October 2025 arXiv papers — page 82

Xiaoxi Huang, Daniel A. Pharis, Hang Zhou, Zishen Tian

To achieve efficient anti-damping switching of nanoscale magnetic memories with perpendicular magnetic anisotropy using spin-orbit torque requires that the anti-damping spin-orbit torque have a strong out-of-plane component. The spin anomalous Hall effect and the planar Hall effect spin current produced by a ferromagnetic layer are candidate mechanisms for p

Tomonori Sadamoto, Takashi Tanaka

We study the policy gradient method (PGM) for the linear quadratic Gaussian (LQG) dynamic output-feedback control problem using an input-output-history (IOH) representation of the closed-loop system. First, we show that any dynamic output-feedback controller is equivalent to a static partial-state feedback gain for a new system representation characterized b

Youngjae Jeong

This paper develops a novel econometric framework for static discrete choice games with costly information acquisition. In traditional discrete games, players are assumed to perfectly know their own payoffs when making decisions, ignoring that information acquisition can be a strategic choice. In the proposed framework, I relax this assumption by allowing pl

Sohyeon Jeon, Hyung-Chul Lee

Despite the rapid expansion of Large Language Models (LLMs) in healthcare, robust and explainable evaluation of their ability to assess clinical trial reporting according to CONSORT standards remains an open challenge. In particular, uncertainty calibration and metacognitive reliability of LLM reasoning are poorly understood and underexplored in medical auto

Ziyi Zhang, Shaogang Ren, Xiaoning Qian, Nick Duffield

Granger causality is widely used for causal structure discovery in complex systems from multivariate time series data. Traditional Granger causality tests based on linear models often fail to detect even mild non-linear causal relationships. Therefore, numerous recent studies have investigated non-linear Granger causality methods, achieving improved performa

K. El Bourakadi, M. Yu. Khlopov, M. Krasnov, H. Chakir

We explore how inflationary features shape the early stages of cosmic structure formation. Using the transfer function formalism, we trace the evolution of primordial perturbations, showing how causal physics and oscillatory signatures from inflation influence the matter power spectrum. The variance of smoothed density fields is then applied to model the col

Krzysztof Siminski

Fuzzy numbers are commonly represented with fuzzy sets. Their objective is to better represent imprecise data. However, operations on fuzzy numbers are not as straightforward as maths on crisp numbers. Commonly, the Zadeh's extension rule is applied to elaborate a result. This can produce two problems: (1) high computational complexity and (2) for some f

Pengzhuo Wang, Jose Sanjuan, Moritz Mehmet, Felipe Guzman

We present a laser frequency stabilization system based on an iodine-filled hollow-core photonic microcell (PMC), which is a sealed version of a hollow-core photonic crystal fiber (HC-PCF). A 532 nm laser is locked to the a1 component of the R(56) 32-0 transition of molecular iodine in the fiber cell, and its frequency stability is compared to that of the sa

Drew B. Thomas

This paper presents a novel methodology for classifying early modern religious images by using Large Language Models (LLMs) and vector databases in combination with Retrieval-Augmented Generation (RAG). The approach leverages the full-page context of book illustrations from the Holy Roman Empire, allowing the LLM to generate detailed descriptions that incorp

Shruti Shirol, Sean van Geldern, Hanzhe Xi, Chen Wang

Physical qubits in a quantum computer are often represented by superposition states of single particles or excitations. Decay of the excitation itself is a fundamental error channel that is difficult to overcome via external drive or control techniques. Quantum error correcting codes, which encode information in superpositions involving multiple excitations,

Philippa Helen McGuinness, Fabian Henssler, Manex Alkorta, Mark Joachim Graf von Westarp

Charge-density-wave (CDW) order and superconductivity coexist in the kagome metals AV$_3$Sb$_5$ (A=K, Cs, Rb), raising fundamental questions about the mechanisms driving their intertwined phases. Here we combine high-resolution inelastic X-ray scattering with first-principles calculations to uncover the origin of CDW formation in CsV$_3$Sb$_5$. Guided by str

Mahmoud Ibrahim, Bart Elen, Chang Sun, Gökhan Ertaylan

We present a novel framework for leveraging synthetic ICU time-series data not only to train but also to rigorously and trustworthily evaluate predictive models, both at the population level and within fine-grained demographic subgroups. Building on prior diffusion and VAE-based generators (TimeDiff, HealthGen, TimeAutoDiff), we introduce \textit{Enhanced Ti

