained results show that Recurrent Neural Networks are competitive compared
-to state-of-the-art classifiers, and may outperform classical approaches in
-presence of low represented and/or highly mixed classes. We also show that
-using the alternative feature representation generated by LSTM can improve the
-performances of standard classifiers.
-",1,0,0,0,0,0
-20919,Disentangling by Factorising,"  We define and address the problem of unsupervised learning of disentangled
-representations on data generated from independent factors of variation. We
-propose FactorVAE, a method that disentangles by encouraging the distribution
-of representations to be factorial and hence independent across the dimensions.
-We show that it improves upon $\beta$-VAE by providing a better trade-off
-between disentanglement and reconstruction quality. Moreover, we highlight the
-problems of a commonly used disentanglement metric and introduce a new metric
-that does not suffer from them.
-",0,0,0,1,0,0
-20920,Symmetries and synchronization in multilayer random networks,"  In the light of the recently proposed scenario of asymmetry-induced
-synchronization (AISync), in which dynamical uniformity and consensus in a
-distributed system would demand certain asymmetries in the underlying network,
-we investigate here the influence of some regularities in the interlayer
-connection patterns on the synchronization properties of multilayer random
-networks. More specifically, by considering a Stuart-Landau model of complex
-oscillators with random frequencies, we report for multilayer networks a
-dynamical behavior that could be also classified as a manifestation of AISync.
-We show, namely, that the presence of certain symmetries in the interlayer
-connection pattern tends to diminish the synchronization capability of the
-whole network or, in other words, asymmetries in the interlayer connections
-would enhance synchronization in such structured networks. Our results might
-help the understanding not only of the AISync mechanism itself, but also its
-possible role in the determination of the interlayer connection pattern of
-multilayer and other structured networks with optimal synchronization
-properties.
-",0,1,0,0,0,0
-20921,Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation,"  As part of autonomous car driving systems, semantic segmentation is an
-essential component to obtain a full understanding of the car's environment.
-One difficulty, that occurs while training neural networks for this purpose, is
-class imbalance of training data. Consequently, a neural network trained on
-unbalanced data in combination with maximum a-posteriori classification may
-easily ignore classes that are rare in terms of their frequency in the dataset.
-However, these classes are often of highest interest. We approach such
-potential misclassifications by weighting the posterior class probabilities
-with the prior class probabilities which in our case are the inverse
-frequencies of the corresponding classes in the training dataset. More
-precisely, we adopt a localized method by computing the priors pixel-wise such
-that the impact can be analyzed at pixel level as well. In our experiments, we
-train one network from scratch using a proprietary dataset containing 20,000
-annotated frames of video sequences recorded from street scenes. The evaluation
-on our test set shows an increase of average recall with regard to instances of
-pedestrians and info signs by $25\%$ and $23.4\%$, respectively. In addition,
-we significantly reduce the non-detection rate for instances of the same
-classes by $61\%$ and $38\%$.
-",1,0,0,1,0,0
-20922,Muchnik degrees and cardinal characteristics,"  We provide a pair of dual results, each stating the coincidence of highness
-properties from computability theory. We provide an analogous pair of dual
-results on the coincidence of cardinal characteristics within ZFC.
-A mass problem is a set of functions on $\omega$. For mass problems $\mathcal
-C, \mathcal D$, one says that $\mathcal C$ is Muchnik reducible to $\mathcal D$
-if each function in $\mathcal D$ computes a function in $\mathcal C$. In this
-paper we view highness properties as mass problems, and compare them with
-respect to Muchnik reducibility and its uniform strengthening, Medvedev
-reducibility.
-Let $\mathcal D(p)$ be the mass problem of infinite bit sequences $y$ (i.e.,
-0,1 valued functions) such that for each computable bit sequence $x$, the
-asymptotic lower density $\underline \rho$ of the agreement bit sequence $x
-\leftrightarrow y$ is at most $p$ (this sequence takes the value 1 at a bit
-position iff $x$ and $y$ agree).
-We show that all members of this family of mass problems parameterized by a
-real $p$ with $0 < p<1/2 $ have the same complexity in the sense of Muchnik
-reducibility. This also yields a new version of Monin's affirmative answer to
-the ""Gamma question"", whether $\Gamma(A)< 1/2$ implies $\Gamma(A)=0$ for each
-Turing oracle $A$.
-We also show, together with Joseph Miller, that for any order function~$g$
-there exists a faster growing order function $h $ such that $\mathrm{IOE}(g) $
-is strictly Muchnik below $\mathrm{IOE}(h)$.
-We study cardinal characteristics analogous to the highness properties above.
-For instance, $\mathfrak d (p)$ is the least size of a set $G$ of bit sequences
-so that for each bit sequence $x$ there is a bit sequence $y$ in $G$ so that
-$\underline \rho (x \leftrightarrow y) >p$. We prove within ZFC all the
-coincidences of cardinal characteristics that are the analogs of the results
-above.
-",0,0,1,0,0,0
-20923,Existence of solutions for a semirelativistic Hartree equation with unbounded potentials,"  We prove the existence of a solution to the semirelativistic Hartree equation
-$$\sqrt{-\Delta+m^2}u+ V(x) u = A(x)\left( W * |u|^p \right) |u|^{p-2}u $$
-under suitable growth assumption on the potential functions $V$ and $A$. In
-particular, both can be unbounded from above.
-",0,0,1,0,0,0
-20924,Lattice Operations on Terms over Similar Signatures,"  Unification and generalization are operations on two terms computing
-respectively their greatest lower bound and least upper bound when the terms
-are quasi-ordered by subsumption up to variable renaming (i.e., $t_1\preceq
-t_2$ iff $t_1 = t_2\sigma$ for some variable substitution $\sigma$). When term
-signatures are such that distinct functor symbols may be related with a fuzzy
-equivalence (called a similarity), these operations can be formally extended to
-tolerate mismatches on functor names and/or arity or argument order. We
-reformulate and extend previous work with a declarative approach defining
-unification and generalization as sets of axioms and rules forming a complete
-constraint-normalization proof system. These include the Reynolds-Plotkin
-term-generalization procedures, Maria Sessa's ""weak"" unification with partially
-fuzzy signatures and its corresponding generalization, as well as novel
-extensions of such operations to fully fuzzy signatures (i.e., similar functors
-with possibly different arities). One advantage of this approach is that it
-requires no modification of the conventional data structures for terms and
-substitutions. This and the fact that these declarative specifications are
-efficiently executable conditional Horn-clauses offers great practical
-potential for fuzzy information-handling applications.
-",1,0,0,0,0,0
-20925,Towards Physically Safe Reinforcement Learning under Supervision,"  This paper addresses the question of how a previously available control
-policy $\pi_s$ can be used as a supervisor to more quickly and safely train a
-new learned control policy $\pi_L$ for a robot. A weighted average of the
-supervisor and learned policies is used during trials, with a heavier weight
-initially on the supervisor, in order to allow safe and useful physical trials
-while the learned policy is still ineffective. During the process, the weight
-is adjusted to favor the learned policy. As weights are adjusted, the learned
-network must compensate so as to give safe and reasonable outputs under the
-different weights. A pioneer network is introduced that pre-learns a policy
-that performs similarly to the current learned policy under the planned next
-step for new weights; this pioneer network then replaces the currently learned
-network in the next set of trials. Experiments in OpenAI Gym demonstrate the
-effectiveness of the proposed method.
