November 2025 arXiv papers — page 61

Ming Zhong, Yuanlei Wang, Liuzhou Zhang, Ruichuan An

While Multimodal Large Language Models (MLLMs) excel on benchmarks, their processing paradigm differs from the human ability to integrate visual information. Unlike humans who naturally bridge details and high-level concepts, models tend to treat these elements in isolation. Prevailing evaluation protocols often decouple low-level perception from high-level

Hannuo Zhang, Zhixiang Chi, Yang Wang, Xinxin Zuo

Recent learning-based multi-view stereo (MVS) methods are data-driven and have achieved remarkable progress due to large-scale training data and advanced architectures. However, their generalization remains sub-optimal due to fixed model parameters trained on limited training data distributions. In contrast, optimization-based methods enable scene-specific a

Amirhossein Nazerian, MAlbor Asllani, Melvyn Tyloo, Francesco Sorrentino

This paper presents a unified framework for analyzing the input-output behavior of discrete time complex networks viewed as open systems. Importantly, we focus on systems that are inherently modeled in discrete time-such as opinion dynamics, Markov chains, diffusion on networks, and population models-reflecting their natural formulation in many real-world co

Thomas Bothner, Fei Wei

The Riemann-Hilbert method is employed to carry out an asymptotic analysis of a family of $\sigma$-Painlev\'e V functions associated with Hankel determinants involving the confluent hypergeometric function of the second kind. In the large-matrix limit, this family degenerates to a family of $\sigma$-Painlev\'e III$'$ functions, whose precise asymptotic behav

Levon Mahseredjian

This paper develops a theoretical mesoscopic model of the limit order book driven by multivariate Hawkes processes, designed to capture temporal self-excitation and the spatial propagation of order flow across price levels. In contrast to classical zero-intelligence or Poisson based queueing models, the proposed framework introduces mathematically defined mi

Yuheng Shao, Lizhang Wang, Changhao Li, Peixian Chen

Zero-Shot Anomaly Detection (ZSAD) aims to identify and localize anomalous regions in images of unseen object classes. While recent methods based on vision-language models like CLIP show promise, their performance is constrained by existing prompt engineering strategies. Current approaches, whether relying on single fixed, learnable, or dense dynamic prompts

Wenyu Li, Sidun Liu, Peng Qiao, Yong Dou

We present Muskie, a native multi-view vision backbone designed for 3D vision tasks. Unlike existing models, which are frame-wise and exhibit limited multi-view consistency, Muskie is designed to process multiple views simultaneously and introduce multi-view consistency in pre-training stage. Muskie is trained to reconstruct heavily masked content in one vie

Wanqi Wang, Chun Yang, Jianbo Shao, Yaokai Zhang

Pediatric liver tumors are one of the most common solid tumors in pediatrics, with differentiation of benign or malignant status and pathological classification critical for clinical treatment. While pathological examination is the gold standard, the invasive biopsy has notable limitations: the highly vascular pediatric liver and fragile tumor tissue raise c

Itay Hazan, Yael Mathov, Guy Shtar, Ron Bitton

Securing AI agents powered by Large Language Models (LLMs) represents one of the most critical challenges in AI security today. Unlike traditional software, AI agents leverage LLMs as their "brain" to autonomously perform actions via connected tools. This capability introduces significant risks that go far beyond those of harmful text presented in a chatbot

Lin Chen, Yifei Zhao

Given an oriented $2$-manifold $M$, a locally constant sheaf of lattices $\Lambda$ over $M$, and a pointed morphism $q : \textsf B^2\Lambda \rightarrow \textsf B^4\mathbf C^{\times}$, we define an $\mathbb E_M$-category $\mathrm{Rep}_q(\check T)$ which we call the "quantum torus" at level $q$. We explain why this terminology is deserved and calculate the fac

Alexander Yakimenko

We propose and analyze an autocatalytic mechanism in which bursts of Hawking radiation from evaporating micro-primordial black holes (PBHs) trigger the collapse of near-critical plasma overdensities. In a primordial plasma seeded with such patches, this feedback self organizes into a traveling ignition front that successively forms new PBHs and then self-que

Onofre Rojas

While true phase transitions are forbidden in one-dimensional systems with short-range interactions, several models have recently been shown to exhibit sharp yet analytic thermodynamic anomalies that mimic thermal phase transitions. We show that this behavior arises from transfer matrices that are mathematically irreducible but possess a nearly block-diagona

