March 2026 arXiv papers — page 44

Tibor Jordán, Shin-ichi Tanigawa

A pair $\{u,v\}$ of vertices is said to be globally linked in a $d$-dimensional framework $(G,p)$ if there exists no other framework $(G,q)$ with the same edge lengths, in which the distance between the points corresponding to $u$ and $v$ is different from that in $(G,p)$. We say that $\{u,v\}$ is globally linked in $G$ in $\R^d$ if $\{u,v\}$ is globally lin

Renata O. Figueira, Mahendra Panthee

In this paper, we obtain new lower bounds for the evolution of the radius of analyticity of solutions to two initial value problems (IVPs) with initial data belonging to the class of analytic functions $H^{\sigma,s}(\mathbb{R})$ defined via a hyperbolic cosine weight. First, we consider the IVP for the modified Korteweg-de Vries (mKdV) equation. For this pro

Nirmalya Jana, Atasi Chakraborty, Anamitra Mukherjee, Amit Agarwal

Two-dimensional metallic altermagnets are rare, and no correlated 2D material has been established to host large nonrelativistic spin splitting. Here we show that spontaneous orbital order, driven by electronic correlations and Fermi surface nesting, provides a general microscopic route to two-dimensional metallic altermagnetism. Antiferro-orbital ordering b

Ding-Hui Xu, Zhen-hua Zhao, Tian-Rui Wang

Within the framework of the left-right symmetric model (LRSM) and under the assumption of a diagonal Dirac neutrino mass matrix $M_{\rm D}$, this paper systematically investigates 20 types of three-zero textures in the Majorana neutrino mass matrix $M_{\rm R}$. The study reveals that only five three-zero textures of $M_{\rm R}$ satisfy the constraints of the

Zhi Zeng, Yifei Yang, Jiaying Wu, Xulang Zhang

The rise of micro-videos has reshaped how misinformation spreads, amplifying its speed, reach, and impact on public trust. Existing benchmarks typically focus on a single deception type, overlooking the diversity of real-world cases that involve multimodal manipulation, AI-generated content, cognitive bias, and out-of-context reuse. Meanwhile, most detection

Erkan Gunes, Christoffer Florczak, Tevfik Murat Yildirim

Recent developments in text classification using Large Language Models (LLMs) in the social sciences suggest that costs can be cut significantly, while performance can sometimes rival existing computational methods. However, with a wide variance in performance in current tests, we move to the question of how to maximize performance. In this paper, we focus o

George Eskandar, Fengyi Shen, Mohammad Altillawi, Dong Chen

Recent progress in video-to-video (V2V) translation has enabled realistic resimulation of embodied AI demonstrations, a capability that allows pretrained robot policies to be transferable to new environments without additional data collection. However, prior works can only operate on a single view at a time, while embodied AI tasks are commonly captured from

Xu Huang, Zhejian Lai, Zixian Huang, Jiajun Chen

Large Language Models (LLMs) have demonstrated remarkable proficiency in English mathematical reasoning, yet a significant performance disparity persists in multilingual contexts, largely attributed to deficiencies in language understanding. To bridge this gap, we introduce Translation-Augmented Policy Optimization (TAPO), a novel reinforcement learning fram

Andrea Tosin

Homogeneous Boltzmann-type equations are an established tool for modelling interacting multi-agent systems in sociophysics by means of the principles of statistical mechanics and kinetic theory. A customary implicit assumption is that interactions are "all-to-all", meaning that every pair of randomly sampled agents may potentially interact. However, this leg

Jie Zhu, Hanlin Song, Zhenwei Lyu, Hao Li

The constancy of the gravitational constant $G$ is a cornerstone of the strong equivalence principle and of general relativity, yet its possible temporal variation remains a key target in tests of fundamental physics. Gravitational-wave (GW) astronomy, especially when combined with electromagnetic observations, provides an unprecedented new opportunity to pr

Xunguang Wang, Yuguang Zhou, Qingyue Wang, Zongjie Li

Large language models increasingly rely on explicit chain-of-thought reasoning to solve complex tasks, yet the safety of the reasoning process itself remains largely unaddressed. Existing work focuses predominantly on content safety (i.e., detecting harmful, biased, or factually incorrect outputs), while treating the underlying reasoning chain as an opaque i

Yuan-Hao Wei

We propose SAHMM-VAE, a source-wise adaptive Hidden Markov prior variational autoencoder for unsupervised blind source separation. Instead of treating the latent prior as a single generic regularizer, the proposed framework assigns each latent dimension its own adaptive regime-switching prior, so that different latent dimensions are pulled toward different s