-",1,0,0,1,0,0
-20926,Complex Valued Risk Diversification,"  Risk diversification is one of the dominant concerns for portfolio managers.
-Various portfolio constructions have been proposed to minimize the risk of the
-portfolio under some constrains including expected returns. We propose a
-portfolio construction method that incorporates the complex valued principal
-component analysis into the risk diversification portfolio construction. The
-proposed method is verified to outperform the conventional risk parity and risk
-diversification portfolio constructions.
-",0,0,0,0,0,1
-20927,An Overview on Application of Machine Learning Techniques in Optical Networks,"  Today's telecommunication networks have become sources of enormous amounts of
-widely heterogeneous data. This information can be retrieved from network
-traffic traces, network alarms, signal quality indicators, users' behavioral
-data, etc. Advanced mathematical tools are required to extract meaningful
-information from these data and take decisions pertaining to the proper
-functioning of the networks from the network-generated data. Among these
-mathematical tools, Machine Learning (ML) is regarded as one of the most
-promising methodological approaches to perform network-data analysis and enable
-automated network self-configuration and fault management. The adoption of ML
-techniques in the field of optical communication networks is motivated by the
-unprecedented growth of network complexity faced by optical networks in the
-last few years. Such complexity increase is due to the introduction of a huge
-number of adjustable and interdependent system parameters (e.g., routing
-configurations, modulation format, symbol rate, coding schemes, etc.) that are
-enabled by the usage of coherent transmission/reception technologies, advanced
-digital signal processing and compensation of nonlinear effects in optical
-fiber propagation. In this paper we provide an overview of the application of
-ML to optical communications and networking. We classify and survey relevant
-literature dealing with the topic, and we also provide an introductory tutorial
-on ML for researchers and practitioners interested in this field. Although a
-good number of research papers have recently appeared, the application of ML to
-optical networks is still in its infancy: to stimulate further work in this
-area, we conclude the paper proposing new possible research directions.
-",0,0,0,1,0,0
-20928,Study of Minor Actinides Transmutation in PWR MOX fuel,"  The management of long-lived radionuclides in spent fuel is a key issue to
-achieve the closed nuclear fuel cycle and the sustainable development of
-nuclear energy. Partitioning-Transmutation is supposed to be an efficient
-method to treat the long-lived radionuclides in spent fuel. Some Minor
-Actinides (MAs) have very long half-lives among the radionuclides in the spent
-fuel. Accordingly, the study of MAs transmutation is a significant work for the
-post-processing of spent fuel.
-In the present work, the transmutations in Pressurized Water Reactor (PWR)
-mixed oxide (MOX) fuel are investigated through the Monte Carlo based code RMC.
-Two kinds of MAs, $^{237}$Np and five MAs ($^{237}$Np, $^{241}$Am, $^{243}$Am,
-$^{244}$Cm and $^{245}$Cm) are incorporated homogeneously into the MOX fuel
-assembly. The transmutation of MAs is simulated with different initial MOX
-concentrations.
-The results indicate an overall nice efficiency of transmutation in both
-initial MOX concentrations, especially for the two kinds of MAs primarily
-generated in the UOX fuel, $^{237}$Np and $^{241}$Am. In addition, the
-inclusion of $^{237}$Np in MOX has no large influence for other MAs, while the
-transmutation efficiency of $^{237}$Np is excellent. The transmutation of MAs
-in MOX fuel depletion is expected to be a new, efficient nuclear spent fuel
-management method for the future nuclear power generation.
-",0,1,0,0,0,0
-20929,Undesired parking spaces and contractible pieces of the noncrossing partition link,"  There are two natural simplicial complexes associated to the noncrossing
-partition lattice: the order complex of the full lattice and the order complex
-of the lattice with its bounding elements removed. The latter is a complex that
-we call the noncrossing partition link because it is the link of an edge in the
-former. The first author and his coauthors conjectured that various collections
-of simplices of the noncrossing partition link (determined by the undesired
-parking spaces in the corresponding parking functions) form contractible
-subcomplexes. In this article we prove their conjecture by combining the fact
-that the star of a simplex in a flag complex is contractible with the second
-author's theory of noncrossing hypertrees.
-",0,0,1,0,0,0
-20930,Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization,"  Due to their simplicity and excellent performance, parallel asynchronous
-variants of stochastic gradient descent have become popular methods to solve a
-wide range of large-scale optimization problems on multi-core architectures.
-Yet, despite their practical success, support for nonsmooth objectives is still
-lacking, making them unsuitable for many problems of interest in machine
-learning, such as the Lasso, group Lasso or empirical risk minimization with
-convex constraints.
-In this work, we propose and analyze ProxASAGA, a fully asynchronous sparse
-method inspired by SAGA, a variance reduced incremental gradient algorithm. The
-proposed method is easy to implement and significantly outperforms the state of
-the art on several nonsmooth, large-scale problems. We prove that our method
-achieves a theoretical linear speedup with respect to the sequential version
-under assumptions on the sparsity of gradients and block-separability of the
-proximal term. Empirical benchmarks on a multi-core architecture illustrate
-practical speedups of up to 12x on a 20-core machine.
-",1,0,1,1,0,0
-20931,Sparsity-promoting and edge-preserving maximum a posteriori estimators in non-parametric Bayesian inverse problems,"  We consider the inverse problem of recovering an unknown functional parameter
-$u$ in a separable Banach space, from a noisy observation $y$ of its image
-through a known possibly non-linear ill-posed map ${\mathcal G}$. The data $y$
-is finite-dimensional and the noise is Gaussian. We adopt a Bayesian approach
-to the problem and consider Besov space priors (see Lassas et al. 2009), which
-are well-known for their edge-preserving and sparsity-promoting properties and
-have recently attracted wide attention especially in the medical imaging
-community.
-Our key result is to show that in this non-parametric setup the maximum a
-posteriori (MAP) estimates are characterized by the minimizers of a generalized
-Onsager--Machlup functional of the posterior. This is done independently for
-the so-called weak and strong MAP estimates, which as we show coincide in our
-context. In addition, we prove a form of weak consistency for the MAP
-estimators in the infinitely informative data limit. Our results are remarkable
-for two reasons: first, the prior distribution is non-Gaussian and does not
-meet the smoothness conditions required in previous research on non-parametric
-MAP estimates. Second, the result analytically justifies existing uses of the
-MAP estimate in finite but high dimensional discretizations of Bayesian inverse
-problems with the considered Besov priors.
