July 2023 arXiv papers — page 13

Hao Chen, Weikang Wu, Kangtai Sun, Shengyuan A. Yang

Chiral phonons have attracted increasing attention, as they play important roles in many different systems and processes. However, a method to control phonon chirality by external fields is still lacking. Here, we propose that in displacement-type ferroelectric materials, an external electric field can reverse the chirality of chiral phonons via ferroelectri

Suman Sourav, Binbin Chen

For industrial control systems (ICS), many existing defense solutions focus on detecting attacks only when they make the system behave anomalously. Instead, in this work, we study how to detect attackers who are still in their hiding phase. Specifically, we consider an off-path false-data-injection attacker who makes the original sensor's readings unavailabl

Sergey Khrapov, Alexander Khoperskov, Natalia Zaitseva, Anatoly Zasov

Recent studies show the possibility of the formation of fairly regular and global spiral patterns in dwarf galaxies (dS type). Our sample of observed dwarf objects of this class also includes galaxies with a central stellar bar. The analysis of the observational data provides a small rotation velocity and a small disk component mass for dS galaxies, which is

Jian Wang, Yong Wang, Tong Wu

Recently Dabrowski etc. \cite{DL} obtained the metric and Einstein functionals by two vector fields and Laplace-type operators over vector bundles, giving an interesting example of the spinor connection and square of the Dirac operator. Pf$\ddot{a}$ffle and Stephan \cite{PS1} considered orthogonal connections with arbitrary torsion on compact Riemannian mani

Chen Wu, Wenjun Guo

In this work, we study the properties of neutron stars using the linear Relativistic Mean-Field (RMF) theory and consider multiple degrees of freedom inside neutron stars, including hyperons and $\Delta$ resonances. We investigate different coupling parameters $x_{\sigma \Delta}$ between $\Delta$ resonances and nucleons and compare the differences between ne

Jian Wang, Yong Wang

This paper aims to provide an explicit computation of the equivariant noncommutative residue density of which yield the metric and Einstein tensors on even-dimensional Riemannian manifolds. A considerable contribution of this paper is the development of the spectral Einstein functionals by two vector fields and the equivariant Bismut Laplacian over spinor bu

Neelima Agarwal, Sourav Pal, Aditya Srivastav, Anurag Tripathi

Logarithm of the soft function can be organized into sets of Feynman diagrams known as Cwebs. We introduced a new formalism in~\cite{Agarwal:2022wyk}, that allows to determine several of the building blocks of Cweb mixing matrices without explicit computations. In~\cite{Agarwal:2022xec} we used this formalism to obtain the diagonal blocks of four general cla

Gerson J. Ferreira, Boyu Wang, Jiyong Fu, Roberto Raimondi

The reciprocal interconversion between spin polarization and charge current (CSC) is the focus of intensive theoretical and experimental investigation in spintronics research. Its physical origin stems from the Rashba spin-orbit coupling (SOC) induced by the breaking of the structure inversion symmetry. The steady-state interconversion efficiency is the resu

Yingya Guo, Qi Tang, Yulong Ma, Han Tian

Traffic Engineering (TE) is an efficient technique to balance network flows and thus improves the performance of a hybrid Software Defined Network (SDN). Previous TE solutions mainly leverage heuristic algorithms to centrally optimize link weight setting or traffic splitting ratios under the static traffic demand. Note that as the network scale becomes large

Jian Wang, Yong Wang, Tong Wu, Yuchen Yang

For two one-forms and the Dirac operator, Dabrowski etc. recovered the spectral Einstein functionals by computing their noncommutative residue in Theorem 4.1 \cite{DL}. In this paper, we generalize the results of Dabrowski etc. to the cases of four dimensional spin manifolds with boundary.

Elena-Simona Apostol, Alin-Georgian Pisică, Ciprian-Octavian Truică

The increasing volume of online reviews has made possible the development of sentiment analysis models for determining the opinion of customers regarding different products and services. Until now, sentiment analysis has proven to be an effective tool for determining the overall polarity of reviews. To improve the granularity at the aspect level for a better

Mehran Hosseinzadeh Dizaj

Energy is a necessity and the basis of human life. With the increase in the need for energy supply in recent years, the use of fossil fuels has intensified. Environment is a basic principle for human beings. In our dear country Iran, with an average sundial of approximately 4.5 hours per day, there is the highest potential for the use of photovoltaic systems

