March 2020 arXiv papers — page 124

Nazanin Roshandel Tavana

In this paper, a computably definable predicate is defined and characterized. Then, it is proved that every separable infinite-dimensional Hilbert structure in an effectively presented language is computable. Moreover, every definable predicate in these structures is computable.

Isadora Veeren, Fernando de Melo

Our knowledge of quantum mechanics can satisfactorily describe simple, microscopic systems, but is yet to explain the macroscopic everyday phenomena we observe. Here we aim to shed some light on the quantum-to-classical transition as seen through the analysis of uncertainty relations. We employ entropic uncertainty relations to show that it is only by the in

Stefan Typel, Diana Alvear Terrero

The relativistic density functional with minimal density dependent nucleon-meson couplings for nuclei and nuclear matter is extended to include tensor couplings of the nucleons to the vector mesons. The dependence of the minimal couplings on either vector or scalar densities is explored. New parametrisations are obtained by a fit to nuclear observables with

Rodrigo Bañuelos, Fabrice Baudoin, Li Chen, Yannick Sire

We develop a new approach to prove multiplier theorems in various geometric settings. The main idea is to use martingale transforms and a Gundy-Varopoulos representation for multipliers defined via a suitable extension procedure. Along the way, we provide a probabilistic proof of a generalization of a result by Stinga and Torrea, which is of independent inte

Yu Hamada, Kengo Kikuchi

We propose a formalism to obtain the electroweak sphaleron, which is one of the static classical solutions, using the gradient flow method. By adding a modification term to the gradient flow equation, we can obtain the sphaleron configuration as a stable fixed point of the flow in the large flow time. Applying the method to the $SU(2)$-Higgs model (the Weinb

Rita Wysoczańska, Piotr A. Dybczyński, Magdalena Polińska

The second Gaia data release (Gaia DR2) provided us with the precise five-parameter astrometry for 1.3 billion of sources. As stars passing close to the Solar System are thought to be responsible for influencing the dynamical history of long period comets, we update and extend the list of stars that could potentially perturb motion of these comets. We aim to

Debottam Nandi

We construct a class of viable bouncing models that are conformally related to cosmological inflation. There are three main difficulties in constructing such a model: (i) A stable (attractor) solution, (ii) A non-singular bounce, and (iii) to bypass the no-go theorem that states that simultaneously maintaining the observational bounds on the tensor-to-scalar

Giuseppe Gaeta

The COVID-19 epidemics, started in China in January 2020, was recognized to have reached Italy around February 20; recent estimates show that most probably the virus circulated in the country already in January, but was not recognized. Data for the early dynamics of COVID-19 in Northern Italy are analyzed.

Richard Teague, Marija R. Jankovic, Thomas J. Haworth, Chunhua Qi

Unraveling the 3D physical structure, the temperature and density distribution, of protoplanetary discs is an essential step if we are to confront simulations of embedded planets or dynamical instabilities. In this paper we focus on Submillimeter Array observations of the edge-on source, Gomez's Hamburger, believed to host an over-density hypothesised to

Lorenzo Buffoni, Michele Campisi

The D-wave processor is a partially controllable open quantum system which exchanges energy with its surrounding environment (in the form of heat) and with the external time dependent control fields (in the form of work). Despite being rarely thought as such, it is a thermodynamic machine. Here we investigate the properties of the D-Wave quantum annealers fr

Aymen Mir, Thiemo Alldieck, Gerard Pons-Moll

In this paper, we present a simple yet effective method to automatically transfer textures of clothing images (front and back) to 3D garments worn on top SMPL, in real time. We first automatically compute training pairs of images with aligned 3D garments using a custom non-rigid 3D to 2D registration method, which is accurate but slow. Using these pairs, we

Jianbo Wang, Zhiwang Yu, Yuyu Wang

Following Brooks's calculation of the $\hat{A}$-genus of complete intersections, a new and more computable formula about the $\hat{A}$-genus and $α$-invariant will be described as polynomials of multi-degree and dimension. We also give an iterated formula of $\hat{A}$-genus and the necessary and sufficient conditions for the vanishing of $\hat{A}$-genus

Anni Hakanen, Ville Junnila, Tero Laihonen, María Luz Puertas

Resolving sets were originally designed to locate vertices of a graph one at a time. For the purpose of locating multiple vertices of the graph simultaneously, $\{\ell\}$-resolving sets were recently introduced. In this paper, we present new results regarding the $\{\ell\}$-resolving sets of a graph. In addition to proving general results, we consider $\{2\}

