April 2026 arXiv papers — page 52

Qiuli Wang, Xinhuan Sun, Fengxi Chen, Yongxu Liu

Gadoxetate disodium-enhanced MRI is essential for the detection and characterization of hepatocellular carcinoma. However, acquisition of the hepatobiliary phase (HBP) requires a prolonged post-contrast delay, which reduces workflow efficiency and increases the risk of motion artifacts. In this study, we propose a Triple-Phase Sequential Fusion Network (TriP

Yongxiang Lian, Yueyang Cang, Pingge Hu, Yuchen He

Objective: Decoding visual information from electroencephalography (EEG) is an important problem in neuroscience and brain-computer interface (BCI) research. Existing methods are largely restricted to natural images and categorical representations, with limited capacity to capture structural features and to differentiate objective perception from subjective

Thomas Colcombet, Olivier Idir

We present a new algebraic characterisation of Eve-positionality for $\omega$-regular languages. It involves only a limited number of elementary local properties to be checked. An $\omega$-regular language is Eve-positional if, in all games with this language as objective, the existential player (Eve) can play optimally without keeping any information concer

Hyungjun Lee, Changhui Lee, Jaehyeok Ryu, Geunpil An

A Lead-Scintillating Fiber (Pb/SciFi) prototype for the Barrel Imaging Calorimeter (BIC) at the Electron--Ion Collider (EIC) was tested with electron beams at the CERN PS T10 beam line in August 2024. The prototype consisted of unit modules with a sampling structure of lead sheets and scintillating fibers, corresponding to a total depth of approximately $10.

Lloyd Lobo, Junze Liu, Hang Yang, Yasin Karimi

Phase sensitive detection in spectral domain optical coherence tomography (SD-OCT) is a powerful method for functional imaging of biological events with high spatiotemporal resolution. The depth-dependent signal-to-noise ratio (SNR) is a limiting factor on the minimum detectable phase changes of phase in shot noise-limited SD-OCT systems. The SNR over a dept

Anvarbek Meirmanov, Akbota Senkebayeva

We study a mathematical model of in situ leaching of rare metals, in which the joint filtration of two liquids is governed by the microscopic model $\mathbb{A}^{\varepsilon}$. A key difficulty is the unknown (free) boundary $Γ(r)$ between solid and liquid components, determined by an additional condition on $Γ(r)$; no standard methods exist for this nonlinea

Naohiro Yoshida

In this paper, we propose and analyze a novel one-dimensional inhomogeneous random walk model that combines spatial decay of transition probabilities with a temporal renewal structure for each excursion. In this model, the probability of moving to the right from each state creats a spatial inhomogeneity that causes a stronger pull-back toward the origin as t

Hülya Argüz, Dominic Joyce

We generalize complex manifolds to manifolds with corners $X$, and to manifolds with generalized corners (g-corners) in the sense of the second author arXiv:1501.00401, using complex structures on the b-tangent bundle (log tangent bundle) ${}^bTX$. We prove a formal Newlander-Nirenberg type theorem showing that along each corner stratum of $X$, the b-complex

Esteban Henríquez, Miroslav Kuchta, Jeonghun J. Lee, Sander Rhebergen

We propose parameter-robust preconditioners for the statically condensed linear system arising from a hybridizable discontinuous Galerkin discretization of the coupled Stokes--Darcy system. The design strategy relies on first applying the operator-preconditioning framework [Numer. Linear Algebra Appl., 18(1):1--40, 2011] to construct a preconditioner for the

Zaheed Ahmed, Emmanuel Charleson Dapaah, Philip Makedonski, Jens Grabowski

Mutants support testing and debugging in two roles: (i) as test goals and (ii) as substitutes for real faults. Hard-to-kill mutants provide better guidance for test improvement, while realism is essential when mutants are used to simulate real bugs. Building on these roles, selective mutation for deep learning (DL) aims to reduce the cost of mutant generatio

Lukáš Hrdonka, Martin Jureček

Malware development and detection have undergone significant changes in recent years as modern concepts, such as machine learning, have been used for both adversarial attacks and defense. Despite intensive research on Windows Portable Executable (PE) files, there is minimal work on Linux Executable and Linkable Format (ELF). In this work, we summarize the ac

Naohiro Yoshida

In this paper, we propose a novel stochastic process that serves as a natural discrete-time counterpart to the continuous-time model known as the ``Poisson hyperbolic staircase'' proposed by Levikson et al. (1999), and clarify its analytical properties. The proposed model is a Markov chain on the state space $(0,1]$. Its transition rule states that at each t

