March 2026 arXiv papers — page 38

Zhijun Jiang, Amin Milani Fard

Achieving high availability and robust security in Kubernetes requires more than reactive scaling and standard perimeter firewalls. Traditional autoscalers, such as HPA, often fail to react quickly to traffic spikes and cannot distinguish between legitimate flash crowds and DDoS attacks. We present an open-source toolchain to provide a traffic-aware autoscal

Irfan Qaisar, Kailai Sun, Qingshan Jia, Qianchuan Zhao

Accurate occupancy information is essential for closed-loop occupant-centric control (OCC) in smart buildings. However, existing vision-based occupancy measurement methods often struggle to provide stable and accurate measurements in real indoor environments, and their implications for downstream HVAC control remain insufficiently studied. To achieve Net Zer

Leilei Cui, Richard D. Braatz

A gradient-based method is proposed for solving the linear quadratic regulator (LQR) problem for linear systems with nonlinear dependence on time-invariant probabilistic parametric uncertainties. The approach explicitly accounts for model uncertainty and ensures robust performance. By leveraging polynomial chaos theory (PCT) in conjunction with policy optimi

Zhihan Chen, Yuhuan Zhao, Yijie Zhu, Xinyu Yao

Subject-driven text-to-image diffusion models have achieved remarkable success in preserving single identities, yet their ability to compose multiple interacting subjects remains largely unexplored and highly challenging. Existing evaluation protocols typically rely on global CLIP metrics, which are insensitive to local identity collapse and fail to capture

Takehiro Ishikawa

Background and Aims: Reproducible external benchmarks for pneumothorax-related lung ultrasound (LUS) AI are scarce, and binary lung-sliding classification may obscure clinically important signs. We therefore developed a manifest-based external benchmark and used it to test both cross-domain generalization and task validity. Methods: We curated 280 clips from

Yufan Zhou, Zhiyuan Li, Óscar Jiménez-Arranz, Santi Roca-Fàbrega

Although stellar bars are prevalent in local galaxies, unbarred galaxies constitute a significant fraction, particularly at high redshifts. While some galaxies are unbarred by nature due to stability against the bar instability, several mechanisms capable of transforming barred galaxies into unbarred systems have also been proposed, such as central mass conc

Darryl Teo, Adharsha Sam, Chuan Shen Marcus Koh, Rakesh Nagi

Documentation of airport operations is inherently complex due to extensive technical terminology, rigorous regulations, proprietary regional information, and fragmented communication across multiple stakeholders. The resulting data silos and semantic inconsistencies present a significant impediment to the Total Airport Management (TAM) initiative. This paper

Kailai Sun, Yuebing Liang, Mingyi He, Yunhan Zheng

Urban development has been a defining force in human history, shaping cities for centuries. However, past studies mostly analyze such development as predictive tasks, failing to reflect its generative nature. Therefore, this study designs a multimodal generative AI framework to envision sustainable urban development at a global scale. By integrating prompts

Leonel Bixano, Tonatiuh Matos

We present a generalized Ernst-type framework for stationary, axisymmetric spacetimes in which a scalar field is coupled to the electrodynamic field, with a particular focus on the ModMax theory. Our approach relies on the Weyl stationary-axisymmetric ansatz and explicitly allows for a nonzero rotational metric function, $\omega\neq 0$. The resulting setup i

Junse Lee, François Baccelli

Non-terrestrial networks (NTNs) are considered a promising technology for seamless, universal communication in the 6G era. However, signals from NTN elements to ground users are often blocked by high-rise buildings in dense urban environments. To quantify this blocking effect, in this paper, we propose a novel analytical framework by modeling the location of

Kyungwon Kim, Dosik Hwang

Accurate survival prediction from multimodal medical data is essential for precision oncology, yet clinical deployment faces a persistent challenge: modalities are frequently incomplete due to cost constraints, technical limitations, or retrospective data availability. While recent methods attempt to address missing modalities through feature alignment or jo

Bilguun Bayarsaikhan, Jarah Evslin

We analytically sum the leading bubble diagrams that contribute to the elastic scattering amplitude of a kink and a meson in the $\phi^4$ double-well model. We find a single peak, corresponding to the unstable kink state in which the shape mode is excited twice. The peak has the usual Breit-Wigner form, and its imaginary part agrees with the shape mode decay

Woonghee Cho, Kiwan Nam, Yeochan An, You Young Kim

We report a systematic study of the composition-dependent bulk properties in $Co_{1/3(1\pmδ)}NbS_{2}$ single crystals across a series of precisely controlled cobalt compositions with -4%<$δ$<8%. By tuning the cobalt stoichiometry, we find that the topological Hall effect is critically sensitive to the intercalant cobalt composition and is completely suppress

