October 2025 arXiv papers — page 216

Jiashuo Sun, Shixuan Liu, Zhaochen Su, Xianrui Zhong

Prevailing methods for training Large Language Models (LLMs) as text encoders rely on contrastive losses that treat the model as a black box function, discarding its generative and reasoning capabilities in favor of static embeddings. We introduce GRACE (Generative Representation Learning via Contrastive Policy Optimization), a novel framework that reimagine

Gábor Benedek, Tibor Krisztin

This paper considers a class of delay differential equations with unimodal feedback and describes the structure of certain unstable sets of stationary points and periodic orbits. These unstable sets consist of heteroclinic connections from stationary points and periodic orbits to stable stationary points, stable periodic orbits and some more complicated comp

Zijing Hu, Yunze Tong, Fengda Zhang, Junkun Yuan

Diffusion models have achieved impressive results in generating high-quality images. Yet, they often struggle to faithfully align the generated images with the input prompts. This limitation is associated with synchronous denoising, where all pixels simultaneously evolve from random noise to clear images. As a result, during generation, the prompt-related re

Shuai Zhao, Xinyi Wu, Shiqian Zhao, Xiaobao Wu

During fine-tuning, large language models (LLMs) are increasingly vulnerable to data-poisoning backdoor attacks, which compromise their reliability and trustworthiness. However, existing defense strategies suffer from limited generalization: they only work on specific attack types or task settings. In this study, we propose Poison-to-Poison (P2P), a general

Chiun-Chuan Chen, Ting-Yang Hsiao, Shun-Chieh Wang

We investigate traveling wave solutions in the two-species reaction-diffusion Lotka-Volterra competition system under weak competition. For the strict weak competition regime $(b<a<1/c,\,d>0)$, we construct refined upper and lower solutions combined with the Schauder fixed point theorem to establish the existence of traveling waves for all wave speeds $s\geq

Qiao Wang, Adnan Labib, Robert Swier, Michael Hofmeyr

GenQuest is a generative text adventure game that leverages Large Language Models (LLMs) to facilitate second language learning through immersive, interactive storytelling. The system engages English as a Foreign Language (EFL) learners in a collaborative "choose-your-own-adventure" style narrative, dynamically generated in response to learner choices. Game

Jyotirmoy Ganguly, Digjoy Paul, Amritanshu Prasad, K N Raghavan

A Kronecker coefficient is the multiplicity of an irreducible representation of a finite group $G$ in a tensor product of irreducible representations. We define Kronecker Hecke algebras and use them as a tool to study Kronecker coefficients in finite groups. We show that the number of simultaneous conjugacy classes in a finite group $G$ is equal to the sum o

Wenda Xie, Chao Guo, Yanqing Jing, Junle Wang

Although LLMs have been widely adopted for creative content generation, a single-pass process often struggles to produce high-quality long narratives. How to effectively revise and improve long narrative scripts like scriptwriters remains a significant challenge, as it demands a comprehensive understanding of the entire context to identify global structural

Vandan Patel, Juhi Oudichhya, Ajay Kumar Rai

In this paper, we explore the mass spectra of $qq\bar{q}\bar{q}$, $ss\bar{s}\bar{s}$ and $qq\bar{s}\bar{s}$ tetraquarks by employing Regge phenomenology. We calculate the range for ground state masses of $qq\bar{s}\bar{s}$ tetraquarks, and estimate the Regge parameters for their trajectories in $(J,M^2)$ plane. Using these Regge parameters we have calculated

Yingche Liu, Mengyang Li

The massive digital footprints generated by bike-sharing systems in megacities like Shanghai offer a novel perspective on the urban socio-economic fabric. This study investigates whether these daily mobility patterns can quantitatively map the city's underlying social stratification. To overcome the persistent challenge of acquiring fine-grained socio-econom

Napasorn Tevarut, Brittany Reid, Yutaro Kashiwa, Pattara Leelaprute

Trivial packages, small modules with low functionality, are common in the npm ecosystem and can pose security risks despite their simplicity. This paper refines existing definitions and introduce data-only packages that contain no executable logic. A rule-based static analysis method is developed to detect trivial and data-only packages and evaluate their pr

Ningzhi Tang, David Meininger, Gelei Xu, Yiyu Shi

Code modification requires developers to comprehend code, plan changes, articulate intent, and validate outcomes, making it cognitively demanding. While natural language (NL) code summaries offer a promising external representation of this process, existing approaches remain limited. Systems grounded in exploratory data analysis are restricted to narrow doma

