November 2025 arXiv papers — page 30

Ernest Górka, Dariusz Baran, Gabriela Wojak, Michał Ćwiąkała

Artificial intelligence improves enterprise decision-making by accelerating data analysis, reducing human error, and supporting evidence-based choices. A quantitative survey of 92 companies across multiple industries examines how AI adoption influences managerial performance, decision efficiency, and organizational barriers. Results show that 93 percent of f

Wenwei Li, Lingyi Cai, Hui Gong, Qingming Luo

Accurately registering in-vivo two-photon and ex-vivo fluorescence micro-optical sectioning tomography images of individual neurons is critical for structure-function analysis in neuroscience. This task is profoundly challenging due to a significant cross-modality appearance gap, the scarcity of annotated data and severe tissue deformations. We propose a nov

Flurin Arquint, Oscar Castañeda, Gian Marti, Christoph Studer

We present the first ASIC implementation of jammer-resilient multi-antenna time synchronization. The ASIC implements a recent algorithm that mitigates jamming attacks on synchronization signals using multi-antenna processing. Our design supports synchronization between a single-antenna transmitter and a 16-antenna receiver while mitigating smart jammers with

Per Austrin, Johan Håstad, Björn Martinsson

A Boolean predicate $A$ is defined to be promise-useful if $\operatorname{PCSP}(A,B)$ is tractable for some non-trivial $B$ and otherwise it is promise-useless. We initiate investigations of this notion and derive sufficient conditions for both promise-usefulness and promise-uselessness (assuming $\text{P} \ne \text{NP}$). While we do not obtain a complete c

Rebeka Toth, Tamas Bisztray, Nils Gruschka

In this paper, we introduce a metadata-enriched generation framework (PhishFuzzer) that seeds real emails into Large Language Models (LLMs) to produce 23,100 diverse, structurally consistent email variants across controlled entity and length dimensions. Unlike prior corpora, our dataset features strict three-class labels (Phishing, Spam, Valid), provides ful

Seyyed Sadegh Kazemipoor, Hadiseh Ebrahimi

We prove the existence of at least one positive solution for the Laplacian system\\ -\Delta v=\lambda a(x)|v|^{q-2}v+\beta\frac{\beta}{\alpha+\beta}b(x)|u|^{\alpha}|v|^{\beta-2}v&$for~$x\in\Omega$$ $$ \end{array}\right.$$ On a bounded region $\Omega$ by using the Nehari manifold and the fibering maps associated with the Euler functional for the system.

L. Q. Lai

We investigate the nonequilibrium dynamics of interacting bosons in a two-leg ring ladder pierced by an artificial magnetic flux, where the particles are initially localized in the central sites of both rings, and the ac-driven local energy shifts are applied to the remaining lattice sites. Within the mean-field approximation, we demonstrate the emergence of

Yesom Park, Kelvin Kan, Thomas Flynn, Yi Huang

Implicit Neural Representations (INRs) provide a powerful continuous framework for modeling complex visual and geometric signals, but spectral bias remains a fundamental challenge, limiting their ability to capture high-frequency details. Orthogonal to existing remedy strategies, we introduce Dynamical Implicit Neural Representations (DINR), a new INR modeli

Zhe Jiang, Jiong Wang, Xiaoyu Yue, Zijie Guo

Extreme weather events pose escalating risks to global society, underscoring the urgent need to unravel their underlying physical mechanisms. Yet the prevailing expert-driven, labor-intensive diagnostic paradigm has created a critical analytical bottleneck, stalling scientific progress. While AI for Earth Science has achieved notable advances in prediction,

Sebastian Kilde-Westberg, Andreas Johansson, Anna Pearson, Jonas Enger

Quantum physics education at the upper-secondary level traditionally follows a historical approach, rarely extending beyond early 20th-century ideas, leaving students unprepared for comprehending modern quantum technologies central to everyday life and many facets of modern industry. To address this gap, we investigated how upper-secondary students and pre-s

Alheydis Geiger, Francesca Zaffalon

We introduce the self-projecting Grassmannian, an irreducible subvariety of the Grassmannian parametrizing linear subspaces that satisfy a generalized self-duality condition. We study its relation to classical moduli spaces, such as the moduli spaces of pointed curves of genus $g$, as well as to other natural subvarieties of the Grassmannian. We further tran

Sohan Malkaruge Costa, Benjamin Cohen-Stead, Steven Johnston

The precise role of e-ph coupling in graphene and related materials on a honeycomb lattice is not yet fully understood, despite extensive research on these systems. Here, we perform sign-problem-free determinant quantum Monte Carlo (DQMC) simulations of the optical Su-Schrieffer-Heeger (oSSH)-Hubbard model on the honeycomb lattice, focusing on the parameters

