Papers
Research papers from arXiv and related sources
Instance-optimal estimation of L2-norm
The $L_2$-norm, or collision norm, is a core entity in the analysis of distributions and probabilistic algorithms. Batu and Canonne (FOCS 2017) presented an extensive analysis of algorithmic aspect...
Tomer Adar
A Framework for Cross-Domain Generalization in Coronary Artery Calcium Scoring Across Gated and Non-Gated Computed Tomography
Coronary artery calcium (CAC) scoring is a key predictor of cardiovascular risk, but it relies on ECG-gated CT scans, restricting its use to specialized cardiac imaging settings. We introduce an au...
Mahmut S. Gokmen, Moneera N. Haque, Steve W. Leung, Caroline N. Leach, Seth Parker, Stephen B. Ho...
Small Wins Big: Comparing Large Language Models and Domain Fine-Tuned Models for Sarcasm Detection in Code-Mixed Hinglish Text
Sarcasm detection in multilingual and code-mixed environments remains a challenging task for natural language processing models due to structural variations, informal expressions, and low-resource ...
Bitan Majumder, Anirban Sen
Geometry-as-context: Modulating Explicit 3D in Scene-consistent Video Generation to Geometry Context
Scene-consistent video generation aims to create videos that explore 3D scenes based on a camera trajectory. Previous methods rely on video generation models with external memory for consistency, o...
JiaKui Hu, Jialun Liu, Liying Yang, Xinliang Zhang, Kaiwen Li, Shuang Zeng, Yuanwei Li, Haibin Hu...
Robust Kaczmarz methods for nearly singular linear systems
The Kaczmarz method is an efficient iterative algorithm for large-scale linear systems. However, its linear convergence rate suffers from ill-conditioned problems and is highly sensitive to the sma...
Yunying Ke, Hao Luo
A generalized Riemann problem-based compact reconstruction method for finite volume schemes
We present a Generalized Riemann Problem-based reconstruction method (GRPrec) for high-order finite volume schemes applied to hyperbolic partial differential equations. The method constructs spatia...
Gino I. Montecinos, Eleuterio F. Toro, Lucas O. Müller
Jackknife Inference for Fixed Effects Models
This paper develops a general method of inference for fixed effects models which is (i) automatic, (ii) computationally inexpensive, and (iii) highly model agnostic. Specifically, we show how to co...
Ayden Higgins
Computing Nonequilibrium Transport from Short-Time Transients: From Lorentz Gas to Heat Conduction in One Dimensional Chains
We test the Transient Time Correlation Function (TTCF) method to compute nonequilibrium transport coefficients, highlighting its conceptual and practical difference from the standard time-average a...
Davide Carbone, Vincenzo Di Florio, Stefano Lepri, Lamberto Rondoni
EmoOmni: Bridging Emotional Understanding and Expression in Omni-Modal LLMs
The evolution of Omni-Modal Large Language Models~(Omni-LLMs) has revolutionized human--computer interaction, enabling unified audio-visual perception and speech response. However, existing Omni-LL...
Wenjie Tian, Zhixian Zhao, Jingbin Hu, Huakang Chen, Haohe Liu, Binshen Mu, Lei Xie
A task-based data-flow methodology for programming heterogeneous systems with multiple accelerator APIs
Heterogeneous nodes that combine multi-core CPUs with diverse accelerators are rapidly becoming the norm in both high-performance computing (HPC) and AI infrastructures. Exploiting these platforms,...
Aleix Boné, Alejandro Aguirre, David Álvarez, Pedro J. Martinez-Ferrer, Vicenç Beltran
APFuzz: Towards Automatic Greybox Protocol Fuzzing
Greybox protocol fuzzing is a random testing approach for stateful protocol implementations, where the input is protocol messages generated from mutations of seeds, and the search in the input spac...
Yu Wang, Yang Xiang, Chandra Thapa, Hajime Suzuki
2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support
Across a growing number of fields, human decision making is supported by predictions from AI models. However, we still lack a deep understanding of the effects of adoption of these technologies. In...
Otto Nyberg, Fausto Carcassi, Giovanni Cinà
ExpLang: Improved Exploration and Exploitation in LLM Reasoning with On-Policy Thinking Language Selection
Current large reasoning models (LRMs) have shown strong ability on challenging tasks after reinforcement learning (RL) based post-training. However, previous work mainly focuses on English reasonin...
Changjiang Gao, Zixian Huang, Kaichen Yang, Jiajun Chen, Jixing Li, Shujian Huang
The Dawes Review: A Decade of Ultra-Diffuse Galaxies
It has been 10 years since the initial discovery of Ultra-Diffuse Galaxies (UDGs) in the Coma cluster and the revelation that large, low surface brightness galaxies may constitute a greater fractio...
Jonah S. Gannon, Anna Ferré-Mateu, Duncan A. Forbes
Personalized Graph-Empowered Large Language Model for Proactive Information Access
Since individuals may struggle to recall all life details and often confuse events, establishing a system to assist users in recalling forgotten experiences is essential. While numerous studies hav...
Chia Cheng Chang, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen
Distill and Align Decomposition for Enhanced Claim Verification
Complex claim verification requires decomposing sentences into verifiable subclaims, yet existing methods struggle to align decomposition quality with verification performance. We propose a reinfor...
Jabez Magomere, Elena Kochkina, Samuel Mensah, Simerjot Kaur, Fernando Acero, Arturo Oncevay, Cha...
Understanding Annotation Error Propagation and Learning an Adaptive Policy for Expert Intervention in Barrett's Video Segmentation
Accurate annotation of endoscopic videos is essential yet time-consuming, particularly for challenging datasets such as dysplasia in Barrett's esophagus, where the affected regions are irregular an...
Lokesha Rasanjalee, Jin Lin Tan, Dileepa Pitawela, Rajvinder Singh, Hsiang-Ting Chen
FewMMBench: A Benchmark for Multimodal Few-Shot Learning
As multimodal large language models (MLLMs) advance in handling interleaved image-text data, assessing their few-shot learning capabilities remains an open challenge. In this paper, we introduce Fe...
Mustafa Dogan, Ilker Kesen, Iacer Calixto, Aykut Erdem, Erkut Erdem
Meta-FC: Meta-Learning with Feature Consistency for Robust and Generalizable Watermarking
Deep learning-based watermarking has made remarkable progress in recent years. To achieve robustness against various distortions, current methods commonly adopt a training strategy where a \underli...
Yuheng Li, Weitong Chen, Chengcheng Zhu, Jiale Zhang, Chunpeng Ge, Di Wu, Guodong Long
JSAM: Privacy Straggler-Resilient Joint Client Selection and Incentive Mechanism Design in Differentially Private Federated Learning
Differentially private federated learning faces a fundamental tension: privacy protection mechanisms that safeguard client data simultaneously create quantifiable privacy costs that discourage part...
Ruichen Xu, Ying-Jun Angela Zhang, Jianwei Huang