April 2020 arXiv papers — page 32

Robert Laterveer

We prove Bloch's conjecture for numerical Campedelli surfaces with fundamental group of order $9$.

Chandra Thapa, M. A. P. Chamikara, Seyit Camtepe, Lichao Sun

Federated learning (FL) and split learning (SL) are two popular distributed machine learning approaches. Both follow a model-to-data scenario; clients train and test machine learning models without sharing raw data. SL provides better model privacy than FL due to the machine learning model architecture split between clients and the server. Moreover, the spli

Luhong Diao, Jinying Gao1, Manman Deng

As a kind of basic machine learning method, clustering algorithms group data points into different categories based on their similarity or distribution. We present a clustering algorithm by finding hyper-planes to distinguish the data points. It relies on the marginal space between the points. Then we combine these hyper-planes to determine centers and numbe

Jacopo Banchetti, Paolo Luchini, Maurizio Quadrio

This work studies the effects of skin-friction drag reduction in a turbulent flow over a curved wall, with a view to understanding the relationship between the reduction of friction and changes to the total aerodynamic drag. Direct numerical simulations (DNS) are carried out for an incompressible turbulent flow in a channel where one wall has a small bump; t

Manjul Bhargava, John Cremona, Tom Fisher

We consider the proportion of genus one curves over $\mathbb{Q}$ of the form $z^2=f(x,y)$ where $f(x,y)\in\mathbb{Z}[x,y]$ is a binary quartic form (or more generally of the form $z^2+h(x,y)z=f(x,y)$ where also $h(x,y)\in\mathbb{Z}[x,y]$ is a binary quadratic form) that have points everywhere locally. We show that the proportion of these curves that are loca

Jannis Born, Gabriel Brändle, Manuel Cossio, Marion Disdier

With the rapid development of COVID-19 into a global pandemic, there is an ever more urgent need for cheap, fast and reliable tools that can assist physicians in diagnosing COVID-19. Medical imaging such as CT can take a key role in complementing conventional diagnostic tools from molecular biology, and, using deep learning techniques, several automatic syst

Stefano Dafarra, Sylvain Bertrand, Robert J. Griffin, Giorgio Metta

Performing large step-ups is a challenging task for a humanoid robot. It requires the robot to perform motions at the limit of its reachable workspace while straining to move its body upon the obstacle. This paper presents a non-linear trajectory optimization method for generating step-up motions. We adopt a simplified model of the centroidal dynamics to gen

Qing-Le Meng, Huai-An Diao, Zheng-Jian Bai

In this paper, we present explicit expressions for the mixed and componentwise condition numbers of the truncated total least squares (TTLS) solution of $A\boldsymbol{x} \approx \boldsymbol{b} $ under the genericity condition, where $A$ is a $m\times n$ real data matrix and $\boldsymbol{b}$ is a real $m$-vector. Moreover, we reveal that normwise, componentwi

Tapas Si

Automatic programming (AP) is an important area of Machine Learning (ML) where computer programs are generated automatically. Swarm Programming (SP), a newly emerging research area in AP, automatically generates the computer programs using Swarm Intelligence (SI) algorithms. This paper presents two grammar-based SP methods named as Grammatical Moth-Flame Opt

Harshvardhan Sikka

The ability to recognize objects despite there being differences in appearance, known as Core Object Recognition, forms a critical part of human perception. While it is understood that the brain accomplishes Core Object Recognition through feedforward, hierarchical computations through the visual stream, the underlying algorithms that allow for invariant rep

Zhe Sun, Zihao Huang, Feng Duan, Yu Liu

Brain-computer interface (BCI) technologies have been widely used in many areas. In particular, non-invasive technologies such as electroencephalography (EEG) or near-infrared spectroscopy (NIRS) have been used to detect motor imagery, disease, or mental state. It has been already shown in literature that the hybrid of EEG and NIRS has better results than th

Ion Spanulescu, Anca Gheorghiu

In this paper the general principles and categories of mixed economy that currently exist in almost all countries of the world are presented. The paper also presents an Advanced Model of Mixed Economy with Threshold (AMMET), which is characterized by a reduced value (approx. 10-15%) of the State and public sector participation in the national economy and pro

Niciolas Boulanger, Fabien Buisseret

Since the pioneering works of Newton $(1643-1727)$, Mechanics has been constantly reinventing itself: reformulated in particular by Lagrange $(1736-1813)$ then Hamilton $(1805-1865)$, it now offers powerful conceptual and mathematical tools for the exploration of the most complex dynamical systems, essentially via the action-angle variables formulation and m

