May 2022 arXiv papers — page 54

Alejandro P. Riascos, Francisco Hernández Padilla

In this paper, we present a framework to compare the differences in the occupation probabilities of two random walk processes, which can be generated by modifications of the network or the transition probabilities between the nodes of the same network. We explore a dissimilarity measure defined in terms of the eigenvalues and eigenvectors of the normalized L

Rida Benhaddou, Qing Liu

We investigate the nonparametric bivariate additive regression estimation in the random design and long-memory errors and construct adaptive thresholding estimators based on wavelet series. The proposed approach achieves asymptotically near-optimal convergence rates when the unknown function and its univariate additive components belong to Besov space. We co

José Morano, Álvaro S. Hervella, José Rouco, Jorge Novo

Age-related Macular Degeneration (AMD) is the predominant cause of blindness in developed countries, specially in elderly people. Moreover, its prevalence is increasing due to the global population ageing. In this scenario, early detection is crucial to avert later vision impairment. Nonetheless, implementing large-scale screening programmes is usually not v

Jiquan Li, Junliang Guo, Yongxin Zhu, Xin Sheng

The task of Grammatical Error Correction (GEC) has received remarkable attention with wide applications in Natural Language Processing (NLP) in recent years. While one of the key principles of GEC is to keep the correct parts unchanged and avoid over-correction, previous sequence-to-sequence (seq2seq) models generate results from scratch, which are not guara

Morteza Hasanvand

Let $G$ be a simple graph and let $p$ and $q$ be two integer-valued functions on $V(G)$ with $p< q$ in which for each $v\in V(G)$, $q(v) \ge \frac{1}{2}d_G(v)$ and $p(v) \ge \frac{1}{2} q(v)-2$. In this note, we show that $G$ has an orientation such that for each vertex $v$, $d^+_G(v)\in\{p(v),p(v)+1,q(v)-1,q(v)\}$ if and only if it has an orientation such t

Sheng Tian

All-sky imagers (ASIs) are used to record auroral activities from the ground but are often contaminated by the moon. Here, we studied the THEMIS ASIs data and developed an algorithm to eliminate the moon which can be generalized to other types of ASIs. With our algorithm, the ASI pixels within the moon's surface are typically saturated and thus removed by th

Natalí S. M. de Santi, L. Raul Abramo

Cosmological covariance matrices are fundamental for parameter inference, since they are responsible for propagating uncertainties from the data down to the model parameters. However, when data vectors are large, in order to estimate accurate and precise matrices we need huge numbers of observations, or rather costly simulations - neither of which may be via

Raphael Yuster

Let $H$ be a directed acyclic graph other than a rooted star. It is known that there are constants $c(H)$ and $C(H)$ such that the following holds for the complete directed graph $D_n$. There are at most $C\log n$ directed acyclic subgraphs of $D_n$ that cover every $H$-copy of $D_n$, while fewer than $c\log n$ directed acyclic subgraphs of $D_n$ do not cove

Alec M. Dunton, Benjamin W. Priest, Amanda Muyskens

Gaussian processes (GPs) are Bayesian non-parametric models useful in a myriad of applications. Despite their popularity, the cost of GP predictions (quadratic storage and cubic complexity with respect to the number of training points) remains a hurdle in applying GPs to large data. We present a fast posterior mean prediction algorithm called FastMuyGPs to a

Zelong Zeng, Zheng Wang, Fan Yang, Shin'ichi Satoh

The large variation of viewpoint and irrelevant content around the target always hinder accurate image retrieval and its subsequent tasks. In this paper, we investigate an extremely challenging task: given a ground-view image of a landmark, we aim to achieve cross-view geo-localization by searching out its corresponding satellite-view images. Specifically, t

S. Kundu, A. Pal, Amit Chauhan, K. Patro

Double perovskite-based magnets wherein frustration and competition between emergent degrees of freedom are at play can lead to novel electronic and magnetic phenomena. Herein, we report the electronic structure and magnetic properties of an ordered double perovskite material Ho2CoMnO6. In the double perovskite with general class A2BB'O6, the octahedral B an

M. Grilli, C. Di Castro, G. Seibold, S. Caprara

The strange metal behavior, usually characterized by a linear-in-temperature (T) resistivity, is a still unsolved mystery in solid-state physics. Usually it is associated with the proximity to a quantum critical point (a second order transition at temperature T = 0) focusing on the related divergent order parameter correlation length. Here, we propose a para

