October 2025 arXiv papers — page 175

T. V. Iser, E. G. S. Luna

We investigate the high-energy behavior of the total cross section, $\sigma_{\text{tot}}$, and the ratio of the real to imaginary parts of the scattering amplitude, $\rho$, in both proton-proton and antiproton-proton channels. Our analysis is based on a QCD-inspired model in which the rise of the cross sections is predominantly driven by semihard processes i

Xiaofeng Cao, Mingwei Xu, Xin Yu, Jiangchao Yao

Learning with high-resource data has demonstrated substantial success in artificial intelligence (AI); however, the costs associated with data annotation and model training remain significant. A fundamental objective of AI research is to achieve robust generalization with limited-resource data. This survey employs agnostic active sampling theory within the P

Guojian Li, Qijie Shao, Zhixian Zhao, Shuiyuan Wang

Speaking Style Recognition (SSR) identifies a speaker's speaking style characteristics from speech. Existing style recognition approaches primarily rely on linguistic information, with limited integration of acoustic information, which restricts recognition accuracy improvements. The fusion of acoustic and linguistic modalities offers significant potential t

Takumi Otani, Dongjian Wu

We study stability conditions on the derived category of a finite connected acyclic quiver. We prove that, for any stability condition on the derived category, its heart can be obtained from an algebraic heart by a rotation of phases. Consequently, we establish the connectedness of the space of stability conditions. Furthermore, we prove that every stability

YaoKun Lei, MaoDong Li, Yi Isaac Yang

Crystallization, a fundamental phase transition process governing material formation in natural and industrial contexts, involves the spontaneous emergence of long-range structural order from disordered phases. This long-range periodicity involves spatial and molecular orientation order. Molecular dynamics (MD) simulations of crystallization require collecti

Jinxin Shi, Zongsheng Cao, Runmin Ma, Yusong Hu

The deep-research framework orchestrates external tools to perform complex, multi-step scientific reasoning that exceeds the native limits of a single large language model. However, it still suffers from context pollution, weak evidentiary support, and brittle execution paths. To address these issues, we propose DualResearch, a retrieval and fusion framework

Wenshuo Wang, Ziyou Jiang, Junjie Wang, Mingyang Li

Internet memes have emerged as a popular multimodal medium, yet they are increasingly weaponized to convey harmful opinions through subtle rhetorical devices like irony and metaphor. Existing detection approaches, including Multimodal Large Language Model (MLLM)-based techniques, struggle with these implicit expressions, leading to frequent misjudgments. Thi

Ashish Kattamuri, Ishita Prasad, Meetu Malhotra, Arpita Vats

Current Text-to-SQL methods are evaluated and only focused on executable queries, overlooking the semantic alignment challenge -- both in terms of the semantic meaning of the query and the correctness of the execution results. Even execution accuracy itself shows significant drops when moving from English to other languages, with an average decline of 6 perc

Zirui Liao

Cognitive neuroscience research indicates that humans leverage cues to activate entity-centered memory traces (engrams) for complex, multi-hop recollection. Inspired by this mechanism, we introduce EcphoryRAG, an entity-centric knowledge graph RAG framework. During indexing, EcphoryRAG extracts and stores only core entities with corresponding metadata, a lig

Lande Ma, Zhaokun Ma

For any real polynomial $p(x)$ of even degree $n$, Shapiro [{\it Arnold Math. J.} 1(1) (2015), 91--99] conjectured that the sum of the number of real zeros of $(n-1)(p')^2 - np p''$ and the number of real zeros of $p$ is positive. We resolve this conjecture completely: it holds in nine mutually exclusive cases and fails in four, as characterized by the root

Mobina Noori, Mahasweta Chakraborti, Amy X Zhang, Seth Frey

We study how open source communities describe participation and control through version controlled governance documents. Using a corpus of 710 projects with paired snapshots, we parse text into actors, rules, actions, and objects, then group them and measure change with entropy for evenness, richness for diversity, and Jensen Shannon divergence for drift. Pr

Nisha Pillai

Modern agricultural operations increasingly rely on integrated monitoring systems that combine multiple data sources for farm optimization. Aerial drone-based animal health monitoring serves as a key component but faces limited data availability, compounded by scene-specific issues such as small, occluded, or partially visible animals. Transfer learning appr

Junjie Lu, Xin Wu

In a quantum theory of gravity, a renormalization group improved Kerr metric is obtained from the Kerr metric, where the Newton gravitational constant is modified as a function of the radial distance. The motion of neutral test particles in this metric is integrable. However, the dynamics of charged test particles is nonintegrable when an external asymptotic

