December 2020 arXiv papers — page 77

Nir Shlezinger, Jay Whang, Yonina C. Eldar, Alexandros G. Dimakis

Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations that represent the underlying physics, prior information and additional domain knowledge. Simple classical models are useful but sensitive to inaccuracies and may lead to poor perfor

Sujay K. Ashok, Jan Troost

We revisit the calculation of the thermal free energy for string theory in three-dimensional anti-de Sitter spacetime with Neveu-Schwarz-Neveu-Schwarz flux. The path integral calculation is exploited to confirm the off-shell Hilbert space and we find that the Casimir of the discrete representations of the isometry group takes values in a half-open interval.

N. S. Mankoč Borštnik, H. F. B. Nielsen, M. Y. Khlopov, D. Lukman

The contribution contains the preface to the Proceedings to the 22nd Workshop "What Comes Beyond the Standard Models", Bled, July 06 - July 14, 2019, published in Bled workshops in physics, Vol.20, No. 2, DMFA-Zalo\v{z}nistvo, Ljubljana, Dec. 2019, links to (most of) the published contributions and section (by M.Yu. Khlopov) on VIA at Bled 2019.

Marcus Venzke, Daniel Klisch, Philipp Kubik, Asad Ali

In this paper we investigate the usage of machine learning for interpreting measured sensor values in sensor modules. In particular we analyze the potential of artificial neural networks (ANNs) on low-cost micro-controllers with a few kilobytes of memory to semantically enrich data captured by sensors. The focus is on classifying temporal data series with a

David Veysset, Jae-Hwang Lee, Mostafa Hassani, Steven E. Kooi

High-velocity microparticle impacts are relevant to many fields from space exploration to additive manufacturing and can be used to help understand the physical and chemical behaviors of materials under extreme dynamic conditions. Recent advances in experimental techniques for single microparticle impacts have allowed fundamental investigations of dynamical

Michael Balynskiy, Howard Chiang, David Gutierrez, Alexander Kozhevnikov

Quantum computing is an emerging field of science which will eventually lead us to new and powerful logic devices with capabilities far beyond the limits of current transistor-based technology. There are certain types of problems which quantum computers can solve fundamentally faster than the tradition digital computers. There are quantum algorithms which re

Ferran Cedó, Jan Okniński

We study involutive non-degenerate set-theoretic solutions (X,r) of the Yang-Baxter equation on a finite set X. The emphasis is on the case where (X,r) is indecomposable, so the associated permutation group acts transitively on X. One of the major problems is to determine how such solutions are built from the imprimitivity blocks; and also how to characteriz

Andreas L Opdahl, Vimala Nunavath

The Internet of Things, crowdsourcing, social media, public authorities, and other sources generate bigger and bigger data sets. Big and open data offers many benefits for emergency management, but also pose new challenges. This chapter will review the sources of big data and their characteristics. We then discuss potential benefits of big data for emergency

Adrian J. Brown

Mars Sample Return consists of three separate missions, the first of which is the Mars2020 rover which will land at Jezero crater on February 18, 2021. We describe here our remote sensing study of a particular unit that outcrops in Jezero crater that is likely to be part of the return sample suite. We report on our efforts to characterize the olivine unit us

Ahana Ghoshal, Sreetama Das, Amit Kumar Pal, Aditi Sen De

We show that quantum absorption refrigerators, which have traditionally been studied as of three qubits, each of which is connected to a thermal reservoir, can also be constructed by using three qubits and two thermal baths, where two of the qubits, including the qubit to be locally cooled, are connected to a common bath. With a careful choice of the system,

Deepak Ravikumar, Sangamesh Kodge, Isha Garg, Kaushik Roy

Deep neural networks have found widespread adoption in solving complex tasks ranging from image recognition to natural language processing. However, these networks make confident mispredictions when presented with data that does not belong to the training distribution, i.e. out-of-distribution (OoD) samples. In this paper we explore whether the property of V

Yunyi Shen, Claudia Solis-Lemus

Microbiome data require statistical models that can simultaneously decode microbes' reaction to the environment and interactions among microbes. While a multiresponse linear regression model seems like a straight-forward solution, we argue that treating it as a graphical model is flawed given that the regression coefficient matrix does not encode the conditi

Wenjie Qin, Xiang Li, Yuhui Sun, Deyi Xiong

In this paper, we propose a robust neural machine translation (NMT) framework. The framework consists of a homophone noise detector and a syllable-aware NMT model to homophone errors. The detector identifies potential homophone errors in a textual sentence and converts them into syllables to form a mixed sequence that is then fed into the syllable-aware NMT.

