October 2025 arXiv papers — page 27

Anchang Bao, Enya Shen, Jianmin Wang

Point containment queries on trimmed surfaces are fundamental to CAD modeling, solid geometry processing, and surface tessellation. Existing approaches such as ray casting and generalized winding numbers often face limitations in robustness and computational efficiency. We propose a fast and numerically stable method for performing containment queries on tri

Asish Jyoti Boruah, Liza Devi, Biplob Sarkar

Accretion near black holes (BHs) is multidimensional, with turbulence, resistivity, thermal transport, and dust dynamics all playing essential roles. In cold accretion discs (ADs) or the region of an AD where magnetic fields (MFs) are negligible (or absent), hydrodynamic (HD) turbulence is probably dominating. However, Magneto-rotational instability (MRI) is

Gautam A. Viruthagiri, Arnuv Tandon, Gerald G. Fuller, Vinny Chandran Suja

Thin film interferometry is a powerful technique for non-invasively measuring liquid film thickness with applications in ophthalmology, but its clinical translation is hindered by the challenges in reconstructing thickness profiles from interference patterns - an ill-posed inverse problem complicated by phase periodicity, imaging noise and ambient artifacts.

Jophy Lin

Diabetic retinopathy (DR), a microvascular complication of diabetes and a leading cause of preventable blindness, is projected to affect more than 130 million individuals worldwide by 2030. Early identification is essential to reduce irreversible vision loss, yet current diagnostic workflows rely on methods such as fundus photography and expert review, which

Guang Yang, Wei Zheng, Xiang Chen, Dong Liang

Code generation has emerged as a critical research area at the intersection of Software Engineering (SE) and Artificial Intelligence (AI), attracting significant attention from both academia and industry. Within this broader landscape, Verilog, as a representative hardware description language (HDL), plays a fundamental role in digital circuit design and ver

Thomas Dunn, Rebecca McElroy, Mirko Krumpe, Scott M. Croom

We investigate the long-term variability of the known Changing Look Active Galactic Nuclei (CL AGN) Mrk 1018, whose second change we discovered as part of the Close AGN Reference Survey (CARS). Collating over a hundred years worth of photometry from scanned photographic plates and five modern surveys we find a historic outburst between ~1935-1960, with varia

Hannah Comiskey, Niamh Cahill, Leontine Alkema, David Fraizer

Engaging the private sector in contraceptive method supply is critical for creating equitable, sustainable, and accessible healthcare systems. To achieve this, it is essential to understand where women obtain their modern contraceptives. While national-level estimates provide valuable insights into overall trends in contraceptive supply, they often obscure v

Anchang Bao, Jie Xu, Enya Shen, Jianmin Wang

Stochastic PDE solvers have emerged as a powerful alternative to traditional discretization-based methods for solving partial differential equations (PDEs), especially in geometry processing and graphics. While off-centered estimators enhance sample reuse in WoS-type Monte Carlo solvers, they introduce correlation artifacts and bias when Green's functions ar

Shreyas Gopal, Ashutosh Anshul, Haoyang Li, Yue Heng Yeo

Discrete audio representations are gaining traction in speech modeling due to their interpretability and compatibility with large language models, but are not always optimized for noisy or real-world environments. Building on existing works that quantize Whisper embeddings for speech-to-unit modeling, we propose disentangling semantic speech content from bac

Justin Lawrence, Nicholas Rouse, Ben Williams

We give criteria to determine when a degree-2 Azumaya algebra with $C_2$-action over a dense open subvariety of a curve extends to the entire curve as an algebra with $C_2$-action. These consist of conditions for the extension of the algebra, combined with a new condition for the extension of the algebra with the action. The new condition is testable by comp

Aayush Aluru, Myra Malik, Samarth Patankar, Spencer Kim

Multi-agent systems (MAS) often achieve higher reasoning accuracy than single models, but their reliance on repeated debates across agents makes them computationally expensive. We introduce SMAGDi, a distillation framework that transfers the debate dynamics of a five-agent Llama-based MAS into a compact Socratic decomposer-solver student. SMAGDi represents d

Katsuki Yamagishi, Norihiro Yoshida, Erina Makihara, Katsuro Inoue

Many cloud services provide REST API accessible to client applications. However, developers often identify specification violations only during testing, as error messages typically lack the detail necessary for effective diagnosis. Consequently, debugging requires trial and error. This study proposes dcFix, a method for detecting and automatically repairing