S. S. Agaev, K. Azizi, H. Sundu

We review our investigations devoted to the analysis of the resonances $ Z_{c}(3900)$, $Z_{c}(4430)$, $Z_{c}(4100)$, $X(4140)$, $X(4274)$, $a_1(1420)$ , $Y(4660)$, $X(2100)$, $X(2239)$ and $Y(2175)$ discovered in various processes by Belle, BaBar, BESIII, D0, CDF, CMS, LHCb and COMPASS collaborations. These resonances are considered as serious candidates to

E. A. Konovalova, Yu. A. Demidov, M. G. Kozlov

The precision measurements of the ratio of hyperfine structure constants for $s_{1/2}$ and $p_{1/2}$ states allow us to estimate the difference between hyperfine magnetic anomalies for these levels. We calculate the atomic factor in order to recover the absolute values of the hyperfine magnetic anomalies from their difference. Taking into account the hyperfi

Vladimir. P. Koshcheev, Dmitry. A. Morgun, Yuriy. N. Shtanov

The Fourier component of the potential energy of interaction of an atom with an atom is represented as a polynomial of the fourth degree from the atomic form factor. A numerical calculation was performed for the atomic form factor in the approximation of hydrogen-like wave functions. It is shown that taking the Pauli principle into account leads to two local

Lucas Lamata

Quantum machine learning has emerged as an exciting and promising paradigm inside quantum technologies. It may permit, on the one hand, to carry out more efficient machine learning calculations by means of quantum devices, while, on the other hand, to employ machine learning techniques to better control quantum systems. Inside quantum machine learning, quant

Chunhua Li, Yoshinori Nishii, Yuji Sagawa, Hideaki Sunagawa

We consider the initial value problem for cubic derivative nonlinear Schr\"odinger equation in one space dimension. Under a suitable weakly dissipative condition on the nonlinearity, we show that the small data solution has a logarithmic time decay in $L^2$.

Xiao-Xiong Zeng, Hai-Qing Zhang, Hongbao Zhang

We investigate the shadows and photon spheres of the four-dimensional Gauss-Bonnet black hole with the static and infalling spherical accretions. We show that for both cases, the shadow and photon sphere are always present. The radii of the shadow and photon sphere are independent of the profiles of accretion for a fixed Gauss-Bonnet constant, implying that

Sho Takase, Sosuke Kobayashi

In neural network-based models for natural language processing (NLP), the largest part of the parameters often consists of word embeddings. Conventional models prepare a large embedding matrix whose size depends on the vocabulary size. Therefore, storing these models in memory and disk storage is costly. In this study, to reduce the total number of parameter

Kimmo Luoma, Walter T. Strunz, Jyrki Piilo

We solve two long standing problems for stochastic descriptions of open quantum system dynamics. First, we find the classical stochastic processes corresponding to non-Markovian quantum state diffusion and non-Markovian quantum jumps in projective Hilbert space. Second, we show that the diffusive limit of non-Markovian quantum jumps can be taken on the proje

Diego Sánchez-Moreno, Vivian F. López Batista, M. Dolores Muñoz Vicente, Ana B. Gil González

In recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make available to users. This enormous availability means that recommendation mechanisms that help users to select the music they like need to be incorporated. However, developing reliable recommender systems in the music field involves de

Zhong Meng, M Umair Bin Altaf, Biing-Hwang, Juang

Active authentication refers to a new mode of identity verification in which biometric indicators are continuously tested to provide real-time or near real-time monitoring of an authorized access to a service or use of a device. This is in contrast to the conventional authentication systems where a single test in form of a verification token such as a passwo

Zhou Yu, Yuhao Cui, Jun Yu, Meng Wang

Designing effective neural networks is fundamentally important in deep multimodal learning. Most existing works focus on a single task and design neural architectures manually, which are highly task-specific and hard to generalize to different tasks. In this paper, we devise a generalized deep multimodal neural architecture search (MMnas) framework for vario

Shilin Zhu, Zexiang Xu, Henrik Wann Jensen, Hao Su

Recently, deep learning-based denoising approaches have led to dramatic improvements in low sample-count Monte Carlo rendering. These approaches are aimed at path tracing, which is not ideal for simulating challenging light transport effects like caustics, where photon mapping is the method of choice. However, photon mapping requires very large numbers of tr

