September 2020 arXiv papers — page 3

Daniele Garzoni

We prove that the invariably generating graph of a finite group can have an arbitrarily large number of connected components with at least two vertices.

Stephan Teichtmeister, Marc-Andre Keip

The paper presents a versatile framework for solids which undergo nonisothermal processes with irreversibly changing microstructure at large strains. It outlines rate-type and incremental variational principles for the full thermomechanical coupling in gradient-extended dissipative materials. It is shown that these principles yield as Euler equations essenti

Olivier Dauloudet, Izaak Neri, Jean-Charles Walter, Jérôme Dorignac

Translation is one of the main steps in the synthesis of proteins. It consists of ribosomes that translate sequences of nucleotides encoded on mRNA into polypeptide sequences of amino acids. Ribosomes bound to mRNA move unidirectionally, while unbound ribosomes diffuse in the cytoplasm. It has been hypothesized that finite diffusion of ribosomes plays an imp

D. Meyer, V. Roddatis, J. P. Bange, S. Lopatin

We present experimental and theoretical investigations of phonon thermal transport in (LaMnO$_3$)$_m$/(SrMnO$_3$)$_n$ superlattices (LMO/SMO SLs) with the thickness of individual layers $m,n = 3 - 10\;$ u.c. and the thickness ratio $m/n = 1, 2$. Optical transient thermal reflectivity measurements reveal a pronounced difference in the thermal conductivity bet

Vincent Bertin, Zaicheng Zhang, Rodolphe Boisgard, Christine Grauby-Heywang

We present contactless atomic-force microscopy measurements of the hydrodynamic interactions between a rigid sphere and an air bubble in water at the micro-scale. The size of the bubble is found to have a significant effect on the response due to the long-range capillary deformation of the air-water interface. To rationalize the experimental data, we develop

David Milec, Jakub Černý, Viliam Lisý, Bo An

Solution concepts of traditional game theory assume entirely rational players; therefore, their ability to exploit subrational opponents is limited. One type of subrationality that describes human behavior well is the quantal response. While there exist algorithms for computing solutions against quantal opponents, they either do not scale or may provide stra

Alberto Lanconelli, Ramiro Scorolli

We investigate some probabilistic aspects of the unique global strong solution of a two dimensional system of stochastic differential equations describing a prey-predator model perturbed by Gaussian noise. We first establish, for any fixed $t> 0$, almost sure upper and lower bounds for the components $X(t)$ and $Y(t)$ of the solution vector: these explicit e

Rob Klabbers, Jules Lamers

Three decades ago, Inozemtsev found an isotropic long-range spin chain with elliptic pair potential that interpolates between the Heisenberg and Haldane-Shastry (HS) spin chains while admitting an exact solution throughout, based on a connection with the elliptic quantum Calogero-Sutherland model. Though Inozemtsev's spin chain is widely believed to be q

Qiaoyun Wu, Xiaoxi Gong, Kai Xu, Dinesh Manocha

We present a target-driven navigation system to improve mapless visual navigation in indoor scenes. Our method takes a multi-view observation of a robot and a target as inputs at each time step to provide a sequence of actions that move the robot to the target without relying on odometry or GPS at runtime. The system is learned by optimizing a combinational

Eugene A. Koval, Oksana A. Koval

The impact of the short-range interaction on the resonances occurrence in the anisotropic dipolar scattering in a plane was numerically investigated for the arbitrarily oriented dipoles and for a wide range of collision energies. We revealed the strong dependence of the cross section of the 2D dipolar scattering on the radius of short-range interaction, whic

Feilong Zhang

In this brief, the current robust numerical solution to the inverse kinematics based on Levenberg-Marquardt (LM) method is reanalyzed through control theory instead of numerical method. Compared to current works, the robustness of computation and convergence performance of computational error are analyzed much more clearly by analyzing the control performanc

Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto

We give methods for Bayesian inference of directed acyclic graphs, DAGs, and the induced causal effects from passively observed complete data. Our methods build on a recent Markov chain Monte Carlo scheme for learning Bayesian networks, which enables efficient approximate sampling from the graph posterior, provided that each node is assigned a small number $

Pratik Joshi, Somak Aditya, Aalok Sathe, Monojit Choudhury

Pre-trained Transformer-based neural architectures have consistently achieved state-of-the-art performance in the Natural Language Inference (NLI) task. Since NLI examples encompass a variety of linguistic, logical, and reasoning phenomena, it remains unclear as to which specific concepts are learnt by the trained systems and where they can achieve strong ge