Xiaoyu Zhou, Jingqi Wang, Yuang Jia, Yongtao Wang

Current 3D scene understanding methods are limited by offline-collected multi-view data or pre-constructed 3D geometry. In this paper, we present ExtractAnything3D (EA3D), a unified online framework for open-world 3D object extraction that enables simultaneous geometric reconstruction and holistic scene understanding. Given a streaming video, EA3D dynamicall

Giancarlo Maldonado Cardenas, Diana C. Gonzalez, Judy C. Guevara, Carlos A. Astudillo

Preamble collision in the random access channel (RACH) is a major bottleneck in massive machine-type communication (mMTC) scenarios, typical of cellular IoT (CIoT) deployments. This work proposes a machine learning-based mechanism for early collision detection during the random access (RA) procedure. A labeled dataset was generated using the RA procedure mes

Kaya Alpturer, Kushal Babel, Aditya Saraf

Optimistic responsiveness -- the ability of a consensus protocol to operate at the speed of the network -- is widely used in consensus protocol design to optimize latency and throughput. However, blockchain applications incentivize validators to play timing games by strategically delaying their proposals, since increased block time correlates with greater re

Mingze Xia, Yuxiao Li, Pu Jiao, Bei Wang

Scientific simulations and observations are producing vast amounts of time-varying vector field data, making it hard to store them for archival purposes and transmit them for analysis. Lossy compression is considered a promising approach to reducing these data because lossless compression yields low compression ratios that barely mitigate the problem. Howeve

Jayant Biradar, Smit Shah, Tanmay Naik

In this paper, we propose a novel hybrid deep learning architecture that synergistically combines Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs), and multi-head attention mechanisms to significantly enhance cybersecurity intrusion detection capabilities. By leveraging the comprehensive UNSW-NB15 dataset containing diverse network traffic patt

Yihang Sun, Encheng Xie, Tao Feng, Jiaxuan You

Large Language Model (LLM) routing has demonstrated strong capability in balancing response quality with computational cost. As users exhibit diverse preferences, personalization has attracted increasing attention in LLM routing, since even identical queries may require different models to generate responses tailored to individual needs. However, existing ap

Wan Jiang, Jing Yan, Xiaojing Chen, Lin Shen

Diffusion models have recently achieved remarkable photorealism, making it increasingly difficult to distinguish real images from generated ones, raising significant privacy and security concerns. In response, we present a key finding: structural edits enhance the reconstruction of real images while degrading that of generated images, creating a distinctive

Kun Chen, Peng Shi, Haibo Qiu, Zhixiong Zeng

Reinforcement learning (RL) with verifiable rewards has recently catalyzed a wave of "MLLM-r1" approaches that bring RL to vision language models. Most representative paradigms begin with a cold start, typically employing supervised fine-tuning (SFT), to initialize the policy before RL. However, SFT-based cold start adopts the reasoning paradigm intertwined

Malaisree P, Youwai S, Kitkobsin T, Janrungautai S

Object detection in civil engineering applications is constrained by limited annotated data in specialized domains. We introduce DINO-YOLO, a hybrid architecture combining YOLOv12 with DINOv3 self-supervised vision transformers for data-efficient detection. DINOv3 features are strategically integrated at two locations: input preprocessing (P0) and mid-backbo

Matthew Edmonds, Lewis A. Williamson, Matthew J. Davis

We study the dynamics of polar core vortices in the easy plane phase of an atomic spin-1 Bose-Einstein condensate confined in a two-dimensional disc potential. A single vortex moves radially outward due to its interaction with background flows that arise from boundary effects. Pairs of opposite sign vortices, which tend to attract, move either radially inwar

Yuxiang Yan, Zhiyuan Zhou, Xin Gao, Guanghao Li

Manipulation in cluttered environments is challenging due to spatial dependencies among objects, where an improper manipulation order can cause collisions or blocked access. Existing approaches often overlook these spatial relationships, limiting their flexibility and scalability. To address these limitations, we propose OrderMind, a unified spatial-aware ma

Ayush Chopra, Santanu Bhattacharya, DeAndrea Salvador, Ayan Paul

Artificial Intelligence is reshaping America's \$9.4 trillion labor market, with cascading effects that extend far beyond visible technology sectors. When AI transforms quality control tasks in automotive plants, consequences spread through logistics networks, supply chains, and local service economies. Yet traditional workforce metrics cannot capture these

Yang Jiao

We develop a first-principles theory for the vibrational density of states (VDOS) and thermal properties of network materials built on stationary correlated disordered point configurations. For scalar (mass--spring) models whose dynamical matrix is a distance-weighted graph Laplacian, we prove that the limiting spectral measure is the pushforward of Lebesgue

