April 2019 arXiv papers — page 101

ATLAS Collaboration

This letter describes the observation of the light-by-light scattering process, $γγ\rightarrowγγ$, in Pb+Pb collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV. The analysis is conducted using a data sample corresponding to an integrated luminosity of 1.73 nb$^{-1}$, collected in November 2018 by the ATLAS experiment at the LHC. Light-by-light scattering candida

Yifan Zhu, Fan Dai, Ranjan Maitra

We develop methodology for visualization of labeled mixed-featured datasets. We first investigate datasets with continuous features where our Max-Ratio Projection (MRP) method utilizes the group information in high dimensions to provide distinctive lower-dimensional projections that are then displayed using Radviz3D. Our methodology is extended to datasets w

Taposh Banerjee, Prudhvi Gurram, Gene Whipps

Theory and algorithms are developed for detecting changes in the distribution of statistically periodic random processes. The statistical periodicity is modeled using independent and periodically identically distributed processes, a new class of stochastic processes proposed by us. An algorithm is developed that is minimax asymptotically optimal as the false

Elizaveta V. Okorokova, James M. Goodman, Nicholas G. Hatsopoulos, Sliman J. Bensmaia

The hand, a complex effector comprising dozens of degrees of freedom of movement, endows us with the ability to flexibly, precisely, and effortlessly interact with objects. The neural signals associated with dexterous hand movements in primary motor cortex (M1) and somatosensory cortex (SC) have received comparatively less attention than have those that are

Arun Parthasarathy, Shaloo Rakheja

The equation of motion of the staggered order parameter is derived in a step-by-step manner from the coupled Landau-Lifshitz-Gilbert dynamics of bipartite spin moments in the limit of strong antiferromagnetic exchange coupling.

Ben Hayes

We give many examples of algebraic actions which are factors of Bernoulli shifts. These include certain harmonic models over left orderable groups of large enough growth, as well as algebraic actions associated to certain lopsided elements in any left orderable group. For many of our examples, the acting group is amenable so these actions are Bernoulli (and

Artur O. Lopes, Victor Vargas

We are interested in the study of Gibbs and equilbrium probabilities on the lattice $\mathbb{R}^{\mathbb{N}}$. Consider the unilateral full-shift defined on the non-compact set $\mathbb{R}^{\mathbb{N}}$ and an $α$-Hölder continuous potential $A$ from $\mathbb{R}^{\mathbb{N}}$ into $\mathbb{R}$. From a suitable class of a priori probability measures $ν$ (over

Md Mahmudul Hasan, Md. Noor-E-Alam, Mehul Rakeshkumar Patel, Alicia Sasser Modestino

Overdose related to prescription opioids have reached an epidemic level in the US, creating an unprecedented national crisis. This has been exacerbated partly due to the lack of tools for physicians to help predict the risk of whether a patient will develop opioid use disorder. Little is known about how machine learning can be applied to a big-data platform

Roee Levy Leshem, Raja Giryes

This paper introduces Taco-VC, a novel architecture for voice conversion based on Tacotron synthesizer, which is a sequence-to-sequence with attention model. The training of multi-speaker voice conversion systems requires a large number of resources, both in training and corpus size. Taco-VC is implemented using a single speaker Tacotron synthesizer based on

Yongkuan Cheng, Juncheng Wei

We consider the following quasilinear Schr\"{o}dinger equations of the form \begin{equation*} \triangle u-\varepsilon V(x)u+u\triangle u^2+u^{p}=0,\ u>0\ \mbox{in}\ \mathbb{R}^N\ \mbox{and}\ \underset{|x|\rightarrow \infty}{\lim} u(x)=0, \end{equation*} where $N\geq 3,$ $p>\frac{N+2}{N-2},$ $\varepsilon>0$ and $V(x)$ is a positive function. By imposing appro

S. S. Melnyk, V. A. Yampol'skii, O. V. Usatenko

We study the non-Markovian random continuous processes described by the Mori-Zwanzig equation. As a starting point, we use the Markovian Gaussian Ornstein-Uhlenbeck process and introduce an integral memory term depending on the past of the process into expression for the higher-order transition probability function and stochastic differential equation. We sh

Gireesh C. Joshi

The spectroscopic and photometric analysis of sample Am stars are carried out to determine the stellar characteristics of each studied star. The CCD photometric analysis of HD 98851 and HD 207561 show clear evidence of pulsation variability of 1.55 hr and 5.8 min respectively. Similarly, a clear evidence of the photometric variability is also found for an Am

