November 2020 arXiv papers — page 42

William N. Faugno, Tongzhou Zhao, Ajit C. Balram, Thierry Jolicoeur

A recent work [Balram, Jain, and Barkeshli, Phys. Rev. Res. ${\bf 2}$, 013349 (2020)] has suggested that an unconventional state describing $\mathbb{Z}_{n}$ superconductivity of composite bosons, which supports excitations with charge $1/(3n)$ of the electron charge, is energetically better than the Laughlin wave function at $\nu=7/3$ in GaAs systems. All ex

Lasha Berezhiani, Michael Zantedeschi

Quantum dynamics of coherent states is studied within quantum field theory using two complementary methods: by organizing the evolution as a Taylor series in elapsed time and by perturbative expansion in coupling within the interaction-picture formalism. One of the important aspects of our analysis consists in utilizing the operators and the vacuum of intera

Nikita Mehrotra, Navdha Agarwal, Piyush Gupta, Saket Anand

Code clones are duplicate code fragments that share (nearly) similar syntax or semantics. Code clone detection plays an important role in software maintenance, code refactoring, and reuse. A substantial amount of research has been conducted in the past to detect clones. A majority of these approaches use lexical and syntactic information to detect clones. Ho

Y. Ichinohe, A. Simionescu, N. Werner, M. Markevitch

We analysed the deep archival Chandra observations of the high-temperature galaxy cluster Abell 2319 to investigate the prominent cold front in its core. The main sharp arc of the front shows wiggles, or variations of the radius of the density jump along the arc. At the southern end of the arc is a feature that resembles a Kelvin-Helmholtz (KH) eddy, beyond

Shingo Tagami, Maya Takechi, Jun Matsui, Tomotsugu Wakasa

{\bf Background:} Using the chiral (Kyushu) $g$-matrix folding model with the densities calculated with GHFB+AMP, we determined $r_{\rm skin}^{208}=0.25$fm from the central values of $\sigma_{\rm R}$ of p+$^{208}$Pb scattering in $E_{\rm in}=40-81$MeV. The high-resolution $E1$ polarizability experiment ($E1$pE) yields $r_{\rm skin}^{48}(E1{\rm pE}) =0.14-0.2

Ankit Murarka, Balaji Radhakrishnan, Sushma Ravichandran

Given the current social distancing regulations across the world, social media has become the primary mode of communication for most people. This has resulted in the isolation of many people suffering from mental illnesses who are unable to receive assistance in person. They have increasingly turned to social media to express themselves and to look for guida

Manik Dhar, Zeev Dvir

A Kakeya set $S \subset (\mathbb{Z}/N\mathbb{Z})^n$ is a set containing a line in each direction. We show that, when $N$ is any square-free integer, the size of the smallest Kakeya set in $(\mathbb{Z}/N\mathbb{Z})^n$ is at least $C_{n,\epsilon} N^{n - \epsilon}$ for any $\epsilon$ -- resolving a special case of a conjecture of Hickman and Wright. Previously,

Mostafa Peysokhan, Saeid Rostami, Esmaeil Mobini, Alexander R. Albrecht

Laser cooling of a solid is achieved when a coherent laser illuminates the material, and the heat is extracted by resulting anti-Stokes fluorescence. Over the past year, net solid-state laser cooling was successfully demonstrated for the first time in Yb-doped silica glass in both bulk samples and fibers. Here, we improve the previously published results by

Nazar Emirov, Cheng Cheng, Qiyu Sun, Zhihua Qu

Eigenvectors of matrices on a network have been used for understanding spectral clustering and influence of a vertex. For matrices with small geodesic-width, we propose a distributed iterative algorithm in this letter to find eigenvectors associated with their given eigenvalues. We also consider the implementation of the proposed algorithm at the vertex/agen

Kwang-Sung Jun, Lalit Jain, Blake Mason, Houssam Nassif

We propose improved fixed-design confidence bounds for the linear logistic model. Our bounds significantly improve upon the state-of-the-art bound by Li et al. (2017) via recent developments of the self-concordant analysis of the logistic loss (Faury et al., 2020). Specifically, our confidence bound avoids a direct dependence on $1/\kappa$, where $\kappa$ is

Xianjin Chao, Yanrui Bin, Wenqing Chu, Xuan Cao

Human motion prediction aims to predict future 3D skeletal sequences by giving a limited human motion as inputs. Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend, but motion details such as limb movement may be lost. To predict more accurate future human motion, we propose an Adversarial Re

Michael Finkelberg, Mykola Matviichuk, Alexander Polishchuk

We study the elliptic zastava spaces, their versions (twisted, Coulomb, Mirkovic local spaces, reduced) and relations with monowalls moduli spaces and Feigin-Odesskii moduli spaces of $G$-bundles with parabolic structure on an elliptic curve.

