April 2026 arXiv papers — page 108

Ziao Wang, Fei Xia, Logan G. Wright, Tatsuhiro Onodera

Deep learning has triggered explosive growth in the demand for specialized hardware processors, thus motivating the development of scalable and reconfigurable computing substrates. Optical processors offer a fundamentally different computing paradigm, combining massive parallelism and ultrahigh bandwidth with the potential for substantial energy savings. How

Andjela Mladenovic, Aaron Courville, Gauthier Gidel

In recent years, with the advancement of frontier AI, we have observed certain dynamics in open-sourcing and closed-sourcing decisions. We propose a game-theoretic model to analyze these dynamics in the current landscape of the AI race. Our model builds on an R&D race framework under a winner-takes-all setting, and it accounts for the cases where the players

Ramit Pahwa, Apoorva Beedu, Parivesh Priye, Rutu Gandhi

Voice assistants increasingly rely on Speech Language Models (SpeechLMs) to interpret spoken queries and execute complex tasks, yet existing benchmarks lack domain breadth, acoustic diversity, and compositional reasoning complexity to evaluate tool-calling performance. We introduce Audio2Tool, a large-scale dataset comprising approximately 30,000 queries des

Djordje Bogdanović, Marija Dimitrijević Ćirić, Richard J. Szabo

We construct cubic scalar field theory on $\lambda$-Minkowski space by combining the Batalin-Vilkovisky formalism with harmonic analysis, and produce two inequivalent noncommutative quantum field theories. The braided theory is based on a braided $L_\infty$-algebra whereby covariance dictates a spectral decomposition into cylindrical Bessel functions that di

Shiran Dudy, Jan Simson, Yanan Long

As a relatively new forum, ACM FAccT has become a key space for activists and scholars to critically examine emerging AI and ML technologies. It brings together academics, civil society members, and government representatives from diverse fields to explore the broader societal impacts of both deployed and proposed technologies. We report a large-scale partic

Reem Vitale, Masatoshi Hirabayashi

Ceres, the dwarf planet in the main asteroid belt, hosts heavily cratered surfaces where craters are continuously eroded mainly due to impact bombardment with a limited influence by non-impact processes. Over continuous bombardment, such regions experience both crater production and erasure, eventually ceasing the crater population growth. This end-state, kn

Kiran Kumar Challa, Alok Kumar Bharati, Venkataramana Ajjarapu

With a steady increase in the inverter technology integration to the grid, frequency response of the large inter-connection system becomes more unpredictable. This leads to a significant change in the boundaries of the coherent region, which highly depends on the changing disturbance locations and operating conditions. While most of the existing coherency id

Gerta Rücker, Annabel L. Davies, Guido Schwarzer

We show that the covariance matrix of the treatment effect estimates in a network meta-analysis can be obtained without matrix inversion using a geometric series of diffusion matrices. This property extends to the hat matrix and provides a connection between parameter estimation in regression analysis and random walks on the network graph. We also provide a

Haoran Zhang, Livia Betti, Konstantin Klemmer, Esther Rolf

In computer vision and machine learning for geographic data, out-of-domain generalization is a pervasive challenge, arising from uneven global data coverage and distribution shifts across geographic regions. Though models are frequently trained in one region and deployed in another, there is no principled method for determining when this cross-region adaptat

Percy S. Zhai, Mladen Kolar, Wei Biao Wu

For time series with long-range temporal dependence, inference for covariance and precision matrices is non-trivial. We propose a Berry-Esseen type Gaussian approximation result that gives a finite-sample bound for the Kolmogorov distance between the infinity norms of the estimation error of sample covariance matrix and the corresponding Gaussian approximati

Yanli Wang, Peng Kuang, Xiaoyu Han, Kaidi Xu

Large language models are increasingly deployed in settings where reliability matters, yet output-level uncertainty signals such as token probabilities, entropy, and self-consistency can become brittle under calibration--deployment mismatch. Conformal prediction provides finite-sample validity under exchangeability, but its practical usefulness depends on th

Members of the HRL Quantum Team, Collaborators, :, Michael Abraham

Commercially-relevant quantum computers will require large numbers of high-performing qubits that can be manufactured, integrated, and controlled at scale. Silicon exchange-only (EO) qubits are a strong candidate modality due to their control-signal simplicity and compatibility with advanced semiconductor manufacturing, but questions remain around the achiev

Maurício Corrêa, Alex Massarenti

Let $(X,Δ)$ be a smooth complex projective simple normal crossing pair of dimension $n\ge 3$ endowed with an everywhere nondegenerate logarithmic conformal tensor. If $K_X+Δ$ is not nef, then exactly one of the following occurs: $Δ=\varnothing$ and $X\simeq Q^n$; $X\simeq\mathbb{P}^n$ and $Δ$ is a hyperplane; or $n=2m$ and $(X,Δ)$ admits a $(K_X+Δ)$-negative

