December 2023 arXiv papers — page 3

Haihua Shi, Qianliang Wu

Efficiently identifying accurate correspondences between point clouds is crucial for both rigid and non-rigid point cloud registration. Existing methods usually rely on geometric or semantic feature embeddings to establish correspondences and then estimate transformations or flow fields. Recently, several state-of-the-art methods have adopted RAFT-like itera

Hanbo Cheng, Chenyu Liu, Pengfei Hu, Zhenrong Zhang

The Handwritten Mathematical Expression Recognition (HMER) task is a critical branch in the field of OCR. Recent studies have demonstrated that incorporating bidirectional context information significantly improves the performance of HMER models. However, existing methods fail to effectively utilize bidirectional context information during the inference stag

Zhouhan Lin, Cheng Deng, Le Zhou, Tianhang Zhang

Large language models (LLMs) have achieved huge success for their general knowledge and ability to solve a wide spectrum of tasks in natural language processing (NLP). Due to their impressive abilities, LLMs have shed light on potential inter-discipline applications to foster scientific discoveries of a specific domain by using artificial intelligence (AI fo

Benjamin Anwasia, Diogo Arsénio

We show that a measurable function $g:\mathbb{S}^{d-1}\to\mathbb{R}$, with $d\geq 3$, satisfies the functional relation \begin{equation*} g(\omega)+g(\omega_*)=g(\omega')+g(\omega_*'), \end{equation*} for all admissible $\omega,\omega_*,\omega',\omega_*'\in\mathbb{S}^{d-1}$ in the sense that \begin{equation*} \omega+\omega_*=\omega'+\omega_*', \end{equation*

Xiaofan Zhou, Jingtao Fan, Suotang Jia

We investigate the magnetic order and related strongly-correlated effects in an one-dimensional Ising-Kondo lattice with transverse field. This model is the anisotropic limit of the conventional isotropic Kondo lattice model, in the sense that the itinerant electrons interact with the localized magnetic moments via only longitudinal Kondo exchange. Adopting

Tiange Xiang, Adam Sun, Scott Delp, Kazuki Kozuka

Rendering the visual appearance of moving humans from occluded monocular videos is a challenging task. Most existing research renders 3D humans under ideal conditions, requiring a clear and unobstructed scene. Those methods cannot be used to render humans in real-world scenes where obstacles may block the camera's view and lead to partial occlusions. In this

Weijian Mai, Jian Zhang, Pengfei Fang, Zhijun Zhang

In the era of Artificial Intelligence Generated Content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image, text-to-video, text-to-audio, etc) are gradually reshaping the natural content in the real world. The key to multimodal synthesis technology is to establish the mapping relationship between different modalities. Brain signals, s

Aijia Liu, Shiqing Liu, Xiaobing Pei

In today's era, users have increasingly high expectations regarding the performance and efficiency of communication networks. Network operators aspire to achieve efficient network planning, operation, and optimization through Digital Twin Networks (DTN). The effectiveness of DTN heavily relies on the network model, with graph neural networks (GNN) playing a

Jaebak Kim

Experimental particle physics uses machine learning for many tasks, where one application is to classify signal and background events. This classification can be used to bin an analysis region to enhance the expected significance for a mass resonance search. In natural language processing, one of the leading neural network architectures is the transformer. I

Shohei Nakamura, Hiroshi Tsuji

We prove that the functional volume product for even functions is monotone increasing along the Fokker--Planck heat flow. This in particular yields a new proof of the functional Blaschke--Santal\'{o} inequality by K. Ball and also Artstein-Avidan--Klartag--Milman in the even case. This result is the consequence of a new understanding of the regularizing prop

Chaojie Wang, Yishi Xu, Zhong Peng, Chenxi Zhang

Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, especially for question answering. However, in the face of problems beyond the scope of knowledge, these LLMs tend to talk nonsense with a straight face, where the potential solution could be incorporating an Information Retrieval (IR) modul

Athanasios Chatzistavrakidis, Noriaki Ikeda, Larisa Jonke

We study twisted Courant sigma models, a class of topological field theories arising from the coupling of 3D 0-/2-form BF theory and Chern-Simons theory and containing a 4-form Wess-Zumino term. They are examples of theories featuring a nonlinearly open gauge algebra, where products of field equations appear in the commutator of gauge transformations, and th

Shijue Huang, Libo Qin, Bingbing Wang, Geng Tu

Multi-modal intent detection aims to utilize various modalities to understand the user's intentions, which is essential for the deployment of dialogue systems in real-world scenarios. The two core challenges for multi-modal intent detection are (1) how to effectively align and fuse different features of modalities and (2) the limited labeled multi-modal inte

