November 2025 arXiv papers — page 3

Thibault Damour, Tamanna Jain, Ulrich Sperhake

We study the scattering of boson-star binaries, taking into account three effects: point-mass gravitational, tidal, and short-range scalar-field interactions. We compare analytic results to the scattering angle extracted from four sequences of numerical-relativity simulations at fixed energy and varying impact parameter. The very good agreement exhibits the

An Yang, Chenyu Liu, Jun Du, Jianqing Gao

3D Gaussian Splatting (3D-GS) has emerged as an efficient 3D representation and a promising foundation for semantic tasks like segmentation. However, existing 3D-GS-based segmentation methods typically rely on high-dimensional category features, which introduce substantial memory overhead. Moreover, fine-grained segmentation remains challenging due to label

J. Kluson

In this short note we study Born-Infeld Inspired Gravity together with an action functional for ideal fluid. We obtain corresponding equations of motion and also determine canonical form of this action.

Khevna Rajput, Mark Zuidgeest, Philipp Fröhlich, Stephane Hess

This research tries to understand how seat capacity on routes connecting 267 African cities is distributed across airlines by analysing supply decisions between 2016 and 2022. The paper compiles an Africa-specific database on seat capacity by origin-destination pair and year, using revealed preference data for one week in November each year extracted from th

Gregory Hellbourg

The rapid expansion of satellite constellations is transforming the radio-frequency environment around the Earth. At the same time, radio astronomy is entering a new era of sensitivity and survey capability, requiring unprecedented control of interference. This primer introduces satellite operators, engineers, spectrum managers and policy makers to the basic

Susmit Agrawal, Krishn Vishwas Kher, Saksham Mittal, Swarnim Maheshwari

Organisms constantly pivot between tasks such as evading predators, foraging, traversing rugged terrain, and socializing, often within milliseconds. Remarkably, they preserve knowledge of once-learned environments sans catastrophic forgetting, a phenomenon neuroscientists hypothesize, is due to a singular neural circuitry dynamically overlayed by neuromodula

Nayesha Gandotra, Itamar Mishani, Maxim Likhachev

Recent progress in contact-rich robotic manipulation has been striking, yet most deployed systems remain confined to simple, scripted routines. One of the key barriers is the lack of motion planning algorithms that can provide verifiable guarantees for safety, efficiency and reliability. To address this, a family of algorithms called Constant-Time Motion Pla

Ahmed Mustafa Younes

Transformer models have significantly advanced Natural Language Processing (NLP), demonstrating strong performance in English. However, their effectiveness in Arabic, particularly for Named Entity Recognition (NER), remains limited, even with larger pre-trained models. This performance gap stems from multiple factors, including tokenisation, dataset quality,

Lars Nippert

Arabic text-to-speech (TTS) remains challenging due to limited resources and complex phonological patterns. We present reproducible baselines for Arabic TTS built on the FastPitch architecture and introduce cepstral-domain metrics for analyzing oversmoothing in mel-spectrogram prediction. While traditional Lp reconstruction losses yield smooth but over-avera

Keita Otani, Tatsuya Harada

Grounding complex, compositional visual queries with multiple objects and relationships is a fundamental challenge for vision-language models. While standard phrase grounding methods excel at localizing single objects, they lack the structural inductive bias to parse intricate relational descriptions, often failing as queries become more descriptive. To addr

Hunseok Kang, Doowon Koh, Dung The Tran

We extend Raimi's classical partition theorem to the continuous setting of the circle and $n$-dimensional torus. Building on recent work of Hegyv\'ari, Pach, and Pham in finite groups, we prove that there exist measurable partitions of the $n$-dimensional torus $\mathbb{T}^n$ with the property that for any finite measurable cover, some translated part of the

Giuseppina Guatteri, Federica Masiero, Lukas Wessels

We extend Peng's maximum principle to the case of stochastic delay differential equations of mean-field type. More precisely, the coefficients of our control problem depend on the state, on the past trajectory and on its expected value. Moreover, the control enters the noise coefficient and the control domain may be non-convex. Our approach is based on a lif

Erick I. Duque

Fermions are coupled to the Einstein-Cartan system in the canonical formulation, including the cosmological, the Barbero-Immirzi, and the non-minimal coupling constants. The resulting ten first-class constraints generate gauge transformations that are on-shell equivalent to spacetime diffeomorphisms and SL(2,C) transformations. The gravitational second-class

