November 2025 arXiv papers — page 97

Manuel Gonzalez-Espinoza, Ramón Herrera, Giovanni Otalora, Carlos Ríos

In this work, we investigate late-time interacting cosmologies within the framework of generalized Rastall gravity, where the interaction arises naturally from the non-conservation of the energy-momentum tensor. We formulate the background evolution of the dark sector as an autonomous dynamical system, defining interaction terms $Q_1=\alpha\,\dot{f}$ and $Q_

Adam Caulfield, Muhammad Wasif Kamran, N. Asokan

Run-time integrity enforcement in real-time systems presents a fundamental conflict with availability. Existing approaches in real-time systems primarily focus on minimizing the execution-time overhead of monitoring. After a violation is detected, prior works face a trade-off: (1) prioritize availability and allow a compromised system to continue to ensure a

Jun Ding, Shang Gao

Medical image segmentation underpins computer-aided diagnosis and therapy by supporting clinical diagnosis, preoperative planning, and disease monitoring. While U-Net style convolutional neural networks perform well due to their encoder-decoder structures with skip connections, they struggle to capture long-range dependencies. Transformer-based variants addr

Yue Zhang, Zun Wang, Han Lin, Jialu Li

Despite recent progress in 3D-LLMs, they remain limited in accurately grounding language to visual and spatial elements in 3D environments. This limitation stems in part from training data that focuses on language reasoning rather than spatial understanding due to scarce 3D resources, leaving inherent grounding biases unresolved. To address this, we propose

Jun Liu, Jianhua Zhang, Yongxin Yao, Kai-Ming Ho

We apply correlation matrix renormalization theory (CMRT) to cerium nitride (CeN) under pressure. For B1 (NaCl-type) phase, CMRT gives an equation of state consistent with ambient pressure experiments. It produces electronic density-of-state (DOS) characterized by a sharp 4f quasi-particle resonance peak pinned at the Fermi level and two subbands formed by s

Iden Kalemaj, Luca Melis, Maxime Boucher, Ilya Mironov

Differential privacy (DP) auditing is essential for evaluating privacy guarantees in machine learning systems. Existing auditing methods, however, pose a significant challenge for large-scale systems since they require modifying the training dataset -- for instance, by injecting out-of-distribution canaries or removing samples from training. Such interventio

Zhonghao Liu, Hanxue Gu, Qihang Li, Michael Fox

To develop and validate a fully automated, deep-learning pipeline for measuring glenoid bone loss on 3D CT scans using linear-based, en-face view, and best-circle method. Shoulder CT scans of 81 patients were retrospectively collected between January 2013 and March 2023. Our algorithm consists of three main stages: (1) Segmentation, where we developed a U-Ne

Yao Qin, Yangyang Yan, YuanChao Yang, Jinhua Pang

Deep learning models have achieved remarkable success in medical image analysis but are fundamentally constrained by the requirement for large-scale, meticulously annotated datasets. This dependency on "big data" is a critical bottleneck in the medical domain, where patient data is inherently difficult to acquire and expert annotation is expensive, particula

Shang-Wei Li, Yuan-Heng Tseng, Kai-Wei Huang, Fu-Jiun Jiang

We study the phase transitions of the two-dimensional antiferromagnetic Ising model with nearest $J_1$ and next-to-nearest $J_2$ interactions on the triangular lattice for $J_2/J_1 = 0.1, 0.5$ and 1.0. The method of supervised neural networks (NN) is employed for the investigation. While supervised NN is used, no real spin configurations are needed for the t

Chao Zhang, Tao Zhu

The $\gamma$-metric, also known as Zipoy-Voorhees spacetime, is a static, axially symmetric vacuum solution to Einstein's field equations characterized by two parameters: mass and the deformation parameter $\gamma$. It reduces to the Schwarzschild metric when $\gamma = 1$. In this paper, we explore potential signatures of the $\gamma$-metric on periodic orbi

Janarbek Yuanbek, Bruno Tenorio, Yusuf Turek

In quantum metrology, measurements are usually treated as passive readout processes. Here we investigate whether post-selected von Neumann measurements (PVNMs) can be used as an active resource to reshape the nonclassical properties of a two-mode entangled coherent state (ECS). By analyzing the finite-coupling post-selected state, we show that PVNMs can enha

Yihong Liang, Emine Celiker, Ping Lin

This paper presents a phase-field model for simulating the three-dimensional deformation of vesicle membranes, incorporating area-difference elasticity, with constraints on bulk volume and surface area. We develop efficient numerical schemes based on the Fourier-spectral method for spatial discretization and temporal evolution. The model successfully capture

