March 2026 arXiv papers — page 34

David Harbater, Julia Hartmann, George McNinch

We discuss local-global principles for the existence of Levi factors (i.e., complements to the unipotent radical) for linear algebraic groups over one-variable function fields. We give examples of disconnected groups that fail the local-global principle, and prove a strong local-global principle in the presence of Levi descent.

Sourayan Banerjee, Oliver Lorscheid, Alejandro Martínez Méndez, Alejandro Vargas

In this text, we outline a theory of schemes associated with a site, which generalizes a variety of geometries, such as manifolds, schemes, analytic spaces, simplicial complexes, and more. We present an abstract process of gluing model spaces via sheaf theory and recover a posteriori the underlying topological spaces that are often present in the constructio

Karen Hambardzumyan, Nicolas Baldwin, Edan Toledo, Rishi Hazra

Existing research has identified three structural performance bottlenecks in AI research agents: (1) synchronous single-GPU execution constrains sample throughput, limiting the benefit of search; (2) a generalization gap where validation-based selection causes overfitting and performance to degrade over extended search horizons; and (3) the limited capabilit

Konstantinos Papaioannou, Thaleia Dimitra Doudali

Multimodal Large Language Models (MLLMs) power platforms like ChatGPT, Gemini, and Copilot, enabling richer interactions with text, images, and videos. These heterogeneous workloads introduce additional inference stages, such as vision preprocessing and encoding, that inflate latency and memory demand. Existing LLM serving systems, optimized for text-only wo

Dalila Ressi, Alvise Spanò, Matteo Rizzo, Lorenzo Benetollo

Reentrancy remains one of the most critical classes of vulnerabilities in Ethereum smart contracts, yet widely used detection tools and datasets continue to reflect outdated patterns and obsolete Solidity versions. This paper adopts a dependability-oriented perspective on reentrancy detection in Solidity 0.8+, assessing how reliably state-of-the-art static a

Mengrui Zhang, Bang Huang, Yunxin Xu, Haiying Huang

As networking systems become increasingly complex, achieving disruptive innovation grows more challenging. At the same time, recent progress in Large Language Models (LLMs) has shown strong potential for scientific hypothesis formation and idea generation. Nevertheless, applying LLMs effectively to networking research remains difficult for two main reasons:

M. Ibarra-Meneses, A. Martín-Ruiz

We study electromagnetic radiation from classical sources near a planar interface separating a topological and a trivial insulator, modeled within axion electrodynamics. The system features a piecewise constant $\theta$-term that encodes the magnetoelectric response of topological surface states. Treating this coupling perturbatively, we derive analytical co

Nathan Roll

Quantum language models have shown competitive performance on sequential tasks, yet whether trained quantum circuits exploit genuinely quantum resources -- or merely embed classical computation in quantum hardware -- remains unknown. Prior work has evaluated these models through endpoint metrics alone, without examining the memory strategies they actually le

Zhisu Liu, Giulio Romani, Yu Su

We study a nonlinear Schr\"odinger equation with mixed dispersion in the mass competition regime, namely mass-supercritical for the Laplacian and mass-subcritical for the Bilaplacian. In this setting, the existence of a critical value of the mass $c_\varepsilon$, which divides existence and nonexistence of energy ground state solutions, was established in [B

Jean Paul Schemeil

We study the tensor-triangular geometry of the category of Voevodsky motives generated by real quadrics. At the prime 2, we determine its Balmer spectrum, and find that it is a countably infinite, non-Noetherian space of Krull dimension 2. We detail the relationship between this space, the real Artin-Tate spectrum computed by Balmer-Gallauer, and Vishik's is

Wing Fung Chong, Runhuan Feng, Kenneth Tsz Hin Ng

This article proposes a new class of risk-sharing rules by exploring the relationship between capital allocation and risk sharing. While the former is concerned with ex-ante allocating capitals to different lines of business within a corporation based on the relationship among the individual risks, often also through the aggregate risk, the latter is an arra

Marcello Calvanese Strinati, Giancarlo Calvanese Strinati

Topological properties of solid-state materials arise when crossings occur in their band-structure eigenvalues, which give rise to discontinuities in the associated Bloch-function eigenvectors once these are mapped over the whole Brillouin zone. These nonanalytic properties have direct consequences on the spatial decay of the corresponding Wannier functions,