K. R. Rajagopal, Casey Rodriguez

Motivated by recent strain-limiting models for solids and biological fibers, we introduce the first intrinsic set of nonlinear constitutive relations, between the geometrically exact strains and the components of the contact force and contact couple, describing a uniform, hyperelastic, strain-limiting special Cosserat rod. After discussing some attractive fe

Morteza Hasanvand

Let $G$ be a graph and let $f$ be a positive integer-valued function on $V(G)$ satisfying $2m\le f\le b$, where $b$ and $m$ are two positive integers with $b\ge 4m^2$. In this paper, we show that if $G$ is $b^2$-tough and $|V(G)|\ge b^2$, then it has an $m$-tree-connected factor $H$ such that for each vertex $v$, $$d_H(v)\in \{f(v), f(v)+1\}.$$ Next, we gene

Corentin Dancette, Matthieu Cord

Transformers have been matching deep convolutional networks for vision architectures in recent works. Most work is focused on getting the best results on large-scale benchmarks, and scaling laws seem to be the most successful strategy: bigger models, more data, and longer training result in higher performance. However, the reduction of network complexity and

Usman Mahmood, Daniel Pimentel-Alarcón

This paper introduces {\em fusion subspace clustering}, a novel method to learn low-dimensional structures that approximate large scale yet highly incomplete data. The main idea is to assign each datum to a subspace of its own, and minimize the distance between the subspaces of all data, so that subspaces of the same cluster get {\em fused} together. Our met

Morteza Hasanvand

In this paper, we show that every $O(m)$-edge-connected simple graph $G$ of size divisible by $m$ with minimum degree at least $2^{O(m)}$ has an edge-decomposition into isomorphic copies of any given tree $T$ of size $m$. Moreover, the minimum degree condition can be dropped for graphs $G$ with girth greater than the diameter of $T$. These results improve tw

Niyati Desai, Jorge Llop-Sayson, Nemanja Jovanovic, Garreth Ruane

For direct imaging of exoplanets, Scalar Vortex Coronagraphs (SVCs) are an attractive alternative to the popularly used Vector Vortex Coronagraphs (VVCs). This is primarily because they are able to induce the same phase ramp regardless of the incoming light's polarization state. We tested a set of stepped SVC staircase masks in the Exoplanet Technology Labor

Erik Rosenberg, Steven Gratton, George Efstathiou

We present angular power spectra and cosmological parameter constraints derived from the Planck PR4 (NPIPE) maps of the Cosmic Microwave Background. NPIPE, released by the Planck Collaboration in 2020, is a new processing pipeline for producing calibrated frequency maps from Planck data. We have created new versions of the CamSpec likelihood using these maps

Qingfeng Lan, Yangchen Pan, Jun Luo, A. Rupam Mahmood

Artificial neural networks are promising for general function approximation but challenging to train on non-independent or non-identically distributed data due to catastrophic forgetting. The experience replay buffer, a standard component in deep reinforcement learning, is often used to reduce forgetting and improve sample efficiency by storing experiences i

Klara Suchan, Justus Just, Pascal Becker, Carolin Rehermann

Photo- and charge-carrier induced ion migration is a major challenge when utilizing metal halide perovskite semiconductors for optoelectronic applications. For mixed iodide/bromide perovskites, the compositional instability due to light- or electrical bias induced phase- segregation restricts the exploitation of the entire bandgap range. Previous experimenta

Paola Merlo, Aixiu An, Maria A. Rodriguez

Current successes of machine learning architectures are based on computationally expensive algorithms and prohibitively large amounts of data. We need to develop tasks and data to train networks to reach more complex and more compositional skills. In this paper, we illustrate Blackbird's language matrices (BLMs), a novel grammatical dataset developed to test

Daniel Guterding

We present a method for the arbitrage-free interpolation of plain-vanilla option prices and implied volatilities, which is based on a system of integral equations that relates terminal density and option prices. Using a discretization of the terminal density, we write these integral equations as a system of linear equations. We show that the kernel matrix of

Adnan Ben Mansour, Gaia Carenini, Alexandre Duplessis, David Naccache

Federated Learning has been recently proposed for distributed model training at the edge. The principle of this approach is to aggregate models learned on distributed clients to obtain a new more general "average" model (FedAvg). The resulting model is then redistributed to clients for further training. To date, the most popular federated learning algorithm

Sobhan Kazempour, Amin Rezaei Akbarieh, Hossein Motavalli, Lijing Shao

We introduce the cosmological analysis of the Dirac-Born-Infeld dRGT massive gravity theory which is a new extension of de Rham-Gabadadze-Tolley (dRGT) massive gravity. In this theory, we consider the Dirac-Born-Infeld (DBI) scalar field which is coupled to the graviton field. Moreover, we perform the cosmological background equations, and we demonstrate the

Fedele Lizzi, Luca Scala, Patrizia Vitale

We consider the $\varrho$-Minkowski spacetime, a model with linear noncommutativity involving the time and the azimuthal angle. We study its quantum symmetries, the $\varrho$-Poincar\'e quantum group, and analyse the concepts of localizability and quantum observers.