Yingjie Luo, Eduard P. Kontar, Debesh Bhattacharjee

Solar flares effectively accelerate particles to non-thermal energies. These accelerated electrons are responsible for energy transport and subsequent emissions in HXR, radio, and UV/EUV radiation. Due to the steeply decreasing electron spectrum, the electron population and consequently the overall flare energetics, are predominantly influenced by low-energy

Qiqi Xiao, Ziqi Ye, Yinghui He, Jianwei Liu

To promote the practicality of deep learning-based localization, existing studies aim to address the issue of scenario dependence through meta-learning. However, these studies primarily focus on variations in environmental layouts while overlooking the impact of changes in device configurations, such as bandwidth, the number of access points (APs), and the n

Ajesh Koyatan Chathoth, Stephen Lee

Sensor data-based recognition systems are widely used in various applications, such as gait-based authentication and human activity recognition (HAR). Modern wearable and smart devices feature various built-in Inertial Measurement Unit (IMU) sensors, and such sensor-based measurements can be fed to a machine learning-based model to train and classify human a

Zijin Su, Huanzhu Lyu, Yuren Niu, Yiming Liu

Multi-label sentiment classification plays a vital role in natural language processing by detecting multiple emotions within a single text. However, existing datasets like GoEmotions often suffer from severe class imbalance, which hampers model performance, especially for underrepresented emotions. To address this, we constructed a balanced multi-label senti

Narumasa Tsutsumida, Rei Mitsuhashi, Yoshito Sawada, Akira Kato

Spaceborne Light Detection and Ranging (LiDAR) systems, such as NASA's Global Ecosystem Dynamics Investigation (GEDI), provide forest structure for global carbon assessments. However, geolocation uncertainties (typically 5-15 m) propagate systematically through derived products, undermining forest profile estimates, including carbon stock assessments. Existi

Geonwoo Bang, DongMyung Kim, Hayoung Oh

Large Language Models (LLMs) hold great potential for web-based interactive applications, including browser games, online education, and digital storytelling platforms. However, LLM-based conversational agents suffer from spatiotemporal distortions when responding to variant user inputs, failing to maintain consistency with provided scenarios. We propose SNA

Jingyu Lei, Gaoang Wang, Der-Horng Lee

Large Vision-Language Models (LVLMs) usually suffer from prohibitive computational and memory costs due to the quadratic growth of visual tokens with image resolution. Existing token compression methods, while varied, often lack a high-level semantic understanding, leading to suboptimal merges, information redundancy, or context loss. To address these limita

Saviz Saei, Nazanin Tajik

A key principle in resilience thinking is Embracing Change because change is, indeed, inevitable. In the face of a growing number of disasters, natural and human-made disasters, our critical infrastructures (CIs) are being challenged like never before. This recent trend has sparked a wave of interest among both practitioners and researchers in understanding

Junsik Kim, Gun Bang, Soowoong Kim

Hierarchical LiDAR geometry compression encodes voxel occupancies from low to high bit-depths, yet prior methods treat each depth independently and re-estimate local context from coordinates at every level, limiting compression efficiency. We present ELiC, a real-time framework that combines cross-bit-depth feature propagation, a Bag-of-Encoders (BoE) select

Zhuoqing Zheng, Tao Liu, Xuyang Wu

Dual ascent (DA) and the method of multipliers (MM) are fundamental methods for solving linear equality-constrained convex optimization problems, and their dual updates can be viewed as the minimization of a proximal linear surrogate function of the negative Lagrange dual and augmented Lagrange dual function, respectively. However, the proximal linear surrog

Peiqi Li, Jie Chen

In energy science, Darcy flow in heterogeneous porous media is a central problem in reservoir sim-ulation. However, the pronounced multiscale characteristics of such media pose significant challenges to conventional numerical methods in terms of computational demand and efficiency. The Mixed Generalized Multiscale Finite Element Method (MGMsFEM) provides an

Vladimir Rosenhaus, Natalia Vladimirova, Gregory Falkovich

The Gross-Pitaevskii (GP) model, also known as the nonlinear Schr\"odinger equation, is arguably the most universal model in classical and quantum physics, describing spectrally narrow or long-wavelength distributions of interacting waves or particles. Modern applications -- from oceanic and atmospheric flows to photonics and cold atoms -- predominantly invo

Zhiheng Cai, Si Liu, Hengfeng Wei, Yuxing Chen

Strong isolation guarantees, such as serializability and snapshot isolation, are essential for maintaining data consistency and integrity in modern databases. Verifying whether a database upholds its claimed guarantees is increasingly critical, as these guarantees form a contract between the vendor and its users. However, this task is challenging, particular

Zdzislaw Brzezniak, Qi Li, Tusheng Zhang

In this paper, we establish an exponential ergodicity for stochastic evolution equations with reflection in an infinite dimensional ball. As an application, we obtain the exponential ergodicity of stochastic Navier-Stokes equations with reflection. A coupling method plays an important role.

