October 2020 arXiv papers — page 78

Ye-Won Luke Cho, Kang-Tae Kim

The main purpose of this article is to present a generalization of Forelli's theorem for the functions holomorphic along a general pencil of holomorphic discs. This generalizes the main result of \cite{JKS13} and the original Forelli's theorem, and furthermore, answers one of the problems posed in \cite{Chirka06}.

Noman Akbar, Emil Bjornson, Nan Yang, Erik G. Larsson

In this paper, we investigate optimal downlink power allocation in massive multiple-input multiple-output (MIMO) networks with distributed antenna arrays (DAAs) under correlated and uncorrelated channel fading. In DAA massive MIMO, the base station (BS) consists of multiple antenna sub-arrays. Notably, the antenna sub-arrays are deployed in arbitrary locatio

Ajit C. Balram

Fascinating structures have arisen from the study of the fractional quantum Hall effect (FQHE) at the even denominator fraction of $5/2$. We consider the FQHE at another even denominator fraction, namely $\nu=2+3/8$, where a well-developed and quantized Hall plateau has been observed in experiments. We examine the non-Abelian state described by the "$\bar{3}

Zewei Sun, Mingxuan Wang, Hao Zhou, Chengqi Zhao

This paper does not aim at introducing a novel model for document-level neural machine translation. Instead, we head back to the original Transformer model and hope to answer the following question: Is the capacity of current models strong enough for document-level translation? Interestingly, we observe that the original Transformer with appropriate training

P. Jachimowicz, M. Kowal, J. Skalski

We systematically determine ground-state and saddle-point shapes and masses for 1305 heavy and superheavy nuclei with $Z=98-126$ and $N=134-192$, including odd-$A$ and odd-odd systems. From these, we derive static fission barrier heights, one- and two-nucleon separation energies, and $Q_α$ values for g.s. to g.s transitions. Our study is performed within the

Mark V Lawson, Aidan Sims, Alina Vdovina

We construct a family of groups from suitable higher rank graphs which are analogues of the finite symmetric groups. We introduce homological invariants showing that many of our groups are, for example, not isomorphic to $nV$, when $n \geq 2$.

Rintaro Ikeshita, Tomohiro Nakatani, Shoko Araki

In this paper, we address the problem of extracting all super-Gaussian source signals from a linear mixture in which (i) the number of super-Gaussian sources $K$ is less than that of sensors $M$, and (ii) there are up to $M - K$ stationary Gaussian noises that do not need to be extracted. To solve this problem, independent vector extraction (IVE) using a maj

Farzad Fatehi, Richard J Bingham, Eric C Dykeman, Peter G Stockley

Within-host models of COVID-19 infection dynamics enable the merits of different forms of antiviral therapy to be assessed in individual patients. A stochastic agent-based model of COVID-19 intracellular dynamics is introduced here, that incorporates essential steps of the viral life cycle targeted by treatment options. Integration of model predictions with

Ivailo Hartarsky, Bernardo N. B. de Lima

We consider the Constrained-degree percolation model on the hypercubic lattice, $\mathbb L^d=(\mathbb Z^d,\mathbb E^d)$ for $d\geq 3$. It is a continuous time percolation model defined by a sequence, $(U_e)_{e\in\mathbb E^d}$, of i.i.d. uniform random variables in $[0,1]$ and a positive integer (constraint) $\kappa$. Each bond $e\in\mathbb E^d$ tries to open

Sofie Tilborghs, Tom Dresselaers, Piet Claus, Jan Bogaert

Semantic segmentation using convolutional neural networks (CNNs) is the state-of-the-art for many medical segmentation tasks including left ventricle (LV) segmentation in cardiac MR images. However, a drawback is that these CNNs lack explicit shape constraints, occasionally resulting in unrealistic segmentations. In this paper, we perform LV and myocardial s

Panpan Ren, Feng-Yu Wang

The following type exponential convergence is proved for (non-degenerate or degenerate) McKean-Vlasov SDEs: $$W_2(\mu_t,\mu_\infty)^2 +{\rm Ent}(\mu_t|\mu_\infty)\le c {\rm e}^{-\lambda t} \min\big\{W_2(\mu_0, \mu_\infty)^2,{\rm Ent}(\mu_0|\mu_\infty)\big\},\ \ t\ge 1,$$ where $c,\lambda>0$ are constants, $\mu_t$ is the distribution of the solution at time $