Jinyan Su, Claire Cardie, Jennifer Healey

Multi-hop question answering is a challenging task for both large language models (LLMs) and humans, as it requires recognizing when multi-hop reasoning is needed, followed by reading comprehension, logical reasoning, and knowledge integration. To better understand how humans might collaborate effectively with AI, we evaluate the performance of crowd workers

Zhou Zhang, Yizhu Wang, Saman Atapattu, Sumei Sun

Caching is crucial in hybrid satellite-terrestrial networks to reduce latency, optimize throughput, and improve data availability by storing frequently accessed content closer to users, especially in bandwidth-limited satellite systems, requiring strategic Medium Access Control (MAC) layer. This paper addresses throughput optimization in satellite-terrestria

Akshay Govind Srinivasan, Vikas Dwivedi, Balaji Srinivasan

Partial differential equation (PDE) solvers are fundamental to engineering simulation. Classical mesh-based approaches (finite difference/volume/element) are fast and accurate on high-quality meshes but struggle with higher-order operators and complex, hard-to-mesh geometries. Recently developed physics-informed neural networks (PINNs) and their variants are

Jiachen Xu, Jian Qian, Zijun Gao

We study the design of experiments with multiple treatment levels, a setting common in clinical trials and online A/B/n testing. Unlike single-treatment studies, practical analyses of multi-treatment experiments typically first select a winning treatment, and then only estimate the effect therein. Motivated by this analysis paradigm, we propose a design for

Edward Y. Chang, Ethan Y. Chang

Multi-agent debate often wastes compute by using a fixed adversarial stance, aggregating without deliberation, or stopping on heuristics. We introduce MACI, an active controller with two independent dials that decouple information from behavior: an information dial that gates evidence by quality, and a behavior dial that schedules contentiousness from explor

Aditya Gulati, Yao-Ting Lin, Tomoyuki Morimae, Shogo Yamada

Pseudorandom functions (PRFs) are one of the most fundamental primitives in classical cryptography. On the other hand, in quantum cryptography, it is possible that PRFs do not exist but their quantum analogues could exist, and still enabling many applications including SKE, MACs, commitments, multiparty computations, and more. Pseudorandom unitaries (PRUs) [

Tomas Basile, Natascha Leijnse, Malte Slot Lauridsen, Younes Farhangi Barooji

Distinguishing active from passive dynamics is a fundamental challenge in understanding the motion of living cells and other active matter systems. Here, we introduce a framework that combines physical modeling, analytical theory, and machine learning to identify and characterize active fluctuations from trajectory data. We train a long short-term memory (LS

Hao Fang, Zechao Zhan, Weixin Feng, Ziwei Huang

Recent advances in image generation and editing technologies have enabled state-of-the-art models to achieve impressive results in general domains. However, when applied to e-commerce scenarios, these general models often encounter consistency limitations. To address this challenge, we introduce TBStar-Edit, an new image editing model tailored for the e-comm

Xia Liao, Xiping Zhang

In this paper we give a characterization of strongly Euler homogeneous singular points on a reduced complex projective hypersurface $D=V(f)\subset \PP^n$ using the Jacobian syzygies of $f$. The characterization compares the ranks of the first syzygy matrices of the global Jacobian ideal $J_f$ and its quotient $J_f/(f)$. When $D$ has only isolated singulariti

Fahed Quttainah

This paper explores the distinctions and connections between cybersecurity and ethical hacking, two vital disciplines in the protection of digital systems. It defines each field, outlines their goals and methodologies, and compares the academic and professional paths available to aspiring students. Cybersecurity is presented as a defensive discipline focused

Otis Chodosh

We discuss applications of minimal surfaces to comparison geometry.

Yunuo Cen, Zixuan Wang, Jintao Zhang, Zhiwei Zhang

The Constraint-satisfaction problem (CSP) is fundamental in mathematics, physics, and theoretical computer science. Continuous local search (CLS) solvers, as recent advancements, can achieve highly competitive results on certain classes of Boolean satisfiability (SAT) problems. Motivated by these advances, we extend the CLS framework from Boolean SAT to gene

Nonghai Zhang, Zeyu Zhang, Jiazi Wang, Yang Zhao

Vision-Language Models (VLMs) have achieved significant progress in multimodal understanding tasks, demonstrating strong capabilities particularly in general tasks such as image captioning and visual reasoning. However, when dealing with specialized cultural heritage domains like 3D vase artifacts, existing models face severe data scarcity issues and insuffi

