March 2020 arXiv papers — page 68

Gabriel de Albuquerque Gleizer, Manuel Mazo

This paper addresses the problem of modeling and scheduling the transmissions generated by multiple event-triggered control (ETC) loops sharing a network. We present a method to build a symbolic traffic model of periodic ETC (PETC), which by construction provides an exact simulation of such traffic. The model is made in such a way as to avoid the combinatori

Chuan-Hung Chen, Takaaki Nomura

A nonconventional $U(1)_{e-\mu}$ gauge model is proposed to explain the unexpected anomalous magnetic moments of the electron and muon (lepton $g-2$), where only the right-handed electron and muon in the standard model carry the $U(1)_{e-\mu}$ charge. Although the light lepton masses are suppressed when the gauge symmetry is spontaneously broken, they can be

Hu Zhang, Linchao Zhu, Yi Zhu, Yi Yang

Deep neural networks are known to be susceptible to adversarial noise, which are tiny and imperceptible perturbations. Most of previous work on adversarial attack mainly focus on image models, while the vulnerability of video models is less explored. In this paper, we aim to attack video models by utilizing intrinsic movement pattern and regional relative mo

Irene Bouw, Nirvana Coppola, Pınar Kılıçer, Sabrina Kunzweiler

We study a 3-dimensional stratum $\mathcal{M}_{3,V}$ of the moduli space $\mathcal{M}_3$ of curves of genus $3$ parameterizing curves $Y$ that admit a certain action of $V= C_2\times C_2$. We determine the possible types of the stable reduction of these curves to characteristic different from $2$. We define invariants for $\mathcal{M}_{3,V}$ and characterize

Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin, Christopher J. Anders

With the broader and highly successful usage of machine learning in industry and the sciences, there has been a growing demand for Explainable AI. Interpretability and explanation methods for gaining a better understanding about the problem solving abilities and strategies of nonlinear Machine Learning, in particular, deep neural networks, are therefore rece

Masoud Ahmadi

Lithium-ion batteries (LIBs) of high energy density and light-weight design, have found wide applications in electronic devices and systems. Degradation mechanisms that caused by lithiation is a main challenging problem for LIBs with high capacity electrodes like silicon (Si), which eventually can reduce the lifetime of batteries. In this paper, a hybrid pha

Huafei Zhu, Zengxiang Li, Mervyn Cheah, Rick Siow Mong Goh

This paper studies privacy-preserving weighted federated learning within the oracle-aided multi-party computation (MPC) framework. The contribution of this paper mainly comprises the following three-fold: In the first fold, a new notion which we call weighted federated learning (wFL) is introduced and formalized inspired by McMahan et al.'s seminal paper

Hai-Bing Fu, Wei Cheng, Long Zeng, Dan-Dan Hu

In this paper, we make a detailed study about the $D\to V$ helicity form factors (HFFs) within the framework of QCD light-cone sum rule (LCSR) up to twist-4 accuracy. After extrapolating the LCSR predictions of HFFs to the whole physical $q^2$-region, we get the longitudinal, transverse and total $|V_{cq}|$-independent decay widths of semileptonic decay $D\t

Paulo C. Lima, Ricardo Lopes de Jesus, Aldo Procacci

We analyze the d-dimensional Blume-Emery-Griffiths model in the disordered region of parameters and we show that its free energy can be explicitly written in term of a series which is absolutely convergent at any temperature in an unbounded portion of this region. As a byproduct we also obtain an upper bound for the number of d-dimensional fixed polycubes of

Clara Fernandez-Labrador, Ajad Chhatkuli, Danda Pani Paudel, Jose J. Guerrero

Automatic discovery of category-specific 3D keypoints from a collection of objects of some category is a challenging problem. One reason is that not all objects in a category necessarily have the same semantic parts. The level of difficulty adds up further when objects are represented by 3D point clouds, with variations in shape and unknown coordinate frames

Vladislav Sovrasov, Dmitry Sidnev

This work considers the problem of domain shift in person re-identification.Being trained on one dataset, a re-identification model usually performs much worse on unseen data. Partially this gap is caused by the relatively small scale of person re-identification datasets (compared to face recognition ones, for instance), but it is also related to training ob

Shuang Yu, Xinyu Liu, Guoqiang Zhao, Yi Peng

In this paper we report successful synthesis and magnetic properties of (Ca,Na)(Zn,Mn)2Sb2 as a new ferromagnetic dilute magnetic semiconductor (DMS). In this DMS material the concentration of magnetic moments can be controlled independently from the concentration of electric charge carriers that are required for mediating magnetic interactions between these

