April 2020 arXiv papers — page 4

Oriol Artime, Valeria d'Andrea, Riccardo Gallotti, Pier Luigi Sacco

Online social networks are the perfect test bed to better understand large-scale human behavior in interacting contexts. Although they are broadly used and studied, little is known about how their terms of service and posting rules affect the way users interact and information spreads. Acknowledging the relation between network connectivity and functionality

Zdenek Straka, Tomas Svoboda, Matej Hoffmann

Predictive coding, currently a highly influential theory in neuroscience, has not been widely adopted in machine learning yet. In this work, we transform the seminal model of Rao and Ballard (1999) into a modern deep learning framework while remaining maximally faithful to the original schema. The resulting network we propose (PreCNet) is tested on a widely

Robert Santos

In this work the generalized Collatz problem $qn+1$ ($q$ odd) is studied. As a natural generalization of the original $3n+1$ problem, it consists of a discrete dynamical system of an arithmetical kind. Using standard methods of number theory and dynamical systems, general properties are established, such as the existence of finitely many periodic sequences f

Laura Burdick, Jonathan K. Kummerfeld, Rada Mihalcea

Word embeddings are powerful representations that form the foundation of many natural language processing architectures, both in English and in other languages. To gain further insight into word embeddings, we explore their stability (e.g., overlap between the nearest neighbors of a word in different embedding spaces) in diverse languages. We discuss linguis

Nicolas Girard, Dmitriy Smirnov, Justin Solomon, Yuliya Tarabalka

While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To help bridge the gap between deep network output and the format used in downstream tasks, we add a frame field output to a deep segmentation model for extracting buildings from remot

Ben Saunders, Necati Cihan Camgoz, Richard Bowden

The goal of automatic Sign Language Production (SLP) is to translate spoken language to a continuous stream of sign language video at a level comparable to a human translator. If this was achievable, then it would revolutionise Deaf hearing communications. Previous work on predominantly isolated SLP has shown the need for architectures that are better suited

Eugene Gorsky, José Simental, Monica Vazirani

In this note we explicitly construct an action of the rational Cherednik algebra $H_{1,m/n}(S_n,\mathbb{C}^n)$ corresponding to the permutation representation of $S_n$ on the $\mathbb{C}^{*}$-equivariant homology of parabolic Hilbert schemes of points on the plane curve singularity $\{x^{m} = y^{n}\}$ for coprime $m$ and $n$. We use this to construct actions

Cole Franks, Michael Walter

Consider the action of a connected complex reductive group on a finite-dimensional vector space. A fundamental result in invariant theory states that the orbit closure of a vector v is separated from the origin if and only if some homogeneous invariant polynomial is nonzero on v, i.e. v is not in the null cone. Thus, efficiently finding the minimum distance

Libo Qin, Minheng Ni, Yue Zhang, Wanxiang Che

Spoken language understanding has been addressed as a supervised learning problem, where a set of training data is available for each domain. However, annotating data for each domain is both financially costly and non-scalable so we should fully utilize information across all domains. One existing approach solves the problem by conducting multi-domain learni

Samson Tan, Shafiq Joty, Lav R. Varshney, Min-Yen Kan

Inflectional variation is a common feature of World Englishes such as Colloquial Singapore English and African American Vernacular English. Although comprehension by human readers is usually unimpaired by non-standard inflections, current NLP systems are not yet robust. We propose Base-Inflection Encoding (BITE), a method to tokenize English text by reducing

Clementa Alonso-González, Miguel Ángel Navarro-Pérez, Xaro Soler-Escrivà

In this paper we study a class of multishot network codes given by families of nested subspaces (flags) of a vector space $\mathbb{F}_q^n$, being $q$ a prime power and $\mathbb{F}_q$ the finite field of $q$ elements. In particular, we focus on flag codes having maximum minimum distance (optimum distance flag codes). We explore the existence of these codes fr

Pragya Shukla

Multi-parametric chiral random matrix ensembles are important tools to analyze the statistical behavior of generic complex systems with chiral symmetry. A recent study \cite{psmulti} of the former maps them to the chiral Brownian ensemble (Ch-BE) that appears as a non-equilibrium state of a single parametric crossover between two stationary chiral Hermitian