Alessandro V. M. Oliveira, Moises D. Vassallo

This study investigates how the layout and seat density in aircraft cabins influence the pricing of airline tickets on domestic flights. The analysis is based on microdata from boarding passes linked to face-to-face interviews with passengers, allowing us to relate the price paid to the location on the aircraft seat map, as well as market characteristics and

Futian Wang, Fan Zhang, Xiao Wang, Mengqi Wang

Event cameras produce asynchronous event streams that are spatially sparse yet temporally dense. Mainstream event representation learning algorithms typically use event frames, voxels, or tensors as input. Although these approaches have achieved notable progress, they struggle to address the undersampling problem caused by spatial sparsity. In this paper, we

Simon Süwer, Kester Bagemihl, Sylvie Baier, Lucia Dicunta

Repurposing approved drugs offers a time-efficient and cost-effective alternative to traditional drug development. However, in silico prediction of repurposing candidates is challenging and requires the effective collaboration of specialists in various fields, including pharmacology, medicine, biology, and bioinformatics. Fragmented, specialized algorithms a

Oğuz Kağan Hitit, Leander Girrbach, Zeynep Akata

Model merging combines multiple fine-tuned checkpoints into a single model without additional training, offering an attractive approach to reusing models and efficiently improving performance. However, it remains unclear whether the advantages reported for settings where all merged experts have distinct roles and are tuned on clearly separated tasks also hol

Marcin Piotr Pruszczyk, Andrea Gambassi

We describe the random motion of a particle immersed in a thermally fluctuating medium and harmonically trapped at a certain distance from a wall. The medium, modeled by a Gaussian field with a tunable correlation length $\xi$, is linearly coupled to the particle and evolves according to dissipative relaxational dynamics. Dirichlet boundary conditions impose

Wolfgang Paul

The question about the existence of so-called ``hidden'' variables in quantum mechanics and the perception of the completeness of quantum mechanics are two sides of the same coin. Quantum analytical mechanics constitutes a completion of standard quantum mechanics based on the concept of stochastic trajectories in the configuration space of a quantum system.

Fabrizio André Farfán Prado, William César Pérez Campos, Steisy Anahi Carreño Tacuri, Favio David Cabrera Alva

This work describes the design and implementation of a low-power wireless communication system for transmitting text using ESP32 modules and the LoRa DXLR01. The proposal arises as a solution to connectivity and energy-efficiency problems commonly found in rural areas and certain urban environments where Wi-Fi or mobile networks are unavailable or operate wi

Peter Iwer Hoedt Karstensen, Roberto Galeazzi

This paper addresses resilient collaborative localization in multi-agent systems exposed to spoofed radio frequency measurements. Each agent maintains multiple hypotheses of its own state and exchanges selected information with neighbors using covariance intersection. Geometric reductions based on distance tests and convex hull structure limit the number of

Lu Zhao, Rong Shi, Shaoqing Zhang, Yueqiang Chen

The training of large-scale Mixture of Experts (MoE) models faces a critical memory bottleneck due to severe load imbalance caused by dynamic token routing. This imbalance leads to memory overflow on GPUs with limited capacity, constraining model scalability. Existing load balancing methods, which cap expert capacity, compromise model accuracy and fail on me

Filip Hejda

We consider collisional Penrose process for charged, rotating black holes together with a simple model of pair creation, in which two oppositely charged particles are produced in a collision of two neutral particles. We highlight that significant energy extraction is possible without assuming fine-tuning or extremality as long as the escaping particles are s

Jiajie Zhang, Sören Schwertfeger, Alexander Kleiner

We present a novel unsupervised framework to unlock vast unlabeled human demonstration data from continuous industrial video streams for Vision-Language-Action (VLA) model pre-training. Our method first trains a lightweight motion tokenizer to encode motion dynamics, then employs an unsupervised action segmenter leveraging a novel "Latent Action Energy" metr

Fei Ma

Graph is considered neutral if its assortativity coefficient $r$ is equal to zero. In this paper, we address an outstanding conjecture, i.e., whether is there a neutral graph on $n$ vertices? First, we show that for $n\geq7$, there is at least one neutral tree, which suggests that we find a representative of any order neutral graph. Additionally, we obtain t

Muhammad Bilal, Ashwin Renganathan

We develop a convolutional regularized least squares ($\texttt{CRLS}$) framework for reduced-order modeling of transonic flows with shocks. Conventional proper orthogonal decomposition (POD) based reduced models are attractive because of their optimality and low online cost; however, but they perform poorly when snapshots contain parameter-dependent disconti