Q. He, X. Que, L. Zhou, M. Isobe

Tuning many-body electronic phases by an external handle is of both fundamental and practical importance in condensed matter science. The tunability mirrors the underlying interactions, and gigantic electric, optical and magnetic responses to minute external stimuli can be anticipated in the critical region of phase change. The excitonic insulator is one of

François Huveneers, François Simenhaus

We study the behavior of a tracer particle driven by a one-dimensional fluctuating potential, defined initially as a Brownian motion, and evolving in time according to the heat equation. We obtain two main results. First, in the short time limit, we show that the fluctuations of the particle become Gaussian and sub-diffusive, with dynamical exponent $3/4$. S

Rubén Sánchez-Janssen, Francois Hammer, Simon Morris, Jean-Gabriel Cuby

MOSAIC is the planned multi-object spectrograph for the 39m Extremely Large Telescope (ELT). Conceived as a multi-purpose instrument, it offers both high multiplex and multi-IFU capabilities at a range of intermediate to high spectral resolving powers in the visible and the near-infrared. MOSAIC will enable unique spectroscopic surveys of the faintest source

Jan Kristanto Wibisono, Hsueh-Ming Hang

In this paper, we address the design of lightweight deep learning-based edge detection. The deep learning technology offers a significant improvement on the edge detection accuracy. However, typical neural network designs have very high model complexity, which prevents it from practical usage. In contrast, we propose a Fast Inference Network for Edge Detecti

Catherine Medlock, Alan Oppenheim

The Receiver Operating Characteristic (ROC) is a well-established representation of the tradeoff between detection and false alarm probabilities in binary hypothesis testing. In many practical contexts ROC's are generated by thresholding a measured score variable -- applying score variable threshold tests (SVT's). In many cases the resulting curve is differe

Ferdinand Ihringer

We provide an abundance of strongly regular graphs (SRGs) for certain parameters $(n, k, \lambda, \mu)$ with $n < 100$. For this we use Godsil-McKay (GM) switching with a partition of type $4,n-4$ and Wang-Qiu-Hu (WQH) switching with a partition of type $3,3,n-6$ or $4,4,n-8$. In most cases, we start with a highly symmetric graph which belongs to a finite ge

Michele Benzi, Igor Simunec

In this paper we propose a method to compute the solution to the fractional diffusion equation on directed networks, which can be expressed in terms of the graph Laplacian $L$ as a product $f(L^T) \boldsymbol{b}$, where $f$ is a non-analytic function involving fractional powers and $\boldsymbol{b}$ is a given vector. The graph Laplacian is a singular matrix,

Kuang Huang, Xu Chen, Xuan Di, Qiang Du

This paper aims to answer the research question as to optimal design of decision-making processes for autonomous vehicles (AVs), including dynamical selection of driving velocity and route choices on a transportation network. Dynamic traffic assignment (DTA) has been widely used to model travelers's route choice or/and departure-time choice and predict dynam

Cynthia C. S. Liem, Annibale Panichella

Machine learning (ML) has been widely used in the literature to automate software engineering tasks. However, ML outcomes may be sensitive to randomization in data sampling mechanisms and learning procedures. To understand whether and how researchers in SE address these threats, we surveyed 45 recent papers related to three predictive tasks: defect predictio

Michael Seitz, Marc Meléndez, Nerea Alcázar-Cano, Daniel N. Congreve

Transient microscopy is of vital importance in understanding the dynamics of optical excited states in optoelectronic materials, as it allows for a direct visualization of the movement of energy carriers in space and time. Important information on the influence of trap-states can be obtained using this technique, typically observed as a slow-down of the ener

Rui Gong, Dengxin Dai, Yuhua Chen, Wen Li

One challenge of object recognition is to generalize to new domains, to more classes and/or to new modalities. This necessitates methods to combine and reuse existing datasets that may belong to different domains, have partial annotations, and/or have different data modalities. This paper formulates this as a multi-source domain adaptation and label unificat

Yuhan Liu, Hassan Shapourian, Paolo Glorioso, Shinsei Ryu

Boundary theories of static bulk topological phases of matter are obstructed in the sense that they cannot be realized on their own as isolated systems. The obstruction can be quantified/characterized by quantum anomalies, in particular when there is a global symmetry. Similarly, topological Floquet evolutions can realize obstructed unitary operators at thei

