December 2020 arXiv papers — page 37

Xinwei Zhang, Wotao Yin, Mingyi Hong, Tianyi Chen

Federated learning (FL) is a recently proposed distributed machine learning paradigm dealing with distributed and private data sets. Based on the data partition pattern, FL is often categorized into horizontal, vertical, and hybrid settings. Despite the fact that many works have been developed for the first two approaches, the hybrid FL setting (which deals

Yassine Maalej, Elyes Balti

This paper presents a novel Advanced Activity-Aware (AAA) scheme to optimize and improve Multi-Channel Operations based on the IEEE 1609.4 standard in wireless access vehicular environments (WAVE). The proposed scheme relies on the awareness of the vehicular safety load to dynamically find an optimal setup for switching between service channel intervals (SCH

Xingyi Yang

Deep neural networks (DNN) are typically optimized using stochastic gradient descent (SGD). However, the estimation of the gradient using stochastic samples tends to be noisy and unreliable, resulting in large gradient variance and bad convergence. In this paper, we propose \textbf{Filter Gradient Decent}~(FGD), an efficient stochastic optimization algorithm

Sebastien Vievard, Steven P. Bos, Frederic Cassaing, Thayne Currie

Focal plane wavefront sensing is an elegant solution for wavefront sensing since near-focal images of any source taken by a detector show distortions in the presence of aberrations. Non-Common Path Aberrations and the Low Wind Effect both have the ability to limit the achievable contrast of the finest coronagraphs coupled with the best extreme adaptive optic

Sébastien Vievard, Elsa Huby, Sylvestre Lacour, Kevin Barjot

FIRST, the Fibered Imager foR a Single Telescope, is a spectro-imager using single-mode fibers for pupil remapping, allowing measurements beyond the telescope diffraction limit. Integrated on the Subaru Coronagraphic Extreme Adaptive Optics instrument at the Subaru Telescope, it benefits from a very stable visible light wavefront allowing to acquire long exp

Tianshi Li, Camille Cobb, Jackie, Yang

Smartphone-based contact-tracing apps are a promising solution to help scale up the conventional contact-tracing process. However, low adoption rates have become a major issue that prevents these apps from achieving their full potential. In this paper, we present a national-scale survey experiment ($N = 1963$) in the U.S. to investigate the effects of app de

Wentao Yuan, Zhaoyang Lv, Tanner Schmidt, Steven Lovegrove

We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotation. Recent work has shown that neural networks are surprisingly effective at the task of compressing many views of a scene into a learned function which maps from a viewing ray to

Jie Yu, Saskia Hekker, Timothy R. Bedding, Dennis Stello

Mass loss by red giants is an important process to understand the final stages of stellar evolution and the chemical enrichment of the interstellar medium. Mass-loss rates are thought to be controlled by pulsation-enhanced dust-driven outflows. Here we investigate the relationships between mass loss, pulsations, and radiation, using 3213 luminous Kepler red

Jelica Vasiljević, Friedrich Feuerhake, Cédric Wemmert, Thomas Lampert

The application of supervised deep learning methods in digital pathology is limited due to their sensitivity to domain shift. Digital Pathology is an area prone to high variability due to many sources, including the common practice of evaluating several consecutive tissue sections stained with different staining protocols. Obtaining labels for each stain is

Gerard McCaul, Andreas Mershin, Denys I. Bondar

In this work we consider the question of whether a simple diffusive model can explain the scent tracking behaviours found in nature. For this behaviour to occur, both the concentration of a scent and its gradient must be above some threshold. Applying these conditions to the solutions of various diffusion equations, we find that a purely diffusive model cann

Ilya G. Ovodov

This paper proposes an optical Braille recognition method that uses an object detection convolutional neural network to detect whole Braille characters at once. The proposed algorithm is robust to the deformation of the page shown in the image and perspective distortions. It makes it usable for recognition of Braille texts being shoot on a smartphone camera,

Rob B. N. Scharff, Guoxin Fang, Yingjun Tian, Jun Wu

Real-time proprioception is a challenging problem for soft robots, which have almost infinite degrees-of-freedom in body deformation. When multiple actuators are used, it becomes more difficult as deformation can also occur on actuators caused by interaction between each other. To tackle this problem, we present a method in this paper to sense and reconstruc

