December 2020 arXiv papers — page 104

Kristian Stølevik Olsen

Biological and synthetic microswimmers display a wide range of swimming trajectories depending on driving forces and torques. In this paper we consider a simple overdamped model of self-propelled particles with a constant self-propulsion speed, but an angular velocity that varies in time. Specifically, we consider the case of both deterministic and stochasti

Yangming Li, Kaisheng Yao

Encoder-decoder has been widely used in neural machine translation (NMT). A few methods have been proposed to improve it with multiple passes of decoding. However, their full potential is limited by a lack of appropriate termination policies. To address this issue, we present a novel architecture, Rewriter-Evaluator. It consists of a rewriter and an evaluato

Peter Schwerdtfeger, Antony Burrows, Odile R. Smits

Analytical formulae are derived for the zero-point vibrational energy and anharmonicity corrections of the cohesive energy and the mode Gr\"{u}neisen parameter within the Einstein model for the cubic lattices (sc, bcc and fcc) and for the hexagonal close-packed structure. This extends the work done by Lennard Jones and Ingham in 1924, Corner in 1939 and Wall

Peng Zhou, Jihong Zhu, Shengzeng Huo, David Navarro-Alarcon

Soft object manipulation has recently gained popularity within the robotics community due to its potential applications in many economically important areas. Although great progress has been recently achieved in these types of tasks, most state-of-the-art methods are case-specific; They can only be used to perform a single deformation task (e.g. bending), as

Zach Vander Missen, Sergey Macheret, Abbas Semnani, Dimitrios Peroulis

This paper introduces a new technology for a high-speed, high-power mobile form-factor tuner utilizing gas discharge tube plasma cells as switching components. To the best of our knowledge, this represents the first plasma-enabled RF matching network. Technology development is reviewed, the fabrication and measurement of a proof-of-concept switched stub impe

Victor Croisfelt Rodrigues, Abolfazl Amiri, Taufik Abrão, Elisabeth de Carvalho

Massive multiple-input-multiple-output (M-MIMO) features a capability for spatial multiplexing of large number of users. This number becomes even more extreme in extra-large (XL-MIMO), a variant of M-MIMO where the antenna array is of very large size. Yet, the problem of signal processing complexity in M-MIMO is further exacerbated by the XL size of the arra

George N. Wojcik, Thomas G. Rizzo

In this paper, we present a model which attempts to unify a new dark sector force with a local $SU(3)$ flavor symmetry. Dark Matter (DM) and its potential interactions with the Standard Model (SM) continue to present a rich framework for model building. In the case of thermal DM of a mass between a few MeV and a few GeV, a compelling and much-explored framew

Angus McLure, Ben O'Neill, Helen Mayfield, Colleen Lau

Pooled testing (also known as group testing), where diagnostic tests are performed on pooled samples, has broad applications in the surveillance of diseases in animals and humans. An increasingly common use case is molecular xenomonitoring (MX), where surveillance of vector-borne diseases is conducted by capturing and testing large numbers of vectors (e.g. m

Van C. Nguyen, Oana Veliche

In a paper in 1962, Golod proved that the Betti sequence of the residue field of a local ring attains an upper bound given by Serre if and only if the homology algebra of the Koszul complex of the ring has trivial multiplications and trivial Massey operations. This is the origin of the notion of Golod ring. Using the Koszul complex components he also constru

Yangyang Li, Zhihan Wang

In this paper, we show that every $8$-dimensional closed Riemmanian manifold with $C^\infty$-generic metrics admits a smooth minimal hypersurface. This generalized previous results by N. Smale and Chodosh-Liokumovich-Spolaor. Different from their local perturbation techniques, our construction is based on a global perturbation argument in and a novel geometr

Jeffrey D. Cain, Sehoon Oh, Amin Azizi, Scott Stonemeyer

Imposing additional confinement in two-dimensional (2D) materials can yield further control over the associated electronic, optical, and topological properties. However, synthesis of ultra-narrow nanoribbons (NRs) remains a challenge, particularly for the transition metal dichalcogenides (TMDs), and synthesizing TMD NRs narrower than 50 nm has remained elusi

Jason M. Altschuler, Enric Boix-Adsera

Multimarginal Optimal Transport (MOT) is the problem of linear programming over joint probability distributions with fixed marginals. A key issue in many applications is the complexity of solving MOT: the linear program has exponential size in the number of marginals k and their support sizes n. A recent line of work has shown that MOT is poly(n,k)-time solv