E. Tognelli, P. G. Prada Moroni, S. Degl'Innocenti, M. Salaris

The cosmological lithium problem, that is, the discrepancy between the lithium abundance predicted by the Big Bang nucleosynthesis and the one observed for the stars of the 'Spite plateau', is one of the long standing problems of modern astrophysics. Recent hints for a possible solution involve lithium burning induced by protostellar mass accretion on Spite

Christoph Alt, Aleksandra Gabryszak, Leonhard Hennig

TACRED (Zhang et al., 2017) is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE). But, even with recent advances in unsupervised pre-training and knowledge enhanced neural RE, models still show a high error rate. In this paper, we investigate the questions: Have we reached a performance ceiling or is there still room for

Cornelia Spee, Costantino Budroni, Otfried Gühne

The correlations arising from sequential measurements on a single quantum system form a polytope. This is defined by the arrow-of-time (AoT) constraints, meaning that future choices of measurement settings cannot influence past outcomes. We discuss the resources needed to simulate the extreme points of the AoT polytope, where resources are quantified in term

Abhishek Kumar Jha, Supratik Mukherjee, Bindu A. Bambah

We investigate and quantify various measures of bipartite and tripartite entanglement in the context of two and three flavor neutrino oscillations. The bipartite entanglement is analogous to the entanglement swapping resulting from a beam splitter in quantum optics. For the three neutrino systems various measures of tripartite entanglement are explored. The

Wolfgang Gregor Hollik, Cheng Li, Gudrid Moortgat-Pick, Steven Paasch

The current challenges in High Energy Physics and Cosmology are to build coherent particle physics models to describe the phenomenology at colliders in the laboratory and the observations in the universe. From these observations, the existence of an inflationary phase in the early universe gives guidance for particle physics models. We study a supersymmetric

Abhik Ghosh, Magne Thoresen

Variable selection in ultra-high dimensional regression problems has become an important issue. In such situations, penalized regression models may face computational problems and some pre screening of the variables may be necessary. A number of procedures for such pre-screening has been developed; among them the sure independence screening (SIS) enjoys some

Ella Peltonen, Mehdi Bennis, Michele Capobianco, Merouane Debbah

In this white paper we provide a vision for 6G Edge Intelligence. Moving towards 5G and beyond the future 6G networks, intelligent solutions utilizing data-driven machine learning and artificial intelligence become crucial for several real-world applications including but not limited to, more efficient manufacturing, novel personal smart device environments

Vittorio De Falco, Emmanuele Battista, Salvatore Capozziello, Mariafelicia De Laurentis

We derive the equations of motion of a test particle in the equatorial plane around a static and spherically symmetric wormhole influenced by a radiation field including the general relativistic Poynting-Robertson effect. From the analysis of this dynamical system, we develop a diagnostic to distinguish a black hole from a wormhole, which can be timely suppo

Momchil Hardalov, Ivan Koychev, Preslav Nakov

Detecting the user's intent and finding the corresponding slots among the utterance's words are important tasks in natural language understanding. Their interconnected nature makes their joint modeling a standard part of training such models. Moreover, data scarceness and specialized vocabularies pose additional challenges. Recently, the advances in pre-trai

Bernard Dousset, Josiane Mothe

COVID-19 is one of the most important topic these days, specifically on search engines and news. While fake news are easily shared, scientific papers are reliable sources where information can be extracted. With about 24,000 scientific publications on COVID-19 and related research on PUBMED, automatic computer-assisted analysis is required. In this paper, we

Jacques Pienaar

According to the subjective Bayesian interpretation of quantum mechanics (QBism), the instruments used to measure quantum systems are to be regarded as an extension of the senses of the agent who is using them, and quantum states describe the agent's expectations for what they will experience through these extended senses. How can QBism then account for the

Elizabeth Nielsen, Mark Steedman, Sharon Goldwater

Prosody is a rich information source in natural language, serving as a marker for phenomena such as contrast. In order to make this information available to downstream tasks, we need a way to detect prosodic events in speech. We propose a new model for pitch accent detection, inspired by the work of Stehwien et al. (2018), who presented a CNN-based model for

Vaiva Vasiliauskaite, Fernando E. Rosas

Network theory provides tools which are particularly appropriate for assessing the complex interdependencies that characterise our modern connected world. This article presents an introduction to network theory, in a way that doesn't require a strong mathematical background. We explore how network theory unveils commonalities in the interdependency profiles

