November 2025 arXiv papers — page 103

Fei Kong

Denoising diffusion models have emerged as a dominant paradigm in image generation. Discretizing image data into tokens is a critical step for effectively integrating images with Transformer and other architectures. Although the Denoising Diffusion Codebook Models (DDCM) pioneered the use of pre-trained diffusion models for image tokenization, it strictly re

Tino Laidin

We present a novel multiscale numerical approach that combines parallel-in-time computation with hybrid domain adaptation for linear collisional kinetic equations in the diffusive regime. The method addresses the computational challenges of kinetic simulations by integrating two complementary strategies: a parareal temporal parallelization method and a dynam

M. Olshanii, G. Aupetit-Diallo, S. G. Jackson, P. Vignolo

We investigate the Lieb--Liniger model of interacting one-dimensional bosons coupled to a localized impurity, modeled by a delta barrier. While the Lieb--Liniger gas is integrable, the impurity breaks integrability and induces a transition towards quantum chaos. We show that the low-energy spectrum exhibits random-matrix statistics, in striking contrast to t

Catalin Dumitrescu

This study explores the potential impact of introducing a Central Bank Digital Currency (CBDC) on financial stability in an emerging dual-currency economy (Romania), where the domestic currency (RON) coexists with the euro. It develops an integrated analytical framework combining econometrics, machine learning, and behavioural modelling. CBDC adoption probab

Anumita Mukhopadhyay, Shibdas Roy, Arun Kumar Pati

The fidelity estimation between two quantum states is crucial for quantum computation and information science. However, an efficacious method for this, especially for mixed states and higher-dimensional density matrices, remains elusive. While there are many existing algorithms on computing the fidelity between two pure states, there is not much work on how

Deng-Shan Wang, Xiaodong Zhu

This work investigates the long-time asymptotic behaviors of initial value problem for the good Boussinesq equation and the modified Boussinesq equation in Painlev\'{e} region. The Deift-Zhou steepest descent method is used to deform the associated $3 \times 3$ Riemann-Hilbert problem to the Painlev\'e IV model. Then asymptotic formulas for the modified Bous

Siyu Zhu, Mouxiao Bian, Yue Xie, Yongyu Tang

With the rapid rise of large language models (LLMs) in medicine, a key question is whether they can function as competent pediatricians in real-world clinical settings. We developed PEDIASBench, a systematic evaluation framework centered on a knowledge-system framework and tailored to realistic clinical environments. PEDIASBench assesses LLMs across three di

Olivier Bisson, Xavier Pennec

We develop a self-contained theory of log-Euclidean Lie groups: smooth manifolds diffeomorphic to finite-dimensional vector spaces, equipped with the pullback of a constant Euclidean metric. This framework encompasses symmetric positive-definite (SPD) matrices S+(n) and full-rank correlation matrices Cor+(n), and explains why many seemingly different log-Euc

Ettore Marmo, David Riley, Thomas Weigel

It is shown that the Zassenhaus restricted $\mathbb F_p$-Lie algebra of a (pro-p) group G can be presented by the Magnus Lie algebra of G. For the class of (pro-p) groups for which the terms of the lower central series are torsion-free, the Zassenhaus restricted $\mathbb F_p$-Lie algebra can be explicitly computed from the Magnus Lie algebra. These results a

Carlo Teo Pedretti, Davide Picca, Dario Rodighiero

Diagrams are crucial yet underexplored tools in many disciplines, demonstrating the close connection between visual representation and scholarly reasoning. However, their iconic form poses obstacles to visual studies, intermedial analysis, and text-based digital workflows. In particular, Charles S. Peirce consistently advocated the use of diagrams as essenti

Amélie Canin, Cédric Févotte, Nicolas Dobigeon, Dries Van De Putte

MIRI is the imager and spectrograph covering wavelengths from $4.9$ to $27.9$ $\mu$m onboard the James Webb Space Telescope (JWST). The Medium-Resolution Spectrometer (MRS) consists of four integral field units (IFU), each of which has three sub-channels. The twelve resulting spectral data cubes have different fields of view, spatial, and spectral resolution

Sebastian Kebrich, Luisa Kamp, Jochen Linßen, Heidi Heinrichs

Climate change is one of the 21st centurys major challenges. However, the progress in reducing greenhouse gas emissions is perceived as being too slow. Hence, more radical technologies such as stratospheric aerosol injection are entering discussions to limit climate change. This study presents a methodology for evaluating the effects of injecting 20Mt of SO$

