March 2026 arXiv papers — page 85

Zhe Zhang, Jun Li, Biwei Jiang, He Zhao

Accurate distance measurements to supernova remnants (SNRs) are crucial for understanding their physical properties and evolution. We present a novel method that combines CO line observations with three-dimensional (3D) extinction maps to determine distances to SNRs (G93.7$-$0.2, G109.1$-$1.0, G156.2+5.7, and G166.0+4.3) through their associated molecular cl

Wagno Cesar e Silva, João Paulo S. Melo, José A. Helayël-Neto

This work sets out to compute the corrections to the Euler-Heisenberg effective action that arise from spacetime-dependent background anisotropies that violate Lorentz symmetry. To accomplish our task, we evaluate the functional determinant of the modified Dirac operator using the spectral regularization method. Within the framework of the Standard Model Ext

G. H. M. Araújo, O. A. Krzysik, H. De Sterck

Standard gradient-based iteration algorithms for optimization, such as gradient descent and its various proximal-based extensions to nonsmooth problems, are known to converge slowly for ill-conditioned problems, sometimes requiring many tens of thousands of iterations in practice. Since these iterations are computed sequentially, they may present a computati

Abhisha Garg, Suraj Srivastava, Akash Kumar, Nimish Yadav

A unified beamforming and channel estimation framework relying on Bayesian learning is conceived. Recognizing the limitations imposed by low-resolution analog-to-digital converter (ADCs) and frequency-dependent propagation effects occurring in the Terahertz (THz) band, we formulate a dual-wideband channel model incorporating root raised cosine (RRC) pulse sh

Wim Michiels, Federico Bribiesca-Argomedo, Jean Auriol

The paper addresses the boundary control of a class of hyperbolic PDEs, based on an equivalent representation in terms of an integral-difference equation. The situation is considered where direct compensation of reflection terms induces a fragile closed-loop system, in the sense of lack of strong stability. This is theoretically resolved by adding a low-pass

Sergei Konyagin, Kristina Oganesyan

Let $\|n\|$ stand for the integer complexity of the number $n$, i.e. for the least number of $1$'s needed to write $n$ using arbitrary many additions, multiplications, and parentheses. The two-sided inequality $3\log_3 n\leq\|n\|\leq 3\log_2 n$ for all $n$ is well known and reveals the logarithmic behaviour of the complexity function $\|n\|$. While the lower

Dinh Loc Duong

We propose an approach to induce nontrivial bands with non-zero Chern numbers by utilizing strong spin-orbit coupling in transition metal dichalcogenides with dopants. We demonstrate that a doped state near the valence-band edge induces band inversion with the hybridized host band, leading to topologically non-trivial properties. Calculations for V-doped WSe

Keshav Aggarwal, R. K. Choudhary, Abhirup Datta, Soumyaneal Banerjee

We present a turbulence index independent framework for simultaneously deriving solar wind velocity and coronal electron density in the near-Sun region using the spectral broadening of spacecraft radio signals. The formulation accommodates arbitrary turbulence spectral indices ($p$), providing a direct analytical link between the observed Doppler spectra and

A. C. Kutluay, J. Southworth

BK Peg is a double-lined detached eclipsing binary containing two late-F stars in an orbit with small eccentricity. We use light curves from the Transiting Exoplanet Survey Satellite (TESS) and spectroscopic measurements from previous studies to measure the physical properties of the companions to a high precision. We obtain masses of $1.411 \pm 0.004$\Msun\

Rustem Khasanov, Thomas J. Hicken, Igor Plokhikh, Ekaterina Pomjakushina

We report muon-spin rotation measurements of the pressure dependence of the oxygen-isotope ($^{16}$O/$^{18}$O) effect on the spin-density wave (SDW) transition in the trilayer Ruddlesden-Popper nickelate Pr$_4$Ni$_3$O$_{10}$. At ambient pressure, the SDW transition shows a finite isotope shift, with $^{16}T_{\rm SDW}=158.04(5)$ K and $^{18}T_{\rm SDW}=159.81

Wenda Zhang, Wenfei Yu

Neutron stars serve as unique laboratories for studying ultra-dense nuclear matter. The equation of state of neutron star matter can be effectively constrained by their masses and radii. Particular attention has been paid to rapidly rotating neutron stars, where strong relativistic effects leave imprints on their electromagnetic emission. To model the emissi

Tianyou Lai, Wentao Yue, Jiayi Zhou, Chaoyuan Hao

Financial time-series forecasting in real-world high-frequency markets is often hindered by delayed or partially stale observations caused by asynchronous data acquisition and transmission latency. To better reflect such practical conditions, we investigate a simulated delay setting where a portion of historical signals is corrupted by a Zero-Order Hold (ZOH

