December 2025 arXiv papers — page 108

Bowei Zhang, Jin Xiao, Guanglei Yue, Qianyu He

Quotation recommendation aims to enrich writing by suggesting quotes that complement a given context, yet existing systems mostly optimize surface-level topical relevance and ignore the deeper semantic and aesthetic properties that make quotations memorable. We start from two empirical observations. First, a systematic user study shows that people consistent

Yi Luo, Luping Xiang, Cheng Luo, Kun Yang

The evolution toward sixth-generation (6G) wireless networks demands high-performance transceiver architectures capable of handling complex and dynamic environments. Conventional orthogonal frequency-division multiplexing (OFDM) receivers rely on cascaded discrete Fourier transform (DFT) and demodulation blocks, which are prone to inter-stage error propagati

Hyunki Seong, Jeong-Kyun Lee, Heesoo Myeong, Yongho Shin

Learning interactive motion behaviors among multiple agents is a core challenge in autonomous driving. While imitation learning models generate realistic trajectories, they often inherit biases from datasets dominated by safe demonstrations, limiting robustness in safety-critical cases. Moreover, most studies rely on open-loop evaluation, overlooking compoun

Ben-Yang Zhu, Yun-Feng Liang, Xiaoyuan Huang

A recent analysis of pulsar timing data has reported evidence for a massive ($\sim 6 \times 10^7 M_{\odot}$) dark matter subhalo located only $\sim 0.8$ kpc from Earth. This candidate implies an exceptionally large $J$-factor of $\sim 10^{23}\,{\rm GeV^2\,cm^{-5}}$, exceeding that of known classical dwarf spheroidal galaxies by orders of magnitude and rivali

Hugo Roger Paz

Both student retention in higher education and artificial intelligence governance face a common structural challenge: the application of linear regulatory frameworks to complex adaptive systems. Risk-based approaches dominate both domains, yet systematically fail because they assume stable causal pathways, predictable actor responses, and controllable system

Faizuddin Ahmed, Edilberto O. Silva

We investigate the thermodynamic microstructure of electrically charged AdS black holes in Einstein-Kalb-Ramond bumblebee gravity in the presence of a spherically symmetric cloud of strings. Employing Weinhold and Ruppeiner thermodynamic geometries in complementary thermodynamic representations, we show that curvature singularities consistently track the spi

André E. Piatti

A recent model prediction claimed that exists a correlation between the formation scenarios of globular clusters, i.e., whether they formed in situ, or in dark matter halos that were accreted into the Milky way, with some properties of their tidal tails, particularly, their widths ($w$), their dispersion in the z-component of the angular momentum ($\sigma$$_

Victor P. Goncalves, Juciene T. de Souza, Diego Spiering

In this paper we propose the analysis of the heavy quark photoproduction associated with a leading neutron in hadronic collisions at the LHC as an alternative to probe the pion gluon distribution in a kinematical range not covered by previous experiments. We perform an exploratory study of the charm and bottom photoproduction associated with a leading neutro

Bilal Kousar, Selma Franca, David Perconte, Anton Khvalyuk

Reproducibility and quantization in quantum spin Hall platforms is a persisting challenge, limiting their use in hybrid realizations of topological superconductivity. We report robust and reproducible quantized transport in a graphene quantum Hall topological insulator, stabilized at low magnetic fields by screening long-range Coulomb interactions with a met

Yunhong Min, Juil Koo, Seungwoo Yoo, Minhyuk Sung

We introduce B\'ezierFlow, a lightweight training approach for few-step generation with pretrained diffusion and flow models. B\'ezierFlow achieves a 2-3x performance improvement for sampling with $\leq$ 10 NFEs while requiring only 15 minutes of training. Recent lightweight training approaches have shown promise by learning optimal timesteps, but their scop

Pamela Klaassen, Matthew Kenworthy, Eric Mamajek, Nienke van der Marel

We report on ALMA Band 7 continuum observations towards the star 1SWASP J140747.93-394542.6 taken in mid 2024. These observations were a follow-up of a previous detection of an emission source in the J1407 field of view at an unexpected position in 2017. Proper motion analysis indicated that if this were the object responsible for the 2007 eclipse of J1407,

Tao Li, Wenshuo Ge, Zhichao Wang, Zihao Cui

Codec-based language models (LMs) have revolutionized text-to-speech (TTS). However, standard codecs entangle timbre and prosody, which hinders independent control in continuation-based LMs. To tackle this challenge, we propose DisCo-Speech, a zero-shot controllable TTS framework featuring a disentangled speech codec (DisCodec) and an LM-based generator. The