Benjamin Maas, Erfan Zabeh, Soroush Arabshahi

Non-invasive techniques such as magnetic resonance imaging (MRI) are widely employed in brain tumor diagnostics. However, manual segmentation of brain tumors from 3D MRI volumes is a time-consuming task that requires trained expert radiologists. Due to the subjectivity of manual segmentation, there is low inter-rater reliability which can result in diagnosti

Michal Zajaček, Bożena Czerny, Mary Loli Martinez-Aldama, Mateusz Rałowski

Using the six years of the spectroscopic monitoring of the luminous quasar HE 0435-4312 ($z=1.2231$) with the Southern African Large Telescope (SALT), in combination with the photometric data (CATALINA, OGLE, SALTICAM, and BMT), we determined the rest-frame time-delay of $296^{+13}_{-14}$ days between the MgII broad-line emission and the ionizing continuum u

Shahid Sattar, J. Andreas Larsson

The possibility to achieve charge-to-spin conversion via Rashba spin-orbit effects provide stimulating opportunities toward the development of nanoscale spintronics. Here we use first-principles calculations to study the electronic and spintronic properties of Tl$_2$O/PtS$_2$ heterostructure, for which we have confirmed the dynamical stability by its positiv

E. C. Shaw, P. A. R. Ade, S. Akers, M. Amiri

In this work we describe upgrades to the Spider balloon-borne telescope in preparation for its second flight, currently planned for December 2021. The Spider instrument is optimized to search for a primordial B-mode polarization signature in the cosmic microwave background at degree angular scales. During its first flight in 2015, Spider mapped ~10% of the s

Kevin A. Thomas, Dominik Krzemiński, Łukasz Kidziński, Rohan Paul

Objective: We evaluate a fully-automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin echo (MESE) MRI. We have open sourced this model and corresponding segmentations. Methods: We trained a neural network to segment femoral cartilage from MESE MRIs. Cartilage was divided into 12 subreg

Vasili K. Semenov, Evan B. Golden, Sergey K. Tolpygo

Superconductor electronics fabrication technology developed at MIT Lincoln Laboratory enables the development of VLSI digital circuits with millions of Josephson junctions per square centimeter. However, conventional DC and multi-phase AC biasing techniques already encounter serious challenges for scaling circuits above several hundred thousand junctions. In

Uğur Teğin, Mustafa Yıldırım, İlker Oğuz, Christophe Moser

Today's heavy machine learning tasks are fueled by large datasets. Computing is performed with power hungry processors whose performance is ultimately limited by the data transfer to and from memory. Optics is one of the powerful means of communicating and processing information and there is intense current interest in optical information processing for real

Savva Morozov, Parker C. Lusk, Brett T. Lopez, Jonathan P. How

Model predictive control (MPC) is an effective method for control of constrained systems but is susceptible to the external disturbances and modeling error often encountered in real-world applications. To address these issues, techniques such as Tube MPC (TMPC) utilize an ancillary offline-generated robust controller to ensure that the system remains within

Yun Chen, Bin Yang, Ming Liang, Raquel Urtasun

In this paper, we tackle the problem of depth completion from RGBD data. Towards this goal, we design a simple yet effective neural network block that learns to extract joint 2D and 3D features. Specifically, the block consists of two domain-specific sub-networks that apply 2D convolution on image pixels and continuous convolution on 3D points, with their ou

Sonal Doomra, Naman Kohli, Shounak Athavale

Driving etiquette takes a different flavor for each locality as drivers not only comply with rules/laws but also abide by local unspoken convention. When to have the turn signal (indicator) on/off is one such etiquette which does not have a definitive right or wrong answer. Learning this behavior from the abundance of data generated from various sensor modal

Chen Shen, Derrick Van Gennep, Alexander F. Siegenfeld, Yaneer Bar-Yam

The infection fatality rate (IFR) of COVID-19 is one of the measures of disease impact that can be of importance for policy making. Here we show that many of the studies on which these estimates are based are scientifically flawed for reasons which include: nonsensical equations, unjustified assumptions, small sample sizes, non-representative sampling (syste

Shuzhou Wang, Zhenhua Wang

In this paper, we investigate the relative operator entropies in the more general settings of C*-algebras, real C*-algebras and JC-algebras. We show that all the operator inequalities on relative operator entropies still hold in these broader settings. In addition, we improve the lower and upper bounds of the relative operator $(\alpha, \beta)$-entropy estab