Mariana Dória Prata Lima, Gilson Antonio Giraldi, Jaime S. Cardoso

Semantic segmentation consists of assigning a semantic label to each pixel according to predefined classes. This process facilitates the understanding of object appearance and spatial relationships, playing an important role in the global interpretation of image content. Although modern deep learning approaches achieve high accuracy, they often ignore ordina

Tangtangfang Fang, Yang Jiao, Xiangjian He, Jingxi Hu

Low-dose computed tomography (LDCT) reduces radiation exposure but also introduces substantial noise and structural degradation, making it difficult to suppress noise without erasing subtle anatomical details. In this paper, we present TAFG-MAN, a latent diffusion framework for efficient and high-quality LDCT image denoising. The framework combines a percept

Hossein Javidnia

Recent interpretability work often treats a feature as a single global direction, dictionary atom, or latent coordinate shared across contexts. We argue that this ontology can fail in obstructed representation spaces, where locally coherent meanings need not assemble into one globally consistent feature. We introduce an atlas-native replacement object, the s

Amit Dey

A system, comprised of a qubit pair coupled to a common cavity, is studied with the aim of establishing qubit entanglement. This study is the sequel of the paper Phys. Rev. A 111, 043705 (2025), where similar model was investigated for an initially vacuum cavity. In the present manuscript the cavity with finite initial occupancy is considered and the effect

Koushik Brahma, Takeshi Ikeda, Shinsuke Iwao, Yi Yang

We develop neutral-fermionic constructions for the factorial $gp$-and $gq$-functions introduced by Nakagawa and Naruse, which are respectively dual to the factorial $GQ$- and $GP$-functions of Ikeda and Naruse. In particular, we realize the factorial $GP$-, $GQ$- and $gq$-functions as vacuum expectation values. As applications, we obtain, Jacobi--Trudi type

Daoqiang Liu

We introduce a refined notion of relative $\widehat{A}$-cowaist, extending the framework of Cecchini--Zeidler to complete manifolds, which may be noncompact and have compact boundary. We establish a sharp inequality relating this invariant to the scalar curvature and the bottom spectrum of the Laplacian. As a consequence, we partially answer a question raise

Rishav Roshan

We show that accounting for a non-instantaneous reheating phase after inflation can significantly modify the charged lepton Yukawa equilibration temperature in the early Universe. This finding calls for revisiting the role of lepton flavors in leptogenesis models where right-handed neutrinos are produced and decay during the extended reheating period. Our an

Wenjing Cao, Yafei Wang, Jinshuo Zhang, Xiaofan Xu

This paper investigates new efficient transmission architectures for multi-satellite massive multiple-input multiple-output (MIMO). We study the weighted sum-rate maximization problem in a multi-satellite system where multiple satellites transmit independent data streams to multi-antenna user terminals, thereby achieving higher throughput. We first adopt a m

Luciano Melodia

The homology of an ample groupoid is computed from the complex of compactly supported continuous functions on the nerve. Two hypotheses routinely imposed on this complex behave in opposite ways. We show that the comparison map from the integral chain complex tensored with the coefficient group to the chain complex with coefficients is always injective, and t

Xander Coetzer, Arné Schreuder, Anna Sergeevna Bosman

Transfer learning with models pretrained on ImageNet has become a standard practice in computer vision. Transfer learning refers to fine-tuning pretrained weights of a neural network on a downstream task, typically unrelated to ImageNet. However, pretrained weights can become saturated and may yield insignificant gradients, failing to adapt to the downstream

Zhaoxing Chen, Wei Liu, Ziqing Xie, Wenfan Yi

This paper presents a class of efficient manifold optimization algorithms for computing the ground state solutions of a semilinear elliptic system, which are unstable saddle points of the variational functional. Variational arguments show that these unstable saddle points can be characterized as the local minimizers of the variational functional constrained

Boris Slautin, Kamyar Barakati, Utkarsh Pratiush, Christopher D. Lowe

The real-world implementation of materials prediction algorithms remains limited by persistent characterization bottlenecks in materials discovery, where photon-based probe techniques (e.g., XRD or Raman) impose long acquisition times and access latencies, restricting exploration to quasi-ternary composition spaces typically realized as compositional librari

Han Jiao, Jiakai Sun, Lei Zhao, Zhanjie Zhang

3D Gaussian Splatting has demonstrated remarkable real-time rendering capabilities and superior visual quality in novel view synthesis for static scenes. Building upon these advantages, researchers have progressively extended 3D Gaussians to dynamic scene reconstruction. Deformation field-based methods have emerged as a promising approach among various techn