Thomas H. Geisser, Takashi Suzuki

The purpose of this paper is to give a formula for the leading coefficient at $s=1$ of the $L$-function of one-motives over function fields in terms of Weil-étale cohomology, generalizing the Weil-étale version of the Birch and Swinnerton-Dyer conjecture in the authors' previous work. As a consequence we express the Tamagawa number of a torus introduced

Ruishuang Liu, Weiwei Wan, Keisuke Koyama, Kensuke Harada

This paper develops a flexible and robust robotic system for autonomous drawing on 3D surfaces. The system takes 2D drawing strokes and a 3D target surface (mesh or point clouds) as input. It maps the 2D strokes onto the 3D surface and generates a robot motion to draw the mapped strokes using visual recognition, grasp pose reasoning, and motion planning. The

Nicolas Nagel, Martin Schäfer, Tino Ullrich

We provide a new upper bound for sampling numbers $(g_n)_{n\in \mathbb{N}}$ associated to the compact embedding of a separable reproducing kernel Hilbert space into the space of square integrable functions. There are universal constants $C,c>0$ (which are specified in the paper) such that $$ g^2_n \leq \frac{C\log(n)}{n}\sum\limits_{k\geq \lfloor cn \rfloor}

Kazuhiro Sato

We consider a reconstruction problem of a reduced stable positive network system with the preservation of the original interconnection structure based on an $H^2$ optimal model reduction problem with constraints. To this end, we define an important set using the Perron--Frobenius theory of nonnegative matrices such that all elements of the set are stable and

Anton Glushchenko, Vladislav Petrov, Konstantin Lastochkin

The scope of this research is a problem of parameters identification of a linear time-invariant (LTI) plant, which 1) input signal is not frequency-rich, 2) is subjected to initial conditions and external disturbances. The memory regressor extension (MRE) scheme, in which a specially derived differential equation is used as a filter, is applied to solve the

R. Desapogu, G. Le Marchand, R. Smith, P. Ray

The vast majority of the microscopy methods currently available to study biological samples require staining prior to imaging. Nevertheless, the ability to reveal specific cell structures or organelles in a label-free manner remains desirable in different contexts. Polarization microscopy has long been considered as an interesting alternative to fluorescence

Constantin Shramov, Vadim Vologodsky

We show the boundedness of finite subgroups in any anisotropic reductive algebraic group over a perfect field that contains all roots of 1. Also, we provide explicit bounds for orders of finite subgroups of automorphism groups of Severi-Brauer varieties and quadrics over such fields.

Zhonghua Liu, Ting Ye, Baoluo Sun, Mary Schooling

Standard Mendelian randomization analysis can produce biased results if the genetic variant defining the instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the treatment. We provide novel identification conditions for the causal effect of a treatment in presence of unmeasured confoun

Chen Shen, Marko Jusup, Lei Shi, Zhen Wang

We study the evolutionary dynamics of the prisoner's dilemma game in which cooperators and defectors interact with another actor type called exiters. Rather than being exploited by defectors, exiters exit the game in favour of a small payoff. We find that this simple extension of the game allows cooperation to flourish in well-mixed populations when iter

Ashleigh C. Myall, Robert L. Peach, Andrea Y. Weiße, Frances Davies

Hospitals constitute highly interconnected systems that bring into contact an abundance of infectious pathogens and susceptible individuals, thus making infection outbreaks both common and challenging. In recent years, there has been a sharp incidence of antimicrobial-resistance amongst healthcare-associated infections, a situation now considered endemic in

Mathias Mikkelsen, Thomás Fogarty, Thomas Busch

We investigate the relationship between information scrambling and work statistics after a quench for the paradigmatic example of short-range interacting particles in a one-dimensional harmonic trap, considering up to five particles numerically. In particular, we find that scrambling requires finite interactions, in the presence of which the long-time averag

Tamás Baranyai

Elasticity ellipses or central ellipses have been long used in graphic statics to capture the elastic behaviour of structural elements. The paper gives a generalisation the concept both in dimensions and in the possibility of degenerate conics / quadrics. The effect of projective transformations of these quadrics is also given, such that the entire mechanica

Jang Soo Kim, Dennis Stanton

A combinatorial theory for type $R_I$ orthogonal polynomials is given. The ingredients include weighted generalized Motzkin paths, moments, continued fractions, determinants, and histories. Several explicit examples in the Askey scheme are given.