Yemao Xu, Dezun Dong, Yawei Zhao, Weixia Xu

Intensive communication and synchronization cost for gradients and parameters is the well-known bottleneck of distributed deep learning training. Based on the observations that Synchronous SGD (SSGD) obtains good convergence accuracy while asynchronous SGD (ASGD) delivers a faster raw training speed, we propose Several Steps Delay SGD (SSD-SGD) to combine th

Zhaofeng Wu, Hao Peng, Noah A. Smith

For natural language processing systems, two kinds of evidence support the use of text representations from neural language models "pretrained" on large unannotated corpora: performance on application-inspired benchmarks (Peters et al., 2018, inter alia), and the emergence of syntactic abstractions in those representations (Tenney et al., 2019, inter alia).

Jason Yoo, Tony Joseph, Dylan Yung, S. Ali Nasseri

There are currently many barriers that prevent non-experts from exploiting machine learning solutions ranging from the lack of intuition on statistical learning techniques to the trickiness of hyperparameter tuning. Such barriers have led to an explosion of interest in automated machine learning (AutoML), whereby an off-the-shelf system can take care of many

Marco Mazzucchelli, Marco Radeschi

We consider convex contact spheres $Y$ all of whose Reeb orbits are closed. Any such $Y$ admits a stratification by the periods of closed Reeb orbits. We show that $Y$ "resembles" a contact ellipsoid: any stratum of $Y$ is an integral homology sphere, and the sequence of Ekeland-Hofer spectral invariants of $Y$ coincides with the full sequence of action valu

L. S. Moriggi, G. M. Peccini, M. V. T. Machado

The transverse momentum spectra of hadrons is calculated from the unintegrated gluon distribution (UGD) within the $k_T$-factorization framework at small $x$. Starting from $pp$ collisions, the modification caused by the nuclear medium is incorporated in the UGD at high energies, which is related to the nuclear shadowing phenomenon. Moreover, we consider tha

Karmel S. Shehadeh, Emily L. Tucker

We consider the problem of preparing for a disaster season by determining where to open warehouses and how much relief item inventory to preposition in each. Then, after each disaster, prepositioned items are distributed to demand nodes during the post-disaster phase, and additional items are procured and distributed as needed. There is often uncertainty in

Norita Kawanaka, Shin Mineshige

Existence of relatively cool ($k_B T \lesssim 10~{\rm keV}$) and optically thick ($\tau \gtrsim 3$) coronae are inferred above super-Eddington accretion flow such as ultraluminous X-ray sources (ULXs), GRS 1915+105, and narrow-line Seyfert 1 galaxies (NLS1), which contrasts the cases in sub-Eddington accretion flows, which are associated with coronae with $k

S. Gill Williamson

Our main result, Theorem 2.5, shows the existence of a vast infinity of subset sum problems solvable in polynomial time. The only proof we have of this result uses the ZFC independent Jump Free Theorem of Harvey Friedman, thus putting Theorem 2.5 in what we call ZFC limbo. The mathematics we use is elementary and at the level of a good undergraduate course i

N. J. Klingler, A. Lien, S. R. Oates, J. A. Kennea

On 2020 February 24, during their third observing run ("O3"), the Laser Interferometer Gravitational-wave Observatory and Virgo Collaboration (LVC) detected S200224ca: a candidate gravitational wave (GW) event produced by a binary black hole (BBH) merger. This event was one of the best-localized compact binary coalescences detected in O3 (with 50%/90% error

Alberto Dayan, Brett D. Wick, Shengkun Wu

We study almost sure separating and interpolating properties of random sequences in the polydisc and the unit ball. In the unit ball, we obtain the 0-1 Komolgorov law for a sequence to be interpolating almost surely for all the Besov-Sobolev spaces $B_{2}^{\sigma}\left(\mathbb{B}_{d}\right)$, in the range $0 < \sigma\leq1 / 2$. For those spaces, such interpo

Xiangyi Meng

The Keldysh formalism is capable of describing driven-dissipative dynamics of open quantum systems as nonunitary effective field theories that are not necessarily thermodynamical, thus often exhibiting new physics. Here, we introduce a general Keldysh action that maximally obeys Weinbergian constraints, including locality, Poincar\'e invariance, and two "$CP

Tien Chih, Laura Scull

We develop a theory of $\times$-homotopy, fundamental groupoids and covering spaces that apply to non-simple graphs, generalizing existing results for simple graphs. We prove that $\times$-homotopies from finite graphs can be decomposed into moves which adjust at most one vertex at a time, generalizing the spider lemma of \cite{CS1}. We define a notion of ho