Jun Zeng, Xing-Gang Wu, Xu-Chang Zheng, Jian-Ming Shen

The momentum space subtraction (MOM) scheme is one of the most frequently used renormalization schemes in perturbative QCD (pQCD) theory. In the paper, we make a detailed discussion on the gauge dependence of the pQCD prediction under the MOM scheme. Conventionally, there is renormalization scale ambiguity for the fixed-order pQCD predictions, which assigns

Zhong Meng, Hu Hu, Jinyu Li, Changliang Liu

We propose a novel neural label embedding (NLE) scheme for the domain adaptation of a deep neural network (DNN) acoustic model with unpaired data samples from source and target domains. With NLE method, we distill the knowledge from a powerful source-domain DNN into a dictionary of label embeddings, or l-vectors, one for each senone class. Each l-vector is a

W. Zhang, Z. W. Ma, H. W. Zhang

Two different definitions of the safety factor are applied to investigate the evolution of the safety factor profile during normal sawteeth, the stationary state, and the incomplete reconnection. It is found that the safety factor profiles from the old definition are sometimes inconsistent with the Poincare plots of the magnetic field during sawteeth. The ol

Xiaojuan Chen, Qiang Tu, Ni Xiang

In this paper, we consider a Class of Hessian quotient equations in Euclidean space. Under some sufficient condition, we obtain an existence result by the standard degree theory based on the a prior estimates for the solutions to the Hessian quotient equations.

Kourosh Nozari, Milad Hajebrahimi

By considering the back-reaction of the spacetime through the spherically symmetric quantum fluctuations of the background metric, Kazakov and Solodukhin removed the singularity of the Schwarzschild black hole. This regular Schwarzschild black hole has a spherical central region with a radius of the order of the Planck length. On the other hand, due to the p

B. Schmookler, M. Duer, A. Schmidt, O. Hen

The atomic nucleus is made of protons and neutrons (nucleons), that are themselves composed of quarks and gluons. Understanding how the quark-gluon structure of a nucleon bound in an atomic nucleus is modified by the surrounding nucleons is an outstanding challenge. Although evidence for such modification, known as the EMC effect, was first observed over 35

Di Zhuang, Keyu Chen, J. Morris Chang

Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in skin lesion analysis. Compared with single CNN classifier, combining the results of multiple classifiers via fusion approaches shows to be more effective and robust. Since the skin lesion datasets are usually limited and statistically biased, while designing an effective f

David Gamarnik, Aukosh Jagannath, Alexander S. Wein

We consider the problem of finding nearly optimal solutions of optimization problems with random objective functions. Two concrete problems we consider are (a) optimizing the Hamiltonian of a spherical or Ising $p$-spin glass model, and (b) finding a large independent set in a sparse Erd\H{o}s-R\'{e}nyi graph. The following families of algorithms are conside

Xiaoxi Guo, Sujoy Sikdar, Haibin Wang, Lirong Xia

In multi-type resource allocation (MTRA) problems, there are p $\ge$ 2 types of items, and n agents, who each demand one unit of items of each type, and have strict linear preferences over bundles consisting of one item of each type. For MTRAs with indivisible items, our first result is an impossibility theorem that is in direct contrast to the single type (

Sebastian A. Nugroho, Ahmad F. Taha, and Vu Hoang

Numerous state-feedback and observer designs for nonlinear dynamic systems (NDS) have been developed in the past three decades. These designs assume that NDS nonlinearities satisfy one of the following function set classifications: bounded Jacobian, Lipschitz continuity, one-sided Lipschitz, quadratic inner-boundedness, and quadratic boundedness. These funct

Ammar Alammari, Ammar Alkahtani, Mohd Riduan, Fuad Noman

A lightning mapping system based on perpendicular crossed baseline interferometer (ITF) technology has been developed rapidly in recent years. Several processing methods have been proposed to estimate the temporal location and spatial map of lightning strikes. In this paper, a single very high frequency (VHF) interferometer is used to simulate and augment th

Snehasish Bhattacharjee

$f(T,B)$ teleparallel gravity is a recently proposed straightforward generalization of the popular $f(T)$ teleparallel gravity by the incorporation of a boundary term $B=\frac{2}{e}\partial_{i}(e T ^{i}) = \bigtriangledown_{i}T^{i}$ where $T$ denote the torsion scalar \cite{ftb13}. In this work, I investigate the viability of some well motivated $f(T,B)$ tel

