May 2025 arXiv papers — page 37

Seongwan Park, Taeklim Kim, Youngjoong Ko

Despite their strong performance, Dense Passage Retrieval (DPR) models suffer from a lack of interpretability. In this work, we propose a novel interpretability framework that leverages Sparse Autoencoders (SAEs) to decompose previously uninterpretable dense embeddings from DPR models into distinct, interpretable latent concepts. We generate natural language

Zhongyi Zhou, Yichen Zhu, Junjie Wen, Chaomin Shen

Vision-language-action (VLA) models have emerged as the next generation of models in robotics. However, despite leveraging powerful pre-trained Vision-Language Models (VLMs), existing end-to-end VLA systems often lose key capabilities during fine-tuning as the model adapts to specific robotic tasks. We argue that a generalizable VLA model should retain and e

Mo Zhou, Keren Ye, Viraj Shah, Kangfu Mei

Preserving face identity is a critical yet persistent challenge in diffusion-based image restoration. While reference faces offer a path forward, existing reference-based methods often fail to fully exploit their potential. This paper introduces a novel approach that maximizes reference face utility for improved face restoration and identity preservation. Ou

Pardis Taghavi, Tian Liu, Renjie Li, Reza Langari

Instance segmentation demands costly per-pixel annotations and computationally expensive models. We introduce CAST, a semi-supervised knowledge distillation (SSKD) framework that compresses pre-trained vision foundation models (VFM) into compact experts using limited labeled and abundant unlabeled data. CAST unfolds in three stages: (1) domain adaptation of

Ruihao Zheng, Zhenkun Wang, Yin Wu, Maoguo Gong

The ideal objective vector, which comprises the optimal values of the $m$ objective functions in an $m$-objective optimization problem, is an important concept in evolutionary multi-objective optimization. Accurate estimation of this vector has consistently been a crucial task, as it is frequently used to guide the search process and normalize the objective

Zhenyu Jiang, Xuemei Lu, Congcong Wang, Yingjie Huang

Silicon carbide detectors exhibit good detection performance and are being considered for detection applications. However, the presence of surface electrode of detector limits the application of low-penetration particle detectors, photodetectors and heavy-ion detection. A graphene-optimized 4H-SiC detector has been fabricated to expand the application of SiC

Zhixing Huang, Bing Xue, Mengjie Zhang, Jeremy S. Ronney

Machine learning techniques play an important role in analyzing spectral data. The spectral data of fish biomass is useful in fish production, as it carries many important chemistry properties of fish meat. However, it is challenging for existing machine learning techniques to comprehensively discover hidden patterns from fish biomass spectral data since the

Hyukpyo Hong, Diego Rojas La Luz, Gheorghe Craciun

Living systems maintain stable internal states despite environmental fluctuations. Absolute concentration robustness (ACR) is a striking homeostatic phenomenon in which the steady-state concentration of a species remains invariant despite changes in total supply. Although experimental studies have reported approximate-but not exact-robustness in steady-state

Rui Li, Jianfei Liu, Zhilin Yang, Peichang Shi

Existing serverless workflow orchestration systems are predominantly designed for a single-cloud FaaS system, leading to vendor lock-in. This restricts performance optimization, cost reduction, and availability of applications. However, orchestrating serverless workflows on Jointcloud FaaS systems faces two main challenges: (1) additional overhead caused by

Rennai Qiu, Chen Qian, Ran Li, Yufan Dang

Recent advancements in Large Language Models (LLMs) and autonomous agents have demonstrated remarkable capabilities across various domains. However, standalone agents frequently encounter limitations when handling complex tasks that demand extensive interactions and substantial computational resources. Although Multi-Agent Systems (MAS) alleviate some of the

Wang Mengjie, Zhu Huiping, Li Jian, Shi Wenxiu

With the advancement of autonomous and assisted driving technologies, higher demands are placed on the ability to understand complex driving scenarios. Multimodal general large models have emerged as a solution for this challenge. However, applying these models in vertical domains involves difficulties such as data collection, model training, and deployment

Jianchao Jiang, Haofeng Zhang

Few-Shot Medical Image Segmentation (FSMIS) has been widely used to train a model that can perform segmentation from only a few annotated images. However, most existing prototype-based FSMIS methods generate multiple prototypes from the support image solely by random sampling or local averaging, which can cause particularly severe boundary blurring due to th

Lingfeng Yao, Chenpei Huang, Shengyao Wang, Junpei Xue

With the surge of social media, maliciously tampered public speeches, especially those from influential figures, have seriously affected social stability and public trust. Existing speech tampering detection methods remain insufficient: they either rely on external reference data or fail to be both sensitive to attacks and robust to benign operations, such a

