October 2020 arXiv papers — page 115

Han Lin Shang, Kaiying Ji, Ufuk Beyaztas

We study causality between bivariate curve time series using the Granger causality generalized measures of correlation. With this measure, we can investigate which curve time series Granger-causes the other; in turn, it helps determine the predictability of any two curve time series. Illustrated by a climatology example, we find that the sea surface temperat

Zeno Schätzle, Jan Hermann, Frank Noé

Variational quantum Monte Carlo (QMC) is an ab-initio method for solving the electronic Schrödinger equation that is exact in principle, but limited by the flexibility of the available ansatzes in practice. The recently introduced deep QMC approach, specifically two deep-neural-network ansatzes PauliNet and FermiNet, allows variational QMC to reach the accur

Hongjie Dong, Yan Guo, Zhimeng Ouyang

We consider the Vlasov-Poisson-Landau system, a classical model for a dilute collisional plasma interacting through Coulombic collisions and with its self-consistent electrostatic field. We establish global stability and well-posedness near the Maxwellian equilibrium state with decay in time and some regularity results for small initial perturbations, in any

Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik

We consider the problem of controlling a partially-observed dynamic process on a graph by a limited number of interventions. This problem naturally arises in contexts such as scheduling virus tests to curb an epidemic; targeted marketing in order to promote a product; and manually inspecting posts to detect fake news spreading on social networks. We formulat

Yucheng Yang, Zhong Zheng, Weinan E

The lack of interpretability and transparency are preventing economists from using advanced tools like neural networks in their empirical research. In this paper, we propose a class of interpretable neural network models that can achieve both high prediction accuracy and interpretability. The model can be written as a simple function of a regularized number

Aanjaneya Kumar, Peter Grassberger, Deepak Dhar

Chase-escape percolation is a variation of the standard epidemic spread models. In this model, each site can be in one of three states: unoccupied, occupied by a single prey, or occupied by a single predator. Prey particles spread to neighboring empty sites at rate $p$, and predator particles spread only to neighboring sites occupied by prey particles at rat

Guangxu Ju, Dongwei Xu, Carol Thompson, Matthew J. Highland

Miscut surfaces of layered crystals can exhibit a stair-like sequence of terraces having periodic variation in their atomic structure. For hexagonal close-packed and related crystal structures with an αβαβ stacking sequence, there have been long-standing questions regarding how the differences in adatom attachment kinetics at the steps separating the terrace

Mohammad Salahshour

The evolution of cooperation has remained an important problem in evolutionary theory and social sciences. In this regard, a curious question is why consistent cooperative and defective personalities exist and if they serve a role in the evolution of cooperation? To shed light on these questions, here, I consider a population of individuals who possibly play

Mousomi Bhakta, Souptik Chakraborty, Olimpio H. Miyagaki, Patrizia Pucci

In this paper we study positive solutions to the following nonlocal system of equations: \begin{equation*} \left\{\begin{aligned} &(-Δ)^s u = \fracα{2_s^*}|u|^{α-2}u|v|^β+f(x)\;\;\text{in}\;\mathbb{R}^{N}, &(-Δ)^s v = \fracβ{2_s^*}|v|^{β-2}v|u|^α+g(x)\;\;\text{in}\;\mathbb{R}^{N}, & \qquad u, \, v >0\, \mbox{ in }\,\mathbb{R}^{N}, \end{aligned} \right. \end{

Aaram J. Kim, Nikolay V. Prokof'ev, Boris V. Svistunov, Evgeny Kozik

The major obstacle preventing Feynman diagrammatic expansions from accurately solving many-fermion systems in strongly correlated regimes is the series slow convergence or divergence problem. Several techniques have been proposed to address this issue: series resummation by conformal mapping, changing the nature of the starting point of the expansion by shif

Abdel Missa, Chrif Youssfi

This article introduces an intuitive function MY that simplifies solving cubic equations without venturing into the complex space. To many, it's quite strange that cubic root(s) are expressed using trigonometric functions in the three-real-roots case versus real-radicals in the one-real-root case. Yet, the MY function provides a different perspective to

Dmitriy Stolyarov

Let $\mathcal{W}$ be a closed dilation and translation invariant subspace of the space of $\mathbb{R}^\ell$-valued Schwartz distributions in $d$ variables. We show that if the space $\mathcal{W}$ does not contain distributions of the type $a\otimes δ_0$, $δ_0$ being the Dirac delta, then the inequality $\|\mathbb{I}_α[f]\|_{L_{p,1}}\lesssim \|f\|_{L_1}$, $\f