Csaba Both, Andrew Yen-Jong Chen, Ting-Ting Gao, Niek Mooij

Materials, at their essence, are networks defined by homogeneity: uniform bonds, fixed thicknesses, and discrete length scales. Mechanical metamaterials, while representing structurally more diverse microstructures, remain defined by the homogeneity of their unit cells, pore sizes, or repeating features. In contrast, as network science has revealed, real-wor

Yegon Kim, Hyunsu Kim, Gyeonghoon Ko, Juho Lee

Accurately solving partial differential equations (PDEs) is critical to understanding complex scientific and engineering phenomena, yet traditional numerical solvers are computationally expensive. Surrogate models offer a more efficient alternative, but their development is hindered by the cost of generating sufficient training data from numerical solvers. I

Hou Jian, Meng Tan, Tian Maozai

We develop a convex framework for spatially varying coefficient quantile regression that, for each predictor, separates a location-invariant \emph{global} effect from a \emph{spatial deviation}. An adaptive group penalty selects whether a predictor varies over space, while a graph\textendash Laplacian quadratic promotes spatial continuity of the deviations o

Purvish Jajal, Nick John Eliopoulos, Benjamin Shiue-Hal Chou, George K. Thiruvathukal

Modern transformer architectures achieve remarkable performance across tasks and domains but remain rigid in how they allocate computation at inference time. Real-world deployment often requires models to adapt to diverse hardware and latency constraints, yet most approaches to dynamic computation focus on a single axis -- such as reducing the number of toke

Rohan Sarkar, Tuhin Subhra Pal, Sharmistha Maji, Srijaya Nandi

Finding food is a fundamental activity for survival of all living organisms. Free-ranging dogs have been known to use their olfaction to assess the quality and type of available food but their use of visual ability in foraging is not well-documented. In the current study, we seek to remedy that by testing free-ranging dogs in a food-based choice test. We tes

Xiaohong Liu, Xiufeng Song, Huayu Zheng, Lei Bai

The proliferation of videos generated by diffusion models has raised increasing concerns about information security, highlighting the urgent need for reliable detection of synthetic media. Existing methods primarily focus on image-level forgery detection, leaving generic video-level forgery detection largely underexplored. To advance video forensics, we prop

Adrien Banse, Alessandro Abate, Raphaël M. Jungers

Labeled Markov Chains (or LMCs for short) are useful mathematical objects to model complex probabilistic languages. A central challenge is to compare two LMCs, for example to assess the accuracy of an abstraction or to quantify the effect of model perturbations. In this work, we study the recently introduced Cantor-Kantorovich (or CK) distance. In particular

Aditya Chinchure, Sahithya Ravi, Pushkar Shukla, Vered Shwartz

Current text-to-video models (T2V) can generate high-quality, temporally coherent, and visually realistic videos. Nonetheless, errors still often occur, and are more nuanced and local compared to the previous generation of T2V models. While current evaluation paradigms assess video models across diverse dimensions, they typically evaluate videos holistically

Bhargav Bhatt, Bjorn Poonen

Diophantine subsets of $\mathbb{Z}$ play a key role in the negative answer to Hilbert's tenth problem. The definition of diophantine set generalizes in several ways to other commutative rings. We compare these definitions. Along the way, we prove that for every finitely presented scheme $Y$ over a ring $R$, there exists an affine $R$-scheme $X$ with a finite

Nagi Arai, Shinpei Ogata, Hikofumi Suzuki, Kozo Okano

In preparation for constructing or modifying information networks, network engineers develop configuration procedures for network devices according to network configuration specifications. However, as engineers typically create these procedures manually, the generated configuration procedures frequently diverge from the specified requirements. To improve thi

Chandrasekhar Gokavarapu, D. Madhusudhana Rao

Classical shortest-path methods rely on binary tropical semirings $(\min,+)$, whose dyadic structure limits them to pairwise cost interactions. However, many real-world systems, including logistics, supply chains, communication networks, and reliability-aware infrastructures, exhibit inherently ternary dependencies among cost, time, and risk that cannot be d

More Aayush Babasaheb, Shamik Sural

This paper presents an empirical investigation into the capabilities of Large Language Models (LLMs) to perform automated Attribute-based Access Control (ABAC) policy mining. While ABAC provides fine-grained, context-aware access management, the increasing number and complexity of access policies can make their formulation and evaluation rather challenging.