F. S. Cuesta, G. A. Ptitcyn, M. S. Mirmoosa, S. A. Tretyakov

Inhomogeneous metasurfaces have shown possibilities for unprecedented control of wave propagation and scattering. While it is conventional to shine a single incident plane wave from one side of these metastructures, illuminating by several waves simultaneously from both sides may enhance possibilities to control scattered waves, which results in additional f

Valentin Goranko

I consider the following generic scenario: an abstract model M of some 'real' system is only partially presented, or partially known to us, and we have to ensure that the actual system satisfies a given specification, formalised in some logical language. This scenario has at least two essentially different interpretations, leading to two, essentially differe

Ming Liang, Bin Yang, Yun Chen, Rui Hu

In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. Our experiments show that all these tasks are complementary and help the network learn bett

P. Maris, E. Epelbaum, R. J. Furnstahl, J. Golak

We present a systematic investigation of few-nucleon systems and light nuclei using the current LENPIC interactions comprising semilocal momentum-space regularized two- and three-nucleon forces up to third chiral order (N$^2$LO). Following our earlier study utilizing the coordinate-space regularized interactions, the two low-energy constants entering the thr

Wenjie Luo, Bin Yang, Raquel Urtasun

In this paper we propose a novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor. By jointly reasoning about these tasks, our holistic approach is more robust to occlusion as well as sparse data at range. Our approach performs 3D convolutions across space and time over

Stefano Giovanni Rizzo, Linsey Pang, Yixian Chen, Sanjay Chawla

A new method for outlier detection and generation is introduced by lifting data into the space of probability distributions which are not analytically expressible, but from which samples can be drawn using a neural generator. Given a mixture of unknown latent inlier and outlier distributions, a Wasserstein double autoencoder is used to both detect and genera

Lorenzo Gotta, Leonardo Mazza, Pascal Simon, Guillaume Roux

We show that a simple one-dimensional model of spinless fermions with pair hopping displays a phase in which a Luttinger liquid of paired fermions coexists with a Luttinger liquid of unpaired fermions. Our results are based on extensive numerical density-matrix renormalisation group calculations and are supported by a two-fluid model that captures the essenc

R. Hložek, K. A. Ponder, A. I. Malz, M. Dai

Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition which

Claudio Cacciapuoti, Domenico Finco, Diego Noja

We study the well posedness of the nonlinear Schr\"odinger (NLS) equation with a point interaction and power nonlinearity in dimension two and three. Behind the autonomous interest of the problem, this is a model of the evolution of so called singular solutions that are well known in the analysis of semilinear elliptic equations. We show that the Cauchy prob

Jonathan Wells, Chris Robertson, Vincent Marmara, Alan Yeung

Faced with the 2020 SARS-CoV2 epidemic, public health officials have been seeking models that could be used to predict not only the number of new cases but also the levels of hospitalisation, critical care and deaths. In this paper we present a stochastic compartmental model capable of real-time monitoring and forecasting of the pandemic incorporating multip

Shahrokh Hamidi, Safieddin Safavi-Naeini

In this paper, we present a high resolution microwave imaging technique using a compact and low cost single channel Frequency Modulated Continuous Wave (FMCW) radar based on Circular Synthetic Aperture Radar (CSAR) technique. We develop an algorithm to reconstruct the image from the raw data and analyse different aspects of the system analytically. Furthermo

I-Hsuan Kao, Ryan Muzzio, Hantao Zhang, Menglin Zhu

Spin-orbit torque (SOT) driven deterministic control of the magnetization state of a magnet with perpendicular magnetic anisotropy (PMA) is key to next generation spintronic applications including non-volatile, ultrafast, and energy efficient data storage devices. But, field-free deterministic switching of perpendicular magnetization remains a challenge beca

Ronghuai Qi, Hamed Jamshidifar, Amir Khajepour

Cable-driven parallel robots (CDPRs) are widely investigated and applied in the worldwide; however, traditional configurations make them to be limited in reaching their maximum workspace duo to constraints such as the maximum allowable tensions of cables. In this paper, we introduce auxiliary counterbalances to tackle this problem and focus on workspace anal

Michele Bonnin, Fabio Lorenzo Traversa, Fabrizio Bonani

The frenetic growth of the need for computation performance and efficiency, along with the intrinsic limitations of the current main solutions, is pushing the scientific community towards unconventional, and sometimes even exotic, alternatives to the standard computing architectures. In this work we provide a panorama of the most relevant alternatives, both

L. G. Gheorghe

We describe all triangles that shares either circumcircle and pedal circle or circumcircle and negative-pedal circle. Neither of these pairs is poristic; nevertheless, the negative-pedal curve of the pedal-circle is a (very) special i-conic that points toward a poristic solution. Subsequently, other poristic pairs show up and the choreography swiftly begins.

