November 2025 arXiv papers — page 196

Liwei Luo, Shuaitengyuan Li, Dongwei Ren, Qilong Wang

Recently, remarkable progress has been made in large-scale pre-trained model tuning, and inference efficiency is becoming more crucial for practical deployment. Early exiting in conjunction with multi-stage predictors, when cooperated with a parameter-efficient fine-tuning strategy, offers a straightforward way to achieve an inference-efficient model. Howeve

Fei Zhao, Zhong-Qiu Wang

Acoustic echo cancellation (AEC) remains challenging in real-world environments due to nonlinear distortions caused by low-cost loudspeakers and complex room acoustics. To mitigate these issues, we introduce a dual-microphone configuration, where an auxiliary reference microphone is placed near the loudspeaker to capture the nonlinearly distorted far-end sig

Miguel Costa, Arthur Vandervoort, Martin Drews, Karyn Morrissey

Climate change will cause an increase in the frequency and severity of flood events, prompting the need for cohesive adaptation policymaking. Designing effective adaptation policies, however, depends on managing the uncertainty of long-term climate impacts. Meanwhile, such policies can feature important normative choices that are not always made explicit. We

Aristides Moustakas, Ioannis N Vogiatzakis

Island endemism is shaped by complex interactions among environmental, ecological, and evolutionary factors, yet the relative contributions of topography, climate, and land cover remain incompletely quantified. We investigated the drivers of endemic plant richness across Crete, a Mediterranean biodiversity hotspot, using spatially explicit data on species di

Gang Bao, Yaohua Zang

Solving inverse problems governed by partial differential equations (PDEs) is central to science and engineering, yet remains challenging when measurements are sparse, noisy, or when the underlying coefficients are high-dimensional or discontinuous. Existing deep learning approaches either require extensive labeled datasets or are limited to specific measure

Tianliang Feng, Ziwei Xu

Understanding the role of cap structure during the nucleation and growth of single-walled carbon nanotubes (SWCNTs) is essential for achieving chirality-controlled synthesis. In this work, we propose a novel and intuitive algorithm to determine the chirality of nascent carbon caps by tracking the relative shifts of six pentagons within a topological coordina

Philipp Reis, Philipp Rigoll, Christian Steinhauser, Jacob Langner

Modern AI systems are increasingly constrained not by model capacity but by the quality and diversity of their data. Despite growing emphasis on data-centric AI, most datasets are still gathered in an open-loop manner which accumulates redundant samples without feedback from the current coverage. This results in inefficient storage, costly labeling, and limi

Miguel Costa, Arthur Vandervoort, Martin Drews, Karyn Morrissey

Urban flooding is expected to increase in frequency and severity as a consequence of climate change, causing wide-ranging impacts that include a decrease in urban Quality of Life (QoL). Meanwhile, policymakers must devise adaptation strategies that can cope with the uncertain nature of climate change and the complex and dynamic nature of urban flooding. Rein

Xuan Ouyang, Senan Wang, Bouzhou Wang, Siyuan Xiahou

Short-video platforms have become a central medium in the modern Internet landscape, where efficient information delivery and strong interactivity are reshaping user engagement and cultural dissemination. Among the various forms of user interaction, comments play a vital role in fostering community participation and enabling content re-creation. However, gen

Souvik Rana, Arul Menezes, Ashish Kulkarni, Chandra Khatri

Tokenizers play a crucial role in determining the performance, training efficiency, and the inference cost of Large Language Models (LLMs). Designing effective tokenizers for multilingual LLMs is particularly challenging due to diverse scripts and rich morphological variation. While subword methods like Byte Pair Encoding (BPE) are widely adopted, their effe

Alberto Abadie, Mehrdad Ghadiri, Ali Jadbabaie, Mahyar JafariNodeh

This article introduces a leave-one-out regression adjustment (LOORA) for estimating average treatment effects in randomized controlled trials. In finite samples, LOORA removes the bias of conventional regression adjustment and yields exact variance formulas for regression-adjusted Horvitz-Thompson and difference-in-means estimators. Ridge regularization cur

Yi-Fei Liu, Yi-Long Lu, Di He, Hang Zhang

Psychological constructs within individuals are widely believed to be interconnected. We investigated whether and how Large Language Models (LLMs) can model the correlational structure of human psychological traits from minimal quantitative inputs. We prompted various LLMs with Big Five Personality Scale responses from 816 human individuals to role-play thei

Seokbeom Kim, Taite LaGrange, Mathieu Rundström, Arpan Sadhukhan

We extend the list of tournaments $S$ for which the complete structural description for tournaments excluding $S$ as a subtournament is known. Specifically, let $\Delta(1, 2, 2)$ be a tournament on five vertices obtained from a cyclic triangle by substituting a two-vertex tournament for two of its vertices. In this paper, we show that tournaments excluding $