Cheng Ouyang, Moeen Ul Islam, Dong Chen, Kaixiang Zhang

Soft robots offer significant advantages in safety and adaptability, yet achieving precise and dynamic control remains a major challenge due to their inherently complex and nonlinear dynamics. Recently, Data-enabled Predictive Control (DeePC) has emerged as a promising model-free approach that bypasses explicit system identification by directly leveraging in

Zhihan Zhang, Xunkai Li, Yilong Zuo, Henan Sun

Text-attributed graphs (TAGs) have become a key form of graph-structured data in modern data management and analytics, combining structural relationships with rich textual semantics for diverse applications. However, the effectiveness of analytical models, particularly graph neural networks (GNNs), is highly sensitive to data quality. Our empirical analysis

Yingtie Lei, Zimeng Li, Chi-Man Pun, Yupeng Liu

Ultra-high-field 7T MRI offers enhanced spatial resolution and tissue contrast that enables the detection of subtle pathological changes in neurological disorders. However, the limited availability of 7T scanners restricts widespread clinical adoption due to substantial infrastructure costs and technical demands. Computational approaches for synthesizing 7T-

Li-Yue Zhang, Chao-Jian Wu, Xuan Fang, Wei Zhang

We report multi-fiber, medium-resolution spectroscopy of the Rosette Nebula with full spatial coverages, and present a table of the nebular parameters based on the spatially-resolved measurements of emission lines. These new observations were conducted through the Medium-Resolution Spectroscopic Survey of Nebulae (MRS-N) on the Large Sky Area Multi-Object Fi

Zhen Yang, Yansong Ma, Lei Chen

Trustworthy medical image segmentation aims at deliver accurate and reliable results for clinical decision-making. Most existing methods adopt the evidence deep learning (EDL) paradigm due to its computational efficiency and theoretical robustness. However, the EDL-based methods often neglect leveraging uncertainty maps rich in attention cues to refine ambig

Siyuan Chen, Minghao Guo, Caoliwen Wang, Anka He Chen

Biomolecular interaction modeling has been substantially advanced by foundation models, yet they often produce all-atom structures that violate basic steric feasibility. We address this limitation by enforcing physical validity as a strict constraint during both training and inference with a uniffed module. At its core is a differentiable projection that map

Shin-nosuke Ishikawa, Masato Todo, Taiki Ogihara, Hirotsugu Ohba

We present a tendency of large language models (LLMs) to generate absurd patterns despite their clear inappropriateness in a simple task of identifying regularities in number series. Several approaches have been proposed to apply LLMs to complex real-world tasks, such as providing knowledge through retrieval-augmented generation and executing multi-step task

Haroon Gharwi, Kai Shu

Real-world time series data exhibit non-stationary behavior, regime shifts, and temporally varying noise (heteroscedastic) that degrade the robustness of standard regression models. We introduce the Variability-Aware Recursive Neural Network (VARNN), a novel residual-aware architecture for supervised time-series regression that learns an explicit error memor

Jean Pierre Ndabakuranye, Inge W. G. Last, Kay Weng Choy, Peter Thurgood

Objective The concentration of bilirubin in blood or serum is useful for assessing liver function as well as monitoring treatment. This study evaluates the clinical performance of a novel point-of-care (PoC) device for the detection of bilirubin in serum. The PoC device incorporates an integrated miniature optoelectronic sensing module and a microfluidic tes

Jiaming Wang, Zhe Tang, Zehao Jin, Hefei Chen

As large language models (LLMs) are widely deployed as domain-specific agents, many benchmarks have been proposed to evaluate their ability to follow instructions and make decisions in real-world scenarios. However, business scenarios often involve complex standard operating procedures (SOPs), and the evaluation of LLM capabilities in such contexts has not b

WenHao Ma, Siqi Yang, Mingzhe Xie, Minghui Liu

Over the years, comprehensive experiments have shown a fact that the nucleons, such as the proton and neutron, are formed by not only the "valence" up and down quarks which were thought to comprise the nucleons in a simple constituent picture, but also "sea" quarks which can be any other flavour. However, it is still unknown how sea quarks are generated insi

Abel Beyene, Zhongpan Wu, Yunus Dawji, Karim Hammad

Hand-sized Deoxyribonucleic acid (DNA) sequencing machines are of growing importance in several life sciences fields as their small footprints enable a broader range of use cases than their larger, stationary counterparts. However, as currently designed, they lack sufficient embedded computing to process the large volume of measurements generated by their in