Valeri Alexiev

Developing a robust generalization measure for the performance of machine learning models is an important and challenging task. A lot of recent research in the area focuses on the model decision boundary when predicting generalization. In this paper, as part of the "Predicting Generalization in Deep Learning" competition, we analyse the complexity of decisio

Hang Wang, Sen Lin, Hamid Jafarkhani, Junshan Zhang

This paper studies distributed Q-learning for Linear Quadratic Regulator (LQR) in a multi-agent network. The existing results often assume that agents can observe the global system state, which may be infeasible in large-scale systems due to privacy concerns or communication constraints. In this work, we consider a setting with unknown system models and no c

Reno Kriz, Marianna Apidianaki, Chris Callison-Burch

Text simplification systems generate versions of texts that are easier to understand for a broader audience. The quality of simplified texts is generally estimated using metrics that compare to human references, which can be difficult to obtain. We propose Simple-QE, a BERT-based quality estimation (QE) model adapted from prior summarization QE work, and sho

Onur Mutlu

Computing is bottlenecked by data. Large amounts of application data overwhelm storage capability, communication capability, and computation capability of the modern machines we design today. We argue that an intelligent architecture should be designed to handle data well. We show that handling data well requires designing architectures based on three key pr

Hongkai Zhao, Yimin Zhong

The photoacoustic tomography (PAT) is a hybrid modality that combines the optics and acoustics to obtain high resolution and high contrast imaging of heterogeneous media. In this work, our objective is to study the inverse problem in the quantitative step of PAT which aims to reconstruct the optical coefficients of the governing radiative transport equation

Mohammed Bouhassoune, Samir Lounis

Magnetic skyrmions are spin swirling solitonic defects that can play a major role in information technology. Their future in applications and devices hinges on their efficient manipulation and detection. Here, we explore from ab-initio their nature as magnetic inhomongeities in an otherwise unperturbed magnetic material, Fe layer covered by a thin Pd film an

Ivana Orlitova

The rate of star formation varies between galaxy types and evolves with redshift. Most stars in the universe have formed in episodes of an exceptionally high star-forming activity, commonly called a starburst. We here summarize basic definitions and general properties of starbursts, together with their observational signatures.We overviewthe main types of st

Namdar Homayounfar, Wei-Chiu Ma, Justin Liang, Xinyu Wu

One of the fundamental challenges to scale self-driving is being able to create accurate high definition maps (HD maps) with low cost. Current attempts to automate this process typically focus on simple scenarios, estimate independent maps per frame or do not have the level of precision required by modern self driving vehicles. In contrast, in this paper we

Catherine Babecki

Graphical designs are an extension of spherical designs to functions on graphs. We connect linear codes to graphical designs on cube graphs, and show that the Hamming code in particular is a highly effective graphical design. We show that even in highly structured graphs, graphical designs are distinct from the related concepts of extremal designs, maximum s

Magdalena Solà-Garcia, Kelly W. Mauser, Matthias Liebtrau, Toon Coenen

Photon bunching in incoherent cathodoluminescence (CL) spectroscopy originates from the fact that a single high-energy electron can generate multiple photons when interacting with a material, thus revealing key properties of electron-matter excitation. Contrary to previous works based on Monte-Carlo modelling, here we present a fully analytical model describ

Hengguang Li, Peimeng Yin, Zhimin Zhang

In this paper, we study the biharmonic equation with the Navier boundary conditions in a polygonal domain. In particular, we propose a method that effectively decouples the 4th-order problem into a system of Poisson equations. Different from the usual mixed method that leads to two Poisson problems but only applies to convex domains, the proposed decompositi

Chunjin Song, Yuchi Zhang, Willis Peng, Parmis Mohaghegh

A long-standing goal in the field of sensory substitution is to enable sound perception for deaf and hard of hearing (DHH) people by visualizing audio content. Different from existing models that translate to hand sign language, between speech and text, or text and images, we target immediate and low-level audio to video translation that applies to generic e