Jeffrey Näf, Riana Valera Mbelson, Markus Meierer

Predicting customers' long-term revenue from sparse and irregular transaction data is central to marketing resource allocation in non-contractual settings, yet existing approaches face a trade-off. Traditional probabilistic customer base models deliver robust long-horizon forecasts by imposing strong structural assumptions, while flexible machine-learning mo

Chris Karpinski

An action of a group $G$ on a set $X$ is called ``decent'' if every subgroup of $G$ with a finite orbit in $X$ fixes a point in $X$ and every finitely generated subgroup of $G$ such that every element of the subgroup fixes a point of $X$ must itself have a global fixed point. In this article, we study conditions on when actions of groups on restricted produc

Yan-Chen Bi, Yu-Mei Wu, Qing-Guo Huang

Primordial black holes (PBHs) provide a compelling interpretation for the binary black holes (BBHs) observed by ground-based gravitational-wave (GW) detectors, especially for those BBHs in the theoretical mass gap. In the early Universe, the scalar perturbations required to produce such PBHs inevitably generate scalar-induced GWs (SIGWs). These SIGWs peak in

Huan Qing

The Gaussian mixture model is widely used in unsupervised learning, owing to its simplicity and interpretability. However, a fundamental limitation of the classical Gaussian mixture model is that it forces each observation to belong to exactly one component. In many practical applications, such as genetics, social network analysis, and text mining, an observ

Kristijan Kilassa Kvaternik

For the family of Lozi maps $L_{a,b}$, we consider parameter pairs for which the f\mbox{}ixed point $X$ has no homoclinic points and the period-two orbit $\{P,P'\}$ is attracting. For such parameters, let $\ell$ be the set of accumulation points of the unstable manifold $W_X^u$ that do not lie on $W_X^u$. We construct a polygon $\mathcal{D}$ whose forward im

Felix Herron, Solange Rossato, Alexandre Allauzen, François Portet

Modern automatic speech recognition (ASR) systems have been observed to function better for certain speaker groups (SGs) than others, despite recent gains in overall performance. One potential impediment to progress towards fairer ASR is a more nuanced understanding of the types of modeling errors that speech encoder models make, and in particular the differ

Weichan Liu, Xiangxiang Nie, Simón Piga, Bjarne Schülke

For $k$-graphs $F$ and $H_0$ the $F$-bootstrap percolation process (or $F$-process) starting with $H_0$ is a sequence $(H_i)_{i\geq0}$ of $k$-graphs such that $H_{i+1}$ is obtained from $H_i$ by adding all those $e\in V(H_0)^{(k)}\setminus E(H_i)$ as edges that complete a new copy of $F$. The running time of this $F$-process, denoted by $M_F(H_0)$, is the sm

Tomáš Kalný, Martin Jureček, Mark Stamp

This work proposes a structural approach to concept drift detection in malware classification using decision tree rulesets. Classifiers are trained across temporal windows on the EMBER2024 dataset, and drift is quantified by comparing extracted rule representations using feature importance, prediction agreement, activation stability, and coverage metrics. Th

Ritajit Kundu, Ajit C. Balram

We present identities relating the pair-correlation functions and static structure factors of states in the maximal spin multiplet. This allows us to compute these density-density correlation functions of all members of the multiplet using just these correlation functions of the highest-weight state. We apply these relations to obtain energies for many fract

Shuai Zeng

Joint measurements on multiple copies of a quantum state provide access to nonlinear observables such as $\operatorname{tr}(ρ^t)$, but whether replica number marks a sharp information-theoretic resource boundary has remained unclear. For every fixed order $t\ge 3$, existing protocols show that $\lceil t/2\rceil$ replicas already suffice for polynomial-sample

Yasmin Rodrigues Sobrinho, João Renato Ribeiro Manesco, João Paulo Papa

The integration of quantum machine learning with classical deep learning offers promising avenues for medical image analysis by mapping data into high-dimensional Hilbert spaces. However, effectively unifying these distinct paradigms remains challenging due to common optimization asymmetries. In this paper, a novel hybrid quantum-classical architecture for b

Yilun Jiang, Haihao Lu, Zedong Peng, Jinwen Yang

We present FlashFolio, a GPU-accelerated solver for single-period and multi-period portfolio optimization with factor-based risk modeling, bid-offer spread costs, and nonlinear market impact. These models are widely used in portfolio construction and optimal execution, but become computationally challenging at large scale, especially in the multi-period sett

Meshal Alharbi, Munther A. Dahleh, Gioele Zardini

Many engineered systems must balance competing objectives, such as performance and safety, cost and reliability, or efficiency and sustainability, and are naturally modeled as compositions of interacting subsystems. We study online multi-objective decision-making in monotone co-design, where functionalities and resources are partially ordered, and the goal i

Szőllősi Tamás-Géza

In certain modified theories of gravity, non-minimal couplings between matter and geometry lead to the nonconservation of the energy-momentum tensor. By interpreting this as an effective dissipative process, we formulate a general class of f(R, Matter) theories with the Herglotz variational principle, a variational approach designed for dissipative systems.