Dimitra Karabali, V. P. Nair

An effective action for the bulk dynamics of quantum Hall effect in arbitrary even spatial dimensions was obtained some time ago in terms of a Chern-Simons term associated with the Dolbeault index theorem. Here we explore further properties of this action, showing how electronic band structures can be incorporated, obtaining Hall currents and conductivity (f

Alwyn Jose Raja, R. Ganesh

Frustrated magnets typically possess a large space of classical ground states. If this degeneracy is not protected by symmetry, thermal fluctuations may `select' certain states via order-by-disorder. In this article, we examine a precursor effect where all ground states are sampled, but with different weights. Geometry plays a key role in determining the wei

Jin Cao, Bo-Yuan Wang, Huan Yang, Zhi-Jie Fan

Photoassociation of ultracold atoms is a resonant light-assisted collision process, in which two colliding atoms absorb a photon and form an excited molecule. Since the first observation about three decades ago, the photoassociation of ultracold atoms has made a significant impact on the study of ultracold atoms and molecules. Extending the photoassociation

Jingwei Zuo, Wenbin Li, Michele Baldo, Hakim Hacid

Air Quality Monitoring and Forecasting has been a popular research topic in recent years. Recently, data-driven approaches for air quality forecasting have garnered significant attention, owing to the availability of well-established data collection facilities in urban areas. Fixed infrastructures, typically deployed by national institutes or tech giants, of

Jin Wang, Zishan Huang, Hui Xiao, Yinhao Xiao

Software vulnerabilities pose significant risks to computer systems, impacting our daily lives, productivity, and even our health. Identifying and addressing security vulnerabilities in a timely manner is crucial to prevent hacking and data breaches. Unfortunately, current vulnerability identification methods, including classical and deep learning-based appr

Snehinh Sen

We classify fields having finitely many finite non-commutative (not necessarily central) division algebras over them. In the process, we introduce the notion of anti-closure of a field and also make comments on fields having a linear lattice of finite field extensions over them.

Igor Pereira, Felipe Araújo, Filip Korzeniowski, Richard Vogl

In this paper, we introduce the MoisesDB dataset for musical source separation. It consists of 240 tracks from 45 artists, covering twelve musical genres. For each song, we provide its individual audio sources, organized in a two-level hierarchical taxonomy of stems. This will facilitate building and evaluating fine-grained source separation systems that go

Govind Krishnan.

The field of interpretation of quantum mechanics emerged in an attempt to solve the measurement problem. This turned on the perception that Niels Bohr avoided addressing the measurement problem by taking an instrumentalist view of quantum mechanics. I argue that this view is mistaken and Bohr's interpretation of quantum mechanics is realist. Moreover, Bohr's

Richard W. Longman, Shuo Liu, Tarek A. Elsharhawy

Various spacecraft have sensors that repeatedly perform a prescribed scanning maneuver, and one may want high precision. Iterative Learning Control (ILC) records previous run tracking error, adjusts the next run command, aiming for zero tracking error in the real world, not our model of the world. In response to a command, feedback control systems perform a

Stephen DiAdamo, Janis Nötzel

This work explores entanglement-assisted communication, where quantum entanglement resources enable the transmission of classical information at an enhanced rate. We consider a scenario where entanglement is distributed ahead of time based on network traffic levels, and simulate a setting where idle nodes generate and store entanglement to later transmit mes

Xiaoshan Lin, Abbasali Koochakzadeh, Yasin Yazicioglu, Derya Aksaray

We propose an automata-theoretic approach for reinforcement learning (RL) under complex spatio-temporal constraints with time windows. The problem is formulated using a Markov decision process under a bounded temporal logic constraint. Different from existing RL methods that can eventually learn optimal policies satisfying such constraints, our proposed appr

Duc-Viet Vu

We prove that the partial derivative of the volume function of big classes along any real divisor in a compact Kaehler manifold is equal to the numerical restricted volume of that class to the divisor. A consequence of our main result is that the divisorial components of the non-Kaehler locus of a big class lie in fact in the null locus of that class.