Felipe A. Torres, Alejandro Weinstein, Jesus M. Cortes, Wael El-Deredy

The collective frequency that emerges from synchronized neuronal populations--the network resonance--shows a systematic relationship with brain size: whole-brain's large networks oscillate slowly, whereas finer parcellations of fixed volume exhibit faster rhythms. This resonance-size scaling has been reported in delayed neural mass models and human neuroimag

Kelin Ren, Chan-Yang Ju, Dong-Ho Lee

Medication recommendation systems play a crucial role in assisting clinicians with personalized treatment decisions. While existing approaches have made significant progress in learning medication representations, they suffer from two fundamental limitations: (i) treating medical entities as independent features without modeling their synergistic effects on

Dongyang Jin, Ryan Xu, Jianhao Zeng, Rui Lan

Recently, autoregressive (AR) models have shown strong potential in image generation, offering better scalability and easier integration with unified multi-modal systems compared to diffusion-based methods. However, extending AR models to general image editing remains challenging due to weak and inefficient conditioning, often leading to poor instruction adh

Richard F. Murray

Game engines such as Unity and Unreal Engine have become popular tools for creating perceptual and behavioral experiments in complex, interactive environments. They are often used with flat-panel displays, and also with head-mounted displays. Here I describe and test a mathematical model of luminance and color in Unity's High Definition Render Pipeline (HDRP

Shenglin Zhang, Ziang Chen, Zijing Que, Yilun Liu

Log anomaly detection, which is critical for identifying system failures and preempting security breaches, detects irregular patterns within large volumes of log data, and impacts domains such as service reliability, performance optimization, and database log analysis. Modern log anomaly detection methods rely on training deep learning models on clean, anoma

Hanlin Ren, Yichuan Wang, Yan Zhong

Given a circuit $G: \{0, 1\}^n \to \{0, 1\}^m$ with $m > n$, the *range avoidance* problem ($\text{Avoid}$) asks to output a string $y\in \{0, 1\}^m$ that is not in the range of $G$. Besides its profound connection to circuit complexity and explicit construction problems, this problem is also related to the existence of *proof complexity generators* -- circu

Yifan Li, Qin Li, Min Zhang, Min Zhang

Assessing the reasoning ability of Large Language Models (LLMs) over data remains an open and pressing research question. Compared with LLMs, human reasoning can derive corresponding modifications to the output based on certain kinds of changes to the input. This reasoning pattern, which relies on abstract rules that govern relationships between changes of d

Arpita Roddanavar, Satoshi Inoue, Keiji Hayashi, Ju Jing

Solar active region 11283 produced an X2.1 flare associated with a solar eruption on September 6, 2011. Observations revealed a preflare sigmoidal structure and a circular flare ribbon surrounding the typical two ribbon structure, along with remote brightenings located at a considerable distance from the main flare site. To interpret these observations in te

Jianbing Dong, Jianbin Chang

Training Large Language Models (LLMs) typically involves a two-stage pipeline at the output layer: hidden states are projected into vocabulary logits via a linear transformation (lm_head), followed by cross-entropy loss computation against target tokens. While conceptually simple, this design incurs substantial overhead. The intermediate logits tensor, with

E. Saleh, S. Ghaffari, J. H. Curtis, L. Patel

Key aerosol properties that shape climate -- such as CCN activity, scattering and absorption, and ice nucleation efficiency -- are difficult to infer from measurements that typically capture only a part of the aerosol state. We develop a conditional generative framework that maps a label (a vector of partial observations) to an ensemble of plausible aerosol

Feng Yu, Mingao Yuan

Subgraph counting is a fundamental task that underpins several network analysis methodologies, including community detection and graph two-sample tests. Counting subgraphs is a computationally intensive problem. Substantial research has focused on developing efficient algorithms and strategies to make it feasible for larger unweighted graphs. Implementing th

Xianghe Liu, Jiajia Liu, Chuxian Xu, Minghan Wang

In precision sports such as archery, athletes' performance depends on both biomechanical stability and psychological resilience. Traditional motion analysis systems are often expensive and intrusive, limiting their use in natural training environments. To address this limitation, we propose a machine learning-based multimodal framework that integrates wearab