S. A. K. Leeney, T. Gessey-Jones, W. J. Handley, E. de Lera Acedo

For frontier experiments operating at the edge of detectability, instrument design directly determines the probability of discovery. We introduce Conditional Neural Bayes Ratio Estimation (cNBRE), which extends neural Bayes ratio estimation by conditioning on design parameters, enabling a single trained network to estimate Bayes factors across a continuous d

Mriganka Nath, Anurag Das, Jiahao Xie, Bernt Schiele

Large vision-language models (LVLMs) tend to hallucinate, especially when visual inputs are corrupted at test time. We show that such corruptions act as additional distribution shifts, significantly amplifying hallucination rates in real-world applications. To address this, we propose CLIP-guided Test-Time Training (ClipTTT), a method to adapt LVLMs under de

Wenze Lan, Anton Lögl, Meryem Benelajla, Clemens Schäfermeier

Spin-orbit interactions of light couple polarization and spatial degrees of freedom, underpinning phenomena such as the spin Hall effect of light. Although widely explored at interfaces and in tightly focused beams, their impact in nominally paraxial confocal systems remains largely unexamined. Here we show that a single quarter-wave plate embedded in a simp

George Barmpalias, Nan Fang, Wolfgang Merkle, Ivan Titov

An approximation of a real is a sequence of rational numbers that converges to the real. An approximation is left-c.e. if it is computable and nondecreasing and is d.c.e. if it is computable and has bounded variation. A real is computably approximable if it has some computable approximation, and left-c.e. and d.c.e. reals are defined accordingly. An approxim

Mostafa Anoosha, Dhavalkumar Thakker, Kuniko Paxton, Koorosh Aslansefat

Privacy-preserving medical inference must balance data locality, diagnostic reliability, and deployment efficiency. This paper presents EcoFair, a simulated vertically partitioned inference framework for dermatological diagnosis in which raw image and tabular data remain local and only modality-specific embeddings are transmitted for server-side multimodal f

Deepika Gurung, Lala Shakti Swarup Ray, Mengxi Liu, Bo Zhou

Real time sensor based applications in pervasive computing require edge deployable models to ensure low latency privacy and efficient interaction. A prime example is sensor based human activity recognition where models must balance accuracy with stringent resource constraints. Yet many deep learning approaches treat temporal sensor signals as black box seque

Weihong Pan, Xiaoyu Zhang, Zhuang Zhang, Zhichao Ye

High-quality 4D reconstruction enables photorealistic and immersive rendering of the dynamic real world. However, unlike static scenes that can be fully captured with a single camera, high-quality dynamic scenes typically require dense arrays of tens or even hundreds of synchronized cameras. Dependence on such costly lab setups severely limits practical scal

Thomas R. Michel, Mathias Steinhuber, Juan Diego Urbina, Peter Schlagheck

The presence of chaos in classical Hamiltonian systems is witnessed by its maximal Lyapunov exponent, that quantifies the instability of motion through the exponential growth of indicators such as the trace of the stability matrix or the out-of-time-ordered correlator. On the other hand, integrable dynamics near unstable fixed points, which are in turn chara

Peng-Yu Niu, Qian Wang, Qiang Zhao

In this work the heavy flavor conserving (HFC) hadronic weak decays of bottom baryons are studied in the framework of the nonrelativistic constituent quark model (NRCQM). We show that the pole terms play an indispensable role in the description of the branching ratio of $\Xi_b^-\to \Lambda_b^0 \pi^-$. With the pole terms included we can make reliable predict

Ron Taieb, Yoel Greenberg, Barak Sober

Motifs often recur in musical works in altered forms, preserving aspects of their identity while undergoing local variation. This paper investigates how such motivic transformations occur within their musical context in symbolic music. To support this analysis, we develop a probabilistic framework for modeling motivic transformations and apply it to Beethove

Bastian Castorene, Martin HvE Groves, Francisco J. Peña, Nicolas Vidal-Silva

We investigate the Stirling-cycle performance of a Heisenberg--Kitaev magnonic medium with Dzyaloshinskii--Moriya (DM) interactions. Using linear spin-wave theory, we show the DM interaction preserves spectral symmetry, yielding even caloric responses and symmetric Stirling engine efficiency. In contrast, bond-dependent Kitaev exchange asymmetrically distort

Fadoua Amri-Jouidel, Emmanuel Kemel, Stéphane Mussard

Explainability and fairness have mainly been considered separately, with recent exceptions trying the explain the sources of unfairness. This paper shows that the Shapley value can be used to both define and explain unfairness, under standard group fairness criteria. This offers an integrated framework to estimate and derive inference on unfairness as-well-a