Merlin Christ

This paper concerns spherical adjunctions of stable $\infty$-categories and their relation to monadic adjunctions. We begin with a proof of the 2/4 property of spherical adjunctions in the setting of stable $\infty$-categories. The proof is based on the description of spherical adjunctions as 4-periodic semiorthogonal decompositions given by Halpern-Leistner

C. A. Fonseca-Mora

In this work we introduce a theory of stochastic integration with respect to general cylindrical semimartingales defined on a locally convex space $Φ$. Our construction of the stochastic integral is based on the theory of tensor products of topological vector spaces and the property of good integrators of real-valued semimartingales. This theory is further d

Xin Guo, Huyên Pham, Xiaoli Wei

We establish Itô's formula along flows of probability measures associated with general semimartingales; this generalizes existing results for flows of measures on Itô processes. Our approach is to first establish Itô's formula for cylindrical functions and then extend it to the general case via function approximation and localization techniques. This

Giuseppe Arbia, Vincenzo Nardelli

The Big Data revolution is challenging the state-of-the-art statistical and econometric techniques not only for the computational burden connected with the high volume and speed which data are generated, but even more for the variety of sources through which data are collected (Arbia, 2021). This paper concentrates specifically on this last aspect. Common ex

Alexander Tyulenev

We construct explicit examples of Frostman-type measures concentrated on arbitrary planar rectifiable curves of positive length. Based on such constructions we obtain for each $p \in (1,\infty)$ an exact description of the trace space of the first-order Sobolev space $W^{1}_{p}(\mathbb{R}^{2})$ to an arbitrary planar rectifiable curve $Γ\subset \mathbb{R}^{2

Manuel Hertzog, Battulga Munkhbat, Denis G. Baranov, Timur O. Shegai

Vibrational strong coupling is emerging as a promising tool to modify molecular properties, by making use of hybrid light-matter states known as polaritons. Fabry-Perot cavities filled with organic molecules are typically used, and the molecular concentration limits the maximum reachable coupling strength. Developing methods to increase the coupling strength

Vadim Kaushansky, Christoph Reisinger, Mykhaylo Shkolnikov, Zhuo Qun Song

The supercooled Stefan problem and its variants describe the freezing of a supercooled liquid in physics, as well as the large system limits of systemic risk models in finance and of integrate-and-fire models in neuroscience. Adopting the physics terminology, the supercooled Stefan problem is known to feature a finite-time blow-up of the freezing rate for a

Muhsin Aljaf, Daniele Gregoris, Martiros Khurshudyan

In this paper, we will deepen the understanding of some fluid models proposed by other authors for the description of dark energy. Specifically, we will show that the so-called (Modified) Berthelot fluid is the hydrodynamic realization of the free Dirac-Born-Infeld theory and that the Dieterici fluid admits a non-relativistic $k$-essence formulation; for the

Gad Koren, Anna Eyal, Leonid Iomin, Yuval Nitzav

Nickelate films have recently attracted broad attention due to the observation of superconductivity in the infinite layer phase of $Nd_{0.8}Sr_{0.2}NiO_2$ (obtained by reducing Sr doped $NdNiO_3$ films) and their similarity to the cuprates high temperature superconductors. Here we report on the observation of a new type of transport in oxygen poor $Nd_{0.8}S

Claire Voisin

We prove that the integral cohomology modulo torsion of a rationally connected threefold comes from the integral cohomology of a smooth curve via the cylinder homomorphism associated to a family of $1$-cycles. Equivalently, it is of strong coniveau 1 in the sense of Benoist-Ottem.

Maruan Al-Shedivat, Jennifer Gillenwater, Eric Xing, Afshin Rostamizadeh

Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that possess local data and specify different parts of the global objective. We present an alternative perspective and formulate federated learning as a posterior inference problem, wher

Ziyi Wu, Yueqi Duan, He Wang, Qingnan Fan

Point cloud is an important 3D data representation widely used in many essential applications. Leveraging deep neural networks, recent works have shown great success in processing 3D point clouds. However, those deep neural networks are vulnerable to various 3D adversarial attacks, which can be summarized as two primary types: point perturbation that affects

Moritz Walden, Stefan Weinzierl

We report on an implementation within GiNaC to evaluate iterated integrals related to elliptic Feynman integrals numerically to arbitrary precision within the region of convergence of the series expansion of the integrand. The implementation includes iterated integrals of modular forms as well as iterated integrals involving the Kronecker coefficient functio