Julen Expósito-Márquez, Arianna Di Cintio, Chris Brook, Jorge Sarrato-Alós

We investigate whether combining gas and stellar kinematic maps provides measurable advantages in recovering galaxy mass profiles, compared to using single-component maps alone. While traditional methods struggle to integrate multi-tracer data effectively, we test whether deep learning models can leverage this joint information. We develop a probabilistic co

Michael I. Tribelsky

A systematic analysis of the Eckhaus instability in the one-dimensional Ginzburg-Landau equation is presented. The analysis is based on numerical integration of the equation in a large (xt)-domain. The initial conditions correspond to a stationary, unstable spatially periodic solution perturbed by "noise." The latter consists of a set of spatially pe

Elias Stenhede, Agnar Martin Bjørnstad, Arian Ranjbar

Millions of clinical ECGs exist only as paper scans, making them unusable for modern automated diagnostics. We introduce a fully automated, modular framework that converts scanned or photographed ECGs into digital signals, suitable for both clinical and research applications. The framework is validated on 37,191 ECG images with 1,596 collected at Akershus Un

H. Cu-Castillo, A. Bañón Navarro, G. Merlo, F. Reimold

Gyrokinetic simulations reveal that microtearing mode (MTM) turbulence dominates transport in a Wendelstein 7-X (W7-X) discharge characterized by large density gradients, moderate temperature gradients, and low plasma beta. This conclusion is supported by the close agreement between simulated and experimentally measured heat and particle fluxes. The emergenc

Yaozhong W. Qiu

We continue the program initiated by [J. Éc. Polytech., Math. 12, 1083-1160 (2025)] and show that the Pleijel theorem holds unconditionally on all but four $H$-type groups.

Veena Krishnaraj, Adrian E. Bayer, Christian Kragh Jespersen, Peter Melchior

Machine learning enables powerful cosmological inference but typically requires many high-fidelity simulations covering many cosmological models. Transfer learning offers a way to reduce the simulation cost by reusing knowledge across models. We show that pre-training on the standard model of cosmology, $Λ$CDM, and fine-tuning on various beyond-$Λ$CDM scenar

Ana Paula Gomes Ferreira, Aleksandar Anžel, Izabel Oliva Marcilio de Souza, Helen Hughes

Case definitions are essential for effectively communicating public health threats. However, the absence of a standardized, machine-readable format poses significant challenges to interoperability, epidemiological research, the exchange of qualitative data, and the effective application of computational analysis methods, including artificial intelligence (AI

Zinan Tang, Xin Gao, Qizhi Pei, Zhuoshi Pan

Supervised Fine-Tuning (SFT) Large Language Models (LLM) fundamentally rely on high-quality training data. While data selection and data synthesis are two common strategies to improve data quality, existing approaches often face limitations in static dataset curation that fail to adapt to evolving model capabilities. In this paper, we introduce Middo, a self

Mahdi Abdollahpour, Marco Bertuletti, Yichao Zhang, Yawei Li

Artificial intelligence approaches for base-band processing for radio receivers have demonstrated significant performance gains. Most of the proposed methods are characterized by high compute and memory requirements, hindering their deployment at the edge of the Radio Access Networks (RAN) and limiting their scalability to large bandwidths and many antenna 6

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

By analyzing $(2367.0\pm11.1)\times10^6$ $ψ(3686)$ events collected in $e^+e^-$ collisions at $\sqrt{s}=3.686~\rm GeV$ with the BESIII detector at the BEPCII collider, we report the first search for the charged lepton flavor violating decay $ψ(3686)\to e^{\pm}μ^{\mp}$. No signal is found. An upper limit on the branching fraction $\mathcal{B}(ψ(3686)\to e^{\p

Oluwaseyi Giwa, Muhammad Ahmed Mohsin, Folarin Jubril Adesola, Muhammad Ali Jamshed

Reliable link adaptation is critical for efficient wireless communications in dynamic fading environments. However, reinforcement learning (RL) solutions often suffer from unstable convergence due to poorly conditioned policy gradients, hindering their practical application. We propose the quantum-preconditioned policy gradient (QPPG) algorithm, which levera

Zahra Khatti, Daniel P. Robinson, Frank E. Curtis

A new strategy for fair supervised machine learning is proposed. The main advantages of the proposed strategy as compared to others in the literature are as follows. (a) We introduce a new smooth nonconvex surrogate to approximate the Heaviside functions involved in discontinuous unfairness measures. The surrogate is based on smoothing methods from the optim

Fabia Bayer, Remco I. Leine

The Koopman-Hill projection method offers an efficient approach for stability analysis of linear time-periodic systems, and thereby also for the Floquet stability analysis of periodic solutions of nonlinear systems. However, its accuracy has previously been supported only by numerical evidence, lacking rigorous theoretical guarantees. This paper presents the