Mazen Ali

We show how local bounded interactions in an unbounded Hamiltonian lead to eigenfunctions with favorable low-rank properties. To this end, we utilize ideas from quantum entanglement of multi-particle spin systems. We begin by analyzing the connection between entropy area laws and low-rank approximability. The characterization for 1D chains such as Matrix Pro

V. Manuilov

For two discrete metric spaces, $X$ and $Y$ we consider metrics on $X\sqcup Y$ compatible with the metrics on $X$ and $Y$. As morphisms from $X$ to $Y$ we consider the Roe bimodules, i.e. the norm closures of bounded finite propagation operators from $l^2(X)$ to $l^2(Y)$. We study the corresponding category $\mathcal M$, which is also a 2-category. We show t

Yuemeng Li, Yong Fan

Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans. It remains challenging to build nodule detection deep learning models with good generalization performance due to unbalanced positive and negative samples. In order to overcome this problem and further improve state-of-the-art nodule dete

Sidhanth Mohanty, Ryan O'Donnell

Let $X$ be an infinite graph of bounded degree; e.g., the Cayley graph of a free product of finite groups. If $G$ is a finite graph covered by $X$, it is said to be $X$-Ramanujan if its second-largest eigenvalue $λ_2(G)$ is at most the spectral radius $ρ(X)$ of $X$, and more generally $k$-quasi-$X$-Ramanujan if $λ_k(G)$ is at most $ρ(X)$. In case $X$ is the

D. M. Busiello, D. Gupta, A. Maritan

The entropy production is one of the most essential features for systems operating out of equilibrium. The formulation for discrete-state systems goes back to the celebrated Schnakenberg's work and hitherto can be carried out when for each transition between two states also the reverse one is allowed. Nevertheless, several physical systems may exhibit a

Fatma P. Aslan, Matthias Burkardt, Marc Schlegel

While twist-2 GPDs allow for a determination of the distribution of partons on the transverse plane, twist-3 GPDs contain quark-gluon correlations that provide information about the average transverse color Lorentz force acting on quarks. We demonstrate how twist-3 GPDs can be used to provide transverse position information about that force.

Xin Wang, Jiawei Wu, Junkun Chen, Lei Li

We present a new large-scale multilingual video description dataset, VATEX, which contains over 41,250 videos and 825,000 captions in both English and Chinese. Among the captions, there are over 206,000 English-Chinese parallel translation pairs. Compared to the widely-used MSR-VTT dataset, VATEX is multilingual, larger, linguistically complex, and more dive

Mihran Papikian, Fu-Tsun Wei

Let $F_\infty=\mathbb{F}_q(\!(1/T)\!)$ be the completion of $\mathbb{F}_q(T)$ at $1/T$. We develop a theory of Fourier expansions for harmonic cochains on the edges of the Bruhat-Tits building of $\mathrm{PGL}_r(F_\infty)$, $r\geq 2$, generalizing an earlier construction of Gekeler for $r=2$. We then apply this theory to study modular units on the Drinfeld s

Giuseppe Pernagallo, Benedetto Torrisi

The efficient market hypothesis has been considered one of the most controversial arguments in finance, with the academia divided between who claims the impossibility of beating the market and who believes that it is possible to gain over the average profits. If the hypothesis holds, it means, as suggested by Burton Malkiel, that a blindfolded monkey selecti

Themos Stafylakis, Johan Rohdin, Oldrich Plchot, Petr Mizera

Contrary to i-vectors, speaker embeddings such as x-vectors are incapable of leveraging unlabelled utterances, due to the classification loss over training speakers. In this paper, we explore an alternative training strategy to enable the use of unlabelled utterances in training. We propose to train speaker embedding extractors via reconstructing the frames

Yuqian Zhou, Jianbo Jiao, Haibin Huang, Yang Wang

Discriminative learning-based image denoisers have achieved promising performance on synthetic noises such as Additive White Gaussian Noise (AWGN). The synthetic noises adopted in most previous work are pixel-independent, but real noises are mostly spatially/channel-correlated and spatially/channel-variant. This domain gap yields unsatisfied performance on i

Huu Le, Thanh-Toan Do, Tuan Hoang, Ngai-Man Cheung

This paper presents a novel randomized algorithm for robust point cloud registration without correspondences. Most existing registration approaches require a set of putative correspondences obtained by extracting invariant descriptors. However, such descriptors could become unreliable in noisy and contaminated settings. In these settings, methods that direct