Wanlin Cai, Yuxuan Liang, Xianggen Liu, Jianshuai Feng

Multivariate time series forecasting poses an ongoing challenge across various disciplines. Time series data often exhibit diverse intra-series and inter-series correlations, contributing to intricate and interwoven dependencies that have been the focus of numerous studies. Nevertheless, a significant research gap remains in comprehending the varying inter-s

Dehua Peng, Zhipeng Gui, Huayi Wu

The characteristics of data like distribution and heterogeneity, become more complex and counterintuitive as dimensionality increases. This phenomenon is known as curse of dimensionality, where common patterns and relationships (e.g., internal pattern and boundary pattern) that hold in low-dimensional space may be invalid in higher-dimensional space. It lead

Xingyuan Li, Yang Zou, Jinyuan Liu, Zhiying Jiang

With the rapid progression of deep learning technologies, multi-modality image fusion has become increasingly prevalent in object detection tasks. Despite its popularity, the inherent disparities in how different sources depict scene content make fusion a challenging problem. Current fusion methodologies identify shared characteristics between the two modali

Samarth Mishra, Carlos D. Castillo, Hongcheng Wang, Kate Saenko

In cross-domain retrieval, a model is required to identify images from the same semantic category across two visual domains. For instance, given a sketch of an object, a model needs to retrieve a real image of it from an online store's catalog. A standard approach for such a problem is learning a feature space of images where Euclidean distances reflect simi

Pierre Bergé, Mari Chaikovskaia, Jean-Philippe Gayon, Alain Quilliot

We consider here the MultiBot problem for the scheduling and the resource parametrization of jobs related to the production or the transportation of different products inside a given time horizon. Those jobs must meet known in advance demands. The time horizon is divided into several discrete identical periods representing each the time needed to proceed a j

Sascha Kurz

We consider locally recoverable codes (LRCs) and aim to determine the smallest possible length $n=n_q(k,d,r)$ of a linear $[n,k,d]_q$-code with locality $r$. For $k\le 7$ we exactly determine all values of $n_2(k,d,2)$ and for $k\le 6$ we exactly determine all values of $n_2(k,d,1)$. For the ternary field we also state a few numerical results. As a general r

Shijin Ding, Zhilin Lin

In this paper, we study the stability for 2-D plane Poiseuille flow $(1-y^2,0)$ in a channel $\mathbb{T}\times (-1,1)$ with Navier-slip boundary condition. We prove that if the initial perturbation for velocity field $u_0$ satisfies that $\|u_0\|_{H^{\frac{7}{2}+}} \leq \epsilon_1 \nu^{2/3}$ for some suitable small $0<\epsilon_1 \ll 1$ independent of viscosi

Licai Sun, Zheng Lian, Kexin Wang, Yu He

Video-based facial affect analysis has recently attracted increasing attention owing to its critical role in human-computer interaction. Previous studies mainly focus on developing various deep learning architectures and training them in a fully supervised manner. Although significant progress has been achieved by these supervised methods, the longstanding l

Wenxing Hu, Minglei Tong

Traditional models focus on horizontal table detection but struggle in rotating contexts, limiting progress in table recognition. This paper introduces a new task: detecting table regions and localizing head-tail parts in rotation scenarios. We propose corresponding datasets, evaluation metrics, and methods. Our novel method, 'Adaptively Bounded Rotation,' a

Yonathan Sarmiento, Debraj Das, Édgar Roldán

Using martingale theory, we compute, in very few lines, exact analytical expressions for various first-exit-time statistics associated with one-dimensional biased diffusion. Examples include the distribution for the first-exit time from an interval, moments for the first-exit site, and functionals of the position, which involve memory and time integration. A

Zhongzhou Du, Gaoli Zhao, Zhanpeng Hua, Na Ye

This paper presents a novel method for estimating the temperature of magnetic nanoparticles (MNPs) based on AC magnetization harmonics of MNPs dominated by Brownian relaxation. The difference in the AC magnetization response and magnetization harmonic between the Fokker-Planck equation and the Langevin function was analyzed, and we studied the relationship b

Xiaoxuan Han, Songlin Yang, Wei Wang, Ziwen He

Deep neural networks have significantly improved the performance of face forgery detection models in discriminating Artificial Intelligent Generated Content (AIGC). However, their security is significantly threatened by the injection of triggers during model training (i.e., backdoor attacks). Although existing backdoor defenses and manual data selection can