Karam Mawas, Mehdi Maboudi, Pedro Achanccaray, Markus Gerke

The architecture, engineering and construction (AEC) industry is constantly evolving to meet the demand for sustainable and effective design and construction of the built environment. In the literature, two primary deposition techniques for large-scale 3D concrete printing (3DCP) have been described, namely extrusion-based (Contour Crafting-CC) and shotcrete

Neeraj Kumar, Ankur Srivastav, Phongpichit Channuie

In this article, we performed Simpson-Visser (SV)-regularization scheme to Anti-de Sitter (AdS) black holes and then studied thermal properties of the resulting spacetime geometry. We considered the validity of the first law of black hole thermodynamics in this case and derived an entropy formula consistent with this new regular geometry. Next, we carried ou

Guo-Jian Qiao, Zhi-Lei Zhang, Xin Yue, C. P. Sun

Majorana fermions (zero modes) are predicted to emerge in nanowire-superconductor heterostructures. This theoretical prediction typically relies on an oversimplified model, where both the nanowire and the superconductor are idealized as one-dimensional systems. In reality, heterostructures have finite sizes that deviate from this idealization-and as a result

Adeela Islam, Stefano Fiorini, Manuel Lecha, Theodore Tsesmelis

3D reassembly is a fundamental geometric problem, and in recent years it has increasingly been challenged by deep learning methods rather than classical optimization. While learning approaches have shown promising results, most still rely primarily on geometric features to assemble a whole from its parts. As a result, methods struggle when geometry alone is

Rene Marczinzik

We answer a question raised by Auslander and Bridger by showing that not every 2-reflexive module is reflexive.

Norman Do, Paul Norbury

Okuyama introduced a family of polynomials, whose coefficients depend on a parameter $q$, in his study of correlators in the double-scaled SYK model. He verified in small cases that their coefficients can be expressed in terms of certain $q$-zeta values and that the polynomials recover the Weil-Petersson volumes of moduli spaces studied by Mirzakhani under a

Futian Wang, Mengqi Wang, Xiao Wang, Haowen Wang

Remote sensing change captioning is an emerging and popular research task that aims to describe, in natural language, the content of interest that has changed between two remote sensing images captured at different times. Existing methods typically employ CNNs/Transformers to extract visual representations from the given images or incorporate auxiliary tasks

Wen-Yuan Yu, Hou-Jun Lü, Xiao Tian, Liang-Jun Chen

GRB 250114A is a long-duration gamma-ray burst (GRB) which triggered the Swift/BAT with a spectroscopic high-redshift at $z = 4.732$. The light curve of the prompt emission is composed of three distinct emission episodes, which are separated by quiescent gaps ranging from tens to hundreds of seconds. While the X-ray light curve exhibits the canonical X-ray e

Michael Kapralov, Cameron Musco, Kshiteej Sheth

Hankel matrices are an important class of highly-structured matrices, arising across computational mathematics, engineering, and theoretical computer science. It is well-known that positive semidefinite (PSD) Hankel matrices are always approximately low-rank. In particular, a celebrated result of Beckermann and Townsend shows that, for any PSD Hankel matrix

Mia Müßig, Jan Johannsen

In pseudo-boolean solving the currently most successful unit propagation strategy is a hybrid mode combining the watched literal scheme with the counting method. This short paper introduces new heuristics for this hybrid decision, which are able to drastically outperform the current method in the RoundingSAT solver.

Kaifeng Hong, Yinglong Zhang, Xiaoying Hong, Xuewen Xia

Text-attributed graphs require models to effectively combine strong textual understanding with structurally informed reasoning. Existing approaches either rely on GNNs--limited by over-smoothing and hop-dependent diffusion--or employ Transformers that overlook graph topology and treat nodes as isolated sequences. We propose Odin (Oriented Dual-module INtegra

Mingue Park, Prin Phunyaphibarn, Phillip Y. Lee, Minhyuk Sung

We introduce DiverseVAR, a framework that enhances the diversity of text-conditioned visual autoregressive models (VAR) at test time without requiring retraining, fine-tuning, or substantial computational overhead. While VAR models have recently emerged as strong competitors to diffusion and flow models for image generation, they suffer from a critical limit

Muzi Xu, Jiaqi Zhang, Chaoqun Dong, Zibo Zhang

Human skin acts as a dynamic biomechanical interface that conveys critical physiological and behavioural information through spatiotemporally distributed deformations. Due to the limited capabilities of current sensing technologies, the spatiotemporal diversity of its mechanical cues has remained underutilised to date, preventing these mechanisms from being