Naomichi Nakajima, Toru Ohmoto

The dually flat structure introduced by Amari-Nagaoka is highlighted in information geometry and related fields. In practical applications, however, the underlying pseudo-Riemannian metric may often be degenerate, and such an excellent geometric structure is rarely defined on the entire space. To fix this trouble, in the present paper, we propose a novel gen

Amir Afshar, Dong Meng

The issue of ice accumulation at low-temperature circumstances causes multiple problems and serious damages in many civil infrastructures which substantially influence human daily life. However, despite the significant consideration in manufacturing anti-icing or icephobic surfaces, it is still demanding to design surfaces with well ice-repellent properties.

Asuka Ito

We reveal all linear order inertial and gravitational effects on a non-relativistic Dirac particle (mass $m$) on the Earth up to the order of $1/m$ in the Foldy-Wouthuysen-like expansion. Applying the result to Penning trap experiments where a Dirac particle experiences the cyclotron motion and the spin precession in a cavity, i.e., a geonium atom, we study

Igor V. Bondarev, Chandra M. Adhikari

We present a theoretical study of the collective quasiparticle excitations responsible for the electromagnetic response of ultrathin plane-parallel homogeneous periodic single-wall carbon nanotube arrays and weakly inhomogeneous single-wall carbon nanotube films. We show that in addition to varying film composition, the collective response can be controlled

Huaxin Lin

Let $A$ be a separable simple exact ${\cal Z}$-stable $C^*$-algebra. We show that the unitay group of ${\tilde A}$ has the cancellation property. If $A$ has continuous scale, the Cuntz semigroup of $\tilde A$ has the strict comparison property and a weak cancellation property. Let $C$ be a 1-dimensional non-commutative CW complex with $K_1(C)=\{0\}.$ Suppose

Marin Bukov, Markus Schmitt, Maxime Dupont

Strongly interacting quantum systems described by non-stoquastic Hamiltonians exhibit rich low-temperature physics. Yet, their study poses a formidable challenge, even for state-of-the-art numerical techniques. Here, we investigate systematically the performance of a class of universal variational wave-functions based on artificial neural networks, by consid

Davide Ravotti

Let $G$ be a connected semisimple Lie group with finite centre, and let $M= \Gamma \backslash G$ be a compact homogeneous manifold. Under a spectral gap assumption, we show that smooth time-changes of any unipotent flow on $M$ have polynomial decay of correlations. Our result applies also in the case where $M$ is a finite volume, non-compact quotient under s

James Bartusek, Andrea Coladangelo, Dakshita Khurana, Fermi Ma

We construct the first constant-round protocols for secure quantum computation in the two-party (2PQC) and multi-party (MPQC) settings with security against malicious adversaries. Our protocols are in the common random string (CRS) model. - Assuming two-message oblivious transfer (OT), we obtain (i) three-message 2PQC, and (ii) five-round MPQC with only thre

Anushri Dixit, Mohamadreza Ahmadi, Joel W. Burdick

We consider the problem of risk-sensitive motion planning in the presence of randomly moving obstacles. To this end, we adopt a model predictive control (MPC) scheme and pose the obstacle avoidance constraint in the MPC problem as a distributionally robust constraint with a KL divergence ambiguity set. This constraint is the dual representation of the Entrop

Qing Zhu, Qisen Shang, Han Hu, Haojia Yu

Photogrammetric mesh models obtained from aerial oblique images have been widely used for urban reconstruction. However, the photogrammetric meshes also suffer from severe texture problems, especially on the road areas due to occlusion. This paper proposes a structure-aware completion approach to improve the quality of meshes by removing undesired vehicles o

Yong Wang

In this paper, we introduce semi-symmetric metric Koszul forms and semi-symmetric non-metric Koszul forms on singular semi-Riemannian manifolds. Semi-symmetric metric Koszul forms and semi-symmetric non-metric Koszul forms and their curvature of semi-regular warped products are expressed in terms of those of the factor manifolds. We also introduce Koszul for