Deepak Kumar, Abhishek Pratap Singh, Puneet Kumar, Xiaobai Li

Understanding affective dynamics in real-world social systems is fundamental to modeling and analyzing human-human interactions in complex environments. Group affect emerges from intertwined human-human interactions, contextual influences, and behavioral cues, making its quantitative modeling a challenging computational social systems problem. However, compu

Matthew Frazier, Kostadin Damevski, Lori Pollock

Secondary school students enrolled in the AP Computer Science Principles (CSP) course commonly utilize web resources (e.g., tutorials, Q\&A sites) to better understand key concepts in the curriculum. The primary obstacle to using these resources is finding information appropriate for the learning task and student's background. In addition to web search, conv

William Fritsch, Noah Walton, Justin Loring, Jacob Forbes

This work investigates the use of resonance statistics for resonance evaluation to inform spin group assignment and an alternative fitting objective function beyond the commonly used chi-squared statistic. Resonance statistics -informed methods are applied to the automated resonance fitting framework, developed by N. Walton et al. In this automated framework

Xiaohui Zhang, Liaoyuan Yang, Peng Yang

This article proposes a data-driven framework to verify the distributed conditions that guarantee the system-wide stability for interconnected power systems. To guarantee system wide stability, the dynamics of each bus are required to satisfy an output differential passivity (ODP) condition with a sufficient index. These ODP indices uniformly quantify the im

Mattia Morgavi, Peter Majcen, Marco Rigobello, Simone Montangero

We introduce a method for the selective preparation and detection of quasiparticle wave packets, based on creation operators that generate dressed, localized excitations on top of interacting vacua of (quasi-)one-dimensional quantum lattice theories. This method exploits maximally localized Wannier functions (MLWFs) constructed from quasiparticle bands at in

Jason Cusati, Chris Brown

Software engineering research has experienced rapid growth in both output and participation over the past decades. Yet concerns persist about the field's ability to accumulate, integrate, and reuse knowledge in ways that support long-term progress. To better understand how the community itself perceives these challenges, we analyze responses from the ICSE 20

Hao Wang, Beichen Zhang, Yanpei Gong, Shaoyi Fang

As forgery types continue to emerge consistently, Incremental Face Forgery Detection (IFFD) has become a crucial paradigm. However, existing methods typically rely on data replay or coarse binary supervision, which fails to explicitly constrain the feature space, leading to severe feature drift and catastrophic forgetting. To address this, we propose AIFIND,

Vitalii Makogin, Evgeny Spodarev, Ilja Sukhanov

We propose a method for the prediction of stationary max--stable random fields with $\alpha$-Fr\'echet marginal distribution $H_\alpha$. The method is suitable to cope with heavy tails for $\alpha\in(0,2)$ and is (approximately) exact in marginal distributions. It is based on a recent extrapolation approach via level sets which requires no moment assumptions

Takashi Yoshino, Supanut Chaidee

We consider a new treatment for making polyhedron nets referred to as ``apple peel unfolding'': drawing the nets as if we were peeling off appleskins. We define apple peel unfolding strictly and implement a program that derives the sequential selection of the polyhedral faces for a target polyhedron in accordance with the definition. Consequently, the progra

Stephan Bark, Waqas Ahmed Malik, Maryna Prus, Hans-Peter Piepho

In variety testing, multi-environment trials (MET) are essential for evaluating the genotypic performance of crop plants. A persistent challenge in the statistical analysis of MET data is the estimation of variance components, which are often still inaccurately estimated or shrunk to exactly zero when using residual (restricted) maximum likelihood (REML) app

Alberto Hijano, Tero T. Heikkilä

Proportional-Integral-Derivative (PID) control is used for automatically regulating a measurable quantity to a desired setpoint. It is widely used in different types of classical control electronics. Here, we show how extending the feedback theory in quantum systems to include the derivative and integral parts influences both the transient and steady-state b

Nikhil Behari, Diego Rivero, Luke Apostolides, Suman Ghosh

Consumer LiDARs in mobile devices and robots typically output a single depth value per pixel. Yet internally, they record full time-resolved histograms containing direct and multi-bounce light returns; these multi-bounce returns encode rich non-line-of-sight (NLOS) cues that can enable perception of hidden objects in a scene. However, severe hardware limitat

Muhammad Z. Alam, Larry Stetsiuk, Arooba Zeshan

This paper presents a novel saturation aware space variant blind image deblurring framework designed to address challenges posed by saturated pixels in deblurring under high dynamic range and low light conditions. The proposed approach effectively segments the image based on blur intensity and proximity to saturation, leveraging a pre estimated Light Spread