David Richard, Karina González-López, Geert Kapteijns, Robert Pater

It has been recently established that the low-frequency spectrum of simple computer glass models is populated by soft, quasilocalized nonphononic vibrational modes whose frequencies $\omega$ follow a gapless, universal distribution ${\cal D}(\omega)\!\sim\!\omega^4$. While this universal nonphononic spectrum has been shown to be robust to varying the glass h

Silvia Bonfanti, Roberto Guerra, Chandana Mondal, Itamar Procaccia

It was recently shown that different simple models of glass formers with binary interactions define a universality class in terms of the density of states of their quasi-localized low-frequency modes. Explicitly, once the hybridization with standard Debye (extended) modes is avoided, a number of such models exhibit a universal density of state, depending on

Peter Hästö, Saminathan Ponnusamy

Assume that $f$ lies in the class of starlike functions of order $\alpha \in [0,1)$, that is, which are regular and univalent for $|z|<1$ and such that $${\rm Re} \left (\frac{zf'(z)}{f(z)} \right ) > \alpha ~\mbox{ for } |z|<1. $$ In this paper we show that for each $\alpha \in [0,1)$, the following sharp inequality holds: $$ |f(re^{i\theta})|^{-1} \int_{0}

Axel Böhm, Stephen J. Wright

We study minimization of a structured objective function, being the sum of a smooth function and a composition of a weakly convex function with a linear operator. Applications include image reconstruction problems with regularizers that introduce less bias than the standard convex regularizers. We develop a variable smoothing algorithm, based on the Moreau e

Nayana Das, Ritajit Majumdar

The first two party Quantum Key Agreement (QKA) protocol, based on quantum teleportation, was proposed by Zhou et al. (Electronics Letters 40.18 (2004): 1149-1150). In this protocol, to obtain the key bit string, one of the parties use a device to obtain inner product of two quantum states, one being unknown, and the other one performs Bell measurement. Howe

Varinder Singh

We present a detailed study of a three-level quantum heat engine operating at maximum efficient power function, a trade-off objective function defined by the product of the efficiency and power output of the engine. First, for near equilibrium conditions, we find general expression for the efficiency and establish universal nature of efficiency at maximum po

José Ruiz-Franco, Fabrizio Camerin, Nicoletta Gnan, Emanuela Zaccarelli

We study colloidal gels formed by competing electrostatic repulsion and short-range attraction by means of extensive numerical simulations under external shear. We show that, upon varying the repulsion strength, the gel structure and its viscoelastic properties can be largely tuned. In particular, the gel fractal dimension can be either increased or decrease

Michael R. Metel, Akiko Takeda

This paper presents an extension of stochastic gradient descent for the minimization of Lipschitz continuous loss functions. Our motivation is for use in non-smooth non-convex stochastic optimization problems, which are frequently encountered in applications such as machine learning. Using the Clarke $\epsilon$-subdifferential, we prove the non-asymptotic co

Daniel Arnström, Daniel Axehill

In model predictive control (MPC) an optimization problem has to be solved at each time step, which in real-time applications makes it important to solve these optimization problems efficiently and to have good upper bounds on worst-case solution time. Often for linear MPC problems, the optimization problem in question is a quadratic program (QP) that depend

Ruifeng Shi, Deming Zhai, Xianming Liu, Junjun Jiang

Plant diseases serve as one of main threats to food security and crop production. It is thus valuable to exploit recent advances of artificial intelligence to assist plant disease diagnosis. One popular approach is to transform this problem as a leaf image classification task, which can be then addressed by the powerful convolutional neural networks (CNNs).

Malik Magdon-Ismail

We present a robust data-driven machine learning analysis of the COVID-19 pandemic from its early infection dynamics, specifically infection counts over time. The goal is to extract actionable public health insights. These insights include the infectious force, the rate of a mild infection becoming serious, estimates for asymtomatic infections and prediction

Nikolaos S. Papageorgiou, Patrick Winkert

We consider a nonlinear Dirichlet problem driven by the $(p,q)$-Laplacian and with a reaction having the combined effects of a singular term and of a parametric $(p-1)$-superlinear perturbation. We prove a bifurcation-type result describing the changes in the set of positive solutions as the parameter $\lambda>0$ varies. Moreover, we prove the existence of a

Willem Esterhuizen, Karl Worthmann, Stefan Streif

We consider sampled-data Model Predictive Control (MPC) of nonlinear continuous-time control systems. We derive sufficient conditions to guarantee recursive feasibility and asymptotic stability without stabilising costs and/or constraints. Moreover, we present formulas to explicitly estimate the required length of the prediction horizon based on the concept