Christian Hamster, Hermen Jan Hupkes

In this paper we establish the meta-stability of travelling waves for a class of reaction-diffusion equations forced by a multiplicative noise term. In particular, we show that the phase-tracking technique developed in [hamster2017,hamster2020] can be maintained over timescales that are exponentially long with respect to the noise intensity. This is achieved

Christian Ufrecht, Enno Giese

Light-pulse atom interferometers are powerful quantum sensors, however, their accuracy for example in tests of the weak equivalence principle is limited by various spurious influences like magnetic stray fields or blackbody radiation. Pushing the accuracy therefore requires a detailed assessment of the size of such deleterious effects. Here, we present a sys

Francesco Bei, Paolo Piazza

Let $M$ be a compact complex manifold. In this paper we give a simple proof of the bimeromorphic invariance of the higher Todd genera of $M$, a result first proved implicitly by Brasselet-Schürmann-Yokura using algebraic methods.

Debasmita Mondal, Ameya G. Prabhune, Sriram Ramaswamy, Prerna Sharma

Microorganisms swimming through viscous fluids imprint their propulsion mechanisms in the flow fields they generate. Extreme confinement of these swimmers between rigid boundaries often arises in natural and technological contexts, yet measurements of their mechanics in this regime are absent. Here, we show that strongly confining the microalga Chlamydomonas

Haotian Zhang, Jianyong Sun, Zongben Xu

This paper proposes the first-ever algorithmic framework for tuning hyper-parameters of stochastic optimization algorithm based on reinforcement learning. Hyper-parameters impose significant influences on the performance of stochastic optimization algorithms, such as evolutionary algorithms (EAs) and meta-heuristics. Yet, it is very time-consuming to determi

Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal

We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. Our approach, deterministic uncertainty quantification (DUQ), builds upon ideas of RBF networks. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy

Yuri F. Saporito, Zhaoyu Zhang

In this paper, we propose a novel numerical method for Path-Dependent Partial Differential Equations (PPDEs). These equations firstly appeared in the seminal work of Dupire [2009], where the functional Itô calculus was developed to deal with path-dependent financial derivatives contracts. More specificaly, we generalize the Deep Galerking Method (DGM) of Sir

Matthew W. Thompson, Justin B. Gilmer, Ray A. Matsumoto, Co D. Quach

Systems composed of soft matter (e.g., liquids, polymers, foams, gels, colloids, and most biological materials) are ubiquitous in science and engineering, but molecular simulations of such systems pose particular computational challenges, requiring time and/or ensemble-averaged data to be collected over long simulation trajectories for property evaluation. P

Artur O. Lopes, Jairo K. Mengue

It is well known that in Information Theory and Machine Learning the Kullback-Leibler divergence, which extends the concept of Shannon entropy, plays a fundamental role. Given an {\it a priori} probability kernel $\hatν$ and a probability $π$ on the measurable space $X\times Y$ we consider an appropriate definition of entropy of $π$ relative to $\hatν$, whic

R. M. P. Neves, F. F. Santos, F. A. Brito

We consider a brane cosmology scenario by taking an inflating 3D domain wall immersed in a five-dimensional Minkowski space in the presence of a stack of $N$ parallel domain walls. They are static BPS solutions of the bosonic sector of a 5D supergravity theory. However, one can move towards each other due to an attractive force in between driven by bulk part

Mikolaj Jankowski, Deniz Gunduz, Krystian Mikolajczyk

We propose a joint feature compression and transmission scheme for efficient inference at the wireless network edge. Our goal is to enable efficient and reliable inference at the edge server assuming limited computational resources at the edge device. Previous work focused mainly on feature compression, ignoring the computational cost of channel coding. We i

Snigdha Ghosh, Arghya Mukherjee, Nilanjan Chaudhuri, Pradip Roy

In this work the neutral meson properties have been investigated in the presence of thermo-magnetic background using two-flavor Nambu--Jona-Lasinio model. Mass, spectral function and dispersion relations are obtained in the scalar ($σ$) and pseudo-scalar ($π^0$) channels as well as in the vector ($ρ^0$) and axial vector ($a^0_1$) channels. The general Lorent

Filippo Bracci, Daniela Kraus, Oliver Roth

In this paper we establish several invariant boundary versions of the (infinitesimal) Schwarz-Pick lemma for conformal pseudometrics on the unit disk and for holomorphic selfmaps of strongly convex domains in $\mathbb C^N$ in the spirit of the boundary Schwarz lemma of Burns-Krantz. Firstly, we focus on the case of the unit disk and prove a general boundary