Reuben R. W. Wang, John L. Bohn

We study the fully itinerant dynamics of ultracold but nondegenerate polar molecules with a spin-$1/2$ degree of freedom encoded into two of their electric field dressed rotational states. Center of mass molecular motion is constrained to two-dimensions via tight confinement with a one-dimensional optical lattice, but remains mostly unconstrained within the

Cameron Gordon, Yiping Ji, Hemanth Saratchandran, Paul Albert

Resource-constrained weight deployment is a task of immense practical importance. Recently, there has been interest in the specific task of \textit{Delta Compression}, where parties each hold a common base model and only communicate compressed weight updates. However, popular parameter efficient updates such as Low Rank Adaptation (LoRA) face inherent repres

Yuanyuan Liu, Yuanbiao Yang, Jing Cheng, Zhuo-Xu Cui

Dynamic MRI suffers from limited spatiotemporal resolution due to long acquisition times. Undersampling k-space accelerates imaging but makes accurate reconstruction challenging. Supervised deep learning methods achieve impressive results but rely on large fully sampled datasets, which are difficult to obtain. Recently, implicit neural representations (INR)

Xunpeng Huang, Yingyu Lin, Nikki Lijing Kuang, Hanze Dong

Continuous diffusion models have demonstrated remarkable performance in data generation across various domains, yet their efficiency remains constrained by two critical limitations: (1) the local adjacency structure of the forward Markov process, which restricts long-range transitions in the data space, and (2) inherent biases introduced during the simulatio

Arooj Zaidi, Giulia Barbareschi, Kai Kunze, Yun Suen Pai

Tangible User Interfaces have shown potential in supporting the acquisition of key concepts in computing and mathematics while fostering engagement in young learners, but these approaches are less commonly utilised in the context of geometry. In this paper we introduce TIEboard, an interactive device to promote early learning of basic geometry concepts. TIEb

Sunil Kumar Narayanan, Lingjun Zhao, Lu Gan, Yongsheng Chen

Hyperspectral imaging (HSI) has been widely used in agricultural applications for non-destructive estimation of plant nutrient composition and precise quantification of sample nutritional elements. Recently, 3D reconstruction methods, such as Neural Radiance Field (NeRF), have been used to create implicit neural representations of HSI scenes. This capability

Tianyu Guo, Hande Dong, Yichong Leng, Feng Liu

Large language models (LLMs) are often used for infilling tasks, which involve predicting or generating missing information in a given text. These tasks typically require multiple interactions with similar context. To reduce the computation of repeated historical tokens, cross-request key-value (KV) cache reuse, a technique that stores and reuses intermediat

Guangming Liu, Hsing-Ta Chen

In this work, we systematically investigate the spectral and transport properties of exciton-polaritons under the explicit influence of intermolecular vibrational coupling, which introduces dynamic disorder. In the context of a one-dimensional molecular chain strongly interacting with a cavity photon, we demonstrate the polaritonic characteristics of the spe

Ahmed Heakl, Yahia Salaheldin Shaaban, Martin Takac, Salem Lahlou

Robust routing under uncertainty is central to real-world logistics, yet most benchmarks assume static, idealized settings. We present SVRPBench, the first open benchmark to capture high-fidelity stochastic dynamics in vehicle routing at urban scale. Spanning more than 500 instances with up to 1000 customers, it simulates realistic delivery conditions: time-

Haotian Chen, Xishuo Wei, Hongxuan Zhu, Zhihong Lin

Global gyrokinetic simulations find a strong suppression of ion temperature gradient (ITG) turbulence by zonal flows in stellarators optimized for neoclassical transport. The reduction of the ITG transport by the zonal flows in quasi-helicalsymmetric (QH) and quasi-isodynamic (QI) stellarators are much larger than a quasi-axisymmetric (QA) stellarator or a t

Rouzbeh Allahverdi, James B. Dent, Ngo Phuc Duc Loc, Tao Xu

We present a scenario in which primordial black holes (PBHs) form in a post-inflationary radiation-dominated (RD) phase and then experience significant accretion during a phase of early matter dominated (EMD). We show that PBH masses could grow by up to two orders of magnitude. Restricting to the linear perturbation regime, we compute the gravitational wave

Junhong Liu, Qinfei Long, Rong-Peng Liu, Wenjie Liu

The proliferation of distributed renewable energy triggers the peer-to-peer (P2P) energy market formations. To make profits, prosumers equipped with photovoltaic (PV) panels and even the energy storage system (ESS) can actively participate in the real-time P2P energy market and trade energy. However, in real situations, system states such as energy demands a