Zachary Morrow, Michael Penwarden, Brian Chen, Aurya Javeed

Deep neural networks (DNNs) and Kolmogorov-Arnold networks (KANs) are popular methods for function approximation due to their flexibility and expressivity. However, they typically require a large number of trainable parameters to produce a suitable approximation. Beyond making the resulting network less transparent, overparameterization creates a large optim

Tim Trappen, Robert Keßler, Roland Pabel, Viktor Achter

Due to rising demands for Artificial Inteligence (AI) inference, especially in higher education, novel solutions utilising existing infrastructure are emerging. The utilisation of High-Performance Computing (HPC) has become a prevalent approach for the implementation of such solutions. However, the classical operating model of HPC does not adapt well to the

Siyu Li, Ian Marquette, Yao-Zhong Zhang

We give a systematic construction of new quasi-exactly solvable systems via Bethe ansatz and supersymmetric quantum mechanics (SUSYQM). Methods based on the intertwining of supercharges have been extensively used in the literature for exactly solvable systems. We generalize the state-deleting (Krein-Adler) supersymmetric transformations to quasi-exactly exac

Jungyeon Koh, Hyeonho Noh, Hyun Jong Yang

In this letter, we propose a group-wise semantic splitting multiple access framework for multi-user semantic communication in downlink scenarios. The framework begins by applying a balanced clustering mechanism that groups users based on the similarity of their semantic characteristics, enabling the extraction of group-level common features and user-specific

Bhavik Agarwal, Nidhi Bendre, Viktoria Rojkova

Pharmaceutical manufacturers generate thousands of batch manufacturing records (BMRs) each year under FDA 21 CFR Part 211 and EU GMP rules. These long documents combine tables, calculations, images, and handwritten notes, and are usually digitized by hand with hours of expert review per record. We present an AI workflow that converts unstructured BMRs into s

Mariia Kyrychenko, Mykyta Mudryi, Markiyan Chaklosh

The rapid advancement of general-purpose AI models has increased concerns about copyright infringement in training data, yet current regulatory frameworks remain predominantly reactive rather than proactive. This paper examines the regulatory landscape of AI training data governance in major jurisdictions, including the EU, the United States, and the Asia-Pa

Hang Ma, Jiajun Linghu, Nannan Han, Ying Liang

Experiments have evidenced proton pairing in Y-doped BaZrO3. However, the nature of proton pairing and its impact on conduction remain insufficiently understood theoretically. Here, through quantitative computational analysis of proton-proton interactions in Y-doped BaZrO3, we identify lattice-distortion-mediated elastic interaction as the key factor determi

Wouter Visser, Jelmer M. Wolterink

Neural fields are increasingly used as a light-weight, continuous, and differentiable signal representation in (bio)medical imaging. However, unlike discrete signal representations such as voxel grids, neural fields cannot be easily extended. As neural fields are, in essence, neural networks, prior signals represented in a neural field will degrade when the

Frederico Wieser, Martin Benfeghoul, Haitham Bou Ammar, Jun Wang

The rigid, uniform allocation of computation in standard Transformer (TF) architectures can limit their efficiency and scalability, particularly for large-scale models and long sequences. Addressing this, we introduce Subjective Depth Transformers (SDT) and Subjective Timescale Transformers (STT), two distinct architectures that leverage Bayesian surprise si

Emilia R. Schütz, Martin Majewski, Olivier J. J. Ronsin, Jens Harting

Large perovskite crystals with reduced defect density enable superior charge transport and stability. Therefore, controlling their nucleation and growth is key to advancing high-performance optoelectronic devices based on perovskite semiconductors. Millimeter-scale perovskite crystals can be synthesized as a continuous film through methylamine treatment, wit

Mariia Kyrychenko, Mykyta Mudryi, Markiyan Chaklosh

The rapid advancement of general-purpose AI models has increased concerns about copyright infringement in training data, yet current regulatory frameworks remain predominantly reactive rather than proactive. This paper examines the regulatory landscape of AI training data governance in major jurisdictions, including the EU, the United States, and the Asia-Pa

Ioannis Prapas, Nikolaos Papadopoulos, Nikolaos-Ioannis Bountos, Dimitrios Michail

Forecasting wildfires weeks to months in advance is difficult, yet crucial for planning fuel treatments and allocating resources. While short-term predictions typically rely on local weather conditions, long-term forecasting requires accounting for the Earth's interconnectedness, including global patterns and teleconnections. We introduce TeleViT, a Teleconn