-",0,0,1,1,0,0
-20932,Non-LTE line formation of Fe in late-type stars IV: Modelling of the solar centre-to-limb variation in 3D,"  Our ability to model the shapes and strengths of iron lines in the solar
-spectrum is a critical test of the accuracy of the solar iron abundance, which
-sets the absolute zero-point of all stellar metallicities. We use an extensive
-463-level Fe atom with new photoionisation cross-sections for FeI as well as
-quantum mechanical calculations of collisional excitation and charge transfer
-with neutral hydrogen; the latter effectively remove a free parameter that has
-hampered all previous line formation studies of Fe in non-local thermodynamic
-equilibrium (NLTE). For the first time, we use realistic 3D NLTE calculations
-of Fe for a quantitative comparison to solar observations. We confront our
-theoretical line profiles with observations taken at different viewing angles
-across the solar disk with the Swedish 1-m Solar Telescope. We find that 3D
-modelling well reproduces the observed centre-to-limb behaviour of spectral
-lines overall, but highlight aspects that may require further work, especially
-cross-sections for inelastic collisions with electrons. Our inferred solar iron
-abundance is log(eps(Fe))=7.48+-0.04.
-",0,1,0,0,0,0
-20933,"Transmission clusters in the HIV-1 epidemic among men who have sex with men in Montreal, Quebec, Canada","  Background. Several studies have used phylogenetics to investigate Human
-Immunodeficiency Virus (HIV) transmission among Men who have Sex with Men
-(MSMs) in Montreal, Quebec, Canada, revealing many transmission clusters. The
-Quebec HIV genotyping program sequence database now includes viral sequences
-from close to 4,000 HIV-positive individuals classified as MSMs. In this paper,
-we investigate clustering in those data by comparing results from several
-methods: the conventional Bayesian and maximum likelihood-bootstrap methods,
-and two more recent algorithms, DM-PhyClus, a Bayesian algorithm that produces
-a measure of uncertainty for proposed partitions, and the Gap Procedure, a fast
-distance-based approach. We estimate cluster growth by focusing on recent cases
-in the Primary HIV Infection (PHI) stage. Results. The analyses reveal
-considerable overlap between cluster estimates obtained from conventional
-methods. The Gap Procedure and DM-PhyClus rely on different cluster definitions
-and as a result, suggest moderately different partitions. All estimates lead to
-similar conclusions about cluster expansion: several large clusters have
-experienced sizeable growth, and a few new transmission clusters are likely
-emerging. Conclusions. The lack of a gold standard measure for clustering
-quality makes picking a best estimate among those proposed difficult. Work
-aiming to refine clustering criteria would be required to improve estimates.
-Nevertheless, the results unanimously stress the role that clusters play in
-promoting HIV incidence among MSMs.
-",0,0,0,1,0,0
-20934,Conformal blocks attached to twisted groups,"  The aim of this paper is to generalize the notion of conformal blocks to the
-situation in which the Lie algebra they are attached to is not defined over a
-field, but depends on covering data of curves. The result will be a sheaf of
-conformal blocks on the Hurwitz stack parametrizing Galois coverings of curves.
-Many features of the classical sheaves of conformal blocks are proved to hold
-in this more general setting, in particular the fusion rules, the propagation
-of vacua and the WZW connection.
-",0,0,1,0,0,0
-20935,Statistical study on propagation characteristics of Omega signals (VLF) in magnetosphere detected by the Akebono satellite,"  This paper shows a statistical analysis of 10.2 kHz Omega broadcasts of an
-artificial signal broadcast from ground stations, propagated in the
-plasmasphere, and detected using an automatic detection method we developed. We
-study the propagation patterns of the Omega signals to understand the
-propagation characteristics that are strongly affected by plasmaspheric
-electron density and the ambient magnetic field. We show the unique propagation
-patterns of the Omega 10.2 kHz signal when it was broadcast from two
-high-middle-latitude stations. We use about eight years of data captured by the
-Poynting flux analyzer subsystem on board the Akebono satellite from October
-1989 to September 1997. We demonstrate that the signals broadcast from almost
-the same latitude (in geomagnetic coordinates) propagated differently depending
-on the geographic latitude. We also study propagation characteristics as a
-function of local time, season, and solar activity. The Omega signal tended to
-propagate farther on the nightside than on the dayside and was more widely
-distributed during winter than during summer. When solar activity was at
-maximum, the Omega signal propagated at a lower intensity level. In contrast,
-when solar activity was at minimum, the Omega signal propagated at a higher
-intensity and farther from the transmitter station.
-",0,1,0,0,0,0
-20936,"Thermally induced stresses in boulders on airless body surfaces, and implications for rock breakdown","  This work investigates the macroscopic thermomechanical behavior of lunar
-boulders by modeling their response to diurnal thermal forcing. Our results
-reveal a bimodal, spatiotemporally-complex stress response. During sunrise,
-stresses occur in the boulders' interiors that are associated with large-scale
-temperature gradients developed due to overnight cooling. During sunset,
-stresses occur at the boulders' exteriors due to the cooling and contraction of
-the surface. Both kinds of stresses are on the order of 10 MPa in 1 m boulders
-and decrease for smaller diameters, suggesting that larger boulders break down
-more quickly. Boulders <30 cm exhibit a weak response to thermal forcing,
-suggesting a threshold below which crack propagation may not occur. Boulders of
-any size buried by regolith are shielded from thermal breakdown. As boulders
-increase in size (>1 m), stresses increase to several 10s of MPa as the
-behavior of their surfaces approaches that of an infinite halfspace. As the
-thermal wave loses contact with the boulder interior, stresses become limited
-to the near-surface. This suggests that the survival time of a boulder is not
-only controlled by the amplitude of induced stress, but also by its diameter as
-compared to the diurnal skin depth. While stresses on the order of 10 MPa are
-enough to drive crack propagation in terrestrial environments, crack
-propagation rates in vacuum are not well constrained. We explore the
-relationship between boulder size, stress, and the direction of crack
-propagation, and discuss the implications for the relative breakdown rates and
-estimated lifetimes of boulders on airless body surfaces.
-",0,1,0,0,0,0
-20937,Some observations about generalized quantifiers in logics of imperfect information,"  We analyze the definitions of generalized quantifiers of imperfect
-information that have been proposed by F.Engström. We argue that these
-definitions are just embeddings of the first-order generalized quantifiers into
-team semantics, and fail to capture an adequate notion of team-theoretical
-generalized quantifier, save for the special cases in which the quantifiers are
-applied to flat formulas. We also criticize the meaningfulness of the
-monotone/nonmonotone distinction in this context. We make some proposals for a
-more adequate definition of generalized quantifiers of imperfect information.
-",0,0,1,0,0,0
-20938,Predicting Demographics of High-Resolution Geographies with Geotagged Tweets,"  In this paper, we consider the problem of predicting demographics of
-geographic units given geotagged Tweets that are composed within these units.
-Traditional survey methods that offer demographics estimates are usually
-limited in terms of geographic resolution, geographic boundaries, and time
-intervals. Thus, it would be highly useful to develop computational methods
-that can complement traditional survey methods by offering demographics
-estimates at finer geographic resolutions, with flexible geographic boundaries
-(i.e. not confined to administrative boundaries), and at different time
-intervals. While prior work has focused on predicting demographics and health
-statistics at relatively coarse geographic resolutions such as the county-level
-or state-level, we introduce an approach to predict demographics at finer
-geographic resolutions such as the blockgroup-level. For the task of predicting
-gender and race/ethnicity counts at the blockgroup-level, an approach adapted
-from prior work to our problem achieves an average correlation of 0.389
-(gender) and 0.569 (race) on a held-out test dataset. Our approach outperforms
-this prior approach with an average correlation of 0.671 (gender) and 0.692
-(race).
