December 2025 arXiv papers — page 82

Junwei Ma, Bo Li, Xiangpeng Li, Chenyue Liu

Lifeline infrastructure underpins the continuity of daily life, yet conventional criticality assessments remain largely asset-centric, inferring importance from physical capacity or network topology rather than actual behavioral reliance. This disconnect frequently obscures the true societal cost of disruption, particularly in underserved communities where r

Qizhou Chen, Chengyu Wang, Taolin Zhang, Xiaofeng He

Large Language Models (LLMs) have become indispensable tools in science, technology, and society, enabling transformative advances across diverse fields. However, errors or outdated information within these models can undermine their accuracy and restrict their safe deployment. Developing efficient strategies for updating model knowledge without the expense

Justin Jiang

Images are a substantial portion of the internet, making efficient compression important for reducing storage and bandwidth demands. This study investigates the use of Singular Value Decomposition and low-rank matrix approximations for image compression, evaluating performance using relative Frobenius error and compression ratio. The approach is applied to b

Akihiro Sugawara

This paper determines almost symmetric numerical semigroups with maximal reduced type completely. In addition, this paper classifies MED-semigroups with maximal reduced type.

Yanyu Cheng, Yujian Hu, Haoran Liu, Hua Zhong

In this paper, we propose a simultaneous secrecy and covert communications (SSACC) scheme in a reconfigurable intelligent surface (RIS)-aided network with a cooperative jammer. The scheme enhances communication security by maximizing the secrecy capacity and the detection error probability (DEP). Under a worst-case scenario for covert communications, we cons

Ziqi Ding, Shangzhi Xu, Wei Song, Yuekang Li

CAPTCHAs are widely employed for distinguishing humans from automated bots online. However, current vision based CAPTCHAs face escalating security risks: traditional attacks continue to bypass many deployed CAPTCHA schemes, and recent breakthroughs in AI, particularly large scale vision models, enable machine solvers to significantly outperform humans on man

Kevin J. Kelly, Mudit Rai

Accelerator-based neutrino experiments offer a competitive environment to search for long-lived particles with sub-GeV masses. Yet, many theoretical models involving such particles predict very similar phenomenology and nearly identical final-state signatures. In view of this, we study the capabilities of upcoming experiments -- specifically the DUNE near de

Lorenzo Nava, Ye Chen, Maximillian Van Wyk de Vries

Predicting geohazard runout is critical for protecting lives, infrastructure and ecosystems. Rapid mass flows, including landslides and avalanches, cause several thousand deaths across a wide range of environments, often travelling many kilometres from their source. The wide range of source conditions and material properties governing these flows makes their

Alexis Hibbler, Kevin J. McGown, Enrique Treviño

Heilbronn gave a sufficient condition for a number field with a totally ramified prime to fail to be norm-Euclidean. We say that Heilbronn's criterion applies to a polynomial $f$ if it applies to the number field $K=\mathbb{Q}[x]/(f)$ generated by $f$. Suppose $n\geq 3$ is odd and $p\geq 5$ is prime with $\gcd(p-1,n)=1$. Let ${F}_{p,n}$ denote the collection

Zhihao Zhang, Xuejun Yang, Weihua Liu, Mouquan Shen

Single-view novel view synthesis (NVS) models based on diffusion models have recently attracted increasing attention, as they can generate a series of novel view images from a single image prompt and camera pose information as conditions. It has been observed that in diffusion models, certain high-quality initial noise patterns lead to better generation resu

Sun Lee, Panayotis G. Kevrekidis, Wenrui Hao

In this work, we are motivated by a recent variant of the nonlinear Schrodinger (NLS) equation describing cold, dilute atomic condensates with quantum fluctuation effects. Our goal is to develop robust numerical methods capable of uncovering diverse stationary solutions in such NLS models. Specifically, and in line with recent theoretical and experimental in

Adam S. Wilkinson, Leo J. A. Parry, Jorma Louko, William G. Unruh

We investigate the back-action from a spatially pointlike particle detector on a quantum scalar field, as characterised by the expectation value of the field's stress-energy tensor, without conditioning on a measurement of the detector. First, assuming the field to be initially in a zero-mean Gaussian Hadamard state in a globally hyperbolic spacetime, we eva

Lingxiao Li, Haiyan Su, He Zhang, Weiying Zheng

The Stokes equations play an important role in the incompressible flow simulation. In this paper, a novel divergence-free parametric mixed finite element method is proposed for solving three-dimensional Stokes equations on domains with piecewise smooth boundaries. The flow velocity and pressure are discretized with high-order parametric Brezzi-Douglas-Marini

