March 2026 arXiv papers — page 55

Cansu Sancaktar, David Zhang, Gabriel Synnaeve, Taco Cohen

Reinforcement learning (RL) has emerged as a powerful paradigm for improving large language models beyond supervised fine-tuning, yet sustaining performance gains at scale remains an open challenge, as data diversity and structure, rather than volume alone, become the limiting factor. We address this by introducing a scalable multi-turn synthetic data genera

Roberto Casarin, Matteo Iacopini, Antonio Peruzzi

A new dynamic latent space eigenmodel (LSM) is proposed for weighted temporal networks. The model accommodates integer-valued weights, excess of zeros, time-varying node positions (features), and time-varying network sparsity. The latent positions evolve according to a vector autoregressive process that accounts for lagged and contemporaneous dependence acro

Viktor Csimma

Using agda2hs and ad-hoc Haskell FFI bindings, writing Qt applications in C++ with Agda- or Haskell-based backends (possibly including correctness proofs) is already possible. However, there was no repeatable methodology to do so, nor to use arbitrary Haskell built-in libraries in Agda code. We present a well-documented, general methodology to address this,

Yushuai Song, Weize Quan, Weining Wang, Jiahui Sun

Recent advances in generative super-resolution (SR) have greatly improved visual realism, yet existing evaluation and optimization frameworks remain misaligned with human perception. Full-Reference and No-Reference metrics often fail to reflect perceptual preference, either penalizing semantically plausible details due to pixel misalignment or favoring visua

Isabella Graßl

The first generation of students is learning to program alongside GenAI (Generative Artificial Intelligence) tools, raising questions about how young learners critically engage with them and perceive ethical responsibilities. While prior research has focused on university students or developers, little is known about secondary school novices, who represent t

Mathias Braun, Marta Sálamo Candal

We prove a globally hyperbolic spacetime with locally Lipschitz continuous metric and timelike distributional Ricci curvature bounded from below obeys the timelike measure contraction property. The remarkable class of examples of spacetimes that are covered by this result includes impulsive gravity waves, thin shells, and matched spacetimes. As applications,

Giuseppe Cosma Brusca, Davide Donati, Sergio Scalabrino, Chiara Trifone

We prove compactness with respect to $\Gamma$-convergence for a general class of non-local energies modelled after the ones considered in [Gobbino, CPAM (1998)]. We give an integral representation result for the limits, which are free discontinuity functionals defined on the space of generalised special functions of bounded variation. We then characterise th

Isabella Graßl, Birgit Penzenstadler

Mental health and well-being are major concerns in higher education and professional fields such as software engineering, yet are often overlooked in curricula. This paper describes our approach to include mental health, well-being, and sustainability in software engineering education in two ways: (1) well-being-focused software projects that ask students to

Daniel Doehring, Jesse Chan, Hendrik Ranocha, Michael Schlottke-Lakemper

We introduce the concept of volume term adaptivity for high-order discontinuous Galerkin (DG) schemes solving time-dependent partial differential equations. Termed v-adaptivity, we present a novel general approach that exchanges the discretization of the volume contribution of the DG scheme at every Runge-Kutta stage based on suitable indicators. Depending o

Peter Vassilev Danchev, Truong Cong Quynh, Jan Žemlička

We characterize in certain basic cases when a module over a ring is either {\it hereditarily Bassian} or {\it super Bassian} in the sense that either each its proper submodule is Bassian or, respectively, each its proper epimorphic image is Bassian. We prove several structural criteria for both hereditarily Bassian and super Bassian modules over non-primitiv

Fedor Shuklin, Khristina Albitskaya, Sergei Solovyov, Alexander Chernov

We investigate spin-wave modes in confined ferromagnetic resonators with spherical and cylindrical geometries across the exchange-dominated, dipole-exchange, and dipolar interaction regimes. Starting from the linearized Landau-Lifshitz-Gilbert equation, we show that the projection of the total angular momentum and mirror parity are conserved quantities in th

Shengyu Duan, Marcos L. L. Sartori, Rishad Shafik, Alex Yakovlev

Increasing demands for adaptability, privacy, and security at the edge have persistently pushed the frontiers for a new generation of machine learning (ML) algorithms with training and inference capabilities on-chip. Weightless Neural Network (WNN) is such an algorithm that is principled on lookup table based simple neuron structures. As a result, it offers