Keerthi Kumaran, Manas Sajjan, Bibek Pokharel, Kevin Wang

Observing superdiffusive scaling in the spin transport of the integrable 1D Heisenberg model is one of the key discoveries in non-equilibrium quantum many-body physics. Despite this remarkable theoretical development and the subsequent experimental observation of the phenomena in KCuF$_3$, real materials are often imperfect and contain integrability breaking

S. J. Gomez Alvarado, J. R. Chamorro, D. Rout, J. Hielscher

Frustration of long-range order via lattice geometries serves to amplify fluctuations of the order parameter and generate unconventional ground states that are highly sensitive to perturbations. Traditionally, this concept of geometric frustration is used to engineer unconventional magnetic states in a variety of materials; however, the charge degree of free

Franklin G. Horowitz

Some geophysical observations commonly collect only one component (1C) of a three component (3C) vector field. For example, Distributed Acoustic Sensing (DAS) records seismograms derived from displacement differences along the axis of segments of a fiber optic cable. In practice, multiple observations from such 3C vector fields are available, but commonly al

Minseok Cho, Ki-Hong Lee, Jaewon Song

We classify four-dimensional $\mathcal{N}=1$ supersymmetric gauge theories with a simple gauge group admitting a large $N$ limit that flow to non-trivial superconformal fixed points in the infrared. We focus on the cases where the large $N$ limit can be taken while keeping the flavor symmetry fixed so that the putative holographic dual has a fixed gauge grou

Carlos A. P. C. Junior, Leandro O. Nascimento, Van Sérgio Alves

We investigate generalized quantum electrodynamics (GQED), a higher-derivative extension of quantum electrodynamics in (3+1) dimensions. We perform a dimensional reduction of this theory to (2+1)D by confining the Dirac current to a plane while allowing the gauge-field to propagate outside the plane. The resulting effective theory, which we denote as Pseudo

Camila Angulo-Valdez, Rosa L. Becerra, Ramandeep Gill, Noémie Globus

We present the photometric and spectroscopic analysis of the short-duration GRB 250221A ($T_{90}=1.80\pm0.32$ s), using a data set from the optical facilities COLIBR\'I, the Harlingten 50~cm Telescope, and the Very Large Telescope. We complement these observations with data from the Neil Gehrels Swift Observatory and the Einstein Probe, as well as radio obse

Faizuddin Ahmed, Ahmad Al-Badawi, İzzet Sakallı, Sara Kanzi

The discovery of gravitational waves and advances in black hole imaging have opened new opportunities to probe exotic physics in strong-field regimes. Building upon a recent black hole solution in Einstein gravity coupled with nonlinear electrodynamics and exotic matter sources-specifically a cloud of strings and a quintessence field--we study the perturbati

Rhiannon Cameron, Emma Griffiths, Damion Dooley, William Hsiao

Contextual metadata is the unsung hero of research data. When done right, standardized and structured vocabularies make your data findable, shareable, and reusable. When done wrong, they turn a well intended effort into data cleanup and curation nightmares. In this paper we tackle the surprisingly tricky process of vocabulary standardization with a mix of pr

Valentin Noël

Different transformer architectures implement identical linguistic computations via distinct connectivity patterns, yielding model imprinted ``computational fingerprints'' detectable through spectral analysis. Using graph signal processing on attention induced token graphs, we track changes in algebraic connectivity (Fiedler value, $\Delta\lambda_2$) under v

Andres Garcia-Medina

Covariance matrices estimated from short, noisy, and non-Gaussian financial time series are notoriously unstable. Empirical evidence suggests that such covariance structures often exhibit power-law scaling, reflecting complex, hierarchical interactions among assets. Motivated by this observation, we introduce a power-law covariance model to characterize coll

Qiancheng Fu, Hongwei Xi, Ankush Das

We present TLLC which extends the Two-Level Linear dependent type theory (TLL) with session-based concurrency. Equipped with Martin-L\"{o}f style dependency, the session types of TLLC allow protocols to specify properties of communicated messages. When used in conjunction with the dependent type machinery already present in TLL, dependent session types facil

Mehran Ghafarian Tamizi, Homayoun Honari, Amir Mehdi Soufi Enayati, Aleksey Nozdryn-Plotnicki

Path planning for a robotic system in high-dimensional cluttered environments needs to be efficient, safe, and adaptable for different environments and hardware. Conventional methods face high computation time and require extensive parameter tuning, while prior learning-based methods still fail to generalize effectively. The primary goal of this research is

