October 2020 arXiv papers — page 52

Mohammad M. Jalalzai, Jianyu Niu, Chen Feng, Fangyu Gai

The HotStuff protocol is a breakthrough in Byzantine Fault Tolerant (BFT) consensus that enjoys both responsiveness and linear view change. It creatively adds an additional round to classic BFT protocols (like PBFT) using two rounds. This brings us to an interesting question: Is this additional round really necessary in practice? In this paper, we answer thi

C. Furtado, J. R. Nascimento, A. Yu. Petrov, P. J. Porfírio

In this work we calculate the angular deflection of light in the strong field limit in two spacetimes which were previously studied within the Eddington-inspired Born-Infeld gravity (EiBI), namely, a black hole and a wormhole, both with topological charge. We show that the presence of the parameters characterizing EiBI and the topological charge promote sign

Xiao-Ming Zhao, Cui-Xian Guo, Su-Peng Kou, Lin-Zhuang

Topological stability is an important property for topological materials. However, the non-Hermitian effects may change this situation. Here, we investigate the robustness of edge states in the non-Hermitian Kitaev chain with imbalanced tunneling term and superconducting pairing term. By defining the similarity of Majorana zero modes (MZMs) and magnetic fact

Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni, Manolis Zampetakis

A soft-max function has two main efficiency measures: (1) approximation - which corresponds to how well it approximates the maximum function, (2) smoothness - which shows how sensitive it is to changes of its input. Our goal is to identify the optimal approximation-smoothness tradeoffs for different measures of approximation and smoothness. This leads to nov

Andrew H. Song, Demba Ba, Emery N. Brown

To capture the slowly time-varying spectral content of real-world time-series, a common paradigm is to partition the data into approximately stationary intervals and perform inference in the time-frequency domain. However, this approach lacks a corresponding nonstationary time-domain generative model for the entire data and thus, time-domain inference occurs

Xu T. Liu, Jesun Firoz, Andrew Lumsdaine, Cliff Joslyn

This paper considers structures of systems beyond dyadic (pairwise) interactions and investigates mathematical modeling of multi-way interactions and connections as hypergraphs, where captured relationships among system entities are set-valued. To date, in most situations, entities in a hypergraph are considered connected as long as there is at least one com

Junkun Chen, Mingbo Ma, Renjie Zheng, Liang Huang

End-to-end Speech-to-text Translation (E2E-ST), which directly translates source language speech to target language text, is widely useful in practice, but traditional cascaded approaches (ASR+MT) often suffer from error propagation in the pipeline. On the other hand, existing end-to-end solutions heavily depend on the source language transcriptions for pre-

Felix Schwock, Shima Abadi

We present and analyze an alternative, more robust approach to the Welch's overlapped segment averaging (WOSA) spectral estimator. Our method computes sample percentiles instead of averaging over multiple periodograms to estimate power spectral densities (PSDs). Bias and variance of the proposed estimator are derived for varying sample sizes and arbitrary pe

Longjiang Li, Bingchuan Ma, Jianjun Yang, Yonggang Li

Modern cryptography is essential to communication and information security for performing all kinds of security actions, such as encryption, authentication, and signature. However, the exposure possibility of keys poses a great threat to almost all modern cryptography. This article proposes a key-fusing framework, which enables a high resilience to key expos

Łukasz Bożyk, Jan Derbisz, Tomasz Krawczyk, Jana Novotná

A permutation graph can be defined as an intersection graph of segments whose endpoints lie on two parallel lines $l_1$ and $l_2$, one on each. A bipartite permutation graph is a permutation graph which is bipartite. In this paper we study the parameterized complexity of the bipartite permutation vertex deletion problem, which asks, for a given n-vertex grap

Jaber Valinejad, Lamine Mili, Natalie van der Wal

In the literature, smart grids are modeled as cyber-physical power systems without considering the computational social aspects. However, end-users are playing a key role in their operation and response to disturbances via demand response and distributed energy resources. Therefore, due to the critical role of active and passive end-users and the intermitten

Jianfeng Wang, Xuelei Sui, Wenhui Duan, Feng Liu

To efficiently integrate cutting-edge terahertz technology into compact devices, the highly confined terahertz plasmons are attracting intensive attentions. Compared to plasmons at visible frequencies in metals, terahertz plasmons, typically in lightly doped semiconductors or graphene, are sensitive to carrier density (n) and thus have an easy tunability, wh