Jorge A. Zavala, Andreas L. Faisst, Manuel Aravena, Caitlin M. Casey

We exploit a new sample of around 400 bright dusty galaxies from the ALMA CHAMPS Large Program, together with the rich JWST multi-band data products in the COSMOS field, to explore and validate new selection methods for identifying dusty star-forming galaxies (DSFGs). Here, we present an effective empirical selection criterion based on a newly defined parame

Jianming Liu, Ren Zhu, Jian Xu, Kun Ding

Solving Partial Differential Equations (PDEs) is a cornerstone of engineering and scientific research. Traditional methods for PDE solving are cumbersome, relying on manual setup and domain expertise. While Physics-Informed Neural Network (PINNs) introduced end-to-end neural network-based solutions, and frameworks like DeepXDE further enhanced automation, th

Amit Vishwakarma, K. S. Subrahamanian Moosath

Three-dimensional point clouds provide highly accurate digital representations of objects, essential for applications in computer graphics, photogrammetry, computer vision, and robotics. However, comparing point clouds faces significant challenges due to their unstructured nature and the complex geometry of the surfaces they represent. Traditional geometric

Boris Huang, Wolfgang K. Schief, Jan Techter

Based on a novel discretization procedure which has recently been proposed and applied in the construction of a canonical discrete analogue of confocal coordinate systems, an explicit method of constructing discrete analogues of ellipsoids is recorded. These discrete ellipsoids are entirely composed of planar quadrilaterals and come in pairs of combinatorial

Jia-Yong Xie, Jun-ichi Nakashima, Yong Zhang

We investigated whether "velocity excess" in circumstellar maser lines can diagnose the earliest evolutionary phases of Water Fountains (WFs). Here we define "velocity excess" as maser emission (e.g., H$_2$O 22.235 GHz or OH 1665/1667 MHz) detected at velocities outside the velocity range of the OH 1612 MHz line, which traces the terminal expansion velocity

Yusuke Tamura, Kairi Masuda, Yu Kumagai

The unexpected emergence of ferroelectricity in HfO2 at reduced dimensions has attracted considerable attention, as it provides a pathway toward the realization of ultrasmall ferroelectric devices. Ab initio calculations suggest that this effect arises from a unique mode coupling, in which an antipolar displacement mode stabilizes a robust polar distortion.

Sachin R. Pendse, Darren Gergle, Rachel Kornfield, Kaylee Kruzan

Throughout history, a prevailing paradigm in mental healthcare has been one in which distressed people may receive treatment with little understanding around how their experience is perceived by their care provider, and in turn, the decisions made by their provider around how treatment will progress. Paralleling this offline model of care, people who seek me

Yuting Hu, Lei Zhuang, Hua Xiang, Jinjun Xiong

As integrated circuit (IC) dimensions shrink below the lithographic wavelength, optical lithography faces growing challenges from diffraction and process variability. Model-based optical proximity correction (OPC) and inverse lithography technique (ILT) remain indispensable but computationally expensive, requiring repeated simulations that limit scalability.

M. V. Egorov

This work presents three-body calculations of the cross sections for the $^3$He+T $\to$ n+p+$^4$He fusion reaction. These calculations were performed by solving a set of six coupled Faddeev integral equations, utilizing a cluster representation of the target nucleus, the triton, as a bound neutron-deuteron system. Short-range interactions within the two-body

S. Bala, P. Veres, A. Goldstein, R. Sonawane

The minimum variability timescale (MVT) is a key observable used to probe the central engines of Gamma-Ray Bursts (GRBs) by constraining the emission region size and the outflow Lorentz factor. However, its interpretation is often ambiguous: statistical noise and analysis choices can bias measurements, making it difficult to distinguish genuine source variab

Zilin Wang, Sangwoo Mo, Stella X. Yu, Sima Behpour

Adaptive categorization of visual scenes is essential for AI agents to handle changing tasks. Unlike fixed common categories for plants or animals, ad-hoc categories are created dynamically to serve specific goals. We study open ad-hoc categorization: Given a few labeled exemplars and abundant unlabeled data, the goal is to discover the underlying context an

Sarosij Bose, Ravi K. Rajendran, Biplob Debnath, Konstantinos Karydis

Radiology Report Generation (RRG) is a critical step toward automating healthcare workflows, facilitating accurate patient assessments, and reducing the workload of medical professionals. Despite recent progress in Large Medical Vision-Language Models (Med-VLMs), generating radiology reports that are both visually grounded and clinically accurate remains a s