Feilong Wang, Jinlin Fan, Ruolin Chai, Zhibin Zhao

We investigate the mean-field scattering dynamics of a quasi-one-dimensional Bose--Einstein condensate interacting with a Rosen--Morse potential. For specific potential and nonlinearity parameters, we derive analytically exact, degenerate scattering states (doubly or triply degenerate) exhibiting perfect transmission. Using the Bogoliubov--de Gennes approach

Jie Zhu, Hao Li

The static spherical vacuum solution in a bumblebee gravity model where the bumblebee field $B_\mu$ has a two-component space-like, light-like, and time-like vacuum expectation value $b_\mu$ is studied. Based on the results, we present a comprehensive classification of the static spherical vacuum solutions in bumblebee gravity with general vacuum expectation

Zhi-Bin Zhu, Hao-Lei Chen, Xu-Guang Huang

We study chiral symmetry breaking and restoration in accelerating and rotating frames using low-energy effective models. By analyzing the chiral condensate in Rindler coordinates, we show that different renormalization schemes lead to distinct conclusions in accelerating frame: the scheme with subtracting divergences in Rindler vacuum supports an acceleratio

Yong Huang, Zhibo Dong, Xiaoguang Yang, Dalong Zhang

Due to the openness of the wireless medium, smartphone users are susceptible to user privacy attacks, where user privacy information is inferred from encrypted Wi-Fi wireless traffic. Existing attacks are limited to recognizing mobile apps and their actions and cannot infer the smartphone user identity, a fundamental part of user privacy. To overcome this li

Shantanu Agarwal, Joel Barry, Elizabeth Boschee, Scott Miller

Machine Translation for English Retrieval of Information in Any Language (MATERIAL) is an IARPA initiative targeted to advance the state of cross-lingual information retrieval (CLIR). This report provides a detailed description of Information Sciences Institute's (ISI's) Summarization and domain-Adaptive Retrieval Across Language's (SARAL's) effort for MATER

Alexander Htet Kyaw, Lenin Ravindranath Sivalingam

We present a node-based storytelling system for multimodal content generation. The system represents stories as graphs of nodes that can be expanded, edited, and iteratively refined through direct user edits and natural-language prompts. Each node can integrate text, images, audio, and video, allowing creators to compose multimodal narratives. A task selecti

Tomonori Uesugi, Yoshihiro Ishi, Yoshiharu Mori

We propose a novel acceleration scheme for fixed-field accelerators (FFAs), in which RF buckets with harmonic numbers $h = 1$ and $h = 2$ are time-sequenced to form a single, continuous acceleration path. This approach completes acceleration in two RF frequency sweeps, thereby reducing the total frequency sweep range and shortening the repetition period. The

Manjunath Bestha, Thirupathi Sivarani, Bachar Wehbe, Amirul Hasan

Atmospheric dispersion causes light from celestial objects with different wavelengths to refract at varying angles as it passes through Earth's atmosphere. This effect results in an elongated image at the focal plane of a telescope and diminishes fiber coupling efficiency into spectrographs. We propose an optical design that incorporates a Rotational Atmosph

Nobuo Iida, Tatsumasa Suzuki

We prove that an infinite family of three-strand pretzel knots is not squeezed. In particular, we show that $P(4, -3, 5)$ is not squeezed. This answers a question posed by Lewark (2024). Our proof is obtained by comparing the Rasmussen invariant with the $q_M$-invariant introduced by Iida and Taniguchi.

Yaohong Chen, Luchi Zhang, Yiju Deng, Yanze Yu

The accurate prediction of airfoil pressure distribution is essential for aerodynamic performance evaluation, yet traditional methods such as computational fluid dynamics (CFD) and wind tunnel testing have certain bottlenecks. This paper proposes a hybrid deep learning model combining a Convolutional Neural Network (CNN) and a Chebyshev-enhanced Kolmogorov-A

Yang Guo, Stefan Streif

We propose a moving horizon estimation (MHE) scheme for general nonlinear constrained systems with parametric or static nonlinear uncertainties and a predetermined state feedback controller that is assumed to robustly stabilize the system in the absence of estimation errors. Leveraging integral quadratic constraints (IQCs), we introduce a new notion of detec

Tianhao Mao, Le Liang, Jie Yang, Hao Ye

This paper presents Multimodal-Wireless, a large-scale open-source dataset for multimodal sensing and communication research. The dataset is generated through an integrated and customizable data pipeline built upon the CARLA simulator and Sionna framework, and features high-resolution communication channel state information (CSI) fully synchronized with five

Pengyu Jie, Wanquan Liu, Rui He, Yihui Wen

Augmentation for dense prediction typically relies on either sample mixing or generative synthesis. Mixing improves robustness but misaligned masks yield soft label ambiguity. Diffusion synthesis increases apparent diversity but, when trained as common samples, overlooks the structural benefit of mask conditioning and introduces synthetic-real domain shift.