Vipul Dinesh Pawar

In e-commerce ranking, implicit user feedback is systematically confounded by Position Bias -- the strong propensity of users to interact with top-ranked items regardless of relevance. While Deep Learning architectures (e.g., Two-Tower Networks) are the standard solution for de-biasing, we demonstrate that in High-Bias Regimes, state-of-the-art Deep Ensemble

Juil Koo, Daehyeon Choi, Sangwoo Youn, Phillip Y. Lee

Vision Language Models (VLMs) excel at visual question answering (VQA) but remain limited to snapshot vision, reasoning from static images. In contrast, embodied agents require ambulatory vision, actively moving to obtain more informative views. We introduce Visually Grounded Active View Selection (VG-AVS), a task that selects the most informative next viewp

Shuanglin Jing, Yuting Huang, Hai-Feng Huo

The accurate estimation of time-varying transmission rates is fundamental for understanding infectious disease dynamics and implementing effective public health interventions. To this end, we propose an improved inverse method for estimating time-varying transmission rates in low-prevalence settings, where conventional data preprocessing approaches often fai

Tomasz Kania, Natalia Maślany

We study Raja's covering index $\Theta_X(n)$ for classical $L_p$-spaces and their non-commutative counterparts. For infinite-dimensional Hilbert spaces we compute the covering index exactly, proving \[ \Theta_H(n)=n^{-1/2}\qquad(n\in\mathbb N); \] in particular $\Theta_H(2)=1/\sqrt2$, thus answering a question of Raja about the precise two-piece covering ind

Olivia Hefti, Marco Clementi, Enrico Melani, Jean-Etienne Tremblay

Frequency-degenerate photon pairs generated using nonlinear photonic integrated devices are a crucial resource for scalable quantum information processing and metrology. However, their realization is hindered by unwanted parametric processes occurring within the same phase matching band, which degrade the signal-to-noise ratio and reduce the purity of the as

P. D. Grigoriev, S. I. Pesotskii, R. B. Lyubovskii, S. A. Torunova

The giant magnetoresistance with a huge hysteresis is observed in the organic metal k-(BEDTTTF)2Hg(SCN)2Br at low temperature in a pressure interval around 3 kbar of a width ~1 kbar. The hysteretic magnetoresistance is isotropic with respect to the direction of magnetic field, which excludes the orbital effect of magnetic field as its origin. The observed te

Wei-Chen Lee, Martin Bullinger, Alessandro Abate, Michael Wooldridge

We consider a scheduling problem of strategic agents representing jobs of different weights. Each agent has to decide on one of a finite set of identical machines to get their job processed. In contrast to the common and exclusive focus on makespan minimization, we want the outcome to be fair under strategic considerations of the agents. Two natural properti

Sumeyra Hassan, Bin Li, Yalcin Sadi, Erdal Panayirci

In this paper, a reconfigurable intelligent surface (RIS) assisted cell free massive MIMO (CFmMIMO) framework is designed to enhance physical layer security (PLS) and mitigate multi user (MU) interference in next generation wireless networks. A channel state information (CSI) based precoder is designed at the access point (AP) to suppress MU interference, en

Sebastiaan L. D. ten Haaf, Sebastian Miles, Qingzhen Wang, A. Mert Bozkurt

The Fermi-Hubbard model and its rich phase diagram naturally emerges as a description for a wide range of electronic systems. Recent advances in semiconductor-superconductor hybrid quantum dot arrays have allowed to realize degenerate quantum systems in a controllable way, e.g., allowing to observe robust zero-bias peaks in Kitaev chains, indicative for Majo

Jerome Baray

Inverse power-law interaction forms, such as the inverse-square law, recur across a wide range of physical, social, and spatial systems. While traditionally derived from specific microscopic mechanisms, the ubiquity of these laws suggests a more general organizing principle. This article proposes a statistical-mechanics framework in which such interaction la

Zihui Zhao, Zechang Li

Direct Preference Optimization (DPO) has emerged as a lightweight and effective alternative to Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with AI Feedback (RLAIF) for aligning large language and vision-language models. However, the standard DPO formulation, in which both the chosen and rejected responses are generated by the

Eddy Kiomba Kambilo, Nicolas Herbaut, Irina Rychkova, Carine Souveyet

Blockchain technology is gaining momentum across many sectors. Whereas blockchain solutions have important positive effects on the business domain, they also introduce constraints and may cause delayed or unforeseen negative effects, undermining business strategies. The diversity of blockchain patterns and lack of standardized frameworks linking business goa