Kohei Kawabata, Nobuyuki Okuma, Masatoshi Sato

Non-Hermitian Hamiltonians are generally sensitive to boundary conditions, and their spectra and wave functions under open boundary conditions are not necessarily predicted by the Bloch band theory for periodic boundary conditions. To elucidate such a non-Bloch feature, recent works have developed a non-Bloch band theory that works even under arbitrary bound

Jing Li, Benjamin Venitucci, Yann-Michel Niquet

Spin-phonon interactions are one of the mechanisms limiting the lifetime of spin qubits made in semiconductor quantum dots. At variance with other mechanisms such as charge noise, phonons are intrinsic to the device and can hardly be mitigated. They set, therefore fundamental limits to the relaxation time of the qubits. Here we introduce a general framework

Aldo Battista, Remi Monasson

We consider the additive superimposition of an extensive number of independent Euclidean Random Matrices in the high-density regime. The resolvent is computed with techniques from free probability theory, as well as with the replica method of statistical physics of disordered systems. Results for the spectrum and eigenmodes are shown for a few applications r

Jun-Xu Lu, Chun-Hua Zeng, En Wang, Ju-Jun Xie

Recently, the Belle collaboration measured the ratios of the branching fractions of the newly observed $\Omega(2012)$ excited state. They did not observe significant signals for the $\Omega(2012) \to \bar{K} \Xi^*(1530) \to \bar{K} \pi \Xi$ decay, and reported an upper limit for the ratio of the three body decay to the two body decay mode of $\Omega(2012) \t

Florent Barret, Olivier Raimond

We study diffusion processes and stochastic flows which are time-changed random perturbations of a deterministic flow on a manifold. Using non-symmetric Dirichlet forms and their convergence in a sense close to the Mosco-convergence, we prove that, as the deterministic flow is accelerated, the diffusion process converges in law to a diffusion defined on a di

Yihao Luo, Min Xu, Caihong Yuan, Xiang Cao

Recently spiking neural networks (SNNs), the third-generation of neural networks has shown remarkable capabilities of energy-efficient computing, which is a promising alternative for deep neural networks (DNNs) with high energy consumption. SNNs have reached competitive results compared to DNNs in relatively simple tasks and small datasets such as image clas

Assaf Rinot, Jing Zhang

We study the existence of transformations of the transfinite plane that allow one to reduce Ramsey-theoretic statements concerning uncountable Abelian groups into classical partition relations for uncountable cardinals. To exemplify: we prove that for every inaccessible cardinal $\kappa$, if $\kappa$ admits a stationary set that does not reflect at inaccessi

Natsumi Ikeno, Genaro Toledo, Eulogio Oset

We conduct a study of the interaction of the $\bar K Ξ^*$, $ηΩ$($s$-wave) and $\bar K Ξ$($d$-wave) channels within a coupled channel unitary approach where the transition potential between the $\bar K Ξ^*$ and $ηΩ$ channels is obtained from chiral Lagrangians. The transition potential between $\bar K Ξ^*$, $ηΩ$ and $\bar K Ξ$ is taken in terms of free parame

Md Hasanujjaman, Mahfuzur Rahaman, Abhijit Bhattacharyya, Jan-e Alam

We have used second order relativistic hydrodynamics equipped with equation of state which includes the critical point to study the propagation of perturbation in a relativistic QCD fluid. Dispersion relation for the sound wave has been derived to ascertain the fate of the perturbation in the fluid near the QCD critical end point (CEP). We observe that the t

Yue Wang, Esha Sarkar, Wenqing Li, Michail Maniatakos

Recent work has shown that the introduction of autonomous vehicles (AVs) in traffic could help reduce traffic jams. Deep reinforcement learning methods demonstrate good performance in complex control problems, including autonomous vehicle control, and have been used in state-of-the-art AV controllers. However, deep neural networks (DNNs) render automated dri

Riccardo Ciolfi

The first multimessenger observation of a binary neutron star (BNS) merger in August 2017 demonstrated the huge scientific potential of these extraordinary events. This breakthrough led to a number of discoveries and provided the best evidence that BNS mergers can launch short gamma-ray burst (SGRB) jets and are responsible for a copious production of heavy

Frank Calegari, Naser T. Sardari

We prove that, for fixed tame level (N,p) = 1, there are only finitely many Hecke eigenforms f of level Gamma_1(N) and even weight with a_p(f) = 0 which are not CM.