-",1,0,0,1,0,0
-20939,Text Compression for Sentiment Analysis via Evolutionary Algorithms,"  Can textual data be compressed intelligently without losing accuracy in
-evaluating sentiment? In this study, we propose a novel evolutionary
-compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression),
-which makes use of Parts-of-Speech tags to compress text in a way that
-sacrifices minimal classification accuracy when used in conjunction with
-sentiment analysis algorithms. An analysis of PARSEC with eight commercial and
-non-commercial sentiment analysis algorithms on twelve English sentiment data
-sets reveals that accurate compression is possible with (0%, 1.3%, 3.3%) loss
-in sentiment classification accuracy for (20%, 50%, 75%) data compression with
-PARSEC using LingPipe, the most accurate of the sentiment algorithms. Other
-sentiment analysis algorithms are more severely affected by compression. We
-conclude that significant compression of text data is possible for sentiment
-analysis depending on the accuracy demands of the specific application and the
-specific sentiment analysis algorithm used.
-",1,0,0,1,0,0
-20940,Training large margin host-pathogen protein-protein interaction predictors,"  Detection of protein-protein interactions (PPIs) plays a vital role in
-molecular biology. Particularly, infections are caused by the interactions of
-host and pathogen proteins. It is important to identify host-pathogen
-interactions (HPIs) to discover new drugs to counter infectious diseases.
-Conventional wet lab PPI prediction techniques have limitations in terms of
-large scale application and budget. Hence, computational approaches are
-developed to predict PPIs. This study aims to develop large margin machine
-learning models to predict interspecies PPIs with a special interest in
-host-pathogen protein interactions (HPIs). Especially, we focus on seeking
-answers to three queries that arise while developing an HPI predictor. 1) How
-should we select negative samples? 2) What should be the size of negative
-samples as compared to the positive samples? 3) What type of margin violation
-penalty should be used to train the predictor? We compare two available methods
-for negative sampling. Moreover, we propose a new method of assigning weights
-to each training example in weighted SVM depending on the distance of the
-negative examples from the positive examples. We have also developed a web
-server for our HPI predictor called HoPItor (Host Pathogen Interaction
-predicTOR) that can predict interactions between human and viral proteins. This
-webserver can be accessed at the URL:
-this http URL.
-",1,0,0,1,0,0
-20941,A Useful Solution of the Coupon Collector's Problem,"  The Coupon Collector's Problem is one of the few mathematical problems that
-make news headlines regularly. The reasons for this are on one hand the immense
-popularity of soccer albums (called Paninimania) and on the other hand that no
-solution is known that is able to take into account all effects such as
-replacement (limited purchasing of missing stickers) or swapping. In previous
-papers we have proven that the classical assumptions are not fulfilled in
-practice. Therefore we define new assumptions that match reality. Based on
-these assumptions we are able to derive formulae for the mean number of
-stickers needed (and the associated standard deviation) that are able to take
-into account all effects that occur in practical collecting. Thus collectors
-can estimate the average cost of completion of an album and its standard
-deviation just based on elementary calculations. From a practical point of view
-we consider the Coupon Collector's problem as solved.
------
-Das Sammelbilderproblem ist eines der wenigen mathematischen Probleme, die
-regelmä{\ss}ig in den Schlagzeilen der Nachrichten vorkommen. Dies liegt
-einerseits an der gro{\ss}en Popularität von Fu{\ss}ball-Sammelbildern
-(Paninimania genannt) und andererseits daran, dass es bisher keine Lösung
-gibt, die alle relevanten Effekte wie Nachkaufen oder Tauschen
-berücksichtigt. Wir haben bereits nachgewiesen, dass die klassischen Annahmen
-nicht der Realität entsprechen. Deshalb stellen wir neue Annahmen auf, die
-die Praxis besser abbilden. Darauf aufbauend können wir Formeln für die
-mittlere Anzahl benötigter Bilder (sowie deren Standardabweichung) ableiten,
-die alle in der Praxis relevanten Effekte berücksichtigen. Damit können
-Sammler die mittleren Kosten eines Albums sowie deren Standardabweichung nur
-mit Hilfe von elementaren Rechnungen bestimmen. Für praktische Zwecke ist das
-Sammelbilderproblem damit gelöst.
-",0,0,1,0,0,0
-20942,Accretion driven turbulence in filaments I: Non-gravitational accretion,"  We study accretion driven turbulence for different inflow velocities in star
-forming filaments using the code ramses. Filaments are rarely isolated objects
-and their gravitational potential will lead to radially dominated accretion. In
-the non-gravitational case, accretion by itself can already provoke
-non-isotropic, radially dominated turbulent motions responsible for the complex
-structure and non-thermal line widths observed in filaments. We find that there
-is a direct linear relation between the absolute value of the total density
-weighted velocity dispersion and the infall velocity. The turbulent velocity
-dispersion in the filaments is independent of sound speed or any net flow along
-the filament. We show that the density weighted velocity dispersion acts as an
-additional pressure term supporting the filament in hydrostatic equilibrium.
-Comparing to observations, we find that the projected non-thermal line width
-variation is generally subsonic independent of inflow velocity.
-",0,1,0,0,0,0
-20943,Contextual Outlier Interpretation,"  Outlier detection plays an essential role in many data-driven applications to
-identify isolated instances that are different from the majority. While many
-statistical learning and data mining techniques have been used for developing
-more effective outlier detection algorithms, the interpretation of detected
-outliers does not receive much attention. Interpretation is becoming
-increasingly important to help people trust and evaluate the developed models
-through providing intrinsic reasons why the certain outliers are chosen. It is
-difficult, if not impossible, to simply apply feature selection for explaining
-outliers due to the distinct characteristics of various detection models,
-complicated structures of data in certain applications, and imbalanced
-distribution of outliers and normal instances. In addition, the role of
-contrastive contexts where outliers locate, as well as the relation between
-outliers and contexts, are usually overlooked in interpretation. To tackle the
-issues above, in this paper, we propose a novel Contextual Outlier
-INterpretation (COIN) method to explain the abnormality of existing outliers
-spotted by detectors. The interpretability for an outlier is achieved from
-three aspects: outlierness score, attributes that contribute to the
-abnormality, and contextual description of its neighborhoods. Experimental
-results on various types of datasets demonstrate the flexibility and
-effectiveness of the proposed framework compared with existing interpretation
-approaches.