Florian Jakobs, Thomas Ostler, Charles-Henri Lambert, Yang Yang

Many questions are still open regarding the physical mechanisms behind the magnetic switching in GdFeCo alloys by single optical pulses. Phenomenological models suggest a femtosecond scale exchange relaxation between sublattice magnetization as the driving mechanism for switching. The recent observation of thermally induced switching in GdFeCo by using both

Federico Bianchi, Gaetano Rossiello, Luca Costabello, Matteo Palmonari

Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we introduce the reader to the concept of knowledge graph embeddings by explaining what they are, how they can be generated and how they can be evaluated. We summarize the state-of-the-ar

Lodovica Marchesi, Michele Marchesi, Roberto Tonelli

The Blockchain and the programs running on it, called Smart Contracts, are more and more applied in all fields requiring trust and strong certifications. In this work we compare public and permissioned blockchains for industrial applications. We propose a complete, original solution based on Ethereum to implement a decentralized application. This solution is

Dehua Chen, Amir Jalilifard, Adriano Veloso, Nivio Ziviani

A pharmacological effect of a drug on cells, organs and systems refers to the specific biochemical interaction produced by a drug substance, which is called its mechanism of action. Drug repositioning (or drug repurposing) is a fundamental problem for the identification of new opportunities for the use of already approved or failed drugs. In this paper, we p

Hitomi Yanaka, Koji Mineshima, Daisuke Bekki, Kentaro Inui

Despite the success of language models using neural networks, it remains unclear to what extent neural models have the generalization ability to perform inferences. In this paper, we introduce a method for evaluating whether neural models can learn systematicity of monotonicity inference in natural language, namely, the regularity for performing arbitrary in

Yun Chen, Yang Liu, Guanhua Chen, Xin Jiang

Despite its original goal to jointly learn to align and translate, prior researches suggest that Transformer captures poor word alignments through its attention mechanism. In this paper, we show that attention weights DO capture accurate word alignments and propose two novel word alignment induction methods Shift-Att and Shift-AET. The main idea is to induce

Sven Knuefer, Matthias A. Mueller

The present work provides two alternatives to formulate time-discounted incremental input/output-to-state stability (i-IOSS) as a suitable detectability notion for general nonlinear systems with non-additive disturbances. Both formulations are related to existing i-IOSS notions which result as special cases. Previous results that provide a sufficient Lyapuno

Kevin Batz, Sebastian Junges, Benjamin Lucien Kaminski, Joost-Pieter Katoen

IC3 has been a leap forward in symbolic model checking. This paper proposes PrIC3 (pronounced pricy-three), a conservative extension of IC3 to symbolic model checking of MDPs. Our main focus is to develop the theory underlying PrIC3. Alongside, we present a first implementation of PrIC3 including the key ingredients from IC3 such as generalization, repushing

Dongnam Ko, Enrique Zuazua

We model, simulate and control the guiding problem for a herd of evaders under the action of repulsive drivers. The problem is formulated in an optimal control framework, where the drivers (controls) aim to guide the evaders (states) to a desired region of the Euclidean space. The numerical simulation of such models quickly becomes unfeasible for a large num

Mohammed Osman, Yan V. Fyodorov

Motivated by recent studies of the phenomenon of Coherent Perfect Absorption, we develop the random matrix theory framework for understanding statistics of the zeros of the (subunitary) scattering matrices in the complex energy plane, as well as of the recently introduced Refection Time Difference (RTD). The latter plays the same role for S-matrix zeros as t

Deepak Baby, Arthur Van Den Broucke, Sarah Verhulst

Auditory models are commonly used as feature extractors for automatic speech-recognition systems or as front-ends for robotics, machine-hearing and hearing-aid applications. Although auditory models can capture the biophysical and nonlinear properties of human hearing in great detail, these biophysical models are computationally expensive and cannot be used

Markus A. Ebert, Stella T. Schindler, Iain W. Stewart, Yong Zhao

Transverse momentum dependent parton distribution functions (TMDPDFs) provide a unique probe of the three-dimensional spin structure of hadrons. We construct spin-dependent quasi-TMDPDFs that are amenable to lattice QCD calculations and that can be used to determine spin-dependent TMDPDFs. We calculate the short-distance coefficients connecting spin-dependen

Jan Bouwe van den Berg, Jonathan Jaquette, J. D. Mireles James

This paper develops validated computational methods for studying infinite dimensional stable manifolds at equilibrium solutions of parabolic PDEs, synthesizing disparate errors resulting from numerical approximation. To construct our approximation, we decompose the stable manifold into three components: a finite dimensional slow component, a fast-but-finite

Andreas Kreuml

The minimizers of the anisotropic fractional isoperimetric inequality with respect to the convex body $K$ in $\mathbb{R}^n$ are shown to be equivalent to star bodies whenever $K$ is strictly convex and unconditional. From this a P\'olya-Szeg\"o principle for anisotropic fractional seminorms is derived by using symmetrization with respect to star bodies.