Jad Hamza, Nicolas Voirol, Viktor Kunčak

We present the design, implementation, and foundation of a verifier for higher-order functional programs with generics and recursive data types. Our system supports proving safety and termination using preconditions, postconditions and assertions. It supports writing proof hints using assertions and recursive calls. To formalize the soundness of the system w

Bob Coecke

In previous work we gave a mathematical foundation, referred to as DisCoCat, for how words interact in a sentence in order to produce the meaning of that sentence. To do so, we exploited the perfect structural match of grammar and categories of meaning spaces. Here, we give a mathematical foundation, referred to as DisCoCirc, for how sentences interact in te

Qiuqiang Kong, Yin Cao, Turab Iqbal, Yong Xu

The Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge focuses on audio tagging, sound event detection and spatial localisation. DCASE 2019 consists of five tasks: 1) acoustic scene classification, 2) audio tagging with noisy labels and minimal supervision, 3) sound event localisation and detection, 4) sound event detection in

Philipp Werner, Martin Burger, Jan-Frederik Pietschmann

The aim of this paper is to further develop mathematical models for bleb formation in cells, including cell-membrane interactions with linker proteins. This leads to nonlinear reaction-diffusion equations on a surface coupled to fluid dynamics in the bulk. We provide a detailed mathematical analysis and investigate some singular limits of the model, connecti

Gerard McCaul, Alexander Pechen, Denys I. Bondar

Applying the theory of self-adjoint extensions of Hermitian operators to Koopman von Neumann classical mechanics, the most general set of probability distributions is found for which entropy is conserved by Hamiltonian evolution. A new dynamical phase associated with such a construction is identified. By choosing distributions not belonging to this class, we

Pier Luigi Ventre, Stefano Salsano, Marco Polverini, Antonio Cianfrani

Fixed and mobile telecom operators, enterprise network operators and cloud providers strive to face the challenging demands coming from the evolution of IP networks (e.g. huge bandwidth requirements, integration of billions of devices and millions of services in the cloud). Proposed in the early 2010s, Segment Routing (SR) architecture helps face these chall

Nikolaj Tatti

Decomposing a graph into a hierarchical structure via $k$-core analysis is a standard operation in any modern graph-mining toolkit. $k$-core decomposition is a simple and efficient method that allows to analyze a graph beyond its mere degree distribution. More specifically, it is used to identify areas in the graph of increasing centrality and connectedness,

Olivier Benoist

The moduli space of stable curves of Deligne and Mumford is a compactification of the moduli space of smooth curves of genus >=2 that parametrizes certain nodal curves. It is a powerful tool for the study of algebraic curves. Higher-dimensional analogues were constructed by Kollár, Shepherd-Barron and Alexeev in dimension 2, and by Viehweg in the case of smo

Yoshihito Kuno, Takahiro Orito, Ikuo Ichinose

We study disorder-free many-body localization in the flat-band Creutz ladder, which was recently realized in cold-atoms in an optical lattice. In a non-interacting case, the flat-band structure of the system leads to a Wannier wavefunction localized on four adjacent lattice sites. In the flat-band regime both with and without interactions, the level spacing

Wenjing Zhao, Gongpu Wang, Saman Atapattu, Ruisi He

Ambient backscatter, an emerging green communication technology, has aroused great interest from both academia and industry. One open problem for ambient backscatter communication (AmBC) systems is channel estimation for a massive-antenna reader. In this paper, we focus on channel estimation problem in AmBC systems with uniform linear array (ULA) at the read

Shashwat Bhattacharya, Ravi Samtaney, Mahendra K. Verma

We derive scaling relations for the thermal dissipation rate in the bulk and in the boundary layers for moderate and large Prandtl number (Pr) convection. Using direct numerical simulations of Rayleigh-Bénard convection, we show that the thermal dissipation in the bulk is suppressed compared to passive scalar dissipation. The suppression is stronger for larg

Joonho Kim, Seok Kim, Jaewon Song

We study the asymptotic behavior of the (modified) superconformal index for 4d $\mathcal{N} = 1$ gauge theory. By considering complexified chemical potential, we find that the `high-temperature limit' of the index can be written in terms of the conformal anomalies $3c-2a$. We also find macroscopic entropy from our asymptotic free energy when the Hofman-M