Haishan Ye

Rank-based zeroth-order (ZO) optimization -- which relies only on the ordering of function evaluations -- offers strong robustness to noise and monotone transformations, and underlies many successful algorithms such as CMA-ES, natural evolution strategies, and rank-based genetic algorithms. Despite its widespread use, the theoretical understanding of rank-ba

Jerrin Bright, Zhibo Wang, Dmytro Klepachevskyi, Yuhao Chen

We present Avatar4D, a real-world transferable pipeline for generating customizable synthetic human motion datasets tailored to domain-specific applications. Unlike prior works, which focus on general, everyday motions and offer limited flexibility, our approach provides fine-grained control over body pose, appearance, camera viewpoint, and environmental con

Hongliang Lu, Yunmeng Liu, Junjie Yang

Human decision-making heavily relies on active sensing, a well-documented cognitive behaviour for evidence gathering to accommodate ever-changing environments. However, its operational mechanism in the real world remains non-trivial. Currently, an in-laboratory paradigm, called evidence accumulation modelling (EAM), points out that human decision-making invo

Zhuoyuan Lu, Kirill Koshelev, Pavel Tonkaev, Ziyu Chen

Recently introduced concept of Mie voids allows to enhance the field localization inside air cavities embedded in high-index materials. Mie voids provide an alternative approach to conventional dielectric resonators that confine optical fields within bulk high-index materials. Building on this concept, here we present a hybrid photonic platform that integrat

Sean Doan, Sahil D. Patel, Yilin Chen, Jordan A. Gusdorff. Mark E. Turiansky

Color centers hosted in hexagonal boron nitride have emerged as a highly promising platform for single-photon emission and spin-photon technologies relevant to quantum communication and quantum networking. As a wide-bandgap van der Waals material, hBN can host optically active quantum defects across a broad spectral range. Here, we demonstrate a simple and s

J. Trey Diulus, Ashley R. Head, Jorge Anibal Boscoboinik, Carles Corbella Roca

In plasma X ray photoelectron spectroscopy emerges as a powerful platform for real time, in situ chemical analysis under conditions relevant to semiconductor processing and other plasma enabled technologies. This study investigates the origins of binding energy shifts and satellite peaks formation observed during plasma XPS measurements across conductive, di

Kohei Kitamura

In this paper we investigate multiple polylogarithms with non-positive multi-indices (nonpositive MPLs) from a combinatorial and algebraic viewpoint. By introducing a correspondence between non-positive multiple polylogarithms and Magnus polynomials in a free associative algebra, we obtain an explicit Magnus-type representation of products of mono-indexed no

Benjamin Lowe, Bernard Field, Dhaneesh Kumar, Daniel Moreno Cerrada

Two-dimensional (2D) materials with frustrated crystal geometries can host strongly correlated electrons, potentially leading to a range of exotic many-body quantum phases such as Mott insulators, quantum spin-liquids, and Kondo lattices. The ability to control exchange-coupling within these systems is therefore highly desirable. Here, we use an atomically s

Henry Fontana

We compute the Harder-Narasimhan Filtration of the normal bundle $N_{C/\mathbb{P}^{g-1}}$ where $C$ is a general tetragonal canonical curve of genus $g$.

Sahil Kalra, Niraj K. Shukla

This paper explores the perfect reconstruction property of filter banks based on Ramanujan sums and their applications in signal recovery. Originally introduced by Srinivasa Ramanujan, Ramanujan sums serve as powerful tools for extracting periodic components from signals and form the foundation of Ramanujan filter banks. We investigate the perfect reconstruc

Joonsung Kang

In quantum mechanics, observation actively shapes the system, paralleling the statistical notion of Missing Not At Random (MNAR). This study introduces a unified framework for \textbf{robust causal directionality inference} in quantum engineering, determining whether relations are system$\to$observation, observation$\to$system, or bidirectional. The method i

Jianping Mei, Siqi Ai, Ye Yuan

Spatial transcriptomics enables genome-wide expression analysis within native tissue context, yet identifying spatial domains remains challenging due to complex gene-spatial interactions. Existing methods typically process spatial and feature views separately, fusing only at output level - an "encode-separately, fuse-late" paradigm that limits multi-scale se

Giedrius Žlabys, Wen-Bin He, Domantas Burba, Sarika Sasidharan Nair

We investigate topological phenomena in a spatially modulated Dirac-$\delta$ lattice, where the scattering potential varies periodically in space. Changing the potential modulation frequency leads to Hofstadter's butterfly-like energy spectrum and enables the emergence of topological transport regimes characterized by non-trivial Chern numbers. We show how t

