November 2025 arXiv papers — page 14

Patrick Souza Lima, Paulo Roberto Santana dos Reis, Alex Álisson Bandeira Santos, Ehecatl Antonio del Río Chanona

We propose a multi-fidelity Bayesian optimization (MF-BO) framework that integrates computational fluid dynamics (CFD) evaluations with Gaussian-process surrogates to efficiently navigate the accuracy-cost trade-off induced by mesh resolution. The design vector x = [h, l, s] (height, length, and mesh element size) defines a continuous fidelity index Z(h, l,

Kyle Broder, Dan Popovici

We generalise the notions of scalar-valued holomorphic $p$-contact and $s$-symplectic structures introduced recently on compact complex manifolds by the second-named author jointly with H. Kasuya and L. Ugarte to their analogues with values in a holomorphic line bundle. We then study the resulting holomorphic $p$-contact and $s$-symplectic manifolds which, u

Songyan Li, Hongchang Li, Haiyue Jiang, Yudong Zhang

This letter proposes a targeted-subharmonic-eliminating pulse density modulation (PDM) method for series-series (SS) compensated wireless power transfer (WPT) systems. The subharmonic frequency components which excite current abnormal oscillations in PDM controlled WPT systems are eliminated through a specially designed noise transfer function (NTF). The pro

Amirreza Zamani, Ayfer Özgür, Mikael Skoglund

We study the statistical design of a fair mechanism that attains equalized odds, where an agent uses some useful data (database) $X$ to solve a task $T$. Since both $X$ and $T$ are correlated with some latent sensitive attribute $S$, the agent designs a representation $Y$ that satisfies an equalized odds, that is, such that $I(Y;S|T) =0$. In contrast to our

Parisa Hamedi, Hamed Jelodar, Samita Bai, Mohammad Meymani

Assembly-to-source code translation is a critical task in reverse engineering, cybersecurity, and software maintenance, yet systematic benchmarks for evaluating large language models on this problem remain scarce. In this work, we present the first comprehensive evaluation of five state-of-the-art large language models on assembly-to-source translation. We a

Natalie Neumeyer, Leonie Selk

We consider the error distribution in functional linear models with scalar response and functional covariate. Different asymptotic expansions of the empirical distribution function and the empirical characteristic function based on estimated residuals under different model assumptions are discussed. The results are applied for simple and composite goodness-o

Yujiao Yang, Jing Lian, Linhui Li

The complex reasoning ability of Large Language Models (LLMs) poses a critical bottleneck for their practical applications. Test-time expansion methods such as Tree-of-Thought (ToT) and Graph-of-Thought (GoT) enhance reasoning by introducing intermediate reasoning structures, tree search, or graph-based exploration mechanisms. However, their reasoning strate

Julian P. Merkofer, Antonia Kaiser, Anouk Schrantee, Oliver J. Gurney-Champion

This study systematically compared data-driven and model-based strategies for metabolite quantification in magnetic resonance spectroscopy (MRS), focusing on resilience to out-of-distribution (OoD) effects and the balance between accuracy, robustness, and generalizability. A neural network designed for MRS quantification was trained using three distinct stra

Saqlain Afroz, Titir Mukherjee, Raj Prince

The Fermi Large Area Telescope (Fermi-LAT) has detected more than 7,000 gamma-ray sources, a significant fraction of which are identified as blazars, while a comparable number remain classified as blazars of uncertain type (BCUs) or are unassociated with counterparts at other wavelengths. The absence of complete multi-wavelength spectral information presents

Jayita Lahiri, Tania Robens, Krzysztof Rolbiecki

In this work, we analyze experimental exclusion bounds that have been derived within a specific new physics realization, the two Higgs-doublet model with a pseudoscalar singlet (2HDMa), and their application to a different model, the Inert Doublet Model (IDM), that features the same final state. In this context, we discuss the sensitivity of the ATLAS search

Alexei Morozov, Hasib Sifat

A powerful approach to the celebrated Wess-Zumino-Witten (WZW) model is provided by its free-field realization. However, explicit calculations of conformal blocks are not described in the literature in full detail. We begin this study with the simplest cases of the $\hat{sl}(2)_k$ and $\hat{sl}(3)_k$ WZW models, with special emphasis on their global $sl(2)$

Manas Chaudhary, Chandradeep Pokhariya, Rahul Narain

The position-based dynamics (PBD) algorithm is a popular and versatile technique for real-time simulation of deformable bodies, but is only applicable to forces that can be expressed as linearly compliant constraints. In this work, we explore a generalization of PBD that is applicable to arbitrary nonlinear force models. We do this by reformulating the impli

Andreas Debrouwere, Lenny Neyt

We establish a Pelczy\'nski-Vogt decomposition result for (PLS)-type power series spaces of infinite type. By combining this result with the theory of Gabor frames, we obtain sequence space representations for multiplier spaces of Gelfand-Shilov spaces of Beurling type.