Sujoy Majumder, Nabadwip Sarkar, Molla Basir Ahamed

Logarithmic coefficients play a crucial role in the theory of univalent functions. In this study,we focus on the classes $\mathcal{S}_e^\ast$ and $\mathcal{C}_e$ of starlike and convex functions, respectively, \begin{align*} \mathcal{S}_e^\ast := \left\{ f \in \mathcal{S} : \frac{zf'(z)}{f(z)} \prec e^z, \ z \in \mathbb{D} \right\}, \end{align*} and \begin{a

Md Ashad Alam

RKUM is an R package developed for implementing robust kernel-based unsupervised methods. It provides functions for estimating the robust kernel covariance operator (CO) and the robust kernel cross-covariance operator (CCO) using generalized loss functions instead of the conventional quadratic loss. These operators form the foundation of robust kernel learni

Muneer Ahmad, M Sadik Batcha

The present study attempts to highlight the research output generated in Russia in coronary artery disease (CAD) research during the period 1990-2019 to understand the distribution of research output, top journals for publications, and most prolific authors, authorship pattern, and citation pattern. This study is based on secondary data extracted from the Sc

Wenchang Lei, Ping Zou, Yue Wang, Feng Sun

Large language models (LLMs) exhibit strong semantic understanding, yet struggle when user instructions involve ambiguous or conceptually misaligned terms. We propose the Language Graph Model (LGM) to enhance conceptual clarity by extracting meta-relations-inheritance, alias, and composition-from natural language. The model further employs a reflection mecha

Pengcheng Su, Haibo Cheng, Ping Wang

The shuffle model, which anonymizes data by randomly permuting user messages, has been widely adopted in both cryptography and differential privacy. In this work, we present the first systematic study of the Bayesian advantage in re-identifying a user's message under the shuffle model. We begin with a basic setting: one sample is drawn from a distribution $P

Ruting Cheng, Boyuan Feng, Yijiang Zheng, Chuhui Qiu

Accurately assessing the risk of cesarean section (CS) delivery is critical, especially in settings with limited medical resources, where access to healthcare is often restricted. Early and reliable risk prediction allows better-informed prenatal care decisions and can improve maternal and neonatal outcomes. However, most existing predictive models are tailo

Qiankun Shi, Xiao Wang

In this paper, we study nonconvex constrained optimization problems with both equality and inequality constraints, covering deterministic and stochastic settings. We propose a novel first-order algorithm framework that employs a decomposition strategy to balance objective reduction and constraint satisfaction, together with adaptive update of stepsizes and m

Patel Adam Burhansab, M Sadik Batcha, Muneer Ahmad

The main aim of this study was to assess and evaluate user satisfaction with library resources and services among library users associated with Solapur University. The current research shows the level of users satisfaction with different library resources and services offered by college libraries. The research found that a vast number of respondents were ple

Hao-Xuan Sun, Liu-Yang Cheng, Shi-Guo Peng, Yan-Qiang Li

We theoretically investigate the stable mechanism of a ring soliton in two-dimensional Fermi superfluids by solving the Bogoliubov-de Gennes equations and their time-dependent counterparts. In the uniform situation, we discover that the ring soliton is always driven away from its initial location, and moves towards the edge due to a curvature-induced effecti

Yuan Qiu, Ke-Xiong Yan, Jun-Hao Lin, Jie Song

In this manuscript, we analyze the mechanism of the superradiant phase transition in the anisotropic Rabi model under the classical oscillator limit using the pattern picture. By expanding the anisotropic Rabi model Hamiltonian in operator space, we obtained three patterns, and we find that the phase transition arises from the competition between patterns. T

Kuei-Chun Kao, Hsu Tzu-Yin, Yunqi Hong, Ruochen Wang

Recently, Multimodal Large Language Models (MLLMs) encounter two key issues in multi-image contexts: (1) a lack of fine-grained perception across disparate images, and (2) a diminished capability to effectively reason over and synthesize information from multiple visual inputs. However, while various prompting methods aim to describe visual content, many exi

Li-Li Ye, Nathan Vigne, Fan-Yi Lin, Hui Cao

The inevitable random frequency differences among semiconductor lasers present an obstacle to achieving their collective coherence, but previous worked showed that fully (all-to-all) coupled networks can still be synchronized even in the weakly coupling regime. An outstanding question is whether sparsely coupled network structures exist that lead to strong s