Junyi Guo, Pan Du, Xiantao Fan, Yahui Li

We investigate conditional neural fields (CNFs), mesh-agnostic, coordinate-based decoders conditioned on a low-dimensional latent, for spatial dimensionality reduction of turbulent flows. CNFs are benchmarked against Proper Orthogonal Decomposition and a convolutional autoencoder within a unified encoding-decoding framework and a common evaluation protocol t

Qingdong Cai, Charith Abhayaratne

Class Activation Mapping (CAM) methods are widely applied in weakly supervised learning tasks due to their ability to highlight object regions. However, conventional CAM methods highlight only the most discriminative regions of the target. These highlighted regions often fail to cover the entire object and are frequently misaligned with object boundaries, th

Binxuan Ru

In this paper, we study the local existence and uniqueness of strong solutions for Cauchy problem of three-dimensional inhomogeneous incompressible Navier-Stokes-Vlasov equations, which are influenced by Young-Pil Choi, Bongsuk Kwon [London Mathematical Society 28 (2015), pp. 3309-3336]\cite{12L}. As for the global well-posedness of the solution of the inhom

Ao-Xiang Chang, Yu Su, Zi-Fan Zhu, Yao Wang

We introduce the \textit{Phase-Coupled Caldeira-Leggett} (PCL) model of quantum dissipation and develop an exact framework for its dynamics. Unlike the conventional Caldeira-Leggett model with linear system-bath coupling $H_{\mathrm{SB}}\propto\hat F$, the PCL model features an exponential interaction $H_{\mathrm{SB}}\propto e^{i\lambda \hat F}$, where $\hat

Jialong Sun, Xinpeng Ling, Jiaxuan Zou, Jiawen Kang

The inherent autocorrelation of time series data presents an ongoing challenge to multivariate time series prediction. Recently, a widely adopted approach has been the incorporation of frequency domain information to assist in long-term prediction tasks. Many researchers have independently observed the spectral bias phenomenon in neural networks, where model

Chao Song, Zhiyuan Liu, Han Huang, Liang Wang

Designing enzyme backbones with substrate-specific functionality is a critical challenge in computational protein engineering. Current generative models excel in protein design but face limitations in binding data, substrate-specific control, and flexibility for de novo enzyme backbone generation. To address this, we introduce EnzyBind, a dataset with 11,100

Hisayoshi Muramatsu

In linear time-invariant systems, the sensitivity function to disturbances is designed under a sensitivity tradeoff known as the waterbed effect. To compensate for a quasiperiodic disturbance, a quasiperiodic disturbance observer using time delays was proposed. Its sensitivity function avoids the sensitivity tradeoff, achieving wideband harmonic suppression

Yongjin Han, Suhyun Kim

Lipschitz constant is a fundamental property in certified robustness, as smaller values imply robustness to adversarial examples when a model is confident in its prediction. However, identifying the worst-case adversarial examples is known to be an NP-complete problem. Although over-approximation methods have shown success in neural network verification to a

Xiyu Zhang, Chong Bao, Yipeng Chen, Hongjia Zhai

3D reconstruction of indoor and urban environments is a prominent research topic with various downstream applications. However, existing geometric priors for addressing low-texture regions in indoor and urban settings often lack global consistency. Moreover, Gaussian Splatting and implicit SDF fields often suffer from discontinuities or exhibit computational

Uzair Akbar, Niki Kilbertus, Hao Shen, Krikamol Muandet

The technique of data augmentation (DA) is often used in machine learning for regularization purposes to better generalize under i.i.d. settings. In this work, we present a unifying framework with topics in causal inference to make a case for the use of DA beyond just the i.i.d. setting, but for generalization across interventions as well. Specifically, we a

Fan Liu, Behrooz Farkiani, Patrick Crowley

Accurate network-traffic forecasting enables proactive capacity planning and anomaly detection in Internet Service Provider (ISP) networks. Recent advances in time-series foundation models (TSFMs) have demonstrated strong zero-shot and few-shot generalization across diverse domains, yet their effectiveness for computer networking remains unexplored. This pap

Jin Seong, Jiyun Park, Wencke Liermann, Hongseok Choi

The dynamic nature of information necessitates continuously updating large vision-language models (LVLMs). While recent knowledge editing techniques hint at promising directions, they often focus on editing a single modality (vision or language) in isolation. This prevalent practice neglects the inherent multimodality of LVLMs and the continuous nature of kn