Yequan Zhao, Xian Xiao, Xinling Yu, Ziyue Liu

Solving partial differential equations (PDEs) numerically often requires huge computing time, energy cost, and hardware resources in practical applications. This has limited their applications in many scenarios (e.g., autonomous systems, supersonic flows) that have a limited energy budget and require near real-time response. Leveraging optical computing, thi

Terutoshi Sakakura, Yoshihisa Ishikawa, Shunji Kishimoto, Yasuyuki Takenaka

The theoretically observable limit of electron density distribution by single-crystal X-ray diffraction is discussed. When F_{orb} and {\delta}F are defined as, respectively, the partial structure factor for an orbital and the deviation of the observed F from the true F, the accuracy of electron density attributable to F_{orb} is chiefly determined by the nu

Hongge Xu, Ning Yu, Zuman Zhang

We fit the invariant mass distribution of ${X(3872)}\rightarrow{J}/{\psi}\pi^+\pi^-$ from LHCb using the propagator for S-wave near-threshold states in effective field theory. In this way, we can directly determine the $Z$ which measures the projection of the bound state on the compact state in ${X(3872)}$. Consequently, the structure of ${X(3872)}$ can be e

Yihan Liu, Michael Chertkov

We study the Floating Offshore Wind Turbine (FOWT) dynamic response to high wind velocity scenarios utilizing an extensive Markov Chain Monte Carlo simulation involving 10,000 trials of a reduced model from \cite{betti_development_2014} with a blade-pitch PID controller. The research emphasizes analysis of extreme events in surge, pitch and heave of FOWT, id

Xinrun Mi, Kunya Yang, Yuhan Gan, Long Zhang

The interplay between lattice geometry, band topology and electronic correlations in the newly discovered kagome compounds AV$_3$Sb$_5$ (A=K, Rb, Cs) makes this family a novel playground to investigate emergent quantum phenomena, such as unconventional superconductivity, chiral charge density wave and electronic nematicity. These exotic quantum phases natura

Ruoqi Yin, Jianqin Yin

Human Interaction Recognition is the process of identifying interactive actions between multiple participants in a specific situation. The aim is to recognise the action interactions between multiple entities and their meaning. Many single Convolutional Neural Network has issues, such as the inability to capture global instance interaction features or diffic

Xinwei Liu, Xiaojun Jia, Jindong Gu, Yuan Xun

The field of few-shot learning (FSL) has shown promising results in scenarios where training data is limited, but its vulnerability to backdoor attacks remains largely unexplored. We first explore this topic by first evaluating the performance of the existing backdoor attack methods on few-shot learning scenarios. Unlike in standard supervised learning, exis

Hoon Hong, Jing Yang

In this paper, we tackle the following problem: compute the gcd for several univariate polynomials with parametric coefficients. It amounts to partitioning the parameter space into ``cells'' so that the gcd has a uniform expression over each cell and constructing a uniform expression of gcd in each cell. We tackle the problem as follows. We begin by making a

Yunfeng Fan, Wenchao Xu, Haozhao Wang, Jiaqi Zhu

Federated learning (FL) underpins advancements in privacy-preserving distributed computing by collaboratively training neural networks without exposing clients' raw data. Current FL paradigms primarily focus on uni-modal data, while exploiting the knowledge from distributed multimodal data remains largely unexplored. Existing multimodal FL (MFL) solutions ar

Katsuya T. Abe, Hiroyuki Tashiro

Some theoretical models for the early Universe predict a spike-type enhancement in the primordial power spectrum on a small scale, which would result in forming early-formed dark matter halos~(EFHs). Some recent studies have claimed to have placed limits on such small scales, which, however, involve uncertainties, such as the physics of substructures and the

Esther Enhui Ye, John Enzhou Ye, Joseph Ye, Jacob Ye

Introduction: In the realm of human-computer interaction and behavioral research, accurate real-time gaze estimation is critical. Traditional methods often rely on expensive equipment or large datasets, which are impractical in many scenarios. This paper introduces a novel, geometry-based approach to address these challenges, utilizing consumer-grade hardwar

Qirui Wu, Daniel Ritchie, Manolis Savva, Angel X. Chang

Single-view 3D shape retrieval is a challenging task that is increasingly important with the growth of available 3D data. Prior work that has studied this task has not focused on evaluating how realistic occlusions impact performance, and how shape retrieval methods generalize to scenarios where either the target 3D shape database contains unseen shapes, or

