November 2019 arXiv papers — page 57

Xiaoyu Wang, Ye Deng, Jinjun Wang

Recently the Generative Adversarial Network has become a hot topic. Considering the application of GAN in multi-user environment, we propose Distributed-GAN. It enables multiple users to train with their own data locally and generates more diverse samples. Users don't need to share data with each other to avoid the leakage of privacy. In recent years, co

Natasha Hurley-Walker, Paul J. Hancock, Thomas M. O. Franzen, Joseph R. Callingham

This work makes available a further 2,860deg$^2$ of the GLEAM survey, covering half of the accessible Galactic Plane, across twenty frequency bands sampling $72-231$MHz, with resolution $4'-2'$. Unlike previous GLEAM data releases, we used multi-scale clean to better deconvolve large-scale Galactic structure. For the Galactic longitude ranges $345^\c

Natasha Hurley-Walker, Miroslav D. Filipovic, Bryan M. Gaensler, Denis A. Leahy

We have detected 27 new supernova remnants (SNRs) using a new data release of the GLEAM survey from the Murchison Widefield Array (MWA) telescope, including the lowest surface-brightness SNR ever detected, G0.1-9.7. Our method uses spectral fitting to the radio continuum to derive spectral indices for 26/27 candidates, and our low-frequency observations prob

Momchil Hardalov, Ivan Koychev, Preslav Nakov

We study the problem of finding fake online news. This is an important problem as news of questionable credibility have recently been proliferating in social media at an alarming scale. As this is an understudied problem, especially for languages other than English, we first collect and release to the research community three new balanced credible vs. fake n

Natasha Hurley-Walker, Bryan M. Gaensler, Denis A. Leahy, Miroslav D. Filipovic

We examined the latest data release from the GaLactic and Extragalactic All-sky Murchison Widefield Array (GLEAM) survey covering $345^\circ < l < 60^\circ$, $180^\circ < l < 240^\circ$, using these data and that of the Widefield Infrared Survey Explorer to follow up proposed candidate Supernova Remnants from other sources. Of the 101 candidates proposed in

Xiao-Qin Yu, Zhen-Gang Zhu, Gang Su

We theoretically investigate the temperature gradient-dependent or unidirectional Seebeck effect (USE) in a magnetic/ nonmagnetic topological insulator (TI) heterostructure with in-plane magnetization in terms of the semiclassical electron dynamics and Fermi golden rule. The USE has a quantum origin arising from the magnon asymmetric scattering of surface Di

Qiang Ren, Shaohua Shang, Lianghua He

Capsule network is the most recent exciting advancement in the deep learning field and represents positional information by stacking features into vectors. The dynamic routing algorithm is used in the capsule network, however, there are some disadvantages such as the inability to stack multiple layers and a large amount of computation. In this paper, we prop

Minh-Thang Luong, Preslav Nakov, Min-Yen Kan

We propose a language-independent approach for improving statistical machine translation for morphologically rich languages using a hybrid morpheme-word representation where the basic unit of translation is the morpheme, but word boundaries are respected at all stages of the translation process. Our model extends the classic phrase-based model by means of (1

Todor Mihaylov, Preslav Nakov

There are different definitions of what a troll is. Certainly, a troll can be somebody who teases people to make them angry, or somebody who offends people, or somebody who wants to dominate any single discussion, or somebody who tries to manipulate people&#39;s opinion (sometimes for money), etc. The last definition is the one that dominates the public disc

Hongwei Zeng, Zhuo Zhi, Jun Liu, Bifan Wei

In this paper, we study automatic question generation, the task of creating questions from corresponding text passages where some certain spans of the text can serve as the answers. We propose an Extended Answer-aware Network (EAN) which is trained with Word-based Coverage Mechanism (WCM) and decodes with Uncertainty-aware Beam Search (UBS). The EAN represen

M. M. Sharma, P. Rani, Lina Sang, X. L. Wang

We report crystal growth and below 2.5K superconductivity of Nb0.25Bi2Se3. These crystals are grown by self flux method. The X-ray diffraction (XRD) pattern of as grown crystal flake shows (00l) plane (c-orientation) growth. The Rietveld refinement of crushed crystal powder XRD (PXRD) pattern confirms the phase purity of the studied sample having R-3m space

Guanghua Shi, Dongfeng Yan

A generalized Benjamin-Bona-Mahony (gBBM) equation subject to the periodic boundary condition is studied in this paper. Based on a new infinite dimensional Kolomogorov-Arnold-Moser (KAM) theorem with normal frequencies of finite limit-points, it is shown that the gBBM equation admits plenty of time-quasi-periodic solutions with two frequencies of high modes.