Hongli Zhao, Mihai Anitescu, Sen Na

We present an optimize-then-discretize framework for solving linear-quadratic optimal control problems (OCP) governed by time-inhomogeneous ordinary differential equations (ODEs). Our method employs a modified overlapping Schwarz decomposition based on the Pontryagin Minimum Principle, partitioning the temporal domain into overlapping intervals and independe

Soo Yong Kim, Suin Cho, Vincent-Daniel Yun, Gyeongyeon Hwang

Bridging clinical diagnostic reasoning with AI remains a central challenge in medical imaging. We introduce MedCLM, an automated pipeline that converts detection datasets into large-scale medical visual question answering (VQA) data with Chain-of-Thought (CoT) reasoning by linking lesion boxes to organ segmentation and structured rationales. These contextual

Tomas Figliolia, Nicholas Alonso, Rishi Iyer, Quentin Anthony

Multi-headed Attention's (MHA) quadratic compute and linearly growing KV-cache make long-context transformers expensive to train and serve. Prior works such as Grouped Query Attention (GQA) and Multi-Latent Attention (MLA) shrink the cache, speeding decode, but leave compute, which determines prefill and training speed, largely unchanged. We introduce Compre

Matheus M. Castro, Gary Froyland

Let $\Omega$ and $M$ be compact smooth manifolds and let $\Theta:\Omega\times M\to\Omega\times M$ be a $\mathcal C^{1+\alpha}$ skew-product diffeomorphism over a transitive Anosov base. We show that $\Theta$ has at most countably many ergodic hyperbolic measures of maximal relative entropy. When $\dim M=2$, if $\Theta$ has positive relative topological entro

Gloria Xiaodan Zhang, Yijia Wang, Taro Leo Nakajima, Katie Seaborn

Mobile games have gained immense popularity due to their accessibility, allowing people to play anywhere, anytime. Dark patterns and deceptive designs (DPs) have been found in these and other gaming platforms within certain cultural contexts. Here, we explored DPs in the onboarding experiences of free-to-play mobile games from China and Japan. We identified

Mohamed El-Dosuky

The Internet of Things (IoT) has revolutionized various applications including agriculture, but it still faces challenges in data collection and understanding. This paper proposes a real-time framework with three additional semantic layers to help IoT devices and sensors comprehend data meaning and source. The framework consists of six layers: perception, se

Gang Li, Yan Chen, Ming Lin, Tianbao Yang

Recent large reasoning models (LRMs) driven by reinforcement learning algorithms (e.g., GRPO) have achieved remarkable performance on challenging reasoning tasks. However, these models suffer from overthinking, generating unnecessarily long and redundant reasoning even for simple questions, which substantially increases computational cost and response latenc

Baber Jan, Saeed Anwar, Aiman H. El-Maleh, Abdul Jabbar Siddiqui

Camouflaged object detection segments objects with intrinsic similarity and edge disruption. Current detection methods rely on accumulated complex components. Each approach adds components such as boundary modules, attention mechanisms, and multi-scale processors independently. This accumulation creates a computational burden without proportional gains. To m

Khartik Uppalapati, Shakeel Abdulkareem, Bora Yimenicioglu

We propose RareGraph-Synth, a knowledge-guided, continuous-time diffusion framework that generates realistic yet privacy-preserving synthetic electronic-health-record (EHR) trajectories for ultra-rare diseases. RareGraph-Synth unifies five public resources: Orphanet/Orphadata, the Human Phenotype Ontology (HPO), the GARD rare-disease KG, PrimeKG, and the FDA

Carlos A. Alfaro, Jesús Uriel Medrano, Iván Téllez Téllez

Graham-Lov\'asz-Pollak \cite{GL,GP} obtained the celebrated formula $$\det({\sf D}(T_{n+1}))=(-1)^nn2^{n-1},$$ for the determinant of the distance matrix ${\sf D}(T_{n+1})$ for any tree $T_{n+1}$ with $n+1$ vertices. Later, Hou and Woo \cite{HW} extended this formula to the Smith normal form (SNF) obtaining that $\SNF({\sf D}(T_{n+1}))={\sf I}_2\oplus 2{\sf

Quan Tran, Suresh S. Muknahallipatna, Dongliang Duan, Nga Nguyen

Dynamic contingency screening is a challenging task in dynamic security assessment, when traditional numerical approaches are computationally intensive and often not able to repeatedly solve full AC power flow for all possible contingencies in real time, especially for large-scale power grids. Moreover, the severity caused by a contingency is not identical f