Marwan Haddara, Howard M. Wiseman, Eric G. Cavalcanti

The assumption of a deterministic local hidden variable model constrains the experimentally accessible statistics in a Bell experiment to be contained in the Bell-local polytope. But what if the outputs for only a subset of the measurements at each site are predetermined by the model? In this work, we thoroughly explore this concept of `partial determinism',

Jiarong Du, Zhan Jin, Peijun Yang, Juan Liu

Audio-visual speech enhancement (AVSE) is a task that uses visual auxiliary information to extract a target speaker's speech from mixed audio. In real-world scenarios, there often exist complex acoustic environments, accompanied by various interfering sounds and reverberation. Most previous methods struggle to cope with such complex conditions, resulting in

Yuen Chen, Yulun Wu, Samuel Sharpe, Igor Melnyk

Many real-world prediction tasks, particularly those involving entities such as customers or patients, involve both {sequential} and {relational} data. Each entity maintains its own sequence of events while simultaneously engaging in relationships with others. Existing methods in sequence and graph modeling often overlook one modality in favor of the other.

Davit Aslanyan

Thunderstorm Ground Enhancements (TGEs) are bursts of high-energy particle fluxes detected at Earth's surface, linked to the Relativistic Runaway Electron Avalanche (RREA) mechanism within thunderclouds. Accurate detection of TGEs is vital for advancing atmospheric physics and radiation safety, but event selection methods heavily rely on expert-defined thres

Woojin Cho, Kookjin Lee, Noseong Park, Donsub Rim

We present a data-driven dimensionality reduction method that is well-suited for physics-based data representing hyperbolic wave propagation. The method utilizes a specialized neural network architecture called low rank neural representation (LRNR) inside a hypernetwork framework. The architecture is motivated by theoretical results that rigorously prove the

Jiahong Chen, Jing Wang, Long Chen, Chuwei Cai

Vision-language-action (VLA) models have significantly advanced robotic manipulation by integrating vision-language models (VLMs), and action decoders into a unified architecture. However, their deployment on resource-constrained edge devices, such as mobile robots or embedded systems (e.g., Jetson Orin Nano), remains challenging due to high computational de

Yutong Zheng, Qingna Li

Adversarial learning has been attracting more and more attention thanks to the fast development of machine learning and artificial intelligence. However, due to the complicated structure of most machine learning models, the mechanism of adversarial attacks is not well interpreted. How to measure the effect of attacks is still not quite clear. In this paper,

Wenyan Xu, Dawei Xiang, Tianqi Ding, Weihai Lu

Misinformation and disinformation demand fact checking that goes beyond simple evidence-based reasoning. Existing benchmarks fall short: they are largely single modality (text-only), span short time horizons, use shallow evidence, cover domains unevenly, and often omit full articles -- obscuring models' real-world capability. We present MMM-Fact, a large-sca

Noa Kemp, Cynthia Tarlao, Catherine Guastavino, B. Suresh Krishna

It is known that listeners lose the ability to discriminate the direction of motion of a revolving sound (clockwise vs. counterclockwise) beyond a critical velocity ("the upper limit"), likely due to degraded front-back discrimination. Little is known about how this ability is affected by simultaneously present distractor sounds, despite the real-life import

Zhentong Shao, Nanpeng Yu, Daniel Wong

With the rapid growth of artificial intelligence (AI) and cloud services, data centers have become critical infrastructures driving digital economies, with increasing energy demand heightening concerns over electricity use and carbon emissions, emphasizing the need for carbon-aware infrastructure planning. Most studies assume static power systems, focus only

Ruichen Qiu, Jiajun Tan, Jiayue Pu, Honglin Wang

Large Language Models (LLMs) demonstrate remarkable capabilities, but their training on massive corpora poses significant risks from memorized sensitive information. To mitigate these issues and align with legal standards, unlearning has emerged as a critical technique to selectively erase specific knowledge from LLMs without compromising their overall perfo

Idriss Nguepi Nguefack, Mara Finkelstein, Toadoum Sari Sakayo

This research article examines the effectiveness of various pretraining strategies for developing machine translation models tailored to low-resource languages. Although this work considers several low-resource languages, including Afrikaans, Swahili, and Zulu, the translation model is specifically developed for Lingala, an under-resourced African language,

Drake Brown, Trevor Garrity, Daniel Perkins, Davis Hunter

We develop a cost functional and state-space equations to model the problem of herding m sheep to the origin using n dogs. Our initial approach uses solve_bvp to approximate optimal control trajectories. But this method often fails to converge due to the system's high dimensionality and nonlinearity. However, with a well-chosen initial guess and carefull

