December 2024 arXiv papers — page 91

Vyacheslav P. Spiridonov

The univariate elliptic beta integral is represented as a bilinear combination of infinite $_{10}V_9$ very-well-poised elliptic hypergeometric series representing the sum of residues of the integrand poles. Convergence of this combination of series for some particular choice of parameters is discussed. Additionally, the asymptotics of the Frenkel--Turaev sum

Dongyue Wu, Zilin Guo, Li Yu, Nong Sang

In recent years, semantic segmentation has flourished in various applications. However, the high computational cost remains a significant challenge that hinders its further adoption. The filter pruning method for structured network slimming offers a direct and effective solution for the reduction of segmentation networks. Nevertheless, we argue that most exi

Andrey Alekseevich Butkevich, Marcel Hecker, Toni Seiler, Martijn Kemerink

Polarization reversal within a ferroelectric material is commonly described as a progression of smaller switching events, giving rise to crackling or Barkhausen noise. While studies on Barkhausen noise, and particularly the associated event size distribution, allow for better understanding of switching processes in ferroelectrics, they were not yet conducted

I. Bailleul, N. Moench

The concept of concrete regularity structure gives the algebraic backbone of the operations involved in the local expansions used in the regularity structure approach to singular stochastic partial differential equations. The spaces and the details of the structures depend on each equation. We introduce here a parameter-dependent universal algebraic regulari

Guilin Zhu, Dongyue Wu, Changxin Gao, Runmin Wang

Class incremental semantic segmentation (CISS) aims to segment new classes during continual steps while preventing the forgetting of old knowledge. Existing methods alleviate catastrophic forgetting by replaying distributions of previously learned classes using stored prototypes or features. However, they overlook a critical issue: in CISS, the representatio

Lei Xin, Caiyun Huang, Hao Li, Shihong Huang

With the rapid development of high-throughput sequencing platforms, an increasing number of omics technologies, such as genomics, metabolomics, and transcriptomics, are being applied to disease genetics research. However, biological data often exhibit high dimensionality and significant noise, making it challenging to effectively distinguish disease subtypes

Abderrezzaq Sendjasni, Seif-Eddine Benkabou, Mohamed-Chaker Larabi

This article identifies and addresses a fundamental bottleneck in data-driven 360-degree image quality assessment (IQA): the lack of intelligent, sample-level data selection. Hence, we propose a novel framework that introduces a critical refinement step between patches sampling and model training. The core of our contribution is an embedding similarity-based

Andrey Alekseevich Butkevich, Fabian T. Thome, Toni Seiler, Marcel Hecker

Upon a polarization reversal within a ferroelectric material, one stable state changes into another which is typically described by a progression of switching events of smaller fractions of the material. These events give rise to crackling or Barkhausen noise and follow a characteristic distribution in their sizes. Barkhausen noise has been studied to better

Prasad Padhye, Kajol Paithankar, Sanved Kolekar

The black hole shadow is fundamentally connected to the structure of light rings and the photon region in the background geometry. We investigate the photon region boundary in a generic asymptotically flat, stationary, axisymmetric black hole spacetime that admits spherical photon orbits (SPOs). Explicit expressions possessing real solutions are provided for

Lajos Győrffy, András London, Gábor V. Nagy, András Pluhár

We study the problem of partitioning the edge set of the complete graph into bipartite subgraphs under certain constraints defined by forbidden subgraphs. These constraints lead to both classical problems, such as partitioning into independent matchings or complete bipartite subgraphs, and novel variants motivated by structural restrictions. Our theoretical

Gevin von Witte, Aaron Himmler, Konstantin Tamarov, Jani O. Moilanen

Dynamic nuclear polarization (DNP) can increase nuclear magnetic resonance (NMR) signals by orders of magnitude. DNP in diamond proceeds through different DNP mechanisms with a possible temperature-dependence. We report on 13C dynamic nuclear polarization (DNP) experiments in diamonds at 3.4 T and 7 T between 300 K and 1.7 K. Nuclear polarization enhancement

Longjun Xiang, Lei Zhang, Jun Chen, Fuming Xu

Traditionally, thermal noise in electric currents, arising from thermal agitation, is expected to increase with temperature $T$ and disappear as $T$ approaches zero. Contrary to this expectation, we discover that the resonant DC thermal noise (DTN) in photocurrents not only persists at $T=0$ but also exhibits a divergence proportional to $1/T$. This thermal