Asmaou S. Ouedraogo, Donald J. Docimo

This paper presents a unified optimization framework for phase change material (PCM) based cooling systems. Thermal management is critical in applications such as photovoltaic (PV) modules, battery packs, and power electronics, where excessive heat reduces performance and lifespan. Designing such systems is challenging because energy dynamics, capacity, heat

Jia Li, Ruiqi Bai, Yangkang Luo, Yiran Zhang

Code generation refers to automatically producing executable programs from user requirements. Recently, researchers have explored approaches to enhance the correctness of generated code with advanced large language models. Although achieving improvements, existing approaches focus on designing reasoning strategies or post-refinement methods to enhance code g

Kaito Murakami, Taiki Kawamuro, Ryota Tomaru, Hirokazu Odaka

We present an analysis of XRISM and NuSTAR data obtained for the nearby low-Eddington active galactic nucleus NGC 7213. Our goal is to examine whether its He-like and H-like iron emission lines can be reproduced by photoionization or collisional ionization processes. Using the broad-band energy coverage of our data (2-60 keV), we first constrained the contin

Maximilian Hollendonner, Fedor Dzmitryevich Hrunski, Daniel Scheller, Kim Ullerich

Silicon vacancy (VSi) centers in 4H silicon carbide have emerged as a highly promising platform for semiconductor-based quantum technologies, combining excellent spin and optical properties with an industrial-grade, CMOS-compatible material. As these defects are increasingly integrated into practical quantum devices, they inevitably encounter lattice strain.

Leo Ferres, Laetitia Gauvin

Call detail records (CDR) from mobile phone networks are widely used to study human mobility however CDR data from a single mobile operator are inherently biased because the observed users do not mirror the population distribution. Using data from a major Chilean carrier in Santiago, we observe the user base is skewed by socioeconomic group, so aggregate met

Edward Rothberg

This paper takes an empirical look at asymptotic runtime growth rates for the most widely used algorithms for solving linear programming (LP) problems across a set of six optimization application areas that are known to produce large and difficult LP models. On the algorithm side, we consider the simplex method, interior-point methods, and PDHG. On the model

Linlin Ye, Zhaoqi Wu, Shao-Ming Fei

Quantum coherence plays a pivotal role in quantum algorithms. We study the coherence dynamics of the evolved states in Simon's quantum algorithm based on Tsallis relative $\alpha$ entropy and $l_{1,p}$ norm. We prove that the coherences of the first register and the second register both rely on the dimension $N$ of the state spaces of the $n$ qubit systems,

Wieland Schöllkopf, Sandy Gewinner, Marco De Pas, Heinz Junkes

We report on the design and performance of a two-color dual-oscillator infrared free-electron laser (FEL). The mid-infrared (MIR) FEL at the Fritz Haber Institute (FHI FEL) has been upgraded to include a second oscillator FEL beamline that permits lasing in the far-infrared (FIR) regime from 4.5 {\mu}m to 175 {\mu}m. In addition, a 500 MHz kicker cavity has

Nino Bašić, Ivan Damnjanović, Dragan Stevanović, Ivan Stošić

Let $k \in \mathbb{N}$ and let $H_1, H_2, \ldots, H_k$ be simple graphs such that for each $j \in \{ 1, 2, \ldots, k \}$, the vertex set of $H_j$ is $\{ 0, 1, 2, \ldots, n_j - 1 \}$ for some $n_j \in \mathbb{N}$. The ordered Ramsey number $R_\mathrm{ord}(H_1, H_2, \ldots, H_k)$ is the smallest $n \in \mathbb{N}$ for which every $k$-edge-coloring of the compl

Takumi Shimasue, Shota Kisaka, Aya Bamba, Shinpei Shibata

The pulsar beaming fraction is a fundamental quantity for connecting the observed pulsar population to the intrinsic Galactic population and for constraining pulsar emission geometry. In this study, we estimate the beaming fraction in each observational band (radio, $\gamma$-ray, and X-ray) and for each TeV survey (H.E.S.S., HAWC, and LHAASO) using TeV-selec

José Francisco Perles-Ribes

We propose a descriptive, realization-centred framework for detecting and characterising explosive and co-explosive behaviour in economic time series, which we term path-explosive behaviour. Departing from the data-generating-process (DGP) perspective that underlies recursive unit root testing, the approach operates directly on observable path properties of

Shaoqing Liu, Mushuang Liu

Computational complexity has been a major challenge in game-theoretic model predictive control (GT-MPC), as real-time solutions to a game (e.g., Nash equilibria (NEs)) have to be computed at each sampling instant of an MPC. This challenge is especially critical in autonomous driving, where interactions may involve many agents, and decisions must be made at f