Di Zhuang, Nam Nguyen, Keyu Chen, J. Morris Chang

As the advancement of deep learning (DL), the Internet of Things and cloud computing techniques for biomedical and healthcare problems, mobile healthcare systems have received unprecedented attention. Since DL techniques usually require enormous amount of computation, most of them cannot be directly deployed on the resource-constrained mobile and IoT devices

Mohamed H. Abdelpakey, Mohamed S. Shehata

We propose NullSpaceNet, a novel network that maps from the pixel level input to a joint-nullspace (as opposed to the traditional feature space), where the newly learned joint-nullspace features have clearer interpretation and are more separable. NullSpaceNet ensures that all inputs from the same class are collapsed into one point in this new joint-nullspace

Wanjun Zhong, Duyu Tang, Nan Duan, Ming Zhou

We study question answering over a dynamic textual environment. Although neural network models achieve impressive accuracy via learning from input-output examples, they rarely leverage various types of knowledge and are generally not interpretable. In this work, we propose a graph-based approach, where a heterogeneous graph is automatically built with factua

Sara Tahery, Xurong Chen

To study drag force on a moving heavy quark through a plasma, we use a deformed AdS space-time, in which deformation parameter $c$ describes non-conformality in AdS/QCD. In this case the quark is mapped to a probe string in the AdS space. Considering probable contribution of deformation parameter in the probe string, we apply a general form of c-dependent st

Estari Mamidala, Rakesh Davella, Swapna Gurrapu, Pujala Shivakrishna

The COVID-19 pandemic triggered by SARS-CoV-2 is a worldwide health disaster. Main protease is an attractive drug target among coronaviruses, due to its vital role in processing the polyproteins that are translated from the viral RNA. There is presently no exact drug or treatment for this diseases caused by SARS-CoV-2. In the present study, we report the pot

Tianyuan Yang, Jibin Zheng, Tao Su, Hongwei Liu

In radar accurate localization of unmanned aerial vehicle (UAV) swarms, the high density, similar motion parameters, small radar cross-section (RCS), strong noise and far range put forward high requirements on radar resolution and transmitting power. In this paper, by using advantages of the long-time integration (LTI) technique and gridless sparse method, w

Daniel Mas Montserrat, Arvind Kumar, Carlos Bustamante, Alexander Ioannidis

With declining sequencing costs a promising and affordable tool is emerging in cancer diagnostics: genomics. By using association studies, genomic variants that predispose patients to specific cancers can be identified, while by using tumor genomics cancer types can be characterized for targeted treatment. However, a severe disparity is rapidly emerging in t

Zachary D. Walton, Bernard S. McNamara, Tommaso Toffoli

We bring together two topics (quantum mechanics and time passage) with the goal of clarifying questions about each. Specifically, we claim that the formalism of quantum mechanics provides an answer to the question: "What is time passage?".

Sicong Du, Hengkai Guo, Yao Chen, Yilun Lin

Initialization is essential to monocular Simultaneous Localization and Mapping (SLAM) problems. This paper focuses on a novel initialization method for monocular SLAM based on planar features. The algorithm starts by homography estimation in a sliding window. It then proceeds to a global plane optimization (GPO) to obtain camera poses and the plane normal. 3

Xiang-Gao Wang, Long Li, Yuan-Pei Yang, Jia-Wei Luo

The physical origin of fast radio bursts (FRBs) is unknown. Young magnetars born from gamma-ray bursts (GRBs) have been suggested to be a possible central engine of FRBs. We test such a hypothesis by systematically searching for GRB-FRB spatial associations from 110 FRBs and 1440 GRBs. We find that one FRB event, FRB 171209, discovered by the Parkes telescop

Angel Gutierrez-Rubio, Peter Stano, Daniel Loss

We address the interaction-time optimization for frequency estimation in a two-level system. The goal is to estimate with maximum precision a stochastic perturbation. Our approach is valid for any figure of merit used to define optimality, and is illustrated for the variance and entropy. For the entropy, we clarify the connection to maximum-likelihood estima

Liang Wang, Zhenghan Wang

Anyon models are algebraic structures that model universal topological properties in topological phases of matter and can be regarded as mathematical characterization of topological order in two spacial dimensions. It is conjectured that every anyon model, or mathematically unitary modular tensor category, can be realized as the representation category of so

