October 2025 arXiv papers — page 26

Daniele Nikzad, Alexander Zhilkin, Juha Harviainen, Jack Kuipers

Bayesian inference of Bayesian network structures is often performed by sampling directed acyclic graphs along an appropriately constructed Markov chain. We present two techniques to improve sampling. First, we give an efficient implementation of basic moves, which add, delete, or reverse a single arc. Second, we expedite summing over parent sets, an expensi

Amul Ojha, Shubhit Sardana, Arnab Ghosh

We build on the foundational work of Deffner and Zurek [S. Deffner and W. H. Zurek, New J. Phys. 18, 063013 (2016)] to show how central equilibrium structures of statistical mechanics can be understood within standard quantum mechanics using the concept of envariance (environment-assisted invariance). In particular, we show how the Binomial, Poisson, and Gau

Samuele Ferri, Chiara Giraudo, Valerio Loi, Miroslav Kuchta

In the present paper, we analyze in detail the spectral features of the matrix sequences arising from the Taylor-Hood $\mathbb{P}_2$-$\mathbb{P}_1$ approximation of variable viscosity for $2d$ Stokes problem under weak assumptions on the regularity of the diffusion. Localization and distributional spectral results are provided, accompanied by numerical tests

Charlotte Dombrowsky

We study the rank of the modular curve $X_0(49)$ over quadratic extensions. Assuming the Birch and Swinnerton-Dyer Conjecture, we show that the rank over $\mathbb{Q}(\sqrt{d})$ is positive if and only if the number of solutions of two explicit ternary quadratic forms is the same. Following the approach of Tunnell, we apply a theorem due to Waldpurger which r

Anjelin Mariya Johnson, James A. Sellers, S. N. Fathima

Let $a_k(n)$ denote the number of partitions of $n$ wherein even parts come in only one color, while the odd parts may be ``colored" with one of $k$ colors, for fixed $k$. In this note, we find some congruences for $a_k(n)$ in the spirit of Ramanujan's congruences. We prove a number of results for $a_k(n)$ modulo powers of $2$ for infinitely many values of $

Xi-Wei Pan, Huan-Hai Zhou, Yi-Ming Lu, Jin-Guo Liu

Rydberg atom arrays are a promising platform for quantum optimization, encoding computationally hard problems by reducing them to independent set problems with unit-disk graph topology. In Nguyen et al., PRX Quantum 4, 010316 (2023), a systematic and efficient strategy was introduced to encode multiple problems into a special unit-disk graph: the King's subg

Li He

We give an internal description of constructible objects in an $\infty$-topos. More precisely, $P$-consctructible objects are locally constant objects internal to Fun($P$,An), for any noetherian poset $P$.

Haichao Xu, Xinwu Cao, Yanan Wang, Andrzej A. Zdziarski

X-ray observations of the tidal disruption event (TDE) candidate AT 2019avd show drastic variabilities in flux and spectral shape over hundreds of days, providing clues on the accretion disc-corona evolution. We utilize a disc-corona model, in which a fraction of the gravitational energy released in the disc is transported into the hot corona above/below. So

Yuan Guo, Wen Chen, Qingqing Wu, Yang Liu

Integrated sensing and communication (ISAC) is a promising solution for the future sixth-generation (6G) system. However, classical fixed-position antenna (FPA) ISAC systems fail to fully utilize spatial degrees of freedom (DoFs), resulting in limited gains for both radar sensing and communication functionalities. This challenge can be addressed by the emerg

Alexander Kuznetsov

Given a linear space $U \subset \mathrm{Sym}^2V^\vee$ of quadrics in a projective space $\mathbb{P}(V)$ whose intersection is empty, we consider the corresponding Clifford space -- the projective space $\mathbb{P}(U)$ endowed with the even part of Clifford algebras as a sheaf of algebras. We show that the derived category of a Clifford space is generated by

Wenjie Zhou, Bohan Wang, Wei Chen, Xueqi Cheng

Recent studies \citep{gur2018gradient,song2024does, wen2024understanding} highlight a fundamental dichotomy in deep learning optimization: Although parameter updates along the top eigendirections of the loss Hessian (Dom-space) capture most of the update magnitude, they often contribute minimally to loss reduction. In contrast, updates in the orthogonal comp

Akansha Rautela, Deepak U. Patil, Ameer Mulla, Indra Narayan Kar

A problem of achieving minimum time consensus for a set of $N$ second-order LTI system agents with bounded inputs and fuel constraints is considered. Unlike our other works, here the damping effect in agent dynamics is included. First, the attainable set for each agent with fuel budget constraints is characterized, and its boundary equations are derived. The