Yunlong Wang, Shihao Ru, Feiran Wang, Pei Zhang

Quantum gates are essential for the realization of quantum computer and have been implemented in various types of two-level systems. However, high-dimensional quantum gates are rarely investigated both theoretically and experimentally even that high-dimensional quantum systems exhibit remarkable advantages over two-level systems for some quantum information

Shihao Ru, Weidong Tang, Yunlong Wang, Feiran Wang

Contextuality provides one of the fundamental characterizations of quantum phenomena, and can be used as a resource in lots of quantum information processing. In this paper, we summarize and derive some equivalent noncontextual inequalities from different noncontextual models of the proofs for Kochen-Specker theorem based on Greenberger-Horne-Zeilinger state

Shauli Ravfogel, Yanai Elazar, Jacob Goldberger, Yoav Goldberg

Contextualized word representations, such as ELMo and BERT, were shown to perform well on various semantic and syntactic tasks. In this work, we tackle the task of unsupervised disentanglement between semantics and structure in neural language representations: we aim to learn a transformation of the contextualized vectors, that discards the lexical semantics

Xiao Wang, Qi Lei, Ioannis Panageas

Sampling is a fundamental and arguably very important task with numerous applications in Machine Learning. One approach to sample from a high dimensional distribution $e^{-f}$ for some function $f$ is the Langevin Algorithm (LA). Recently, there has been a lot of progress in showing fast convergence of LA even in cases where $f$ is non-convex, notably [53],

Made Tantrawan, Denny H. Leung, Niushan Gao

The study of the Banach-Saks property in Banach spaces has a long and illustrious history. Of late, motivated by applications in financial mathematics, interest has arisen in the Banach-Saks type properties with respect to order convergence. This paper presents a study of order Banach-Saks properties in Banach function spaces, and in particular in rearrangem

Teng Wang, Xuefeng Zhang, Jingyu Feng, Xinyu Yang

Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) has been proposed as an excel

Michelangelo Bin, Lorenzo Marconi

In this paper we consider the joint problems of state estimation and model identification for a class of continuous-time nonlinear systems in output-feedback canonical form. An adaptive observer is proposed that combines an extended high-gain observer and a discrete-time identifier. The extended observer provides the identifier with a data set permitting the

Jian Liang, Yuren Cao, Shuang Li, Bing Bai

Authentication is the task of confirming the matching relationship between a data instance and a given identity. Typical examples of authentication problems include face recognition and person re-identification. Data-driven authentication could be affected by undesired biases, i.e., the models are often trained in one domain (e.g., for people wearing spring

Yong-Liang Zhao, Alexander Ostermann, Xian-Ming Gu

Fractional Ginzburg-Landau equations as the generalization of the classical one have been used to describe various physical phenomena. In this paper, we propose a numerical integration method for solving space fractional Ginzburg-Landau equations based on a dynamical low-rank approximation. We first approximate the space fractional derivatives by using a fra

Masanao Ozawa, Andrei Khrennikov

We continue to analyze basic constraints on the human decision making from the viewpoint of quantum measurement theory (QMT). As it has been found, the conventional QMT based on the projection postulate cannot account for the combination of the question order effect (QOE) and the response replicability effect (RRE). This was alarming finding for quantum-like

Jiyang Xie, Zhanyu Ma, and Jianjun Lei, Guoqiang Zhang

Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs). We propose advanced dropout, a model-free methodology, to mitigate overfitting and improve the performance of DNNs. The advanced dropout technique applies a model-free and easily implemented distribution with parametric prior, and adaptively adjust

Debaditya Pal, Harsh Sharma, Kaustubh Chaudhari

Relational databases are among the most widely used architectures to store massive amounts of data in the modern world. However, there is a barrier between these databases and the average user. The user often lacks the knowledge of a query language such as SQL required to interact with the database. The NL2SQL task aims at finding deep learning approaches to

Bogdan Grechuk, Alexander N. Gorban, Ivan Y. Tyukin

Phenomenon of stochastic separability was revealed and used in machine learning to correct errors of Artificial Intelligence (AI) systems and analyze AI instabilities. In high-dimensional datasets under broad assumptions each point can be separated from the rest of the set by simple and robust Fisher's discriminant (is Fisher separable). Errors or cluste