Leonid Bunimovich, Kirill Kovalenko

Traditionally, Probability theory was dealing with limit theorems where 'limit" means that time tends to infinity. Questions about finite time dynamics (evolution) were always considered as, although important for practical applications, but untreatable rigorously (mathematically). The same attitude was in the theory of strongly chaotic dynamical systems, wh

Gourab Ghatak

We propose the \emph{weighted K-harmonic means} (WKHM) clustering algorithm, a regularized variant of K-harmonic means designed to ensure numerical stability while enabling soft assignments through inverse-distance weighting. Unlike classical K-means and constrained K-means, WKHM admits a direct interpretation in wireless networks: its weights are exactly eq

Mengfan Shen, Kangqi Song, Xindi Wang, Wei Jia

Structured information extraction from police incident announcements is crucial for timely and accurate data processing, yet presents considerable challenges due to the variability and informal nature of textual sources such as social media posts. To address these challenges, we developed a domain-adapted extraction pipeline that leverages targeted prompt en

Hao Li, Yubing Ren, Yanan Cao, Yingjie Li

With the rapid development of cloud-based services, large language models have become increasingly accessible through various web platforms. However, this accessibility has also led to growing risks of model abuse. LLM watermarking has emerged as an effective approach to mitigate such misuse and protect intellectual property. Existing watermarking algorithms

Matthias Goerner

We introduce a new tiling algorithm for hyperbolic 3-manifolds. We use it to compute the maximal cusp area matrix; this completely characterizes the space of all embedded and disjoint cusp neighborhoods. As another application of our work, we find the Epstein-Penner decomposition answering a challenge of Sakuma and Weeks. We furthermore provide the refinemen

Zilal Eiz AlDin, John Wu, Jeffrey Paul Fung, Jennifer King

Despite rare diseases affecting 1 in 10 Americans, their differential diagnosis remains challenging. Due to their impressive recall abilities, large language models (LLMs) have been recently explored for differential diagnosis. Existing approaches to evaluating LLM-based rare disease diagnosis suffer from two critical limitations: they rely on idealized clin

Sourabh Dube, Nilanjana Kumar, Shriyansh Ranjan

This paper explores a simplified extension of the standard model featuring a neutral fermion quintuplet and a scalar quadruplet, which together generate neutrino masses through tree and loop level mechanisms. The quintuplet fermions decay into standard model gauge bosons via the scalars, producing unique collider signatures at the LHC characterized by multil

M. Oltan Sevinc, Liao Wu, Francisco Cruz

Although traditional cameras are the primary sensor for end-to-end driving, their performance suffers greatly when the conditions of the data they were trained on does not match the deployment environment, a problem known as the domain gap. In this work, we consider the day-night lighting difference domain gap. Instead of traditional cameras we propose event

Junggu Choi, Tak Hur, Seokhoon Jeong, Kyle L. Jung

Effective molecular representations are essential for ligand-based virtual screening. We investigate how quantum data embedding strategies can improve this task by developing and evaluating a family of quantum-classical hybrid embedding approaches. These approaches combine classical neural networks with parameterized quantum circuits in different ways to gen

Kaito Kobayashi

In this paper, we study independent (Bernoulli) bond percolation in dimensions $d \ge 2$, focusing on the maximum diameter of finite clusters in the non-critical regime ($p\neq p_c$). We prove that the maximum diameter $R_n$ satisfies $R_n / \log n \to \varkappa(p)$ almost surely, where $\varkappa(p)$ is determined by the exponential decay rate $\xi(p)$ of $

Khalid N. Anindya, Hong Guo

Establishing symmetry-protected topological (SPT) phases with interactions in chemically realistic systems remains an open challenge. We show that a single, synthetically plausible organic one-dimensional chain, tunable via chemical modification of its radical sites, hosts two such phases: an odd-Haldane phase of a dimerized $S=\tfrac{1}{2}$ Heisenberg chain

Botong Li, Bobby G. Duersch, Hunter Ellis, Imteaz Rahaman

The thermal stability and structural evolution of a GaN high-electron-mobility transistor (HEMT) heterostructure grown on a Si (111) substrate were investigated using in situ high-temperature X-ray diffraction (HT-XRD), reciprocal space mapping (RSM), Raman spectroscopy, and rocking-curve (RC) analysis at varying temperatures. The heterostructure, consisting

Karthikeyan K, Philip Wu, Xin Tang, Alexandre Alves

The Science Consultant Agent is a web-based Artificial Intelligence (AI) tool that helps practitioners select and implement the most effective modeling strategy for AI-based solutions. It operates through four core components: Questionnaire, Smart Fill, Research-Guided Recommendation, and Prototype Builder. By combining structured questionnaires, literature-