Haotian Deng, Siyuan He, Songlin Jia, Yuyan Bao

We present a flow-sensitive effect system for reachability types that supports explicit memory management, including Rust-style move semantics, in higher-order impure functional languages. Our system refines the existing reachability qualifier with polymorphic \emph{use} and \emph{kill} effects that record how references are read, written, transferred, and d

Zhen Yang, Yansong Ma, Lei Chen

Traditional Evidence Deep Learning (EDL) methods rely on static hyperparameter for uncertainty calibration, limiting their adaptability in dynamic data distributions, which results in poor calibration and generalization in high-risk decision-making tasks. To address this limitation, we propose the Meta-Policy Controller (MPC), a dynamic meta-learning framewo

Omer Gokalp Serbetci, Lei Chu, Andreas F. Molisch

Cognitive radio (CR) is an important technique for improving spectral efficiency, letting a secondary system operate in a wireless spectrum when the primary system does not make use of it. While it has been widely explored over the past 25 years, many common assumptions are not aligned with the realities of 5G networks. In this paper, we consider the CR prob

Connor Weinhouse, Jameson Augustin

Wildfires are becoming increasingly frequent and devastating, and therefore the technology to combat them must adapt accordingly. Modern predictive models have failed to balance predictive accuracy and operational viability, resulting in consistently delayed or misinformed fire suppression and public safety efforts. The present study addresses this gap by de

Zixi Yang, Jiapeng Li, Muxi Diao, Yinuo Jing

Recently, Multi-modal Large Language Models (MLLMs) have demonstrated significant performance across various video understanding tasks. However, their robustness, particularly when faced with manipulated video content, remains largely unexplored. In this paper, we introduce Ro-Bench, the first benchmark for evaluating MLLMs on dynamic out-of-distribution (OO

Yingyi Zhang, Pengyue Jia, Derong Xu, Yi Wen

Retrieval-Augmented Generation (RAG) critically depends on effective query expansion to retrieve relevant information. However, existing expansion methods adopt uniform strategies that overlook user-specific semantics, ignoring individual expression styles, preferences, and historical context. In practice, identical queries in text can express vastly differe

Ruixin Bai, Swarnav Mukhopadhyay, Michael Elliott, Ryan Gilbert

In this work, we demonstrate a passivation-free Ga-polar recessed-gate AlGaN/GaN HEMT on a sapphire substrate for W-band operation, featuring a 5.5 nm Al0.35Ga0.65N barrier under the gate and a 31 nm Al0.35Ga0.65N barrier in the gate access regions. The device achieves a drain current density of 1.8 A/mm, a peak transconductance of 750 mS/mm, and low gate le

Ashish Kattamuri, Harshwardhan Fartale, Arpita Vats, Rahul Raja

Data contamination poses a significant challenge to reliable LLM evaluation, where models may achieve high performance by memorizing training data rather than demonstrating genuine reasoning capabilities. We introduce RADAR (Recall vs. Reasoning Detection through Activation Representation), a novel framework that leverages mechanistic interpretability to det

Ruixuan Sun, Junyuan Wang, Sanjali Roy, Joseph A. Konstan

Natural language-based user profiles in recommender systems have been explored for their interpretability and potential to help users scrutinize and refine their interests, thereby improving recommendation quality. Building on this foundation, we introduce a human-AI collaborative profile for a movie recommender system that presents editable personalized int

Yunrui Guan, Krishnakumar Balasubramanian, Shiqian Ma

We study generative modeling on convex domains using flow matching and mirror maps, and identify two fundamental challenges. First, standard log-barrier mirror maps induce heavy-tailed dual distributions, leading to ill-posed dynamics. Second, coupling with Gaussian priors performs poorly when matching heavy-tailed targets. To address these issues, we propos

Yushuo Zheng, Zicheng Zhang, Xiongkuo Min, Huiyu Duan

Existing benchmarks for large multimodal models (LMMs) often fail to capture their performance in real-time, adversarial environments. We introduce LM Fight Arena (Large Model Fight Arena), a novel framework that evaluates LMMs by pitting them against each other in the classic fighting game Mortal Kombat II, a task requiring rapid visual understanding and ta

Yu Fu, Berndt Müller, Chathuranga Sirimanna

We present a study of medium-induced modifications to the energy-energy correlator (EEC) for jets in cold nuclear matter. For electron-nucleus collisions, at leading order in the QCD coupling and in the jet-medium interaction, we derive an analytic expression for the EEC modification as a function of the opening angle and show that the modification is strong