Susobhan Mandal, S. Shankaranarayanan

The accelerated expansion of the universe poses a significant challenge to General Relativity. Non-local modifications to gravity have emerged as a compelling class of theories to address this dark energy puzzle. Building upon earlier proposals, we investigate a specific non-local modified gravity action incorporating terms like $R\Box^{-2}R$, $R^{\mu\nu}\Bo

Zhongwei Shen

Consider the Dirichlet-to-Neumann map $\Lambda_\beta$ associated with the Schr\"odinger operator $(D+\beta \A)^2$ with a magnetic potential in a bounded Lipschitz domain $\Omega$, where $\beta>1$ is the field strength parameter. Assume that the magnetic field $\B=\nabla \times \A$ is of finite type. We show that if $\beta>\beta_0$, the ground state for $\Lam

Fabio L. Braghin, Marcelo Loewe, Cristian Villavicencio

Effective pion-constituent quark couplings induced by relatively weak magnetic fields are calculated in the framework based in Weinberg's large Nc Effective Field Theory. These couplings (form factors) vanish in the vacuum. In particular, single-pion couplings to a scalar and a vector constituent quark currents are investigated. These couplings might corresp

Amirreza Mehrabi, Jason W. Morphew, Breejha Quezada, N. Sanjay Rebello

Adaptive learning often diagnoses precisely yet intervenes weakly, yielding help that is mistimed or misaligned. This study presents evidence supporting an instructor-governed feedback loop that converts concept-level assessment evidence into vetted micro-interventions. The adaptive learning algorithm contains three safeguards: adequacy as a hard guarantee o

Xiang Chen, Ming-Min Zhao, An Liu, Min Li

In this paper, we investigate a multi-target sensing system in multipath environment, where inter-target scattering gives rise to first-order reflected paths whose angles of departure (AoDs) and angles of arrival (AoAs) coincide with the direct-path angles of different targets. Unlike other multipath components, these first-order paths carry structural infor

Liqian Qin, Aviv Gibali, Cuijie Zhang, Yuchao Tang

In this paper, we introduce three novel splitting algorithms for solving structured monotone inclusion problems involving the sum of a maximally monotone operator, a monotone and Lipschitz continuous operator and a cocoercive operator. Each proposed method extends one of the classical schemes: the semi-forward-reflected-backward splitting algorithm, the semi

Semen Leontev

Small and medium-sized enterprises (SMEs) represent 99.9% of U.S. businesses yet remain systematically excluded from AI due to a mismatch between their operational scale and modern machine learning's data requirements. This paper introduces SmallML, a Bayesian transfer learning framework achieving enterprise-level prediction accuracy with datasets as small a

Zifan Wang, Georgios Pantazis, Sergio Grammatico, Michael M. Zavlanos

We study a class of distributionally robust games where agents are allowed to heterogeneously choose their risk aversion with respect to distributional shifts of the uncertainty. In our formulation, heterogeneous Wasserstein ball constraints on each distribution are enforced through a penalty function leveraging a Lagrangian formulation. We then formulate th

Tausif Parvez, S. Shankaranarayanan

We present the first exact, non-singular black hole solution in General Relativity sourced by a Dirac-Born-Infeld (DBI) scalar field. Crucially, the solution is exclusively supported by \emph{the phantom branch of the DBI action}, dynamically replacing the central singularity with a regular core. The solution is asymptotically flat, possesses non-trivial sca

Li-Cheng Sheng, Yu-Jie Tang, Yu-Xin Wan, Rui Chen

The internal structure of the exotic $P_c$ and $P_{cs}$ pentaquarks remains an open question. To address this, we demonstrate that pion emission serves as a sensitive probe by calculating its properties within a molecular scenario using the chiral quark model and coupled-channel effects. Our results reveal a strong dependence of the decay widths on the inter

Yule Liu, Heyi Zhang, Jinyi Zheng, Zhen Sun

Reinforcement Learning with Verifiable Rewards (RLVR) has become a core training stage in recent large language models (LLMs). Its reliance on non-public, high-value prompt sets raises concerns about unauthorized data use, creating a need for exposure auditing. A natural tool is membership inference attacks (MIAs), but existing methods detect fitting to a fi

Cutter Beck, Evan Smith, Khagendra Katuwal, Rudra Kafle

Coronal holes (CHs) are low-activity, low-density solar coronal regions with open magnetic field lines (Cranmer 2009). In the extreme ultraviolet (EUV) spectrum, CHs appear as dark patches. Using daily hand-drawn maps from the Space Weather Prediction Center (SWPC), we developed a semi-automated pipeline to digitize the SWPC maps into binary segmentation mas