Deyang Yu, Chenchen Liu, Chuanjie Zhang, Xiao Fang

The application of Magnetic Random-Access Memory (MRAM) in computing-in-memory (CIM) has gained significant attention. However, existing designs often suffer from high energy consumption due to their reliance on complex analog circuits for computation. In this work, we present a Spin-Orbit- Torque MRAM(SOT-MRAM)-based CIM macro that employs an event-driven s

Zeqi Ye, Qijie Zhu, Molei Tao, Minshuo Chen

Diffusion models have achieved remarkable success across diverse domains, but they remain vulnerable to memorization -- reproducing training data rather than generating novel outputs. This not only limits their creative potential but also raises concerns about privacy and safety. While empirical studies have explored mitigation strategies, theoretical unders

Hassan Wasswa, Hussein Abbass, Timothy Lynar

In an effort to counter the increasing IoT botnet-based attacks, state-of-the-art deep learning methods have been proposed and have achieved impressive detection accuracy. However, their computational intensity restricts deployment on resource-constrained IoT devices, creating a critical need for lightweight detection models. A common solution to this challe

Boning Li, Xufan Li, Yifan Quan, Avetik R Harutyunyan

Molecular spin systems are promising candidates for quantum information processing and nanoscale sensing, yet their characterization at room temperature remains challenging due to fast spin decoherence. In this work, we use $T_1$ relaxometry of shallow nitrogen-vacancy (NV) centers in diamond to probe the electron spin ensemble of a polycrystalline copper ph

Boning Li, Yifan Quan, Xufan Li, Guoqing Wang

Molecular spin qubits are promising candidates for quantum technologies, but their performance is limited by decoherence arising from diverse mechanisms. The complexity of the environment makes it challenging to identify the main source of noise and target it for mitigation. Here we present a systematic experimental and theoretical framework for analyzing th

Jiawei Zhou, Amy Z. Chen, Darshi Shah, Laura M. Schwab-Reese

Recent advances in large language models (LLMs) have brought public and scholarly attention to their potential in generating low-quality information. While widely acknowledged as a risk, low-quality information remains a vaguely defined concept, and little is known about how it manifests in LLM outputs or how these outputs differ from those of traditional in

Maryam Alipourhajiagha, Pierre-Louis Lemaire, Youssef Diouane, Julie Carreau

Climate models are limited by heavy computational costs, often producing outputs at coarse spatial resolutions, while many climate change impact studies require finer scales. Statistical downscaling bridges this gap, and we adapt the probabilistic U-Net for this task, combining a deterministic U-Net backbone with a variational latent space to capture aleator

Kenji Sahay, Snigdha Pandya, Rohan Nagale, Anna Lin

Large language models (LLMs) often generate fluent but factually incorrect statements despite having access to relevant evidence, a failure mode rooted in how they allocate attention between contextual and parametric knowledge. Understanding and steering this internal behavior is key both for trustworthy deployment and for scientific interpretability of mode

Feng Wu, Tsai Hor Chan, Fuying Wang, Guosheng Yin

Various data modalities are common in real-world applications (e.g., electronic health records, medical images and clinical notes in healthcare). It is essential to develop multimodal learning methods to aggregate various information from multiple modalities. The main challenge is how to appropriately align and fuse the representations of different modalitie

Ting-Wen Lan, J. Xavier Prochaska, J. Aguilar, S. Ahlen

We study the multi-phase circumgalactic medium (CGM) of emission line galaxies (ELGs) at $z\sim1.5$, traced by MgII$\lambda2796$, $\lambda2803$ and CIV$\lambda1548$, $\lambda1550$ absorption lines, using approximately 7,000 ELG-quasar pairs from the Dark Energy Spectroscopic Instrument. Our results show that both the mean rest equivalent width ($W_{0}$) prof

Le Xue, Gang Feng, Wenbo Zhang, Yichi Zhang

Publicly available, large-scale medical imaging datasets are crucial for developing and validating artificial intelligence models and conducting retrospective clinical research. However, datasets that combine functional and anatomical imaging with detailed clinical reports across multiple cancer types remain scarce. Here, we present PETWB-REP, a curated data

Gonzalo Mena, Arun Kumar Kuchibhotla, Larry Wasserman

Rectified flow (Liu et al., 2022; Liu, 2022; Wu et al., 2023) is a method for defining a transport map between two distributions, and enjoys popularity in machine learning, although theoretical results supporting the validity of these methods are scant. The rectified flow can be regarded as an approximation to optimal transport, but in contrast to other tran