Minchen Li, Zachary Ferguson, Teseo Schneider, Timothy Langlois

Recent advances in the simulation of frictionally contacting elastodynamics with the Incremental Potential Contact (IPC) model have enabled inversion and intersection-free simulation via the application of mollified barriers, filtered line-search, and optimization-based solvers for time integration. In its current formulation the IPC model is constructed via

Ye Tao, Wanwei Liu, Fu Song, Zhen Liang

Deep neural networks, (DNNs, a.k.a. NNs), have been widely used in various tasks and have been proven to be successful. However, the accompanied expensive computing and storage costs make the deployments in resource-constrained devices a significant concern. To solve this issue, quantization has emerged as an effective way to reduce the costs of DNNs with li

Julia Kekelakova, Boris Tomasik

Since 2005, the global flagship of outreach activities in high-energy physics have been the International Particle Physics Masterclasses. We report on a survey performed among the participants from Slovakia and the Czech Republic, where we have studied the impact of Masterclasses on their further careers and their attitude towards science and especially part

Gaurav Srivastava, Mahesh Jangid

The complexity of high-dimensional datasets presents significant challenges for machine learning models, including overfitting, computational complexity, and difficulties in interpreting results. To address these challenges, it is essential to identify an informative subset of features that captures the essential structure of the data. In this study, the aut

Aayush Dhakal, Adeel Ahmad, Subash Khanal, Srikumar Sastry

We propose a weakly supervised approach for creating maps using free-form textual descriptions. We refer to this work of creating textual maps as zero-shot mapping. Prior works have approached mapping tasks by developing models that predict a fixed set of attributes using overhead imagery. However, these models are very restrictive as they can only solve hig

Fuqing Gao, Yunshi Gao, Lingjiong Zhu

The Hawkes process is a counting process that has self- and mutually-exciting features with many applications in various fields. In recent years, there have been many interests in the mean-field results of the Hawkes process and its extensions. It is known that the mean-field limit of a multivariate nonlinear Hawkes process is a time-inhomogeneous Poisson pr

Karlijn Kruiswijk, Mathieu Lamoureux, Gwenhaël de Wasseige

We present the results of searches for astrophysical neutrinos of few GeV energy from compact binary mergers detected during the first months of the fourth observing run of the LIGO, Virgo, and KAGRA interferometers. We describe our method, based on a selection of $0.5-5$ GeV neutrino events in IceCube, where we search for a statistically significant increas

Lun Qu, Zhidong Gu, Chenyang Li, Yuan Qin

We present a study on photonic vertical cavities consisting of nonlinear materials embedded in photonic crystals (PhCs) for resonantly enhancing second harmonic generation (SHG). Previous attempts at SHG in such structures have been limited to efficiencies of 10$^{-7}$ to 10$^{-5}$, but we demonstrate here a high SHG efficiency of 0.28% by constructing a ver

Kanhu Kishore Nanda, Sunil Kumar Sake, Sandip P. Trivedi

We discuss the canonical quantisation of JT gravity in de Sitter space, following earlier work by Henneaux, with particular attention to the problem of time. Choosing the dilaton as the physical clock, we define a norm and operator expectation values for states and explore the classical limit. We find that requiring a conserved and finite norm and well-defin

Nicolas Crouseilles, Xue Hong

In this work, an exponential Discontinuous Galerkin (DG) method is proposed to solve numerically Vlasov type equations. The DG method is used for space discretization which is combined exponential Lawson Runge-Kutta method for time discretization to get high order accuracy in time and space. In addition to get high order accuracy in time, the use of Lawson m

Sen Fang, Bowen Gao, Yangjian Wu, Teik Toe Teoh

Multimodal large models have been recognized for their advantages in various performance and downstream tasks. The development of these models is crucial towards achieving general artificial intelligence in the future. In this paper, we propose a novel universal language representation learning method called UniBriVL, which is based on Bridging-Vision-and-La

Byeong-Cheol Lee, Hee-Jin Do, Myeong-Gu Park, Beomdu Lim

Context. Certain periodic variations of radial velocities (RV) of wobbling giants originate from exoplanets. Indeed, a number of exoplanets have been discovered around giant stars. Aims. The purpose of our study is to find low-amplitude and long-period RV variations around bright M (super) giants in the RGB (or AGB) stage, which are long-period variables (LP

Fanze Kong, Michael Ward, Juncheng Wei

We analyze the existence, linear stability, and slow dynamics of localized 1D spike patterns for a Keller--Segel model of chemotaxis that includes the effect of logistic growth of the cellular population. Our analysis of localized patterns for this two-component reaction-diffusion (RD) model is based, not on the usual limit of a large chemotactic drift coeff

Peng Jiang, Ruizhe Huang, Ding Li, Yao Guo

System auditing is a crucial technique for detecting APT attacks. However, attackers may try to compromise the system auditing frameworks to conceal their malicious activities. In this paper, we present a comprehensive and systematic study of the super producer threat in auditing frameworks, which enables attackers to either corrupt the auditing framework or