Siddharth Srivastava, Adam Smith, Scott Brooks, Jack Bacon

Automating white blood cell classification for diagnosis of leukaemia is a promising alternative to time-consuming and resource-intensive examination of cells by expert pathologists. However, designing robust algorithms for classification of rare cell types remains challenging due to variations in staining, scanning and inter-patient heterogeneity. We propos

Adam Andrews, Jens Jasche, Guilhem Lavaux, William Coulton

Local primordial non-Gaussianity, parameterised as $f_{\rm NL}^{\rm local}$, will be stringently constrained using state-of-the-art methods applied to next-generation galaxy redshift survey data. In this paper, in preparation for the upcoming data sets, we demonstrate for the first time the joint field-level inference of $f_{\rm NL}^{\rm local}$, nuisance pa

Saken Tukenov

Kazakh, a Turkic language spoken by over 22 million people, remains underserved by existing multilingual language models, which allocate minimal capacity to low-resource languages and employ tokenizers ill-suited to agglutinative morphology. We present SozKZ, a family of Llama-architecture language models (50M-600M parameters) trained entirely from scratch o

Sarah C. Lotspeich, P. D. Anh. Nguyen, Layla Parast

Evaluating treatment effects is critical in clinical trials but sometimes involves lengthy, invasive, or costly follow-up procedures. In these cases, surrogate markers, which provide intermediate measures of the long-term treatment effect, allow clinicians to obtain results faster and more efficiently than would have otherwise been possible. Prior to adoptio

Shanshan Wang, Derek F. Wong, Jingming Yao, Lidia S. Chao

ChatGPT has demonstrated remarkable capabilities on both poetry generation and translation, yet its ability to truly understand poetry remains unexplored. Previous poetry-related work merely analyzed experimental outcomes without addressing fundamental issues of comprehension. This paper introduces a comprehensive framework for evaluating ChatGPT's understan

Chad Vanderbilt, Gabriele Campanella, Siddharth Singi, Swaraj Nanda

Computational biomarkers (CBs) are histopathology-derived patterns extracted from hematoxylin-eosin (H&E) whole-slide images (WSIs) using artificial intelligence (AI) to predict therapeutic response or prognosis. Recently, slide-level multiple-instance learning (MIL) with pathology foundation models (PFMs) has become the standard baseline for CB development.

Ruixin Zhang, Wuyang Dai, Hung Viet Pham, Gias Uddin

The rapid integration of Large Language Models (LLMs) into software development workflows has given rise to a new class of AI-assisted coding tools, such as Claude-Code, Codex, and Gemini CLIs. While promising significant productivity gains, the engineering process of building these tools, which sit at the complex intersection of traditional software enginee

Tuo Wu, Jianchao Zheng

This paper develops a comprehensive analytical framework for the outage probability of fluid antenna system (FAS)-aided communications by modeling the antenna as a continuous aperture and approximating the Jakes (Bessel) spatial correlation with a Gaussian kernel $\rho_G(\delta) = e^{-\pi^2\delta^2}$. Three complementary analytical strategies are pursued. Fi

Ivano Ciardelli, Juha Kontinen

Inquisitive logic is a research program that extends the scope of logic to cover not only statements, but also questions. In the context of this program, a logic that plays a prominent role is inquisitive first-order logic, InqBQ, which extends classical first-order logic with a question-forming disjunction and a question-forming existential quantifier. This

Salima Jaoua, Daniel Temko, Hélène Ruffieux

Large-scale longitudinal molecular profiling is now firmly established in biomedical research, prompted by the need to uncover coordinated biomarker trajectories reflecting the dynamics of underlying biological mechanisms and characterise patient heterogeneity in disease progression. While a range of statistical tools exist for either longitudinal modelling

Wenbo Xu, Yue He, Yunhai Wang, Xingxuan Zhang

Causal discovery has been widely studied, yet many existing methods rely on strong assumptions or fall into two extremes: either depending on costly interventional signals or partial ground truth as strong priors, or adopting purely data driven paradigms with limited guidance, which hinders practical deployment. Motivated by real-world scenarios where only c

Tuo Wu

Fluid antenna systems (FAS) achieve spatial diversity by dynamically switching among $N$ densely packed ports, but the resulting spatially correlated Rayleigh channels render exact outage analysis intractable. Existing block-correlation models (BCM) impose structural approximations on the channel covariance matrix that can introduce optimistic performance bi