E. N. Tsiok, Yu. D. Fomin, E. A. Gaiduk, V. N. Ryzhov

Using molecular dynamics simulation we have investigated the influence of random pinning on the phase diagram and melting scenarios of a two-dimensional (2D) system with the Hertz potential for $α=5/2$. For the first time it has been shown that random pinning can cardinally change the mechanism of first-order transition between the different crystalline phas

Tomas Dutko, Carlo Mercuri, Teresa Megan Tyler

We study a nonlinear Schrödinger-Poisson system which reduces to the nonlinear and nonlocal equation \[- Δu+ u + λ^2 \left(\frac{1}{ω|x|^{N-2}}\star ρu^2\right) ρ(x) u = |u|^{q-1} u \quad x \in \mathbb R^N, \] where $ω= (N-2)|\mathbb{S}^{N-1}|,$ $λ>0,$ $q\in(2,2^{\ast} -1),$ $ρ:\mathbb R^N \to \mathbb R$ is nonnegative and locally bounded, $N=3,4,5$ and $2^*

P. O. Kazinski, G. Yu. Lazarenko

The explicit expression for the inclusive probability to record a photon created in transition radiation from a one Dirac particle wave packet traversing an ideally conducting plate is derived in the leading order of perturbation theory. The anomalous magnetic moment of the Dirac particle is taken into account. It is shown that the quantum corrections to tra

Isaac Ronald Ward, Jack Joyner, Casey Lickfold, Yulan Guo

Graph neural networks (GNNs) have recently grown in popularity in the field of artificial intelligence (AI) due to their unique ability to ingest relatively unstructured data types as input data. Although some elements of the GNN architecture are conceptually similar in operation to traditional neural networks (and neural network variants), other elements re

Bernhard Heim, Markus Neuhauser

Horizontal and vertical generating functions and recursion relations have been investigated by Comtet for triangular double sequences. In this paper we investigate the horizontal and vertical log-concavity of triangular sequences assigned to polynomials which show up in combinatorics, number theory and physics. This includes Laguerre polynomials, the Pochham

Carl Merrigan, Cristiano Nisoli, Yair Shokef

Competing ground states may lead to topologically constrained excitations such as domain walls or quasiparticles, which govern metastable states and their dynamics. Domain walls and more exotic topological excitations are well studied in magnetic systems such as artificial spin ice, in which nanoscale magnetic dipoles are placed on geometrically frustrated l

Cris R. Hasan, Hinke M. Osinga, Claire M. Postlethwaite, Alastair M. Rucklidge

Heteroclinic-induced spiral waves may arise in systems of partial differential equations that exhibit robust heteroclinic cycles between spatially uniform equilibria. Robust heteroclinic cycles arise naturally in systems with invariant subspaces and their robustness is considered with respect to perturbations that preserve these invariances. We make use of p

Alexander Sakhnovich

An important representation of the general-type fundamental solutions of the canonical systems corresponding to matrix string equations is established using linear similarity of a certain class of Volterra operators to the squared integration. Explicit fundamental solutions of these canonical systems are also constructed via the GBDT version of Darboux trans

Nabarun Chakrabarty

The present work introduces new scalar and fermionic degrees of freedom to the Standard Model. While the scalar sector is augmented by a complex scalar triplet and a doubly charged scalar singlet, the fermionic sector is extended by two copies of vector-like leptons. Of these, one copy is an $SU(2)_L$ singlet while the other, an $SU(2)_L$ doublet. We explain

Tsukasa Ishibashi, Shunsuke Kano

We give a characterization of generic pseudo-Anosov mapping classes purely in terms of their expressions in the shear coordinates, thus giving an answer to a problem raised by Papadopoulos--Penner [PP93]. This characterization has a cluster algebraic generalization called the sign stability introduced in [IK21]. By combining with the results in [IK21], we se

Zhuotao Tian, Xin Lai, Li Jiang, Shu Liu

Training semantic segmentation models requires a large amount of finely annotated data, making it hard to quickly adapt to novel classes not satisfying this condition. Few-Shot Segmentation (FS-Seg) tackles this problem with many constraints. In this paper, we introduce a new benchmark, called Generalized Few-Shot Semantic Segmentation (GFS-Seg), to analyze

Sanjin Gumbarević, Bojan Milovanović, Mergim Gaši, Marina Bagarić

Heat losses through the building envelope is one of the key factors in the calculation of the building energy balance. If steady-state heat conduction is observed, which is commonly used to assess the heat losses in building, there is an analytical solution for one-dimensional problem. For two and three-dimensional problems, especially for the complex geomet

Qi Wang

We determined the $τ$-tilting finiteness of Schur algebras over an algebraically closed field of arbitrary characteristic, except for a few small cases.