Roman R. Rafikov

Astrometry and spectroscopy of the S-stars in the Galactic Center provide a unique way to probe the properties of the central supermassive black hole, as well as the post-Newtonian effects caused by its gravity, e.g. gravitational redshift and general relativistic precession. It has also been suggested that the photometry of S-stars can be used for studying

Siwei Zou, Linhua Jiang, Yue Shen, Jin Wu

We present a study of strong intervening absorption systems in the near-IR spectra of 31 luminous quasars at $z>5.7$. The quasar spectra were obtained with {\it Gemini} GNIRS that provide continuous wavelength coverage from $\sim$0.9 to $\sim$2.5 $\mu$m. We detect 32 strong Mg II doublet absorbers with rest-frame equivalent width $W_r$ ($\lambda2796$) $>1.0$

Klée Pollock, Ge Wang, Eric Chitambar

The entanglement of assistance quantifies the amount of entanglement that can be concentrated among a group of spatially-separated parties using the assistance of some auxiliary system. In this paper we study the entanglement of assistance in the simplest scenario of three qubits. We consider how much entanglement is lost when the helper party becomes uncoup

Huifang Yuan

In this work, we apply a fast and accurate numerical method for solving fractional reaction-diffusion equations in unbounded domains. By using the Fourier-like spectral approach in space, this method can effectively handle the fractional Laplace operator, leading to a fully diagonal representation of the fractional Laplacian. To fully discretize the underlyi

Qiujia Li, David Qiu, Yu Zhang, Bo Li

For various speech-related tasks, confidence scores from a speech recogniser are a useful measure to assess the quality of transcriptions. In traditional hidden Markov model-based automatic speech recognition (ASR) systems, confidence scores can be reliably obtained from word posteriors in decoding lattices. However, for an ASR system with an auto-regressive

Weizhou Cai, Jiaxiu Han, Ling Hu, Yuwei Ma

The ability to manipulate quantum systems lies at the heart of the development of quantum technology. The ultimate goal of quantum control is to realize arbitrary quantum operations (AQuOs) for all possible open quantum system dynamics. However, the demanding extra physical resources impose great obstacles. Here, we experimentally demonstrate a universal app

Daisuke Nagakura

Recently, Ghysels, Hill, and Motegi (2020) proposed a test for examining whether a large number of coefficients in linear regression models is zero. The test is called the max test. The test statistic is calculated by first running multiple ordinary least squares (OLS) regressions, each including only one of key regressors, whose coefficients are supposed to

Angelo Casulli, Leonardo Robol

We consider the computation of roots of polynomials expressed in the Chebyshev basis. We extend the QR iteration presented in [Eidelman, Y., Gemignani, L., and Gohberg, I., Numer. Algorithms, 47.3 (2008): pp. 253-273] introducing an aggressive early deflation strategy, and showing that the rank-structure allows to parallelize the algorithm avoiding data depe

Ruize Gao, Feng Liu, Jingfeng Zhang, Bo Han

The maximum mean discrepancy (MMD) test could in principle detect any distributional discrepancy between two datasets. However, it has been shown that the MMD test is unaware of adversarial attacks -- the MMD test failed to detect the discrepancy between natural and adversarial data. Given this phenomenon, we raise a question: are natural and adversarial dat

Di Wang, Jiahao Ding, Lijie Hu, Zejun Xie

(Gradient) Expectation Maximization (EM) is a widely used algorithm for estimating the maximum likelihood of mixture models or incomplete data problems. A major challenge facing this popular technique is how to effectively preserve the privacy of sensitive data. Previous research on this problem has already lead to the discovery of some Differentially Privat

Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi

Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions, and theory of mind, i.e., what others are thinking. This makes predicting human decision-making challenging to be treated agnostically to the underlying psychological mechanisms. We propose here to use a

Hong Shen, Wesley Hanwen Deng, Aditi Chattopadhyay, Zhiwei Steven Wu

Recently, there have been increasing calls for computer science curricula to complement existing technical training with topics related to Fairness, Accountability, Transparency, and Ethics. In this paper, we present Value Card, an educational toolkit to inform students and practitioners of the social impacts of different machine learning models via delibera

Yoshihiko Okamoto, Kenta Niki, Rikuto Mitoka, Koshi Takenaka

We report the electrical resistivity, thermoelectric power, and thermal conductivity of single-crystalline and sintered samples of the 5d pyrochlore oxide CsW2O6. The electrical resistivity of the single crystal is 3 mohm cm at 295 K and gradually increases with decreasing temperature above 215 K (Phase I). The thermoelectric power of the single-crystalline