E. Mamonova, A. F. Kowalski, K. Herbst, S. Wedemeyer

Context. Stellar flares can significantly influence the atmospheres and habitability of orbiting exoplanets, especially around young and active M dwarfs. Understanding the temporally and spectrally resolved activity of such stars is essential for assessing their impact on planetary environments. Aims. We aim to examine in detail state-of-the-art concepts of

Yao Peng, Tingting Liu, Chenyang Yang

In user-centric cell-free multi-antenna systems, pilot contamination degrades spectral efficiency (SE) severely. To mitigate pilot contamination, existing works jointly optimize pilot assignment and power allocation by assuming fixed pilot length, which fail to balance pilot overhead against the contamination. To maximize net-SE, we jointly optimize pilot le

Andreas Henrik Frederiksen, Ole Sigmund, Federico Ferrari

We present a Matlab code for modelling and topology optimization of hyperelastic structures, including contact modelled by the Third Medium Contact (TMC) approach. By using the so-called HuHu-regularization we penalize the skew distortion of the bilinear finite elements discretizing void regions, thus promoting convergence of the nonlinear solver. First, we

Hongfei Zhang, Kanghao Chen, Zixin Zhang, Harold Haodong Chen

This paper presents DualCamCtrl, a novel end-to-end diffusion model for camera-controlled video generation. Recent works have advanced this field by representing camera poses as ray-based conditions, yet they often lack sufficient scene understanding and geometric awareness. DualCamCtrl specifically targets this limitation by introducing a dual-branch framew

Fouad Trad, Ali Chehab

Few-shot prompting has emerged as a practical alternative to fine-tuning for leveraging the capabilities of large language models (LLMs) in specialized tasks. However, its effectiveness depends heavily on the selection and quality of in-context examples, particularly in complex domains. In this work, we examine retrieval-augmented prompting as a strategy to

Shohei Yamamoto, Rebecca McDonald, Daniel Read

This paper examines how outcome modality in intertemporal choice influences time preferences and whether the process differs across cultures, specifically Japan and the United States. Uni-modal choices are those when the outcomes being compared over time are very similar, and cross-modal choices are those when the outcomes are very different. The cross-modal

Cameron A. Nurse, Kelly Breen, Matthew McGuire, Sara Prokup

Gait rehabilitation interventions targeting paretic propulsion can improve walking speed and function in individuals post-stroke. Previous work has demonstrated that real-time biofeedback targeting anterior ground reaction forces (AGRFs) can increase propulsion in individuals post-stroke, however this work was confined to lab-based treadmills, limiting pract

Galina Weinstein

Einstein's 1935 critique of quantum mechanics is often associated with the Einstein-Podolsky-Rosen (EPR) argument, yet his private correspondence from that year reveals a more exact conceptual structure guiding his claim that the $\psi$-function is incomplete. This paper reconstructs Einstein's reasoning in his letters to Schr\"odinger and Popper and examine

Yanqi Cheng, Chun-Wun Cheng, Jim Denholm, Thiago Lima

Medical imaging pipelines critically rely on robust denoising to stabilise downstream tasks such as segmentation and reconstruction. However, many existing denoisers depend on large annotated datasets or supervised learning, which restricts their usability in clinical environments with heterogeneous modalities and limited ground-truth data. To address this l

Juan Felipe Ariza Mejía, Ionuţ Chifan, Denis Osin, Bin Sun

Developing new techniques at the interface of geometric group theory and von Neumann algebras, we identify the first examples of ICC groups $G$ whose von Neumann algebras are McDuff and exhibit a new rigidity phenomenon, termed McDuff superrigidity: an arbitrary group $H$ satisfying $\mathcal{L}(H)\cong \mathcal{L}(G)$ decomposes as $H \cong G\times A$ for a

Fatima Nasser, Fouad Trad, Ammar Mohanna, Ghada El-Hajj Fuleihan

Systematic reviews require the use of rigorously designed search strategies to ensure both comprehensive retrieval and minimization of bias. Conventional manual approaches, although methodologically systematic, are resource-intensive and susceptible to subjectivity, whereas heuristic and automated techniques frequently under-perform in recall unless suppleme

Ruibing Wang, Shuhan Guo, Zeen Li, Zhen Wang

Traffic Signal Control (TSC) involves a challenging trade-off: classic heuristics are efficient but oversimplified, while Deep Reinforcement Learning (DRL) achieves high performance yet suffers from poor generalization and opaque policies. Online Large Language Models (LLMs) provide general reasoning but incur high latency and lack environment-specific optim