Nao Yoshimura, Hiroshi Oyama, Takuya Azumi

The diversification of functionalities and the development of the IoT are making embedded systems larger and more complex in structure. Ensuring system reliability, especially in terms of security, necessitates selecting an appropriate programming language. As part of existing research, TECS/Rust has been proposed as a framework that combines Rust and compon

Hao Huang, Geeta Chandra Raju Bethala, Shuaihang Yuan, Congcong Wen

Whole-body humanoid motion represents a fundamental challenge in robotics, requiring balance, coordination, and adaptability to enable human-like behaviors. However, existing methods typically require multiple training samples per motion, rendering the collection of high-quality human motion datasets both labor-intensive and costly. To address this, we propo

Moritz Lucas, Hamid Ebrahimy, Viacheslav Barkov, Ralf Pecenka

Trees Outside Forests (TOF) play an important role in agricultural landscapes by supporting biodiversity, sequestering carbon, and regulating microclimates. Yet, most studies have treated TOF as a single class or relied on rigid rule-based thresholds, limiting ecological interpretation and adaptability across regions. To address this, we evaluate deep learni

Qianqian Qiao, DanDan Zheng, Yihang Bo, Bao Peng

Video aesthetic assessment, a vital area in multimedia computing, integrates computer vision with human cognition. Its progress is limited by the lack of standardized datasets and robust models, as the temporal dynamics of video and multimodal fusion challenges hinder direct application of image-based methods. This study introduces VADB, the largest video ae

Yinqi Cai, Jichang Li, Zhaolun Li, Weikai Chen

Recent advances in deep generative models have made it easier to manipulate face videos, raising significant concerns about their potential misuse for fraud and misinformation. Existing detectors often perform well in in-domain scenarios but fail to generalize across diverse manipulation techniques due to their reliance on forgery-specific artifacts. In this

Yuchang Lin, Qianqian Zhu, Guodong Li

There are many time series in the literature with high dimension yet limited sample sizes, such as macroeconomic variables, and it is almost impossible to obtain efficient estimation and accurate prediction by using the corresponding datasets themselves. This paper fills the gap by introducing a novel representation-based transfer learning framework for vect

Toshio Irino, Ayako Yamamoto, Fuki Miyazaki

Age-related hearing loss (HL) reduces speech intelligibility (SI) in older adults (OAs). However, deficits in central and cognitive processing also substantially impact SI. Understanding these contributions is essential for explaining individual differences and developing effective assistive hearing strategies. This study presents a framework that distinguis

Yuxiang Mao, Zhijie Zhang, Zhiheng Zhang, Jiawei Liu

Expressions are fundamental to conveying human emotions. With the rapid advancement of AI-generated content (AIGC), realistic and expressive 3D facial animation has become increasingly crucial. Despite recent progress in speech-driven lip-sync for talking-face animation, generating emotionally expressive talking faces remains underexplored. A major obstacle

Jee Won Lee, Hansol Lim, Sooyeun Yang, Jongseong Brad Choi

Vision-based control systems, such as image-based visual servoing (IBVS), have been extensively explored for precise robot manipulation. A persistent challenge, however, is maintaining robust target tracking under partial or full occlusions. Classical methods like Lucas-Kanade (LK) offer lightweight tracking but are fragile to occlusion and drift, while deep

Tianxi Wan, Jiaming Luo, Siyuan Chen, Kunyao Lan

Psychiatric comorbidity is clinically significant yet challenging due to the complexity of multiple co-occurring disorders. To address this, we develop a novel approach integrating synthetic patient electronic medical record (EMR) construction and multi-agent diagnostic dialogue generation. We create 502 synthetic EMRs for common comorbid conditions using a

Rushuang Zhou, Yuan-Ting Zhang, M. Jamal Deen, Yining Dong

Deploying advanced cardiac artificial intelligence for daily cardiac monitoring is hindered by its reliance on extensive medical data and high computational resources. Low-cost cardiac intelligence (LCCI) offers a promising alternative by using wearable device data, such as 1-lead electrocardiogram (ECG), but it suffers from a significant diagnostic performa

Milton Lin, Toan Pham, Jize Yu

Let $G$ be a complex reductive group. For a smooth affine spherical $G$-variety $X$, assume that the unramified relative local Langlands conjecture of Ben-Zvi-Sakellaridis-Venkatesh for $X$ holds, the loop space $LX$ is an $L^+G$--placid ind--scheme, and there exists a dimension theory for $LX$, we give a spectral description of a full subcategory of Iwahori