Pengchuan Jiang, Quanzhong Li, Lifeng Mai, Qi Zhang

A rotatable antenna, which is able to dynamically adjust its deflection angle, is promising to achieve better physical layer security performance for wireless communications. In this paper, considering practical scenarios with non-real-time rotatable antenna adjustment, we investigate the average secrecy rate maximization problem of a rotatable antenna-assis

Subhajyoti Sahoo, Ameeya Kumar Nayak

We develop a nonlinear model for electroosmotic transport in a constricted microchannel with a compliant lower wall, with applications to soft microfluidics, bio-inspired sensing, and energy harvesting. The formulation couples electroosmotic slip-driven flow under a globally constrained electric field with pressure-driven lubrication and elastic wall deforma

A. Leviatan

Quantum phase transitions (QPTs) in odd-mass Nb isotopes are investigated in the framework of the interacting boson-fermion model with configuration mixing. A quantum analysis reveals a Type I QPT (gradual shape-evolution within the intruder configuration) superimposed on a Type II QPT (abrupt crossing of normal and intruder states), thus demonstrating the o

Daijiro Kabata, Takumi Imai

Background: Non-inferiority studies based on non-randomised data are increasingly used in clinical research but remain prone to unmeasured confounding. The classical E-value offers a simple way to quantify such bias but has been applied almost exclusively with respect to the statistical null. We reformulated the E-value framework to make explicit its applica

Fulong Yao, Wanqing Zhao, Matthew Forshaw

Predictive control approaches based on deep reinforcement learning (DRL) have gained significant attention in microgrid energy optimization. However, existing research often overlooks the issue of uncertainty stemming from imperfect prediction models, which can lead to suboptimal control strategies. This paper presents a new error temporal difference (ETD) a

Sebastian Dingler, Philip Rehkop, Florian Mayer, Ralf Muenzenberger

Automated Driving Systems (ADS), including Advanced Driver Assistance Systems (ADAS), must fulfill not only high functional expectations but also stringent timing constraints mandated by international regulations and standards. Regulatory frameworks such as UN regulations, NCAP standards, ISO norms, and NHTSA guidelines impose strict bounds on system reactio

Nalinikanta Pradhan, Rina Kanamoto, M. Bhattacharya, Pankaj Kumar Mishra

The Josephson diode, a non-reciprocal quantum element analogous to the familiar semiconductor p-n junction diode, has been realized in solid-state systems but remains unexplored in tunable atomtronic circuits. In this work, we propose and numerically demonstrate the realization of the Josephson diode effect in an atomtronic circuit consisting of a ring-shape

Mingwei He, Tongda Xu, Xingtong Ge, Ming Sun

Perceptual image super-resolution (SR) methods restore degraded images and produce sharp outputs. In practice, those outputs are usually recompressed for storage and transmission. Ignoring recompression is suboptimal as the downstream codec might add additional artifacts to restored images. However, jointly optimizing SR and recompression is challenging, as

Ibrahim Alsarraj, Yuhao Wang, Abdalla Swikir, Cesare Stefanini

Tendon-driven continuum robots offer intrinsically safe and contact-rich interactions owing to their kinematic redundancy and structural compliance. However, their perception often depends on external sensors, which increase hardware complexity and limit scalability. This work introduces a unified multi-dynamics modeling framework for tendon-driven continuum

Jinxin Wu, Dan Wang, Qingqing Liang, Jianhua Hu

Light carrying transverse orbital angular momentum (T-OAM) in the form of spatiotemporal optical vortices (STOVs) is opening new degrees of freedom for structured light manipulation. Such spatiotemporal wavepackets hold significant potential for optical trapping, analog optical computing, studying photonic symmetry and topology, among others. Up to now, synt

Miguel Lourenço, António Grilo

Unmanned Aerial Vehicle (UAV) swarms represent a key advancement in autonomous systems, enabling coordinated missions through inter-UAV communication. However, their reliance on wireless links makes them vulnerable to jamming, which can disrupt coordination and mission success. This work investigates whether a UAV swarm can effectively overcome jamming while

Yuxuan Wu, Guangming Wang, Zhiheng Yang, Tianchen Deng

Vision-language-action (VLA) models show promising knowledge accumulation ability from pretraining, yet continual learning in VLA remains challenging, especially for efficient adaptation. Existing continual imitation learning (CIL) methods often rely on additional parameters or external modules, limiting scalability for large VLA models. We propose Stellar V

Dou Liu, Ying Long, Sophia Zuoqiu, Kaipeng Xie

Large language models (LLMs) are increasingly adopted in clinical decision support, yet aligning them with the multifaceted reasoning pathways of real-world medicine remains a major challenge. Using more than 8,000 infertility treatment records, we systematically evaluate four alignment strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization

Md Abdullah Al Kafi, Raka Moni, Sumit Kumar Banshal

Background and Objective: Deep learning models have high computational needs and lack interpretability but are often the first choice for medical image classification tasks. This study addresses whether complex neural networks are essential for the simple binary classification task of malaria. We introduce the Extracted Morphological Feature Engineered (EMFE