Hritik Bansal, Daniel Israel, Siyan Zhao, Shufan Li

Recent advancements in mixed-modal generative have opened new avenues for developing unified biomedical assistants capable of analyzing biomedical images, answering complex questions about them, and generating multimodal patient reports. However, existing datasets face challenges such as small sizes, limited coverage of biomedical tasks and domains, and a re

Shuangping Huang, Hao Liang, Qingfeng Wang, Chulong Zhong

Recently, developing unified medical image segmentation models gains increasing attention, especially with the advent of the Segment Anything Model (SAM). SAM has shown promising binary segmentation performance in natural domains, however, transferring it to the medical domain remains challenging, as medical images often possess substantial inter-category ov

Kun Cheng, Chengli Li, Feng Liu

A non-complete graph $G$ is said to be $t$-tough if for every vertex cut $S$ of $G$, the ratio of $|S|$ to the number of components of $G-S$ is at least $t$. The toughness $\tau(G)$ of the graph $G$ is the maximum value of $t$ such that $G$ is $t$-tough. A graph $G$ is said to be minimally $t$-tough if $\tau(G)=t$ and $\tau(G-e)<t$ for every $e\in E(G)$. In

Paul Baconnier, Margot H. Teunisse, Martin van Hecke

Models of coupled binary elements capture memory effects in complex dissipative materials, such as transient responses or sequential computing, when their interactions are chosen appropriately. However, for random interactions, self-loops - cyclic transition sequences incompatible with dissipative dynamics - dominate the response and undermine statistical ap

Baptiste Cerclé

Based on the intrinsic connection between Gaussian Free Fields and the Heisenberg vertex algebra, we study some aspects of the correspondence between probability theory and $W$-algebras. This is first achieved by providing a construction of the $W$-algebra associated to a complex simple Lie algebra $\mathfrak g$ by means of Gaussian Free Fields. This corresp

Mingfei Cheng, Yuan Zhou, Xiaofei Xie

Simulation-based testing plays a critical role in evaluating the safety and reliability of autonomous driving systems (ADSs). However, one of the key challenges in ADS testing is the complexity of preparing and configuring simulation environments, particularly in terms of compatibility and stability between the simulator and the ADS. This complexity often re

Jean Fromentin, Pierre-Louis Giscard, Yohan Hosten

We build upon a recent theoretical breakthrough by employing novel algorithms to accurately compute the fractions $F_p$ of all closed walks on the infinite square lattice whose the last erased loop corresponds is any one of the $762, 207, 869, 373$ self-avoiding polygons $p$ of length at most 38. Prior to this work, only 6 values of $F_p$ had been calculated

Chengyan Liu, Linglan Zhao, Fan Lyu, Kaile Du

Few-Shot Class-Incremental Learning (FSCIL) defines a practical but challenging task where models are required to continuously learn novel concepts with only a few training samples. Due to data scarcity, existing FSCIL methods resort to training a backbone with abundant base data and then keeping it frozen afterward. However, the above operation often causes

Yunpeng Song, Jiawei Li, Yiheng Bian, Zhongmin Cai

Enhancing the intelligence of smart systems, such as smart home, and smart vehicle, and smart grids, critically depends on developing sophisticated planning capabilities that can anticipate the next desired function based on historical interactions. While existing methods view user behaviors as sequential data and apply models like RNNs and Transformers to p

Andrew James Bruce, Janusz Grabowski

We introduce and examine the notion of principal $\mathbb{Z}_2^n$-bundles, i.e., principal bundles in the category of $\mathbb{Z}_2^n$-manifolds. The latter are higher graded extensions of supermanifolds in which a $\mathbb{Z}_2^n$-grading replaces $\mathbb{Z}_2$-grading. These extensions have opened up new areas of research of great interest in both physics

Huachao Yan, Kailing Guo, Shiwei Song, Yihai Dai

Diagnosing seizure onset zone (SOZ) is a challenge in neurosurgery, where stereoelectroencephalography (sEEG) serves as a critical technique. In sEEG SOZ identification, the existing studies focus solely on the intra-patient representation of epileptic information, overlooking the general features of epilepsy across patients and feature interdependencies bet

Yiming Ji, Kaijie Yun, Yang Liu, Zongwu Xie

Q-learning is a widely used reinforcement learning technique for solving path planning problems. It primarily involves the interaction between an agent and its environment, enabling the agent to learn an optimal strategy that maximizes cumulative rewards. Although many studies have reported the effectiveness of Q-learning, it still faces slow convergence iss