Hallie Elich, Aaron Barrett, Varun Shankar, Aaron L. Fogelson

The transport of lymph through the lymphatic vasculature is the mechanism for returning excess interstitial fluid to the circulatory system, and it is essential for fluid homeostasis. Collecting lymphatic vessels comprise a significant portion of the lymphatic vasculature and are divided by valves into contractile segments known as lymphangions. Despite its

Irina Holmes, Sergei Treil, Alexander Volberg

Let $\bfT$ is a certain tensor product of simple dyadic shifts defined below. We prove here that for dyadic bi-parameter commutator the following equivalence holds $ \|\bfT b-b \bfT \| \asymp \|b\|_{bmo^d}$. This result is well-known for many types of bi-parameter commutators, see \cite{FS}, \cite{DLWY} and \cite{DPSK} for more details.

Patrick Tinsley, Adam Czajka, Patrick Flynn

Generative adversarial networks (GANs) are able to generate high resolution photo-realistic images of objects that &#34;do not exist.&#34; These synthetic images are rather difficult to detect as fake. However, the manner in which these generative models are trained hints at a potential for information leakage from the supplied training data, especially in t

Ali Hasan Md. Linkon, Md. Mahir Labib, Faisal Haque Bappy, Soumik Sarker

Automatic detection and recognition of banknotes can be a very useful technology for people with visual difficulties and also for the banks itself by providing efficient management for handling different paper currencies. Lightweight models can easily be integrated into any handy IoT based gadgets/devices. This article presents our experiments on several sta

Manal, Ali Hasan Md. Linkon, Md. Mahir Labib, Marium-E-Jannat

In today&#39;s world, Neural Style Transfer (NST) has become a trendsetting term. NST combines two pictures, a content picture and a reference image in style (such as the work of a renowned painter) in a way that makes the output image look like an image of the material, but rendered with the form of a reference picture. However, there is no study using the

Jirô Akahori, Yuuki Ida, Maho Nishida, Shuji Tamada

We introduce a collective model for life insurance where the heterogeneity of each insured, including the health state, is modeled by a diffusion process. This model is influenced by concepts in statistical mechanics. Using the proposed framework, one can describe the total pay-off as a functional of the diffusion process, which can be used to derive a level

Barry P. Mant, Franco A. Gianturco, Roland Wester, Ersin Yurtsever

The dicarbon molecular anion is currently of interest as a candidate for laser cooling due to its electronic structure and favorable branching ratios to the ground electronic and vibrational states. Helium has been proposed as a buffer gas to cool the molecule&#39;s internal motion. We calculate the cross sections and corresponding rates for rovibrational in

G. Bruce Berriman, William O&#39;Mullane, Arik Mitschang, Ivelina Momcheva

Commercial cloud platforms are a powerful technology for astronomical research. Despite the benefits of cloud computing -- such as on-demand scalability and reduction of systems management overhead -- confusion over how to manage costs remains, for many, one of the biggest barriers to entry. This confusion is exacerbated by the rapid growth in services offer

Tetiana Vakaliuk, Dmitry Antoniuk, Andrii Morozov, Mariia Medvedieva

The paper proposes the use of green IT as a tool for designing a cloud-oriented sustainable learning environment for a higher education institution. The article substantiates the expediency of designing such an environment as a prerequisite for the sustainable development of Ukraine. It is established that one of the goals of Ukraine&#39;s sustainable develo

Michael Fromm, Evgeniy Faerman, Max Berrendorf, Siddharth Bhargava

Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work. At the same time, it is a time-consuming process and increasing interest in emerging fields often results in a high review workload, especially for senior researchers in this area. How to cope with this problem is an open question

Steven Horng, Ashley O&#39;Donoghue, Tenzin Dechen, Matthew Rabesa

Importance: Alternative methods for hospital utilization forecasting, essential information in hospital crisis planning, are necessary in a novel pandemic when traditional data sources such as disease testing are limited. Objective: Determine whether mandatory daily employee symptom attestation data can be used as syndromic surveillance to forecast COVID-19

Rakesh Saini, Bikash K. Behera

It is well-known that decoherence is a crucial barrier in realizing various quantum information processing tasks; on the other hand, it plays a pivotal role in explaining how a quantum system&#39;s fragile state leads to the robust classical state. Zurek [Nat. Phys. 5, 181-188 (2009)] has developed the theory which successfully describes the emergence of cla