Hongcheng Liu, Charles Hernandez, Hung Yi Lee

This paper concerns a high-dimensional stochastic programming problem of minimizing a function of expected cost with a matrix argument. To this problem, one of the most widely applied solution paradigms is the sample average approximation (SAA), which uses the average cost over sampled scenarios as a surrogate to approximate the expected cost. Traditional SA

Sounak Dey, Pau Riba, Anjan Dutta, Josep Llados

In this paper, we investigate the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recogn

D. S. Rabinovich, I. V. Bobkova, A. M. Bobkov, M. A. Silaev

We describe resistive states of the system combining two types of orderings - superconducting and ferromagnetic one. It is shown that in the presence of magnetization dynamics such systems become inherently dissipative and in principle cannot sustain any amount of the superconducting current because of the voltage generated by the magnetization dynamics. We

Huaxing Xu, Shaohua Wang, Yang Huang, Yaqi Song

In the letter, we propose an anti-quantum-channel disturbance decoder for quantum key distribution (QKD). The decoder is based on a quarter-wave plate reflector-Michelson (Q-M) interferometer, with which the QKD system can be free of polarization disturbance caused by quantum channel and optical devices in the system. The theoretical analysis indicates that

Hao Sun, Xu Tan, Jun-Wei Gan, Hongzhi Liu

Grapheme-to-phoneme (G2P) conversion is an important task in automatic speech recognition and text-to-speech systems. Recently, G2P conversion is viewed as a sequence to sequence task and modeled by RNN or CNN based encoder-decoder framework. However, previous works do not consider the practical issues when deploying G2P model in the production system, such

Łukasz Struski, Michał Sadowski, Tomasz Danel, Jacek Tabor

Interpolating between points is a problem connected simultaneously with finding geodesics and study of generative models. In the case of geodesics, we search for the curves with the shortest length, while in the case of generative models we typically apply linear interpolation in the latent space. However, this interpolation uses implicitly the fact that Gau

Brennan Ireland, Ofek Birnholtz, Hiroyuki Nakano, Eric West

We integrate the third and a half post-Newtonian equations of motion for a fully generic binary black hole system, allowing both for non-circular orbits, and for one or both of the black holes to spin, in any orientation. Using the second post-Newtonian order expression beyond the leading order quadrupole formula, we study the gravitational waveforms produce

Maral Salajegheh, S. Mohammad Moosavi Nejad, Abolfazl Mirjalili, S. Atashbar Tehrani

In this work, using the Laplace transformation technique we present our results for non-singlet quark distributions as well as nucleon structure function $F_2(x,Q^2)$ in unpolarized case at next-to-next-to-leading order (NNLO) QCD accuracy. We shall particularly compare our results for the sets of valence-quark parton distribution functions with the contempo

Konrad Zolna, Negar Rostamzadeh, Yoshua Bengio, Sungjin Ahn

Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we consider a challenging setting where an agent and an expert use different actions from each other. We assume that the agent has access to a sparse reward function and state-only expert observations. We propose a method which gradual

Valentin Niess, Anne Barnoud, Cristina Cârloganu, Olivier Martineau-Huynh

TURTLE is a C library providing utilities allowing to navigate through a topography described by a Digital Elevation Model (DEM). The library has been primarily designed for the Monte Carlo transport of particles scattering over medium to long ranges, e.g. atmospheric muons. But, it can also efficiently handle ray tracing problems with very large DEMs ($10^9

Yitong Wu, Liangliang Ji, Xuesong Geng, Qin Yu

We propose a new approach based on an all-optical set-up for generating relativistic polarized electron beams via vortex Laguerre-Gaussian (LG) laser-driven wakefield acceleration. Using a pre-polarized gas target, we find that the topology of the vortex wakefield resolves the depolarization issue of the injected electrons. In full three-dimensional particle

Michael Blomqvist, Hélion du Mas des Bourboux, Nicolás G. Busca, Victoria de Sainte Agathe

We present a measurement of the baryon acoustic oscillation (BAO) scale at redshift $z=2.35$ from the three-dimensional correlation of Lyman-$α$ (Ly$α$) forest absorption and quasars. The study uses 266,590 quasars in the redshift range $1.77<z<3.5$ from the Sloan Digital Sky Survey (SDSS) Data Release 14 (DR14). The sample includes the first two years of ob