Xinqiang Ding, Bin Zhang

Coarse-grained models have proven helpful for simulating complex systems over long timescales to provide molecular insights into various processes. Methodologies for systematic parameterization of the underlying energy function, or force field that describes the interactions among different components of the system are of great interest for ensuring simulati

Jaakko Harjuhahto, Vesa Hirvisalo

We study machine learning systems for real-time industrial quality control. In many factory systems, production processes must be continuously controlled in order to maintain product quality. Especially challenging are the systems that must balance in real-time between stringent resource consumption constraints and the risk of defective end-product. There is

Megha Vagadia, Jaya Prakash Sahoo, Ankit Kumar, Suman Sardar

In past few decades, Rashba spin-orbit coupling (SOC) has been successfully employed for the emergence of exotic phenomena at the quantum oxide interfaces. In these systems, the combined effect of charge transfer, broken symmetries and SOC yields intriguing interfacial magnetism and transport properties. Here, we provide an insight to control and tune interf

Qiang Wen, Haocheng Zhong

The balanced partial entanglement (BPE) was observed to give the reflected entropy and the entanglement wedge cross-section (EWCS) for various mixed states in different theories \cite{Wen:2021qgx,Camargo:2022mme}. It can be calculated in different purifications, and is conjectured to be independent from purifications. In this paper we calculate the BPE and t

Han Wang, Ruiliu Fu, Xuejun Zhang, Jun Zhou

Lifelong Language Learning (LLL) aims to train a neural network to learn a stream of NLP tasks while retaining knowledge from previous tasks. However, previous works which followed data-free constraint still suffer from catastrophic forgetting issue, where the model forgets what it just learned from previous tasks. In order to alleviate catastrophic forgetti

Hideki Arahari, Sota Konishi, Kodai Takaoka, Seiji Akita

We propose an optomechanical system utilizing luminescence-induced optical forces (LIOFs). Anisotropic dielectric structures enhance the recoil force from the luminescence. The optomechanical resonator consists of a composite film with a dielectric membrane, luminescent nanofilm, and a metallic substrate. The LIOF causes a mechanical frequency shift in the o

Sergio España, Vijanti Ramautar

Over the years ethical, social and environmental accounting (ESEA) has become a common practice among responsible organisations. ESEA entails assessing and reporting organisations" performance on environmental, social and governance topics. In this report, we present a textual grammar for specifying ESEA methods. With the grammar ESEA models can be created.

Xiangrui Cheng, Yiliang Liu, Zhou Su, Wei Wang

This paper investigates physical layer security (PLS) in the intelligent reflecting surface (IRS)-assisted multiple-user uplink channel. Since the instantaneous eavesdropper's channel state information (CSI) is unavailable, the secrecy rate can not be measured. In this case, existing investigations usually focus on the maximization of the minimum (max-min) o

Psaridi A., Bouchy F., Lendl M., Grieves N.

The detection and characterization of exoplanets and brown dwarfs (BDs) around massive AF-type stars is essential to investigate and constrain the impact of stellar mass on planet properties. However, such targets are still poorly explored in radial velocity (RV) surveys because they only feature a small number of stellar lines and those are usually broadene

Wan Mohamad Husni Wan Mokhtar, Jorma Louko

We analyse the energy cost of building or demolishing a wall for a massless Dirac field in (1+1)-dimensional Minkowski spacetime and the response of an Unruh-DeWitt particle detector to the generated radiation. For any smoothly-evolving wall, both the field's energy density and the detector's response are finite. In the limit of rapid wall creation or demoli

Zhen Bi, Siyuan Cheng, Jing Chen, Xiaozhuan Liang

Transformers have achieved remarkable performance in widespread fields, including natural language processing, computer vision and graph mining. However, vanilla Transformer architectures have not yielded promising improvements in the Knowledge Graph (KG) representations, where the translational distance paradigm dominates this area. Note that vanilla Transf

