May 2025 arXiv papers — page 80

Jiaxuan Lu, Yuhui Lin, Junyan Shi, Fang Yan

Whole Slide Images (WSIs) in histopathology pose a significant challenge for extensive medical image analysis due to their ultra-high resolution, massive scale, and intricate spatial relationships. Although existing Multiple Instance Learning (MIL) approaches like Graph Neural Networks (GNNs) and Transformers demonstrate strong instance-level modeling capabi

S. Sundar

These lecture notes on $C^{*}$-algebras were prepared for a couple of courses given by the author at IMSc and also at IIT Gandhinagar. The topics covered are: Gelfand-Naimark theorems, universal C*-algebras, Hilbert C*-modules, crossed products, Morita equivalence, K-theory.

Youliang Yuan, Wenxiang Jiao, Yuejin Xie, Chihao Shen

Human safety awareness gaps often prevent the timely recognition of everyday risks. In solving this problem, a proactive safety artificial intelligence (AI) system would work better than a reactive one. Instead of just reacting to users' questions, it would actively watch people's behavior and their environment to detect potential dangers in advance. Our Pro

Hyosoon Jang, Yunhui Jang, Sungjae Lee, Jungseul Ok

Large language models (LLMs) have shown impressive performance by generating reasoning paths before final answers, but learning such a reasoning path requires costly human supervision. To address this issue, recent studies have explored self-training methods that improve reasoning capabilities using pseudo-labels generated by the LLMs themselves. Among these

Menglan Liu, Cenxi Yuan

Since Mayer and Jensen employed the single-particle shell model to interpret the magic numbers, various microscopic nuclear models have been developed to study the nuclear force and structure. The confguration-interaction shell model (CISM), performed in truncated model space with the inclusion of the residual interaction, is one widely-used nuclear structur

Ran Zhang, Meng Liu, Zhenping Yi, Hao Yuan

In large-scale galaxy surveys, particularly deep ground-based photometric studies, galaxy blending is inevitable and poses a potential primary systematic uncertainty for upcoming surveys. Current deblenders predominantly rely on analytical modeling of galaxy profiles, facing limitations due to inflexible and imprecise models. We present a novel approach usin

Zhining Liu, Zihao Li, Ze Yang, Tianxin Wei

Class-imbalanced learning (CIL) on tabular data is important in many real-world applications where the minority class holds the critical but rare outcomes. In this paper, we present CLIMB, a comprehensive benchmark for class-imbalanced learning on tabular data. CLIMB includes 73 real-world datasets across diverse domains and imbalance levels, along with unif

Shoichi Kawamoto, Da-Shin Lee, Chen-Pin Yeh

In this note, we reexamine decoherence effects in quantum field theories with gravity duals. The thought experiment proposed in \cite{DSW_22, DSW_23}, which reveals novel decoherence patterns associated with black holes, also manifests itself from the perspective of the boundary theory. In particular, we consider a moving mirror coupled to quantum critical t

Cody Kommers, Drew Hemment, Maria Antoniak, Joel Z. Leibo

This position paper argues that large language models (LLMs) can make cultural context, and therefore human meaning, legible at an unprecedented scale in AI-based sociotechnical systems. We argue that such systems have previously been unable to represent human meaning because they rely on thin descriptions (numerical representations that enforce standardizat

Inpyo Song, Jangwon Lee

This paper addresses the problem of anticipating traffic accidents, which aims to forecast potential accidents before they happen. Real-time anticipation is crucial for safe autonomous driving, yet most methods rely on computationally heavy modules like optical flow and intermediate feature extractors, making real-world deployment challenging. In this paper,

Bhanuka Gamage, Adnan Labib, Aisha Joomun, Chern Hong Lim

Following the rising popularity of YouTube, there is an emerging problem on this platform called clickbait, which provokes users to click on videos using attractive titles and thumbnails. As a result, users ended up watching a video that does not have the content as publicized in the title. This issue is addressed in this study by proposing an algorithm call

Qi Zhang, Shouqing Yang, Lirong Gao, Hao Chen

Large language models (LLMs) have demonstrated impressive capabilities in reasoning with the emergence of reasoning models like OpenAI-o1 and DeepSeek-R1. Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL) approaches, while the correctness of

Shunsuke Kando, Yusuke Miyao, Shinnosuke Takamichi

The purpose of speech tokenization is to transform a speech signal into a sequence of discrete representations, serving as the foundation for speech language models (SLMs). While speech tokenization has many options, their effect on the performance of SLMs remains unclear. This paper investigates two key aspects of speech tokenization: the segmentation width