Wencheng Ye, Tianshi Wang, Lei Zhu, Fengling Li

Recent Vision-Language-Action (VLA) models have shown impressive flexibility and generalization, yet their deployment in robotic manipulation remains limited by heavy computational overhead and inference latency. In this work, we present ActDistill, a general action-guided self-derived distillation framework that transfers the action prediction capability of

Zijing Li

The Broad Learning System (BLS) has gained significant attention for its computational efficiency and less network parameters compared to deep learning structures. However, the standard BLS relies on the pseudoinverse solution, which minimizes the mean square error with $L_2$-norm but lacks robustness against sensor noise and outliers common in industrial en

Konstantinos Dialektopoulos, Theodoros Papanikolaou, Vasilios Zarikas

We propose a natural mechanism for cosmic acceleration driven by primordial black holes (PBHs) with repulsive behavior, within a ''Swiss Cheese'' cosmological framework. Considering regular black hole spacetimes such as Hayward, Bardeen, and Dymnikova-as well as the singular Schwarzschild-de Sitter case-we consistently find a robust PBH-driven cosmic acceler

Atiye Zeynali, Zahra Bakhshi

The transition from monolithic to distributed multi-chip quantum architectures has fundamentally altered the circuit compilation landscape, introducing challenges in managing temporal noise variations and minimizing expensive inter-chip operations. We present DeepQMap, a deep reinforcement learning framework that integrates a bidirectional Long Short-Term Me

Kexin Li, Mandar Chitre

Accurate modeling of time-varying underwater acoustic channels is essential for the design, evaluation, and deployment of reliable underwater communication systems. Conventional physics models require detailed environmental knowledge, while stochastic replay methods are constrained by the limited diversity of measured channels and often fail to generalize to

Fermat Leukam, Rock Stephane Koffi, Prudence Djagba

This research proposes an enhancement to the innovative portfolio optimization approach using the G-Learning algorithm, combined with parametric optimization via the GIRL algorithm (G-learning approach to the setting of Inverse Reinforcement Learning) as presented by. The goal is to maximize portfolio value by a target date while minimizing the investor's pe

Jianhang Yao, Yongbin Zheng, Siqi Lu, Wanying Xu

To identify objects beyond predefined categories, open-vocabulary aerial object detection (OVAD) leverages the zero-shot capabilities of visual-language models (VLMs) to generalize from base to novel categories. Existing approaches typically utilize self-learning mechanisms with weak text supervision to generate region-level pseudo-labels to align detectors

Vladimir Dotsenko, Andrea Solotar

We examine the Hochschild cohomology for triangular algebras that capture some aspects of geometry and topology of the torus and of the quadric surface, and for deformations of these algebras. In particular, this shows that the cup product on the Hochschild cohomology of a triangular algebra does not generally follow the intuition coming from monomial algebr

Rasmus Kleist Hørlyck Sørensen, Margherita Guido, Allan Peter Engsig-Karup, Daniel Kressner

Efficient simulation of nonlinear and dispersive free-surface flows governed by the incompressible Navier-Stokes equations remains a central challenge in ocean and coastal engineering. The computational bottleneck arises from solving a time-dependent discretized Poisson problem at every time step to enforce divergence free flow. This is crucial to ensure con

Yusheng Qiu, Jinggang Tan, Aliang Xia

We investigate the quantitative unique continuation property for solutions to $$\Delta^2_{X} u = V u,$$ where $\Delta_{X} = \Delta_{x} + |x|^{2\beta} \Delta_{y}$ ($0 < \beta \leq 1$), with $x \in \mathbb{R}^{m}$ and $y \in \mathbb{R}^{n}$, denotes a class of subelliptic operators of Baouendi-Grushin type. The potential $V$ is assumed to be bounded and satisf

Pranav Kumar, Fritz Körmann, Kaveh Edalati, Blazej Grabowski

The kinetics of hydrogen diffusion in C15 cubic and C14 hexagonal TiCr$_2$H$_x$ (0 < $x$ <= 4) Laves-phase hydrogen storage alloys is investigated with density functional theory (DFT) and machine learning interatomic potentials (MLIPs). Generalized solid-state nudged elastic band calculations are conducted based on DFT for all symmetrically inequivalent path

Si-Hong Liu, Ying-Xin Lai, Di Wang

The doubly charmed baryon was first observed by LHCb via the non-leptonic decay $\Xi_{cc}^{++}\to \Lambda^+_cK^-\pi^+\pi^+$ in 2017. Subsequently, ongoing efforts have been made to identify other doubly charmed baryons. However, there is no systematic analysis of the topological decomposition for non-leptonic decays of doubly charmed baryons. In this work, w