Krystofer Baker, Dmytro Savchuk

We construct explicit finite generating sets for the stabilizers in Thompson's group $F$ of rational points of a unit interval or a Cantor set. Our technique is based on the Reidemeister-Schreier procedure in the context of Schreier graphs of such stabilizers in $F$. It is well known that the stabilizers of dyadic rational points are isomorphic to $F\times F

Yunfeng Fan, Wenchao Xu, Haozhao Wang, Fushuo Huo

Selecting proper clients to participate in each federated learning (FL) round is critical to effectively harness a broad range of distributed data. Existing client selection methods simply consider the mining of distributed uni-modal data, yet, their effectiveness may diminish in multi-modal FL (MFL) as the modality imbalance problem not only impedes the col

Andi Mankolli, Aaron Angerami, Ritu Arora, Steffen Bass

Considerable information about the early-stage dynamics of heavy-ion collisions is encoded in the rapidity dependence of measurements. To leverage the large amount of experimental data, we perform a systematic analysis using three-dimensional hydrodynamic simulations of multiple collision systems -- large and small, symmetric and asymmetric. Specifically, we

Yuefeng Xu, Rui Zhong, Chao Zhang, Jun Yu

Various popular multiplayer battle royale games share a lot of common elements. Drawing from our observations, we summarized these shared characteristics and subsequently proposed a novel heuristic algorithm named multiplayer battle game-inspired optimizer (MBGO). The proposed MBGO streamlines mainstream multiplayer battle royale games into two discrete phas

Julie Jiang, Emilio Ferrara

The proliferation of social network data has unlocked unprecedented opportunities for extensive, data-driven exploration of human behavior. The structural intricacies of social networks offer insights into various computational social science issues, particularly concerning social influence and information diffusion. However, modeling large-scale social netw

Masakiyo Kitazawa, Ryo Ashikawa, Shinji Ejiri, Kazuyuki Kanaya

We perform a finite-size scaling analysis of the critical point in the heavy-quark region of QCD at nonzero temperature. Our previous analysis on the Binder cumulant at $N_t=4$ is extended to finer lattices with $N_t=6$ and $8$. The aspect ratio is also extended up to $15$ to suppress the non-singular contribution. High-precision analysis of the Binder cumul

Shun Inoue, Teruaki Enoto, Kosuke Namekata, Yuta Notsu

We conducted 4-night multiwavelength observations of an active M-dwarf star EV Lac on 2022 October 24$-$27 with simultaneous coverage of soft X-rays (NICER; 0.2$-$12 $\mathrm{keV}$, Swift XRT; 0.2$-$10 $\mathrm{keV}$), near-ultraviolet (Swift UVOT/UVW2; 1600$-$3500 \r{A}), optical photometry (TESS; 6000$-$10000 \r{A}), and optical spectroscopy (Nayuta/MALLS;

Yuxun Zhang, Jiang Zhou

In this paper, we obtain two interpolation theorems on convex-set valued Lebesgue spaces, which generalize the Marcinkiewicz interpolation theorem and Riesz-Thorin interpolation theorem on classical Lebesgue spaces, respectively. As applications, we obtain the boundedness of convex-set valued fractional averaging operators and fractional maximal operators, a

Pankaj Gautam, Vellaichamy Vetrivel

In this work, we introduce the notion of warped Yosida regularization and study the asymptotic behavior of the orbit of dynamical systems generated by warped Yosida regularization, which includes Douglas-Rachford dynamical system. We analyze an algorithm where the inclusion problem is first approximated by a regularized one and then the preconditioned regula

Cheng Niu, Yuanhao Wu, Juno Zhu, Siliang Xu

Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs). Despite the integration of RAG, LLMs may still present unsupported or contradictory claims to the retrieved contents. In order to develop effective hallucination prevention strategies under RAG, it is important to create benchmark

Lulu Kang, Yuanxing Cheng, Yiwei Wang, Chun Liu

The Gaussian process (GP) regression model is a widely employed surrogate modeling technique for computer experiments, offering precise predictions and statistical inference for the computer simulators that generate experimental data. Estimation and inference for GP can be performed in both frequentist and Bayesian frameworks. In this chapter, we construct t

Ze Li, Yezhou Yi, Lifeng Zhao

We consider the wave maps from $\mathbb{R}^{1+2}$ into $\mathbb{S}^2\subset \mathbb{R}^3.$ Under an additional assumption of $k$-corotational symmetry, the problem reduces to the one dimensional semilinear wave equation: \begin{equation*} \partial_t^2 u-\partial_r^2 u-\frac{\partial_r u}{r}+k^2 \frac{\sin(2u)}{2r^2}=0. \end{equation*} Given any integer $k\ge

Rahatara Ferdousi, Chunsheng Yang, M. Anwar Hossain, Fedwa Laamarti

Recent advancements in cognitive computing, with the integration of deep learning techniques, have facilitated the development of intelligent cognitive systems (ICS). This is particularly beneficial in the context of rail defect detection, where the ICS would emulate human-like analysis of image data for defect patterns. Despite the success of Convolutional

Vigleik Angeltveit

We improve the upper bound on the Ramsey number R(3,10) from 42 to 41. Hence R(3,10) is equal to 40 or 41.