Elkhan Ismayilzada, Yufei Zhang, Zijun Cui

Significant advancements made in reconstructing hands from images have delivered accurate single-frame estimates, yet they often lack physics consistency and provide no notion of how confidently the motion satisfies physics. In this paper, we propose a novel physics-aware conditional diffusion framework that refines noisy pose sequences into physically plaus

Tianrui Lou, Siyuan Liang, Jiawei Liang, Yuze Gao

Physical adversarial camouflage poses a severe security threat to autonomous driving systems by mapping adversarial textures onto 3D objects. Nevertheless, current methods remain brittle in complex dynamic scenarios, failing to generalize across diverse geometric (e.g., viewing configurations) and radiometric (e.g., dynamic illumination, atmospheric scatteri

Zhuoyu Cheng, Kohei Hatano, Eiji Takimoto

We study a class of adversarial bandit optimization problems in which the loss functions may be non-convex and non-smooth. In each round, the learner observes a loss that consists of an underlying linear component together with an additional perturbation applied after the learner selects an action. The perturbations are measured relative to the linear losses

Bo Chen, Jia Liu

We develop a preference elicitation method for a Von Neumann-Morgenstern (VNM)-type decision-maker from pairwise comparison data in the presence of response errors. We apply the maximum likelihood estimation (MLE) method to jointly elicit the non-parametric systematic VNM utility function and the scale parameter of the response error, assuming a Gumbel distr

Andrew Lauziere, Jonathan Daugherty, Taisa Kushner

As large language models (LLMs) continue to improve and see further integration into software systems, so does the need to understand the conditions in which they will perform. We contribute a statistical framework for understanding the impact of specific prompt features on LLM performance. The approach extends previous explainable artificial intelligence (X

Peiyuan Jiang, Yao Liu, Yanglei Gan, Jiaye Yang

Non-contact automatic deception detection remains challenging because visual and auditory deception cues often lack stable cross-subject patterns. In contrast, galvanic skin response (GSR) provides more reliable physiological cues and has been widely used in contact-based deception detection. In this work, we leverage stable deception-related knowledge in GS

Maya Ma, Rimika Jaiswal, Murphy Yuezhen Niu

Quantum error mitigation is essential for extracting trustworthy results from noisy intermediate-scale quantum (NISQ) processors. Yet, current approaches face a core scalability bottleneck: unbiased methods such as probabilistic error cancellation (PEC) incur exponential sampling overhead, while approximate techniques like zero-noise extrapolation trade accu

Manisha Keim, Sarmad Chandio, Osama Khalid, Rishab Nithyanand

Research on conspiracy theories has largely focused on belief formation, exposure, and diffusion, while paying less attention to how their meanings change over time. This gap persists partly because conspiracy-related terms are often treated as stable lexical markers, making it difficult to separate genuine semantic changes from surface-level vocabulary chan

Johannes Storn

We introduce an iterative scheme for discrete convex minimization problems of $p$-Laplace type such as variational graph $p$-Laplace problems and $\ell^p$ regression. In each iteration, the scheme solves only a weighted least-squares problem. We verify linear convergence for suitably regularized problems and derive convergence to any prescribed tolerance.

Ryoma Yamamoto, Akira Takahashi, Kei Terayama, Yu Kumagai

Large language models (LLMs) exhibit substantial potential across diverse scientific disciplines, including materials science. A property prediction framework, ZEBRA-Prop (Zero-Shot Embedding-Based Rapid and Accessible Regression Model for Materials Properties), is presented here as an extension of LLM-Prop. In contrast to LLM-Prop, which requires task-speci

Nobuaki Naganuma, Kaito Yura

In the present paper, we introduce and analyze elephant random walks (ERWs) on bipartite periodic lattices arising as coverings of dipole graphs. We focus on lattices whose admissible step directions in the two parts of the bipartition are negatives of each other and disjoint. On such graphs, we define an ERW in which each step is chosen by referring to the

Haoshuo Fu

The Weil representation is a particularly significant linear representation of the metaplectic group, used in the study of theta correspondence. In this paper, I introduce a derived category version of the Weil representation in the local field case. For the dual pair $ (\mathrm{GL}_n,\mathrm{GL}_m) $, I give a coherent description of this category, in the p