Peixiang Zhong, Yong Liu, Hao Wang, Chunyan Miao

We study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast. Solving this problem enables the application of conversational agents in many real-world scenarios, e.g., recommendation and psychotherapy. The dominant paradigm for t

Dwight Nwaigwe

We compute the determinant of $\sum_{n=1}^{N} \vec{A}^{(n)} \otimes \vec{B}^{(n)}$, where $\vec{A}^{(n)}$ is square and ${\vec{B}^{(n)}=\vec{x}^{(n)}{\vec{y}^{(n)}}^T}$ where $\vec{x}^{(n)}$ and $\vec{y}^{(n)}$ have length $N$.

Stratis Skoulakis, Tanner Fiez, Ryann Sim, Georgios Piliouras

The predominant paradigm in evolutionary game theory and more generally online learning in games is based on a clear distinction between a population of dynamic agents that interact given a fixed, static game. In this paper, we move away from the artificial divide between dynamic agents and static games, to introduce and analyze a large class of competitive

Xikai Shan, Chengliang Wei, Bin Hu

Gravitational waves (GWs) may be magnified or de-magnified due to lensing. This phenomenon will bias the distance estimation based on the matched filtering technique. Via the multi-sphere ray-tracing technique, we study the GW magnification effect and selection effect with particular attention to the stellar-mass binary black holes (BBHs). We find that, for

Jevgenijs Ivanovs

We provide a novel expression of the scale function for a L\'evy processes with negative phase-type jumps. It is in terms of a certain transition rate matrix which is explicit up to a single positive number. A monotone iterative scheme for the calculation of the latter is presented and it is shown that the error decays exponentially fast. Our numerical examp

Vladimir Shenmaier

The maximum traveling salesman problem (Max TSP) consists of finding a Hamiltonian cycle with the maximum total weight of the edges in a given complete weighted graph. This problem is APX-hard in the general metric case but admits polynomial-time approximation schemes in the geometric setting, when the edge weights are induced by a vector norm in fixed-dimen

Yifeng Xiong, Daryus Chandra, Soon Xin Ng, Lajos Hanzo

Quantum error mitigation (QEM) is a promising technique of protecting hybrid quantum-classical computation from decoherence, but it suffers from sampling overhead which erodes the computational speed. In this treatise, we provide a comprehensive analysis of the sampling overhead imposed by QEM. In particular, we show that Pauli errors incur the lowest sampli

Xin Shu, Sameera Sansre, Di Jin, Xiangxiang Zeng

Leukocyte differential test is a widely performed clinical procedure for screening infectious diseases. Existing hematology analyzers require labor-intensive work and a panel of expensive reagents. Here we report an artificial-intelligence enabled reagent-free imaging hematology analyzer (AIRFIHA) modality that can accurately classify subpopulations of leuko

Peixiang Zhong, Di Wang, Pengfei Li, Chen Zhang

Rationality and emotion are two fundamental elements of humans. Endowing agents with rationality and emotion has been one of the major milestones in AI. However, in the field of conversational AI, most existing models only specialize in one aspect and neglect the other, which often leads to dull or unrelated responses. In this paper, we hypothesize that comb

N. S. Mankoc Borstnik

Fifty years ago the {\it standard model} offered an elegant new step towards understanding elementary fermion and boson fields, making several assumptions, suggested by experiments. The assumptions are still waiting for an explanation. There are many proposals in the literature for the next step. The {\it spin-charge-family} theory, proposing a simple starti

Amedeo Altavilla

Given a quaternionic slice regular function $f$, we give a direct and effective way to compute the coefficients of its spherical expansion at any point. Such coefficients are obtained in terms of spherical and slice derivatives of the function itself. Afterwards, we compare the coefficients of $f$ with those of its slice derivative $\partial_{c}f$ obtaining

Hemang Chawla, Matti Jukola, Shabbir Marzban, Elahe Arani

Spatial scene-understanding, including dense depth and ego-motion estimation, is an important problem in computer vision for autonomous vehicles and advanced driver assistance systems. Thus, it is beneficial to design perception modules that can utilize crowdsourced videos collected from arbitrary vehicular onboard or dashboard cameras. However, the intrinsi