Moritz Vandenhirtz, Samuel Ruipérez-Campillo, Simon Böhi, Sonia Laguna

Electrocardiograms (ECGs) are among the most widely available clinical signals and play a central role in cardiovascular diagnosis. While recent foundation models (FMs) have shown promise for learning transferable ECG representations, most existing pretraining approaches treat leads as independent channels and fail to explicitly leverage their strong structu

B. Meirose, D. Milstead

We present a general, discovery-grade framework for searching for weakly coupled new particles emitted in nuclear de-excitation following neutron capture. Rather than relying on isolated spectral features, the method exploits correlated ``satellite-line combs'': multiple weak $\gamma$-ray lines appearing at a common energy offset $\Delta$ below known capture

Martin Hoffmann Petersen, Steen Lysgaard, Arghya Bhowmik, Kedar Hippalgaonkar

Machine learning interatomic potentials (MLIPs) enable large-scale atomistic simulations but remain challenged in describing mixed-valence materials where charge ordering strongly influences thermodynamic stability. Here we investigate the role of electronic entropy in MLIP structural optimization of the battery cathode material \ce{NaFePO4}. We show that co

Andrius Žemaitis, Paulius Gečys, Mindaugas Gedvilas

Laser-induced lift-off of functional surface layers is a key process in micro- and nano-fabrication; however, optimization criteria for maximizing the lifted-off area remain insufficiently defined. In analogy to the well-established theory of efficient laser ablation, where the maximum ablated volume per pulse is achieved at a peak fluence of F_0^{\mathrm{op

Doğaç Eldenk, Stephen Xia

Developing and evaluating distributed inference algorithms remains difficult due to the lack of standardized tools for modeling heterogeneous devices and networks. Existing studies often rely on ad-hoc testbeds or proprietary infrastructure, making results hard to reproduce and limiting exploration of hypothetical hardware or network configurations. We prese

Ka Hei Carrie Lau, Enkelejda Kasneci

Webcam-based eye tracking is a cost-effective, scalable method for remote research that effectively reaches broader populations. However, uncontrolled environments and hardware diversity lead to inconsistent data quality in crowdsourcing. To assess current practices, we conducted a scoping review of crowdsourced eye-tracking from 2011-2025. The review confir

John Bateman, Andy M. Tyrrell, Jihong Zhu

Programming robots to perform complex tasks is often difficult and time consuming, requiring expert knowledge and skills in robot software and sometimes hardware. Imitation learning is a method for training robots to perform tasks by leveraging human expertise through demonstrations. Typically, the assumption is that those demonstrations are performed by a s

Zhuo Li, Dianxi Li, Tao Teng, Quentin Rouxel

Developing general-purpose robots capable of autonomously operating in human living environments requires the ability to adapt to continuously evolving task conditions. However, adapting high-dimensional coordinated bimanual skills to novel task variations at deployment remains a fundamental challenge. In this work, we present BiSAIL (Bimanual Skill Adaptati

Zhixuan Cao, Yishu Xu, Xuang WU

DNA sequence classification requires not only high predictive accuracy but also the ability to uncover latent site interactions, combinatorial regulation, and epistasis-like higher-order dependencies. Although the standard Transformer provides strong global modeling capacity, its softmax attention is continuous, dense, and weakly constrained, making it bette

Christian Mugisho Zagabe, Sebastian Peitz

In this paper, we present a Koopman autoencoder-based least-squares policy iteration (KAE-LSPI) algorithm in reinforcement learning (RL). The KAE-LSPI algorithm is based on reformulating the so-called least-squares fixed-point approximation method in terms of extended dynamic mode decomposition (EDMD), thereby enabling automatic feature learning via the Koop

ATLAS Collaboration

Entanglement is a key property of quantum systems. In this Letter the first measurements of quantum entanglement between spins in pairs of $Z$ bosons are reported, using proton-proton collision data from the Large Hadron Collider (LHC) at center-of-mass energies of 13 TeV and 13.6 TeV, recorded with the ATLAS detector. Measurements of angular observables sen

Jiaxiang Li, Jun Yan, Daniel Watzenig, Huilin Yin

Trajectory prediction systems are critical for autonomous vehicle safety, yet remain vulnerable to adversarial attacks that can cause catastrophic traffic behavior misinterpretations. Existing attack methods require white-box access with gradient information and rely on rigid physical constraints, limiting real-world applicability. We propose DTP-Attack, a d