Mafoya Landry Dassoundo

Pre-anti-flexible family algebras are introduced and linked with the notions of relative anti-flexible algebras, left and right pre-Lie family algebras and relative Lie algebras which are for mostly newly defined. Relative pre-anti-flexible algebras are given and their underlying algebras structures such as pre-anti-flexible family algebras, left and right p

Zeping Zhu

This paper is concerned with the spectral characteristics of quaternionic positive definite functions on the real line. We generalize the Stone's theorem to the case of a right quaternionic linear one-parameter unitary group via two different types of functional calculus. From the generalized Stone's theorems we obtain a correspondence between continuous qua

Xiaolun Jia, Xiangyun Zhou, Dusit Niyato, Jun Zhao

Bistatic backscatter communication (BackCom) allows passive tags to transmit over extended ranges, but at the cost of having carrier emitters either transmitting at high powers or being deployed very close to tags. In this paper, we examine how the presence of an intelligent reflecting surface (IRS) could benefit the bistatic BackCom system. We study the tra

Martin Andersson, Pierre-Antoine Guihéneuf

We study Birkhoff averages along trajectories of smooth reparameterizations of irrational linear flows of the two torus with two stopping points, say $\mathbf p$ and $\mathbf q$, of quadratic order. The limiting behaviour of such averages is independent of the starting point in a set of full Haar-Lebesgue measure and depends in an intricate way on the Diopha

Hong-Xiang Chen, Kunhong Li, Zhiheng Fu, Mengyi Liu

A main challenge for tasks on panorama lies in the distortion of objects among images. In this work, we propose a Distortion-Aware Monocular Omnidirectional (DAMO) dense depth estimation network to address this challenge on indoor panoramas with two steps. First, we introduce a distortion-aware module to extract calibrated semantic features from omnidirectio

Joseph Resch, Abhyuday Mandal, Pritam Ranjan

In this paper we consider a dynamic computer simulator that produces a time-series response $y_t(x)$ over $L$ time points, for every given input parameter $x$. We propose a method for solving inverse problems, which refer to the finding of a set of inputs that generates a pre-specified simulator output. Inspired by the sequential approach of contour estimati

Tomohiro Okuma

For a given topological type of a normal surface singularity, there are various types of complex structures which realize it. We are interested in the following problem: Find the maximum of the geometric genus and a condition for that the maximal ideal cycle coincides with the undamental cycle on the minimal good resolution. In this paper, we study weighted

Zhi-Gang Wang

In this article, we restudy the mass spectrum of the ground state triply-heavy baryon states with the QCD sum rules by carrying out the operator product expansion up to the vacuum condensates of dimension 6 in a consistent way and preforming a novel analysis. It is the first time to take into account the three-gluon condensates in the QCD sum rules for the t

Xu-Guang Huang, Jinfeng Liao, Qun Wang, Xiao-Liang Xia

Heavy ion collisions generate strong fluid vorticty in the produced hot quark-gluon matter which could in turn induce measurable spin polarization of hadrons. We review recent progress on the vorticity formation and spin polarization in heavy ion collisions with transport models. We present an introduction to the fluid vorticity in non-relativistic and relat

Han Xiao, Qizhi Fang

The arboricity of a graph is the minimum number of forests required to cover all its edges. In this paper, we examine arboricity from a game-theoretic perspective and investigate cost-sharing in the minimum forest cover problem. We introduce the arboricity game as a cooperative cost game defined on a graph. The players are edges, and the cost of each coaliti

Alex Arash Sand Kalaee, Andreas Wacker

Entropy production is a key concept of thermodynamics and allows one to analyze the operation of engines. For the Scovil-Schulz-DuBois heat engine, the archetypal three-level thermal maser coupled to thermal baths, it was argued that the common definition of heat flow may provide negative entropy production for certain parameters [E. Boukobza and D. J. Tanno

Mahmoud I. Banat, Belal H. Sababha, Sami Al-Hamdan

Reliability and availability analysis are essential in dependable critical embedded systems. The classical implementation of dependability for an embedded system relies on merging both fundamental structures with the required dependability techniques to form one composite structure. The separation of the basic system components from the dependability compone

Santosh Ranga, Achintya Kumar Dutta

We present the theory and implementation of a core-valence separated similarity transformed EOM-CCSD (STEOM-CCSD) method for K-edge core excitation spectra. The method can select an appropriate active space using CIS natural orbitals and near black box to use. The second similarity transformation Hamiltonian is diagonalized in the space of single excitation.