Yahong Yang, Sun Lee, Jeff Calder, Wenrui Hao

We derive an energy-based continuum limit for $\varepsilon$-graphs endowed with a general connectivity functional. We prove that the discrete energy and its continuum counterpart differ by at most $O(\varepsilon)$; the prefactor involves only the $W^{1,1}$-norm of the connectivity density as $\varepsilon\to0$, so the error bound remains valid even when that

Di Zhang

This paper introduces the Neural Differential Manifold (NDM), a novel neural network architecture that explicitly incorporates geometric structure into its fundamental design. Departing from conventional Euclidean parameter spaces, the NDM re-conceptualizes a neural network as a differentiable manifold where each layer functions as a local coordinate chart,

Di Zhang

This paper introduces a novel paradigm for the analysis and verification of concurrent programs -- the Singularity Theory. We model the execution space of a concurrent program as a branched topological space, where program states are points and state transitions are paths. Within this framework, we characterize deadlocks as attractors and livelocks as non-co

Jose G Rivera Lizarralde, Aditya Potnis, Abhishek Saha

The influence of flow non-uniformity and unsteadiness on premixed flames is of considerable interest due to its direct relevance to practical combustion systems. The steady counterflow flame has long served as a canonical configuration for investigating flame dynamics under controlled, spatially non-uniform conditions. A commonly studied variation, referred

Marko Medvedev, Kaifeng Lyu, Zhiyuan Li, Nathan Srebro

We consider training and testing on mixture distributions with different training and test proportions. We show that in many settings, and in some sense generically, distribution shift can be beneficial, and test performance can improve due to mismatched training proportions, even if the components are unrelated and with no transfer between components. In a

Rui Fang, Richard Tsai

Hamiltonian systems with widely separated timescales arise in molecular dynamics, classical mechanics, and plasma physics. Long-time simulation of such systems is expensive because standard direct integrators generally need to resolve the fastest dynamics even when the quantities of interest evolve on much slower scales. The cost is particularly severe for l

Hai Zhu

For fixed positive integers $n,m$, let $\mathrm{Mat}_{n\times m}(\mathbb{C})$ be the affine space consisting of all $n\times m$ complex matrices, and let $\mathbb{C}[\mathbf{x}_{n\times m}]$ be its coordinate ring. For $0\le r\le\min\{m,n\}$, we apply the orbit harmonics method to the finite matrix loci $\mathcal{Z}_{n,m,r}$ of rook placements with exactly $

Mihai Mazilu, Luca Giacomoni, George Parisis

Learning-based congestion control (CC), including Reinforcement-Learning, promises efficient CC in a fast-changing networking landscape, where evolving communication technologies, applications and traffic workloads pose severe challenges to human-derived, static CC algorithms. Learning-based CC is in its early days and substantial research is required to und

Yong-Chao Shen

Recently, Andrews and EI Bachraoui obtained several iden tities on two-colored partitions. While solving open problems they posed, Chen and Zhou derived a number of identities using analytic methods and asked for combinatorial proofs. In this note, we provide the requested combinatorial proofs. Additionally, we derive several new identities and provide combi

Minghai Lu, Zhe Zhou, Danning Xie, Songlin Jia

Formal verification via theorem proving enables the expressive specification and rigorous proof of software correctness, but it is difficult to scale due to the significant manual effort and expertise required. While Large Language Models (LLMs) show potential in proof generation, they frequently produce incorrect proofs on the first attempt and require addi

Zhuo Chen, Fei Wang, Zixuan Li, Zhao Zhang

Knowledge Base Question Answering (KBQA) aims to answer natural-language questions over a structured Knowledge Base (KB). Recent work improves KBQA by adopting an agentic reasoning paradigm, in which Large Language Models (LLMs) iteratively decompose a question, generate its corresponding logical queries, and interact with the KB to derive the answer. Howeve

Liangjun Wu

This study presents new geochronological constraints for the Niluo Member within the slope-basin facies of the Late Triassic Nanpanjiang Basin, eastern Tethys. The basin underwent a significant marine-to-continental transition during this period. Previous biostratigraphic studies on platform facies were hindered by inconclusive conodont zonation, leaving the

Ziying Ma, Xiaoxia Zhang, Taotao Fang, Junfeng Wang

Detecting gaseous debris disks around white dwarfs offers a unique window into the ultimate fate of planetary systems and the composition of accreted planetary material. Here we present a systematic search for such disks through the Ca II infrared triplet using the Dark Energy Spectroscopic Instrument (DESI) Early Data Release. From a parent sample of 2706 s