Yi Zhu

The global existence of solutions to incompressible viscoelastic flows has been a longstanding open problem, even for the global weak solution. Under some special structure ("div-curl" condition) the global small smooth solution was obtained in \cite{llzhou, cz}. However, the result with large initial data remains unknown up to now, and it is studied in this

Irene Burghardt, Rémi Carles, Clotilde Fermanian Kammerer, Benjamin Lasorne

We consider composite quantum-dynamical systems that can be partitioned into weakly interacting subsystems, similar to system-bath type situations. Using a factorized wave function ansatz, we mathematically characterize dynamical scale separation.Specifically, we investigate a coupling r{\'e}gime that is partially flat, i.e., slowly varying with respect to o

Z. Pleunis, D. Michilli, C. G. Bassa, J. W. T. Hessels

FRB 20180916B is a well-studied repeating fast radio burst source. Its proximity (~150 Mpc), along with detailed studies of the bursts, have revealed many clues about its nature -- including a 16.3-day periodicity in its activity. Here we report on the detection of 18 bursts using LOFAR at 110-188 MHz, by far the lowest-frequency detections of any FRB to dat

Jianwei Hu, Jingfei Zhang, Jianhua Guo, Ji Zhu

In this paper, we study limiting laws and consistent estimation criteria for the extreme eigenvalues in a spiked covariance model of dimension $p$. Firstly, for fixed $p$, we propose a generalized estimation criterion that can consistently estimate, $k$, the number of spiked eigenvalues. Compared with the existing literature, we show that consistency can be

Marc Bezem, Thierry Coquand, Peter Dybjer, Martín Escardó

We give a new syntax independent definition of the notion of a generalized algebraic theory as an initial object in a category of categories with families (cwfs) with extra structure. To this end we define inductively how to build a valid signature $\Sigma$ for a generalized algebraic theory and the associated category of cwfs with a $\Sigma$-structure and c

Maxime Ingremeau

In this paper, we consider a sequence of open quantum graphs, with uniformly bounded data, and we are interested in the asymptotic distribution of their scattering resonances. Supposing that the number of leads in our quantum graphs is small compared to the total number of edges, we show that most resonances are close to the real axis. More precisely, the as

Elvio Di Salvo, Ziad Jean Ajaltouni

We propose for the spin density matrix two parametrizations which automatically fulfil the non-negativity conditions, without setting any bound on the parameters. The first one relies on a theorem, that we prove, and it is rather simple and easily adaptable to some specific reactions, where, for example, parity is conserved or angular momentum conservation e

Oana Ivanovici

We consider the wave equation with Dirichlet boundary conditions in the exterior of the unit ball $B_{d}(0,1)$ of $\mathbb{R}^d$. For $d=3$, we obtain a global in time parametrix and derive sharp dispersive estimates, matching the $\mathbb{R}^{3}$ case, for all frequencies (low and high). For $d\geq 4$, we provide an explicit solution at large frequency $1/h

Maximilian Schwenger

Cyber-physical systems are inherently safety-critical. The deployment of a runtime monitor significantly increases confidence in their safety. The effectiveness of the monitor can be maximized by considering it an integral component during its development. Thus, in this paper, I given an overview over recent work regarding a development process for runtime m

Lars Andersson, Jérémie Joudioux, Marius A. Oancea, Ayush Raj

The propagation of high-frequency gravitational waves can be analyzed using the geometrical optics approximation. In the case of large but finite frequencies, the geometrical optics approximation is no longer accurate, and polarization-dependent corrections at first order in wavelength modify the propagation of gravitational waves via a spin-orbit coupling m

Yanyan Bu, Tuna Demircik, Michael Lublinsky

An effective action for diffusion of a conserved $U(1)$ charge is derived to all orders in the derivative expansion within a holographic model dual to the Schwinger-Keldysh closed time path. A systematic approach to solution of the 5D Maxwell equations in a doubled Schwarzschild-AdS$_5$ black brane geometry is developed. Constitutive relation for the stochas

Federico Binda, Alberto Merici

The goal of this paper is to extend the work of Voevodsky and Morel on the homotopy $t$-structure on the category of motivic complexes to the context of motives for logarithmic schemes. To do so, we prove an analogue of Morel's connectivity theorem and show a purity statement for $(\mathbf{P}^1, \infty)$-local complexes of sheaves with log transfers. The hom