Yutong Nie, Chenhe Zhang

The authors give an approximation method for Bayesian inference in arena model, which is focused on paired comparisons with eliminations and bifurcations. The approximation method simplifies the inference by reducing parameters and introducing normal distribution functions into the computation of posterior distribution, which is largely based on an important

Dan Cheng, Armin Schwartzman

Let $\{X(t), t\in M\}$ and $\{Z(t&#39;), t&#39;\in M&#39;\}$ be smooth Gaussian random fields parameterized on Riemannian manifolds $M$ and $M&#39;$, respectively, such that $X(t) = Z(f(t))$, where $f: M \to M&#39;$ is a diffeomorphic transformation. We study the expected number and height distribution of the critical points of $X$ in connection with those o

Vladimir Vasilyev

We consider special classes of linear bounded operators in Banach spaces and suggest certain operator variant of symbolic calculus. It permits to formulate an index theorem and to describe Fredholm properties of elliptic pseudo-differential operator on manifolds with non-smooth boundaries.

Kangfu Mei, Juncheng Li, Jiajie Zhang, Haoyu Wu

Neural-networks based image restoration methods tend to use low-resolution image patches for training. Although higher-resolution image patches can provide more global information, state-of-the-art methods cannot utilize them due to their huge GPU memory usage, as well as the instable training process. However, plenty of studies have shown that global inform

Julian Faraone, Martin Kumm, Martin Hardieck, Peter Zipf

Low-precision arithmetic operations to accelerate deep-learning applications on field-programmable gate arrays (FPGAs) have been studied extensively, because they offer the potential to save silicon area or increase throughput. However, these benefits come at the cost of a decrease in accuracy. In this article, we demonstrate that reconfigurable constant coe

Zi-Qing Xia, Yun-Feng Liang, Lei Feng, Qiang Yuan

The conversion between photons and axionlike particles (ALPs) in the Milky Way magnetic field could result in the detectable oscillation phenomena in $γ$-ray spectra of Galactic sources. In this work, the GeV (Fermi-LAT) and TeV (MAGIC/VERITAS/H.E.S.S.) data of three bright supernova remnants (SNRs, ie. IC443, W51C and W49B) have been adopted together to sea

Taichi Kato

The post-outburst rebrightening phenomenon in dwarf novae and X-ray novae is still one of the most challenging subjects for theories of accretion disks. It has been widely recognized that post-outburst rebrightenings are a key feature of WZ Sge-type dwarf novae, which predominantly have short ($\lesssim$0.06 d) orbital periods. I found four post-outburst reb

Javier Echauz, Keith Kenemer, Sarfaraz Hussein, Jay Dhaliwal

Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors. As automated classifiers are increasingly used in industrial control systems and machinery, these adversarial errors could grow to be a serious problem. Despite numerous studies over the past few years, the field of adversarial ML is still considered alch

Lenny Fukshansky, Yingqi Shi

Frobenius problem and its many generalizations have been extensively studied in several areas of mathematics. We study semigroups of totally positive algebraic integers in totally real number fields, defining analogues of the Frobenius numbers in this context. We use a geometric framework recently introduced by Aliev, De Loera and Louveaux to produce upper b

Senbei Du, Gary P. Zank, Fan Guo, Xiaocan Li

It is well known that collisionless systems are dissipation free from the perspective of particle collision and thus conserve entropy. On the other hand, processes such as magnetic reconnection and turbulence appear to convert large-scale magnetic energy into heat. In this paper, we investigate the energization and heating of collisionless plasma. The dissip

Ilias Diakonikolas, Sushrut Karmalkar, Daniel Kane, Eric Price

We study high-dimensional sparse estimation tasks in a robust setting where a constant fraction of the dataset is adversarially corrupted. Specifically, we focus on the fundamental problems of robust sparse mean estimation and robust sparse PCA. We give the first practically viable robust estimators for these problems. In more detail, our algorithms are samp

Min Hoon Kim, Se-Goo Kim, Taehee Kim

We address the primary decomposition of the knot concordance group in terms of the solvable filtration and higher-order von Neumann $ρ$-invariants by Cochran, Orr, and Teichner. We show that for a nonnegative integer n, if the connected sum of two n-solvable knots with coprime Alexander polynomials is slice, then each of the knots has vanishing von Neumann $