Anthony Suen

In this paper, we study the Navier-Stokes equations of compressible, barotropic flow posed in a bounded set in $\mathbb{R}^3$ with different boundary conditions. Specifically, we prove that the local-in-time smooth solution of the Navier-Stokes equations with Navier boundary condition converges to the smooth solution of the Navier-Stokes equations with no-sl

Cao H. Nam

We propose a general flavor-independent extension of the Standard Model (SM) with the minimal particle content, based on the symmetry $SU(3)_C\times SU(2)_L\times U(1)_{Y'}\times U(1)_X\times Z_2$. In this scenario, the charge operator is identified in terms of the charges of two $U(1)$ gauge symmetries. The light neutrino masses are generated via Type-I see

Raimundo N. Aráujo dos Santos, Eder L. Sanchez Quiceno

In this paper we construct new classes of mixed singularities that provide realizations of real algebraic links in the $3$-sphere. Classifications and characterizations of real algebraic links are still open. These new classes of mixed singularities may help to shed light on the Benedetti-Shiota conjecture, which state that any fibered link on the $3$-sphere

M Mehnert, W Oates, P Steinmann

Molecular photo-switches as, e.g., azobenzene molecules allow, when embedded into a polymeric matrix, for photo-active polymer compounds responding mechanically when exposed to light of certain wavelength. Photo-mechanics, i.e. light-matter interaction in photo-active polymers holds great promise for, e.g., remote and contact-free activation of photo-driven

Dongyan Guo, Yanyan Shao, Ying Cui, Zhenhua Wang

Siamese network based trackers formulate the visual tracking task as a similarity matching problem. Almost all popular Siamese trackers realize the similarity learning via convolutional feature cross-correlation between a target branch and a search branch. However, since the size of target feature region needs to be pre-fixed, these cross-correlation base me

Kenn Migan Vincent C. Gumonan, Aleta C. Fabregas

The study aims to develop an application that will serve as an alternative learning tool for learning Asian Studies. The delivery of lessons into a virtual reality game depends on the pace of students. The developed application comprises several more features that enable users to get valuable information from an immersive environment. The researchers used Ra

Reza Babanezhad, Simon Lacoste-Julien

Mirror-prox (MP) is a well-known algorithm to solve variational inequality (VI) problems. VI with a monotone operator covers a large group of settings such as convex minimization, min-max or saddle point problems. To get a convergent algorithm, the step-size of the classic MP algorithm relies heavily on the problem dependent knowledge of the operator such as

Marcel Keller, Ke Sun

Softmax is widely used in deep learning to map some representation to a probability distribution. As it is based on exp/log functions that are relatively expensive in multi-party computation, Mohassel and Zhang (2017) proposed a simpler replacement based on ReLU to be used in secure computation. However, we could not reproduce the accuracy they reported for

Wei Yu, Wenxin Chen, Songhenh Yin, Steve Easterbrook

Recent works in video prediction have mainly focused on passive forecasting and low-level action-conditional prediction, which sidesteps the learning of interaction between agents and objects. We introduce the task of semantic action-conditional video prediction, which uses semantic action labels to describe those interactions and can be regarded as an inver

Yandong Li, Xuhui Jia, Ruoxin Sang, Yukun Zhu

This paper is concerned with ranking many pre-trained deep neural networks (DNNs), called checkpoints, for the transfer learning to a downstream task. Thanks to the broad use of DNNs, we may easily collect hundreds of checkpoints from various sources. Which of them transfers the best to our downstream task of interest? Striving to answer this question thorou

Mehrdad Farajtabar, Andrew Lee, Yuanjian Feng, Vishal Gupta

Estimating individual and average treatment effects from observational data is an important problem in many domains such as healthcare and e-commerce. In this paper, we advocate balance regularization of multi-head neural network architectures. Our work is motivated by representation learning techniques to reduce differences between treated and untreated dis

Ruth Moufang, John Stillwell

This is an English translation of Moufang's notes "Grundlagen der Geometrie" at the University of Frankfurt in 1948, with added notes by the translator.