Oana Ivanovici

We prove global in time dispersion for the wave and the Klein-Gordon equation inside the Friedlander domain by taking full advantage of the space-time localization of caustics and a precise estimate of the number of waves that may cross at a given, large time. Moreover, we uncover a significant difference between Klein-Gordon and the wave equation in the low

Martino Grasselli, Andrea Mazzoran, Andrea Pallavicini

We analyze the VIX futures market with a focus on the exchange-traded notes written on such contracts, in particular we investigate the VXX notes tracking the short-end part of the futures term structure. Inspired by recent developments in commodity smile modelling, we present a multi-factor stochastic-local volatility model that is able to jointly calibrate

Shahar Hod

The hoop conjecture, introduced by Thorne almost five decades ago, asserts that black holes are characterized by the mass-to-circumference relation $4\pi {\cal M}/{\cal C}\geq1$, whereas horizonless compact objects are characterized by the opposite inequality $4\pi {\cal M}/{\cal C}<1$ (here ${\cal C}$ is the circumference of the smallest ring that can engul

Maria Arduca, Cosimo Munari

We study the range of prices at which a rational agent should contemplate transacting a financial contract outside a given securities market. Trading is subject to nonproportional transaction costs and portfolio constraints and full replication by way of market instruments is not always possible. Rationality is defined in terms of consistency with market pri

Steven Gilmore, Khai T. Nguyen

The SBV regularity of weak entropy solutions to the Burgers-Poisson equation is considered. We show that the derivative of a solution consists of only the absolutely continuous part and the jump part.

Willy Garcia, Simon Mendez, Baptiste Fray, Alexandre Nicolas

This work aims to assess the risks of Covid-19 disease spread in diverse daily-life situations (referred to as scenarios) involving crowds of maskless pedestrians, mostly outdoors. More concretely, we develop a method to infer the global number of new infections from patchyobservations of pedestrians. The method relies on ad hoc spatially resolved models for

Michael Fleermann, Werner Kirsch, Gabor Toth

We define a multi-group version of the mean-field spin model, also called Curie-Weiss model. It is known that, in the high temperature regime of this model, a central limit theorem holds for the vector of suitably scaled group magnetisations, that is the sum of spins belonging to each group. In this article, we prove a local central limit theorem for the gro

Inés Pastor-Marazuela, Liam Connor, Joeri van Leeuwen, Yogesh Maan

Fast radio bursts (FRBs) are extragalactic astrophysical transients whose brightness requires emitters that are highly energetic, yet compact enough to produce the short, millisecond-duration bursts. FRBs have thus far been detected between 300 MHz and 8 GHz, but lower-frequency emission has remained elusive. A subset of FRBs is known to repeat, and one of t

Andrew Trask, Emma Bluemke, Teddy Collins, Ben Garfinkel Eric Drexler

Successful collaboration involves sharing information. However, parties may disagree on how the information they need to share should be used. We argue that many of these concerns reduce to 'the copy problem': once a bit of information is copied and shared, the sender can no longer control how the recipient uses it. From the perspective of each collaborator,

Sander Borst, Daniel Dadush, Sophie Huiberts, Samarth Tiwari

For a binary integer program (IP) ${\rm max} ~ c^\mathsf{T} x, Ax \leq b, x \in \{0,1\}^n$, where $A \in \mathbb{R}^{m \times n}$ and $c \in \mathbb{R}^n$ have independent Gaussian entries and the right-hand side $b \in \mathbb{R}^m$ satisfies that its negative coordinates have $\ell_2$ norm at most $n/10$, we prove that the gap between the value of the line

Junzhe Cao, Sha Liu, Chengwen Zhong

In this study, the kinetic inviscid flux (KIF) is improved and an implicit strategy is coupled. The recently proposed KIF is a kind of inviscid flux, whose microscopic mechanism makes it good at solving shock waves, with advantages against the shock instability phenomenon. When developing the implicit KIF, a phenomenon is noticed that the kinetic flux vector

Félix Cabello Sánchez, Valentin Ferenczi, Beata Randrianantoanina

The article is a survey related to a classical unsolved problem in Banach space theory, appearing in Banach's famous book in 1932, and known as the Mazur rotations problem. Although the problem seems very difficult and rather abstract, its study sheds new light on the importance of norm symmetries of a Banach space, demonstrating sometimes unexpected connect

Fabio Bernasconi, János Kollár

We prove Grauert-Riemenschneider-type vanishing theorems for excellent dlt threefolds pairs whose closed points have perfect residue fields of positive characteristic $p>5$. Then we discuss applications to dlt singularities and to Mori fiber spaces of threefolds.