Si-wen Li, Shu Lin

The spin polarization for baryon in a hydrodynamic medium has been extensively studied in the weakly coupled regime using quantum kinetic theory. As a first study of this problem in the strongly coupled regime, we investigate holographically the spectral function of a probe baryon in the fluid-gravity background. This is done by carefully performing gradient

Ruijie Li, Xiang Zhao, Qiao Ning, Shikai Guo

In tennis tournaments, momentum, a critical yet elusive phenomenon, reflects the dynamic shifts in performance of athletes that can decisively influence match outcomes. Despite its significance, momentum in terms of effective modeling and multi-granularity analysis across points, games, sets, and matches in tennis tournaments remains underexplored. In this s

Rui Guan, Junjie Liu, Jian-Hua Jiang

Multipartite quantum correlated systems (MQCSs) are widely utilized in diverse quantum information tasks, where their sophisticated control inherently incurs energetic costs. However, the fundamental characteristics of these control costs remain elusive, largely due to the lack of thermodynamic descriptions capable of capturing the full complexities of MQCSs

Yu-Lun Song, Chung-En Tsern, Che-Cheng Wu, Yu-Ming Chang

This study presents an innovative approach to urban mobility simulation by integrating a Large Language Model (LLM) with Agent-Based Modeling (ABM). Unlike traditional rule-based ABM, the proposed framework leverages LLM to enhance agent diversity and realism by generating synthetic population profiles, allocating routine and occasional locations, and simula

Weiting Liu, Jiaxu Cui, Jiao Hu, En Wang

In science, we are interested not only in forecasting but also in understanding how predictions are made, specifically what the interpretable underlying model looks like. Data-driven machine learning technology can significantly streamline the complex and time-consuming traditional manual process of discovering scientific laws, helping us gain insights into

Wenjie Jiang, Mingjian Cheng, Lixin Guo, Xiang Yi

Atmospheric turbulence degrades the performance of free-space optical (FSO) communication and remote sensing systems by introducing phase and intensity distortions. While a majority of research focuses on mitigating these effects to ensure robust signal transmission, an underexplored alternative is to leverage the transformation of structured light to charac

Hongyao Chen, Tianyang Xu, Xiaojun Wu, Josef Kittler

Batch Normalisation (BN) is widely used in conventional deep neural network training to harmonise the input-output distributions for each batch of data. However, federated learning, a distributed learning paradigm, faces the challenge of dealing with non-independent and identically distributed data among the client nodes. Due to the lack of a coherent method

Yerin Doh, Joonhyung Bae

This study introduces the media artwork Dear Passenger, Please Wear a Mask, designed to offer a layered exploration of single-use mask waste, which escalated during the COVID-19 pandemic. The piece reframes underappreciated ecological concerns by interweaving digital nostalgia and airline travel recollections of Millennials and Gen Z with a unique fantasy na

Bhanuka Gamage, Leona Holloway, Nicola McDowell, Thanh-Toan Do

Over the past decade, considerable research has been directed towards assistive technologies to support people with vision impairments using machine learning, computer vision, image enhancement, and/or augmented/virtual reality. However, this has almost totally overlooked a growing demographic: people with Cerebral Visual Impairment (CVI). Unlike Ocular Visi

Ruiguo Yu, Yiyang Zhang, Yuan Tian, Yujie Diao

Medical image segmentation methods generally assume that the process from medical image to segmentation is unbiased, and use neural networks to establish conditional probability models to complete the segmentation task. This assumption does not consider confusion factors, which can affect medical images, such as complex anatomical variations and imaging moda

Jie Gao, Jun Li, Jing Hu, Shanzhuo Zhang

Protein binder design is central to therapeutics, diagnostics, and synthetic biology, yet practical deployment remains challenging due to fragmented workflows, high computational costs, and complex tool integration. We present HelixDesign-Binder, a production-grade, high-throughput platform built on HelixFold3 that automates the full binder design pipeline,

George R. Nahass, Zhu Wang, Homa Rashidisabet, Won Hwa Kim

Machine unlearning aims to remove the influence of specific training samples from a trained model without full retraining. While prior work has largely focused on privacy-motivated settings, we recast unlearning as a general-purpose tool for post-deployment model revision. Specifically, we focus on utilizing unlearning in clinical contexts where data shifts,

Jaume Llibre, Yilei Tang, Jiang Yu, Pengyu Zhou

In this paper we obtain the global dynamics and phase portraits of quadratic and cubic quasi-homogeneous but non-homogeneous systems. We first prove that all planar quadratic and cubic quasi-homogeneous but non-homogeneous polynomial systems can be reduced to three homogeneous ones. Then for the homogeneous systems, we employ blow-up method, normal sector me