Laura W. Brenneman, Daniel R. Wilkins, Anna Ogorzałek, Daniele Rogantini

We present a time-averaged spectral analysis of the 2024 XRISM observation of the narrow-line Seyfert-1 galaxy MCG--6-30-15, taken contemporaneously with XMM-Newton and NuSTAR. Our analysis leverages a unique combination of broadband and high-resolution X-ray spectroscopy to definitively isolate and characterize both broad and narrow emission and absorption

Han Hu, Zhuoran Zheng, Chen Lyu

Knowledge distillation (KD) attacks pose a significant threat to deep model intellectual property by enabling adversaries to train student networks using a teacher model's outputs. While recent defenses in image classification have successfully disrupted KD by perturbing output probabilities, extending these methods to image restoration is difficult. Unlike

Jonah Botvinick-Greenhouse, Wael H. Ali, Mouhacine Benosman, Saviz Mowlavi

We introduce adaptive-basis physics-informed neural networks (AB-PINNs), a novel approach to domain decomposition for training PINNs in which existing subdomains dynamically adapt to the intrinsic features of the unknown solution. Drawing inspiration from classical mesh refinement techniques, we also modify the domain decomposition on-the-fly throughout trai

Yu Fu, Tharun Krishna, Weiyao Ke, Steffen A. Bass

We develop a comprehensive model for heavy-quark evolution in a realistic QGP, from their production in the initial collision to hadronic freeze-out. Heavy-quark transport is described by a Langevin approach including medium-induced radiation, coupled to a 2+1D viscous hydrodynamic bulk evolution. Transport coefficients are obtained from non-perturbative $T$

Yufei Song, Ziqi Zhou, Qi Lu, Hangtao Zhang

Segmentation models exhibit significant vulnerability to adversarial examples in white-box settings, but existing adversarial attack methods often show poor transferability across different segmentation models. While some researchers have explored transfer-based adversarial attack (i.e., transfer attack) methods for segmentation models, the complex contextua

Tianhao Shao, Jinhui Chen, Yu-Gang Ma, Josef Pochodzalla

An electron scattering experiment to search for the trineutron state $^3n$ by reaction ${\rm ^4He}(e,~e'p\pi^{+})^{3}n$ is designed for the A1 facility at Mainzer Microtron. The detailed principles, setup, and simulation of this experiment are presented. With the momenta of the scattered electron, the produced proton and $\pi^+$ from the reaction measured by

Chunsong Chen, Yichen Hou, Huan Chen, Junlin Li

Rapid urbanization in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has created urgent demand for high-resolution, building-level functional data to support sustainable spatial planning. Existing land use datasets suffer from coarse granularity and difficulty in capturing intra-block heterogeneity. To this end, we present the Greater Bay Area Urban Bu

Yuting Yang, Gang Mei, Zhengjing Ma, Nengxiong Xu

Small Earth data are geoscience observations with limited short-term monitoring variability, providing sparse but meaningful measurements, typically exhibiting spatiotemporal correlations. Spatiotemporal forecasting on such data is crucial for understanding geoscientific processes despite their small scale. However, conventional deep learning models for spat

Daiki Yoshikawa, Takashi Matsubara

Vision-language models have achieved remarkable success in multi-modal representation learning from large-scale pairs of visual scenes and linguistic descriptions. However, they still struggle to simultaneously express two distinct types of semantic structures: the hierarchy within a concept family (e.g., dog $\preceq$ mammal $\preceq$ animal) and the compos

Brandon Lit, Edward Crowder, Daniel Vogel, Hassan Khan

AI chatbots are an emerging security attack vector, vulnerable to threats such as prompt injection, and rogue chatbot creation. When deployed in domains such as corporate security policy, they could be weaponized to deliver guidance that intentionally undermines system defenses. We investigate whether users can be tricked by a compromised AI chatbot in this

Hideaki Kim, Tomoharu Iwata

The representer theorem is a cornerstone of kernel methods, which aim to estimate latent functions in reproducing kernel Hilbert spaces (RKHSs) in a nonparametric manner. Its significance lies in converting inherently infinite-dimensional optimization problems into finite-dimensional ones over dual coefficients, thereby enabling practical and computationally

Nicholas Deas, Kathleen McKeown

We introduce and study artificial impressions--patterns in LLMs' internal representations of prompts that resemble human impressions and stereotypes based on language. We fit linear probes on generated prompts to predict impressions according to the two-dimensional Stereotype Content Model (SCM). Using these probes, we study the relationship between impressi