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Based on data samples collected with the BESIII detector at the BEPCII collider, the process $e^{+}e^{-} \to \Sigma^{+}\bar{\Sigma}^{-}$ is studied at center-of-mass energies $\sqrt{s}$ = 2.3960, 2.6454, and 2.9000 GeV. Using a fully differential angular description of the final state particles, both the relative magnitude and phase information of the $\Sigm

Xinyang Yi, Shao-Chuan Wang, Ruining He, Hariharan Chandrasekaran

The last decade has witnessed many successes of deep learning-based models for industry-scale recommender systems. These models are typically trained offline in a batch manner. While being effective in capturing users' past interactions with recommendation platforms, batch learning suffers from long model-update latency and is vulnerable to system biases, ma

Hengshuai Yao

Gradient Temporal Difference (GTD) algorithms (Sutton et al., 2008, 2009) are the first $O(d)$ ($d$ is the number features) algorithms that have convergence guarantees for off-policy learning with linear function approximation. Liu et al. (2015) and Dalal et. al. (2018) proved the convergence rates of GTD, GTD2 and TDC are $O(t^{-\alpha/2})$ for some $\alpha

Mojtaba Mohareri, Behrooz Mashayekhy

In this paper, we present upper bounds for the depth of some classes of polyhedra, including: polyhedra with finite fundamental group, polyhedra $P$ with abelian or free $\pi_1(P)$ and finitely generated $H_i(tilde{P};\mathbb{Z}$, 2-dimensional polyhedra with abelian or free fundamental group, and 2-dimensional polyhedra with elementary amenable fundamental

Yan Li, Guanghui Lan

We adopt a policy optimization viewpoint towards policy evaluation for robust Markov decision process with $\mathrm{s}$-rectangular ambiguity sets. The developed method, named first-order policy evaluation (FRPE), provides the first unified framework for robust policy evaluation in both deterministic (offline) and stochastic (online) settings, with either ta

Weijie Huang, Yao Yao

We investigate the quench dynamics of interacting bosons on a two-leg ladder in presence of a uniform Abelian gauge field. The model hosts a variety of emergent quantum phases, and we focus on the superfluid biased-ladder phase breaking the $Z_{2}$ symmetry of two legs. We observe an asymmetric spreading of vortex current and particle density, i.e., the curr

Gustavo García, Marcelo Salgado

In this work we analyze the existence of electrically charged scalar clouds which are bound states of a complex-valued massive scalar field in the background of subextremal and extremal Kerr-Newman black holes (BH). In particular, we reanalyze neutral (uncharged) clouds in extremal Kerr BH's. For the extremal scenarios we have implemented a novel technique w

Baskın Şenbaşlar, Gaurav S. Sukhatme

Collision-free navigation in cluttered environments with static and dynamic obstacles is essential for many multi-robot tasks. Dynamic obstacles may also be interactive, i.e., their behavior varies based on the behavior of other entities. We propose a novel representation for interactive behavior of dynamic obstacles and a decentralized real-time multi-robot

Sanjay Chakraborty, Saroj Kumar Pandey, Saikat Maity, Lopamudra Dey

Attackers are now using sophisticated techniques, like polymorphism, to change the attack pattern for each new attack. Thus, the detection of novel attacks has become the biggest challenge for cyber experts and researchers. Recently, anomaly and hybrid approaches are used for the detection of network attacks. Detecting novel attacks, on the other hand, is a

Soonsik Kwon, Kiyeon Lee, Changhun Yang

In this paper, we consider the asymptotic behaviors of small solutions to the semi-relativistic Hartree equations in two dimension. The nonlinear term is convolved with the Coulomb potential 1/|x|, and it produces the long-range interaction in the sense of scattering phenomenon. From this observation, one anticipates that small solutions converge to a modifi

Yuehai Chen, Jing Yang, Badong Chen, Hua Gang

Counting objects in crowded scenes remains a challenge to computer vision. The current deep learning based approach often formulate it as a Gaussian density regression problem. Such a brute-force regression, though effective, may not consider the annotation displacement properly which arises from the human annotation process and may lead to different distrib

Lei Ren, Xin Tao, Lu Zhang, Ming-Jiu Ni

We present an experimental study of Rayleigh-B\'enard convection using liquid metal alloy gallium-indium-tin as the working fluid with a Prandtl number of $Pr=0.029$. The flow state and the heat transport were measured in a Rayleigh number range of $1.2\times10^{4} \le Ra \le 1.3\times10^{7}$. The temperature fluctuation at the cell centre is used as a proxy