Chandrachur Bhattacharya, Sibendu Som

AI Scientific Assistant Core (AISAC) is a transparent, modular multi-agent runtime developed at Argonne National Laboratory to support long-horizon, evidence-grounded scientific reasoning. Rather than proposing new agent algorithms or claiming autonomous scientific discovery, AISAC contributes a governed execution substrate that operationalizes key requireme

Mara Daniels, Philippe Rigollet

We introduce a highly expressive class of function approximators called Splat Regression Models. Model outputs are mixtures of heterogeneous and anisotropic bump functions, termed splats, each weighted by an output vector. The power of splat modeling lies in its ability to locally adjust the scale and direction of each splat, achieving both high interpretabi

You Zhou

The Moon-forming giant impact significantly influenced the initial thermal state of Earth's mantle by generating a global magma ocean, marking the onset of mantle evolution. Recent Smoothed Particle Hydrodynamics (SPH) simulations indicate that such a collision would produce a superheated core, whose cooling would strongly influence subsequent mantle dynamic

Loucif Hebbache, Dariush Amirkhani, Mohand Saïd Allili, Jean-François Lapointe

Anomaly object detection and classification are one of the main challenging tasks in computer vision and pattern recognition. In this paper, we propose a new method to automatically detect, localize and classify defects in concrete bridge structures using drone imagery. This framework is constituted of two main stages. The first stage uses saliency for defec

Logan J. Prust, Lars Bildsten, Samuel J. Boos

White dwarfs which explode by the double-detonation mechanism may have a binary white dwarf donor which is subsequently ignited by its collision with the ejecta. This results in the destruction of the donor via either the triple- or quadruple-detonation mechanism, adding significant mass to the resulting ejecta as well as modifying its structure and composit

Lijie Chen, Yang Hu, Hanlin Ren

The *algebrization barrier*, proposed by Aaronson and Wigderson (STOC '08, ToCT '09), captures the limitations of many complexity-theoretic techniques based on arithmetization. Notably, several circuit lower bounds that overcome the relativization barrier (Buhrman--Fortnow--Thierauf, CCC '98; Vinodchandran, TCS '05; Santhanam, STOC '07, SICOMP '09) remain su

Hesam Mojtahedi, Reza Akhavian

This paper presents a BIM-discrepancy-driven active sensing framework for cooperative navigation between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in dynamic construction environments. Traditional navigation approaches rely on static Building Information Modeling (BIM) priors or limited onboard perception. In contrast, our framework

Gyuha Lim, Deborah Levin

We investigate facility effects on a reduced-scale gridded ion thruster plume using a fully kinetic, three-dimensional Particle-in-Cell/Monte Carlo Collision (PIC-MCC) solver coupled with a Direct Simulation Monte Carlo (DSMC) neutral background. This approach enables detailed examination of key plasma processes governing beam neutralization and wall interac

Michael Y. Grudić, Dávid Guszejnov, Philip F. Hopkins, Stella S. R. Offner

The model for the Planck-mean dust opacity $\kappa_{P}$ given in Appendix C of the STARFORGE simulations methods paper does not extrapolate well to low radiation temperature $T_{\rm rad}$, so we provide an updated calculation suitable for general use. We also clarify the role of the dust and radiation temperatures in setting the dust opacity, and provide cod

Michael Schuckers, Austin Hayes

Using data on 103 recent P4 college football hires, we built a statistical model for predicting a coach's success at their new school. For each hire, we collected data about their background and experiences, the previous success as a head coach or coordinator and their success since hiring. Over 50 variables on these factors were recorded though we used 29 o

Sidharth Gat

Many economic theories have been introduced over the course of history to articulate our understanding of the economy. Classical theories by Adam Smith and David Ricardo's Comparative Advantage have been foundational for the last century's work. Improvements have been achieved over time, incorporating insights from many disparate fields of study: contemporar

Sun Han Neo, Sachith Seneviratne, Herath Mudiyanselage Viraj Vidura Herath, Abhishek Saha

Flood prediction is critical for emergency planning and response to mitigate human and economic losses. Traditional physics-based hydrodynamic models generate high-resolution flood maps using numerical methods requiring fine-grid discretization; which are computationally intensive and impractical for real-time large-scale applications. While recent studies h

Kunal Mukherjee

Digital content distribution and propitiatory research driven industries face persistent risks from intellectual property theft and unauthorized redistribution. Conventional encryption schemes such as AES, TDES, ECC, and ElGamal provide strong cryptographic guarantees, but they remain fundamentally agnostic to where decryption takes place. In practice, this