Zhen Cao, F. Aharonian, Y. X. Bai, Y. W. Bao

The quest for the origin of cosmic ray (CRs) is a fundamental issue in astrophysics. Shocks of supernova remnants (SNRs) have been considered as the dominant contributors to Galactic CRs below the spectral knee near $\sim 3$ petaelectronvolt (PeV). Whether SNRs are efficient accelerators of particles beyond PeV energies has long been debated. Here we report

Alessandro Foradori, Ilya Auslender, Stefano Biasi, Stefano Gretter

The rapid scaling of artificial neural networks has exposed fundamental limitations of conventional von Neumann computing architectures. In these systems, the physical separation between memory and processing creates a bottleneck, as computational capabilities outpace the ability of memory and interconnects to supply and retrieve data. In contrast, biologica

Pieter Colpaert, Piotr Sowinski

RDF-based systems increasingly operate in event-driven and streaming settings, where producers and consumers exchange data as discrete units of communication rather than as freely mergeable RDF statements. As existing RDF semantics and tooling do not provide an interoperable notion of what statements belong together as one message, developers often rely on o

Jose Geraldo Fernandes, Luiz Facury, Pedro Robles Dutenhefner, Wagner Meira

Self-supervised learning in healthcare has largely relied on invariance-based objectives, which maximize similarity between different views of the same patient. While effective for static anatomy, this paradigm is fundamentally misaligned with clinical diagnosis, as it mathematically compels the model to suppress the transient pathological changes it is inte

Jeeban Kumar Nayak, Ebru Buhara, Olivier J. F. Martin

Accurate characterization of polarization dependent light matter interactions in nanostructured systems is paramount for the development of chiral metasurfaces. It is also often challenging, because multiple anisotropic mechanisms, such as linear and circular diattenuation, birefringence, and depolarization can coexist and couple with one another. Convention

Costanza Benassi, Marta Dell'Atti

This work is a thorough investigation of skew-orthogonal polynomials with respect to a quartic Freud weight. We provide an explicit method to evaluate skew-orthogonal polynomials of any degree as linear combinations of orthogonal polynomials. The coefficients of these combinations can be evaluated via novel recursive relations. Moreover, we observe that skew

Chengyang Li, Kaiyi Xiong, Yuan Xu, Lei Qian

Embodied foundation models have achieved significant breakthroughs in robotic manipulation, yet they still depend heavily on large-scale robot demonstrations. Although recent works have explored leveraging human data to alleviate this dependency, effectively extracting transferable knowledge remains a significant challenge due to the inherent embodiment gap

Jae-Mo Lihm, Minsu Ghim, Seung-Ju Hong, Cheol-Hwan Park

The momentum-space derivatives of Bloch wavefunctions are essential for studying quantum geometry and the equilibrium and response properties of solids. In practical first-principles calculations, these derivatives are obtained via Wannier interpolation of position and related composite matrices. These matrices are initially evaluated on a coarse k-point gri

Athira Arayath, Sabrina Pasterski

We use the framework of generalized entanglement wedges to revisit the connected wedge theorem (CWT). This construction identifies an entanglement wedge associated for any bulk region and allows us to rephrase the CWT in terms of the entanglement entropies of bulk regions. We establish new upper and lower bounds on the mutual information of boundary decision

Paolo Ribeca, Alejandro Castro, Alejandro Lage-Castellanos, Alisa Sergeeva

In epistatic fitness landscapes, the fitness effect of a mutation depends on the genetic background and may even switch between deleterious and beneficial depending on the presence of another mutation. Epistatic interactions may cause both mutations to change the sign of each other's fitness effects (reciprocal sign epistasis) or only one mutation to do so (

Maryam Mustafa, Imaan Hameed, Amna Shahnawaz, Bilal A Mateen

Despite steady global advances, maternal mortality remains alarmingly high in Pakistan (155 deaths per 100,000 live births in 2023); largely as a consequence of fragmented paper records, low literacy, poor access to quality healthcare, and gendered barriers that compromise care continuity. Over three years, we designed, deployed, and iteratively developed Aw

Zoran Ristivojevic

The Lieb-Liniger model describes one-dimensional bosons with contact interactions. This many-body system admits an exact solution in terms of the Bethe ansatz. Some of the exact and perturbative results for this model are reviewed. Particular attention is devoted to the explicit evaluation, in terms of the interaction parameter, of physical quantities that c

Mátyás Domokos, Botond Miklósi

The degeneration order of simultaneous similarity classes of $3\times 3$ nilpotent matrix tuples is determined, and is shown to be given by rank conditions.