Riccardo Pinciroli, Lishan Yang, Jacob Alter, Evgenia Smirni

Data center downtime typically centers around IT equipment failure. Storage devices are the most frequently failing components in data centers. We present a comparative study of hard disk drives (HDDs) and solid state drives (SSDs) that constitute the typical storage in data centers. Using a six-year field data of 100,000 HDDs of different models from the sa

Charlie Halberg, Hemanshu Kaul, Andrew Liu, Jeffrey A. Mudrock

DP-coloring (also called correspondence coloring) is a generalization of list coloring that has been widely studied in recent years after its introduction by Dvo\v{r}\'{a}k and Postle in 2015. As the analogue of the chromatic polynomial $P(G,m)$, the DP color function of a graph $G$, denoted $P_{DP}(G,m)$, counts the minimum number of DP-colorings over all p

Nicola Bellomo, Diletta Burini, Giovanni Dosi, Livio Gibelli

This paper is a first step to chase the ambitious objective of developing a mathmatical theory of living systems. The contents refer modeling large systems of interacting living entities with the aim of describing their collective behaviors by differential models. The contents is in three parts. Firstly, we derive the mathematical method; subsequently, we sh

Iryna Egorova, Johanna Michor

We rigorously derive the long-time asymptotics of the Toda shock wave in a middle region where the solution is asymptotically finite gap. In particular, we describe the influence of the discrete spectrum in the spectral gap on the shift of the phase in the theta-function representation for this solution. We also study the effect of possible resonances at the

Hengguang Li, Xiang Wan, Peimeng Yin, Lewei Zhao

We study the elliptic equation with a line Dirac delta function as the source term subject to the Dirichlet boundary condition in a two-dimensional domain. Such a line Dirac measure causes different types of solution singularities in the neighborhood of the line fracture. We establish new regularity results for the solution in a class of weighted Sobolev spa

Daniel Lemire, Colin Bartlett, Owen Kaser

The integer division of a numerator n by a divisor d gives a quotient q and a remainder r. Optimizing compilers accelerate software by replacing the division of n by d with the division of c * n (or c * n + c) by m for convenient integers c and m chosen so that they approximate the reciprocal: c/m ~= 1/d. Such techniques are especially advantageous when m is

Theodoros Tsiligkaridis, Jay Roberts

Deep neural networks are easily fooled by small perturbations known as adversarial attacks. Adversarial Training (AT) is a technique that approximately solves a robust optimization problem to minimize the worst-case loss and is widely regarded as the most effective defense. Due to the high computation time for generating strong adversarial examples in the AT

Soumyadip Ghosh, Mark Squillante

Seeking to improve model generalization, we consider a new approach based on distributionally robust learning (DRL) that applies stochastic gradient descent to the outer minimization problem. Our algorithm efficiently estimates the gradient of the inner maximization problem through multi-level Monte Carlo randomization. Leveraging theoretical results that sh

Dongsheng Wang, Casper Hansen, Lucas Chaves Lima, Christian Hansen

The state of the art in learning meaningful semantic representations of words is the Transformer model and its attention mechanisms. Simply put, the attention mechanisms learn to attend to specific parts of the input dispensing recurrence and convolutions. While some of the learned attention heads have been found to play linguistically interpretable roles, t

Lina Reichenberg, Fredrik Hedenus, Niclas Mattsson, Vilhelm Verendel

Long distance transmission within continents has been shown to be one of the most important variation management strategies in renewable energy systems, where allowing for transmission expansion will reduce system cost by around 20%. In this paper, we test whether the system cost further decreases when transmission is extended to intercontinental connections

Samuel C. Gutekunst, David P. Williamson

Facet-defining inequalities of the symmetric Traveling Salesman Problem (TSP) polytope play a prominent role in both polyhedral TSP research and state-of-the-art TSP solvers. In this paper, we introduce a new class of facet-defining inequalities, the \emph{circlet inequalities}. These inequalities were first conjectured in Gutekunst and Williamson \cite{Gut1

Anatoly Konechny

We consider RG interfaces for boundary RG flows in two-dimensional QFTs. Such interfaces are particular boundary condition changing operators linking the UV and IR conformal boundary conditions. We refer to them as RG operators. In this paper we study their general properties putting forward a number of conjectures. We conjecture that an RG operator is alway