Robert Aufschläger, Sebastian Wilhelm, Michael Heigl, Martin Schramm

This work introduces ClustEm4Ano, an anonymization pipeline that can be used for generalization and suppression-based anonymization of nominal textual tabular data. It automatically generates value generalization hierarchies (VGHs) that, in turn, can be used to generalize attributes in quasi-identifiers. The pipeline leverages embeddings to generate semantic

Tobias Becher, Simon Torka

Traditional rule-based cybersecurity systems have proven highly effective against known malware threats. However, they face challenges in detecting novel threats. To address this issue, emerging cybersecurity systems are incorporating AI techniques, specifically deep-learning algorithms, to enhance their ability to detect incidents, analyze alerts, and respo

Nicolaj Betz, Gregory McMurtrie, Max Hänze, Vivek Krishnakumar Rajathilakam

A system's internal dynamics and its interaction with the environment can be determined by tracking how external perturbations affect its transition rates between states. Quantitative measurements of these rates are crucial for optimizing quantum systems at the atomic scale but are challenging, as these dynamics are often faster than experimental observation

Anthony Francis, Issaku Kanamori, C. -J. David Lin, Wayne Morris

The transverse momentum dependent (TMD) soft function and Collins-Soper (CS) kernel may be obtained by formulating the Wilson line in terms of auxiliary one-dimensional fermion fields on the lattice. Our computation takes place in the region of the lattice that corresponds to the "spacelike" region in Minkowski space in order to obtain the Collins soft funct

Jiahui Li, Roman Klinger

Prompt engineering has made significant contributions to the era of large language models, yet its effectiveness depends on the skills of a prompt author. This paper introduces $\textit{iPrOp}$, a novel interactive prompt optimization approach, to bridge manual prompt engineering and automatic prompt optimization while offering users the flexibility to asses

Mufan Xu, Kehai Chen, Xuefeng Bai, Muyun Yang

Large language models (LLMs) based on generative pre-trained Transformer have achieved remarkable performance on knowledge graph question-answering (KGQA) tasks. However, LLMs often produce ungrounded subgraph planning or reasoning results in KGQA due to the hallucinatory behavior brought by the generative paradigm. To tackle this issue, we propose READS to

Zijin Liu, Xiang Zhao, You Song

Spatiotemporal data imputation plays a crucial role in various fields such as traffic flow monitoring, air quality assessment, and climate prediction. However, spatiotemporal data collected by sensors often suffer from temporal incompleteness, and the sparse and uneven distribution of sensors leads to missing data in the spatial dimension. Among existing met

Hanyu Zhang, Boyu Qiu, Yuhao Feng, Shuqi Li

Large language models (LLMs) have demonstrated strong capabilities in language understanding, generation, and reasoning, yet their potential in finance remains underexplored due to the complexity and specialization of financial knowledge. In this work, we report the development of the Baichuan4-Finance series, including a comprehensive suite of foundational

Geetanjali Bihani, Julia Rayz

The advent of pre-trained language models (PLMs) has enabled significant performance gains in the field of natural language processing. However, recent studies have found PLMs to suffer from miscalibration, indicating a lack of accuracy in the confidence estimates provided by these models. Current evaluation methods for PLM calibration often assume that lowe

Xiangxiang Gao, Weisheng Xie, Yiwei Xiang, Feng Ji

Striking an optimal balance between minimal drafting latency and high speculation accuracy to enhance the inference speed of Large Language Models remains a significant challenge in speculative decoding. In this paper, we introduce Falcon, an innovative semi-autoregressive speculative decoding framework fashioned to augment both the drafter's parallelism and

Ahmed Becir, Mustafa Moumni

We study the two dimensional system influenced by a non-central potential consisting of a Kratzer potential with a dipole moment, along with a vector potential of the Aharonov-Bohm (AB) effect. We explore various information theoretic measures, including Fisher information, Shannon entropy, Tsallis entropy and Renyi entropy. our numerical results show that t

S. M. Koksbang

The convergence of the third order general cosmographic expansion of the luminosity distance is examined using several versions of a semi-realistic model of our local cosmic neighborhood, based on publicly available density and velocity fields from CosmicFlows-4. The study supports earlier findings that the general cosmographic expansion diverges at surprisi

Yu Xi, Haoyu Li, Xiaoyu Gu, Hao Li

Connectionist Temporal Classification (CTC), a non-autoregressive training criterion, is widely used in online keyword spotting (KWS). However, existing CTC-based KWS decoding strategies either rely on Automatic Speech Recognition (ASR), which performs suboptimally due to its broad search over the acoustic space without keyword-specific optimization, or on K