Jie Zhao, Jingshu Zhang, Dongdong Li, Dong Wang

Unmanned aerial vehicles (UAV) have been widely used in various fields, and their invasion of security and privacy has aroused social concern. Several detection and tracking systems for UAVs have been introduced in recent years, but most of them are based on radio frequency, radar, and other media. We assume that the field of computer vision is mature enough

Yubo Xie, Junze Li, Pearl Pu

This paper introduces AFEC, an automatically curated knowledge graph based on people's day-to-day casual conversations. The knowledge captured in this graph bears potential for conversational systems to understand how people offer acknowledgement, consoling, and a wide range of empathetic responses in social conversations. For this body of knowledge to be co

Kazuhiro Horihata

In my previous paper I have contrived a Ginzburg-Landau heat flow with a time-dependent parameter and by using it, I constructed a harmonic heat flow into spheres with a monotonical inequality and a reverse Poincar\'{e} inequality. This paper establishes these two energy inequalities near the boundary and then by making the best of them, we discuss a partial

Jingwei Yi, Fangzhao Wu, Huishuai Zhang, Bin Zhu

Federated learning (FL) enables multiple clients to collaboratively train models without sharing their local data, and becomes an important privacy-preserving machine learning framework. However, classical FL faces serious security and robustness problem, e.g., malicious clients can poison model updates and at the same time claim large quantities to amplify

M. Hamed Mohammady

Thermal channels -- the free processes allowed in the resource theory of quantum thermodynamics -- are generalised to thermal instruments, which we interpret as implementing thermodynamically free quantum measurements; a Maxwellian demon using such measurements never violates the second law of thermodynamics. Further properties of thermal instruments are inv

Federica Bertolotti, Roberto Frigerio

Let $M$ be a closed orientable manifold. We introduce two numerical invariants, called filling volumes, on the mapping class group $\mathrm{MCG}(M)$ of $M$, which are defined in terms of filling norms on the space of singular boundaries on $M$, both with real and with integral coefficients. We show that filling volumes are length functions on $\mathrm{MCG}(M

P. Février, J. Basset, J. Estève, M. Aprili

We study the optical rectification in a metallic tunnel junction. We consider a planar junction in a Kretschmann configuration and measure the photon-assisted tunneling under infrared illumination at $\lambda= 1.5\, \mu \mathrm{m}$. To address the microscopic mechanism at the origin of the optical rectification, we compare the photon assisted current and the

Soo Hong Chew, Wenqian Wang

Two dynamic game forms are said to be behaviorally equivalent if they share the "same" profiles of structurally reduced strategies (Battigalli et al., 2020). In the context of dynamic implementation, behaviorally equivalent game forms are interchangeable under a wide range of solution concepts for the purpose of implementing a social choice function. A gradu

Yincen Qu, Ningyu Zhang, Hui Chen, Zelin Dai

In e-commerce, the salience of commonsense knowledge (CSK) is beneficial for widespread applications such as product search and recommendation. For example, when users search for ``running'' in e-commerce, they would like to find products highly related to running, such as ``running shoes'' rather than ``shoes''. Nevertheless, many existing CSK collections r

Vijay Keswani, L. Elisa Celis

In real-world classification settings, such as loan application evaluation or content moderation on online platforms, individuals respond to classifier predictions by strategically updating their features to increase their likelihood of receiving a particular (positive) decision (at a certain cost). Yet, when different demographic groups have different featu

Joshua Spisak, Andrew Saba, Nayana Suvarna, Brian Mao

Race cars are routinely driven to the edge of their handling limits in dynamic scenarios well above 200mph. Similar challenges are posed in autonomous racing, where a software stack, instead of a human driver, interacts within a multi-agent environment. For an Autonomous Racing Vehicle (ARV), operating at the edge of handling limits and acting safely in thes

Arnaud Deleruyelle, Cristian Versari, John Klein

Background: Accurate segmentation of microscopic structures such as bio-artificial capsules in microscopy imaging is a prerequisite to the computer-aided understanding of important biomechanical phenomenons. State-of-the-art segmentation performances are achieved by deep neural networks and related data-driven approaches. Training these networks from only a

Changchong Sheng, Gangyao Kuang, Liang Bai, Chenping Hou

Visual speech, referring to the visual domain of speech, has attracted increasing attention due to its wide applications, such as public security, medical treatment, military defense, and film entertainment. As a powerful AI strategy, deep learning techniques have extensively promoted the development of visual speech learning. Over the past five years, numer