André Saimon S. Sousa, Otto Pires, Frank Acasiete, Oscar M. Granados

Data plays a fundamental role in consolidating markets, services, and products in the digital financial ecosystem. However, the use of real data, especially in the financial context, can lead to privacy risks and access restrictions, affecting institutions, research, and modeling processes. Although not all financial datasets present such limitations, this w

Toru Harada, Yoshiharu Hirabayashi

We study the production of $^3_\Lambda$H and $^4_\Lambda$H in the $^{3,4}$He($K^-$,$\pi^0$) reactions at $p_{K^-}=1.0$~GeV/$c$ within the distorted-wave impulse approximation, using the optimal Fermi-averaged $K^-p\to\pi^0\Lambda$ amplitude. Because the $^3_\Lambda$H ground state is extremely weakly bound, the $d$--$\Lambda$ wave function becomes spatially e

Yongjun Li, Minghao Song

We report the experimental implementation of a Data-Driven Chaos Indicator (DDCI) [Y.~Li \emph{et al.}, Nucl.\ Instrum.\ Methods Phys.\ Res.\ A \textbf{1024} (2022) 166060] for online optimization of the National Synchrotron Light Source II (NSLS-II) storage ring. The DDCI quantifies the predictability of electron beam dynamics using turn-by-turn beam positi

Nis-Luca van Hülst, Mario Guillaume Cecile, Hai-Yen Van, Tomohiro Hashizume

Turbulent thermal convection governs heat transport in systems ranging from stellar interiors to industrial heat exchangers. Two-dimensional Rayleigh-B\'enard convection serves as a paradigm for these flows, reproducing key features such as thin boundary layers, large-scale circulation, and sustained plume dynamics. While Matrix Product State (MPS) methods h

Euclid Collaboration, A. A. Tumborang, K. I. Caputi, P. Rinaldi

Little Red Dots (LRDs) are some the most intriguing galaxy populations recently identified at z>~4 with JWST. They constitute the most extreme class of a more abundant population of sources with `V-shaped' spectral energy distributions (SEDs) and compact morphologies, which includes also Little Blue Dots (LBDs). Finding brighter analogues to these sources re

Lorenzo Beltrame, Jules Salzinger, Filip Svoboda, Jasmin Lampert

We present a three-stage progressive shadow-removal pipeline for the CVPR2026 NTIRE WSRD+ challenge. Built on OmniSR, our method treats deshadowing as iterative direct refinement, where later stages correct residual artefacts left by earlier predictions. The model combines RGB appearance with frozen DINOv2 semantic guidance and geometric cues from monocular

Julia Gehrlein, Jaime Hoefken Zink, Pedro A. N. Machado, João Paulo Pinheiro

At long-baseline neutrino experiments, neutral-current (NC) events accumulate in large numbers but are seldom exploited for new physics searches. We demonstrate their potential using non-standard neutrino interactions (NSI) with quarks as a case study. Charged-current (CC) analyses constrain NSI through matter effects on neutrino propagation, which probe alm

Yi Lin, Yihao Ding, Yonghui Wu, Yifan Peng

Automated 3D radiology report generation often suffers from clinical hallucinations and a lack of the iterative verification found in human practice. While recent Vision-Language Models (VLMs) have advanced the field, they typically operate as monolithic "black-box" systems without the collaborative oversight characteristic of clinical workflows. To address

Matthew S. Winnel, Sergio Juárez, Chithrabhanu Perumangatt, Taofiq Paraiso

Quantum protocols require classical signaling, and when classical signals propagate faster than quantum ones, standard rate-loss limits can be surpassed. We introduce an all-photonic measurement-device-independent quantum key distribution protocol that exceeds the single-repeater bound without error correction. When quantum signals travel at two-thirds the c

Emma R. Bouckley, Sam F. Lewin, Adrien Lefauve

We study the onset of two-dimensional secondary shear instability (SSI) in the braid regions connecting primary Kelvin-Helmholtz billows in stratified shear flows. While strain induced by the billows stabilises the braids, it also compresses their tilted isopycnals, enhancing baroclinic shear that enables rapid perturbation growth. By modifying the classical

Yeganeh Abdollahinejad, Ahmad Mousavi, Naeemul Hassan, Kai Shu

The widespread dissemination of multimodal content on social media has made misinformation detection increasingly challenging, as misleading narratives often arise not only from textual or visual content alone, but also from semantic inconsistencies between modalities and their evolution over time. Existing multimodal misinformation detection methods typical