-",1,0,0,1,0,0
-20944,Game Theory for Secure Critical Interdependent Gas-Power-Water Infrastructure,"  A city's critical infrastructure such as gas, water, and power systems, are
-largely interdependent since they share energy, computing, and communication
-resources. This, in turn, makes it challenging to endow them with fool-proof
-security solutions. In this paper, a unified model for interdependent
-gas-power-water infrastructure is presented and the security of this model is
-studied using a novel game-theoretic framework. In particular, a zero-sum
-noncooperative game is formulated between a malicious attacker who seeks to
-simultaneously alter the states of the gas-power-water critical infrastructure
-to increase the power generation cost and a defender who allocates
-communication resources over its attack detection filters in local areas to
-monitor the infrastructure. At the mixed strategy Nash equilibrium of this
-game, numerical results show that the expected power generation cost deviation
-is 35\% lower than the one resulting from an equal allocation of resources over
-the local filters. The results also show that, at equilibrium, the
-interdependence of the power system on the natural gas and water systems can
-motivate the attacker to target the states of the water and natural gas systems
-to change the operational states of the power grid. Conversely, the defender
-allocates a portion of its resources to the water and natural gas states of the
-interdependent system to protect the grid from state deviations.
-",1,0,0,0,0,0
-20945,Implicit Entity Linking in Tweets,"  Over the years, Twitter has become one of the largest communication platforms
-providing key data to various applications such as brand monitoring, trend
-detection, among others. Entity linking is one of the major tasks in natural
-language understanding from tweets and it associates entity mentions in text to
-corresponding entries in knowledge bases in order to provide unambiguous
-interpretation and additional con- text. State-of-the-art techniques have
-focused on linking explicitly mentioned entities in tweets with reasonable
-success. However, we argue that in addition to explicit mentions i.e. The movie
-Gravity was more ex- pensive than the mars orbiter mission entities (movie
-Gravity) can also be mentioned implicitly i.e. This new space movie is crazy.
-you must watch it!. This paper introduces the problem of implicit entity
-linking in tweets. We propose an approach that models the entities by
-exploiting their factual and contextual knowledge. We demonstrate how to use
-these models to perform implicit entity linking on a ground truth dataset with
-397 tweets from two domains, namely, Movie and Book. Specifically, we show: 1)
-the importance of linking implicit entities and its value addition to the
-standard entity linking task, and 2) the importance of exploiting contextual
-knowledge associated with an entity for linking their implicit mentions. We
-also make the ground truth dataset publicly available to foster the research in
-this new research area.
-",1,0,0,0,0,0
-20946,Non-cocompact Group Actions and $π_1$-Semistability at Infinity,"  A finitely presented 1-ended group $G$ has {\it semistable fundamental group
-at infinity} if $G$ acts geometrically on a simply connected and locally
-compact ANR $Y$ having the property that any two proper rays in $Y$ are
-properly homotopic. This property of $Y$ captures a notion of connectivity at
-infinity stronger than ""1-ended"", and is in fact a feature of $G$, being
-independent of choices. It is a fundamental property in the homotopical study
-of finitely presented groups. While many important classes of groups have been
-shown to have semistable fundamental group at infinity, the question of whether
-every $G$ has this property has been a recognized open question for nearly
-forty years. In this paper we attack the problem by considering a proper {\it
-but non-cocompact} action of a group $J$ on such an $Y$. This $J$ would
-typically be a subgroup of infinite index in the geometrically acting
-over-group $G$; for example $J$ might be infinite cyclic or some other subgroup
-whose semistability properties are known. We divide the semistability property
-of $G$ into a $J$-part and a ""perpendicular to $J$"" part, and we analyze how
-these two parts fit together. Among other things, this analysis leads to a
-proof (in a companion paper) that a class of groups previously considered to be
-likely counter examples do in fact have the semistability property.
-",0,0,1,0,0,0
-20947,Robust Distributed Control of DC Microgrids with Time-Varying Power Sharing,"  This paper addresses the problem of output voltage regulation for multiple
-DC/DC converters connected to a microgrid, and prescribes a scheme for sharing
-power among different sources. This architecture is structured in such a way
-that it admits quantifiable analysis of the closed-loop performance of the
-network of converters; the analysis simplifies to studying closed-loop
-performance of an equivalent {\em single-converter} system. The proposed
-architecture allows for the proportion in which the sources provide power to
-vary with time; thus overcoming limitations of our previous designs.
-Additionally, the proposed control framework is suitable to both centralized
-and decentralized implementations, i.e., the same control architecture can be
-employed for voltage regulation irrespective of the availability of common
-load-current (or power) measurement, without the need to modify controller
-parameters. The performance becomes quantifiably better with better
-communication of the demanded load to all the controllers at all the converters
-(in the centralized case); however guarantees viability when such communication
-is absent. Case studies comprising of battery, PV and generic sources are
-presented and demonstrate the enhanced performance of prescribed optimal
-controllers for voltage regulation and power sharing.
-",1,0,1,0,0,0
-20948,Quantum Lower Bounds for Tripartite Versions of the Hidden Shift and the Set Equality Problems,"  In this paper, we study quantum query complexity of the following rather
-natural tripartite generalisations (in the spirit of the 3-sum problem) of the
-hidden shift and the set equality problems, which we call the 3-shift-sum and
-the 3-matching-sum problems.
-The 3-shift-sum problem is as follows: given a table of $3\times n$ elements,
-is it possible to circularly shift its rows so that the sum of the elements in
-each column becomes zero? It is promised that, if this is not the case, then no
-3 elements in the table sum up to zero. The 3-matching-sum problem is defined
-similarly, but it is allowed to arbitrarily permute elements within each row.
-For these problems, we prove lower bounds of $\Omega(n^{1/3})$ and
-$\Omega(\sqrt n)$, respectively. The second lower bound is tight.
-The lower bounds are proven by a novel application of the dual learning graph
-framework and by using representation-theoretic tools.
-",1,0,0,0,0,0
-20949,A Parallel Direct Cut Algorithm for High-Order Overset Methods with Application to a Spinning Golf Ball,"  Overset methods are commonly employed to enable the effective simulation of
-problems involving complex geometries and moving objects such as rotorcraft.
-This paper presents a novel overset domain connectivity algorithm based upon
-the direct cut approach suitable for use with GPU-accelerated solvers on
-high-order curved grids. In contrast to previous methods it is capable of
-exploiting the highly data-parallel nature of modern accelerators. Further, the
-approach is also substantially more efficient at handling the curved grids
-which arise within the context of high-order methods. An implementation of this
-new algorithm is presented and combined with a high-order fluid dynamics code.
-The algorithm is validated against several benchmark problems, including flow
-over a spinning golf ball at a Reynolds number of 150,000.
-",0,1,0,0,0,0
-20950,A data assimilation algorithm: the paradigm of the 3D Leray-alpha model of turbulence,"  In this paper we survey the various implementations of a new data
-assimilation (downscaling) algorithm based on spatial coarse mesh measurements.
-As a paradigm, we demonstrate the application of this algorithm to the 3D
-Leray-$\alpha$ subgrid scale turbulence model. Most importantly, we use this
-paradigm to show that it is not always necessary that one has to collect coarse
-mesh measurements of all the state variables, that are involved in the
-underlying evolutionary system, in order to recover the corresponding exact
-reference solution. Specifically, we show that in the case of the 3D
-Leray$-\alpha$ model of turbulence the solutions of the algorithm, constructed
-using only coarse mesh observations of any two components of the
-three-dimensional velocity field, and without any information of the third
-component, converge, at an exponential rate in time, to the corresponding exact
-reference solution of the 3D Leray$-\alpha$ model. This study serves as an
-addendum to our recent work on abridged continuous data assimilation for the 2D
-Navier-Stokes equations. Notably, similar results have also been recently
-established for the 3D viscous Planetary Geostrophic circulation model in which
-we show that coarse mesh measurements of the temperature alone are sufficient
-for recovering, through our data assimilation algorithm, the full solution;
-viz. the three components of velocity vector field and the temperature.