Rong Zhang, Jinxiao Li, Jingnan Wang, Zhiwen Zuo

Garment-centric fashion image generation aims to synthesize realistic and controllable human models dressing a given garment, which has attracted growing interest due to its practical applications in e-commerce. The key challenges of the task lie in two aspects: (1) faithfully preserving the garment details, and (2) gaining fine-grained controllability over

Sungik Choi, Hankook Lee, Moontae Lee

AI-generated image detection has become crucial with the rapid advancement of vision-generative models. Instead of training detectors tailored to specific datasets, we study a training-free approach leveraging self-supervised models without requiring prior data knowledge. These models, pre-trained with augmentations like RandomResizedCrop, learn to produce c

Kristoffer Eggestad, Marc Vila, Sverre M. Selbach, Sinéad M. Griffin

Nonrelativistic spin splitting (NRSS) in compensated magnetic materials is drawing considerable attention due to its potential impact in next-generation spintronic devices. While NRSS is typically restricted to materials with particular symmetry constraints, here we demonstrate, using density functional theory (DFT) and tight-binding transport calculations,

Md Shazid Islam, Shreyangshu Bera, Sudipta Paul, Amit K. Roy-Chowdhury

Although active learning (AL) in segmentation tasks enables experts to annotate selected regions of interest (ROIs) instead of entire images, it remains highly challenging, labor-intensive, and cognitively demanding due to the blurry and ambiguous boundaries commonly observed in medical images. Also, in conventional AL, annotation effort is a function of the

Junjie Wu, Yumeng Fu, Nan Yu, Guohong Fu

Recent advancements in multimodal out-of-context (OOC) misinformation detection have made remarkable progress in checking the consistencies between different modalities for supporting or refuting image-text pairs. However, existing OOC misinformation detection methods tend to emphasize the role of internal consistency, ignoring the significant of external co

Lisa Hartung, Andreas Klippel, Christian Mönch

We study the extreme value statistics of the zero-average Gaussian free field (GFF) on random $r$-regular graphs and the Gaussian free field on $r$-regular trees. For random $r$-regular graphs of diverging size, for every fixed $r\ge3$, we show that the rescaled extremal point process of the field is asymptotically distributed, in the annealed sense, as a Po

Loujun Yu, Yuejian Peng

Let $F_l$ be the fan graph obtained by joining a vertex with a path on $l-1$ vertices. Yu, Li and Peng [Discrete Math. 346 (2023)] conjectured that if the number of edges of $G$ is $m$ and the spectral radius $\lambda(G)>\frac{k-1+\sqrt{4m-k^2+1}}{2}$, then $G$ contains a $F_{2k+1}$ and $F_{2k+2}$, unless $G=K_{k}\vee (\frac{m}{k}-\frac{k-1}{2})K_1$. The cas

Jaskirat Singh, Rohan Chandra

Multi-robot systems are increasingly being used for critical applications such as rescuing injured people, delivering food and medicines, and monitoring key areas. These applications usually involve navigating at high speeds through constrained spaces such as small gaps. Navigating such constrained spaces becomes particularly challenging when the space is cr

Chiharu Hagiwara, Naoki Nonaka, Yuhta Hashimoto, Ryu Uchimido

Triage is a critically important decision-making process in mass casualty incidents (MCIs) to maximize victim survival rates. While the role of AI in such situations is gaining attention for making optimal decisions within limited resources and time, its development and performance evaluation require benchmark datasets of sufficient quantity and quality. How

Pradeep Kumar Sharma, Ishaan Puri, Mantinder Jit Singh, Swapnil Shivaprasad

Modern codebases evolve continuously: files are renamed or deleted; public APIs drift; behavior shifts within otherwise familiar modules. A model trained yesterday to map a developer's natural-language question to the exact set of repository file paths that matter will degrade tomorrow, even if the questions themselves look unchanged. In this paper we study,

SangHyuk Kim, Daniel Haehn, Sumientra Rampersad

Humans can easily identify anatomical planes (axial, coronal, and sagittal) on a 2D MRI slice, but automated systems struggle with this task. Missing plane orientation metadata can complicate analysis, increase domain shift when merging heterogeneous datasets, and reduce accuracy of diagnostic classifiers. This study develops a classifier that accurately gen

J. F. Feinstein, Alexander J. Izzo

We prove the existence of a nontrivial uniform algebra that is logmodular and regular on the Cantor set. As a consequence, we obtain that for every compact metrizable space X without isolated points there exists a nontrivial essential uniform algebra that is logmodular and regular on X. In particular, there exists a nontrivial essential uniform algebra that