Miguel Lerma, Mirtha Lucas

The Grad-CAM algorithm provides a way to identify what parts of an image contribute most to the output of a classifier deep network. The algorithm is simple and widely used for localization of objects in an image, although some researchers have point out its limitations, and proposed various alternatives. One of them is Grad-CAM++, that according to its auth

Raphael Bensadoun, Shir Gur, Nitsan Blau, Tom Shenkar

Inverse kinematic (IK) methods recover the parameters of the joints, given the desired position of selected elements in the kinematic chain. While the problem is well-defined and low-dimensional, it has to be solved rapidly, accounting for multiple possible solutions. In this work, we propose a neural IK method that employs the hierarchical structure of the

Ankang Sun, Bo Li

In this work, we revisit the problem of fairly allocating a number of indivisible items that are located on a line to multiple agents. A feasible allocation requires that the allocated items to each agent are connected on the line. The items can be goods on which agents have non-negative utilities, or chores on which the utilities are non-positive. Our objec

Christos Baziotis, Mikel Artetxe, James Cross, Shruti Bhosale

Multilingual machine translation suffers from negative interference across languages. A common solution is to relax parameter sharing with language-specific modules like adapters. However, adapters of related languages are unable to transfer information, and their total number of parameters becomes prohibitively expensive as the number of languages grows. In

Fabrizio Montecchiani, Giacomo Ortali, Tommaso Piselli, Alessandra Tappini

We study the parameterized complexity of the $s$-Club Cluster Edge Deletion problem: Given a graph $G$ and two integers $s \ge 2$ and $k \ge 1$, is it possible to remove at most $k$ edges from $G$ such that each connected component of the resulting graph has diameter at most $s$? This problem is known to be NP-hard already when $s = 2$. We prove that it admi

Yixiao Cao, Jingchen Hu

The large number of publicly available survey datasets of wide variety, albeit useful, raise respondent-level privacy concerns. The synthetic data approach to data privacy and confidentiality has been shown useful in terms of privacy protection and utility preservation. This paper aims at illustrating how synthetic data can facilitate the dissemination of hi

Nikolai Larkin

An initial-boundary value problem for the n-dimensional ($n$ is a natural number from the interval [2,7]) Kuramoto-Sivashinsky-Zakharov-Kuznetsov equation posed on smooth bounded domains in $\mathbb{R}^n$ was considered. The existence and uniqueness of global regular solutions as well as their exponential decay have been established. A connection between an

Xiao-Wu Chen, Zengqiang Lin, Yu Zhou

We reformulate a result of Bernhard Keller on extensions of $t$-structures and give a detailed proof. In the study of hereditary $t$-structures, the notions of regular $t$-structures and global dimensions arise naturally.

Md. Ariful Haque, Rajesh Palit

Focus on Deep Neural Network based malicious and normal computer Network Traffic classification. (such as attacks, phishing, any other illegal activity and normal traffic identification). In this paper, the main idea is to review, existed Neural Network based network traffic classification. Which indicates intrusion activity classification and detection. It

Gargee Roy, Ritu Gupta, Satya Ranjan Sahoo, Sumit Saha

Ferrocene, since its discovery in 1951, has been extensively exploited as a redox probe in a variety of processes ranging from solution chemistry, medicinal chemistry, supramolecular chemistry, surface chemistry to solid-state molecular electronic and spintronic circuit elements to unravel electrochemical charge-transfer dynamics. Ferrocene represents an ext

Ismail Nasri, Mohammed Karrouchi, Kamal Kassmi, Abdelhafid Messaoudi

Driver Drowsiness is one of the most factors of road accidents, leading to severe injuries and deaths every year. Drowsiness means difficulty staying awake, which can lead to falling asleep. This paper introduces a literature review of driver drowsiness detection systems based on an analysis of physiological signals, facial features, and driving patterns. Th

Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard, Caroline Brun

Recently, very large pre-trained models achieve state-of-the-art results in various natural language processing (NLP) tasks, but their size makes it more challenging to apply them in resource-constrained environments. Compression techniques allow to drastically reduce the size of the models and therefore their inference time with negligible impact on top-tie

Yucheng Liu, Lawrence Ong, Phee Lep Yeoh, Parastoo Sadeghi

Information leakage to a guessing adversary in index coding is studied, where some messages in the system are sensitive and others are not. The non-sensitive messages can be used by the server like secret keys to mitigate leakage of the sensitive messages to the adversary. We construct a deterministic linear coding scheme, developed from the rank minimizatio

Mikołaj Lewandowski, Joanna Polcyn, Christian Reiher

Bermond and Thomassen conjectured that every digraph with minimum outdegree at least $2k-1$ contains $k$ vertex disjoint cycles. So far the conjecture was verified for $k\le 3$. Here we generalise the question asking for all outdegree sequences which force $k$ vertex disjoint cycles and give the full answer for $k\le 2$.