Francesco Ragusa, Michele Mazzamuto, Rosario Forte, Irene D'Ambra

We present Ego-EXTRA, a video-language Egocentric Dataset for EXpert-TRAinee assistance. Ego-EXTRA features 50 hours of unscripted egocentric videos of subjects performing procedural activities (the trainees) while guided by real-world experts who provide guidance and answer specific questions using natural language. Following a ``Wizard of OZ'' data collect

Arnab Sharma

Neural Encoders are frequently used in the NLP domain to perform dense retrieval tasks, for instance, to generate the candidate documents for a given query in question-answering tasks. However, sparse annotation and label noise in the training data make it challenging to train or fine-tune such retrieval models. Although existing works have attempted to miti

Mounir Nisse

We study the affine cone over a reducible nodal curve $X$ obtained by gluing three projective lines along three pairs of points to form a connected curve of arithmetic genus \(1\). We endow \(X\) with a line bundle \(L\) of multidegree \((4,3,3)\), and we show that \(L\) is very ample, giving an embedding into \( \mathbb{P}^9\). We then analyze in detail the

Jianyuan Bo, Yuan Fang

In the rapidly evolving field of self-supervised learning on graphs, generative and contrastive methodologies have emerged as two dominant approaches. Our study focuses on masked feature reconstruction (MFR), a generative technique where a model learns to restore the raw features of masked nodes in a self-supervised manner. We observe that both MFR and graph

Hao Kang, Rui Peng, Maolin Zhou

In this paper, we investigate an eigenvalue problem associated with an age-structured operator incorporating random diffusion and advection. Our primary focus is on examining the asymptotic behaviors of the principal eigenvalue with respect to large advection and small or large diffusion rates. We subsequently apply these results to a nonlinear age-structure

Mojtaba Joodaki, Idriz Pelaj

A non-destructive, real-time method for estimating the volume fraction of a dielectric mixture inside a resonant cavity is presented. A convolutional neural network (CNN)-based approach is used to estimate the fractional composition of two-phase dielectric mixtures inside a resonant cavity using scattering parameter (S-parameter) measurements. A rectangular

Jérôme Coville, Léo Girardin

This paper is concerned with the study of a class of nonlinear nonlocal functional evolution problems defined in an abstract Banach algebra. We introduce an abstract functional setting that encompasses a wide range of structured population models appearing in biomathematical literature. Within this framework, we analyze the well-posedness of the Cauchy probl

Vesa Kuikka, Kosti Koistinen, Kimmo K Kaski

Overlapping communities are key characteristics of the structure and function analysis of complex networks. Shared or overlapping nodes within overlapping communities can form either subcommunities or act as intersections between larger communities. Nodes at the intersections that do not form subcommunities can be identified as overlapping nodes or as part o

Lucia Benito-Barca, Carlos del Pozo-Rojas, Sandra Montalvo-Quiros, Ramiro Perezzan Rodriguez

The cell nucleus is increasingly recognized as a mechanosensitive organelle whose mesoscale mechanical heterogeneity (100 nm 10 um) is inseparable from genome regulation yet remains weakly integrated into systems biology and omics frameworks. Here we synthesize experimental and theoretical advances that define a nuclear mechanome: a multidimensional state of

Mischa Huisman, Erjen Lefeber, Nathan van de Wouw, Carlos Murguia

As cyber-physical systems (CPSs) become more dependent on data and communication networks, their vulnerability to false data injection (FDI) attacks has raised significant concerns. Among these, stealthy attacks, those that evade conventional detection mechanisms, pose a critical threat to closed-loop performance. This paper introduces a controller-oriented

Melvin Barbaux, Samia Boukir

Semi-supervised classification leverages both labeled and unlabeled data to improve predictive performance, but existing software support remains fragmented across methods, learning settings, and data modalities. We introduce ModSSC, an open source Python framework for inductive and transductive semi-supervised classification designed to support reproducible

Sarit Khirirat, Abdurakhmon Sadiev, Yury Demidovich, Peter Richtárik

The use of momentum in stochastic optimization algorithms has shown empirical success across a range of machine learning tasks. Recently, a new class of stochastic momentum algorithms has emerged within the Linear Minimization Oracle (LMO) framework--leading to state-of-the-art methods, such as Muon, Scion, and Gluon, that effectively solve deep neural netwo

Haijun Yang

This proceedings presents a concise overview of the Higgs boson's charge-conjugation and parity (CP) properties and constraints on Effective Field Theory (EFT) operators, derived from the ATLAS experiment at the Large Hadron Collider (LHC). Using proton$\textendash$proton collision data with integrated luminosities of up to 140 fb$^{-1}$ at $\sqrt{s} = 13$ T