Shulin He, Zhong-Qiu Wang

Blind speech separation (BSS) aims to recover multiple speech sources from multi-channel, multi-speaker mixtures under unknown array geometry and room impulse responses. In unsupervised setup where clean target speech is not available for model training, UNSSOR proposes a mixture consistency (MC) loss for training deep neural networks (DNN) on over-determine

Xiao-Tong Chen, Guang-Xing Li

Young stars form in clusters within molecular clouds, but older stars are evenly distributed across the galactic disk, necessitating an explanation for cluster dissolution. We analytically study tidal forces from cold molecular clouds as a key mechanism for accelerated cluster disruption. Cloud tides, caused by the gravitational pull of the parent cloud alon

Jijie Zhou, Yuhan Hu

Recently, large language models have facilitated the emergence of highly intelligent conversational AI capable of engaging in human-like dialogues. However, a notable distinction lies in the fact that these AI models predominantly generate responses rapidly, often producing extensive content without emulating the thoughtful process characteristic of human co

Guoqiang Liu, Ruijun Xie

Amid China's dual-carbon transition, the synergistic alignment of green finance with green-technology innovation is pivotal for co-controlling pollution and CO2 emissions. Using panel data for 266 Chinese prefecture-level cities over 2007-2023, We construct the coupling coordination index system of green finance and green technology innovation via a coupling

Maoxin Ji, Tong Wang, Qiong Wu, Pingyi Fan

Addressing the problem of Age of Information (AoI) deterioration caused by packet collisions and vehicle speed-related channel uncertainties in Semi-Persistent Scheduling (SPS) for the Internet of Vehicles (IoV), this letter proposes an optimization approach based on Large Language Models (LLM) and Deep Deterministic Policy Gradient (DDPG). First, an AoI cal

Min Huang, Ying-Ying Li, Yandong Liu, Hao Zhang

Sphaleron dynamics in the Standard Model at high-energy particle collisions remains experimentally unobserved, with theoretical predictions hindered by its nonperturbative real-time nature. In this work, we investigate a quantum simulation approach to this challenge. Taking the $1+1$D $O(3)$ model as a protocol towards studying dynamics of sphaleron in the e

Qiyuan Zhang, Juncheng Guo, Juchang Zou, Rongxiang Luo

We investigate how gravity influences negative differential thermal resistance (NDTR) in fluids modeled by multiparticle collision dynamics. In the integrable case, we derive the heat flux formula for the system exhibiting the NDTR effect, and show that by introducing a gravity along the direction of the thermodynamic force, the temperature difference requir

Di Zhang

The stochastic multi-armed bandit (MAB) problem is one of the most fundamental models in sequential decision-making, with the core challenge being the trade-off between exploration and exploitation. Although algorithms such as Upper Confidence Bound (UCB) and Thompson Sampling, along with their regret theories, are well-established, existing analyses primari

Xin Liu, Runsong Zhao, Pengcheng Huang, Xinyu Liu

Context compression is an advanced technique that accelerates large language model (LLM) inference by converting long inputs into compact representations. Existing methods primarily rely on autoencoding tasks to train special compression tokens to represent contextual semantics. While autoencoding tasks enable compression tokens to acquire compression capabi

Timothy Gargett, Igor Samsonov

We develop the analytic action principle in the $\mathcal{N}=2$ AdS$_4$ harmonic superspace and apply it for studying the component structure of the free $q$-hypermultiplet model.

Min Zhao, Jiangang Qi, Xiao Chen

In this paper, further to the point interaction method for inverse Sturm-Liouville problems on finite intervals firstly proposed in our previous work, we will continue to generalize this method to the inverse eigenvalue problems for singular Sturm-Liouville problems on the half real axis.

Xing-Yu Wang, En-Qi Bao, Su-Yang Shen, Jun-Hui Yuan

Two-dimensional (2D) kagome materials have drawn extensive research interest due to their unique electronic properties, like flat bands, magnetic frustration, and topological quantum states, which enable precise quantum state control and novel device innovation. Yet, simultaneously achieving high stability, tunability, and multifunctionality in 2D kagome sys

Tengxiao Lv, Ling Luo, Juntao Li, Yanhua Wang

Accurate recognition of biomedical named entities is critical for medical information extraction and knowledge discovery. However, existing methods often struggle with nested entities, entity boundary ambiguity, and cross-lingual generalization. In this paper, we propose a unified Biomedical Named Entity Recognition (BioNER) framework based on Large Language