Hsuan-I, Chou, Naomi Hirano, Masayuki Yamaguchi

We have observed the low-mass molecular cloud core G204.4-11.3A2-NE (G204NE) in the direction of Orion B giant molecular cloud with the Atacama Large Millimeter/submillimeter Array in Band 6. The $\rm 1.3\ mm$ continuum images and visibilities unveil a compact central structure with a radius of $\sim$12 au, while showing no signature of binarity down to 18 a

Shin Seong Kim, Mingi Kwon, Jaeseok Jeong, Youngjung Uh

Rectified flow is a generative model that learns smooth transport mappings between two distributions through an ordinary differential equation (ODE). Unlike diffusion-based generative models, which require costly numerical integration of a generative ODE to sample images with state-of-the-art quality, rectified flow uses an iterative process called reflow to

Chihiro Nagashima, Akira Takahashi, Zhi Zhong, Shusuke Takahashi

This paper explores the integration of AI technologies into the artistic workflow through the creation of Studies for, a generative sound installation developed in collaboration with sound artist Evala (https://www.ntticc.or.jp/en/archive/works/studies-for/). The installation employs SpecMaskGIT, a lightweight yet high-quality sound generation AI model, to g

Quang-Khai Bui-Tran, Thanh-Huy Nguyen, Hoang-Thien Nguyen, Ba-Thinh Lam

Source-Free Domain Adaptation (SFDA) is emerging as a compelling solution for medical image segmentation under privacy constraints, yet current approaches often ignore sample difficulty and struggle with noisy supervision under domain shift. We present a new SFDA framework that leverages Hard Sample Selection and Denoised Patch Mixing to progressively align

Aniruddha Saha, Soujanya Hazra, Sanjay Ghosh

Brain disorders are an umbrella term for a group of neurological and psychiatric conditions that have a major effect on thinking, feeling, and acting. These conditions encompass a wide range of conditions. The illnesses in question pose significant difficulties not only for individuals, but also for healthcare systems all across the world. In this study, we

Miao Zhang, Junpeng Li, Changchun Hua, Yana Yang

Positive--Unlabeled (PU) learning considers settings in which only positive and unlabeled data are available, while negatives are missing or left unlabeled. This situation is common in real applications where annotating reliable negatives is difficult or costly. Despite substantial progress in PU learning, the multi-class case (MPU) remains challenging: many

Shota Nakada, Kazuhiro Saito, Yuchi Ishikawa, Hokuto Munakata

We propose a novel task, hallucination localization in video captioning, which aims to identify hallucinations in video captions at the span level (i.e. individual words or phrases). This allows for a more detailed analysis of hallucinations compared to existing sentence-level hallucination detection task. To establish a benchmark for hallucination localizat

Ziyi Liu, Bahar Sarrafzadeh, Pei Zhou, Longqi Yang

While Large Language Models (LLMs) are increasingly used in agentic frameworks to assist individual users, there is a growing need for agents that can proactively manage complex, multi-party collaboration. Systematic evaluation methods for such proactive agents remain scarce, limiting progress in developing AI that can effectively support multiple people tog

Kun Ouyang, Haoyu Wang, Dong Fang

Event log data, recording fine-grained user actions and system events, represent one of the most valuable assets for modern digital services. However, the complexity and heterogeneity of industrial event logs--characterized by large scale, high dimensionality, diverse data types, and intricate temporal or relational structures--make feature engineering extre

Ian M. Church, Lyndon Drake, Mark Harris

One of the emergent challenges of student work submitted for assessment is the widespread use of large language models (LLMs) to support and even produce written work. This particularly affects subjects where long-form written work is a key part of assessment. We propose a novel approach to addressing this challenge, using LLMs themselves to support the asse

Riki Toshio, Kaito Kishi, Jun Fujisaki, Hirotaka Oshima

The realization of fault-tolerant quantum computers hinges on the construction of high-speed, high-accuracy, real-time decoding systems. The persistent challenge lies in the fundamental trade-off between speed and accuracy: efforts to improve the decoder's accuracy often lead to unacceptable increases in decoding time and hardware complexity, while attempts

Shiyu Qin, Zhihao Cai, Kaixuan Wang, Lin Qi

Photometric stereo is a technique aimed at determining surface normals through the utilization of shading cues derived from images taken under different lighting conditions. However, existing learning-based approaches often fail to accurately capture features at multiple stages and do not adequately promote interaction between these features. Consequently, t

Zhijie Lin, Zhuofeng Li, Chenglei Dai, Wentian Bao

In a multi-stage recommendation system, reranking plays a crucial role in modeling intra-list correlations among items. A key challenge lies in exploring optimal sequences within the combinatorial space of permutations. Recent research follows a two-stage (generator-evaluator) paradigm, where a generator produces multiple feasible sequences, and an evaluator