Shuyang Cao, Karthik Radhakrishnan, David Rosenberg, Steven Lu

Retrieval-augmented generation (RAG) generally enhances large language models' (LLMs) ability to solve knowledge-intensive tasks. But RAG may also lead to performance degradation due to imperfect retrieval and the model's limited ability to leverage retrieved content. In this work, we evaluate the robustness of LLMs in practical RAG setups (henceforth retrie

Takeki Komatsu, Masaaki Umehara

Using Konderak's representation formula, we construct an entire zero-mean curvature graph of mixed-type in Lorentz-Minkowski 3-space over a space-like plane, which does not belong to the class of "Kobayashi surfaces". We also point out the existence of an entire zero-mean curvature graph of mixed-type in Lorentz-Minkowski space over a light-like plane. These

Guiping Cao, Wenjian Huang, Xiangyuan Lan, Jianguo Zhang

Small Object Detection (SOD) poses significant challenges due to limited information and the model's low class prediction score. While Transformer-based detectors have shown promising performance, their potential for SOD remains largely unexplored. In typical DETR-like frameworks, the CNN backbone network, specialized in aggregating local information, strugg

Bhaktipriya Radharapu, Manon Revel, Megan Ung, Sebastian Ruder

The increasing use of LLMs as substitutes for humans in ``aligning'' LLMs has raised questions about their ability to replicate human judgments and preferences, especially in ambivalent scenarios where humans disagree. This study examines the biases and limitations of LLMs in three roles: answer generator, judge, and debater. These roles loosely correspond t

Yusuke Tajima, Kenshin Inamura, Sebun Masaki, Takumi Yamazaki

We report on epitaxial growth in thin-film synthesis of a polar magnetic semiconductor AgCrSe2 on lattice-matched yttria-stabilized zirconia (111) substrate by pulsed-layer deposition (PLD). By using Ag-rich PLD target to compensate for Ag deficiency in thin films, the nucleation of impurity phases is suppressed, resulting in the c-axis-oriented and single-p

Guozhen Zhu, Yuqian Hu, Weihang Gao, Wei-Hsiang Wang

WiFi sensing has emerged as a compelling contactless modality for human activity monitoring by capturing fine-grained variations in Channel State Information (CSI). Its ability to operate continuously and non-intrusively while preserving user privacy makes it particularly suitable for health monitoring. However, existing WiFi sensing systems struggle to gene

J. Kára, S. Zharikov, M. Wolf, N. Vaidman

Context: We present results of time-resolved optical spectroscopy and photometry of the short-orbital period dwarf nova EI Psc. Aims: This study aims to determine fundamental system parameters of EI Psc, study properties of accretion structures in the system, and investigate its origin and current evolution state. Methods: We analyse newly obtained time-reso

Mengda Xu, Han Zhang, Yifan Hou, Zhenjia Xu

We present DexUMI - a data collection and policy learning framework that uses the human hand as the natural interface to transfer dexterous manipulation skills to various robot hands. DexUMI includes hardware and software adaptations to minimize the embodiment gap between the human hand and various robot hands. The hardware adaptation bridges the kinematics

Shikhhar Siingh, Abhinav Rawat, Chitta Baral, Vivek Gupta

Publicly significant images from events hold valuable contextual information, crucial for journalism and education. However, existing methods often struggle to extract this relevance accurately. To address this, we introduce GETReason (Geospatial Event Temporal Reasoning), a framework that moves beyond surface-level image descriptions to infer deeper context

Chenhui Zhao, Yiwei Lyu, Asadur Chowdury, Edward Harake

The scalability of current language-image pre-training for 3D medical imaging, such as CT and MRI, is constrained by the need for radiologists to manually curate raw clinical studies. In this work, we pioneer pre-training directly on uncurated studies, which both aligns more closely with the radiologist's workflow and provides a natural path to scalability.