Minghu Wang, Shuliang Zhao, Yuanyuan Zhao, Hongxia Xu

The static nature of knowledge within Large Language Models (LLMs) makes it difficult for them to adapt to evolving information, rendering knowledge editing a critical task. However, existing methods struggle with challenges of scalability and retrieval efficiency, particularly when handling complex, multi-hop questions that require multi-step reasoning. To

Erum Mushtaq, Anil Ramakrishna, Satyapriya Krishna, Sattvik Sahai

Recent work has shown that fine-tuning on insecure code data can trigger an emergent misalignment (EMA) phenomenon, where models generate malicious responses even to prompts unrelated to the original insecure code-writing task. Such cross-domain generalization of harmful behavior underscores the need for a deeper understanding of the algorithms, tasks, and d

Saeed Akbari, Damiano Lombardi, Hessam Babaee

Developing efficient solvers for large-scale multi-term linear matrix equations remains a central challenge in numerical linear algebra and is still largely unresolved. This paper introduces a methodology leveraging CUR decomposition for solving large-scale generalized Sylvester as well as non-Sylvester multi-term equations on low-rank matrix manifolds. The

Xianming Gu, Lihui Wang, Ying Cao, Zeyu Deng

Multi-contrast magnetic resonance imaging (MRI) super-resolution intends to reconstruct high-resolution (HR) images from low-resolution (LR) scans by leveraging structural information present in HR reference images acquired with different contrasts. This technique enhances anatomical detail and soft tissue differentiation, which is vital for early diagnosis

Bifei Mao, Lanqing Hong

As a capability coming from computation, how does AI differ fundamentally from the capabilities delivered by rule-based software program? The paper examines the behavior of artificial intelligence (AI) from engineering points of view to clarify its nature and limits. The paper argues that the rationality underlying humanity's impulse to pursue, articulate, a

Pranendu Darbar

We improve the range of uniformity in the double-exponential decay of the tail of the distribution established by Lumley~\cite{Lumley} for the quadratic Dirichlet $L$-function $L(1, \chi_D)$ over the ensemble of hyperelliptic curves of genus~$g$ defined over a fixed finite field~$\mathbb{F}_q$, in the limit as $g \to \infty$. Furthermore, we apply a long res

Dong-En Lu, Li-Ming Wang

The recent observation of the X(2600) resonance by the BESIII Collaboration has motivated a renewed interest in the spectroscopy of light mesons, particularly pseudotensor states. However, a significant theoretical gap exists in the poorly explored spectrum of isoscalar pseudotensor mesons, where several predicted radial excitations remain unobserved. To add

Chenchen Kuai, Zihao Li, Braden Rosen, Stephanie Paal

Post-disaster reconnaissance reports contain critical evidence for understanding multi-hazard interactions, yet their unstructured narratives make systematic knowledge transfer difficult. Large language models (LLMs) offer new potential for analyzing these reports, but often generate unreliable or hallucinated outputs when domain grounding is absent. This st

Halle C. Braun, Kushin Mukherjee, Seth R. Gorelik, Karen B. Schloss

Research on affective visualization design has shown that color is an especially powerful feature for influencing the emotional connotation of visualizations. Associations between colors and emotions are largely driven by lightness (e.g., lighter colors are associated with positive emotions, whereas darker colors are associated with negative emotions). Desig

Samantha C. Wu, Daichi Tsuna, Brenna Mockler, Anthony L. Piro

Delayed radio emission has been associated with a growing proportion of tidal disruption events (TDEs). For many events, the radio synchrotron emission is inferred to originate from the interaction of mildly-relativistic outflows, launched with delay times of $\sim 100$--$1000$ d after the TDE optical peak. The mechanism behind these outflows remains uncerta

Kaiyuan Hu, Hong Kang, Yili Jin, Junhua Liu

Volumetric video has emerged as a key paradigm in eXtended Reality (XR) and immersive multimedia because it enables highly interactive, spatially consistent 3D experiences. However, the transport-layer security for such 3D content remains largely unaddressed. Existing volumetric streaming pipelines inherit uniform encryption schemes from 2D video, overlookin

Taijing Chen, Sateesh Kumar, Junhong Xu, Georgios Pavlakos

Service robots must retrieve objects in dynamic, open-world settings where requests may reference attributes ("the red mug"), spatial context ("the mug on the table"), or past states ("the mug that was here yesterday"). Existing approaches capture only parts of this problem: scene graphs capture spatial relations but ignore temporal grounding, temporal reaso

Quoc Viet Vo, Tashreque M. Haq, Paul Montague, Tamas Abraham

Certified defenses promise provable robustness guarantees. We study the malicious exploitation of probabilistic certification frameworks to better understand the limits of guarantee provisions. Now, the objective is to not only mislead a classifier, but also manipulate the certification process to generate a robustness guarantee for an adversarial input cert