R. F. Maldonado, E. Villaver, A. J. Mustill, M. Chávez

We investigate the origin of close-in planets and related phenomena orbiting white dwarfs (WDs), which are thought to originate from orbits more distant from the star. We use the planetary architectures of the 75 multiple-planet systems (four, five and six planets) detected orbiting main-sequence stars to build 750 dynamically analogous templates that we evo

Lu Chen

In the present paper, we consider the following reversible system \begin{equation*} \begin{cases} \dot{x}=\omega_0+f(x,y),\\ \dot{y}=g(x,y), \end{cases} \end{equation*} where $x\in\mathbf{T}^{d}$, $y\backsim0\in \mathbf{R}^{d}$, $\omega_0$ is Diophantine, $f(x,y)=O(y)$, $g(x,y)=O(y^2)$ and $f$, $g$ are reversible with respect to the involution G: $(x,y)\maps

Jianwen Yin, Chenghao Liu, Weiqing Wang, Jianling Sun

Sequential user behavior modeling plays a crucial role in online user-oriented services, such as product purchasing, news feed consumption, and online advertising. The performance of sequential modeling heavily depends on the scale and quality of historical behaviors. However, the number of user behaviors inherently follows a long-tailed distribution, which

Sidra Mehtab, Jaydip Sen

Designing robust and accurate predictive models for stock price prediction has been an active area of research for a long time. While on one side, the supporters of the efficient market hypothesis claim that it is impossible to forecast stock prices accurately, many researchers believe otherwise. There exist propositions in the literature that have demonstra

Vaikkunth Mugunthan, Vignesh Gokul, Lalana Kagal, Shlomo Dubnov

Generative Adversarial Networks (GANs) are deep learning architectures capable of generating synthetic datasets. Despite producing high-quality synthetic images, the default GAN has no control over the kinds of images it generates. The Information Maximizing GAN (InfoGAN) is a variant of the default GAN that introduces feature-control variables that are auto

Zhexin Zhao, Xiao-Qi Sun, Shanhui Fan

The modulation and engineering of the free-electron wave function bring new ingredients to the electron-matter interaction. We study the dynamics of a free-electron passing by a two-level system fully quantum mechanically and emphasize the enhancement of interaction from the modulation of the free-electron wave function. In presence of modulation of the free

Xie Chen, Yu Wu, Zhenghao Wang, Shujie Liu

Recently, Transformer based end-to-end models have achieved great success in many areas including speech recognition. However, compared to LSTM models, the heavy computational cost of the Transformer during inference is a key issue to prevent their applications. In this work, we explored the potential of Transformer Transducer (T-T) models for the fist pass

Scott M. Mills, Dmitri V. Averin, Xu Du

We report localization of fractional quantum Hall (QH) quasiparticles on graphene antidots. By studying coherent tunneling through the localized QH edge modes on the antidot, we measured the QH quasiparticle charges to be approximately $\pm e/3$ at fractional fillings of $\nu = \pm 1/3$. The Dirac spectrum in graphene allows large energy scales and robust qu

Lei Lin, Feng Shi, Weizi Li

COVID-19 is affecting every social sector significantly, including human mobility and subsequently road traffic safety. In this study, we analyze the impact of the pandemic on traffic accidents using two cities, namely Los Angeles and New York City in the U.S., as examples. Specifically, we have analyzed traffic accidents associated with various demographic

Wei Fu, Mingrui Xu, Xianwen Liu, Chang-Ling Zou

In the development of quantum microwave-to-optical (MO) converters, excessive noise induced by the parametric optical drive remains a major challenge at milli-Kelvin temperatures. Here we study the extraneous noise added to an electro-optic transducer in its quantum ground state under an intense pulsed optical excitation. The integrated electro-optical trans

Bahareh Tolooshams, Satish Mulleti, Demba Ba, Yonina C. Eldar

We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution. In this problem, each channel's measurements are given as convolution of a common source signal and sparse filter. Unlike prior works where the compression is achieved either through random projections or by applying a fixed struct

R. Conceição, L. Peres, M. Pimenta, B. Tomé

Novel methods to reconstruct the slant depth of the maximum of the longitudinal profile (\Xmax) of high-energy showers initiated by gamma-rays as well as their energy ($E_0$) are presented. The methods were developed for gamma rays with energies ranging from a few hundred GeV to $\sim 10$ TeV. An estimator of \Xmax is obtained, event-by-event, from its corre

