arXiv papers from May 2021

Daniel Rakita, Bilge Mutlu, Michael Gleicher

In this work, we present a novel sampling-based path planning method, called SPRINT. The method finds solutions for high dimensional path planning problems quickly and robustly. Its efficiency comes from minimizing the number of collision check samples. This reduction in sampling relies on heuristics that predict the likelihood that samples will be useful in

Jiaao Chen, Dinghan Shen, Weizhu Chen, Diyi Yang

Fine-tuning large pre-trained models with task-specific data has achieved great success in NLP. However, it has been demonstrated that the majority of information within the self-attention networks is redundant and not utilized effectively during the fine-tuning stage. This leads to inferior results when generalizing the obtained models to out-of-domain dist

Hsi-Wei Yen, Bo Zhao, Patrick M. Koch, Aashish Gupta

We compared the sizes and fluxes of a sample of protostellar disks in Orion A measured with the ALMA 0.87 mm continuum data from the VANDAM survey with the physical properties of their ambient environments on the core scale of 0.6 pc estimated with the GBT GAS NH3 and JCMT SCUPOL polarimetric data. We did not find any significant dependence of the disk radii

Grisell Díaz Leines, Jutta Rogal

We present an atomistic study of heterogeneous nucleation in Ni employing transition path sampling, which reveals a template precursor-mediated mechanism of crystallization. Most notably, we find that the ability of tiny templates to modify the structural features of the liquid and promote the formation of precursor regions with enhanced bond-orientational o

Atsuhisa Ota, Hee-Jong Seo, Shun Saito, Florian Beutler

The next generation of galaxy surveys like the Dark Energy Spectroscopic Instrument (DESI) and Euclid will provide datasets orders of magnitude larger than anything available to date. Our ability to model nonlinear effects in late time matter perturbations will be a key to unlock the full potential of these datasets, and the area of initial condition reconst

Tedo Vrbanec, Ana Mestrovic

Paraphrase detection is important for a number of applications, including plagiarism detection, authorship attribution, question answering, text summarization, text mining in general, etc. In this paper, we give a performance overview of various types of corpus-based models, especially deep learning (DL) models, with the task of paraphrase detection. We repo

Robert Mieth, Yury Dvorkin, Miguel A. Ortega-Vazquez

Current contingency reserve criteria ignore the likelihood of individual contingencies and, thus, their impact on system reliability and risk. This paper develops an iterative approach, inspired by the current security-constrained unit commitment (SCUC) practice, enabling system operators to determine risk-cognizant contingency reserve requirements and their

Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov

Most studies on word-level Quality Estimation (QE) of machine translation focus on language-specific models. The obvious disadvantages of these approaches are the need for labelled data for each language pair and the high cost required to maintain several language-specific models. To overcome these problems, we explore different approaches to multilingual, w

Ujun Jeong, Kaize Ding, Huan Liu

The growing use of social media has led to drastic changes in our decision-making. Especially, Facebook offers marketing API which promotes business to target potential groups who are likely to consume their items. However, this service can be abused by malicious advertisers who attempt to deceive people by disinformation such as propaganda and divisive opin

Shamaria Engram, Tyler Kaczmarek, Alice Lee, David Bigelow

Data provenance analysis has been used as an assistive measure for ensuring system integrity. However, such techniques are typically reactive approaches to identify the root cause of an attack in its aftermath. This is in part due to fact that the collection of provenance metadata often results in a deluge of information that cannot easily be queried and ana

Mahmoud Elhebeary, Samer Hanna, Sudhakar Pamarti, Danijela Cabric

This paper presents an always-on low-power wake-up receiver (WuRx) that activates the remainder of the system when a wake-up signal is detected. The proposed receiver has two phases of waking up. The first phase uses an integrated CMOS Schottky diodes to detect the signal power at a low bias current. The approach dissipates low quiescent power and allows the

Jiaming Shen, Jialu Liu, Tianqi Liu, Cong Yu

Pre-trained text encoders such as BERT and its variants have recently achieved state-of-the-art performances on many NLP tasks. While being effective, these pre-training methods typically demand massive computation resources. To accelerate pre-training, ELECTRA trains a discriminator that predicts whether each input token is replaced by a generator. However,

Min-Seok Seo

The Cohen-Kaplan-Nelson (CKN) bound formulates the condition that black hole is not produced by the low energy effective field theory dynamics. In de Sitter space it also constrains the maximal size of the matter distribution to be smaller than the cosmological horizon determined by black hole. On the other hand, the swampland distance conjecture (SDC) predi