Daniel Zhao, Daniel Beaglehole, Taylor Berg-Kirkpatrick, Julian McAuley

Controllable music generation remains a significant challenge, with existing methods often requiring model retraining or introducing audible artifacts. We introduce MusicRFM, a framework that adapts Recursive Feature Machines (RFMs) to enable fine-grained, interpretable control over frozen, pre-trained music models by directly steering their internal activat

Keyuan Wu, Tenghan Zhong, Yuxuan Ouyang

We present a fast and robust calibration method for stochastic volatility models that admit Fourier-analytic transform-based pricing via characteristic functions. The design is structure-preserving: we keep the original pricing transform and (i) split the pricing formula into data-independent inte- grals and a market-dependent remainder; (ii) precompute thos

Harshita Gandhi, Huw Morgan

Understanding how active-region properties influence coronal mass ejection (CME) dynamics is essential for constraining eruption models and improving space-weather prediction. Magnetic diagnostics derived above polarity inversion lines (PILs), including the critical height ($h_{\rm crit}$) of torus instability onset, the overlying field strength ($B_{\rm t}$

Elaina Rohlfing, Azim Ahmadzadeh, V Aparna

Solar-flare forecasting has been extensively researched yet remains an open problem. In this paper, we investigate the contributions of elastic distance measures for detecting patterns in the solar-flare dataset, SWAN-SF. We employ a simple $k$-medoids clustering algorithm to evaluate the effectiveness of advanced, high-dimensional distance metrics. Our resu

Thomas Blommel, Enrico Perfetto, Gianluca Stefanucci, Vojtech Vlcek

We present a unified many-body perturbation theory for open quantum systems, that treats dissipation, correlations, and external driving on equal footing. Using a Keldysh-Lindblad formalism, we introduce diagrammatic treatment of dissipative interaction lines representing quasiparticle flows and fluctuations. Two new Feynman rules render the evaluation of di

Muhammad Ahsan Razaq, Claudio Altafini

This paper investigates the wisdom of crowds of linear opinion dynamics models evolving on signed networks. Conditions are given under which models such as the DeGroot, Friedkin-Johnsen (FJ) and concatenated FJ models improve or undermine collective wisdom. The extension to dependent initial opinions is also presented, highlighting how the correlation struct

Etash Guha, Tianxiao Jiang, Andrew Deng, Jian Zhang

Mapping a dataflow-graph of an ML model onto a reconfigurable system is difficult, as different mappings have different throughputs and consume resource constraints differently. To solve this, a model to evaluate the throughput of mappings is necessary as measuring throughput completely is expensive. Many use a hand-designed analytical model, relying on prox

Azim Ahmadzadeh, Mahsa Khazaei, Elaina Rohlfing

Time series are high-dimensional and complex data objects, making their efficient search and indexing a longstanding challenge in data mining. Building on a recently introduced similarity measure, namely Multiscale Dubuc Distance (MDD), this paper investigates its comparative strengths and limitations relative to the widely used Dynamic Time Warping (DTW). M

Sai Teja Erukude, Viswa Chaitanya Marella, Suhasnadh Reddy Veluru

Deep Learning (DL) holds enormous potential for improving medical imaging diagnostics, yet the lack of interpretability in most models hampers clinical trust and adoption. This paper presents an explainable deep learning framework for detecting brain tumors in MRI scans and pneumonia in chest X-ray images using two leading Convolutional Neural Networks, ResN

Haoyue Liu, Sheng Liu, Mingyao Qi

Matching demand with supply in crowdsourcing logistics platforms must contend with uncertain worker participation. Motivated by this challenge, we study a two-stage "recommend-to-match" problem under stochastic supplier rejections, where each demand is initially recommended to multiple potential suppliers prior to final matching decisions. We formulate a sto

Behnam Seyedi, Octavian Postolache

The rapid growth of the Internet of Things (IoT) has transformed industries by enabling seamless data exchange among connected devices. However, IoT networks remain vulnerable to security threats such as denial of service (DoS) attacks, anomalous traffic, and data manipulation due to decentralized architectures and limited resources. To address these issues,

Sepehr Hajebi

Dallard, Milani\v{c}, and \v{S}torgel conjectured that for a hereditary graph class $\mathcal{G}$, if there is some function $f:\mathbb{N}\to\mathbb{N}$ such that every graph $G\in \mathcal{G}$ with clique number $\omega(G)$ has treewidth at most $f(\omega(G))$, then there is a polynomial function $f$ with the same property. Chudnovsky and Trotignon refuted