Inpyo Song, Hyemin Hwang, Jangwon Lee

In the United States, as of 2023, pet ownership has reached 66% of households and continues to rise annually. This trend underscores the critical need for effective pet identification and monitoring methods, particularly as nearly 10 million cats and dogs are reported stolen or lost each year. However, traditional methods for finding lost animals like GPS ta

Zilong Zhao, Nikolay Solodovchenko, Chao Sun, Mingzhao Song

We study Fabry-Perot bound states in the continuum (FP-BIC) in the GHz frequency range, formed by two ceramic discs placed inside a metallic-walled rectangular waveguide, that act as perfect reflectors at the resonant frequency. The energy becomes perfectly trapped between the discs, forming a FP-BIC, when the distance between them matches the Fabry-Perot qu

Elfarouk Harb, Yousef Yassin, Chandra Chekuri

We study the problem of minimizing or maximizing the average value $ f(S)/|S| $ of a submodular or supermodular set function $ f: 2^V \to \mathbb{R} $ over non-empty subsets $ S \subseteq V $. This generalizes classical problems such as Densest Subgraph (DSG), Densest Supermodular Set (DSS), and Submodular Function Minimization (SFM). Motivated by recent app

Hao Jing, Anhong Wang, Yifan Zhang, Donghan Bu

Regarding intelligent transportation systems, low-bitrate transmission via lossy point cloud compression is vital for facilitating real-time collaborative perception among connected agents, such as vehicles and infrastructures, under restricted bandwidth. In existing compression transmission systems, the sender lossily compresses point coordinates and reflec

Can Rager, Chris Wendler, Rohit Gandikota, David Bau

Refusal discovery is the task of identifying the full set of topics that a language model refuses to discuss. We introduce this new problem setting and develop a refusal discovery method, Iterated Prefill Crawler (IPC), that uses token prefilling to find forbidden topics. We benchmark IPC on Tulu-3-8B, an open-source model with public safety tuning data. Our

Hefei Mei, Zirui Wang, Shen You, Minjing Dong

Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding and generation, yet their vulnerability to adversarial attacks raises significant robustness concerns. While existing effective attacks always focus on task-specific white-box settings, these approaches are limited in the context of LVLMs, which are des

Weijia Jin

This study designs an efficient and equitable humanitarian supply chain dynamically by using reinforcement learning, PPO, and compared with heuristic algorithms. This study demonstrates the model of PPO always treats average satisfaction rate as the priority.

Minghao Lu, Xiyu Fan, Bowen Xu, Zexuan Yan

High-speed obstacle avoidance of uncrewed aerial vehicles (UAVs) in cluttered environments is a significant challenge. Existing UAV planning and obstacle avoidance systems can only fly at moderate speeds or at high speeds over empty or sparse fields. In this article, we propose a hyper-efficient perception and planning system for the high-speed obstacle avoi

Yuanshao Zhu, James Jianqiao Yu, Xiangyu Zhao, Xiao Han

The widespread adoption of mobile devices and data collection technologies has led to an exponential increase in trajectory data, presenting significant challenges in spatio-temporal data mining, particularly for efficient and accurate trajectory retrieval. However, existing methods for trajectory retrieval face notable limitations, including inefficiencies

Cheng Peng, Kai Zhang, Mengxian Lyu, Hongfang Liu

To advance biomedical vison-language model capabilities through scaling up, fine-tuning, and instruction tuning, develop vision-language models with improved performance in handling long text, explore strategies to efficiently adopt vision language models for diverse multi-modal biomedical tasks, and examine the zero-shot learning performance. We developed t

Hongyi Henry Jin, Zijun Ding, Dung Daniel Ngo, Zhiwei Steven Wu

In recent years, multicalibration has emerged as a desirable learning objective for ensuring that a predictor is calibrated across a rich collection of overlapping subpopulations. Existing approaches typically achieve multicalibration by discretizing the predictor's output space and iteratively adjusting its output values. However, this discretization approa

Guanzhou Lan, Yuqi Yang, Anup Teejo Mathew, Feiping Nie

Goal-conditioned dynamic manipulation is inherently challenging due to complex system dynamics and stringent task constraints, particularly in deformable object scenarios characterized by high degrees of freedom and underactuation. Prior methods often simplify the problem to low-speed or 2D settings, limiting their applicability to real-world 3D tasks. In th

Fangxin Liu, Ning Yang, Junping Zhao, Tao Yang

Large language models (LLMs) have achieved significant progress in natural language processing but face challenges in deployment due to high memory and computational requirements. Weight quantization is a common approach to address these issues, yet achieving effective low-bit compression remains challenging. This paper presents LCD, which unifies the learni