Chacha Chen, Junjie Liang, Fenglong Ma, Lucas M. Glass

Successful health risk prediction demands accuracy and reliability of the model. Existing predictive models mainly depend on mining electronic health records (EHR) with advanced deep learning techniques to improve model accuracy. However, they all ignore the importance of publicly available online health data, especially socioeconomic status, environmental f

George Boateng

Introductory hands-on courses such as our smartphone-based coding course, SuaCode require a lot of support for students to accomplish learning goals. Online environments make it even more difficult to get assistance especially more recently because of COVID-19. Given the multilingual context of SuaCode students - learners across 42 African countries that are

Dawei Ding, George Karabatsos

We propose Dirichlet Process Mixture (DPM) models for prediction and cluster-wise variable selection, based on two choices of shrinkage baseline prior distributions for the linear regression coefficients, namely the Horseshoe prior and Normal-Gamma prior. We show in a simulation study that each of the two proposed DPM models tend to outperform the standard D

Gabriele Pergola, Lin Gui, Yulan He

The flexibility of the inference process in Variational Autoencoders (VAEs) has recently led to revising traditional probabilistic topic models giving rise to Neural Topic Models (NTMs). Although these approaches have achieved significant results, surprisingly very little work has been done on how to disentangle the latent topics. Existing topic models when

Nguyen T. V. Hang, M. Ebrahim Sarabi

Second-order sufficient conditions for local optimality have been playing an important role in local convergence analysis of optimization algorithms. In this paper, we demonstrate that this condition alone suffices to justify the linear convergence of the primal-dual sequence, generated by the augmented Lagrangian method for piecewise linear-quadratic compos

Md Masud Rana, Victoria E. Howle, Katharine Long, Ashley Meek

A new preconditioner based on a block $LDU$ factorization with algebraic multigrid subsolves for scalability is introduced for the large, structured systems appearing in implicit Runge-Kutta time integration of parabolic partial differential equations. This preconditioner is compared in condition number and eigenvalue distribution, and in numerical experimen

Zaid Nabulsi, Andrew Sellergren, Shahar Jamshy, Charles Lau

Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to build specific

Bin Chen, Weidong Wang, Peibiao Zhao

Lutwak, Yang and Zhang [23] introduced the concept of Lp dual curvature measure for convex bodies and star bodies, and studied the Minkowski problem. We in this paper establish a new unified concept, in briefly, the (p,q)-mixed quermassintegrals, via (p,q)-dual mixed curvature measure, and further have a deep discussion on Minkowski problem with respect to t

Yajing Zhou, Zhengchun Zhou, Zilong Liu, Yang Yang

This paper introduces a novel class of code sets, called "symmetrical Z-complementary code sets (SZCCSs)" , whose aperiodic auto- and cross- correlation sums exhibit zero-correlation zones (ZCZs) at both the front-end and tail-end of the entire correlation window. Three constructions of (optimal) SZCCSs based on general Boolean functions are presented. As a

Terezinha K. A. Ribeiro, Silvia L. P. Ferrari

Beta regression models are widely used for modeling continuous data limited to the unit interval, such as proportions, fractions, and rates. The inference for the parameters of beta regression models is commonly based on maximum likelihood estimation. However, it is known to be sensitive to discrepant observations. In some cases, one atypical data point can

Charilaos I. Kanatsoulis, Nicholas D. Sidiropoulos

Knowledge graphs (KGs) are powerful tools that codify relational behaviour between entities in knowledge bases. KGs can simultaneously model many different types of subject-predicate-object and higher-order relations. As such, they offer a flexible modeling framework that has been applied to many areas, including biology and pharmacology -- most recently, in

Joshua Vendrow, Jamie Haddock, Elizaveta Rebrova, Deanna Needell

Fully unsupervised topic models have found fantastic success in document clustering and classification. However, these models often suffer from the tendency to learn less-than-meaningful or even redundant topics when the data is biased towards a set of features. For this reason, we propose an approach based upon the nonnegative matrix factorization (NMF) mod

Junzi Zhang, Jongho Kim, Brendan O'Donoghue, Stephen Boyd

Policy gradient methods are among the most effective methods for large-scale reinforcement learning, and their empirical success has prompted several works that develop the foundation of their global convergence theory. However, prior works have either required exact gradients or state-action visitation measure based mini-batch stochastic gradients with a di