Oded Ovadia, Adar Kahana, Eli Turkel

We propose an accurate numerical scheme for approximating the solution of the two dimensional acoustic wave problem. We use machine learning to find a stencil suitable even in the presence of high wavenumbers. The proposed scheme incorporates physically informed elements from the field of optimized numerical schemes into a convolutional optimization machine

Animesh Karnewar, Tobias Ritschel, Oliver Wang, Niloy J. Mitra

In many recent works, multi-layer perceptions (MLPs) have been shown to be suitable for modeling complex spatially-varying functions including images and 3D scenes. Although the MLPs are able to represent complex scenes with unprecedented quality and memory footprint, this expressive power of the MLPs, however, comes at the cost of long training and inferenc

Olga Y. Kushel

In this paper, we study the class of relatively $D$-stable matrices and provide the conditions, sufficient for relative $D$-stability. We generalize the well-known Hadamard inequality, to provide upper bounds for the determinants of relatively $D$-stable and relatively additive $D$-stable matrices. For some classes of $D$-stable matrices, we estimate the sec

Li Du, Xiao Ding, Yue Zhang, Kai Xiong

Predicting the subsequent event for an existing event context is an important but challenging task, as it requires understanding the underlying relationship between events. Previous methods propose to retrieve relational features from event graph to enhance the modeling of event correlation. However, the sparsity of event graph may restrict the acquisition o

Yucheng Liu, Lawrence Ong, Phee Lep Yeoh, Parastoo Sadeghi

We study the information leakage to a guessing adversary in index coding with a general message distribution. Under both vanishing-error and zero-error decoding assumptions, we develop lower and upper bounds on the optimal leakage rate, which are based on the broadcast rate of the subproblem induced by the set of messages the adversary tries to guess. When t

Takeo Minamikawa, Reiko Sakaguchi, Yoshinori Harada, Hideharu Hase

We demonstrated remote plasmonic enhancement (RPE) by a dense random array of Ag nanoislands (AgNIs) that were partially gold-alloyed and attached with column-structured silica (CSS) overlayer of more than 100 nm in thickness. The physical and chemical protection of the CSS layer could lead to reducing the mutual impact between analyte molecules and metal na

Tanja Schoger, Benjamin Spreng, Gert-Ludwig Ingold, Paulo A. Maia Neto

For the Casimir interaction between two nearby objects, the plane-wave basis proves convenient for numerical calculations as well as for analytical considerations leading to an optical interpretation of the relevant scattering processes of electromagnetic waves. We review work on the proximity-force approximation and corrections to it within the plane-wave b

Guang-Liang Li, Yi Qiao, Junpeng Cao, Wen-Li Yang

The exact solution of the $D^{(1)}_2$ quantum spin chain with generic non-diagonal boundary reflections is obtained. It is found that the generating functional of conserved quantities of the system can be factorized as the product of transfer matrices of two anisotropic $XXZ$ spin chains with open boundary conditions. By using the factorization identities an

Shihong Zhang

In this paper, we propose a sharp and quantitative criterion, which focuses solely on $Q$ curvature, to demonstrate the Chern-Gauss-Bonnet integral. In contrast to the previous results [4,5,10], we use a new approach that involves estimating the singular integral. Furthermore, we derive the asymptotic formula for the solution to the general $Q$ curvature equ

Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum

Imitation learning aims to extract high-performance policies from logged demonstrations of expert behavior. It is common to frame imitation learning as a supervised learning problem in which one fits a function approximator to the input-output mapping exhibited by the logged demonstrations (input observations to output actions). While the framing of imitatio

Xuekai Wei, Mingliang Zhou, Heqiang Wang, Haoyan Yang

Video coding is a video compression technique that compresses the original video sequence to produce a smaller archive file or reduce the transmission bandwidth under constraints on the visual quality loss. Rate control (RC) plays a critical role in video coding. It can achieve stable stream output in practical applications, especially real-time video applic