Gayana Jayasinghe, Alex R. Taylor, Xinran Yu

We investigate the relationship between two interpretations of equivariant Riemann-Roch defects of complex spaces with conic singularities; as (i) equivariant $\eta_{T}$ and $\xi_{T}$ invariants, and as (ii) supertraces over local cohomology groups. This leads to a novel threefold partitioning of the $L^{2}$-spinor space on the link and a corresponding split

Taotao He, Zhongqi Wu, Yating Zhang

We study logit-based multi-purchase choice models and develop an exact solution methodology for the resulting assortment optimization problems, which we show are NP-hard to approximate. We introduce a hypergraph representation that captures general bundle-based choice structures and subsumes several models in the literature, including the BundleMVL-K and mul

Nathaniel Oh, Paul Attie

Language models detect false premises when asked directly but absorb them under conversational pressure, producing authoritative professional output built on errors they already identified. This failure - order-gap hallucination - is invisible to output inspection because the error migrates into the activation space of the safety circuit, suppressed but not

Qizhi Xie, Kun Yuan, Yunpeng Qu, Ming Sun

Accurately estimating humans' subjective feedback on video fluency, e.g., motion consistency and frame continuity, is crucial for various applications like streaming and gaming. Yet, it has long been overlooked, as prior arts have focused on solving it in the video quality assessment (VQA) task, merely as a sub-dimension of overall quality. In this work, we

Connor Rudy Sullivan, Amin Mamandipoor, Cole Ridge Strickler, Heechul Yun

Modern multicore system-on-chips (SoCs) share off-chip DRAM across cores, where bank-level interference can significantly degrade performance and threaten real-time guarantees. While prior work has focused on per-core bandwidth regulation, these approaches treat main memory as a monolithic resource and overlook DRAM's inherent bank-level parallelism. We show

Borislav Pavlov, Jiajin Li, Jun Fang, Yuntao Wang

Human routines structure daily life, yet remain challenging for computational systems to understand. This paper presents the first systematic review of routine computing, a previously implicit but increasingly recognized field that focuses on computationally sensing and modeling human behaviors. It synthesizes 203 studies published up to August 2025. The pap

Seine A. Shintani

Arithmetic benchmarks are often reduced to a single held-out score, but that score can conflate qualitatively different failures. We study a controlled minimal GPT trained on exhaustive 2-digit addition, where all local digit transitions are already present in training, and ask why 3-digit generalization still fails. The failure is staged. First, there is a

Wonsuk Lee, Jehoshua Bruck

Unlike the von Neumann architecture, which separates computation from memory, the brain tightly integrates them, an organization that large language models increasingly resemble. The crucial difference lies in the ratio of energy spent on computation versus data access: in the brain, most energy fuels compute, while in von Neumann architectures, data movemen

Zizhao Chen, Ping Wei, Ziyang Ren, Huan Li

As multimodal misinformation becomes more sophisticated, its detection and grounding are crucial. However, current multimodal verification methods, relying on passive holistic fusion, struggle with sophisticated misinformation. Due to 'feature dilution,' global alignments tend to average out subtle local semantic inconsistencies, effectively masking the very

Jingru Lu, Zhenbin Yang, Jianming Zheng

We formulate the Gao-Jafferis-Wall traversable wormhole protocol as a quantum channel and compute its quantum channel capacity. We show that this capacity is governed by the time derivative of an out-of-time-ordered correlator, hence by operator size growth in the holographic dual, and that its growth is bounded above by the Einstein gravity limit. The chann

Dianyu Zhong, Tian Xing, Kailai Sun, Xu Yang

Heating, ventilation, and air conditioning (HVAC) systems account for a substantial share of building energy consumption. Environmental uncertainty and dynamic occupancy behavior bring challenges in decarbonized HVAC control. Reinforcement learning (RL) can optimize long-horizon comfort-energy trade-offs but suffers from exponential action-space growth and i

Kang Liu, Zhuoqi Ma, Siyu Liang, Yunan Li

Despite recent advances in medical vision-language pretraining, existing models still struggle to capture the diagnostic workflow: radiographs are typically treated as context-agnostic images, while radiologists' gaze -- a crucial cue for visual reasoning -- remains largely underexplored by existing methods. These limitations hinder the modeling of disease-s

Haoming Shi, Eric C. Chi, Hengrui Luo

We study rank selection for low-rank tensor regression under random covariates design. Under a Gaussian random-design model and some mild conditions, we derive population expressions for the expected training-testing discrepancy (optimism) for both CP and Tucker decomposition. We further demonstrate that the optimism is minimized at the true tensor rank for

Hexu Liu

Given a pair $(V,C)$ that admits a stable minimal model with fixed dimension, fixed coefficient set, and bounded relative volume, we study when the linear system $|m(K_V+C)|$ induces an Iitaka fibration, assuming the Iitaka volume of $K_V+C$ is sufficiently large.