H. Balthasar, C. Denker, A. Diercke, S. J. González Manrique

The photospheric Evershed flow is normally oriented radially outward, yet sometimes opposite velocities are observed not only in the chromosphere but also in the photospheric layers of the penumbra. We study the velocity field in a special case of an active region with two mature sunspots, where one of them formed several days later than the main one. Betwee

Prakrit Timilsina, Anuj Nepal, Rajan Kadel, Robin Doss

Large Language Models (LLMs) face significant challenges in distributed healthcare, including consolidating specialized domain knowledge across institutions while maintaining privacy, reducing computational overhead, and preventing catastrophic forgetting during model updates.This paper presents a systematic evaluation of six parameter-space merging techniqu

Caroline Baumgartner, Eleanor Spens, Neil Burgess, Petru Manescu

How do large language models solve spatial navigation tasks? We investigate this by training GPT-2 models on three spatial learning paradigms in grid environments: passive exploration (Foraging Model- predicting steps in random walks), goal-directed planning (generating optimal shortest paths) on structured Hamiltonian paths (SP-Hamiltonian), and a hybrid mo

Jiajun Zhang, Huanyuan Shan

The Epoch of Reionization (EoR) is a unique phase in cosmic history, marked by the ionization of neutral hydrogen by the first luminous sources. The global neutral hydrogen fraction (x_HI) is a key observable for probing this era. This paper presents a novel, statistically robust method to extract the evolution of x_HI from the challenging noise-dominated da

Lilou Soulas, Lorenzo Lucchini, Maurizio Napolitano, Sebastiano Bontorin

The heterogeneity of Point of Interest (POI) taxonomies is a persistent challenge for the integration of urban datasets and the development of location-based services. OpenStreetMap (OSM) adopts a flexible, community-driven tagging system, while Foursquare (FS) relies on a curated hierarchical structure. Here we present an openly available benchmark and mapp

Angelina Parfenova, Alexander Denzler, Juergen Pfeffer

Large language models (LLMs) are increasingly deployed in collaborative settings, yet little is known about how they coordinate when treated as black-box agents. We simulate 7500 multi-agent, multi-round discussions in an inductive coding task, generating over 125000 utterances that capture both final annotations and their interactional histories. We introdu

Kajetan Dymkiewicz, Ivan Vulic, Helen Yannakoudakis, Eilam Shapira

Large language models (LLMs) perform strongly across tasks and languages, yet how improvements in one task or language affect other tasks and languages remains poorly understood. We conduct a controlled LoRA fine-tuning study across multiple open-weight LLM families and scales, using a standardised grid of 11 languages and four benchmarks. We fine-tune each

Niels Wernicke, Alexander Bähr, Hannah Danhel, Florian Heinrich

Depleted p-channel field effect transistor detectors with repetitive-non-destructive readout (RNDR-DEPFETs) achieve a deep sub-electron noise by averaging several independent measurements of one single event. During the repetitive readout collected electrons are transferred between two readout nodes within each pixel to enable electron number-resolved measur

Akram Heidari, Mark Podolskij

We study the local asymptotic normality (LAN) property for the likelihood function associated with discretely observed $d$-dimensional McKean-Vlasov stochastic differential equations over a fixed time interval. The model involves a joint parameter in both the drift and diffusion coefficients, introducing challenges due to its dependence on the process distri

Ruijun Deng, Zhihui Lu, Qiang Duan

Split inference (SI) enables users to access deep learning (DL) services without directly transmitting raw data. However, recent studies reveal that data reconstruction attacks (DRAs) can recover the original inputs from the smashed data sent from the client to the server, leading to significant privacy leakage. While various defenses have been proposed, the

Hangxu Liu, Yifei Zhu, Zhenggang Lan

A major challenge in nonadiabatic molecular dynamics is to automatically and objectively identify the key reaction coordinates that drive molecules toward distinct excited-state decay channels. Traditional manual analyses are inefficient and rely heavily on expert intuition, creating a bottleneck for interpreting complex photochemical processes. To overcome

Régis Duvigneau

A mixed continuous / discontinuous Galerkin scheme is introduced for the simulation of fluid-structure interaction problems in an isogeometric analysis framework. The properties of Non-Uniform Rational B-Spline basis functions are leveraged to enable an exact transfer of the structural displacement to the fluid domain, while using different discretizations a

Xiayan Xu, Xiaomeng Chen, Dawei Shi, Ling Shi

High communication costs create a major bottleneck for distributed resource allocation over unbalanced directed networks. Conventional dual gradient tracking methods, while effective for problems on unbalanced digraphs, rely on periodic communication that creates significant overhead in resource-constrained networks. This paper introduces a novel event-trigg