Tomáš Roubíček

A mechanical interaction of compressible viscoelastic fluids with viscoelastic solids in Kelvin-Voigt rheology using the concept of higher-order (so-called 2nd-grade multipolar) viscosity is investigated in a quasistatic variant. The no-slip contact between fluid and solid is considered and the Eulerian-frame return-mapping technique is used for both the flu

Huaqi Zhou, Ting Gao, Fengli Yan

In this paper, we consider the orthogonal product set (OPS) with strong quantum nonlocality. Based on the decomposition of plane geometry, we present a sufficient condition for the triviality of orthogonality-preserving POVM on fixed subsystem and partially answer an open question given by Yuan et al. Phys. Rev. A \textbf{102}, 042228 (2020)}. The connection

Tanja Schoger, Benjamin Spreng, Gert-Ludwig Ingold, Astrid Lambrecht

We study universal Casimir interactions in two configurations which appear as dual to each other. The first involves spheres described by the Drude model and separated by vacuum while the second involves dielectric spheres immersed in a salted solution at distances larger than the Debye screening length. In both cases, the long-distance limit, equivalently t

Arup Bose, Priyanka Sen

Let $S=XX^T$ be the (unscaled) sample covariance matrix where $X$ is a real $p \times n$ matrix with independent entries. It is well known that if the entries of $X$ are independent and identically distributed (i.i.d.) with enough moments and $p/n \to y\neq 0$, then the limiting spectral distribution (LSD) of $\frac{1}{n}S$ converges to a Mar$\check{\text{c}

Nickos Papadatos

The Laplace transform is a useful and powerful analytic tool with applications to several areas of applied mathematics, including differential equations, probability and statistics. Similarly to the inversion of the Fourier transform, inversion formulae for the Laplace transform are of central importance; such formulae are old and well-known (Fourier-Mellin

Olivera Miskovic, Rodrigo Olea, Yoel Parra-Cisterna

We provide new formulas for the energy of black hole solutions in the anti-de Sitter (AdS) sector of a generic gravity theory with curvature-squared terms. This is achieved by the addition of counterterms of the extrinsic type (\emph{Kounterterms}) which produces a finite variation of the total action. The procedure is compatible with the boundary conditions

Ameth Ndiaye, Zehra Özdemir

In this paper, we define three types of 2-Ruled hypersurfaces in the Minkowski 4-space $\mathbb{E}^4_1$. We obtain Gaussian and mean curvatures of the 2-ruled hypersurfaces of type-1 and type-2, and some characterizations about its minimality. We also deal with the first Laplace-Beltrami operators of these types of 2-Ruled hypersurfaces in $\mathbb{E}^4_1$.

Hui Chen, Tarig Ballal, Mohammed E. Eltayeb, Tareq Y. Al-Naffouri

Direction-of-arrival (DOA) information is vital for multiple-input-multiple-output (MIMO) systems to complete localization and beamforming tasks. Switched antenna arrays have recently emerged as an effective solution to reduce the cost and power consumption of MIMO systems. Switch-based array architectures connect a limited number of radio frequency chains t

Irshad A. Meer, Woong-Hee Lee, Mustafa Ozger, Cicek Cavdar

Feasibility of using unlicensed spectrum for ultra reliable low latency communications (URLLC) is still a question for beyond 5G wireless networks. Low latency access to the channel and efficiently sharing spectrum among the multiple users are the main requirements for exploiting unlicensed spectrum for URLLC. Listen before talk and back-off procedures imple

Frank Wilczek

C. N. Yang's ideas about local gauge symmetry and non-integrable phases have been enormously fertile sources of inspiration in fundamental physics and in the quantum theory of matter. They also arise naturally in describing the dynamics of deformable bodies. Here I extend previous discussions of the gauge symmetry of deformable bodies in several directions,

Souhail Aboulfadl, Fouad Mellouki, Btissam Ameur, Chafika Faraj

Aedes albopictus has been newly recorded in Agdal district at Rabat in Morocco. The establishment of this invasive mosquito could affect the public health by causing serious epidemics despite of its high nuisance in urban and sub-urban areas. Vector control is mainly based on environmental management but chemical insecticides can be used to reduce adult mosq

Fayçal Aït Aoudia, Jakob Hoydis, Sebastian Cammerer, Matthijs Van Keirsbilck

We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) and carrier frequency offset (CFO) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT). The introduced NN architecture leverages residual convolutional networks as well as knowledge of the preamble struct