Shahar Hod

The asymptotically decaying tails that characterize the late-time dynamics of collapsing self-gravitating charged massless scalar fields were studied three decades ago by Hod and Piran (HP). In particular, it was shown, both analytically and numerically, that the late-time behavior of these collapsing charged massless scalar fields is governed by oscillatory

Cristiana Punzo, Italo Napolitano, Cinzia Tomaselli, Mario di Bernardo

This paper investigates decentralized shepherding in cluttered environments, where a limited number of herders must guide a larger group of non-cohesive, diffusive targets toward a goal region in the presence of static obstacles. A hierarchical control architecture is proposed, integrating a high-level target assignment rule, where each herder is paired with

Manuel Grumet, Takeru Miyagawa, Olivier Pittet, Paolo Pegolo

Fast ionic conduction is a defining property of solid electrolytes for all-solid-state batteries. Previous studies have suggested that liquid-like cation motion associated with fast ionic transport can disrupt crystalline symmetry, thereby lifting Raman selection rules. Here, we exploit the resulting low-frequency, diffusive Raman scattering as a spectral si

Chi Zhang

In order to detect the quantum nature of gravity, the quantum gravity induced entanglement of masses(QGEM) has been proposed both in flat and curved spacetime. In this paper we propose an analogous QGEM protocol using photons produced in astronomical processes as the quantum systems. Unlike massive particles, the gravitational interaction between photons-int

Zhifeng Hao, Qibin Song, Ruichu Cai, Boyan Xu

Recent divide-and-conquer reasoning approaches, particularly those based on Chain-of-Thought (CoT), have substantially improved the Text-to-SQL capabilities of Large Language Models (LLMs). However, when applied to complex enterprise databases, such methods struggle to maintain coherent reasoning due to limited context capacity, unreliable schema linking, an

Antonín Jarolím, Martin Fajčík, Lucia Makaiová

Misinformation frequently spreads in user comments under online news articles, highlighting the need for effective methods to detect factually incorrect information. To strongly support or refute claims extracted from such comments, it is necessary to identify relevant documents and pinpoint the exact text spans that justify or contradict each claim. This pa

Joshua Fonseca Rivera, David Demitri Africa

Activation steering -- adding a vector to a model's residual stream to modify its behavior -- is widely used in safety evaluations as if the model cannot detect the intervention. We test this assumption, introducing steering awareness: a model's ability to infer, during its own forward pass, that a steering vector was injected and what concept it encodes. Af

Jiayuan Zhang, Kaiquan Chen, Zhihao Lu, Enshen Zhou

Web automation employs intelligent agents to execute high-level tasks by mimicking human interactions with web interfaces. Despite the capabilities of recent Large Language Model (LLM)-based web agents, navigating complex, real-world webpages efficiently remains a significant hurdle due to the prohibitively large size of Document Object Model (DOM) structure

Rafał Gruszczyński, Paula Menchón, William Zuluaga

The ternary extended contact relation was introduced in (Ivanova, 2020) as a more expressive counterpart of the standard binary contact relation. The class of Boolean algebras expanded with the relation was named Extended Contact Algebras (ECAs). In this work, we take an algebraic perspective on ECAs, interpreting the ternary relation as a form of entailment

Qixun Wang, Yang Shi, Yifei Wang, Yuanxing Zhang

"Thinking with images" has emerged as an effective paradigm for advancing visual reasoning, extending beyond text-only chains of thought by injecting visual evidence into intermediate reasoning steps. However, existing methods fall short of human-like abstract visual thinking, as their flexibility is fundamentally limited by external tools. In this work, we

Hongji Li, Junchi yao, Manjiang Yu, Priyanka Singh

Machine unlearning aims to erase requested data from trained models without full retraining. For Reasoning Multimodal Large Language Models (RMLLMs), this is uniquely challenging: intermediate chain-of-thought steps can still leak sensitive information even when final answers are forgotten, and overly aggressive interventions easily damage general reasoning

Guoxiao Zhang, Tan Qu, Ao Li, DongLin Ni

Reranking improves recommendation quality by modeling item interactions. However, existing methods often decouple ranking and reranking, leading to weak listwise evaluation models that suffer from combinatorial sparsity and limited representational power under strict latency constraints. In this paper, we propose RIA (Ranking-Infused Architecture), a unified

Yusong Zhang, Zeng-Qi Wang

In this study, we propose the lopsided HSS (LHSS) iteration method for solving a class of complex symmetric indefinite systems of linear equations. This method employs an alternating iterative scheme, where each iteration entails solving two systems of equations with symmetric real coefficient matrices. This design is intended to reduce the high computationa