Peter Robinson

Atomic Crosschain Transaction technology allows composable programming across private Ethereum blockchains. It allows for inter-contract and inter-blockchain function calls that are both synchronous and atomic: if one part fails, the whole call graph of function calls is rolled back. Traditional Ethereum contract functions can limit which accounts can call t

Ping-Ping Wang, Liang Li, Guang-Hui Cheng

Tensor completion is a challenging problem with various applications. Many related models based on the low-rank prior of the tensor have been proposed. However, the low-rank prior may not be enough to recover the original tensor from the observed incomplete tensor. In this paper, we prose a tensor completion method by exploiting both the low-rank and sparse

Emre Sen

For a given irreducible projective variety $X$, the closure of the set of all hyperplanes containing tangents to $X$ is the projectively dual variety $X^{\vee}$. We study the singular locus of projectively dual varieties of certain Segre-Plücker embeddings in series of papers. In this work we give a classification of the irreducible components of the singula

Zhongxin Bai, Xiao-Lei Zhang, Jingdong Chen

Deep embedding based text-independent speaker verification has demonstrated superior performance to traditional methods in many challenging scenarios. Its loss functions can be generally categorized into two classes, i.e., verification and identification. The verification loss functions match the pipeline of speaker verification, but their implementations ar

Hongxing Gao, Wei Tao, Dongchao Wen, Tse-Wei Chen

Deploying deep models on embedded devices has been a challenging problem since the great success of deep learning based networks. Fixed-point networks, which represent their data with low bits fixed-point and thus give remarkable savings on memory usage, are generally preferred. Even though current fixed-point networks employ relative low bits (e.g. 8-bits),

Yu-Xuan Li, Jin-Yuan Liu, Liang Li, Xiang Guan

Recurrent neural networks have been widely used in sequence learning tasks. In previous studies, the performance of the model has always been improved by either wider or deeper structures. However, the former becomes more prone to overfitting, while the latter is difficult to optimize. In this paper, we propose a simple new model named Thick-Net, by expandin

Young-Jin Kim

Mobile energy storage devices (MESDs) operate as medium- or large-sized batteries that can be loaded onto electric trucks and connected to charging stations to provide various ancillary services for distribution grids. This paper proposes a new strategy for MESD operation, in which their power outputs and paths are co-optimally scheduled to minimize the tota

Yingru Liu, Xuewen Yang, Dongliang Xie, Xin Wang

Multi-task learning (MTL) is a common paradigm that seeks to improve the generalization performance of task learning by training related tasks simultaneously. However, it is still a challenging problem to search the flexible and accurate architecture that can be shared among multiple tasks. In this paper, we propose a novel deep learning model called Task Ad

Xiangliang Kong, Fan Guo, Chengcai Shen, Bin Chen

Nonthermal loop-top sources in solar flares are the most prominent observational signature that suggests energy release and particle acceleration in the solar corona. Although several scenarios for particle acceleration have been proposed, the origin of the loop-top sources remains unclear. Here we present a model that combines a large-scale magnetohydrodyna

Xin Li, Dongya Wu

We study minimax rates for high-dimensional linear regression with additive errors under the $\ell_p\ (1\leq p<\infty)$-losses, where the regression parameter is of weak sparsity. Our lower and upper bounds agree up to constant factors, implying that the proposed estimator is minimax optimal.

Xin Li, Dongya Wu, Chong Li, Jinhua Wang

In this paper, we analyse the recovery properties of nonconvex regularized $M$-estimators, under the assumption that the true parameter is of soft sparsity. In the statistical aspect, we establish the recovery bound for any stationary point of the nonconvex regularized $M$-estimator, under restricted strong convexity and some regularity conditions on the los

Mario S. Cabrera, Craig W. McMurtry, William J. Forrest, Judith L. Pipher

The University of Rochester infrared detector group is working together with Teledyne Imaging Sensors to develop HgCdTe 15 $μm$ cutoff wavelength detector arrays for future space missions. To reach the 15 $μm$ cutoff goal, we took an intermediate step by developing four $\sim$13 $μm$ cutoff wavelength arrays to identify any unforeseen effects related to incr

Nishant Parashar, Sawan S. Sinha, Balaji Srinivasan

The understanding of the dynamics of the velocity gradients in turbulent flows is critical to understanding various non-linear turbulent processes. The pressure-Hessian and the viscous-Laplacian govern the evolution of the velocity-gradients and are known to be non-local in nature. Over the years, several simplified dynamical models have been proposed that m