Zhaojiang Lin, Zihan Liu, Genta Indra Winata, Samuel Cahyawijaya

Personalized dialogue systems are an essential step toward better human-machine interaction. Existing personalized dialogue agents rely on properly designed conversational datasets, which are mostly monolingual (e.g., English), which greatly limits the usage of conversational agents in other languages. In this paper, we propose a multi-lingual extension of P

Parham Rezaee, Hamid Reza Naeij

In order to separate a mixture of hydrogen ($H_2$) and carbon dioxide ($CO_2$) gases, we have proposed a new approach employing the graphdiyne-like membrane (GDY-H) using density functional theory (DFT) calculations and molecular dynamics (MD) simulations. GDY-H is constructed by removing one-third diacetylenic ($-$C$\equiv$C$-$C$\equiv$C$-$) bonds linkages

Ping Miao, Zhijian Tan, Sanghyun Lee, Yoshihisa Ishikawa

The layered perovskite PrBaCo2O5.5+x demonstrates a strong negative thermal expansion (NTE) which holds potential for being fabricated into composites with zero thermal expansion. The NTE was found to be intimately associated with the spontaneous magnetic ordering, known as magnetovolume effect (MVE). Here we report with compelling evidences that the continu

Yu Yang, Seshadhri Srinivasan, Guoqiang Hu, Costas J. Spanos

This paper studies a scalable control method for multi-zone heating, ventilation and air-conditioning (HVAC) systems to optimize the energy cost for maintaining thermal comfort and indoor air quality (IAQ) (represented by CO2) simultaneously. This problem is computationally challenging due to the complex system dynamics, various spatial and temporal coupling

JEDI Collaboration, F. Müller, M. Zurek, Z. Bagdasarian

A measurement of vector analyzing powers in elastic deuteron-carbon scattering has been performed at the Cooler Synchrotron COSY of Forschungszentrum J\"ulich, Germany. Seven kinetic beam energies between 170 and 380 MeV have been used. A vector-polarized beam from a polarized deuteron source was injected, accelerated to the final desired energy and stored i

Jerrin Thomas Panachakel, Jeena R. S

Cerebrovascular accident (CVA) or stroke is the rapid loss of brain function due to disturbance in the blood supply to the brain. Statistically, stroke is the second leading cause of death. This has motivated us to suggest a two-tier system for predicting stroke; the first tier makes use of Artificial Neural Network (ANN) to predict the chances of a person s

Tengiz Kopaliani, Nino Samashvili, Shalva Zviadadze

In this paper we generalize Bochkariev's theorem, which states that for any uniformly bounded orthonormal system $\Phi$, there exists a Lebesgue integrable function such that the Fourier series of it with respect to system $\Phi$ diverge on the set of positive measure. We characterize the class of variable exponent Lebesgue spaces $L^{p(\cdot)}[0;1]$, $1<p(x

J. Ebel, T. Joas, M. Schalk, A. Angerer

We demonstrate dispersive readout of the spin of an ensemble of Nitrogen-Vacancy centers in a high-quality dielectric microwave resonator at room temperature. The spin state is inferred from the reflection phase of a microwave signal probing the resonator. Time-dependent tracking of the spin state is demonstrated, and is employed to measure the T1 relaxation

Di Wu, Yihao Chen, Xianbiao Qi, Yongjian Yu

How can we effectively utilise the 2D monocular image information for recovering the 6D pose (6-DoF) of the visual objects? Deep learning has shown to be effective for robust and real-time monocular pose estimation. Oftentimes, the network learns to regress the 6-DoF pose using a naive loss function. However, due to a lack of geometrical scene understanding

Chantelle Blachut, Cecilia González-Tokman

Coherent structures are spatially varying regions which disperse minimally over time and organise motion in non-autonomous systems. This work develops and implements algorithms providing multilayered descriptions of time-dependent systems which are not only useful for locating coherent structures, but also for detecting time windows within which these struct

Kürşat Tekbıyık, Özkan Akbunar, Ali Rıza Ekti, Ali Görçin

Spectrum sensing is one of the means of utilizing the scarce source of wireless spectrum efficiently. In this paper, a convolutional neural network (CNN) model employing spectral correlation function which is an effective characterization of cyclostationarity property, is proposed for wireless spectrum sensing and signal identification. The proposed method c

Xichen Shi, Patrick Spieler, Ellande Tang, Elena-Sorina Lupu

Fixed-wing vertical take-off and landing (VTOL) aircraft pose a unique control challenge that stems from complex aerodynamic interactions between wings and rotors. Thus, accurate estimation of external forces is indispensable for achieving high performance flight. In this paper, we present a composite adaptive nonlinear tracking controller for a fixed-wing V