Qiang Dong, Shuang-Shuang Xie, Wen-Jun Li

As we all know, users and item-providers are two main parties of participants in recommender systems. However, most existing research efforts on recommendation were focused on better serving users and overlooked the purpose of item-providers. This paper is devoted to improve the item exposure fairness for item-providers' objective, and keep the recommend

J. K. Terry, Benjamin Black, Nathaniel Grammel, Mario Jayakumar

This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle ("AEC") games model. PettingZoo is a library of diverse sets of multi-agent environments with a universal, elegant Python API. PettingZoo was developed with the goal of accelerating research in Multi-Agent Reinforcement Learning ("MARL"), by making work

Yusheng Jiao, Feng Ling, Sina Heydari, Nicolas Heess

Fish swim by undulating their bodies. These propulsive motions require coordinated shape changes of a body that interacts with its fluid environment, but the specific shape coordination that leads to robust turning and swimming motions remains unclear. To address the problem of underwater motion planning, we propose a simple model of a three-link fish swimmi

Kotaro Kawatani

We show that the existence of locally finite stability conditions on the bounded derived category $\mathbf{D}^{b}(X)$ of coherent sheaves on an affine Noetherian scheme $X$ is equivalent to $\dim X=0$. We also study the spaces of stability conditions on the category of morphisms $\mathbf M_{X}$ in the derived category of the scheme $X$ and show that the spac

Sparsh Mishra, Shun Tamura, Akito Kobayashi, Yukio Tanaka

We study a Kitaev chain model, which is the simplest model of topological superconductors hosting Majorana fermion, appearing as a zero-energy state at the edge. We analytically calculate the Green's function of the semi-infinite Kitaev chain with a delta-function-type impurity potential within the quasi-classical regime to obtain the spatial dependence

Subhojeet Pramanik, Shashank Mujumdar, Hima Patel

Recent approaches in literature have exploited the multi-modal information in documents (text, layout, image) to serve specific downstream document tasks. However, they are limited by their - (i) inability to learn cross-modal representations across text, layout and image dimensions for documents and (ii) inability to process multi-page documents. Pre-traini

Weihao Tan, Devdhar Patel, Robert Kozma

Spiking neural networks (SNNs) have great potential for energy-efficient implementation of Deep Neural Networks (DNNs) on dedicated neuromorphic hardware. Recent studies demonstrated competitive performance of SNNs compared with DNNs on image classification tasks, including CIFAR-10 and ImageNet data. The present work focuses on using SNNs in combination wit

Shouhei Ma

We prove that the degree k unramified cohomology with torsion coefficients of a smooth complex projective variety X with small CH_0(X) has a filtration of length [k/2], whose first piece is the torsion part of the quotient of the degree k+1 integral singular cohomology by its coniveau 2 subgroup, and whose next graded piece is controlled by the Griffiths gro

Sébastien Bubeck, Yuanzhi Li, Dheeraj Nagaraj

We initiate the study of the inherent tradeoffs between the size of a neural network and its robustness, as measured by its Lipschitz constant. We make a precise conjecture that, for any Lipschitz activation function and for most datasets, any two-layers neural network with $k$ neurons that perfectly fit the data must have its Lipschitz constant larger (up t

Shuhang Chen, Adithya Devraj, Andrey Bernstein, Sean Meyn

The ODE method has been a workhorse for algorithm design and analysis since the introduction of the stochastic approximation. It is now understood that convergence theory amounts to establishing robustness of Euler approximations for ODEs, while theory of rates of convergence requires finer analysis. This paper sets out to extend this theory to quasi-stochas

Yonghwan Kim, Yoshiaki Katayama, Koichi Wada

Programmable matter (PM) is a form of matter capable of dynamically altering its physical properties, such as shape or density, through programmable means. From a robotics perspective, PM can be realized as a distributed system consisting of numerous small computational entities working collaboratively to achieve specific objectives. Although autonomous mobi