Min Hoon Kim, Se-Goo Kim, Taehee Kim

Let {T_n} be the bipolar filtration of the smooth concordance group of topologically slice knots, which was introduced by Cochran, Harvey, and Horn. It is known that for each n not equal to 1 the quotient group T_n/T_{n+1} has infinite rank and T_1/T_2 has positive rank. In this paper, we show that T_1/T_2 also has infinite rank. Moreover, we prove that ther

XiangRui Xu, YaQin Li, Cao Yuan

Deep neural network (DNN) with the state of art performance has emerged as a viable and lucrative business service. However, those impressive performances require a large number of computational resources, which comes at a high cost for the model creators. The necessity for protecting DNN models from illegal reproducing and distribution appears salient now.

Akash Srivastava, Jessie Rosenberg, Dan Gutfreund, David D. Cox

This paper presents a simulator-assisted training method (SimVAE) for variational autoencoders (VAE) that leads to a disentangled and interpretable latent space. Training SimVAE is a two-step process in which first a deep generator network(decoder) is trained to approximate the simulator. During this step, the simulator acts as the data source or as a teache

Yang Zhang, Yu Wang, Yu Liu, Xiao-Lin Zeng

Circular polarized photocurrent is observed near the electrodes on a few-layer ReS2sample at room temperature. For both electrodes, the spatial distribution of the circular polarized photocurrent shows a feature of two wings, with one positive and the other negative. We suggest that this phenomenon arises from the inverse spin Hall effect due to local electr

Yixuan Qiu, Jing Lei, Kathryn Roeder

Sparse principal component analysis (PCA) is an important technique for dimensionality reduction of high-dimensional data. However, most existing sparse PCA algorithms are based on non-convex optimization, which provide little guarantee on the global convergence. Sparse PCA algorithms based on a convex formulation, for example the Fantope projection and sele

J. S. Avva, P. A. R. Ade, Z. Ahmed, A. J. Anderson

The cosmic microwave background (CMB) encodes information about the content and evolution of the universe. The presence of light, weakly interacting particles impacts the expansion history of the early universe, which alters the temperature and polarization anisotropies of the CMB. In this way, current measurements of the CMB place interesting constraints on

C. C. Gu, J. Hu, X. L. Chen, Z. P. Guo

Tunable symmetry breaking plays a crucial role for the manipulation of topological phases of quantum matter. Here, through combined high-pressure magneto-transport measurements, Raman spectroscopy, and X-ray diffraction, we demonstrate a pressure-induced topological phase transition in nodal-line semimetal ZrSiS. Symmetry analysis and first-principles calcul

Yulun Xu, Qi Bao, Zhongzhi Zhang

The $k$-power domination problem is a problem in graph theory, which has applications in many areas. However, it is hard to calculate the exact $k$-power domination number since determining k-power domination number of a generic graph is a NP-complete problem. We determine the exact $k$-power domination number in two graphs which have the same number of vert

Niufa Fang, Jiazu Zhou

We define the functional LYZ ellipsoid of log-concave functions. Then we give notes appended to [6].

Alvin Chan, Yew-Soon Ong

Deep learning models have recently shown to be vulnerable to backdoor poisoning, an insidious attack where the victim model predicts clean images correctly but classifies the same images as the target class when a trigger poison pattern is added. This poison pattern can be embedded in the training dataset by the adversary. Existing defenses are effective und

Bingfeng Zhang, Jimin Xiao, Yunchao Wei, Mingjie Sun

Weakly supervised semantic segmentation is a challenging task as it only takes image-level information as supervision for training but produces pixel-level predictions for testing. To address such a challenging task, most recent state-of-the-art approaches propose to adopt two-step solutions, \emph{i.e. } 1) learn to generate pseudo pixel-level masks, and 2)

Zhaoyang Zhang, Rong Wang, Yiqi Zhang, Yaroslav V. Kartashov

Edge states emerge in diverse areas of science, offering new opportunities for the development of novel electronic or optoelectronic devices, sound and light propagation controls in acoustics and photonics. Previous experiments on edge states and exploration of topological phases in photonics were carried out mostly in linear regimes, but the current belief

Matteo Mitrano, Sangjun Lee, Ali A. Husain, Minhui Zhu

We use femtosecond resonant soft x-ray scattering to measure the ultrafast optical melting of charge-order correlations in La$_{1.875}$Ba$_{0.125}$CuO$_4$. By analyzing both the energy-resolved and energy-integrated order parameter dynamics, we find evidence of a short-lived nonequilibrium state, whose features are compatible with a sliding charge density wa