Alexandra Dragomir, Ioana Pintilie, Antonio Barbalau, Marius Dragoi

Adapter-based methods have become a cost-effective approach to continual learning (CL) for Large Language Models (LLMs), by sequentially learning a low-rank update matrix for each task. To mitigate catastrophic forgetting, state-of-the-art approaches impose constraints on new adapters with respect to the previous ones, by targeting either subspace or coordin

Toby Perrett, Matthew Bouchard, William McCarthy

We introduce neuralCAD-Edit, the first benchmark for editing 3D CAD models collected from expert CAD engineers. Instead of text conditioning as in prior works, we collect realistic CAD editing requests by capturing videos of professional designers, interacting directly with CAD models in CAD software, while talking, pointing and drawing. We recruited ten con

Yongsheng Zhang

We extend a key result in [Zha26], by establishing the obstruction that the minimal product structure (for minimal submanifolds or stationary currents in spheres) automatically makes all cones over (non-trivial) minimal products fail to be calibrated by any global defined smooth calibration in Euclidean spaces.

Georgi P. Petrov, Svetoslav Botev, Antoniya Valcheva, Petko Nedialkov

We examine the dependence of the ellipticity of globular clusters in the Milky Way on their X-ray luminosity using two modern catalogs and combine them with optical and X-ray data from the literature. Kolmogorov-Smirnov tests applied across multiple subsets reveal statistically significant differences in the ellipticity distributions when both $L_{\rm X}$ an

Giuseppe Puglisi, Avinash Anand, Marina Migliaccio

We introduce an innovative approach employing Cycle Generative Adversarial Networks (Cycle-GANs) to accurately simulate Carbon Monoxide (CO) emissions by learning features identified in thermal dust emission maps from the Planck satellite alongside HI data from HI4PI survey. Our training dataset is complemented by the targets represented by the two rotationa

Peter F. Wyper, Jonathan Squire, Etienne Pariat, Oleksiy V. Agapitov

Magnetic switchbacks are large amplitude deflections of the magnetic field within the solar wind. They are Alfv\'enic in character and so are associated with a spike in velocity and a generally small variation in local plasma density. Early orbits of Parker Solar Probe revealed that the solar wind near the Sun is dominated by these structures, and therefore,

Cameron Paterson, Jasminder S. Sidhu, Thomas Brougham, Sarah E. McCarthy

Space-based entanglement distribution has the potential to extend the range of quantum communication beyond that achievable through optical fibres that are constrained by exponential losses. Quantum repeaters have been proposed to mitigate the effects of channel losses for both fibre and satellite networks. Although quantum repeaters can improve entanglement

Jongyeop Kim, Jinki Kim, Doyun Lee

Structural health monitoring plays a critical role in ensuring structural safety by analyzing vibration responses from engineering systems. This paper proposes a Spectro-Temporal Alignment framework and a Hybrid Spectro-Temporal Fusion framework that integrate arrival-time interval descriptors with spectral features to capture both fine-scale and coarse-scal

Long Xiong, Xiaoyang Wang, Xiaoxia Cai, Xiao Yuan

Nonlinear spectroscopy is a cornerstone of quantum science, providing unique access to multi-point correlations, quantum coherence, and couplings that are invisible to linear methods. However, classical simulation of these phenomena is fundamentally limited by the exponential growth of the Hilbert space, and practical quantum algorithms for the nonlinear reg

Henry O. Velesaca, David Freire-Obregon, Abel Reyes-Angulo, Steven Araujo

Penalty kicks in soccer are decided under extreme time constraints, where goalkeepers benefit from anticipating shot direction from the kickers motion before or around ball contact. In this paper, MambaKick is presented as a learning-based framework for penalty direction prediction that leverages pretrained human action recognition (HAR) embeddings extracted

Maks Pečnik Bambič, Nuno A. M. Araújo, Giorgio Volpe

Collective rotations are common in active matter, enhancing cohesion, transport, and mixing. They are typically attributed to chiral non-reciprocal dynamics due to intrinsic particle chirality, torque-generating interactions among units, or geometric confinement. Here, we uncover a different mechanism for rotational order in active matter where a dynamic env

Seil Kang, Woojung Han, Junhyeok Kim, Jinyeong Kim

We present an amortized framework for real-time visual attribution streaming in multimodal thinking models. When these models generate code from a screenshot or solve math problems from images, their long reasoning traces should be grounded in visual evidence. However, verifying this reliance is challenging: faithful causal methods require costly repeated ba

Luay Gharzeddine, Samer Saab

Long-horizon tool-using tasks sometimes benefit from revisiting earlier subtasks, but explicit revisitation also adds routing, coordination, and token cost. We study complete cyclic subtask graphs for large language model (LLM) agents: a workflow controller in which executable subtasks are fully connected and a unified state-analysis-and-routing agent select