Chuxun Liu, Debo Cheng, Qingfeng Chen, Jiangzhang Gan

Graph neural networks (GNNs) excel on relational data by passing messages over node features and structure, but they can amplify training data biases, propagating discriminatory attributes and structural imbalances into unfair outcomes. Many fairness methods treat bias as a single source, ignoring distinct attribute and structure effects and leading to subop

Aleksandra Urbańczyk, Krzysztof Czech, Piotr Urbańczyk, Marek Kisiel-Dorohinicki

This paper introduces the Trust-Based Optimization (TBO), a novel extension of the island model in evolutionary computation that replaces conventional periodic migrations with a flexible, agent-driven interaction mechanism based on trust or reputation. Experimental results demonstrate that TBO generally outperforms the standard island model evolutionary algo

Chanhyeong Yang, Taehoon Song, Jihwan Park, Hyunwoo J. Kim

Zero-shot Human-Object Interaction detection aims to localize humans and objects in an image and recognize their interaction, even when specific verb-object pairs are unseen during training. Recent works have shown promising results using prompt learning with pretrained vision-language models such as CLIP, which align natural language prompts with visual fea

Hyunsik Yoo, Ting-Wei Li, SeongKu Kang, Zhining Liu

While large language models (LLMs) achieve strong performance in recommendation, they face challenges in continual learning as users, items, and user preferences evolve over time. Existing LoRA-based continual methods primarily focus on preserving performance on previous tasks, but this overlooks the unique nature of recommendation: the goal is not to predic

Weijia Zhang, Zijia Liu, Haoru Li, Haoqi Chen

Recent advances in text-only large language models (LLMs), such as DeepSeek-R1, demonstrate remarkable reasoning ability. However, these models remain fragile or entirely incapable when extended to multi-modal tasks. Existing approaches largely rely on single-form captions, which lack diversity and often fail to adapt across different types of Visual Questio

Peilin Tan, Liang Xie, Churan Zhi, Dian Tu

Stock movement prediction remains fundamentally challenging due to complex temporal dependencies, heterogeneous modalities, and dynamically evolving inter-stock relationships. Existing approaches often fail to unify structural, semantic, and regime-adaptive modeling within a scalable framework. This work introduces H3M-SSMoEs, a novel Hypergraph-based MultiM

Sascha Schediwy, Aliesha Aden, Benjamin Dix-Matthews, Alex Frost

TeraNet is a new three-node OGS network that has been established in Western Australia. The network is built to support a broad range of space missions operating between LEO and the Moon, using both conventional and advanced optical technologies developed at UWA. It is designed to be spacecraft and mission agnostic, able to be adapted for compatibility with

Jon M. Miller, Liyi Gu, John Raymond, Laura Brenneman

GRS 1915$+$105 was the stellar-mass black hole that best reproduced key phenomena that are also observed in Type-1 active galactic nuclei. In recent years, however, it has evolved to resemble a Type-2 or Compton-thick AGN. Herein, we report on the first XRISM observation of GRS 1915$+$105. The high-resolution Resolve calorimeter spectrum reveals that a sub-E

Wencai Yan, Wanming Hao, Yajun Fan, Yabo Guo

In this paper, we investigate a six-dimensional movable antenna (6DMA)-enabled wideband terahertz (THz) communication system with sub-connected hybrid beamforming architecture at the base station (BS). In particular, the three-dimensional (3D) position and 3D rotation of each 6DMA surface can be flexibly reconfigured to mitigate the beam squint effects inste

Nourah M Salem, Elizabeth White, Michael Bada, Lawrence Hunter

Coreference resolution in biomedical texts presents unique challenges due to complex domain-specific terminology, high ambiguity in mention forms, and long-distance dependencies between coreferring expressions. In this work, we present a comprehensive evaluation of generative large language models (LLMs) for coreference resolution in the biomedical domain. U

Guibin Sun, Jinhu Lü, Kexin Liu, Zhenqian Wang

Swarms evolving from collective behaviors among multiple individuals are commonly seen in nature, which enables biological systems to exhibit more efficient and robust collaboration. Creating similar swarm intelligence in engineered robots poses challenges to the design of collaborative algorithms that can be programmed at large scales. The assignment-based

Xiang liu, Zhaoxiang Liu, Huan Hu, Zipeng Wang

Recent advancements in personalized image generation have significantly improved facial identity preservation, particularly in fields such as entertainment and social media. However, existing methods still struggle to achieve precise control over facial attributes in a per-subject-tuning-free (PSTF) way. Tuning-based techniques like PreciseControl have shown