Michael te Vrugt, Gyula I. Tóth, Raphael Wittkowski

Wigner functions, allowing for a reformulation of quantum mechanics in phase space, are of central importance for the study of the quantum-classical transition. A full understanding of the quantum-classical transition, however, also requires an explanation for the absence of macroscopic superpositions to solve the quantum measurement problem. Stochastic refo

Anuj Mahajan, Mikayel Samvelyan, Lei Mao, Viktor Makoviychuk

Reinforcement Learning in large action spaces is a challenging problem. Cooperative multi-agent reinforcement learning (MARL) exacerbates matters by imposing various constraints on communication and observability. In this work, we consider the fundamental hurdle affecting both value-based and policy-gradient approaches: an exponential blowup of the action sp

Mohannad Alkhraijah, Carlos Menendez, Daniel K. Molzahn

Power system operators are increasingly looking toward distributed optimization to address various challenges facing electric power systems. To assess their capabilities in environments with nonideal communications, this paper investigates the impacts of data quality on the performance of distributed optimization algorithms. Specifically, this paper compares

Xuxi Chen, Zhenyu Zhang, Yongduo Sui, Tianlong Chen

Deep generative adversarial networks (GANs) have gained growing popularity in numerous scenarios, while usually suffer from high parameter complexities for resource-constrained real-world applications. However, the compression of GANs has less been explored. A few works show that heuristically applying compression techniques normally leads to unsatisfactory

Maayan Shvo, Zhiming Hu, Rodrigo Toro Icarte, Iqbal Mohomed

Human beings, even small children, quickly become adept at figuring out how to use applications on their mobile devices. Learning to use a new app is often achieved via trial-and-error, accelerated by transfer of knowledge from past experiences with like apps. The prospect of building a smarter smartphone - one that can learn how to achieve tasks using mobil

Zhifeng Kong, Wei Ping

In this work, we propose FastDPM, a unified framework for fast sampling in diffusion probabilistic models. FastDPM generalizes previous methods and gives rise to new algorithms with improved sample quality. We systematically investigate the fast sampling methods under this framework across different domains, on different datasets, and with different amount o

Yaling Tao, Kentaro Takagi, Kouta Nakata

Clustering is one of the most fundamental tasks in machine learning. Recently, deep clustering has become a major trend in clustering techniques. Representation learning often plays an important role in the effectiveness of deep clustering, and thus can be a principal cause of performance degradation. In this paper, we propose a clustering-friendly represent

Rui Meng, Khushboo Thaker, Lei Zhang, Yue Dong

Faceted summarization provides briefings of a document from different perspectives. Readers can quickly comprehend the main points of a long document with the help of a structured outline. However, little research has been conducted on this subject, partially due to the lack of large-scale faceted summarization datasets. In this study, we present FacetSum, a

Mahyar Mahinzaeim, Gen Qi Xu, Hai E Zhang

We deal with the as yet unresolved exponential stability problem for Beck's Problem on a metric star graph with three identical edges. The edges are stretched Euler--Bernoulli beams which are simply supported with respect to the outer vertices. At the inner vertex we have viscoelastic damping acting on the slopes of the edges. We carry out a complete spectra

Helen Xu, Sean Fraser, Charles E. Leiserson

This paper presents algorithms for the included-sums and excluded-sums problems used by scientific computing applications such as the fast multipole method. These problems are defined in terms of a $d$-dimensional array of $N$ elements and a binary associative operator~$\oplus$ on the elements. The included-sum problem requires that the elements within overl

Tidor-Vlad Pricope

Algorithmic stock trading has become a staple in today's financial market, the majority of trades being now fully automated. Deep Reinforcement Learning (DRL) agents proved to be to a force to be reckon with in many complex games like Chess and Go. We can look at the stock market historical price series and movements as a complex imperfect information enviro

Yuan Wang, Sebin Gracy, Hideaki Ishii, Karl Henrik Johansson

This paper considers the susceptible-infected-susceptible (SIS) epidemic model with an underlying network structure among subpopulations and focuses on the effect of social distancing to regulate the epidemic level. We demonstrate that if each subpopulation is informed of its infection rate and reduces interactions accordingly, the fraction of the subpopulat