A. R. Ziyaee, M. Mohsenzadeh, E. Yusofi

Krein approach is used to study the particle creation during quasi-de Sitter inflation in different background space-times. In the conventional method for calculating the created particles spectrum, the background space-time is automatically considered flat. Selecting a flat background poses two fundamental problems: First, the method of calculating is not c

Stav Ben-Nun, Shay Golan, Tomasz Kociumaka, Matan Kraus

We consider the problem of finding, given two documents of total length $n$, a longest string occurring as a substring of both documents. This problem, known as the Longest Common Substring (LCS) problem, has a classic $O(n)$-time solution dating back to the discovery of suffix trees (Weiner, 1973) and their efficient construction for integer alphabets (Fara

Viktor Holubec, Zhuolin Ye

We analytically derive maximum efficiency at given cooling power for Carnot-type low-dissipation refrigerators. The corresponding optimal cycle duration depends on a single parameter, which is a specific combination of irreversibility parameters and bath temperatures. For a slight decrease in power with respect to its maximum value, the maximum efficiency ex

Ze Cui, Jing Wang, Shangyin Gao, Bo Bai

With the development of deep learning techniques, the combination of deep learning with image compression has drawn lots of attention. Recently, learned image compression methods had exceeded their classical counterparts in terms of rate-distortion performance. However, continuous rate adaptation remains an open question. Some learned image compression metho

Takami Kuroda, Almudena Arcones, Tomoya Takiwaki, Kei Kotake

We present results of three-dimensional (3D), radiation-magnetohydrodynamics (MHD) simulations of core-collapse supernovae in full general relativity (GR) with spectral neutrino transport. In order to study the effects of progenitor's rotation and magnetic fields, we compute three models, where the precollapse rotation rate and magnetic fields are includ

Long Huang, Xiaofeng Yang, Xiang Liu

It may be determined by non-parametric method if the dark energy evolves with time. We propose a method of combining PCA and biased estimation on the basis of ridge regression analysis to reconstruct parameters, meanwhile we present an interesting principal component selection criterion to avoid the arbitrariness of principal component selections, and use nu

Ghislain Fourier, Gabriele Nebe

Building upon the application of flags to network coding introduced by Liebhold, Nebe, and Vazquez-Castro, we develop a variant of this coding technique that uses degenerate flags. The information set is a metric affine space isometric to the space of upper triangular matrices endowed with the flag rank metric. This suggests the development of a theory for f

Quentin Bletery, Olivier Cavalié, Jean-Mathieu Nocquet, Théa Ragon

The North Anatolian Fault (NAF) has produced numerous major earthquakes. After decades of quiescence, the $\rm{M_w}$ 6.8 Elazığ earthquake (January 24, 2020) has recently reminded us that the East Anatolian Fault (EAF) is also capable of producing significant earthquakes. To better estimate the seismic hazard associated with these two faults, we jointly inve

Mouez Dimassi, Masaki Kawamoto, Vesselin Petkov

In the presence of the homogeneous electric field ${\bf E}$ and the homogeneous perpendicular magnetic field ${\bf B}$, the classical trajectory of a quantum particle on ${\mathbb R}^2$ moves with drift velocity $α$ which is perpendicular to the electric and magnetic fields. For such Hamiltonians the absence of the embedded eigenvalues of perturbed Hamiltoni

Victor Garcia Satorras, Max Welling

A graphical model is a structured representation of locally dependent random variables. A traditional method to reason over these random variables is to perform inference using belief propagation. When provided with the true data generating process, belief propagation can infer the optimal posterior probability estimates in tree structured factor graphs. How

Michael Klute, Horia-Eugen Porteanu, Ilija Stefanovic, Wolfgang Heinrich

Microwave and radio frequency driven plasmas jets play an important role in many technical applications. They are usually operated in a capacitive mode known as E-mode. As a new plasma source the MMWICP (Miniature Micro Wave ICP) has been proposed, a small scale plasma jet with inductive coupling based on a specially designed resonator that acts as an LC-res

Alexander Rieder, Francisco-Javier Sayas, Jens Markus Melenk

We consider the approximation to an abstract evolution problem with inhomogeneous side constraint using $A$-stable Runge-Kutta methods. We derive a priori estimates in norms other than the underlying Banach space. Most notably, we derive estimates in the graph norm of the generator. These results are used to study convolution quadrature based discretizations

