February 2024 arXiv papers — page 160

Mohammad Majid Akhtar, Navid Shadman Bhuiyan, Rahat Masood, Muhammad Ikram

The detection of automated accounts, also known as "social bots", has been an increasingly important concern for online social networks (OSNs). While several methods have been proposed for detecting social bots, significant research gaps remain. First, current models exhibit limitations in detecting sophisticated bots that aim to mimic genuine OSN users. Sec

Jie Xiao, Han Xu

In [Tame_quivers_and_affine_bases_I], we give a Ringel-Hall algebra approach to the canonical bases in the symmetric affine cases. In this paper, we extend the results to general symmetrizable affine cases by using Ringel-Hall algebras of representations of a valued quiver. We obtain a bar-invariant basis $\mathbf{B}'=\{C(\mathbf{c},t_\lambda)|(\mathbf{c},t_

Yuxu Lu, Dong Yang, Yuan Gao, Ryan Wen Liu

Scattering and attenuation of light in no-homogeneous imaging media or inconsistent light intensity will cause insufficient contrast and color distortion in the collected images, which limits the developments such as vision-driven smart urban, autonomous vehicles, and intelligent robots. In this paper, we propose an all-in-one scene recovery network via mult

Apurv Shukla

We consider a high-dimensional stochastic contextual linear bandit problem when the parameter vector is $s_{0}$-sparse and the decision maker is subject to privacy constraints under both central and local models of differential privacy. We present PrivateLASSO, a differentially private LASSO bandit algorithm. PrivateLASSO is based on two sub-routines: (i) a

Jinghui Xue, Jiongzhi Zheng, Mingming Jin, Kun He

The Maximum s-Bundle Problem (MBP) addresses the task of identifying a maximum s-bundle in a given graph. A graph G=(V, E) is called an s-bundle if its vertex connectivity is at least |V|-s, where the vertex connectivity equals the minimum number of vertices whose deletion yields a disconnected or trivial graph. MBP is NP-hard and holds relevance in numerous

Joy Krishan Das, Saikat Mondal, Chanchal K. Roy

Issue tracking systems serve as the primary tool for incorporating external users and customizing a software project to meet the users' requirements. However, the limited number of contributors and the challenge of identifying the best approach for each issue often impede effective resolution. Recently, an increasing number of developers are turning to AI to

Anupama Swain, Kshitij Singh Rathore, Pushpendra Gupta, Abhisek Mishra

Spin pumping has significant implications for spintronics, providing a mechanism to manipulate and transport spins for information processing. Understanding and harnessing spin currents through spin pumping is critical for the development of efficient spintronic devices. The use of a magnetic insulator with low damping, enhances the signal-to-noise ratio in

Haximjan Abdusattar, Shi-Bei Kong

In this paper, within the framework of modified gravity involving a conformal scalar field, we investigate the Joule-Thomson expansion of the FRW universe to identify cooling and heating regions. Notably, we observe that the Joule-Thomson coefficient, denoted as $\mu$, diverges at $R_A=\sqrt{-2\alpha}$ when $\alpha<0$, aligning with the thermodynamic singula

Huiling Tu, Shuo Yu, Vidya Saikrishna, Feng Xia

Knowledge graphs (KGs) have garnered significant attention for their vast potential across diverse domains. However, the issue of outdated facts poses a challenge to KGs, affecting their overall quality as real-world information evolves. Existing solutions for outdated fact detection often rely on manual recognition. In response, this paper presents DEAN (De

Wenbin Lin

We calculate the metric for a self-gravitating and collapsing infinitely-thin spherical shell under the theory of post-Newtonian approximation, and successfully recover the shell's energy-momentum tensor from the achieved metric. The post-Newtonian dynamic equations of the test particles inside and outside the shell are achieved respectively. It is demonstra

Chun Liu, Cheng Wang, Yiwei Wang

In this paper, we provide a detailed theoretical analysis of the numerical scheme introduced in J. Comput. Phys. 436 (2021) 110253 for the reaction kinetics of a class of chemical reaction networks that satisfies detailed balance condition. In contrast to conventional numerical approximations, which are typically constructed based on ordinary differential eq

Feng Qu

On two subspaces of the Bruhat-Tits tree, effective actions are calculated. The limits of these effective field theories are found to be the same conformal field theory over p-adic numbers when subspaces are taken to the boundary of the tree. Their relations to the p-adic version of AdS/CFT are also discussed.