Isar Lemeire, Yee Wei Law, Sang-Heon Lee, William Meakin

Synthetic aperture radar (SAR) enables versatile, all-time, all-weather remote sensing. Coupled with automatic target recognition (ATR) leveraging machine learning (ML), SAR is empowering a wide range of Earth observation and surveillance applications. However, the surge of attacks based on adversarial perturbations against the ML algorithms underpinning SAR

Huihui Zeng

For the physical vacuum free boundary problem of the damped compressible Euler equations in both 2D and 3D, we prove the global existence of smooth solutions and justify their time-asymptotic equivalence to the corresponding Barenblatt self-similar solutions derived from the porous media equation under Darcy's law approximation, provided the initial data are

Mingtao Zhang, Guoli Yang, Zhanxing Zhu, Mengzhu Wang

Attention mechanisms have been extensively employed in various applications, including time series modeling, owing to their capacity to capture intricate dependencies; however, their utility is often constrained by quadratic computational complexity, which impedes scalability for long sequences. In this work, we propose a novel linear attention mechanism des

Zeqing Zhang, Weifeng Lu, Lei Yang, Wei Jing

This paper explores a physical human-robot collaboration (pHRC) task involving the joint insertion of a board into a frame by a sightless robot and a human operator. While admittance control is commonly used in pHRC tasks, it can be challenging to measure the force/torque applied by the human for accurate human intent estimation, limiting the robot's ability

Jonghae Park, Daesol Cho, Jusuk Lee, Dongseok Shim

Unsupervised skill discovery in reinforcement learning (RL) aims to learn diverse behaviors without relying on external rewards. However, current methods often overlook the periodic nature of learned skills, focusing instead on increasing the mutual dependence between states and skills or maximizing the distance traveled in latent space. Considering that man

Yiru Chen, Sally Fang, Sai Sree Harsha, Dan Luo

Generative AI assistants offer significant potential to enhance productivity, streamline information access, and improve user experience in enterprise contexts. In this work, we present Summit Concierge, a domain-specific AI assistant developed for Adobe Summit. The assistant handles a wide range of event-related queries and operates under real-world constra

A. D. Kulkarni, R. M. Shannon, D. J. Reardon, M. T. Miles

The turbulent nature of the ionised interstellar medium (IISM) causes dispersion measure (DM) and scattering variations in pulsar timing measurements. To improve precision of gravitational wave measurements, pulsar timing array (PTA) collaborations have begun the use of sophisticated and intricate noise modelling techniques such as modelling stochastic varia

I. Araya, M. Curé, N. Machuca, R. O. J. Venero

Massive stars play a fundamental role in galactic evolution through their strong stellar winds, chemical enrichment, and feedback mechanisms. Accurate modelling of their atmospheres and winds is critical for understanding their physical properties and evolutionary pathways. Traditional spectroscopic analyses often rely on the $\beta$-law approximation for wi

Travis Kwan

We survey the localization theory of random Schr\"odinger operators with singular single-site distributions, focusing on two regimes: (i) H\"older-continuous laws, where quantitative Wegner estimates enable the classical multiscale analysis (MSA); and (ii) purely atomic (Bernoulli) laws, where the failure of spectral averaging is overcome via quantitative un

Vinaik Chhetri, Moghis Fereidouni, A. B Siddique, Umar Farooq

Large language models (LLMs) for code are increasingly used in software development, but they remain static after pretraining while APIs and software libraries continue to evolve. Model editing offers a lightweight alternative to retraining for incorporating API updates, yet it remains unclear whether existing editing methods can induce correct API migration

Rewida Ali, Cristian C. Beltran-Hernandez, Weiwei Wan, Kensuke Harada

Human-robot cooperation is essential in environments such as warehouses and retail stores, where workers frequently handle deformable objects like paper, bags, and fabrics. Coordinating robotic actions with human assistance remains difficult due to the unpredictable dynamics of deformable materials and the need for adaptive force control. To explore this cha

Varun Kumar, George Em Karniadakis

The engineering design process often demands expertise from multiple domains, leading to complex collaborations and iterative refinements. Traditional methods can be resource-intensive and prone to inefficiencies. To address this, we formalize the engineering design process through a multi-agent AI framework that integrates structured design and review loops

Shreyas C. Dhake, Jiayuan Huang, Runlong He, Danyal Z. Khan

Anticipating forthcoming surgical events is vital for real-time assistance in endonasal transsphenoidal pituitary surgery, where visibility is limited and workflow changes rapidly. Most visual question answering (VQA) systems reason on isolated frames with static vision language alignment, providing little support for forecasting next steps or instrument nee