Byakatonda Denis

Differential Equations are among the most important Mathematical tools used in creating models in the science, engineering, economics, mathematics, physics, aeronautics, astronomy, dynamics, biology, chemistry, medicine, environmental sciences, social sciences, banking and many other areas [7]. A differential equation that has only one independent variable i

Michele Weiland, Bernhard Homoelle

Byte-addressable persistent memory (B-APM) presents a new opportunity to bridge the performance gap between main memory and storage. In this paper, we present the usage scenarios for this new technology, based on the capabilities of Intel&#39;s DCPMM. We outline some of the basic performance characteristics of DCPMM, and explain how it can be configured and

Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin

The utilisation of artificial intelligence in medicine and healthcare has led to successful clinical applications in several domains. The conflict between data usage and privacy protection requirements in such systems must be resolved for optimal results as well as ethical and legal compliance. This calls for innovative solutions such as privacy-preserving m

Shuteng Niu, Meryl Liu, Yongxin Liu, Jian Wang

Medical image processing is one of the most important topics in the field of the Internet of Medical Things (IoMT). Recently, deep learning methods have carried out state-of-the-art performances on medical image tasks. However, conventional deep learning have two main drawbacks: 1) insufficient training data and 2) the domain mismatch between the training da

B. Acar, G. Adamov, C. Adloff, S. Afanasiev

As part of its HL-LHC upgrade program, the CMS Collaboration is developing a High Granularity Calorimeter (CE) to replace the existing endcap calorimeters. The CE is a sampling calorimeter with unprecedented transverse and longitudinal readout for both electromagnetic (CE-E) and hadronic (CE-H) compartments. The calorimeter will be built with $\sim$30,000 he

Ayush Goel

From face recognition systems installed in phones to self-driving cars, the field of AI is witnessing rapid transformations and is being integrated into our everyday lives at an incredible pace. Any major failure in these system&#39;s predictions could be devastating, leaking sensitive information or even costing lives (as in the case of self-driving cars).

Renate Thiede, Nada Abdelatif, Inger Fabris-Rotelli, Raeesa Manjoo-Docrat

OBJECTIVES: Estimates of the basic reproduction number (R0) of COVID-19 vary across countries. This paper aims to characterise the spatial variability in R0 across the first six months of the global COVID-19 outbreak, and to explore social factors that impact R0 estimates at national and regional level. METHODS: We searched PubMed, LitCOVID and the WHO COVID

Elisa Bertino, Finale Doshi-Velez, Maria Gini, Daniel Lopresti

The rise of Artificial Intelligence (AI) will bring with it an ever-increasing willingness to cede decision-making to machines. But rather than just giving machines the power to make decisions that affect us, we need ways to work cooperatively with AI systems. There is a vital need for research in &#34;AI and Cooperation&#34; that seeks to understand the way

Gheorghe Craciun, Abhishek Deshpande, Badal Joshi, Polly Y. Yu

Autocatalytic systems are very often incorporated in the &#34;origin of life&#34; models, a connection that has been analyzed in the context of the classical hypercycles introduced by Manfred Eigen. We investigate the dynamics of certain networks called bimolecular autocatalytic systems. In particular, we consider the dynamics corresponding to the relative p

Nancy Iskander, Aurelien Simoni, Eloi Alonso, Maxim Peter

In recent years, Reinforcement Learning (RL) has seen increasing popularity in research and popular culture. However, skepticism still surrounds the practicality of RL in modern video game development. In this paper, we demonstrate by example that RL can be a great tool for Artificial Intelligence (AI) design in modern, non-trivial video games. We present ou

William Ballinger

By considering a version of Khovanov homology incorporating both the Lee and $E(-1)$ differentials, we construct a $1$-parameter family of concordance homomorphisms similar to the Upsilon invariant from knot Floer homology. This invariant gives lower bounds on the slice genus and can be used to prove that certain infinite families of pretzel knots are linear

Yasas Senarath, Uthayasanker Thayasivam

In this paper, we present an experiment on using deep learning and transfer learning techniques for emotion analysis in tweets and suggest a method to interpret our deep learning models. The proposed approach for emotion analysis combines a Long Short Term Memory (LSTM) network with a Convolutional Neural Network (CNN). Then we extend this approach for emoti