Yuichi Sudo, Fukuhito Ooshita, Sayaka Kamei

In this paper, we present two self-stabilizing algorithms that enable a single (mobile) agent to explore graphs. Starting from any initial configuration, \ie regardless of the initial states of the agent and all nodes, as well as the initial location of the agent, the algorithms ensure the agent visits all nodes. We evaluate the algorithms based on two metri

Shuang Du, Weihua Wang

The two radio pulses (TRPs) from SGR 1935+2154 detected by \cite{BKRMHB} and \cite{2020arXiv200510324T} have similar features to that of cosmological fast radio bursts (FRBs). Many authors directly call the TRPs as FRB 200428 without consider two questions carefully. (1) Are the TRPs just two brighter subpulses of a normal radio pulse liking the normal radio

Keehang Kwon

Computability logic(CoL) is a powerful computational model. In this paper, we show that CoL naturally supports multi-agent programming models where resources (coffee for example) are involved. To be specific, we discuss an implementation of the Starbucks based on CoL (CL2 to be exact).

Shin-woo Park, Byung Jun Bae, Jinyoung Yeo, Seung-won Hwang

Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved superior performance in tasks such as node classification. However, analyzing heterogeneous graph of different types of nodes and links still brings great challenges for injecting the heterogeneity into a graph neural network. A general remedy is to manually

Matthew Kwan, Lisa Sauermann

Let $M_{n}$ denote a random symmetric $n\times n$ matrix, whose entries on and above the diagonal are i.i.d. Rademacher random variables (taking values $\pm 1$ with probability $1/2$ each). Resolving a conjecture of Vu, we prove that the permanent of $M_{n}$ has magnitude $n^{n/2+o(n)}$ with probability $1-o(1)$. Our result can also be extended to more gener

Vektor Dewanto, George Dunn, Ali Eshragh, Marcus Gallagher

Reinforcement learning is important part of artificial intelligence. In this paper, we review model-free reinforcement learning that utilizes the average reward optimality criterion in the infinite horizon setting. Motivated by the solo survey by Mahadevan (1996a), we provide an updated review of work in this area and extend it to cover policy-iteration and

Xiaoyu Xiang, Qian Lin, Jan P. Allebach

Due to the limits of bandwidth and storage space, digital images are usually down-scaled and compressed when transmitted over networks, resulting in loss of details and jarring artifacts that can lower the performance of high-level visual tasks. In this paper, we aim to generate an artifact-free high-resolution image from a low-resolution one compressed with

Xiuqi Ma, Wilbur Shirley, Meng Cheng, Michael Levin

2+1D multi-component $U(1)$ gauge theories with a Chern-Simons (CS) term provide a simple and complete characterization of 2+1D Abelian topological orders. In this paper, we extend the theory by taking the number of component gauge fields to infinity and find that they can describe interesting types of 3+1D "fractonic" order. "Fractonic" describes the peculi

C. Matthew Evans

In this paper we study prime spectra of commutative BCK-algebras. We give a new construction for commutative BCK-algebras using rooted trees, and determine both the ideal lattice and prime ideal lattice of such algebras. We prove that the spectrum of any commutative BCK-algebra is a locally compact generalized spectral space which is compact if and only if t

Tongzhou Zhao, William N. Faugno, Songyang Pu, Ajit C. Balram

The nature of the fractional quantum Hall effect at $ν=1/2$ observed in wide quantum wells almost three decades ago is still under debate. Previous studies have investigated it by the variational Monte Carlo method, which makes the assumption that the transverse wave function and the gap between the symmetric and antisymmetric subbands obtained in a local de

Xu-Dong Huang, Xing-Gang Wu, Qing Yu, Xu-Chang Zheng

In the paper, we make a detailed study on the generalized Crewther Relation (GCR) between the Adler function ($D$) and the Gross-Llewellyn Smith sum rules coefficient ($C^{\rm GLS}$) by using the newly suggested single-scale approach of the principle of maximum conformality (PMC). The resultant GCR is scheme-independent, whose residual scale dependence due t

Stefan Antusch, A. Hammad, Ahmed Rashed

We investigate the sensitivity of electron-proton ($ep$) colliders for charged lepton flavor violation (cLFV) in an effective theory approach, considering a general effective Lagrangian for the conversion of an electron into a muon or a tau via the effective coupling to a neutral gauge boson or a neutral scalar field. For the photon, the $Z$ boson and the Hi