Takumi Anzawa

Jarossay (arXiv math.NT1412.5099) introduced adjoint multiple zeta values and, by using Racinet's dual formulation of the generating series of multiple zeta values, found $\mathbb{Q}$-algebraic relations among them, referred to as the \textit{adjoint double shuffle relations}. Additionally, Jarossay defined the affine scheme $\mathrm{AdDMR}_0$ determined by

L. Theerthagiri, Balakrishnan Viswanathan, C. M. Chandrashekar

Induced-coherence interferometry, first introduced in the Zou-Wang-Mandel (ZWM) setup, enables retrieval of object information from the interference pattern of light that never interacted with the object. This scheme relies on two identically correlated photon pairs and the absence of "which-way" information about the photons illuminating the object to induc

Jiawei Zhou, Lei Zhang, Mei Li, Benjamin D Horne

Representation shapes public attitudes and behaviors. With the recent advances and rapid adoption of LLMs, the way these systems are introduced will negotiate societal expectations for their role in high-stakes domains like health. Yet it remains unclear whether current narratives present a balanced view. We analyzed five prominent discourse channels (news,

Arsalan Muhammad, Wasiu Akande Ahmed, Omada Friday Ojonugwa, Paul Puspendu Biswas

The rapid growth of cislunar activities, including lunar landings, the Lunar Gateway, and in-space refueling stations, requires advances in cost-efficient trajectory design and reliable integration of navigation and remote sensing. Traditional Earth-Moon transfers suffer from rigid launch windows and high propellant demands, while Earth-based GNSS systems pr

Mahasweta Bhattacharya, Aditya S. Mondal, Biplab Raychaudhuri, Gulab C. Dewangan

We report on the results obtained by the analysis of persistent and type-I thermonuclear X-ray burst emission observed from the periodic burster 4U 1323-62. These analyses are based on the NuSTAR observation performed on 2024 August 7 for a total exposure of around 90 ks. The persistent emission is well described by an absorbed thermal Comptonization model.

Xuanxiang Huang, Yacine Izza, Alexey Ignatiev, Joao Marques-Silva

Formal explainable artificial intelligence (XAI) offers unique theoretical guarantees of rigor when compared to other non-formal methods of explainability. However, little attention has been given to the validation of practical implementations of formal explainers. This paper develops a novel methodology for validating formal explainers and reports on the as

Tingzhu Bi, Yicheng Pan, Xinrui Jiang, Huize Sun

Uncovering cause-effect relationships from observational time series is fundamental to understanding complex systems. While many methods infer static causal graphs, real-world systems often exhibit dynamic causality-where relationships evolve over time. Accurately capturing these temporal dynamics requires time-resolved causal graphs. We propose UnCLe, a nov

Tamas Almos Vami, Danyi Zhang

A comprehensive search for microscopic black holes and electroweak sphalerons is presented, using proton-proton collision data collected by the CMS detector during 2016-2018, corresponding to an integrated luminosity of $138~\mathrm{~fb}^{-1}$. A novel tool has been developed to identify collider events with distinct kinematic features, based on the phase-sp

Xin Liu, Jinze Wu, Yinghui Li, Chenkun Qi

Multi-legged robots offer enhanced stability to navigate complex terrains with their multiple legs interacting with the environment. However, how to effectively coordinate the multiple legs in a larger action exploration space to generate natural and robust movements is a key issue. In this paper, we introduce a motion prior-based approach, successfully appl

Kevin Wang, Subre Abdoul Moktar, Jia Li, Kangshuo Li

Large Language Models (LLMs) have become increasingly pervasive, finding applications across many industries and disciplines. Ensuring the trustworthiness of LLM outputs is paramount, where Uncertainty Estimation (UE) plays a key role. In this work, a comprehensive empirical study is conducted to examine the robustness and effectiveness of diverse UE measure

Raj Surya Rajendran Kathirvel, Zach A Chavis, Stephen J. Guy, Karthik Desingh

We introduce SENT-Map, a semantically enhanced topological map for representing indoor environments, designed to support autonomous navigation and manipulation by leveraging advancements in foundational models (FMs). Through representing the environment in a JSON text format, we enable semantic information to be added and edited in a format that both humans

Qi Ding, Shoumik Chowdhury, Agustin Di Paolo, Réouven Assouly

Achieving high-fidelity single- and two-qubit gates is essential for executing arbitrary digital quantum algorithms and for building error-corrected quantum computers. We propose a theoretical framework for implementing quantum gates using frequency- and amplitude-modulated microwave control, which extends conventional amplitude modulation by introducing fre