Alberto G. Matachana, Kenneth T. Co, Luis Muñoz-González, David Martinez

Neural network compression methods like pruning and quantization are very effective at efficiently deploying Deep Neural Networks (DNNs) on edge devices. However, DNNs remain vulnerable to adversarial examples-inconspicuous inputs that are specifically designed to fool these models. In particular, Universal Adversarial Perturbations (UAPs), are a powerful cl

Jinwook Huh, Volkan Isler, Daniel D. Lee

This paper presents c2g-HOF networks which learn to generate cost-to-go functions for manipulator motion planning. The c2g-HOF architecture consists of a cost-to-go function over the configuration space represented as a neural network (c2g-network) as well as a Higher Order Function (HOF) network which outputs the weights of the c2g-network for a given input

Shangrui Wu, Chavit Denninnart, Xiangbo Li, Yang Wang

Serverless clouds allocate multiple tasks (e.g., micro-services) from multiple users on a shared pool of computing resources. This enables serverless cloud providers to reduce their resource usage by transparently aggregate similar tasks of a certain context (e.g., video processing) that share the whole or part of their computation. To this end, it is crucia

Jing Zhang, Yuchao Dai, Xin Yu, Mehrtash Harandi

Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. However, those methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem. Thus, state-of-the-art SOD networks are prone to be overconfident. In other words, the predicted co

Matthew Roughan

The Tukey-$λ$ distribution has interesting properties including (i) for some parameters values it has finite support, and for others infinite support, and (ii) it can mimic several other distributions such that parameter estimation for the Tukey distribution is a method for identifying an appropriate class of distribution to model a set of data. The Tukey-$λ

Vincenzo Liguori

This paper introduces two architectures for the inference of convolutional neural networks (CNNs). Both architectures exploit weight sparsity and compression to reduce computational complexity and bandwidth. The first architecture uses multiply-accumulators (MACs) but avoids unnecessary multiplications by skipping zero weights. The second architecture exploi

Rafia Malik, Mai Vu

Wireless charging coupled with computation offloading in edge networks offers a promising solution for realizing power-hungry and computation intensive applications on user devices. We consider a multi-access edge computing (MEC) system with collocated MEC server and base-station/access point, each equipped with a massive MIMO antenna array, supporting multi

Ibrahim Ahmed, Marcos Quinones-Grueiro, Gautam Biswas

This paper discusses an Enhanced Model-Agnostic Meta-Learning (E-MAML) algorithm that generates fast convergence of the policy function from a small number of training examples when applied to new learning tasks. Built on top of Model-Agnostic Meta-Learning (MAML), E-MAML maintains a set of policy parameters learned in the environment for previous tasks. We

Chaya Keller, Micha A. Perles

For a family $\mathcal{F}$ of sets in $\mathbb{R}^d$, the Krasnoselskii number of $\mathcal{F}$ is the smallest $m$ such that for any $S \in \mathcal{F}$, if every $m$ points of $S$ are visible from a common point in $S$, then any finite subset of $S$ is visible from a single point. More than 35 years ago, Peterson asked whether there exists a Krasnoselskii

Bruno Magalhaes, Felix Schürmann

Graph-based representations underlie a wide range of scientific problems. Graph connectivity is typically represented as a sparse matrix in the Compressed Sparse Row format. Large-scale graphs rely on distributed storage, allocating distinct subsets of rows to compute nodes. Efficient matrix transpose is an operation of high importance, providing the reverse

David Handelman

Some occurrences of $n$ can be replaced by $n-1$ in a special case of the Shapley-Folkman lemma.

Eric Bunch, Qian You, Glenn Fung, Vikas Singh

Recently, neural network architectures have been developed to accommodate when the data has the structure of a graph or, more generally, a hypergraph. While useful, graph structures can be potentially limiting. Hypergraph structures in general do not account for higher order relations between their hyperedges. Simplicial complexes offer a middle ground, with

Liang Han, Zhaozheng Yin, Zhurong Xia, Li Guo

Different from shopping in physical stores, where people have the opportunity to closely check a product (e.g., touching the surface of a T-shirt or smelling the scent of perfume) before making a purchase decision, online shoppers rely greatly on the uploaded product images to make any purchase decision. The decision-making is challenging when selling or pur

Liang Han, Zhaozheng Yin, Zhurong Xia, Mingqian Tang

This paper presents an intelligent price suggestion system for online second-hand listings based on their uploaded images and text descriptions. The goal of price prediction is to help sellers set effective and reasonable prices for their second-hand items with the images and text descriptions uploaded to the online platforms. Specifically, we design a multi