Qixia Zhang

In this paper, we consider optimal control problems derived by stochastic systems with delay, where control domains are non-convex and the diffusion coefficients depend on control variables. By an estimate of the integral of x_{1}(t)x_{1}(t-\delta) term, we obtain a general maximum principle for the optimal control problems with a standard spike variational

Yandong Sun, Yanguang Zhou, Ming Hu, G. Jeffrey Snyder

Thermal management is extremely important for designing high-performance devices. The lattice thermal conductivity of materials is strongly dependent on the structural defects at different length scales, particularly point defects like vacancies, line defects like dislocations, and planar defects such as grain boundaries. Traditionally, the McKelvey-Shockley

Thao Minh Le, Vuong Le, Svetha Venkatesh, Truyen Tran

Video QA challenges modelers in multiple fronts. Modeling video necessitates building not only spatio-temporal models for the dynamic visual channel but also multimodal structures for associated information channels such as subtitles or audio. Video QA adds at least two more layers of complexity - selecting relevant content for each channel in the context of

Tien D. Vo-Huu, Triet D. Vo-Huu, Guevara Noubir

Secure ranging is poised to play a critical role in several emerging applications such as self-driving cars, unmanned aerial systems, wireless IoT devices, and augmented reality. In this paper, we propose a design of a secure broadcast ranging systems with unique features and techniques. Its spectral-flexibility, and low-power short ranging bursts enable co-

Vipul Gupta, Dhruv Choudhary, Ping Tak Peter Tang, Xiaohan Wei

In this paper, we consider hybrid parallelism -- a paradigm that employs both Data Parallelism (DP) and Model Parallelism (MP) -- to scale distributed training of large recommendation models. We propose a compression framework called Dynamic Communication Thresholding (DCT) for communication-efficient hybrid training. DCT filters the entities to be communica

Lei Zhang

This paper aims to investigate effectivity problems of pluricanonical systems on varieties of general type in positive characteristic. In practice, we will consider a sub-linear system $|S^0_{-}(X, K_X + nK_X)| \subseteq |H^0(X, K_X +nK_X)|$ generated by certain Frobenius stable sections, and prove that for a minimal terminal threefold $X$ of general type wi

Qingjun Tong, Mingxing Chen, Feiping Xiao, Hongyi Yu

Recent experimental progresses have demonstrated the great potential of electronic and excitonic moiré superlattices in transition metal dichalcogenides (TMDs) for quantum many-body simulations and quantum optics applications. Here we reveal that the moiré potential landscapes in the TMDs heterostructures have an electrostatic origin from the spontaneous cha

Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu

The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural operators that learn mappings between function spaces. For partial differential equations (PDEs), neural operators directly learn the mapping from any functional parametric dependen

Tianhui Zhou, Guangyu Tong, Fan Li, Laine E. Thomas

Propensity score weighting is an important tool for comparative effectiveness research.Besides the inverse probability of treatment weights (IPW), recent development has introduced a general class of balancing weights, corresponding to alternative target populations and estimands. In particular, the overlap weights (OW) lead to optimal covariate balance and

Aayam Shrestha, Stefan Lee, Prasad Tadepalli, Alan Fern

We study an approach to offline reinforcement learning (RL) based on optimally solving finitely-represented MDPs derived from a static dataset of experience. This approach can be applied on top of any learned representation and has the potential to easily support multiple solution objectives as well as zero-shot adjustment to changing environments and goals.

Ioannis P. Antoniades, Giuseppe Brandi, L. G. Magafas, T. Di Matteo

The dynamical evolution of multiscaling in financial time series is investigated using time-dependent Generalized Hurst Exponents (GHE), $H_q$, for various values of the parameter $q$. Using $H_q$, we introduce a new visual methodology to algorithmically detect critical changes in the scaling of the underlying complex time-series. The methodology involves th

Christoph Koutschan, Elaine Wong

We showcase a collection of practical strategies to deal with a problem arising from an analysis of integral estimators derived via quasi-Monte Carlo methods. The problem reduces to a triple binomial sum, thereby enabling us to open up the holonomic toolkit, which contains tools such as creative telescoping that can be used to deduce a recurrence satisfied b

Andrei Ol'khovatov

Some examples of the events associated with falls of rocks and iron terrestrial origin from the sky are considered (in scientific publications they are often called as meteor-wrongs or pseudo-meteorites). Their possible connections with other natural phenomena (like a whirlwind and a ball-lightning) are considered. Some compilation of info connected with the