Xinjie Dai, Baiping Zhang, Diancong Jin

In this paper, we investigate the asymptotic distribution of the normalized error for the Mittag--Leffler Euler (MLE) method applied to a class of multidimensional fractional stochastic differential equations. These equations are reformulated as stochastic Volterra equations (SVEs) featuring a non-diagonal, matrix-valued kernel $K(u)=u^{\alpha-1}E_{\alpha,\a

Yujin Park, Haejun Chung, Ikbeom Jang

Pairwise comparison labeling is emerging as it yields higher inter-rater reliability than conventional classification labeling, but exhaustive comparisons require quadratic cost. We propose Dodgersort, which leverages CLIP-based hierarchical pre-ordering, a neural ranking head and probabilistic ensemble (Elo, BTL, GP), epistemic--aleatoric uncertainty decomp

Birva Sevak, Shrenik Jadhav, Van-Hai Bui

Cascading failures in power grids pose severe risks to infrastructure reliability, yet real-time prediction of their progression remains an open challenge. Physics-based simulators require minutes to hours per scenario, while existing graph neural network approaches treat cascading failures as static classification tasks, ignoring temporal evolution and phys

ATLAS Collaboration

A search is conducted in proton-proton collisions at the Large Hadron Collider for photon-induced production $pp\rightarrow pp+ γγ, (γγ\rightarrow VX)$ of a visible particle $V$ decaying into a pair of same-flavour charged leptons ($e^+e^-$ or $μ^+μ^-$) and an undetected invisible component $X$. Measurements of the outgoing proton energies by the ATLAS forwa

Wenjun Huang, Shenghao Fu, Yian Jin, Yang Ni

RAW images captured by different camera sensors exhibit substantial domain shifts due to varying spectral responses, noise characteristics, and tone behaviors, complicating their direct use in downstream computer vision tasks. Prior methods address this problem by training domain-specific RAW-to-RAW translators for each source-target pair, but such approache

M Vijay Kumar, Saujatya Mandal, Siddhant Jain, Saptarshi Basu

Frontal polymerization (FP) enables rapid curing of thermosets via a self-sustaining thermal wave, but its propagation mechanism can shift dramatically depending on processing conditions. In this study, we investigate the effect of trigger direction and monomer viscosity - controlled via hold time - on the front velocity in frontal ring-opening metathesis po

Zhenguo Liang, Zhiyan Zhao

We introduce the concept of {\it generalized reducibility}, which provides a flexible framework for analyzing the long-time behavior of solutions to quadratic quantum Hamiltonians. As an application of this notion, for many prescribed sub-exponential growth rates $f(t)$, either monotone or oscillatory, we explicitly construct time-decaying perturbations of t

Steven Johnson

As AI agent ecosystems grow, agents need mechanisms to monitor relevant knowledge in real time. Semantic publish-subscribe systems address this by matching new content against vector subscriptions. However, in multi-agent settings where agents operate under different data handling policies, unrestricted semantic subscriptions create policy violations: agents

Xiaohui Gao

Laser-driven ion acceleration from nanostructured targets offers a promising route to compact, high-energy ion sources. In this work, we demonstrate through particle-in-cell simulations that rectangular nanoring targets significantly enhance energy absorption and increase the cutoff energy of laser-accelerated ions. The nanoring geometry enables strong field

Dongchen Li, Dmitry Turaev

A blender is a hyperbolic basic set such that the projection of its stable/unstable set onto some center subspace has a higher topological dimension than the set itself. We prove that, for any $C^s$ symplectic diffeomorphism (where $s=2,\dots\infty,\omega$), if it has a one-dimensional whiskered torus with a homoclinic orbit, then a symplectic blender can be

Yunhui Liu, Yue Liu, Yongchao Liu, Tao Zheng

Attributed Graph Clustering (AGC) is a fundamental unsupervised task that partitions nodes into cohesive groups by jointly modeling structural topology and node attributes. While the advent of graph neural networks and self-supervised learning has catalyzed a proliferation of AGC methodologies, a widening chasm persists between academic benchmark performance

Xiefan Guo, Xinzhu Ma, Haoxiang Ma, Zihao Zhou

Text-to-image diffusion models have achieved remarkable fidelity in synthesizing images from explicit text prompts, yet exhibit a critical deficiency in processing implicit prompts that require deep-level world knowledge, ranging from natural sciences to cultural commonsense, resulting in counter-factual synthesis. This paper traces the root of this limitati

Kevin Qiu, Kyle Walker, Mike Y. Michelis, Marek Cygan

We present Swim2Real, a pipeline that calibrates a 16-parameter robotic fish simulator from swimming videos using vision-language model (VLM) feedback, requiring no hand-designed search stages. Calibrating soft aquatic robots is particularly challenging because nonlinear fluid-structure coupling makes the parameter landscape chaotic, simplified fluid models