Valentina G. Klochkova, Anatoly S. Miroshnichenko, Victoria N. Komarova, Nonna S. Tavolzhanskaya

The atlas of the spectrum of the B[e] star CI Cam obtained with the 6-meter BTA telescope in combination with the NES spectrograph in the wavelength interval of 395$\div$780 nm with a resolution of $\lambda/\Delta\lambda\ge60\,000$ is presented. The atlas contains about 400 spectral features and illustrates the diversity of spectral features of the unique st

Md Akil Raihan Iftee, Syed Md. Ahnaf Hasan, Mir Sazzat Hossain, Rakibul Hasan Rajib

Test-time adaptation (TTA) in federated learning (FL) is crucial for handling unseen data distributions across clients, particularly when faced with domain shifts and skewed class distributions. Class Imbalance (CI) remains a fundamental challenge in FL, where rare but critical classes are often severely underrepresented in individual client datasets. Althou

Szil\' ard Gy. R\' ev\' esz, Imre Z. Ruzsa

An upper mean here is a subadditive functional $\overline M$ defined on bounded functions on a commutative group which has, beside some natural requirements, the property we call restricted additivity: if $g(x)= f(x)+f(x+t)$, then $\overline{M} (g)= 2 \overline{M} (f)$. This tries to grasp that it should not depend on local properties. This naturally induces

Gabriela Fernandes

Cervical adenocarcinoma in situ (AIS) is a critical premalignant lesion whose accurate histopathological diagnosis is challenging. Early detection is essential to prevent progression to invasive cervical adenocarcinoma. In this study, we developed a deep learning-based virtual pathology assistant capable of distinguishing AIS from normal cervical gland histo

Manas Nagda, Priyam Kumar De, Amber Jain

Fewest switches surface hopping (FSSH) is a well benchmarked dynamical method for simulating nonadiabatic systems. In particular, the literature shows that for the spin-Boson model Hamiltonian, FSSH with appropriate corrections usually captures the detailed balance well and obtains rate constants within a factor of 2 compared to numerically exact results. In

Imran Khan, Mohammad Yasir, Vikrant Saxena

Recently, using two-dimensional particle-in-cell simulations, it has been demonstrated that in laser based proton acceleration with micro-structured targets, a single rectangular groove on the target front offers significant proton cut-off enhancement with linearly polarised laser pulses. In the present work, three-dimensional investigations are carried out

Francesca Romana Crucinio, Sahani Pathiraja

Wasserstein-Fisher-Rao (WFR) gradient flows have been recently proposed as a powerful sampling tool that combines the advantages of pure Wasserstein (W) and pure Fisher-Rao (FR) gradient flows. Existing algorithmic developments implicitly make use of operator splitting techniques to numerically approximate the WFR partial differential equation, whereby the W

Wei Song, Guowei Liu, Hanbin Deng, Tianyu Yang

The pyrochlore lattice materials can exhibit geometrical frustration, while the related many-body electronic states remain elusive. In this work, we performed scanning tunneling microscopy measurements on the pyrochlore superconductor CsBi2 and spin liquid Pr2Ir2O7 at 0.3 K. For the first time, we obtained atomically resolved images of their (111) surfaces,

Wei Huang, Zhitong Xiong, Chenying Liu, Xiao Xiang Zhu

The performance of deep learning models in remote sensing (RS) strongly depends on the availability of high-quality labeled data. However, collecting large-scale annotations is costly and time-consuming, while vast amounts of unlabeled imagery remain underutilized. To address this challenge, we propose a Hierarchical Semi-Supervised Active Learning (HSSAL) f

Ahmed Gamal Eldin

Current models treat physiological signals as noise corrupting neural computation. Previously, we showed that removing these "artifacts" eliminates 70% of predictive correlation, suggesting body signals functionally drive cognition. Here, we investigate the mechanism using high-density EEG (64 channels, 10 subjects, 500+ trials) during P300 target recognitio

Maximilien Dreveton, Matthias Grossglauser, Daichi Kuroda, Patrick Thiran

Hierarchical clustering seeks to uncover nested structures in data by constructing a tree of clusters, where deeper levels reveal finer-grained relationships. Traditional methods, including linkage approaches, face three major limitations: (i) they always return a hierarchy, even if none exists, (ii) they are restricted to binary trees, even if the true hier