Zhisheng Zhao, Sayan Banerjee, Debankur Mukherjee

The Join-the-Shortest Queue (JSQ) policy is a classical benchmark for the performance of many-server queueing systems due to its strong optimality properties. While the exact analysis of the JSQ policy is an open question to date, even under Markovian assumption on the service requirements, recently, there has been a significant progress in understanding its

Daniel T. Chang

Probabilistic deep learning is deep learning that accounts for uncertainty, both model uncertainty and data uncertainty. It is based on the use of probabilistic models and deep neural networks. We distinguish two approaches to probabilistic deep learning: probabilistic neural networks and deep probabilistic models. The former employs deep neural networks tha

V. V. Klimenko, A. V. Ivanchik, P. Petitjean, P. Noterdaeme

The linear increase of the cosmic microwave background (CMB) temperature with cosmological redshift, $T_{\rm CMB} = T_0(1 + z)$, is a prediction of the standard cosmological $\Lambda$CDM model. There are currently two methods to measure this dependence at redshift $z>0$, and that is equally important to estimate the CMB temperature $T_0$ at the present epoch

Marek Lassak

We present a spherical version of the theorem of Blaschke that every body of constant width $w < \frac{\pi}{2}$ can be approximated as well as we wish in the sense of the Hausdorff distance by a body of constant width $w$ whose boundary consists only of pieces of circles of radius $w$. This is a special case of our theorem about approximation of spherical re

Siavash Rajabpour, Alexander Vera, Wen He, Benjamin N. Katz

Chemically stable quantum-confined 2D metals are of interest in next-generation nanoscale quantum devices. Bottom-up design and synthesis of such metals could enable the creation of materials with tailored, on-demand, electronic and optical properties for applications that utilize tunable plasmonic coupling, optical non-linearity, epsilon-near-zero behavior,

Henrique Ferraz de Arruda, Luciano da Fontoura Costa

To a good extent, words can be understood as corresponding to patterns or categories that appeared in order to represent concepts and structures that are particularly important or useful in a given time and space. Words are characterized by not being completely general nor specific, in the sense that the same word can be instantiated or related to several di

Mehdi Cherti, Jenia Jitsev

Increasing model, data and compute budget scale in the pre-training has been shown to strongly improve model generalization and transfer learning in vast line of work done in language modeling and natural image recognition. However, most studies on the positive effect of larger scale were done in scope of in-domain setting, with source and target data being

Waleed Mustafa, Yunwen Lei, Antoine Ledent, Marius Kloft

In machine learning we often encounter structured output prediction problems (SOPPs), i.e. problems where the output space admits a rich internal structure. Application domains where SOPPs naturally occur include natural language processing, speech recognition, and computer vision. Typical SOPPs have an extremely large label set, which grows exponentially as

Patrizia Berti, Emanuela Dreassi, Fabrizio Leisen, Luca Pratelli

Let $X=(X_1,X_2,\ldots)$ be a sequence of random variables with values in a standard space $(S,\mathcal{B})$. Suppose \begin{gather*} X_1\sim\nu\quad\text{and}\quad P\bigl(X_{n+1}\in\cdot\mid X_1,\ldots,X_n\bigr)=\frac{\theta\nu(\cdot)+\sum_{i=1}^nK(X_i)(\cdot)}{n+\theta}\quad\quad\text{a.s.} \end{gather*} where $\theta>0$ is a constant, $\nu$ a probability

Satoru Hayami, Ryota Yambe

We theoretically study the stability of magnetic skyrmion crystals (SkXs) in chiral cubic antiferromagnets with the EuPtSi hosting unconventional nanometric SkXs in mind. By performing numerical simulations for a minimal effective spin model with the long-range Dzyaloshinskii-Moriya interaction and the multiple-spin interaction arising from itinerant nature

Ke-Jun Xu, Mark Barber, Eric Yue Ma, Jing Xia

Since its discovery as a Kondo insulator 50 years ago, SmB6 recently received a revival of interest due to detection of unexpected quantum oscillations in the insulating state, discovery of disorder-immune bulk transport, and proposals of correlation-driven topological physics. While recent transport results attribute the anomalous low temperature conduction

Kiyoharu Kawana, Ke-Pan Xie

We propose a novel primordial black hole (PBH) formation mechanism based on a first-order phase transition (FOPT). If a fermion species gains a huge mass in the true vacuum, the corresponding particles get trapped in the false vacuum as they do not have sufficient energy to penetrate the bubble wall. After the FOPT, the fermions are compressed into the false