Dong-Hui Xu, Cong-Mian Zhen, Deng-Lu Hou, Li Ma

We investigate the magnetic and transport evolution in Mn$_{3+x}$Ga$_{1-x}$C ($0 \le x \le 0.60$), where Mn substitution at corner Ga sites induces lattice contraction and suppresses the antiferromagnetic order of Mn$_3$GaC. As $x$ increases, the magnetic ground state of the system undergoes a sequential transition from an antiferromagnetic state, via a cant

Isabella Graßl

When software is designed by people from diverse identities and experiences, it is more likely to be inclusive and address a broader range of user needs. However, for transgender and non-binary students in software engineering, the path to becoming such creators may be marked by unique challenges. While existing research explores gender minorities in profess

Mattia Moroder, Felix C. Binder, John Goold

Thermodynamic computing harnesses the relaxation dynamics of physical systems to perform matrix operations. A key limitation of such approaches is the often long thermalization time required for the system to approach equilibrium with sufficient accuracy. Here, we introduce a hybrid digital-thermodynamic algorithm that substantially accelerates relaxation th

Ian D Sharp

Presented here is an analysis of over 66,000 magnitude measurements, made by the author, of the cataclysmic variable SS Cygni in two photometric filters (Cousins R and Johnson V). This large number of measurements, along with their cadence, is sufficient to analyse the light curve for quasi-periodic oscillations (QPOs) ranging from a few minutes to a few hou

Adhemar de Senneville, Xavier Bou, Jérémy Anger, Rafael Grompone

Current Large Vision Language Models (LVLMs) excel at many zero-shot tasks like image captioning, visual question answering and OCR. However, these same models suffer from poor performance at image classification tasks, underperforming against CLIP-based methods. Notably, this gap is surprising because many LVLMs use CLIP-pretrained vision encoders. Yet LVLM

Akshay Vayal Parambath, Jose Flordelis, Venkatesh Tentu, Charitha Madapatha

We propose an alternating optimization framework for maximizing energy efficiency (EE) in reconfigurable intelligent surface (RIS) assisted distributed MIMO (D-MIMO) systems under both coherent and non-coherent reception modes. The framework jointly optimizes access point (AP) power allocation and RIS phase configurations to improve EE under per-AP power and

Matteo Magi, Francesca Lepori, Julian Adamek

The standard weak lensing formalism assumes that the lensing map relating the observed image of a source to its intrinsic shape depends only on the deflection angle. We show that this description is incomplete beyond linear perturbation theory, even when only scalar perturbations are present at first order. Using the Jacobi map formalism, we derive expressio

Wanying Qu, Jianxiong Gao, Wei Wang, Yanwei Fu

Capturing dynamic spatiotemporal neural activity is essential for understanding large-scale brain mechanisms. Functional magnetic resonance imaging (fMRI) provides high-resolution cortical representations that form a strong basis for characterizing fine-grained brain activity patterns. The high acquisition cost of fMRI limits large-scale applications, theref

Milad Ahanjideh, István Kovács

We verify Tutte's $3$-flow conjecture in the class of Cayley graphs on solvable groups of order $2n$, where $n$ is square-free. The proof relies on a new necessary and sufficient condition for a simple $5$-valent graph to admit a nowhere-zero $3$-flow in terms of a pseudoforest decomposition.

Tommaso Furieri, Stefano Bonora

Multi-Conjugate Adaptive Optics (MCAO) is essential for increasing the corrected Field-of-View (FoV) in astronomical imaging and potentially for free-space optical communications, particularly for small-aperture, transportable systems. We demonstrate the viability and performance of a Refractive-MCAO system utilizing a novel multi-actuator Deformable Lens (D

Ilya Karzhemanov

We establish a couple of dynamical properties of surjective rational maps $f: X \dashrightarrow X$ for smooth projective surfaces $X$. We also give a numerical condition for regularity of $f$ in the case when $X$ is a del Pezzo surface. Some explicit constructions and calculations, related to the topological entropy of $f$, are provided.

Martin Herrmann, Oussama Draissi, Christian Niesler, Ahmad-Reza Sadeghi

Microarchitectural vulnerabilities increasingly undermine the assumption that hardware can be treated as a reliable root of trust. Prevention mechanisms often lag behind evolving attack techniques, leaving deployed systems unable to assume continued trustworthiness. We propose a shift from prevention to detection through microarchitectural-aware remote attes

Francesco Vitali, Andrea Francesco Battaglia, Luca Belluzzi, Svetlana Berdyugina

Spectropolarimetric observations of solar flares in the He I D$_3$ line at 5876 {\AA} are extremely rare, and their diagnostic potential remains largely unexplored. We report the first unambiguous detection of linear polarization in He I D$_3$ during a solar flare. Using the high-precision ZIMPOL polarimeter at the IRSOL observatory in Locarno (Switzerland),