Jerome Droniou, Beniamin Goldys, Kim-Ngan Le

*The gradient discretisation method (GDM) is a generic framework, covering many classical methods (Finite Elements, Finite Volumes, Discontinuous Galerkin, etc.), for designing and analysing numerical schemes for diffusion models. In this paper, we study the GDM for a general stochastic evolution problem based on a Leray--Lions type operator. The problem con

Keisuke Imoto, Seisuke Kyochi

A limited number of types of sound event occur in an acoustic scene and some sound events tend to co-occur in the scene; for example, the sound events "dishes" and "glass jingling" are likely to co-occur in the acoustic scene "cooking". In this paper, we propose a method of sound event detection using graph Laplacian regularization with sound event co-occurr

John X. Morris, Eli Lifland, Jack Lanchantin, Yangfeng Ji

State-of-the-art attacks on NLP models lack a shared definition of a what constitutes a successful attack. We distill ideas from past work into a unified framework: a successful natural language adversarial example is a perturbation that fools the model and follows some linguistic constraints. We then analyze the outputs of two state-of-the-art synonym subst

Ragav Sachdeva, Frank Neumann, Markus Wagner

Many real-world optimisation problems involve dynamic and stochastic components. While problems with multiple interacting components are omnipresent in inherently dynamic domains like supply-chain optimisation and logistics, most research on dynamic problems focuses on single-component problems. With this article, we define a number of scenarios based on the

Alexander E Patkowski

We obtain some Bailey pairs associated with indefinite quadratic forms with the $\beta_n$ connected to a finite sum. A new general identity is given, which provides identities for $q$-hypergeometric series, including mock theta functions.

Kenneth Joseph, Jonathan H. Morgan

Social biases are encoded in word embeddings. This presents a unique opportunity to study society historically and at scale, and a unique danger when embeddings are used in downstream applications. Here, we investigate the extent to which publicly-available word embeddings accurately reflect beliefs about certain kinds of people as measured via traditional s

Behnam Sherafat, Abbas Rashidi, Siyuan Song

Several studies have been conducted to automatically recognize activities of construction equipment using their generated sound patterns. Most of these studies are focused on single-machine scenarios under controlled environments. However, real construction job sites are more complex and often consist of several types of equipment with different orientations

Siyuan Huang, Brian D. Hoskins, Matthew W. Daniels, Mark D. Stiles

The movement of large quantities of data during the training of a Deep Neural Network presents immense challenges for machine learning workloads. To minimize this overhead, especially on the movement and calculation of gradient information, we introduce streaming batch principal component analysis as an update algorithm. Streaming batch principal component a

Yomna Safaa El-Din, Mohamed N. Moustafa, Hani Mahdi

Biometric presentation attack detection is gaining increasing attention. Users of mobile devices find it more convenient to unlock their smart applications with finger, face or iris recognition instead of passwords. In this paper, we survey the approaches presented in the recent literature to detect face and iris presentation attacks. Specifically, we invest

Heedong Do, Namyoon Lee, Angel Lozano

This paper establishes an upper bound on the capacity of line-of-sight multiantenna channels over all possible antenna arrangements and shows that uniform linear arrays (ULAs) with an SNR-dependent rotation of transmitter or receiver can closely approach such capacity---and in fact achieve it at low and high SNR, and asymptotically in the numbers of antennas

M. Belkhatir

Due to the difficulty of automatically mapping visual features with semantic descriptors, state-of-the-art frameworks have exhibited poor performance in terms of coverage and effectiveness for indexing the visual content. This prompted us to investigate the use of both the Web as a large information source from where to extract relevant contextual linguistic

K. Moskovtsev, M. I. Dykman

We present the results of large-scale numerical simulations of the mobility of a two-dimensional electron liquid on the helium surface in the presence of a one-dimensional periodic potential. Even where the potential is much weaker than the electron-electron interaction, it can strongly change the mobility. The effect depends on the interrelation between the

Walter Bridges

We prove an asymptotic formula for the number of partitions of $n$ into distinct parts where the largest part is at most $t\sqrt{n}$ for fixed $t \in \mathbb{R}$. Our method follows a probabilistic approach of Romik, who gave a simpler proof of Szekeres' asymptotic formula for distinct parts partitions when instead the number of parts is bounded by $t\sqrt{n

Bhawani Selvaretnam, Mohammed Belkhatir

In this paper, we propose a linguistically-motivated query expansion framework that recognizes and en-codes significant query constituents that characterize query intent in order to improve retrieval performance. Concepts-of-Interest are recognized as the core concepts that represent the gist of the search goal whilst the remaining query constituents which s