Matthew Daws

We develop two approaches to Quantum (or Non-commutative) Graphs based on arbitrary von Neumann algebras $M\subseteq\mathcal B(H)$: one looking at operator bimodules of Hilbert--Schmidt (instead of bounded) operators, and the second looking at Quantum Adjacency Operators. Hilbert--Schmidt Quantum Graphs relate to Weaver's picture of Quantum Graphs in a compl

Pankhil Gawade, Adam Izdebski, Myriam Lizotte, Kevin R. Moon

Pretrained transformers provide rich, general-purpose embeddings, which are transferred to downstream tasks. However, current transfer strategies: fine-tuning and probing, either distort the pretrained geometric structure of the embeddings or lack sufficient expressivity to capture task-relevant signals. These issues become even more pronounced when supervis

Mengjie Liu, Jiahui Peng, Wenchang Ning, Pei Chu

High-quality main content extraction from web pages is a critical prerequisite for constructing large-scale training corpora. While traditional heuristic extractors are efficient, they lack the semantic reasoning required to handle the structural heterogeneity of the modern web. Conversely, well-pretrained generative Large Language Models (LLMs) offer superi

Ruibing Wang, Shuhan Guo, Haotong Du, Quanming Yao

Multi-scenario recommendation is pivotal for optimizing user experience across diverse contexts. While Multi-gate Mixture-of-Experts (MMOE) thrives in ranking, its transfer to the matching stage is hindered by the blind optimization inherent to independent two-tower architectures and the parameter dominance of head scenarios. To address these structural and

Stefaniya Kozhevnikova, Denis Yukhnenko, Giulio Scola, Seena Fazel

Purpose To conduct a systematic review of machine learning models for predicting violent behaviour by synthesising and appraising their validity, usefulness, and performance. Methods We systematically searched nine bibliographic databases and Google Scholar up to September 2025 for development and/or validation studies on machine learning methods for predict

Meng-Die Zhao, Yu-Yan Wang, Ke-Jian He, Guo-Ping Li

In this paper, we study the observable signatures of Lorentz violation (LV) in low-energy Horava gravity by simulating the images and polarization features of rotating LV black holes using a backward ray-tracing method. Within a thin-disk accretion model and the ZAMO framework, we numerically solve the geodesics equation of photon and simulate the correspond

Alfred Galichon, Antoine Jacquet, Georgy Salakhutdinov

We present a general framework for matching with transferable utility (TU) that accommodates arbitrary heterogeneity without relying on the logit structure. The optimal assignment problem is characterized by tractable linear programming formulation, allowing flexible error distributions and correlation patterns. We introduce an iterative algorithm that solve

Zibo Zhou, Zhengjun Zhai, Huimin Chen, Wei Dai

Image emotion classification (IEC) is a longstanding research field that has received increasing attention with the rapid progress of deep learning. Although recent advances have leveraged the knowledge encoded in pre-trained visual models, their effectiveness is constrained by the "affective gap" , limits the applicability of pre-training knowledge for IEC

Ankit Gupta, Mustafa Khammash

Stochastic reaction networks (SRNs) are a general class of continuous-time Markov jump processes used to model a wide range of systems, including biochemical dynamics in single cells, ecological and epidemiological populations, and queueing or communication networks. Yet analyzing their dynamics remains challenging because these processes are high-dimensiona

Siqi Chen, Ke Hong, Tianchen Zhao, Ruiqi Xie

Scaling Diffusion Transformer (DiT) inference via sequence parallelism is critical for reducing latency in visual generation, but is severely hampered by workload imbalance when applied to models employing block-wise sparse attention. The imbalance stems from the inherent variation in sparsity across attention heads and the irregular distribution of dense bl

Yuandong Wang, Yao Cui, Yuxin Zhao, Zhen Yang

Recent advances in Vision-Language Models (VLMs) have achieved impressive progress in multimodal mathematical reasoning. Yet, how much visual information truly contributes to reasoning remains unclear. Existing benchmarks report strong overall performance but seldom isolate the role of the image modality, leaving open whether VLMs genuinely leverage visual u

Franz Ciceri, Henning Samtleben

The IKKT matrix model, from the holographic perspective, arises at the p=-1 endpoint of the family of dualities relating type II supergravities on near-horizon Dp-brane geometries to (p+1)-dimensional super Yang-Mills theories with sixteen supercharges. In this work, we detail and expand results reported in a recent letter by establishing the holographic dic