Dongxue Qu, Cong Zhang

Previous analyses of shocks generated by shell-crossing singularities are affected by inappropriate gauge choices, and no systematic method is available for selecting a consistent gauge. To address this issue, we focus on the shock dynamics, which can be effectively described by a thin dust shell interacting with the surrounding dust. As a first step toward

Xu Zhang, Zhe Chen, Jing Zhang, Dacheng Tao

Most referring object detection (ROD) models, especially the modern grounding detectors, are designed for data-rich conditions, yet many practical deployments, such as robotics, augmented reality, and other specialized domains, would face severe label scarcity. In such regimes, end-to-end grounding detectors need to learn spatial and semantic structure from

Quentin Rible, Stéphane Seuret

We prove that, given a wavelet $\psi$, it is possible to choose some multi-integers $(p_j=(p_{j,1},...,p_{j,d}))_{j \in \mathbb{Z}} \in \mathbb{Z}^d$ such that, for every $x=(x_1,...,x_d) \in \mathbb{R}^d$, for infinitely many integers $j$, the tensorized wavelet $\prod_{i=1}^d \psi(2^j x_i-p_{j,i})$ does not vanish at $x$. This non-vanishing property is ess

Carlos Herdeiro, Hyat Huang, Jutta Kunz, Meng-Yun Lai

We revisit scalarized black holes in Einstein-scalar-Gauss-Bonnet gravity and analyze the thermodynamic phase transition between the Schwarzschild solution of general relativity and scalarized black holes. Restricting to spherically symmetric configurations, we investigate several classes of scalar-Gauss-Bonnet coupling functions. For the simplest quadratic

Moritz Schönherr, Friedemann Schuricht

The paper treats density measures as typical examples of finitely additive measures in $\mathbb{R}^n$. We study their structure and derive basic properties. In addition, estimates for related integrals are provided. The results are applied to the precise representative of general integrable functions and then they are specialized to functions of bounded vari

S. A. Kadam, V. A. Kshirsagar, Santosh Kumar Yadav

This study explores the bouncing solutions within the framework of modified $f(Q, L_m)$ gravity. We examine four prominent bouncing models, the symmetric bounce, super bounce, oscillatory bounce, and matter bounce, each of which has been extensively analyzed in the context of modified gravity theories. Our investigation focuses on the behavior of the Hubble

El-Mehdi El Arar, Massimiliano Fasi, Silviu-Ioan Filip, Mantas Mikaitis

Stochastic rounding (SR) is a probabilistic rounding mode that mitigates errors in large-scale numerical computations, especially when prone to stagnation effects. Beyond numerical analysis, SR has shown significant benefits in practical applications such as deep learning and climate modelling. The definition of classical SR requires that results of arithmet

Shengcheng Yu, Yuchen Ling, Chunrong Fang, Zhenyu Chen

The rapid proliferation and increasing complexity of software demand robust quality assurance, with graphical user interface (GUI) testing playing a pivotal role. Crowdsourced testing has proven effective in this context by leveraging the diversity of human testers to achieve rich, scenario-based coverage across varied devices, user behaviors, and usage envi

X. Chang, D. R. Xiong, Chenxu Liu, J. R. Xu

On $2024$ October $5$, BL Lacertae ($2200+420$) experienced one of its brightest gamma-ray flares. We conducted simultaneous follow-up observations in the $u$, $v$, $g$, $r$, $i$, and $z$ bands from $2024$ October $17$ to November $21$ using the Mephisto telescope and its two $50$ cm twin auxiliary photometric telescopes of Yunnan University. Intraday variab

Cecilia Kuan, Aditya Kamlesh Parikh, Henk van den Heuvel

Medical conversations offer insights into clinical communication often absent from Electronic Health Records. However, developing reliable clinical Natural Language Processing (NLP) models is hampered by the scarcity of domain-specific datasets, as clinical data are typically inaccessible due to privacy and ethical constraints. To address these challenges, w

S. Bellucci, M. Shatnev, L. Zazunov

We investigate the entanglement entropy of a massive scalar field using the spherical shell lattice model introduced by Das and Shankaranarayanan. A systematic numerical analysis is performed to study the dependence of the entropy on the field mass and on the size of the entangling region for both ground and excited states. For the ground state, we find that

Akash Ghosh, Tajamul Ashraf, Rishu Kumar Singh, Numan Saeed

Multimodal agentic pipelines are transforming human-computer interaction by enabling efficient and accessible automation of complex, real-world tasks. However, recent efforts have focused on short-horizon or general-purpose applications (e.g., mobile or desktop interfaces), leaving long-horizon automation for domain-specific systems, particularly in healthca