Chi Zhang

Quantum entanglement, as one of the fundamental concepts in quantum mechanics, has garnered significant attention over the past few decades for its extraordinary nonlocality. With the advancement of quantum technology, quantum entanglement holds promising application for exploring fundamental physical theories. The experimental scheme of Quantum Gravity Indu

Hannes Thiel

We show that the distance from an element of a C*-algebra to the set of regular elements is the infimum of the $δ>0$ for which the $δ$-cut-down of the element admits a polar decomposition within the algebra. This parallels results of Pedersen and Brown-Pedersen describing the distance to invertible and quasi-invertible elements through polar decompositions o

Francesco Balducci, Sergio Segura de León

Our objective is to prove existence of a solution to the Dirichlet problem for an equation arising in the theory of radiation hydrodynamics to deal with the radiating energy in transparent media. We study its stationary equation with $L^1$--datum in a bounded domain. This problem was addressed in [11] for regular data (data belonging to $L^N(\Omega)$) and a

Guoxiao Zhang, Ao Li, Tan Qu, Qianlong Xie

Online platforms usually suffer from user experience degradation due to near-duplicate items with similar visuals and text. While Multimodal Large Language Models (MLLMs) enable multimodal embedding, existing methods treat representations as black boxes, ignoring structural relationships (e.g., primary vs. auxiliary elements), leading to local structural col

Chi Zhang, Lin Wang

Real-time, physically-consistent predictions on low-power edge devices is critical for the next generation embodied AI systems, yet it remains a major challenge. Physics-Informed Neural Networks (PINNs) combine data-driven learning with physics-based constraints to ensure the model's predictions are with underlying physical principles.However, PINNs are ener

Chengshu Li

The Kibble-Zurek effect is studied around a tricritical point, where the adiabatic-impulse scenario breaks down. Several new scaling forms are also proposed.

Andrés E. Quintero, Vinícius A. Lacerda, Oriol Gomis-Bellmunt, Moisés J. B. B. Davi

This paper investigates how grid-forming (GFM) and grid-following (GFL) control strategies in inverter-based resources (IBRs) influence line distance and differential protection in converter-dominated transmission systems. A modified IEEE 39-bus system is evaluated with GFM and GFL units equipped with low-voltage ride-through logic, current limiting, and pos

Alexandros Groutides

We construct an Euler system attached to general-type cohomological cuspidal automorphic representations of $\mathrm{GSp}(4)$ twisted by a Groessencharacter of an imaginary quadratic field. We then use this to bound strict Selmer groups under standard hypotheses. In addition, our approach gives a way of extending the $\mathrm{GSp}(4)\times\mathrm{GL}(2)$ Eul

Victor Ziapkoff, François Boulogne, Anniina Salonen, Emmanuelle Rio

We present a practical white-light interferometric method, supported by an open-source Python library \textit{optifik} for automated spectrum-to-thickness deduction, enabling foam film measurements down to a few nanometers. We describe three typical spectral scenarii encountered in this method: spectra exhibiting numerous interference fringes, spectra with a

Xuan Qiu

While Large Language Models (LLMs) have been extensively tested in dyadic game-theoretic scenarios, their collective behavior within complex network games remains surprisingly unexplored. To bridge this gap, we present NetworkGames, a framework connecting Generative Agents and Geometric Deep Learning. By formalizing social simulation as a message-passing pro

Bei Chu, Yang Feng, Kui Liu, Zhaoqiang Guo

Automated unit test generation is critical for software quality but traditional structure-driven methods often lack the semantic understanding required to produce realistic inputs and oracles. Large language models (LLMs) address this limitation by leveraging their extensive data-driven knowledge of code semantics and programming patterns. To analyze the sta

Demerson N. Gonçalves, João T. Dias

This paper presents a group-theoretic framework for structured channel estimation in Orthogonal Frequency Division Multiplexing (OFDM). By modeling subcarriers as the cyclic group \(\mathbb{Z}_N\), we show that nulling a subgroup \(H \subseteq \mathbb{Z}_N\) constrains the channel impulse response to its annihilator \(H^\perp\) in the dual domain. A low-comp

Jianling Gao, Chongyang Tao, Xuelian Lin, Junfeng Liu

Semi-supervised anomaly detection (AD) has shown great promise by effectively leveraging limited labeled data. However, existing methods are typically structured around scoring individual points or simple pairs. Such {point- or pair-centric} view not only overlooks the contextual nature of anomalies, which are defined by their deviation from a collective gro