Shinsaku Sakaue

Contextual recommendation is a variant of contextual linear bandits in which the learner observes an (optimal) action rather than a reward scalar. Recently, Sakaue et al. (2025) developed an efficient Online Newton Step (ONS) approach with an $O(d\log T)$ regret bound, where $d$ is the dimension of the action space and $T$ is the time horizon. In this paper,

Hanlin Xiao, Rainer Breitling, Eriko Takano, Mauricio A. Álvarez

Recent advances in general-purpose foundation models have stimulated the development of large biological sequence models. While natural language shows symbolic granularity (characters, words, sentences), biological sequences exhibit hierarchical granularity whose levels (nucleotides, amino acids, protein domains, genes) further encode biologically functional

Phongsakorn Sereewat, David Senjaya, Piyabut Burikham

We investigate the thermodynamic, optical, and dynamical properties of Reissner-Nordstrom-de Sitter black holes in a noncommutative spacetime with a minimal length scale Theta. Within a two-horizon framework, we formulate an effective first law of thermodynamics and introduce an entropy capturing correlations between the event and cosmological horizons. Impo

Derya Akkaynak, Michael S. Brown

Consumer cameras are ubiquitous in aquatic sciences because they are affordable and easy to use, generating vast collections of underwater imagery for ecosystem surveys, monitoring, mapping, and animal behavior studies. Yet when color is the variable of interest, such as in coral-bleaching research, most of these images cannot be used quantitatively if captu

Tamunonye Cheetham-West, Xiaoyu Xu

For any two-bridge link or 3-tangle Montesinos link $L\subset S^3$ (including knot), this paper proves that $\pi_1(S^3-L)$ is profinitely rigid among the fundamental groups of compact orientable 3-manifolds.

David Hokken, Mahya Mehrabdollahei, Berend Ringeling

Let $\chi_{-f}$ be the odd quadratic Dirichlet character of conductor $f$, and let $\mathrm{m}(P)$ denote the Mahler measure of a polynomial $P$. In 1984, Chinburg conjectured that for any such $\chi_{-f}$ there exist an integral bivariate rational function $P$ (and, in the strong form, an integral polynomial) such that $\mathrm{m}(P)$ is a rational multiple

Hanqiao Ye, Yuzhou Liu, Yangdong Liu, Shuhan Shen

While structure-based relocalizers have long strived for point correspondences when establishing or regressing query-map associations, in this paper, we pioneer the use of planar primitives and 3D planar maps for lightweight 6-DoF camera relocalization in structured environments. Planar primitives, beyond being fundamental entities in projective geometry, al

Kazuharu Yanagimoto

This paper investigates the barriers to gender convergence using Japan as a salient environment to explore the interactive effects of labor market structures and social norms. I develop a quantitative model of household labor supply where couples jointly decide their occupations and working hours. The model features a labor market with inflexible "regular" j

Zihao Shi, Dongling Wang

Neural networks have demonstrated significant potential in solving partial differential equations (PDEs). While global approaches such as Physics-Informed Neural Networks (PINNs) offer promising capabilities, they often lack inherent temporal causality, which can limit their accuracy and stability for time-dependent problems. In contrast, local training fram

Xiaohan Wang, Nan Zhang, Sulene Han, Keguang Tang

The pharmaceutical industry is facing challenges with quality management such as high costs of compliance, slow responses and disjointed knowledge. This paper presents GMPilot, a domain-specific AI agent that is designed to support FDA cGMP compliance. GMPilot is based on a curated knowledge base of regulations and historical inspection observations and uses

Wankai He, Chengjie Yu

In this paper, we obtain sharp Faber-Krahn inequalities for the first Dirichlet eigenvalue of the combinatorial $p$-Laplacian on connected graphs with a fixed number of vertices or with a fixed number of edges. More precisely, we show that the minimum of the first $p$-Dirichlet eigenvalues of connected graphs with boundary that consist of $n$ vertices or $n$

Renata Wentzcovitch, Laura Cobden, Christine Houser, Grace Shephard

The Earth's lower mantle hosts a subtle but pervasive quantum phenomenon: the pressure-induced spin crossover of iron in its dominant minerals, bridgmanite and ferropericlase. In this transition, iron ions gradually shift from high-spin to low-spin electronic states without structural change, altering their volume, compressibility, and elastic properties. Al