R. Sarkar, Ph. Schlender, V. Grinenko, E. Haeussler

Rare-earth delafossites were recently proposed as promising candidates for the realization of an effective $S$=1/2 quantum spin liquid (QSL) on the triangular lattice. In contrast to the most actively studied triangular-lattice antiferromagnet YbMgGaO$_4$, which is known for considerable structural disorder due to site intermixing, NaYbS$_2$ delafossite real

Bo Zhao, Cheng Guo, Christina A. C. Garcia, Prineha Narang

Objects around us constantly emit and absorb thermal radiation. The emission and absorption processes are governed by two fundamental radiative properties: emissivity and absorptivity.For reciprocal systems, the emissivity and absorptivity are restricted to be equal by Kirchhoff&#39;s law of thermal radiation. This restriction limits the degree of freedom to

Uri Erez, Gregory W. Wornell

A rateless transmission architecture is developed for communication over Gaussian intersymbol interference channels, based on the concept of super-Nyquist (SNQ) signaling. In such systems, the signaling rate is chosen significantly higher than the Nyquist rate of the system. We show that such signaling, when used in conjunction with good &#34;off-the-shelf&#

Wolfgang Jeltsch

Blockchains are becoming increasingly relevant in a variety of fields, such as finance, logistics, and real estate. The fundamental task of a blockchain system is to establish data consistency among distributed agents in an open network. Blockchain consensus protocols are central for performing this task. Since consensus protocols play such a crucial role in

Abenezer Girma, Xuyang Yan, Abdollah Homaifar

Despite advancements in vehicle security systems, over the last decade, auto-theft rates have increased, and cyber-security attacks on internet-connected and autonomous vehicles are becoming a new threat. In this paper, a deep learning model is proposed, which can identify drivers from their driving behaviors based on vehicle telematics data. The proposed Lo

Haien Zeng, Hanjiang Lai, Jian Yin

Fine-grained image hashing is a challenging problem due to the difficulties of discriminative region localization and hash code generation. Most existing deep hashing approaches solve the two tasks independently. While these two tasks are correlated and can reinforce each other. In this paper, we propose a deep fine-grained hashing to simultaneously localize

Takako Nemoto, Michael Rathjen

Independence of premise principles play an important role in characterizing the modified realizability and the Dialectica interpretations. In this paper we show that a great many intuitionistic set theories are closed under the corresponding independence of premise rule for finite types over $\mathbb{N}$. It is also shown that the existence property (or exis

M. Tokieda, K. Hagino

Couplings of a system to other degrees of freedom (that is, environmental degrees of freedom) lead to energy dissipation when the number of environmental degrees of freedom is large enough. Here we discuss quantal treatments for such energy dissipation. To this end, we discuss two different time-dependent methods. One is to introduce an effective time-depend

Baokun He, Guihong Wan, Haim Schweitzer

Outlier based Robust Principal Component Analysis (RPCA) requires centering of the non-outliers. We show a &#34;bias trick&#34; that automatically centers these non-outliers. Using this bias trick we obtain the first RPCA algorithm that is optimal with respect to centering.

Lingfei Jin, Gaojun Luo, Chaoping Xing

In modern practical data centers, storage nodes are usually organized into equally sized groups, which is called racks. The cost of cross-rack communication is much more expensive compared with the intra-rack communication cost. The codes for this system are called rack-aware regenerating codes. Similar to standard minimum storage regenerating (MSR) codes, i

Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco

We study how to estimate a nearly low-rank Toeplitz covariance matrix $T$ from compressed measurements. Recent work of Qiao and Pal addresses this problem by combining sparse rulers (sparse linear arrays) with frequency finding (sparse Fourier transform) algorithms applied to the Vandermonde decomposition of $T$. Analytical bounds on the sample complexity ar

Manoj Settipalli, Sanghamitra Neogi

We analyze the cross-plane miniband transport in n-doped [001] silicon (Si)/germanium (Ge) superlattices using an effective mass approximation (EMA) approach that correctly accounts for the indirect nature of the Si and Ge band gaps. Direct-gap based EMA has been employed so far to investigate the electronic properties of these superlattices, that does not a

Robert Mieth, Yury Dvorkin

Distribution locational marginal prices (DLMPs) facilitate the efficient operation of low-voltage electric power distribution systems. We propose an approach to internalize the stochasticity of renewable distributed energy resources (DERs) and risk tolerance of the distribution system operator in DLMP computations. This is achieved by means of applying conic