Roy D. Jara, Dennis F. Salinel, Jayson G. Cosme

We pinpoint the conditions necessary for discrete time crystal (DTC) formation in fully connected spin-cavity systems from the perspective of parametric resonance by mapping these systems onto oscillator like models. We elucidate the role of nonlinearity and dissipation by mapping the periodically driven open Dicke model onto effective linear and nonlinear o

Hossein Rajaby Faghihi, Parisa Kordjamshidi

This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming (ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions' prior probab

Xilu Zhu

We find an example to illustrate that the first nondegeneracy condition of (1.2) is actually needed in proving the global exsitence of 2D multispeed Klein-Gordon system with small initial data (See [3]). We construct a collection of special Klein-Gordon dispersive relations and by iterating the corresponding profiles we can find a blowup in finite time.

Dongxia Wu, Tsuyoshi Idé, Aurélie Lozano, Georgios Kollias

We address the problem of learning Granger causality from asynchronous, interdependent, multi-type event sequences. In particular, we are interested in discovering instance-level causal structures in an unsupervised manner. Instance-level causality identifies causal relationships among individual events, providing more fine-grained information for decision-m

Yang-Yang Tang

Logarithmic negativity is a widely used entanglement measure in quantum information theories, which can also be efficiently computed in quantum many-body systems by replica trick or by relating to correlation matrices. In this paper, we demonstrate that in free-fermion systems with conserved charge, R\'enyi and logarithmic negativity can be expanded by conne

Daiki Miwa, Tomohiro Shiraishi, Vo Nguyen Le Duy, Teruyuki Katsuoka

In this study, we consider the reliability assessment of anomaly detection (AD) using Variational Autoencoder (VAE). Over the last decade, VAE-based AD has been actively studied in various perspective, from method development to applied research. However, when the results of ADs are used in high-stakes decision-making, such as in medical diagnosis, it is nec

Alfredo Rivero, ShahRukh Athar, Zhixin Shu, Dimitris Samaras

Creating controllable 3D human portraits from casual smartphone videos is highly desirable due to their immense value in AR/VR applications. The recent development of 3D Gaussian Splatting (3DGS) has shown improvements in rendering quality and training efficiency. However, it still remains a challenge to accurately model and disentangle head movements and fa

Sebastian Debus, Charu Goel, Salma Kuhlmann, Cordian Riener

The equivariant nonnegativity versus sums of squares question has been solved for any infinite series of essential reflection groups but type A. As a first step to a classification, we analyse $A_n$-invariant quartics. We prove that the cones of invariant sums of squares and nonnegative forms are equal if and only if the number of variables is at most 3 or o

Nicolas Harvey Chapman, Feras Dayoub, Will Browne, Chris Lehnert

Deep-learning and large scale language-image training have produced image object detectors that generalise well to diverse environments and semantic classes. However, single-image object detectors trained on internet data are not optimally tailored for the embodied conditions inherent in robotics. Instead, robots must detect objects from complex multi-modal

Rui Li, Jiwei Li, Jiawei Han, Guoyin Wang

Text-attributed graphs (TAGs) present unique challenges for direct processing by Language Learning Models (LLMs), yet their extensive commonsense knowledge and robust reasoning capabilities offer great promise for node classification in TAGs. Prior research in this field has grappled with issues such as over-squashing, heterophily, and ineffective graph info

Jing-Cheng Pang, Heng-Bo Fan, Pengyuan Wang, Jia-Hao Xiao

The rise of large language models (LLMs) has revolutionized the way that we interact with artificial intelligence systems through natural language. However, LLMs often misinterpret user queries because of their uncertain intention, leading to less helpful responses. In natural human interactions, clarification is sought through targeted questioning to uncove

Juan A. Paredes, Jhon Manuel Portella, Dennis S. Bernstein, Ankit Goel

Extremum seeking control (ESC) constitutes a powerful technique for online optimization with theoretical guarantees for convergence to the neighborhood of the optimizer under well-understood conditions. However, ESC requires a nonconstant perturbation signal to provide persistent excitation to the target system to yield convergent results, which usually resu

Vuong D. Nguyen, Samiha Mirza, Pranav Mantini, Shishir K. Shah

Current state-of-the-art Video-based Person Re-Identification (Re-ID) primarily relies on appearance features extracted by deep learning models. These methods are not applicable for long-term analysis in real-world scenarios where persons have changed clothes, making appearance information unreliable. In this work, we deal with the practical problem of Video

Yoonho Lee, Michelle S. Lam, Helena Vasconcelos, Michael S. Bernstein

The standard way to teach models is by feeding them lots of data. However, this approach often teaches models incorrect ideas because they pick up on misleading signals in the data. To prevent such misconceptions, we must necessarily provide additional information beyond the training data. Prior methods incorporate additional instance-level supervision, such