S. Wang, Z. P. Wang, J. D. Zhang

By means of the technique of integration within an ordered product of operators and Dirac notation, we introduce a new kind of asymmetric integration projection operators in entangled state representations. These asymmetric projection operators are proved to be the Hermitian operator. Then, we rigorously demonstrate that they correspond to a parity measureme

Sebabrata Mukherjee, Mikael C. Rechtsman

A salient feature of solid-state topological materials in two dimensions is the presence of conducting electronic edge states that are insensitive to scattering by disorder. Such unidirectional edge states have been explored in many experimental settings beyond solid-state electronic systems, including in photonic devices, mechanical and acoustic structures,

Abdulelah Alharbi, Shutang You

Power systems usually go through electromagnetic and electromechanical transient processes after different disturbances. Learning the characteristics and the differences between them are important but not easy for students majoring in power systems. This paper presents a simulation-based approach to comprehensively study the two types of transient waves, con

Marc Besson, Jiuzu Hong

Let $\mathscr{G}$ be a special parahoric group scheme of twisted type over the ring of formal power series over $\mathbb{C}$, excluding the absolutely special case of $A_{2\ell}^{(2)}$. Using the methods and results of Zhu, we prove a duality theorem for general $\mathscr{G}$ : there is a duality between the level one twisted affine Demazure modules and the

Yuya Murakami

In this paper, we prove Hurwitz-Eichler type formulas for Hurwitz class numbers with each level $ M $ when the modular curve $ X_0(M) $ has genus zero. A key idea is to calculate intersection numbers of modular correspondences with the level in two different ways. A generalization of Atkin-Lehner involutions for $ Γ_0(M) $ and its subgroup $ Γ_0^{(M')}(M

Shreyas Malakarjun Patil, Constantine Dovrolis

Methods that sparsify a network at initialization are important in practice because they greatly improve the efficiency of both learning and inference. Our work is based on a recently proposed decomposition of the Neural Tangent Kernel (NTK) that has decoupled the dynamics of the training process into a data-dependent component and an architecture-dependent

Jason J. Bramburger, J. Nathan Kutz, Steven L. Brunton

Periodic orbits are among the simplest non-equilibrium solutions to dynamical systems, and they play a significant role in our modern understanding of the rich structures observed in many systems. For example, it is known that embedded within any chaotic attractor are infinitely many unstable periodic orbits (UPOs) and so a chaotic trajectory can be thought

Mohammad Esmaeilpour, Patrick Cardinal, Alessandro Lameiras Koerich

In this paper we propose a novel defense approach against end-to-end adversarial attacks developed to fool advanced speech-to-text systems such as DeepSpeech and Lingvo. Unlike conventional defense approaches, the proposed approach does not directly employ low-level transformations such as autoencoding a given input signal aiming at removing potential advers

Nur Ahmed, Muntasir Wahed

Increasingly, modern Artificial Intelligence (AI) research has become more computationally intensive. However, a growing concern is that due to unequal access to computing power, only certain firms and elite universities have advantages in modern AI research. Using a novel dataset of 171394 papers from 57 prestigious computer science conferences, we document

Niya Stoimenova, Rebecca Price

Solutions relying on artificial intelligence are devised to predict data patterns and answer questions that are clearly defined, involve an enumerable set of solutions, clear rules, and inherently binary decision mechanisms. Yet, as they become exponentially implemented in our daily activities, they begin to transcend these initial boundaries and to affect t

Zhiyuan Fu, Yuanming Lu, Donald DeAngelis, Bo Zhang

Understanding how to limit biological invasion is critical, especially in the context of accelerating anthropogenic ecological changes. Although biological invasion success could be explained by the lack of natural enemies in new regions, recent studies have revealed that resident herbivores often do have a substantial effect on both native and invasive plan

Andreas Savin

Model Hamiltonians with long-range interaction yield energies that are corrected taking into account the universal behavior of the electron-electron interaction at short range. Although the intention of the paper is to explore the foundations of using density functionals combined with range separation, the approximations presented can be used without them, a

Alireza M. Javid, Sandipan Das, Mikael Skoglund, Saikat Chatterjee

We propose ReDense as a simple and low complexity way to improve the performance of trained neural networks. We use a combination of random weights and rectified linear unit (ReLU) activation function to add a ReLU dense (ReDense) layer to the trained neural network such that it can achieve a lower training loss. The lossless flow property (LFP) of ReLU is t