Ronan John, Anis Chihoub, Ryan Meegan, Gina Sidelli

Change monitoring is an essential task for cranberry farming as it provides both breeders and growers with the ability to analyze growth, predict yield, and make treatment decisions. However, this task is often done manually, requiring significant time on the part of a cranberry grower or breeder. Deep learning based change monitoring holds promise, despite

Zeyu Wang, Tianyi Jiang, Huanchang Ma, Yao Lu

AI-assisted molecular property prediction has become a promising technique in early-stage drug discovery and materials design in recent years. However, due to high-cost and complex wet-lab experiments, real-world molecules usually experience the issue of scarce annotations, leading to limited labeled data for effective supervised AI model learning. In light

Junxi Yin, Haisen Luo, Zhenyu Li, Yihua Liu

While Reinforcement Learning with Verifiable Rewards (RLVR) enhances complex reasoning in LLMs, current methods struggle to balance exploration and exploitation. This leads to critical issues like inaccurate credit assignment for intermediate steps and premature entropy collapse, limiting model performance. To address this, we introduce Attribution-based Con

Soumojit Das, Dilshanie Deepawansa, Partha Lahiri

Many countries measure poverty based only on income or consumption. However, there is a growing awareness of measuring poverty through multiple dimensions that captures a more reasonable status of poverty. Estimating poverty measure(s) for small geographical areas, commonly referred to as poverty mapping, is challenging due to small or no sample for the smal

Wei Luo, Sihan Deng, Muting Xie, Junyi Ji

Ferroelectricity is a cornerstone of functional materials research, enabling diverse technologies from non-volatile memory to optoelectronics. Recently, type-I integer quantum ferroelectricity (IQFE), unconstrained by symmetry, has been proposed and experimentally demonstrated; however, as it arises from ionic displacements of an integer lattice vector, the

Suming Qiu, Jing Li, Zhicheng Zhou, Junjie Huang

We present HES-SQL, a novel hybrid training framework that advances Text-to-SQL generation through the integration of thinking-mode-fused supervised fine-tuning (SFT) with Group Relative Policy Optimization (GRPO). Our approach introduces three key innovations: (1) a skeleton-completeness scoring mechanism that enhances preference alignment between generated

Christian Borgs, Karissa Huang, Geng Zhao

As the world grows increasingly connected, infectious disease transmission and outbreaks have become a pressing global concern for public health officials and policymakers. While policy interventions to contain and prevent the spread of disease have been proposed and implemented, there has been little rigorous quantitative analysis of the effectiveness of su

Christopher J. Paciorek, Daniel Cooley

Weather extremes produce major impacts on society and ecosystems and are likely to change in likelihood and magnitude with climate change. However, very low probability events are hard to characterize statistically using observations or even climate model output because of short records/runs. For precipitation, consideration of such events arises in quantify

Haomin Zhuang, Yujun Zhou, Taicheng Guo, Yue Huang

Reinforcement Learning has demonstrated substantial improvements in the reasoning abilities of Large Language Models (LLMs), exhibiting significant applicability across various domains. Recent research has identified that tokens within LLMs play distinct roles during reasoning tasks, categorizing them into high-entropy reasoning tokens and low-entropy knowle

Xiangsen Qin

This paper establishes quantitative Carleman-type inequalities for holomorphic sections of Hermitian vector bundles over bounded domains in $\mathbb{C}^n$ with $n \geq 2$. We first prove a Sobolev-type inequality with explicit constants for the Laplace operator, which leads to quantitative Carleman-type estimates for holomorphic functions. These results are

Yashodip Dharmendra Jagtap, Aaditya Ganesh Bagul

Electronic waste (e-waste) is a rapidly growing global problem caused by shorter device lifecycles and rising consumption. India ranks third globally in e-waste generation, producing over 1.7 million tonnes in 2023-24, of which less than half is formally processed. To address this, we propose Green Grid, an integrated AI-powered e-waste management platform c

Zijian Zhang, Mingyao Cui

In recent years, densifying multiple-input multiple-output (MIMO) has attracted much attention from the communication community. Thanks to the subwavelength antenna spacing, the strong correlations among densifying antennas provide sufficient prior knowledge about channel state information (CSI). This inspires the careful design of observation matrices (e.g.