J. Chauhan, J. C. A. Miller-Jones, W. Raja, J. R. Allison

We present HI absorption spectra of the black hole candidate X-ray binary (XRB) MAXI J1348-630 using the Australian Square Kilometre Array Pathfinder (ASKAP) and MeerKAT. The ASKAP HI spectrum shows a maximum negative radial velocity (with respect to the local standard of rest) of $-31\pm4$ km s$^{-1}$ for MAXI J1348-630, as compared to $-50\pm4$ km s$^{-1}$

Yuqi Zhou, Ahmed S. Zamzam, Andrey Bernstein, Hao Zhu

Operations of substation circuit breakers are important for maintenance needs and topology reconfiguration in power systems. Bus splitting is one type of topology change where the two bus bars at a substation can become electrically disconnected under certain actions of circuit breakers. Because these events involve detailed substation modeling, they are typ

Qi Qian, Hao Li, Juhua Hu

Knowledge distillation is an effective way for model compression in deep learning. Given a large model (i.e., teacher model), it aims to improve the performance of a compact model (i.e., student model) by transferring the information from the teacher. Various information for distillation has been studied. Recently, a number of works propose to transfer the p

Zi-Yu Chen, Ronghao Hu

Vector beam, with a spatial nonuniform polarization distribution, is important for many applications due to its unique field characteristics and novel effects when interacting with matter. Here through three-dimensional particle-in-cell simulations, we demonstrate that intense vector beams in the extreme-ultraviolet to x-ray spectral region can be generated

Hiroyuki Maehara, Yuta Notsu, Kousuke Namekata, Satoshi Honda

In this paper, we present the results from spectroscopic and photometric observations of the M-type flare star YZ CMi in the framework of the Optical and Infrared Synergetic Telescopes for Education and Research (OISTER) collaborations during the Transiting Exoplanet Survey Satellite (TESS) observation period. We detected 145 white-light flares from the TESS

Viresh Ranjan, Boyu Wang, Mubarak Shah, Minh Hoai

We present a method for image-based crowd counting, one that can predict a crowd density map together with the uncertainty values pertaining to the predicted density map. To obtain prediction uncertainty, we model the crowd density values using Gaussian distributions and develop a convolutional neural network architecture to predict these distributions. A ke

Fanxu Meng, Hao Cheng, Ke Li, Huixiang Luo

Pruning has become a very powerful and effective technique to compress and accelerate modern neural networks. Existing pruning methods can be grouped into two categories: filter pruning (FP) and weight pruning (WP). FP wins at hardware compatibility but loses at the compression ratio compared with WP. To converge the strength of both methods, we propose to p

Tao Jiang, Hei Victor Cheng, Wei Yu

Intelligent reflecting surface (IRS), which consists of a large number of tunable reflective elements, is capable of enhancing the wireless propagation environment in a cellular network by intelligently reflecting the electromagnetic waves from the base-station (BS) toward the users. The optimal tuning of the phase shifters at the IRS is, however, a challeng

Wei Zhao, Ishan Patil, Bin Han, Yong Yang

Background and purpose: To propose a novel machine learning-based method for reliable and accurate modeling of linac beam data applicable to the processes of linac commissioning and QA. Materials and methods: We hypothesize that the beam data is a function of inherent linac features and percentage depth doses (PDDs) and profiles of different field sizes are

Lauren Kennedy, Katharine Khanna, Daniel Simpson, Andrew Gelman

Accounting for sex and gender is a challenge in social science research. While other methodology papers consider issues surrounding appropriate measurement, we consider the problem of adjustment for survey nonresponse and generalization from samples to populations in the context of the recent push toward measuring sex or gender as a non-binary construct. Thi

Aleksander Horawa

We propose an action of a certain motivic cohomology group on the coherent cohomology of Hilbert modular varieties, extending conjectures of Venkatesh, Prasanna, and Harris. The action is described in two ways: on cohomology modulo $p$ and over $\mathbb C$, and we conjecture that they both lift to an action on cohomology with integral coefficients. The conje

Mingyang Zhang, Yi Zhou, Li Zhao, Haizhou Li

This paper presents a novel framework to build a voice conversion (VC) system by learning from a text-to-speech (TTS) synthesis system, that is called TTS-VC transfer learning. We first develop a multi-speaker speech synthesis system with sequence-to-sequence encoder-decoder architecture, where the encoder extracts robust linguistic representations of text,

Alberto Bietti, Francis Bach

Deep networks are often considered to be more expressive than shallow ones in terms of approximation. Indeed, certain functions can be approximated by deep networks provably more efficiently than by shallow ones, however, no tractable algorithms are known for learning such deep models. Separately, a recent line of work has shown that deep networks trained wi