Ting Cai, Peyman Mohtat, Anna G. Stefanopoulou, Jason B. Siegel

Internal short circuits are a leading cause of battery thermal runaway, and hence a major safety issue for electric vehicles. An internal short circuit with low resistance is called a hard internal short, which causes a high internal current flow that leads to an extremely fast temperature rise, gas generation, cell swelling, and ultimately battery rupture a

Tristan Sylvain, Linda Petrini, R Devon Hjelm

Zero-shot classification is a generalization task where no instance from the target classes is seen during training. To allow for test-time transfer, each class is annotated with semantic information, commonly in the form of attributes or text descriptions. While classical zero-shot learning does not explicitly forbid using information from other datasets, t

Gabriele Scrivanti, Luca Calatroni, Serena Morigi, Lindsay Nicholson

We propose a non-convex variational model for the super-resolution of Optical Coherence Tomography (OCT) images of the murine eye, by enforcing sparsity with respect to suitable dictionaries learnt from high-resolution OCT data. The statistical characteristics of OCT images motivate the use of α-stable distributions for learning dictionaries, by considering

Fabio Tonini

Given a quasi-compact category fibered in groupoids $\mathcal{X}$ and a monoidal subcategory $\mathcal{C}$ of its category of locally free sheaves $\text{Vect}(\mathcal{X})$, we are going to introduce the stack of fiber functors $\text{Fib}_{\mathcal{X},\mathcal{C}}$ with source $\mathcal{C}$, which comes equipped with a map $\mathcal{P}_{\mathcal{C}}\colon\

Simon H. Tindemans, Goran Strbac

Thermostatically controlled loads such as refrigerators are exceptionally suitable as a flexible demand resource. This paper derives a decentralised load control algorithm for refrigerators. It is adapted from an existing continuous time control approach, with the aim to achieve low computational complexity and an ability to handle discrete time steps of var

Sergey Bezuglyi, Palle E. T. Jorgensen

We present recent advances in harmonic analysis on infinite graphs. Our approach combines combinatorial tools with new results from the theory of unbounded Hermitian operators in Hilbert space, geometry, boundary constructions, and spectral invariants. We focus on particular classes of infinite graphs, including such weighted graphs which arise in electrical

Panayiota Touloupou, Renata Retkute, T Deirdre Hollingsworth, Simon E. F. Spencer

Infectious diseases remain one of the major causes of human mortality and suffering. Mathematical models have been established as an important tool for capturing the features that drive the spread of the disease, predicting the progression of an epidemic and hence guiding the development of strategies to control it. Another important area of epidemiological

David Hu, Alec Sun

Probabilistic zero-forcing is a coloring process on a graph. In this process, an initial set of vertices is colored blue, and the remaining vertices are colored white. At each time step, blue vertices have a non-zero probability of forcing white neighbors to blue. The expected propagation time is the expected amount of time needed for every vertex to be colo

Fiorenza Morini, Marco Antonio Pellegrini

Let $m,n,s,k$ be integers such that $4\leq s\leq n$, $4\leq k \leq m$ and $ms=nk$. Let $λ$ be a divisor of $2ms$ and let $t$ be a divisor of $\frac{2ms}λ$. In this paper we construct magic rectangles $MR(m,n;s,k)$, signed magic arrays $SMA(m,n;s,k)$ and integer $λ$-fold relative Heffter arrays ${}^λH_t(m,n;s,k)$ where $s,k$ are even integers. In particular,

Matej Ulicny, Vladimir A. Krylov, Rozenn Dahyot

We show how parameter redundancy in Convolutional Neural Network (CNN) filters can be effectively reduced by pruning in spectral domain. Specifically, the representation extracted via Discrete Cosine Transform (DCT) is more conducive for pruning than the original space. By relying on a combination of weight tensor reshaping and reordering we achieve high lev

Teresa White, Jesse Wheeler, Colton Lindstrom, Randall Christensen

Unmanned aerial vehicles (UAV) often rely on GPS for navigation. GPS signals, however, are very low in power and easily jammed or otherwise disrupted. This paper presents a method for determining the navigation errors present at the beginning of a GPS-denied period utilizing data from a synthetic aperture radar (SAR) system. This is accomplished by comparing