Shomir Wilson

Computing faculty at research universities are often expected to guide the work of undergraduate and graduate student researchers. This guidance is typically called advising or mentoring, but these terms belie the complexity of the relationship, which includes several related but distinct roles. I examine the guidance of student researchers in computing (abb

Alexandre Lopes, Catarina Barata, Plinio Moreno

In-Hand Manipulation, as many other dexterous tasks, remains a difficult challenge in robotics by combining complex dynamic systems with the capability to control and manoeuvre various objects using its actuators. This work presents the application of a previously developed hybrid Reinforcement Learning (RL) Framework to In-Hand Manipulation task, verifying

Anupam Gupta, Roie Levin

In the submodular cover problem, we are given a monotone submodular function $f$, and we want to pick the min-cost set $S$ such that $f(S) = f(N)$. Motivated by problems in network monitoring and resource allocation, we consider the submodular cover problem in an online setting. As a concrete example, suppose at each time $t$, a nonnegative monotone submodul

Haolin Liu, Chen-Yu Wei, Julian Zimmert

We study decision making with structured observation (DMSO). Previous work (Foster et al., 2021b, 2023a) has characterized the complexity of DMSO via the decision-estimation coefficient (DEC), but left a gap between the regret upper and lower bounds that scales with the size of the model class. To tighten this gap, Foster et al. (2023b) introduced optimistic

Gregory Snyder, Chrisy Xiyu Du

Patchy particles have proven to be a prominent model for studying the self-assembly behavior of various systems, ranging from finite clusters to bulk crystal assemblies, and from synthetic colloidal particles to viruses. The patchy particle model is flexible, but it also comes with its own pitfalls -- the potential design space is infinite. Many efforts have

Baoshan Song, Xiao Xia, Penggao Yan, Yihan Zhong

Accurate calibration of intrinsic (odometer scaling factors) and extrinsic parameters (IMU-odometer translation and rotation) is essential for autonomous ground vehicle localization. Existing GNSS-aided approaches often rely on positioning results or raw measurements without ambiguity resolution, and their observability properties remain underexplored. This

Yuxuan Jiang, Zehua Chen, Zeqian Ju, Yusheng Dai

Text-to-audio (TTA) generation with fine-grained control signals, e.g., precise timing control or intelligible speech content, has been explored in recent works. However, constrained by data scarcity, their generation performance at scale is still compromised. In this study, we recast controllable TTA generation as a multi-task learning problem and introduce

Arpit Narechania, Alex Endert, Clio Andris

When creating choropleth maps, mapmakers often bin (i.e., group, classify) quantitative data values into groups to help show that certain areas fall within a similar range of values. For instance, a mapmaker may divide counties into groups of high, middle, and low life expectancy (measured in years). It is well known that different binning methods (e.g., nat

Md Habibur Rahman, Md Sharif Hossen, Nathan H. Stephenson, Vijay K. Shah

The open radio access network (O-RAN) enables modular, intelligent, and programmable 5G network architectures through the adoption of software-defined networking, network function virtualization, and implementation of standardized open interfaces. However, one of the security concerns for O-RAN, which can severely undermine network performance, is jamming at

M. Boboqambarova, A. V. Nazarov

We have created an innovative natural thermostat algorithm to mimic the direct impact of temperature on interatomic distances in both a perfect crystal and a system containing a vacancy. Unlike previous research, our findings demonstrate that in a system with a defect, the radii of the initial ten coordination spheres increase almost linearly with temperatur

Kostiantyn Bevziuk, Andrii Fatula, Svetozar Lashin Yaroslav Opanasenko, Anna Tukhtarova

We present a repository decomposition system that converts large software repositories into a vectorized knowledge graph which mirrors project architectural and semantic structure, capturing semantic relationships and allowing a significant level of automatization of further repository development. The graph encodes syntactic relations such as containment, i

Zongze Wu, Yani Guo, Churong Liang, Runnan Li

Despite remarkable advances in Large Language Model capabilities, tool retrieval for agent-based systems remains fundamentally limited by reliance on semantic similarity, which fails to capture functional viability. Current methods often retrieve textually relevant but functionally inoperative tools due to parameter mismatches, authentication failures, and e

N. Nazari Pooya

In this study, we employ a two-step method to analyze models of holographic dark energy (HDE) and interacting holographic dark energy (IHDE), incorporating three distinct dark energy (DE)-dark matter (DM) interaction terms. First, using the latest background dataset, we conduct a Markov chain Monte Carlo (MCMC) analysis to constrain the free parameters of th

Benjamin Brock, Renato Golin

Many important applications across science, data analytics, and AI workloads depend on distributed matrix multiplication. Prior work has developed a large array of algorithms suitable for different problem sizes and partitionings including 1D, 2D, 1.5D, and 2.5D algorithms. A limitation of current work is that existing algorithms are limited to a subset of p