Kyomin Hwang, Nojun Kwak

Recent advancements in Large Language Models (LLMs) have played a significant role in reducing human workload across various domains, a trend that is increasingly extending into the medical field. In this paper, we propose an automated pipeline designed to alleviate the burden on nurses by automatically extracting clinical observations from nurse dictations.

Eric Yocam, Varghese Vaidyan, Yong Wang

We present H-Node Adversarial Noise Cancellation (H-Node ANC), a mechanistic framework that identifies, exploits, and defends hallucination representations in transformer-based large language models (LLMs) at the level of individual hidden-state dimensions. A logistic regression probe trained on last-token hidden states localizes hallucination signal to a sm

Sambartha Ray Barman, Andrey Starenky, Sophia Bodnar, Nikhil Narasimhan

Why do we forget? Why do we remember things that never happened? The conventional answer points to biological hardware. We propose a different one: geometry. Here we show that high-dimensional embedding spaces, subjected to noise, interference, and temporal degradation, reproduce quantitative signatures of human memory with no phenomenon-specific engineering

Yu Hashimoto

We prove that for each fixed $m \ge 2$, there are only finitely many disjoint covering systems with minimum modulus at least $3$ in which precisely one modulus is repeated, namely the largest modulus, and it occurs exactly $m$ times.

Ally Qin, Jian Wan, Sarat Mudunuri, Srinivasan Manoharan

We evaluate speculative decoding with EAGLE3 as an inference-time optimization for PayPal's Commerce Agent, powered by a fine-tuned llama3.1-nemotron-nano-8B-v1 model. Building on prior work (NEMO-4-PAYPAL) that reduced latency and cost through domain-specific fine-tuning, we benchmark EAGLE3 via vLLM against NVIDIA NIM on identical 2xH100 hardware across 40

A. N. Osipov, I. G. Savenko, Sergej Flach, A. V. Yulin

Flat bands in driven-dissipative systems offer a route to engineer strongly localized, long-lived excitations, yet their selective population via incoherent pumping remains an open challenge. We study a one-dimensional chain of coupled lasing dimers arranged in a cross-stitch geometry and show that the synchronization regime of the individual dimers, control

Daiqiang Li, Zihao Pan, Zeyu Zhang, Xuyang Liu

In recent years, GUI agents have demonstrated strong potential in navigation tasks. However, preserving complete historical screenshots introduces substantial computational overhead. This paper investigates how token pruning, a plug-and-play inference acceleration technique, can be effectively applied to GUI agent navigation scenarios. Firstly, we address th

Giorgi Japaridze

Computability logic (CoL) provides a semantic foundation in which formulas represent interactive computational problems and validity corresponds to uniform algorithmic solvability. Building on this foundation, clarithmetics -- CoL-based axiomatic number theories -- combine the full arithmetical strength of Peano arithmetic (PA) with explicit control over com

Mingxin Ma, Peter V. Gordon, Robert Roussarie, Peipei Shang

In this paper we consider a one-dimensional reaction-diffusion model with piecewise continuous reaction term that describes propagation of autoignition fronts in reactive co-flow jets in a certain parametric regime. The model is reduced to a free boundary problem with two interfaces. It is shown that this problem admits permanent traveling front solution whi

Kutub Uddin, Nusrat Tasnim, Byung Tae Oh

Multimedia data, particularly images and videos, is integral to various applications, including surveillance, visual interaction, biometrics, evidence gathering, and advertising. However, amateur or skilled counterfeiters can simulate them to create deepfakes, often for slanderous motives. To address this challenge, several forensic methods have been develop

Wenbo Gao, Renxi Liu, Xian Wang, Fang Guo

Autonomous agents powered by large language models (LLMs) perform complex tasks through long-horizon reasoning and tool interaction, where a fundamental trade-off arises between execution efficiency and reasoning robustness. Models at different capability-cost levels offer complementary advantages: lower-cost models enable fast execution but may struggle on

Haijian Liang, Zenghao Niu, Junjie Wu, Changwang Zhang

Retrieval-Augmented Generation (RAG) expands the knowledge of Large Language Models (LLMs), yet current static retrieval methods struggle with complex, multi-hop problems. While recent dynamic retrieval strategies offer improvements, they face two key challenges: 1) irrelevant retrieved noise can misdirect the reasoning process, and 2) processing full docume