Zhengyi Zhao, Shubo Zhang, Yuxi Zhang, Yanxi Zhao

Memes have emerged as a popular form of multimodal online communication, where their interpretation heavily depends on the specific context in which they appear. Current approaches predominantly focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express di

Matthew Di Meglio, Chris Heunen

This article introduces Hilbert $*$-categories: an abstraction of categories having algebraic and analytic properties similar to those of the categories of real, complex, and quaternionic Hilbert spaces and bounded linear maps. Other examples include categories of Hilbert W*-modules and of unitary representations of groupoids. Hilbert $*$-categories are &#34

Boyuan Li, Yicheng Luo, Zhen Liu, Junhao Zheng

Irregular multivariate time series (IMTS) are characterized by irregular time intervals within variables and unaligned observations across variables, posing challenges in learning temporal and variable dependencies. Many existing IMTS models either require padded samples to learn separately from temporal and variable dimensions, or represent original samples

Yongkang Yang, Jian Zhao, Tengfei Yang

We present SEvoBench, a modern C++ framework for evolutionary computation (EC), specifically designed to systematically benchmark evolutionary single-objective optimization algorithms. The framework features modular implementations of Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms, organized around three core components: (1) alg

Seyed Naseh Sajadi, Supakchai Ponglertsakul

Higher curvature gravity usually has complicated field equations, and solving them analytically is strenuous. In this work, we obtain an analytical charged black hole (BH) solution in higher curvature gravity using the thermodynamics of black holes and employing the continued fraction expansion. We investigate the thermodynamics of static black holes using t

Fangzheng Shi, Matteo Guainazzi, Yijun Wang

Accretion onto supermassive black hole (SMBH) can release energy via radiation, jets or winds, providing feedback effects on the circumnuclear gas environment. However, not all active galactic nuclei (AGNs) exhibit clear signature of such feedback, and the dynamics of accreting gas on the inner sub-parsec scales remains poorly understood. Using high-resoluti

Zhengyi Zhao, Shubo Zhang, Zezhong Wang, Huimin Wang

Recent advances in Large Language Models (LLMs) have demonstrated remarkable performance in Contextual Question Answering (CQA). However, prior approaches typically employ elaborate reasoning strategies regardless of question complexity, leading to low adaptability. Recent efficient test-time scaling methods introduce budget constraints or early stop mechani

Rui Wang, Qianguo Sun, Tianrong Chen, Zhiyun Zeng

The emergence of multi-codebook neutral audio codecs such as Residual Vector Quantization (RVQ) and Group Vector Quantization (GVQ) has significantly advanced Large-Language-Model (LLM) based Text-to-Speech (TTS) systems. These codecs are crucial in separating semantic and acoustic information while efficiently harnessing semantic priors. However, since sema

Wei Jie Yeo, Rui Mao, Moloud Abdar, Erik Cambria

Multimodal models like CLIP have gained significant attention due to their remarkable zero-shot performance across various tasks. However, studies have revealed that CLIP can inadvertently learn spurious associations between target variables and confounding factors. To address this, we introduce \textsc{Locate-Then-Correct} (LTC), a contrastive framework tha

A. Rehman, Tayyab Naseer, Baiju Dayanandan

Regardless of the adequate descriptions of complexity in distinct alternative gravity theories, its elaboration in the framework of $f(R,\mathcal{L}_{m},\mathcal{T})$ theory remains uncertain. The orthogonal splitting of the curvature tensor yields the complexity factor as suggested by Herrera \cite {herrera2018new}. To commence our study, the inner spacetim

Chuan Qin

Motivated by the recent work of Aubert-Xu and the techniques in G. Muic's article, we provide examples of computations of the Aubert-Zelevinsky duality functor for the principal and mediate series of the exceptional group $G_2$, and deduce corresponding results regarding the involution on the Hecke algebra side. These computations also allow us to confirm se

Zhen Qiao, Jiang Xue, Junkai Zhang, Guanzhang Liu

With the widespread deployment of fifth-generation (5G) wireless networks, research on sixth-generation (6G) technology is gaining momentum. Artificial Intelligence (AI) is anticipated to play a significant role in 6G, particularly through integration with the physical layer for tasks such as channel estimation. Considering resource limitations in real syste

Ning Yang, Fangxin Liu, Junjie Wang, Tao Yang

Large language models (LLMs) have achieved remarkable performance across a wide range of NLP tasks. However, their substantial inference cost poses a major barrier to real-world deployment, especially in latency-sensitive scenarios. To address this challenge, we propose \textbf{DASH}, an adaptive layer-skipping framework that dynamically selects computation