Yilun Zhu, Clayton Scott, Darren Holland, George Landon

Many nuclear safety applications need fast, portable, and accurate imagers to better locate radiation sources. The Rotating Scatter Mask (RSM) system is an emerging device with the potential to meet these needs. The main challenge is the under-determined nature of the data acquisition process: the dimension of the measured signal is far less than the dimensi

Simon J. Devitt

The development of quantum computing systems has been a staple of academic research since the mid-1990s when the first proposal for physical platforms were proposed using Nuclear Magnetic Resonance and Ion-Trap hardware. These first proposals were very basic, essentially consisting of identifying a physical qubit (two-level quantum system) that could be isol

Benedict Estrella

Almost difference sets have emerged as a fascinating and important area of research as they can produce functions with optimal nonlinearity, cyclic codes, and binary sequences with optimal autocorrelation. This study aims to investigate the existence of almost difference sets from the union of suitable cyclotomic classes of order 14 (with and without the res

Guo Yu, Pengjie Wang, Ayelet J. Uzan, Yanyu Jia

While Landau's Fermi liquid theory provides the standard description for two- and three-dimensional (2D/3D) conductors, the physics of interacting one-dimensional (1D) conductors is governed by the distinct Luttinger liquid (LL) theory. Can a LL-like state, in which electronic excitations are fractionalized modes, emerge in a 2D system as a stable zero-tempe

Zhendong Yang, Ailing Zeng, Chun Yuan, Yu Li

Whole-body pose estimation localizes the human body, hand, face, and foot keypoints in an image. This task is challenging due to multi-scale body parts, fine-grained localization for low-resolution regions, and data scarcity. Meanwhile, applying a highly efficient and accurate pose estimator to widely human-centric understanding and generation tasks is urgen

V. A. Melent'ev

The method is based on the preliminary transformation of the traditionally used matrices or adjacency lists in the graph theory into refined projections free from redundant information, and their subsequent use in constructing shortest paths. Unlike adjacency matrices and lists based on enumerating binary adjacency relations, the refined projection is based

Chetraj Pandey, Rafal A. Angryk, Berkay Aydin

This paper contributes to the growing body of research on deep learning methods for solar flare prediction, primarily focusing on highly overlooked near-limb flares and utilizing the attribution methods to provide a post hoc qualitative explanation of the model's predictions. We present a solar flare prediction model, which is trained using hourly full-disk

Ruquan You, Shuming Zhang, Tinglin Kong, Haiwang Li

In order to solve the problem of the difficult direct measurement of temperature field in fluid machinery under high-speed compressible conditions, this study combines high-order finite difference numerical format, Weighted Essentially Non-Oscillatory (WENO) discontinuity detection, and traditional Physics-Informed Neural Network (PINN) to develop a high-ord

Ke Feng, Dahai Liu, Yongxin Liu, Hong Liu

The current National Airspace System (NAS) is reaching capacity due to increased air traffic, and is based on outdated pre-tactical planning. This study proposes a more dynamic airspace configuration (DAC) approach that could increase throughput and accommodate fluctuating traffic, ideal for emergencies. The proposed approach constructs the airspace as a con

Armaan V. Goyal, Fei Dai, Songhu Wang

Super-Earths within the same close-in, compact planetary system tend to exhibit a striking degree of uniformity in their radius, mass, and orbital spacing, and this 'peas-in-a-pod' phenomenon itself serves to provide one of the strongest constrains on planet formation at large. While it has been recently demonstrated from independent samples that such planet

Jian Gong, Carlos Murguia, Anggera Bayuwindra, Jinde Cao

This paper proposes a vehicular platoon control approach under Denial-of-Service (DoS) attacks and external disturbances. DoS attacks increase the service time on the communication network and cause additional transmission delays, which consequently increase the risk of rear-end collisions of vehicles in the platoon. To counter DoS attacks, we propose a resi

Ye Lu, Satyajit Mojumder, Jiachen Guo, Yangfan Li

This paper introduces an extended tensor decomposition (XTD) method for model reduction. The proposed method is based on a sparse non-separated enrichment to the conventional tensor decomposition, which is expected to improve the approximation accuracy and the reducibility (compressibility) in highly nonlinear and singular cases. The proposed XTD method can

Hicham Messaoudi, Ahror Belaid, Douraied Ben Salem, Pierre-Henri Conze

Over the last decade, convolutional neural networks have emerged and advanced the state-of-the-art in various image analysis and computer vision applications. The performance of 2D image classification networks is constantly improving and being trained on databases made of millions of natural images. However, progress in medical image analysis has been hinde