Mats J. Stensrud, Miguel A. Hernán, Eric J. Tchetgen Tchetgen, James M. Robins

In competing event settings, a counterfactual contrast of cause-specific cumulative incidences quantifies the total causal effect of a treatment on the event of interest. However, effects of treatment on the competing event may indirectly contribute to this total effect, complicating its interpretation. We previously proposed the separable effects (Stensrud

Shangzhi Hong, Yuqi Sun, Hanying Li, Henry S. Lynn

Missing data are common in data analyses in biomedical fields, and imputation methods based on random forests (RF) have become widely accepted, as the RF algorithm can achieve high accuracy without the need for specification of data distributions or relationships. However, the predictions from RF do not contain information about prediction uncertainty, which

Filippo Agnelli, Pierre Margerit, Paolo Celli, Chiara Daraio

The multi-scale nature of architectured materials raises the need for advanced experimental methods suitable for the identification of their effective properties, especially when their size is finite and they undergo extreme deformations. The present work demonstrates that state-of-the art image processing methods combined with numerical and analytical model

Shoetsu Sato, Jin Sakuma, Naoki Yoshinaga, Masashi Toyoda

Neural network methods exhibit strong performance only in a few resource-rich domains. Practitioners, therefore, employ domain adaptation from resource-rich domains that are, in most cases, distant from the target domain. Domain adaptation between distant domains (e.g., movie subtitles and research papers), however, cannot be performed effectively due to mis

Peter beim Graben, Ronald Römer, Werner Meyer, Markus Huber

Speech-controlled user interfaces facilitate the operation of devices and household functions to laymen. State-of-the-art language technology scans the acoustically analyzed speech signal for relevant keywords that are subsequently inserted into semantic slots to interpret the user's intent. In order to develop proper cognitive information and communication

Lei Jiang, Haijian Zhang, Lei Yu

Time-frequency distributions (TFDs) play a vital role in providing descriptive analysis of non-stationary signals involved in realistic scenarios. It is well known that low time-frequency (TF) resolution and the emergency of cross-terms (CTs) are two main issues, which make it difficult to analyze and interpret practical signals using TFDs. In order to addre

Matthew D. Koslovsky, Kristi L. Hoffman, Carrie R. Daniel, Marina Vannucci

One of the major research questions regarding human microbiome studies is the feasibility of designing interventions that modulate the composition of the microbiome to promote health and cure disease. This requires extensive understanding of the modulating factors of the microbiome, such as dietary intake, as well as the relation between microbial compositio

Scott Davidson, Felix A. Pollock, Erik M. Gauger

Adapting techniques from the field of information geometry, we show that open quantum system models of Frenkel exciton transport, a prevalent process in photosynthetic networks, belong to a class of mathematical models known as 'sloppy'. Performing a Fisher-information-based multi-parameter sensitivity analysis to investigate the full dynamical evolution of

Liying Cheng, Dekun Wu, Lidong Bing, Yan Zhang

Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description. Existing datasets, such as WIKIBIO, WebNLG, and E2E, basically have a good alignment between an input triple/pair set and its output text. However, in practice, the input knowle

Roberto Interdonato, Matteo Magnani, Diego Perna, Andrea Tagarelli

Multilayer networks have been widely used to represent and analyze systems of interconnected entities where both the entities and their connections can be of different types. However, real multilayer networks can be difficult to analyze because of irrelevant information, such as layers not related to the objective of the analysis, because of their size, or b

Kimet Jusufi

In this letter we present an exact spherically symmetric and magnetically charged black hole solution with exponential model of nonlinear electrodynamics [S. Kruglov, Annals Phys. 378, 59-70 (2017)] in the context of 4D Einstein-Gauss-Bonnet (EGB) gravity. We show that our $-$ve branch, in the limit of GB coupling coefficient $\alpha \rightarrow 0$ and the n