Ansgar Denner, Giovanni Pelliccioli

We present the QCD radiative corrections to the full off-shell $\rm t\bar{t}W^+$ production, considering a final state with three charged leptons, two b jets and missing energy. All interferences, off-shell effects and spin correlations are included in the calculation. Beyond presenting integrated and differential results for the full off-shell process, we c

Jean-Guy Caputo, Gustavo Cruz-Pacheco, Benoit Sarels

We revisit the problem of pinning a reaction-diffusion front by a defect, in particular by a reaction-free region. Using collective variables for the front and numerical simulations, we compare the behaviors of a bistable and monostable front. A bistable front can be pinned as confirmed by a pinning criterion, the analysis of the time independant problem and

Ricardo Zarzuela, Daniel Hill, Jairo Sinova, Yaroslav Tserkovnyak

We study the onset of spin superfluidity, namely coherent spin transport mediated by a topological spin texture, in frustrated exchange-dominated magnetic systems, engendered by an external magnetic field. We show that for typical device geometries used in nonlocal magnetotransport experiments, the magnetic field stabilizes a spin superflow against fluctuati

B. Broerman, M. Laubenstein, S. Nagorny, N. Song

A measurement of hafnium foil using a modified ultra-low-background high purity detector with optimized sample-to-detector geometry was performed at Laboratori Nazionale del Gran Sasso. Radiopurity of the stock Hf foil was studied in detail, in addition to an analysis of data collected over 310 days to search for rare processes that can occur in natural Hf i

Shuya Nagayasu, Sumio Watanabe

Bayesian inference is a widely used statistical method. The free energy and generalization loss, which are used to estimate the accuracy of Bayesian inference, are known to be small in singular models that do not have a unique optimal parameter. However, their characteristics are not yet known when there are multiple optimal probability distributions. In thi

Viviana del Barco, Andrei Moroianu

We introduce the notion of metric Lie algebras of Killing type, which are characterized by the fact that all conformal Killing symmetric tensors are sums of Killing tensors and multiples of the metric tensor. We show that if a Lie algebra is either 2-step nilpotent, or 2- or 3-dimensional, or 4-dimensional non-solvable, or 4-dimensional solvable with 1-dimen

Bing Luo, Xiang Li, Shiqiang Wang, Jianwei Huang

Federated learning (FL) is a distributed learning paradigm that enables a large number of devices to collaboratively learn a model without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on

Bernd Finkbeiner, Stefan Oswald, Noemi Passing, Maximilian Schwenger

The safety of cyber-physical systems rests on the correctness of their monitoring mechanisms. This is problematic if the specification of the monitor is implemented manually or interpreted by unreliable software. We present a verifying compiler that translates specifications given in the stream-based monitoring language Lola to implementations in Rust. The g

Mariano Cadoni, Andrea P. Sanna

We investigate anisotropic fluid cosmology in a situation where the spacetime metric back-reacts in a local, time-dependent way to the presence of inhomogeneities. We derive exact solutions to the Einstein field equations describing Friedmann-Lemaitre-Robertson-Walker (FLRW) large scale cosmological evolution in the presence of local inhomogeneities and time

Nikita Durasov, Timur Bagautdinov, Pierre Baque, Pascal Fua

Deep neural networks have amply demonstrated their prowess but estimating the reliability of their predictions remains challenging. Deep Ensembles are widely considered as being one of the best methods for generating uncertainty estimates but are very expensive to train and evaluate. MC-Dropout is another popular alternative, which is less expensive, but als

Chenfeng Cao, Xin Wang

Quantum autoencoder is an efficient variational quantum algorithm for quantum data compression. However, previous quantum autoencoders fail to compress and recover high-rank mixed states. In this work, we discuss the fundamental properties and limitations of the standard quantum autoencoder model in more depth, and provide an information-theoretic solution t

Caterina Caracciolo, Sophie Aubin, Clement Jonquet, Emna Amdouni

In this paper, we report on the outputs and adoption of the Agrisemantics Working Group of the Research Data Alliance (RDA), consisting of a set of recommendations to facilitate the adoption of semantic technologies and methods for the purpose of data interoperability in the field of agriculture and nutrition. From 2016 to 2019, the group gathered researcher