Jiazheng Xing, Chao Xu, Hangjie Yuan, Mengmeng Wang

Multimodal Large Language Models (MLLMs) have propelled the field of few-shot action recognition (FSAR). However, preliminary explorations in this area primarily focus on generating captions to form a suboptimal feature->caption->feature pipeline and adopt metric learning solely within the visual space. In this paper, we propose FSAR-LLaVA, the first end-to-

Shashie Dilhara Batan Arachchige, Hassan Jameel Asghar, Benjamin Zi Hao Zhao, Dinusha Vatsalan

Large Language Models (LLMs) generate responses based on user prompts. Often, these prompts may contain highly sensitive information, including personally identifiable information (PII), which could be exposed to third parties hosting these models. In this work, we propose a new method to sanitize user prompts. Our mechanism uses the randomized response mech

Harshitha Voleti, Charalambos Poullis

Prolonged mid-air interaction in virtual reality (VR) causes arm fatigue and discomfort, negatively affecting user experience. Incorporating ergonomic considerations into VR user interface (UI) design typically requires extensive human-in-the-loop evaluation. Although biomechanical models have been used to simulate human behavior in HCI tasks, their applicat

Zhangyong Liang, Huanhuan Gao

In practical structural design and solid mechanics simulations, material properties inherently exhibit random variations within bounded intervals. However, evaluating mechanical responses under continuous material uncertainty remains a persistent challenge. Traditional numerical approaches, such as the Finite Element Method (FEM), incur prohibitive computati

Runshi Tang, Yuefeng Han, Anru R. Zhang

We study estimation and detection of high-order moment and cumulant tensors from $n$ i.i.d.\ observations of a $p$-dimensional random vector, with performance measured in tensor spectral norm. Under sub-Gaussianity, we show that the minimax rate for estimating the order-$d$ moment and cumulant tensors is $\sqrt{p/n}\wedge 1$. In contrast to covariance estima

Zhaoyang Wang, Ping Lin

In this paper, we construct novel first- and second-order decoupled schemes for the Navier-Stokes equations based on the penalty method and the sequential regularization method (SRM), respectively. These schemes do not require the boundary condition on the pressure and thus preserve the original velocity boundary conditions. By using the idea of the scalar a

Ian Milne, Lachlan Astfalck, Matthew Zed, Jack Lee-Kopij

A framework for probabilistic forecasting of vessel motion is developed and validated for a semisubmersible operating in long period swell. Bayesian statistical methods are applied to predictions of the heave response from a physics model using numerical wave spectra and measured motion data. Model diagnoses motivate an additional level of complexity require

Alex Glushkovsky

Identification of causal directionality in bivariate numerical data is a fundamental research problem with important practical implications. This paper presents two alternative methods to identify direction of causation by considering conditional distributions: (1) Anticipated Asymmetric Geometries (AAG) and (2) Monotonicity Index (MI). The AAG method compar

Haoyue Bai, Dong Wang, Long Chen, Bingguang Hao

Large language model-based web agents have demonstrated strong performance on realistic web interaction tasks. However, existing evaluations are predominantly conducted under relatively stable and well-behaved interaction conditions, which may overestimate agent robustness. High task success in such idealized settings does not necessarily reflect performance

Linzheng Wang, Jason Chen, Nicolas Tricard, Zituo Chen

Digital twins of complex physical systems are expected to infer unobserved states from sparse measurements and predict their evolution in time, yet these two functions are typically treated as separate tasks. Here we present GLU, a Global-Local-Uncertainty framework that formulates sparse reconstruction and dynamic forecasting as a unified state-representati

Jack Jon Hinsch, Kazushi Fujimoto

Proton exchange membrane fuel cells (PEMFCs) are a promising clean energy technology, offering high efficiency and near-zero operational emissions for stationery and automotive applications. However, their widespread adoption remains limited by insufficient durability, driven by the degradation of the catalyst layer and proton exchange membrane under realist

Junta Kamiya

We introduce tropical singular intersection homologies (non-GM and GM) with the tropical coefficients on rational polyhedral spaces using their filtrations. We investigate their fundamental properties. In the non-GM case, we give a Poincar\'e duality and two bilinear pairings analogous to the cup and cap products under some assumptions. We compute the homolo

Yaoyu Fang, Jiahe Qian, Xinkun Wang, Lee A. Cooper

Spatial Transcriptomics (ST) provides spatially resolved gene expression profiles within intact tissue architecture, enabling molecular analysis in histological context. However, the high cost, limited throughput, and restricted data sharing of ST experiments result in severe data scarcity, constraining the development of robust computational models. To addr