Kien Nguyen, Clinton Fookes, Sridha Sridharan, Arun Ross

In this work, we design a fully complex-valued neural network for the task of iris recognition. Unlike the problem of general object recognition, where real-valued neural networks can be used to extract pertinent features, iris recognition depends on the extraction of both phase and magnitude information from the input iris texture in order to better represe

Weiwei Liu, Haobo Wang, Xiaobo Shen, Ivor W. Tsang

Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with an extremely large number of classes or labels;

Tie Zhang, Shangyou Zhang

In this paper, we present and analyze a weak Galerkin finite element (WG) method for solving the symmetric hyperbolic systems. This method is highly flexible by allowing the use of discontinuous finite elements on element and its boundary independently of each other. By introducing special weak derivative, we construct a stable weak Galerkin scheme and deriv

Hai-Rui Wei, Wen-Qiang Liu, Leong-Chuan Kwek

We introduce a nonunitary partial-swap gate for fusing arbitrary small-sized photonic W-states into a large-scale entangled network of W-state efficiently without ancillary photons. A partial-swap gate is designed in an optical architecture based on linear optics elements. By introducing auxiliary degree of freedom, this gate provides a higher success probab

Mohammad Golbabaee, Clarice Poon

We propose a novel numerical approach to separate multiple tissue compartments in image voxels and to estimate quantitatively their nuclear magnetic resonance (NMR) properties and mixture fractions, given magnetic resonance fingerprinting (MRF) measurements. The number of tissues, their types or quantitative properties are not a-priori known, but the image i

Sebastián Nájera, Roberto A. Sussman

We examine the evolution of peculiar velocities of cold dark matter (CDM) in localized arrays of inhomogeneous cosmic structures in a $\Lambda$CDM background that can be identified as a frame comoving with the Cosmic Microwave (CMB). These arrays are constructed by smoothly matching to this cosmological background regions of Szekeres-II models whose source i

Kunming Li, Mao Shan, Karan Narula, Stewart Worrall

Seamlessly operating an autonomous vehicle in a crowded pedestrian environment is a very challenging task. This is because human movement and interactions are very hard to predict in such environments. Recent work has demonstrated that reinforcement learning-based methods have the ability to learn to drive in crowds. However, these methods can have very poor

Kunming Li, Stuart Eiffert, Mao Shan, Francisco Gomez-Donoso

Autonomous vehicle navigation in shared pedestrian environments requires the ability to predict future crowd motion both accurately and with minimal delay. Understanding the uncertainty of the prediction is also crucial. Most existing approaches however can only estimate uncertainty through repeated sampling of generative models. Additionally, most current p

David Fernandez-Gutierrez, Wim M. van Rees

Ray-finned fish are able to adapt the curvature of their fins through musculature at the base of the fin. In this work we numerically investigate the effects of such leading-edge curvature actuation on the hydrodynamic performance of a heaving and pitching fin. We present a geometric and numerical framework for constructing the shape of ray-membrane type fin

Rick Archibald, Edmond Chow, Eduardo D'Azevedo, Jack Dongarra

This paper presents some of the current challenges in designing deep learning artificial intelligence (AI) and integrating it with traditional high-performance computing (HPC) simulations. We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems efficiently, identify challenges, and propose new a

Zhengda Mo, Sam Qunell, Anush Tserunyan, Jenna Zomback

We study the ratio, in a finite graph, of the sizes of the largest matching in any pair of disjoint matchings with the maximum total number of edges and the largest possible matching. Previously, it was shown that this ratio is between 4/5 and 1, and the class of graphs achieving 4/5 was completely characterized. In this paper, we first show that graph decom

Ziliang Zhong, Muhang Zheng, Huafeng Mai, Jianan Zhao

Computer-aided diagnosis establishes methods for robust assessment of medical image-based examination. Image processing introduced a promising strategy to facilitate disease classification and detection while diminishing unnecessary expenses. In this paper, we propose a novel metastatic cancer image classification model based on DenseNet Block, which can eff

Menglan Liao, Bin Guo, Xiangyu Zhu

The goal of the present paper is to study the asymptotic behavior of solutions for the viscoelastic wave equation with variable exponents \[ u_{tt}-\Delta u+\int_0^tg(t-s)\Delta u(s)ds+a|u_t|^{m(x)-2}u_t=b|u|^{p(x)-2}u\] under initial-boundary condition, where the exponents $p(x)$ and $m(x)$ are given functions, and $a,~b>0$ are constants. More precisely, un

James Gigliotti, Xin Li, Suresh Sundaram, Dogukan Deniz

Realizing high-performance nanoelectronics requires control of materials at the nanoscale. Methods to produce high quality epitaxial graphene (EG) nanostructures on silicon carbide are known. The next step is to grow Van der Waals semiconductors on top of EG nanostructures. Hexagonal boron nitride (h-BN) is a wide bandgap semiconductor with a honeycomb latti