Guanyiman Fu, Fengchao Xiong, Jianfeng Lu, Jun Zhou

Hyperspectral images (HSIs) often suffer from noise arising from both intra-imaging mechanisms and environmental factors. Leveraging domain knowledge specific to HSIs, such as global spectral correlation (GSC) and non-local spatial self-similarity (NSS), is crucial for effective denoising. Existing methods tend to independently utilize each of these knowledg

Wei-Jer Chang, Francesco Pittaluga, Masayoshi Tomizuka, Wei Zhan

Evaluating the performance of autonomous vehicle planning algorithms necessitates simulating long-tail safety-critical traffic scenarios. However, traditional methods for generating such scenarios often fall short in terms of controllability and realism; they also neglect the dynamics of agent interactions. To address these limitations, we introduce SAFE-SIM

Paul K. Mandal, Cole Leo, Connor Hurley

In the modern world, the amount of visual data recorded has been rapidly increasing. In many cases, data is stored in geographically distinct locations and thus requires a large amount of time and space to consolidate. Sometimes, there are also regulations for privacy protection which prevent data consolidation. In this work, we present federated implementat

Shiyue Peng, Xu-Guang Zhou, Yasuhiro H. Matsuda, Qian Chen

The investigation of transport characteristics in high-temperature superconductors under magnetic fields is one of the most important topics in condensed matter physics and transport applications. For YBa$_2$Cu$_3$O$_7$ (YBCO), the measurements of magnetoresistance under a high magnetic field are technically challenging because the required magnetic field ($

Shreyas Verma, Manoj Parmar, Palash Choudhary, Sanchita Porwal

Answering questions using pre-trained language models (LMs) and knowledge graphs (KGs) presents challenges in identifying relevant knowledge and performing joint reasoning.We compared LMs (fine-tuned for the task) with the previously published QAGNN method for the Question-answering (QA) objective and further measured the impact of additional factual context

Jian-Jie Qiu, Yong Zhang, Jun-ichi Nakashima, Jiang-Shui Zhang

It has been more than 30 years since the enigmatic 21 {\mu}m emission feature was first discovered in protoplanetary nebulae (PPNs). Although dozens of different dust carrier candidates have been proposed, there is as yet no widely accepted one. We present the results of molecular observations toward 21{\mu}m objects using the 10m Submillimeter Telescope of

József Balogh, Ce Chen, Sean English

In the Constructor-Blocker game, two players, Constructor and Blocker, alternatively claim unclaimed edges of the complete graph $K_n$. For given graphs $F$ and $H$, Constructor can only claim edges that leave her graph $F$-free, while Blocker has no restrictions. Constructor's goal is to build as many copies of $H$ as she can, while Blocker attempts to stop

Lei Dai, Xiaojie Wang

To obtain strong convergence rates of numerical schemes, an overwhelming majority of existing works impose a global monotonicity condition on coefficients of SDEs. Nevertheless, there are still many SDEs from applications that do not have globally monotone coefficients. As a recent breakthrough, the authors of [Hutzenthaler, Jentzen, Ann. Probab., 2020] orig

Akinori Suenaga, Takeru Utsugi, Rui Asaoka, Yuuki Tokunaga

Cavity-mediated adiabatic transfer (CMAT) is a robust way to perform a two-qubit gate between trapped atoms inside an optical cavity. In the previous study by Goto and Ichimura [H. Goto and K. Ichimura, Phys. Rev. A 77, 013816 (2008).], the upper bound of success probability of CMAT was shown where the operation is adiabatically slow. For practical applicati

Enas Altarawneh, Ameeta Agrawal, Michael Jenkin, Manos Papagelis

Understanding and predicting the emotional trajectory in multi-party multi-turn conversations is of great significance. Such information can be used, for example, to generate empathetic response in human-machine interaction or to inform models of pre-emptive toxicity detection. In this work, we introduce the novel problem of Predicting Emotions in Conversati