Chenxing Meng, Wuzhou Quan, Yingjie Cai, Liqun Cao

Cloud occlusion severely degrades the semantic integrity of optical remote sensing imagery. While incorporating Synthetic Aperture Radar (SAR) provides complementary observations, achieving efficient global modeling and reliable cross-modal fusion under cloud interference remains challenging. Existing methods rely on dense global attention to capture long-ra

Marcos Lacasa-Cazcarra

We study the employment effects of the 22% increase in the Spanish minimum wage in 2019, focusing on young workers. Using census-grade administrative tax data covering the universe of formal wage bills and employment (Models 190/390 linked to personal income tax records), we construct several measures of treatment intensity, including two structurally ground

Enguang Wang, Qiang Wang, Yuanchen Wu, Ke Yan

While Multimodal Large Language Models (MLLMs) excel at vision-language tasks, the cost of their language-driven training on internal visual foundational competence remains unclear. In this paper, we conduct a detailed diagnostic analysis to unveil a pervasive issue: visual representation degradation in MLLMs. Specifically, we find that compared to the initi

Yandan Zheng, Haoran Luo, Zhenghong Lin, Wenjin Liu

Benchmarks are the de facto standard for tracking progress in large language models (LLMs), yet static test sets can rapidly saturate, become vulnerable to contamination, and are costly to refresh. Scalable evaluation of open-ended items often relies on LLM judges, introducing additional sources of bias and prompt sensitivity. We argue that evaluation must e

Raul Suzuki, Rodrigo Moreira, Pedro Henrique A. Damaso de Melo, Larissa F. Rodrigues Moreira

Detecting Internet routing instability is a critical yet challenging task, particularly when relying solely on endpoint active measurements. This study introduces TRACE, a MachineLearning (ML)pipeline designed to identify route changes using only traceroute latency data, thereby ensuring independence from control plane information. We propose a robust featur

Yifeng Zheng

Multi-label fundus diagnosis requires features that capture both fine-grained lesions and large-scale retinal structure. Many multi-scale medical vision models address this challenge through explicit frequency decomposition, but our ablation studies show that such heuristics provide limited benefit in this setting: replacing the proposed simple dual-resoluti

Anastasios Giannopoulos, Sotirios Spantideas, Panagiotis Trakadas

The evolution of Open Radio Access Networks (O-RAN) enables programmable and intelligent control of radio resources through disaggregated architectures and open interfaces. However, existing solutions typically rely on isolated control loops and fail to jointly address end-to-end optimization objectives across multiple timescales. Thus, it remains a key chal

Qunchao Jin, Yiliao Song, Qi Wu

Vision-Language Navigation (VLN) systems are fundamentally constrained by partial observability, as an agent can only accumulate knowledge from locations it has personally visited. As multiple robots increasingly coexist in shared environments, a natural question arises: can agents navigating the same space benefit from each other's observations? In this wor

Stephen Wiggins

The Out-of-Time-Order Correlator (OTOC) is a standard algebraic diagnostic of quantum information scrambling, but it offers limited direct geometric intuition. In this note, we propose a Bohmian, trajectory-based framework for constructing a geometric diagnostic of scrambling-related sensitivity using Lagrangian Descriptors (LDs). To avoid the uncertainty-pr

Yudong W. Xu, Wenhao Li, Scott Sanner, Elias B. Khalil

Neural networks are being increasingly used as heuristics for constraint satisfaction. These neural methods are often recurrent, learning to iteratively refine candidate assignments. In this work, we make explicit the connection between such iterative neural heuristics and Large Neighborhood Search (LNS), and adapt an existing neural constraint satisfaction

Libo Zhang, Chilong Liu, Guixu Xie, Haolan Yuan

Collective dynamics in engineered quantum systems offer a unique and versatile platform for exploring how many-body correlations bridge microscopic entanglement and macroscopic behavior. In this work, we report collective Dicke dynamics of acoustic modes in a macroscopic high-overtone bulk acoustic resonator (HBAR). To achieve this, we engineer a hybrid quan

Kaiyuan Li, Jing-Cheng Pang, Yang Yu

Reinforcement learning from verifiable rewards (RLVR) stimulates the thinking processes of large language models (LLMs), substantially enhancing their reasoning abilities on verifiable tasks. It is often assumed that similar gains should transfer to general question answering (GQA), but this assumption has not been thoroughly validated. To assess whether RLV

Junwei Gong, Xiao Shen, Zhihao Chen, Shirui Pan

Open-set node classification (OSNC) allows unlabeled test data to contain novel classes previously unseen in the labeled data. The goal is to classify in-distribution (ID) nodes into corresponding known classes and reject out-of-distribution (OOD) nodes as unknown class. Despite recent notable progress in OSNC, two challenges remain less explored, i.e., how