Huizhi Liang, Yichao Shen, Yu Deng, Sicheng Xu

Achieving human-like spatial intelligence for vision-language models (VLMs) requires inferring 3D structures from 2D observations, recognizing object properties and relations in 3D space, and performing high-level spatial reasoning. In this paper, we propose a principled hierarchical framework that decomposes the learning of 3D spatial understanding in VLMs

Zhiwei Song, Lin Chen

The spin alignment conjecture was originally formulated in connection with the additivity of coherent information for a class of quantum channels known as platypus channels. Recently, a stronger majorization-based version was proposed by M. A. Alhejji and E. Knill [Commun. Math. Phys. 405, 119, 2024], asserting that the spectrum of the alignment operator is

Philip Goyal

Quantum theory brings into question the compatibility of the twin desiderata of exact knowability of the present state of the physical world and perfect predictability of its future states. Bohr's coordination-causality complementarity principle transforms this tension into one between properties (as ordinarily understood in classical physics) and determinis

Bedri Keskin, Özgür Baştürk

We focus on the automated classification of eclipsing binary stars using deep learning methods to handle the vast data generated by large-scale photometric sky surveys. These surveys produce extensive datasets that are impractical for manual analysis. By using machine learning to classify eclipsing binary stars based on light curve morphology, this study aim

Mark L. Lewis, Lucia Morotti, Emanuele Pacifici, Lucia Sanus

We consider finite groups having a conjugacy class that is the difference of two normal subgroups. That is, suppose $G$ is a group and $M$ and $N$ are normal subgroups so that $N < M$, and suppose that there is an element $g \in G$ so that the conjugacy class of $g$ is $M \setminus N$. We find a character-theoretic characterization of this condition, and we

Guangyu Zhao, Ceyao Zhang, Chengdong Ma, Tao Wu

Long-horizon tabletop games pose a distinct systems challenge for robotics: small perceptual or execution errors can invalidate accumulated task state, propagate across decision-making modules, and ultimately derail interaction. This paper studies how to maintain internal state consistency in turn-based, multi-human robotic tabletop games through deliberate

M. W. AlMasri

In this work, we study the resurgence program in holomorphic quantum mechanics. As a specific problem, we investigate the resurgence in the quartic anharmonic oscillator within holomorphic quantum mechanics, using the Bargmann representation of bosonic operators. In this framework, the perturbative energy series is shown to be Gevrey-1 and Borel summable onl

Q. C. Dong, Zi-Xuan Su, Qing Huo Liu, Wen Chen

This paper proposes a physics-informed neural operator (PINO) framework for solving inverse scattering problems, enabling rapid and accurate reconstructions under diverse measurement conditions. In the proposed approach, the dielectric property is represented as a learnable tensor, while a neural operator is employed to predict the induced current distributi

Eyal Hadad, Mordechai Guri

On-device Vision-Language Models (VLMs) promise data privacy via local execution. However, we show that the architectural shift toward Dynamic High-Resolution preprocessing (e.g., AnyRes) introduces an inherent algorithmic side-channel. Unlike static models, dynamic preprocessing decomposes images into a variable number of patches based on their aspect ratio

Noboru Ito, Mayuko Kon

An A-type coefficient polynomial introduced by Kawauchi recovers the HOMFLY-PT polynomial as a formal power series within skein theory. A notable feature of this construction is that each coefficient defines a link invariant, yielding an infinite sequence of invariants, while the low-degree coefficients are relatively easy to compute. In this paper, we exten

Manh Hung Le, Andrea Simonetto

We study nonsmooth convex minimization through a continuous-time dynamical system that can be seen as a high-resolution ODE of Nesterov Accelerated Gradient (NAG) adapted to the nonsmooth case. We apply a time-varying Moreau envelope smoothing to a proper convex lower semicontinuous objective function and introduce a controlled time-rescaling of the gradient

Yijie Bi, Yifan Gao, Xinyi Li

We study the one-arm probability in the level-set percolation of the discrete and metric-graph Gaussian free field (GFF) defined on a box with Dirichlet boundary conditions. For the metric-graph case, we establish asymptotic estimates on two one-arm probabilities of interest. For the discrete case, we show up-to-constants bounds on the point-to-bulk probabil

Niccolò Cavagnero, Narges Norouzi, Gijs Dubbelman, Daan de Geus

Vision Foundation Models (VFMs) pre-trained at scale enable a single frozen encoder to serve multiple downstream tasks simultaneously. Recent VFM-based encoder-only models for image and video segmentation, such as EoMT and VidEoMT, achieve competitive accuracy with remarkably low latency, yet they require finetuning the encoder, sacrificing the multi-task en