Xiongfeng Zhan, Xueyi Huang, Jin-Xin Zhou

This paper establishes new eigenvalue bounds for combinatorial Laplacians of simplicial complexes, extending previous results for flag complexes by Lew (2024) and general complexes by Shukla and Yogeshwaran (2020). Using elementary matrix-theoretic methods, we derive lower bounds for the eigenvalues of the combinatorial Laplacian in terms of the graph Laplac

Way Wang, Zhongshui Ma, Hai-qing Lin

Understanding the pairing mechanism of unconventional superconductors remains a core challenge in condensed matter physics, particularly the ongoing debate over whether the related effects caused by electron-electron interactions unify various unconventional superconductors (UcSs). To address this challenge, it is necessary to establish a universal quantitat

Yuepeng Guan, Masatoshi Yamada

We investigate the QCD chiral phase transition at finite temperature and finite baryon density using the functional Renormalization Group (fRG). While conventional fRG studies often employ techniques such as dynamical bosonization to regularize divergences, we instead pursue the weak solution of the fRG equations which allows for non-analytic behavior in the

Will Wolf

Card games are widely used to study sequential decision-making under uncertainty, with real-world analogues in negotiation, finance, and cybersecurity. These games typically fall into three categories based on the flow of control: strictly sequential (players alternate single actions), deterministic response (some actions trigger a fixed outcome), and unboun

Albert Espinal, V. Sanchez Padilla, Yesenia Cevallos

The performance of Dropbox, Google Drive, and OneDrive cloud storage services was evaluated under Wi-Fi and LTE network conditions during multimedia file uploads. Traffic was captured using Wireshark, and key metrics (including delay, jitter, bandwidth, and packet loss) were analyzed. Google Drive maintained the most consistent performance across both types

Jihye Hwang, Patricio Sanhueza, Josep Miquel Girart, Ian W. Stephens

We investigate the magnetic field orientation and strength in the massive star-forming region G35.20-0.74N (G35), using polarized dust emission data obtained with the Atacama Large Millimeter/submillimeter Array (ALMA) as part of the Magnetic fields in Massive star-forming Regions (MagMaR) survey. The G35 region shows a filamentary structure (a length of $\s

Fahimeh Orvati Nia, Amirmohammad Mohammadi, Salim Al Kharsa, Pragati Naikare

In this work, we introduce Neighborhood Feature Pooling (NFP), a novel pooling layer designed to enhance texture-aware representation learning for remote sensing image classification. The proposed NFP layer captures relationships between neighboring spatial features by aggregating local similarity patterns across feature dimensions. Implemented using standar

Yanfang Zhang, Shu-Qin Zhang

It is well known that if a metric space is uniformly disconnected, then its conformal dimension is zero. First, we characterize when a self-affine sponge of Lalley-Gatzouras type is uniformly disconnected. Thanks to this characterization, we show that a self-affine sponge of Lalley-Gatzouras type has conformal dimension zero if and only if it is uniformly di

Keisuke Imoto

Annotating time boundaries of sound events is labor-intensive, limiting the scalability of strongly supervised learning in audio detection. To reduce annotation costs, weakly-supervised learning with only clip-level labels has been widely adopted. As an alternative, partial label learning offers a cost-effective approach, where a set of possible labels is pr

Sai Likhith Karri, Ansh Saxena

This study examines the effectiveness of spatio-temporal modeling and the integration of spatial attention mechanisms in deep learning models for underwater object detection. Specifically, in the first phase, the performance of temporal-enhanced YOLOv5 variant T-YOLOv5 is evaluated, in comparison with the standard YOLOv5. For the second phase, an augmented v

Andrew Clark, Jack Moursounidis, Osmaan Rasouli, William Gan

We introduce Bounded Numerical Differentiation (BOND), a perturbative method for estimating the gradients of black-box functions. BOND is distinguished by its formulation, which adaptively bounds perturbations to ensure accurate sign estimation, and by its implementation, which operates at black-box interfaces. This enables BOND to be more accurate and scala

Emily Dunn, Bavand Keshavarz, Earl Dowell

The interaction of 'walking droplets' and capillary waves in a weakly subcritical Faraday wave experiment has been studied as a hydrodynamic analog to Bohmian quantum mechanics (see "Hydrodynamic Quantum Analogs", J. Bush and A. Oza, Rep. Prog. Physics (2021)). We report here experimental results of walking droplets interacting with supercritical Faraday wav