Md Habibur Rahman, Md Sharif Hossen, Nathan H. Stephenson, Vijay K. Shah

The open radio access network (O-RAN) enables modular, intelligent, and programmable 5G network architectures through the adoption of software-defined networking (SDN), network function virtualization (NFV), and implementation of standardized open interfaces. It also facilitates closed loop control and (non/near) real-time optimization of radio access networ

Haoran Jin, Jirong Yang, Yunpeng Liu, Barry Lyu

Modern AI acceleration faces a fundamental challenge: conventional assumptions about memory requirements, batching effectiveness, and latency-throughput tradeoffs are systemwide generalizations that ignore the heterogeneous computational patterns of individual neural network operators. However, going towards network-level customization and operator-level het

Pedro Ramonetti, Melissa Floca, Kate O'Laughlin, Amarnath Gupta

As demand for AI literacy and data science education grows, there is a critical need for infrastructure that bridges the gap between research data, computational resources, and educational experiences. To address this gap, we developed a first-of-its-kind Education Hub within the National Data Platform. This hub enables seamless connections between collabora

Priyobrata Mondal, Faizanuddin Ansari, Swagatam Das

Class imbalance, where certain classes have insufficient data, poses a critical challenge for robust classification, often biasing models toward majority classes. Distribution calibration offers a promising avenue to address this by estimating more accurate class distributions. In this work, we propose Rebalancing with Calibrated Sub-classes (RCS) - a novel

Siqi Zhu, David Zhang, Pedro Cisneros-Velarde, Jiaxuan You

Large Language Models (LLMs) have achieved remarkable progress in reasoning, yet sometimes produce responses that are suboptimal for users in tasks such as writing, information seeking, or providing practical guidance. Conventional alignment practices typically assume that maximizing model reward also maximizes user welfare, but this assumption frequently fa

Sven Cats, John Michael Clark, Charlotte Dombrowsky, Mar Curco Iranzo

In this short note, we give a method for computing a non-torsion point of smallest canonical height on a given elliptic curve $E/\mathbb{Q}$ over all number fields of a fixed degree. We then describe data collected using this method, and investigate related conjectures of Lehmer and Lang using these data.

Krzysztof Mrozinski, Minji Kang, Ahmed Khota, Vincent Michael Sutanto

Quality estimation (QE) reranking is a form of quality-aware decoding which aims to improve machine translation (MT) by scoring and selecting the best candidate from a pool of generated translations. While known to be effective at the sentence level, its application to the increasingly prominent domain of document-level translation remains underexplored. In

Nicholas LaHaye, Thilanka Munashinge, Hugo Lee, Xiaohua Pan

This work demonstrates the possibilities for improving wildfire and air quality management in the western United States by leveraging the unprecedented hourly data from NASA's TEMPO satellite mission and advances in self-supervised deep learning. Here we demonstrate the efficacy of deep learning for mapping the near real-time hourly spread of wildfire fr

Aashish Dhawan, Pankaj Bodani, Vishal Garg

The output of image the segmentation process is usually not very clear due to low quality features of Satellite images. The purpose of this study is to find a suitable Conditional Random Field (CRF) to achieve better clarity in a segmented image. We started with different types of CRFs and studied them as to why they are or are not suitable for our purpose.

Aflatoun Amouzandeh, Klaus Jansen, Lis Pirotton, Rob van Stee

We consider the problem of minimizing the weighted makespan on a single machine with restarts. Restarts are similar to preemptions but weaker: a job can be interrupted, but then it has to be run again from the start instead of resuming at the point of interruption later. The objective is to minimize the weighted makespan, defined as the maximum weighted comp

Bernardo Araneda, James Lucietti

We prove that the only smooth, Ricci flat, ALE instanton with a toric Hermitian non-Kähler structure is the Eguchi-Hanson instanton. The proof is analogous to the classification of toric Hermitian ALF instantons by Biquard and Gauduchon, although we avoid the use of toric Kähler geometry and instead perform a direct global analysis of the Tod form of the met

Kranthi Kumar Bestha, Manaswini Sahoo, Niccolò Francini, Robert Kluge

We report a rich anisotropic magnetic phase diagram of Na$_3$Co$_2$SbO$_6$, a previously proposed cobaltate Kitaev candidate, based on field- and temperature-dependent magnetization, specific heat, and magnetocaloric effect studies. At low temperatures, our experiments uncover a low-lying $j_{\textrm{eff}} = \frac{1}{2}$ state with an antiferromagnetic groun