Sagar Nagaraj Simha, Juliette Ortholand, Dave Dongelmans, Jessica D. Workum

In this applied paper, we address the difficult open problem of when to discharge patients from the Intensive Care Unit. This can be conceived as an optimal stopping scenario with three added challenges: 1) the evaluation of a stopping strategy from observational data is itself a complex causal inference problem, 2) the composite objective is to minimize the

Valentina Zalbertus, Max Pfeffer, Alexander Schmeding

Riemannian structures on infinite-dimensional manifolds arise naturally in shape analysis and shape optimization. These applications lead to optimization problems on manifolds which are not modeled on Banach spaces. The present article develops the basic framework for optimization via gradient descent on weak Riemannian manifolds leading to the notion of a H

Qisheng Zhao, Meng Guo, Hengxuan Du, Lars Lindemann

Multi-robot systems can be extremely efficient for accomplishing team-wise tasks by acting concurrently and collaboratively. However, most existing methods either assume static task features or simply replan when environmental changes occur. This paper addresses the challenging problem of coordinating multi-robot systems for collaborative tasks involving dyn

Changhee Shin, Bom Kim, Seungsoo Lee

Serverless computing is increasingly adopted for AI-driven workloads due to its automatic scaling and pay-as-you-go model. However, its function-based architecture creates significant security risks, including excessive privilege allocation and poor permission management. In this paper, we present ALPS, an automated framework for enforcing least privilege in

Toshiki Matsusaka

In 1999, Arakawa and Kaneko introduced a zeta function whose special values at negative integers yield the poly-Bernoulli numbers and investigated its relation to multiple zeta values. Since the poly-Bernoulli numbers appear in this function essentially by design, it is natural to ask whether they arise as special values of more intrinsic zeta-type objects.

Zeynep Begüm Kara, Aytekin İsmail, Ece Ateş, İzgi Nur Tamcı

Every software organization faces the onboarding challenge: helping newcomers navigate complex codebases, compensate for insufficient documentation, and comprehend code they did not author. Expert walkthroughs are among the most effective forms of support, yet they are expensive, repetitive, and do not scale. We present Lacy, a hybrid human-AI onboarding sys

Yingmei Zhang, Wangtao Bao, Yong Yang, Weiguo Wan

Infrared small target detection (IRSTD) aims to identify and distinguish small targets from complex backgrounds. Leveraging the powerful multi-scale feature fusion capability of the U-Net architecture, IRSTD has achieved significant progress. However, U-Net suffers from semantic degradation when transferring high-level features from deep to shallow layers, l

Shengbin Guo, Hang Zhao, Senqiao Yang, Chenyang Jiang

Multimodal dataset distillation aims to construct compact synthetic datasets that enable efficient compression and knowledge transfer from large-scale image-text data. However, existing approaches often fail to capture the complex, dynamically evolving knowledge embedded in the later training stages of teacher models. This limitation leads to degraded studen

Nikki Zabel, D. J. Pisano, Sushma Kurapati, Omri Scannell

We present a detailed analysis of the neutral atomic gas (HI) in the dwarf galaxy UGCA 320, observed with the MeerKAT telescope as part of the MHONGOOSE (MeerKAT HI Observations of Nearby Galactic Objects: Observing Southern Emitters) programme. In a small group consisting of three dwarf galaxies, all of which contain HI, it is the most massive. Detailed kin

Selim An, Il hong Suh, Yeseong Kim

Quantization techniques such as BitsAndBytes, AWQ, and GPTQ are widely used as a standard method in deploying large language models but often degrades accuracy when using low-bit representations, e.g., 4 bits. Low-rank correction methods (e.g., LQER, QERA, ASER) has been proposed to mitigate this issue, however, they restore all layers and insert error-corre

Chia-Hsiang Lin, Si-Sheng Young

As most optical satellites remotely acquire multispectral images (MSIs) with limited spatial resolution, multispectral unmixing (MU) becomes a critical signal processing technology for analyzing the pure material spectra for high-precision classification and identification. Unlike the widely investigated hyperspectral unmixing (HU) problem, MU is much more c

Jeannie Chung, Hanna Jang, Ingyeong Yang, Uiwon Hwang

CLIP aligns image and text embeddings via contrastive learning and demonstrates strong zero-shot generalization. Its large-scale architecture requires substantial computational and memory resources, motivating the distillation of its capabilities into lightweight student models. However, existing CLIP distillation methods do not explicitly model multi-direct

Yanmei Jiao, Anpeng Lu, Wenhan Hu, Rong Xiong

Object-goal visual navigation requires robots to reason over semantic structure and act effectively under partial observability. Recent approaches based on object-level topological maps enable long-horizon navigation without dense geometric reconstruction, but their execution remains limited by the gap between global topological guidance and local perception