Junnan Li, Caiming Xiong, Steven Hoi

Semi-supervised learning has been an effective paradigm for leveraging unlabeled data to reduce the reliance on labeled data. We propose CoMatch, a new semi-supervised learning method that unifies dominant approaches and addresses their limitations. CoMatch jointly learns two representations of the training data, their class probabilities and low-dimensional

A. M. Masullo

Building on ideas of Berthelot, we develop a crystalline cohomology formalism over divided power rings $(A, I_0, \eta)$ for any ring $A$, allowing $\mathbf{Z}$-flat $A$. For a smooth $A$-scheme $Y$ and a closed subscheme $X$ of $Y$ for which $\eta$ extends to $I_0 \mathscr{O}_X$, a (quasi-coherent) crystal $\mathscr{F}$ on $(X/A)_{\rm{cris}}$ is equivalent t

Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo

In this work, we examine the security of InstaHide, a scheme recently proposed by [Huang, Song, Li and Arora, ICML'20] for preserving the security of private datasets in the context of distributed learning. To generate a synthetic training example to be shared among the distributed learners, InstaHide takes a convex combination of private feature vectors and

Safa Alsafari, Samira Sadaoui, Malek Mouhoub

Deep neural networks have been adopted successfully in hate speech detection problems. Nevertheless, the effect of the word embedding models on the neural network's performance has not been appropriately examined in the literature. In our study, through different detection tasks, 2-class, 3-class, and 6-class classification, we investigate the impact of both

William Graham Hoover, Carol Griswold Hoover

Following Berni Alder [1] and Francis Ree [2], Douglas Henderson was the third of Bill's California coworkers from the 1960s to die in 2020. Motivated by Doug's death we undertook better to understand Lyapunov instability and the breaking of time symmetry in continuum and atomistic simulations. Here we have chosen to extend our explorations of an interesting

Tie Zhang

The $q$-fractional differential equation usually describe the physics process imposed on the time scale set $T_q$. In this paper, we first propose a difference formula for discretizing the fractional $q$-derivative $^cD_q^\alpha x(t)$ on the time scale set $T_q$ with order $0<\alpha<1$ and scale index $0<q<1$. We establish a rigours truncation error boundnes

Fan Yin, Jieying Jiao, Guanyu Hu, Jun Yan

Basketball shot location data provide valuable summary information regarding players to coaches, sports analysts, fans, statisticians, as well as players themselves. Represented by spatial points, such data are naturally analyzed with spatial point process models. We present a novel nonparametric Bayesian method for learning the underlying intensity surface

Huan Qing, Jingli Wang

Community detection in network analysis is an attractive research area recently. Here, under the degree-corrected mixed membership (DCMM) model, we propose an efficient approach called mixed regularized spectral clustering (Mixed-RSC for short) based on the regularized Laplacian matrix. Mixed-RSC is designed based on an ideal cone structure of the variant fo

Paul A. Roediger

This work provides documentation for a suite of R functions contained in gonogo.R. The functions provide sensitivity testing practitioners and researchers with an ability to conduct, analyze and simulate various sensitivity experiments involving binary responses and a single stimulus level (e.g., drug dosage, drop height, velocity, etc.). Included are the mo

Yu Sun, Jian Zhang

With the rapid development of the internet technology, dirty data are commonly observed in various real scenarios, e.g., owing to unreliable sensor reading, transmission and collection from heterogeneous sources. To deal with their negative effects on downstream applications, data cleaning approaches are designed to preprocess the dirty data before conductin

Erich Cavalcanti

The renormalon singularities are a known source of the divergent behavior of asymptotic perturbative series from field theoretical models. These singularities live in the Borel plane and are responsible for ambiguities in the physical plane. We propose that field theories can have renormalons beyond the usual first Borel plane. We show an example with a scal

Shiv Ram Dubey

The content based image retrieval aims to find the similar images from a large scale dataset against a query image. Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have been investigated based on the visual cu

Lauren Mancia, Jin Yang, Jean-Sebastien Spratt, Jonathan R. Sukovich

Characterization of soft materials is challenging due to their high compliance and the strain-rate dependence of their mechanical properties. The inertial microcavitation-based high strain-rate rheometry (IMR) method [Estrada et al., J. Mech. Phys. Solids, 2018, 112, 291-317] combines laser-induced cavitation measurements with a model for the bubble dynamics

Dmitriy Drusvyatskiy, Lin Xiao

Stochastic optimization problems often involve data distributions that change in reaction to the decision variables. This is the case for example when members of the population respond to a deployed classifier by manipulating their features so as to improve the likelihood of being positively labeled. Recent works on performative prediction have identified an