Michał Ryczkowski

Despite the remarkable success of the Standard Model in describing fundamental interactions, unresolved phenomena such as dark matter, dark energy, and matter-antimatter asymmetry strongly suggest the existence of physics beyond the Standard Model. The absence of new particle discoveries at the LHC indicates that such New Physics may be significantly heavier

Lim Chi Tung James, Ivo R. Peters, Swathi Krishna

Bubbles released from a needle show shape deformations that depend on the surfactant concentration of the surrounding liquid. We develop a model that predicts the surfactant concentration based on experimental early-stage observations of these deformations. Using high-speed imaging, we examine bubbles within the first 144 ms of ascent, corresponding to a ver

Sheza Munir, Ratna Kandala, Anamta Khan, Deepti

Health misinformation remains one of the most pressing challenges on social media, particularly when cultural traditions intersect with scientific-sounding claims. These dynamics are not only global but also deeply local, manifesting in culturally specific controversies that require careful analysis. Motivated by this, we examine 100 YouTube transcripts that

McKenzie A. Myers, Claire B. Campbell, Kelly M. Patton, Segen BenZvi

Prior to core collapse, the neutrino emission from red supergiants (RSGs) is so large that a nearby ($\lesssim1$kpc) RSG will become visible in current and near-future neutrino detectors. The rate of emission and the spectra of the pre-supernova (pre-SN) neutrinos from RSGs are sensitive to the temperature, density, and detailed isotopic composition of the c

Ariel Yuhan Ong, Iain Livingstone, Caroline Kilduff, Mertcan Sevgi

Clinicians often face workflow problems that are perceived as either too bespoke or low stakes to attract commercial attention. Historically, most do not have the technical knowledge to address these problems, but the recent emergence of "vibe coding" presents a transformative opportunity. Vibe coding refers to the co-development of software using natural la

Matheus Stolet, Simon Peter, Antoine Kaufmann

Conventional cloud network virtualization sends packets through multiple guest and host layers, inflating CPU cost and tail latency. Shared host datapaths collapse this layering into one optimized path across tenants, but existing shared stacks are fixed-function: tenants cannot specialize their protocols. eBPF is the natural vehicle for restoring programmab

Jay Yu, Shunfan Zhou, Hang Yin, Brian Seong

Blockchain wallets conventionally follow an ownership model where possession of a private key grants unilateral control. However, this assumption is brittle for emerging settings such as AI agent wallets, organizational custody, and enterprise payroll, where multiple actors must coordinate without exposing secrets or leaking internal activity. We present PAS

Md Erfan, Md Kamal Hossain Chowdhury, Ahmed Ryan, Md Rayhanur Rahman

Large Language Models (LLMs) show promise in automated software engineering, yet their guarantee of correctness is frequently undermined by erroneous or hallucinated code. To enforce model honesty, formal verification requires LLMs to synthesize implementation logic alongside formal specifications that are subsequently proven correct by a mathematical verifi

Peter O. Hess, Sahila Chopra

The study of the structure of the 0+ spectrum in heavy nuclei has drawn much attention in the last two decades. In this contribution we study their properties from a microscopic point of view. The pseudo-SU(3) model (\tilde{SU}(3)) is applied to some rare earth nuclei, namely to Sm, Gd, Dy, Er, Yb and Hf isotopes. It is shown that the 0+ spectrum, and the ac

Kang-Fa Cheng, Kai-Xian Luo, Xiao-Hong Zhao, Jirong Mao

In gamma-ray bursts (GRBs), the electron pitch angle ($\alpha$) is usually assumed to be isotropically distributed. However, recent numerical simulations indicate that only the high-energy electrons (with Lorentz factors $\gamma>\gamma_{iso}$) are distributed isotropically, whereas the low-energy electrons (with $\gamma<\gamma_{iso}$) follow an energy-depend

Erez Yosef, Oron Anschel, Shunit Haviv Hakimi, Asaf Gendler

Recent advancements in large language models have led to significant improvements across various tasks, including mathematical reasoning, which is used to assess models' intelligence in logical reasoning and problem-solving. Models are evaluated on mathematical reasoning benchmarks by verifying the correctness of the final answer against a ground truth answe