Sai Teja Erukude, Suhasnadh Reddy Veluru, Viswa Chaitanya Marella

Identifying images generated by Generative Adversarial Networks (GANs) has become a significant challenge in digital image forensics. This research presents a wavelet-based detection method that uses discrete wavelet transform (DWT) preprocessing and a ResNet50 classification layer to differentiate the StyleGAN-generated images from real ones. Haar and Daube

Eyad Gad, Mustafa Abou Khatwa, Mustafa A. Elattar, Sahar Selim

Breast cancer is a leading cause of death among women worldwide, emphasizing the need for early detection and accurate diagnosis. As such Ultrasound Imaging, a reliable and cost-effective tool, is used for this purpose, however the sensitive nature of medical data makes it challenging to develop accurate and private artificial intelligence models. A solution

Valentin Noël

Large language models achieve impressive results but distinguishing factual reasoning from hallucinations remains challenging. We propose a spectral analysis framework that models transformer layers as dynamic graphs induced by attention, with token embeddings as signals on these graphs. Through graph signal processing, we define diagnostics including Dirich

Jaesung Bae, Cameron Churchwell, Mitchell Hermon, Tsun-An Hsieh

This paper investigates how large language models (LLMs) behave when faced with discrepancies between their parametric knowledge and conflicting information contained in a prompt. Building on prior question-answering (QA) research, we extend the investigation of knowledge conflicts to the realm of code generation. We propose a domain-agnostic framework for c

Felix Michalak, Steven Abreu

We demonstrate complete functional segregation in hybrid SSM-Transformer architectures: retrieval depends exclusively on self-attention layers. Across RecurrentGemma-2B/9B and Jamba-Mini-1.6, attention ablation causes catastrophic retrieval failure (0% accuracy), while SSM layers show no compensatory mechanisms even with improved prompting. Conversely, spars

Sharath Kumar Manjeshwar Sathyanath, Anna L. Ravensburg, Vassilios Kapaklis, Jan Rusz

When aiming for atomic resolution electron magnetic circular dichroism (EMCD) in STEM mode, the high convergence angle of the electron probe can lead to unforeseen artefacts and strong reductions of the signal to noise ratio (SNR) in the measurement of magnetic magnitudes. In this work, the EMCD signal is measured in STEM mode at semi-convergence angles rang

Erica Cai, Benjamin A. Miller, Olga Simek, Christopher L. Smith

Node-ranking methods that focus on structural importance are widely used in a variety of applications, from ranking webpages in search engines to identifying key molecules in biomolecular networks. In real social, supply chain, and terrorist networks, one definition of importance considers the impact on information flow or network productivity when a given n

Jean-Christophe Wallet

The status of several representative gauge theories on various quantum space-times, mainly focusing on Yang-Mills type extensions together with a few matrix model formulations is overviewed. The common building blocks are derivation based differential calculus possibly twisted and noncommutative analog of the Koszul connection. The star-products related to t

Andreas Winter

Hayashi's Pinching Inequality, which establishes a matrix inequality between a semidefinite matrix and a multiple of its "pinched" version via a projective measurement, has found many applications in quantum information theory and beyond. Here, we show a very simple proof of it, which lends itself immediately to natural generalisations where the different pr

Archer Dodson, Ritabrata Dutta

Modern weather forecasting has increasingly transitioned from numerical weather prediction (NWP) to data-driven machine learning forecasting techniques. While these new models produce probabilistic forecasts to quantify uncertainty, their training and evaluation may remain hindered by conventional scoring rules, primarily MSE, which are designed for single t

Eyad Gad, Seif Soliman, M. Saeed Darweesh

In the realm of medical diagnostics, rapid advancements in Artificial Intelligence (AI) have significantly yielded remarkable improvements in brain tumor segmentation. Encoder-Decoder architectures, such as U-Net, have played a transformative role by effectively extracting meaningful representations in 3D brain tumor segmentation from Magnetic resonance imag

Andrea Sottosanti, Davide Risso, Francesco Denti

Spatial transcriptomics measures the expression of thousands of genes in a tissue sample while preserving its spatial structure. This class of technologies has enabled the investigation of the spatial variation of gene expressions and their impact on specific biological processes. Identifying genes with similar expression profiles is of utmost importance, th

Aliakbar Mehdizadeh, Martin Hilbert

We investigate how peer pressure influences the opinions of Large Language Model (LLM) agents across a spectrum of cognitive commitments by embedding them in social networks where they update opinions based on peer perspectives. Our findings reveal key departures from traditional conformity assumptions. First, agents follow a sigmoid curve: stable at low pre

Matthew Raffel, Adwaith Renjith, Lizhong Chen

Kolmogorov-Arnold Networks (KANs) replace scalar weights with per-edge vectors of basis coefficients, thereby increasing expressivity and accuracy while also resulting in a multiplicative increase in parameters and memory. We propose MetaCluster, a framework that makes KANs highly compressible without sacrificing accuracy. Specifically, a lightweight meta-le