Anna K. Yanchenko, Mohammadreza Soltani, Robert J. Ravier, Sayan Mukherjee

Understanding the features learned by deep models is important from a model trust perspective, especially as deep systems are deployed in the real world. Most recent approaches for deep feature understanding or model explanation focus on highlighting input data features that are relevant for classification decisions. In this work, we instead take the perspec

Jong Gwang Kim

This paper presents a new primal-dual method for computing an equilibrium of generalized (continuous) Nash game (referred to as generalized Nash equilibrium problem (GNEP)) where each player's feasible strategy set depends on the other players' strategies. The method is based on a new form of Lagrangian function with a quadratic approximation. First, we refo

Leron Borsten, Hyungrok Kim, Christian Saemann

We define a generalized form of $L_\infty$-algebras called $E_2L_\infty$-algebras. As we show, these provide the natural algebraic framework for generalized geometry and the symmetries of double field theory as well as the gauge algebras arising in the tensor hierarchies of gauged supergravity. Our perspective shows that the kinematical data of the tensor hi

Terence Lines, Ana Basiri

3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This paper proposes and implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) ma

Efrain Gonzalez, Moad Abudia, Michael Jury, Rushikesh Kamalapurkar

This manuscript revisits theoretical assumptions concerning dynamic mode decomposition (DMD) of Koopman operators, including the existence of lattices of eigenfunctions, common eigenfunctions between Koopman operators, and boundedness and compactness of Koopman operators. Counterexamples that illustrate restrictiveness of the assumptions are provided for eac

Marco Bertolini, Ilarion V. Melnikov, M. Ronen Plesser

We discuss renormalization group flows in two-dimensional quantum field theories with (0,2) supersymmetry. We focus on theories with UV described by a Landau-Ginzburg Lagrangian and use the chiral algebra to constrain the IR dynamics. We present examples where the structure of the chiral algebra is incompatible with unitarity of the IR superconformal theory

Yumo Xu, Mirella Lapata

The availability of large-scale datasets has driven the development of neural models that create summaries from single documents, for generic purposes. When using a summarization system, users often have specific intents with various language realizations, which, depending on the information need, can range from a single keyword to a long narrative composed

Moad Abudia, Tejasvi Channagiri, Joel A. Rosenfeld, Rushikesh Kamalapurkar

This manuscript presents an algorithm for obtaining an approximation of a nonlinear high order control affine dynamical system. Controlled trajectories of the system are leveraged as the central unit of information via embedding them in vector-valued reproducing kernel Hilbert space (vvRKHS). The trajectories are embedded as the so-called higher order contro

Juraj Visnovsky, Ondrej Kassak, Michal Kompan, Maria Bielikova

Cold-start problem, which arises upon the new users arrival, is one of the fundamental problems in today's recommender approaches. Moreover, in some domains as TV or multime-dia-items take long time to experience by users, thus users usually do not provide rich preference information. In this paper we analyze the minimal amount of ratings needs to be done by

Rachid Bellaama, Benharrat Belaïdi

In this article, we deal with the order of growth of solutions of non-homogeneous linear differential-difference equation \begin{equation*} \sum_{i=0}^{n}\sum_{j=0}^{m}A_{ij}f^{(j)}(z+c_{i})=F(z), \end{equation*} where $A_{ij},$ $F\left( z\right) $ are entire or meromorphic functions and $c_{i}$ $\left( 0,1,...,n\right) $ are non-zero distinct complex number

L. Baldini, J. Ballet, D. Bastieri, J. Becerra Gonzalez

We present the first Fermi Large Area Telescope (LAT) catalog of long-term $\gamma$-ray transient sources (1FLT). This comprises sources that were detected on monthly time intervals during the first decade of Fermi-LAT operations. The monthly time scale allows us to identify transient and variable sources that were not yet reported in other Fermi-LAT catalog

Harsh Satija, Philip S. Thomas, Joelle Pineau, Romain Laroche

We study the problem of Safe Policy Improvement (SPI) under constraints in the offline Reinforcement Learning (RL) setting. We consider the scenario where: (i) we have a dataset collected under a known baseline policy, (ii) multiple reward signals are received from the environment inducing as many objectives to optimize. We present an SPI formulation for thi