Imane Jaaouine, Ross D. King

Large language models (LLMs) produce context inconsistency hallucinations, which are LLM generated outputs that are misaligned with the user prompt. This research project investigates whether prompt engineering (PE) methods can mitigate context inconsistency hallucinations in zero-shot LLM summarisation of scientific texts, where zero-shot indicates that the

Somangchan Park, Heesang Ann, Min-hwan Oh

We study the multi-objective linear contextual bandit problem, where multiple possible conflicting objectives must be optimized simultaneously. We propose \texttt{MOL-TS}, the \textit{first} Thompson Sampling algorithm with Pareto regret guarantees for this problem. Unlike standard approaches that compute an empirical Pareto front each round, \texttt{MOL-TS}

Antonio Ponno, Giacomo Gradenigo, Marco Baldovin, Angelo Vulpiani

We present a non-perturbative, mean-field theory for the Fermi-Pasta-Ulam-Tsingou model with quartic interaction, capturing the quasiperiodic features shown by the system at all energies in the thermodynamic limit. Starting from the true Hamiltonian $H$ of the system with $N$ degrees of freedom, we introduce a mean-field Hamiltonian $\mathcal{H}$ such that t

Jiajun Cao, Qinggang Zhang, Yunbo Tang, Zhishang Xiang

Multimodal keyphrase generation (MKP) aims to extract a concise set of keyphrases that capture the essential meaning of paired image-text inputs, enabling structured understanding, indexing, and retrieval of multimedia data across the web and social platforms. Success in this task demands effectively bridging the semantic gap between heterogeneous modalities

Wentao Qu, Xiaoshui Huang, Liang Xiao

Most existing learning-based point cloud descriptors for point cloud registration focus on perceiving local information of point clouds to generate distinctive features. However, a reasonable and broader receptive field is essential for enhancing feature distinctiveness. In this paper, we propose a Local Attentive Hashing Network for point cloud registration

Shulan Li, Jian-Pin Wu, Xian-Hui Ge

We construct a new non-singular cosmological model matched to a Minkowski-core regular black hole by means of a modified Oppenheimer--Snyder framework. Its dynamics is studied in both dust-only and scalar-field scenarios, and compared with that of two other non-singular models as well as the classical standard cosmology. The results show that, although all t

Shaoxun Wang, Xingjun Zhang, Kun Xia, Qianyang Li

Accurate Multivariate Time Series (MTS) forecasting is crucial for collaborative design of complex systems, Digital Twin building, and maintenance ahead of time. However, the collaborative industrial environment presents new challenges for MTS forecasting models: models should decouple complex inter-variable dependencies while addressing non-stationary distr

George Metcalfe

This chapter presents a state-of-the-art survey of relationships, traditionally referred to as `bridges', between interpolation properties for propositional logics -- including superintuitionistic, modal, and substructural logics -- and amalgamation properties for corresponding classes of algebraic structures. These bridges are developed in the framework of

J. M. Z. Choquehuanca

Quantum thermodynamics has emerged as a central field for understanding how energy conversion processes occur in microscopic systems. In these systems, effects such as coherence, entanglement, and non-Markovianity play key roles. In this thesis, we explore different ways to describe quantum thermodynamics, using two main approaches: one based on entropy and

Qian Wan, Yehui Hou, Minyong Guo

Horizon-scale images of black holes provide a potential probe of fundamental physics, including tests of gravity and black hole hair. To assess the impact of scalar hair on accretion-flow imaging self-consistently, we construct an analytical model of a geometrically thick, magnetized disk around a rotating hairy black hole in Horndeski theory and analyze its

Sotirios D. Nikolopoulos

Evaluating rare-event forecasts is challenging because standard metrics collapse as event prevalence declines. Measures such as F1-score, AUPRC, MCC, and accuracy induce degenerate thresholds -- converging to zero or one -- and their values become dominated by class imbalance rather than tail discrimination. We develop a family of rare-event-stable (RES) met

John Paul P. Miranda, Joseph Alexander Bansil, Emerson Q. Fernando, Almer B. Gamboa

This study examined generative AI usage among Philippine college students particularly on frequency, devices, reasons, knowledge, trust, perceptions, and challenges. Most students used free AI tools on smartphones due to financial constraints. They used it primarily for homework, idea generation, and research. Less than half felt confident with AI and expres

Junwoo Chang, Minwoo Park, Joohwan Seo, Roberto Horowitz

Group symmetries provide a powerful inductive bias for reinforcement learning (RL), enabling efficient generalization across symmetric states and actions via group-invariant Markov Decision Processes (MDPs). However, real-world environments almost never realize fully group-invariant MDPs; dynamics, actuation limits, and reward design usually break symmetries