Luis Manuel Rivera, Gerardo Vazquez Briones

The $2$-token graph $F_2(G)$ of a graph $G$ is the graph whose set of vertices consists of all the $2$-subsets of $V(G)$, where two vertices are adjacent if and only if their symmetric difference is an edge in $G$. Let $G$ be the join graph of $E_n$ and $H$, where $H$ is any graph. In this paper, we give a method to construct an independent set ${\mathcal I}

Rudrajit Choudhuri, Bianca Trinkenreich, Rahul Pandita, Eirini Kalliamvakou

Generative AI (genAI) tools promise productivity gains, yet miscalibrated trust and usage friction still hinder adoption. Moreover, genAI can be exclusionary, failing to adequately support diverse users. One such aspect of diversity is cognitive diversity, which leads to diverging interaction styles (e.g., a risk-averse developer may gate genAI outputs behin

Zeyu Liu, Yan Li, Yunquan Zhang, Boyang Zhang

Training large language models typically demands extensive GPU memory and substantial financial investment, which poses a barrier for many small- to medium-sized teams. In this paper, we propose a full-parameter pre-training and fine-tuning framework based on block coordinate descent (BCD), enhanced with engineering optimizations, to enable efficient trainin

Alan Dao, Dinh Bach Vu, Huy Hoang Ha, Tuan Le Duc Anh

The rapid growth of voice assistants powered by large language models (LLM) has highlighted a need for speech instruction data to train these systems. Despite the abundance of speech recognition data, there is a notable scarcity of speech instruction data, which is essential for fine-tuning models to understand and execute spoken commands. Generating high-qu

Yanli Jin, Chunpei Li, Peng Fan, Peng Liu

Smart contracts are a key component of the Web 3.0 ecosystem, widely applied in blockchain services and decentralized applications. However, the automated execution feature of smart contracts makes them vulnerable to potential attacks due to inherent flaws, which can lead to severe security risks and financial losses, even threatening the integrity of the en

Yun Wang, Katherine Freese

Using DESI DR2 baryon acoustic oscillation (BAO) distance measurements and Planck cosmic microwave background distance priors, we have measured the dark energy density $\rho_X(z)$ and dark energy equation of state w_X(z) as free functions of redshift (smoothly interpolated from values at {z_i}={0, 1/3, 2/3, 1, 4/3, 2.33}, and find both to be consistent with

Fiaz Hossain, Nilanjan Ray Chaudhuri

A dynamic phasor (DP) framework for time-domain and frequency-domain analyses of grid-forming converters (GFCs) connected to series-compensated transmission lines is proposed. The proposed framework can capture the behavior of GFCs subjected to unbalanced short circuit faults in presence of different current limiting strategies. Moreover, the linearizability

Xinxian Fan, Mengye Lyu

This study explores the use of text-prompted MRI image generation with the Stable Diffusion (SD) model to address challenges in acquiring real MRI datasets, such as high costs, limited rare case samples, and privacy concerns. The SD model, pre-trained on natural images, was fine-tuned using the 3T fastMRI dataset and the 0.3T M4Raw dataset, with the goal of

Niranjan Chebrolu, Gerard Christopher Yeo, Kokil Jaidka

Large Language Models (LLMs) demonstrate increasing conversational fluency, yet instilling them with nuanced, human-like emotional expression remains a significant challenge. Current alignment techniques often address surface-level output or require extensive fine-tuning. This paper demonstrates that targeted activation engineering can steer LLaMA 3.1-8B to

Shuang Wu, Youtian Lin, Feihu Zhang, Yifei Zeng

Generating high-resolution 3D shapes using volumetric representations such as Signed Distance Functions (SDFs) presents substantial computational and memory challenges. We introduce Direct3D-S2, a scalable 3D generation framework based on sparse volumes that achieves superior output quality with dramatically reduced training costs. Our key innovation is the

Natsuo Yamashita, Masaaki Yamamoto, Hiroaki Kokubo, Yohei Kawaguchi

Generative error correction (GER) with large language models (LLMs) has emerged as an effective post-processing approach to improve automatic speech recognition (ASR) performance. However, it often struggles with rare or domain-specific words due to limited training data. Furthermore, existing LLM-based GER approaches primarily rely on textual information, n