Rebecca Adaimi, Abdelkareem Bedri, Jun Gong, Richard Kang

Wearable sensors have permeated into people's lives, ushering impactful applications in interactive systems and activity recognition. However, practitioners face significant obstacles when dealing with sensing heterogeneities, requiring custom models for different platforms. In this paper, we conduct a comprehensive evaluation of the generalizability of moti

Belle II Collaboration, I. Adachi, L. Aggarwal, H. Ahmed

We describe a measurement of charge-parity ($CP$) violation asymmetries in $B^0\to\eta'K^0_S$ decays using Belle II data. We consider $\eta'\to\eta(\to\gamma\gamma)\pi^+\pi^-$ and $\eta'\to\rho(\to\pi^+\pi^-)\gamma$ decays. The data were collected at the SuperKEKB asymmetric-energy $e^+e^-$ collider between the years 2019 and 2022, and contain $(387\pm 6) \t

Pingal Pratyush Nath, Debashis Saha, Dipankar Home, Urbasi Sinha

We theoretically formulate and experimentally demonstrate a secure scheme for semi-device-independent quantum random number generation by utilizing Leggett-Garg inequality violations, within a loophole-free photonic architecture. The quantification of the generated randomness is rigorously estimated by analytical as well as numerical approaches, both of whic

Bharadwaj Vedula, M. A. Moore, Auditya Sharma

One of the key predictions of Parisi's broken replica symmetry theory of spin glasses is the existence of a phase transition in an applied field to a state with broken replica symmetry. This transition takes place at the de Almeida-Thouless (AT) line in the $h-T$ plane. We have studied this line in the power-law diluted Heisenberg spin glass in which the pro

Xilin Jiang, Cong Han, Yinghao Aaron Li, Nima Mesgarani

In daily life, we encounter a variety of sounds, both desirable and undesirable, with limited control over their presence and volume. Our work introduces "Listen, Chat, and Remix" (LCR), a novel multimodal sound remixer that controls each sound source in a mixture based on user-provided text instructions. LCR distinguishes itself with a user-friendly text in

Jhon Manuel Portella Delgado, Mohammad Mirtaba, Ankit Goel

This paper presents a model-based, adaptive, nonlinear controller for the bicopter stabilization and trajectory-tracking problem. The nonlinear controller is designed using the backstepping technique. Due to the non-invertibility of the input map, the bicopter system is first dynamically extended. However, the resulting dynamically extended system is in the

Pengming Feng, Mingjie Xie, Hongning Liu, Xuanjia Zhao

Fine-grained ship instance segmentation in satellite images holds considerable significance for monitoring maritime activities at sea. However, existing datasets often suffer from the scarcity of fine-grained information or pixel-wise localization annotations, as well as the insufficient image diversity and variations, thus limiting the research of this task

Michelle Blom, Peter J. Stuckey, Vanessa Teague, Damjan Vukcevic

Single Transferable Vote (STV) elections are a principled approach to electing multiple candidates in a single election. Each ballot has a starting value of 1, and a candidate is elected if they gather a total vote value more than a defined quota. Votes over the quota have their value reduced by a transfer value so as to remove the quota, and are passed to t

Mohammed Musthafa Kummali, David Cole, Siddharth Gautam

Deciphering the interplay between confinement effects and intermolecular interactions in zeolites is crucial for understanding diverse diffusion behaviors of confined molecules. Recent studies explored the impact of water and CO$_2$ on hydrocarbon dynamics in nanoporous materials. However, differing nanoporous materials, as used in these studies complicate t

Shenghai Yuan, Yizhuo Yang, Thien Hoang Nguyen, Thien-Minh Nguyen

In response to the evolving challenges posed by small unmanned aerial vehicles (UAVs), which possess the potential to transport harmful payloads or independently cause damage, we introduce MMAUD: a comprehensive Multi-Modal Anti-UAV Dataset. MMAUD addresses a critical gap in contemporary threat detection methodologies by focusing on drone detection, UAV-type

Qi Zhou, Dongxia Wang, Tianlin Li, Zhihong Xu

Guided image synthesis methods, like SDEdit based on the diffusion model, excel at creating realistic images from user inputs such as stroke paintings. However, existing efforts mainly focus on image quality, often overlooking a key point: the diffusion model represents a data distribution, not individual images. This introduces a low but critical chance of