Bin Ma, Viktor Nikitin, Xi Wang, Tekin Bicer

ADMM-FFT is an iterative method with high reconstruction accuracy for laminography but suffers from excessive computation time and large memory consumption. We introduce mLR, which employs memoization to replace the time-consuming Fast Fourier Transform (FFT) operations based on an unique observation that similar FFT operations appear in iterations of ADMM-F

Ruibo Hou, Shiyu Teng, Jiaqing Liu, Shurong Chai

Multimodal deep learning has shown promise in depression detection by integrating text, audio, and video signals. Recent work leverages sentiment analysis to enhance emotional understanding, yet suffers from high computational cost, domain mismatch, and static knowledge limitations. To address these issues, we propose a novel Retrieval-Augmented Generation (

Kaichen Ouyang, Yezhi Xia

Synthetic Benchmark Problems (SBPs) are commonly used to evaluate the performance of metaheuristic algorithms. However, these SBPs often contain various unrealistic properties, potentially leading to underestimation or overestimation of algorithmic performance. While several benchmark suites comprising real-world problems have been proposed for various types

Shaoshan Liu, Anina Schwarzenbach, Yiyu Shi

AI is displacing tasks, mediating high-stakes decisions, and flooding communication with synthetic content, unsettling work, identity, and social trust. We argue that the decisive human countermeasure is resilience. We define resilience across three layers: psychological, including emotion regulation, meaning-making, cognitive flexibility; social, including

Quang-Khai Bui-Tran, Thanh-Huy Nguyen, Manh D. Ho, Thinh B. Lam

Medical image segmentation models face severe performance drops under domain shifts, especially when data sharing constraints prevent access to source images. We present a novel Uncertainty-aware Progressive Pseudo-label Denoising (UP2D) framework for source-free domain adaptation (SFDA), designed to mitigate noisy pseudo-labels and class imbalance during ad

Sreejita Bhaduri, Mohammed Ibrahim Sugarno, Ashoke De

The self-sustaining oscillations in cavity flows enhance fluid mixing and promote energy and momentum transport. However, the associated oscillation frequencies can amplify acoustic loading, potentially damaging surrounding structures. Hence, understanding cavity dynamics across geometries and freestream conditions and developing strategies to regulate these

Tiancheng Zhang, Li Lei, Changhao Ding, Fanhao Meng

The orbital angular momentum (OAM) of light is a versatile degree of freedom with transformative impact across optical communication, imaging, and micromanipulation. These applications have motivated a growing demand for compact, reconfigurable vortex arrays with tunable topological charge, yet integrating these functionalities into nanophotonic platforms re

Shane A. Caldwell, Moein Khazraee, Elena Agostini, Tom Lassiter

We propose an architecture, called NVQLink, for connecting high-performance computing (HPC) resources to the control system of a quantum processing unit (QPU) to accelerate workloads necessary to the operation of the QPU. We aim to support every physical modality of QPU and every type of QPU system controller (QSC). The HPC resource is optimized for real-tim

Lei Han, Jinhao Zhang, Jinhui Liu, Zhiyong Yu

Frequent natural disasters cause significant losses to human society, and timely, efficient collection of post-disaster environmental information is the foundation for effective rescue operations. Due to the extreme complexity of post-disaster environments, existing sensing technologies such as mobile crowdsensing suffer from weak environmental adaptability,

Amith Khandakar, David Michelson, Shaikh Golam Rabbani, Fariya Bintay Shafi

It's important to monitor road issues such as bumps and potholes to enhance safety and improve road conditions. Smartphones are equipped with various built-in sensors that offer a cost-effective and straightforward way to assess road quality. However, progress in this area has been slow due to the lack of high-quality, standardized datasets. This paper discu

Stefano Civelli, Pietro Bernardelle, Nardiena A. Pratama, Gianluca Demartini

Large language models (LLMs) are increasingly used in content moderation systems, where ensuring fairness and neutrality is essential. In this study, we examine how persona adoption influences the consistency and fairness of harmful content classification across different LLM architectures, model sizes, and content modalities (language vs. vision). At first

Haoran Deng, Yingyu Lin, Zhenghao Lin, Xiao Liu

Long-context language models unlock advanced capabilities in reasoning, code generation, and document summarization by leveraging dependencies across extended spans of text. However, a significant portion of readily available long-text data lacks meaningful long-distance dependencies; most spans can be predicted using only local context. Training on such dat