Hunter Blanton, Scott Workman, Nathan Jacobs

Estimating camera pose from a single image is a fundamental problem in computer vision. Existing methods for solving this task fall into two distinct categories, which we refer to as direct and indirect. Direct methods, such as PoseNet, regress pose from the image as a fixed function, for example using a feed-forward convolutional network. Such methods are d

Paulo Carrillo Rouse, Bai-Ling Wang, Hang Wang

We give a complete solution, for discrete countable groups, to the problem of defining and computing a geometric pairing between the left hand side of the Baum-Connes assembly map, given in terms of geometric cycles associated to proper actions on manifolds, and cyclic periodic cohomology of the group algebra. Indeed, for any such group $\Gamma$ (without any

Pieter Collins

The aim of this paper is to present an elementary computable theory of random variables, based on the approach to probability via valuations. The theory is based on a type of lower-measurable sets, which are controlled limits of open sets, and extends existing work in this area by providing a computable theory of conditional random variables. The theory is b

M. Bousder, Z. Sakhi, M. Bennai

We propose a new unified model that describes~dark energy and dark matter in the context of $f(R,\phi )$ gravity using a massive scalar field in five dimensions. The scalar field is considered in the bulk that surrounds the 3-brane in branworld model. We show that for a specific choice of the $% f(R,\phi )$ function, we can describe the Einstein gravitation

Igor Leite Freire

Lower order conservation laws and symmetries of a family of hyperbolic equations having the Camassa-Holm equation as a particular member are obtained. We show that the equation has two conservation laws with zeroth order characteristics and that its symmetries are generated by translations in the independent variables and a certain scaling, as well as some i

Qing Ye, Weijun Xie

As an important problem in modern data analytics, classification has witnessed varieties of applications from different domains. Different from conventional classification approaches, fair classification concerns the issues of unintentional biases against the sensitive features (e.g., gender, race). Due to high nonconvexity of fairness measures, existing met

F. Hammad, P. Sadeghi, N. Fleury, A. Leblanc

It is well known that the Klein-Gordon equation in curved spacetime is conformally noninvariant, both with and without a mass term. We show that such a noninvariance provides nontrivial physical insights at different levels, first within the fully relativistic regime, then in the nonrelativistic regime leading to the Schr\"odinger equation, and then within t

Siamak Dadras, Robert M. Pettit, Danika R. Luntz-Martin, Kewen Xiao

We report on the injection locking of an optically levitated nanomechanical oscillator (a silica nanosphere) to resonant intensity modulations of an external optical signal. We explore the characteristic features of injection locking in this system, e.g. the phase pull-in effect and the injection-induced reduction of the oscillation linewidth. Our measuremen

Ehud de Shalit, José Gutiérrez

This is a largely expository paper, providing a self-contained account on the results of [Sch-Si1, Sch-Si2], in the cases denoted there 2Q and 2M. These papers of Sch\"afke and Singer supplied new proofs to the main theorems of [Bez-Bou, Ad-Be], on the rationality of power series satisfying a pair of independent q-difference, or Mahler, equations. We emphasi

Letitia Parcalabescu, Albert Gatt, Anette Frank, Iacer Calixto

We investigate the reasoning ability of pretrained vision and language (V&L) models in two tasks that require multimodal integration: (1) discriminating a correct image-sentence pair from an incorrect one, and (2) counting entities in an image. We evaluate three pretrained V&L models on these tasks: ViLBERT, ViLBERT 12-in-1 and LXMERT, in zero-shot and finet

Sung Chan Choi

Parrondo games with spatial dependence were introduced by Toral (2001) and have been studied extensively. In Toral's model, $N$ players are arranged in a circle. The players play either game $A$ or game $B$. In game $A$, a randomly chosen player wins or loses one unit according to the toss of a fair coin. In game $B$, which depends on parameters $p_0,p_1,p_2

Ming Dong

Today, the adoption of new technologies has increased power system dynamics significantly. Traditional long-term planning studies that most utility companies perform based on discrete power levels such as peak or average values cannot reflect system dynamics and often fail to accurately predict system reliability deficiencies. As a result, long-term future c