Pallabi Manna, Peter J. Cameron, Ranjit Mehatari

The undirected power graph (or simply power graph) of a group $G$, denoted by $P(G)$, is a graph whose vertices are the elements of the group $G$, in which two vertices $u$ and $v$ are connected by an edge between if and only if either $u=v^i$ or $v=u^j$ for some $i$, $j$. A number of important graph classes, including perfect graphs, cographs, chordal graph

Stanislav Grishin, Ilya Karzhemanov, with an Appendix by Ming-chang Kang

We prove rationality of the quotient $\mathbb{C}^n / H_n$ for the finite Heisenberg group $H_n$, any $n \ge 1$, acting on $\mathbb{C}^n$ via its irreducible representation.

S. N. Iablokov, A. V. Kuznetsov

A coordinate-space representation for a charged scalar particle propagator in a constant magnetic field was obtained as a series over the Landau levels. Using the recently developed modified Fock-Schwinger method, an intermediate expression was constructed and symmetrized, thus, allowing for factorization of the series terms into two factors. The first one,

Jiuyang Liang, Zixuan Gao, Zhenli Xu

Approximation of interacting kernels by sum of Gaussians (SOG) is frequently required in many applications of scientific and engineering computing in order to construct efficient algorithms for kernel summation or convolution problems. In this paper, we propose a kernel-independent SOG method by introducing the de la Vallée-Poussin sum and Chebyshev polynomi

Xiaolei Yi, Xiangzhuo Xing, Lingyao Qin, Jiajia Feng

We report the successful synthesis of FeSe$_{1-x}$S$_{x}$ single crystals with $x$ ranging from 0 to 1 via a hydrothermal method. A complete phase diagram of FeSe$_{1-x}$S$_{x}$ has been obtained based on resistivity and magnetization measurements. The nematicity is suppressed with increasing $x$, and a small superconducting dome appears within the nematic p

Ioannis Diamantis

In this paper we study the theory of {\it pseudo knots}, which are knots with some missing crossing information, and we introduce and study the theory of {\it pseudo tied links} and the theory of {\it pseudo knotoids}. In particular, we first present a braiding algorithm for pseudo knots and we then introduce the $L$-moves in that setting, with the use of wh

Qi Zhang, Yilin Chen, Ziyi Yang, Eric Darve

In this paper, we study the problem of large-strain consolidation in poromechanics with deep neural networks (DNN). Given different material properties and different loading conditions, the goal is to predict pore pressure and settlement. We propose a novel method "multi-constitutive neural network" (MCNN) such that one model can solve several different cons

Moaaz AlQady, Riley Chabot, William Dudarov, Linus Ge

We study Erd\H os's distinct distances problem under $\ell_p$ metrics with integer $p$. We improve the current best bound for this problem from $\Omega(n^{4/5})$ to $\Omega(n^{6/7-\epsilon})$, for any $\epsilon>0$. We also characterize the sets that span an asymptotically minimal number of distinct distances under the $\ell_1$ and $\ell_\infty$ metrics.

Cristian Martinez, Masato Nozawa

We discuss various novel features of $n(\ge 4)$-dimensional spacetimes sourced by a massless (non-)phantom scalar field in general relativity. Assuming that the metric is a warped product of static two-dimensional Lorentzian spacetime and an $(n-2)$-dimensional Einstein space $K^{n-2}$ with curvature $k=0, \pm 1$, and that the scalar field depends only on th

Shiping Cao, Hua Qiu

We construct a strongly local regular Dirichlet form on the golden ratio Sierpinski gasket, which is a self-similar set without any finitely ramified cell structure, via a study on the trace of electrical networks on an infinite graph. The Dirichlet form is self-similar in the sense of an infinite iterated function system, and is decimation invariant with re

Zhengxian Lin, Kim-Ho Lam, Alan Fern

We investigate a deep reinforcement learning (RL) architecture that supports explaining why a learned agent prefers one action over another. The key idea is to learn action-values that are directly represented via human-understandable properties of expected futures. This is realized via the embedded self-prediction (ESP)model, which learns said properties in

Dibakar Roychowdhury

The present Letter derives multispin nonrelativistic magnon spectrum considering various near BPS corners within $ SU(1,2|3) $ Spin-Matrix theory (SMT) limit of strings on $ AdS_5 \times S^5 $. In particular, we focus on some typical rotating string solutions those correspond to two spin as well as three spin configurations in the bulk and identify the assoc