Donald Brinkman, Jonathan Grudin

For generative AI to succeed, how engaging a conversationalist must it be? For almost sixty years, some conversational agents have responded to any question or comment to keep a conversation going. In recent years, several utilized machine learning or sophisticated language processing, such as Tay, Xiaoice, Zo, Hugging Face, Kuki, and Replika. Unlike generat

Pedro Emerick, Luan Arjuna Belmonte

In this paper, we investigate the concept of infinite dense-lineability recently introduced by M. Calder\'on-Moreno, P. Gerlach-Mena and J. Prado-Bassas. We answer a question posed by the authors about the equivalence between infinite (pointwise) dense-lineability and (pointwise) dense-lineability. We prove that the equivalence always holds in first-countabl

Hui Wei, Chenyue Feng, Jianning Zhang

At the intersection of computation and cognitive science, graph theory is utilized as a formalized description of complex relationships and structures. Traditional graph models are often static, lacking dynamic and autonomous behavioral patterns. They rely on algorithms with a global view, significantly differing from biological neural networks, in which, to

Kenric P. Nelson

Nonextensive Statistical Mechanics has developed into an important framework for modeling the thermodynamics of complex systems and the information of complex signals. Upon the 80th birthday of the field's founder, Constantino Tsallis, a review of open problems that can stimulate future research is provided. Over the thirty-year development of NSM a variety

Manlan Li, Huifu Xu

In this paper, we revisit the LAPUE model with a different focus: we begin by adopting a new penalty function which gives a smooth transition of the boundary between lateness and no lateness and demonstrate the LAPUE model based on the new penalty function has a unique equilibrium and is stable with respect to (w.r.t.) small perturbation of probability distr

Xiaoyu Zhang, Juan Zhai, Shiqing Ma, Chao Shen

Deep Learning models have become an integrated component of modern software systems. In response to the challenge of model design, researchers proposed Automated Machine Learning (AutoML) systems, which automatically search for model architecture and hyperparameters for a given task. Like other software systems, existing AutoML systems suffer from bugs. We i

Peter Rudzis

We study the ergodic properties of two classes of random dynamical systems: a type of Markov chain which we call the \textit{alternating random walk} and a certain stochastic billiard system which describes the motion of a free-moving rough disk bouncing between two parallel rough walls. Our main results characterize the types of Markov transition kernels wh

Mei Ming

We prove a weighted a priori energy estimate for the two dimensional water-waves problem with contact points in the absence of gravity and surface tension. When the surface graph function and its time derivative have some decay near the contact points, we show that there is corresponding decay for the velocity, the pressure and other quantities in a short ti

Yue-Yue Chang, Jun-Qing Cheng, Hui Shao, Dao-Xin Yao

We investigate the magnetic excitations of the trimerized Heisenberg models with intra-trimer interaction $J_1$ and inter-trimer interaction $J_2$ on four different two-dimensional lattices using a combination of stochastic series expansion quantum Monte Carlo (SSE QMC) and stochastic analytic continuation methods (SAC), complemented by cluster perturbation

Liyuan Tan, Yang Yang, Li Fang, David J. Cappelleri

Flagellated microorganisms can swim at low Reynolds numbers and adapt to changes in their environment. Specifically, the flagella can switch their shapes or modes through gene expression. In the past decade, efforts have been made to fabricate and investigate rigid types of microrobots without any adaptation to the environments. More recently, obtaining adap

Haiyang Liu, Zihao Zhu, Giorgio Becherini, Yichen Peng

We propose EMAGE, a framework to generate full-body human gestures from audio and masked gestures, encompassing facial, local body, hands, and global movements. To achieve this, we first introduce BEAT2 (BEAT-SMPLX-FLAME), a new mesh-level holistic co-speech dataset. BEAT2 combines a MoShed SMPL-X body with FLAME head parameters and further refines the model

Lingyin Luo, Deqing Fang, Ke Han, Di Huang

With the expanding reach of physics, xenon-based detectors such as PandaX-4T in the China Jinping Underground Laboratory aim to cover an energy range from sub-keV to multi-MeV. A linear response of the photomultiplier tubes (PMTs) is required for both scintillation and electroluminescence signals. Through a dedicated bench test, we investigated the cause of

Bo Fan, Antonio M. García-García

We study the quench dynamics of a two dimensional superconductor in a lattice of size up to $200\times 200$ employing the self-consistent time dependent Bogoliubov-de Gennes (BdG) formalism. In the clean limit, the dynamics of the order parameter for short times, characterized by a fast exponential growth and an oscillatory pattern, agrees with the Bardeen-C