Benjamin Billot, Douglas Greve, Koen Van Leemput, Bruce Fischl

We present a deep learning strategy that enables, for the first time, contrast-agnostic semantic segmentation of completely unpreprocessed brain MRI scans, without requiring additional training or fine-tuning for new modalities. Classical Bayesian methods address this segmentation problem with unsupervised intensity models, but require significant computatio

Igor Buzhinsky, Arseny Nerinovsky, Stavros Tripakis

Recent studies have shown that modern deep neural network classifiers are easy to fool, assuming that an adversary is able to slightly modify their inputs. Many papers have proposed adversarial attacks, defenses and methods to measure robustness to such adversarial perturbations. However, most commonly considered adversarial examples are based on $\ell_p$-bo

Zhuo Chen, Honglei Lang, Zhangju Liu

A strict Lie $2$-algebra $\Gamma(\wedge^\bullet A) \stackrel{T}{\rightarrow} \mathfrak{X}_{\mathrm{mult}}^\bullet(\mathcal{G})$ is associated with any Lie groupoid $\mathcal{G}$. Here, $\Gamma(\wedge^\bullet A)$ is the Schouten algebra of the tangent Lie algebroid $A$ of $\mathcal{G}$ and $\mathfrak{X}_{\mathrm{mult}}^\bullet(\mathcal{G})$ is the space of mu

Fabian Wolf, Gernot A. Fink

Word spotting is a popular tool for supporting the first exploration of historic, handwritten document collections. Today, the best performing methods rely on machine learning techniques, which require a high amount of annotated training material. As training data is usually not available in the application scenario, annotation-free methods aim at solving th

Michael Carr, Siamak F. Shahandashti

In this work we analyse five popular commercial password managers for security vulnerabilities. Our analysis is twofold. First, we compile a list of previously disclosed vulnerabilities through a comprehensive review of the academic and non-academic sources and test each password manager against all the previously disclosed vulnerabilities. We find a mixed p

Moritz Drescher, Manfred Salmhofer, Tilman Enss

We investigate a Bose-Einstein condensate in strong interaction with a single impurity particle. While this situation has received considerable interest in recent years, the regime of strong coupling remained inaccessible to most approaches due to an instability in Bogoliubov theory arising near the resonance. We present a nonlocal extension of Gross-Pitaevs

Annalisa Cesaroni, Marco Cirant

In this paper we consider an optimal control problem for a large population of interacting agents with deterministic dynamics, aggregating potential and constraints on reciprocal distances, in dimension 1. We study existence and qualitative properties of periodic in time optimal trajectories of the finite agents optimal control problem, with particular inter

S. C. Inan, A. V. Kisselev

The virtual production of axion-like particles (ALPs) in the light-by-light scattering at the CLIC collider is studied. Both differential and total cross sections are calculated, assuming interaction of the ALP with photons via CP-odd term in the Lagrangian. The 95\% C.L. exclusion regions for the ALP mass and its coupling constant are given. By comparing ou

Kristoffer Andersson, Cornelis Oosterlee

In this paper, we propose a neural network-based method for approximating expected exposures and potential future exposures of Bermudan options. In a first phase, the method relies on the Deep Optimal Stopping algorithm, which learns the optimal stopping rule from Monte-Carlo samples of the underlying risk factors. Cashflow-paths are then created by applying

Felisia Angela Chiarello, Giuseppe Maria Coclite

We prove the well-posedness of entropy weak solutions for a class of space-discontinuous scalar conservation laws with non-local flux arising in traffic modeling. We approximate the problem adding a viscosity term and we provide $L^\infty$ and BV estimates for the approximate solutions. We use the doubling of variable technique to prove the stability with re

Hery Randriamaro

Varchenko introduced in 1993 a distance function on the chambers of a hyperplane arrangement that gave rise to a determinant whose entry in position $(C, D)$ is the distance between the chambers $C$ and $D$, and computed that determinant. In 2017, Aguiar and Mahajan provided a generalization of that distance function, and computed the corresponding determina

Ashley Scillitoe, Pranay Seshadri, Mark Girolami

Data-driven turbulence modelling approaches are gaining increasing interest from the CFD community. However, the introduction of a machine learning (ML) model introduces a new source of uncertainty, the ML model itself. Quantification of this uncertainty is essential since the predictive capability of a data-driven model diminishes when predicting physics no