Sourabh Karmakar, Cameron J. Turner

Accurate calibration of a Stewart platform is important for their precise and efficient operation. However, the calibration of these platforms using forward kinematics is a challenge for researchers because forward kinematics normally generates multiple feasible and unfeasible solutions for any pose of the moving platform. The complex kinematic relations amo

Ravi Kiran, A. Taraphder

Chiral phonons, originally identified in two-dimensional hexagonal lattices and later extended to kagome, square, and other lattices, have been extensively studied as manifestations of broken inversion and time-reversal symmetries in vibrational dynamics. In this work, we investigate the vibrational dynamics of the two-dimensional square-octagon lattice usin

Manjunath Prasad Holenarasipura Rajiv, B. M. Vidyavathi

Zero-shot scene understanding in real-world settings presents major challenges due to the complexity and variability of natural scenes, where models must recognize new objects, actions, and contexts without prior labeled examples. This work proposes a vision-language integration framework that unifies pre-trained visual encoders (e.g., CLIP, ViT) and large l

Gabin Taibi, Lucia Gomez

Traditional approaches to semantic polarity in computational linguistics treat sentiment as a unidimensional scale, overlooking the multidimensional structure of language. This work introduces TOPol (Topic-Orientation POLarity), a semi-unsupervised framework for reconstructing and interpreting multidimensional narrative polarity fields under human-on-the-loo

Tarhik Wallace, Patrick Antolin

Magnetic reconnection is a proposed mechanism for nanojets associated with coronal heating. We investigate the characteristics of reconnection-driven nanojets just before and during a prominence eruption using the High Resolution Imager (HRI) of the Extreme Ultraviolet imager (EUI) aboard Solar Orbiter during its perihelion on September 30, 2024. Extreme UV

Yusen Peng, Sachin Kumar

Recently, the advances in vision-language models, including contrastive pretraining and instruction tuning, have greatly pushed the frontier of multimodal AI. However, owing to the large-scale and hence expensive pretraining, the efficiency concern has discouraged researchers from attempting to pretrain a vision language model from scratch. In this work, we

Arnav Sood, James Best

A sender persuades a strategically naive decisionmaker (DM) by committing privately to an experiment. Sender's choice of experiment is unknown to the DM, who must form her posterior beliefs nonparametrically by applying some learning rule to an IID sample of (state, message) realizations. We show that, given mild regularity conditions, the empirical payoff f

Chul-Moon Yoo, Masashi Kimura, Akihiro Ishibashi, Rikuto Ohashi

We examine the possible characterization of ringdown waves in a dynamical Vaidya spacetime using the Penrose limit geometry around the dynamical photon sphere. In the case of a static spherically symmetric black hole spacetime, it is known that the quasinormal frequency in the eikonal limit can be characterized by the angular velocity and the Lyapunov expone

Chengyu Wu, Jiaqing Yang

In this paper, we extend and simplify the methods in [13] to improve the results on uniqueness of the boundary determination for the Maxwell equation. In particular, we show that the electromagnetic parameters are uniquely determined to infinite order at the boundary from the local admittance map, disregarding the presence of an unknown obstacle, where actua

Jingzhou Liu

In this work, we study the nonlinear dynamics of a shallow neural network trained with mean-squared loss and leaky ReLU activation. Under Gaussian inputs and equal layer width k, (1) we establish, based on the equivariant gradient degree, a theoretical framework, applicable to any number of neurons k>= 4, to detect bifurcation of critical points with associa

Ken Kamano

We give a formula for a $q$-analogue of Boyadzhiev-Mneimneh-type binomial sums of finite multi-polylogarithms. In the limit as $q\to 1$, this formula reduces to an identity equivalent to the Sakugawa-Seki identities. We also give a formula for Boyadzhiev-Mneimneh-type sums corresponding to the Cauchy binomial theorem.

Andriy Myronenko, Dong Yang, Yufan He, Daguang Xu

In this work, we describe our solution to the BraTS 2023 cluster of challenges using Auto3DSeg from MONAI. We participated in all 5 segmentation challenges, and achieved the 1st place results in three of them: Brain Metastasis, Brain Meningioma, BraTS-Africa challenges, and the 2nd place results in the remaining two: Adult and Pediatic Glioma challenges.

Robin Maisch, Larissa Schmid, Timur Sağlam, Nils Niehues

Plagiarism detection in programming education faces growing challenges due to increasingly sophisticated obfuscation techniques, particularly automated refactoring-based attacks. While code plagiarism detection systems used in education practice are resilient against basic obfuscation, they struggle against structural modifications that preserve program beha