Hiroshi Kontani, Youichi Yamakawa, Rina Tazai, Seiichiro Onari

Unconventional symmetry-breaking phenomena due to nontrivial order parameters attract increasing attention in strongly correlated electron systems. Here, we predict theoretically the occurrence of nanoscale spontaneous spin-current, called the spin loop-current (sLC) order, as a promising origin of the pseudogap and electronic nematicity in cuprates. We reve

Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary C. Lipton

Under label shift, the label distribution p(y) might change but the class-conditional distributions p(x|y) do not. There are two dominant approaches for estimating the label marginal. BBSE, a moment-matching approach based on confusion matrices, is provably consistent and provides interpretable error bounds. However, a maximum likelihood estimation approach,

Yong Zhao, Kunpeng Yuan, Yinqiao Liu, Steph-Yves Louis

Materials representation plays a key role in machine learning based prediction of materials properties and new materials discovery. Currently both graph and 3D voxel representation methods are based on the heterogeneous elements of the crystal structures. Here, we propose to use electronic charge density (ECD) as a generic unified 3D descriptor for materials

Bo Zhang, Stephane Popinet, Yue Ling

In the present study, detailed numerical simulations are performed to investigate the primary breakup of a gasoline surrogate jet under non-evaporative &#34;Spray G&#34; operating conditions. The Spray G injector and operating conditions, developed by the Engine Combustion Network (ECN), represent the early phase of spray-guided gasoline injection. To focus

Peyman Eslami, Carlangelo Liverani

We establish sharp bounds on the mixing rates of a class of two dimensional non-uniformly hyperbolic symplectic maps. This provides a primer on how to investigate such questions in a concrete example and, at the same time, it solves a controversy between previous rigorous results and numerical experiments.

Linlin Zhong, Qi Gu, Bingyu Wu

Numerical modelling is an essential approach to understanding the behavior of thermal plasmas in various industrial applications. We propose a deep learning method for solving the partial differential equations in thermal plasma models. In this method a deep feed-forward neural network is constructed to surrogate the solution of the model. A loss function is

Tran Ngoc Thang, Vijender Kumar Solanki, Tuan Anh Dao, Nguyen Thi Ngoc Anh

In this article, we use the monotonic optimization approach to propose an outcome-space outer approximation by copolyblocks for solving strictly quasiconvex multiobjective programming problems and especially in the case that the objective functions are nonlinear fractional. After the algorithm is terminated, with any given tolerance, we obtain an approximati

Kazuto Hasegawa, Kai Fukami, Takaaki Murata, Koji Fukagata

We propose a method to construct a reduced order model with machine learning for unsteady flows. The present machine-learned reduced order model (ML-ROM) is constructed by combining a convolutional neural network autoencoder (CNN-AE) and a long short-term memory (LSTM), which are trained in a sequential manner. First, the CNN-AE is trained using direct numer

Jiajia Zhou, Masao Doi

A filament of liquid is usually unstable and breaks up into small droplets, while a filament of polymer solution is known to be quite stable against such instability, and they form a stable configuration of filament connecting two spherical droplets. If the droplets are fixed in space, the liquid flows from the filament region to the droplet region to reduce

Zhaonan Qu, Isabella Qian, Zhengyuan Zhou

With the rise of the digital economy and an explosion of available information about consumers, effective personalization of goods and services has become a core business focus for companies to improve revenues and maintain a competitive edge. This paper studies the personalization problem through the lens of policy learning, where the goal is to learn a dec

Norihiro Hanihara

Given a negatively graded Calabi-Yau algebra, we regard it as a DG algebra with vanishing differentials and study its cluster category. We show that this DG algebra is sign-twisted Calabi-Yau, and realize its cluster category as a triangulated hull of an orbit category of a derived category, and as the singularity category of a finite dimensional Iwanaga-Gor

Changliang Li, Chao Feng, Bocheng Jiang

Generation of extreme bright coherent synchrotron radiation in a short wavelength range is of remarkable interest in the synchrotron light source community. In this paper, a novel technique is adopted to produce the coherent radiation which uses an angular dispersion to enhance the micro-bunch by a tiny amplitude of energy modulation. This scheme can be inse

Wei Hu, Qianjiang Hu, Zehua Wang, Xiang Gao

3D dynamic point clouds provide a natural discrete representation of real-world objects or scenes in motion, with a wide range of applications in immersive telepresence, autonomous driving, surveillance, \etc. Nevertheless, dynamic point clouds are often perturbed by noise due to hardware, software or other causes. While a plethora of methods have been propo