Wen Huang, Kevin Labille, Xintao Wu, Dongwon Lee

Personalized recommendation based on multi-arm bandit (MAB) algorithms has shown to lead to high utility and efficiency as it can dynamically adapt the recommendation strategy based on feedback. However, unfairness could incur in personalized recommendation. In this paper, we study how to achieve user-side fairness in personalized recommendation. We formulat

Sinho Chewi, Julien Clancy, Thibaut Le Gouic, Philippe Rigollet

We propose a new method for smoothly interpolating probability measures using the geometry of optimal transport. To that end, we reduce this problem to the classical Euclidean setting, allowing us to directly leverage the extensive toolbox of spline interpolation. Unlike previous approaches to measure-valued splines, our interpolated curves (i) have a clear

Andrea Rovere, Riccardo Piccoli, Andrea Bertoncini, Young-Gyun Jeong

Ultrashort photoemitted electron bunches can provide high electron currents within sub-picosecond timeframes, enabling time-resolved investigations of ultrafast physical processes with nanoscale resolution. Non-resonant conductive nanotips are typically employed to realize nanoscale photoelectron sources with high brightness. However, such emitters require c

Farzan Shenavarmasouleh, Hamid R. Arabnia

Bad statistics make research papers unreproducible and misleading. For the most part, the reasons for such misusage of numerical data have been found and addressed years ago by experts and proper practical solutions have been presented instead. Yet, we still see numerous instances of statistical fallacies in modern researches which without a doubt play a sig

Michael Fauß, H. Vincent Poor

A nonparametric variant of the Kiefer--Weiss problem is proposed and investigated. In analogy to the classical Kiefer--Weiss problem, the objective is to minimize the maximum expected sample size of a sequential test. However, instead of taking the maximum over a parametric family of distributions, it is taken over all distributions defined on the given samp

Ravi Tejwani, Boris Katz, Cynthia Breazeal

The migration of conversational AI agents across different embodiments in order to maintain the continuity of the task has been recently explored to further improve user experience. However, these migratable agents lack contextual understanding of the user information and the migrated device during the dialog conversations with the user. This opens the quest

Jordan Purdy, Brian Glass

The algorithmic fairness of predictive analytic tools in the public sector has increasingly become a topic of rigorous exploration. While instruments pertaining to criminal recidivism and academic admissions, for example, have garnered much attention, the predictive instruments of Child Welfare jurisdictions have received considerably less attention. This is

Debasmit Das, J. H. Moon, C. S. George Lee

In this paper, we extend the traditional few-shot learning (FSL) problem to the situation when the source-domain data is not accessible but only high-level information in the form of class prototypes is available. This limited information setup for the FSL problem deserves much attention due to its implication of privacy-preserving inaccessibility to the sou

Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Stefan Lee

Imitation learning is a popular approach for teaching motor skills to robots. However, most approaches focus on extracting policy parameters from execution traces alone (i.e., motion trajectories and perceptual data). No adequate communication channel exists between the human expert and the robot to describe critical aspects of the task, such as the properti

Ramin Okhrati, Aldo Lipani

Shapley values are great analytical tools in game theory to measure the importance of a player in a game. Due to their axiomatic and desirable properties such as efficiency, they have become popular for feature importance analysis in data science and machine learning. However, the time complexity to compute Shapley values based on the original formula is exp

Edward Raff, Bobby Filar, James Holt

False positives (FPs) have been an issue of extreme importance for anti-virus (AV) systems for decades. As more security vendors turn to machine learning, alert deluge has hit critical mass with over 20% of all alerts resulting in FPs and, in some organizations, the number reaches half of all alerts. This increase has resulted in fatigue, frustration, and, w

Long Cheng, Chenmu Zhang, Yuanyue Liu

Atomically thin (two-dimensional, 2D) semiconductors have shown great potential as the fundamental building blocks for next-generation electronics. However, all the 2D semiconductors that have been experimentally made so far have room-temperature electron mobility lower than that of bulk silicon, which is not understood. Here, by using first-principles calcu

Mohd Sabra, Anindya Maiti, Murtuza Jadliwala

Due to recent world events, video calls have become the new norm for both personal and professional remote communication. However, if a participant in a video call is not careful, he/she can reveal his/her private information to others in the call. In this paper, we design and evaluate an attack framework to infer one type of such private information from th

Daiki Shirafuji, Hiromichi Kameya, Rafal Rzepka, Kenji Araki

There are many discussions held during political meetings, and a large number of utterances for various topics is included in their transcripts. We need to read all of them if we want to follow speakers\' intentions or opinions about a given topic. To avoid such a costly and time-consuming process to grasp often longish discussions, NLP researchers work

Hongshuo Chen, Rupert L. Frank, Heinz Siedentop

The purpose of this note is to give an elementary derivation of a lower bound on the relativistic Thomas-Fermi-Weizsäcker-Dirac functional of Thomas-Fermi type and to apply it to get an upper bound on the excess charge of this model.