Robert J. Archbold, Ilja Gogić, Leonel Robert

We study variants of the Dixmier property that apply to elements of a unital C*-algebra, rather than to the C*-algebra itself. By a Dixmier element in a C*-algebra we understand one that can be averaged into a central element by means of a sequence of unitary mixing operators. Examples include all self-commutators and all quasinilpotent elements. We do a par

Roberto Vila, Helton Saulo, Jamer Roldan

In this work, we derive some novel properties of the bimodal normal distribution. Some of its mathematical properties are examined. We provide a formal proof for the bimodality and assess identifiability. We then discuss the maximum likelihood estimates as well as the existence of these estimates, and also some asymptotic properties of the estimator of the p

Edward B. Jenkins, Todd M. Tripp

Interstellar thermal pressures can be measured using C I absorption lines that probe the pressure-sensitive populations of the fine-structure levels of its ground state. In a survey of C I absorption toward Galactic hot stars, Jenkins & Tripp (2011) found evidence of small amounts ($\sim 0.05\%$) of gas at high pressures ($p/k \gg 10^4{\rm cm^{-3}K}$) mixed

Ruslan Magdiev, Artem Semidetnov

In this article, we study geometric properties of nilpotent groups. We find a geometric criterion for the word problem for the finitely generated free nilpotent groups. By geometric criterion, we mean a way to determine whether two words represent the same element in a free nilpotent group of rank $r$ and class $k$ by analyzing their behavior on the Cayley g

Zabir Al Nazi, Sayed Mohammed Tasmimul Huda

Speech synthesis is one of the challenging tasks to automate by deep learning, also being a low-resource language there are very few attempts at Bangla speech synthesis. Most of the existing works can't work with anything other than simple Bangla characters script, very short sentences, etc. This work attempts to solve these problems by introducing Byakta, t

Hansheng Diao, Giovanni Rosso, Ju-Feng Wu

The aim of this paper is twofold. We first present a construction of the overconvergent automorphic sheaves for Siegel modular forms by generalising the perfectoid method, originally introduced by Chojecki--Hansen--Johansson for automorphic forms on compact Shimura curves over $\mathbf{Q}$. The global sections of these automorphic sheaves are precisely the o

Gilad Chase, Yuval Filmus, Dor Minzer, Elchanan Mossel

For a function $g\colon\{0,1\}^m\to\{0,1\}$, a function $f\colon \{0,1\}^n\to\{0,1\}$ is called a $g$-polymorphism if their actions commute: $f(g(\mathsf{row}_1(Z)),\ldots,g(\mathsf{row}_n(Z))) = g(f(\mathsf{col}_1(Z)),\ldots,f(\mathsf{col}_m(Z)))$ for all $Z\in\{0,1\}^{n\times m}$. The function $f$ is called an approximate polymorphism if this equality hold

Kushal Chakrabarti, Nikhil Chopra

Accelerated gradient-based methods are being extensively used for solving non-convex machine learning problems, especially when the data points are abundant or the available data is distributed across several agents. Two of the prominent accelerated gradient algorithms are AdaGrad and Adam. AdaGrad is the simplest accelerated gradient method, which is partic

Kamesh Munagala, Zeyu Shen, Kangning Wang

We consider the algorithmic question of choosing a subset of candidates of a given size $k$ from a set of $m$ candidates, with knowledge of voters' ordinal rankings over all candidates. We consider the well-known and classic scoring rule for achieving diverse representation: the Chamberlin-Courant (CC) or $1$-Borda rule, where the score of a committee is the

Zhikun Liu, Yuanpeng Liu, Yuan Hong, Jinwen Meng

This study aimed to develop a classifier of prognosis after resection or liver transplantation (LT) for HCC by directly analysing the ubiquitously available histological images using deep learning based neural networks. Nucleus map set was used to train U-net to capture the nuclear architectural information. Train set included the patients with HCC treated b

Fernando Gama, Brendon G. Anderson, Somayeh Sojoudi

Graph neural networks (GNNs) have been successfully employed in a myriad of applications involving graph signals. Theoretical findings establish that GNNs use nonlinear activation functions to create low-eigenvalue frequency content that can be processed in a stable manner by subsequent graph convolutional filters. However, the exact shape of the frequency c

Henrique de Oliveira, Yuhta Ishii, Xiao Lin

An agent makes decisions based on multiple sources of information. In isolation, each source is well understood, but their correlation is unknown. We study the agent's robustly optimal strategies -- those that give the best possible guaranteed payoff, even under the worst possible correlation. With two states and two actions, we show that a robustly optimal

Robert L Wolpert

For each $\lambda>0$ and every square-integrable infinitely-divisible (ID) distribution there exists at least one stationary stochastic process $t\mapsto X_t$ with the specified distribution for $X_1$ and with first-order autoregressive (AR(1)) structure in the sense that the autocorrelation of $X_s$ and $X_t$ is $\exp(-\lambda|s-t|)$ for all indices $s,t$.