Erfan Nozari, Maxwell A. Bertolero, Jennifer Stiso, Lorenzo Caciagli

A central challenge in the computational modeling of neural dynamics is the trade-off between accuracy and simplicity. At the level of individual neurons, nonlinear dynamics are both experimentally established and essential for neuronal functioning. An implicit assumption has thus formed that an accurate computational model of whole-brain dynamics must also

Zecheng He, Srinivas Sunkara, Xiaoxue Zang, Ying Xu

As mobile devices are becoming ubiquitous, regularly interacting with a variety of user interfaces (UIs) is a common aspect of daily life for many people. To improve the accessibility of these devices and to enable their usage in a variety of settings, building models that can assist users and accomplish tasks through the UI is vitally important. However, th

Giacomo M. Leccese, Valentino Magnani

We present a complete study of measure-theoretic area formulas in metric spaces, providing different measurability conditions.

Christian Beck, Martin Hutzenthaler, Arnulf Jentzen, Benno Kuckuck

It is one of the most challenging problems in applied mathematics to approximatively solve high-dimensional partial differential equations (PDEs). Recently, several deep learning-based approximation algorithms for attacking this problem have been proposed and tested numerically on a number of examples of high-dimensional PDEs. This has given rise to a lively

Ojas Parekh, Kevin Thompson

The quantum k-Local Hamiltonian problem is a natural generalization of classical constraint satisfaction problems (k-CSP) and is complete for QMA, a quantum analog of NP. Although the complexity of k-Local Hamiltonian problems has been well studied, only a handful of approximation results are known. For Max 2-Local Hamiltonian where each term is a rank 3 pro

Riu Naito, Toshihiro Yamada

The paper introduces a very simple and fast computation method for high-dimensional integrals to solve high-dimensional Kolmogorov partial differential equations (PDEs). The new machine learning-based method is obtained by solving a stochastic weighted minimization with stochastic gradient descent which is inspired by a high-order weak approximation scheme f

Sung Chan Choi

Game theory has been applied in many fields of study, especially economics and political science. Arce M. and Sandler (2005) analyzed counter-terrorism using non-cooperative game theory (the players are, for example, the US and the EU), which assumes that communication among the players is not allowed, or, if it is allowed, then there is no mechanism to enfo

Geovani Rizk, Igor Colin, Albert Thomas, Moez Draief

Experimental design is an approach for selecting samples among a given set so as to obtain the best estimator for a given criterion. In the context of linear regression, several optimal designs have been derived, each associated with a different criterion: mean square error, robustness, \emph{etc}. Computing such designs is generally an NP-hard problem and o

José Luis Sainz-Pardo, José Valero

The experience of Singapur and South Korea makes it clear that under certain circumstances massive testing is an effective way for containing the advance of the COVID-19. In this paper, we propose a modified SEIR model which takes into account tracing and massive testing, proving theoretically that more tracing and testing implies a reduction of the total nu

Damiano Archetti, Neophytos Neophytou

In this work we theoretically explore the effect of dimensionality on the thermoelectric power factor of InAs nanowires by coupling atomistic tight-binding calculations to the Linearized Boltzmann transport formalism. We consider nanowires with diameters from 40nm (bulk-like) down to 3nm (1D), which allows for the proper exploration of the power factor withi

I. Takyi, B. Barnes, J. Ackora-Prah

We compute the one-loop quantum corrections to the kink energies of the sinh-deformed $\phi^{4}$ and $\varphi^{6}$ models in one space and one time dimensions. These models are constructed from the well-known polynomial $\phi^{4}$ and $\varphi^{6}$ models by a deformation procedure. We also compute the vacuum polarization energy to the non-polynomial functio

Aleksey Cherman, Aditya Dhumuntarao

Gauge theories with confining phases at low temperatures tend to deconfine at high temperatures. In some cases, for example in supersymmetric theories, confinement can persist for all temperatures provided the partition function includes a grading by $(-1)^F$. When it is possible to define partition functions which smoothly interpolate between no grading and

Jiaming Wang, Weishi Yuan, Takashi Imai, Philip M. Singer

We investigate the structural and magnetic properties of a Kitaev spin liquid candidate material Ag$_3$LiIr$_2$O$_6$ based on $^7$Li nuclear magnetic resonance line shape, Knight shift and spin-lattice relaxation rate $1/T_1$. The first sample A shows signatures of magnetically ordered spins, and exhibits one sharp $^7$Li peak with FWHM increasing significan