P. Bernát Szabó, Zeno Schätzle, Frank Noé

A faithful description of chemical processes requires exploring extended regions of the molecular potential energy surface (PES), which remains challenging for strongly correlated systems. Transferable deep-learning variational Monte Carlo (VMC) offers a promising route by efficiently solving the electronic Schr\"odinger equation jointly across molecular geo

Gal Binyamini, Oded Carmon, Dmitry Novikov

We extend the theory of complex cells introduced by Binyamini and Novikov to the sharply o-minimal setting, obtaining cellular preparation and parameterization theorems which are polynomially effective in the degrees of the relevant sets. Our constructions are definable, and so applying them to sets in a given reduct of R_an yields cells and cellular maps de

Peng Gang

Does structured intent representation generalize across languages and models? We study PPS (Prompt Protocol Specification), a 5W3H-based framework for structured intent representation in human-AI interaction, and extend prior Chinese-only evidence along three dimensions: two additional languages (English and Japanese), a fourth condition in which a user's si

Yuhang Dai, Haopeng Lin, Jiale Qian, Ruiqi Yan

Large Audio-Language Models (LALMs) have demonstrated remarkable performance in end-to-end speaker diarization and recognition. However, their speaker discriminability remains limited due to the scarcity of large-scale conversational data and the absence of explicit speaker representation optimization. To address this, we propose GLSC-SDR, a paradigm that jo

Gaby Brenot, Benoit Clément, Hanno Filter-Pieler, Daniel Galbinski

We report the results of the second iteration of an experiment searching for neutron-hidden-neutron oscillations in a beam of ultracold neutrons, conducted at the PF2 facility of the Institut Laue Langevin (ILL). Oscillations were tested via neutron disappearance as a function of an applied magnetic field, in the context of a phenomenological two-parameter m

Abdelmalek Boumali, Abdelmalek Bouzenada, Edilberto O. Silva

We present a unified analytical and numerical study of the one-dimensional Feshbach--Villars (FV) equation for spin-0 particles in the presence of several representative external potentials. Starting from the FV formulation of the Klein--Gordon equation, we derive the corresponding one-dimensional master equation and analyse its solutions for Coulomb, power-

Dimitris Stripelis, Patrick Foley, Mohammad Naseri, William Lindskog-Münzing

RAG typically assumes centralized access to documents, which breaks down when knowledge is distributed across private data silos. We propose a secure Federated RAG system built using Flower that performs local silo retrieval, while server-side aggregation and text generation run inside an attested, confidential compute environment, enabling confidential remo

Bangchen Yin, Jian Ouyang, Zhen Fan, Kailai Lin

Local curvature of potential energy surfaces is critical for predicting certain experimental observables of molecules and materials from first principles, yet it remains far beyond reach for complex systems. In this work, we introduce a Hessian-informed Machine Learning Interatomic Potential (Hi-MLIP) that captures such curvature reliably, thereby enabling a

Louis Lehmann, Florian N. Brünig, Jonathan Scherlitzki, Morten Lehmann

Aqueous solutions of HF and HCl behave very differently at intermediate concentrations: HCl dissociates completely, whereas HF remains only partially dissociated and forms bifluoride (HF$_2^-$). This should lead to different excess-proton spectra in HF and HCl solutions, in contrast to experimental reports. Using ab initio molecular dynamics, we show that in

Tianlin Yang, Hailiang Du, Louis Aslett

Probabilistic forecasting provides a principled framework for uncertainty quantification in dynamical systems by representing predictions as probability distributions rather than deterministic trajectories. However, existing forecasting approaches, whether physics-based or neural-network-based, remain fundamentally trajectory-oriented: predictive distributio

Riccardo Cristoferi, Jakob Deutsch, Luca Pignatelli

In this paper, we study the prototypical model of liquid-liquid phase separation, the Cahn-Hilliard functional, in a highly irregular setting. Specifically, we analyze potentials with low regularity vanishing on space-dependent wells. Under remarkably weak hypotheses, we establish a robust compactness result. Strengthening the regularity of the wells and of

Yamamoto Hirofumi

In this paper, we explicitly determine the local $2$-adic component of a non-selfdual automorphic representation $\Pi$ of $\mathrm{GL}_3$ constructed by van Geemen and Top. We prove that $\Pi_2$ is a parabolically induced representation of $\mathrm{GL}_3(\mathbb{Q}_2)$ given by $\Pi_2 = \mathrm{Ind}_P^{\mathrm{GL}_3(\mathbb{Q}_2)}(\pi\boxtimes \chi)$, where