Binghan He, Gray C. Thomas, Nicholas Paine, Luis Sentis

In this paper we consider a class of exoskeletons designed to amplify the strength of humans through feedback of sensed human-robot interactions and actuator forces. We define an amplification error signal based on a reference amplification rate, and design a linear feedback compensator to attenuate this error. Since the human operator is an integral part of

Bowen Yi, Romeo Ortega, Houria Siguerdidjane, Juan E. Machado

Probing signal injection is a well-established technique to extract additional information from a weakly (or non) observable dynamical system. Using averaging theory, a framework to analyse such schemes for general nonlinear systems has been recently proposed in [Combes et. al., 2016], where it is shown that the signal injection may be used to generate a new

Stylianos Ploumpis, Evangelos Ververas, Eimear O&#39; Sullivan, Stylianos Moschoglou

Three-dimensional Morphable Models (3DMMs) are powerful statistical tools for representing the 3D shapes and textures of an object class. Here we present the most complete 3DMM of the human head to date that includes face, cranium, ears, eyes, teeth and tongue. To achieve this, we propose two methods for combining existing 3DMMs of different overlapping head

Olivia J. Erdélyi, Gábor Erdélyi

Certainty around the regulatory environment is crucial to enable responsible AI innovation and foster the social acceptance of these powerful new technologies. One notable source of uncertainty is, however, that the existing legal liability system is inapt to assign responsibility where a potentially harmful conduct and/or the harm itself are unforeseeable,

Hyunsoo Kim, Rathinasamy Sakthivel, Amar Debbouche, Delfim F. M. Torres

In this article, exact traveling wave solutions of a Wick-type stochastic nonlinear Schrödinger equation and of a Wick-type stochastic fractional Regularized Long Wave-Burgers (RLW-Burgers) equation have been obtained by using an improved computational method. Specifically, the Hermite transform is employed for transforming Wick-type stochastic nonlinear par

Sangwoo Park, Michaela Fraser, Lynne M. Weber, Cassie Meeker

We studied the performance of a robotic orthosis designed to assist the paretic hand after stroke. It is wearable and fully user-controlled, serving two possible roles: as a therapeutic tool that facilitates device mediated hand exercises to recover neuromuscular function or as an assistive device for use in everyday activities to aid functional use of the h

Sen Wang, Jiaqi Chen, Xuanliang Deng, Seth Hutchinson

Chinese calligraphy is a unique art form with great artistic value but difficult to master. In this paper, we formulate the calligraphy writing problem as a trajectory optimization problem, and propose an improved virtual brush model for simulating the real writing process. Our approach is inspired by pseudospectral optimal control in that we parameterize th

Amir Dembo, Eyal Lubetzky, Ofer Zeitouni

We study dynamics for asymmetric spin glass models, proposed by Hertz et al. and Sompolinsky et al. in the 1980&#39;s in the context of neural networks: particles evolve via a modified Langevin dynamics for the Sherrington--Kirkpatrick model with soft spins, whereby the disorder is i.i.d. standard Gaussian rather than symmetric. Ben Arous and Guionnet (1995)

Atul K. Divakarla, Eric Thrane, Paul D. Lasky, Bernard F. Whiting

A variety of gravitational-wave transient sources can be modeled in the Fourier domain using a power law. This simple power-law model provides a reasonable approximation for gravitational-wave bursts from cosmic string cusps, cosmic string kinks, and the memory effect. Each of these sources is described using a different spectral index. In this work, we simu

Byung Gyu Chae

We analyze the aliasing phenomenon for digital Fresnel hologram with an enhanced numerical aperture (NA). The enhanced-NA digital hologram acquired computationally or optically at a closer distance from the object has an aliased fringe generated by undersampling process of the Fresnel prefactor. The point spread function known as Fresnel factor reveals a sel

ArgoNeuT Collaboration, R. Acciarri, C. Adams, J. Asaadi

A search for millicharged particles, a simple extension of the standard model, has been performed with the ArgoNeuT detector exposed to the Neutrinos at the Main Injector beam at Fermilab. The ArgoNeuT Liquid Argon Time Projection Chamber detector enables a search for millicharged particles through the detection of visible electron recoils. We search for an