Brian Lester, Daniel Pressel, Amy Hemmeter, Sagnik Ray Choudhury

Most state-of-the-art models in natural language processing (NLP) are neural models built on top of large, pre-trained, contextual language models that generate representations of words in context and are fine-tuned for the task at hand. The improvements afforded by these "contextual embeddings" come with a high computational cost. In this work, we e

Braden Kronheim, Michelle Kuchera, Harrison Prosper

TensorBNN is a new package based on TensorFlow that implements Bayesian inference for modern neural network models. The posterior density of neural network model parameters is represented as a point cloud sampled using Hamiltonian Monte Carlo. The TensorBNN package leverages TensorFlow's architecture and training features as well as its ability to use mo

Xiaoyu Nie, Fan Yang, Xiangpei Liu, Xingchen Zhao

We propose a computational ghost imaging scheme using customized pink noise speckle pattern illumination. By modulating the spatial frequency amplitude of the speckles, we generate speckle patterns with a significant positive spatial correlation. We experimentally reconstruct images using our synthesized speckle patterns in the presence of a variety of noise

Ahmed Roushdy Elkordy, A. Salman Avestimehr

Secure model aggregation across many users is a key component of federated learning systems. The state-of-the-art protocols for secure model aggregation, which are based on additive masking, require all users to quantize their model updates to the same level of quantization. This severely degrades their performance due to lack of adaptation to available band

David H. Meyer, Paul D. Kunz, Kevin C. Cox

We demonstrate an atomic radio-frequency (RF) receiver and spectrum analyzer based on thermal Rydberg atoms coupled to a planar microwave waveguide. We use an off-resonant RF heterodyne technique to achieve continuous operation for carrier frequencies ranging from DC to 20 GHz. The system achieves an intrinsic sensitivity of up to -120(2) dBm/Hz, DC coupling

Atefeh Sohrabizadeh, Cody Hao Yu, Min Gao, Jason Cong

Adopting FPGA as an accelerator in datacenters is becoming mainstream for customized computing, but the fact that FPGAs are hard to program creates a steep learning curve for software programmers. Even with the help of high-level synthesis (HLS), accelerator designers still have to manually perform code reconstruction and cumbersome parameter tuning to achie

Wenkui Ding, Xiaoguang Wang

Rotating wave approximation in a quantum spin system driven by a linearly polarized alternating magnetic field with quadrupole interaction presents is investigated in detail in this paper. The conventional way to employ the rotating wave approximation is to assume the dynamics being restricted in the reduced Hilbert space. However, when the driving strength

Ahmadreza Mosallanezhad, Yasin N. Silva, Michelle V. Mancenido, Huan Liu

Face images are rich data items that are useful and can easily be collected in many applications, such as in 1-to-1 face verification tasks in the domain of security and surveillance systems. Multiple methods have been proposed to protect an individual's privacy by perturbing the images to remove traces of identifiable information, such as gender or race

Xiao Liang, Shao-Jun Dong, Lixin He

Neural networks have been used as variational wave functions for quantum many-particle problems. It has been shown that the correct sign structure is crucial to obtain the high accurate ground state energies. In this work, we propose a hybrid wave function combining the convolutional neural network (CNN) and projected entangled pair states (PEPS), in which t

Yoshimasa Hidaka, Muneto Nitta, Ryo Yokokura

We investigate a higher-group structure of massless axion electrodynamics in $(3+1)$ dimensions. By using the background gauging method, we show that the higher-form symmetries necessarily have a global semistrict 3-group (2-crossed module) structure, and exhibit 't Hooft anomalies of the 3-group. In particular, we find a cubic mixed 't Hooft anomaly

Matias D. Cattaneo, Michael Jansson, Xinwei Ma

This paper investigates the large sample properties of local regression distribution estimators, which include a class of boundary adaptive density estimators as a prime example. First, we establish a pointwise Gaussian large sample distributional approximation in a unified way, allowing for both boundary and interior evaluation points simultaneously. Using

Max Engelstein, Robin Neumayer, Luca Spolaor

On any closed Riemannian manifold of dimension $n\geq 3$, we prove that if a function nearly minimizes the Yamabe energy, then the corresponding conformal metric is close, in a quantitative sense, to a minimizing Yamabe metric in the conformal class. Generically, this distance is controlled quadratically by the Yamabe energy deficit. Finally, we produce an e

Andean Medjedovic

In this paper we look at the history behind Chowla's problem on the solutions to $L(1,f) = 0$ for periodic $f$. We focus on the results given by Baker, Birch and Wirsing on the topic. We briefly discuss recent results due to Chatterjee, Murty and Pathak which give a full solution when combined with the work of Baker, Birch and Wirsing.