Mengdi Liu, Qiang Li, Weizhi Nie, Shaopeng Zhang

Type A Aortic Dissection (TAAD) is a life-threatening cardiovascular emergency that demands rapid and precise preoperative evaluation. While key anatomical and pathological features are decisive for surgical planning, current research focuses predominantly on improving segmentation accuracy, leaving the reliable, quantitative extraction of clinically actiona

Danny Abraham, Nikhil Kamalkumar Advani, Arun Das, Nikil Dutt

Accurate 3D lane segment detection and topology reasoning are critical for structured online map construction in autonomous driving. Recent transformer-based approaches formulate this task as query-based set prediction, yet largely inherit decoder designs originally developed for compact object detection. However, lane segments are continuous polylines embed

Siqiao Xue, Zhaoyang Zhu, Wei Zhang, Rongyao Cai

Time series forecasting is critical across finance, healthcare, and cloud computing, yet progress is constrained by a fundamental bottleneck: the scarcity of large-scale, high-quality benchmarks. To address this gap, we introduce \textsc{QuitoBench}, a regime-balanced benchmark for time series forecasting with coverage across eight trend$\times$seasonality$\

Weirong Dong, Kai Zhou, Zhen Kong, Zhengke Yang

RRAM-based in-memory computing (IMC) offers high energy efficiency but suffers from conductance drift that severely degrades long-term accuracy. Existing approaches including retraining, noise-aware training, and Batch Normalization (BN)-based calibration either require RRAM rewriting, demand large storage overhead, or rely on online correction. We propose V

Rakib Hossain Sajib, Md Kishor Morol, Rajan Das Gupta, Mohammad Sakib Mahmood

Human age estimation from facial images represents a challenging computer vision task with significant applications in biometrics, healthcare, and human-computer interaction. While traditional deep learning approaches require extensive labeled datasets and domain-specific training, recent advances in large vision-language models (LVLMs) offer the potential f

Naruki Murahashi, Mitsuhiro Nakamura, Megumi Nakao

Cone-beam computed tomography (CBCT) images are problematic in clinical medicine because of their low contrast and high artifact content compared with conventional CT images. Although there are some studies to improve image quality, in regions subject to organ deformation, the anatomical structure may change after such image quality improvement. In this stud

Sina Bagheri Nezhad

Multilingual NLP is often treated as a route to global inclusion, but linguistic coverage and cultural competence frequently diverge. This paper synthesizes over 50 papers spanning multilingual performance inequality, cross-lingual transfer, culture-aware evaluation, cultural alignment, multimodal benchmarks, benchmark-design critique, and community-grounded

Ziming Li, Hongji Li, Jialin Wang, Pan Hui

The recent emergence and popularity of consumer-grade augmented reality (AR) glasses from major technology companies highlight their potential to become the next daily computing platform. A dominant design trend in this context is the integration of a front-facing camera to deliver a first-person perspective. While this approach is intuitive, there is limite

Stanislav Stoupin, David Sagan

The imaging problem of a specular reflector is revisited. Retaining terms through second order in the reflector surface expansion, we derive the form of the aberration-limiting aperture for arbitrary magnification assuming no bandwidth limitations. A permissible relative aperture size of the reflector is limited by a set relative aberration tolerance and sca

Xi Lu, Di Hu, An T. Nguyen, Brad Morse

Patient-controlled data-sharing systems are increasingly promoted as a way to empower patients with greater autonomy over their health data. Yet it remains unclear how different stakeholders, especially patients and health system leaders, perceive the benefits and challenges of enabling granular control over the sharing of de-identified medical data for rese

Yimeng Sun, Zhuoyuan Wang, Xiaole Zhang, Heng Ping

Accurate risk assessment is essential for safety-critical autonomous and control systems under uncertainty. In many real-world settings, stochastic dynamics exhibit asymmetric jumps and long-range memory, making long-term risk probabilities difficult to estimate across varying system dynamics, initial conditions, and time horizons. Existing sampling-based me

Mahesh Bhosale, Abdul Wasi, Shantam Srivastava, Shifa Latif

While powerful in image-conditioned generation, multimodal large language models (MLLMs) can display uneven performance across demographic groups, highlighting fairness risks. In safety-critical clinical settings, such disparities risk producing unequal diagnostic narratives and eroding trust in AI-assisted decision-making. While fairness has been studied ex