Jiyang Zheng, Islam Nassar, Thanh Vu, Xu Zhong

Medical coding converts free-text clinical notes into standardized diagnostic and procedural codes, which are essential for billing, hospital operations, and medical research. Unlike ordinary text classification, it requires multi-step reasoning: extracting diagnostic concepts, applying guideline constraints, mapping to hierarchical codebooks, and ensuring c

Enrico Santamato, Francesco De Martini

The traditional Standard Quantum Mechanics (SQM) theory is unable to solve the Spin-s problem, i.e., to justify the utterly important "Pauli Exclusion Principle". A complete and straightforward solution of the Spin-Statistics problem is presented based on the "Weyl Integrable Quantum Mechanics" (WIQM) theory. This theory provides a Weyl-gauge invariant formu

Yuhang Wang, Yanxu Zhu, Jitao Sang

The advanced reasoning capabilities of Large Reasoning Models enable them to thoroughly understand and apply safety policies through deliberate thought processes, thereby improving the models' safety. Beyond safety, these models must also be able to reflect the diverse range of human values across various cultures. This paper presents the Cultural Norm-based

Reetikaa Reddy Munnangi, Barbara Giunti

Art is a deeply personal and expressive medium, where each artist brings their own style, technique, and cultural background into their work. Traditionally, identifying artistic styles has been the job of art historians or critics, relying on visual intuition and experience. However, with the advancement of mathematical tools, we can explore art through more

Lukas Bentkamp, Michael Wilczek

One challenge in developing a statistical field theory of turbulence is the analysis of the functional equations that govern the complete statistics of the flow field. Simplified models of turbulence may help to develop such a statistical framework. Here, we consider the advection and stretching of an incompressible passive vector field by a spatially linear

Dmitry Moskalev

Exploring relationships across data sources is a crucial optimization for entities recognition. Since databases can store big amount of information with synthetic and organic data, serving all quantity of objects correctly is an important task to deal with. However, the decision of how to construct entity relationship model is associated with human factor. I

Lei Wang, Yulong Tian, Hao Han, Fengyuan Xu

Backdoor attacks pose severe threats to machine learning systems, prompting extensive research in this area. However, most existing work focuses on single-target All-to-One (A2O) attacks, overlooking the more complex All-to-X (A2X) attacks with multiple target classes, which are often assumed to have low attack success rates. In this paper, we first demonstr

Tsviatko V. Rangelov, George D. Manolis, Petia S. Dineva

This paper presents an analytical derivation of a frequency-dependent fundamental solution plus a Green's function for the uni-dimensional, hexagonal quasicrystal sheet subjected to elastic waves under anti-plane strain conditions. Furthermore, closed-form solutions for the free-fields developing in this sheet for propagating shear waves are also obtained. T

Lucas Gabriel Telesco, Danila Nejamkin, Estefanía Mata, Francisco Filizzola

Retinal image quality assessment (RIQA) supports computer-aided diagnosis of eye diseases. However, most tools classify only overall image quality, without indicating acquisition defects to guide recapture. This gap is mainly due to the high cost of detailed annotations. In this paper, we aim to mitigate this limitation by introducing a hybrid semi-supervise

Jules Berry

We derive a priori error estimates for semidiscrete finite element approximations of stable solutions to time-dependent mean field game systems with Dirichlet boundary conditions. Expressing solutions to the MFG system as zeros of a nonlinear abstract mapping, we show that the stability of solutions is equivalent to the invertibility of its differential. Thi

Xinlan Wu, Bin Zhu, Feng Han, Pengkun Jiao

Food analysis has become increasingly critical for health-related tasks such as personalized nutrition and chronic disease prevention. However, existing large multimodal models (LMMs) in food analysis suffer from catastrophic forgetting when learning new tasks, requiring costly retraining from scratch. To address this, we propose a novel continual learning f

Alexandre Nicolas, Nilankur Dutta, Léo Bulckaen

In many large metropolitan areas, cars cruising for parking significantly contribute to congestion and pollution. At the same time, parking restrictions are contemplated to encourage the use of greener transport alternatives. We give a short overview of a versatile modelling framework for parking search, which can be solved by both numerical simulations and

A. A. Norton, T. L. Duvall,, J. Schou, R. S. Bogart

We report a point spread function (PSF) and deconvolution procedure to remove stray light from the Helioseismic and Magnetic Imager (HMI) data. Pre-launch calibration observations, post-launch Venus transit and lunar transit data were used to develop the PSF and evaluate how well it reproduced the observed scattering. The PSF reported differs from previous s