Thibaut Modrzyk, Ane Etxebeste, Élie Bretin, Voichita Maxim

In this paper, we present a novel variational plug-and-play algorithm for Poisson inverse problems. Our approach minimizes an explicit functional which is the sum of a Kullback-Leibler data fidelity term and a regularization term based on a pre-trained neural network. By combining classical likelihood maximization methods with recent advances in gradient-bas

Harsh Yadav, Tobias Meisen

Current trajectory prediction models are primarily trained in an open-loop manner, which often leads to covariate shift and compounding errors when deployed in real-world, closed-loop settings. Furthermore, relying on static datasets or non-reactive log-replay simulators severs the interactive loop, preventing the ego agent from learning to actively negotiat

Daniel Bloch

This paper introduces a transformative framework for managing path-dependent financial risk by shifting from traditional distribution-centric models to a geometry-based approach. We propose the SigSwap as a new regulatory instrument that allows market participants to decompose complex risk into terminal price law and the underlying texture of the price path.

Lucien M. Vidagbandji, Alexandre Berred, Cyrille Bertelle, Laurent Amanton

Quantile regression (QR) relies on the estimation of conditional quantiles and explores the relationships between independent and dependent variables. At high probability levels, classical QR methods face extrapolation difficulties due to the scarcity of data in the tail of the distribution. Another challenge arises when the number of predictors is large and

Ali Muhammad, Imtiaz Khan, Tianjun Li, Shabbar Raza

Driven by the growing agreement between the experimentally measured muon anomalous magnetic moment and its SM prediction, we reexamine phenomenological consequences of the MSSM, which is embedded in the supersymmetric $SU(4)_C \times SU(2)_L \times SU(2)_R$ Pati-Salam model. In contrast to earlier studies that predominantly favored a specific sign for the Hi

Zdzislaw Burda, Mario Kieburg, Tomasz Maciocha

We discuss the order statistics of the particle positions of a gas of $N$ identical independent particles performing Brownian motion in one dimension in a potential that asymptotically behaves like $V(x) \sim x^γ$ for $x\rightarrow+\infty$, with a positive power $γ>0$. We show that in the stationary state, the order statistics that describe how the leaders a

Rami Luisto

Antonyms, or opposites, are sometimes defined as \emph{word pairs that have all of the same contextually relevant properties but one}. Seeing how transformer models seem to encode concepts as directions, this begs the question if one can detect ``antonymity'' in the geometry of the embedding vectors of word pairs, especially based on their difference vectors

Vasyl Semenov, Nataliya Vasylyeva

In this paper, given a certain regularity of a function $v$, we derive an explicit formula relating the order $\nu_0\in(0,1)$ of the leading fractional derivative in a fractional differential operator $\mathbf{D_t}$ with the variable coefficients $r_i=r_i(x,t)$ and the function $v$ on which this operator acts. Moreover, we discuss application of this result

Hisao Hayakawa, Satoshi Takada

We present an exactly solvable model of the Mpemba effect in an overdamped Langevin system confined to a two-dimensional, radially symmetric bistable potential. The potential is constructed as a piecewise quadratic-logarithmic function that is continuous and differentiable at the matching radii, enabling an exact mapping of the corresponding Fokker-Planck op

Jacob Aarup Dalsgaard, Filipi Nascimento Silva, Jin AI

Funding acknowledgments in scholarly publications provide large-scale trace data on organizations that support scientific research. We present a dataset for linking global science funding organizations to research publications by systematically disambiguating unique funding acknowledgment strings extracted from publication metadata. Funder names are matched

Jaehun Bang, Jinhyeok Kim, Minji Kim, Seungheon Jeong

Open-vocabulary 3D scene understanding enables users to segment novel objects in complex 3D environments through natural language. However, existing approaches remain slow, memory-intensive, and overly complex due to iterative optimization and dense per-Gaussian feature assignments. To address this, we propose LightSplat, a fast and memory-efficient training

Subir Majumder, Gilad Orr, Paul Ben-Ishai

Traces of water can profoundly alter the dielectric response of functional oxides, yet such effects have remained largely unrecognized in systems where colossal dielectric behaviour has been widely reported. Here, we investigate the impact of sub-percent hydration ($<$1 wt\%) on the dielectric relaxation, charge transport, and interfacial polarization proper