Jason J. Wang, J. Kent Wallace, Nemanja Jovanovic, Olivier Guyon

Differential atmospheric refraction (DAR) limits the amount of light that can be coupled into a single mode fiber and provides additional complications for any fiber tracking system. We present an atmospheric dispersion corrector (ADC) design based off of two counter-rotating prisms to fit the needs of exoplanet spectroscopy for the Keck Planet Imager and Ch

Christiann Bartelt, Sascha Marton, Heiner Stuckenschmidt

We present xRAI an approach for extracting symbolic representations of the mathematical function a neural network was supposed to learn from the trained network. The approach is based on the idea of training a so-called interpretation network that receives the weights and biases of the trained network as input and outputs the numerical representation of the

Taoan Huang, Bistra Dilkina, Sven Koenig

Conflict-Based Search (CBS) is a state-of-the-art algorithm for multi-agent path finding. At the high level, CBS repeatedly detects conflicts and resolves one of them by splitting the current problem into two subproblems. Previous work chooses the conflict to resolve by categorizing the conflict into three classes and always picking a conflict from the highe

Anton Banta, Romain Cosentino, Mathews M John, Allison Post

We propose the Nonlinear Regression Convolutional Encoder-Decoder (NRCED), a novel framework for mapping a multivariate input to a multivariate output. In particular, we implement our algorithm within the scope of 12-lead surface electrocardiogram (ECG) reconstruction from intracardiac electrograms (EGM) and vice versa. The goal of performing this task is to

Huihui Liu, Yiding Yang, Xinchao Wang

Catastrophic forgetting refers to the tendency that a neural network &#34;forgets&#34; the previous learned knowledge upon learning new tasks. Prior methods have been focused on overcoming this problem on convolutional neural networks (CNNs), where the input samples like images lie in a grid domain, but have largely overlooked graph neural networks (GNNs) th

Zaoxing Liu, Hun Namkung, Anup Agarwal, Antonis Manousis

Sketching algorithms or sketches have emerged as a promising alternative to the traditional packet sampling-based network telemetry solutions. At a high level, they are attractive because of their high resource efficiency and accuracy guarantees. While there have been significant recent advances in various aspects of sketching for networking tasks, many fund

Thomas P. Quinn, Stephan Jacobs, Manisha Senadeera, Vuong Le

Our title alludes to the three Christmas ghosts encountered by Ebenezer Scrooge in \textit{A Christmas Carol}, who guide Ebenezer through the past, present, and future of Christmas holiday events. Similarly, our article will take readers through a journey of the past, present, and future of medical AI. In doing so, we focus on the crux of modern machine lear

Mohammad Ayoub Khan

Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diag

Redouane Qesmi, Aayah Hammoumi

The main focus of this chapter is on public health control strategies which are currently the main way to mitigate COVID-19 pandemic. We introduce and compare compartmental models of increasing complexity for COVID-19 transmission to describe dynamics of the disease spread. We begin by considering an SEAIR model including basic characteristics related to COV

Atefeh Keshavarzi Zafarghandi, Rineke Verbrugge, Bart Verheij

Abstract dialectical frameworks (ADFs) have been introduced as a formalism for modeling and evaluating argumentation allowing general logical satisfaction conditions. Different criteria used to settle the acceptance of arguments are called semantics. Semantics of ADFs have so far mainly been defined based on the concept of admissibility. However, the notion

Francesco Fanelli, Eduard Feireisl

We show that the Euler system of gas dynamics in $\mathbb{R}^d$, $d=2,3$, with positive far field density and arbitrary far field entropy, admits infinitely many steady solutions with compactly supported velocity. The same proof yields a similar result for the incompressible Euler system with variable density. In particular, these are examples of global in t

Weijie Li, Jingwei Mu, Shaoyun Huang, Dong Pan

A linear triple quantum dot (TQD) integrated with a quantum dot (QD) charge sensor is realized. The TQD and the charge sensor are built from two adjacent InAs nanowires by fine finger gate technique. The charge state configurations of the nanowire TQD are studied by measurements of the direct transport signals of the TQD and by detection of the charge state

Chelsey Edge, Md Jahidul Islam, Christopher Morse, Junaed Sattar

In this paper, we introduce a generative model for image enhancement specifically for improving diver detection in the underwater domain. In particular, we present a model that integrates generative adversarial network (GAN)-based image enhancement with the diver detection task. Our proposed approach restructures the GAN objective function to include informa