R. Baggio, E. Arbib, P. Biscari, S. Conti

We show that nonlinear continuum elasticity can be effective in modeling plastic flows in crystals if it is viewed as Landau theory with an infinite number of equivalent energy wells whose configuration is dictated by the symmetry group GL(3,Z). Quasi-static loading can be then handled by athermal dynamics, while lattice based discretization can play the rol

Somnath Mazumdar, Alberto Scionti

There are increasing number of works addressing the design challenges of fast, scalable solutions for the growing number of new type of applications. Recently, many of the solutions aimed at improving processing element capabilities to speed up the execution of machine learning application domain. However, only a few works focused on the interconnection subs

Weichao Guo, Guoping Zhao

This paper is devoted to the study of the relatively compact sets in Quasi-Banach function spaces, providing an important improvement of the known results. As an application, we take the final step in establishing a relative compactness criteria for function spaces with any weight without any assumption.

Lei Qi, Lei Wang, Jing Huo, Luping Zhou

Unsupervised cross-domain person re-identification (Re-ID) faces two key issues. One is the data distribution discrepancy between source and target domains, and the other is the lack of labelling information in target domain. They are addressed in this paper from the perspective of representation learning. For the first issue, we highlight the presence of ca

Shin Kiriki, Yushi Nakano, Teruhiko Soma

Inspired by a recent work by Berger, we introduce the concept of pointwise emergence. This concept provides with a new quantitative perspective into the study of non-existence of averages for dynamical systems. We show that high pointwise emergence on a large set appears for abundant dynamical systems: Any continuous maps on a compact metric space with the s

Piotr Bielak, Kamil Tagowski, Maciej Falkiewicz, Tomasz Kajdanowicz

Representation learning on graphs has emerged as a powerful mechanism to automate feature vector generation for downstream machine learning tasks. The advances in representation on graphs have centered on both homogeneous and heterogeneous graphs, where the latter presenting the challenges associated with multi-typed nodes and/or edges. In this paper, we con

Marijan Markovic

In this paper we give a generalization and improvement of the Pavlović result on the characterization of continuously differentiable functions in the Bloch space on the unit ball in $\mathbb{R}^m$. Then we derive a Holland--Walsh type theorem for analytic normal mappings on the unit disk.

Enric Corona, Albert Pumarola, Guillem Alenyà, Francesc Moreno-Noguer

The problem of predicting human motion given a sequence of past observations is at the core of many applications in robotics and computer vision. Current state-of-the-art formulate this problem as a sequence-to-sequence task, in which a historical of 3D skeletons feeds a Recurrent Neural Network (RNN) that predicts future movements, typically in the order of

Christos-Raent Onti

In this note we provide a direct proof of the complete classification of conformally flat isoparametric submanifolds of Euclidean space.

Luca Sanguinetti, Emil Björnson, Jakob Hoydis

Since the seminal paper by Marzetta from 2010, Massive MIMO has changed from being a theoretical concept with an infinite number of antennas to a practical technology. The key concepts are adopted in 5G and base stations (BSs) with $M=64$ full-digital transceivers have been commercially deployed in sub-6\,GHz bands. The fast progress was enabled by many soli

Jin Zeng, Yanfeng Tong, Yunmu Huang, Qiong Yan

The growing availability of commodity RGB-D cameras has boosted the applications in the field of scene understanding. However, as a fundamental scene understanding task, surface normal estimation from RGB-D data lacks thorough investigation. In this paper, a hierarchical fusion network with adaptive feature re-weighting is proposed for surface normal estimat

Lu Bail, Lixin Cui, Yuhang Jiao, Luca Rossi

In this paper, we develop a novel Backtrackless Aligned-Spatial Graph Convolutional Network (BASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-sized backtrackless aligned grid structures and define a new spatial graph convolution operation associated with the grid structures. We sho

D. A. Balakin, A. V. Belinsky

We consider a modification of the classical ghost imaging scheme where an image of the research object is formed and acquired in the object arm. It is used alongside the ghost image to produce an estimate of the transmittance distribution of the object. It is shown that it allows to weaken the deterioration of image quality caused by nonunit quantum efficien

Victoria de Sainte Agathe, Christophe Balland, Hélion du Mas des Bourboux, Nicolás G. Busca

We measure the imprint of primordial baryon acoustic oscillations (BAO) in the correlation function of Ly$α$ absorption in quasar spectra from the Baryon Oscillation Spectroscopic Survey (BOSS) and the extended BOSS (eBOSS) in Data Release 14 (DR14) of the Sloan Digital Sky Survey (SDSS)-IV. In addition to 179,965 spectra with absorption in the Lyman-$α$ (Ly