Yun-Chen Lin, Jiayuan Huang, Hanyuan Zhang, Sergi Kavtaradze

Accurate detection and delineation of anatomical structures in medical imaging are critical for computer-assisted interventions, particularly in laparoscopic liver surgery where 2D video streams limit depth perception and complicate landmark localization. While recent works have leveraged monocular depth cues for enhanced landmark detection, challenges remai

Yijing Chu, Qinxuan Xiang, Sipei Zhao, Ming Wu

In decentralized active noise control (ANC) systems, crosstalk between multichannel secondary sources and error microphones significantly degrades control accuracy. Moreover, prefiltering reference signals in filtered-x (Fx) type algorithms may further introduce modeling errors. A theoretical analysis of the Fx-based decentralized control algorithm was perfo

Aniket Jivani, Cosmin Safta, Beckett Y. Zhou, Xun Huan

We present a bifidelity Karhunen-Lo\`eve expansion (KLE) surrogate model for field-valued quantities of interest (QoIs) under uncertain inputs. The approach combines the spectral efficiency of the KLE with polynomial chaos expansions (PCEs) to preserve an explicit mapping between input uncertainties and output fields. By coupling inexpensive low-fidelity (LF

Calvin W. Johnson, Austin Keller

The configuration-interaction shell model is an effective and widely-used approach to the nuclear many-body problem, whose main drawback is the exponential growth of the basis dimension. An useful way to character nuclear shell-model Hamiltonians is through traces, including traces in subspaces defined by orbital occupations. Such traces, or energy centroids

Everton M. C. Abreu, Henrique Boschi-Filho, Rafael A. Costa-Silva

Recently, Barrow proposed an extension of the Bekenstein-Hawking black hole entropy to include the effects of fractal geometry through a parameter $\Delta$. Since then, several interesting issues related to this extended entropy have been explored in the literature. In this work, we investigate the effects of the fractal parameter $\Delta$ on the inversion t

Shuting Qiu, Fang Dong, Siyu Tan, Ruiting Zhou

Mobile edge computing (MEC) can pre-cache deep neural networks (DNNs) near end-users, providing low-latency services and improving users' quality of experience (QoE). However, caching all DNN models at edge servers with limited capacity is difficult, and the impact of model loading time on QoE remains underexplored. Hence, we introduce dynamic DNNs in edge s

Changqing Lu, Ganggang Xu, Junho Yang, Yongtao Guan

Preferential sampling has attracted considerable attention in geostatistics since the pioneering work of Diggle et al. (2010). A variety of likelihood-based approaches have been developed to correct estimation bias by explicitly modelling the sampling mechanism. While effective in many applications, these methods are often computationally expensive and can b

Bryce-Allen Bagley, Navin Khoshnan

The complexity of human cognition has meant that psychology makes more use of theory and conceptual models than perhaps any other biomedical field. To enable precise quantitative study of the full breadth of phenomena in psychological and psychiatric medicine as well as cognitive aspects of AI safety, there is a need for a mathematical formulation which is b

Yi Zhou, Chunyu Luo, Zhengren Wang, Zhang-Hua Fu

A graph with $n$ vertices is an $f(\cdot)$-dense graph if it has at least $f(n)$ edges, $f(\cdot)$ being a well-defined function. The notion $f(\cdot)$-dense graph encompasses various clique models like $\gamma$-quasi cliques, $k$-defective cliques, and dense cliques, arising in cohesive subgraph extraction applications. However, the $f(\cdot)$-dense graph m

Sagar Shrestha, Gopal Sharma, Luowei Zhou, Suren Kumar

Personalizing text-to-image diffusion models has traditionally relied on subject-specific fine-tuning approaches such as DreamBooth~\cite{ruiz2023dreambooth}, which are computationally expensive and slow at inference. Recent adapter- and encoder-based methods attempt to reduce this overhead but still depend on additional fine-tuning or large backbone models

Zhefan Wang, Guokai Yan, Jinbei Yu, Siyu Gu

Recommender systems in multi-behavior domains, such as advertising and e-commerce, aim to guide users toward high-value but inherently sparse conversions. Leveraging auxiliary behaviors (e.g., clicks, likes, shares) is therefore essential. Recent progress on generative recommendations has brought new possibilities for multi-behavior sequential recommendation

Natchaphon Leungbootnak, Zihao Li, Zihang Wei, Dominique Lord

The ability of existing headway distributions to accurately reflect the diverse behaviors and characteristics in heterogeneous traffic (different types of vehicles) and mixed traffic (human-driven vehicles with autonomous vehicles) is limited, leading to unsatisfactory goodness of fit. To address these issues, we modified the exponential function to obtain a