G. Bruce Berriman, The IVOA Executive Committee, The IVOA Technical Coordination Group, The IVOA Community

The International Virtual Observatory Alliance (IVOA) develops the technical standards needed for seamless discovery of and access to astronomy data worldwide, according to the Findable, Accessible, Interoperable and Reusable (FAIR) principles, with the goal of realizing the Virtual Observatory (VO). There are 21 member organizations. The Netherlands VO appl

Christopher Bogart, James Howison, James Herbsleb

Software is increasingly produced in the form of ecosystems, collections of interdependent components maintained by a distributed community. These ecosystems act as network organizations, not markets, and thus often lack actionable price-like signals about how the software is used and what impact it has. We introduce a tool, the Scientific Software Network M

Christopher Griffin

We identify a counter-example to the consensus result given in [J. Semonsen et al. Opinion dynamics in the presence of increasing agreement pressure. \textit{IEEE Trans. Cyber.}, 49(4): 1270-1278, 2018]. We resolve the counter-example by replacing Lemma 5 in the given reference with a novel variation of the Banach Fixed Point theorem which explains both the

Christian Elsholtz, Stefan Planitzer

We prove new upper bounds on the number of representations of rational numbers $\frac{m}{n}$ as a sum of $4$ unit fractions, giving five different regions, depending on the size of $m$ in terms of $n$. In particular, we improve the most relevant cases, when $m$ is small, and when $m$ is close to $n$. The improvements stem from not only studying complete para

Haoteng Tang, Guixiang Ma, Lifang He, Heng Huang

Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for graph level tasks such as graph classification. However, current HGPNNs do not take full advantage of the graph&#39;s intrinsic structures (e.g., community structure). Moreover, the

Chenyang Lu, Gijs Dubbelman

This work presents the first convolutional neural network that learns an image-to-graph translation task without needing external supervision. Obtaining graph representations of image content, where objects are represented as nodes and their relationships as edges, is an important task in scene understanding. Current approaches follow a fully-supervised appr

Michael Moy, Robert Cardona, Robert Green, Jacob Cleveland

Motivated by efforts to incorporate sheaves into networking, we seek to reinterpret pathfinding algorithms in terms of cellular sheaves, using Dijkstra&#39;s algorithm as an example. We construct sheaves on a graph with distinguished source and sink vertices, in which paths are represented by sections. The first sheaf is a very general construction that can

Yan-Cheng Chao, Thomas M. Braun, Roy N. Tamura, Kelley M. Kidwell

A small n, sequential, multiple assignment, randomized trial (snSMART) is a small sample, two-stage design where participants receive up to two treatments sequentially, but the second treatment depends on response to the first treatment. The treatment effect of interest in an snSMART is the first-stage response rate, but outcomes from both stages can be used

Brian Kidd, Matthias Katzfuss

In spatial statistics, it is often assumed that the spatial field of interest is stationary and its covariance has a simple parametric form, but these assumptions are not appropriate in many applications. Given replicate observations of a Gaussian spatial field, we propose nonstationary and nonparametric Bayesian inference on the spatial dependence. Instead

Corey J. Maley

Relative to digital computation, analog computation has been neglected in the philosophical literature. To the extent that attention has been paid to analog computation, it has been misunderstood. The received view -- that analog computation has to do essentially with continuity -- is simply wrong, as shown by careful attention to historical examples of disc

Joshua Feinberg, Roman Riser

We study small oscillations of highly connected systems, which represent a limit opposite to the more familiar case of disordered crystals. As a concrete example we analyze the vibrational spectra of composite pendula. Remarkably, these spectra exhibit universal behavior with non-vanishing zero-frequency limit of the density of phonon states. This universali

Rok Hribar, Timotej Hrga, Gregor Papa, Gašper Petelin

In this paper, we consider the symmetric multi-type non-negative matrix tri-factorization problem (SNMTF), which attempts to factorize several symmetric non-negative matrices simultaneously. This can be considered as a generalization of the classical non-negative matrix tri-factorization problem and includes a non-convex objective function which is a multiva

Yael Sde-Chen, Yoav Y. Schechner, Vadim Holodovsky, Eshkol Eytan

We present 3DeepCT, a deep neural network for computed tomography, which performs 3D reconstruction of scattering volumes from multi-view images. Our architecture is dictated by the stationary nature of atmospheric cloud fields. The task of volumetric scattering tomography aims at recovering a volume from its 2D projections. This problem has been studied ext