Pallavi Borkar, Chen Chen, Mohamadreza Rostami, Nikhilesh Singh

Timing vulnerabilities in processors have emerged as a potent threat. As processors are the foundation of any computing system, identifying these flaws is imperative. Recently fuzzing techniques, traditionally used for detecting software vulnerabilities, have shown promising results for uncovering vulnerabilities in large-scale hardware designs, such as proc

Zhirong Luan, Yujun Lai, Rundong Huang, Yan Yan

Despite their powerful semantic understanding and code generation capabilities, Large Language Models (LLMs) still face challenges when dealing with complex tasks. Multi agent strategy generation and motion control are highly complex domains that inherently require experts from multiple fields to collaborate. To enhance multi agent strategy generation and mo

Yeaju Kim, Junggu Choi, Bora Kim, Yongwan Park

Academic achievement is a critical measure of intellectual ability, prompting extensive research into cognitive tasks as potential predictors. Neuroimaging technologies, such as functional near-infrared spectroscopy (fNIRS), offer insights into brain hemodynamics, allowing understanding of the link between cognitive performance and academic achievement. Here

Manish Bansal, Pramsu Srivastava, J. Harshan

In Vehicle-to-Everything networks that involve multi-hop communication, the Road Side Units (RSUs) typically aim to collect location information from the participating vehicles to provide security and network diagnostics features. While the vehicles commonly use the Global Positioning System (GPS) for navigation, they may refrain from sharing their precise G

Lingxiao Zhao, Xueying Ding, Lijun Yu, Leman Akoglu

Discrete diffusion models have seen a surge of attention with applications on naturally discrete data such as language and graphs. Although discrete-time discrete diffusion has been established for a while, only recently Campbell et al. (2022) introduced the first framework for continuous-time discrete diffusion. However, their training and sampling processe

Tica Lin, Hanspeter Pfister, Jui-Hsien Wang

The rapid development of generative AI (GenAI) models in computer vision necessitates effective evaluation methods to ensure their quality and fairness. Existing tools primarily focus on dataset quality assurance and model explainability, leaving a significant gap in GenAI output evaluation during model development. Current practices often depend on develope

Zhirong Luan, Yujun Lai, Rundong Huang, Xiaruiqi Lan

Despite the remarkable code generation abilities of large language models LLMs, they still face challenges in complex task handling. Robot development, a highly intricate field, inherently demands human involvement in task allocation and collaborative teamwork . To enhance robot development, we propose an innovative automated collaboration framework inspired

Zach Furman, Edmund Lau

The \textit{local learning coefficient} (LLC) is a principled way of quantifying model complexity, originally derived in the context of Bayesian statistics using singular learning theory (SLT). Several methods are known for numerically estimating the local learning coefficient, but so far these methods have not been extended to the scale of modern deep learn

Oleg Gutik, Inna Pozdniakova

We study the semigroup of non-injective monoid endomorphisms of the semigroup $\boldsymbol{B}_{\omega}^{\mathscr{F}}$ with a two-elements family $\mathscr{F}$ of inductive nonempty subsets of $\omega$. We describe the structure of elements of the semigroup $\boldsymbol{End}^*_0(\boldsymbol{B}_{\omega}^{\mathscr{F}})$ of non-injective monoid endomorphisms of

Nishchal Sapkota, Yejia Zhang, Sirui Li, Peixian Liang

Male infertility accounts for about one-third of global infertility cases. Manual assessment of sperm abnormalities through head morphology analysis encounters issues of observer variability and diagnostic discrepancies among experts. Its alternative, Computer-Assisted Semen Analysis (CASA), suffers from low-quality sperm images, small datasets, and noisy cl

Hsin-Yu Chen, Philippe Landry, Jocelyn S. Read, Daniel M. Siegel

Observations of GW170817 strongly suggest that binary neutron star (BNS) mergers produce rapid neutron-capture nucleosynthesis (r-process) elements. However, it remains an open question whether these mergers can account for all the r-process element enrichment in the Milky Way's history. Here, we constrain the contributions of the BNS channel using astrophys

Nishchal Sapkota, Yejia Zhang, Susan M. Motch Perrine, Yuhan Hsi

Studying the morphological development of cartilaginous and osseous structures is critical to the early detection of life-threatening skeletal dysmorphology. Embryonic cartilage undergoes rapid structural changes within hours, introducing biological variations and morphological shifts that limit the generalization of deep learning-based segmentation models t

Shinan Liu, Ted Shaowang, Gerry Wan, Jeewon Chae

Network traffic analysis increasingly uses complex machine learning models as the internet consolidates and traffic gets more encrypted. However, over high-bandwidth networks, flows can easily arrive faster than model inference rates. The temporal nature of network flows limits simple scale-out approaches leveraged in other high-traffic machine learning appl