H. C. Travaglini, J. J. Cuozzo, K. R. Sapkota, I. A. Leahy

The recent demonstration of the superconducting diode effect (SDE) has generated renewed interests in superconducting electronics in which devices such as compact superconducting diodes that can perform signal rectification where low-energy operations are needed. In this article, we present our results of robust and symmetric-in-magnetic-field SDE in asymmet

Soroush Omidvartehrani, Arash Dargahi Nobari, Davood Rafiei

Describing real-world entities can vary across different sources, posing a challenge when integrating or exchanging data. We study the problem of joinability under syntactic transformations, where two columns are not equi-joinable but can become equi-joinable after some transformations. Discovering those transformations is a challenge because of the large sp

Vishakh Padmakumar, Zichao Wang, David Arbour, Jennifer Healey

While large language models (LLMs) are increasingly capable of handling longer contexts, recent work has demonstrated that they exhibit the "lost in the middle" phenomenon (Liu et al., 2024) of unevenly attending to different parts of the provided context. This hinders their ability to cover diverse source material in multi-document summarization, as noted i

Jiangjie Zhou, Baosheng Liang

Panel count data arise in clinical trials when patients are asked to report their occurrences of events of interest periodically but the exact event times are unknown, only the count of events between two successive examinations are observed. Ordinal panel count data goes even further as the exact event counts are not observed, the only information available

Mijung Park

We revisit the classical, full-fledged Bayesian model averaging (BMA) paradigm to ensemble pre-trained and/or lightly-finetuned foundation models to enhance the classification performance on image and text data. To make BMA tractable under foundation models, we introduce trainable linear classifiers that take frozen features from the pre-trained foundation m

Jarah Evslin, Hui Liu

The one-loop tension of the domain wall in the 3+1 dimensional $\phi^4$ double-well model was derived long ago using dimensional regularization. The methods used can only be applied to solitons depending on a single dimension. In the past few months, domain wall tensions have been recalculated using spectral methods with Born subtractions and also linearized

Jiseung Yoo, Curran Mahowald, Meiyu Li, Wei Ai

Large Language Models (LLMs) are transforming information extraction from academic literature, offering new possibilities for knowledge management. This study presents an LLM-based system designed to extract detailed information about research instruments used in the education field, including their names, types, target respondents, measured constructs, and

Palur Venkata Raghuvamsi, Siyuan Brandon Loh, Prasanta Bhattacharya, Joses Ho

The COVID-19 pandemic response relied heavily on statistical and machine learning models to predict key outcomes such as case prevalence and fatality rates. These predictions were instrumental in enabling timely public health interventions that helped break transmission cycles. While most existing models are grounded in traditional epidemiological data, the

Jun Chen, Xinke Li, Mingyue Xu, Chongshou Li

Gradient-based adversarial attacks are widely used to evaluate the robustness of 3D point cloud classifiers, yet they often rely on uniform update rules that neglect point-wise heterogeneity, leading to perceptible perturbations. We propose two complementary strategies to improve both the effectiveness and imperceptibility of the attack. \textbf{WAAttack} em

Qianxue Shan, Ziqiang Yu, Baiyan Jiang, Jian Hou

Purpose: Recent studies have shown that spin-lock MRI can simplify quantitative magnetization transfer (MT) by eliminating its dependency on water pool parameters, removing the need for a T1 map in macromolecular proton fraction (MPF) quantification. However, its application is often limited by the requirement for long radiofrequency (RF) pulse durations, wh

Akifumi Wachi, Kohei Miyaguchi, Takumi Tanabe, Rei Sato

A longstanding goal in safe reinforcement learning (RL) is a method to ensure the safety of a policy throughout the entire process, from learning to operation. However, existing safe RL paradigms inherently struggle to achieve this objective. We propose a method, called Provably Lifetime Safe RL (PLS), that integrates offline safe RL with safe policy deploym

Sunshine Jiang, Xiaolin Fang, Nicholas Roy, Tomás Lozano-Pérez

Recent advances in diffusion$/$flow-matching policies have enabled imitation learning of complex, multi-modal action trajectories. However, they are computationally expensive because they sample a trajectory of trajectories: a diffusion$/$flow trajectory of action trajectories. They discard intermediate action trajectories, and must wait for the sampling pro

Keren Zhou, Mario Lezcano, Adam Goucher, Akhmed Rakhmati

Efficient tensor computation is a cornerstone of modern deep learning (DL) workloads, yet existing approaches struggle to achieve flexible and performant design and implementation of tensor layouts -- mappings between logical tensors and hardware resources. The increasing complexity of DL algorithms and hardware demands a generic and systematic approach to h

Xinxing Ren, Qianbo Zang, Zekun Guo

Recent advances in large language models (LLMs) have shown impressive performance in mathematical reasoning and code generation. However, LLMs still struggle in the simulation domain, particularly in generating Simulink models, which are essential tools in engineering and scientific research. Our preliminary experiments indicate that LLM agents often fail to

Yanbei Jiang, Yihao Ding, Chao Lei, Jiayang Ao

Current Multimodal Large Language Models (MLLMs) excel in general visual reasoning but remain underexplored in Abstract Visual Reasoning (AVR), which demands higher-order reasoning to identify abstract rules beyond simple perception. Existing AVR benchmarks focus on single-step reasoning, emphasizing the end result but neglecting the multi-stage nature of re