Karl Friedrich Siburg, Christopher Strothmann

Given two random variables $X$ and $Y$, stochastic monotonicity describes a monotone influence of $X$ on $Y$. We prove two different characterizations of stochastically monotone $2$-copulas using the isomorphism between $2$-copulas and Markov operators. The first approach establishes a one-to-one correspondence between stochastically monotone copulas and mon

Tianyi Wei, Dongdong Chen, Wenbo Zhou, Jing Liao

Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high-quality trimap itself is arduous, thus restricting the application of these methods. Recently, some trimap-free methods have emerged, h

Roberto Millon, Emmanuel Frati, Enzo Rucci

The increasing complexity in today's systems and the limited market times demand new development tools for FPGA. Currently, in addition to traditional hardware description languages (HDLs), there are high-level synthesis (HLS) tools that increase the abstraction level in system development. Despite the greater simplicity of design and testing, HLS has some d

W. L. Zhang, T. Grismayer, K. M. Schoeffler, R. A. Fonseca

The laser interaction with an electron-positron-ion mixed plasma is studied, from the perspective of the associated high-order harmonic generation. For an idealized mixed plasma which is assumed with a sharp plasma-vacuum interface and uniform density distribution, when it is irradiated by a weakly relativistic laser pulse, well-defined signals at harmonics

Magali Brunet, Benoit Malard, Nicolas Ratel-Ramond, Christophe Deshayes

The understanding of long-term aging of aeronautical materials, in particular aluminium alloys used in the fuselage and structure of aircraft is of extreme importance for airline fleets. In this work, a plate from an old aircraft (Breguet) was retrieved and studied in terms of microstructure and mechanical properties. A comparison was made between this natur

Sebastian Müller, Andreas Penzkofer, Darcy Camargo, Olivia Saa

Voting algorithms have been widely used as consensus protocols in the realization of fault-tolerant systems. These algorithms are best suited for distributed systems of nodes with low computational power or heterogeneous networks, where different nodes may have different levels of reputation or weight. Our main contribution is the construction of a fair voti

Deepak Kumar, Nalin Kumar, Subhankar Mishra

Hate speech, quite common in the age of social media, at times harmless but can also cause mental trauma to someone or even riots in communities. Image of a religious symbol with derogatory comment or video of a man abusing a particular community, all become hate speech with its every modality (such as text, image, and audio) contributing towards it. Models

Tony Lelièvre, Dorian Le Peutrec, Boris Nectoux

We consider the first exit point distribution from a bounded domain $\Omega$ of the stochastic process $(X_t)_{t\ge 0}$ solution to the overdamped Langevin dynamics $$d X_t = -\nabla f(X_t) d t + \sqrt{h} \ d B_t$$ starting from deterministic initial conditions in $\Omega$, under rather general assumptions on $f$ (for instance, $f$ may have several critical

Tristan Guillot, Leigh Fletcher

Observations from the Juno and Cassini missions provide essential constraints on the internal structures and compositions of Jupiter and Saturn, resulting in profound revisions of our understanding of the interior and atmospheres of Gas Giant planets. The next step to understand planetary origins in our Solar System requires a mission to their Ice Giant sibl

Yves Benoist, Dominique Hulin

We prove that a harmonic quasi-isometric map between pinched Hadamard surfaces is a quasi-conformal diffeomorphism.

Benjamin Helmich-Paris

We present a trust-region augmented Hessian implementation (TRAH-SCF) for restricted and unrestricted Hartree-Fock and Kohn-Sham methods. With TRAH-SCF convergence can always be achieved with tight convergence thresholds, which requires just a modest number of iterations. Our convergence benchmark study and our illustrative applications focus on open-shell m

Hajer Bahouri, Isabelle Gallagher

It was proved by H. Bahouri, P. G{\'e}rard and C.-J. Xu in [9] that the Schr{\"o}dinger equation on the Heisenberg group $\mathbb{H}^d$, involving the sublaplacian, is an example of a totally non-dispersive evolution equation: for this reason global dispersive estimates cannot hold. This paper aims at establishing local dispersive estimates on $\mathbb{H}^d$

Hyeryung Jang, Nicolas Skatchkovsky, Osvaldo Simeone

Artificial Neural Network (ANN)-based inference on battery-powered devices can be made more energy-efficient by restricting the synaptic weights to be binary, hence eliminating the need to perform multiplications. An alternative, emerging, approach relies on the use of Spiking Neural Networks (SNNs), biologically inspired, dynamic, event-driven models that e