Weihua Liu

Classical distributional symmetries can be described as invariance under the actions of semigroups (or groups) of matrix structures, and subsequently under the coactions of continuous functions on the matrix semigroups (or groups) generated by entry functions. By considering noncommutative entry functions on matrix structures, Woronowicz introduced coreprese

Danilo F. Schafaschek, Giovani L. Vasconcelos, Antônio M. S. Macêdo

Nelson's stochastic mechanics formulates quantum dynamics as a real-time conservative diffusion process in which a particle undergoes Brownian-like motion with a fluctuation amplitude fixed by Planck's constant. While being mathematically equivalent to the Schr\"odinger formulation, this approach provides an alternative dynamical framework that enables the s

Davide Rovere

The aim of this Thesis is twofold. On the one hand, we find the necessary and sufficient conditions for a maximally supersymmetric supergravity theory in 3D to be a solution of 11D supergravity (but the result is general and also holds for 10D supergravities), with 8 dimensions compactified into a coset space. The used method is based on the formalism of gen

Katie Ansaldi, Dayane Lira, Maral Mostafazadehfard, Kumari Saloni

We investigate the special fibers associated with certain coordinate sections of Hankel determinantal ideals. We provide explicit descriptions of their defining equations, showing that these equations admit a natural matrix structure. In particular, we prove that they are Cohen-Macaulay and cannot, in general, be minimally generated only by quadrics and cubi

Chao Li, Dasha Hu, Chengyang Li, Yuming Jiang

Unsupervised Domain Adaptation transfers knowledge from a labeled source domain to an unlabeled target domain. Directly deploying Vision-Language Models (VLMs) with prompt tuning in downstream UDA tasks faces the signifi cant challenge of mitigating domain discrepancies. Existing prompt-tuning strategies primarily align marginal distribu tion, but neglect co

Ryan M. Shannon, N. D. Ramesh Bhat, Aurelien Chalumeau, Siyuan Chen

Pulsar timing arrays (PTAs) are ensembles of millisecond pulsars observed for years to decades. The primary goal of PTAs is to study gravitational-wave astronomy at nanohertz frequencies, with secondary goals of undertaking other fundamental tests of physics and astronomy. Recently, compelling evidence has emerged in established PTA experiments for the prese

Avishek Basu, Vanessa Graber, Marcus E. Lower, Marco Antonelli

Matter inside neutron stars is compressed to densities several times greater than nuclear saturation density, while maintaining low temperatures and large asymmetries between neutrons and protons. Neutron stars, therefore, provide a unique laboratory for testing physics in environments that cannot be recreated on Earth. To uncover the highly uncertain nature

Joseph D. Gelfand, C. -Y. Ng, B. Posselt, Mallory S. E. Roberts

Produced by the interaction between the ``pulsar wind'' powered by the rotational energy of a neutron star and its surroundings, the study of pulsar wind nebulae (PWNe) provides vital insight into the physics of neutron star magnetospheres and ultra-relativistic outflows. Spatially-resolved studies of the continuum and polarized radio emission of these sourc

Jun Xu, J. L. Han, Weicong Jing, The SKA Pulsar Science Working Group

The magnetic field structure of the Milky Way can offer critical insights into the origin of galactic magnetic fields. Measurements of magnetic structures of the Milky Way are still sparse in far regions of the Galactic disk and halo. Pulsars are the best probes for the three-dimensional structure of the Galactic magnetic field, primarily owing to their high

C. Tiburzi, M. T. Lam, D. J. Reardon, N. K. Porayko

The ionised media that permeate the Milky Way have been active topics of research since the discovery of pulsars in 1967. In fact, pulsars allow one to study several aspects of said plasma, such as their column density, turbulence, scattering measures, and discrete, intervening structures between the neutron star and the observer, as well as aspects of the m

L. S. Oswald, A. Basu, M. Chakraborty, B. C. Joshi

The SKA telescopes will bring unparalleled sensitivity across a broad radio band, a wide field of view across the Southern sky, and the capacity for sub-arraying, all of which make them the ideal instruments for studying the pulsar magnetosphere. This paper describes the advances that have been made in pulsar magnetosphere physics over the last decade, and d

L. Levin, M. Bagchi, M. Burgay, A. T. Deller

The known population of non-accreting neutron stars is ever growing and currently consists of more than 3500 sources. Pulsar surveys with the SKAO telescopes will greatly increase the known population, adding radio pulsars to every subgroup in the radio-loud neutron star family. These discoveries will not only add to the current understanding of neutron star