Daisuke Inoue, Akihisa Okada, Tadayoshi Matsumori, Kazuyuki Aihara

The spread of intelligent transportation systems in urban cities has caused heavy computational loads, requiring a novel architecture for managing large-scale traffic. In this study, we develop a method for globally controlling traffic signals arranged on a square lattice by means of a quantum annealing machine, namely the D-Wave quantum annealer. We first f

Sunho Kim, Byungjai Kim, HyunWook Park

Medical image analysis using deep neural networks has been actively studied. Deep neural networks are trained by learning data. For accurate training of deep neural networks, the learning data should be sufficient, of good quality, and should have a generalized property. However, in medical images, it is difficult to acquire sufficient patient data because o

Hamid Hosseinianfar, Maite Brandt-Pearce

Visible light communication (VLC) systems are promising candidates for future indoor access and peer-to-peer networks. The performance of these systems, however, is vulnerable to the line of sight (LOS) link blockage due to objects inside the room. In this paper, we develop a probabilistic object detection method that takes advantage of the blockage status o

Shoupu Wan

Manacher&#39;s algorithm has been shown to be optimal to the longest palindromic substring problem. Many of the existing implementations of this algorithm, however, unanimously required in-memory construction of an augmented string that is twice as long as the original string. Although it has found widespread use, we found that this preprocessing is neither

Mengjiao Yang, Bo Dai, Hanjun Dai, Dale Schuurmans

Recently there has been growing interest in modeling sets with exchangeability such as point clouds. A shortcoming of current approaches is that they restrict the cardinality of the sets considered or can only express limited forms of distribution over unobserved data. To overcome these limitations, we introduce Energy-Based Processes (EBPs), which extend en

Kazumasa Okabayashi, Nobuyuki Asaka, Ken-ichi Nakao

The so-called black hole shadow is not a silhouette of a black hole but an image of a collapsing object or a white hole. Hence it is non-trivial whether black hole shadows merge with each other when black holes coalesce with each other. In this paper, by analyzing the null geodesic generators of the event horizon in Kastor-Traschen spacetime which describes

Tony Feng, Aaron Landesman, Eric M. Rains

Fix a positive integer $n$ and a finite field $\mathbb F_q$. We study the joint distribution of the rank of $E$, the $n$-Selmer group of $E$, and the $n$-torsion in the Tate-Shafarevich group of $E$ as $E$ varies over elliptic curves of fixed height $d \geq 2$ over $\mathbb F_q(t)$. We compute this joint distribution in the large $q$ limit. We also show that

Boris Aronov, Mark de Berg, Joachim Gudmundsson, Michael Horton

Let $V$ be a set of $n$ points in $\mathbb{R}^d$, called voters. A point $p\in \mathbb{R}^d$ is a plurality point for $V$ when the following holds: for every $q\in\mathbb{R}^d$ the number of voters closer to $p$ than to $q$ is at least the number of voters closer to $q$ than to $p$. Thus, in a vote where each $v\in V$ votes for the nearest proposal (and vote

S. M. Zhang, X. Z. Zhang, L. Jin, Z. Song

We employ the intertwining operator technique to synthesize a supersymmetric (SUSY) array of arbitrary size $N$. The synthesized SUSY system is equivalent to a spin-$(N-1)/2$ under an effective magnetic field. By considering an additional imaginary magnetic field, we obtain a generalized parity-time-symmetric non-Hermitian Hamiltonian that describes a SUSY a

Mariana B. Sánchez, Gonzalo C. de Elía, Juan José Downes

Recent observational results show that very low mass stars and brown dwarfs are able to host close-in rocky planets. Low-mass stars are the most abundant stars in the Galaxy and the formation efficiency of their planetary systems is relevant in the computation of a global probability of finding Earth-like planets inside habitable zones. Tidal forces and rela

Santiago López de Medrano

For a family of polytopes of even dimension $2p$, known as \textit{dual-neighborly}, it has been shown for $p\ne 2$ that the associated intersection of quadrics is a connected sum of sphere products $S^p\times S^p$. In this article we give formulas for the number of terms in that connected sum. Certain combinatorial operations produce new polytopes whose ass

Wei Liu, Pingping Zhang, Xiaolin Huang, Jie Yang

Edge-preserving image smoothing is a fundamental procedure for many computer vision and graphic applications. There is a tradeoff between the smoothing quality and the processing speed: the high smoothing quality usually requires a high computational cost which leads to the low processing speed. In this paper, we propose a new global optimization based metho

Paul Dütting, Federico Fusco, Philip Lazos, Stefano Leonardi

A celebrated impossibility result by Myerson and Satterthwaite (1983) shows that any truthful mechanism for two-sided markets that maximizes social welfare must run a deficit, resulting in a necessity to relax welfare efficiency and the use of approximation mechanisms. Such mechanisms in general make extensive use of the Bayesian priors. In this work, we inv