Kangxiang Xia, Bingshen Mu, Xian Shi, Jin Xu

Achieving natural full-duplex interaction in spoken dialogue systems (SDS) remains a challenge due to the difficulty of accurately detecting user interruptions. Current solutions are polarized between "trigger-happy" VAD-based methods that misinterpret backchannels and robust end-to-end models that exhibit unacceptable response delays. Moreover, the absence

Heng Wu, Junjie Wang, Benzhuo Lu

Neural operator learning directly constructs the mapping relationship from the equation parameter space to the solution space, enabling efficient direct inference in practical applications without the need for repeated solution of partial differential equations (PDEs) -- an advantage that is difficult to achieve with traditional numerical methods. In this wo

Gabriele Amante, Klaudia Mrazikova, Gabriele Centi, Sylvie Roke

Why kinetically stable oil droplets in water spontaneously acquire a negative charge remains one of the most vigorously debated questions in interfacial science. Here, we combine neural-network based deep potential molecular dynamics with a data-driven and information theory approach to probe the real-space electron density at an extended decane-water interf

Fedor B. Lyudogovskiy

We study the degree landscape of the partition graph $G_n$, whose vertices are the integer partitions of $n$ and whose edges correspond to elementary transfers of one unit between parts, followed by reordering. Using the previously established local degree formula, we introduce the degree layers $D_d(n)$, the degree spectrum $Spec_D(n)$, and the numerical in

Marco Damele

We prove that if $(B,+,\cdot)$ is a two-sided skew brace whose additive group is solvable, then every finite quotient of the multiplicative group $(B,\cdot)$ is solvable. In particular, our result recovers Nasybullov's theorem in the finite case ~\cite[Theorem~4.3(1)]{Nas} and extends it to arbitrary two-sided skew braces of solvable type.

Vojtěch Novák, Tomáš Bezděk, Ivan Zelinka, Swagatam Das

This paper provides a historical analysis of the IEEE CEC Single Objective Optimization competition results (2010-2024). We analyze how benchmark functions shaped winning algorithms, identifying the 2014 introduction of dense rotation matrices as a key performance filter. This design choice introduced parameter non-separability, reduced effectiveness of coor

Lukas Theiner, Maik Pfefferkorn, Yongpeng Zhao, Sebastian Hirt

Tuning control policies manually to meet high-level objectives is often time-consuming. Bayesian optimization provides a data-efficient framework for automating this process using numerical evaluations of an objective function. However, many systems, particularly those involving humans, require optimization based on subjective criteria. Preferential Bayesian

Patrick Noble, Mathieu Rosenbaum, Saad Souilmi

We introduce a practical, interactive simulator of the limit order book for large-tick assets, designed to produce realistic execution, costs, and P&L. The book state is projected onto a tractable representation based on spread and volume imbalance, enabling robust estimation from market data. Event timing is calibrated to reproduce the fine-scale temporal s

Gangyi Zhang, Runzhe Teng, Chongming Gao

Large Language Models (LLMs) have shown strong potential in generating natural language explanations for recommender systems. However, existing methods often overlook the sequential dynamics of user behavior and rely on evaluation metrics misaligned with practical utility. We propose SELLER (SEquence-aware LLM-based framework for Explainable Recommendation),

Michael N. Stavropoulos, André V. G. Cavalieri, Lutz Lesshafft, Peter Jordan

We consider a round turbulent jet grazing a rectangular plate angled at $45^\circ$. Through sound pressure measurements, the tonal dynamics associated with jet-edge interaction are explored in a parameter space comprising jet Mach number, $M_j$, and plate radial position, $R/D$. A variety of spectral signatures are observed and classified. The classification

Haoyu Ji, Bowen Chen, Zhihao Yang, Wenze Huang

Skeleton-based Temporal Action Segmentation (STAS) seeks to densely segment and classify diverse actions within long, untrimmed skeletal motion sequences. However, existing STAS methodologies face challenges of limited inter-class discriminability and blurred segmentation boundaries, primarily due to insufficient distinction of spatio-temporal patterns betwe

Dries Dirckx, Jan Swevers, Wilm Decré

Generating time-optimal, collision-free trajectories for autonomous mobile robots involves a fundamental trade-off between guaranteeing safety and managing computational complexity. State-of-the-art approaches formulate spline-based motion planning as a single Optimal Control Problem (OCP) but often suffer from high computational cost because they include se

Shubham Kumar Nigam, Suparnojit Sarkar, Piyush Patel

Conversational artificial intelligence has the potential to assist users in preliminary medical consultations, particularly in settings where access to healthcare professionals is limited. However, many existing medical dialogue systems operate in a single-turn question--answering paradigm or rely on template-based datasets, limiting conversational realism a