Amit Moryossef, Yoav Goldberg, Ido Dagan

Data-to-text generation can be conceptually divided into two parts: ordering and structuring the information (planning), and generating fluent language describing the information (realization). Modern neural generation systems conflate these two steps into a single end-to-end differentiable system. We propose to split the generation process into a symbolic t

Kai-Xing Lu, Ying-Ke Huang, Zhi-Xiang Zhang, Kai Wang

We compiled a sample of 73 active galactic nuclei (AGNs) with reverberation mapping (RM) observations from RM campaigns including our ongoing campaign of monitoring super-Eddington accreting massive black holes (SEAMBHs). This sample covers a large range of black hole (BH) mass $(M_{\bullet}=10^{6-9}~M_\odot)$, dimensionless accretion rates $(\dot{\mathscr{M

Shaofeng Cai, Yao Shu, Gang Chen, Beng Chin Ooi

Convolutional Neural networks (CNNs) based applications have become ubiquitous, where proper regularization is greatly needed. To prevent large neural network models from overfitting, dropout has been widely used as an efficient regularization technique in practice. However, many recent works show that the standard dropout is ineffective or even detrimental

Sulaiman Khan, Hazrat Ali, Zahid Ullah, Nasru Minallah

This paper presents a recognition system for handwritten Pashto letters. However, handwritten character recognition is a challenging task. These letters not only differ in shape and style but also vary among individuals. The recognition becomes further daunting due to the lack of standard datasets for inscribed Pashto letters. In this work, we have designed

F. C. T. Barbato, G. Barbarino, A. Boiano, R. de Asmundis

With the observation of the gravitational wave event of August 17th 2017 and then with those of the extragalactic neutrino of September 22nd, the multi messenger astronomy era has definitely begun. With the opening of this new panorama, it is necessary to have a perfect coordination of the several observatories. Crystal Eye is an experiment aimed at the expl

Francesco Sanna Passino, Nicholas A. Heard

Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space, with optimal theoretical guarantees. The embedding can be used to estimate the community structure of the network, with strong consistency results in the stochastic blockmodel framework. One of the m

Sijie Song, Wei Zhang, Jiaying Liu, Tao Mei

In this paper, we address unsupervised pose-guided person image generation, which is known challenging due to non-rigid deformation. Unlike previous methods learning a rock-hard direct mapping between human bodies, we propose a new pathway to decompose the hard mapping into two more accessible subtasks, namely, semantic parsing transformation and appearance

Jinjin Gu, Hannan Lu, Wangmeng Zuo, Chao Dong

Deep learning based methods have dominated super-resolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. Most of these methods assume that the blur kernel during downsampling is predefined/known (e.g., bicubic). However, the blur kernels involved in real applications are complicated and unknown, resulting in severe

Fernando Reynoso

Under this method second order \textbf{partial differential equations (PDE&#39;s)} can be reduce to first order PDE&#39;s, simplifying the Initial value problem \textbf{IVP} or Border value Problem \textbf{BVP} for most cases of second-order differential equation, letting easiest proofs for existence and uniqueness of the PVI and BVP problems.

Mengjia Yu, Xiaohui Chen

We consider the problem of change point detection for high-dimensional distributions in a location family when the dimension can be much larger than the sample size. In change point analysis, the widely used cumulative sum (CUSUM) statistics are sensitive to outliers and heavy-tailed distributions. In this paper, we propose a robust, tuning-free (i.e., fully

Nouha Dziri, Ehsan Kamalloo, Kory W. Mathewson, Osmar Zaiane

Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers. Automatic metrics such as BLEU correlate weakly with human annotations, resulting in a significant bias across different models and datasets. Some researchers resort to human judgment experimentation for assessing response quality, which is expensive, time

Aftab Anjum, Fengyang Sun, Lin Wang, Jeff Orchard

Neuro-encoded expression programming(NEEP) that aims to offer a novel continuous representation of combinatorial encoding for genetic programming methods is proposed in this paper. Genetic programming with linear representation uses nature-inspired operators (e.g., crossover, mutation) to tune expressions and finally search out the best explicit function to