Khouloud Oueslati, Maxime Lamothe, Foutse Khomh

Large Language Models (LLMs) have substantially influenced various software engineering tasks. Indeed, in the case of software refactoring, traditional LLMs have shown the ability to reduce development time and enhance code quality. However, these LLMs often rely on static, detailed instructions for specific tasks. In contrast, LLM-based agents can dynamical

Srishti Yadav, Jasmina Gajcin, Erik Miehling, Elizabeth Daly

Understanding how different stakeholders perceive risks in AI systems is essential for their responsible deployment. This paper presents a framework for stakeholder-grounded risk assessment by using LLMs, acting as judges to predict and explain risks. Using the Risk Atlas Nexus and GloVE explanation method, our framework generates stakeholder-specific, inter

Jingbo Wang, Jintao Xie, Jing Zou, Jianfei Tang

We present an analysis of 4 nulling pulsars with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). For PSR J1649+2533, our results suggest mode changing rather than subpulse drifting as previously reported at lower frequencies. For PSR J1752+2359, we confirm its quasi-periodic switching between distinct emission states, but further show that

Atif Hassan, Tarun Kumar, Ashish Mishra, Sergey Serebryakov

Forecasting anomalies (anomaly prediction) in multivariate time series from different real-world, dynamic, and complex systems is vital for preempting critical failures, leading to a substantial minimization in operational costs and human labor. Yet, existing methods are limited to specific systems while failing to generalize to evolving anomaly patterns ove

Paria Mehrbod, Pedro Vianna, Geraldin Nanfack, Guy Wolf

Domain adaptation is a key strategy for enhancing the generalizability of deep learning models in real-world scenarios, where test distributions often diverge significantly from the training domain. However, conventional approaches typically rely on prior knowledge of the target domain or require model retraining, limiting their practicality in dynamic or re

Haotian Yin, Przemyslaw Musialski

Neural signed distance functions (SDFs) have become a powerful representation for geometric reconstruction from point clouds, yet they often require both gradient- and curvature-based regularization to suppress spurious warp and preserve structural fidelity. FlatCAD introduced the Off-Diagonal Weingarten (ODW) loss as an efficient second-order prior for CAD

Kaiyuan Zhang, Chenghao Yang, Zhoufutu Wen, Sihang Yuan

As reasoning models scale rapidly, the essential role of multimodality in human cognition has come into sharp relief, driving a growing need to probe vision-centric cognitive behaviors. Yet, existing multimodal benchmarks either overemphasize textual reasoning or fall short of systematically capturing vision-centric cognitive behaviors, leaving the cognitive

André Casajus, Yukihiko Funaki, Frank Huettner

We consider fair and consistent extensions of the Shapley value for games with externalities. Based on the restriction identified by Casajus et al. (2024, Games Econ. Behavior 147, 88-146), we define balanced contributions, Sobolev's consistency, and Hart and Mas-Colell's consistency for games with externalities, and we show that these properties lead to cha

Jonathan Claros, Emanuel Gallo

In this article, we extend our previously presented analytical formulas (Phys.Rev.D 109 (2024) 12, 124055) for describing light rays passing near or emitted in the vicinity of compact objects to a broader class of spherically symmetric, static spacetimes, including the Johansen-Psaltis and Rezzolla-Zhidenko metric families. The generalized formulas retain th

Erfan Shayegani, Jina Suh, Andy Wilson, Nagu Rangan

Empathy is a critical factor in fostering positive user experiences in conversational AI. While models can display empathy, it is often generic rather than tailored to specific tasks and contexts. In this work, we introduce a novel framework for developing and evaluating context-specific empathetic large language models (LLMs). We first analyze a real-world

Rohit Satish Patil, Zhilin Han, Sofia G. Mogilevskaya

The paper presents the Isogeometric Boundary Element Method (IGABEM) algorithm for solving the plane strain problem of an isotropic linearly elastic matrix containing an open material surface of arbitrary shape. Theoretical developments are based on the use of the Gurtin-Murdoch model of material surfaces. The governing equations and the boundary conditions

Xu-Fan Hu, Hong-Xuan Jiang, Yosuke Mizuno, Christian M. Fromm

Recent polarimetric observations of blazars indicate the development of current-driven (CD) kink instability after passing the recollimation shocks in the relativistic jets and association with quasi-periodic oscillations (QPOs). To investigate multi-wavelength polarized features of CD kink instability in jets, we develop {\tt RaptorP}, a new special relativ

Chong Zou, Qiwei Zhang, Guangyuan Luan, Hongyi Wu

The gamma-ray total absorption facility (GTAF) composed of 40 BaF2 detection units is designed to measure the cross section data of neutron radiation capture reaction online, in order to comply with the experimental nuclear data sheet.We have found that one of the most important sources of experimental background is the initial alpha particles emitted by the