Denis Werth, Lucas Pinol, Sébastien Renaux-Petel

Cosmological correlators hold the key to high-energy physics as they probe the earliest moments of our Universe, and conceal hidden mathematical structures. However, even at tree-level, perturbative calculations are limited by technical difficulties absent in flatspace Feynman diagrammatics. In this paper, we introduce CosmoFlow: a new accurate open source P

Yuxuan Dai, Shouxing Zhao, Min He

It has been traditionally hypothesized that the heavy quark (charm, $c$ and bottom, $b$) fragmentation is universal across different collision systems, based on the notion that hadronization as a soft process should occur at the characteristic non-perturbative QCD scale, $\Lambda_{QCD}$. However, this universality hypothesis has recently been challenged by t

Shayla Lee, Wendy Ju

This research explores whether the interaction between adversarial robots and creative practitioners can push artists to rethink their initial ideas. It also explores how working with these robots may influence artists' views of machines designed for creative tasks or collaboration. Many existing robots developed for creativity and the arts focus on compleme

Changwoon Choi, Jaeah Lee, Jaesik Park, Young Min Kim

While free-hand sketching has long served as an efficient representation to convey characteristics of an object, they are often subjective, deviating significantly from realistic representations. Moreover, sketches are not consistent for arbitrary viewpoints, making it hard to catch 3D shapes. We propose 3Dooole, generating descriptive and view-consistent sk

Byungjoon Min, Eun-Kyu Park, Sang-Hwan Gwak, K. -I. Goh

No-exclaves percolation (NExP) is a nonlocal percolation process in which the components are formed not only by the connected occupied nodes but also by the agglomeration of empty nodes completely surrounded by the occupied nodes. It has been studied in low dimensions, displaying such novel phenomena as the discontinuous transition to complete percolation. H

Lei Yu, Meng Han, Yiming Li, Changting Lin

Vertical Federated Learning (VFL) is a federated learning paradigm where multiple participants, who share the same set of samples but hold different features, jointly train machine learning models. Although VFL enables collaborative machine learning without sharing raw data, it is still susceptible to various privacy threats. In this paper, we conduct the fi

Xinyu Li, Ruiyang Zhou, Zachary C. Lipton, Liu Leqi

Personalized large language models (LLMs) are designed to tailor responses to individual user preferences. While Reinforcement Learning from Human Feedback (RLHF) is a commonly used framework for aligning LLMs with human preferences, vanilla RLHF assumes that all human preferences share the same distribution, preventing fine-tuned LLMs from generating person

Lingxiao Zhao, Xueying Ding, Leman Akoglu

Graph generation has been dominated by autoregressive models due to their simplicity and effectiveness, despite their sensitivity to ordering. Yet diffusion models have garnered increasing attention, as they offer comparable performance while being permutation-invariant. Current graph diffusion models generate graphs in a one-shot fashion, but they require e

Soumya Sanyal, Tianyi Xiao, Jiacheng Liu, Wenya Wang

Making inferences in text comprehension to understand the meaning is essential in language processing. This work studies the entailment verification (EV) problem of multi-sentence premises that requires a system to make multiple inferences implicitly. Studying EV for such complex premises is important because modern NLP problems, such as detecting inconsiste

Chao Yang, Zhujun Zhang

In this paper, we show that the friends-and-strangers problem is PSPACE-complete by reduction from the Ncl (non-deterministic constraint logic) problem.

Changcheng Jing, Shuxun Tian, Zong-Hong Zhu

Early dark energy (EDE), introduced at the epoch of matter-radiation equality to alleviate the Hubble tension, has posed a new coincidence problem: why EDE appears at matter-radiation equality when their physics are completely unrelated? To solve this coincidence problem, we propose a new EDE model based on scalar-tensor gravity with the idea that EDE is tri

J. Jon Ryu, Gregory W. Wornell

A confidence sequence (CS) is a sequence of confidence sets that contains a target parameter of an underlying stochastic process at any time step with high probability. This paper proposes a new approach to constructing CSs for means of bounded multivariate stochastic processes using a general gambling framework, extending the recently established coin toss

Gregory J. Parker

Given a $\mathbb Z_2$-harmonic spinor satisfying some genericity assumptions, this article constructs a 1-parameter family of two-spinor Seiberg-Witten monopoles converging to it after renormalization. The proof is a gluing construction beginning with model solutions on a neighborhood of the $\mathbb Z_2$-harmonic spinor's singular set. The gluing is complic