Devashish Gaikwad, Wil M. P. van der Aalst, Gyunam Park

Process anomaly detection is an important application of process mining for identifying deviations from the normal behavior of a process. Neural network-based methods have recently been applied to this task, learning directly from event logs without requiring a predefined process model. However, since anomaly detection is a purely statistical task, these mod

Valentinian Lungu, Anish Dhir, Mark van der Wilk, Ioannis Kontoyiannis

We investigate the asymptotic properties of Bayesian bivariate causal discovery for Gaussian Linear Structural Equation Models (SEMs) with heteroscedastic noise. We demonstrate that with purely observational data, the posterior distribution over the models fails to consistently identify the true causal structure - a consequence of the fundamental non-identif

Andrew Strominger, Hongji Wei

Recently a one-parameter family of deformed $ \mathcal{L} w_{1+\infty}$ soft symmetry algebras, denoted $ \mathcal{L}_{\Lambda}w_{1+\infty}$, acting on tree-level gravitational theories in AdS$_4$ has been discovered. Here we show that all CFT$_3$s, including those dual to quantum gravity on AdS$_4$, admit an $\mathcal{L}_{\Lambda}w_{1+\infty}$ action genera

Rui Liu

Can an expensive AI model effectively direct a cheap one to solve software engineering tasks? We study this question by introducing ManagerWorker, a two-agent pipeline where an expensive "manager" model (text-only, no code execution) analyzes issues, dispatches exploration tasks, and reviews implementations, while a cheap "worker" model (with full repo acces

Maria Villanueva-Blanco, Javier Yeste, Julia Ingles-Cerrillo, Pablo Ibañez-Romero

Cd(Zn)O stands out as probably the best plasmonic material in the mid-IR, but it is usually grown on sapphire or other passive substrates. In this work we introduce SiC as a novel, highly polar, dopable substrate for Cd(Zn)O. The Cd(Zn)O/SiC system is analyzed as a function of the Zn concentration and thin film thickness, and the results are compared to thos

Ying Zheng, Yangfan Jiang, Kian-Lee Tan

Fair data pre-processing is a widely used strategy for mitigating bias in machine learning. A promising line of research focuses on calibrating datasets to satisfy a designed fairness policy so that sensitive attributes influence outcomes only through clearly specified legitimate causal pathways. While effective on clean and information-rich data, these meth

Horia D. Cornean, Mikkel H. Thorn

We combine our previous results on magnetic pseudo-differential operators for H\"ormander symbols dominated by tempered weights [arXiv:2511.07184] with the magnetic Weyl super calculus of Lee and Lein [arXiv:2201.11487, arXiv:2405.19964]. This allows us to extend some previous results on the semi-super and super Moyal algebra, as well as to prove boundedness

Wen-Xuan Long, Marco Moretti, Giacomo Bacci, Luca Sanguinetti

This letter investigates the channel estimation problem in THz wireless communications where a RIS is employed to assist wireless transmission between different devices. Unlike existing studies, we consider a novel scenario where specific devices are all located in the radiative NF region of the RIS. Meanwhile, we also account for the impact on channel estim

Tatsuro Hikawa

This paper studies an analog of the classical Schwartz space $ \mathscr{S}(\mathbb{R}^N) $ in the framework of $ (k, a) $-deformed harmonic analysis associated with the $ (k, a) $-generalized Fourier transform $ \mathscr{F}_{k, a} $. Motivated by the observation that $ \mathscr{S}(\mathbb{R}^N) $ coincides with the space of smooth vectors for the Segal--Shal

Peter Mlkvik, Nicola A. Spaldin, Claude Ederer

We present a combined density-functional theory and dynamical mean-field theory (DFT+DMFT) study of the full structural phase space of rutile-based vanadium dioxide (VO$_2$), including also the less studied M2 and T phases, using an unconventional bond-centered orbital basis. The use of bond-centered orbitals allows us to treat all main phases of VO$_2$, and

Marco Saretta, Enrica Raheli, Jalal Kazempour

Despite hydrogen being central to Europe's decarbonisation strategy, only a small share of renewable hydrogen projects reached final investment decision. A key barrier is uncertainty about how future hydrogen markets will be designed and operated, particularly under Renewable Fuels of Non-Biological Origin requirements. This study investigates the extent to