Adam Parusiński, Armin Rainer

We prove lifting theorems for complex representations $V$ of finite groups $G$. Let $σ=(σ_1,\dots,σ_n)$ be a minimal system of homogeneous basic invariants and let $d$ be their maximal degree. We prove that any continuous map $\overline{f} \colon {\mathbb R}^m \to V$ such that $f = σ\circ \overline{f}$ is of class $C^{d-1,1}$ is locally of Sobolev class $W^{

Ren Yang, Fabian Mentzer, Luc Van Gool, Radu Timofte

In this paper, we propose a Hierarchical Learned Video Compression (HLVC) method with three hierarchical quality layers and a recurrent enhancement network. The frames in the first layer are compressed by an image compression method with the highest quality. Using these frames as references, we propose the Bi-Directional Deep Compression (BDDC) network to co

Bart Jacobs, Sam Staton

This paper reformulates a classical result in probability theory from the 1930s in modern categorical terms: de Finetti's representation theorem is redescribed as limit statement for a chain of finite spaces in the Kleisli category of the Giry monad. This new limit is used to identify among exchangeable coalgebras the final one.

Bo Tai, Weikang Wu, Xiaolong Feng, Yalong Jiao

The interplay between magnetism, band topology, and electronic correlation in low dimensions has been a fascinating subject of research. Here, we propose two-dimensional (2D) material systems which demonstrate such an interesting interplay. Based on first-principles calculations and structural search algorithms, we identify three lowest energy 2D CoSe struct

Morteza Raeisi, Florent Bonneu, Edith Gabriel

Spatial and spatio-temporal single-structure point process models are widely used in epidemiology, biology, ecology, seismology... . However, most natural phenomena present multiple interaction structure or exhibit dependence at multiple scales in space and/or time, leading to define new spatial and spatio-temporal multi-structure point process models. In th

Juan Vidal Alegría, Fredrik Rusek, Jesús Rodríguez Sánchez, Ove Edfors

Typical massive multiple-input multiple-output (MIMO) architectures consider a centralized approach, in which all baseband data received by each antenna has to be sent to a central processing unit (CPU) to be processed. Due to the enormous amount of antennas expected in massive MIMO base stations (BSs), the number of connections to the CPU required in centra

Mats Andersson, Håkan Samuelsson Kalm

We prove that any smooth mapping between reduced analytic spaces induces a natural pullback operation on smooth differential forms.

Federico Simonetta, Stavros Ntalampiras, Federico Avanzini

This paper describes an open-source Python framework for handling datasets for music processing tasks, built with the aim of improving the reproducibility of research projects in music computing and assessing the generalization abilities of machine learning models. The framework enables the automatic download and installation of several commonly used dataset

Shou-Long Li, Wen-Di Tan, Puxun Wu, Hongwei Yu

In this paper, we adopt the so-called Buonanno-Kidder-Lehner (BKL) recipe to estimate the final spin of a rotating binary black hole merger in STU supergravity. According to the BKL recipe, the final spin can be viewed as the sum of the individual spins plus the orbital angular momentum of the binary system which could be approximated as the angular momentum

Duraivelan Palanisamy, Wouter K. den Otter

The deviatoric stresses of colloidal suspensions are routinely calculated using the expression introduced by Batchelor [J. Fluid Mech. 83, 97--117 (1977)]. We show by example that the central feature in its derivation, the thermodynamic force driving particles down the density gradient, is inconsistent with the motion of the colloids. A new expression for th

Takuya Machida

We focus on a 2-period time-dependent quantum walk on the half line in this paper. The quantum walker launches at the edge of the half line in a localized superposition state and its time evolution is carried out with two unitary operations which are alternately cast to the quantum walk. As a result, long-time limit finding probabilities of the quantum walk

Pau Tallada, Jorge Carretero, Jordi Casals, Carles Acosta-Silva

We present CosmoHub (https://cosmohub.pic.es), a web application based on Hadoop to perform interactive exploration and distribution of massive cosmological datasets. Recent Cosmology seeks to unveil the nature of both dark matter and dark energy mapping the large-scale structure of the Universe, through the analysis of massive amounts of astronomical data,

Wei Guo Foo, Joel Merker, The-Anh Ta

Applying Lie's theory, we show that any $\mathcal{C}^ω$ hypersurface $M^5 \subset \mathbb{C}^3$ in the class $\mathfrak{C}_{2,1}$ carries Cartan-Moser chains of orders $1$ and $2$. Integrating and straightening any order $2$ chain at any point $p \in M$ to be the $v$-axis in coordinates $(z, ζ, w = u + i\, v)$ centered at $p$, we show that there exists a