Timothy H. McNicholl, Diego A. Rojas

We establish a framework for the study of the effective theory of weak convergence of measures. We define two effective notions of weak convergence of measures on $\mathbb{R}$: one uniform and one non-uniform. We show that these notions are equivalent. By means of this equivalence, we prove an effective version of the Portmanteau Theorem, which consists of m

Clara Meister, Ryan Cotterell

We propose an alternate approach to quantifying how well language models learn natural language: we ask how well they match the statistical tendencies of natural language. To answer this question, we analyze whether text generated from language models exhibits the statistical tendencies present in the human-generated text on which they were trained. We provi

Luke Lippstreu

We generalize Zwanziger's pairwise little group to include a boost subgroup. We do so by working in the celestial sphere representation of scattering amplitudes. We propose that due to late time soft photon and graviton exchanges, matter particles in the asymptotic states in massless QED and gravity transform under the Poincare group with an additional pair

Daniel Engel, Maurice Herlihy

Automated market makers (AMMs) are automata that trade electronic assets at rates set by mathematical formulas. AMMs are usually implemented by smart contracts on blockchains. In practice, AMMs are often composed: and outputs from AMMs can be directed into other compatible AMMs. This paper proposes a mathematical model for AMM composition. We define sequenti

Chunjie Wang, Michael Friedrichs, Jens Oberrath, Ralf Peter Brinkmann

Active Plasma Resonance Spectroscopy (APRS) is a well-established plasma diagnostic method: a radio frequency signal is coupled into the plasma via a probe or antenna, excites it to oscillate, and the response is evaluated through a mathematical model. The majority of APRS probes are invasive and perturb the plasma by their physical presence. The planar Mult

Hubert Balsam

In this paper we prove matching upper and lower bounds for the transition density function of the subordinate reflected Brownian motion on fractals.

Vitalii Konarovskyi, Victor Marx, Max von Renesse

Introducing an interpolation method we derive lower bounds for the spectral gap for Brownian motion on general domains with sticky-reflecting boundary diffusion associated to the first nontrivial eigenvalue for the Laplace operator with corresponding Wentzell-type boundary condition. In the manifold case our proofs involve novel applications of the celebrate

Adiv Paradise, Kristen Menou, Christopher Lee, Bo Lin Fan

Inferring the climate and surface conditions of terrestrial exoplanets in the habitable zone is a major goal for the field of exoplanet science. This pursuit will require both statistical analyses of the population of habitable planets as well as in-depth analyses of the climates of individual planets. Given the close relationship between habitability and su

Yuri Georgievskii, Stephen J. Klippenstein

The important, and often dominant, role of tunneling in low temperature kinetics has resulted in numerous theoretical explorations into the methodology for predicting it. Nevertheless, there are still key aspects of the derivations that are lacking, particularly for non-separable systems in the low temperature regime, and further explorations of the physical

Nicolas Steven Holliman

We present a case study in the use of machine+human mixed intelligence for visualization quality assessment, applying automated visualization quality metrics to support the human assessment of data visualizations produced as coursework by students taking higher education courses. A set of image informatics algorithms including edge congestion, visual salienc

Sebastian Cygert, Bartłomiej Wróblewski, Karol Woźniak, Radosław Słowiński

While recent computer vision algorithms achieve impressive performance on many benchmarks, they lack robustness - presented with an image from a different distribution, (e.g. weather or lighting conditions not considered during training), they may produce an erroneous prediction. Therefore, it is desired that such a model will be able to reliably predict its

Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner

Bayesian phylogenetic inference is often conducted via local or sequential search over topologies and branch lengths using algorithms such as random-walk Markov chain Monte Carlo (MCMC) or Combinatorial Sequential Monte Carlo (CSMC). However, when MCMC is used for evolutionary parameter learning, convergence requires long runs with inefficient exploration of