Yuan Gong, Jian Yang, Jacob Huber, Mitchell MacKnight

This paper introduces a new database of voice recordings with the goal of supporting research on vulnerabilities and protection of voice-controlled systems (VCSs). In contrast to prior efforts, the proposed database contains both genuine voice commands and replayed recordings of such commands, collected in realistic VCSs usage scenarios and using modern voic

David M. Hernandez

Symplectic integrators are a foundation to the study of dynamical $N$-body phenomena, at scales ranging from from planetary to cosmological. These integrators preserve the Poincaré invariants of Hamiltonian dynamics. The $N$-body Hamiltonian has another, perhaps overlooked, symmetry: it is smooth, or, in other words, it has infinite differentiability class o

Zbigniew Palmowski, José Luis Pérez, Budhi Arta Surya, Kazutoshi Yamazaki

We revisit the optimal capital structure model with endogenous bankruptcy first studied by Leland \cite{Leland94} and Leland and Toft \cite{Leland96}. Differently from the standard case, where shareholders observe continuously the asset value and bankruptcy is executed instantaneously without delay, we assume that the information of the asset value is update

Chen Chen, Xiaopeng Liu, Meng Ding, Junfeng Zheng

Brain tumor segmentation plays a pivotal role in medical image processing. In this work, we aim to segment brain MRI volumes. 3D convolution neural networks (CNN) such as 3D U-Net and V-Net employing 3D convolutions to capture the correlation between adjacent slices have achieved impressive segmentation results. However, these 3D CNN architectures come with

Manasvi Lingam

An Earth-like planetary magnetic field has been widely invoked as a requirement for habitability as it purportedly mitigates the fluxes of ionizing radiation reaching the surface and the escape of neutrals and ions from the atmosphere. Recent paleomagnetic evidence indicates that the nucleation of Earth&#39;s inner core, followed perhaps by an increase in ge

Nabeel Gillani, Roger Levy

Word embeddings trained on large-scale historical corpora can illuminate human biases and stereotypes that perpetuate social inequalities. These embeddings are often trained in separate vector space models defined according to different attributes of interest. In this paper, we develop a unified dynamic embedding model that learns attribute-specific word emb

Yue-Hui Zhou, Fen Zou, Xi-Ming Fang, Jin-Feng Huang

We study the spectrum of single-photon emission and scattering in a mixed optomechanical model which consists of both linear and quadratic optomechanical interactions. The spectra are calculated based on the exact long-time solutions of the single-photon emission and scattering processes in this system. We find that there exist some phonon sideband peaks in

Zhen Zhu, Tengteng Huang, Baoguang Shi, Miao Yu

This paper proposes a new generative adversarial network for pose transfer, i.e., transferring the pose of a given person to a target pose. The generator of the network comprises a sequence of Pose-Attentional Transfer Blocks that each transfers certain regions it attends to, generating the person image progressively. Compared with those in previous works, o

Abram Magner, Wojciech Szpankowski

Numerous networks in the real world change over time, in the sense that nodes and edges enter and leave the networks. Various dynamic random graph models have been proposed to explain the macroscopic properties of these systems and to provide a foundation for statistical inferences and predictions. It is of interest to have a rigorous way to determine how we

Chao Lu, Bo Zeng, Shixin Liu

The block relocation problem (BRP) is a fundamental operational issue in modern warehouse and yard management, which, however, is very challenging to solve. In this paper, to advance our understanding on this problem and to provide a substantial assistance to practice, we (i) introduce a classification scheme and present a rather comprehensive review on all

Long Zhao, Xi Peng, Yu Tian, Mubbasir Kapadia

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node. To address these limitations, we propose Semantic Graph Convolutional Networks (SemGCN), a novel neural network archit

Xiaoyu Wang, Ya-xiang Yuan

We present a stochastic trust-region model-based framework in which its radius is related to the probabilistic models. Especially, we propose a specific algorithm, termed STRME, in which the trust-region radius depends linearly on the latest model gradient. The complexity of STRME method in non-convex, convex and strongly convex settings has all been analyze

Charles L. Fefferman, Karol W. Hajduk, James C. Robinson

We approximate functions defined on smooth bounded domains by elements of the eigenspaces of the Laplacian or the Stokes operator in such a way that the approximations are bounded and converge in both Sobolev and Lebesgue spaces. We prove an abstract result referred to fractional power spaces of positive, self-adjoint, compact-inverse operators on Hilbert sp