Chengpeng Li, Farnaz Behrang, August Shi, Peng Liu

Flaky tests that non-deterministically pass or fail waste developer time and slow release cycles. While large language models (LLMs) show promise for automatically repairing flaky tests, existing approaches like FlakyDoctor fail in industrial settings due to the context problem: providing either too little context (missing critical production code) or too mu

William Zhao, Guy Van den Broeck, Benjie Wang

Bayesian networks (BNs) are a widely used class of probabilistic graphical models employed in numerous application domains. However, inferring the network's graphical structure from data remains challenging. Bayesian structure learners approach this problem by inferring a posterior distribution over the possible directed acyclic graphs underlying the BN. The

Pedro Rosario, A. Cidrim, R. Bachelard

Postselection is a non-deterministic mechanism to entangle subsystems, often used in weakly-excited systems. We here show how highly-excited ensembles of two-level emitters can be entangled by photon detection. A collective spin is formed, characterized by a squeezing parameter detected by far-field measurements. While decoherence is detrimental to this cond

Katherine M. FitzGerald, Michelle Riedlinger, Axel Bruns, Stephen Harrington

Interactive chat systems that build on artificial intelligence frameworks are increasingly ubiquitous and embedded into search engines, Web browsers, and operating systems, or are available on websites and apps. Researcher efforts have sought to understand the limitations and potential for harm of generative AI, which we contribute to here. Conducting a syst

Akbar Anbar Jafari, Cagri Ozcinar, Gholamreza Anbarjafari

Contemporary machine learning models, including large language models, exhibit remarkable capabilities in static tasks yet falter in non-stationary environments due to rigid architectures that hinder continual adaptation and lifelong learning. Building upon the nested learning paradigm, which decomposes models into multi-level optimization problems with fixe

Marco Acerbis, Swarnadip Chatterjee, Christophe Avenel, Joakim Lindblad

Computational cytology faces two major challenges: i) instance-level labels are unreliable and prohibitively costly to obtain, ii) witness rates are extremely low. We propose SLAM-AGS, a Slide-Label-Aware Multitask pretraining framework that jointly optimizes (i) a weakly supervised similarity objective on slide-negative patches and (ii) a self-supervised co

T. J. Taiwo, A. D. Alhaidari, U. Al Khawaja

We introduce a perturbative formulation for a nonlinear extension of the J-matrix method of scattering in two dimensions. That is, we obtain the scattering matrix for the time-independent nonlinear Schrödinger equation in two dimensions with circular symmetry. The formulation relies on the linearization of products of orthogonal polynomials and on the utiliz

Hanyu Cheng, Liangqi Cheng, Xiwen Bai

In the maritime sector, tramp shipping companies manage fleets to maximize profit while navigating market uncertainties. The International Maritime Organization (IMO) recently introduced the Carbon Intensity Indicator (CII) to reduce greenhouse gas emissions, further complicating deployment decisions. This paper introduces a novel two-stage stochastic progra

Emilio A. Lauret, Fiorela Rossi Bertone, Alejandro Tolcachier

We study the smallest positive eigenvalue $λ_1$ of the Laplace-Beltrami operator associated with any compact strongly isotropy irreducible space. We provide an explicit expression for all simply connected cases. Furthermore, every strongly isotropy irreducible space is automatically an Einstein manifold, and we prove for each of them that $E<λ_1\leq 16E$, wh

Ravi Shankar Prasad, Nandani Sharma, Dinesh Singh

In forensic craniofacial identification and in many biomedical applications, craniometric landmarks are important. Traditional methods for locating landmarks are time-consuming and require specialized knowledge and expertise. Current methods utilize superimposition and deep learning-based methods that employ automatic annotation of landmarks. However, these

Thibault Ghesquière-Diérickx, Jean-François Molinari, Guillaume Anciaux

Dynamic fragmentation simulations are essential for predicting material response at high strain rates, yet explicit dynamic simulations that combine an extrinsic cohesive-zone model (CZM) with penalty-based contact often exhibit severe instabilities. In a two-dimensional benchmark, we observe exponential energy growth and resulting artificial fragmentation u

M. R. Povolotskiy, A. S. Slavich, G. A. Ermolaev, D. V. Grudinin

The development of nanophotonics is hindered by a fundamental trade-off between a material&#39;s refractive index (n) and its electronic bandgap (Eg), which severely restricts the choice of materials for short-wavelength applications. This challenge is particularly acute in the visible and ultraviolet (UV) spectra, where high-performance devices require mate