Sascha Xu, Jan Bauer, Benjamin Axmann

The quality of visual input is very important for both human and machine perception. Consequently many processing techniques exist that deal with different distortions. Usually image processing is applied freely and lacks redundancy regarding safety. We propose a novel image comparison method called the Combined Distances of Contrast Distributions (CD2) to p

Nikolaos Flemotomos, Panayiotis Georgiou, Shrikanth Narayanan

Speaker diarization relies on the assumption that speech segments corresponding to a particular speaker are concentrated in a specific region of the speaker space; a region which represents that speaker's identity. These identities are not known a priori, so a clustering algorithm is typically employed, which is traditionally based solely on audio. Under noi

Qian Wang, Toby P. Breckon

Automatic crowd behaviour analysis is an important task for intelligent transportation systems to enable effective flow control and dynamic route planning for varying road participants. Crowd counting is one of the keys to automatic crowd behaviour analysis. Crowd counting using deep convolutional neural networks (CNN) has achieved encouraging progress in re

Ashwin Kallingal Joshy, Wei Le

Software development is inherently incremental. Nowadays, many software companies adopt an agile process and a shorter release cycle, where software needs to be delivered faster with quality assurances. On the other hand, the majority of existing program analysis tools still target single versions of programs and are slow and inflexible to handle changes. In

Mohamed Ndaoud, Suzanne Sigalla, Alexandre B. Tsybakov

We establish sufficient conditions of exact and almost full recovery of the node partition in Bipartite Stochastic Block Model (BSBM) using polynomial time algorithms. First, we improve upon the known conditions of almost full recovery by spectral clustering algorithms in BSBM. Next, we propose a new computationally simple and fast procedure achieving exact

Shivam Kalra, Mohammed Adnan, Graham Taylor, Hamid Tizhoosh

Many real-world tasks such as classification of digital histopathology images and 3D object detection involve learning from a set of instances. In these cases, only a group of instances or a set, collectively, contains meaningful information and therefore only the sets have labels, and not individual data instances. In this work, we present a permutation inv

Ekagra Ranjan, Soumya Sanyal, Partha Pratim Talukdar

Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classification. There has been some recent progress in defining the notion of pooling in graphs whereby the model tries to generate a graph level representation by downsampling and summarizin

J. D. Quigley

The $2$-primary homotopy $\beta$-family, defined as the collection of Mahowald invariants of Mahowald invariants of $2^i$, $i \geq 1$, is an infinite collection of periodic elements in the stable homotopy groups of spheres. In this paper, we calculate $\mathit{tmf}$-based approximations to this family. Our calculations combine an analysis of the Atiyah-Hirze

Venkata Gandikota, Daniel Kane, Raj Kumar Maity, Arya Mazumdar

In this work, we present a family of vector quantization schemes \emph{vqSGD} (Vector-Quantized Stochastic Gradient Descent) that provide an asymptotic reduction in the communication cost with convergence guarantees in first-order distributed optimization. In the process we derive the following fundamental information theoretic fact: $\Theta(\frac{d}{R^2})$

Zhen Xiang, David J. Miller, Hang Wang, George Kesidis

Recently, a backdoor data poisoning attack was proposed, which adds mislabeled examples to the training set, with an embedded backdoor pattern, aiming to have the classifier learn to classify to a target class whenever the backdoor pattern is present in a test sample. Here, we address post-training detection of innocuous perceptible backdoors in DNN image cl

Xizhi Liu, Dhruv Mubayi

A fundamental barrier in extremal hypergraph theory is the presence of many near-extremal constructions with very different structures. Indeed, the classical constructions due to Kostochka imply that the notorious extremal problem for the tetrahedron exhibits this phenomenon assuming Tur\'an's conjecture. Our main result is to construct a finite family of tr

Jindong Gu, Volker Tresp

Convolutional neural networks (CNNs) achieve translational invariance by using pooling operations. However, the operations do not preserve the spatial relationships in the learned representations. Hence, CNNs cannot extrapolate to various geometric transformations of inputs. Recently, Capsule Networks (CapsNets) have been proposed to tackle this problem. In

Cheng Li, Abdul Dakkak, Jinjun Xiong, Wen-mei Hwu

The past few years have seen a surge of applying Deep Learning (DL) models for a wide array of tasks such as image classification, object detection, machine translation, etc. While DL models provide an opportunity to solve otherwise intractable tasks, their adoption relies on them being optimized to meet latency and resource requirements. Benchmarking is a k