Shuo Zheng, Shuowen Zhang

The sixth-generation (6G) wireless networks will rely on ultra-dense multi-cell deployment to meet the high rate and connectivity demands. However, frequency reuse leads to severe inter-cell interference, particularly for cell-edge users, which limits the communication performance. To overcome this challenge, we investigate a beyond diagonal reconfigurable i

Enzo Monino, Daria Drwal, Pavel Beran, Michał Hapka

The projection-based wave function (WF)-in-DFT embedding enables an efficient description of both the energetics and properties of large and complex chemical systems, with accuracy exceeding that of pure DFT. Recently, we have proposed using the density matrix renormalization group (DMRG) as the WF method for molecules containing strongly correlated fragment

Nicolás Andruskiewitsch, Dirceu Bagio, Saradia Della Flora, Daiana Flôres

Let $\Bbbk$ be an algebraically closed field of characteristic $2$ and let $\mathfrak{fsl}(2)$ be the unique, up to isomorphism, $3$-dimensional simple Lie algebra over $\Bbbk$. Denote by $\mathfrak{m}$ the minimal $2$-envelope of $\mathfrak{fsl}(2)$ and by $\mathfrak{u}(\mathfrak{m})$ its corresponding restricted enveloping algebra. The non-isomorphic finit

Ori Meiraz, Sharon Shalev, Avishai Weizman

This paper presents a novel Mixture-of-Experts framework for object detection, incorporating adaptive routing among multiple YOLOv9-T experts to enable dynamic feature specialization and achieve higher mean Average Precision (mAP) and Average Recall (AR) compared to a single YOLOv9-T model.

A Cormier, David Roqui, Fabrice Surma, Martin Labouré

Heritage materials are already affected by climate change, and increasing climatic variations reduces the lifespan of monuments. As weathering depends on many factors, it is also difficult to link its progression to climatic changes. To predict weathering, it is essential to gather climatic data while simultaneously monitoring the progression of deterioratio

Zihe Yan, Kai Luo, Haoyu Yang, Yang Yu

In modern software development workflows, the open-source software supply chain contributes significantly to efficient and convenient engineering practices. With increasing system complexity, using open-source software as third-party dependencies has become a common practice. However, the lack of maintenance for underlying dependencies and insufficient commu

Tirtharaj Barman, Swarandeep Sahoo

This study examines the onset of rotating magnetoconvection under an axially imposed magnetic field in the presence of partial thermal stable stratification. Three stratification models-fully unstable, weakly stable, and strongly stable-are analyzed across Ekman numbers $E = 10^{-3}, 10^{-4}$, and $10^{-5}$ (rotation rates) and Roberts numbers $q = 0.01, 1$

Han Meng, Gang Mei, Hong Tian, Nengxiong Xu

Rock discontinuities critically govern the mechanical behavior and stability of rock masses. Their internal distributions remain largely unobservable and are typically inferred from surface-exposed discontinuities using generative prediction approaches. However, surface-exposed observations are inherently sparse, and existing generative prediction approaches

Yunze Leng, Rohan Ghosh, Mehul Motani

Supervised learning with tabular data presents unique challenges, including low data sizes, the absence of structural cues, and heterogeneous features spanning both categorical and continuous domains. Unlike vision and language tasks, where models can exploit inductive biases in the data, tabular data lacks inherent positional structure, hindering the effect

Anna van Es, Eva Cantoni

We study Gaussian-copula models with discrete margins, with primary emphasis on low-count (Poisson) data. Our goal is exact yet computationally efficient maximum likelihood (ML) estimation in regimes where many observations contain small counts, which imperils both identifiability and numerical stability. We develop three novel Kendall's tau-based approaches

Maolin Li, Feng Shu, Minghao Chen, Cunhua Pan

Programmable metasurfaces and adjustable antennas are promising technologies. The security of a rotatable array system is investigated in this paper. A dual-base-station (BS) architecture is adopted, in which the BSs collaboratively perform integrated sensing of the eavesdropper (the target) and communication tasks. To address the security challenge when the

Maram Alharbi, Salmane Chafik, Saad Ezzini, Ruslan Mitkov

The hospitality industry in the Arab world increasingly relies on customer feedback to shape services, driving the need for advanced Arabic sentiment analysis tools. To address this challenge, the Sentiment Analysis on Arabic Dialects in the Hospitality Domain shared task focuses on Sentiment Detection in Arabic Dialects. This task leverages a multi-dialect,

Florent Segonne

Diversification is a cornerstone of robust portfolio construction, yet its application remains fraught with challenges due to model uncertainty and estimation errors. Practitioners often rely on sophisticated, proprietary heuristics to navigate these issues. Among recent advancements, Agnostic Risk Parity introduces eigenrisk parity (ERP), an innovative appr