Rezvan Ghanbari, Thorsten Buettner, Wolfgang Hillert, Karol Kasprzak

The variation of the resonance frequency and intrinsic quality factor of superconducting radio-frequency cavities during the transition from the superconducting to the normal-conducting state provides essential insight into the fundamental superconducting properties of the cavity material. Investigating these transition dynamics is crucial for the continued

Angshul Majumdar

This paper studies the computational difficulty of clustering problems that are defined directly on a continuous probability density. Rather than working with finite samples, we assume the density is given as a polynomial and ask whether it contains certain cluster structures. Four natural questions are examined. First, do there exist several points with hig

Hyo Jin Jon, Longbin Jin, Eun Yi Kim

CLIP has demonstrated strong generalization in visual domains through natural language supervision, even for video action recognition. However, most existing approaches that adapt CLIP for action recognition have primarily focused on temporal modeling, often overlooking spatial perception. In real-world scenarios, visual challenges such as low-light environm

Philippe Flores, Julien Flamant, Nicolas Le Bihan

This paper discusses the left and right ranks of quaternion matrices with Hankel structure. While they are in general different for arbitrary quaternion matrices, we show that the left and right ranks of quaternion Hankel matrices are equal. Moreover, we establish the relation between Hankel matrices and the existence of linear recurrence relations with quat

Jan Bulling, Marit O. E. Steiner, Julen S. Pedernales, Martin B. Plenio

Proposed experiments for gravitationally induced entanglement (GIE) typically suppress direct electromagnetic interactions between two massive particles by inserting a conducting Faraday shield. For superconducting particles, their large diamagnetism requires additional magnetic shielding to screen magnetic dipolar interactions. Here, we analyze the effect o

M. Murakami

We present a unified theoretical and numerical framework for self-similar multi-shock implosions achieving ultrahigh compression in a uniform solid spherical target. Extending the classical Guderley model to N stacked, spherically converging shocks, we derive selfsimilar solutions and the scaling law for the final density. One dimensional Lagrangian hydrodyn

Yuhao Zhang, Borong Zhang, Jiaming Fan, Jiachen Shen

The real-world deployment of Vision-Language-Action (VLA) models remains limited by the risk of unpredictable and irreversible physical harm. However, we currently lack effective mechanisms to proactively detect these physical safety risks before deployment. To address this gap, we propose \textbf{RedVLA}, the first red teaming framework for physical safety

Manuel V. Gnann, Christina Lienstromberg, Katerina Nik

We consider a power-law thin-film equation for strongly shear-thinning fluids. Weak solutions to this equation have been constructed more than twenty years ago by Ansini and Giacomelli. Here, we pass over to analyzing strong solutions with nonzero contact angle (partial-wetting regime), and place emphasis on studying the behavior of solutions near points whe

Scott Congreve, Alice Hodson, Anwesh Pradhan

In this article, we develop the $C^1$-nonconforming $C^0$-conforming virtual element method (VEM) for the vanishing moment approximation of the second-order fully nonlinear Monge-Amp\`ere equation in two dimensions. In the vanishing moment equation an artificial biharmonic term is introduced which produces a quasilinear fourth order problem. We derive optima

Paul Aigner, Jasmin Matti, Maria Flors Mor-Ruiz, Julius Wallnöfer

We develop analytical and algorithmic techniques that enable efficient simulation of a broad class of noisy stabilizer circuits. We derive closed-form expressions of expectation values for tensor product of Paulis in circuits with non-deterministic Pauli measurements, yielding an efficient strong simulation method that avoids explicit density matrix construc

Clement Leroy, Tarak Arbi, Benoit Geller, Olivier Rioul

We study the secrecy of wireless channels in the presence of an eavesdropper, where the channels are random and the transmitter only has knowledge of the channel statistics. We investigate the optimal input distribution with respect to several secrecy metrics: the Secrecy Outage Probability (SOP), defined as the probability that the coding rate $r$ exceeds t

Ze Chen, Lan Chen, Yuanhang Li, Qi Mao

We propose FlowAnchor, a training-free framework for stable and efficient inversion-free, flow-based video editing. Inversion-free editing methods have recently shown impressive efficiency and structure preservation in images by directly steering the sampling trajectory with an editing signal. However, extending this paradigm to videos remains challenging, o

Rasmus Jouttijärvi, Klaus Kroencke, Louis Yudowitz

We prove dynamical stability and instability theorems for asymptotically hyperbolic static solutions of Einstein's equation with $\Lambda<0$, viewed as self-similar solutions of the Ricci-harmonic flow. More precisely, we show that static metrics are dynamically stable if and only if a positive mass type theorem holds for nearby metrics. Our key tool is a ne

Arjun Bagchi, Arthur Lipstein, Saikat Mondal, Alex Jiayi Zhang

A natural approach for constructing a concrete example of flat space holography is to take the flat space limit of a well-understood example of AdS/CFT, such as the one relating M-theory in AdS$_4$ times an orbifolded 7-sphere to a certain three dimensional superconformal Chern-Simons-matter theory known as the ABJM theory living in the boundary of AdS$_4$.