Kenny Vilella, Shunichi Kamata

The effect of melting in planetary mantles plays a key role in their thermo-chemical evolution. Because of the laterally heterogeneous nature of melting, 3D numerical simulations are in principle necessary prohibiting us from exploring wide ranges of conditions. To overcome this issue, we propose a new analytical framework allowing to estimate the amount and

Yu-Yu Zhang, Zi-Xiang Hu, Libin Fu, Hong-Gang Luo

The interplay of interactions, symmetries and gauge fields usually leads to intriguing quantum many-body phases. To explore the nature of emerging phases, we study a quantum Rabi triangle system as an elementary building block for synthesizing an artificial magnetic field. We develop an analytical approach to study the rich phase diagram and the associated q

Yayun Zheng, Fang Yang, Jinqiao Duan, Jürgen Kurths

Mathematical models for complex systems under random fluctuations often certain uncertain parameters. However, quantifying model uncertainty for a stochastic differential equation with an $\alpha$-stable L\'evy process is still lacking. Here, we propose an approach to infer all the uncertain non-Gaussian parameters and other system parameters by minimizing t

Youngdae Lee, Suk Kim, Soo-Chang Rey, Jiwon Chung

We present the properties of galaxies in filaments around the Virgo cluster with respect to their vertical distance from the filament spine using the NASA-Sloan Atlas catalog. The filaments are mainly composed of low-mass, blue dwarf galaxies. We observe that the g - r color of galaxies becomes blue and stellar mass decreases with increasing vertical filamen

Yang Li

We propose a new collapsing mechanism for $G_2$-metrics, with the generic region admitting a circle bundle structure over a K3 fibration over a Riemann surface. The adiabatic description involves a weighted version of the maximal submanifold equation. In a local smooth setting we prove the existence of formal power series solutions, and the problem of compac

Edward Kim, Maryam Daniali, Jocelyn Rego, Garrett T. Kenyon

Research has shown that neurons within the brain are selective to certain stimuli. For example, the fusiform face area (FFA) region is known by neuroscientists to selectively activate when people see faces over non-face objects. However, the mechanisms by which the primary visual system directs information to the correct higher levels of the brain are curren

Julio César Vera-Hernández

Bimetric gravity theories describes gravitational interactions in the presence of an extra spin-2 field. The Hassan-Rosen (HR) nonlinear massive minimal bigravity theory is a ghost-free bimetric theory formulated with respect a flat, dynamical reference metric. In this work the deformation quantization formalism is applied to a HR cosmological model in the m

Charlotte Strandkvist, Pavel Chvykov, Mikhail Tikhonov

Complex systems with many degrees of freedom are typically intractable, but some of their behaviors may admit simpler effective descriptions. The question of when such effective descriptions are possible remains open. The paradigmatic approach where such "emergent simplicity" can be understood in detail is the renormalization group (RG). Here, we show that f

Theo Johnson-Freyd

There is exactly one bosonic (3+1)-dimensional topological order whose only nontrivial particle is an emergent boson: pure $\mathbb{Z}_2$ gauge theory. There are exactly two (3+1)-dimensional topological orders whose only nontrivial particle is an emergent fermion: pure "spin-$\mathbb{Z}_2$" gauge theory, in which the dynamical field is a spin structure; and

Jiequan Cui, Shu Liu, Liwei Wang, Jiaya Jia

Previous adversarial training raises model robustness under the compromise of accuracy on natural data. In this paper, we reduce natural accuracy degradation. We use the model logits from one clean model to guide learning of another one robust model, taking into consideration that logits from the well trained clean model embed the most discriminative feature

Kenny Vilella, Frederic Deschamps

Earth's continents are characterized by a strong enrichment in long-lived radioactive isotopes. Recent estimates suggest that they contribute to 33\% of the heat released at the surface of the Earth, while occupying less than 1\% of the mantle. This distinctive feature has profound implications for the underlying mantle by impacting its thermal structure and

Siavash Mollaebrahim, Baltasar Beferull-Lozano

Implementing linear transformations is a key task in the decentralized signal processing framework, which performs learning tasks on data sets distributed over multi-node networks. That kind of network can be represented by a graph. Recently, some decentralized methods have been proposed to compute linear transformations by leveraging the notion of graph shi