Joonhyung Bae

Thief of Truth is a first-person perspective Virtual Reality (VR) comic that explores the relationship between humans and artificial intelligence (AI). The work tells the story of a mind-uploaded human being reborn as a new subject while interacting with an AI that is looking for the meaning of life. In order to experiment with the expandability of VR comics

Bo Tang, Junyi Zhu, Chenyang Xi, Yunhang Ge

Traditional search engines struggle to synthesize fragmented information for complex queries, while generative AI search engines face challenges in relevance, comprehensiveness, and presentation. To address these limitations, we introduce Xinyu AI Search, a novel system that incorporates a query-decomposition graph to dynamically break down complex queries i

Jingqi Xu, Chenghao Li, Yuke Zhang, Peter A. Beerel

Diffusion models have demonstrated remarkable potential in generating high-quality images. However, their tendency to replicate training data raises serious privacy concerns, particularly when the training datasets contain sensitive or private information. Existing mitigation strategies primarily focus on reducing image duplication, modifying the cross-atten

Xuwei Xu, Yang Li, Yudong Chen, Jiajun Liu

We reveal that feedforward network (FFN) layers, rather than attention layers, are the primary contributors to Vision Transformer (ViT) inference latency, with their impact signifying as model size increases. This finding highlights a critical opportunity for optimizing the efficiency of large-scale ViTs by focusing on FFN layers. In this work, we propose a

Noémie Globus, Roger Blandford

Ultra High Energy Cosmic Rays, UHECR, are charged particles with energies between $\sim10^{18}\,{\rm eV}$ and $\sim3\times10^{20}\,{\rm eV}\sim50\,{\rm J}$. They exhibit fundamental physics at energies inaccessible to terrestrial accelerators, challenge experimental physics and connect strongly to astronomical observations through electromagnetic, neutrino a

Lingfei Zhao, Hadeel Soliman, Kevin S. Xu, Subhadeep Paul

Temporal networks observed continuously over time through timestamped relational events data are commonly encountered in application settings including online social media communications, financial transactions, and international relations. Temporal networks often exhibit community structure and strong dependence patterns among node pairs. This dependence ca

Mehrdad Noori, David Osowiechi, Gustavo Adolfo Vargas Hakim, Ali Bahri

Recently, test-time adaptation has attracted wide interest in the context of vision-language models for image classification. However, to the best of our knowledge, the problem is completely overlooked in dense prediction tasks such as Open-Vocabulary Semantic Segmentation (OVSS). In response, we propose a novel TTA method tailored to adapting VLMs for segme

Vladyslava I. Marsakova, Ivan L. Andronov, Victoriia O. Borshchenko, Illia. A. Garbazhii-Romanchenko

A group of poorly studied eclipsing variables (the classification of which is marked as uncertain and/or the period of brightness changes is uncertain) has been studied with the using of the photometric observations of the TESS mission and NSVS, ASAS-SN sky surveys. We also obtained some observations covering the brightness minima of our variables by our gro

Filippos Fotiadis, Kyriakos G. Vamvoudakis

We propose a physics-informed neural networks (PINNs) framework to solve the infinite-horizon optimal control problem of nonlinear systems. In particular, since PINNs are generally able to solve a class of partial differential equations (PDEs), they can be employed to learn the value function of the infinite-horizon optimal control problem via solving the as

Jiahui Zhu, Kihyun Yu, Dabeen Lee, Xin Liu

Online safe reinforcement learning (RL) plays a key role in dynamic environments, with applications in autonomous driving, robotics, and cybersecurity. The objective is to learn optimal policies that maximize rewards while satisfying safety constraints modeled by constrained Markov decision processes (CMDPs). Existing methods achieve sublinear regret under s

Kristian Gjorgjieski, Jutta Kunz, Petya Nedkova

We study circular orbits and accretion structures around symmetric wormholes. As exemplary solutions we choose three different wormhole spacetimes, namely rotating traversable wormholes from the Teo class, the rotating Simpson-Visser metric with the parameter spectrum corresponding to wormholes and a static wormhole from beyond Horndeski theories. We show th

Dmitrii Zagorulia, Hsien Shang, Ruben Krasnopolsky

We extend the analytic expressions for polytropic wind-driven bubbles and their shock structures, formulated initially in Koo and McKee 1992(a,b), focusing on spherically symmetric configurations in astrophysical environments with $\rho\propto r^{-2}$, which arises naturally in the star-forming environment and has applications to winds flowing into a preexis