Md Afsar Reja, Arka Bandyopadhyay, Awadhesh Narayan

The quantum metric -- which quantifies the distance between quantum states -- is a fundamental component of the quantum geometric tensor, playing a crucial role in a wide range of physical phenomena. Its direct detection and control remains a challenge, requiring suitable material candidates. In this work, we present the emergence of a tunable quantum metric

Zirui Lin, Haris Gulzar, Monnika Roslianna Busto, Akiko Masaki

Dialect Identification (DI) is a task to recognize different dialects within the same language from a speech signal. DI can help to improve the downstream speech related tasks even when speakers have a strong dialect. However, fine-tuning a speech model for tasks like DI is expensive in terms of computation cost and memory requirement. Recent studies have ex

Daniil N. Chistikov, Pavel M. Radzikovitsky, Dmitry S. Popov, Ivan V. Dudakov

The methaniminium cation, CH$_2$NH$_2^+$, plays an important role in Titan's N$_2$--CH$_4$ atmospheric chemistry. As the simplest protonated Schiff base (PSB), it also serves as a model for studying the nonadiabatic dynamics of retinal PSB, the chromophore central to vertebrate vision. While previous studies have established CN bond cleavage and photoisomeri

Abdelghafour Halimi, Ali Alibrahim, Didier Barradas-Bautista, Ronell Sicat

This study presents a comprehensive deep learning pipeline for the automated classification of foraminifera species using 2D micro-CT slices derived from 3D scans. We curated a scientifically rigorous dataset of 97 micro-CT scanned specimens spanning 27 species, from which we selected 12 representative species with sufficient specimen counts (at least four 3

Yuhao Shan, Qianyi Yuan, Jingguo Liu, Shigang Li

Panoramic cameras, capable of capturing a 360-degree field of view, are crucial in robotic vision, particularly in environments with sparse features. However, non-upright panoramas due to unstable robot postures hinder downstream tasks. Traditional IMU-based correction methods suffer from drift and external disturbances, while vision-based approaches offer a

Jochen Wengenroth

The main aim of this note is to prove a version of a celebrated theorem of Effros about transitive group actions in a non-metrizable setting, these parts have been formalized and verified with Lean by Lara Toledano. We do not claim any originality since the given proof is in fact very close to one of van Mill. Our presentation is however completely self-cont

Alireza Javanmardi, Pragati Jaiswal, Tewodros Amberbir Habtegebrial, Christen Millerdurai

Recent advancements in diffusion models have significantly improved the realism and generalizability of character-driven animation, enabling the synthesis of high-quality motion from just a single RGB image and a set of driving poses. Nevertheless, generating temporally coherent long-form content remains challenging. Existing approaches are constrained by co

Xinzhu Chen, Xuesheng Li, Zhongxiang Sun, Weijie Yu

Reinforcement Learning with Verifiable Rewards (RLVR) has become a central approach for improving the reasoning ability of large language models. Recent work studies RLVR through token entropy, arguing that high-entropy tokens drive exploration and should receive stronger updates. However, they overlook the fact that most of a reasoning trajectory consists o

Chengjin Du, Federico Bernabei, Zhengyin Du, Sergio Decherchi

Soft robots are powerful tools for manipulating delicate objects, yet their adoption is hindered by two gaps: the lack of integrated tactile sensing and sensor signal distortion caused by actuator deformations. This paper addresses these challenges by introducing the SoftMag actuator: a magnetic tactile-sensorized soft actuator. Unlike systems relying on att

M. Özgün Güleç, Koray K. Şafak

This study presents mechatronic design, dynamic modeling, simulations and real-time control experiments of a new movable scaffolding system. The proposed system consists of a 3 degrees-of-freedom movable platform, which can be positioned on the outer surface of buildings. The platform is supported and driven by cords that are wound on pulleys and coupled to

Aashna P. Shah, James A. Diao, Emma Pierson, Chirag J. Patel

The use of coarse demographic adjustments in clinical equations has been increasingly scrutinized. In particular, adjustments for race have sparked significant debate with several medical professional societies recommending race-neutral equations in recent years. However, current approaches to remove race from clinical equations do not address the underlying

Xingyu Zhu, Beier Zhu, Yunfan Li, Junfeng Fang

Image clustering is a classic problem in computer vision, which categorizes images into different groups. Recent studies utilize nouns as external semantic knowledge to improve clustering performance. However, these methods often overlook the inherent ambiguity of nouns, which can distort semantic representations and degrade clustering quality. To address th