Jun Yang, Qing Li, Yixuan Sun

Tailings ponds are places for storing industrial waste. Once the tailings pond collapses, the villages nearby will be destroyed and the harmful chemicals will cause serious environmental pollution. There is an urgent need for a reliable forecast model, which could investigate the variation trend of stability coefficient of tailing dam and issue early warning

Weijian Jiao, Stefano Gonella

In weakly nonlinear systems, the main effect of cubic nonlinearity on wave propagation is an amplitude-dependent correction of the dispersion relation. This phenomenon can manifest either as a frequency shift or as a wavenumber shift depending on whether the excitation is prescribed as a initial condition or as a boundary condition, respectively. Several mod

Steven Blaber, David A. Sivak

Free energy differences are a central quantity of interest in physics, chemistry, and biology. We develop design principles that improve the precision and accuracy of free energy estimators, which has potential applications to screening for targeted drug discovery. Specifically, by exploiting the connection between the work statistics of time-reversed protoc

Jesse Silliman

In this paper, we study the action of diamond operators on Hilbert modular forms with coefficients in a general commutative ring. In particular, we generalize a result of Chai on the surjectivity of the constant term map for Hilbert modular forms with nebentypus to the setting of group ring valued modular forms. As an application, we construct certain Hilber

Liangkai Liu, Sidi Lu, Ren Zhong, Baofu Wu

The recent proliferation of computing technologies (e.g., sensors, computer vision, machine learning, and hardware acceleration), and the broad deployment of communication mechanisms (e.g., DSRC, C-V2X, 5G) have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multipl

Rodolfo M. Favaretto, Roberto R. Santos, Marcio Ballotin, Paulo Knob

This paper presents a study regarding group behavior in a controlled experiment focused on differences in an important attribute that vary across cultures -- the personal spaces -- in two Countries: Brazil and Germany. In order to coherently compare Germany and Brazil evolutions with same population applying same task, we performed the pedestrian Fundamental

D. K. Sunko

Many-fermion Hilbert space has the algebraic structure of a free module generated by a finite number of antisymmetric functions called shapes. Physically, each shape is a many-body vacuum, whose excitations are described by symmetric functions (bosons). The infinity of bosonic excitations accounts for the infinity of Hilbert space, while all shapes can be ge

Eric Benhamou, David Saltiel, Sandrine Ungari, Abhishek Mukhopadhyay

While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum variance, maximum diversification or equal risk parity, in parallel, another community in machine learning has started working on reinforcement learning and more particularly deep rein

Eric Benhamou, David Saltiel, Sandrine Ungari, Abhishek Mukhopadhyay

Can an agent learn efficiently in a noisy and self adapting environment with sequential, non-stationary and non-homogeneous observations? Through trading bots, we illustrate how Deep Reinforcement Learning (DRL) can tackle this challenge. Our contributions are threefold: (i) the use of contextual information also referred to as augmented state in DRL, (ii) t

Zijie Qu, Dominik Schildknecht, Shahriar Shadkhoo, Enrique Amaya

Biological systems achieve precise control over ambient fluids through the self-organization of active protein structures including flagella, cilia, and cytoskeletal networks. In active structures individual proteins consume chemical energy to generate force and motion at molecular length scales. Self-organization of protein components enables the control an

Marlesson R. O. Santana, Luckeciano C. Melo, Fernando H. F. Camargo, Bruno Brandão

Recommender Systems are especially challenging for marketplaces since they must maximize user satisfaction while maintaining the healthiness and fairness of such ecosystems. In this context, we observed a lack of resources to design, train, and evaluate agents that learn by interacting within these environments. For this matter, we propose MARS-Gym, an open-

Ellis Kessler, Moeti Masiane, Awad Abdelhalim

Tracking of occupants within buildings has become a topic of interest in the past decade. Occupant tracking has been used in the public safety, energy conservation, and marketing fields. Various methods have been demonstrated which can track people outside of and inside buildings; including GPS, visual-based tracking using surveillance cameras, and vibration

Andrea Fiandro, Jeanpierre Francois, Isabeau Oliveri, Simone Leonardi

This paper presents the POLINKS solution to the RecSys Challenge 2020 that ranked 6th in the final leaderboard. We analyze the performance of our solution that utilizes the click-through rate value to address the challenge task, we compare it with a gradient boosting model, and we report the quality indicators utilized for computing the final leaderboard.