Arthur Hernandez, M. Cristina Marchetti

Using the Poisson bracket method, we derive continuum equations for a fluid of deformable particles in two dimensions. Particle shape is quantified in terms of two continuum fields: an anisotropy density field that captures the deformations of individual particles from regular shapes and a shape tensor density field that quantifies both particle elongation a

Hong Lin, Lidan Shou, Ke Chen, Gang Chen

Federated Learning (FL) is a promising distributed learning paradigm, which allows a number of data owners (also called clients) to collaboratively learn a shared model without disclosing each client's data. However, FL may fail to proceed properly, amid a state that we call negative federated learning (NFL). This paper addresses the problem of negative fede

Wen-Xiong Song, Qiongyan Tang, Jin Zhao, Muriel Veron

While phase-change materials (PCMs) composed of chalcogenide have different crystallization mechanisms (CM), such as nucleation-dominated Ge2Sb2Te5 (GST) and growth-dominated GeTe (GT), revealing the essential reason of CM as well as the tuned properties is still a long-standing issue. Here, we remarkably find the distinct stability of Te-terminated (111) bo

Alexander T. Miller, Anil V. Rao

A parameterization is described for quantifying translational motion of a point in three-dimensional Euclidean space. The parameterization is similar to well-known parameterizations such as spherical coordinates in that both position and velocity are decoupled into magnitude and orientation components. Unlike these standard parameterizations, where principal

D. Bazeia, Adalto R. Gomes, Fabiano C. Simas

In this work we consider model of asymmetric kinks, where the behavior of the solution in one side is different from the other side. Also, the models depend of an integer $n$ and, with the increase of $n$, the constructed kink assumes a hybrid character: a compactlike profile on one side and a kinklike profile on the other side. We investigate numerically th

Divya Shanmugam, Davis Blalock, Guha Balakrishnan, John Guttag

Test-time augmentation -- the aggregation of predictions across transformed versions of a test input -- is a common practice in image classification. Traditionally, predictions are combined using a simple average. In this paper, we present 1) experimental analyses that shed light on cases in which the simple average is suboptimal and 2) a method to address t

Hao Zhu, Yang Yuan, Guosheng Hu, Xiang Wu

Deep embedding learning is expected to learn a metric space in which features have smaller maximal intra-class distance than minimal inter-class distance. In recent years, one research focus is to solve the open-set problem by discriminative deep embedding learning in the field of face recognition (FR) and person re-identification (re-ID). Apart from open-se

Maëva Ostermann

We prove that, for $M>0$ and $n,m\ge2$, we can find two $10\times10$ matrices $A$ and $B$ with identical pseudospectra such that we have simultaneously $\|A^n\|/\|B^n\|>M$ and $\|A^m\|/\|B^m\|>M$. We also prove that, under certain conditions, this result holds for two more general functions of $A$ and $B$.

Roman Pokora, Susan Kutschbach, Matthias Weigl, Detlef Braun

Since May 2020, several COVID-19 outbreaks have occurred in the German meat industry despite various protective measures, and temperature and ventilation conditions were considered as possible high-risk factors. This cross-sectional study examined meat and poultry plants to examine possible risk factors. Companies completed a self-administered questionnaire

Zeke Xie, Zhiqiang Xu, Jingzhao Zhang, Issei Sato

Weight decay is a simple yet powerful regularization technique that has been very widely used in training of deep neural networks (DNNs). While weight decay has attracted much attention, previous studies fail to discover some overlooked pitfalls on large gradient norms resulted by weight decay. In this paper, we discover that, weight decay can unfortunately

Jayant Gupchup, Ashkan Aazami, Yaran Fan, Senja Filipi

Large software systems tune hundreds of 'constants' to optimize their runtime performance. These values are commonly derived through intuition, lab tests, or A/B tests. A 'one-size-fits-all' approach is often sub-optimal as the best value depends on runtime context. In this paper, we provide an experimental approach to replace constants with learned contextu

Zeyd Boukhers, Danniene Wete, Steffen Staab

Processing sequential multi-sensor data becomes important in many tasks due to the dramatic increase in the availability of sensors that can acquire sequential data over time. Human Activity Recognition (HAR) is one of the fields which are actively benefiting from this availability. Unlike most of the approaches addressing HAR by considering predefined activ

Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien

Recently, structure learning of directed acyclic graphs (DAGs) has been formulated as a continuous optimization problem by leveraging an algebraic characterization of acyclicity. The constrained problem is solved using the augmented Lagrangian method (ALM) which is often preferred to the quadratic penalty method (QPM) by virtue of its standard convergence re