-Consequently, this proves the Charney conjecture for the 3D Planetary
-Geostrophic model; namely, that the history of the large spatial scales of
-temperature is sufficient for determining all the other quantities (state
-variables) of the model.
-",0,1,1,0,0,0
-20951,Playing a true Parrondo's game with a three state coin on a quantum walk,"  Playing a Parrondo's game with a qutrit is the subject of this paper. We show
-that a true quantum Parrondo's game can be played with a 3 state coin(qutrit)
-in a 1D quantum walk in contrast to the fact that playing a true Parrondo's
-game with a 2 state coin(qubit) in 1D quantum walk fails in the asymptotic
-limits.
-",1,1,0,0,0,0
-20952,Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data,"  We analyse multimodal time-series data corresponding to weight, sleep and
-steps measurements. We focus on predicting whether a user will successfully
-achieve his/her weight objective. For this, we design several deep long
-short-term memory (LSTM) architectures, including a novel cross-modal LSTM
-(X-LSTM), and demonstrate their superiority over baseline approaches. The
-X-LSTM improves parameter efficiency by processing each modality separately and
-allowing for information flow between them by way of recurrent
-cross-connections. We present a general hyperparameter optimisation technique
-for X-LSTMs, which allows us to significantly improve on the LSTM and a prior
-state-of-the-art cross-modal approach, using a comparable number of parameters.
-Finally, we visualise the model's predictions, revealing implications about
-latent variables in this task.
-",1,0,0,1,0,0
-20953,Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions,"  The key idea of variational auto-encoders (VAEs) resembles that of
-traditional auto-encoder models in which spatial information is supposed to be
-explicitly encoded in the latent space. However, the latent variables in VAEs
-are vectors, which can be interpreted as multiple feature maps of size 1x1.
-Such representations can only convey spatial information implicitly when
-coupled with powerful decoders. In this work, we propose spatial VAEs that use
-feature maps of larger size as latent variables to explicitly capture spatial
-information. This is achieved by allowing the latent variables to be sampled
-from matrix-variate normal (MVN) distributions whose parameters are computed
-from the encoder network. To increase dependencies among locations on latent
-feature maps and reduce the number of parameters, we further propose spatial
-VAEs via low-rank MVN distributions. Experimental results show that the
-proposed spatial VAEs outperform original VAEs in capturing rich structural and
-spatial information.
-",1,0,0,1,0,0
-20954,Optimal Ramp Schemes and Related Combinatorial Objects,"  In 1996, Jackson and Martin proved that a strong ideal ramp scheme is
-equivalent to an orthogonal array. However, there was no good characterization
-of ideal ramp schemes that are not strong. Here we show the equivalence of
-ideal ramp schemes to a new variant of orthogonal arrays that we term augmented
-orthogonal arrays. We give some constructions for these new kinds of arrays,
-and, as a consequence, we also provide parameter situations where ideal ramp
-schemes exist but strong ideal ramp schemes do not exist.
-",1,0,0,0,0,0
-20955,Do Neural Nets Learn Statistical Laws behind Natural Language?,"  The performance of deep learning in natural language processing has been
-spectacular, but the reasons for this success remain unclear because of the
-inherent complexity of deep learning. This paper provides empirical evidence of
-its effectiveness and of a limitation of neural networks for language
-engineering. Precisely, we demonstrate that a neural language model based on
-long short-term memory (LSTM) effectively reproduces Zipf's law and Heaps' law,
-two representative statistical properties underlying natural language. We
-discuss the quality of reproducibility and the emergence of Zipf's law and
-Heaps' law as training progresses. We also point out that the neural language
-model has a limitation in reproducing long-range correlation, another
-statistical property of natural language. This understanding could provide a
-direction for improving the architectures of neural networks.
-",1,0,0,0,0,0
-20956,Super-speeds with Zero-RAM: Next Generation Large-Scale Optimization in Your Laptop!,"  This article presents the novel breakthrough general purpose algorithm for
-large scale optimization problems. The novel algorithm is capable of achieving
-breakthrough speeds for very large-scale optimization on general purpose
-laptops and embedded systems. Application of the algorithm to the Griewank
-function was possible in up to 1 billion decision variables in double precision
-took only 64485 seconds (~18 hours) to solve, while consuming 7,630 MB (7.6 GB)
-or RAM on a single threaded laptop CPU. It shows that the algorithm is
-computationally and memory (space) linearly efficient, and can find the optimal
-or near-optimal solution in a fraction of the time and memory that many
-conventional algorithms require. It is envisaged that this will open up new
-possibilities of real-time large-scale problems on personal laptops and
-embedded systems.
-",1,0,0,0,0,0
-20957,Recoverable Energy of Dissipative Electromagnetic Systems,"  Ambiguities in the definition of stored energy within distributed or
-radiating electromagnetic systems motivate the discussion of the well-defined
-concept of recoverable energy. This concept is commonly overlooked by the
-community and the purpose of this communication is to recall its existence and
-to discuss its relationship to fractional bandwidth. Using a rational function
-approximation of a system's input impedance, the recoverable energy of lumped
-and radiating systems is calculated in closed form and is related to stored
-energy and fractional bandwidth. Lumped circuits are also used to demonstrate
-the relationship between recoverable energy and the energy stored within
-equivalent circuits produced by the minimum phase-shift Darlington's synthesis
-procedure.
-",0,1,0,0,0,0
-20958,Elliptic Hall algebra on $\mathbb{F}_1$,"  We construct Hall algebra of elliptic curve over $\mathbb{F}_1$ using the
-theory of monoidal scheme due to Deitmar and the theory of Hall algebra for
-monoidal representations due to Szczesny. The resulting algebra is shown to be
-a specialization of elliptic Hall algebra studied by Burban and Schiffmann.
-Thus our algebra is isomorphic to the skein algebra for torus by the recent
-work of Morton and Samuelson.
-",0,0,1,0,0,0
-20959,Approximate Bayesian inference with queueing networks and coupled jump processes,"  Queueing networks are systems of theoretical interest that give rise to
-complex families of stochastic processes, and find widespread use in the
-performance evaluation of interconnected resources. Yet, despite their
-importance within applications, and in comparison to their counterpart
-stochastic models in genetics or mathematical biology, there exist few relevant
-approaches for transient inference and uncertainty quantification tasks in
-these systems. This is a consequence of strong computational impediments and
-distinctive properties of the Markov jump processes induced by queueing
-networks. In this paper, we offer a comprehensive overview of the inferential
-challenge and its comparison to analogue tasks within related mathematical
-domains. We then discuss a model augmentation over an approximating network
-system, and present a flexible and scalable variational Bayesian framework,
-which is targeted at general-form open and closed queueing systems, with varied
-service disciplines and priorities. The inferential procedure is finally
-validated in a couple of uncertainty quantification tasks for network service
-rates.