Zhang Youjun, Hou Mingqiang, Liu Guangtao, Zhang Chengwei

The amount of heat transport from the core, which constrains the dynamics and thermal evolution of the region, depends on the transport properties of iron. Ohta et al.(2016) and Konopkova et al.(2016) measured electrical resistivity and thermal conductivity of iron, respectively, in laser-heated diamond anvil cells (DACs) at relevant Earth's core pressure-te

M. Maree, A. Kmail, M. Belkhatir

The rapid development of the Internet has led to introducing new methods for e-recruitment and human resources management. These methods aim to systematically address the limitations of conventional recruitment procedures through incorporating natural language processing tools and semantics-based methods. In this context, for a given job post, applicant resu

Ian D. Roberts, Laura C. Parker

The Coma cluster is the nearest massive ($M \gtrsim 10^{15}\,\mathrm{M_\odot}$) galaxy cluster, making it an excellent laboratory to probe the influence of the cluster environment on galaxy star formation. Here, we present a sample of 41 galaxies with disturbed morphologies consistent with ram pressure stripping. These galaxies are identified visually using

Onur Barut, Yan Luo, Tong Zhang, Weigang Li

Classifying network traffic is the basis for important network applications. Prior research in this area has faced challenges on the availability of representative datasets, and many of the results cannot be readily reproduced. Such a problem is exacerbated by emerging data-driven machine learning based approaches. To address this issue, we provide three ope

Sangrok Lee, Eunsoo Park, Hongsuk Yi, Sang Hun Lee

Vehicle re-identification aims to obtain the same vehicles from vehicle images. This is challenging but essential for analyzing and predicting traffic flow in the city. Although deep learning methods have achieved enormous progress for this task, their large data requirement is a critical shortcoming. Therefore, we propose a synthetic-to-real domain adaptati

Zakaria Aldeneh, Anushree Prasanna Kumar, Barry-John Theobald, Erik Marchi

We present an introspection of an audiovisual speech enhancement model. In particular, we focus on interpreting how a neural audiovisual speech enhancement model uses visual cues to improve the quality of the target speech signal. We show that visual cues provide not only high-level information about speech activity, i.e., speech/silence, but also fine-grain

Mehrdad S. Dizaji, Devin K. Harris, Bernie Kassner, Jeffrey C. Hill

A collaborative investigation between the University of Virginia (UVA) and the Virginia Transportation Research Council was performed on the Mecklenburg Bridge (I-85 over Route 1 in Mecklenburg County). The research team aided the Virginia Department of Transportation - Richmond District in the characterization of the bridge behavior of one of the bridge bea

Thomas Hales, Rodrigo Raya

This article gives an elementary computational proof of the group law for Edwards elliptic curves. The associative law is expressed as a polynomial identity over the integers that is directly checked by polynomial division. Unlike other proofs, no preliminaries such as intersection numbers, Bezout's theorem, projective geometry, divisors, or Riemann Roch are

Mrugen Deshmukh, Md Moin Uddin Chowdhury, Sung Joon Maeng, Alphan Sahin

Partially-overlapping tones (POT) are known to help mitigate co-channel interference in uncoordinated multi-carrier networks by introducing intentional frequency offsets (FOs) to the transmitted signals. In this paper, we explore the use of (POT) with reinforcement learning (RL) in dense networks where multiple links access time-frequency resources simultane

Jixiong Wang, Ashish Patel, James M. S. Wason, Paul J. Newcombe

High-dimensional biomarkers such as genomics are increasingly being measured in randomized clinical trials. Consequently, there is a growing interest in developing methods that improve the power to detect biomarker-treatment interactions. We adapt recently proposed two-stage interaction detecting procedures in the setting of randomized clinical trials. We al

Daniel Mas Montserrat, Hanxiang Hao, S. K. Yarlagadda, Sriram Baireddy

Altered and manipulated multimedia is increasingly present and widely distributed via social media platforms. Advanced video manipulation tools enable the generation of highly realistic-looking altered multimedia. While many methods have been presented to detect manipulations, most of them fail when evaluated with data outside of the datasets used in researc

Georges Bastin, Jean-Michel Coron, Amaury Hayat

We give sufficient conditions for Input-to-State Stability in $C^{1}$ norm of general quasilinear hyperbolic systems with boundary input disturbances. In particular the derivation of explicit Input-to-State Stability conditions is discussed for the special case of $2\times 2$ systems.