Shiping Cao

We study the convergence of resistance metrics and resistance forms on a converging sequence of spaces. As an application, we study the existence and uniqueness of self-similar Dirichlet forms on Sierpinski gaskets with added rotated triangles. The fractals depend on a parameter in a continuous way. When the parameter is irrational, the fractal is not post c

Pablo Borys, Oleg Kolokoltsev, Naser Qureshi, Martin L. Plumer

We demonstrate using micromagnetic simulations and a theoretical model that a gradient in the saturation magnetization ($M_s$) of a perpendicularly magnetized ferromagnetic film induces a non-reciprocal spin wave propagation and, consequently an asymmetric dispersion relation. The $M_s$ gradient adds a linear potential to the spin wave equation of motion con

John Leventides, Nick Poulios, Costas Poulios

We introduce the concept of $\epsilon$-uncontrollability for random linear systems, i.e. linear system in which the usual matrices have been replaced by random matrices. We also estimate the $\epsilon$-uncontrollability in the case where the matrices come from the Gaussian orthogonal ensemble. Our proof utilizes tools from systems theory, probability theory

Alejandro Cholaquidis, Ricardo Fraiman, Leonardo Moreno

The lens depth of a point has been recently extended to general metric spaces, which is not the case for most depths. It is defined as the probability of being included in the intersection of two random balls centred at two random points X and Y, with the same radius d(X, Y). We study the consistency in Hausdorff and measure distance, of the level sets of th

David E. Alvarez-Castillo

Transition of a compact star into the third family for an equation of state (EoS) featuring mass twins is considered. The energy released at a baryon number conserving transition for static compact stars configurations is computed for two sets of models for comparison. The EoS of choice is the density dependent functional DD2 EoS with excluded model correcti

Taeyoung An, A. Yavuz Oruc

The proliferation of number of processing elements (PEs) in parallel computer systems, along with the use of more extensive parallelization of algorithms causes the interprocessor communications dominate VLSI chip space. This paper proposes a new architecture to overcome this issue by using simple crosspoint switches to pair PEs instead of a complex intercon

C. D. Macrae, K. Bongs, M. Holynski

A method for the agile generation of the optical frequencies required for laser cooling and atom interferometry of rubidium is demonstrated. It relies on fiber Bragg grating technology to filter the output of an electro-optic modulator and was demonstrated in a robust, alignment-free, single-seed, frequency-doubled, telecom fiber laser system. The system was

Amrita Acharyya, Allen Williams

The Directed Power Graph of a group is a graph whose vertex set is the elements of the group, with an edge from $x$ to $y$ if $y$ is a power of $x$. The \textit{Power Graph} of a group can be obtained from the directed power graph by disorienting its edges. This article discusses properties of cliques, cycles, paths, and coloring in power graphs of finite gr

G. Berkolaiko, P. Kuchment

We consider a compact perturbation $H_0 = S + K_0^* K_0$ of a self-adjoint operator $S$ with an eigenvalue $\lambda^\circ$ below its essential spectrum and the corresponding eigenfunction $f$. The perturbation is assumed to be "along" the eigenfunction $f$, namely $K_0f=0$. The eigenvalue $\lambda^\circ$ belongs to the spectra of both $H_0$ and $S$. Let $S$

Marcelo Bongarti, Sutthirut Charoenphon, Irena Lasiecka

The (third-order in time) JMGT equation \cite{Jordan2,HCP} is a nonlinear (quasi-linear) Partial Differential Equation (PDE) model introduced to describe a nonlinear propagation of sound in an acoustic medium. The important feature is that the model avoids the infinite speed of propagation paradox associated with a classical second order in time equation ref

Alejandro Cholaquidis, Ricardo Fraiman, Fabrice Gamboa, Leonardo Moreno

Starting with Tukey's pioneering work in the 1970's, the notion of depth in statistics has been widely extended especially in the last decade. These extensions include high dimensional data, functional data, and manifold-valued data. In particular, in the learning paradigm, the depth-depth method has become a useful technique. In this paper we extend the not

Jie Gao, Mushu Li, Weihua Zhuang, Xuemin

In the second part of this paper, we develop a centralized packet transmission scheduling scheme to pair with the protocol designed in Part I and complete our medium access control (MAC) design for machine-type communications in the industrial internet of things. For the networking scenario, fine-grained scheduling that attends to each device becomes necessa