Florian Lengyel

We exhibit a symmetric promonoidal kernel on the simplex category $\Delta$ with Cartesian unit, yielding on representable functors a Hadamard natural transformation $\Delta^p\times\Delta^q\to\Delta^{pq}$ based on pointwise multiplication of nondecreasing maps. Specializing to $q=1$ yields a simplicial homotopy contracting $\Delta^n$ to its $0$-vertex. The co

David R. Junior, Gastão Krein, Luis E. Oxman, Bruno R. Soares

Lattice simulations and theoretical analyses consistently identify center vortices and monopoles as key nonperturbative configurations in Yang-Mills theory. In the continuum, the effective representation of mixed oriented and nonoriented center vortices showed that these degrees of freedom generate a confining flux tube with $N$-ality. Independently, studies

John Worley, Marina Orio, Andrej Dobrotka, Jozef Magdolen

Nova Scorpii 2023 was first detected as a luminous supersoft X-ray source (SSS) 93 days after outburst and continued emitting soft X-rays for over two months, until it was too close to the Sun to observe. The nova was monitored with the Swift X-ray Telescope (XRT) and the Neutron Star Interior Composition Explorer (NICER) on the International Space Station,

Matthew Jiang, Shipeng Liu, Feifei Qian

Legged robots can sense terrain through force interactions during locomotion, offering more reliable traversability estimates than remote sensing and serving as scouts for guiding wheeled rovers in challenging environments. However, even legged scouts face challenges when traversing highly deformable or unstable terrain. We present Safe Active Exploration fo

Seunghyun Bang, Seonghyeon Kang, Hyunjong Lee, Hyungsik Kim

Glass materials play an increasingly important role in advanced technologies due to their superior physical properties. However, precise machining of glass remains a major challenge because of its brittleness and sensitivity to thermal and mechanical stresses. In this study, we present a novel approach that combines spherical-aberration-assisted filamentatio

Yaning Jia, Chunhui Zhang, Xingjian Diao, Xiangchi Yuan

Curriculum learning (CL) - ordering training data from easy to hard - has become a popular strategy for improving reasoning in large language models (LLMs). Yet prior work employs disparate difficulty metrics and training setups, leaving open fundamental questions: When does curriculum help? Which direction - forward or reverse - is better? And does the answ

Zhitong He, Yaobin Chen, Brian King, Lingxi Li

Ensuring the safety of vulnerable road users (VRUs), including pedestrians, cyclists, electric scooter riders, and motorcyclists, remains a major challenge for advanced driver assistance systems (ADAS) and connected and automated vehicles (CAV) technologies. Real-world VRU tests are expensive and sometimes cannot capture or repeat rare and hazardous events.

Long Li, Mourad Sini

It is well known, in the acoustic model, that highly contrasting transmission leads to the so-called Minnaert subwavelength resonance. In this work, we show that such highly contrasting transmissions create not only one resonance but a family of infinite resonances located near the real axis where the first one (i.e. the smallest) is indeed the Minnaert one.

Alain Couvreur, Rakhi Pratihar

In 2021, Augot, Couvreur, Lavauzelle and Neri introduced a new class of rank metric codes which can be regarded as rank metric counterparts of Reed-Muller codes. Given a finite Galois extension $\mathbb{L} / \mathbb{K}$, these codes are defined as some specific $\mathbb{L}$-subspaces of the twisted group algebra $\mathbb{L} [\textrm{G}]$. We investigate the

Jaewon Lim, Alex Luedtke

Estimating the causal dose-response function is challenging, particularly when data from a single source are insufficient to estimate responses precisely across all exposure levels. To overcome this limitation, we propose a data fusion framework that leverages multiple data sources that are partially aligned with the target distribution. Specifically, we der

Atli Kosson, Jeremy Welborn, Yang Liu, Martin Jaggi

Transferring the optimal learning rate from small to large neural networks can enable efficient training at scales where hyperparameter tuning is otherwise prohibitively expensive. To this end, the Maximal Update Parameterization (muP) proposes a learning rate scaling designed to keep the update dynamics of internal representations stable across different mo

Albie Chan, Zheng Shi, Jorge Miguel-Ramiro, Luca Dellantonio

Quantum networks are a backbone of future quantum technologies thanks to their role in communication and scalable quantum computing. However, their performance is challenged by noise and decoherence. We propose a self-configuring approach that integrates superposed quantum paths with variational quantum optimization techniques. This allows networks to dynami