Wenqi Yan, Toby Murray, Benjamin I. P. Rubinstein, Van-Thuan Pham

We present DynamiQ, a full-fledged and optimized successor to AFLTeam that supports dynamic and adaptive parallel fuzzing. Unlike most existing approaches that treat individual seeds as tasks, DynamiQ leverages structural information from the program's call graph to define tasks and continuously refines task allocation using runtime feedback. This design sig

Asif Mohammed Samir, Mohammad Masudur Rahman

Despite decades of research, software bug localization remains challenging due to heterogeneous content and inherent ambiguities in bug reports. Existing methods, such as Information Retrieval (IR)-based approaches, often attempt to match source documents to bug reports, overlooking the context and semantics of the source code. On the other hand, Large Langu

Liang Wu, Yunhong Che, Richard D. Braatz, Jan Drgona

Minimizing both the worst-case and average execution times of optimization algorithms is equally critical in real-time optimization-based control applications such as model predictive control (MPC). Most MPC solvers have to trade off between certified worst-case and practical average execution times. For example, our previous work [1] proposed a full-Newton

Ian Baxter, Hamid Pahlavan, Pedram Hassanzadeh, Katharine Rucker

Physics-based atmosphere-land models with prescribed sea surface temperature have notable successes but also biases in their ability to represent atmospheric variability compared to observations. Recently, AI emulators and hybrid models have emerged with the potential to overcome these biases, but still require systematic evaluation against metrics grounded

Zhiping Zhang, Yi Evie Zhang, Freda Shi, Tianshi Li

LLM agents require personal information for personalization in order to effectively act on users&#39; behalf, but this raises privacy concerns that can discourage data sharing, limiting both the autonomy levels at which agents can operate and the effectiveness of personalization. Yet the expanded design space of agent autonomy also presents opportunities to

Tonghui Qi

I establish nonparametric identification results in first- and second-price auctions when transaction prices are truncated by a binding reserve price under a range of information structures. When the number of potential bidders is fixed and known across all auctions, if only the transaction price is observed, the bidders' private-value distribution is identi

Takashi Maekaku, Keita Goto, Jinchuan Tian, Yusuke Shinohara

Self-Supervised Learning (SSL) has gained traction for its ability to learn rich representations with low labeling costs, applicable across diverse downstream tasks. However, assessing the downstream-task performance remains challenging due to the cost of extra training and evaluation. Existing methods for task-agnostic evaluation also require extra training

Qi-Pei Liu, Zheng-Yuan Xue

Scalable quantum computation demands high-fidelity two-qubit gates. However, decoherence and control errors are inevitable, which can decrease the quality of implemented quantum operations. We propose a robust iSWAP gate protocol for semiconductor spin qubits, which is a promising platform for scalable quantum computing. Our scheme uses only local microwave

Yuntian Wang, Lizhu Sun, Changjiang Bu

In 1959, Erd\H{o}s and Gallai established two classic theorems, which determine the maximum number of edges in an $n$-vertex graph with no cycles of length at least $k$, and in an $n$-vertex graph with no paths on $k$ vertices, respectively. Subsequently, generalized and spectral versions of the Erd\H{o}s-Gallai theorems have been investigated. A concept of

Bobby Shi, Kevin Tian, Matthew S. Zhang

We survey different perspectives on the stochastic localization process of Eldan, a powerful construction that has had many exciting recent applications in high-dimensional probability and algorithm design. Unlike prior surveys on this topic, our focus is on giving a self-contained presentation of all known alternative constructions of Eldan's stochastic loc

Samuel A. Verburg, Efren Fernandez-Grande, Peter Gerstoft

Sound field reconstruction involves estimating sound fields from a limited number of spatially distributed observations. This work introduces a differentiable physics approach for sound field reconstruction, where the initial conditions of the wave equation are approximated with a neural network, and the differential operator is computed with a differentiabl

Alexander Kushkuley

We introduce a weighted sum of irreducible character ratios as an estimator for commutator probabilities. The estimator yields Frobenius formula when applied to a regular representation

Tomasz Górecki, Mirosław Krzyśko, Felix Gnettner, Piotr Kokoszka

In classical canonical correlation analysis (CCA), the goal is to determine the linear transformations of two random vectors into two new random variables that are most strongly correlated. Canonical variables are pairs of these new random variables, while canonical correlations are correlations between these pairs. In this paper, we propose and study two ge

Akira Kitaoka

Data-driven inverse optimization for mixed-integer linear programs (MILPs), which seeks to learn an objective function and constraints consistent with observed decisions, is important for building accurate mathematical models in a variety of domains, including power systems and scheduling. However, to the best of our knowledge, existing data-driven inverse o