Richard D. Charlesworth

Modelling across engineering, systems science, and formal methods remains limited by binary relations, implicit semantics, and diagram-centred notations that obscure multilevel structure and hinder mechanisation. Hypernetwork Theory (HT) addresses these gaps by treating the n-ary relation as the primary modelling construct. Each relation is realised as a typ

Chaojun Ni, Cheng Chen, Xiaofeng Wang, Zheng Zhu

Vision-Language-Action (VLA) models built on pretrained Vision-Language Models (VLMs) show strong potential but are limited in practicality due to their large parameter counts. To mitigate this issue, using a lightweight VLM has been explored, but it compromises spatiotemporal reasoning. Although some methods suggest that incorporating additional 3D inputs c

Yebo Wu, Jingguang Li, Zhijiang Guo, Li Li

Federated fine-tuning offers a promising solution for adapting Large Language Models (LLMs) to downstream tasks while safeguarding data privacy. However, its high computational and communication demands hinder its deployment on resource-constrained devices. In this paper, we propose SmartFed, a resource-efficient federated fine-tuning framework. SmartFed int

Mingzhou Deng, Yan Fu

This paper discusses several p-value-free multiple hypothesis testing methods proposed in recent years and organizes them by introducing a unified framework termed competition test. Although existing competition tests are effective in controlling the False Discovery Rate (FDR), they struggle with handling data with strong heterogeneity or dependency structur

Vadim Ohanyan, Lusik Amiraghyan, Michael Sekania, Marcus Kollar

We investigate flat magnonic bands in a generalized sawtooth-chain model in which three sets of exchange parameters (symmetric Heisenberg exchange, axial Ising anisotropy, and antisymmetric Dzyaloshinskii-Moriya (DM) exchange) are assigned independently to each side of the triangular plaquette. If the effective Dzyaloshinskii-Moriya (DM) interaction paramete

Raka Dabhade, Jebraan Mudholkar, Siddhesh Durgude, Arpit Kottur

The search for potentially habitable exoplanets is a primary objective in modern astrophysics, yet the vast number of candidates discovered by missions like Kepler and TESS presents a significant challenge for detailed follow-up characterization. An efficient and reliable method for prioritizing the most promising targets is therefore essential. In this pape

Kfir Eliaz, Ran Spiegler

A profit-maximizing monopolist curates a database for users seeking to learn a parameter. There are two user types: "Nowcasters" wish to learn the parameter's current value, while "forecasters" target its long-run value. Data storage involves a constant marginal cost. The monopolist designs a menu of contracts described by fees and data-access levels. The pr

Jiang Zhou, Changjiang Bu

In this paper, we give the relationship between spectral radius and local structures of graphs and hypergraphs. Our work shows that certain local subgraphs (subhypergraphs) must occur when the spectral radius ratio is large. We also give spectral bounds on the local vector chromatic number in terms of tensor eigenvalues of graphs.

Leandro Lyra Braga Dognini

This paper generalizes the entropy maximization problem leading to the Boltzmann-Gibbs distribution through the nonadditive entropy $S_{q,s}(p)=k_{s}\sum^{W}_{i\geq1}p_{i}\ln_{q}1/p_{i}$, $q\in(0,1)$, which is a rescaled version of $S_{q}$ \cite{Tsallis1988} by a factor $k_{s}=k^{q}(e_{\max}/(W^{\sigma}-1))^{1-q}$, $\sigma>0$, varying according to the underl

Kundan Mukhia, Buddha Nath Sharma, Salam Rabindrajit Luwang, Md. Nurujjaman

We study how the 2024 U.S. presidential election, viewed as a major political risk event, affected cryptocurrency markets by distinguishing human-driven peer-to-peer stablecoin transactions from automated algorithmic activity. Using structural break analysis, we find that human-driven Ethereum Request for Comment 20 (ERC-20) transactions shifted on November

Jacob Mannhardt, Lukas Kunz, Giovanni Sansavini

Energy system optimization often relies on time series aggregation to ensure computational tractability. Aggregation generally loses the chronology of time steps, which renders the storage level representation challenging. Typically, this challenge is addressed by using representative days (RD) to utilize intra-day chronology, even though representative hour

Yiyu Wang, Xuyang Liu, Xiyan Gui, Xinying Lin

Streaming Video Large Language Models (VideoLLMs) have demonstrated impressive performance across various video understanding tasks, but they face significant challenges in real-time deployment due to the high computational cost of processing dense visual tokens from continuous video streams. In streaming video scenarios, the primary bottleneck lies in the V