Junsheng Zhou, Xingyu Shi, Haichuan Song, Yi Fang

Point clouds captured by scanning sensors are often perturbed by noise, which have a highly negative impact on downstream tasks (e.g. surface reconstruction and shape understanding). Previous works mostly focus on training neural networks with noisy-clean point cloud pairs for learning denoising priors, which requires extensively manual efforts. In this work

Koustav De

We are given a bipartite graph $G = \left( A \cup B, E \right)$. In the one-sided model, every $a \in A$ (often called agents) ranks its neighbours $z \in N_{a}$ strictly, and no $b \in B$ has any preference order over its neighbours $y \in N_{b}$, and vertices in $B$ abstain from casting their votes to matchings. In the two-sided model with one-sided ties,

Yusheng Xiong, Kaveh Delfanazari

The continuous miniaturisation of metal-oxide-semiconductor field-effect transistors (MOSFETs) from long- to short-channel architectures has advanced beyond the predictions of Moore's Law. Continued advances in semiconductor electronics, even near current scaling and performance boundaries under cryogenic conditions, are driving the development of innovative

Yisong Fu, Zezhi Shao, Chengqing Yu, Yujie Li

Benefiting from high capacity for capturing complex temporal patterns, deep learning (DL) has significantly advanced time series forecasting (TSF). However, deep models tend to suffer from severe overfitting due to the inherent vulnerability of time series to noise and anomalies. The prevailing DL paradigm uniformly optimizes all timesteps through the MSE lo

Tianqianjin Lin, Xi Zhao, Xingyao Zhang, Rujiao Long

Reinforcement learning (RL) can refine the reasoning abilities of large language models (LLMs), but critically depends on a key prerequisite: the LLM can already generate high-utility reasoning paths with non-negligible probability. For tasks beyond the LLM's current competence, such reasoning path can be hard to sample, and learning risks reinforcing famili

Yuyang Xia, Zibo Liang, Liwei Deng, Yan Zhao

Autonomous driving is an emerging technology that is expected to bring significant social, economic, and environmental benefits. However, these benefits come with rising energy consumption by computation engines, limiting the driving range of vehicles, especially electric ones. Perception computing is typically the most power-intensive component, as it relie

Qianyun Wu, Orr Levy, Yoed N. Kenett, Yukie Sano

Examining emotion interactions as an emotion network in social media offers key insights into human psychology, yet few studies have explored how fluctuations in such emotion network evolve during crises and normal times. This study proposes a novel computational approach grounded in network theory, leveraging large-scale Japanese social media data spanning

Y. Balaji, M. Surendran, X. Li, A. Kemelbay

Superconducting qubits have motivated the exploration of Josephson-junction technologies beyond quantum computing, with emerging applications in low-energy photon and phonon detection for astrophysics and dark matter searches. Achieving sensitivity at the THz (meV) scale requires materials with smaller superconducting gaps than those of conventional aluminum

Vishal Patil, Kavya Bhand, Kaustubh Mukdam, Kavya Sharma

The proposed system aims to use various machine learning algorithms to enhance financial prediction and generate highly accurate analyses. It introduces an AI-driven platform which offers inflation-analysis, stock market prediction, and E-learning module powered by a chatbot. It has achieved high accuracy where the Inflation Analysis depicts 0.8% MAE, 1.2% R

Philani Rodney Majozi

We study quasi-modular pseudometric spaces as asymmetric refinements of modular metric structures. To each such space we associate canonical forward and backward quasi-uniformities and the corresponding directional topologies. We introduce directional notions of convergence, completeness, total boundedness, and compactness, and show that these properties are

Manisha More, Kavya Bhand, Kaustubh Mukdam, Kavya Sharma

Early diagnosis of critical diseases can significantly improve patient survival and reduce treatment costs. However, existing diagnostic techniques are often costly, invasive, and inaccessible in low-resource regions. This paper presents a multimodal artificial intelligence (AI) diagnostic framework integrating image analysis, thermal imaging, and audio sign

E. Cappellazzo, J. R. Dawson, Mark Wardle, Trey V. Wenger

Observational studies of HII region-molecular cloud interactions constrain models of feedback and quantify its impact on the surrounding environment. A recent hypothesis proposes that a characteristic spectral signature in ground state hyperfine lines of hydroxyl (OH) -- the OH flip -- may trace gas that is dynamically interacting with an expanding HII regio

Raghda Abdel Khaleq, Madeleine J. Zurowski

The nature of dark matter (DM) remains one of the biggest mysteries in physics today. Dark matter direct detection experiments look for nuclear recoil signals from DM-nucleus elastic scattering, which can be used to characterise DM. Nuclear modelling of the target nucleus may impact the predicted DM-nucleus scattering rates, and affect interpretation of expe