Sungho Shin, Mihai Anitescu, Victor M. Zavala

We present an overlapping Schwarz decomposition algorithm for constrained quadratic programs (QPs). Schwarz algorithms have been traditionally used to solve linear algebra systems arising from partial differential equations, but we have recently shown that they are also effective at solving structured optimization problems. In the proposed scheme, we conside

Benjamin Ackerman, Catherine R. Lesko, Juned Siddique, Ryoko Susukida

Randomized trials are considered the gold standard for estimating causal effects. Trial findings are often used to inform policy and programming efforts, yet their results may not generalize well to a relevant target population due to potential differences in effect moderators between the trial and population. Statistical methods have been developed to impro

L. Chekhov, M. Shapiro

Using Fock--Goncharov higher Teichm\"uller space variables we derive Darboux coordinate representation for entries of general symplectic leaves of the $\mathcal A_n$ groupoid of upper-triangular matrices and, in a more general setting, of higher-dimensional symplectic leaves for algebras governed by the reflection equation with the trigonometric $R$-matrix.

Guo Liang, Sojin Han, Xubo Hu, Felix Martinez Solis

We provide the first demonstration of nonlinear self-trapping of light in solutions of food coloring dyes, leading to dirt cheap soliton waveguides. The optical self-focusing nonlinearity observed in such solutions is attributed to the optical absorption force acting on dye molecules, as the thermal effect at relatively higher power results in a self-defocus

Ajitesh Srivastava, Naifeng Zhang, Rajgopal Kannan, Viktor K. Prasanna

Writing high-performance code requires significant expertise in the programming language, compiler optimizations, and hardware knowledge. This often leads to poor productivity and portability and is inconvenient for a non-programmer domain-specialist such as a Physicist. More desirable is a high-level language where the domain-specialist simply specifies the

Kahkashan Afrin, Ashif S. Iquebal, Mostafa Karimi, Allyson Souris

In recent biomedical scientific problems, it is a fundamental issue to integratively cluster a set of objects from multiple sources of datasets. Such problems are mostly encountered in genomics, where data is collected from various sources, and typically represent distinct yet complementary information. Integrating these data sources for multi-source cluster

Shi-Xue Zhang, Xiaobin Zhu, Jie-Bo Hou, Chang Liu

Arbitrary shape text detection is a challenging task due to the high variety and complexity of scenes texts. In this paper, we propose a novel unified relational reasoning graph network for arbitrary shape text detection. In our method, an innovative local graph bridges a text proposal model via Convolutional Neural Network (CNN) and a deep relational reason

Yuichi Sudo, Masahiro Shibata, Junya Nakamura, Yonghwan Kim

In the population protocol model, many problems cannot be solved in a self-stabilizing way. However, global knowledge, such as the number of nodes in a network, sometimes allows us to design a self-stabilizing protocol for such problems. In this paper, we investigate the effect of global knowledge on the possibility of self-stabilizing population protocols i

Zheng Zhang, Ryuichi Takanobu, Qi Zhu, Minlie Huang

Due to the significance and value in human-computer interaction and natural language processing, task-oriented dialog systems are attracting more and more attention in both academic and industrial communities. In this paper, we survey recent advances and challenges in task-oriented dialog systems. We also discuss three critical topics for task-oriented dialo

Hui-Min Yang, Hua-Xing Chen

We investigate $P$-wave bottom baryons of the $SU(3)$ flavor $\mathbf{6}_F$, and systematically study their $D$-wave decays into ground-state bottom baryons and pseudoscalar mesons. Together with [PRD91, 054034 (2015); PRD92, 114007 (2015); PRD95, 094008 (2017); EPJC80, 80 (2020)], a rather complete study is performed on both mass spectra and decay propertie

Dmitry Tonkonog, Umut Varolgunes

This article uses relative symplectic cohomology, recently studied by the second author, to understand rigidity phenomena for compact subsets of symplectic manifolds. As an application, we consider a symplectic crossings divisor in a Calabi-Yau symplectic manifold $M$ whose complement is a Liouville manifold. We show that, for a carefully chosen Liouville st

Mingjian He, Hong Qi, Yatao Ren, Yijun Zhao

In this letter, active control of near-field radiative heat transfer (NFRHT) between two isotropic materials is realized by a coating-twisting method. The two slabs are coated with graphene gratings, and then the NFRHT can be not only enhanced, but also weakened, by tuning the twisted angle between the two gratings. The physical mechanism is attributed to th