Meiling Wang, Juan Wang, Yan Li, Franco Dalfovo

We investigate parametric excitation and pattern formation in a harmonically trapped two-component Bose-Einstein condensate. We assume the condensate to be in the miscible phase, but near the miscible-immiscible phase transition, where total density and spin density excitations are decoupled. By periodically modulating the atomic scattering lengths, Faraday

Ilkyoo Choi, Alexandr V. Kostochka, Matthew Yancey

The following measure of sparsity of multigraphs refining the maximum average degree: For $a>0$ and an arbitrary real $b$, a multigraph $H$ is \emph{$(a,b)$-sparse} if it is loopless and for every $A\subseteq V(H)$ with $|A|\geq 2$, the induced subgraph $H[A]$ has at most $a|A|+b$ edges. Forests are exactly $(1,-1)$-sparse multigraphs. It is known that the v

Zhi Rui Tam, Cheng-Kuang Wu, Yu Ying Chiu, Chieh-Yen Lin

Large reasoning models (LRMs) have demonstrated impressive performance across a range of reasoning tasks, yet little is known about their internal reasoning processes in multilingual settings. We begin with a critical question: {\it In which language do these models reason when solving problems presented in different languages?} Our findings reveal that, des

Enyi Jiang, Changming Xu, Nischay Singh, Tian Qiu

While Chain-of-Thought (CoT) prompting has become a cornerstone for complex reasoning in Large Language Models (LLMs), the faithfulness of the generated reasoning remains an open question. We investigate the Decoupling Hypothesis: that correct answers often mask fragile, post-hoc rationalizations that are not causally tied to the model's prediction. To syste

Jie Wen, Chenyu Jia, Guangshu Xia

Aiming at the state of health (SOH) prediction of lithium-ion batteries (LiBs) for electric vehicles (EVs), this paper proposes a fusion model of a dual-module bidirectional gated recurrent unit (BiGRU) and sparrow search algorithm (SSA) with full parameter domain optimization. With the help of Spearman correlation analysis and ablation experiments, the indi

Kaicheng Zhang, Sinian Zhang, Doudou Zhou, Yidong Zhou

Transfer learning is a powerful paradigm for leveraging knowledge from source domains to enhance learning in a target domain. However, traditional transfer learning approaches often focus on scalar or multivariate data within Euclidean spaces, limiting their applicability to complex data structures such as probability distributions. To address this limitatio

Chunguo Duan, Qian Gou, Tie Liu, Fengwei Xu

High-mass star formation involves complex processes, with the hot core phase playing a crucial role in chemical enrichment and the formation of complex organic molecules. However, molecular inventories in hot cores remain limited. Using data from the ALMA Three-millimeter Observations of Massive Star-forming regions survey (ATOMS), the molecular composition

Mahmoud Chick Zaouali, Todd Charter, Homayoun Najjaran

High-fidelity 3D reconstruction is critical for aerial inspection tasks such as infrastructure monitoring, structural assessment, and environmental surveying. While traditional photogrammetry techniques enable geometric modeling, they lack semantic interpretability, limiting their effectiveness for automated inspection workflows. Recent advances in neural re

Shan Gao, Zhiqiang Wu, Yawen Niu, Xiaotao Li

While deep neural network (DNN)-based video denoising has demonstrated significant performance, deploying state-of-the-art models on edge devices remains challenging due to stringent real-time and energy efficiency requirements. Computing-in-Memory (CIM) chips offer a promising solution by integrating computation within memory cells, enabling rapid matrix-ve

Yingpeng Du, Tianjun Wei, Zhu Sun, Jie Zhang

Large Language Models (LLMs) have been widely adopted in ranking systems such as information retrieval (IR) systems and recommender systems (RSs). To alleviate the latency of auto-regressive decoding, some studies explore the single (first) token decoding for ranking approximation, but they suffer from severe degradation in tail positions. Although speculati

Chuan Qin

We give two generalizations of the Alvis-Curtis duality for Hecke algebras: an unequal parameter version for the affine Hecke algebras, based on S.-I. Kato's work, and a relative version for finite Hecke algebras, based on Howlett-Lehrer's work. Our results for the finite case focus on the involution theorem for finite Hecke algebras that appear in Howlett-L

Jingyu Liu, Yanglei Song

We study the stochastic linear bandit problem with multiple arms over $T$ rounds, where the covariate dimension $d$ may exceed $T$, but each arm-specific parameter vector is $s$-sparse. We begin by analyzing the sequential estimation problem in the single-arm setting, focusing on cumulative mean-squared error. We show that Lasso estimators are provably subop