João Castelo-Branco, José Santos-Victor, Alexandre Bernardino

Autonomous object search is challenging for mobile robots operating in indoor environments due to partial observability, perceptual uncertainty, and the need to trade off exploration and navigation efficiency. Classical probabilistic approaches explicitly represent uncertainty but typically rely on handcrafted action-selection heuristics, while deep reinforc

Changjiang Bu, Jueru Liu, Haotian Zeng

Zykov's theorem shows that $r$-partite Tur\'{a}n graph uniquely has the maximum number of $K_t$ among all $n$-vertex $K_{r+1}$-free graphs for $2\le t\le r$. The clique tensor is a high-order extension of the adjacency matrix of a graph. Yu and Peng \cite{peng1} gave a spectral version of the Zykov's theorem via clique tensor. In this paper, we give some upp

Nadav Cohen, Itzik Klein

Accurate post-processing navigation is essential for applications such as survey and mapping, where the full measurement history can be exploited to refine past state estimates. Fixed-interval smoothing algorithms represent the theoretically optimal solution under Gaussian assumptions. However, loosely coupled INS/GNSS systems fundamentally inherit the syste

Renaud Boussarie, Luigi Delle Rose, Michael Fucilla, Alessandro Papa

Continuing our previous study of Deeply Virtual Meson Production (DVMP) at twist-3 accuracy, we derive compact expressions for all helicity amplitudes. We perform a phenomenological analysis of the helicity-amplitude ratio $\mathcal{A}^{11}/\mathcal{A}^{00}$ and of the spin-density matrix element $r_{00}^{04}$ within the Color Glass Condensate framework. Sma

Minjia Shi, Xuan Wang, Junmin An, Jon-Lark Kim

We study linear codes over Gaussian integers equipped with the Mannheim distance. We develop Mannheim-metric analogues of several classical bounds. We derive an explicit formula for the volume of Mannheim balls, which yields a sphere packing bound and constraints on the parameters of two-error-correcting perfect codes. We prove several other useful bounds, a

Melanie Rupflin, Sebastian Woodward

We consider the question of quantitative stability of minimisers for a well-known variational problem for which the infimum of the energy is not achieved in the classical sense, namely for the Dirichlet energy of degree $1$ maps from closed surfaces $(\Sigma,g_{\Sigma})$ of positive genus into the unit sphere $S^2\subset \mathbb{R}^3$. For this variational p

Ethan Sanchez Hidalgo, Diego Zafra Bono, Guillermo Encinas Lago, J. Xavier Salvat Lozano

Efficient entanglement distribution is the foundational challenge in realizing large-scale Quantum Networks. However, state-of-the-art solutions are frequently limited by restrictive operational assumptions, prohibitive computational complexities, and performance metrics that misalign with practical application needs. To overcome these barriers, this paper a

T. Popa, N. Sandulescu, D. Gambacurta

We investigate the impact of isovector and isoscalar proton-neutron pairing correlations on the ground-state properties of even-even N=Z nuclei with mass numbers between A=16 and A=120. Nuclear mean fields are generated using the quark-meson coupling (QMC) energy density functional, while pairing correlations are treated within the quartet condensation model

Shinnosuke Onishi, Oliver Hahn, Ryuji Takagi

Importance sampling based on quasi-probability decomposition is the backbone of many widely used techniques, such as error mitigation, circuit knitting, and, more generally, virtual quantum resource distillation, as it allows one to simulate operations that are not accessible in a given setting. However, this class of protocols faces a fundamental problem --

Yang Yue, Nan Li, Xin Zhang, Chenhao Wang

Machine learning (ML) is shaping our exploration of topological matter, whose existence is inherently tied to the geometry of quantum states or energy spectra. In non-Hermitian systems, distinctive spectral geometry can lead to topological braiding of complex-energy bands, yet directly probing this topology-geometry interplay remains challenging. Here, we in

Yinhan Zhang, Yue Ma, Bingyuan Wang, Kunyu Feng

We propose InstanceAnimator, a novel Diffusion Transformer framework for multi-instance sketch video colorization. Existing methods suffer from three core limitations: inflexible user control due to heavy reliance on single reference frames, poor instance controllability leading to misalignment in multi-character scenarios, and degraded detail fidelity in fi

Yunus E. Zeytuncu

Arithmetic puzzle games provide a controlled setting for studying difficulty in mathematical reasoning tasks, a core challenge in adaptive learning systems. We investigate the structural determinants of difficulty in a class of integer arithmetic puzzles inspired by number games. We formalize the problem and develop an exact dynamic-programming solver that e

Arijit Maiti, Mudit K. Srivastava, Vipin Kumar, Bhaveshkumar Mistry

We present the development of ProtoPol - a medium resolution echelle spectro-polarimeter for the PRL 1.2m and 2.5m telescopes at Mt Abu observatory, India. In this second and final part of the paper series, we report on the development of a dedicated data reduction pipeline of ProtoPol along with several characterization, performance evaluation, and scientif

Masami Ichikawa

In recent years, fuzzing has been widely applied not only to application software but also to system software, including the Linux kernel and firmware, and has become a powerful technique for vulnerability discovery. Among these approaches, Coverage-based grey-box fuzzing, which utilizes runtime code coverage information, has become the dominant methodology.