Mitia Duerinckx, Christopher Shirley

Motivated by the long-time transport properties of quantum waves in weakly disordered media, the present work puts random Schr\"odinger operators into a new spectral perspective. Based on a stationary random version of a Floquet type fibration, we reduce the description of the quantum dynamics to a fibered family of abstract spectral perturbation problems on

Takashi Tsuchimochi, Yuto Mori, Seiichiro L. Ten-no

Although spin is a core property in fermionic systems, its symmetry can be easily violated in a variational simulation, especially when strong correlation plays a vital role therein. In this study, we will demonstrate that the broken spin-symmetry can be restored exactly in a quantum computer, with little overhead in circuits, while delivering additional str

E. Aprà, E. J. Bylaska, W. A. de Jong, N. Govind

Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principledriven methodologies

Gang Liu, Jing Wang

There are two famous function decomposition methods in math: Taylor Series and Fourier Series. Fourier series developed into Fourier spectrum, which was applied to signal decomposition\analysis. However, because the Taylor series whose function without a definite functional expression cannot be solved, Taylor Series has rarely been used in engineering. Here,

Zhuolin Jiang, Manaj Srivastava, Sanjay Krishna, David Akodes

Graph-based extractive document summarization relies on the quality of the sentence similarity graph. Bag-of-words or tf-idf based sentence similarity uses exact word matching, but fails to measure the semantic similarity between individual words or to consider the semantic structure of sentences. In order to improve the similarity measure between sentences,

Guanyu Hu, Yishu Xue, Zhihua Ma

In regional economics research, a problem of interest is to detect similarities between regions, and estimate their shared coefficients in economics models. In this article, we propose a mixture of finite mixtures (MFM) clustered regression model with auxiliary covariates that account for similarities in demographic or economic characteristics over a spatial

Ryotaro Koshio, Yuta Kozakai

Support $\tau$-tilting modules correspond to some classes of categorical objects bijectively, such as two-term tilting complexes for any finite dimensional symmetric algebra. This fact motivates us to classify support $\tau$-tilting modules over blocks of finite groups. Therefore we classify support $\tau$-tilting modules over particular blocks of finite gro

M. A. Caprio, A. E. McCoy, P. J. Fasano

The need to enforce fermionic antisymmetry in the nuclear many-body problem commonly requires use of single-particle coordinates, defined relative to some fixed origin. To obtain physical operators which nonetheless act on the nuclear many-body system in a Galilean-invariant fashion, thereby avoiding spurious center-of-mass contributions to observables, it i

Niladri S. Chatterji, Philip M. Long

We prove bounds on the population risk of the maximum margin algorithm for two-class linear classification. For linearly separable training data, the maximum margin algorithm has been shown in previous work to be equivalent to a limit of training with logistic loss using gradient descent, as the training error is driven to zero. We analyze this algorithm app

Morgan F. Wofford, Bernadette M. Boscoe, Christine L. Borgman, Irene V. Pasquetto

Citing data and software is a means to give scholarly credit and to facilitate access to research objects. Citation principles encourage authors to provide full descriptions of objects, with stable links, in their papers. As Jupyter notebooks aggregate data, software, and other objects, they may facilitate or hinder citation, credit, and access to data and s

Atiyeh Fotoohinasab, Toby Hocking, Fatemeh Afghah

Electrocardiogram (ECG) signal is the most commonly used non-invasive tool in the assessment of cardiovascular diseases. Segmentation of the ECG signal to locate its constitutive waves, in particular the R-peaks, is a key step in ECG processing and analysis. Over the years, several segmentation and QRS complex detection algorithms have been proposed with dif

Jun Horiuchi, Kazuma Shimomoto

In this article, we give a few examples of local rings in relation to weak normality and seminormality in mixed characteristic. It is known that two concepts can differ in the equal prime characteristic case, while they coincide in the equal characteristic zero case. No explicit examples seem to be documented in the existing literature in the mixed character

Zhuolin Jiang, Amro El-Jaroudi, William Hartmann, Damianos Karakos

Multiple neural language models have been developed recently, e.g., BERT and XLNet, and achieved impressive results in various NLP tasks including sentence classification, question answering and document ranking. In this paper, we explore the use of the popular bidirectional language model, BERT, to model and learn the relevance between English queries and f