M. V. S. Saketh, Rajes Ghosh, Anuj Mishra

Gravitational-wave (GW) lensing can encode valuable information about the properties of the intervening lens, but most existing studies remain restricted to the small-deflection, weak-field regime. To bridge this crucial gap, this work presents the first systematic analysis of strong-field, wave-optical GW lensing by a Kerr black hole (BH), extending recent

Madalina Erascu

This paper proposes a novel hybrid neuro-symbolic framework for the optimal and scalable deployment of component-based applications in the Cloud. The challenge of efficiently mapping application components to virtual machines (VMs) across diverse VM Offers from Cloud Providers is formalized as a constrained optimization problem (COP), considering both genera

Hridya P, Mangilal Choudhary

Paschen law relates the breakdown voltage of a gas to the product of gas pressure and inter-electrode distance, predicting a characteristic minimum voltage at a specific pd value. In this study, the role of electrode configurations (symmetric and asymmetric) and inter-electrode spacing on the gas breakdown processes (or Paschen Law) is examined. Numerous set

Viviana del Barco, Andrei Moroianu

The (reduced) characteristic group of a locally conformally product manifold is obtained by restricting the action of its fundamental group to the non-flat factor of the universal cover, and taking the connected component of the identity in the closure of this restriction. It was shown by Kourganoff that this group is abelian, but it is currently unknown whe

Zhenyu Xu, Xiaoqi Shen, Haotian Nan, Xinyu Zhang

Instruction-based image editing enables intuitive manipulation through natural language commands. However, text instructions alone often lack the precision required for fine-grained control over edit intensity. We introduce NumeriKontrol, a framework that allows users to precisely adjust image attributes using continuous scalar values with common units. Nume

Maria Lugaro, Giulia C. Cinquegrana, Balázs Szányi, James M. Ball

Bulk meteoritic data show isotopic variability of $slow$-neutron-capture ($s$-process) origin in a several elements heavier than Fe. One peculiar feature is that the lighter $s$-process elements (e.g., Zr and Mo) present larger anomalies than the heavier $s$-process elements (e.g., Nd and W). To address this observation, we compared Zr and Nd data to model p

Guoqiang Lei, Zhihua Wang, Lijing Zhou, D. Exposito

Physics-informed neural networks (PINNs) have been applied to simulate multiphase flows, yet they are limited in modeling phase changes and sharp interfaces due to optimization conflicts in the strongly coupled Allen-Cahn, Cahn-Hilliard, and Navier-Stokes equations and the intrinsic smoothness bias of neural representations near discontinuities. To mitigate

Francesco Di Cursi, Chiara Boldrini, Marco Conti, Andrea Passarella

We evaluate large language models (LLMs) for automatic personality prediction from text under the binary Five Factor Model (BIG5). Five models -- including GPT-4 and lightweight open-source alternatives -- are tested across three heterogeneous datasets (Essays, MyPersonality, Pandora) and two prompting strategies (minimal vs. enriched with linguistic and psy

Gennaro Auricchio, Adelaide Emma Bernardelli, Paolo Giudici, Giuseppe Toscani

Evaluating the reliability of machine learning classifications remains a fundamental challenge in Artificial Intelligence (AI), particularly when the target variable is multidimensional. Classification variables can be expressed by means of a categorical scale which, at best, is ordinal. Because ordinal data lack a natural metric structure in their underlyin

Michal Čertík, Andreas Emil Feldmann, Jaroslav Nešetřil, Paweł Rzążewski

An ordered graph is a graph enhanced with a linear order on the vertex set. An ordered graph is a core if it does not have an order-preserving homomorphism to a proper subgraph. We say that $H$ is the core of $G$ if (i) $H$ is a core, (ii) $H$ is a subgraph of $G$, and (iii) $G$ admits an order-preserving homomorphism to $H$. We study complexity aspects of s

Thomas Rolland, Alberto Abad

While supervised fine-tuning of adult pre-trained models for children's ASR has shown promise, it often fails to capture group-specific characteristics and variations among children. To address this, we introduce GRoup-Aware PARtial model Merging, a parameter-efficient approach that combines unsupervised clustering, partial fine-tuning, and model merging. Ou

Georgios Papasotiropoulos, Zein Pishbin

This paper bridges two perspectives: it studies the multi-secretary problem through the fairness lens of social choice, and examines multi-winner elections from the viewpoint of online decision making. After identifying the limitations of the prominent proportionality notion of Extended Justified Representation (EJR) in the online domain, the work proposes a

Esrafil Ali Molla

We study the average shifted convolution sum $$ B(H,N):= \frac{1}{H} \sum_{h \sim H} \sum_{n \sim N} A_{\pi_1}(n)\, A_{\pi_2}(n+h), $$ where $A_{\pi_i}(n)$ denotes the Fourier coefficients of a Hecke--Maass cusp form $\pi_i$ for $\mathrm{SL}(d_i,\mathbb{Z})$ with $d_i\ge 4$, $i=1,2$. We establish a nontrivial power-saving bound of $B(H,N)$ for the range of t