Daniel Cortild, Coralia Cartis

We investigate the Stochastic Krasnoselskii-Mann iterations for expected nonexpansive fixed-point problems in a real Hilbert space. We establish convergence guarantees under significantly weaker assumptions on the variance than those typically used in the literature. In particular, instead of a uniform bound on the variance of the stochastic oracle, we only

Younes Essafouri, Laure Raynaud, Luciano Drozda, Laurent Risser

As the demand to integrate Artificial Intelligence into high-stakes environments continues to grow, explaining the reasoning behind neural-network predictions has shifted from a theoretical curiosity to a strict operational requirement. Our work is motivated by the explanations of autoregressive neural predictions on dynamic physical fields, as in weather fo

Coenraad Mouton, Randle Rabe, Niklas C. Koser, Nicolai Krekiehn

We study whether deep networks for medical imaging learn useful nonrobust features - predictive input patterns that are not human interpretable and highly susceptible to small adversarial perturbations - and how these features impact test performance. We show that models trained only on nonrobust features achieve well above chance accuracy across five MedMNI

Alessandro Dioguardi Burgio, Manuel Mancini, Tim Van der Linden

In this article, we investigate the representability of actions of the category $\mathsf{Nil}_2(\mathsf{Grp})$ of $2$-nilpotent groups. We first provide an algebraic characterisation of derived actions in $\mathsf{Nil}_2(\mathsf{Grp})$ by determining a universal strict general actor of an object $X$, which turns out to be the group $\operatorname{Aut}_c(X)$

Manyi Zhang, Ji-Fu Li, Zhongao Sun, Xiaohao Liu

Autonomous agent systems such as OpenClaw introduce significant efficiency challenges due to long-context inputs and multi-turn reasoning. This results in prohibitively high computational and monetary costs in real-world development. While quantization is a standard approach for reducing cost and latency, its impact on agent performance in realistic scenario

Lucas Hegerath, David Philipp Klüner, Philipp Pelcz, Viswanatha Reddy Batchu

In software-defined vehicles, automotive middleware plays a fundamental role in enabling efficient communication, integration, and coordination among software components. This paper examines how well two of the currently most popular middleware frameworks, ROS 2 Jazzy and AUTOSAR Adaptive Platform R24-11, meet practical requirements elicited from automotive

Yuqi Pan, Jinghao Zhuang, Yupeng Feng, Fangzhi Zhong

Scaling context length is reshaping large-model development, yet full-attention Transformers suffer from prohibitive computation and inference bottlenecks at long sequences. A key challenge is to design foundation models that maintain performance and long-context efficiency with minimal training overhead. We introduce SpikingBrain2.0 (SpB2.0), a 5B model tha

Xabier Pérez-Couto, Santiago Torres, Nuria Miret-Roig, Friedrich Anders

Understanding the large-scale dynamics of molecular clouds (MCs) is crucial for constraining the processes that govern star formation and the structure and evolution of the Galaxy. While gas tracers have traditionally been used to map MC kinematics, stellar tracers such as young stellar objects (YSOs) and open clusters (OCs) provide a complementary approach

Bartłomiej Brudnowski, Kamil Zembaczyński, Aleksander Filip Żarnecki

The physics program of the Higgs factory will focus on measurements of the 125 GeV Higgs boson, with the Higgs-strahlung process being the dominant production channel at 250 GeV. However, similar production of exotic light scalars, in a scalar-strahlug process, is still not excluded by the existing experimental data, provided their coupling to the SM gauge b

Luca M. Possati

Classical design theory treats the type of an object as a given: the designer decides in advance that this will be a cup, then optimizes its parameters. This paper argues that object type is not a presupposition but an inference, something that can be determined from physical data and functional requirements jointly. We call this problem requirement-steered

William Dawson, Louis Beal, Yoann Curé, Giuseppe Fisicaro

Large language models (LLMs) and agentic systems have recently demonstrated potential for automating scientific workflows, including atomistic simulations. However, their deployment in high-performance computing (HPC) environments remains limited by the lack of mechanisms ensuring correctness, reproducibility, and safe interaction with computational resource

Henry Shugart

Borwein and Wiersma [SIAM J. Optim. 18(3) (2007), 946-960] asked if the set of acyclic monotone operators is closed under addition. We answer this question in the negative.