Ariful Islam, Md Rifat Hossen, Abir Ahmed, B M Taslimul Haque

Aspect-Based Sentiment Analysis (ABSA) has emerged as a critical tool for extracting fine-grained sentiment insights from user-generated content, particularly in e-commerce and social media domains. However, research on Bangla ABSA remains significantly underexplored due to the absence of comprehensive datasets and specialized frameworks for triplet extracti

Jingyi Chen, Xiaoyan Guo, Songqiang Chen, Shing-Chi Cheung

Automating the adaptation of software engineering (SE) research artifacts across datasets is essential for scalability and reproducibility, yet it remains largely unstudied. Recent advances in large language model (LLM)-based multi-agent systems, such as GitHub Copilot's agent mode, promise to automate complex development workflows through coordinated reason

Yixia Zhang, Panyue Zhou

Let $\mathcal{A}$ be an additive category and let $T\colon \mathcal{A}\rightarrow \mathcal{A}$ be an additive functor equipped with a natural transformation $\omega\colon \mathrm{Id}_{\mathcal{A}}\rightarrow T$. We prove that the homotopy category of $n$-fold matrix factorizations of $\omega$, denoted ${\rm HFact}_{n}(\mathcal{A},T,\omega)$, admits a natural

Jungi Lee, Jungkwon Kim, Chi Zhang, Kwangsun Yoo

Handling contaminated data poses a critical challenge in anomaly detection, as traditional models assume training on purely normal data. Conventional methods mitigate contamination by relying on fixed contamination ratios, but discrepancies between assumed and actual ratios can severely degrade performance, especially in noisy environments where normal and a

Ben Anson, Laurence Aitchison

Stability of neural network weights is critical when training transformer models. The query and key weights are particularly problematic, as they tend to grow large without any intervention. Applying normalization to queries and keys, known as `QK norm', fixes stability issues in practice, but is not always applicable. For example, QK norm is not compatible

Lukas Pin, Stef Baas, Gianmarco Caruso, David S. Robertson

Response-Adaptive Randomization (RAR) is recognized for its potential to deliver improvements in patient benefit. However, the utility of RAR is contingent on regularization methods to mitigate early instability and preserve statistical integrity. A standard regularization approach is the ''burn-in'' period, an initial phase of equal randomization before tre

Xin Gu, Haoji Zhang, Qihang Fan, Jingxuan Niu

Spatio-temporal video grounding (STVG) requires localizing a target object in untrimmed videos both temporally and spatially from natural language descriptions. Despite their strong language understanding, multimodal large language models (MLLMs) underperform on STVG due to misaligned training objectives and weak fine-grained region-word alignment in standar

Faizuddin Ahmed, Edilberto O. Silva

We investigate the extended phase-space thermodynamics and $P$--$V$ criticality of electrically charged anti-de Sitter (AdS) black holes in Kalb--Ramond bumblebee gravity in the presence of a spherically symmetric cloud of strings. The background Kalb--Ramond field induces Lorentz symmetry violation through a dimensionless parameter $\ell$, while the string

Ryo B. Tanaka, Goro Oohata, Takayuki Uozumi

We theoretically investigated how the quantum entanglement between the spin of the photoelectron and the polarization of the emitted X-ray photon depends on the excited core-level, using the 3$d\rightarrow\ $2$p$ and 3$d\rightarrow\ $3$p$ SPR-XEPECS (spin- and polarization-resolved XEPECS) processes for $\rm Ti_{2}O_{3}$-type system, and the 4$f\rightarrow\

Alessandro Cannone, Silvia Cingolani, Minbo Yang, Shunneng Zhao

In this paper, we study the qualitative properties of single blow-up solutions to the nonlocal equations with slightly subcritical exponents \begin{equation*} -\Delta u=(|x|^{-(n-2)}\ast u^{p-\epsilon})u^{p-1-\epsilon}\quad \mbox{in}~~\Omega,~~ u=0\quad \mbox{on}~~\partial\Omega, \end{equation*} where $\Omega$ is a smooth bounded domain in $\mathbb{R}^n$ for

Yichen Liu, Hongyu Wu, Bo Liu

Large language models (LLMs) have gained increasing attention in power grids for their general-purpose capabilities. Meanwhile, anomaly detection (AD) remains critical for grid resilience, requiring accurate and interpretable decisions based on multivariate telemetry. Yet the performance of LLMs on large-scale numeric data for AD remains largely unexplored.