Manjari Bagchi, Federico Abbate, Vishnu Balakrishnan, Miquel Colom i Bernadich

Because of their extreme stellar densities, globular clusters are highly efficient factories of X-ray binaries and radio pulsars: per unit of stellar mass, they contain about 1000 times more of these exotic objects. Thus far, 345 radio pulsars have been found in globular clusters. These can be used as precision probes of the structure, gas content, magnetic

E. F. Keane, V. Graber, L. Levin, C. M. Tan

Most of the pulsar science case with the Square Kilometre Array (SKA) depends on long-term precision pulsar timing of a large number of pulsars, as well as astrometric measurements of these using very long baseline interferometry (VLBI). But before we can time them, or VLBI them, we must first find them. Here, we describe the considerations and strategies on

Bhal Chandra Joshi, Aris Karastergiou, Marta Burgay, The SKA pulsar science working group

The large instantaneous sensitivity, a wide frequency coverage and flexible observation modes with large number of beams in the sky are the main features of the SKA observatory's two telescopes, the SKA-Low and the SKA-Mid, which are located on two different continents. Owing to these capabilities, the SKAO telescopes are going to be a game-changer for radio

Liu Qian, Yue Li, Ying Xie, Jian Zhang

Catalyst design is crucial for materials synthesis, especially for complex reaction networks. Strategies like collaborative catalytic systems and multifunctional catalysts are effective but face challenges at the nanoscale. Carbon nanotube synthesis contains complicated nanoscale catalytic reactions, thus achieving high-density, high-quality semiconducting C

Muzeeb Mohammad

Microservice based systems underpin modern distributed computing environments but remain vulnerable to partial failures, cascading timeouts, and inconsistent recovery behavior. Although numerous resilience and recovery patterns have been proposed, existing surveys are largely descriptive and lack systematic evidence synthesis or quantitative rigor. This pape

Wenbo Qiao, Shuaixian Wang, Peng Zhang, Yan Ming

Data re-uploading quantum circuits (DRQC) are a key approach to implementing quantum neural networks and have been shown to outperform classical neural networks in fitting high-frequency functions. However, their practical application is limited by the scalability of current quantum hardware. In this paper, we introduce the mathematical paradigm of DRQC into

Sihan Liu, Andrej Košmrlj

The physics and morphology of biomolecular condensates formed through liquid-liquid phase separation underpin diverse biological processes, exemplified by the nested organization of nucleoli that facilitates ribosome biogenesis. Here, we develop a theoretical and computational framework to understand and predict multiphase morphologies in DNA-nanostar soluti

Victor Stasiuc

Safety alignment can make frontier LMs overly conservative, degrading collaboration via hedging or false refusals. We present a lightweight toolkit with three parts: (1) Victor Calibration (VC), a multi-pass protocol that elicits a scalar confidence proxy T (T0<T1<T2) through iterative evidence re-evaluation; (2) FD-Lite, a behavior-only phenomenology audit

Hao Chen, Zhexin Hu, Jiajun Chai, Haocheng Yang

Training LLMs to invoke tools and leverage retrieved information necessitates high-quality, diverse data. However, existing pipelines for synthetic data generation often rely on tens of thousands of real API calls to enhance generalization, incurring prohibitive costs while lacking multi-hop reasoning and self-reflection. To address these limitations, we int

Zhisheng Hu, Jiacheng Shen, Ming-Chang Yang

Disaggregated memory (DM) is a promising data center architecture that decouples CPU and memory into independent resource pools to improve resource utilization. Building on DM, memory-disaggregated key-value (KV) stores are adopted to efficiently manage remote data. Unfortunately, existing approaches suffer from poor performance due to two critical issues: 1

Yash Bhaskar, Sankalp Bahad, Parameswari Krishnamurthy

Social media platforms, while enabling global connectivity, have become hubs for the rapid spread of harmful content, including hate speech and fake narratives \cite{davidson2017automated, shu2017fake}. The Faux-Hate shared task focuses on detecting a specific phenomenon: the generation of hate speech driven by fake narratives, termed Faux-Hate. Participants

Muzeeb Mohammad

Apache Kafka has become a foundational platform for high throughput event streaming, enabling real time analytics, financial transaction processing, industrial telemetry, and large scale data driven systems. Despite its maturity and widespread adoption, consolidated research on reusable architectural design patterns and reproducible benchmarking methodologie

Pengyu Wang, Shuchang Ye, Usman Naseem, Jinman Kim

Medical report generation aims to automatically produce radiology-style reports from medical images, supporting efficient and accurate clinical decision-making.However, existing approaches predominately rely on token-level likelihood training, which favors local lexical matching and leaves clinical correctness under-specified in the training objective. This