Philipp del Hougne, K. Brahima Yeo, Philippe Besnier, Matthieu Davy

Non-Hermitian photonic systems capable of perfectly absorbing incident radiation recently attracted much attention both because fundamentally they correspond to an exotic scattering phenomenon (a real-valued scattering matrix zero) and because their extreme sensitivity holds great technological promise. The sharp reflection dip is a hallmark feature underlyi

Jianzhong Wu, Mengyang Gu

This chapter provides a tutorial overview of first principles methods to describe the properties of matter at the ground state or equilibrium. It begins with a brief introduction to quantum and statistical mechanics for predicting the electronic structure and diverse static properties of of many-particle systems useful for practical applications. Pedagogical

Yuri Arenberg

We show that an elementary proof of Fermat's Last Theorem (FLT) exists. Our paper also extends the scope of FLT from integers to all rational numbers.

Haosu Zhou, Qingfeng Xu, Nan Li

In the design optimization of metal forming, it is increasingly significant to use surrogate models to analyse the finite element analysis (FEA) simulations. However, traditional surrogate models using scalar based machine learning methods (SBMLMs) fall in short of accuracy and generalizability. This is because SBMLMs fail to harness the location information

Yu. G. Ignat'ev

A mathematical model of the cosmological evolution of statistical systems of scalarly charged particles with Higgs scalar interaction is formulated and investigated. Examples are given of numerical modeling of such systems, revealing their very remarkable properties, in particular, the formation of paired bursts of cosmological acceleration.

Mingtao Xia, Sihong Shao, Tom Chou

When using spectral methods, a question arises as how to determine the expansion order, especially for time-dependent problems in which emerging oscillations may require adjusting the expansion order. In this paper, we propose a frequency-dependent $p$-adaptive technique that adaptively adjusts the expansion order based on a frequency indicator. Using this $

Junfeng Hu, Xiaosa Li, Yuru Xu, Shaowu Wu

In this paper, company investment value evaluation models are established based on comprehensive company information. After data mining and extracting a set of 436 feature parameters, an optimal subset of features is obtained by dimension reduction through tree-based feature selection, followed by the 5-fold cross-validation using XGBoost and LightGBM models

Danny Blom, Rudi Pendavingh, Frits C. R. Spieksma

In this paper, we introduce an optimization problem posed by the Music Building Eindhoven (MBE) to deal with the economical consequences of the COVID-19 pandemic for theatre halls. We propose a model for maximizing the number of guests in a theatre hall that respects social distancing rules, and is based on trapezoid packings. Computational results show that

Seyed Saeed Ahmadisoleymani, Samy Missoum

Chains of resonators in the form of spring-mass systems have long been known to exhibiting interesting properties such as band gaps. Such features can be leveraged to manipulate the propagation of waves such as the filtering of specific frequencies and, more generally, mitigate vibrations and impact. Adding nonlinearities to the system can also provide furth

Michael te Vrugt, Jens Bickmann, Raphael Wittkowski

In response to the worldwide outbreak of the coronavirus disease COVID-19, a variety of nonpharmaceutical interventions such as face masks and social distancing have been implemented. A careful assessment of the effects of such containment strategies is required to avoid exceeding social and economical costs as well as a dangerous "second wave" of th

Yuliana Jiménez-Gaona, María José Rodríguez-Álvarez, Vasudevan Lakshminarayanan

This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis (CAD) systems, which make use of new deep learning methods to automatically recognize images and improve the accuracy of diagnosis made by radio

Artur Lewis Gower, Gerhard Kristensson

How do you take a reliable measurement of a material whose microstructure is random? When using wave scattering, the answer is often to take an ensemble average (average over time or space). By ensemble averaging we can calculate the average scattered wave and the effective wavenumber. To date, the literature has focused on calculating the effective wavenumb