Khaled Alharbi, Sabine Riemann, Ayash Alrashdi, Gudrid Moortgat-Pick

In the future the International Linear Collider (ILC), a helical undulator-based polarized positron source, is expected to be chosen. A high energy electron beam passes through a superconducting helical undulator in order to create circularly polarized photons which will be directed to a conversion target, resulting in electron-positron pairs. The resulting

F. Escalante

In introductory level electromagnetism courses the calculation of electrostatic potential and electric field in an arbitrary point is a very common exercise. One of the most viewed cases is the calculation of electrostatic potential and electric field in the symmetry axis of a centered ring and it has been widely studied the potential off the axis of a charg

Jacob Brown, Tanujay Saha, Niraj K. Jha

Internet-of-Things (IoT) and cyber-physical systems (CPSs) may consist of thousands of devices connected in a complex network topology. The diversity and complexity of these components present an enormous attack surface, allowing an adversary to exploit security vulnerabilities of different devices to execute a potent attack. Though significant efforts have

Shixiang Zhu, Alexander Bukharin, Liyan Xie, Khurram Yamin

Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to local health departments to limit the spread of the disease. Understanding the spatio-temporal dynamics of hotspot events is of great importan

Anton Tselishchev

The one-sided Littlewood--Paley inequality for arbitrary intervals was proved by Rubio de Francia. Later, N. Osipov proved its analogue for the system of Walsh functions. In this paper, this inequality is proved for more general Vilenkin systems.

K. A. Vyatkina, A. N. Panov

We present the general construction of the $U$-projector (the homomorphism of the algebra into its field of $U$-invariants identical on the subalgebra of $U$-invariants). It is shown how to apply $U$-projector to find the systems of free generators of the fields of $U$-invariants for representations of reductive groups.

Gionni Marchetti, Marco Patriarca, Els Heinsalu

We study the recently introduced Bayesian naming game model, in which the one-shot learning of the minimal naming game is replaced by a more realistic learning process defined according to Bayesian inference. The results are compared with those obtained from the minimal naming game model. We focus on the dynamics of the bilingual population, providing analyt

Chao Wang, Pengcheng An

Current research on Explainable AI (XAI) heavily targets on expert users (data scientists or AI developers). However, increasing importance has been argued for making AI more understandable to nonexperts, who are expected to leverage AI techniques, but have limited knowledge about AI. We present a mobile application to support nonexperts to interactively mak

Ikechukwu Obi-Okoye, Alejandro Sarria

It has been established that solutions to the inviscid Proudman-Johnson equation subject to a homogeneous three-point boundary condition can develop singularities in finite time. In this paper, we consider the possibility of singularity formation in solutions of the generalized, inviscid Proudman-Johnson equation with damping subject to the same homogeneous

Niels M. Israelsen, Peter John Rodrigo, Christian R. Petersen, Getinet Woyessa

We report on Mid-infrared (MIR) OCT at 4 $\mu$m based on collinear sum-frequency upconversion and promote the A-scan scan rate to 3 kHz. We demonstrate the increased imaging speed for two spectral realizations, one providing an axial resolution of 8.6 $\mu$m, and one providing a record axial resolution of 5.8 $\mu$m. Image performance is evaluated by sub-sur

Ninad Hogade, Sudeep Pasricha, Howard Jay Siegel

Cloud service providers are distributing data centers geographically to minimize energy costs through intelligent workload distribution. With increasing data volumes in emerging cloud workloads, it is critical to factor in the network costs for transferring workloads across data centers. For geo-distributed data centers, many researchers have been exploring

Aaron Barbosa, Elijah Pelofske, Georg Hahn, Hristo N. Djidjev

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or QUBO (quadratic unconstrained binary optimization) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the curr

Bao D. Tran, Zdzislaw E. Musielak

A new formulation of relativistic quantum mechanics is presented and applied to a free, massive, and spin zero elementary particle in the Minkowski spacetime. The reformulation requires that time and space, as well as the timelike and spacelike intervals, are treated equally, which makes the new theory fully symmetric and consistent with the Special Theory o

Adrien Rimélé, Philippe Grangier, Michel Gamache, Michel Gendreau

A Robotic Mobile Fulfillment System is a robotised parts-to-picker system that is particularly well-suited for e-commerce warehousing. One distinguishing feature of this type of warehouse is its high storage modularity. Numerous robots are moving shelves simultaneously, and the shelves can be returned to any open location after the picking operation is compl