Julian Frattini, Jannik Fischbach, Davide Fucci, Michael Unterkalmsteiner

Variance theories quantify the variance that one or more independent variables cause in a dependent variable. In software engineering (SE), variance theories are used to quantify -- among others -- the impact of tools, techniques, and other treatments on software development outcomes. To acquire variance theories, evidence from individual empirical studies n

Muchen Ju, Junjie Ni, Kaixin Wang, Yihan Xiao

A rooted arborescence of a directed graph is a spanning tree directed towards a particular vertex. A recent work of Chepuri et al. showed that the arborescences of a covering graph of a directed graph G are closely related to the arborescences of G. In this paper, we study the weighted sum of arborescences of a random covering graph and give a formula for th

Zhiyuan Chang, Mingyang Li, Xiaojun Jia, Junjie Wang

Incorporating external knowledge has emerged as a promising way to mitigate outdated knowledge and hallucinations in LLM. However, external knowledge is often imperfect, encompassing substantial extraneous or even inaccurate content, which interferes with the LLM's utilization of useful knowledge in the context. This paper seeks to characterize the features

Supriya Sarker, Iftekharul Islam, Bibek Poudel, Weizi Li

Extreme weather and infrastructure vulnerabilities pose significant challenges to urban mobility, particularly at intersections where signals become inoperative. To address this growing concern, we introduce Beacon, a naturalistic driving dataset capturing traffic dynamics during blackouts at two major intersections in Memphis, TN, USA. The dataset provides

Giovanni Catino, Luciano Mari, Paolo Mastrolia, Alberto Roncoroni

In this paper we prove general criticality criteria for operators $\Delta + V$ on manifolds with more than one end, where $V$ bounds the Ricci curvature, and a related spectral splitting theorem extending Cheeger-Gromoll's one. Our results give new insight on Li-Wang's theory of manifolds with a weighted Poincar\'e inequality. We apply them to study stable a

Lu Wen, Xinyu Lv, Zhiqiang Li

We theoretically study the electronic band structure and local optical conductivity of domain wall solitons in bilayer graphene (as well as twisted bilayer graphene) with arbitrary soliton angle, which characterizes the local strain direction. We demonstrate that the soliton angle provides an important yet underexplored degree of freedom that can strongly mo

Pranav Rajpurkar, Julian N. Acosta, Siddhant Dogra, Jaehwan Jeong

We present a comprehensive evaluation of a2z-1, an artificial intelligence (AI) model designed to analyze abdomen-pelvis CT scans for 21 time-sensitive and actionable findings. Our study focuses on rigorous assessment of the model's performance and generalizability. Large-scale retrospective analysis demonstrates an average AUC of 0.931 across 21 conditions.

Ziheng Zhou, Jinxing Zhou, Wei Qian, Shengeng Tang

In the field of audio-visual learning, most research tasks focus exclusively on short videos. This paper focuses on the more practical Dense Audio-Visual Event Localization (DAVEL) task, advancing audio-visual scene understanding for longer, untrimmed videos. This task seeks to identify and temporally pinpoint all events simultaneously occurring in both audi

Supriya Sarker, Brent Maples, Iftekharul Islam, Muyang Fan

Autonomous driving has rapidly evolved through synergistic developments in hardware and artificial intelligence. This comprehensive review investigates traffic datasets and simulators as dual pillars supporting autonomous vehicle (AV) development. Unlike prior surveys that examine these resources independently, we present an integrated analysis spanning the

Tadesse Destaw Belay, Israel Abebe Azime, Abinew Ali Ayele, Grigori Sidorov

Large Language Models (LLMs) show promising learning and reasoning abilities. Compared to other NLP tasks, multilingual and multi-label emotion evaluation tasks are under-explored in LLMs. In this paper, we present EthioEmo, a multi-label emotion classification dataset for four Ethiopian languages, namely, Amharic (amh), Afan Oromo (orm), Somali (som), and T

Andong Chen, Yuchen Song, Kehai Chen, Muyun Yang

Visual information has been introduced for enhancing machine translation (MT), and its effectiveness heavily relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations. In this paper, we introduce a stable diffusion-based imagination network into a multimodal large language model (MLLM) to explicitly genera

Shiyu Hu, Daizong Liu, Wei Hu

Deep learning models for point clouds have shown to be vulnerable to adversarial attacks, which have received increasing attention in various safety-critical applications such as autonomous driving, robotics, and surveillance. Existing 3D attackers generally design various attack strategies in the white-box setting, requiring the prior knowledge of 3D model