Xiaoyu Yang, Giancarlo La Camera, Gianluigi Mongillo

We investigate the equilibria of a random model network exhibiting extensive chaos. In this regime, a large number of equilibria is present. They are all saddles with low-dimensional unstable manifolds. Surprisingly, despite network's connectivity being completely random, the equilibria are strongly correlated and, as a result, they occupy a very small regio

Ming Han, John Devany, Michel Fruchart, Margaret L. Gardel

Tissue dynamics play a crucial role in biological processes ranging from inflammation to morphogenesis. However, these noisy multicellular dynamics are notoriously hard to predict. Here, we introduce a biomimetic machine learning framework capable of inferring noisy multicellular dynamics directly from experimental movies. This generative model combines grap

Dirk HR Spennemann

This study examines the prompt fidelity of ChatGPT4o / DALL-E3 text-to-image visualisations by analysing whether attributes explicitly specified in autogenously generated prompts are correctly rendered in the resulting images. Using two public-domain datasets comprising 200 visualisations of women working in the cultural and creative industries and 230 visua

Érik Martin-Dorel

This paper presents three closely-related software projects, namely: docker-coq, docker-coq-action, and docker-keeper. It aims at two objectives: provide a high-level description of the available features -- to foster the use of a Docker-based CI/CD for Rocq (formerly known as Coq) or OCaml projects -- and document the underlying requirements and the main de

Jacob Eller, Nicholas D. M. Hine

Machine Learned Interatomic Potentials (MLIPs) offer a powerful combination of abilities for accelerating theoretical spectroscopy calculations utilising both ensemble sampling and trajectory post-processing for inclusion of vibronic effects, which can be very challenging for traditional ab initio MD approaches. We demonstrate a workflow that enables efficie

Valentina Crespi, Carlos R. Argüelles, Eduar A. Becerra-Vergara, Martín F. Mestre

Surrounding Sgr A*, a cluster of young and massive stars coexist with a population of dust-enshrouded objects, whose astrometric data can be used to scrutinize the nature of Sgr A*. An alternative to the black hole (BH) scenario has been recently proposed in terms of a supermassive compact object composed of self-gravitating fermionic dark matter (DM). Such

Joshua Nijiati Alimujiang

As emergent artificial intelligence technologies increasingly assert roles as assistants within intangible cultural heritage contexts, researchers and artists observe existing questions on the theme of agency negotiation, cultural resistance, and technical critique. This research interrogates power dynamics in human-AI sovereignty and entanglement for nomadi

Andrei Katsevich, Igor R. Klebanov, Zimo Sun, Grigory Tarnopolsky

We discuss dimensional continuation of the massless scalar field theory with the $i\phi^5$ interaction term. It preserves the so-called $\mathcal{PT}$ symmetry, which acts by $\phi\rightarrow -\phi$ accompanied by $i\rightarrow -i$. Below its upper critical dimension $10/3$, this theory has interacting infrared fixed points. We argue that the fixed point in

Ketai Qiu, Luca Di Grazia, Leonardo Mariani, Mauro Pezzè

Test suites are inherently imperfect, and testers can always enrich a suite with new test cases that improve its quality and, consequently, the reliability of the target software system. However, finding test cases that explore execution scenarios beyond the scope of an existing suite can be extremely challenging and labor-intensive, particularly when managi

Owen H. Wherry

Aging is the primary risk factor for nearly all forms of human death, yet pharmaceutical interventions hold the potential to prevent it. Combinations of drugs have been shown to increase the lifespan of model organisms more than individual drugs, and geroprotective histone deacetylase (HDAC) inhibitors that have different molecular targets within the longevi

Chen Lu, Chuang Li, Chao Cao, Huiqiu Yuan

Altermagnetic (AM) fluctuations are a new class of collinear spin fluctuations whose role in mediating superconductivity faces a fundamental tension: their $\Gamma$-point peak favors intra-orbital spin-triplet pairing, while their spin compensation favors inter-orbital singlets. Here, we demonstrate that inversion-symmetry-broken AM fluctuations generically

Jacob Folks

"Higher-order Wiener-Wintner averages" were constructed by Assani, Folks, and Moore to quantitatively control multiple recurrence averages. Systems in which these averages converge at a polynomial rate for a sufficiently large subset are termed "higher-order Wiener-Wintner systems of power type", in which properties like pointwise convergence of multiple rec

Shifa Sulaiman, Tobias Busk Jensen, Stefan Hein Bengtson, Simon Bøgh

Recent advances in robotics and autonomous systems have broadened the use of robots in laboratory settings, including automated synthesis, scalable reaction workflows, and collaborative tasks in self-driving laboratories (SDLs). This paper presents a comprehensive development of a mobile manipulator designed to assist human operators in such autonomous lab e