Ji Qiang, Jinyu Wan, Allen Qiang, Yue Hao

Automatic differentiation provides an efficient means of computing derivatives of complex functions with machine precision, thereby enabling differentiable simulation. In this work, we propose the use of the norm of the tangent map, obtained from differentiable tracking of particle trajectories, as a computationally efficient indicator of chaotic behavior in

Shuochuan Li, Zan Wang, Xiaoning Du, Zhuo Wu

Code comment generation aims to produce a generic overview of a code snippet, helping developers understand and maintain code. However, generic summaries alone are insufficient to meet the diverse needs of practitioners; for example, developers expect the implementation insights to be presented in an untangled manner, while users seek clear usage instruction

Pranav Bhandari, Nicolas Fay, Sanjeevan Selvaganapathy, Amitava Datta

Large Language Models exhibit implicit personalities in their generation, but reliably controlling or aligning these traits to meet specific needs remains an open challenge. The need for effective mechanisms for behavioural manipulation of the model during generation is a critical gap in the literature that needs to be fulfilled. Personality-aware LLMs hold

A. E. Petrova, S. Yu. Gavrilkin, V. A. Stepanov, S. S. Khasanov

We studied the magnetoresistance (MR) of well-characterized samples of Co$_{1-x}$Fe$_x$Si at x=0.2, 0.4, and 0.65 at temperatures between 1.8 and 100~K and magnetic fields of 9~T. The quasilinear dependence of MR on the magnetic field at low temperatures and the practically isotropic properties of MR in these compounds are tentatively attributed to the speci

Qi You, Qinghua Huang, Yi-Cheng Lin

Accurate direction-of-arrival (DOA) estimation for sound sources is challenging due to the continuous changes in acoustic characteristics across time and frequency. In such scenarios, accurate localization relies on the ability to aggregate relevant features and model temporal dependencies effectively. In time series modeling, achieving a balance between mod

Zihao Zhou, Zhaolin Wang, Yuanwei Liu

The joint transmit and pinching beamforming design for spectral efficiency (SE) and energy efficiency (EE) tradeoff in pinching-antenna systems (PASS) is proposed, under practical channel and energy consumption models. In the single-user scenario, it is proved that the optimal pinching antenna (PA) positions are independent of the transmit beamforming. Based

Hong Wang, Haiyang Xin, Jie Wang, Xuanze Yang

Pre-training has proven effective in addressing data scarcity and performance limitations in solving PDE problems with neural operators. However, challenges remain due to the heterogeneity of PDE datasets in equation types, which leads to high errors in mixed training. Additionally, dense pre-training models that scale parameters by increasing network width

Hongyu Song, Rishabh Dev Yadav, Cheng Guo, Wei Pan

Autonomous navigation under natural language instructions represents a crucial step toward embodied intelligence, enabling complex task execution in environments ranging from industrial facilities to domestic spaces. However, language-driven 3D navigation for Unmanned Aerial Vehicles (UAVs) requires precise spatial reasoning, a capability inherently lacking

Ramkrishna Joshi, Riddhiman Roy

In the Standard Model, three discovered generations of leptons and quarks are known to date. However, speculations about existence of next generations have a strong foothold. In this study, we sequentially extrapolate the Standard Model to include a fourth generation of leptons (l4, nu4) with a massive Dirac neutrino. We perform MC simulated event generation

Ana M. Rodriguez, Jaime Acosta, Anantaa Kotal, Aritran Piplai

Designing realistic and adaptive networked threat scenarios remains a core challenge in cybersecurity research and training, still requiring substantial manual effort. While large language models (LLMs) show promise for automated synthesis, unconstrained generation often yields configurations that fail validation or execution. We present AgentCyTE, a framewo

Koustav De

For a subset $X$ of the vertex set $\VV(\GG)$ of a graph $\GG$, we denote the set of edges of $\GG$ which have exactly one end in $X$ by $\partial(X)$ and refer to it as the cut of $X$ or edge cut $\partial(X)$. A graph $\GG=(\VV,\EE)$ is called matching covered if $\forall e \in \EE(\GG), ~\exists \text{a perfect matching }M \text{ of }\GG \text{ s. t. } e

Ritesh Sunil Chavan, Jack Mostow

While large language models are trained on massive datasets, this data is heavily skewed towards English. Does their impressive performance reflect genuine ability or just this data advantage? To find out, we tested them in a setting where they could not rely on data abundance: low-resource languages. Building on prior work Agarwal et al. (2025) that used Ne

Surojit Ghosh, Ankit Kumar

We reprove the generalized Nandakumar-Ramana Rao conjecture for the prime case using representation ring-graded Bredon cohomology. Our approach relies solely on the $RO(C_p)$-graded cohomology of configuration spaces, viewed as a module over the $RO(C_p)$-graded Bredon cohomology of a point.