Rahul Islam, Tongze Zhang, Sang Won Bae

MoodCam introduces a novel method for assessing mood by utilizing facial affect analysis through the front-facing camera of smartphones during everyday activities. We collected facial behavior primitives during 15,995 real-world phone interactions involving 25 participants over four weeks. We developed three models for timely intervention: momentary, daily a

Haoran Sun, Michael Galperin

Fast and reliable manipulation with qubits is fundamental for any quantum technology. The implementation of these manipulations in physical systems is the focus of studies involving optimal control theory. Realistic physical devices are open quantum systems. So far, studies in optimal control theory have primarily utilized the Redfield/Lindblad quantum maste

Li Niu, Xi Feng, Xueqian Zhang, Yongchang Lu

Phased arrays are crucial in various technologies, such as radar and wireless communications, due to their ability to precisely control and steer electromagnetic waves. This precise control improves signal processing and enhances imaging performance. However, extending phased arrays to the terahertz (THz) frequency range has proven challenging, especially fo

Iftekharul Islam, Weizi Li

Managing mixed traffic comprising human-driven and robot vehicles (RVs) across large-scale networks presents unique challenges beyond single-intersection control. This paper proposes a reinforcement learning framework for coordinating mixed traffic across multiple interconnected intersections. Our key contribution is a neighbor-aware reward mechanism that en

Weixiong Zheng, Peijian Zeng, Yiwei Li, Hongyan Wu

Large language models (LLMs) excel in various tasks but remain vulnerable to jailbreak attacks, where adversaries manipulate prompts to generate harmful outputs. Examining jailbreak prompts helps uncover the shortcomings of LLMs. However, current jailbreak methods and the target model's defenses are engaged in an independent and adversarial process, resultin

Junjie Wang, Yuze Gao, Dongying Li, Wenxian Yu

Detecting small targets in sea clutter is challenging due to dynamic maritime conditions. Existing solutions either model sea clutter for detection or extract target features based on clutter-target echo differences, including statistical and deep features. While more common, the latter often excels in controlled scenarios but struggles with robust detection

Kuiyuan Zhang, Zhongyun Hua, Rushi Lan, Yushu Zhang

Recent advancements in text-to-speech and speech conversion technologies have enabled the creation of highly convincing synthetic speech. While these innovations offer numerous practical benefits, they also cause significant security challenges when maliciously misused. Therefore, there is an urgent need to detect these synthetic speech signals. Phoneme feat

Parviz Goodarzi

We investigate slow roll inflation and the creation of primordial density fluctuations in the framework of $f(Q,T)$ gravity. Our focus is on constraining the evolution of both the background and perturbations in this theory, specifically using the form $f(Q,T) = \alpha Q + g(T)$, where $g(T)$ is an arbitrary function of the trace of the stress-energy tensor

Jianan Ye, Weiguang Zhao, Xi Yang, Guangliang Cheng

Point cloud anomaly detection under the anomaly-free setting poses significant challenges as it requires accurately capturing the features of 3D normal data to identify deviations indicative of anomalies. Current efforts focus on devising reconstruction tasks, such as acquiring normal data representations by restoring normal samples from altered, pseudo-anom

Jan Eliáš, Gianluca Cusatis

This paper presents a rigorous derivation of equations to evaluate the macroscopic stress tensor, the couple stress tensor, and the flux vector equivalent to underlying microscopic fields in continuous and discrete heterogeneous systems with independent displacements and rotations. Contrary to the classical asymptotic expansion homogenization, finite size re

Ngoc-Duy Dinh, Marc Rodriguez-Garcia, Zenon Toprakcioglu, Yi Shen

Microscale hydrogels comprised of macromolecular networks have increasingly been used for applications involving cell encapsulation, tissue engineering and for the storage and release of active cargo molecules. However, the majority of such microgels are formed from nonbiodegradable synthetic polymers, involving harmful solvents, or using animal proteins, su

Antonio Alarcon, Francisco J. Lopez

The Gauss map of a conformal minimal immersion of an open Riemann surface $M$ into $\mathbb{R}^n$, $n\ge 3$, is a holomorphic map $M\to{\bf Q}^{n-2}\subset \mathbb{CP}^{n-1}$. Denote by ${\rm CMI}_{\rm full}(M,\mathbb{R}^n)$ and $\mathscr{O}_{\rm full}(M,{\bf Q}^{n-2})$ the spaces of full conformal minimal immersions $M\to\mathbb{R}^n$ and full holomorphic m