Shodai Kubota, Ryota Nakayashiki, Ken Shirakawa

In this paper, we consider a class of optimal control problems governed by 1D parabolic state-systems of KWC types with dynamic boundary conditions. The state-systems are based on a phase-field model of grain boundary motion, proposed in [Kobayashi--Warren--Carter, Physica D, 140, 141--150, 2000], and in the context, the dynamic boundary conditions are suppo

Bo Peng, Youde Wang, Guodong Wei

In this paper, we consider the gradient estimates of the positive solutions to the following equation defined on a complete Riemannian manifold $(M, g)$ $$Δu + au(\log u)^{p}+bu=0,$$ where $a, b\in \mathbb{R}$ and $p$ is a rational number with $p=\frac{k_1}{2k_2+1}\geq2$ where $k_1$ and $k_2$ are positive integer numbers. we obtain the gradient bound of a po

G. J. Ehnholm, M. Krusius

Temperature scale and the Boltzmann constant: The newest system of units is based on a compatible set of natural constants with fixed values. An example is the Boltzmann constant k which defines the thermal energy content kT. To express the base unit T, the absolute temperature in kelvin, an international agreement for the temperature scale is needed. The sc

Puning Zhao, Lifeng Lai

We analyze the $\ell_1$ and $\ell_\infty$ convergence rates of k nearest neighbor density estimation method. Our analysis includes two different cases depending on whether the support set is bounded or not. In the first case, the probability density function has a bounded support and is bounded away from zero. We show that kNN density estimation is minimax o

J. M. Saborit-Torres, J. J. Saenz-Gamboa, J. À. Montell, J. M. Salinas

Brain Imaging Data Structure (BIDS) allows the user to organise brain imaging data into a clear and easy standard directory structure. BIDS is widely supported by the scientific community and is considered a powerful standard for management. The original BIDS is limited to images or data related to the brain. Medical Imaging Data Structure (MIDS) was therefo

G. Mouloudakis, P. Lambropoulos

In this work we study the production of electron-positron pairs from vacuum in presence of a field resulting from the collision of two counter-propagating FEL beams that undergo Gaussian amplitude fluctuations. Our work aims to the extension of previous works based on the standing wave hypothesis, by including the inherent stochastic amplitude fluctuations o

Jieon Kim, Sam Nelson, Minju Seo

Quandle coloring quivers are directed graph-valued invariants of oriented knots and links, defined using a choice of finite quandle $X$ and set $S\subset\mathrm{Hom}(X,X)$ of endomorphisms. From a quandle coloring quiver, a polynomial knot invariant known as the \textit{in-degree quiver polynomial} is defined. We consider quandle coloring quiver invariants f

L. A. Castro-Enriquez, A. Martín-Ruiz

We investigate the interaction between a topological insulator nanoparticle and a quantum dot in an impulse magnetic field. Since topological insulators are nonmagnetic, after the impulse has ended only the localised topological surface modes, which are quantised in terms of dipolar bosonic modes, thus coupling dipolarly to the quantum dot. Hence, the hybrid

Maxim Neumann, André Susano Pinto, Xiaohua Zhai, Neil Houlsby

Automatically finding good and general remote sensing representations allows to perform transfer learning on a wide range of applications - improving the accuracy and reducing the required number of training samples. This paper investigates development of generic remote sensing representations, and explores which characteristics are important for a dataset t

Domenico Cotroneo, Luigi De Simone, Pietro Liguori, Roberto Natella

Cloud computing systems fail in complex and unexpected ways due to unexpected combinations of events and interactions between hardware and software components. Fault injection is an effective means to bring out these failures in a controlled environment. However, fault injection experiments produce massive amounts of data, and manually analyzing these data i

Zbigniew J. Jurek

The random integral mappings (some type of functionals of Lévy processes) are continuous homomorphisms between convolution subsemigroups of the semigroup of all infinitely divisible measures. Compositions of those random integrals (mappings) can be always expressed as another single random integral mapping. That fact is illustrated by some old and new exampl

Yan-an Cai, Rencai Lü

In this paper, we classify all simple jet modules for the Neveu-Schwarz algebra $\widehat{\mathfrak{k}}$ and its contact subalgebra $\mathfrak{k}^+$. Based on these results, we give a classification of simple Harish-Chandra modules for $\widehat{\mathfrak{k}}$ and $\mathfrak{k}^+$.