Nicolas Zilberstein, Santiago Segarra, Luiz Chamon

We introduce shielded Langevin Monte Carlo (LMC), a constrained sampler inspired by navigation functions, capable of sampling from unnormalized target distributions defined over punctured supports. In other words, this approach samples from non-convex spaces defined as convex sets with convex holes. This defines a novel and challenging problem in constrained

Lukas Lehnert, Martin Lorenz, Maria Fernanda Juarez, Max Schammer

Modelling the ionic transport in battery cells requires precise parametrization of the involved electrolytes. For carbonate-based electrolytes, however, the evaluation of their parameters suffers from interphase effects between the bulk electrolyte and the Li metal electrode, commonly present in the usual electrochemical polarization experiments. In this wor

T. Coudert, A. Delphin, A. Barrier, E L Barbier

Over the past decade, several studies have explored the potential of magnetic resonance fingerprinting (MRF) for the quantification of brain hemodynamics, oxygenation, and perfusion. Recent advances in simulation models and reconstruction frameworks have also significantly enhanced the accuracy of vascular parameter estimation. This review provides an overvi

Antoine Douai

Above a Laurent polynomial f one makes grow a vector space of vanishing cycles (after the work of Sabbah, singularity setting), a graded Milnor ring (after the work of Kouchnirenko) and an orbifold cohomology ring (after the work of Borisov, Chen and Smith). Under suitable assumptions, these structures are isomorphic and these identifications are interesting

Johannes Schöttner, Qingguang Xie, Gaurav Nath, Jens Harting

Controlling the deposition of filaments, such as nanowires and nanotubes, from evaporating droplets is critical for the performance of emerging technologies like flexible sensors and printed electronics. The final deposit morphology strongly governs functional properties, such as electrical conductivity, yet remains challenging to control. In this work, we n

Eleni Tsaprazi, Giorgio F. Lesci, Federico Marulli, Alan F. Heavens

Despite the success of general relativity (GR), the unexplained nature of dark energy on cosmological scales leaves open the question of whether GR provides a complete description of gravity. This quest is further motivated by growing tensions among cosmological observations when interpreted within $\Lambda$CDM. Gravitational redshifts of cluster member gala

Moisy Amélie, Voisin Hugo, Davy Joëlle, Cathala Bernard

The mechanical performance of plant fibres is linked to the presence of crystalline elements dispersed within an amorphous cohesive matrix. The more the crystalline reinforcement is aligned with the fibre axis, the better the mechanical properties of the fibre. With the aim of developing entirely biobased biomimetic fibres as alternatives to synthetic or res

Jiaqun Wei, Yu Zhou

We introduce the notion of AIR tilting subcategories of extended hearts of $t$-structures on a triangulated category associated with silting subcategories. This notion generalizes $\tau_{[d]}$-tilting pairs of extended finitely generated modules over finite-dimensional algebras to a more general framework, which includes both extended large modules over unit

Lorenzo Sabug, Eric Kerrigan

We revisit the problem of physics-informed regression, and propose a method that directly computes the state at the prediction point, simultaneously with the derivative and curvature information of the existing samples. We frame each prediction as a constrained optimisation problem, leveraging multivariate Taylor series expansions and explicitly enforcing ph

Odile Bellenguez, Nadia Brauner, Christine Solnon, Alexis Tsoukias

This document, intended for computer science teachers, describes a case study that puts into practice a questioning of ethical, societal and environmental issues when designing or implementing a decision support system. This study is based on a very popular application, namely road navigation software that informs users of real-time traffic conditions and su

Yinsong Qu, Yunxiang Li, Shanlin Zhong

Autonomous Mobile Robots (AMRs) have become indispensable in industrial applications due to their operational flexibility and efficiency. Navigation serves as a crucial technical foundation for accomplishing complex tasks. However, navigating AMRs in dense, cluttered, and semi-structured environments remains challenging, primarily due to nonholonomic vehicle

Daria Botvynko, Pierre Haslée, Lucile Gaultier, Bertrand Chapron

We present an end-to-end deep learning framework for short-term forecasting of global sea surface dynamics based on sparse satellite altimetry data. Building on two state-of-the-art architectures: U-Net and 4DVarNet, originally developed for image segmentation and spatiotemporal interpolation respectively, we adapt the models to forecast the sea level anomal

Diego Bolliger, Gabriele Fadini, Markus Bambach, Alisa Rupenyan

Controlling the deformation of flexible objects is challenging due to their non-linear dynamics and high-dimensional configuration space. This work presents a differentiable Material Point Method (MPM) simulator targeted at control applications. We exploit the differentiability of the simulator to optimize a control trajectory in an active damping problem fo