Bowen Qu, Shangkun Sun, Xiaoyu Liang, Wei Gao

Recent advances in text-driven image editing have been significant, yet the task of accurately evaluating these edited images continues to pose a considerable challenge. Different from the assessment of text-driven image generation, text-driven image editing is characterized by simultaneously conditioning on both text and a source image. The edited images of

Gowtham, Sai Rupesh, Sanjay Kumar, Saravanan

High-quality training data is fundamental to large language model (LLM) performance, yet existing preprocessing pipelines often struggle to effectively remove noise and unstructured content from web-scale corpora. This paper presents Blu-WERP, a novel data preprocessing pipeline designed to optimize the quality of Common Crawl WARC files for LLM training. We

Chenxu Feng, Yifan Li

This paper analyzes key properties of networks generated by geometric preferential attachment. We establish that the expected number of triangles is proportional to that of the standard preferential attachment model, with a proportionality constant equal to the ratio of the number of triangles between a random geometric graph and an Erd\H{o}s-R\'enyi graph.

Jilan Mei, Tengjie Zheng, Lin Cheng, Shengping Gong

Sparse dynamics identification is an essential tool for discovering interpretable physical models and enabling efficient control in engineering systems. However, existing methods rely on batch learning with full historical data, limiting their applicability to real-time scenarios involving sequential and partially observable data. To overcome this limitation

Rongji Li, Haichuan Di, Shixiao Willing Jiang

Solving partial differential equations (PDEs) on manifolds defined by randomly sampled point clouds is a challenging problem in scientific computing and has broad applications in various fields. In this paper, we develop a two-step generalized radial basis function-generated finite difference (gRBF-FD) method for solving PDEs on manifolds without boundaries,

Manali Dutta, Gourav Saha, Rahul Singh, Ness B. Shroff

Millimeter-wave (mmWave) networks have the potential to support high throughput and low-latency requirements of 5G-and-beyond communication standards. But transmissions in this band are highly vulnerable to attenuation and blockages from humans, buildings, and foliage, which increase end-to-end packet delays. This work designs dynamic scheduling policies tha

Oren Barkan, Yahlly Schein, Yehonatan Elisha, Veronika Bogina

Explanation fidelity, which measures how accurately an explanation reflects a model's true reasoning, remains critically underexplored in recommender systems. We introduce SPINRec (Stochastic Path Integration for Neural Recommender Explanations), a model-agnostic approach that adapts path-integration techniques to the sparse and implicit nature of recommenda

V. García-Rojas, J. F. Pérez-Torres

The spin-orbital entanglement in $5d^1$ transition metal ions embedded in double perovskites, where anomalous effective magnetic dipole moments are frequently observed, is quantified by the spin-orbital von Neumann entropy $\Delta S_{\rm vN}^{\rm SO}$. The framework is grounded on the relativistic crystal field theory, and is illustrated through a series of

Shreyansh Swami, Ishwardeep Singh, Chinmay Prawah Pant

The exponential growth of the Internet of Things (IoT) ecosystem has amplified concerns regarding device reliability, interoperability, and security assurance. Despite the proliferation of IoT security guidelines, a unified and quantitative approach to measuring trust remains absent. This paper introduces SCI-IoT (Secure Certification Index for IoT), a stand

Tirsi Prebibaj, Fanouria Antoniou, Foteini Asvesta, Hannes Bartosik

The survival of charged particles in synchrotrons requires avoiding setting the beam on machine resonances, the most dangerous of which are the integer and half-integer. Nevertheless, operationally, the transverse tunes may change dynamically, crossing these resonances, and resulting in unwanted beam quality degradation and beam loss. For high intensity beam

Kei Funano

We establish two new universal inequalities for Neumann eigenvalues of the Laplacian on a planar convex domain.

R. R. Kumar, S. Saini, N. R. Vadlamani, A. S. Chamarthi

The two promising methods for capturing high-speed flows are local artificial diffusivity (LAD) and centralised gradient-based reconstruction (C-GBR), the former being computationally economical and the latter being more robust and stable but expensive. While the LAD approach captures discontinuities by adding artificial fluid transport coefficients, C-GBR e

A. Jiménez-Vargas, D. Ruiz-Casternado

Let X and Y be complex Banach spaces, B_X be the open unit ball of X and HL(B_X,Y) be the Banach space of all holomorphic Lipschitz maps f:B_X->Y such that f(0)=0, endowed with the Lipschitz norm. Given a Banach operator ideal A, we use the property of A-compactness by Carl and Stephani to introduce and study the subclass of those functions in HL(B_X,Y) for