Yufei Wang, Zhanyi Sun, Jesse Zhang, Zhou Xian

Reward engineering has long been a challenge in Reinforcement Learning (RL) research, as it often requires extensive human effort and iterative processes of trial-and-error to design effective reward functions. In this paper, we propose RL-VLM-F, a method that automatically generates reward functions for agents to learn new tasks, using only a text descripti

Zhenghan Chen, Kun Yang, Xiaodong Liu

The irregular satellites of Jupiter produce dust particles through the impact of interplanetary micrometeoroids. In this paper, the dynamics of these particles is studied by both high-accuracy numerical simulation and analytical theory, in order to learn their transport, final fate, and spatial distribution. The perturbation forces that are considered in our

Tchinda Franck, Fotso Tachago Joel, Dongho Joseph

In this paper, we study the stochastic-periodic homogenization of Non-stationary Navier-Stokes Type Equations on anisotropic heterogeneous media. More precisely, we are interested in the stochastic-periodic homogenization of its variational formulation. This problematic relies on the notion of dynamical system. It is shown by the stochastic two-scale converg

Yash Shukla, Tanushree Burman, Abhishek Kulkarni, Robert Wright

Reinforcement Learning (RL) has made significant strides in enabling artificial agents to learn diverse behaviors. However, learning an effective policy often requires a large number of environment interactions. To mitigate sample complexity issues, recent approaches have used high-level task specifications, such as Linear Temporal Logic (LTL$_f$) formulas o

Qiaohao Liang, Giacomo Galuppini, Partha M. Gomadam, Prabhakar A. Tamirisa

We present a physics-based model that accurately predicts the performance of Medtronic's implantable medical device battery lithium/carbon monofluoride (CF$_x$) - silver vanadium oxide (SVO) under both low-rate background monitoring and high-rate pulsing currents. The distinct properties of multiple active materials are reflected by parameterizing their ther

Sam P. Vaughan, Jesse van de Sande, A. Fraser-McKelvie, Scott Croom

We use the SAMI galaxy survey to study the the kinematic morphology-density relation: the observation that the fraction of slow rotator galaxies increases towards dense environments. We build a logistic regression model to quantitatively study the dependence of kinematic morphology (whether a galaxy is a fast rotator or slow rotator) on a wide range of param

Chenqing Hua, Connor Coley, Guy Wolf, Doina Precup

Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, and immune defense. As the accuracy of multi-chain protein complex structure prediction improves, the challenge has shifted towards effectively navigating the vast complex universe to identify potential PPIs. Herein, we pr

Jie Xu, Shusen Xie, Hongfei Fu

In this paper, by introducing two temporal-derivative-dependent auxiliary variables, a linearized and decoupled fourth-order compact finite difference method is developed and analyzed for the nonlinear coupled bacterial systems. The temporal-spatial error splitting technique and discrete energy method are employed to prove the unconditional stability and con

Danila O. Revin, Andrei V. Zavarnitsine

As defined by Guralnick and Saxl given a nonabelian simple group $S$ and its nonidentity automorphism $x$, a natural number $\alpha_S(x)$ does not exceed a natural number $m$ if some $m$ conjugates of $x$ in the group $\langle x,S\rangle$ generate a subgroup that includes $S$. The outcome of this paper together with one by Di Martino, Pellegrini, and Zalessk

Yi-Liang Yin, Wen-Bo Dong, Jin-Yi Pang, Shi Pu

We study the spin alignment of neutral rho mesons in a pion gas using spin kinetic or Boltzmann equations. The $\rho\pi\pi$ coupling is given by the chiral effective theory. The collision terms at the leading and next-to-leading order in spin Boltzmann equations are derived. The evolution of the spin density matrix of the neutral rho meson is simulated with

Yi-Chien Lin, Yuyang Chen, Sameh Gobriel, Nilesh Jain

As Graph Neural Networks (GNNs) become popular, libraries like PyTorch-Geometric (PyG) and Deep Graph Library (DGL) are proposed; these libraries have emerged as the de facto standard for implementing GNNs because they provide graph-oriented APIs and are purposefully designed to manage the inherent sparsity and irregularity in graph structures. However, thes

Chuan-Tsung Chan, Hiroshi Itoyama, Reiji Yoshioka

A non-perturbative effect in $\kappa$ (renormalized string coupling) obtained from the large order behavior in the vicinity of the prototypical Argyres-Douglas critical point of $su(2)$, $N_f =2$, $\mathcal{N} =2$ susy gauge theory can be studied in the GWW unitary matrix model with the log term: the one as the work done against the barrier of the effective