Alexandru-Mihai Apostu, Andrei Preda, Alexandra Daniela Damir, Diana Bolocan

Generating thorough natural language explanations for threat detections remains an open problem in cybersecurity research, despite significant advances in automated malware detection systems. In this work, we present AutoMalDesc, an automated static analysis summarization framework that, following initial training on a small set of expert-curated examples, o

Tanmayee Patra, Biplab Ganguli

Phase space trajectories are fundamentally important for understanding and analysing chaotic attractors. This is mostly carried out by direct numerical solution of the dynamical equations. Though the origin of scrolls can be understood from the properties of dynamical equations, their appearance in the phase space can also be inferred from the geometry and r

Tirtharaj Barman, Arpan Das, Swarandeep Sahoo

To explore the combined effects of partial thermal stable stratification and magnetic back-reaction within Earth's tangent cylinder, we study the onset of magnetoconvection in an infinite plane layer subject to horizontal magnetic field imposed perpendicular to the rotation axis. Three stratification models-fully unstable, weakly stable, and strongly stable-

Dhilan Nag, Suhun Kim, Cole Johnson, Collin Sumrell

Quantum computers (QCs) have the potential to solve critical problems significantly faster than today's most advanced supercomputers. One major challenge in realizing this technology is designing robust electrostatic pulses to realize unitaries on qubits. Current practice when calibrating unitaries involves recursive experimentation to find the highest-fidel

Shufan Yang, Zifeng Cheng, Zhiwei Jiang, Yafeng Yin

Embedding-as-a-Service (EaaS) is an effective and convenient deployment solution for addressing various NLP tasks. Nevertheless, recent research has shown that EaaS is vulnerable to model extraction attacks, which could lead to significant economic losses for model providers. For copyright protection, existing methods inject watermark embeddings into text em

Gregory Patellis, Danai Roumelioti, Stelios Stefas, George Zoupanos

The Unification of Conformal and Fuzzy gravities with Internal Interactions is based on the following two facts. The first is that the tangent group of a curved manifold and the manifold itself do not necessarily have the same dimensions. The second is that both gravitational theories considered here have been formulated in a gauge theoretic way. Here we wou

Angelina Parfenova, Andreas Marfurt, Alexander Denzler, Juergen Pfeffer

This paper investigates the automation of qualitative data analysis, focusing on inductive coding using large language models (LLMs). Unlike traditional approaches that rely on deductive methods with predefined labels, this research investigates the inductive process where labels emerge from the data. The study evaluates the performance of six open-source LL

Athanasia Gkogkou, Victor Bonjean, Jean-Luc Starck, Marta Spinelli

Neutral hydrogen (HI) intensity mapping with single-dish experiments is a powerful approach for probing cosmology in the post-reionization epoch. However, the presence of bright foregrounds over four orders of magnitude stronger than the HI signal makes its extraction highly challenging. While all methods perform well when assuming a Gaussian beam degraded t

Juntao Jian, Yi-Lin Wei, Chengjie Mou, Yuhao Lin

Task-oriented dexterous grasping holds broad application prospects in robotic manipulation and human-object interaction. However, most existing methods still struggle to generalize across diverse objects and task instructions, as they heavily rely on costly labeled data to ensure task-specific semantic alignment. In this study, we propose \textbf{ZeroDexGras

Siyao Zhao, Hao Ma, Zhiqiang Pu, Jingjing Huang

Creating offensive advantages during open play is fundamental to football success. However, due to the highly dynamic and long-sequence nature of open play, the potential tactic space grows exponentially as the sequence progresses, making automated tactic discovery extremely challenging. To address this, we propose TacEleven, a generative framework for footb

Devesh Giri, Bhooshan Gadre

We investigate the detectability and waveform systematics of sub-solar mass intermediate mass-ratio inspirals (SSM-IMRIs), characterized by mass ratios $q \sim 10^2-10^4$. Using the black hole perturbation theory surrogate model \textsc{BHPTNRSur1dq1e4} as a reference, we assess the performance of the \textsc{IMRPhenomX} phenomenological family in the high-m

Marianne Bessemoulin-Chatard, Tino Laidin, Thomas Rey

In this article, we study the long-time behavior of a finite-volume discretization for a nonlinear kinetic reaction model involving two interacting species. Building upon the seminal work of [Favre, Pirner, Schmeiser, ARMA, 2023], we extend the discrete exponential convergence to equilibrium result established in [Bessemoulin-Chatard, Laidin, Rey, IMAJNA, 20