Ruikang Zhong, Xiao Liu, Yuanwei Liu, Yue Chen

A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs), while non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. The optimization problem of joint three-dimensional (3D) trajectory design and power allocation is for

Kunal Sawarkar, Sanket Jain

For a successful business, engaging in an effective campaign is a key task for marketers. Most previous studies used various mathematical models to segment customers without considering the correlation between customer segmentation and a campaign. This work presents a conceptual model by studying the significant campaign-dependent variables of customer targe

Huichen Yang, William H. Hsu

We present an approach for adapting convolutional neural networks for object recognition and classification to scientific literature layout detection (SLLD), a shared subtask of several information extraction problems. Scientific publications contain multiple types of information sought by researchers in various disciplines, organized into an abstract, bibli

Haoyue Dai

In recent years convolutional neural networks (CNN) have shown striking progress in various tasks. However, despite the high performance, the training and prediction process remains to be a black box, leaving it a mystery to extract what neurons learn in CNN. In this paper, we address the problem of interpreting a CNN from the aspects of the input image'

Ramesh Narasimman, Izzat Alsmadi

Role based Access control (RBAC) is the cornerstone of security for any modern organization. In this report, we defined a health-care access control structure based on RBAC. We used Alloy formal logic modeling tool to model and validate system functions. We modeled system static and dynamic or temporal behaviours. We focused on evaluating properties such as

Oliver Consa

Quantum Electrodynamics (QED) is considered the most accurate theory in the history of science. However, this precision is limited to a single experimental value: the anomalous magnetic moment of the electron (g-factor). The calculation of the electron g-factor was carried out in 1950 by Karplus and Kroll. Seven years later, Petermann detected and corrected

Spyridon Mastorakis, Xin Zhong, Pei-Chi Huang, Reza Tourani

The onboarding of IoT devices by authorized users constitutes both a challenge and a necessity in a world, where the number of IoT devices and the tampering attacks against them continuously increase. Commonly used onboarding techniques today include the use of QR codes, pin codes, or serial numbers. These techniques typically do not protect against unauthor

Kazi Alam, Swapnil Yadav, K. A. Muttalib

We demonstrate a method to solve a general class of random matrix ensembles numerically. The method is suitable for solving log-gas models with biorthogonal type two-body interactions and arbitrary potentials. We reproduce standard results for a variety of well-known ensembles and show some new results for the Muttalib-Borodin ensembles and recently introduc

Aditya A. Paranjape, Soon-Jo Chung

The dyadic adaptive control architecture evolved as a solution to the problem of designing control laws for nonlinear systems with unmatched nonlinearities, disturbances and uncertainties. A salient feature of this framework is its ability to work with infinite as well as finite dimensional systems, and with a wide range of control and adaptive laws. In this

Jianchao Lu, Xi Zheng, Tianyi Zhang, Michael Sheng

Automated Driving System (ADS) has attracted increasing attention from both industrial and academic communities due to its potential for increasing the safety, mobility and efficiency of existing transportation systems. The state-of-the-art ADS follows the human-in-the-loop (HITL) design, where the driver's anomalous behaviour is closely monitored by the

Aninda Chakraborty

It is known that for an IP^{*} set A in (\mathbb{N},+) and a sequence \left\langle x_{n}\right\rangle _{n=1}^{\infty} in \mathbb{N}, there exists a sum subsystem \left\langle y_{n}\right\rangle _{n=1}^{\infty} of \left\langle x_{n}\right\rangle _{n=1}^{\infty} such that FS\left(\left\langle y_{n}\right\rangle _{n=1}^{\infty}\right)\cup FP\left(\left\langle y

Aninda Chakraborty

In [HLS], N. Hindman, I. Leader and D. Strauss proved the abundance for a matrix with rational entries. In this paper we proved it for the ring of Gaussian integers. We showed the result when the matrix is taken with entries from \mathbb{Q}\left[i\right]. The main obstacle is in the field of complex numbers, no linear order relation exists. We overcome that

Laurence Boxer

We continue the study of freezing sets for digital images introduced in [4, 2, 3]. We prove methods for obtaining freezing sets for digital images (X, c_i) for X \subset Z^2 and i \in {1, 2}. We give examples to show how these methods can lead to the determination of minimal freezing sets.