Mina Dalirrooyfard, Surya Mathialagan, Virginia Vassilevska Williams, Yinzhan Xu

We study finding and listing $k$-cliques in a graph, for constant $k\geq 3$, a fundamental problem of both theoretical and practical importance. Our main contribution is a new output-sensitive algorithm for listing $k$-cliques in graphs, for arbitrary $k\geq 3$, coupled with lower bounds based on standard fine-grained assumptions, showing that our algorithm'

Yang Xu, Yunming Liao, Hongli Xu, Zhipeng Sun

Federated Learning (FL) has emerged to allow multiple clients to collaboratively train machine learning models on their private data at the network edge. However, training and deploying large-scale models on resource-constrained devices is challenging. Fortunately, Split Federated Learning (SFL) offers a feasible solution by alleviating the computation and/o

Vo Si Trong Long, Boris Mordukhovich, Nguyen Mau Nam

This paper focuses on investigating generalized relative interior notions for sets in locally convex topological vector spaces with particular attentions to graphs of set-valued mappings and epigraphs of extended-real-valued functions. We introduce, study, and utilize a novel notion of quasi-near convexity of sets that is an infinite-dimensional extension of

Lesi Chen, Boyuan Yao, Luo Luo

This paper considers stochastic first-order algorithms for minimax optimization under Polyak--{\L}ojasiewicz (PL) conditions. We propose SPIDER-GDA for solving the finite-sum problem of the form $\min_x \max_y f(x,y)\triangleq \frac{1}{n} \sum_{i=1}^n f_i(x,y)$, where the objective function $f(x,y)$ is $\mu_x$-PL in $x$ and $\mu_y$-PL in $y$; and each $f_i(x

Seraphim Jarov, Sarah Thiele, Siddharth Soni, Julian Ding

The Advanced LIGO and Advanced Virgo detectors have enabled the confident detection of dozens of mergers of black holes and neutron stars. However, the presence of detector noise transients (glitches) hinders the search for these gravitational wave (GW) signals. We prototyped a restructuring of Gravity Spy's classification model to distinguish between glitch

Fulin Chen, Xin Huang, Shaobin Tan

In this paper, we construct and study various dual pairs acting on the oscillator modules of the symplectic toroidal Lie algebras coordinated by irrational quantum tori. This extends the classical Howe dual pairs to the toroidal setup.

David N. Pham

Let $G$ be a Lie group, and let $(g,J)$ be a left invariant almost pseudo-Hermitian structure on $G$. It is shown that if $(g,J)$ is also nearly pseudo-K\"{a}hler, then the tangent bundle $TG$ (with its natural Lie group structure induced from $G$) admits a left-invariant nearly pseudo-K\"{a}hler structure.

Na Wang, Wei Yan, Sian-Jheng Lin, Yuliang Huang

The necessity of radix conversion of numeric data is an indispensable component in any complete analysis of digital computation. In this paper, we propose a binary encoding for mixed-radix digits. Second, a variant of rANS coding based on this conversion is given, which supports parallel decoding. The simulations show that the proposed coding in serial mode

Yiren Wang, Peter C B Phillips, Liangjun Su

This paper considers a linear panel model with interactive fixed effects and unobserved individual and time heterogeneities that are captured by some latent group structures and an unknown structural break, respectively. To enhance realism the model may have different numbers of groups and/or different group memberships before and after the break. With the p

Xiaohui Chen, Tie Luo

Depression is a common mental health disorder that can cause consequential symptoms with continuously depressed mood that leads to emotional distress. One category of depression is Concealed Depression, where patients intentionally or unintentionally hide their genuine emotions through exterior optimism, thereby complicating and delaying diagnosis and treatm

Do Sang Kim, Minh Tung Nguyen, Tien Son Pham

In this work, the notions of normal cones at infinity to unbounded sets and limiting and singular subdifferentials at infinity for extended real value functions are introduced. Various calculus rules for these notions objects are established. A complete characterization of the Lipschitz continuity at infinity for lower semi-continuous functions is given. The

Ziheng Huang, Boheng Li, Yan Cai, Run Wang

In recent decades, Generative Adversarial Network (GAN) and its variants have achieved unprecedented success in image synthesis. However, well-trained GANs are under the threat of illegal steal or leakage. The prior studies on remote ownership verification assume a black-box setting where the defender can query the suspicious model with specific inputs, whic