Haoyu Sun, Huichen Will Wang, Jiawei Gu, Linjie Li

Front-end engineering involves a complex workflow where engineers conceptualize designs, translate them into code, and iteratively refine the implementation. While recent benchmarks primarily focus on converting visual designs to code, we present FullFront, a benchmark designed to evaluate Multimodal Large Language Models (MLLMs) \textbf{across the full fron

Justin Lyle, Paolo Mantero

In this paper, we propose a uniform approach to tackle problems about squarefree monomial ideals whose powers have good properties. We employ this approach to achieve a twofold goal: (i) recover and extend several well--known results in the literature, especially regarding Stanley--Reisner ideals of matroids, and (ii) provide short, elementary proofs for the

Zhongheng Cai, Lian Ma, Jingjing Ye, Haitao Pan

Development of effective treatments in pediatric population poses unique scientific and ethical challenges in addition to the small population. In this regard, both the U.S. and E.U. regulations suggest a complementary strategy, pediatric extrapolation, based on assessing the relevance of existing information in the adult population to the pediatric populati

Yawei Gao, Jan Michael Carrillo, Logan T. Kearney, Polyxeni P. Angelopoulou

Understanding how polymer topology influences melt extrudability is critical for advancing material design in extrusion-based additive manufacturing. In this work, we develop a bottom-up, cross-scale modeling framework that integrates coarse-grained molecular dynamics (CGMD) and continuum-scale computational fluid dynamics (CFD) to quantitatively assess the

Gowtham Raj Vuppari, Navarun Gupta, Ahmed El-Sayed, Xingguo Xiong

The critical need for sophisticated detection techniques has been highlighted by the rising frequency and intensity of wildfires in the US, especially in California. In 2023, wildfires caused 130 deaths nationwide, the highest since 1990. In January 2025, Los Angeles wildfires which included the Palisades and Eaton fires burnt approximately 40,000 acres and

Joseph D. Lopes, Benjamin Winterstrain, Fernando Caballero, Amélie Chardac

Competition for resources is a fundamental constraint that guides the self-organization of natural, biological, and human systems, ranging from urban planning and ecosystem development to intracellular pattern formation. Here, we reveal that competition for resources is at the origin of the collective dynamics that emerge in a population of colloids propelle

Changquan Zhao, Yi Zhang, Zhuo Li, Li Jin

Identifying optimal catalyst compositions and reaction conditions is central in catalysis research, yet remains challenging due to the vast multidimensional design spaces encompassing both continuous and categorical parameters. In this work, we present CatBOX, a Bayesian Optimization method for accelerated catalytic experimental design that jointly optimizes

Leon C. C. K, Zeng Hui

In this paper, a novel dual-sensing driver fatigue detection method combining computer vision and physiological signal analysis is proposed. The system exploits the complementary advantages of the two sensing modalities and breaks through the limitations of existing single-modality methods. We introduce an innovative architecture that combines real-time faci

Yuelyu Ji, Rui Meng, Zhuochun Li, Daqing He

Retrieval-augmented generation (RAG) grounds large language models (LLMs) in up-to-date external evidence, yet existing multi-hop RAG pipelines still issue redundant subqueries, explore too shallowly, or wander through overly long search chains. We introduce EVO-RAG, a curriculum-guided reinforcement learning framework that evolves a query-rewriting agent fr

Gauri Kambhatla, Chantal Shaib, Venkata Govindarajan

Fine-grained personas have recently been used for generating 'diverse' synthetic data for pre-training and supervised fine-tuning of Large Language Models (LLMs). In this work, we measure the diversity of persona-driven synthetically generated prompts and responses with a suite of lexical diversity and redundancy metrics. First, we find that synthetic prompt

Jinrong Yang, Kexun Chen, Zhuoling Li, Shengkai Wu

Imitation learning (IL) with human demonstrations is a promising method for robotic manipulation tasks. While minimal demonstrations enable robotic action execution, achieving high success rates and generalization requires high cost, e.g., continuously adding data or incrementally conducting human-in-loop processes with complex hardware/software systems. In

Chen Hu, Kouxiao Zhang

We conduct modeling of the price dynamics following order flow imbalance in market microstructure and apply the model to the analysis of Chinese CSI 300 Index Futures. There are three findings. The first is that the order flow imbalance is analogous to a shock to the market. Unlike the common practice of using Hawkes processes, we model the impact of order f

Boqin Zhuang, Chenxiao Song, Huitong Lu, Jiacheng Qiao

Current Large Language Models (LLMs) exhibit significant limitations, notably in structured, interpretable, and verifiable medical reasoning, alongside practical deployment challenges related to computational resources and data privacy. This report focused on the development of WiNGPT-3.0, the 32-billion parameter LLMs, engineered with the objective of enhan