Aiji Liang, Cheng Chen, Huijun Zheng, Wei Xia

The topological electronic structure plays a central role in the non-trivial physical properties in topological quantum materials. A minimal, hydrogen-atom-like topological electronic structure is desired for researches. In this work, we demonstrate an effort towards the realization of such a system in the intrinsic magnetic topological insulator MnBi2Te4, b

Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong

Self-supervised learning (SSL) has been extensively explored in recent years. Particularly, generative SSL has seen emerging success in natural language processing and other AI fields, such as the wide adoption of BERT and GPT. Despite this, contrastive learning-which heavily relies on structural data augmentation and complicated training strategies-has been

Kunal Pattanayak, Vikram Krishnamurthy, Christopher Berry

Inverse reinforcement learning (IRL) deals with estimating an agent's utility function from its actions. In this paper, we consider how an agent can hide its strategy and mitigate an adversarial IRL attack; we call this inverse IRL (I-IRL). How should the decision maker choose its response to ensure a poor reconstruction of its strategy by an adversary perfo

Tapio Rajala, Zheng Zhu

In this note, we prove that the boundary of a $(W^{1, p}, BV)$-extension domain is of volume zero under the assumption that the domain $\boz$ is $1$-fat at almost every $x\in\partial\boz$. Especially, the boundary of any planar $(W^{1, p}, BV)$-extension domain is of volume zero.

A. R. Kuzmak, V. M. Tkachuk

We consider simulation of the high spins on a quantum computer. The protocols which allow one to measure the mean value of spin and correlations between spins are proposed. As a result, we determine the time dependence of the mean values of spin-1 in the magnetic field prepared on the ibmq-santiago quantum computer. In addition, we study the evolution of two

Nickos Papadatos

Let $X_1,\ldots,X_n$ be a random sample from the Gamma distribution with density $f(x)=\lambda^{\alpha}x^{\alpha-1}e^{-\lambda x}/\Gamma(\alpha)$, $x>0$, where both $\alpha>0$ (the shape parameter) and $\lambda>0$ (the reciprocal scale parameter) are unknown. The main result shows that the uniformly minimum variance unbiased estimator (UMVUE) of the shape pa

Shuo Li, Xiayan Ji, Edgar Dobriban, Oleg Sokolsky

Anomaly detection is essential for preventing hazardous outcomes for safety-critical applications like autonomous driving. Given their safety-criticality, these applications benefit from provable bounds on various errors in anomaly detection. To achieve this goal in the semi-supervised setting, we propose to provide Probably Approximately Correct (PAC) guara

Michael C. Burkhart, Kyle Shan

Given a small set of labeled data and a large set of unlabeled data, semi-supervised learning (SSL) attempts to leverage the location of the unlabeled datapoints in order to create a better classifier than could be obtained from supervised methods applied to the labeled training set alone. Effective SSL imposes structural assumptions on the data, e.g. that n

John E. Gough

We give a tutorial exposition of the analogue of the filtering equation for quantum systems focusing on the quantum probabilistic framework and developing the ideas from the classical theory. Quantum covariances and conditional expectations on von Neumann algebras play an essential part in the presentation.

Wanying Bi, Jingyan Li, Jian Liu, Jie Wu

In this paper, we introduce a persistent (co)homology theory for Cayley digraph grading. We give the algebraic structures of Cayley-persistence object. Specifically, we consider the module structure of persistent (co)homology and show the decomposition of a finitely generated Cayley-persistence module. Moreover, we introduce the persistence-cup product on th

Hideaki Murase, Shunto Arai, Takuro Sato, Kazuya Miyagawa

Glass, a ubiquitous state of matter like a frozen liquid, is a seminal issue across fundamental and applied sciences and has long been investigated in the framework of classical mechanics. A challenge in glass physics is the exploration of the quantum-mechanical behaviour of glass. Experimentally, however, the real quantum manifestation of glass and the rela

Seray Sahin, Patrick Antolin

Recent observations have shown that besides the characteristic multi-million degree component the corona also contains a large amount of cool material called coronal rain, whose clumps are 10 - 100 times cooler and denser than the surroundings and are often organised in larger events termed showers. Thermal instability (TI) within a coronal loop in a state o

Sihao Lin, Hongwei Xie, Bing Wang, Kaicheng Yu

Knowledge distillation becomes a de facto standard to improve the performance of small neural networks. Most of the previous works propose to regress the representational features from the teacher to the student in a one-to-one spatial matching fashion. However, people tend to overlook the fact that, due to the architecture differences, the semantic informat