Benjamin Scully, Christopher D. Matzner, Almog Yalinewich

A new class of transient, which has been hypothesized to accompany the explosion of an aspherical compact supernova, would arise when streams of ejecta collide outside the star. However, conditions that favour the prompt release of radiation from the collision, such as a diffuse stellar envelope, disfavour the creation of non-radial ejecta in the first place

Hotaka Shiokawa, Pradipto Das, Arthur Toth, Justin Chiu

In this paper, we revisit the problem of product item classification for large-scale e-commerce catalogs. The taxonomy of e-commerce catalogs consists of thousands of genres to which are assigned items that are uploaded by merchants on a continuous basis. The genre assignments by merchants are often wrong but treated as ground truth labels in automatically g

Maria-Veronica Ciocanel, Lee Ding, Lucas Mastromatteo, Sarah Reichheld

Identifying unique parameters for mathematical models describing biological data can be challenging and often impossible. Parameter identifiability for partial differential equations models in cell biology is especially difficult given that many established \textit{in vivo} measurements of protein dynamics average out the spatial dimensions. Here, we are mot

M. V. Dolgopolik

Subdifferentials (in the sense of convex analysis) of matrix-valued functions defined on $\mathbb{R}^d$ that are convex with respect to the L\"{o}wner partial order can have a complicated structure and might be very difficult to compute even in simple cases. The aim of this paper is to study subdifferential calculus for such functions and properties of their

Shiqi Yu, Jessie Micallef

The IceCube South Pole Neutrino Observatory is a Cherenkov detector instrumented in a cubic kilometer of ice at the South Pole. IceCube's primary scientific goal is the detection of TeV neutrino emissions from astrophysical sources. At the lower center of the IceCube array, there is a subdetector called DeepCore, which has a denser configuration that makes i

Long Tian, Xiaoping Yang

In this paper, we investigate the quantitative unique continuation, propagation of smallness and measure bounds of nodal sets of solutions to the Buckling type equation $\triangle^2u+\lambda\triangle u-k^2u=0$ in a bounded analytic domain $\Omega\subseteq\mathbb{R}^n$ with the homogeneous boundary conditions $u=0$ and $\frac{\partial u}{\partial\nu}=0$ on $\

Zheyu Yan, Yifan Qin, Wujie Wen, Xiaobo Sharon Hu

Compute-in-Memory (CiM), built upon non-volatile memory (NVM) devices, is promising for accelerating deep neural networks (DNNs) owing to its in-situ data processing capability and superior energy efficiency. Unfortunately, the well-trained model parameters, after being mapped to NVM devices, can often exhibit large deviations from their intended values due

Alexandre Girard

The answer to the question posed in the title is yes if the context (the list of variables defining the motion control problem) is dimensionally similar. This article explores the use of the Buckingham $\pi$ theorem as a tool to encode the control policies of physical systems into a more generic form of knowledge that can be reused in various situations. Thi

Saqib Hussain, Giulia Pagliaroli, Elisabete M. de Gouveia Dal Pino

We compute the contribution from clusters of galaxies to the diffuse neutrino and $\gamma-$ray background. Due to their unique magnetic-field configuration, cosmic rays (CRs) with energy $\leq10^{17}$ eV can be confined within these structures over cosmological time scales, and generate secondary particles, including neutrinos and $\gamma-$rays, through inte

Sevvandi Kandanaarachchi, Kate Smith-Miles

Item Response Theory (IRT) has been proposed within the field of Educational Psychometrics to assess student ability as well as test question difficulty and discrimination power. More recently, IRT has been applied to evaluate machine learning algorithm performance on a single classification dataset, where the student is now an algorithm, and the test questi

Yu-Chu Lin, Haitao Wang, Kung-Chien Wu

Consider the Boltzmann equation in the perturbation regime. Since the macroscopic quantities in the background global Maxwellian are obtained through measurements, there are typically some errors involved. This paper investigates the effect of background variations on the solution for a given initial perturbation. Our findings demonstrate that the solution c

Claudio Corti, Peter Sadowski, Nikolay Nikonov, Marius Potgieter

Galactic cosmic rays (GCRs) are affected by solar modulation while they propagate through the heliosphere. The study of the time variation of GCR spectra observed at Earth can shed light on the underlying physical processes, specifically diffusion and particle drifts. We combine a state-of-the art 3D numerical model of GCR transport in the heliosphere with a

Chaowei Jiang, Aiying Duan, Peng Zou, Zhenjun Zhou

Solar eruptions are explosive disruption of coronal magnetic fields, and often launch coronal mass ejections into the interplanetary space. Intriguingly, many solar eruptions fail to escape from the Sun, and the prevailing theory for such failed eruption is based on ideal MHD instabilities of magnetic flux rope (MFR); that is, a MFR runs into kink instabilit