Han Lin Shang, Lin Han, Stefan Trück

Electricity demand and generation have become increasingly unpredictable with the growing share of variable renewable energy sources in the power system. Forecasting electricity supply by fuel mix is crucial for market operation, ensuring grid stability, optimizing costs, integrating renewable energy sources, and supporting sustainable energy planning. We in

Kateryna Shapovalenko, Quentin Auster

When we hear the word "house", we don't just process sound, we imagine walls, doors, memories. The brain builds meaning through layers, moving from raw acoustics to rich, multimodal associations. Inspired by this, we build on recent work from Meta that aligned EEG signals with averaged wav2vec2 speech embeddings, and ask a deeper question: which layers of pr

Zhifeng Wang, Minghui Wang, Chunyan Zeng, Jialong Yao

In the contemporary landscape, the fusion of information technology and the rapid advancement of artificial intelligence have ushered school education into a transformative phase characterized by digitization and heightened intelligence. Concurrently, the global paradigm shift caused by the Covid-19 pandemic has catalyzed the evolution of e-learning, accentu

Phurich Saengthong, Tomoya Nishida, Kota Dohi, Natsuo Yamashita

Anomalous sound detection (ASD) in the wild requires robustness to distribution shifts such as unseen low-SNR input mixtures of machine and noise types. State-of-the-art systems extract embeddings from an adapted audio encoder and detect anomalies via nearest-neighbor search, but fine tuning on noisy machine sounds often acts like a denoising objective, supp

Yixiang Zhou, Tong Wu, Meixia Tao, Jianhua Mo

This paper addresses the critical challenges of communication overhead, data heterogeneity, and privacy in deep learning for channel state information (CSI) feedback in massive MIMO systems. To this end, we propose Fed-PELAD, a novel federated learning framework that incorporates personalized encoders and a LoRA-adapted shared decoder. Specifically, personal

Utsab Saha, Jeffrey D'Andria, Tyler Menezes

Computer science educators are increasingly integrating open source contributions into classes to prepare students for higher expectations due to GenAI, and to improve employment outcomes in an increasingly competitive job market. However, little is known about how employers view student open source contributions. This paper addresses two research questions

Juan Ren, Mark Dras, Usman Naseem

Agentic methods have emerged as a powerful and autonomous paradigm that enhances reasoning, collaboration, and adaptive control, enabling systems to coordinate and independently solve complex tasks. We extend this paradigm to safety alignment by introducing Agentic Moderation, a model-agnostic framework that leverages specialised agents to defend multimodal

Kazuhiko Seki

Temperature gradients drive asymmetric ion distributions via thermodiffusion (the Soret effect), leading to deviations from the classical Debye--H\"uckel potential.We introduce the Eastman entropy of transfer, $\hat{S}_\pm = \alpha_\pm k_{\rm B}$ for cations and anions, respectively, where $k_{\rm B}$ is the Boltzmann constant, and analyze non-isothermal ele

Mohammadreza Doostmohammadian, Zulfiya R. Gabidullina, Hamid R. Rabiee

In the rapidly evolving research on artificial intelligence (AI) the demand for fast, computationally efficient, and scalable solutions has increased in recent years. The problem of optimizing the computing resources for distributed machine learning (ML) and optimization is considered in this paper. Given a set of data distributed over a network of computing

Yingjie Gao, Yanan Zhang, Zhi Cai, Di Huang

In recent years, test-time adaptive object detection has attracted increasing attention due to its unique advantages in online domain adaptation, which aligns more closely with real-world application scenarios. However, existing approaches heavily rely on source-derived statistical characteristics while making the strong assumption that the source and target

Huadong Tang, Youpeng Zhao, Min Xu, Jun Wang

Prevalent semantic segmentation methods generally adopt a vanilla classifier to categorize each pixel into specific classes. Although such a classifier learns global information from the training data, this information is represented by a set of fixed parameters (weights and biases). However, each image has a different class distribution, which prevents the

Kejing Xia, Jidong Jia, Ke Jin, Yucai Bai

Recently, Gaussian Splatting (GS) has shown great potential for urban scene reconstruction in the field of autonomous driving. However, current urban scene reconstruction methods often depend on multimodal sensors as inputs, \textit{i.e.} LiDAR and images. Though the geometry prior provided by LiDAR point clouds can largely mitigate ill-posedness in reconstr