Olha Jurečková, Martin Jureček, Matouš Kozák, Róbert Lórencz

Machine learning-based malware detectors are increasingly vulnerable to adversarial examples. Traditional defenses, such as one-shot adversarial training, often fail against adaptive attackers who use reinforcement learning to bypass detection. This paper proposes a robust defense framework based on bilevel optimization, explicitly modeling the strategic int

Vasilii Vadimov

We study truncations of hierarchical equations of motion (HEOM) for finite-dimensional open quantum systems. We prove that for finite-dimensional approximations constructed with a Schur-complement type of terminator, the spectrum converges to that of the full HEOM as the truncation depth increases. We also prove that this approximation is free of spectral po

Antoni Lorente, Amin Oueslati, Robin Staes-Polet

Over the last couple of years, AI Agents have gained significant traction due to substantial progress in the capabilities of underlying General Purpose AI (GPAI) models, enhanced scaffolding techniques, and the promise to drive societal transformation. Companies, researchers, and policy makers have started to consider the different effects that AI agents may

Ian Fligler

In strategic games such as the prisoner's dilemma, allowing players to make binding offers of utility transfers before play has been shown to alter incentives and potentially support cooperative outcomes. These preplay exchange mechanisms reshape payoffs by transferring utility while being contingent on actions; however, they typically require side payments

Asim Ukaye, Mubarak Abdu-Aguye, Nurbek Tastan, Karthik Nandakumar

Client contribution estimation in Federated Learning is necessary for identifying clients' importance and for providing fair rewards. Current methods often rely on server-side validation data or self-reported client information, which can compromise privacy or be susceptible to manipulation. We introduce a data-free signal based on the matrix von Neumann (sp

Himanshu Sharma Pathok, Padma Pani Shahu, Himanshu Kalita, Prasanta Kumar Saikia

Ni-doped Cd1-xMnxS (x=0.4) thin films were prepared via a cost-effective chemical bath deposition (CBD) method to investigate their suitability for optoelectronic applications. Incorporation of a secondary transition metal such as Ni is expected to influence lattice strain, defect density, and electronic structure through ionic size effects and sp-d exchange

Gautam Kumar Jain, Carsten Markgraf, Julian Stähler

Graph Visual Question Answering (GVQA) for autonomous driving organizes reasoning into ordered stages, namely Perception, Prediction, and Planning, where planning decisions should remain consistent with the model's own perception. We present a comparative study of cross-stage context passing on DriveLM-nuScenes using two complementary mechanisms. The explici

Muhammad Akmal Husain, Irham Taufik Andika, Mochamad Ikbal Arifyanto

PSO J083.8371+11.8482, a quasar at $z = 6.34$ with a nearby companion galaxy, provides an opportunity to study the impact of active galactic nucleus (AGN) activity on the surrounding environment during the epoch of reionization. We analyze ALMA observations of the [C\,\textsc{ii}] 158~$\mu$m emission line and the far-infrared (FIR) continuum, which trace col

Jichao Wang, Liuyang Bian, Yufeng Zhou, Han Xiao

As Multimodal Large Language Models (MLLMs) mature, GUI agents are evolving from static interactions to complex navigation. While Reinforcement Learning (RL) has emerged as a promising paradigm for training MLLM agents on dynamic GUI tasks, its effective application faces a dilemma. Standard Offline RL often relies on static step-level data, neglecting globa

Anam Hashmi, Mayug Maniparambil, Julia Dietlmeier, Kathleen M. Curran

The emergence of large-scale pretrained foundation models has transformed computer vision, enabling strong performance across diverse downstream tasks. However, their potential for physics-based inverse problems, such as accelerated cardiac MRI reconstruction, remains largely underexplored. In this work, we investigate whether natural-domain foundation model

Robert Doane-Solomon, Isla R. Simpson, Tim Woollings

Precipitation trends can arise from both dynamic factors (changes in atmospheric circulation) and thermodynamic factors (changes in atmospheric moisture content). Disentangling these contributions can aid in understanding regional climate change and improving projections. We compare two approaches which separate dynamic and thermodynamic contributions to pre

Noah Dasanaike, Kosuke Imai

Estimating racial disparity requires individual-level race data, which are often unavailable due to the sensitivity of collecting such information. To address this problem, many researchers utilize Bayesian Improved Surname Geocoding (BISG), which have critically relied on Census surname data. Unfortunately, these data capture race-surname relationships only