Mayssa Soussia, Gita Ayu Salsabila, Mohamed Ali Mahjoub, Islem Rekik

Message passing is a core mechanism in Graph Neural Networks (GNNs), enabling the iterative update of node embeddings by aggregating information from neighboring nodes. Graph Convolutional Networks (GCNs) exemplify this approach by adapting convolutional operations for graph structures, allowing features from adjacent nodes to be combined effectively. Howeve

Chungeng Tian, Fenghua He, Ning Hao

This paper presents an equivariant filter (EqF) transformation approach for visual--inertial navigation. By establishing analytical links between EqFs with different symmetries, the proposed approach enables systematic consistency design and efficient implementation. First, we formalize the mapping from the global system state to the local error-state and pr

Abdalrhaman Koko

Grain boundaries can block slip-band propagation and generate intense local stress and strain fields that influence subsequent deformation and damage initiation in polycrystalline metals. Conventional geometric criteria, such as Schmid factor and slip-transfer parameters, describe crystallographic compatibility but do not quantify the energetic severity of a

Feng Zhao, Kangzheng Liu, Teng Peng, Yu Yang

Accurate representation of multimodal knowledge is crucial for event forecasting in real-world scenarios. However, existing studies have largely focused on static settings, overlooking the dynamic acquisition and fusion of multimodal knowledge. 1) At the knowledge acquisition level, how to learn time-sensitive information of different modalities, especially

Igor Colin, Aurélien Bellet, Stephan Clémençon, Joseph Salmon

In the IoT era, information is more and more frequently picked up by connected smart sensors with increasing, though limited, storage, communication and computation abilities. Whether due to privacy constraints or to the structure of the distributed system, the development of statistical learning methods dedicated to data that are shared over a network is no

Sergio Alvarez-Telena, Marta Diez-Fernandez

This paper introduces Orthogonal Art, a proposed artistic discipline that emerges in dialectical response to artificial intelligence rather than in service of it. Unlike AI-augmented creative practices, Orthogonal Art is structurally defined by occupying the generative and conceptual spaces that current AI systems cannot access. As a founding instantiation o

Aurélien Guerder

In this article, we study a model of random permutations, which we call random standardized permutations, based on a sequence of i.i.d. random variables. This model generalizes others, such as the riffle-shuffle and the major-index-biased permutations. We first establish an exact result on the joint distribution of the number of cycles of given lengths, invo

Jacek Karwowski, Younesse Kaddar, Zihuiwen Ye, Esmeralda S. Whitammer

When language models are trained by reinforcement learning (RL) to write probabilistic programs, they can artificially inflate their marginal-likelihood reward by producing programs whose data distribution fails to normalise instead of fitting the data better. We call this failure likelihood hacking (LH). We formalise LH in a core probabilistic programming l

Mingyi Liu

RLHF-aligned language models exhibit response homogenization: on TruthfulQA (n=790), 40-79% of questions produce a single semantic cluster across 10 i.i.d. samples. On affected questions, sampling-based uncertainty methods have zero discriminative power (AUROC=0.500), while free token entropy retains signal (0.603). This alignment tax is task-dependent: on G

Elena Apresyan, Gor Sarkissian

In this paper we show how the Baxter Q-operator and the product formula for eigenfunctions of two-particle hyperbolic Ruijsenaars system can be derived from the genus one Moore-Seiberg duality identity in two-dimensional Liouville conformal field theory. We expect that this relation would reveal genuine role of the Moore-Seiberg identity in integrable system

Peng-Fei Wang, Lei Huang, Miao-Miao Wei, Hong Yang

We investigate the entanglement dynamics of two giant atoms coupled to a common waveguide. By introducing additional phase modulation at each coupling point, every photon propagation path is jointly controlled by two distinct coupling phases, enabling precise and flexible manipulation of the entanglement evolution. This phase engineering induces destructive

Tingxu Han, Wei Song, Weisong Sun, Hao Wu

With the development of deep learning, Neural Code Models (NCMs) such as CodeBERT and CodeLlama are widely used for code understanding tasks, including defect detection and code classification. However, recent studies have revealed that NCMs are vulnerable to adversarial examples, inputs with subtle perturbations that induce incorrect predictions while remai

Jiawei Zhao, Md Shamim Ahmed, Nicolai Dinh Khang Truong, Verena Schuster

Background: Electronic health records (EHRs) enable machine learning for diagnosis, prognosis, and clinical decision support. However, EHR standards vary by country and hospital, making records often incompatible. This limits large-scale and cross-clinical machine learning. To address such complexity, a metadata repository cataloguing available data elements