Liang Wang, Dawei Dai, Shiyu Fu, Guoyin Wang

In specific scenarios, face sketch can be used to identify a person. However, drawing a face sketch often requires exceptional skill and is time-consuming, limiting its widespread applications in actual scenarios. The new framework of sketch less face image retrieval (SLFIR)[1] attempts to overcome the barriers by providing a means for humans and machines to

Hwayoon Lee, Kyoungkook Kang, Hyeongmin Lee, Seung-Hwan Baek

Recent image restoration methods can be broadly categorized into two classes: (1) regression methods that recover the rough structure of the original image without synthesizing high-frequency details and (2) generative methods that synthesize perceptually-realistic high-frequency details even though the resulting image deviates from the original structure of

Mario De Florio, Adar Kahana, George Em Karniadakis

This paper explores the impact of biologically plausible neuron models on the performance of Spiking Neural Networks (SNNs) for regression tasks. While SNNs are widely recognized for classification tasks, their application to Scientific Machine Learning and regression remains underexplored. We focus on the membrane component of SNNs, comparing four neuron mo

Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang

In this paper, we introduce a novel and simple method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. Unlike existing methods that often depend on multi-stage intermediate pre-training with billions of weakly-supervised text pairs, followed by fine-tuning with a few labeled datasets, our method does not r

Steven W. Su

In this note, we discuss the extension of several important stable square matrices, e.g., D-stable matrices, diagonal dominance matrices, Volterra-Lyapunov stable matrices, to their corresponding non-square matrices. The extension is motivated by some distributed control-related problems, such as decentralized unconditional stability and decentralized integr

Magdalena Wolska, Bernd Fröhlich, Katrin Girgensohn, Sassan Gholiagha

We present an annotation scheme for argumentative and domain-specific aspects of scholarly articles on the theory of International Relations. At argumentation level we identify Claims and Support/Attack relations. At domain level we model discourse content in terms of Theory and Data-related statements. We annotate Waltz's 1993 text on structural realism and

Yuhta Takida, Yukara Ikemiya, Takashi Shibuya, Kazuki Shimada

Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations. It is commonly performed with a variational autoencoding model, VQ-VAE, which can be further extended to hierarchical structures for making high-fidelity reconstructions. However, such hierarchical extensions of VQ-VAE often suffer from the cod

Shaan Ul Haque, Sajad Khodadadian, Siva Theja Maguluri

Stochastic approximation (SA) is an iterative algorithm for finding the fixed point of an operator using noisy samples and widely used in optimization and Reinforcement Learning (RL). The noise in RL exhibits a Markovian structure, and in some cases, such as gradient temporal difference (GTD) methods, SA is employed in a two-time-scale framework. This combin

Hongyu Lu, Han-Qing Wu, Bin-Bin Chen, Kai Sun

Symmetry-breaking orders can not only compete with each other, but also be interwined, and the interwined topological and symmetry-breaking orders make the situation more intriguing. This work examines the archetypal correlated flat band model on a checkerboard lattice at filling $\nu=2/3$ and we find the unique interplay between smectic charge order and top

Hermie Monterde

A vertex in a graph is said to be sedentary if a quantum state assigned on that vertex tends to stay on that vertex. Under mild conditions, we show that the direct product and join operations preserve vertex sedentariness. We also completely characterize sedentariness in blow-up graphs. These results allow us to construct new infinite families of graphs with

Jorge David Castaño-Yepes, Marcelo Loewe, Enrique Muñoz, Juan Cristóbal Rojas

In this work, we study the effects of random temperature fluctuations on the equation of state of a non-interacting, relativistic fermion gas by means of the replica method. This picture provides a conceptual model for a non-equilibrium system, depicted as an ensemble of subsystems at different temperatures, randomly distributed with respect to a given mean

Kanika, A Krishnan, J W Klimes, B Reich

We have built and operated a cryogenic Penning trap arrangement that allows for the efficient production, selection, and long-term storage of highly charged atomic ions. In close similarity to an electron-beam ion trap (EBIT) it works by electron-impact ionisation of atoms inside a dedicated confinement region. The electrons are produced by field emission at

Akash Singha Roy

Let $g_1, \dots , g_M$ be additive functions for which there exist nonconstant polynomials $G_1, \dots , G_M$ satisfying $g_i(p) = G_i(p)$ for all primes $p$ and all $i \in \{1, \dots , M\}$. Under fairly general and nearly optimal hypotheses, we show that the functions $g_1, \dots , g_M$ are jointly equidistributed among the residue classes to moduli $q$ va