-",1,0,0,1,0,0
-20960,Universal features of price formation in financial markets: perspectives from Deep Learning,"  Using a large-scale Deep Learning approach applied to a high-frequency
-database containing billions of electronic market quotes and transactions for
-US equities, we uncover nonparametric evidence for the existence of a universal
-and stationary price formation mechanism relating the dynamics of supply and
-demand for a stock, as revealed through the order book, to subsequent
-variations in its market price. We assess the model by testing its
-out-of-sample predictions for the direction of price moves given the history of
-price and order flow, across a wide range of stocks and time periods. The
-universal price formation model is shown to exhibit a remarkably stable
-out-of-sample prediction accuracy across time, for a wide range of stocks from
-different sectors. Interestingly, these results also hold for stocks which are
-not part of the training sample, showing that the relations captured by the
-model are universal and not asset-specific.
-The universal model --- trained on data from all stocks --- outperforms, in
-terms of out-of-sample prediction accuracy, asset-specific linear and nonlinear
-models trained on time series of any given stock, showing that the universal
-nature of price formation weighs in favour of pooling together financial data
-from various stocks, rather than designing asset- or sector-specific models as
-commonly done. Standard data normalizations based on volatility, price level or
-average spread, or partitioning the training data into sectors or categories
-such as large/small tick stocks, do not improve training results. On the other
-hand, inclusion of price and order flow history over many past observations is
-shown to improve forecasting performance, showing evidence of path-dependence
-in price dynamics.
-",0,0,0,1,0,1
-20961,A New Tracking Algorithm for Multiple Colloidal Particles Close to Contact,"  In this paper, we propose a new algorithm based on radial symmetry center
-method to track colloidal particles close to contact, where the optical images
-of the particles start to overlap in digital video microscopy. This overlapping
-effect is important to observe the pair interaction potential in colloidal
-studies and it appears as additional interaction in the measurement of the
-interaction with conventional tracking analysis. The proposed algorithm in this
-work is simple, fast and applicable for not only two particles but also three
-and more particles without any modification. The algorithm uses gradient
-vectors of the particle intensity distribution, which allows us to use a part
-of the symmetric intensity distribution in the calculation of the actual
-particle position. In this study, simulations are performed to see the
-performance of the proposed algorithm for two and three particles, where the
-simulation images are generated by using fitted curve to experimental particle
-image for different sized particles. As a result, the algorithm yields the
-maximum error smaller than 2 nm for 5.53 {\mu}m silica particles in contact
-condition.
-",0,1,0,0,0,0
-20962,Critical Percolation Without Fine Tuning on the Surface of a Topological Superconductor,"  We present numerical evidence that most two-dimensional surface states of a
-bulk topological superconductor (TSC) sit at an integer quantum Hall plateau
-transition. We study TSC surface states in class CI with quenched disorder.
-Low-energy (finite-energy) surface states were expected to be critically
-delocalized (Anderson localized). We confirm the low-energy picture, but find
-instead that finite-energy states are also delocalized, with universal
-statistics that are independent of the TSC winding number, and consistent with
-the spin quantum Hall plateau transition (percolation).
-",0,1,0,0,0,0
-20963,Low-luminosity stellar wind accretion onto neutron stars in HMXBs,"  Features and applications of quasi-spherical settling accretion onto rotating
-magnetized neutron stars in high-mass X-ray binaries are discussed. The
-settling accretion occurs in wind-fed HMXBs when the plasma cooling time is
-longer than the free-fall time from the gravitational capture radius, which can
-take place in low-luminosity HMXBs with $L_x\lesssim 4\times 10^{36}$ erg/s. We
-briefly review the implications of the settling accretion, focusing on the SFXT
-phenomenon, which can be related to instability of the quasi-spherical
-convective shell above the neutron star magnetosphere due to magnetic
-reconnection from fast temporarily magnetized winds from OB-supergiant. If a
-young neutron star in a wind-fed HMXB is rapidly rotating, the propeller regime
-in a quasi-spherical hot shell occurs. We show that X-ray spectral and temporal
-properties of enigmatic $\gamma$ Cas Be-stars are consistent with failed
-settling accretion regime onto a propelling neutron star. The subsequent
-evolutionary stage of $\gamma$ Cas and its analogs should be the X Per-type
-binaries comprising low-luminosity slowly rotating X-ray pulsars.
-",0,1,0,0,0,0
-20964,Faithful Inversion of Generative Models for Effective Amortized Inference,"  Inference amortization methods share information across multiple
-posterior-inference problems, allowing each to be carried out more efficiently.
-Generally, they require the inversion of the dependency structure in the
-generative model, as the modeller must learn a mapping from observations to
-distributions approximating the posterior. Previous approaches have involved
-inverting the dependency structure in a heuristic way that fails to capture
-these dependencies correctly, thereby limiting the achievable accuracy of the
-resulting approximations. We introduce an algorithm for faithfully, and
-minimally, inverting the graphical model structure of any generative model.
-Such inverses have two crucial properties: (a) they do not encode any
-independence assertions that are absent from the model and; (b) they are local
-maxima for the number of true independencies encoded. We prove the correctness
-of our approach and empirically show that the resulting minimally faithful
-inverses lead to better inference amortization than existing heuristic
-approaches.
-",1,0,0,1,0,0
-20965,A social Network Analysis of the Operations Research/Industrial Engineering Faculty Hiring Network,"  We study the U.S. Operations Research/Industrial-Systems Engineering (ORIE)
-faculty hiring network, consisting of 1,179 faculty origin and destination data
-together with attribute data from 83 ORIE departments. A social network
-analysis of faculty hires can reveal important patterns in an academic field,
-such as the existence of a hierarchy or sociological aspects such as the
-presence of communities of departments. We first statistically test for the
-existence of a linear hierarchy in the network and for its steepness. We find a
-near linear hierarchical order of the departments, proposing a new index for
-hiring networks, which we contrast with other indicators of hierarchy,
-including published rankings. A single index is not capable to capture the full
-structure of a complex network, however, so we next fit a latent exponential
-random graph model (ERGM) to the network, which is able to reproduce its main
-observed characteristics: high incidence of self-hiring, skewed out-degree
-distribution, low density and clustering. Finally, we use the latent variables
-in the ERGM to simplify the network to one where faculty hires take place among
-three groups of departments. We contrast our findings with those reported for
-other related disciplines, Computer Science and Business.