Ehsan Zabardast, Kwabena Ebo Bennin, Javier Gonzalez-Huerta

Context: Technical Debt (TD) discusses the negative impact of sub-optimal decisions to cope with the need-for-speed in software development. Code Technical Debt Items (TDI) are atomic elements of TD that can be observed in code artefacts. Empirical results on open-source systems demonstrated how code-smells, which are just one type of TDIs, are introduced an

Harald Schmid

For linear Hamiltonian $2n\times 2n$ systems $J y'(x) = (λW(x)+H(x))y(x)$ we investigate the problem how the eigenvalues $λ$ depend on the entries of the coefficient matrix $H$. This question turns into a deformation equation for $H$ and a partial differential equation for the eigenvalues $λ$. We apply our results to various examples, including generaliz

Shambel Sahlu, Heba Sami, Anna-Mia Swart, Thato Tsabone

In this paper we study the perturbations of a cosmic multi-fluid medium consisting of radiation, dust and a Chaplygin gas. To do so, we follow the 1 + 3 covariant formalism and derive the evolution equations of the fluctuations in the energy density for each species of fluid in the multi-fluid system. The solutions to these coupled systems of equations are t

Shu Sun, Xiaofeng Li

Scheduling plays a pivotal role in multi-user wireless communications, since the quality of service of various users largely depends upon the allocated radio resources. In this paper, we propose a novel scheduling algorithm with contiguous frequency-domain resource allocation (FDRA) based on deep reinforcement learning (DRL) that jointly selects users and al

Changhan Wang, Yun Tang, Xutai Ma, Anne Wu

We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such as end-to-end speech recognition and speech-to-text translation. It follows fairseq's careful design for scalability and extensibility. We provide end-to-end workflows from data pre-processing, model training to offline (online) inference. We implement state-of-the

Licong Lin, Edgar Dobriban

Modern machine learning methods are often overparametrized, allowing adaptation to the data at a fine level. This can seem puzzling; in the worst case, such models do not need to generalize. This puzzle inspired a great amount of work, arguing when overparametrization reduces test error, in a phenomenon called "double descent". Recent work aimed to u

Ashkan Rezaei, Anqi Liu, Omid Memarrast, Brian Ziebart

Making predictions that are fair with regard to protected group membership (race, gender, age, etc.) has become an important requirement for classification algorithms. Existing techniques derive a fair model from sampled labeled data relying on the assumption that training and testing data are identically and independently drawn (iid) from the same distribut

Su Gao, Liza Jacoby, William Johnson, James Leng

We define a notion of rank for words and subshifts that we call spacer rank, extending the notion of rank-one symbolic shifts of Gao and Hill. We construct infinite words of each finite spacer rank, of unbounded spacer rank, and show there exist words that do not have a spacer rank construction. We consider words that are fixed points of substitutions and gi

Xin-Zhen Weng, Xiao-Lin Chen, Wei-Zhen Deng, Shi-Lin Zhu

In this work, we systematically study the mass spectrum of the fully heavy tetraquark in an extended chromomagnetic model, which includes both color and chromomagnetic interactions. Numerical results indicate that the energy level is mainly determined by the color interaction, which favors the color-sextet $\ket{(QQ)^{6_{c}}(\bar{Q}\bar{Q})^{\bar{6}_{c}}}$ c

Akira Matsumura, Kazuhiro Yamamoto

We investigate the phenomenon of gravity-induced entanglement in optomechanical systems. Assuming photon number conservation and the Newtonian potential expanded up to the quadratic order of the oscillator positions, we exactly solve the dynamics of the optomehcanical systems. Then, we find that the phase difference due to the Newtonian gravity leads to the

J. D. Peñaranda-Rivera, D. L Paipa-León, S. D. Hernández-Charpak, J. E. Forero-Romero

Superclusters are a convenient way to partition and characterize the large scale structure of the Universe. In this Letter we explore the advantages of defining superclusters as watershed basins in the divergence velocity field. We apply this definition on diverse datasets generated from linear theory and N-body simulations, with different grid sizes, smooth

Daisuke Miki, Akira Matsumura, Kazuhiro Yamamoto

We analyze the dynamics of a gravity-induced entanglement for N massive particles. Considering the linear configuration of these particles, we investigate the entanglement between a specific pair of particles under the influence of the gravitational interaction between the massive particles. As the particle number increases, the specific particle pair decohe