Andrea Miglio, Cristina Chiappini, Ted Mackereth, Guy Davies

[Abridged] Ensemble studies of red-giant stars with exquisite asteroseismic, spectroscopic, and astrometric constraints offer a novel opportunity to recast and address long-standing questions concerning the evolution of stars and of the Galaxy. Here, we infer masses and ages for nearly 5400 giants with available Kepler light curves and APOGEE spectra, and di

Antonio Remiro-Azócar, Anna Heath, Gianluca Baio

Population-adjusted indirect comparisons estimate treatment effects when access to individual patient data is limited and there are cross-trial differences in effect modifiers. Popular methods include matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC). There is limited formal evaluation of these methods and whether they can

Michał Kaczor, Paweł Jakubczyk

The Schur-Weyl states belong to a special class of states with a symmetry described by two Young and Weyl tableaux. Representation of physical systems in Hilbert space spanned on these states enables to extract quantum information hidden in nonlocal degrees of freedom. Such property can be very useful in a broad range of problems in Quantum Computations, esp

Yevgeni Berzak, Jonathan Malmaud, Roger Levy

We present STARC (Structured Annotations for Reading Comprehension), a new annotation framework for assessing reading comprehension with multiple choice questions. Our framework introduces a principled structure for the answer choices and ties them to textual span annotations. The framework is implemented in OneStopQA, a new high-quality dataset for evaluati

Jingcai Guo, Song Guo

Zero-shot learning aims at recognizing unseen classes (no training example) with knowledge transferred from seen classes. This is typically achieved by exploiting a semantic feature space shared by both seen and unseen classes, i.e., attribute or word vector, as the bridge. One common practice in zero-shot learning is to train a projection between the visual

Angela Bonifati, Stefania Dumbrava, Haridimos Kondylakis

The continuous and rapid growth of highly interconnected datasets, which are both voluminous and complex, calls for the development of adequate processing and analytical techniques. One method for condensing and simplifying such datasets is graph summarization. It denotes a series of application-specific algorithms designed to transform graphs into more comp

Gal Mendelson

Redundancy-d (R(d)) is a load balancing method used to route incoming jobs to K servers, each with its own queue. Every arriving job is replicated into 2<=d<=K tasks, which are then routed to d servers chosen uniformly at random. When the first task finishes service, the remaining d-1 tasks are cancelled and the job departs the system. Despite the fact that

D. Cogollo, Yohan M. Oviedo-Torres, Yoxara S. Villamizar

In light of the upcoming measurement of the muon anomalous magnetic moment (g-2), we revisit the corrections to g-2 in the context of the $SU(4)_L \times U(1)_X$ gauge symmetry. We investigate three models based on this gauge symmetry and express our results in terms of the energy scale at which the $SU(4)_L \times U(1)_X$ symmetry is broken. To draw solid c

Joshua Ciappara, Geordie Williamson

These notes provide a concise introduction to the representation theory of reductive algebraic groups in positive characteristic, with an emphasis on Lusztig's character formula and geometric representation theory. They are based on the first author's notes from a lecture series delivered by the second author at the Simons Centre for Geometry and Physics in

Aaron Brunk, Maria Lukacova-Medvidova

The aim of this paper is to prove global in time existence of weak solutions for a viscoelastic phase separation. We consider the case with singular potentials and degenerate mobilities. Our model couples the diffusive interface model with the Peterlin-Navier-Stokes equations for viscoelastic fluids. To obtain the global in time existence of weak solutions w

Édouard Bonnet, Eun Jung Kim, Stéphan Thomassé, Rémi Watrigant

Inspired by a width invariant defined on permutations by Guillemot and Marx [SODA '14], we introduce the notion of twin-width on graphs and on matrices. Proper minor-closed classes, bounded rank-width graphs, map graphs, $K_t$-free unit $d$-dimensional ball graphs, posets with antichains of bounded size, and proper subclasses of dimension-2 posets all have b

Yingqiang Gao, Nikola I. Nikolov, Yuhuang Hu, Richard H. R. Hahnloser

We explore the suitability of self-attention models for character-level neural machine translation. We test the standard transformer model, as well as a novel variant in which the encoder block combines information from nearby characters using convolutions. We perform extensive experiments on WMT and UN datasets, testing both bilingual and multilingual trans