Hun Jang, Eun Bok, Hyeunwoo Kim, Byeongjun Yoon

Photon structure function has been a solid platform in testing strong interaction along with nucleon structure function. Strong Interaction has the property that it is perturbatively calculable at high energy but becomes non-perturbative at low energy. This nature makes QCD hard to handle theoretically in factorizing these two regions. The fundamental dimens

Ishaan Gangwani, Aayam Bansal

Zero-shot foundation models (FMs) promise training-free prediction on tabular data, yet their hardware footprint remains poorly characterized. We present a fully reproducible benchmark that reports test accuracy together with wall-clock latency, peak CPU RAM, and peak GPU VRAM on four public datasets: Adult-Income, Higgs-100k, Wine-Quality, and California-Ho

Carlos Rebelo, Gil Rocha, João Daniel Silva, Bruno Martins

Remote sensing image captioning has advanced rapidly through encoder--decoder models, although the reliance on large annotated datasets and the focus on English restricts global applicability. To address these limitations, we propose the first training-free multilingual approach, based on retrieval-augmented prompting. For a given aerial image, we employ a d

Antoine Galet

A field $K$ is $d$-local if there exist fields $K=k_d,...,k_0$ with $k_{i+1}$ complete discrete valuation with residue field $k_i$, and $k_0$ finite of characteristic $p$. By work of Deninger and Wingberg, the Galois cohomology of such fields with finite coefficients satisfies a duality generalizing Tate duality when either $d=0$, $\mathrm{char} k_1=0$ or th

Samuel Kessler, Menglin Xia, Daniel Madrigal Diaz, Dongge Han

A common and effective means for improving language model capabilities involves finetuning a ``student'' language model's parameters on generations from a more proficient ``teacher'' model. Termed ``synthetic data'', these generations are often produced before any student finetuning, but some work has considered generating new synthetic samples as training p

Aayam Bansal, Ishaan Gangwani

We present a comprehensive zero-training temporal drift analysis of transformer-based sentiment models validated on authentic social media data from major real-world events. Through systematic evaluation across three transformer architectures and rigorous statistical validation on 12,279 authentic social media posts, we demonstrate significant model instabil

Xisheng Feng

Vision-Language Models (VLMs) exhibit significant performance plateaus in specialized domains like precision agriculture, primarily due to "Reasoning-Driven Hallucination" where linguistic priors override visual perception. A key bottleneck is the "Modality Gap": visual embeddings fail to reliably activate the fine-grained expert knowledge already encoded in

Aayam Bansal, Ishaan Gangwani

Foundation model reliability assessment typically requires thousands of evaluation examples, making it computationally expensive and time-consuming for real-world deployment. We introduce microprobe, a novel approach that achieves comprehensive reliability assessment using only 100 strategically selected probe examples. Our method combines strategic prompt d

Li Yuan, Qingfei Huang, Bingshan Zhu, Yi Cai

Multimodal Knowledge Editing (MKE) extends traditional knowledge editing to settings involving both textual and visual modalities. However, existing MKE benchmarks primarily assess final answer correctness while neglecting the quality of intermediate reasoning and robustness to visually rephrased inputs. To address this limitation, we introduce MMQAKE, the f

Haijian Shao, Wei Liu, Xing Deng

The rapid growth of multimodal intelligence on resource-constrained and heterogeneous domestic hardware exposes critical bottlenecks: multimodal feature heterogeneity, real-time requirements in dynamic scenarios, and hardware-specific operator redundancy. This work introduces a quantum-inspired geometric framework for neural operators that represents each op

Chunlin Tian, Xuyang Wei, Huanrong Liu, Zhijiang Guo

Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient fine-tuning (PEFT) method for Large Language Models (LLMs), but it still incurs notable overhead and suffers from parameter interference in complex datasets. While recent works decouple LoRA update matrices to exploit matrix-wise asymmetry, training costs remain high. We revisit LoRA from the

Zhening Liu, Rui Song, Yushi Huang, Yingdong Hu

3D Gaussian Splatting (3DGS) has emerged as a revolutionary 3D representation. However, its substantial data size poses a major barrier to widespread adoption. While feed-forward 3DGS compression offers a practical alternative to costly per-scene per-train compressors, existing methods struggle to model long-range spatial dependencies, due to the limited rec

Pavel A. Shcherbakov, Lev A. Smirnov, Vasily A. Kostin, Maxim I. Bolotov

We study the influence of nonuniform motion of oscillators in a ring chain with nonlocal coupling on their collective dynamics and reveal the mechanism behind the emergence of an atypical chimera state in such systems. The mechanism relies on regular spatially inhomogeneous motion of oscillators, which breaks the symmetry of the effective interaction kernel.