Khartik Uppalapati, Bora Yimenicioglu, Shakeel Abdulkareem, Adan Eftekhari

Batten disease (neuronal ceroid lipofuscinosis) is a rare pediatric neurodegenerative disorder whose early MRI signs are subtle and often missed. We propose TinyViT-Batten, a few-shot Vision Transformer (ViT) framework to detect early Batten disease from pediatric brain MRI with limited training cases. We distill a large teacher ViT into a 5 M-parameter Tiny

Xiangchi Yuan, Xiang Chen, Tong Yu, Dachuan Shi

Large Language Models (LLMs) show strong reasoning abilities, often amplified by Chain-of-Thought (CoT) prompting and reinforcement learning (RL). Although RL algorithms can substantially improve reasoning, they struggle to expand reasoning boundaries because they learn from their own reasoning trajectories rather than acquiring external knowledge. Supervise

Jinmin Yi, Ruizhi Liu, Zhi Li

Approximate quantum error correction (AQEC) provides a versatile framework for both quantum information processing and probing many-body entanglement. We reveal a fundamental tension between the error-correcting power of an AQEC and the hardness of code state preparation. More precisely, through a novel application of the Lov\'asz local lemma, we establish a

Nan Meng, Yun-Bin Zhao

Thresholding algorithms for sparse optimization problems involve two key components: search directions and thresholding strategies. In this paper, we use the compressed Newton direction as a search direction, derived by confining the classical Newton step to a low-dimensional subspace and embedding it back into the full space with diagonal regularization. Th

Qiyuan He, Yicong Li, Haotian Ye, Jinghao Wang

Visual autoregressive (AR) generation offers a promising path toward unifying vision and language models, yet its performance remains suboptimal against diffusion models. Prior work often attributes this gap to tokenizer limitations and rasterization ordering. In this work, we identify a core bottleneck from the perspective of generator-tokenizer inconsisten

Dafa Li

In this paper, we propose the average determinant of reduced density matrices for each qubit as a global entanglement measure. By means of the properties of reduced density matrices, we can investigate the present measure. We propose a decomposition law for the present measure, demonstrate that the present measure just measures the average mixedness for each

Shota Shiiku, Jun Ichikawa

Reflecting the recent rise in Japanese basketball's competitiveness and pivotal outcomes in international tournaments, clutch-time performance has become increasingly critical. We examine clutch-time performance in Japan's B.League using play-by-play and box-score data from the 2023-24 season. Defining clutch time as the final five minutes with a score margi

Tomoyuki Morimae, Yuki Shirakawa, Takashi Yamakawa

One-way puzzles (OWPuzzs) introduced by Khurana and Tomer [STOC 2024] are a natural quantum analogue of one-way functions (OWFs), and one of the most fundamental primitives in ''Microcrypt'' where OWFs do not exist but quantum cryptography is possible. OWPuzzs are implied by almost all quantum cryptographic primitives, and imply several important application

Ziyi Chen, Peiran Yu, Heng Huang

This work aims to solve a stochastic nonconvex nonsmooth composite optimization problem. Previous works on composite optimization problem requires the major part to satisfy Lipschitz smoothness or some relaxed smoothness conditions, which excludes some machine learning examples such as regularized ReLU network and sparse support matrix machine. In this work,

Anju Yokoi

In this paper, we introduce a new function, the multiple confluent hypergeometric functions, and establish a functional equation for the $r$-variable Euler--Zagier multiple zeta functions using it. In the case when $r=2$, this functional equation includes the well-known functional equation for the Euler--Zagier double zeta functions obtained by Matsumoto.

Seth Bernstein, Ashfin Rahman, Nadia Sharifi, Ariunjargal Terbish

Generative artificial intelligence (GenAI) has already had a big impact on computing education with prior research identifying many benefits. However, recent studies have also identified potential risks and harms. To continue maximizing AI benefits while addressing the harms and unintended consequences, we conducted a systematic literature review of research

Hikozo Kobayashi, Yu Ohno, Takayuki Okuda, Hiroshi Tamaru

In the context of information geometry, the concept known as left-invariant statistical structure on Lie groups is defined by Furuhata--Inoguchi--Kobayashi (Inf Geom 4(1):177--188, 2021). In this paper, we introduce the notion of the moduli space of left-invariant statistical structures on a Lie group. We study the moduli spaces for three particular Lie grou

Yilun Zhu, Naihao Deng, Naichen Shi, Aditya Gangrade

Domain generalization (DG) is the problem of generalizing from several distributions (or domains), for which labeled training data are available, to a new test domain for which no labeled data is available. For the prevailing benchmark datasets in DG, there exists a single classifier that performs well across all domains. In this work, we study a fundamental