Dominic Horsman, Susan Stepney, Tim Clarke, Viv Kendon

Control systems are ubiquitous in modern technology, comprising an engineered plant to be kept within specific, often fine-tuned, limits, and a separate controller that ensures this is the case. While modern controllers often employ digital computers, other examples are purely mechanical, or even biological. It is an open question whether computation is happ

Grzegorz Bauman, Pavlos Panos, Philipp Latzin, Oliver Bieri

Purpose: To develop a robust deep learning framework for non-contrast-enhanced functional lung MRI, overcoming the limitations of spectral decomposition in the presence of physiological non-stationarity. Methods: We introduce VQ-Wave (Ventilation/Q-perfusion Waveform-based Assessment of Variable Evolutions), a physics-driven spatio-temporal inception neural

Chih-Hsuan Wei, Chi-Ping Day, Zhizheng Wang, Christine C. Alewine

Drug repurposing is often framed as a candidate identification task, but existing approaches provide limited guidance for distinguishing biologically plausible candidates from historically well-connected ones. Here we introduce DrugKLM, a hybrid framework that integrates biomedical knowledge graph structure with large language model-based mechanistic reasoni

Niranjan Nair

Let $G = V, E$ be a simple connected undirected graph. A set $X \subseteq V$ is \emph{geodesically convex} if for any pair of vertices $x, y \in X$, all vertices on all shortest paths in $G$ from $x$ to $y$ are contained in $X$. A set $H \subseteq V$ is said to be a {halfspace} if both $H$ and its complement (denoted by $H^c$) are convex. Given two sets $A,

Max Henning Höth, Kristian Kersting, Björn Deiseroth, Letitia Parcalabescu

Large language models (LLMs) increasingly rely on chain-of-thought (CoT) reasoning to solve complex tasks. Yet ensuring that the reasoning trace both contributes to and faithfully reflects the processes underlying the model's final answer, rather than merely accompanying it, remains challenging. We introduce AtManRL, a method that leverages differentiable at

S. N. Chen, K. Burdonov, W. Yao, J. D. Alvarado-Gómez

Solar coronal mass ejections (CME) are routinely observed, but as of yet there exist few convincing detections of stellar CMEs. A reason for this could be the stronger magnetic fields of these stars, compared to that of our Sun, would prevent CME to form and escape. Here we combined astrophysical simulations, measurements of scaled high-energy laser-driven p

Keyvan Aghababaiyan, Baldomero Coll-Perales, Javier Gozalvez

Future cellular networks will sustainably integrate computing, intelligence and services within a network of networks ecosystem that includes IoT devices and subnetworks for local communications and distributed processing. This integration creates an IoT-edge-cloud continuum that enables opportunistic task offloading across the continuum, enhancing network p

Simone Heisinger, Luca Pulina, Martina Seidl

The QBF Gallery 2023, the last QBF evaluation event, continues the tradition to survey and document the state of the art in solving quantified Boolean formulas (QBFs). It provides a detailed overview by collecting newly developed solvers and formulas as benchmarks. This report documents the solvers and formulas submitted by the community and introduces a new

J. P. Dadario Pereira, Raphael Tromer, Luiz A. Ribeiro Junior, Douglas S. Galvao

We present a trajectory-resolved framework for charge transport in graphene and related two-dimensional carbon systems beyond the ideal ballistic and fully coherent limits. Transport is described by kinetic Monte Carlo hopping on a predefined atomic lattice, allowing the combined treatment of disorder, thermal activation, and external fields. Current and eff

Ruifang Liu, Hongyu Chen, Ao Fan

The binding number $b(G)$ of a graph, introduced by Woodall [J. Combin. Theory, Ser. B, 1973], is a central topic of both structural and extremal graph theory. It is closely related to fundamental combinatorial and structural properties of graphs. The graphs with $b(G)\geq1$ exhibit strong expansion properties and a highly connected global structure. In cont

Jon M. Miller, Xin Xiang, Missagh Mehdipour, Liyi Gu

NGC 4151 is the brightest Seyfert-1 active galaxy in the pass band of the Resolve calorimeter spectrometer aboard XRISM. It has been observed on 14 occasions, resulting in a total exposure of 893 ks. Herein, we report on an analysis of the time-averaged spectrum. The narrow Fe K$_{\alpha}$ emission line complex requires contributions from the torus and the o

Henry O. Velesaca, Luigi Miranda, Angel D. Sappa

This paper presents SWNet, a bimodal end-to-end cross-spectral network specifically engineered for the detection of camouflaged weeds in dense agricultural environments. Plant camouflage, characterized by homochromatic blending where invasive species mimic the phenotypic traits of primary crops, poses a significant challenge for traditional computer vision s