Zafeiria Moumoulidou, Andrew McGregor, Alexandra Meliou

Diversity is an important principle in data selection and summarization, facility location, and recommendation systems. Our work focuses on maximizing diversity in data selection, while offering fairness guarantees. In particular, we offer the first study that augments the Max-Min diversification objective with fairness constraints. More specifically, given

Benjamin Adams, Mark Gahegan

In Spatial Data Infrastructure or Cyber Infrastructure, the description of geographic data semantics is intended to support data discovery, reuse and integration. In the vast majority of cases the producers of these data generate descriptions based on particular understandings of what uses the data are good for. This producer-oriented perspective means that

Giulia Bovolenta, Stefano Bovino, Esteban Vöhringer-Martinez, David A. Saez

The knowledge of the binding energy of molecules on astrophysically relevant ices can help to obtain an estimate of the desorption rate, i.e. the molecules residence time on the surface. This represents an important parameter for astrochemical models, crucial to determine the chemical fate of complex organic molecules formed on dust grains and observed in th

David Céspedes-Berrocal, Heloïse Damas, Sébastien Petit-Watelot, David Maccariello

Spintronics exploits spin-orbit coupling (SOC) to generate spin currents, spin torques, and, in the absence of inversion symmetry, Rashba, and Dzyaloshinskii-Moriya interactions (DMI). The widely used magnetic materials, based on 3d metals such as Fe and Co, possess a small SOC. To circumvent this shortcoming, the common practice has been to utilize the larg

Cainã Passos, Carlos Pedroso, Agnaldo Batista, Michele Nogueira

The evolution of wireless technologies has enabled the creation of networks for several purposes as health care monitoring. The Wireless Body Area Networks (WBANs) enable continuous and real-time monitoring of physiological signals, but that monitoring leads to an excessive data transmission usage, and drastically affects the power consumption of the devices

Dennis Wei, Tian Gao, Yue Yu

This paper re-examines a continuous optimization framework dubbed NOTEARS for learning Bayesian networks. We first generalize existing algebraic characterizations of acyclicity to a class of matrix polynomials. Next, focusing on a one-parameter-per-edge setting, it is shown that the Karush-Kuhn-Tucker (KKT) optimality conditions for the NOTEARS formulation c

Manxi Wu, Saurabh Amin, Asuman Ozdaglar

We study learning dynamics induced by strategic agents who repeatedly play a game with an unknown payoff-relevant parameter. In each step, an information system estimates a belief distribution of the parameter based on the players' strategies and realized payoffs using Bayes' rule. Players adjust their strategies by accounting for an equilibrium stra

Ernest Ma

A model of split left-right symmetry is proposed, where the first family of quarks and leptons transforms under $SU(2)_R$ but the heavier two families do not. The Higgs scalar sector consists only of an $SU(2)_L$ doublet and an $SU(2)_R$ doublet. The $u,d$ quarks and the electron, as well as the neutrinos, are massless at tree level, but become massive radia

Vladimir Müller, Yuri Tomilov

Given a bounded linear operator $T$ on separable Hilbert space, we develop an approach allowing one to construct a matrix representation for $T$ having certain specified algebraic or asymptotic structure. We obtain matrix representations for $T$ with preassigned bands of the main diagonals, with an upper bound for all of the matrix elements, and with entrywi

Zhongxiang Wei, Fan Liu, Christos Masouros, H. Vincent Poor

In the era of big data, anonymity is recognized as an important attribute in privacy-preserving communications. The existing anonymous authentication and routing are applied at higher layers of networks, ignoring physical layer (PHY) also contains privacy-critical information. In this paper, we introduce the concept of PHY anonymity, and reveal that the rece

Francesco Crecchi, Marco Melis, Angelo Sotgiu, Davide Bacciu

Deep neural networks are vulnerable to adversarial examples, i.e., carefully-crafted inputs that mislead classification at test time. Recent defenses have been shown to improve adversarial robustness by detecting anomalous deviations from legitimate training samples at different layer representations - a behavior normally exhibited by adversarial attacks. De

Daniel Cano

A method is proposed to produce atomic ensembles with sub-Poissonian atom number distributions. The method consists of removing the excess atoms using the interatomic interactions induced by Rydberg dressing. The selective removal of atoms occurs via spontaneous decay into untrapped states using an electromagnetically induced transparency scheme. Ensembles w

Amrita Bhattacharjee, Kai Shu, Min Gao, Huan Liu

With the rapid increase in access to internet and the subsequent growth in the population of online social media users, the quality of information posted, disseminated and consumed via these platforms is an issue of growing concern. A large fraction of the common public turn to social media platforms and in general the internet for news and even information