A. M. Kabaeva, A. V. Logachov, A. A. Yambartsev

Bruss's odds theorem \cite{Bruss1} addresses the problem of determining the optimal stopping time for sequences of independent indicator functions. In this note, we derive upper and lower bounds for the success probability under the optimal stopping rule. These bounds depend on the number of independent events under consideration and on a deterministic index

Tingkai Xue, Chin Chun Ooi, Zhengwei Ge, Fong Yew Leong

Numerical simulations provide key insights into many physical, real-world problems. However, while these simulations are solved on a full 3D domain, most analysis only require a reduced set of metrics (e.g. plane-level concentrations). This work presents a hybrid physics-neural model that predicts scalar transport in a complex domain orders of magnitude fast

Peter Lunkenheimer, Konrad Samwer, Alois Loidl

Since more than 100 years, melting is thought to be governed by the Lindemann criterion. It assumes that a crystal melts when, upon heating, the growing atomic vibration amplitudes become sufficiently large to destabilize its crystalline lattice. However, it is unclear why the viscosities eta or the related relaxation times tau of the resulting liquids, meas

Yangle Liu, Fengze Li, Kan Liu, Jieming Ma

Endoscopic (endo) video exhibits strong view-dependent effects such as specularities, wet reflections, and occlusions. Pure photometric supervision misaligns with geometry and triggers early geometric drift, where erroneous shapes are reinforced during densification and become hard to correct. We ask how to anchor geometry early for 4D Gaussian splatting (4D

Dmitri Kovalenko

An autonomous robotic nut tightening system for a serial manipulator equipped with a parallel gripper is proposed. The system features a hierarchical motion-primitive-based planner and a control-switching scheme that alternates between force and position control. Extensive simulations demonstrate the system's robustness to variance in initial conditions. Add

Qing Li, Huifang Feng, Kanle Shi, Yue Gao

Estimating the normal of a point requires constructing a local patch to provide center-surrounding context, but determining the appropriate neighborhood size is difficult when dealing with different data or geometries. Existing methods commonly employ various parameter-heavy strategies to extract a full feature description from the input patch. However, they

Ariful Islam, Md Rifat Hossen, Md. Mahmudul Arif, Abdullah Al Noman

Natural disasters remain a major challenge for Bangladesh, so real-time monitoring and quick response systems are essential. In this study, we present BanglaMM-Disaster, an end-to-end deep learning-based multimodal framework for disaster classification in Bangla, using both textual and visual data from social media. We constructed a new dataset of 5,037 Bang

Kevin Iselborn, David Dembinsky, Adriano Lucieri, Andreas Dengel

The utility of an explanation method critically depends on its fidelity to the underlying machine learning model. Especially in high-stakes medical settings, clinicians and regulators require explanations that faithfully reflect the model's decision process. Existing fidelity metrics such as Infidelity rely on Monte Carlo approximation, which demands numerou

Sachin Manjunath Naik, P. Sam Johnson

The concept of complementability is extended from bounded operators to densely defined operators on Hilbert spaces. By introducing appropriate projections and decomposition techniques, a framework is developed for analyzing complementability in this broader context. The results provide new insights into the structure of unbounded operators, contributing to t

Geraldo Botelho, Ariel Monção

Let $X_1, \ldots, X_m$ be Banach spaces and let $E_1, \ldots, E_m,F$ be Banach lattices. Our main results read as follows: (i) The linear adjoint $A^*$ of a continuous multilinear operator $A \colon X_1 \times \cdots \times X_m \to F$ is $M$-weakly compact if and only if $A$ is $L$-weakly compact. (ii) The linear adjoint $A^*$ of a multilinear operator of or

Sergey D. Traytak, Georgiy A. Babushkin

This study is due to various applications in physics, chemistry and especially in biology, where both bounded configuration domain and chemical anisotropy could play a great part. In fact we generalize the well-known Berg theory, which describes diffusion-controlled reactions occurring within a spherically symmetric absorber-cavity system. The trapping proba

Bram Silue, Santiago Amaya-Corredor, Patrick Mannion, Lander Willem

Adversarial Inverse Reinforcement Learning (AIRL) has shown promise in addressing the sparse reward problem in reinforcement learning (RL) by inferring dense reward functions from expert demonstrations. However, its performance in highly complex, imperfect-information settings remains largely unexplored. To explore this gap, we evaluate AIRL in the context o

Gaurang Agrawal, Matt Wilson

A basic postulate of modern compositional approaches to generalised physical theories is the generalised Born rule, in which probabilities are postulated to be computable from the composition of states and effects. In this paper we consider whether this postulate, and the strength of the identification between scalars and probabilities, can be argued from ba