Farid Bozorgnia, Vyacheslav Kungurtsev, Shirali Kadyrov, Mohsen Yousefnezhad

In this work, we introduce novel algorithms for label propagation and self-training using fractional heat kernel dynamics with a source term. We motivate the methodology through the classical correspondence of information theory with the physics of parabolic evolution equations. We integrate the fractional heat kernel into Graph Neural Network architectures

Lucie Kunitomo-Jacquin, Edison Marrese-Taylor, Ken Fukuda

Quantifying uncertainty in large language models (LLMs) is important for safety-critical applications because it helps spot incorrect answers, known as hallucinations. One major trend of uncertainty quantification methods is based on estimating the entropy of the distribution of the LLM's potential output sequences. This estimation is based on a set of outpu

Kaosar Uddin

We present spd-metrics-id, a Python package for computing distances and divergences between symmetric positive-definite (SPD) matrices. Unlike traditional toolkits that focus on specific applications, spd-metrics-id provides a unified, extensible, and reproducible framework for SPD distance computation. The package supports a wide variety of geometry-aware m

Fangzhe Wu, Dongyang Lyu, Xiaoqi Li

Against the backdrop of deepening digital and intelligent transformation in human resource management, traditional recruitment models struggle to fully meet enterprises' growing demand for precise talent acquisition due to limited efficiency, high costs, and information asymmetry. As a vital tool for optimizing recruitment processes, reducing labor and time

Jushan Chen, Santiago Paternain

Recently, diffusion models have gained popularity and attention in trajectory optimization due to their capability of modeling multi-modal probability distributions. However, addressing nonlinear equality constraints, i.e, dynamic feasibility, remains a great challenge in diffusion-based trajectory optimization. Recent diffusion-based trajectory optimization

Shaofeng H. -C. Jiang, Pan Peng, Haoze Wang

Max-Cut is a fundamental combinatorial optimization problem that has been studied in various computational settings. We initiate the study of its streaming complexity in \emph{general metric spaces} with access to distance oracles. We give a $(1 + \epsilon)$-approximate algorithm for estimating the Max-Cut value in \emph{sliding-window} streams using only po

Grace LeFevre, Qingcheng Zeng, Adam Leif, Jason Jewell

The social impact of Natural Language Processing (NLP) is increasingly important, with a rising community focus on initiatives related to NLP for Social Good (NLP4SG). Indeed, in recent years, almost 20% of all papers in the ACL Anthology address topics related to social good as defined by the UN Sustainable Development Goals (Adauto et al., 2023). In this s

Maria Filipkovska

Abstract differential-algebraic equations (ADAEs) of a semilinear type are studied. Theorems on the existence and uniqueness of solutions and the maximal interval of existence, on the global solvability of the ADAEs, the boundedness of solutions and the blow-up of solutions are presented. Previously, an ADAE is reduced to a system of explicit differential eq

Ziyi Chen, Su Zhang, Heng Huang

Federated learning has attracted increasing attention at recent large-scale optimization and machine learning research and applications, but is also vulnerable to Byzantine clients that can send any erroneous signals. Robust aggregators are commonly used to resist Byzantine clients. This usually requires to estimate the unknown number $f$ of Byzantine client

Rohitash Chandra, Yathin Suresh, Divyansh Raj Sinha, Sanchit Jindal

There is no doubt that there has been a drastic increase in abusive and sexually explicit content in music, particularly in Billboard Music Charts. However, there is a lack of studies that validate the trend for effective policy development, as such content has harmful behavioural changes in children and youths. In this study, we utilise deep learning method

Motoki Osada, Shusaku Imajo, Yuji Seki, Kousuke Ishida

The recently discovered superconducting infinite-layer nickelates offer a novel platform to explore an exotic pairing mechanism in multi-band systems towards high-temperature superconductivity and associated rich quantum phases, contrasting with cuprates. Here, we show that infinite-layer (La,Sr)NiO$_2$ exhibits strong-coupling superconductivity, resilient t

Ziyi Chen, Heng Huang

Performative reinforcement learning is an emerging dynamical decision making framework, which extends reinforcement learning to the common applications where the agent's policy can change the environmental dynamics. Existing works on performative reinforcement learning only aim at a performatively stable (PS) policy that maximizes an approximate value functi

Eduardo O. Schmidt, Laura D. Baravalle, Ana Pichel, Damián Mast

We aim to spectroscopically confirm the nature of VVV-J181258.71-314346.7, a candidate counterpart to the unassociated gamma-ray source 4FGLJ1812.8-3144. This object was selected based on its near-infrared photometric properties and moderate variability, as part of a broader effort to identify active galactic nuclei (AGN) behind the Galactic bulge and disc.