Changyeob Baek, Paul Johanns, Tomohiko G. Sano, Paul Grandgeorge

We present a methodology to simulate the mechanics of knots in elastic rods using geometrically nonlinear, full three-dimensional (3D) finite element analysis. We focus on the mechanical behavior of knots in tight configurations, for which the full 3D deformation must be taken into account. To set up the topology of our knotted structures, we apply a sequenc

Ioannis Anagnostides, Themis Gouleakis, Ali Marashian

This work provides several new insights on the robustness of Kearns' statistical query framework against challenging label-noise models. First, we build on a recent result by \cite{DBLP:journals/corr/abs-2006-04787} that showed noise tolerance of distribution-independently evolvable concept classes under Massart noise. Specifically, we extend their chara

Yuxin Hou, Muhammad Kamran Janjua, Juho Kannala, Arno Solin

We propose a method for fusing stereo disparity estimation with movement-induced prior information. Instead of independent inference frame-by-frame, we formulate the problem as a non-parametric learning task in terms of a temporal Gaussian process prior with a movement-driven kernel for inter-frame reasoning. We present a hierarchy of three Gaussian process

Todd A. Brun, Leonard Mlodinow

Quantum walks on lattices can give rise to one-particle relativistic wave equations in the long-wavelength limit. In going to multiple particles, quantum cellular automata (QCA) are natural generalizations of quantum walks. In one spatial dimension, the quantum walk can be "promoted" to a QCA that, in the long-wavelength limit, gives rise to the Dira

Ryan Burt, Nina N. Thigpen, Andreas Keil, Jose C. Principe

Deep learning architectures are an extremely powerful tool for recognizing and classifying images. However, they require supervised learning and normally work on vectors the size of image pixels and produce the best results when trained on millions of object images. To help mitigate these issues, we propose the fusion of bottom-up saliency and top-down atten

Harish RaviPrakash, Syed Muhammad Anwar, Ulas Bagci

We propose a novel capsule network based variational encoder architecture, called Bayesian capsules (B-Caps), to modulate the mean and standard deviation of the sampling distribution in the latent space. We hypothesized that this approach can learn a better representation of features in the latent space than traditional approaches. Our hypothesis was tested

David Mumford

Neural nets, one of the oldest architectures for AI programming, are loosely based on biological neurons and their properties. Recent work on language applications has made the AI code closer to biological reality in several ways. This commentary examines this convergence and, in light of what is known of neocortical structure, addresses the question of whet

Paritosh Ramanan, Murat Yildirim, Nagi Gebraeel, Edmond Chow

Decentralized methods are gaining popularity for data-driven models in power systems as they offer significant computational scalability while guaranteeing full data ownership by utility stakeholders. However, decentralized methods still require sharing information about network flow estimates over public facing communication channels, which raises privacy c

Shakeeb Ahmad, Rafael Fierro

This paper addresses the problem of real-time vision-based autonomous obstacle avoidance in unstructured environments for quadrotor UAVs. We assume that our UAV is equipped with a forward facing stereo camera as the only sensor to perceive the world around it. Moreover, all the computations are performed onboard. Feasible trajectory generation in this kind o

Karl Bringmann, Philip Wellnitz

In the Subset Sum problem we are given a set of $n$ positive integers $X$ and a target $t$ and are asked whether some subset of $X$ sums to $t$. Natural parameters for this problem that have been studied in the literature are $n$ and $t$ as well as the maximum input number $\rm{mx}_X$ and the sum of all input numbers $Σ_X$. In this paper we study the dense c

Jinying Wang, Kuang-Chung Wang, Tillmann Kubis

Short wavelength ultraviolet (UV-C) light deactivates DNA of any germs, including multiresistive bacteria and viruses like COVID-19. Two-dimensional (2D) material-based UV-C light emitting diodes can potentially be integrated into arbitrary surfaces to allow for shadow-free surface disinfection. In this work, we perform a series of first-principles calculati

Ruikang Zhong, Xiao Liu, Yuanwei Liu, Yue Chen

A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs), while the non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. The optimization problem of joint three-dimensional (3D) trajectory design and power allocation is

Alireza Sepas-Moghaddam, Ali Etemad

Gait recognition refers to the identification of individuals based on features acquired from their body movement during walking. Despite the recent advances in gait recognition with deep learning, variations in data acquisition and appearance, namely camera angles, subject pose, occlusions, and clothing, are challenging factors that need to be considered for