Jacob Fox, Maya Sankar, Michael Simkin, Jonathan Tidor

Degeneracy plays an important role in understanding Tur\'an- and Ramsey-type properties of graphs. Unfortunately, the usual hypergraphical generalization of degeneracy fails to capture these properties. We define the skeletal degeneracy of a $k$-uniform hypergraph as the degeneracy of its $1$-skeleton (i.e., the graph formed by replacing every $k$-edge by a

Akash Singha Roy

We study the distribution of families of multiplicative functions among the coprime residue classes to moduli varying uniformly in a wide range, obtaining analogues of the Siegel--Walfisz Theorem for large classes of multiplicative functions. We extend a criterion of Narkiewicz for families of multiplicative functions that can be controlled by values of poly

Luca Vialetto, Hirotake Sugawara, Savino Longo

In this review, we detail the commonality of mathematical intuitions that underlie three numerical methods used for the quantitative description of electron swarms propagating in a gas under the effect of externally applied electric and/or magnetic fields. These methods can be linked to the integral transport equation, following a common thread much better k

Iyadunni Adenuga, Benjamin Hanrahan

Agency is an important human characteristic that users of automated complex technologies are usually denied. This affects the user's experience leading to decreased satisfaction and productivity. In this paper, we consider the ridesharing context and interviewed 7 drivers to understand the controls that would improve the agency they feel. The results show th

Xinzhi Zhong, Yang Zhou, Soyoung Ahn, Danjue Chen

This paper develops a stochastic and unifying framework to examine variability in car-following (CF) dynamics of commercial automated vehicles (AVs) and its direct relation to traffic-level dynamics. The asymmetric behavior (AB) model by Chen at al. (2012a) is extended to accommodate a range of CF behaviors by AVs and compare with the baseline of human-drive

Ali Hanif, Sajid Ahmed, Tareq Y. Al-Naffouri, Mohamed-Slim Alouin

This review paper examines the concept and advancements in the evolving landscape of Dual-functional Radar Communication (DFRC) systems. Traditionally, radar and communication systems have functioned independently, but current research is actively investigating the integration of these functionalities into a unified platform. This paper discusses the motivat

Zheyang Li, Xi Yu

Exploring nonlinear chemical dynamic systems for information processing has emerged as a frontier in chemical and computational research, seeking to replicate the brain&#39;s neuromorphic and dynamic functionalities. We have extensively explored the information processing capabilities of a nonlinear chemical dynamic system through theoretical modeling by int

Giacomo Aletti, Nancy Flournoy, Caterina May, Chiara Tommasi

This study focuses on the estimation of the Emax dose-response model, a widely utilized framework in clinical trials, agriculture, and environmental experiments. Existing challenges in obtaining maximum likelihood estimates (MLE) for model parameters are often ascribed to computational issues but, in reality, stem from the absence of a MLE. Our contribution

Abhinav Arun, Mehul Soni, Palash Choudhary, Saksham Arora

This study explores the development of an explainable music recommendation system with enhanced user control. Leveraging a hybrid of collaborative filtering and content-based filtering, we address the challenges of opaque recommendation logic and lack of user influence on results. We present a novel approach combining advanced algorithms and an interactive u

Marko Stojanovic, Neven Bilic, Dragoljub D. Dimitrijevic, Goran S. Djordjevic

We study inflation driven by the tachyon field in the holographic braneworld by assuming the second slow-roll parameter $\eta$ is constant. The parameter $\eta$ can be either defined by the tachyon scalar field and the Hubble parameter or by the Hubble parameter only. By assuming a constant $\eta$, we derive and numerically solve a differential equation for

Ahmet Orun, Fatih Kurugollu

Cryptocurrency mining processes always lead to a high energy consumption at considerably high production cost, which is nearly one-third of cryptocurrency (e.g. Bitcoin) price itself. As the core of mining process is based on SHA-256 cryptographic hashing function, by using the alternative quantum computers, hybrid quantum computers or more larger quantum co

Zechuan Zheng

In the realm of contemporary physics, the bootstrap method is typically associated with an optimization-based approach to problem-solving. This method leverages our understanding of a specific physical problem, which is used as the constraints for the optimization problem, to carve out the allowed region of our physical theory. Notably, this method often yie

Matthew B. Day, Trevor Nakamura

We consider normal subgroups $N$ of the braid group $B_n$ such that the quotient $B_n/N$ is an extension of the symmetric group by an abelian group. We show that, if $n\geq 4$, then there are exactly 8 commensurability classes of such subgroups. We define a Specht subgroup to be a subgroup of this form that is maximal in its commensurability class. We give d