Linjie Chen, Yihua Yan, Qiuxiang Fan, Lihong Geng

The very low frequency (VLF) regime below 30 MHz in the electromagnetic spectrum has presently drawing global attentions in radio astronomical research due to its potentially significant science outcomes exploring many unknown extragalactic sources, transients, and so on. However, the non-transparency of the Earth's ionosphere, ionospheric distortion and

Rohan Ramanath, Konstantin Salomatin, Jeffrey D. Gee, Kirill Talanine

One of the most well-established applications of machine learning is in deciding what content to show website visitors. When observation data comes from high-velocity, user-generated data streams, machine learning methods perform a balancing act between model complexity, training time, and computational costs. Furthermore, when model freshness is critical, t

Xin Chen, Yingying Li, Jun Shimada, Na Li

This paper studies the automated control method for regulating air conditioner (AC) loads in incentive-based residential demand response (DR). The critical challenge is that the customer responses to load adjustment are uncertain and unknown in practice. In this paper, we formulate the AC control problem in a DR event as a multi-period stochastic optimizatio

Arup Bose, Shambhu Nath Maurya, Koushik Saha

Consider the $n \times n$ reverse circulant $RC_n(t)$ and symmetric circulant $SC_n(t)$ matrices with independent Brownian motion entries. We discuss the process convergence of the time dependent fluctuations of linear eigenvalue statistics of these matrices as $n \tends \infty$, when the test functions of the statistics are polynomials. The proofs are mainl

Tsung-Yen Yang, Michael Hu, Yinlam Chow, Peter J. Ramadge

While safe reinforcement learning (RL) holds great promise for many practical applications like robotics or autonomous cars, current approaches require specifying constraints in mathematical form. Such specifications demand domain expertise, limiting the adoption of safe RL. In this paper, we propose learning to interpret natural language constraints for saf

Yao-Bei Liu, Stefano Moretti

We investigate the prospects for discovering the Flavour Changing Neutral Current (FCNC) $tqZ$ couplings via two production processes yielding trilepton signals: top quark pair production $pp\to t\bar{t}$ with one top decaying to the $Z$ boson and one light jet and the anomalous single top plus $Z$ boson production process $pp\to tZ$. We study these channels

David Dumas, Andrew Sanders

We study uniformization problems for compact manifolds that arise as quotients of domains in complex flag varieties by images of Anosov homomorphisms. We focus on Anosov homomorphisms with "small" limit sets, as measured by the Riemannian Hausdorff codimension in the flag variety. Under such a codimension hypothesis, we show that all first-order defo

Frédéric Ouimet

In this paper, we prove a local limit theorem for the ratio of the Poisson distribution to the Gaussian distribution with the same mean and variance, using only elementary methods (Taylor expansions and Stirling's formula). We then apply the result to derive an upper bound on the Le Cam distance between Poisson and Gaussian experiments, which gives a com

Yiran Chen, Pengfei Liu, Ming Zhong, Zi-Yi Dou

Neural network-based models augmented with unsupervised pre-trained knowledge have achieved impressive performance on text summarization. However, most existing evaluation methods are limited to an in-domain setting, where summarizers are trained and evaluated on the same dataset. We argue that this approach can narrow our understanding of the generalization

Jianing Li, Shenxing Zhang

We determine the $3$-class groups of $\mathbb{Q}(\sqrt[3]{p})$ and $K=\mathbb{Q}(\sqrt[3]{p},\sqrt{-3})$ when $p\equiv 4,7\bmod 9$ is a prime and $3$ is a cubic modulo $p$. This confirms a conjecture made by Barrucand-Cohn, and proves the last remaining case of a conjecture of Lemmermeyer on the $3$-class group of $K$.

David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng

Mainstream machine learning conferences have seen a dramatic increase in the number of participants, along with a growing range of perspectives, in recent years. Members of the machine learning community are likely to overhear allegations ranging from randomness of acceptance decisions to institutional bias. In this work, we critically analyze the review pro

Ren Zhang, Chenwei Lv, Yangqian Yan, Qi Zhou

Engineering lattice models with tailored inter-site tunnelings and onsite energies could synthesize essentially arbitrary Riemannian surfaces with highly tunable local curvatures. Here, we point out that discrete synthetic Poincaré half-planes and Poincaré disks, which are created by lattices in flat planes, support infinitely degenerate eigenstates for any