Felix Abramovich, Vadim Grinshtein, Tomer Levy

In this paper we consider high-dimensional multiclass classification by sparse multinomial logistic regression. We propose first a feature selection procedure based on penalized maximum likelihood with a complexity penalty on the model size and derive the nonasymptotic bounds for misclassification excess risk of the resulting classifier. We establish also th

Shi-Yi Lan, Jin Ma, Wang Zhou

Using the estimate of the difference between the discrete harmonic function and its corresponding continuous version we derive a rate of convergence of the Loewner driving function for the harmonic explorer to the Brownian motion with speed 4 on the real line. Based on this convergence rate, the derivative estimate for chordal $\mbox{SLE}_4$, and the estimat

A. Adin, T. Goicoa, J. S. Hodges, P. Schnell

Assessing associations between a response of interest and a set of covariates in spatial areal models is the leitmotiv of ecological regression. However, the presence of spatially correlated random effects can mask or even bias estimates of such associations due to confounding effects if they are not carefully handled. Though potentially harmful, confounding

Huy Hoang Nguyen, Simo Saarakkala, Matthew Blaschko, Aleksei Tiulpin

Knee osteoarthritis (OA) is one of the highest disability factors in the world. This musculoskeletal disorder is assessed from clinical symptoms, and typically confirmed via radiographic assessment. This visual assessment done by a radiologist requires experience, and suffers from moderate to high inter-observer variability. The recent literature has shown t

Xiao Luo, Haixin Wang, Daqing Wu, Chong Chen

Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining. Hashing is one of the most widely used methods for its computational and storage efficiency. With the development of deep learning, deep hashing methods

Jie Xiong, Tong-Yi Zhang, San-Qiang Shi

The mechanical properties are essential for structural materials. The analyzed 360 data on four mechanical properties of steels, viz. fatigue strength, tensile strength, fracture strength, and hardness, are selected from the NIMS database, including carbon steels, and low-alloy steels. Five machine learning algorithms were applied on the 360 data to predict

Zhiyong Qiu, Liu Chen, Fulvio Zonca, Ruirui Ma

Zero frequency zonal flow (ZFZF) excitation by trapped energetic electron driven beta-induced Alfven eigenmode (eBAE) is investigated using nonlinear gyrokinetic theory. It is found that, during the linear growth stage of eBAE, resonant energetic electrons (EEs) not only effectively drive eBAE unstable, but also contribute to the nonlinear coupling, leading

Alexander Prolubnikov

We consider a wide class of the discrete optimization problems with interval objective function. We give a generalization of the greedy algorithm for the problems. Using the algorithm, we obtain the set of all possible greedy solutions and the set of all possible values of the objective function for the solutions. For a given probability distribution on inte

Md. Sadrul Islam Toaha, Sakib Bin Asad, Chowdhury Rafeed Rahman, S. M. Shahriar Haque

Most city establishments of developing cities are digitally unlabeled because of the lack of automatic annotation systems. Hence location and trajectory services such as Google Maps, Uber etc remain underutilized in such cities. Accurate signboard detection in natural scene images is the foremost task for error-free information retrieval from such city stree

Aya Iyonaga, Kazufumi Takahashi, Tsutomu Kobayashi

Late-time cosmology in the extended cuscuton theory is studied, in which gravity is modified while one still has no extra dynamical degrees of freedom other than two tensor modes. We present a simple example admitting analytic solutions for the cosmological background evolution that mimics $Λ$CDM cosmology. We argue that the extended cuscuton as dark energy

Mengmou Li, Lanlan Su, Tao Liu

In this work, we address the distributed optimization problem with event-triggered communication by the notion of input feedforward passivity (IFP). First, we analyze the distributed continuous-time algorithm over uniformly jointly strongly connected balanced digraphs in an IFP-based framework. Then, we propose a distributed event-triggered communication mec

Wei Qiu, Henrik Bruus, Per Augustsson

We present three-dimensional measurements of size-dependent acoustophoretic motion of microparticles with diameters from 4.8 um down to 0.5 um suspended in either homogeneous or inhomogeneous fluids inside a glass-silicon microchannel and exposed to a standing ultrasound wave. To study the cross-over from radiation force dominated to streaming dominated moti