Senne Deproost, Dennis Steckelmacher, Ann Nowé

Deep Reinforcement Learning is one of the state-of-the-art methods for producing near-optimal system controllers. However, deep RL algorithms train a deep neural network, that lacks transparency, which poses challenges when the controller has to meet regulations, or foster trust. To alleviate this, one could transfer the learned behaviour into a model that i

Liu Liu, Xueyu Zhu, Zhenyi Zhu

This paper introduces a Bi-fidelity Asymptotic-Preserving Neural Network (BI-APNNs) framework, designed to efficiently solve forward and inverse problems for the semiconductor Boltzmann equation. Our approach builds upon the Asymptotic-Preserving Neural Network (APNNs) methodology \cite{APNN-transport}, which employs a micro-macro decomposition to handle the

Chelsea McMurray, Hayder Tirmazi

Users increasingly rely on large language models (LLMs) for personal, emotionally charged, and socially sensitive conversations. However, prompts sent to cloud-hosted models can contain personally identifiable information (PII) that users do not want logged, retained, or leaked. We observe this to be especially acute when users discuss friends, coworkers, or

Peihao Li

Blockchain technologies are rapidly transforming both academia and industry. However, large-scale blockchain data collection remains prohibitively expensive, as many RPC providers only offer enhanced APIs with high pricing tiers that are unsuitable for budget-constrained research or industrial-scale applications, which has significantly slowed down academic

Hanxiao Xia, Ziming Wang, Jianghua Wu, Yue Fang

BL Lac has entered an active state since 2020, with multiwavelength observations revealing intense flares. In this study, we conducted 12-night multicolor optical monitoring using an 85 cm telescope from 2020 September to 2024 June and collected long-term broad-band archived data from radio to $\gamma$-rays. Intraday variabilities were detected on four night

Olivier Poisson

We consider the discrete anisotropic Maxwell operator DaH0 on a bounded paving $\Omega$ $\subset$ Z3 , where H0 denotes discrete isotropic Maxwell operator and Da a diagonal operator of multiplication containing information about the anisotropy of the medium inside $\Omega$. Letting a complex number $\lambda$ __ = 0 such the Dirichlet-to-Neumann operator $\L

Narthana Sivalingam, Santhirarajah Sivasthigan, Thamayanthi Mahendranathan, G. M. R. I. Godaliyadda

Group activity detection in multi-person scenes is challenging due to complex human interactions, occlusions, and variations in appearance over time. This work presents a computer vision based framework for group activity recognition and action spotting using a combination of deep learning models and graph based relational reasoning. The system first applies

J. Quirola-Vásquez, P. G. Jonker, A. J. Levan, D. B. Malesani

We present a multi-wavelength analysis of the fast X-ray transient EP 241021a, discovered by the Wide-field X-ray Telescope aboard the \emph{Einstein Probe} satellite on 2024 October 21. The event was not detected in gamma-rays. Follow-up observations from $\sim$1.5 to 100 days post-trigger were obtained across X-ray, UV, optical, near-infrared, and radio ba

Sulaiman Muhammad Rashid, Ibrahim Aliyu, Jaehyung Park, Jinsul Kim

The Metaverse promises immersive, real-time experiences; however, meeting its stringent latency and resource demands remains a major challenge. Conventional optimization techniques struggle to respond effectively under dynamic edge conditions and high user loads. In this study, we explore a slice-enabled in-network edge architecture that combines computing-i

Jonas Bode, Raphael Memmesheimer, Sven Behnke

Acting in human environments is a crucial capability for general-purpose robots, necessitating a robust understanding of natural language and its application to physical tasks. This paper seeks to harness the capabilities of diffusion models within a visuomotor policy framework that merges visual and textual inputs to generate precise robotic trajectories. B

Thayla M. G. Iglesias, Alessandro V. M. Oliveira

This article presents an analytical account of the capacity limits and operational challenges of the main airports in the S\~ao Paulo Macrometropolis. Drawing on international examples, such as London Heathrow, it discusses how large hubs combine high traffic generation with severe physical constraints, highlighting how saturation intensifies delays, operati

Kaiwen Cai, Xinze Liu, Xia Zhou, Hengtong Hu

The generation of realistic LiDAR point clouds plays a crucial role in the development and evaluation of autonomous driving systems. Although recent methods for 3D LiDAR point cloud generation have shown significant improvements, they still face notable limitations, including the lack of sequential generation capabilities and the inability to produce accurat