Nicolas Tiltmann

The ALICE experiment will upgrade the innermost three layers of its vertexing detector, the Inner Tracking System (ITS), during the next LHC Long Shutdown (LS3) with a novel, bent, ultra-light MAPS-based tracker. Six wafer-scale sensor chips will be bent into three cylinders, held in place only by carbon foam, leaving no material except for the silicon die i

Lakshmi Kanta Dey, Subhadip Pal

Let $X$ and $Y$ be Banach spaces and let $G \in L(X,Y)$ with $\|G\|=1$. We study the geometry of $G$-(semi-)norm on $L(X,Y)$, defined by \[ \|T\|_G := \inf_{\delta>0}\sup\{\|Tx\|: \|x\|=1, \|Gx\|>1-\delta\}, \] considering it as a norm ($G$-norm), and further explore the associated numerical indices. In particular, we characterize relative spear operators, t

Yuan Cao, Mingyang Wang, Hinrich Schütze

Large language models (LLMs) are increasingly used as knowledge bases, but keeping them up to date requires targeted knowledge editing (KE). However, it remains unclear how edits are implemented inside the model once applied. In this work, we take a mechanistic view of KE using neuron-level knowledge attribution (NLKA). Unlike prior work that focuses on pre-

Oleg Zubelevich

This article examines a family of smooth mappings between Banach spaces and establishes conditions for the existence of their zeros. Applications to fixed-point problems and the Implicit Function Theorem are also discussed.

Haoran Zhu

We show that whenever \[ [\,\cdot,\cdot]_t = [\,\cdot,\cdot]_0 + t[\,\cdot,\cdot]_1,\qquad \alpha_t = \mathrm{id} + t\alpha_1 \] define an infinitesimal Hom--Lie deformation of $\mathfrak{sl}_2(\mathbb K)$ over $\mathbb K[t]/(t^2)$ and $(\mathfrak{sl}_2(\mathbb K),[\,\cdot,\cdot]_0,\alpha_1)$ is a Hom--Lie algebra, then the deformed bracket $[\,\cdot,\cdot]_

Hyomin Lee, Sangwoo Park, Yumin Choi, Sohyun An

While prior red-teaming efforts have focused on eliciting harmful text outputs from large language models (LLMs), such approaches fail to capture agent-specific vulnerabilities that emerge through multi-step tool execution, particularly in rapidly growing ecosystems such as the Model Context Protocol (MCP). To address this gap, we propose a trajectory-aware

Soumyojyoti Dutta, Tushar

Characterizing quantum magic -- the resource enabling computational advantage beyond stabilizer circuits -- is subtle in qubit systems because established measures can give conflicting information about the same state. We introduce C(rho), the l1 distance from a state's discrete Wigner function to the convex hull of stabilizer Wigner functions, and study its

Khoa Nguyen, Bao Duong, Viet Huynh, Thin Nguyen

Recovering Markov boundary -- the minimal set of variables that maximizes predictive performance for a response variable -- is crucial in many applications. While recent advances improve upon traditional constraint-based techniques by scoring local causal structures, they still rely on nonparametric estimators and heuristic searches, lacking theoretical guar

Duncan Adamson, Pamela Fleischmann, Annika Huch

Starting in the 1970s with the fundamental work of Imre Simon, \emph{scattered factors} (also known as subsequences or scattered subwords) have remained a consistently and heavily studied object. The majority of work on scattered factors can be split into two broad classes of problems: given a word, what information, in the form of scattered factors, are con

David Rico Menéndez, Pablo Picazo-Martínez, Antonio de la Oliva, Carlos Jesús Bernardos

Emerging integrated sensing and communication (ISAC) applications require large volumes of data, but collecting such datasets in real networks is costly, time consuming, and often infeasible due to limited access to low level measurements. In this paper we present NextSense, an open and modular semi-synthetic data generation platform that consists of a 5G st

Arno Strouwen, Sebastián Micluţa-Câmpeanu

For most process systems, knowledge of the model structure is incomplete. This missing physics must then be learned from experimental data. Recently, a combination of universal differential equations and symbolic regression has become a popular tool to discover these missing physics. Universal differential equations employ neural networks to represent missin

Maciej Lesniak

We investigate the relationship between uniform polyconvexity of anisotropic geometric integrands and Almgren's uniform ellipticity. We first establish the converse implication for uniform ellipticity with respect to polyhedral test pairs, thereby strengthening earlier results. Our main theorem shows that uniform polyconvexity is equivalent to Almgren's unif

Joachim Kock, Jesper M. Møller

When G is a finite abelian group, we define G-spans of groupoids and their associated matrices with entries in the group ring QG and show that composition of spans corresponds to multiplication of matrices.