Xinlin He, Zetong Li, Congcong Zheng, Sixuan Li

Characterizing non-Markovian quantum dynamics is currently hindered by the self-inconsistency and high computational complexity of existing quantum comb tomography (QCT) methods. In this work, we propose a self-consistent framework that unifies the quantum comb, instrument set, and initial states into a single geometric entity, termed as the Comb-Instrument-

Henrik Danielyan, Arus Harutyunyan, Armen Sedrakian

We compute the thermal conductivity and thermoelectric power (thermopower) of the inner crust of compact stars across a broad temperature-density domain relevant for proto-neutron stars, binary neutron-star mergers, and accreting neutron stars. The analysis covers the transition from a semi-degenerate to a highly degenerate electron gas and assumes temperatu

Haoshen Wang, Lei Chen, Wei-Hua Zhang, Linxia Wu

Cone beam computed tomography (CBCT)-guided puncture has become an established approach for diagnosing and treating early- to mid-stage thoracic tumours, yet the associated radiation exposure substantially elevates the risk of secondary malignancies. Although multiple low-dose CBCT strategies have been introduced, none have undergone validation using large-s

Tim Veenboer, George Yiasemis, Eric Marcus, Vivien Van Veldhuizen

Existing foundation models (FMs) in the medical domain often require extensive fine-tuning or rely on training resource-intensive decoders, while many existing encoders are pretrained with objectives biased toward specific tasks. This illustrates a need for a strong, task-agnostic foundation model that requires minimal fine-tuning beyond feature extraction.

Na Wang, Guowei Ren, Shun Zhang, Tingfeng Yi

Based on the Zwicky Transient Facility (ZTF), we selected 10 blazars as our sample sources. Among these, we found four blazars (J 0923.5+4125, J 1221.3+3010, J 1503.5+4759, and J 1652.7+4024) showing possible indications of quasi periodic oscillations (QPOs) modulation. We conducted a detailed analysis of their optical light curves (g- and r-bands) over the

Sven Groppe, Valter Uotila, Jinghua Groppe

In an era where data underpins decision-making across science, politics, and economics, ensuring high data quality is of paramount importance. Conventional computing algorithms for enhancing data quality, including anomaly detection, demand substantial computational resources, lengthy processing times, and extensive training datasets. This work aims to explo

Miikka Kuutila, Paul Ralph, Huilian Sophie Qiu, Ronnie de Souza Santos

Context: Voluntary turnover is common in the software industry, increasing recruitment and onboarding costs and the risk of losing organizational and tacit knowledge. Objective: This study investigates how job satisfaction, work-life balance, job embeddedness, and their antecedents, including job quality, personality traits, attitudes toward technical and so

Chris Tennant

We propose a small, shared core ontology for particle accelerators that provides a semantic backbone for interoperable data and workflows across facilities. The ontology names key device types, signals, parameters, and regions, and relates them through explicit properties (e.g., hasSetpoint, hasReadback, partOf). Each site contributes a lightweight facility

Obada Kraishan

AI coding assistants have transformed software development, raising questions about transparency and attribution practices. We examine the "AI attribution paradox": how developers strategically balance acknowledging AI assistance with managing community scrutiny. Analyzing 14,300 GitHub commits across 7,393 repositories from 2023-2025, we investigated attrib

Hongjie Dong, Haigang Li, Huaijun Teng, Peihao Zhang

In this paper, we establish higher-order derivative estimates for the Stokes equations in a three-dimensional domain containing two closely spaced rigid inclusions. We construct a sequence of auxiliary functions via an inductive process to isolate the leading singular terms of higher-order derivatives within the narrow region between the inclusions. For a cl

Oleksiy Dovgoshey, Ruslan Shanin

Let $X$ be a set and $2^X$ be a set of all subsets of $X$. The necessary and sufficient conditions under which a mapping $X \to 2^X$ is a closure of one-point sets in some $T_0$-space $(X, \tau)$ are described. It is proved that every $T_0$-Alexandroff space is quasi-metrizable by some equidistant quasi-metric.

Kirill Gubarev, Konstantin Sovit

We construct bi- and uni-vector deformations of 10d heterotic supergravity solutions with the gauged double field theory approach. We construct a generalization of the "open/closed" map for this case and consider some examples of the deformed solutions, particularly for the F1 string solution.