Christo Zietsman

The dominant industry response to AI-generated code quality problems is to deploy AI reviewers. This paper argues that this response is structurally circular when executable specifications are absent: without an external reference, both the generating agent and the reviewing agent reason from the same artefact, share the same training distribution, and exhib

Thanh Nguyen Canh, Thang Tran Viet, Thanh Tuan Tran, Ben Wei Lim

The rise of unmanned ``dark factories'' operating without human presence demands autonomous safety systems capable of detecting and responding to multiple hazard types. We present SafeGuard ASF (Agentic Security Fleet), a comprehensive framework deploying humanoid robots for autonomous hazard detection in industrial environments. Our system integrates multi-

Maciej Błaszak, Krzysztof Marciniak, Błażej M. Szablikowski

In this article we construct evolutionary soliton hierarchies from pencils of Novikov algebras of St\"ackel type. We start by defining a special class of associative Novikov algebras, which we call Novikov algebras of St\"ackel type, as they are associated with classical St\"ackel metrics in Vi\`ete coordinates. We obtain sufficient conditions for pencils of

Maximilian Woehrer

Automatic image rotation estimation is a key preprocessing step in many vision pipelines. This task is challenging because angles have circular topology, creating boundary discontinuities that hinder standard regression methods. We present a comprehensive study of five circular-aware methods for global orientation estimation: direct angle regression with cir

Tim J. Boonen, Engel John C. Dela Vega, Len Patrick Dominic M. Garces

This paper considers an insurer with two collaborating business lines that faces three critical decisions: (1) dividend payout, (2) reinsurance coverage, and (3) capital injection between the lines, in the presence of model uncertainty. The insurer considers the reference model to be an approximation of the true model, and each line has its own robustness pr

Vardan Adibekyan, Olivier Demangeon, Tiago Campante, Nuno Santos

Citations are essential for recognizing scientific contributions, yet citation behavior is shaped by more than just relevance or quality. We analyzed approximately 255,000 refereed astronomy articles published between 2000 and 2025 to investigate how journals are cited relative to their publication volume and authorship context. We find that multidisciplinar

Manuel Úbeda-Flores

This paper studies the degree to which a bivariate copula fails to be symmetric under coordinate permutation, a property known as non-exchangeability. Working within an axiomatic framework that quantifies this asymmetry through a family of $L^p$-based measures, we establish sharp bounds linking non-exchangeability to classical dependence and concordance meas

Luca Asteria, Klaus Sengstock, André Eckardt, Christof Weitenberg

Driven Floquet systems can realize topological phases with no static counterparts. These so-called anomalous Floquet topology breaks the bulk-boundary correspondence based on the Chern number. The number of edge modes in each band gap is instead determined by another integer index, a winding number, which is calculated from the time evolution operator of the

Joy Acharya, Smit Patel, Paawan Sharma, Mohendra Roy

Physically Unclonable Functions (PUFs) provide promising hardware security for IoT authentication, leveraging inherent randomness suitable for resource constrained environments. However, ML/DL modeling attacks threaten PUF security by learning challenge-response patterns. This work introduces a custom resistor-capacitor (RC) based dynamically reconfigurable

Jürg Fröhlich

Various applications of Chern-Simons theory in algebraic topology, in particular knot theory, condensed matter physics and cosmology are reviewed. Special attention is paid to appearances of Chern-Simons actions in the theory of the (integer and fractional) quantum Hall effect. A mechanism related to five-dimensional abelian Chern-Simons theory that may be a

Lucas E. A. Porto, Lucas Tendick, Daniel Cavalcanti, Roope Uola

Measurement incompatibility and bipartite quantum steering are known to display a strong connection: a set of measurements is incompatible if and only if it can lead to bipartite steering. Despite such a close link between these concepts in bipartite scenarios, little is known in the multipartite setting, where notions of genuine multipartite correlations pl

Konstantin Stankevich, Alexander Studenikin, Maksim Vyalkov

We develop a theoretical framework for quantum field description of neutrino evolution in a medium, with a focus on quantum decoherence induced by neutrino scattering on fermions. By deriving a generalized Lindblad master equation that accounts for neutrino momentum-changing transitions, we go beyond the standard treatment that assumes fixed neutrino momentu