Andrew Y. Chen, Carlos M. Portela

Composite materials are used across engineering applications for their superior mechanical performance, a result of efficient load transfer between the structure and matrix phases. However, the inherently two-dimensional structure of laminated composites reduces their robustness to shear and out-of-plane loads, while unpredictable interlaminar failure and fi

Guodong Zhang, Na Li, Hao Zhang, Huaiyi Wang

The focused ultrafast laser, with its ability to initiate nonlinear absorption in transparent materials, has emerged as one of the most effective approaches for micro-nano processing. In this study, we carried out research on the processing of high-aspect-ratio nanovoids on fused silica by using the single-pulse ultrafast Bessel beam. The thermodynamic respo

Tianyu Xie, Shuchen Xue, Zijin Feng, Tianyang Hu

Discrete diffusion models have recently shown great promise for modeling complex discrete data, with masked diffusion models (MDMs) offering a compelling trade-off between quality and generation speed. MDMs denoise by progressively unmasking multiple dimensions from an all-masked input, but their performance can degrade when using few denoising steps due to

Yujiro Kawamata

We construct a non-commutative version of the Grassmann variety $G(2,4)$ as a non-commutative moduli space of linear subspaces in a projective space.

Yuge Ye, Qingna Li

This paper investigates the box-constrained $\ell_0$-regularized sparse optimization problem. We introduce the concept of a $\tau$-stationary point and establish its connection to the local and global minima of the box-constrained $\ell_0$-regularized sparse optimization problem. We utilize the $\tau$-stationary points to define the support set, which we div

Mitsumasa Nakajima, Kohki Shibahara, Kohei Ikeda, Akira Kawai

The explosive growth of global data traffic demands scalable and energy-efficient optical communication systems. Spatial division multiplexing (SDM) using multicore or multimode fibers is a promising solution to overcome the capacity limit of single-mode fibers. However, long-haul SDM transmission faces significant challenges due to modal dispersion, which i

Yinghui Huang, Yuxuan Jiang, Hui Liu, Yixin Cai

Large language models (LLMs) like GPT-4 show potential for scaling motivational interviewing (MI) in addiction care, but require systematic evaluation of therapeutic capabilities. We present a computational framework assessing user-perceived quality (UPQ) through expected and unexpected MI behaviors. Analyzing human therapist and GPT-4 MI sessions via human-

Zichen Wang, Chuanhao Li, Huazheng Wang

We investigate the problem of identifying the optimal scoring rule within the principal-agent framework for online information acquisition problem. We focus on the principal's perspective, seeking to determine the desired scoring rule through interactions with the agent. To address this challenge, we propose two algorithms: OIAFC and OIAFB, tailored for fixe

Itai Keren, Tatiana A. Webb, Shuai Zhang, Jikai Xu

Is it feasible to alter the ground state properties of a material by engineering its electromagnetic environment? Inspired by theoretical predictions, experimental realizations of such cavity-controlled properties without optical excitation are beginning to emerge. Here, we devised and implemented a novel platform to realize cavity-altered materials. Single

Yuta Suzuki, Yoshiki Matsuoka, Satoshi Kikuta, Hisakazu Uchiyama

We report on the statistical analysis of quasar photoevaporation at $z\sim2.2$ by comparing the density of surrounding Ly$\alpha$ Emitters (LAEs) and continuum-selected galaxies, based on the imaging data of Hyper Suprime-Cam (HSC) Subaru Strategic Program (SSP) and CFHT Large Area $U$-band Deep Survey (CLAUDS). We select 18 quasars from Sloan Digital Sky Su

Zhixiang Deng, Rui Ma, Chunxiang Zhang, Boris Malomed

We address the weak interaction of a pair of well-separated pure-quartic solitons (PQSs), which are solutions to a generalized nonlinear Schrodinger equation (NLSE) with the quartic-only dispersion. An asymptotic technique is applied to derive equations for the slow evolution of the temporal separation and phase difference of the PQSs interacting through the

Andrew Lott, Nagendar Reddy Ponagandla

By Maynard's theorem and the subsequent improvements by the Polymath Project, there exists a positive integer $b\leq 246$ such that there are infinitely many primes $p$ such that $p+b$ is also prime. Let $P_1,...,P_t\in \mathbb{Z}[y]$ with $P_1(0)=\cdots=P_t(0)=0$. We use the transference argument of Tao and Ziegler to prove there exist positive integers $x,