Yuanhao Zou, Shengji Jin, Andong Deng, Youpeng Zhao

Effectively applying Vision-Language Models (VLMs) to Video Question Answering (VideoQA) hinges on selecting a concise yet comprehensive set of frames, as processing entire videos is computationally infeasible. However, current frame selection methods face a critical trade-off: approaches relying on lightweight similarity models, such as CLIP, often fail to

Lisen Ding, Mingyi Wang, Dongling Wang

Recently, a class of efficient spectral Monte-Carlo methods was developed in \cite{Feng2025ExponentiallyAS} for solving fractional Poisson equations. These methods fully consider the low regularity of the solution near boundaries and leverage the efficiency of walk-on-spheres algorithms, achieving spectral accuracy. However, the underlying formulation is ess

Magaly Catanzariti, Hugo Aimar, Diego M. Mateos

We introduce the Divergence Phase Index (DPI), a novel framework for quantifying phase differences in one and multidimensional signals, grounded in harmonic analysis via the Riesz transform. Based on classical Hilbert Transform phase measures, the DPI extends these principles to higher dimensions, offering a geometry-aware metric that is invariant to intensi

Bo Li, Ankang Sun, Zunyu Wang, Yu Zhou

We study a fundamental fair allocation problem, where the agent's value is determined by the number of bins either used to pack or cover the items allocated to them. Fairness is evaluated using the maximin share (MMS) criterion. This problem is not only motivated by practical applications, but also serves as a natural framework for studying group fairness. A

Pulak Ranjan Giri

The discrete-time quantum walk on the Johnson graph $J(n,k)$ is a useful tool for performing target vertex searches with high success probability. This graph is defined by $n$ distinct elements, with vertices being all the \(\binom{n}{k}\) $k$-element subsets and two vertices are connected by an edge if they differ exactly by one element. However, most works

Bingkun Guo, Wentian Li, Xiaojian Liu, Jiaqi Luo

To accelerate mechanical design and enhance design quality and innovation, we present a Multidisciplinary Design and Optimization (MDO) Agent driven by Large Language Models (LLMs). The agent semi-automates the end-to-end workflow by orchestrating three core capabilities: (i) natural-language-driven parametric modeling, (ii) retrieval-augmented generation (R

Chaojia Yu, Kaixin Wang, Junle Li, Jingjie Wang

Urban intersections with mixed pedestrian and non-motorized vehicle traffic present complex safety challenges, yet traditional models fail to account for dynamic interactions arising from speed heterogeneity and collision anticipation. This study introduces the Time and Angle Based Social Force Model (TASFM), an enhanced framework extending the classical Soc

Sungwoo Jeong

We study the smallest eigenvalue statistics of the $\beta$-Laguerre and $\beta$-Jacobi ensembles. Using Kaneko's integral formula, we show that the smallest eigenvalue marginal density and distribution functions of the two ensembles for any $\beta>0$ can be represented in terms of multivariate Laguerre and Jacobi polynomials evaluated at a multiple of the id

Pulak Ranjan Giri, Rei Sato, Kazuhiro Saito

Quantum walk has emerged as an essential tool for searching marked vertices on various graphs. Recent advances in the discrete-time quantum walk search algorithm have enabled it to effectively handle multiple marked vertices, expanding its range of applications further. In this article, we propose a novel application of this advanced quantum walk search algo

Weiman Yan, Yi-Chia Chang, Wanyu Zhao

Accurate and efficient circuit behavior modeling is a cornerstone of modern electronic design automation. Among different types of circuits, stiff circuits are challenging to model using previous frameworks. In this work, we propose a new approach using Crossformer, which is a current state-of-the-art Transformer model for time-series prediction tasks, combi

Jihao Liu, Zheng Xu

Assuming the abundance conjecture in dimension $d$, we establish a non-algebraicity criterion of foliations: any log canonical foliation of rank $\le d$ with $\nu\neq\kappa$ is not algebraically integrable, answering question of Ambro--Cascini--Shokurov--Spicer. Under the same hypothesis, we prove abundance for klt algebraically integrable adjoint foliated s

Tesshu Hanaka, Hironori Kiya, Hirotaka Ono

A homeomorphically irreducible spanning tree (HIST) is a spanning tree with no degree-2 vertices, serving as a structurally minimal backbone of a graph. While the existence of HISTs has been widely studied from a structural perspective, the algorithmic complexity of finding them remains less understood. In this paper, we provide a comprehensive investigation