Thong Bach, Thanh Nguyen-Tang, Dung Nguyen, Thao Minh Le

Fine-tuning Large Language Models (LLMs) for downstream tasks often compromises safety alignment, even when using parameter-efficient methods like LoRA. In this work, we uncover a notable property: fine-tuned models preserve the geometric structure of their loss landscapes concerning harmful content, regardless of the fine-tuning method employed. This sugges

Xiaoke Han, Hong Zhu

Testing RESTful API is increasingly important in quality assurance of cloud-native applications. Recent advances in machine learning (ML) techniques have demonstrated that various testing activities can be performed automatically by large language models (LLMs) with reasonable accuracy. This paper develops a multi-agent system called MASTEST that combines LL

Ziyi Guo, Zhou Liu, Wentao Zhang

The manual creation of system architecture diagrams for scientific papers is a time-consuming and subjective process, while existing generative models lack the necessary structural control and semantic understanding for this task. A primary obstacle hindering research and development in this domain has been the profound lack of a standardized benchmark to qu

Giacomo Iannucci, Petros Barmpounakis, Alexandros Beskos, Nikolaos Demiris

This paper presents a real time, data driven decision support framework for epidemic control. We combine a compartmental epidemic model with sequential Bayesian inference and reinforcement learning (RL) controllers that adaptively choose intervention levels to balance disease burden, such as intensive care unit (ICU) load, against socio economic costs. We co

Roberto Tauraso

In the 1980s, Koecher and, independently, Leshchiner found an elegant formula for the generating function of odd zeta values. In this short note, we derive a $q$-analogue of this formula, which provides a $q$-version of the accelerated series for $\zeta(3)$ used by Ap\'ery in his famous proof of irrationality.

Aram Vardanyan

Browser agents enable autonomous web interaction but face critical reliability and security challenges in production. This paper presents findings from building and operating a production browser agent. The analysis examines where current approaches fail and what prevents safe autonomous operation. The fundamental insight: model capability does not limit age

Xiao-Lin Wang, Rui Sun, Li-Ting Wang

With the potential prospects of the $D^{*}_{s}$ at high-luminosity heavy-flavor experiments in the future, we investigated the CKM-favored and tree-dominated leptonic $D^{*}_{s}\to\ell\bar{\nu}_{\ell}$ and semileptonic $D^{*}_{s}\to M\ell\bar{\nu}_{\ell}$ ($M=\phi, \eta^{(\prime)}$ and $\ell=e, \mu$) weak decays in the Standard Model (SM). The theoretical pr

Karl Dilcher, Christophe Vignat

A general integral expression to transform power series is applied to $\arcsin{x}$ and its positive integer powers. We concentrate on the first to the fourth powers and obtain infinite classes of new power series involving central binomial coefficients. Specializing the variable to appropriate simple values leads to different classes of series expansions for

Arun Kumar Das, Sandip Das, Sweta Das, Foivos Fioravantes

Efficient resource (re-)allocation is a critical challenge in optimizing productivity and sustainability within multi-party supply networks. In this work, we introduce the \textsc{Red-Blue Reinforcement} (R-BR) problem, where a service provider under budgetary constraints must minimize client reallocations to reduce the required number of servers they should

Yanxing Liu, Jiancheng Pan, Jianwei Yang, Tiancheng Chen

Few-shot object detection (FSOD) aims to detect novel instances with only a limited number of labeled training samples, presenting a challenge that is particularly prominent in numerous remote sensing applications such as endangered species monitoring and disaster assessment. Existing FSOD methods for remote sensing images (RSIs) have achieved promising prog

O. Debeaupuis

Many biomarker pipelines require patient-level decisions aggregated from instance-level (cell/patch) scores. Thresholds tuned on pooled instances often fail across sites due to hierarchical dependence, prevalence shift, and score-scale mismatch. We present a selection-honest framework for hierarchical thresholding that makes patient-level decisions reproduci

Dylan M. Paré, Zi-Xuan Feng, Yue Hu, Maya A. Petkova

The Galactic Center (GC) is an extreme region of the Milky Way that is host to a complex set of thermal and non-thermal structures. In particular, the GC contains high-density gas and dust that is collectively referred to as the Central Molecular Zone (CMZ). In this work, we study a subset of HNCO filaments identified in band 3 ALMA observations of the GC ob

Chenyu Li, Danfeng Hong, Bing Zhang, Zhaojie Pan

Image super-resolution (SR) is a critical technology for overcoming the inherent hardware limitations of sensors. However, existing approaches mainly focus on directly enhancing the final resolution, often neglecting effective control over error propagation and accumulation during intermediate stages. Recently, Mamba has emerged as a promising approach that