Ashley Melvin, J. C. Mandal

When extended to two-phase flows, weakly compressible models lead to a non-conservative system, which precludes its treatment using standard finite volume techniques. In this paper, a novel HLLC-type path-conservative scheme is formulated for the weakly compressible two-phase model. Furthermore, capillary effects are included in the proposed path-conservativ

Roberto Leporini, Edoardo Provenzi, Michel Berthier

Starting from the foundational axiomatization of the perceptual color space initiated by Schr\"odinger in 1920 and eventually refined by Resnikoff in 1974, Berthier, Provenzi and their collaborators have recently proposed a reformulation of perceptual color attributes within the framework of quantum information. Their work is based on the Jordan algebra form

Jens-Peter Kreiss, Panagiotis Maouris, Efstathios Paparoditis

Estimating the periodicity of a stationary time series via fitting a second order stationary autoregressive (AR(2)) model has been initiated by the seminal paper of Yule(1927).. We investigate properties of this procedure when applied to a general stationary processes possessing a spectral density with a dominant peak at some frequency $\lambda_0\in(0,\pi)$.

Michal Čertík, Andreas Emil Feldmann, Jaroslav Nešetřil, Paweł Rzążewski

Ordered matchings, defined as graphs with linearly ordered vertices, where each vertex is connected to exactly one edge, play a crucial role in the area of ordered graphs and their homomorphisms. Therefore, we consider related problems from the complexity point of view and determine their corresponding computational and parameterized complexities. We show th

David Demitri Africa, Hans Ethan Ting

Self-evaluation is increasingly central to language model training, underpinning techniques from Constitutional AI to self-refinement. We investigate whether coupling self-evaluation to reward signals creates incentives for wireheading, where agents manipulate the measurement process rather than optimizing the task. We first formalize conditions under which

ATLAS Collaboration

The top-quark mass is measured using top-quark decays producing an isolated lepton and $J/\psi$ meson reconstructed in its $\mu^+\mu^-$ decay mode. The data sample was recorded with the ATLAS detector in proton-proton collisions at a centre-of-mass energy of $\sqrt{s}=13$ TeV during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosit

O. A. Kalenyuk, S. I. Futimsky, I. A. Martynenko, A. P. Shapovalov

The response of a MoRe-based superconducting resonator operating near 5 K to pulsed infrared irradiation is investigated, and the underlying physical mechanisms are analyzed. The device exhibits a pronounced nonlinear response dominated by nonequilibrium quasiparticle dynamics rather than uniform thermal heating. Infrared pulses produce strong distortions of

J. Švrčková, P. Harmanec, R. Klement, Th. Rivinius

$\delta$ Circini is known to be a massive multiple system containing a 3.9 d inner eclipsing binary in a slightly elliptical orbit exhibiting slow apsidal motion and a distant tertiary with a probable period of 1644 d. All three components of the system are O- or B-type stars. We carried out a comprehensive study of the system, based on light curves from TES

Akhil Rajeev P, Annarao Kulkarni

The Rigveda, among the oldest Indian texts in Vedic Sanskrit, employs a distinctive pitch-accent system : ud\=atta, anud\=atta, svarita whose marks encode melodic and interpretive cues but are often absent from modern e-texts. This work develops a parallel corpus of accented-unaccented \'slokas and conducts a controlled comparison of three strategies for aut

Muhammad Irfan Khalid, Ilias Pappas, Moatasim Mahmoud, Stamatia Rizou

The metaverse promises unprecedented immersive digital experiences but also raises critical privacy concerns as vast amounts of personal and behavioral data are collected. As immersive technologies blur the boundaries between physical and virtual realms, established privacy standards are being challenged. However, little is known about how the experts of the

Yongseok Hur, Joonhyuk Jung, Juhee Lee

We propose a Bayesian nonparametric (BNP) approach to causal inference using observational data consisting of outcome, treatment, and a set of confounders. The conditional distribution of the outcome given treatment and confounders is modeled flexibly using a dependent nonparametric mixture model, in which both the atoms and the weights vary with the confoun

Yuval Nitzav, Abigail Dishi, Himanshu Lohani, Ittai Sidilkover

Trions, three-body bound states composed of an exciton and an additional charge, are typically fragile and require external excitation to form. Here, we report the spontaneous emergence of a stable trion gas at the surface of the layered semiconductor Ta2NiS5, revealed through angle-resolved photoemission spectroscopy. We observe a sharp, highly localized in