V. Yu. Mylnikov, S. O. Potashin, M. S. Ukhtary, G. S. Sokolovskii

We analytically investigate the switching rate in a two-photon driven Kerr oscillator with finite detuning and two-photon dissipation. This system exhibits quantum bistability and supports a logical manifold for a bosonic qubit. Using Kramer's theory together with the $P$-representation, we derive an analytical expression for the bit-flip error rate within t

Aurélie Tomezzoli, Mathieu Gréau, Charles Pontonnier

Although a growing number of exoskeletons have been developed for occupational applications, wrist exoskeletons remain relatively rare. However, in the meat processing industry, elbow and hand-wrist musculoskeletal disorders are particularly common. The aim of this study was to assess the potential effectiveness and risks of a 670g wrist exoskeleton prototyp

Bowen Ye, Bin Zhang, Hang Zhao

Gaining sustainable performance improvement with scaling data and model budget remains a pivotal yet unresolved challenge in autonomous driving. While autoregressive models exhibited promising data-scaling efficiency in planning tasks, predicting ego trajectories alone suffers sparse supervision and weakly constrains how scene evolution should shape ego moti

Alastair Litterick, Alexei Vernitski, Billy Woods

Automated proof assistants are a technology pre-empting mistakes in mathematics. In our practice we have seen that reasoning about planar diagrams is difficult to both humans and computers. One example that has led to wrong statements in publications is that an orientation-preserving mapping is not always defined by how it acts on triples of elements. In thi

Sumana Hatui, Sanjay Mukherjee, Kamal Lochan Patra

Let $G$ be a finite group and let $\tilde{G}$ be a Schur cover of $G$. The deep commuting graph $\Delta_D(G)$ of $G$ is a simple graph with vertex set $G$, where two distinct vertices are adjacent if their pre-images commute in $\tilde{G}$. The deep commuting graph of a finite group was first introduced in [P. J. Cameron and B. Kuzma, Between the enhanced po

Paul Bratch, M. N. Ellingham, Joanna A. Ellis-Monaghan, Iain Moffatt

Cyclically ordered graphs, or cogs, sit between abstract graphs and cellularly embedded graphs. They arise naturally in topological graph theory, knot theory, and mathematical biology. We develop a formal theory of cogs and establish a number of invariants of cogs. In particular we detail several ways to present cogs and detail how these descriptions can be

Lisa Lehner, Christian Komusiewicz, Luca Pascal Staus

A graph is $c$-closed when every pair of nonadjacent vertices has at most $c-1$ common neighbors. In $c$-Closed Vertex Deletion, the input is a graph $G$ and an integer $k$ and we ask whether $G$ can be transformed into a $c$-closed graph by deleting at most $k$ vertices. We study the classic and parameterized complexity of $c$-Closed Vertex Deletion. We obt

David Muñoz-Lahoz, Pedro Tradacete

We construct and analyze the free Banach $f\!$-algebra $\operatorname{FB{\it f}A}[E]$ generated by a Banach space $E$, extending recent developments on free Banach lattices to the setting of Banach $f\!$-algebras, where multiplication interacts with the lattice structure. Starting from the explicit realization of the free Archimedean $f\!$-algebra as a subla

Kazimierz Chomicz, Miłosz Płatek, Konstanty Smolira, Dylan Wyrzykowski

We consider the following configuration. Let $ABCD$ be a cyclic quadrilateral with circumcenter $O$, and for each vertex $X$, let $H_X$ be the orthocenter of the triangle formed by the other three. Then $A,\;B,\;C,\;D,\;H_A,\;H_B,\;H_C,\;H_D$ all lie on a single conic. In this paper we study a certain generalization of this fact as follows. For an arbitrary

Enhui Ma, Lijun Zhou, Tao Tang, Jiahuan Zhang

End-to-end planning methods are the de facto standard of the current autonomous driving system, while the robustness of the data-driven approaches suffers due to the notorious long-tail problem (i.e., rare but safety-critical failure cases). In this work, we explore whether recent diffusion-based video generation methods (a.k.a. world models), paired with st

Michail Tsagris, Omar Alzeley

Simplicia-simplicial regression concerns statistical modeling scenarios in which both the predictors and the responses are vectors constrained to lie on the simplex. \cite{fiksel2022} introduced a transformation-free linear regression framework for this setting, wherein the regression coefficients are estimated by minimizing the Kullback-Leibler divergence b

Chaowang Lan, Jingxin Wu, Yulong Yuan, Chuxun Liu

Biomarker discovery from high-throughput transcriptomic data is crucial for advancing precision medicine. However, existing methods often neglect gene-gene regulatory relationships and lack stability across datasets, leading to conflation of spurious correlations with genuine causal effects. To address these issues, we develop a causal graph neural network (