Lee Whieldon, Huthaifa Ashqar

Predicting Residential Property Value in Catonsville, Maryland: A Comparison of Multiple Regression Techniques

Marcel K. Goh, Rosie Y. Zhao

For a finite set $A$ of size $n$, an ordering is an injection from $\{1,2,\ldots,n\}$ to $A$. We present results concerning the asymptotic properties of the length $L_n$ of the longest arithmetic subsequence in a random ordering of an additive set $A$. In the torsion-free case where $A = [1,n]^d\subseteq {\bf Z}^d$, we prove that $L_n\sim 2d\log n/\log\log n

Danko Georgiev, Leon Bello, Avishy Carmi, Eliahu Cohen

Complementarity between one- and two-particle visibility in discrete systems can be extended to bipartite quantum-entangled Gaussian states. The meaning of the two-particle visibility originally defined by Jaeger, Horne, Shimony, and Vaidman with the use of an indirect method that first corrects the two-particle probability distribution by adding and subtrac

Jan Greve, Bettina Grün, Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter

Cluster analysis aims at partitioning data into groups or clusters. In applications, it is common to deal with problems where the number of clusters is unknown. Bayesian mixture models employed in such applications usually specify a flexible prior that takes into account the uncertainty with respect to the number of clusters. However, a major empirical chall

Nathan Tsoi, Mohamed Hussein, Olivia Fugikawa, J. D. Zhao

Evaluation of social robot navigation inherently requires human input due to its qualitative nature. Motivated by the need to scale human evaluation, we propose a general method for deploying interactive, rich-client robotic simulations on the web. Prior approaches implement specific web-compatible simulators or provide tools to build a simulator for a speci

Carlos Núñez-Molina, Vladislav Nikolov, Ignacio Vellido, Juan Fernández-Olivares

In this work we propose a goal reasoning method which learns to select subgoals with Deep Q-Learning in order to decrease the load of a planner when faced with scenarios with tight time restrictions, such as online execution systems. We have designed a CNN-based goal selection module and trained it on a standard video game environment, testing it on differen

Yunqiang Li, Jan van Gemert

Unsupervised hashing is important for indexing huge image or video collections without having expensive annotations available. Hashing aims to learn short binary codes for compact storage and efficient semantic retrieval. We propose an unsupervised deep hashing layer called Bi-half Net that maximizes entropy of the binary codes. Entropy is maximal when both

Petra Bačová, Eirini Gkolfi, Laurence G. D. Hawke, Vagelis Harmandaris

Star polymers can exhibit a heterogeneous dynamical behavior due to their internal structure. In this work we employ atomistic molecular dynamics simulations to study translational motion in non-entangled polystyrene and poly(ethylene oxide) star-shaped melts. We focus on the local heterogeneous dynamics originating from the multi-arm star-like architecture

David Nicolas Nenning, Armin Rainer, Gerhard Schindl

We prove sectorial extension theorems for ultraholomorphic function classes of Beurling type defined by weight functions with a controlled loss of regularity. The proofs are based on a reduction lemma, due to the second author, which allows to extract the Beurling from the Roumieu case, which was treated recently by Jim\'{e}nez-Garrido, Sanz, and the third a

Anja Dakić, Markus Hofer, Benjamin Rainer, Stefan Zelenbaba

Future automation and control units for advanced driver assistance systems (ADAS) will exchange sensor and kinematic data with nearby vehicles using wireless communication links to improve traffic safety. In this paper we present an accurate real-time system-level simulation for multi-vehicle communication scenarios to support the development and test of con

Ioan Bâldea

To quantify charge transport through molecular junctions fabricated using the conducting probe atomic force microscopy (CP-AFM) platform, information on the number of molecules $N$ per junction is absolutely necessary. $N$ can be currently obtained only via contact mechanics, and the Young's modulus $E$ of the self-assembled monolayer (SAM) utilized in the k

Ibrahim Yildirim, Fatih Kilinc, Ertugrul Basar, George C. Alexandropoulos

In this letter, we introduce two hybrid transmission schemes combining a passive reconfigurable intelligent surface (RIS) with decode-and-forward relaying in a synergistic manner. The proposed schemes offer a flexible as well as cost- and power-efficient solution for coverage extension in future generation wireless networks. We present closed-form expression