Ivan Homoliak

With the rise of cryptocurrencies, many new applications built on decentralized blockchains have emerged. Blockchains are full-stack distributed systems where multiple sub-systems interact. While many deployed blockchains and decentralized applications need better scalability and performance, security is also critical. Due to their complexity, assessing bloc

Hikaru Awazu

A discrete group $\Gamma$ is called exact if the reduced group C*-algebra ${C_{\lambda}}^{*}(\Gamma)$ is exact as C*-algebras, and a discrete group $\Lambda$ is called residually exact if every nonunital element $g \in \Lambda$ admits a surjective group homomorphism from $\Lambda$ to some exact group $\Gamma$ which maps $g$ to a nonunital element of $\Gamma$

Mounir Nisse

The deformation theory of affine cones over polarized projective varieties, initiated by Pinkham and further developed by Schlessinger and Wahl, is central to the study of singularities and graded deformation functors. For a projective variety \(Y\) with ample line bundle \(\mathcal L\), the affine cone \(C(Y)\) carries a natural \(\mathbb Z\)-grading, and P

Nicolas Fares, Elorri Garcia, Ahmad Badr, Yacine Amarouchene

The unicellular microalga Chlamydomonas reinhardtii is widely recognized as a premier model living microswimmer for physicists and biophysicists. However, the interest around C. reinhardtii goes beyond its swimming capabilities. In fact, light can drastically alter its behavior: under blue illumination, the cell attaches to a nearby surface and intermittentl

Giusi Capobianco, Angelina Zheng

We study the conjecture stated by Jensen and Len on a tropical version on Martens' theorem via the Brill--Noether rank of a tropical curve. We recall Coppens' counterexample of Martens-special chain of cycles, and we generalize the construction defining another class of graphs, Martens-special trees of cycles, for which the conjecture does not hold in a simi

Karina Chichifoi, Fabio Merizzi, Michele Colajanni

Deep learning and federated learning (FL) are becoming powerful partners for next-generation weather forecasting. Deep learning enables high-resolution spatiotemporal forecasts that can surpass traditional numerical models, while FL allows institutions in different locations to collaboratively train models without sharing raw data, addressing efficiency and

Michael Heller, Tomasz Miller, Wiesław Sasin

In this work, we propose a dangerous journey -- a journey through the strong singularity from one universe to another or from inside of a black hole to its 'inverse' as a white hole. Such singularities are hidden in the Friedman and Schwarzschild solutions; we call them malicious singularities. The journey is made possible owing to two generalizations. The f

P. I. Hurtado, J. J. del Pozo, P. L. Garrido

Hydrodynamics provides a universal description of the emergent collective dynamics of vastly different many-body systems, based solely on their symmetries and conservation laws. Here we harness this universality, encoded in the Navier-Stokes-Fourier (NSF) equations, to find general scaling laws for the stationary uniaxial solutions of the compressible NSF pr

Samuel J. Magorrian, Ljiljana Stojanović, Lara Kabalan, Ardita Shkurti

Ultra-high temperature ceramics (UHTCs) represent a class of crystalline materials for extreme environments. They can withstand extremely high temperatures but are mechanically difficult to work with due to their inherent brittleness. Mixture compounds, in particular high-entropy mixtures, offer a pathway to tune the physical properties of UHTCs such as thei

Mounir Nisse

The deformation theory of singular varieties plays a central role in understanding the geometry and moduli of algebraic varieties. For a variety $X$ with possibly singular points, the space of first-order infinitesimal deformations is given by \( T^1_X = \operatorname{Ext}^1_{\mathcal{O}_X}(\Omega_X, \mathcal{O}_X), \) which measures the Zariski tangent spac

M. Juvela, N. Ysard

Dust is an important tracer of the structure of interstellar clouds, as well as a central factor in the thermal balance and chemistry of the clouds. Our knowledge of the dust properties is nevertheless incomplete, especially regarding the dense star-forming clouds. The aim is to study dust evolution in the Orion Molecular Cloud 3 (OMC-3) and how uncertainty

Ingemar Bengtsson, Markus Grassl

A well supported conjecture states that SIC-POVMs -- maximal sets of complex equiangular lines -- with anti-unitary symmetry give rise to an identity expressing some of its overlaps as squares of the (rescaled) components of a suitably chosen fiducial vector. In number theoretical terms the identity essentially expresses Stark units as sums of products of pa

Marc Arnaudon, Jean-François Chassagneux, Sergey Nadtochiy, Adrien Richou

The purpose of this paper is twofold. First, we introduce the notion of a $\Gamma$-martingale on a Euclidean manifold with a boundary (i.e., the closure of an open connected domain in R d ), we provide its equivalent characterization through the $\Gamma$-convex functions, and we establish its connection with the reflected backward stochastic differential equ