H. Minh Lam, V. Nam Do

We investigate the interaction between quantum anomalous Hall (QAH) phases hosted by two atomically thin hexagonal lattices and demonstrate the emergence of topological phases with large Chern numbers. Interlayer coupling between two graphene-like lattices produces band crossings, while relative sliding preserves gapless Dirac points located at generic, low-

Darren J. Edwards

We present a self-contained separation framework for P vs NP developed entirely within ZFC. The approach consists of: (i) a deterministic, radius-1 compilation from uniform polynomial-time Turing computation to local sum-of-squares (SoS) polynomials with polylogarithmic contextual entanglement width (CEW); (ii) a formal Width-to-Rank upper bound for the resu

Ningning Chen, Weicai Ye, Ying Jiang

We introduce HBLLM, a wavelet-enhanced high-fidelity $1$-bit post-training quantization method for Large Language Models (LLMs). By leveraging Haar wavelet transforms to enhance expressive capacity through frequency decomposition, HBLLM significantly improves quantization fidelity while maintaining minimal overhead. This approach features two innovative stru

Shota Komatsu, Pronobesh Maity

Two-dimensional Yang-Mills theory (2d YM) is arguably the simplest confining gauge theory and its large $N_c$ expansion has a structure of the genus expansion in string theory. Nevertheless various aspects of its worldsheet description have not been fully understood. In this paper, we elaborate on a bosonic string dual to large $N_c$ chiral 2d YM at finite '

Praveen Anilkumar Shukla

Modern deep networks are heavily overparameterized yet often generalize well, suggesting a form of low intrinsic complexity not reflected by parameter counts. We study this complexity at initialization through the effective rank of the Neural Tangent Kernel (NTK) Gram matrix, $r_{\text{eff}}(K) = (\text{tr}(K))^2/\|K\|_F^2$. For i.i.d. data and the infinite-

Zhangsheng Li, Jiancheng Qiu, Gao Xu Wu

This paper presents a deep learning-based method for dynamic gear measurement and uncertainty estimation. A twin-system proposed on the Unity platform is utilized to flexibly generate diverse simulated datasets. This effectively addresses the scarcity of real-world gear measurement data and facilitates verification of network performance.The designed Concret

Gonzalo Roa, Manuel Suarez-Roman, Juan Tapiador

This paper studies the problem of Threat Actor (TA) naming convention inconsistency across leading Cyber Threat Intelligence (CTI) vendors. The current decentralized and proprietary nomenclature creates confusion and significant obstacles for researchers, including difficulties in integrating and correlating disparate CTI reports and TA profiles. This paper

Midhun Manoj

Probabilistic forecasting is essential for modern risk management, allowing decision-makers to quantify uncertainty in critical systems. This paper tackles this challenge using the volatile REFIT household dataset, which is complicated by a large structural data gap. We first address this by conducting a rigorous comparative experiment to select a Seasonal I

Miikka Kuutila, Paul Ralph, Huilian Sophie Qiu, Ronnie de Souza Santos

Context: Recent software engineering (SE) research has highlighted the need for sociotechnical research, implying a demand for customized psychometric scales. Objective: We define the concepts of technical and sociotechnical infrastructure in software engineering, and develop and validate a psychometric scale that measures attitudes toward them. Method: Grou

Nguyen Thi Nguyet Nga, Nguyen Huy Thao, Phung Van Dong

Neutral vectorlike fermion as inspired by unified theories might become quasi-Dirac states at TeV due to a violation in lepton-like symmetry. It is shown that such quasi-Dirac fermions can properly achieve radiative neutrino mass generation and dark matter stability. Indeed, the small splitting of quasi-Dirac masses, i.e. $\Delta M/M\ll 1$, suitably suppress

M. I. Bolotov, L. A. Smirnov, V. A. Kostin, G. V. Osipov

We investigate chimera synchronization of internal oscillator states in a ring of interacting particles, using the damped dc-driven Frenkel--Kontorova chain model as an example. In a system with a spatially periodic potential, a dc external force, and dissipation, kinks spontaneously emerge and stabilize. We show that these kinks induce and govern a collecti

Yandong Sun, Qiang Huang, Ziwei Xu, Yiqun Sun

Embedding spaces are fundamental to modern AI, translating raw data into high-dimensional vectors that encode rich semantic relationships. Yet, their internal structures remain opaque, with existing approaches often sacrificing semantic coherence for structural regularity or incurring high computational overhead to improve interpretability. To address these