Akira Tamamori

High-capacity kernel Hopfield networks exhibit a \textit{Ridge of Optimization} characterized by extreme stability. While previously linked to \textit{Spectral Concentration}, its origin remains elusive. Here, we analyze the network dynamics on a statistical manifold, revealing that the Ridge corresponds to the Edge of Stability, a critical boundary where th

Soobin Jeon, Sujong Kim, Dongmahn Seo

The amendments made to the Data 3 Act and impact of COVID-19 have fostered the growth of digital healthcare market and promoted the use of medical data in artificial intelligence in South Korea. Atopic dermatitis, a chronic inflammatory skin disease, is diagnosed via subjective evaluations without using objective diagnostic methods, thereby increasing the ri

Pigi P. Papanikolaou, Dimitrios Bozanis, Sotiris A. Tegos, Panagiotis D. Diamantoulakis

As next-generation wireless networks emerge, security is becoming a critical performance metric. However, conventional multiple-input-multiple-output (MIMO) systems often suffer from severe path loss and are vulnerable to nearby eavesdroppers due to their fixed-antenna configurations. Pinching-antenna systems (PASs) offer a promising alternative, leveraging

Michal Čertík, Andreas Emil Feldmann, Jaroslav Nešetřil, Paweł Rzążewski

We examine ordered graphs, defined as graphs with linearly ordered vertices, from the perspective of homomorphisms (and colorings) and their complexities. We demonstrate the corresponding computational and parameterized complexities, along with algorithms associated with related problems. These questions are interesting, and we show that numerous problems le

Ruosen Zhao, Zhikang Zhang, Jialei Xu, Jiahao Chang

Large vision-language models (VLMs) show strong multimodal understanding but still struggle with 3D spatial reasoning, such as distance estimation, size comparison, and cross-view consistency. Existing 3D-aware methods either depend on auxiliary 3D information or enhance RGB-only VLMs with geometry encoders through shallow feature fusion. We propose SpaceMin

Xiang-Dong Li, Qi Yan

In this paper, we study Perelman' s $ \mathcal{W}$ entropy for mean curvature flow in $\mathbb{R}^{n+1}$. Analogously to Perelman's $\mathcal{W}$-entropy defined for Ricci flow, K. Ecker in \cite{Ecker07} defined a functional $\mathcal{W}$ for the mean curvature flow in $\mathbb{R}^{n+1}$ and the region it encloses, and made the conjecture that this function

Alicia Martín, Tariq Yasin, Deaglan J. Bartlett, Harry Desmond

Dark matter haloes are typically characterised by radial density profiles with fixed forms motivated by simulations (e.g. NFW). However, simulation predictions depend on uncertain dark matter physics and baryonic modelling. Here, we present a method to constrain halo density profiles directly from observations using Exhaustive Symbolic Regression (ESR), a te

Mathieu Ciancone, Clovis Varangot-Reille, Marion Schaeffer

In Retrieval-Augmented Generation applications, the Information Retrieval part is central as it provides the contextual information that enables a Large Language Model to generate an appropriate and truthful response. High quality parsing and chunking are critical as efficient data segmentation directly impacts downstream tasks, i.e. Information Retrieval an

Mikayil Mahmudlu, Oktay Karakuş, Hasan Arkadaş

This study develops a hierarchical Bayesian framework that integrates expert domain knowledge to quantify player-specific effects in expected goals (xG) estimation, addressing a limitation of standard models that treat all players as identical finishers. Using 9,970 shots from StatsBomb's 2015-16 data and Football Manager 2017 ratings, we combine Bayesian lo

Dhruv Nigam, Naman Agarwal, Krishna Murthy, Susmit Saha

User behavior prediction at scale remains a critical challenge for online B2C platforms. Traditional approaches rely heavily on task-specific models and domain-specific feature engineering. This is time-consuming, computationally expensive, and requires domain expertise and therefore, not scalable. We present LUMOS (Large User MOdel Series), a transformer-ba

Hongye Zhu, Xuan Liu, Yanwen Ba, Jingye Xue

Missing modalities consistently lead to significant performance degradation in multimodal models. Existing approaches either synthesize missing modalities at high computational cost or apply prompt-based fine-tuning that relies only on adjacent-layer features and overlooks long-distance contextual information, which may offer additional tolerance to errors w

Rohit Sahasrabuddhe, Renaud Lambiotte

Segregation is a multi-scale phenomenon that requires careful measurement. A segregation index implicitly defines how the demographic compositions of locations are compared. We identify two properties -- mean-minimisation and invariance -- that uniquely characterise the Kullback-Leibler divergence as a measure of demographic difference. Mean-minimiser makes