Ishaan Cherukuri

Predicting whether someone with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD) is crucial in the early stages of neurodegeneration. This uncertainty limits enrollment in clinical trials and delays urgent treatment. The Boundary Sharpness Coefficient (BSC) measures how well-defined the gray-white matter boundary looks on structural

Jinliang Xu, Bingqi Li

Traditional network architectures suffer from severe protocol ossification and structural fragility due to their reliance on static, human-defined rules that fail to adapt to the emergent edge cases and probabilistic reasoning of modern autonomous agents. To address these limitations, this paper proposes DarwinNet, a bio-inspired, self-evolving network archi

Aida Abiad, Gabriel Coutinho, Emanuel Juliano, Vinicius F. dos Santos

In 1981, Lubiw proved that the fixed point free automorphism problem (FPFAut) is NP-complete: given a graph G, determine whether there exists an automorphism that maps no vertex of G to itself. We revisit this problem and prove that FPFAut remains NP-complete when restricted to split, bipartite, k-subdivided, and H-free graphs, if H is not an induced subgrap

Valentina Kuskova, Sonia Howell, Brianna Stines, Brianna Conaghan

National and international policy efforts increasingly promote AI literacy in K--12 education, yet access to AI tools alone is insufficient to ensure equitable and responsible participation, particularly in under-resourced contexts. Without designs that integrate ethical reasoning, human support, and opportunities for judgment, AI initiatives risk reinforcin

Oscar Peralta

This work introduces hybrid stochastic differential equations with memory (mH-SDEs), a new class of stochastic systems where transition rates depend on the joint history of both Euclidean and discrete components. This extends existing hybrid stochastic differential equation models that condition transitions only on the Euclidean process history, enabling ric

Yuriy Kozachenko, Yuriy Mlavets, Oleksandr Mokliachuk

This book develops the theory of quasi-Banach $K_\sigma$-spaces $\mathbf{F}_\psi(\Omega)$, $\mathbf{F}_\psi^*(\Omega)$, and $D_{V,W}(\Omega)$ of random variables and stochastic processes, extending the classical framework of Orlicz spaces, $Sub_\varphi(\Omega)$ and $V(\varphi,\psi)$ spaces. The book consists of eleven chapters. The first two chapters establi

Imtiaz Khan, Muzamil Shah, Ambreen Uzair, Reza Asgari

In this paper, we employ a modified Haldane lattice model to investigate the light-driven, spin- and valley-dependent anomalous Nernst effect in two-dimensional hexagonal topological systems. We demonstrate that two-dimensional buckled materials exhibit a hierarchy of electrically and optically tunable topological phases when subjected to off-resonant circul

Renkang Song, Ziye Chen, Junbo Xu, Zerui Wang

Rhombohedrally stacked transition metal dichalcogenides such as 3R-MoS$_2$ offer an exceptional platform for nonlinear optics, naturally forming Fabry-P\'erot (FP) microcavities due to their giant dielectric contrast with the surrounding media. However, rigorously tracking the evolution of multiple harmonic fields within these unpatterned monolithic crystals

Yicheng Wang, Yupeng Yang, Xinyi Dai, Shuangxi Yi

The big bang quantum cosmology model introduces the trace $J$ of the Schouten tensor as a form of dynamic dark energy. Together with cold dark matter, these components form the so-called $J$CDM cosmology model, proposed by M.H.P.M. van Putten (J. High Energy Astrophys., 45, 2025, 194), which offers a potential resolution to the Hubble tension. We derive the

S. Deodhar, A. Iosevich

We show that spectral synthesis thresholds are governed by a quantitative spectral complexity parameter, the Fourier Ratio, in addition to the geometric size of the Fourier support. In the Euclidean setting, we prove that if a compactly supported measure has finite $\alpha$-dimensional packing measure and the associated Fourier ratio decays with asymptotic e

Dalaver H. Anjum, Shahid Nawaz, Muhammad Saleem

A generalized formulation of non-relativistic quantum mechanics is developed within multidimensional geometric (NG) frameworks characterized by a power-law dispersion relation \(E \propto |p|^{j}\), where \(j = N - 1\). Starting from the generalized Minkowski distance in \(L^j\)-normed spaces, the conventional quadratic kinetic structure of three-dimensional

Yoshiyuki Kagei, Lijuan Wang

We study a three-dimensional gravity-induced flame front model under a Couette flow. By exploiting the enhanced dissipation induced by the Couette flow, we prove global-in-time well-posedness of the Cauchy problem in $\mathbb{R}^3$ and derive decay estimates for the solution and its spatial derivatives in $L^p$ norms for all $p \ge 1$. The analysis is based