Minjia Shi, Xuan Wang, Bouazzaoui Zakariae, Jon-Lark Kim

Wall-Sun-Sun primes (shortly WSS primes) are defined as those primes $p$ such that the period of the Fibonacci recurrence is the same modulo $p$ and modulo $p^2.$ This concept has been generalized recently to certain second order recurrences whose characteristic polynomials admit as a zero the principal unit of $\mathbb{Q}(\sqrt{d}),$ for some integer $d>0.$

Shuoling Liu, Zhiquan Tan, Kun Yi, Hui Wu

Deep Research Agents (DRAs) aim to answer complex questions by searching the web, checking evidence, and synthesizing conclusions across heterogeneous sources. We introduce a category-theoretic framework for evaluating and improving such agents. The framework treats deep research as a structured mapping from user intent to evidence-grounded conclusions, maki

Jian Wang, Xiu-Bin Liu, Ziqi Zeng, Xu-Jie Wang

Resonance fluorescence from a coherently driven two-level emitter is a minimal quantum optical field that combines phase coherence with single-photon-level nonlinearity. Here we show that it can be engineered, using only passive linear interferometry, into energy-time entanglement. By injecting resonance fluorescence from a single quantum dot into an asymmet

Arup Biswas, Satya N Majumdar, Arnab Pal

Stochastic resetting has attracted significant attention in recent years due to its wide-ranging applications across physics, biology, and search processes. In most existing studies, however, resetting events are governed by an external timer and remain decoupled from the system's intrinsic dynamics. In a recent Letter by Biswas et al, we introduced threshol

Satoshi Matsuoka

We show that any multiple-valued function can be represented by a linear lambda term typed in a second-order polymorphic type system, using two distinct styles. The first is a circuit style, which mimics combinational circuits in switching theory. The second is an inductive style, which follows a more traditional mathematical approach. We also discuss severa

Yongsung Kim, Wooseok Song, Jaihyun Lew, Hun Hwangbo

Visual Geometry Grounded Transformer (VGGT) has advanced 3D vision, yet its global attention layers suffer from quadratic computational costs that hinder scalability. Several sparsification-based acceleration techniques have been proposed to alleviate this issue, but they often suffer from substantial accuracy degradation. We hypothesize that the accuracy de

Mohammad AL-Smadi

We present a unified system addressing both Subtask 3 (answer generation) and Subtask 4 (evidence sentence alignment) of the ArchEHR-QA Shared Task. For Subtask 3, we apply two-stage Quantised Low-Rank Adaptation (QLoRA) to Qwen3-4B loaded in 4-bit NF4 quantisation: first on 30,000 samples from the emrQA-MedSQuAD corpus to establish clinical domain competenc

Paul Shepherd, Tasos Dagiuklas, Bugra Alkan, Jonathan Rodriguez

Distributed intelligence in industrial networks increasingly integrates sensing, communication, and computation across heterogeneous and resource constrained devices. Federated learning (FL) enables collaborative model training in such environments, but its reliability is affected by inconsistent client behaviour, noisy sensing conditions, and the presence o

Paulo Roberto de Moura Júnior, Jean Lelong, Annabelle Blangero

The effectiveness of Retrieval-Augmented Generation (RAG) is highly dependent on how documents are chunked, that is, segmented into smaller units for indexing and retrieval. Yet, commonly used "one-size-fits-all" approaches often fail to capture the nuanced structure and semantics of diverse texts. Despite its central role, chunking lacks a dedicated evaluat

Hritwik Bora, Ranjeev Misra, Rukaiya Khatoon, Rupjyoti Gogoi

Flat-Spectrum Radio Quasars (FSRQs) are among the most energetic and powerful active galactic nuclei, often exhibiting jet powers comparable to or exceeding the Eddington luminosity. In this work, we performed broadband spectral energy distribution (SED) modeling of two FSRQs PKS 1441+25 and Ton 599, using Swift-XRT/UVOT, NuSTAR, Fermi-LAT and VERITAS observ

Maria Marina, Alexander Panchenko, Vasily Konovalov

As generative AI tools like ChatGPT enter classrooms, workplaces and everyday thinking, writing is at risk of becoming a formality -- outsourced, automated and stripped of its cognitive value. But writing is not just output; it is how we learn to think. This paper explores what is lost when we let machines write for us, drawing on cognitive psychology, educa