David Faget, José Luis Lisani, Miguel Colom

Planet-scale photo geolocalization involves the intricate task of estimating the geographic location depicted in an image purely based on its visual features. While deep learning models, particularly convolutional neural networks (CNNs), have significantly advanced this field, understanding the reasoning behind their predictions remains challenging. In this

Teodora Răgman, Adrian Bogdan Stânea, Horia Cucu, Adriana Stan

Open-source text-to-speech (TTS) frameworks have emerged as highly adaptable platforms for developing speech synthesis systems across a wide range of languages. However, their applicability is not uniform -- particularly when the target language is under-resourced or when computational resources are constrained. In this study, we systematically assess the fe

Hyunwoo Kim, Heesuk Kim, Wungrak Choi, Jae-Sang Hyun

For accurate glaucoma diagnosis and monitoring, reliable retinal layer segmentation in OCT images is essential. However, existing 2D segmentation methods often suffer from slice-to-slice inconsistencies due to the lack of contextual information across adjacent B-scans. 3D segmentation methods are better for capturing slice-to-slice context, but they require

Chang Wei, Yuchen Fan, Jian Cheng Wong, Chin Chun Ooi

Physics-informed neural networks (PINNs) have emerged as a major research focus. However, today's PINNs encounter several limitations. Firstly, during the construction of the loss function using automatic differentiation, PINNs often neglect information from neighboring points, which hinders their ability to enforce physical constraints and diminishes their

Jonathan Haag, Christian Metzner, Dmitrii Zendrikov, Giacomo Indiveri

On-chip learning is key to scalable and adaptive neuromorphic systems, yet existing training methods are either difficult to implement in hardware or overly restrictive. However, recent studies show that feedback-control optimizers can enable expressive, on-chip training of neuromorphic devices. In this work, we present a proof-of-concept implementation of s

Fabio Punzo, Federico Zucchero

This paper is the second part of the study initiated in a companion work and is devoted to finite-time blow-up and global existence for a semilinear heat equation on infinite weighted graphs. We first establish basic results on mild and classical solutions (which, to the best of our knowledge, were not previously available in the setting of graphs) proving t

Aymen Bouferroum, Valeria Loscri, Abderrahim Benslimane

The Industrial Internet of Things (IIoT) introduces significant security challenges as resource-constrained devices become increasingly integrated into critical industrial processes. Existing security approaches typically address threats at a single network layer, often relying on expensive hardware and remaining confined to simulation environments. In this

Fabio Punzo, Federico Zucchero

We investigate finite-time blow-up of solutions to the Cauchy problem for a semilinear heat equation posed on infinite graphs. Assuming that the initial datum is sufficiently large, we establish a general blow-up criterion valid on arbitrary infinite graphs. We then apply this result to specific classes of graphs, including trees and the integer lattice. The

Iris Dumeur, Jérémy Anger, Gabriele Facciolo

Multi-modal Satellite Image Time Series (SITS) analysis faces significant computational challenges for live land monitoring applications. While Transformer architectures excel at capturing temporal dependencies and fusing multi-modal data, their quadratic computational complexity and the need to reprocess entire sequences for each new acquisition limit their

Hector Blondel, Lucas Drumetz, Thierry Chonavel

Most algorithms for hyperspectral image unmixing produce point estimates of fractional abundances of the materials to be separated. However, in the absence of reliable ground truth, the ability to perform abundance uncertainty quantification (UQ) should be an important feature of algorithms, e.g. to evaluate how hard the unmixing problem is and how much the

Mehdi Bouziane, Silia Bercisse, Abdennasser Chekroun, Simon Girel

This research paper introduces an SQIR-V epidemic model to investigate the transmission of infectious diseases. Particular attention is paid to the roles of vaccination and quarantine (incorporating physical distancing interventions) in protecting susceptible individuals. The model features nonlinear transition rates that depend on the history of infection,

Fan Chen, Shuyin Xia, Yi Wang

Single-source domain generalization for crowd counting is highly challenging because a single labeled source domain may contain heterogeneous latent domains, while unseen target domains often exhibit severe distribution shifts. A central issue is stable latent domain discovery: directly performing flat clustering on evolving sample-level latent features is e

Saba Nasiri, Selin Aviyente, Dorina Thanou

Learning representations from multiplex graphs, i.e., multi-layer networks where nodes interact through multiple relation types, is challenging due to the entanglement of shared (common) and layer-specific (private) information, which limits generalization and interpretability. In this work, we introduce a causal inference-based framework that disentangles c