Ayoub Hammal, Pierre Zweigenbaum, Caio Corro

Recent works proposed test-time alignment methods that rely on a small aligned model as a proxy that guides the generation of a larger base (unaligned) model. The implicit reward approach skews the large model distribution, whereas the nudging approach defers the generation of the next token to the small aligned model when the large base one is unconfident a

Minchul Kang, Changyong Shin, Jinwoo Jeong, Hyunho Lee

Accurate prediction of training time in distributed deep learning is crucial for resource allocation, cost estimation, and job scheduling. We observe that the floating-point precision setting is a key determinant of training time, leading to training time variations of ~2.4x over its minimum. However, existing studies on distributed training time prediction

Zongru Li, Xingsheng Chen, Honggang Wen, Regina Qianru Zhang

Molecular property prediction integrates quantum chemistry, cheminformatics, and deep learning to connect molecular structure with physicochemical and biological behavior. This survey traces four complementary paradigms, including Quantum, Descriptor Machine Learning, Geometric Deep Learning, and Foundation Models, and outlines a unified taxonomy linking mol

C. A. S. Almeida

We propose an effective non-relativistic framework in which wave-function collapse emerges as a deterministic dynamical instability induced by gravitational self-interaction and regulated by short-distance repulsion. The dynamics is described by a nonlinear Schr\"odinger equation supplemented by a phenomenological repulsive sector ensuring regularity at high

Keyvan Aghababaiyan, Baldomero Coll-Perales, Luca Lusvarghi, Javier Gozalvez

Modern vehicles are embedding increasing levels of automation, connectivity, and intelligence, which require advanced in-vehicle networks and computational platforms to support the dependability and deterministic requirements of critical in-vehicle functions. To this end, the automotive industry is shifting towards software-defined vehicles (SDVs) and zonal

Brendan M. Heffernan, James Greenberg, William F. McGrew, Antoine Rolland

Stable dissemination of terahertz (THz) signals over long distances is important for next-generation synchronization networks, radio astronomy, and high-capacity wireless systems. Optical fiber provides a low-loss platform for coherent frequency transfer; however, when a THz carrier is encoded as the difference between two optical wavelengths, chromatic disp

Jiaping Lu

Let I be a finite partially ordered set and let (Sym({\Delta}i),{\Delta}i)i be a sequence of symmetric groups indexed by I. Construct the generalised wreath product (F, {\Delta}) on this sequence of permutation groups. We determine the minimum number d(F) of generators required for this generalised wreath product.

Noureddine Kermiche

We present the Global Neural World Model (GNWM), a self-stabilizing framework that achieves topological quantization through balanced continuous entropy constraints. Operating as a continuous, action-conditioned Joint-Embedding Predictive Architecture (JEPA), the GNWM maps environments onto a discrete 2D grid, enforcing translational equivariance without pix

Grigory A. Starkov, Sharareh Sayyad

The emergence of various types of degeneracies plays a crucial role in optimizing and engineering different physical phenomena in non-Hermitian physics. In our work, we focus on the derogatory Exceptional Points (EPs), which are characterized by multiple Jordan blocks corresponding to the same eigenvalue. We demonstrate that, under certain infinitesimal pert

Fabrizio Nunnari, Siddhant Jain, Patrick Gebhard

We present a dataset and a model for sentiment analysis of German sign language (DGS) fairy tales. First, we perform sentiment analysis for three levels of valence (negative, neutral, positive) on German fairy tales text segments using four large language models (LLMs) and majority voting, reaching an inter-annotator agreement of 0.781 Krippendorff's alpha.

Si Wang, Thomas G. Kiely, Dorothee Tell, Johannes Obermeyer

Symmetry-based classification of quantum phases of matter is one of the most foundational organizing principles in physics; however, an analogous framework for mixed, decohered quantum states has only begun to emerge. A central new concept is strong-to-weak spontaneous symmetry breaking (SW-SSB), a sharp transition in mixed quantum states that is invisible t

Alolika Roy, Amarendra K. Sarma

We theoretically study how quantum measurement noise can be engineered in a hybrid cavitymagnomechanical platform for precision force sensing. The proposed configuration consists of a driven optomechanical cavity, with a movable mirror on one side plus a fixed semi-transparent mirror on the other side, coupled to a magnon mode, with an OPA placed inside the

Yue Jiang, Mingyu Yang, Liuyuxin Yang, Yang Xu

Recent advances in generative motion synthesis have enabled the production of realistic human motions from diverse input modalities. However, synthesizing compound actions from texts, which integrate multiple concurrent actions into coherent full-body sequences, remains a major challenge. We identify two key limitations in current text-to-motion diffusion mo