Huaqing Li, Liang Ran, Lifeng Zheng, Zhe Li

This paper considers a class of noncooperative games in which the feasible decision sets of all players are coupled together by a coupled inequality constraint. Adopting the variational inequality formulation of the game, we first introduce a new local edge-based equilibrium condition and develop a distributed primal-dual proximal algorithm with full informa

Matthew Rupert

We consider the category of generalized weight modules over the unrolled restricted quantum group $\overline{U}_q^H(\mathfrak{g})$ of a finite-dimensional simple complex Lie algebra $\mathfrak{g}$ at root of unity q. Although this category does not admit projective modules, it is filtered by subcategories with enough projectives. We show that the projective

Yanfang Zhang, Yiliu Sun, Yibing Zhan, Dapeng Tao

Recently, increasing attention has been focused on improving the ability of Large Language Models (LLMs) to perform complex reasoning. Advanced methods, such as Chain-of-Thought (CoT) and its variants, are found to enhance their reasoning skills by designing suitable prompts or breaking down complex problems into more manageable sub-problems. However, little

Haoxuan Wang, Yuzhang Shang, Zhihang Yuan, Junyi Wu

The practical deployment of diffusion models is still hindered by the high memory and computational overhead. Although quantization paves a way for model compression and acceleration, existing methods face challenges in achieving low-bit quantization efficiently. In this paper, we identify imbalanced activation distributions as a primary source of quantizati

Yang Du, Kui Li, Jin Niu, Angyi Lin

We present a novel method for multi-color wavefront measurement of high-order harmonic generation beams using the Talbot effect, validated both theoretically and experimentally for the first time. Each harmonic maintains a unique wavefront and produces an independent set of self-images along the optical axis.We achieved the wavefronts reconstruction of three

Yikun Bai, Rocio Diaz Martin, Abihith Kothapalli, Hengrong Du

The Gromov-Wasserstein (GW) distance has gained increasing interest in the machine learning community in recent years, as it allows for the comparison of measures in different metric spaces. To overcome the limitations imposed by the equal mass requirements of the classical GW problem, researchers have begun exploring its application in unbalanced settings.

Aaron Bembenek, Toby Murray

To handle AI tasks that combine perception and logical reasoning, recent work introduces Neurosymbolic Deep Neural Networks (NS-DNNs), which contain -- in addition to traditional neural layers -- symbolic layers: symbolic expressions (e.g., SAT formulas, logic programs) that are evaluated by symbolic solvers during inference. We identify and formalize an int

Mark Mandelkern

The Sylvester-Gallai Theorem, stated as a problem by J. J. Sylvester in 1893, asserts that for any finite, noncollinear set of points on a plane, there exists a line passing through exactly two points of the set. First, it is shown that for the real plane, the theorem is constructively invalid. Then, a well-known classical proof is examined from a constructi

Bohao Qu, Xiaofeng Cao, Qing Guo, Yi Chang

In this study, we present a transductive inference approach on that reward information propagation graph, which enables the effective estimation of rewards for unlabelled data in offline reinforcement learning. Reward inference is the key to learning effective policies in practical scenarios, while direct environmental interactions are either too costly or u

Zhanpeng Zhou, Zijun Chen, Yilan Chen, Bo Zhang

The pretraining-finetuning paradigm has become the prevailing trend in modern deep learning. In this work, we discover an intriguing linear phenomenon in models that are initialized from a common pretrained checkpoint and finetuned on different tasks, termed as Cross-Task Linearity (CTL). Specifically, we show that if we linearly interpolate the weights of t

James Flamino, Mohammed Shahid Modi, Boleslaw K. Szymanski, Brendan Cross

Large Language Models (LLMs) have shown remarkable promise in communicating with humans. Their potential use as artificial partners with humans in sociological experiments involving conversation is an exciting prospect. But how viable is it? Here, we rigorously test the limits of agents that debate using LLMs in a preregistered study that runs multiple debat

Kelvin J. L. Koa, Yunshan Ma, Ritchie Ng, Tat-Seng Chua

Explaining stock predictions is generally a difficult task for traditional non-generative deep learning models, where explanations are limited to visualizing the attention weights on important texts. Today, Large Language Models (LLMs) present a solution to this problem, given their known capabilities to generate human-readable explanations for their decisio