Saumyak Mukherjee, Sayantan Mondal, Biman Bagchi

Here we propose and implement a generalized mathematical model to find the time evolution of population in infectious diseases and apply the model to study the recent COVID-19 pandemic. Our model at the core is a non-local generalization of the widely used Kermack-McKendrick(KM) model where the susceptible(S) population evolves into two other categories, nam

Zhiyong Wu, Yun Chen, Ben Kao, Qun Liu

By introducing a small set of additional parameters, a probe learns to solve specific linguistic tasks (e.g., dependency parsing) in a supervised manner using feature representations (e.g., contextualized embeddings). The effectiveness of such probing tasks is taken as evidence that the pre-trained model encodes linguistic knowledge. However, this approach o

Noboru Ito, Migiwa Sakurai

Vassiliev introduced filtered invariants of knots using an unknotting operation, called crossing changes. Goussarov, Polyak, and Viro introduced other filtered invariants of virtual knots, which order is called GPV-order, using an unknotting operation, called virtualization. We defined other filtered invariants, which order is called $F$-order, of virtual kn

Zachary A. H. Goodwin, Valerio Vitale, Xia Liang, Arash A. Mostofi

A detailed understanding of interacting electrons in twisted bilayer graphene (tBLG) near the magic angle is required to gain insights into the physical origin of the observed broken symmetry phases. Here, we present extensive atomistic Hartree theory calculations of the electronic properties of tBLG in the (semi-)metallic phase as function of doping and twi

Federico Bertacco

We prove existence and uniqueness of a solution for the stochastic Allen-Cahn equation with logarithmic potential and multiplicative Wiener noise, under homogeneous Neumann boundary condition. The existence of a solution is obtained in the variational sense by means of an approximated equation involving a Yosida regularization of the nonlinearity. The noise

Ravishankar Ramanathan, Michał Banacki, Ricard Ravell Rodríguez, Paweł Horodecki

Quantum statistics can be considered from the perspective of postquantum no-signaling theories in which either none or only a certain number of quantum systems are trusted. In these scenarios, the role of states is played by the so-called no-signaling boxes or no-signaling assemblages respectively. It has been shown so far that in the usual Bell non-locality

Bo Wang, Tao Shen, Guodong Long, Tianyi Zhou

Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion of them. Prevalent graph embedding approaches, e.g., TransE, learn structured knowledge via representing graph elements into dense embeddings and capturing their triple-level rela

Gonçalo Oliveira

The goal of this article is to analyze some compartmental models specially designed to model the spread of a disease whose transmission has the same features as COVID-19. The major contributions of this article are: (1) Rigorously find sufficient conditions for the outbreak to only have one peak, i.e. for no second wave of infection to form; (2) Investigate

Bin Zhu, Augusto Ferrante, Johan Karlsson, Mattia Zorzi

This paper concerns a spectral estimation problem for multivariate (i.e., vector-valued) signals defined on a multidimensional domain, abbreviated as M$^2$. The problem is posed as solving a finite number of trigonometric moment equations for a nonnegative matricial measure, which is well known as the \emph{covariance extension problem} in the literature of

Iason Baldes, Quentin Decant, Deanna C. Hooper, Laura Lopez-Honorez

Dark matter coupled solely gravitationally can be produced through the decay of primordial black holes in the early universe. If the dark matter is lighter than the initial black hole temperature, it could be warm enough to be subject to structure formation constraints. In this paper we perform a more precise determination of these constraints. We first eval

Ken-Ichiro Imura, Yositake Takane

Bulk-boundary correspondence is the cornerstone of topological physics. In some non-Hermitian topological system this fundamental relation is broken in the sense that the topological number calculated for the Bloch energy band under the periodic boundary condition fails to reproduce the boundary properties under the open boundary. To restore the bulk-boundar

Alexis Léculier, Jean-Michel Roquejoffre

The question studied here is the existence and uniqueness of a non-trivial bounded steady state of a Fisher-KPP equation involving a fractional Laplacian (--$\Delta$)^$\alpha$ in a domain with Dirichlet conditions outside of the domain. More specifically, we investigate such questions in the case of general fragmented unbounded domains. Indeed, we take advan

Karim Benakli

We point out the peculiar form of the gravitational wave signal expected from a gas of particles carry spin 3/2 produced during preheating. Given the very few ways that gravitinos can manifest themselves in an experimentally observable way, we stress the importance of improving the sensitivity of ultra-high frequency detectors in the future. This review is b