Andrew Adamatzky, Antoni Gandia

Fungal construction materials -- substrates colonised by mycelium -- are getting increased recognition as viable ecologically friendly alternatives to conventional building materials. A functionality of the constructions made from fungal materials would be enriched if blocks with living mycelium, known for their ability to respond to chemical, optical and ta

Haonan Wang, Chang Zhou, Carl Yang, Hongxia Yang

In this paper, we identify and study an important problem of gradient item retrieval. We define the problem as retrieving a sequence of items with a gradual change on a certain attribute, given a reference item and a modification text. For example, after a customer saw a white dress, she/he wants to buy a similar one but more floral on it. The extent of "mor

Sumit A. Raurale, Geraldine B. Boylan, Sean R. Mathieson, William P. Marnane

Electroencephalography (EEG) is an important clinical tool to capture sleep-wake cycling. It can also be used for grading injury, known as hypoxic-ischaemic encephalopathy(HIE), caused by lack of oxygen or blood to the brain during birth. Trac\'e alternant (TA) is a distinctive component of normal quiet sleep which consists of alternating periods of high-vol

Artem E. Shitikov, Valery E. Lobanov, Nikita M. Kondratiev, Andrey S. Voloshin

We experimentally observed self-injection locking regime of the gain-switched laser to high-Q optical microresonator. We revealed that comb generated by the gain-switched laser experiences a dramatic reduce of comb teeth linewidths in this regime. We demonstrated the Lorentzian linewidth of the comb teeth of sub-kHz scale as narrow as for non-switched self-i

M. Torki, H. Hajizadeh, M. Farhang, A. Vafaei Sadr

We develop two parallel machine-learning pipelines to estimate the contribution of cosmic strings (CSs), conveniently encoded in their tension ($G\mu$), to the anisotropies of the cosmic microwave background radiation observed by {\it Planck}. The first approach is tree-based and feeds on certain map features derived by image processing and statistical tools

Bahareh Tolooshams, Demba Ba

The dictionary learning problem, representing data as a combination of a few atoms, has long stood as a popular method for learning representations in statistics and signal processing. The most popular dictionary learning algorithm alternates between sparse coding and dictionary update steps, and a rich literature has studied its theoretical convergence. The

Henning Haahr Andersen

Let $\mathfrak g$ be a simple complex Lie algebra. In this paper we study the BGG category $\mathcal O_q$ for the quantum group $U_q(\mathfrak g)$ with $q$ being a root of unity in a field $K$ of characteristic $p >0$. We first consider the simple modules in $\mathcal O_q$ and prove a Steinberg tensor product theorem for them. This result reduces the problem

Christine Darve, Jimmy Andersen, Sarah Salman, Martin Stankovski

The emergence of new technologies and innovative communication tools permits us to transcend societal challenges. While particle accelerators are essential instruments to improve our quality of life through science and technology, an adequate ecosystem is essential to activate and maximize this potential. Research Infrastructure (RI) and industries supported

Thomas Bott, Dominik Schlechtweg, Sabine Schulte im Walde

This paper presents a comparison of unsupervised methods of hypernymy prediction (i.e., to predict which word in a pair of words such as fish-cod is the hypernym and which the hyponym). Most importantly, we demonstrate across datasets for English and for German that the predictions of three methods (WeedsPrec, invCL, SLQS Row) strongly overlap and are highly

Alastair N. Fletcher, Vyron Vellis

The Decomposition Problem in the class $LIP(\mathbb{S}^2)$ is to decompose any bi-Lipschitz map $f:\mathbb{S}^2 \to \mathbb{S}^2$ as a composition of finitely many maps of arbitrarily small isometric distortion. In this paper, we construct a decomposition for certain bi-Lipschitz maps which spiral around every point of a Cantor set $X$ of Assouad dimension s

Martin Wolfgang Winkler, Tim Linden

In a recent paper we showed that the decay of intermediate $\bar{\Lambda}_b$ baryons can dramatically enhance the antihelium flux from dark matter annihilation. Our antihelium predictions were derived using several implementations of the Pythia and Herwig event generators which were calibrated to existing data on antideuteron and antihelium formation. Kachel

Roman Bedyakin, Nikolay Mikhaylovskiy

This memo describes NTR/TSU winning submission for Low Resource ASR challenge at Dialog2021 conference, language identification track. Spoken Language Identification (LID) is an important step in a multilingual Automated Speech Recognition (ASR) system pipeline. Traditionally, the ASR task requires large volumes of labeled data that are unattainable for most