CMS Collaboration

The first observation of single top quark production in association with a W and a Z boson in proton-proton collisions is reported. The analysis uses data at center-of-mass energies of 13 and 13.6 TeV recorded with the CMS detector at the CERN LHC, corresponding to a total integrated luminosity of 200 fb$^{-1}$. Events with three or four charged leptons, whi

Gabriel Elyas Gama Araujo, Andreia Luisa da Rosa

We present a comprehensive first-principles investigation of the structural, electronic, vibrational, and topological properties of tellurium across its dimensional hierarchy, including bulk trigonal Te-I, two-dimensional tellurene polymorphs, and one-dimensional helical nanowires. Using density functional theory with full inclusion of spin-orbit coupling, w

Zhongyu Jiang, Wenhao Chai, Lei Li, Zhuoran Zhou

In recent years, there has been a growing interest in developing effective alignment pipelines to generate unified representations from different modalities for multi-modal fusion and generation. As an important component of Human-Centric applications, Human Pose representations are critical in many downstream tasks, such as Human Pose Estimation, Action Rec

Shomoita Alam, Nathaniel Dyrkton, Susan Shepherd, Ibrahim Sana

Background: Target trial emulation (TTE) that applies trial design principles to improve the analysis of non-randomized studies is increasingly being used. Applications of TTE to emulate cluster randomized trials (RCTs) have been limited. This study explored how to integrate simulation-guided design into the TTE framework to inform planning of a non-randomiz

Yuting Wu, Kenji Saito, Alex Taylor, Andrei Ganshyn

A development effort to improve the performance of superconducting radio-frequency half-wave resonators (SRF HWRs) is underway at the Facility for Rare Isotope Beams (FRIB), where 220 such resonators are in operation. Our goal was to achieve an intrinsic quality factor (Q0) of >= 2E10 at an accelerating gradient (Ea) of 12 MV/m. FRIB production resonators we

Bonnie Berger, Rohan Goyal, Matthew M. Hong, Yael Tauman Kalai

We show that if a language $L$ admits a public-coin unambiguous interactive proof (UIP) with round complexity $\ell$, where $a$ bits are communicated per round, then the batch language $L^{\otimes k}$, i.e. the set of $k$-tuples of statements all belonging to $L$, has an unambiguous interactive proof with round complexity $\ell\cdot\mathsf{polylog}(k)$, per-

Yilang Liu, Haoxiang You, Ian Abraham

This paper investigates a sample-based solution to the hybrid mode control problem across non-differentiable and algorithmic hybrid modes. Our approach reasons about a set of hybrid control modes as an integer-based optimization problem where we select what mode to apply, when to switch to another mode, and the duration for which we are in a given control mo

Sebastian Nagies, Chiara Capecci, Marcel Seelbach Benkner, Javed Akram

Quantum annealing is a framework for solving combinatorial optimization problems. While it offers a promising path towards a practical application of quantum hardware, its performance in real-world devices is severely limited by environmental noise that can degrade solution quality. We investigate the suppression of local field noise in quantum annealing pro

Tomoki Arita, Keisuke Okumura

Guidance is an emerging concept that improves the empirical performance of real-time, sub-optimal multi-agent pathfinding (MAPF) methods. It offers additional information to MAPF algorithms to mitigate congestion on a global scale by considering the collective behavior of all agents across the entire workspace. This global perspective helps reduce agents' wa

Uri Rolls, Dominic W. Pesce, Paul Tiede, Lindy Blackburn

Using very long baseline interferometry (VLBI) observations at (sub)millimeter wavelengths, the Event Horizon Telescope (EHT) currently achieves the finest angular resolution of any astronomical facility, necessary for imaging the horizon-scale structure around supermassive black holes. A significant calibration challenge for high-frequency VLBI stems from r

Yuyang Miao, Huijun Xing, Danilo P. Mandic, Tony G. Constantinides

This report presents a comprehensive analysis of an unsupervised multi-expert machine learning framework for detecting short ticketing fraud in railway systems. The study introduces an A/B/C/D station classification system that successfully identifies suspicious patterns across 30 high-risk stations. The framework employs four complementary algorithms: Isola

Ali Shakeri Kahnamouei, Saeed Lotfifard

This paper proposes a time-domain fault location identification method for mixed overhead-underground power distribution systems that can handle challenging fault scenarios such as sub-cycle faults, arcing faults and evolving faults. The proposed method is formulated based on differential equations of the system and accounts for the peculiarities of power di