Prime Intellect Team, Mika Senghaas, Fares Obeid, Sami Jaghouar

We present INTELLECT-3, a 106B-parameter Mixture-of-Experts model (12B active) trained with large-scale reinforcement learning on our end-to-end RL infrastructure stack. INTELLECT-3 achieves state of the art performance for its size across math, code, science and reasoning benchmarks, outperforming many larger frontier models. We open-source the model togeth

Yueyang Hu, Haiyong Jiang, Haoxuan Song, Jun Xiao

This work presents a novel framework for few-shot 3D part segmentation. Recent advances have demonstrated the significant potential of 2D foundation models for low-shot 3D part segmentation. However, it is still an open problem that how to effectively aggregate 2D knowledge from foundation models to 3D. Existing methods either ignore geometric structures for

Hoel Queffelec, Anne-Laure Thiel, Emmanuel Wagner

We use the notion of Bridgeland stability condition and its associated metric to endow triangulated categories with extriangulated structures and study their extriangulated Grothendieck groups. This study is motivated by Khovanov-Seidel's categorification of the Burau representation, from which can be extracted the Lawrence-Krammer-Bigelow representation, pr

Ze Yuan, Wenbin Li, Shusen Zhao

We propose a hybrid reconstruction framework for dual-spectral CT (DSCT) that integrates iterative methods with deep learning models. The reconstruction process consists of two complementary components: a knowledge-driven module and a data-driven module. In the knowledge-driven phase, we employ the oblique projection modification technique (OPMT) to reconstr

Ionel Stetcu

I present a first-quantization deterministic algorithm for antisymmetrizing a spatially separated target-projectile system containing $N_T$ and $N_p$ identical fermions, respectively. The method constructs a fully antisymmetric wavefunction from the product of two independently antisymmetrized many-body states, each of which may be a superposition of Slater

S. Fujiyama, K. Ueda, Y. Otsuka

We present large-scale ab initio phonon calculations for the molecular quantum spin liquid X[Pd(dmit)2]2. An unusually low average phonon velocity ( 700 {m/s}) and optical modes below 10 cm^{-1} confine the Debye T^{3} regime to T < 2 K. As the transfer-integral anisotropy approaches the maximally frustrated regime (t'/t \to 1), the lattice stiffens, ruling

Zhisheng Hu, Pengfei Zuo, Junliang Hu, Yizou Chen

Disaggregated memory (DM) separates compute and memory resources, allowing flexible scaling to achieve high resource utilization. To ensure atomic and consistent data access on DM, distributed transaction systems have been adapted, where compute nodes (CNs) rely on one-sided RDMA operations to access remote data in memory nodes (MNs). However, we observe tha

Rudra Sekhri, Rahil N. Valani, Tapio Simula

We experimentally investigate the dynamics of synthetic active particles composed of gravitationally bouncing, superwalking droplets confined within an annular fluid bath. Driven by a topologically pumping dual-frequency waveform, the droplets exhibit alternating active (walking) and dormant (bouncing) phases, producing intermittent azimuthal motion. Trackin

Jian Tian, Shuailong Li, Yang Cao, Wenbo Cui

The evolution of Large Language Model (LLM) serving towards complex, distributed architectures--specifically the P/D-separated, large-scale DP+EP paradigm--introduces distinct scheduling challenges. Unlike traditional deployments where schedulers can treat instances as black boxes, DP+EP architectures exhibit high internal synchronization costs. We identify

Ren Nakagawa, Yang Yang, Risa Shinoda, Hiroaki Santo

This paper introduces a method and application for automatically detecting behavioral interactions between grazing cattle from a single image, which is essential for smart livestock management in the cattle industry, such as for detecting estrus. Although interaction detection for humans has been actively studied, a non-trivial challenge lies in cattle inter

Tian Wang, Nida Haram, Zack Dube, Kyle A. Hamer

Coupled electronic and nuclear motions govern chemical reactions, yet disentangling their interplay during bond rupture remains challenging. Here we follow the light-induced fragmentation of Br$_2$ using a coincidence-based multi-messenger approach. A UV pulse prepares the dissociative state, and strong-field ionization probes the evolving system. Coincident

Li-Wei He, Shun-Li Yu, Jian-Xin Li

The kagome lattice, with its unique geometric structure, has emerged as a leading platform for exploring quantum many-body physics, particularly in the study of quantum spin liquids (QSLs) and unconventional superconductivity. This review highlights recent advancements in the investigations of QSLs, fractional magnetization plateau phases in kagome antiferro