Shabir Ahmad, Sabina Umirzakova, Ghulam Mujtaba, Muhammad Sadiq Amin

We are currently in a post-pandemic era in which life has shifted to a digital world. This has affected many aspects of life, including education and learning. Education 5.0 refers to the fifth industrial revolution in education by leveraging digital technologies to eliminate barriers to learning, enhance learning methods, and promote overall well-being. The

Coulton Boucher, Igor Zhitomirsky, Oleg Rubel

In this study, we investigate the effectiveness of murexide for surface modification of Fe$_3$O$_4$ nanoparticles to enhance the performance of multi-walled carbon nanotube-Fe$_3$O$_4$ supercapacitor anodes. Our experimental results demonstrate significant improvements in electrode performance when murexide is used as a capping or dispersing agent compared t

Sagnik Bhattacharya, Prakash Narayan

Shared information is a measure of mutual dependence among multiple jointly distributed random variables with finite alphabets. For a Markov chain on a tree with a given joint distribution, we give a new proof of an explicit characterization of shared information. The Markov chain on a tree is shown to possess a global Markov property based on graph separati

Edson R. Cardozo de Oliveira, Priscila Vensaus, Galo J. A. A. Soler-Illia, Norberto Daniel Lanzillotti-Kimura

Gigahertz acoustic resonators have the potential to advance data processing and quantum communication. However, they are expensive and lack responsiveness to external stimuli, limiting their use in sensing applications. In contrast, low-cost nanoscale mesoporous materials, known for their high surface-to-volume ratio, have shown promise in various applicatio

Ben Hambly, Renyuan Xu, Huining Yang

We consider two-player non-zero-sum linear-quadratic Gaussian games in which both players aim to minimize a quadratic cost function while controlling a linear and stochastic state process {using linear policies}. The system is partially observable with asymmetric information available to the players. In particular, each player has a private and noisy measure

Qiyao Liang, Mingyu Kang, Ming Li, Yunseong Nam

High-fidelity operation of quantum computers requires precise knowledge of the physical system through characterization. For motion-mediated entanglement generation in trapped ions, it is crucial to have precise knowledge of the motional-mode parameters such as the mode frequencies and the Lamb-Dicke parameters. Unfortunately, the state-of-the-art mode-chara

Marco Russo, Edoardo Giusto, Bartolomeo Montrucchio

Quantum Kernel Estimation (QKE) is a technique based on leveraging a quantum computer to estimate a kernel function that is classically difficult to calculate, which is then used by a classical computer for training a Support Vector Machine (SVM). Given the high number of 2-local operators necessary for realizing a feature mapping hard to simulate classicall

Yeping Wang, Carter Sifferman, Michael Gleicher

We explore task tolerances, i.e., allowable position or rotation inaccuracy, as an important resource to facilitate smooth and effective telemanipulation. Task tolerances provide a robot flexibility to generate smooth and feasible motions; however, in teleoperation, this flexibility may make the user's control less direct. In this work, we implemented a tele

Sina Shaham, Arash Hajisafi, Minh K Quan, Dinh C Nguyen

Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML). Each objective has been independently studied in the literature with the aim of reducing utility loss in achieving them. Despite the significant interest attracted from both academia and industry, there remains an immediate demand f

Yuan Li, Xinhan Liu, Engui Fan

We address the existence of global solutions to the initial value problem for the integrable nonlocal derivative nonlinear Schr\"{o}dinger equation in weighted Sobolev space $H^{2}(\mathbb{R})\cap H^{1,1}(\mathbb{R})$. The key to prove this result is to establish a bijectivity between potential and reflection coefficient by using the inverse scattering trans

Mohammad Khorrami, Amir Aghamohammadi, Cina Aghamohammadi

The two-dimensional motion of an object on a moving rough horizontal plane is investigated. Two cases are studied: the plane having a translational acceleration, and a rotating plane. For the first case, the motions of a point particle and a sphere are studied, and it is shown that the solution to the latter problem can be expressed in terms of the solution

Rachel Cummings, Vitaly Feldman, Audra McMillan, Kunal Talwar

A key challenge in many modern data analysis tasks is that user data are heterogeneous. Different users may possess vastly different numbers of data points. More importantly, it cannot be assumed that all users sample from the same underlying distribution. This is true, for example in language data, where different speech styles result in data heterogeneity.