Christian Boudreault, Nicolas Levasseur

In the presence of a globally conserved charge $N$, a natural question is whether a given separable state can be separated into charge-conserving components. We dub this problem the Symmetric Separability Problem (SSP). On random states, the SSP is answered negatively with probability one for almost all $N$. Using a witness to the failure of symmetric separa

Jiaxin Cheng, Yue Wu, Yicong Zhou

Learning-based edge detection models trained with cross-entropy loss often suffer from thick edge predictions, which deviate from the crisp, single-pixel annotations typically provided by humans. While previous approaches to achieving crisp edges have focused on designing specialized loss functions or modifying network architectures, we show that a carefully

Jiang Liu, Ge Qiu, Hao Fei, Dongdong Xie

With the rapid development of large language models (LLMs), more and more researchers have paid attention to information extraction based on LLMs. However, there are still some spaces to improve in the existing related methods. First, existing multimodal information extraction (MIE) methods usually employ natural language templates as the input and output of

Kuan Liu, Fei Xie, Ming-Yu Ge, Wei Deng

X-ray polarization observations of pulsar wind nebulae (PWNe) provide crucial insights into magnetic field structures and particle acceleration mechanisms. While the Imaging X-ray Polarimetry Explorer (IXPE) has made significant contributions to PWN studies, its limited effective area restricts observations to only the brightest sources, leaving many fainter

Xiaoya Cheng, Long Wang, Yan Liu, Xinyi Liu

We present PiLoT, a unified framework that tackles UAV-based ego and target geo-localization. Conventional approaches rely on decoupled pipelines that fuse GNSS and Visual-Inertial Odometry (VIO) for ego-pose estimation, and active sensors like laser rangefinders for target localization. However, these methods are susceptible to failure in GNSS-denied enviro

Aarush Aggarwal, Akshat Tomar, Amritanshu Tiwari, Sargam Goyal

Robust semantic segmentation is crucial for safe autonomous driving, yet deployed models remain vulnerable to black-box adversarial attacks when target weights are unknown. Most existing approaches either craft image-wide perturbations or optimize patches for a single architecture, which limits their practicality and transferability. We introduce OmniPatch,

V. A. Buts

New conditions for wave-wave interaction are considered. It is shown that using these conditions allows us to discover and describe new features of wave-wave interaction. Specifically, it is shown that an electromagnetic wave in a stationary periodic medium can excite a wave with a different frequency. It is shown that waves with different frequencies can ef

Lukas Junge

We prove a Neumann localization inequality for the Laplacian that includes a spectral gap. This result is obtained by partitioning a cube into overlapping families of subcubes and analysing the associated projection operators. The resulting operator inequality goes through a discrete Neumann Laplacian on the lattice of boxes and yields a quantitative spectra

Zhiren Sun, Sizhong Zhou

Let $G$ be a connected graph with $n$ vertices. The isolated toughness of $G$, denoted by $I(G)$, is defined by $I(G)=\min\left\{\frac{|S|}{i(G-S)}:S\subseteq V(G) \ \mbox{and} \ i(G-S)\geq2\right\}$ if $G$ is not complete, or $I(G)=+\infty$ if $G$ is complete. A graph $G$ is called isolated $r$-tough if $I(G)\geq r$. A spanning subgraph $H$ of $G$ is called

Mao-Sheng Li, Chang-Yue Zhang, Zheng Zheng, Zhihua Chen

The state overlap, quantified via $\tr[\rho \sigma]$, is a metric widely used to assess the closeness between two quantum states $\rho$ and $\sigma$. Although global state overlap alone does not directly capture entanglement properties, we uncover that incorporating local state overlaps provide profound insights into the entanglement characteristics of quant

Wei Lu, Yang Zhong, Sheng-Qin Feng

We investigate the stability and causality of relativistic spin hydrodynamics in the presence of a nonvanishing spin density background, assuming that the spin chemical potential $ω^{μν}$ is of leading order ($ω^{μν} \sim \mathcal{O}(1)$) in the gradient expansion and is treated as a finite background in the linear perturbation analysis. It is found that wit

Sara Aguincha, Emanuel Nunes, Samih Eisa, Miguel L. Pardal

Sensor technologies have evolved to a point where it is now practical to monitor products along the supply chain. The collected data can be stored in a decentralized way using blockchain technology. However, ensuring the reliability of the sensed data is a critical challenge. In other words, we need to trust the data that we write to the blockchain. In this