Jorge Sánchez Canales, Alice Lixuan Xu, Chiara Fusar Bassini, Lynn H. Kaack

Researchers and electricity sector practitioners frequently require the supply curve of electricity markets and the price elasticity of supply for purposes such as price forecasting, policy analyses or market power assessment. It is common practice to construct supply curves from engineering data such as installed capacity and fuel prices. In this study, we

Abhishek Singh, Aaditya Jadhav, Abha Goyal, Jesma V

: One of the prominent challenges being faced by agricultural sciences is the onset of climate change which is adversely affecting every aspect of cropping. Modelling of climate change at macro level have been carried out at large scale and there is ample amount of research publications available for that. But at micro level like at state level or district l

Felipe Akio Matsuoka, Eduardo Moreno J. M. Farina, Augusto Sarquis Serpa, Soraya Monteiro

Generative foundation models can remove visual artifacts through realistic image inpainting, but their impact on medical AI performance remains uncertain. Pediatric hand radiographs often contain non-anatomical markers, and it is unclear whether inpainting these regions preserves features needed for bone age and gender prediction. To evaluate the clinical re

Ben Thornley, Maruf Sarkar, Saptarsi Ghosh, Martin Frentrup

Fabrication of porous GaN distributed Bragg reflectors (DBRs) via the selective electrochemical etching (ECE) of conductive Si-doped layers, separated by non-intentionally doped (NID) layers, provides a straightforward methodology for producing highly reflective DBRs suitable for device overgrowth and integration, which has otherwise proven difficult in the

Serhiy Kapustyan, Pranav Tetey, Thomas Grube, Jochen Linssen

This research presents a Load Profile Generator model for non-road mobile machinery, which depicts the most common operational profiles that reflect real-world conditions. This technological bottom-up model enables users to parameterize specific machines for simulation and observe their power demand at the actuator interfaces. The application of the Load Pro

Angelika Abramiuk-Szurlej

Anticonformity, behaving in deliberate opposition to the group of influence, has long been recognized as a distinct social response, differing both from conformity and from independence. While often treated as a source of noise or contrarianism, anticonformity can play a constructive role in social dynamics by counterbalancing majority pressure and influenci

Yan V. Fyodorov, Bertrand Lacroix-A-Chez-Toine, Pierre Le Doussal

We describe the atypical fluctuations of the ground state energy of the random elastic manifold, a disordered model defined on a lattice of linear size $L$ with internal dimension $0\leq d<4$ embedded in a medium of dimension $N\gg 1$. The ground-state energy results from a competition between confinement, elasticity and disorder. We obtain an exact descript

Jiatong Han

Traditional Chinese Medicine theory is built on imagistic thinking, in which medical principles and diagnostic and therapeutic logic are structured through metaphor and metonymy. However, existing English translations largely rely on literal rendering, making it difficult for target-language readers to reconstruct the underlying conceptual networks and apply

Sidharth Rony, Jack Patman

Standard Occupational Classifiers (SOC) are systems used to categorize and classify different types of jobs and occupations based on their similarities in terms of job duties, skills, and qualifications. Integrating these facets with Big Data from job advertisement offers the prospect to investigate labour demand that is specific to various occupations. This

Paulo J. N. Pinto, Armando J. Pinho, Diogo Pratas

Accurately dating historical texts is essential for organizing and interpreting cultural heritage collections. This article addresses temporal text classification using interpretable, feature-engineered tree-based machine learning models. We integrate five feature categories - compression-based, lexical structure, readability, neologism detection, and distan

Dudekula Kasim Vali

Software testing is critical in the software development lifecycle, yet translating requirements into executable test scripts remains manual and error-prone. While Large Language Models (LLMs) can generate code, they often hallucinate non-existent UI elements. We present the Autonomous QA Agent, a Retrieval-Augmented Generation (RAG) system that grounds Sele

Ruoxuan Zhang, Qiyun Zheng, Zhiyu Zhou, Ziqi Liao

Theory of Mind (ToM) refers to the ability to infer others' mental states, such as beliefs, desires, and intentions. Current vision-language embodied agents lack ToM-based decision-making, and existing benchmarks focus solely on human mental states while ignoring the agent's own perspective, hindering coherent decision and action generation. To address this,

Ying Zhou, Ziwen Wang, Fan Wang, Haoshen Ye

Topology, as a mathematical concept, has been introduced into condensed matter physics since the discovery of quantum Hall effect, which characterizes new physical scenario beyond the Landau theory. The topologically protected physical quantities, such as the dissipationless quantum transport of edge/surface states as well as magnetic/dipole quasi-particles