Wenyuan Zhao, Adithya Balachandran, Chao Tian, Paul Pu Liang

The study of multimodality has garnered significant interest in fields where the analysis of interactions among multiple information sources can enhance predictive modeling, data fusion, and interpretability. Partial information decomposition (PID) has emerged as a useful information-theoretic framework to quantify the degree to which individual modalities i

Jonah S. Gannon, Lucas C. Kimmig, Duncan A. Forbes, Jean P. Brodie

There is increasing observational evidence for a failed galaxy formation pathway for some ultradiffuse galaxies (UDGs) at low redshift however they currently lack simulated counterparts. We attempt to identify dark matter halos at high redshift within the MAGNETICUM cosmological simulations that could plausibly be their progenitors. We build a toy model of p

Hongpei Li, Han Zhang, Huikang Liu, Dongdong Ge

Pipeline parallelism (PP) has become a standard technique for scaling large language model (LLM) training across multiple devices. However, despite recent progress in reducing memory consumption through activation offloading, existing approaches remain largely heuristic and coarse-grained, often overlooking the fine-grained trade-offs between memory, computa

Kazuhito Ohmi, Hitoshi Fukuma, Shinji Terui

In recent electron-positron colliders designed and operated with very low emittance and high current, the energy density of the beam has increased significantly compared to earlier designs. Under these conditions, interactions between the beam and residual materials within the beam pipe exert considerable mutual influence. The residual materials are heated b

Alisha Chromey

CTA 1 is a shell-type supernova remnant (SNR) with a central pulsar wind nebula (PWN), visible at very-high-energy (VHE) from 50 GeV to 100 TeV from a moderately extended emission region. While general consensus concludes the VHE emission originates from relativistic leptons accelerated by the PWN and undergoing inverse Compton scattering, questions remain a

Muhammad Umar Farooq Qaisar, Weijie Yuan, Onur Günlü, Taneli Riihonen

The commencement of the sixth-generation (6G) wireless networks represents a fundamental shift in the integration of communication and sensing technologies to support next-generation applications. Integrated sensing and communication (ISAC) is a key concept in this evolution, enabling end-to-end support for both communication and sensing within a unified fra

Takayuki Hibi, Peter Schenzel

For four elements of a Noetherian ring we construct complexes of free modules of length three (resp. five) by an explicit description of the homomorphisms of the free modules. We provide exactness criteria for them. As an application we use these results in order to describe explicit the minimal free resolution of the Hartshorne--Rao module of a monomial cur

Adam Bene Watts, Charles R. Chen, J. William Helton, Joseph Slote

Parallelization is a major challenge in quantum algorithms due to physical constraints like no-cloning. This is vividly illustrated by the conjecture of Moore and Nilsson from their seminal work on quantum circuit complexity [MN01, announced 1998]: unitaries of a deceptively simple form--controlled-unitary "staircases"--require circuits of minimum depth $\Om

Venkata Bharath Reddy Reddem, Akshay P Sarashetti, Ranjith Merugu, Amit Satish Unde

Blind face restoration (BFR) has attracted increasing attention with the rise of generative methods. Most existing approaches integrate generative priors into the restoration pro- cess, aiming to jointly address facial detail generation and identity preservation. However, these methods often suffer from a trade-off between visual quality and identity fidelit

Samyadip Sarkar, Arunashish Datta, David Yang, Mayukh Nath

In recent decades Human Body Communication has emerged as a promising alternative to traditional radio wave communication, utilizing the body's conductive properties for low-power connectivity among wearables. This method harnesses the human body as an energy-efficient channel for data transmission within the electro-quasistatic frequency range, enabling adv

Pei Liu, Tamara Christiani, Zhijie Wang, Fei Guo

DNA frequently adopts liquid-crystalline conformations in both cells and viruses. The Oseen--Frank framework provides a powerful continuum description of these phases through three elastic moduli: splay ($K_1$), twist or cholesteric ($K_2$), and bending ($K_3$). While $K_1$ is typically assumed to dominate, the relative magnitude of $K_2$ and $K_3$ in confin

Brian Hu Zhang, Ioannis Anagnostides, Tuomas Sandholm

A considerable chasm has been looming for decades between theory and practice in zero-sum game solving through first-order methods. Although a convergence rate of $T^{-1}$ has long been established, the most effective paradigm in practice is counterfactual regret minimization (CFR), which is based on regret matching and its modern variants. In particular, th