Yu Xi, Haoyu Li, Hao Li, Jiaqi Guo

In recent years, there has been a growing interest in designing small-footprint yet effective Connectionist Temporal Classification based keyword spotting (CTC-KWS) systems. They are typically deployed on low-resource computing platforms, where limitations on model size and computational capacity create bottlenecks under complicated acoustic scenarios. Such

Victor Koltalo, Saga Westerberg, Melvin Redon, Gaspard Beaufort

We report pulse energy scaling enabled by the use of Laguerre-Gaussian single-vortex ($\text{LG}_{0,l}$) beams for spectral broadening in a sub-40 cm long Herriott-type bulk multi-pass cell. Beams with orders ${l= 1-3}$ are generated by a spatial light modulator, which facilitates rapid and precise reconfiguration of the experimental conditions. 180 fs pulse

Aman Tiwari, Shiva Krishna Reddy Malay, Vikas Yadav, Masoud Hashemi

Graph databases like Neo4j are gaining popularity for handling complex, interconnected data, over traditional relational databases in modeling and querying relationships. While translating natural language into SQL queries is well-researched, generating Cypher queries for Neo4j remains relatively underexplored. In this work, we present an automated, LLM-Supe

Yun An, Taiping Hu, Quanquan Pang, Shenzhen Xu

Benefiting from the significantly improved energy density and safety, all-solid-state lithium batteries (ASSLBs) are considered one of the most promising next-generation energy technologies. Their practical applications, however, are strongly impeded by the Li dendrite formation. Despite this recognized challenge, a comprehensive understanding of Li dendrite

Gordon Burtch, Alejandro Zentner

Gender bias distorts the economic behavior and outcomes of women and households. We investigate gender biases in property taxes. We analyze records of more than 100,000 property tax appeal hearings and more than 2.7 years of associated audio recordings, considering how panelist and appellant genders associate with hearing outcomes. We first observe that fema

Nianqi Li, Zujie Liang, Siyu Yuan, Jiaqing Liang

Program-of-Thought (PoT), which aims to use programming language instead of natural language as an intermediate step in reasoning, is an important way for LLMs to solve mathematical problems. Since different programming languages excel in different areas, it is natural to use the most suitable language for solving specific problems. However, current PoT rese

Jiayu Liu, Tingting Luo, Cairong Chen, Deren Han

Two common methods for solving absolute value equations (AVE) are SOR-like iteration method and fixed point iteration (FPI) method. In this paper, novel convergence analysis, which result wider convergence range, of the SOR-like iteration and the FPI are given. Based on the new analysis, a new optimal iterative parameter with a analytical form is obtained fo

Farhana A. Simi, Matthew K. Tam

We consider resolvent splitting algorithms for finding a zero of the sum of finitely many maximally monotone operators. The standard approach to solving this type of problem involves reformulating as a two-operator problem in the product-space and applying the Douglas-Rachford algorithm. However, existing results for linear convergence cannot be applied in t

YiFan Zhang, Shanglin Lei, Runqi Qiao, Zhuoma GongQue

The rapidly developing field of large multimodal models (LMMs) has led to the emergence of diverse models with remarkable capabilities. However, existing benchmarks fail to comprehensively, objectively and accurately evaluate whether LMMs align with the diverse needs of humans in real-world scenarios. To bridge this gap, we propose the Multi-Dimensional Insi

Hai Lin, Cheng Huang, Zhihong Chen

Reinforcement learning tasks in real-world scenarios often involve large, high-dimensional action spaces, leading to challenges such as convergence difficulties, instability, and high computational complexity. It is widely acknowledged that traditional value-based reinforcement learning algorithms struggle to address these issues effectively. A prevalent app

Jianbo Cui, Feng-Yu Wang

In the study of geometric surface evolutions, stochastic reaction-diffusion equation provides a powerful tool for capturing and simulating complex dynamics. A critical challenge in this area is developing numerical approximations that exhibit error bounds with polynomial dependence on $\vv^{-1}$, where the small parameter $\vv>0$ represents the diffuse inter

Guangwenjie Zou, Liang Yao, Fan Liu, Chuanyi Zhang

Since high resolution remote sensing image classification often requires a relatively high computation complexity, lightweight models tend to be practical and efficient. Model pruning is an effective method for model compression. However, existing methods rarely take into account the specificity of remote sensing images, resulting in significant accuracy los

Chuye Zhang, Yifei Simon Shao, Harshil Parekh, Junyao Shi

We present a novel approach for enhancing human-robot collaboration using physical interactions for real-time error correction of large language model (LLM) powered robots. Unlike other methods that rely on verbal or text commands, the robot leverages an LLM to proactively executes 6 DoF linear Dynamical System (DS) commands using a description of the scene