Sergei V. Koniakhin, Oleg I. Utesov, Vitaly I. Korepanov, Andrey G. Yashenkin

Ready-to-use numerical toolbox for nanodiamond Raman spectra calculation and fit is presented. The developed theoretical approach allows accounting for arbitrary nanoparticle size-distribution and the microscopic line broadening mechanisms for the optical phonons. The two tools for solving the inverse problem of the nanodiamond properties reconstruction usin

Grigorios Aris Cheimariotis, Antonis Karakottas, Vangelis Chatzis, Angelos Kanlis

Data valuation and monetization are becoming increasingly important across domains such as eXtended Reality (XR) and digital media. In the context of 3D scene reconstruction from a set of images -- whether casually or professionally captured -- not all inputs contribute equally to the final output. Neural Radiance Fields (NeRFs) enable photorealistic 3D reco

Hyunjin Kim, Kunho Kim, Adam Lee, Wonkwang Lee

We present GOATex, a diffusion-based method for 3D mesh texturing that generates high-quality textures for both exterior and interior surfaces. While existing methods perform well on visible regions, they inherently lack mechanisms to handle occluded interiors, resulting in incomplete textures and visible seams. To address this, we introduce an occlusion-awa

Nikita Repnkiov, Vladimir Faerman

This article addresses the development of quantum communication methods in the context of emerging quantum computing threats and emphasizes the importance of key reconciliation in quantum communication systems. The study focuses on the CASCADE protocol and the design of a software prototype intended for research and educational purposes. A parallel error-cor

Priyadarshi Mukherjee, Constantinos Psomas, Ioannis Krikidis

Chaotic dynamical systems have attracted considerable attention due to their inherent randomness and high sensitivity to initial conditions, which makes them ideal for secure wireless communications. Beyond security, these same characteristics also make chaotic signals particularly effective for wireless power transfer (WPT) applications. On the other hand,

Evgenii Volkov, Kseniia Chelidze, Dmitrii Gromushkin, Semen Khokhlov

A Ground Level Enhancement event was observed by neutron detectors designed for the registration of extensive air showers at the Experimental Complex NEVOD. The potential for that was unlocked by a recent modernization of the experimental setup that included implementation of additional channels for measuring neutron flux variation. At 10:15 UT on November 1

Eldar Sultanow, Andreas Hatziiliou

We present a direct, index-free method to recover the side lengths of a planar rectangle the spectrum of its Dirichelet Laplacian, assuming only access to a finite subset of eigenvalues. No modal indices $(m,n)$ are available, and the list may begin at an arbitrary unknown offset; in particular, the lowest eigenvalues may be missing, so classical formulas ba

Shoya Kasai, Shun Okumura, Yukitoshi Motome

Hopfions--three-dimensional topological solitons with knotted spin texture--have recently garnered attention in topological magnetism due to their unique topology characterized by the Hopf number $H$, a topological invariant derived from knot theory. In contrast to two-dimensional skyrmions, which are typically limited to small topological invariants, i.e.,

Yiwei Li, Jiannong Cao, Penghui Ruan, Divya Saxena

Gaussian Splatting has been considered as a novel way for view synthesis of dynamic scenes, which shows great potential in AIoT applications such as digital twins. However, recent dynamic Gaussian Splatting methods significantly degrade when only sparse input views are available, limiting their applicability in practice. The issue arises from the incoherent

Even Marius Nordhagen, Håvard Homleid Haugen, Magnus Sikora Ingstad, Aram Farhad Shafiq Salihi

We present a probabilistic data-driven weather model providing ensembles of high spatial resolution realizations of 87 variables at arbitrary ensemble size and forecast length. The model uses a global stretched grid, dedicating 2.5 km resolution to our Nordic region of interest and 31 km resolution elsewhere, with 6-hour temporal resolution. Unique ensemble

A. K. Likhoded, V. A. Petrov, V. D. Samoylenko

We present a heavy quark fragmentation mechanism based on a modified version of the standard model of quark fragmentation, taking into account peculiarities of the Regge trajectories of quarkonia containing heavy quarks. The modified model describes the experimental data more satisfactorily both for meson and baryon production.

Dong Nguyen, Laura Rosseel

Spelling variation (e.g. funnnn vs. fun) can influence the social perception of texts and their writers: we often have various associations with different forms of writing (is the text informal? does the writer seem young?). In this study, we focus on the social perception of spelling variation in online writing in English and study to what extent this perce

Derek Inman

Long-range attractive fifth forces can lead to exponential instabilities in the early Universe. For fermions with a Yukawa coupling to a sufficiently light scalar mediator, rapid oscillations of the scalar field can lead to a conservative force law with fractional behaviour on sufficiently large scales. We study cosmological systems evolving under both this