Seon Gyeom Kim, Jae Young Choi, Ryan Rossi, Eunyee Koh

The field of Multimodal Large Language Models (MLLMs) has made remarkable progress in visual understanding tasks, presenting a vast opportunity to predict the perceptual and emotional impact of charts. However, it also raises concerns, as many applications of LLMs are based on overgeneralized assumptions from a few examples, lacking sufficient validation of

Kaiwen Wang, Jin Peng Zhou, Jonathan Chang, Zhaolin Gao

In this paper, we propose a simple and efficient method for value model training on long-context reasoning traces. Compared to existing process reward models (PRMs), our method does not require a fine-grained notion of "step," which is difficult to define for long-context reasoning models. By collecting a dataset of 2.5 million reasoning traces, we train a 1

Thomas Jun Jewell, Andrew L. Krause, Philip K. Maini, Eamonn A. Gaffney

Chase-and-run dynamics, in which one population pursues another that flees from it, are found throughout nature, from predator-prey interactions in ecosystems to the collective motion of cells during development. Intriguingly, in many of these systems, the movement is not straight; instead, 'runners' veer off at an angle from their pursuers. This angled move

Sergio Chevtchenko, Nikhil Navas, Rafaella Vale, Franco Ubaudi

Child literacy is a strong predictor of life outcomes at the subsequent stages of an individual's life. This points to a need for targeted interventions in vulnerable low and middle income populations to help bridge the gap between literacy levels in these regions and high income ones. In this effort, reading assessments provide an important tool to measure

Veronica Pasquarella

The present work consists of topics covered through a course currently taught by the author at SIMIS.

Qilin Wang

We argue that long-term forecasting requires learning local Jacobians with explicit spectral structure, going beyond simple conditional mean matching. Our method, Fern, invokes Brenier's theorem to directly parameterize the Jacobian as a symmetric positive semi-definite (SPD) factorization, treating forecasting as the optimal transport of probability mass fr

TaekHyun Park, YoungJun Choi, SeungHoon Shin, Kwangil Lee

LA-RCS (LLM-agent-based robot control system) is a sophisticated robot control system designed to autonomously plan, work, and analyze the external environment based on user requirements by utilizing LLM-Agent. Utilizing a dual-agent framework, LA-RCS generates plans based on user requests, observes the external environment, executes the plans, and modifies

Jerome R. Busemeyer, Masanao Ozawa, Emmanuel M. Pothos, Naotsugu Tsuchiya

An important challenge for quantum theories of cognition and decision concerns the incorporation of memory for recently made judgments and their effects on later judgments. First, we review a general approach to measurement based on system plus environment representations of states and measurement instruments. These more general measurement models provide wa

Gülnaz Boruzanlı Ekinci, Csilla Bujtás, Didem Gözüpek, Sandi Klavžar

For a non-decreasing sequence of positive integers $S=(s_1,s_2,\ldots)$, the $S$-packing chromatic number of a graph $G$ is denoted by $\chi_S(G)$. In this paper, $\chi_S$-critical graphs are introduced as the graphs $G$ such that $\chi_S(H) < \chi_S(G)$ for each proper subgraph $H$ of $G$. Several families of $\chi_S$-critical graphs are constructed, and $2

Mira Nuthakki

Traffic congestion and collisions represent significant economic, environmental, and social challenges worldwide. Traditional traffic management approaches have shown limited success in addressing these complex, dynamic problems. To address the current research gaps, three potential tools are developed: a comprehensive 3D city-wide simulation environment tha

Mohsen Dehghankar, Abolfazl Asudeh

Hierarchical graph-based algorithms such as HNSW have achieved state-of-the-art performance for Approximate Nearest Neighbor (ANN) search in practice, yet they often lack theoretical guarantees on query time or recall due to their heavy use of randomized heuristic constructions. Conversely, existing theoretically grounded structures are typically difficult t

Zichuan Yang, Yongzhi Wang

Medical image classification is critical for clinical decision-making, yet demands for accuracy, interpretability, and generalizability remain challenging. This paper introduces EVM-Fusion, an Explainable Vision Mamba architecture featuring a novel Neural Algorithmic Fusion (NAF) mechanism for multi-organ medical image classification. EVM-Fusion leverages a

Yichi Zhang, Zhihao Duan, Yuning Huang, Fengqing Zhu

Image Coding for Machines (ICM) focuses on optimizing image compression for AI-driven analysis rather than human perception. Existing ICM frameworks often rely on separate codecs for specific tasks, leading to significant storage requirements, training overhead, and computational complexity. To address these challenges, we propose an energy-efficient framewo