Koji Kobayashi, Miku Wada, Tomi Ohtsuki

We study the dynamics of Dirac and Weyl electrons in disordered point-node semimetals. The ballistic feature of the transport is demonstrated by simulating the wave-packet dynamics on lattice models. We show that the ballistic transport survives under a considerable strength of disorder up to the semimetal-metal transition point, which indicates the robustne

Yonghyun Jeong, Hyunjin Choi, Byoungjip Kim, Youngjune Gwon

We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games. Given a partially observed state, DefogGAN generates defogged images of a game as predictive information. Such information can lead to create a strategic agent for the game. DefogGAN is a conditional GAN varian

Chandan Singh, Wooseok Ha, Francois Lanusse, Vanessa Boehm

Machine learning lies at the heart of new possibilities for scientific discovery, knowledge generation, and artificial intelligence. Its potential benefits to these fields requires going beyond predictive accuracy and focusing on interpretability. In particular, many scientific problems require interpretations in a domain-specific interpretable feature space

Anne-Maria Ernvall-Hytönen, Neea Palojärvi

We prove an explicit error term for the $ψ(x,χ)$ function assuming the Generalized Riemann Hypothesis. Using this estimate, we prove a conditional explicit bound for the number of primes in arithmetic progressions.

Meng-Ting Hsieh, Vikram S. Deshpande, Lorenzo Valdevit

We develop a numerical methodology for the calculation of mode-I R-curves of brittle and elastoplastic lattice materials, and unveil the impact of lattice topology, relative density and constituent material behavior on the toughening response of 2D isotropic lattices. The approach is based on finite element calculations of the J-integral on a single-edge-not

Chen-Yu Wei, Haipeng Luo, Alekh Agarwal

We initiate the study of learning in contextual bandits with the help of loss predictors. The main question we address is whether one can improve over the minimax regret $\mathcal{O}(\sqrt{T})$ for learning over $T$ rounds, when the total error of the predictor $\mathcal{E} \leq T$ is relatively small. We provide a complete answer to this question, including

Carsten Schneider

In (S.B. Ekhad and D. Zeilberger, 2020) an exciting case study has been initiated in which experimental mathematics and symbolic computation are utilized to discover new properties concerning the so-called Absent-Minded Passengers Problem. Based on these results, Doron Zeilberger raised some challenging tasks to gain further probabilistic insight. In this no

Jinhyeok Jang, Dohyung Kim, Cheonshu Park, Minsu Jang

Deep learning, based on which many modern algorithms operate, is well known to be data-hungry. In particular, the datasets appropriate for the intended application are difficult to obtain. To cope with this situation, we introduce a new dataset called ETRI-Activity3D, focusing on the daily activities of the elderly in robot-view. The major characteristics of

Helmut Prodinger

A variation of Dyck paths allows for down-steps of arbitrary length, not just one. Credits for this invention are given to Emeric Deutsch. Surprisingly, the enumeration of them is somewhat akin to the analysis of Motzkin-paths; the last section contains a bijection.

Nathan F. Lepora, John Lloyd

This article illustrates the application of deep learning to robot touch by considering a basic yet fundamental capability: estimating the relative pose of part of an object in contact with a tactile sensor. We begin by surveying deep learning applied to tactile robotics, focussing on optical tactile sensors, which help bridge from deep learning for vision t

Debasish Pattanayak, Klaus-Tycho Foerster, Partha Sarathi Mandal, Stefan Schmid

Pattern formation is one of the most fundamental problems in distributed computing, which has recently received much attention. In this paper, we initiate the study of distributed pattern formation in situations when some robots can be \textit{faulty}. In particular, we consider the well-established \textit{look-compute-move} model with oblivious, anonymous

Nobuchika Okada, Satomi Okada, Qaisar Shafi

We consider a U(1)$_X$ gauge symmetry extension of the Standard Model (SM) with a $Z^\prime$-portal Majorana fermion dark matter that allows for a relatively light gauge boson $Z^\prime$ with mass of 10 MeV$-$ a few GeV and a much heavier dark matter through the freeze-in mechanism. In a second scenario the roles are reversed, and the dark matter mass, in th

Jitin Krishnan, Hemant Purohit, Huzefa Rangwala

During the onset of a disaster event, filtering relevant information from the social web data is challenging due to its sparse availability and practical limitations in labeling datasets of an ongoing crisis. In this paper, we hypothesize that unsupervised domain adaptation through multi-task learning can be a useful framework to leverage data from past cris