Samapti Lakshan, Le Tan Phuc, Deepak Pandit, Srijit Bhattacharya

The production of $^{60}$Fe is crucial for nucleosynthesis in massive stars and supernovae. In this work, by using the microscopic EP+IPM (exact pairing plus the independent-particle model) for the nuclear level density (NLD) and extended EP+PDM (exact pairing plus phonon damping model) for the $\gamma$-ray strength function (gSF), we re-evaluate the substan

Tran Quoc Viet, Le Tan Phuc, Tran Vu Dong, Nguyen Ngoc Anh

In this work, we present the ``EP code" (version 1.0), a user-friendly and robust computational tool. It computes the exact pairing eigenvalues and eigenvectors directly from the general nuclear pairing Hamiltonian, represented using SU(2) quasi-spin algebra with basis vectors in binary representation, at zero temperature for both odd and even deformed nucle

Junekey Jeon, Andrej Zlatos

We show that the generalized SQG equation on the plane is locally well-posed in spaces of low regularity solutions (essentially H\"older continuous with H\"older exponents depending on the equation parameter $\alpha\in(0,\frac 12)$) that have $H^2$ level sets (i.e., with $L^2$ curvatures). Moreover, for $\alpha\le\frac 16$ and initial data satisfying some ad

Zi Wang, Dong Zhao, Li Liang, Hengyi Wang

Chromatic dispersion, an inherent wavelength-dependent phenomenon in optical systems, has traditionally been regarded as a detrimental effect to be minimized in imaging and display. Here, we present a paradigm shift by deliberately engineering and harnessing metalens dispersion as a functional mechanism for three-dimensional (3D) near-eye displays. Specifica

Lulu Xue, Shengshan Hu, Linqiang Qian, Peijin Guo

Machine unlearning is a newly popularized technique for removing specific training data from a trained model, enabling it to comply with data deletion requests. While it protects the rights of users requesting unlearning, it also introduces new privacy risks. Prior works have primarily focused on the privacy of data that has been unlearned, while the risks t

Daniela N. Rim, Heeyoul Choi

Traditional Convolutional Neural Networks have been successful in capturing local, position-invariant features in text, but their capacity to model complex transformation within language can be further explored. In this work, we explore a novel approach by integrating Lie Convolutions into Convolutional-based sentence classifiers, inspired by the ability of

Yunfan Zhao, Xiaojing Chen

Let $(X_n)_{n\ge 1}$ be a Markov chain on a measurable state space $X$, and let $S_n = \sum_{k=1}^n f(X_k)$ be the associated Markov walk. For $y>0$, denote by $\tau_y$ the first time at which $y+S_n$ becomes non-positive. Assuming that the centred martingale approximation of $S_n$ lies in the domain of attraction of a strictly $\alpha$-stable law with $\alp

Min Geun Song, Gang Min Kim, Woonmin Kim, Yongsik Kim

Deep learning-based object detection models play a critical role in real-world applications such as autonomous driving and security surveillance systems, yet they remain vulnerable to adversarial examples. In this work, we propose an autoencoder-based denoising defense to recover object detection performance degraded by adversarial perturbations. We conduct

Xiaohui Liu, Wei-Yang Wang, Weicong Jing, Xuelei Chen

The statistical analysis of fast radio burst (FRB) samples from repeaters may suffer from a band-limited selection effect, which can bias the observed distribution. We investigated the impact of this selection bias on the energy function through simulations and then applied our analysis to the particular case of FRB 20220912A. Our simulations show that, in t

Geyuan Chen, Xin Zhong

We investigate the compressible magnetohydrodynamic equations subject to large external potential forces with discontinuous initial data in a three-dimensional bounded domain under Navier-slip boundary conditions. We show the global existence of weak solutions for such an initial-boundary value problem provided the initial energy is suitably small. In partic

Tristan Phillips

Let $M$ and $N$ be positive integers for which the modular curve $X_1(M,MN)$ has genus $0$, and let $p$ be a prime divisor of $MN$. This article gives asymptotic lower bounds for the average size of the $p$-Selmer group of elliptic curves over a number field, with torsion subgroup $\mathbb{Z}/M\mathbb{Z} \oplus \mathbb{Z}/MN\mathbb{Z}$. In many cases, it is

Medet Jumadildayev

This paper studies increasing trees on $n$ labeled vertices, in which labels increase from the root to the leaves. It is known that the number of binary increasing trees coincides with the number of alternating permutations (Euler numbers). Riordan obtained explicit formulas for the numbers of ternary and quaternary trees. This article derives a general form