Jie Lin, Tinggui Wang, Minxuan Cai, Zhen Wan

As the time-domain survey telescope of the highest survey power in the northern hemisphere currently, Wide Field Survey Telescope (WFST) is scheduled to hourly/daily/semi-weekly scan northern sky up to ~23 mag in four optical (ugri) bands. Unlike the observation cadences in the forthcoming regular survey missions, WFST performed "staring" observations toward

Oliver T. Oayda, Vasudev Mittal, Geraint F. Lewis

We present a new approach to constructing and fitting dipoles and higher-order multipoles in synthetic galaxy samples over the sky. Within our Bayesian paradigm, we illustrate that this technique is robust to masked skies, allowing us to make credible inferences about the relative contributions of each multipole. We also show that dipoles can be recovered in

James Allen Fill, Jason Matterer

QuickSelect (aka Find), introduced by Hoare (1961), is a randomized algorithm for selecting a specified order statistic from an input sequence of $n$ objects, or rather their identifying labels usually known as keys. The keys can be numeric or symbol strings, or indeed any labels drawn from a given linearly ordered set. We discuss various ways in which the c

Tina Torabi, Timon S Gutleb, Christoph Ortner

We describe efficient differentiation methods for computing Jacobians and gradients of a large class of matrix functions including the matrix logarithm $\log(A)$ and $p$-th roots $A^{\frac{1}{p}}$. We exploit contour integrals and conformal maps as described by (Hale et al., SIAM J. Numer. Anal. 2008) for evaluation and differentiation and analyze the comput

Niloufar Eghbali, Tuka Alhanai, Mohammad M. Ghassemi

Mechanical Ventilation (MV) is a critical life-support intervention in intensive care units (ICUs). However, optimal ventilator settings are challenging to determine because of the complexity of balancing patient-specific physiological needs with the risks of adverse outcomes that impact morbidity, mortality, and healthcare costs. This study introduces Confo

Shide Du, Zihan Fang, Yanchao Tan, Changwei Wang

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box mod

T. M. Dunster

Bessel and modified Bessel functions of imaginary order $i\nu$ ($\nu >0$) are studied. Asymptotic expansions are derived as $\nu \to \infty$ that are uniformly valid in unbounded complex domains, with error bounds provided. Coupled with appropriate connection formulas the approximations are uniformly valid for all complex argument. The expansions are of two

Wenyu Cong, Jingtao Shi, Bingchang Wang

This paper addresses a linear-quadratic Stackelberg mean field (MF) games and teams problem with arbitrary population sizes, where the game among the followers is further categorized into two types: non-cooperative and cooperative, and the number of followers can be finite or infinite. The leader commences by providing its strategy, and subsequently, each fo

Zipeng Qi, Buhua Liu, Shiyan Zhang, Bao Li

Large diffusion models have become mainstream generative models in both academic studies and industrial AIGC applications. Recently, a number of works further explored how to employ the power of large diffusion models as zero-shot classifiers. While recent zero-shot diffusion-based classifiers have made performance advancement on benchmark datasets, they sti

Zhenhua Li, Tianqi Dou, Yuheng Xie, Weiwen Kong

The mode pairing quantum key distribution (MP-QKD) protocol has attracted considerable attention for its capability to ensure high secure key rates over long distances without requiring global phase locking. However, ensuring symmetric channels for the MP-QKD protocol is challenging in practical quantum communication networks. Previous studies on the asymmet

Jian-Ping Dai, Hai-Bo Li, Shuai Zhao, Zong-Ying Zheng

True muonium, the bound state of a muon and an antimuon ($\mu^+\mu^-$), has long been theoretically predicted but remains experimentally elusive. We investigate the production of true para-muonium in the radiative decay of $J/\psi$ meson,and analyze the prospects for detecting true muonium in current and future high-energy $e^+e^-$ experiments, particularly

Chongyang Tao, Tao Shen, Shen Gao, Junshuo Zhang

Large language models (LLMs) have revolutionized natural language processing by achieving state-of-the-art performance across various tasks. Recently, their effectiveness as embedding models has gained attention, marking a paradigm shift from traditional encoder-only models like ELMo and BERT to decoder-only, large-scale LLMs such as GPT, LLaMA, and Mistral.