Matvey Yorkhov, Vladimir Faerman, Anton Konev

The article discusses a key reconciliation protocol for quantum key distribution (QKD) systems based on Tree Parity Machines (TPM). The idea of transforming key material into neural network weights is presented. Two experiments were conducted to study how the number of synchronization iterations and the amount of leaked information depend on the quantum bit

Hyun-Gi Lee, Jaekyeong Han, Minjun Kwon, Hyeonuk Kwon

As battery technologies advance toward higher stability and energy density, the need for extensive cell-level testing across various component configurations becomes critical. To evaluate performance and understand the operating principles of batteries in laboratory scale, fabrication and evaluation of coin cells are essential processes. However, the convent

Ayrat Abdullin, Umair Bin Waheed, Leo Eisner, Naveed Iqbal

Seismic phase picking is fundamental for microseismic monitoring and subsurface imaging. Manual processing is impractical for real-time applications and large sensor arrays, motivating the use of deep learning-based pickers trained on extensive earthquake catalogs. On a broader scale, these models are generally tuned to perform optimally in high signal-to-no

Chethana Prasad Kabgere, Sudarshan T S B

Advanced Driver Assistance Systems (ADAS) increasingly employ Federated Learning (FL) to collaboratively train models across distributed vehicular nodes while preserving data privacy. Yet, conventional FL aggregation remains susceptible to noise, latency, and security constraints inherent to real-time vehicular networks. This paper introduces Noise-Resilient

Adam Braun, Jean Auriol, Lucas Brivadis

It is well-known that the exponential stability of Integral Difference Equations and Delay Difference Equations, in the usual state space of continuous functions, is equivalent to the location of the roots of its associated characteristic equation strictly in the open left half-plane (see e.g. [16, Chapter 9]). In this paper, we use results from [15, Chapter

Chendong Sun, Ali Mao, Lei Xu, mingmin Chen

Speculative Decoding is a prominent technique for accelerating the autoregressive inference of large language models (LLMs) by employing a fast draft model to propose candidate token sequences and a large target model to verify them in parallel. However, its core component -- the rejection sampling mechanism -- relies on a fixed, context-independent random t

Neevkumar Manavar, Hanno Gerd Meyer, Joachim Waßmuth, Barbara Hammer

Monitoring contact pressure in hospital beds is essential for preventing pressure ulcers and enabling real-time patient assessment. Current methods can predict pressure maps but often lack physical plausibility, limiting clinical reliability. This work proposes a framework that enhances plausibility via Informed Latent Space (ILS) and Weight Optimization Los

I. G. van Rens, O. O. Zheliuk, M. W. de Dreu, K. Mukhuti

We have measured the quantum Hall effect in monolayer graphene samples that were exposed to a cold hydrogen plasma leading to a hydrogenation level of a few percent. Compared to pristine graphene, the Landau level distance significantly decreases in the hydrogenated structures, and its field dependence changes from square root type to linear. From this obser

Zhuo Chen, Chengqun Yang, Zhuo Su, Zheng Lv

Face relighting aims to synthesize realistic portraits under novel illumination while preserving identity and geometry. However, progress remains constrained by the limited availability of large-scale, physically consistent illumination data. To address this, we introduce POLAR, a large-scale and physically calibrated One-Light-at-a-Time (OLAT) dataset conta

Gong Chen, Chaokun Zhang, Pengcheng Lv, Xiaohui Xie

Collaborative perception has garnered significant attention as a crucial technology to overcome the perceptual limitations of single-agent systems. Many state-of-the-art (SOTA) methods have achieved communication efficiency and high performance via intermediate fusion. However, they share a critical vulnerability: their performance degrades under adverse com

Emna Bouzid, Noura Baccar, Kamran Iqbal, Yassine Chaouch

As agriculture faces increasing pressure from water scarcity, especially in regions like Tunisia, innovative, resource-efficient solutions are urgently needed. This work explores the integration of indoor vertical hydroponics with Machine Learning (ML) techniques to optimize basil yield while saving water. This research develops a prediction system that uses

Jin Sob Kim, Hyun Joon Park, Wooseok Shin, Dongil Park

The Automatic Identification System (AIS) enables data-driven maritime surveillance but suffers from reliability issues and irregular intervals. We address vessel destination estimation using global-scope AIS data by proposing a differentiated approach that recasts long port-to-port trajectories as a nested sequence structure. Using spatial grids, this metho