Kun Li, Chengbo Chen, Xiaojun Quan, Qing Ling

Aspect term extraction aims to extract aspect terms from review texts as opinion targets for sentiment analysis. One of the big challenges with this task is the lack of sufficient annotated data. While data augmentation is potentially an effective technique to address the above issue, it is uncontrollable as it may change aspect words and aspect labels unexp

Pierre-Paul De Breuck, Geoffroy Hautier, Gian-Marco Rignanese

In order to make accurate predictions of material properties, current machine-learning approaches generally require large amounts of data, which are often not available in practice. In this work, an all-round framework is presented which relies on a feedforward neural network, the selection of physically-meaningful features and, when applicable, joint-learni

Enzo Tartaglione, Andrea Bragagnolo, Marco Grangetto

Recently, a race towards the simplification of deep networks has begun, showing that it is effectively possible to reduce the size of these models with minimal or no performance loss. However, there is a general lack in understanding why these pruning strategies are effective. In this work, we are going to compare and analyze pruned solutions with two differ

Luca Bayha, Marvin Holten, Ralf Klemt, Keerthan Subramanian

Many-body physics describes phenomena which cannot be understood looking at a systems' constituents alone. Striking manifestations are broken symmetry, phase transitions, and collective excitations. Understanding how such collective behaviour emerges when assembling a system from individual particles has been a vision in atomic, nuclear, and solid-state phys

Ralph Kritzinger

We study the dispersion of digital $(0,m,2)$-nets; i.e. the size of the largest axes-parallel box within such point sets. Digital nets are an important class of low-discrepancy point sets. We prove tight lower and upper bounds for certain subclasses of digital nets where the generating matrices are of triangular form and compute the dispersion of special net

Luca Studer

We show that the biggest possible average set size in the complement $2^{\{1,2,\ldots, n\}} \setminus A$ of a union-closed family $A \subset 2^{\{1,2, \ldots, n\}}$ is $\tfrac{n+1}{2}$. With the same proof we get a sharp upper bound for the average frequency in complements of union-closed families. This implies an asymptotic version of the union-closed sets

Katrine Tjell, Jaron Skovsted Gundersen, Rafael Wisniewski

This work is inspired by the outbreak of COVID-19, and some of the challenges we have observed with gathering data about the disease. To this end, we aim to help collect data about citizens and the disease without risking the privacy of individuals. Specifically, we focus on how to determine the density of the population across the country, how to trace cont

Serhii Havrylov, Ivan Titov

Variational autoencoders (VAEs) are a standard framework for inducing latent variable models that have been shown effective in learning text representations as well as in text generation. The key challenge with using VAEs is the {\it posterior collapse} problem: learning tends to converge to trivial solutions where the generators ignore latent variables. In

Matthew Mirman, Timon Gehr, Martin Vechev

Generative neural networks can be used to specify continuous transformations between images via latent-space interpolation. However, certifying that all images captured by the resulting path in the image manifold satisfy a given property can be very challenging. This is because this set is highly non-convex, thwarting existing scalable robustness analysis me

Pedro R. A. S. Bassi, Romis Attux

Purpose: We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia and normal. Methods: We fine-tuned neural networks pretrained on ImageNet and applied a twice transfer learning approach, using NIH ChestX-ray14 dataset as an intermediate step. We als

Hady Elsahar, Maximin Coavoux, Matthias Gallé, Jos Rozen

We address the problem of unsupervised abstractive summarization of collections of user generated reviews with self-supervision and control. We propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. This setting makes training simpler than previous approaches by relying only on standard log-l

Andrea Maggiordomo, Federico Ponchio, Paolo Cignoni, Marco Tarini

We are witnessing a proliferation of textured 3D models captured from the real world with automatic photo-reconstruction tools. Digital 3D models of this class come with a unique set of characteristics and defects -- especially concerning their parametrization -- setting them starkly apart from 3D models originating from other, more traditional, sources. We

Martin Pömsl, Roman Lyapin

This paper describes the winning contribution to SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection (Subtask 2) handed in by team UG Student Intern. We present an ensemble model that makes predictions based on context-free and context-dependent word representations. The key findings are that (1) context-free word representations are a powerf

Cesar Jauregui, Christoph Stihler, Jens Limpert

This work presents a review on the effect of transverse mode instability in high-power fiber laser systems and the corresponding investigations led worldwide over the last decade. This manuscript includes the description of the experimental observations, the physical origin of this effect, as well as some of the proposed mitigation strategies.