Nicholas Stedmon

This paper will investigate the literature surrounding cyber security threats in the 2020 US Elections. It begins with a brief overview of cyber security and the current state of cyber security regarding elections. In the main body of the paper, the focus will be on the literature review of three main areas: voter suppression, voter fraud, and disinformation

K. Adjallah, K. Adzakpa

This paper proposes important and useful results relating to the minimization of the sum of the flow-time and the tardiness of tasks or jobs with unequal release dates (occurrence date), with application to maintenance planning and scheduling. Firstly, the policy of real-time maintenance is defined for minimizing the cost of tardiness and critical states. Th

Mengzhang Li, Zhanxing Zhu

Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated spatial dependencies and dynamical trends of temporal pattern between different roads. Existing frameworks typically utilize given spatial adjacency graph and sophisticated mechanisms for modeling spatial and temporal correlations. However, limited representations

Karol Desnos, Nicolas Sourbier, Pierre-Yves Raumer, Olivier Gesny

Tangled Program Graph (TPG) is a reinforcement learning technique based on genetic programming concepts. On state-of-the-art learning environments, TPGs have been shown to offer comparable competence with Deep Neural Networks (DNNs), for a fraction of their computational and storage cost. This lightness of TPGs, both for training and inference, makes them an

Kevin Akbar Adhiguna, Firhan Maulana Rusli, Hendy Irawan

A lot of service requires identity of users to mitigate undesirable incidents such as fraud. To cut down probability of potential fraud, ID Card of users are collected to be verified so people can verify users whenever an undesirable activity happens. However, to verify identity through an ID card, a repository for the ID Card is required. To verify ID cards

Mehmet Niyazi Çankaya, Roberto Vila

The maximum ${\log}_q$ likelihood estimation method is a generalization of the known maximum $\log$ likelihood method to overcome the problem for modeling non-identical observations (inliers and outliers). The parameter $q$ is a tuning constant to manage the modeling capability. Weibull is a flexible and popular distribution for problems in engineering. In t

Alain Haraux

The usual equation for both motions of a single planet around the sun and electrons in the deterministic Rutherford-Bohr atomic model is conservative with a singular potential at the origin. When a dissipation is added, new phenomena appear. It is shown that whenever the momentum is not zero, the moving particle does not reach the center in finite time and i

Rong-Gen Cai, Jia-Feng Ding, Zong-Kuan Guo, Shao-Jiang Wang

The increasing tension between the different local direct measurements of the Hubble expansion rate and that inferred from the Cosmic Microwave Background observation by $\Lambda$-Cold-Dark-Matter model could be a smoking gun of new physics, if not caused by either observational systematics or local bias. We generalize previous investigation on the local bia

Benny Avelin, Vesa Julin

In this paper we are concerned with the approximation of functions by single hidden layer neural networks with ReLU activation functions on the unit circle. In particular, we are interested in the case when the number of data-points exceeds the number of nodes. We first study the convergence to equilibrium of the stochastic gradient flow associated with the

H. Moulinec, Pierre Suquet

The local and overall responses of nonlinear composites are classically investigated by the Finite Element Method. We propose an alternate method based on Fourier series which avoids meshing and which makes direct use of microstructure images. It is based on the exact expression of the Green function of a linear elastic and homogeneous comparison material. F

Ron Zhu

Hateful meme detection is a new research area recently brought out that requires both visual, linguistic understanding of the meme and some background knowledge to performing well on the task. This technical report summarises the first place solution of the Hateful Meme Detection Challenge 2020, which extending state-of-the-art visual-linguistic transformers

Hanne Hardering, Benedikt Wirth

We prove quartic convergence of cubic spline interpolation for curves into Riemannian manifolds as the grid size of the interpolation grid tends to zero. In contrast to cubic spline interpolation in Euclidean space, where this result is classical, the interpolation operator is no longer linear. Still, concepts from the linear setting may be generalized to th

Guangxi Li, Zhixin Song, Xin Wang

Classification of quantum data is essential for quantum machine learning and near-term quantum technologies. In this paper, we propose a new hybrid quantum-classical framework for supervised quantum learning, which we call Variational Shadow Quantum Learning (VSQL). Our method in particular utilizes the classical shadows of quantum data, which fundamentally