Maobin Lu, Martin Guay, Telema Harry, Shimin Wang

This paper investigates the robust output regulation problem of second-order nonlinear uncertain systems with an unknown exosystem. Instead of the adaptive control approach, this paper resorts to a robust control methodology to solve the problem and thus avoid the bursting phenomenon. In particular, this paper constructs generic internal models for the stead

Aliasghar Khani, Arianna Rampini, Evan Atherton, Bruno Roy

Motion generation is a cornerstone of computer graphics, animation, gaming, and robotics, enabling the creation of realistic and varied character movements. A significant limitation of existing methods is their reliance on specific skeletal structures, which restricts their versatility across different characters. To overcome this, we introduce UniMoGen, a n

E. Garrido, A. Kievsky, R. Del Grande, L. Serksnyte

The computation of the three-particle correlation function involving three hadrons started just recently after the first publications of ALICE measurements. Key elements to be considered are the correct description of the asymptotics, antisymmetrization issues and, in most cases, the treatment of the Coulomb interaction. In the case of the $ppp$ correlation

R. Unnikrishnan, M. Andriantsaralaza, E. De Beck, L. -Å. Nyman

The circumstellar envelopes (CSEs) of asymptotic giant branch (AGB) stars harbour a rich variety of molecules and are sites of complex chemistry. Our current understanding of the circumstellar chemical processes of carbon-rich AGB stars is predominantly based on observations of a single star, IRC+10216, often regarded as an archetypical carbon star. We aim t

Peng Xu, Gang Chen

We investigate the effective potential and scattering length of ultracold polar molecules under different shielding techniques. First, we derive the effective potential for two polar molecules in the presence of an elliptical polarization field, combined elliptical and linear polarization fields, and combined elliptical polarization and static fields. The ef

Johannes Niederhauser, Aart Middeldorp

We lift the computability path order and its extensions from plain higher-order rewriting to higher-order rewriting on beta-eta-normal forms where matching modulo beta-eta is employed. The resulting order NCPO is shown to be useful on practical examples. In particular, it can handle systems where its cousin NHORPO fails even when it is used together with the

Mayank, K. Hari, Subhajit Barman, Dawood Kothawala

We initiate an investigation into features of vacuum entanglement as probed by accelerated quantum probes in curved spacetime. Focussing specifically on de Sitter (dS) spacetime with curvature $Λ$, we obtain several exact results corresponding to different kinematical set-up of the probes. The interaction with the quantum field creates a non-local correlatio

Andrea Bandini, Ignazio Longhi

Let $\ell$ and $p$ be distinct primes, and let $\G$ be an abelian pro-$p$-group. We study the structure of the algebra $Ł:=\Z_\ell[[\G]]$ and of $Ł$-modules. The algebra $Ł$ turns out to be a direct product of copies of ring of integers of cyclotomic extensions of $\Q_\ell$ and this induces a similar decomposition for a family of $Ł$-modules. Inside this fam

Davood Momeni

This comment critically examines the recent proposal by S.~H.~V\"olkel [Phys. Rev. Lett., arXiv:2505.17186], which asserts that the quasinormal mode (QNM) spectrum of Schwarzschild black holes can be reconstructed from bound states of an inverted Regge--Wheeler potential. We demonstrate that this claim rests on a mathematically invalid spectral mapping and a

Xiangyu Chen, Jing Liu, Ye Wang, Matthew Brand

To reduce model size during post-training, compression methods, including knowledge distillation, low-rank approximation, and pruning, are often applied after fine-tuning the model. However, sequential fine-tuning and compression sacrifices performance, while creating a larger than necessary model as an intermediate step. In this work, we aim to reduce this

Chunchao Wen, Zhichun Qi, Jianfa Zhang, Shiqiao Qin

As the characteristic feature of generalized Kerker effect in Mie theory, directional scattering elimination has been playing a pivotal role in nanophotonics and many other photonic disciplines, such as singular optics and topological photonics. Generally, zero directional scattering can be obtained only for a specific incident polarization, and to make it f

Rohan V. Koodli, Alexander S. Powers, Ayush Pandit, Chiho Im

A common starting point for drug design is to find small chemical groups or "fragments" that form interactions with distinct subregions in a protein binding pocket. The subsequent challenge is to assemble these fragments into a molecule that has high affinity to the protein, by adding chemical bonds between atoms in different fragments. This "molecule assemb