Chang-Goo Kim, Eve C. Ostriker, Drummond B. Fielding, Matthew C. Smith

Galactic outflows have density, temperature, and velocity variations at least as large as that of the multiphase, turbulent interstellar medium (ISM) from which they originate. We have conducted a suite of parsec-resolution numerical simulations using the TIGRESS framework, in which outflows emerge as a consequence of interaction between supernovae (SNe) and

Tianlong Chen, Weiyi Zhang, Jingyang Zhou, Shiyu Chang

Learning to optimize (L2O) has gained increasing attention since classical optimizers require laborious problem-specific design and hyperparameter tuning. However, there is a gap between the practical demand and the achievable performance of existing L2O models. Specifically, those learned optimizers are applicable to only a limited class of problems, and of

Dominik Baumann, Fabian Mager, Ulf Wetzker, Lothar Thiele

Smart manufacturing aims to overcome the limitations of today's rigid assembly lines by making the material flow and manufacturing process more flexible, versatile, and scalable. The main economic drivers are higher resource and cost efficiency as the manufacturers can more quickly adapt to changing market needs and also increase the lifespan of their pr

Alireza Sepas-Moghaddam, Saeed Ghorbani, Nikolaus F. Troje, Ali Etemad

Gait recognition, referring to the identification of individuals based on the manner in which they walk, can be very challenging due to the variations in the viewpoint of the camera and the appearance of individuals. Current methods for gait recognition have been dominated by deep learning models, notably those based on partial feature representations. In th

Mahdi Saleh, Shervin Dehghani, Benjamin Busam, Nassir Navab

3D Point clouds are a rich source of information that enjoy growing popularity in the vision community. However, due to the sparsity of their representation, learning models based on large point clouds is still a challenge. In this work, we introduce Graphite, a GRAPH-Induced feaTure Extraction pipeline, a simple yet powerful feature transform and keypoint d

Anushka Prakash, Harish Tayyar Madabushi

The explosive growth and popularity of Social Media has revolutionised the way we communicate and collaborate. Unfortunately, this same ease of accessing and sharing information has led to an explosion of misinformation and propaganda. Given that stance detection can significantly aid in veracity prediction, this work focuses on boosting automated stance det

Yunling Zheng, Zhijian Li, Jack Xin, Guofa Zhou

As the COVID-19 pandemic evolves, reliable prediction plays an important role for policy making. The classical infectious disease model SEIR (susceptible-exposed-infectious-recovered) is a compact yet simplistic temporal model. The data-driven machine learning models such as RNN (recurrent neural networks) can suffer in case of limited time series data such

Nan Miles Xi

By analyzing the duopoly market of computer graphics cards, we categorized the effects of enterprise's technological progress into two types, namely, cost reduction and product diversification. Our model proved that technological progress is the most effective means for enterprises in this industry to increase profits. Due to the technology-intensive nat

Yizhou Zhang, Guido Salvaneschi, Andrew C. Myers

Pressed by the difficulty of writing asynchronous, event-driven code, mainstream languages have recently been building in support for a variety of advanced control-flow features. Meanwhile, experimental language designs have suggested effect handlers as a unifying solution to programmer-defined control effects, subsuming exceptions, generators, and async--aw

Eleri Sarsfield, Harish Tayyar Madabushi

Much as the social landscape in which languages are spoken shifts, language too evolves to suit the needs of its users. Lexical semantic change analysis is a burgeoning field of semantic analysis which aims to trace changes in the meanings of words over time. This paper presents an approach to lexical semantic change detection based on Bayesian word sense in

Andrea Ottolini

Suppose $k$ balls are dropped into $n$ boxes independently with uniform probability, where $n, k$ are large with ratio approximately equal to some positive real $λ$. The maximum box count has a counterintuitive behavior: first of all, with high probability it takes at most two values $m_n$ or $m_n+1$, where $m_n$ is roughly $\frac{\ln n}{\ln \ln n}$. Moreove

Natalia A. Sadovnikova, Leysan A. Davletshina, Olga A. Zolotareva, Olga O. Lebedinskaya

This article presents the results of a cluster analysis of the regions of the Russian Federation in terms of the main parameters of socio-economic development according to the data presented in the official data sources of the Federal State Statistics Service (Rosstat). Studied and analyzed the domestic and foreign (Eurostat) methodology for assessing the so