Kun Ouyang, Liqiang Jing, Xuemeng Song, Meng Liu

Sarcasm Explanation in Dialogue (SED) is a new yet challenging task, which aims to generate a natural language explanation for the given sarcastic dialogue that involves multiple modalities (\ie utterance, video, and audio). Although existing studies have achieved great success based on the generative pretrained language model BART, they overlook exploiting

Tiannan Wu, Zihao Wang, Shaojie Wang

A new computational method to solve the hyperbolic (Vlasov) equation and the elliptic (Poisson-like) equation at the polar axis is proposed. It is shown that the value of a scalar function at the polar axis can be predicted by its neighbouring values based on the continuity condition. This continuity condition systematically solves the pole problems includin

Duc-Tri Vo, Ionela Prodan, Laurent Lefèvre, Vincent Vanel

This paper addresses the particularities of adaptive optimal control of the uranium extraction-scrubbing operation in the PUREX process. The process dynamics are nonlinear, high dimensional, and have limited online measurements. In addition, analysis and developments are based on a qualified simulation program called PAREX, which was validated with laborator

J. Jon Ryu, Xiangxiang Xu, H. S. Melihcan Erol, Yuheng Bu

Computing eigenvalue decomposition (EVD) of a given linear operator, or finding its leading eigenvalues and eigenfunctions, is a fundamental task in many machine learning and scientific computing problems. For high-dimensional eigenvalue problems, training neural networks to parameterize the eigenfunctions is considered as a promising alternative to the clas

Ricardo de Deijn, Aishwarya Batra, Brandon Koch, Naseef Mansoor

The growth of generative adversarial network (GAN) models has increased the ability of image processing and provides numerous industries with the technology to produce realistic image transformations. However, with the field being recently established there are new evaluation metrics that can further this research. Previous research has shown the Fr\'echet I

Prabhat Kumar Chand, Apurba Das, Anisur Rahaman Molla

Triangle counting in a graph is a fundamental problem with wide-ranging applications. It is crucial for understanding graph structure and serves as a basis for more advanced graph analytics. One key application is truss decomposition, a technique for identifying maximal, highly interconnected subgraphs, revealing structural cohesion and tight-knit communitie

Hui Chen, Dong Yang

It is shown that the gentle one-cycle algebra $\Lambda(n-1,1,1)$ has Hall polynomials. The Hall polynomials are explicitly given for all triples of indecomposable modules, and as a consequence, the Ringel--Hall Lie algebra of $\Lambda(n-1,1,1)$ is shown to be isomorphic to its Riedtmann Lie algebra.

Razieh Shirzadkhani, Shenyang Huang, Elahe Kooshafar, Reihaneh Rabbany

Real-world networks, with their evolving relations, are best captured as temporal graphs. However, existing software libraries are largely designed for static graphs where the dynamic nature of temporal graphs is ignored. Bridging this gap, we introduce TGX, a Python package specially designed for analysis of temporal networks that encompasses an automated p

Wanyue Xiao, Shubo Wang

Pancharatnam-Berry (PB) metasurfaces can be applied to manipulate the phase and polarization of light within subwavelength thickness. The underlying mechanism is attributed to the geometric phase originating from the longitudinal spin of light. Here, we demonstrate a new type of PB geometric phase derived from the intrinsic transverse spin of guided light. U

Duc Thien Nguyen, Konstantinos Slavakis

This paper introduces a novel nonparametric framework for data imputation, coined multilinear kernel regression and imputation via the manifold assumption (MultiL-KRIM). Motivated by manifold learning, MultiL-KRIM models data features as a point cloud located in or close to a user-unknown smooth manifold embedded in a reproducing kernel Hilbert space. Unlike

Jiacheng Lin, Meng Xu, Zhihua Xiong, Huangang Wang

Recent advancements have introduced machine learning frameworks to enhance the Branch and Bound (B\&B) branching policies for solving Mixed Integer Linear Programming (MILP). These methods, primarily relying on imitation learning of Strong Branching, have shown superior performance. However, collecting expert samples for imitation learning, particularly for

Xiaochang Li, Chen Qian, Qineng Wang, Jiangtao Kong

Network traffic refers to the amount of data being sent and received over the Internet or any system that connects computers. Analyzing network traffic is vital for security and management, yet remains challenging due to the heterogeneity of plain-text packet headers and encrypted payloads. To capture the latent semantics of traffic, recent studies have adop

Yu. E. Geints, E. K. Panina

Multilayer microparticles with a liquid core and a polycomposite light-absorbing shell (microcapsules) are important components of modern bio- and medical technologies. Opening of the microcapsule shell and payload release can be realized by optical radiation. The photophoretic force is due to the radiation-stimulated thermal gradient and arises from the tem