Syeda Nyma Ferdous, Ali Dabouei, Jeremy Dawson, Nasser M Nasrabadi

Performance of fingerprint recognition algorithms substantially rely on fine features extracted from fingerprints. Apart from minutiae and ridge patterns, pore features have proven to be usable for fingerprint recognition. Although features from minutiae and ridge patterns are quite attainable from low-resolution images, using pore features is practical only

Mihaela Bornea, Lin Pan, Sara Rosenthal, Radu Florian

Prior work on multilingual question answering has mostly focused on using large multilingual pre-trained language models (LM) to perform zero-shot language-wise learning: train a QA model on English and test on other languages. In this work, we explore strategies that improve cross-lingual transfer by bringing the multilingual embeddings closer in the semant

Lilas Alrahis, Satwik Patnaik, Faiq Khalid, Muhammad Abdullah Hanif

In this paper, we propose GNNUnlock, the first-of-its-kind oracle-less machine learning-based attack on provably secure logic locking that can identify any desired protection logic without focusing on a specific syntactic topology. The key is to leverage a well-trained graph neural network (GNN) to identify all the gates in a given locked netlist that belong

Tomas Baca, Robert Penicka, Petr Stepan, Matej Petrlik

This paper presents a system for autonomous cooperative wall building with a team of Unmanned Aerial Vehicles (UAVs). The system was developed for Challenge 2 of the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. The wall-building scenario of Challenge 2 featured an initial stack of bricks and wall structure where the individual bricks had

Xiang-dong Hou, Christopher Sze

Let $F$ be a field and let $F(X_1,\dots,X_n)$ be the field of rational functions in $n$ variables $X_1,\dots,X_n$ over $F$. Let $T=X_1+\cdots+X_n\in F(X_1,\dots,X_n)$ and let $m$ be a positive integer such that $\text{char}\,F\nmid m$. Is it possible to express each $X_i$ as a rational function in $X_1^m\dots,X_n^m$ and $T$ over $F$? It is not difficult to p

Chenyu Wu, Yangyang Xu

The coordinate descent (CD) method has recently become popular for solving very large-scale problems, partly due to its simple update, low memory requirement, and fast convergence. In this paper, we explore the greedy CD on solving non-negative quadratic programming (NQP). The greedy CD generally has much more expensive per-update complexity than its cyclic

Carlos Gómez, Niamh Belton, Boi Quach, Jack Nicholls

This report is based on the modified NIST challenge, Too Close For Too Long, provided by the SFI Centre for Machine Learning (ML-Labs). The modified challenge excludes the time calculation (too long) aspect. By handcrafting features from phone instrumental data we develop two machine learning models, a GBM and an MLP, to estimate distance between two phones.

A. P. Jezghani, L. J. Broussard, C. B. Crawford

We present here a technique for developing a high-throughput algorithm to fit a combination of template pulse shapes while simultaneously subtracting parameterized background noise. By convolving the psuedoinverse of the least-squares fit design matrix along a regularly sampled waveform trace, the time evolution of the fit parameters for each basis function

Jonathan P Williams, Danica M Ommen, Jan Hannig

One formulation of forensic identification of source problems is to determine the source of trace evidence, for instance, glass fragments found on a suspect for a crime. The current state of the science is to compute a Bayes factor (BF) comparing the marginal distribution of measurements of trace evidence under two competing propositions for whether or not t

J. Cornelison, P. A. R. Ade, Z. Ahmed, M. Amiri

The BICEP3 CMB Polarimeter is a small-aperture refracting telescope located at the South Pole and is specifically designed to search for the possible signature of inflationary gravitational waves in the Cosmic Microwave Background (CMB). The experiment measures polarization on the sky by differencing the signal of co-located, orthogonally polarized antennas

Weixin Li, Yao Qiao, Joel Fenner, Kyle Warren

This work presents a comprehensive investigation of the elastic and fracture behavior of ply-to-ply angle interlock three-dimensional woven composites. The research investigated novel splitting and wedge-driven out-of-plane fracture tests to shed light on the tensile fracture behavior in the thickness direction and to provide estimates of the out-of-plane te

Robert Feldmann

Observations of the interstellar medium are key to deciphering the physical processes regulating star formation in galaxies. However, observational uncertainties and detection limits can bias the interpretation unless carefully modeled. Here I re-analyze star formation rates and gas masses of a representative sample of nearby galaxies with the help of multi-