Gal Metzer, Sagi Polaczek, Ali Mahdavi-Amiri, Raja Giryes

Transformations produced by image and video generation models often evolve in a highly non-linear manner: long stretches where the content barely changes are followed by sudden, abrupt semantic jumps. To analyze and correct this behavior, we introduce a Semantic Progress Function, a one-dimensional representation that captures how the meaning of a given sequ

M. García Olmos, Y. Baba, R. A. Molina, M. Amado

Proximity-induced superconductivity in low-dimensional systems offers a powerful pathway to engineer topological superconducting phases in, otherwise, non-superconducting systems. These exotic phases are of fundamental and technological interest due to the presence of robust zero-energy modes, the Majorana bound states. In this work, we propose a theoretical

Shihui Yan, Ziqi Zhou, Yufei Song, Yifan Hu

Physical adversarial patch attacks critically threaten pedestrian detection, causing surveillance and autonomous driving systems to miss pedestrians and creating severe safety risks. Despite their effectiveness in controlled settings, existing physical attacks face two major limitations in practice: they lack systematic disruption of the multi-stage decision

Mathilde Kappel, Mahdi Khoramshahi, Louis Annabi, Faiz Ben Amar

Thanks to the latest advances in learning and robotics, domestic robots are beginning to enter homes, aiming to execute household chores autonomously. However, robots still struggle to perform autonomous manipulation tasks in open-ended environments. In this context, this paper presents a method that enables a robot to manipulate a wide spectrum of articulat

Yongqi Jiang, Yansong Gao, Boyu Kuang, Chunyi Zhou

Self-supervised learning (SSL) encoders are invaluable intellectual property (IP). However, no existing SSL watermarking for IP protection can concurrently satisfy the following two practical requirements: (1) provide ownership verification capability under black-box suspect model access once the stolen encoders are used in downstream tasks; (2) be robust un

Yunhang He, Cong Xu, Zhangchi Zhu, Hongzhi Yin

Graph filter design is central to spectral collaborative filtering, yet most existing methods rely on manually tuned hyperparameters rather than fully learnable filters. We show that this challenge stems from a bias in traditional recommendation objectives, which induces a spectral phenomenon termed low-frequency explosion, thereby fundamentally hindering th

Cheolhei Lee, Xing Wang, Xiaowei Yue, Jianguo Wu

Problem definition: Data-driven models in machine learning have enabled efficient management of production systems. However, a majority of machine learning models are devoted to modeling the mean response or average pattern, which is inappropriate for studying abnormal extreme events that are often of primary interest in aircraft manufacturing. Since extreme

Xunyao Luo, Xander Peetroons, Tsung-Yeh Yang, Ruben M. Otxoa

Electron spin resonance spectroscopy (ESR) of a single electron in planar Si-MOS quantum dot is reported in the vicinity of a valley level anti-crossing. A number of one and two-photon resonances are observed due to mixing of magnetic spin-flip and electric valley-flip transitions. This allows the reconstruction of the energy-level diagram of a four state sy

K. Goyal, R. S. Lakes

Elastic wave dispersion is studied in an octet truss lattice and compared with a designed rib lattice known to exhibit strong Cosserat elastic effects. Dispersion entails variation of wave speed with frequency. The phenomenon is experimentally investigated by exciting standing waves in specimens of different length at discrete frequencies. At lower frequenci

Kersten Schmidt, Timon Seibel, Sebastian Schöps

We present a domain decomposition formulation based on hybridization which is inspired by hybridized discontinuous Galerkin (HDG) methods, that enhance mixed domain decomposition methods by incorporating stabilization terms. Unlike discontinuous Galerkin methods, our analysis of the proposed finite element method is based on a corresponding consistent variat

Haidong Yuan, Haokun Zhao, Wanshi Xu, Songjun Cao

Large language models (LLMs) often fail to meet the pedagogical needs of K-12 English learners in non-native contexts due to a proficiency mismatch. To address this widespread challenge, we introduce a proficiency-aligned framework that adapts LLM outputs to learner abilities, using China's national curriculum (CSE) as a representative case. Our framework en

Mingfeng He, Fayu Jiang, Junkun Jiao, Mingrun Li

High Energy Physics (HEP) experiments like BESIII produce petabyte-scale data. Extracting physics results requires complex workflows (simulation, reconstruction, statistical analysis, etc.) that traditionally take experts months or years. Current manual methods are labor-intensive, prone to bias, and limit large-scale systematic scans. As data grows, this pa