Fan Xie, Alexander Chowdhury, M. Clara De Paolis Kaluza, Linfeng Zhao

We present a deep imitation learning framework for robotic bimanual manipulation in a continuous state-action space. A core challenge is to generalize the manipulation skills to objects in different locations. We hypothesize that modeling the relational information in the environment can significantly improve generalization. To achieve this, we propose to (i

Li-Ming Wang, Qin-Song Zhou, Cheng-Qun Pang, Xiang Liu

Inspired by the event accumulation around 2.6 GeV in the $η^\primeπ^+π^-$ invariant mass spectrum of $J/ψ\to γη^\primeπ^+π^-$, which was reported by the BESIII Collaboration, we carry out the study of the mass spectrum and decay behavior of four radial excitations in the pseudoscalar meson family, which include $η^{(\prime)}(6S)$ and $η^{(\prime)}(7S)$. Comb

Qiuqiang Kong, Bochen Li, Jitong Chen, Yuxuan Wang

Symbolic music datasets are important for music information retrieval and musical analysis. However, there is a lack of large-scale symbolic datasets for classical piano music. In this article, we create a GiantMIDI-Piano (GP) dataset containing 38,700,838 transcribed notes and 10,855 unique solo piano works composed by 2,786 composers. We extract the names

Sharat Ibrahimpur, Chaitanya Swamy

Motivated by the need for, and growing interest in, modeling uncertainty in data, we introduce and study {\em stochastic minimum-norm optimization}. We have an underlying combinatorial optimization problem where the costs involved are {\em random variables} with given distributions; each feasible solution induces a random multidimensional cost vector, and gi

John Ryan Westernacher-Schneider

We provide a road towards obtaining gravitational waveforms from inspiraling material binaries with an accuracy viable for third-generation gravitational wave detectors, without necessarily advancing computational hardware or massively-parallel software infrastructure. We demonstrate a proof-of-principle 1+1-dimensional numerical implementation that exhibits

Keji Han, Yun Li, Xianzhong Long, Yao Ge

Many works demonstrate that deep learning system is vulnerable to adversarial attack. A deep learning system consists of two parts: the deep learning task and the deep model. Nowadays, most existing works investigate the impact of the deep model on robustness of deep learning systems, ignoring the impact of the learning task. In this paper, we adopt the bina

Da Wu

In this short note, we compute the precise asymptotics for the number of contingency tables with non-uniform margins. More precisely, for parameter $n,δ, B,C>0$, we consider the set of matrices whose first $[n^δ]$ rows and columns have sum $[BCn]$ and the rest $n$ rows and columns have sum $[Cn]$. We compute the precise asymptotics of the cardinality of this

Christopher Parker, Jorge M. Mejia, Franco Pestilli

The implementation of social distancing policies is key to reducing the impact of the current COVID-19 pandemic. However, their effectiveness ultimately depends on human behavior. In the United States, compliance with social distancing policies has widely varied thus far during the pandemic. But what drives such variability? Through six open datasets, includ

Bin-Bin Xu, Gabriele G. de Boo, Brett C. Johnson, Miloš Rančić

Electrodes in close proximity to an active area of a device are required for sufficient electrical control. The integration of such electrodes into optical devices can be challenging since low optical losses must be retained to realise high quality operation. Here, we demonstrate that it is possible to place a metallic shallow phosphorus doped layer in a sil

Ushnish Sengupta, Maximilian L. Croci, Matthew P. Juniper

The estimation of model parameters with uncertainties from observed data is a ubiquitous inverse problem in science and engineering. In this paper, we suggest an inexpensive and easy to implement parameter estimation technique that uses a heteroscedastic Bayesian Neural Network trained using anchored ensembling. The heteroscedastic aleatoric error of the net

David M. W. Powers

Commonly used evaluation measures including Recall, Precision, F-Measure and Rand Accuracy are biased and should not be used without clear understanding of the biases, and corresponding identification of chance or base case levels of the statistic. Using these measures a system that performs worse in the objective sense of Informedness, can appear to perform

David M. W. Powers

There has been considerable interest in boosting and bagging, including the combination of the adaptive techniques of AdaBoost with the random selection with replacement techniques of Bagging. At the same time there has been a revisiting of the way we evaluate, with chance-corrected measures like Kappa, Informedness, Correlation or ROC AUC being advocated. T

David M W Powers

Both empirical and mathematical demonstrations of the importance of chance-corrected measures are discussed, and a new model of learning is proposed based on empirical psychological results on association learning. Two forms of this model are developed, the Informatron as a chance-corrected Perceptron, and AdaBook as a chance-corrected AdaBoost procedure. Co