Wenzhang Du

Deploying spatio-temporal forecasting models across many cities is difficult: traffic networks differ in size and topology, data availability can vary by orders of magnitude, and new cities may provide only a short history of logs. Existing deep traffic models are typically trained per city and backbone, creating high maintenance cost and poor transfer to da

Yunbo Long, Jiaquan Zhang, Xi Chen, Alexandra Brintrup

Clustering non-independent and identically distributed (non-IID) data under local differential privacy (LDP) in federated settings presents a critical challenge: preserving privacy while maintaining accuracy without iterative communication. Existing one-shot methods rely on unstable pairwise centroid distances or neighborhood rankings, degrading severely und

Pascal J. Thomas, Nikolai Nikolov

We study the relationship between the smoothness of a plane curve and that of its evolute, especially in the cases where the parent curve is no more two or three times continuously differentiable, and exhibit the same kind of apparent improvement in regularity: in the generic local situation, the evolute has one order of regularity less than the parent curve

Xiaokun Teng, Yanqing Ren, Weicong Chen, Wankai Tang

Diffractive neural networks, where signal processing is embedded into wave propagation, promise light-speed and energy-efficient computation. However, existing three-dimensional structures, such as stacked intelligent metasurfaces (SIMs), face critical challenges in implementation and integration. In contrast, this work pioneers planar diffractive neural net

Neeraj Anand, Rishabh Jain, Sohan Patnaik, Balaji Krishnamurthy

There is a growing demand for mobile user interface (UI) automation, driven by its broad applications across industries. With the advent of visual language models (VLMs), GUI automation has progressed from generating text-based instructions for humans to autonomously executing tasks, thus optimizing automation workflows. Recent approaches leverage VLMs for t

Giles Winchester, George Parisis, Luc Berthouze

Microservices have transformed software architecture through the creation of modular and independent services. However, they introduce operational complexities in service integration and system management that makes swift and accurate anomaly detection and localisation challenging. Despite the complex, dynamic, and interconnected nature of microservice archi

Camilo Arosemena Serrato

Consider a smooth, locally free, codimension-one action of a higher-rank, simple, split Lie group $G$ on a closed manifold $M$. Let $P$ be a minimal parabolic subgroup of $G$. If the action admits a $P$-invariant probability measure that is mixing, then the action is either equivariantly diffeomorphic to the suspension of a codimension one, locally free acti

Wenzhang Du

Federated learning (FL) enables collaborative training over distributed multimedia data but suffers acutely from statistical heterogeneity and communication constraints, especially when clients deploy large models. Classic parameter-averaging methods such as FedAvg transmit full model weights and can diverge under nonindependent and identically distributed (

Tamanna Jain, Piero Rettegno

Whilst most of the binary configurations in modified theories of gravity are studied under quasi-circular orbit limit, eccentricity effects could play a significant role in future gravitational wave detections. We derive the gravitational radiation-reaction force for nonspinning eccentric orbits within the effective-one-body (EOB) description for the massles

Fabrizio Maturo, Donato Riccio, Andrea Mazzitelli, Giuseppe Bifulco

This paper introduces ARCADIA, an agentic AI framework for causal discovery that integrates large-language-model reasoning with statistical diagnostics to construct valid, temporally coherent causal structures. Unlike traditional algorithms, ARCADIA iteratively refines candidate DAGs through constraint-guided prompting and causal-validity feedback, leading t

Md Muzakkir Quamar, Ali Nasir, Sami ELFerik

This paper presents a scalable and fault-tolerant framework for unmanned aerial vehicle (UAV) mission management in complex and uncertain environments. The proposed approach addresses the computational bottleneck inherent in solving large-scale Markov Decision Processes (MDPs) by introducing a two-stage decomposition strategy. In the first stage, a factor-ba

Emily Howerton, Justin Lessler

Counterfactual scenario modeling exercises that ask "what would happen if?" are one of the most common ways we plan for the future. Despite their ubiquity in planning and decision making, scenario projections are rarely evaluated retrospectively. Differences between projections and observations come from two sources: scenario deviation and model miscalibrati

Adriel Sosa Marco, John Daniel Kirwan, Alexia Toumpa, Simos Gerasimou

Quantifying uncertainty in deep regression models is important both for understanding the confidence of the model and for safe decision-making in high-risk domains. Existing approaches that yield prediction intervals overlook distributional information, neglecting the effect of multimodal or asymmetric distributions on decision-making. Similarly, full or app