Al-Akhir Nayan, Joyeta Saha, Ahamad Nokib Mozumder, Khan Raqib Mahmud

The aim of this research is to detect small objects with low resolution and noise. The existing real time object detection algorithm is based on the deep neural network of convolution need to perform multilevel convolution and pooling operations on the entire image to extract a deep semantic characteristic of the image. The detection models perform better fo

Jefferson Flórez, Jeff S. Lundeen, Maria V. Chekhova

We report the efficient generation of high-gain parametric down-conversion, including pump depletion, with pump powers as low as 100 $\mu$W (energies $0.1$~$\mu$J/pulse) and conversion efficiencies up to 33\%. In our simple configuration, the pump beam is tightly focused into a bulk periodically poled lithium niobate crystal placed in free space. We also obs

Aladji Babacar Niang, Gane Samb Lo, Cherif Mamadou Moctar Traoré

Integrating with respect to functions which are constant on intervals whose bounds are discontinuity points (of those functions) is frequent in many branches of Mathematics, specially in stochastic processes. For such functions and alike extension, a comparison between Riemann-Stieltjes and Lebesgue-Stieltjes integration and the integrals formulas leads to i

Xing Yang, Quanhui Liu, Boldizsár Jankó

We study the recently discovered even-odd effects in the normal state of single-electron devices manufactured at strontium titanium oxide/lanthanum aluminum oxide interfaces (STO/LAO). Within the framework of the number parity-projected formalism and a phenomenological fermion-boson model we find that, in sharp contrast to conventional superconductors, the c

Songyan Li, Daniel Tylavsky, Di Shi, Zhiwei Wang

What has become known as Stahl&#39;s Theorem in power engineering circles has been used to justify a convergence guarantee of the Holormorphic Embedding Method (HEM) as it applies to the power flow (PF) problem. In this two-part paper, we examine in more detail the implications of Stahl&#39;s theorems to both theoretcial and numerical convergence for a wider

Yuxin Lu, Wai Ho Mow

In this paper, we analyze the symbol error rate (SER) performance of the simultaneous wireless information and power transfer (SWIPT) enabled three-node differential decode-and-forward (DDF) relay networks, which adopt the power splitting (PS) protocol at the relay. The use of non-coherent differential modulation eliminates the need for sending training symb

J. L. Subias

A recent model for the stock market calculates future price distributions of a stock as a wave function of a quantum particle confined in an infinite potential well. In such a model the question arose as to how to estimate the classical potential needed for solving the Schrodinger equation. In the present article the method used in that work for evaluating t

Leyla Najafi, Sebastiano Bellani, Andrea Castelli, Milena P. Arciniegas

The design of efficient electrocatalysts for electrochemical water splitting with minimal amount of precious metal is crucial to attain renewable and sustainable energy conversion. Here, we report the use of a network of CdSe branched colloidal nanocrystals, made of a CdSe core and eight CdSe pods (so-called octapods), able to host on their pods Pt particles

Lev Mukhanov, Konstantinos Tovletoglou, Hans Vandierendonck, Dimitrios S. Nikolopoulos

The aggressive scaling of technology may have helped to meet the growing demand for higher memory capacity and density, but has also made DRAM cells more prone to errors. Such a reality triggered a lot of interest in modeling DRAM behavior for either predicting the errors in advance or for adjusting DRAM circuit parameters to achieve a better trade-off betwe

Ashish Verma

The paper proposes to introduce incomplete Srivastava&#39;s triple hypergeometric matrix functions through application of the incomplete Pochhammer matrix symbols. We also derive certain properties such as matrix differential equation, integral formula, reduction formula, recursion formula, recurrence relation and differentiation formula of the incomplete Sr

Marco Baroni

Deep-agent communities developing their own language-like communication protocol are a hot (or at least warm) topic in AI. Such agents could be very useful in machine-machine and human-machine interaction scenarios long before they have evolved a protocol as complex as human language. Here, I propose a small set of priorities we should focus on, if we want t

Jordan Ott

Artificial Intelligence has historically relied on planning, heuristics, and handcrafted approaches designed by experts. All the while claiming to pursue the creation of Intelligence. This approach fails to acknowledge that intelligence emerges from the dynamics within a complex system. Neurons in the brain are governed by local rules, where no single neuron

Xiaodong Wu, Weizhe Lin, Zhilin Wang, Elena Rastorgueva

Online forums and social media platforms provide noisy but valuable data every day. In this paper, we propose a novel end-to-end neural network-based user embedding system, Author2Vec. The model incorporates sentence representations generated by BERT (Bidirectional Encoder Representations from Transformers) with a novel unsupervised pre-training objective, a