Naoto Shiraishi

Two constructive proofs on d-majorization and thermo-majorization are provided. In the first part, we present a diagrammatic proof of the equivalence between d-majorization and the existence of a proper stochastic matrix. We explicitly construct the desired stochastic matrix by using a graphical argument. In the second part, we present a constructive proof o

Bob Coecke, Matthew Leifer

This volume contains the proceedings of the 16th International Conference on Quantum Physics and Logic (QPL 2017), which was held June 10-14, 2019. Quantum Physics and Logic is an annual conference that brings together researchers working on mathematical foundations of quantum physics, quantum computing, and related areas, with a focus on structural perspect

Sepehr Ghader, Jun Zhao, Minha Lee, Weiyi Zhou

The research team has utilized an integrated dataset, consisting of anonymized location data, COVID-19 case data, and census population information, to study the impact of COVID-19 on human mobility. The study revealed that statistics related to social distancing, namely trip rate, miles traveled per person, and percentage of population staying at home have

Raul Quintero, Ignacio Parra, David Fernandez Llorca, Miguel Angel Sotelo

According to several reports published by worldwide organisations, thousands of pedestrians die in road accidents every year. Due to this fact, vehicular technologies have been evolving with the intent of reducing these fatalities. This evolution has not finished yet since, for instance, the predictions of pedestrian paths could improve the current Automatic

Sarath Pathari

Secure distributed storage, which is a rising cloud administration, is planned to guarantee the mystery of re-appropriated data yet also to give versatile data access to cloud customers whose data is out of physical control. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is seen as a champion among the most reassuring frameworks that may be used to ve

Maopeng Wu, Ruiguang Peng, Jingquan Liu, Qian Zhao

The energy band attraction (EBA) caused by the non-orthogonal eigenvectors is a unique phenomenon in the non-Hermitian (NH) system. However, restricted by the required tight-binding approximation and meticulously engineered complex potentials, such effect has not been experimentally demonstrated. Here, an experimentally verifiable model is proposed based on

Farzad Zangeneh-Nejad, Romain Fleury

Depending on the geometry of their Fermi surfaces, Weyl semimetals and their analogues in classical systems have been classified into two types. In type I Weyl semimetals (WSMs), the cone-like spectrum at the Weyl point (WP) is not tilted, leading to a point-like closed Fermi surface. In type II WSMs, on the contrary, the energy spectrum around the WP is str

Alexandre R. Puttick

Let $\mathbb{F}_q$ be a finite field with $q$ elements and let $V$ be a vector space over $\mathbb{F}_q$ of dimension $n>0$. Let $\Omega_V$ be the Drinfeld period domain over $\mathbb{F}_q$. This is an affine scheme of finite type over $\mathbb{F}_q$, and its base change to $\mathbb{F}_q(t)$ is the moduli space of Drinfeld $\mathbb{F}_q[t]$-modules with leve

Ron Peretz, Amnon Schreiber, Ernst Schulte-Geers

The worst-case Lipschitz constant of an $n$-player $k$-action $\delta$-perturbed game, $\lambda(n,k,\delta)$, is given an explicit probabilistic description. In the case of $k\geq 3$, $\lambda(n,k,\delta)$ is identified with the passage probability of a certain symmetric random walk on $\mathbb Z$. In the case of $k=2$ and $n$ even, $\lambda(n,2,\delta)$ is

Rawad Bitar, Lorenz Welter, Ilia Smagloy, Antonia Wachter-Zeh

This paper studies the problem of constructing codes correcting deletions in arrays. Under this model, it is assumed that an $n\times n$ array can experience deletions of rows and columns. These deletion errors are referred to as $(t_r,t_c)$-criss-cross deletions if $t_r$ rows and $t_c$ columns are deleted, while a code correcting these deletion patterns is

Nikolaos S. Papageorgiou, Vicenţiu D. Rădulescu, Dušan D. Repovš

We consider a nonlinear Dirichlet problem driven by a nonhomogeneous differential operator plus an indefinite potential. In the reaction we have the competing effects of a singular term and of concave and convex nonlinearities. In this paper the concave term is parametric. We prove a bifurcation-type theorem describing the changes in the set of positive solu