Wending Mai, Chunxu Mao, Lei Kang, Yifan Chen

Knots have been put forward to explain various physical phenomena because of their topological stability. Nevertheless, few works have reported on the exotic symmetry properties that certain knots possess. Here we reveal an exceptional form of symmetry for a family of knots that are both chiral and three-dimensional (3-D) rotationally symmetric about every a

Wensheng Gan, Jerry Chun-Wei Lin, Han-Chieh Chao, Hamido Fujita

In the field of data mining and analytics, the utility theory from Economic can bring benefits in many real-life applications. In recent decade, a new research field called utility-oriented mining has already attracted great attention. Previous studies have, however, the limitation that they rarely consider the inherent correlation of items among patterns. C

Wentao Teng

In this paper, we will give some results on the support of Dunkl translations on compactly supported functions. Then we will define Dunkl-type $BMO$ space and Riesz transforms for Dunkl transform on $L^\infty$, and prove the boundedness of Riesz transforms from $L^\infty$ to Dunkl-type $BMO$ space under the uniform boundedness assumption of Dunkl translation

Pengyu Liu

We define a bivariate polynomial for unlabeled rooted trees and show that the polynomial of an unlabeled rooted tree $T$ is the generating function of a class of subtrees of $T$. We prove that the polynomial is a complete isomorphism invariant for unlabeled rooted trees. Then, we generalize the polynomial to unlabeled unrooted trees and we show that the gene

Hua Li, Ziping Li, Wenjian Wan, Kang Zhou

Due to its fast and high resolution characteristics, dual-comb spectroscopy has attracted an increasing amount of interest since its first demonstration. In the terahertz frequency range where abundant absorption lines (finger prints) of molecules are located, multiheterodyne spectroscopy that employs the dual-comb technique shows an advantage in real-time s

Ye Wu, Yoonmi Hong, Yuanjing Feng, Dinggang Shen

Diffusion tractography in brain connectomics often involves tracing axonal trajectories across gray-white matter boundaries in gyral blades of complex cortical convolutions. To date, gyral bias is observed in most tractography algorithms with streamlines predominantly terminating at gyral crowns instead of sulcal banks. This work demonstrates that asymmetric

Julius Surya Sumantri, In Kyu Park

360 images represent scenes captured in all possible viewing directions and enable viewers to navigate freely around the scene thereby providing an immersive experience. Conversely, conventional images represent scenes in a single viewing direction with a small or limited field of view (FOV). As a result, only certain parts of the scenes are observed, and va

Alexander Kostenko, Christopher Thompson

This paper continues the approach of Kostenko \& Thompson to calculating quantum electrodynamic processes in the ultrastrong magnetic field near some neutron stars, such as magnetars or merging binary neutron stars. Here we consider electron-positron scattering, the Coulomb scattering of electrons and positrons off ions, and relativistic $e^\pm$-ion bremsstr

Alexander Kostenko, Christopher Thompson

We evaluate several basic electrodynamic processes as modified by the presence of a very strong magnetic field, exceeding $B_{\rm Q} \equiv m^2/e = 4.4\times 10^{13}$ G. These results are needed to build models of dissipative phenomena outside magnetars and some other neutron stars. Differential and total cross sections and rates are presented for electron-p

Emily Alsentzer, John R. Murphy, Willie Boag, Wei-Hung Weng

Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et al., 2018) have dramatically improved performance for many natural language processing (NLP) tasks in recent months. However, these models have been minimally explored on specialty corpora, such as clinical text; moreover, in the clinical domain, no publicly-available pre

Benjamin Plaut

In bandwidth allocation, competing agents wish to transmit data along paths of links in a network, and each agent&#39;s utility is equal to the minimum bandwidth she receives among all links in her desired path. Recent market mechanisms for this problem have either focused on only Nash welfare, or ignored strategic behavior. We propose a nonlinear variant of

Osama Idais, Rainer Schwabe

The gamma model is a generalized linear model for gamma-distributed outcomes. The model is widely applied in psychology, ecology or medicine. In this paper we focus on gamma models having a linear predictor without intercept. For a specific scenario sets of locally D- and A-optimal designs are to be developed. Recently, Gaffke et al. (2018) established a com

Julia E. Reid, Eric Eaton

PURPOSE OF REVIEW: Despite the impressive results of recent artificial intelligence (AI) applications to general ophthalmology, comparatively less progress has been made toward solving problems in pediatric ophthalmology using similar techniques. This article discusses the unique needs of pediatric ophthalmology patients and how AI techniques can address the