Namhyun Kim, Juntaek Han, Jinseok Choi, Ahmed Alkhateeb

In this paper, we propose a precoding framework for frequency division duplex (FDD) integrated sensing and communication (ISAC) systems with multiple-input multiple-output (MIMO). Specifically, we aim to maximize ergodic sum spectral efficiency (SE) while satisfying a sensing beam pattern constraint defined by the mean squared error (MSE). Our method reconst

Yi-Jun Chang, Gopinath Mishra, Hung Thuan Nguyen, Farrel D Salim

In the context of communication complexity, we explore protocols for graph coloring, focusing on the vertex and edge coloring problems in $n$-vertex graphs $G$ with a maximum degree $\Delta$. We consider a scenario where the edges of $G$ are partitioned between two players. Our first contribution is a randomized protocol that efficiently finds a $(\Delta + 1

Yun Luo, Yingjie Li, Xiangkun Hu, Qinglin Qi

As online platforms and recommendation algorithms evolve, people are increasingly trapped in echo chambers, leading to biased understandings of various issues. To combat this issue, we have introduced PerSphere, a benchmark designed to facilitate multi-faceted perspective retrieval and summarization, thus breaking free from these information silos. For each

Qinyu Zhang, Liang Xu, Jianhao Huang, Tao Yang

Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodat

Shen Bian, Yichen Zou

We consider a Keller-Segel model with non-linear porous medium type diffusion and non-local attractive power law interaction, focusing on potentials that are less singular than Newtonian interaction. Here, the nonlinear diffusion is chosen to be $m=2-\frac{2s}{d}$, in which case the steady states are compactly supported. We analyse under what conditions on t

Shanmuka Shivashankara, Hobbes Sprague

Unitarity provides mathematical and physical constraints on quantum information systems. e.g., in entanglement swapping, unitarity requires the same von Neumann entanglement entropy generation for either a particle interaction or an act of measurement. For the first time, the language of non-relativistic quantum mechanics is presented to derive the density m

Yibo Zhao, Jiapeng Zhu, Can Xu, Yao Liu

The rapid growth of social media platforms has raised significant concerns regarding online content toxicity. When Large Language Models (LLMs) are used for toxicity detection, two key challenges emerge: 1) the absence of domain-specific toxic knowledge leads to false negatives; 2) the excessive sensitivity of LLMs to toxic speech results in false positives,

Hanyin Wang, Chufan Gao, Qiping Xu, Bolun Liu

Process-supervised reward models (PRMs) excel at providing step-by-step verification for large language model (LLM) outputs in domains like mathematics and coding. However, their application to fields lacking ground-truth answers, such as clinical note generation, poses significant challenges. We introduce a novel framework for training PRMs to deliver step-

Feng Hao, Zichang Wang, Lei Zhang

Popa and Schnell show that any holomorphic 1-form on a smooth projective variety of general type has zeros. In this article, we show that a smooth good minimal model has a holomorphic 1-form without zero if and only if it admits an analytic fiber bundle structure over a positive dimensional abelian variety.

Haifeng Lu, Jiuyi Chen, Feng Liang, Mingkui Tan

Emotion recognition based on body movements is vital in human-computer interaction. However, existing emotion recognition methods predominantly focus on enhancing classification accuracy, often neglecting the provision of textual explanations to justify their classifications. In this paper, we propose an Emotion-Action Interpreter powered by Large Language M

Tao Wang, Yang Kuang, Ran Zhang, Guanghui Hu

In this paper, a novel $h$-adaptive isogeometric solver utilizing high-order hierarchical splines is proposed to solve the all-electron Kohn--Sham equation. In virtue of the smooth nature of Kohn--Sham wavefunctions across the domain, except at the nuclear positions, high-order globally regular basis functions such as B-splines are well suited for achieving

Zewen Sun, Yujin Zhang, Duanchen Xu, Yiyu Zhang

Apart from forming the backbone of compiler optimization, static dataflow analysis has been widely applied in a vast variety of applications, such as bug detection, privacy analysis, program comprehension, etc. Despite its importance, performing interprocedural dataflow analysis on large-scale programs is well known to be challenging. In this paper, we propo

Luyao Gao, Jianchun Liu, Hongli Xu, Sun Xu

End-cloud collaboration offers a promising strategy to enhance the Quality of Service (QoS) in DNN inference by offloading portions of the inference workload from end devices to cloud servers. Despite the potential, the complex model architectures and dynamic network conditions will introduce numerous bubbles (\ie, idle waiting time) in pipeline execution, r

Sean Kim, Marcus Edwards

The unique features of the hybrid quantum-classical computing model implied by the specification of OpenQASM 3.0 motivate new approaches to quantum program verification. We implement and thoroughly test a QASM 3.0 parser in TypeScript to enable implementations of verification and validation software, compilers, and more. We aim to help the community to forma