Ludovic Pirard, Lorenzo Picinali, Katarina C. Poole

Individual head-related transfer functions (HRTFs) are essential for accurate spatial audio binaural rendering but remain difficult to obtain due to measurement complexity. This study investigates whether photogrammetry-reconstructed (PR) head and ear meshes, acquired with consumer hardware, can provide a practically useful baseline for individual HRTF synth

Zi-Qian Zhou, Zhi-Fan Zhang, Cong Xiao, Hua Jiang

Surface acoustic waves (SAWs) become a popular driving source in modern condensed matter physics, but most existing theories simplify them as electric fields and ignore the non-uniform Brillouin zone folding effect. We develop a semiclassical framework and reconstruct the electron dynamics by treating SAW as a quasi-periodic potential modulating electronic m

Feng Wu, Wei Zuo, Wenliang Yang, Jun Xiao

Zero-shot object navigation requires agents to locate unseen target objects in unfamiliar environments without prior maps or task-specific training which remains a significant challenge. Although recent advancements in vision-language models(VLMs) provide promising commonsense reasoning capabilities for this task, these models still suffer from spatial hallu

Peng Xu, Yapeng Li, Tinghuan Chen, Tsung-Yi Ho

Digital circuits representation learning has made remarkable progress in the electronic design automation domain, effectively supporting critical tasks such as testability analysis and logic reasoning. However, representation learning for analog circuits remains challenging due to their continuous electrical characteristics compared to the discrete states of

Jinkai Gao

In this paper, we consider the existence of positive solutions to the following slightly supercritical Choquard equation \begin{equation*} \begin{cases} -\Delta u=\displaystyle\Big(\int\limits_{\Omega}\frac{u^{2^*_{\alpha}+\varepsilon}(y)}{|x-y|^\alpha}dy\Big)u^{2^*_{\alpha}-1+\varepsilon},\quad u>0\ \ &\mbox{in}\ \Omega, \quad \ \ u=0 \ \ &\mbox{on}\ \parti

Yaya Bello, Yahia Medjahdi, Laurent Clavier, Arthur Louchart

The sub-THz spectrum offers numerous advantages, including massive multiple-input multiple-output (MIMO) technology with large antenna arrays that enhance spectral efficiency (SE) of future systems. Hybrid precoding (HP) thus emerges as a cost-effective alternative to fully digital precoding regarding complexity and energy consumption. However, sub-THz frequ

Xia Gu, Babak Haghighat, Pavel Putrov

We study Knizhnik-Zamolodchikov (KZ) connection in the presence of irregular singularities, that is, poles of higher order. We consider both the case of a universal connection and the case when it is associated with a specific simple Lie algebra, such as $\mathfrak{su}(2)$. We give some general results about the monodromies of such flat connections in the co

Haoyu Ji, Xueting Liu, Yu Gao, Wenze Huang

Skeleton-based Temporal Action Segmentation (STAS) aims to densely parse untrimmed skeletal sequences into frame-level action categories. However, existing methods, while proficient at capturing spatio-temporal kinematics, neglect the underlying physical dynamics that govern human motion. This oversight limits inter-class discriminability between actions wit

Chandra Prakash, Debaprasad Maity

We investigate the imprint of post-inflationary evolution on primordial cosmological correlators by relaxing the standard assumption of instantaneous reheating. We compute the power spectrum and bispectrum for a conformally coupled and non-minimally coupled ($\xi \neq \frac{1}{6},0$) scalar field with cubic self-interaction, across a finite reheating epoch c

Thomas Conway

This work presents a fully discrete, low cost digital isolator requiring no specialized ICs and implemented entirely with general purpose transistors and a two layer PCB embedded air core transformer. The design avoids vendor lock in and long term component obsolescence risks, while providing >1 kV isolation, ~200 ns propagation delay, and validated NRZ data

Pengfei Zhu, Julien Lecompagnon, Philipp Daniel Hirsch, Mathias Ziegler

Snell law is traditionally regarded as a hallmark of phase-propagating phenomena such as optical, acoustic, elastic, electromagnetic, and quantum waves. In contrast, purely diffusive processes, such as Fourier heat conduction and chemical diffusion, are generally considered incapable of exhibiting refractive/reflective behavior. In this letter, we demonstrat

Fei Bai, Zhipeng Chen, Chuan Hao, Ming Yang

Recently, reinforcement learning~(RL) has become an important approach for improving the capabilities of large language models~(LLMs). In particular, reinforcement learning from verifiable rewards~(RLVR) has emerged as a promising paradigm for reasoning tasks. However, existing RL-based training still remains only a rough approximation to human learning. Hum