Junmin Chen, Qian Gao, Yange Lin, Miaofei Huang

Electrolyte design plays an important role in the development of lithium-ion batteries and sodium-ion batteries. Battery electrolytes feature a large design space composed of different solvents, additives, and salts, which is difficult to explore experimentally. High-fidelity molecular simulation can accurately predict the bulk properties of electrolytes by

Chuang Zhao, Hui Tang, Hongke Zhao, Xiaofang Zhou

Accurate healthcare prediction is critical for improving patient outcomes and reducing operational costs. Bolstered by growing reasoning capabilities, large language models (LLMs) offer a promising path to enhance healthcare predictions by drawing on their rich parametric knowledge. However, LLMs are prone to factual inaccuracies due to limitations in the re

Christopher H. Cashen

Biggs gave an explicit construction, using finite colored trees, of finite permutation groups whose Cayley graphs have valence \(C\) and girth tending to infinity as the radius \(R\) of the tree tends to infinity. We show that when the number of colors is at least 3, the group so presented contains the full alternating group on the vertices of the tree. This

S. Yao, P. Peralta-Braz, A. Calderon Hurtado, R. Das

In this study, piezoelectric energy harvesters (PEHs) are designed to offer dual functionality in structural health monitoring (SHM): harvesting electric power from bridge vibrations while serving as intrinsic damage sensors. This strategy utilises the voltage signal directly as the sensing input, eliminating the need for traditional sensing modules and ther

Jea Kwon, Luiz Felipe Vecchietti, Sungwon Park, Meeyoung Cha

Humans display significant uncertainty when confronted with moral dilemmas, yet the extent of such uncertainty in machines and AI agents remains underexplored. Recent studies have confirmed the overly confident tendencies of machine-generated responses, particularly in large language models (LLMs). As these systems are increasingly embedded in ethical decisi

Wenhao Wu, Dan Wu, Bin Wang, Jiabing Hu

This paper presents a novel method for transient stability analysis (TSA) that circumvents the limitations of sequential numerical integration and energy functions. The proposed method begins by constructing a trajectory-dependent stability indicator function to distinguish the system's destiny. To overcome the difficulty in analyzing the asymptotic behavior

Haoyang Hong, Jiajun Yin, Yuan Wang, Jingnan Liu

Multi-agent systems perform well on general reasoning tasks. However, the lack of training in specialized areas hinders their accuracy. Current training methods train a unified large language model (LLM) for all agents in the system. This may limit the performances due to different distributions underlying for different agents. Therefore, training multi-agen

Senan Sekhon

In this paper, we derive exact formulas for generating functions counting the number of $n$-ary words avoiding strictly increasing subwords of length $k$, and provide some applications of these formulas.

Ankur Pandey, Nijjwal Karak

In this paper, we study the necessary and sufficient conditions in the domain for Sobolev-type embedding of the space $W^{1,\Phi(\cdot,\cdot)}(\Omega)$ where $\Phi(x,t):=t^{p(x)}+ a(x) t^{q(x)}\log^{r(x)}(e+t)$ with $1\leq p(x)\leq q(x).$ We have established subcritical embedding in bounded John domains under some regularity assumptions on exponents $p,$ $q,

Yunjie Yu, Jingchen Wu, Junchen Zhu, Chunze Lin

Artistic design, particularly poster design, often demands rapid yet precise modification of textual content while preserving visual harmony and typographic intent, especially across diverse font styles. Although modern image editing models have grown increasingly powerful, they still fall short in fine-grained, font-aware text manipulation, limiting their u

José Roberto Rausell-Campo, Nayem Al Kayed, Daniel Pérez-Lppez, A. Aadhi

The general-purpose programmable photonic processors offer a scalable and reconfigurable solution for a wide range of RF and optical applications. Therefore, implementing photonic Ising machines using programmable processors leverages the advantages of high speed and parallelism, enabling efficient hardware acceleration for finding ground-state solutions to

Jongha Kim, Minseong Bae, Sanghyeok Lee, Jinsung Yoon

Table images present unique challenges for effective and efficient understanding due to the need for question-specific focus and the presence of redundant background regions. Existing Multimodal Large Language Model (MLLM) approaches often overlook these characteristics, resulting in uninformative and redundant visual representations. To address these issues

Kaiwen Wang, Kaili Zheng, Yiming Shi, Chenyi Guo

Multi-person human mesh recovery from a single image is a challenging task, hindered by the scarcity of in-the-wild training data. Prevailing in-the-wild human mesh pseudo-ground-truth (pGT) generation pipelines are single-person-centric, where each human is processed individually without joint optimization. This oversight leads to a lack of scene-level cons