Pankaj Kumar, Pranamesh Chakraborty, Subrahmanya Swamy Peruru

Automated Vehicle (AV) control in mixed traffic, where AVs coexist with human-driven vehicles, poses significant challenges in balancing safety, efficiency, comfort, fuel efficiency, and compliance with traffic rules while capturing heterogeneous driver behavior. Traditional car-following models, such as the Intelligent Driver Model (IDM), often struggle to

Haoyue Zhang, Chang Liu, Xiaotong Wei, Peng Xu

The Taiji mission, a pioneering Chinese space-borne gravitational wave observatory, requires ultra-precise calibration of its gravitational reference sensors (GRSs) to achieve its targeted sensitivity of $3\times10^{-15} \mathrm{\ m\ s^{-2}\ Hz^{-1/2}}$. Maintaining this precision is challenged by time-varying scale factors drifts and dynamic center-of-mass

Canfer Akbulut, Rasmi Elasmar, Abhishek Roy, Anthony Payne

Interest in the concept of AI-driven harmful manipulation is growing, yet current approaches to evaluating it are limited. This paper introduces a framework for evaluating harmful AI manipulation via context-specific human-AI interaction studies. We illustrate the utility of this framework by assessing an AI model with 10,101 participants spanning interactio

Hector Borobia, Elies Seguí-Mas, Guillermina Tormo-Carbó

Weight pruning is a standard technique for compressing large language models, yet its effect on learned internal representations remains poorly understood. We present the first systematic study of how unstructured pruning reshapes the feature geometry of language models, using Sparse Autoencoders (SAEs) as interpretability probes. Across three model families

Rakesh Kumar Malakar, Asim Kumar Ghosh

The introduction of Su-Schrieffer-Heeger model has led to a major breakthrough in the area of one-dimensional topological insulators, even though this model was primarily formulated on an organic polymer called $trans$-polyacetylene in order to explain its anomalous conductivity. In this study, a group of five tight-binding models has been introduced which a

Christian Carimalo

The issue and proof of Gurzadyan theorem are presented concisely, avoiding tedious and unnecessary calculations that would mask what is essential. The goal is to provide a good mathematical and physical understanding of the theorem, making you want to learn more about its use in cosmology.

Wenlong Hou, Sheng Bi, Guangqian Yang, Lihao Liu

Alzheimer's disease (AD) is a growing global health challenge as populations age, and timely, accurate diagnosis is essential to reduce individual and societal burden. However, real-world AD assessment is hampered by incomplete, heterogeneous multimodal data and variability across sites and patient demographics. Although large language models (LLMs) have sho

Erica Bisesi, Giuseppe Murante, Antonello Provenzale, Jost von Hardenberg

Between 2.4 and 0.6 Gy ago, our planet underwent several episodes of global glaciations, including the Snowball Earth case that ended 635 My ago. Causes of this last Snowball event presumably included a decreased greenhouse gas concentration and high continental albedo, both associated with the passage of the super-continent Rodinia at equatorial latitudes.

Konstantinos Chatziandreou, Sven Karbach

We develop a semi-static framework for the variance-optimal hedging of multi-asset derivatives exposed to correlation and covariance risk. The approach combines continuous-time dynamic trading in the underlying assets with a static portfolio of auxiliary contingent claims. Using a multivariate Galtchouk--Kunita--Watanabe decomposition, we show that the resul

Zhekai Chen, Yuqing Wang, Manyuan Zhang, Xihui Liu

Generating images conditioned on multiple visual references is critical for real-world applications such as multi-subject composition, narrative illustration, and novel view synthesis, yet current models suffer from severe performance degradation as the number of input references grows. We identify the root cause as a fundamental data bottleneck: existing da

Márk Somogyvári, Sebastian Reich

In this paper we combine the non-linear filtering capabilities of particle filters with the transdimensional inference of the reversible-jump Markov chain Monte Carlo method for a data assimilation methodology over dynamic problems with variable dimensionality. By using transdimensional MCMC steps for the rejuvenation of the particle filter, the algorithm co

A. Korshunov, M. Alkorta, C. -Y. Lim, F. Ballester

The confinement of electronic wavefunctions in momentum space can give rise to flat electronic bands, where the quenching of kinetic energy enhances the density of states and amplifies interaction effects. Such conditions are fertile ground for emergent quantum phases, as spin, charge and lattice degrees of freedom become strongly entangled. In these regimes

Yunuo Chen, Bing He, Zezheng Lyu, Hongwei Hu

Efficient image compression relies on modeling both local and global redundancy. Most state-of-the-art (SOTA) learned image compression (LIC) methods are based on CNNs or Transformers, which are inherently rigid. Standard CNN kernels and window-based attention mechanisms impose fixed receptive fields and static connectivity patterns, which potentially couple