Diego Espitia, Hernán Larralde

In this work we analyze statistical properties of 91 relatively small texts in 7 different languages (Spanish, English, French, German, Turkish, Russian, Icelandic) as well as texts with randomly inserted spaces. Despite the size (around 11260 different words), the well known universal statistical laws -- namely Zipf and Herdan-Heap&#39;s laws -- are confirm

Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, H. Brendan McMahan

The decentralized nature of federated learning makes detecting and defending against adversarial attacks a challenging task. This paper focuses on backdoor attacks in the federated learning setting, where the goal of the adversary is to reduce the performance of the model on targeted tasks while maintaining good performance on the main task. Unlike existing

Heng Fan, Fan Yang, Peng Chu, Lin Yuan

Generic visual tracking is difficult due to many challenge factors (e.g., occlusion, blur, etc.). Each of these factors may cause serious problems for a tracking algorithm, and when they work together can make things even more complicated. Despite a great amount of efforts devoted to understanding the behavior of tracking algorithms, reliable and quantifiabl

Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu

Normalization methods such as batch [Ioffe and Szegedy, 2015], weight [Salimansand Kingma, 2016], instance [Ulyanov et al., 2016], and layer normalization [Baet al., 2016] have been widely used in modern machine learning. Here, we study the weight normalization (WN) method [Salimans and Kingma, 2016] and a variant called reparametrized projected gradient des

Patrick Hansen, Alexey Vilkin, Yury Khrustalev, James Imber

Convolutional neural networks (CNNs) are now predominant components in a variety of computer vision (CV) systems. These systems typically include an image signal processor (ISP), even though the ISP is traditionally designed to produce images that look appealing to humans. In CV systems, it is not clear what the role of the ISP is, or if it is even required

Zhong-Qiu Wang, Hakan Erdogan, Scott Wisdom, Kevin Wilson

This work introduces sequential neural beamforming, which alternates between neural network based spectral separation and beamforming based spatial separation. Our neural networks for separation use an advanced convolutional architecture trained with a novel stabilized signal-to-noise ratio loss function. For beamforming, we explore multiple ways of computin

Susumu Tanabé, Abuzer Gunduz

We present an effective method to investigate the asymptotic critical value set of a polynomial map. For this purpose we propose a method to construct rational curves with reduced number of terms present in its parametric representation. In this way we show that the asymptotic critical value set contains the critical value of an polynomial associated to so c

Efthymios Tzinis, Scott Wisdom, John R. Hershey, Aren Jansen

Deep learning approaches have recently achieved impressive performance on both audio source separation and sound classification. Most audio source separation approaches focus only on separating sources belonging to a restricted domain of source classes, such as speech and music. However, recent work has demonstrated the possibility of "universal sound separa

Yu-Hsiang Liu

In this paper, we study non-commutative projective schemes whose associated non-commutative graded algebras are finite over their centers. We study their moduli spaces of stable sheaves, and construct a symmetric obstruction theory in the Calabi-Yau-3 case. This allows us to define Donaldson-Thomas type invariants. We also discuss the simplest examples, call

Matthew R. I. Schrecker, Simon Schulz

We prove the existence of relative finite-energy vanishing viscosity solutions of the one-dimensional, isentropic Euler equations under the assumption of an asymptotically isothermal pressure law, that is, $p(ρ)/ρ= O(1)$ in the limit $ρ\to \infty$. This solution is obtained as the vanishing viscosity limit of classical solutions of the one-dimensional, isent

Nariankadu D. Shyamalkumar, Sanvesh Srivastava

Monte Carlo algorithms, such as Markov chain Monte Carlo (MCMC) and Hamiltonian Monte Carlo (HMC), are routinely used for Bayesian inference in generalized linear models; however, these algorithms are prohibitively slow in massive data settings because they require multiple passes through the full data in every iteration. Addressing this problem, we develop

Aviad Rubinstein, Jack Z. Wang, S. Matthew Weinberg

We study the single-choice Prophet Inequality problem when the gambler is given access to samples. We show that the optimal competitive ratio of $1/2$ can be achieved with a single sample from each distribution. When the distributions are identical, we show that for any constant $\varepsilon > 0$, $O(n)$ samples from the distribution suffice to achieve the o

S. Ashwin Renganathan, Romit Maulik, Vishwas Rao

Fluid flow in the transonic regime finds relevance in aerospace engineering, particularly in the design of commercial air transportation vehicles. Computational fluid dynamics models of transonic flow for aerospace applications are computationally expensive to solve because of the high degrees of freedom as well as the coupled nature of the conservation laws