Sungjin Nam, Zoya Bylinskii, Christopher Tensmeyer, Curtis Wigington

A student reads a textbook to learn a new topic; an attorney leafs through familiar legal documents. Each reader may have a different goal for, and prior knowledge of, their reading. A mobile context, which captures interaction behavior, can provide insights about these reading conditions. In this paper, we focus on understanding the different reading condit

Renaud Raquépas

We investigate the behaviour of a family of entropy production functionals associated to stochastic differential equations of the form $\mathrm{d} X_s = -\nabla V(X_s) \, \mathrm{d} s + b(X_s) \, \mathrm{d} s + \sqrt{2\epsilon} \, \mathrm{d} W_s $, where $b$ is a globally Lipschitz nonconservative vector field keeping the system out of equilibrium, with emph

Shunbo Lei, David Hong, Johanna L. Mathieu, Ian A. Hiskens

Commercial building heating, ventilation, and air conditioning (HVAC) systems have been studied for providing ancillary services to power grids via demand response (DR). One critical issue is to estimate the counterfactual baseline power consumption that would have prevailed without DR. Baseline methods have been developed based on whole building electric lo

Anastasios Zachos

We find the equations that allow us to compute the position of the two interior nodes (weighted Fermat-Torricelli points) w.r. to the weighted Steiner problem for four points determining a tetrahedron in R^3. Furthermore, by applying the solution w.r. to the weighted Steiner problem for a boundary tetrahedron, we calculate the twist angle between the two wei

Babacar Mbaye Ndiaye, Lena Tendeng, Diaraf Seck

In this paper, we propose a machine learning technics and SIR models (deterministic and stochastic cases) with numerical approximations to predict the number of cases infected with the COVID-19, for both in few days and the following three weeks. Like in [1] and based on the public data from [2], we estimate parameters and make predictions to help on how to

Andrea Konečná, F. Javier García de Abajo

The interaction between free electrons and optical near fields is attracting increasing attention as a way to manipulate the electron wave function in space, time, and energy. Relying on currently attainable experimental capabilities, we design optical near-field plates to imprint a lateral phase on the electron wave function that can largely correct spheric

Nelson B. Walker, Trevor J. Hefley, Anne E. Ballmann, Robin E. Russell

Binary regression models are commonly used in disciplines such as epidemiology and ecology to determine how spatial covariates influence individuals. In many studies, binary data are shared in a spatially aggregated form to protect privacy. For example, rather than reporting the location and result for each individual that was tested for a disease, researche

Snigdha Panigrahi, Shariq Mohammed, Arvind Rao, Veerabhadran Baladandayuthapani

Integrative analyses based on statistically relevant associations between genomics and a wealth of intermediary phenotypes (such as imaging) provide vital insights into their clinical relevance in terms of the disease mechanisms. Estimates for uncertainty in the resulting integrative models are however unreliable unless inference accounts for the selection o

Álvaro Cartea, Sebastian Jaimungal, Tianyi Jia

We develop the optimal trading strategy for a foreign exchange (FX) broker who must liquidate a large position in an illiquid currency pair. To maximize revenues, the broker considers trading in a currency triplet which consists of the illiquid pair and two other liquid currency pairs. The liquid pairs in the triplet are chosen so that one of the pairs is re

Saurabh Jha, Shengkun Cui, Subho S. Banerjee, Timothy Tsai

Ensuring the safety of autonomous vehicles (AVs) is critical for their mass deployment and public adoption. However, security attacks that violate safety constraints and cause accidents are a significant deterrent to achieving public trust in AVs, and that hinders a vendor's ability to deploy AVs. Creating a security hazard that results in a severe safety co

Oleksandr Koshkarov, Gianmarco Manzini, Gian Luca Delzanno, Cecilia Pagliantini

We discuss the development, analysis, implementation, and numerical assessment of a spectral method for the numerical simulation of the three-dimensional Vlasov-Maxwell equations. The method is based on a spectral expansion of the velocity space with the asymmetrically weighted Hermite functions. The resulting system of time-dependent nonlinear equations is

Juan M. Gomez-Perez, Xian-Peng Zhang, Francesco Calavalle, Maxim Ilyn

Spin-dependent transport at heavy metal/magnetic insulator interfaces is at the origin of many phenomena at the forefront of spintronics research. A proper quantification of the different interfacial spin conductances is crucial for many applications. Here, we report the first measurement of the spin Hall magnetoresistance (SMR) of Pt on a purely ferromagnet