Ifeoluwa Oluwatosin Kunle-John, Segun P. Michaels, Edith N. Okay

Eleyele Lake has enormous economic importance as it is completely surrounded by various communities which discharge their domestic waste directly into the lake. This alters the physical, chemical and biological characteristics of the lake. It is essential to assess the water for its various usage. Twelve (12) samples were collected from various locations of

Riccardo Castagna, Gautam Singh, Cristiano Riminesi, Andrea Di Donato

Refraction, traditionally viewed as a geometric event occurring at material interfaces, is now being re-examined through the lens of coherence. Recent studies in optics and photonics, including coherence tomography, Moire interference, and coherence-engineered diffraction, indicate that phase organization alone can bend light even without index discontinuiti

Hans Jarett J. Ong, Brian Godwin S. Lim, Dominic Dayta, Renzo Roel P. Tan

Unsupervised representation learning seeks to recover latent generative factors, yet standard methods relying on statistical independence often fail to capture causal dependencies. A central challenge is identifiability: as established in disentangled representation learning and nonlinear ICA literature, disentangling causal variables from observational data

Aida Abiad, Jan Meeus

Hoffman proved that a graph $G$ with adjacency eigenvalues $\lambda_1\geq \cdots \geq \lambda_n$ and chromatic number $\chi(G)$ satisfies $\chi(G)\geq 1+\kappa,$ where $\kappa$ is the smallest integer such that $$\lambda_1+\sum_{i=1}^{\kappa}\lambda_{n+1-i}\leq 0.$$ We extend this eigenvalue bound to the distance-$k$ setting, and also show a strengthening of

Khalid Ferji

Machine-learning (ML) models in polymer science typically treat a polymer as a single, perfectly defined molecular graph, even though real materials consist of stochastic ensembles of chains with distributed lengths. This mismatch between physical reality and digital representation limits the ability of current models to capture polymer behaviour. Here we in

Dominik Geißler, Tobias Winkler

Probabilistic programs encode stochastic models as ordinary-looking programs with primitives for sampling numbers from predefined distributions and conditioning. Their applications include, among many others, machine learning and modeling of autonomous systems. The analysis of probabilistic programs is often quantitative - it involves reasoning about numeric

F. Sattin. L. Martinelli

The system made by a charged particle interacting with a single electrostatic wave which propagates perpendicularly to the magnetic field, at a frequency larger than the cyclotron one, has been extensively studied in literature due to its implications with ion heating in magnetized plasmas. It is known that a threshold in the electrostatic potential must be

Alfredo González-Calvin, Juan F. Jiménez, Héctor García de Marina

Path generation, the process of converting high-level mission specifications, such as sequences of waypoints from a path planner, into smooth, executable paths, is a fundamental challenge in mobile robotics. Most path following and trajectory tracking algorithms require the desired path to be defined by at least twice continuously differentiable functions to

Romana Mikušincová, Lorenzo Marra, Hemanth Manikantan, Stefano Bianchi

We present the first X-ray polarimetric measurement of the neutron star low-mass X-ray binary system AX J1745.6-2901 conducted by the Imaging X-ray Polarimetry Explorer (IXPE) satellite. This transient source, located within $ \sim $1.5' of the Galactic center, was observed serendipitously during a MAXI J1744-294 observation with a duration of 150 ks. The co

Giulio Ciraolo, Alberto Farina, Troy Petitt

We study model semilinear equations on complete and non-compact weighted Riemannian manifolds with non-negative Bakry-\'Emery Ricci curvature. Our main goal is to classify positive solutions of the equation at the Sobolev-critical exponent, and furthermore to prove that the existence of such solutions implies rigidity of the manifold and triviality of the we

Émilie Charlier, Savinien Kreczman

Positional numeration systems are a large family of numeration systems used to represent natural numbers. Whether the set of all representations forms a regular language or not is one of the most important questions that can be asked of such a system. This question was investigated in a 1998 article by Hollander. Central to his analysis is a property linking

Funing Liu, Qitao Hu, Anton Sabantsev, Giovanni Di Muccio

Biomolecules exhibit dynamic conformations critical to their functions, yet observing these processes at the single-molecule level under native conditions remains a formidable challenge. While surface immobilization has been widely used to extend observation times, it could disrupt molecular dynamics and impede biological function. Moreover, the study of wea

Katharina Ledebur. Ladislav Bartuska, Klaus Friesenbichler, Peter Klimek

The automotive industry is undergoing transformation, driven by the electrification of powertrains, the rise of software-defined vehicles, and the adoption of circular economy concepts. These trends blur the boundaries between the automotive sector and other industries. Unlike internal combustion engine (ICE) production, where mechanical capabilities dominat