Arkadipta Sarkar, Alok C. Gupta, Varsha R. Chitnis, Paul J. Wiita

We report the detection ($>4σ$) of a Quasi-Periodic Oscillation (QPO) in the $γ$-ray light curve of 3C 454.3 along with a simultaneous marginal QPO detection ($>2.4σ$) in the optical light curves. Periodic flux modulations were detected in both of these wavebands with a dominant period of $\sim 47$ days. The $γ$-ray QPO lasted for over 450 days (from MJD 568

Xuemei Gu, Lijun Chen, Mario Krenn

We introduce the concept of hypergraphs to describe quantum optical experiments with probabilistic multi-photon sources. Every hyperedge represents a correlated photon source, and every vertex stands for an optical output path. Such general graph description provides new insights for producing complex high-dimensional multi-photon quantum entangled states, w

Amin Azizi, Mehmet Dogan, Hu Long, Jeffrey D. Cain

The development of room-temperature sensing devices for detecting small concentrations of molecular species is imperative for a wide range of low-power sensor applications. We demonstrate a room-temperature, highly sensitive, selective, and reversible chemical sensor based on a monolayer of the transition metal dichalcogenide Re0.5Nb0.5S2. The sensing device

Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor

We present a method for provably defending any pretrained image classifier against $\ell_p$ adversarial attacks. This method, for instance, allows public vision API providers and users to seamlessly convert pretrained non-robust classification services into provably robust ones. By prepending a custom-trained denoiser to any off-the-shelf image classifier an

Akshay Subramaniam, Man Long Wong, Raunak D Borker, Sravya Nimmagadda

Generative Adversarial Networks (GANs) have been widely used for generating photo-realistic images. A variant of GANs called super-resolution GAN (SRGAN) has already been used successfully for image super-resolution where low resolution images can be upsampled to a $4\times$ larger image that is perceptually more realistic. However, when such generative mode

Yulin Shao, Soung Chang Liew, Jiaxin Liang

Life-critical warning message, abbreviated as warning message, is a special event-driven message that carries emergency warning information in Vehicle-to-Everything (V2X). Three important characteristics that distinguish warning messages from ordinary vehicular messages are sporadicity, crowding, and ultra-time-criticality. In other words, warning messages c

Pavel V. Kolesnichenko, Qianhui Zhang, Changxi Zheng, Michael S. Fuhrer

Despite 2D materials holding great promise for a broad range of applications, the proliferation of devices and their fulfillment of real-life demands are still far from being realized. Experimentally obtainable samples commonly experience a wide range of perturbations (ripples and wrinkles, point and line defects, grain boundaries, strain field, doping, wate

James Aspnes

Lecture notes for the Yale Computer Science course CPSC 4690/5690 Randomized Algorithms. Suitable for use as a supplementary text for an introductory graduate or advanced undergraduate course on randomized algorithms. Discusses tools from probability theory, including random variables and expectations, union bound arguments, concentration bounds, application

Zhengyang Guo, Yi Li

We study the problem of minimum enclosing rectangle with outliers, which asks to find, for a given set of $n$ planar points, a rectangle with minimum area that encloses at least $(n-t)$ points. The uncovered points are regarded as outliers. We present an exact algorithm with $O(kt^3+ktn+n^2\log n)$ runtime, assuming that no three points lie on the same line.

Phebe Vayanos, Yingxiao Ye, Duncan McElfresh, John Dickerson

We study the problem of eliciting the preferences of a decision-maker through a moderate number of pairwise comparison queries to make them a high quality recommendation for a specific problem. We are motivated by applications in high stakes domains, such as when choosing a policy for allocating scarce resources to satisfy basic needs (e.g., kidneys for tran

Guo-Jie Jason Gao, Fu-Ling Yang, Michael C. Holcomb, Jerzy Blawzdziewicz

We apply a holistic 2D Tetris-like model, where particles move based on prescribed rules, to investigate the flow rate enhancement from a hopper. This phenomenon was originally reported in the literature as a feature of placing an obstacle at an optimal location near the exit of a hopper discharging athermal granular particles under gravity. We find that thi

Preetum Nakkiran, Prayaag Venkat, Sham Kakade, Tengyu Ma

Recent empirical and theoretical studies have shown that many learning algorithms -- from linear regression to neural networks -- can have test performance that is non-monotonic in quantities such the sample size and model size. This striking phenomenon, often referred to as "double descent", has raised questions of if we need to re-think our current