Changjian Xie, Cheng Wang

This work proposes and analyzes a fully discrete numerical scheme for solving the Landau-Lifshitz-Gilbert (LLG) equation, which achieves fourth-order spatial accuracy and third-order temporal accuracy.Spatially, fourth-order accuracy is attained through the adoption of a long-stencil finite difference method, while boundary extrapolation is executed by lever

Benling Li, Wei Zhao

Hilbert's fourth problem seeks the classification of metric geometries where straight lines are shortest paths. Its regular case identifies the projectively flat Finsler manifolds. This broader framework breaks the equivalence between projective flatness and constant curvature that holds in the Riemannian setting, creating a more intricate classification pro

Kewei Wang, Claire Songhyun Lee, Sunwoo Lee, Vishu Gupta

Neural networks are rapidly gaining popularity in scientific research, but training the models is often very time-consuming. Particularly when the training data samples are large high-dimensional arrays, efficient training methodologies that can reduce the computational costs are crucial. To reduce the training cost, we propose a Multi-Resolution Model Fusio

Shinji Watanabe, Tsunetomo Yamada, Hiroyuki Takakura, Nobuhisa Fujita

Quasicrystals (QCs) lack three-dimensional periodicity of atomic arrangement but possess long-range structural order, which are distinct from periodic crystals and random systems. Here, we show how the ferromagnetic (FM) order arises in the icosahedral QC (i-QC) on the basis of the Monte Carlo simulation of the Heisenberg model on the Yb lattice of Cd$_{5.7}

Yasuyuki Hatsuda, Tadashi Okazaki

We study configurations of two $\mathcal{N}=4$ super Yang-Mills theories of unitary gauge groups connected by the BPS interfaces involving line operators. We find strong evidence of S-duality of the configurations as precise matching of the line defect half-indices which enumerate the BPS local operators at the junctions of the interfaces and line operators.

Hayk Stepanyan, Aishwarya Verma, Andrew Zaldivar, Rutledge Chin Feman

Generative models are known to have reduced performance in different global cultural contexts and languages. While continual data updates have been commonly conducted to improve overall model performance, bolstering and evaluating this cross-cultural competence of generative AI models requires data resources to be intentionally expanded to include global con

Zhuojin Li, Marco Paolieri, Leana Golubchik

Given the significant advances in machine learning techniques on mobile devices, particularly in the domain of computer vision, in this work we quantitatively study the performance characteristics of 190 real-world vision transformers (ViTs) on mobile devices. Through a comparison with 102 real-world convolutional neural networks (CNNs), we provide insights

Vinayak M. Kumar

Consider a function that is mildly hard for size-$s$ circuits. For sufficiently large $s$, Impagliazzo's hardcore lemma guarantees a constant-density subset of inputs on which the same function is extremely hard for circuits of size $s'<\!\!<s$. Blanc, Hayderi, Koch, and Tan [FOCS 2024] recently showed that the degradation from $s$ to $s'$ in this lemma is q

Yogesh Thakku Suresh, Vishwajeet Shivaji Hogale, Luca-Alexandru Zamfira, Anandavardhana Hegde

We present a transformer-based multimodal framework for generating clinically relevant captions for MRI scans. Our system combines a DEiT-Small vision transformer as an image encoder, MediCareBERT for caption embedding, and a custom LSTM-based decoder. The architecture is designed to semantically align image and textual embeddings, using hybrid cosine-MSE lo

Wenhao Zheng, Chenwei Sun, Wenbo Zhang, Jiancheng Lv

Deep generative models, such as diffusion models, have shown promising progress in image generation and audio generation via simplified continuity assumptions. However, the development of generative modeling techniques for generating multi-modal data, such as parametric CAD sequences, still lags behind due to the challenges in addressing long-range constrain

Frank C. Porter

Robert Cousins has posted a comment on my manuscript on ``Confidence intervals for the Poisson distribution''. His key point is that one should not include in the likelihood non-physical parameter values, even for frequency statistics. This is my response, in which I contend that it can be useful to do so when discussing such descriptive statistics.

Keidai Iiyama, Grace Gao

This paper presents almanac and ephemeris message representation for lunar navigation satellites supporting the Lunar Augmented Navigation System (LANS). The proposed method combines osculating orbital elements, Chebyshev polynomials, and Fourier series to efficiently represent lunar satellite trajectories subject to complex perturbations from lunar gravity

Hongjin Qian, Zheng Liu

AI search depends on linking large language models (LLMs) with vast external knowledge sources. Yet web pages, PDF files, and other raw documents are not inherently LLM-ready: they are long, noisy, and unstructured. Conventional retrieval methods treat these documents as verbatim text and return raw passages, leaving the burden of fragment assembly and conte