Yu-Ting Lin, Hsin-Po Wang, Venkatesan Guruswami

DNA is an attractive candidate for data storage. Its millennial durability and nanometer scale offer exceptional data density and longevity. Its relevance to medical applications also drives advances in DNA-related biotechnology. To protect our data against errors, a straightforward approach uses one error-correcting code per DNA strand, with a Reed--Solomon

Hassan Oubba

This article investigates the recently introduced three-parameter generalized quaternion algebra (3PGQ), denoted here as $\mathbb{K}_{\lambda_1,\lambda_2,\lambda_3}$ . Our analysis is structured in three parts. First, we demonstrate that every local and 2-local derivation on this algebra is automatically a derivation. Second, we provide a complete characteri

Yifan Luo, Meng Zhang, Jin Xu, Junting Chen

Data streams collected from multiple sources are rarely independent. Values evolve over time and influence one another across sequences. These correlations improve prediction in healthcare, finance, and smart-city control yet violate the record-independence assumption built into most Differential Privacy (DP) mechanisms. To restore rigorous privacy guarantee

Dor Arviv, Yehonatan Elisha, Oren Barkan, Noam Koenigstein

We present a method for extracting \emph{monosemantic} neurons, defined as latent dimensions that align with coherent and interpretable concepts, from user and item embeddings in recommender systems. Our approach employs a Sparse Autoencoder (SAE) to reveal semantic structure within pretrained representations. In contrast to work on language models, monosema

Jun-Xu Lu, Li-Sheng Geng

We present the two-pion exchange contributions to the nucleon-nucleon interaction up to next-to-next-to-next-to leading order (N$^3$LO) in covariant baryon chiral perturbation theory. Both one-loop and two-loop diagrams are calculated with the spectral functional regularization. We show that the phase shifts for partial waves with total angular momentum $3\l

Jingyi Zhao, Linxin Yang, Haohua Zhang, Tian Ding

Dynamic programming (DP) is a cornerstone of combinatorial optimization, yet its inherently sequential structure has long limited its scalability in scenario-based stochastic programming (SP). This paper introduces a GPU-accelerated framework that reformulates a broad class of forward DP recursions as batched min-plus matrix-vector products over layered DAGs

Daniele Amato, Paolo Facchi, Arturo Konderak

In this review we discuss some results on the asymptotic dynamics of finite-dimensional open quantum systems in the Heisenberg picture. Both the spectral and algebraic approaches to this topic are addressed, with particular emphasis on their relationship. The analysis is conducted in both the discrete-time and the continuous-time Markovian settings. In the f

Luca Salasnich

We present a detailed derivation of the quantum and quantum-thermal effective action for non-relativistic systems, starting from the single particle case and extending to the Gross-Pitaevskii (GP) field theory for weakly interacting bosons. In the single-particle framework, we introduce the one-particle irreducible 1PI effective action formalism taking expli

Igor Klep, Victor Magron, Tobias Metzlaff, Jie Wang

Polynomial optimization problems are infinite-dimensional, nonconvex, NP-hard, and are often handled in practice with the moment-sums of squares hierarchy of semidefinite programming bounds. We consider problems where the objective function and constraint polynomials are invariant under the action of a finite group. The present paper simultaneously exploits

G. G. L. Nashed

In this work we examine the internal structure of compact stars within an extended gravitational framework described by the function $f(\mathcal{R},\mathcal{G},\mathcal{T})$. Throughout this work, the quantity $\mathcal{R}$ refers to the curvature scalar formed from the Ricci tensor. The term $\mathcal{G}$ denotes the Gauss--Bonnet curvature invariant, while

Vadim Weinstein

We define a set of restricted Reidemeister moves and show that if $K$ is obtained from $K_0\,\#\,K_1$ using those moves, then the crossing number of $K$ is at least $c(K_0)+c(K_1)$. We also explore topological interpretations of this result.

Mingyu Jeon, Hyobin Kim

Electroencephalogram (EEG)-to-text remains challenging due to high-dimensional noise, subject variability, and error accumulation in autoregressive decoding. We introduce DELTA, which pairs a Residual Vector Quantization (RVQ) EEG tokenizer with a masked language diffusion model (LLaDA). RVQ discretizes continuous EEG into multi-layer tokens to reduce noise

Kacper Dobek, Daniel Jankowski, Krzysztof Krawiec

This work explores Liquid Time-Constant Networks (LTCs) and Closed-form Continuous-time Networks (CfCs) for modeling retinal ganglion cell activity in tiger salamanders across three datasets. Compared to a convolutional baseline and an LSTM, both architectures achieved lower MAE, faster convergence, smaller model sizes, and favorable query times, though with