-",1,0,0,1,0,0
-20966,One-sample aggregate data meta-analysis of medians,"  An aggregate data meta-analysis is a statistical method that pools the
-summary statistics of several selected studies to estimate the outcome of
-interest. When considering a continuous outcome, typically each study must
-report the same measure of the outcome variable and its spread (e.g., the
-sample mean and its standard error). However, some studies may instead report
-the median along with various measures of spread. Recently, the task of
-incorporating medians in meta-analysis has been achieved by estimating the
-sample mean and its standard error from each study that reports a median in
-order to meta-analyze the means. In this paper, we propose two alternative
-approaches to meta-analyze data that instead rely on medians. We systematically
-compare these approaches via simulation study to each other and to methods that
-transform the study-specific medians and spread into sample means and their
-standard errors. We demonstrate that the proposed median-based approaches
-perform better than the transformation-based approaches, especially when
-applied to skewed data and data with high inter-study variance. In addition,
-when meta-analyzing data that consists of medians, we show that the
-median-based approaches perform considerably better than or comparably to the
-best-case scenario for a transformation approach: conducting a meta-analysis
-using the actual sample mean and standard error of the mean of each study.
-Finally, we illustrate these approaches in a meta-analysis of patient delay in
-tuberculosis diagnosis.
-",0,0,0,1,0,0
-20967,QuickCast: Fast and Efficient Inter-Datacenter Transfers using Forwarding Tree Cohorts,"  Large inter-datacenter transfers are crucial for cloud service efficiency and
-are increasingly used by organizations that have dedicated wide area networks
-between datacenters. A recent work uses multicast forwarding trees to reduce
-the bandwidth needs and improve completion times of point-to-multipoint
-transfers. Using a single forwarding tree per transfer, however, leads to poor
-performance because the slowest receiver dictates the completion time for all
-receivers. Using multiple forwarding trees per transfer alleviates this
-concern--the average receiver could finish early; however, if done naively,
-bandwidth usage would also increase and it is apriori unclear how best to
-partition receivers, how to construct the multiple trees and how to determine
-the rate and schedule of flows on these trees. This paper presents QuickCast, a
-first solution to these problems. Using simulations on real-world network
-topologies, we see that QuickCast can speed up the average receiver's
-completion time by as much as $10\times$ while only using $1.04\times$ more
-bandwidth; further, the completion time for all receivers also improves by as
-much as $1.6\times$ faster at high loads.
-",1,0,0,0,0,0
-20968,Contemporary machine learning: a guide for practitioners in the physical sciences,"  Machine learning is finding increasingly broad application in the physical
-sciences. This most often involves building a model relationship between a
-dependent, measurable output and an associated set of controllable, but
-complicated, independent inputs. We present a tutorial on current techniques in
-machine learning -- a jumping-off point for interested researchers to advance
-their work. We focus on deep neural networks with an emphasis on demystifying
-deep learning. We begin with background ideas in machine learning and some
-example applications from current research in plasma physics. We discuss
-supervised learning techniques for modeling complicated functions, beginning
-with familiar regression schemes, then advancing to more sophisticated deep
-learning methods. We also address unsupervised learning and techniques for
-reducing the dimensionality of input spaces. Along the way, we describe methods
-for practitioners to help ensure that their models generalize from their
-training data to as-yet-unseen test data. We describe classes of tasks --
-predicting scalars, handling images, fitting time-series -- and prepare the
-reader to choose an appropriate technique. We finally point out some
-limitations to modern machine learning and speculate on some ways that
-practitioners from the physical sciences may be particularly suited to help.
-",1,1,0,0,0,0
-20969,Uniform diamond coatings on WC-Co hard alloy cutting inserts deposited by a microwave plasma CVD,"  Polycrystalline diamond coatings have been grown on cemented carbide
-substrates with different aspect ratios by a microwave plasma CVD in
-methane-hydrogen gas mixtures. To protect the edges of the substrates from
-non-uniform heating due to the plasma edge effect, a special plateholder with
-pockets for group growth has been used. The difference in heights of the
-substrates and plateholder, and its influence on the diamond film mean grain
-size, growth rate, phase composition and stress was investigated. The substrate
-temperature range, within which uniform diamond films are produced with good
-adhesion, is determined. The diamond-coated cutting inserts produced at
-optimized process exhibited a reduction of cutting force and wear resistance by
-a factor of two, and cutting efficiency increase by 4.3 times upon turning A390
-Al-Si alloy as compared to performance of uncoated tools.
-",0,1,0,0,0,0
-20970,Analysing Soccer Games with Clustering and Conceptors,"  We present a new approach for identifying situations and behaviours, which we
-call ""moves"", from soccer games in the 2D simulation league. Being able to
-identify key situations and behaviours are useful capabilities for analysing
-soccer matches, anticipating opponent behaviours to aid selection of
-appropriate tactics, and also as a prerequisite for automatic learning of
-behaviours and policies. To support a wide set of strategies, our goal is to
-identify situations from data, in an unsupervised way without making use of
-pre-defined soccer specific concepts such as ""pass"" or ""dribble"". The recurrent
-neural networks we use in our approach act as a high-dimensional projection of
-the recent history of a situation on the field. Similar situations, i.e., with
-similar histories, are found by clustering of network states. The same networks
-are also used to learn so-called conceptors, that are lower-dimensional
-manifolds that describe trajectories through a high-dimensional state space
-that enable situation-specific predictions from the same neural network. With
-the proposed approach, we can segment games into sequences of situations that
-are learnt in an unsupervised way, and learn conceptors that are useful for the
-prediction of the near future of the respective situation.
-",1,0,0,0,0,0
-20971,On the Efficient Simulation of the Left-Tail of the Sum of Correlated Log-normal Variates,"  The sum of Log-normal variates is encountered in many challenging
-applications such as in performance analysis of wireless communication systems
-and in financial engineering. Several approximation methods have been developed
-in the literature, the accuracy of which is not ensured in the tail regions.
-These regions are of primordial interest wherein small probability values have
-to be evaluated with high precision. Variance reduction techniques are known to
-yield accurate, yet efficient, estimates of small probability values. Most of
-the existing approaches, however, have considered the problem of estimating the
-right-tail of the sum of Log-normal random variables (RVS). In the present
-work, we consider instead the estimation of the left-tail of the sum of
-correlated Log-normal variates with Gaussian copula under a mild assumption on
-the covariance matrix. We propose an estimator combining an existing
-mean-shifting importance sampling approach with a control variate technique.
-The main result is that the proposed estimator has an asymptotically vanishing
-relative error which represents a major finding in the context of the left-tail
-simulation of the sum of Log-normal RVs. Finally, we assess by various
-simulation results the performances of the proposed estimator compared to
-existing estimators.
-",0,0,1,1,0,0
-20972,Why optional stopping is a problem for Bayesians,"  Recently, optional stopping has been a subject of debate in the Bayesian
-psychology community. Rouder (2014) argues that optional stopping is no problem
-for Bayesians, and even recommends the use of optional stopping in practice, as
-do Wagenmakers et al. (2012). This article addresses the question whether
-optional stopping is problematic for Bayesian methods, and specifies under
-which circumstances and in which sense it is and is not. By slightly varying
-and extending Rouder's (2014) experiment, we illustrate that, as soon as the
-parameters of interest are equipped with default or pragmatic priors - which
-means, in most practical applications of Bayes Factor hypothesis testing -
-resilience to optional stopping can break down. We distinguish between four
-types of default priors, each having their own specific issues with optional
-stopping, ranging from no-problem-at-all (Type 0 priors) to quite severe (Type
-II and III priors).
-",0,0,1,1,0,0