Zhuan Shi, Alireza Dehghanpour Farashah, Rik de Vries, Golnoosh Farnadi

Concept erasure in text-to-image diffusion models seeks to remove undesired concepts while preserving overall generative capability. Localized erasure methods aim to restrict edits to the spatial region occupied by the target concept. However, we observe that suppressing a concept can unintentionally weaken semantically related neighbor concepts, reducing fi

Yi Yang

The interpretation of high-precision cosmic ray spectra is fundamentally bottlenecked by uncertainties in fragmentation cross-sections. Traditional kinematic models, driven by phase-space expansions, typically predict complex, energy-dependent evolutions. However, AMS-02 measurements reveal that at high rigidities ($R > 30$~GV), secondary-to-secondary flux r

Changyang Li, Xueqing Huang, Shin-Fang Chng, Huangying Zhan

While recent feed-forward 3D reconstruction models provide a strong geometric foundation for scene understanding, extending them to 3D instance segmentation typically relies on a disjointed "lift-and-cluster" paradigm. Grouping dense pixel-wise embeddings via non-differentiable clustering scales poorly with the number of views and disconnects representation

Qian Ai, Haiyi Wu, Lalith Krishna Samanth Bonagiri, Kaustubh S. Panse

Liquid structure at solid-liquid interfaces is critical for many natural and engineered processes ranging from biological signal transduction to electrochemical energy conversion. Advanced experimental and computational methods have provided insights into the structure of liquids adjacent to planar substrates at the nanoscale. However, realistic solid-liquid

Snehit Vaddi, Pujith Vaddi

Recent work identifies a sparse set of "hallucination neurons" (H-neurons), less than 0.1% of feed-forward network neurons, that reliably predict when large language models will hallucinate. These neurons are identified on general-knowledge question answering and shown to generalize to new evaluation instances. We ask a natural follow-up question: do H-neuro

Gagan Acharya, Erfan Nozari

Recent advances in neurotechnologies and decades of scientific and clinical research have made closed-loop electrical neuromodulation one of the most promising avenues for the treatment of drug-resistant epilepsy (DRE), a condition that affects over 15 million individuals globally. Yet, with the existing clinical state of the art, only 18% of patients with D

Min-Seok Seo

In quantum gravity, physically meaningful operator is required to be invariant under the diffeomorphisms. Such gauge invariant operator is typically given by the relational observable, the operator localized in relation to some background states. We point out that the relational observable can be comprehensively written in the form of the dressed operator. F

Philippe E. Spiess, Md Muntasir Zitu, Alison Walker, Daniel A. Anaya

Background: More than 80% of U.S. cancer care is delivered in community settings, where survival remains worse than at academic centers. Clinicians must integrate genomics, staging, radiology, pathology, and changing guidelines, creating cognitive burden. We evaluated OncoBrain, an AI clinical reasoning platform for oncology treatment-plan generation, as an

Rohit Chhiber, Yanwen Wang, Arcadi V. Usmanov, William H. Matthaeus

An evolving turbulent flow such as the solar wind can be meaningfully characterized by its "turbulence age" -- an estimate of the number of nonlinear times that have elapsed during a plasma parcel's propagation from the Sun to a given point in space. Recent observations of the near-Sun solar wind by the \textit{Parker Solar Probe} (\textit{PSP}) indicate hig

Leo Hong, Dmitry Kleinbock, Vasiliy Neckrasov

In 1926 Khintchine introduced a topological argument proving the existence of uncountably many nontrivial singular linear forms of $n \geq 2$ variables. Throughout the years, this argument has been extensively modified and generalized. Most recently, Kleinbock et al. (2025) introduced a general framework of Diophantine systems and showed that a certain topol

Gourav Kumawat, Craig O. Heinke, Alison Sills, Haldan N. Cohn

Blue straggler stars (BSSs) are believed to form through mass transfer in binary systems or stellar collisions. The reported presence of double BSS sequences in some globular clusters (GCs) has been interpreted as evidence that these two formation channels produce distinct sequences in color-magnitude diagram (CMD). We reassess this claim using HST UV Globul

Lydia Morley, Emma Lehmberg, Sungsik Kong

Phylogenetic comparative methods (PCMs) are widely used to study trait evolution. However, many evolutionary histories involve reticulate evolutionary scenarios, such as hybridization, that violate core assumptions of these methods. In this study, we evaluate how such violations affect the performance of PCMs. In particular, we focus on the ancestral charact