Minghui Hou, Wei-Hsing Huang, Shaofeng Liang, Daizong Liu

Vision-language models enable the understanding and reasoning of complex traffic scenarios through multi-source information fusion, establishing it as a core technology for autonomous driving. However, existing vision-language models are constrained by the image understanding paradigm in 2D plane, which restricts their capability to perceive 3D spatial infor

Siyuan Shen, Mikhail Khalilov, Lukas Gianinazzi, Timo Schneider

Resource disaggregation is a promising technique for improving the efficiency of large-scale computing systems. However, this comes at the cost of increased memory access latency due to the need to rely on the network fabric to transfer data between remote nodes. As such, it is crucial to ascertain an application's memory latency sensitivity to minimize the

Hongxuan Sun, Tao Wu

Semantic segmentation requires a holistic understanding of the physical world, as it assigns semantic labels to spatially continuous and structurally coherent objects rather than to isolated pixels. However, existing data-free knowledge distillation (DFKD) methods-primarily designed for classification-often disregard this continuity, resulting in significant

Francesco Salvi, Giuseppe Russo, Adam Barla, Vincent Moreau

Meat consumption is a major driver of global greenhouse gas emissions. While pricing interventions have shown potential to reduce meat intake, previous studies have focused on highly constrained environments with limited consumer choice. Here, we present the first large-scale field experiment to evaluate multiple pricing interventions in a real-world, compet

Abdul Rab

We study within-host HIV dynamics using a three--component nonlinear ordinary differential equation model for healthy CD4$^{+}$ T cells, infected CD4$^{+}$ T cells, and free virus. In addition to the baseline model without treatment, we consider two treatment extensions that incorporate antiretroviral therapy: (i) separate efficacy terms for Reverse Transcri

Deepak Ingole, Valentin Bhend, Shiva Ganesh Murali, Oliver Dobrich

Manufacturing processes are often perturbed by drifts in the environment and wear in the system, requiring control re-tuning even in the presence of repetitive operations. This paper presents an iterative learning framework for automatic tuning of Nonlinear Model Predictive Control (NMPC) weighting matrices based on task-level performance feedback. Inspired

Thanh-Danh Luu, Le-Vu Nguyen Dinh, Duc-Thien Tran, Duy-Bao Bui

The growing volume of video data and the introduction of complex retrieval challenges, such as the Temporal Retrieval and Alignment of Key Events (TRAKE) task at the Ho Chi Minh City AI Challenge 2025, expose critical limitations in existing systems. Many methodologies lack scalable, holistic architectures and rely on "frozen" embedding models that fail on o

Haoyu Dong, Pengkun Zhang, Yan Gao, Xuanyu Dong

We introduce FinWorkBench (a.k.a. Finch) for evaluating AI agents on real-world, enterprise-grade finance and accounting workflows that interleave data entry, structuring, formatting, web search, cross-file retrieval, calculation, modeling, validation, translation, visualization, and reporting. Finch is sourced from authentic enterprise workspaces from Enron

Kathrin Bringmann, Jay Jorgenson, Lejla Smajlović

Let $\Gamma\subset PSL_2(\mathbb{R})$ be a Fuchsian group of the first kind which has a cusp $i\infty$ of width one. In this paper, we first consider a generating function formed with the Niebur--Poincar\'e series $\{F_{m,s}(\tau)\}_{m\ge 1}$ associated to $i\infty$. We prove a relation between the continuation of this generating function to $s=1$ with the r

Federico Bonetto, Anthony Popa, Matthew Powell, Peter Chen

In this paper, we study a system of $M$ particles interacting with a reservoir of $N$ particles, where $N >> M$, and compare this setup to one where the $M$-particle system interacts with a thermostat of infinite particles. Our goal is to prove a suitable upper bound, uniform in time, on the distance between the states of these two setups, given an initial M

Jakub Łyskawa, Jakub Lewandowski, Paweł Wawrzyński

Soft Actor-Critic (SAC) is widely used in practical applications and is now one of the most relevant off-policy online model-free reinforcement learning (RL) methods. The technique of n-step returns is known to increase the convergence speed of RL algorithms compared to their 1-step returns-based versions. However, SAC is notoriously difficult to combine wit

Xianchao Guan, Zhiyuan Fan, Yifeng Wang, Fuqiang Chen

The development of clinical-grade artificial intelligence in pathology is limited by the scarcity of diverse, high-quality annotated datasets. Generative models offer a potential solution but suffer from semantic instability and morphological hallucinations that compromise diagnostic reliability. To address this challenge, we introduce a Correlation-Regulate