Samiha Tariq, Weikang Zhang

This study explores the interdependent relationship between consumer credit and consumer confidence in the United States using monthly data from January 1978 to August 2024. Utilizing a Vector Error Correction Model (VECM), the analysis focuses on the interplay between household borrowing behaviour and consumer sentiment while controlling for macroeconomic f

Bowen Chen, Cheng-han Lee, Yixu Chen, Zaixi Shang

We introduce HDRSDR-VQA, a large-scale video quality assessment dataset designed to facilitate comparative analysis between High Dynamic Range (HDR) and Standard Dynamic Range (SDR) content under realistic viewing conditions. The dataset comprises 960 videos generated from 54 diverse source sequences, each presented in both HDR and SDR formats across nine di

James B. Dent, Bhaskar Dutta, Jason Kumar, Danny Marfatia

We consider the formation of Q-balls in false vacuum remnants during a cosmological first-order phase transition. We find that under certain circumstances Q-balls can collapse to form primordial black holes. This scenario can produce multimessenger signals that may be observed at upcoming experiments, including 1-100 nHz gravitational waves from the phase tr

Antonio Orvieto, Robert M. Gower

Understanding the remarkable efficacy of Adam when training transformer-based language models has become a central research topic within the optimization community. To gain deeper insights, several simplifications of Adam have been proposed, such as the signed gradient and signed momentum methods. In this work, we conduct an extensive empirical study - train

Chen Yueh-Han, Guy Davidson, Brenden M. Lake

Do LLMs robustly generalize critical safety facts to novel situations? Lacking this ability is dangerous when users ask naive questions. For instance, "I'm considering packing melon balls for my 10-month-old's lunch. What other foods would be good to include?" Before offering food options, the LLM should warn that melon balls pose a choking hazard to toddler

Yongyi Zang, Zheqi Dai, Mark D. Plumbley, Qiuqiang Kong

We introduce Music Source Restoration (MSR), a novel task addressing the gap between idealized source separation and real-world music production. Current Music Source Separation (MSS) approaches assume mixtures are simple sums of sources, ignoring signal degradations employed during music production like equalization, compression, and reverb. MSR models mixt

Duc P. Truong, Benjamin Nebgen, Derek DeSantis, Dimiter N. Petsev

Accurately evaluating configurational integrals for dense solids remains a central and difficult challenge in the statistical mechanics of condensed systems. Here, we present a novel tensor network approach that reformulates the high-dimensional configurational integral for identical-particle crystals into a sequence of computationally efficient summations.

Parsa Mirtaheri, Ezra Edelman, Samy Jelassi, Eran Malach

Inference-time computation has emerged as a promising scaling axis for improving large language model reasoning. However, despite yielding impressive performance, the optimal allocation of inference-time computation remains poorly understood. A central question is whether to prioritize sequential scaling (e.g., longer chains of thought) or parallel scaling (

Praveen Kumar, Vincent T. Metzger, Scott A. Malec

The global prevalence of diabetes, particularly type 2 diabetes mellitus (T2DM), is rapidly increasing, posing significant health and economic challenges. T2DM not only disrupts blood glucose regulation but also damages vital organs such as the heart, kidneys, eyes, nerves, and blood vessels, leading to substantial morbidity and mortality. In the US alone, t

Louigi Addario-Berry, Serte Donderwinkel, Christina Goldschmidt, Rivka Mitchell

We prove a scaling limit for globally centered discrete snakes on size-conditioned critical Bienaym\'e trees. More specifically, under a global finite variance condition, we prove convergence in the sense of random finite-dimensional distributions of the head of the discrete snake (suitably rescaled) to the head of the Brownian snake driven by a Brownian exc

Renzhi He, Yucheng Li, Brianna Urbina, Jiandi Wan

Two-photon microscopy is a powerful tool for in vivo imaging, but its imaging depth is typically limited to a few hundred microns due to tissue scattering, even with existing scattering correction techniques. Moreover, most active scattering correction methods are restricted to small regions by the optical memory effect. Here, we introduce compressive Fourie

Takashi Ito, Renu Malhotra

Pluto's argument of perihelion is known to librate around $90^\circ$. This libration is related to the secular phenomenon known as the von Zeipel-Lidov-Kozai (vZLK) oscillation. In this work, we make a quantitative assessment of the influence of Neptune's mean motion resonance and of the other giant planets' secular perturbations on the libration of Pluto's

Jing Wang, Zihao Sun, Huaicheng Chen, Gao Wang

Disorder hyperuniform (DHU) systems possess a hidden long-range order manifested as the complete suppression of normalized large-scale density fluctuations like crystals, which endows them with many unique properties. Here, we demonstrate a new organization mechanism for achieving stable DHU structures in active-particle systems via investigating the self-as