Eric W. Fischer, Michael Roemelt

We extend our recent work on the cavity-modified spin Zeeman effect of an effective spin-1/2-system[J. Chem. Phys. 163, 174307 (2025)] to a relativistic Jahn-Teller scenario under strong light-matter coupling. Here, the effective spin-1/2-system is realized via a single electron or a single hole in a doubly-degenerate molecular orbital system of trigonal sym

Pratiksha Gaikwad, Krisztina Zsigmond, Ramon Alain Miranda-Quintana

Geminal wavefunctions, introduced in the late 1950s, have long been recognized for their ability to compactly capture strong electron correlation. Despite their promise, they were historically overshadowed by more computationally efficient methods. Advances in both computational resources and theoretical frameworks have renewed interest in geminal-based appr

Iram Barbaro Rivas Ortiz, Sahar Ranjbar, Piergiorgio Cerello, Emanuele Maria Data

Prompt Gamma Timing (PGT) is a promising technique for in vivo range verification in particle therapy, exploiting the time-of-flight between primary particles and prompt gamma rays emitted by nuclear interactions. PGT distribution is highly sensitive to beam energy and target density, which, under controlled detector positioning, enables real-time monitoring

Mohammed Abbas

We propose a non-holomorphic modular $A_4$ model under the assumption of universal couplings. In this framework, a charged lepton mass hierarchy is not created through parameter fine tuning or hierarchical Yukawa couplings, but instead is determined by the modulus $\tau$, with certain modular weight assignments of right handed charged leptons. The experiment

Yuanhang Luo, Shuxing Fang, Ruijian Han, Yiming Xu

Classical latent-score ranking models often fail to distinguish objects' intrinsic scores from contextual effects, which are typically nonlinear and can dominate the observed outcomes. To address this, we introduce a semiparametric ranking framework in which the log-score of each object is modeled as the sum of a utility parameter and a nonparametric covaria

Luca Ferrari, Billel Habbati, Meriem Guerar, Mariano Ceccato

Mobile application developers are required to disclose how they collect, use, and share user data in compliance with privacy regulations. To support transparency, major app marketplaces have introduced standardized disclosure mechanisms. In 2022, Google mandated the Data Safety Section (DSS) on Google Play, requiring developers to summarize their data practi

Pierre-Andre Chiappori, Dam Linh Nguyen, Bernard Salanie

Since Choo and Siow (2006), a burgeoning literature has analyzed matching markets when utility is perfectly transferable and the joint surplus is separable. We take stock of recent methodological developments in this area. Combining theoretical arguments and simulations, we show that the separable approach is reasonably robust to omitted variables and/or non

Ivan Shilin

We prove that for a generic family of circle diffeomorphisms every parameter value that corresponds to an irrational rotation number is approximated by parameter values for which the diffeomorphisms have arbitrarily large finite numbers of periodic orbits. This phenomenon implies that families where irrational rotation numbers appear are not weakly structura

Diego Torres-García, Wim Michiels

This paper addresses the numerical optimization of proportional-integral-derivative (PID) controllers for linear time-invariant systems with delays, where the derivative action is implemented using a low-pass filter. While performance assessment is often based on the spectral abscissa of the ideal PID-controlled system, the inclusion of a derivative filter f

Karim K. Ben Hicham, Jan G. Rittig, Martin Grohe, Alexander Mitsos

Accurate molecular property prediction is central to drug discovery, catalysis, and process design, yet real-world applications are often limited by small datasets. Molecular foundation models provide a promising direction by learning transferable molecular representations; however, they typically involve task-specific fine-tuning, require machine learning e

Math Dicker

In the winter semester of 1890--1891 Adolf Hurwitz delivered a lecture course at the Albertina University in K\"onigsberg entitled -Theorie der algebraischen Gleichungen-. These lectures contain a particularly clear presentation of the ideas of Evariste Galois and, in particular, a proof of the fundamental theorem of Galois theory formulated in the language

Yueyang Feng, Dipesh Kafle, Vladimir Gladshtein, Vitaly Kurin

Certified program synthesis (aka vericoding) is the process of automatically generating a program, its formal specification, and a machine-checkable proof of their alignment from a natural-language description. Two challenges make vericoding difficult. First, specifications synthesised from natural language are often either too weak to be meaningful or too s

Xibo Li, Liang Zhang

Test-time augmentation (TTA) has become a promising approach for mitigating data sparsity in sequential recommendation by improving inference accuracy without requiring costly model retraining. However, existing TTA methods typically rely on uniform, user-agnostic augmentation strategies. We show that this "one-size-fits-all" design is inherently suboptimal,