Carmelo Rosales-Guzmán, Xiao-Bo Hu, ValeriaRodríguez-Fajardo, Raul I. Hernandez-Aranda

Vector modes represent the most general state of light in which, the spatial and polarisation degrees of freedom are coupled in a non-separable way. Crucially, while polarisation is limited to a bi-dimensional space, the spatial degree of freedom can take any spatial profile. However, most generation and application techniques are mainly limited to spatial m

David Chiang, Chung-chieh Shan

It is natural for probabilistic programs to use conditionals to express alternative substructures in models, and loops (recursion) to express repeated substructures in models. Thus, probabilistic programs with conditionals and recursion motivate ongoing interest in efficient and general inference. A factor graph grammar (FGG) generates a set of factor graphs

Maurice Rahme, Ian Abraham, Matthew L. Elwin, Todd D. Murphey

We present a sim-to-real framework that uses dynamics and domain randomized offline reinforcement learning to enhance open-loop gaits for legged robots, allowing them to traverse uneven terrain without sensing foot impacts. Our approach, D$^2$-Randomized Gait Modulation with Bezier Curves (D$^2$-GMBC), uses augmented random search with randomized dynamics an

Duncan C McElfresh, Michael Curry, Tuomas Sandholm, John P Dickerson

In barter exchanges, participants swap goods with one another without exchanging money; exchanges are often facilitated by a central clearinghouse, with the goal of maximizing the aggregate quality (or number) of swaps. Barter exchanges are subject to many forms of uncertainty--in participant preferences, the feasibility and quality of various swaps, and so

Linjian Ma, Edgar Solomonik

CP tensor decomposition with alternating least squares (ALS) is dominated in cost by the matricized-tensor times Khatri-Rao product (MTTKRP) kernel that is necessary to set up the quadratic optimization subproblems. State-of-art parallel ALS implementations use dimension trees to avoid redundant computations across MTTKRPs within each ALS sweep. In this pape

Mehdi Rezaee, Francis Ferraro

We show how to learn a neural topic model with discrete random variables---one that explicitly models each word's assigned topic---using neural variational inference that does not rely on stochastic backpropagation to handle the discrete variables. The model we utilize combines the expressive power of neural methods for representing sequences of text wit

Chih-Hui Ho, Nuno Vasconcelos

Contrastive learning (CL) is a popular technique for self-supervised learning (SSL) of visual representations. It uses pairs of augmentations of unlabeled training examples to define a classification task for pretext learning of a deep embedding. Despite extensive works in augmentation procedures, prior works do not address the selection of challenging negat

David Chiang, Darcey Riley

We propose the use of hyperedge replacement graph grammars for factor graphs, or factor graph grammars (FGGs) for short. FGGs generate sets of factor graphs and can describe a more general class of models than plate notation, dynamic graphical models, case-factor diagrams, and sum-product networks can. Moreover, inference can be done on FGGs without enumerat

Lukas Gonon, Juan-Pablo Ortega

Echo state networks (ESNs) have been recently proved to be universal approximants for input/output systems with respect to various $L ^p$-type criteria. When $1\leq p< \infty$, only $p$-integrability hypotheses need to be imposed, while in the case $p=\infty$ a uniform boundedness hypotheses on the inputs is required. This note shows that, in the last case,

Vivek Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias

Through the use of carefully tailored convolutional neural network architectures, a deep image prior (DIP) can be used to obtain pre-images from latent representation encodings. Though DIP inversion has been known to be superior to conventional regularized inversion strategies such as total variation, such an over-parameterized generator is able to effective

Yi Xue Chong, Xiaolong Liu, Rahul Sharma, Andrey Kostin

Quantum anomalous Hall (QAH) effect appears in ferromagnetic topological insulators (FMTI) when a Dirac mass gap opens in the spectrum of the topological surface states (SS). Unaccountably, although the mean mass gap can exceed 28 meV (or ~320 K), the QAH effect is frequently only detectable at temperatures below 1 K. Using atomic-resolution Landau level spe