Robin J. Deeley, Magnus Goffeng, Bram Mesland

This monograph studies $KK$-theory in its unbounded model. The central object is the $KK$-bordism group obtained by imposing the $KK$-bordism relation on unbounded $KK$-cycles. In the paradigm of noncommutative geometry, an unbounded $KK$-cycle is a noncommutative geometry in its own right and our approach allow for the study of mildly noncommutative geometr

Raia Abu Ahmad, Max Upravitelev, Aida Usmanova, Veronika Solopova

Automatically verifying climate-related claims against scientific literature is a challenging task, complicated by the specialised nature of scholarly evidence and the diversity of rhetorical strategies underlying climate disinformation. ClimateCheck 2026 is the second iteration of a shared task addressing this challenge, expanding on the 2025 edition with t

S. Li, R. M. Ludlam, M. Sudha, M. C. Brumback

We present the first spectral analysis of Scorpius X-1 (Sco X-1) using intentional stray light (SL) observations taken by NuSTAR. Unlike focused observations that have high telemetry load when observing bright sources, intentional SL observations can help reduce the telemetry and reduce the effect of dead time, thereby maximizing the on-source exposure time;

Shaurjya Mandal, Nutan Sharma, John Galeotti

Human mesh recovery from single images remains challenging due to inherent depth ambiguity and limited generalization across domains. While recent methods combine regression and optimization approaches, they struggle with poor initialization for test-time refinement and inefficient parameter updates during optimization. We propose a novel meta-learning frame

Hsin-Hui Huang, Meguya Ryu, Yoshiaki Nishijima, Haoran Mu

Nanoscale surface analysis of 1 micrometer thick high entropy alloys (HEAs) was carried out using nano-IR for hyperspectral imaging and single point spectroscopy in the 700-1700 1/cm spectral range. Nano-IR is based on the detection of scattered light from an oscillating metal coated nano-tip in one of the arms of the Fourier transform infrared spectrometer

Roland Stenger, Sebastian Löns, Nele Brügge, Feline Hamami

Cervical dystonia (CD) is the most common form of dystonia, yet current assessment relies on subjective clinical rating scales, such as the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS), which requires expertise, is subjective and faces low inter-rater reliability some items of the score. To address the lack of established objective tools for m

Pritam Banerjee, Kowsona Chakraborty, Niles Mondal, Tapobrata Sarkar

Black holes (BHs) and wormholes (WHs) are characterized by distinct spacetime geometries, whose differences become pronounced close to the central objects. A useful way to probe such differences is via the dynamics of stellar tidal disruption events in the regime of strong gravity. Here, using a general relativistic smoothed particle hydrodynamics code inspi

Xiaoming Liu, Borong Zhang, Qingbiao Li, Steven Morad

The prevailing paradigm for image-goal visual navigation often assumes access to large-scale datasets, substantial pretraining, and significant computational resources. In this work, we challenge this assumption. We show that we can collect a dataset, train an in-domain policy, and deploy it to the real world (1) in less than 120 minutes, (2) on a consumer l

Yue Li, Shujuan Chen, Akihiro Shimoda, Ying Jin

Long-term traffic modelling is fundamental to transport planning, but existing approaches often trade off interpretability, transferability, and predictive accuracy. Classical travel demand models provide behavioural structure but rely on strong assumptions and extensive calibration, whereas generic deep learning models capture complex patterns but often lac

Rasched Haidari, Sam Martin, Maxime Allard

Large Genomic Foundation Models have recently achieved remarkable results and in-vivo translation capabilities. However these models quickly grow to over a few Billion of parameters and are expensive to run when compute is limited. To overcome this challenge, we present a distillation framework for transferring mRNA representations from a state of the art ge

Albi Mema, Simon Thomann, Narendra Singh Dhakad, Hussam Amrouch

With the staggering increase of edge compute applications like Internet-of-Things (IoT) and artificial intelligence (AI), the demand for fast, energy-efficient on-chip memory is growing. While the fast and mature static random-access memory (SRAM) technology is the standard choice, its volatility requires a constant supply voltage to operate and store data.

Luca Colagrande, Lorenzo Leone, Chen Wu, Tim Fischer

The exponential increase in Machine Learning (ML) model size and complexity has driven unprecedented demand for high-performance acceleration systems. As technology scaling enables the integration of thousands of computing elements onto a single die, the boundary between distributed and on-chip systems has blurred, making efficient on-chip collective communi

Zixu Han, Kairan Yang, Peng Zhang

The density-based bi-objective topology optimization (TO) has been widely adopted in liquid cooling plate design, where the design domain is treated as porous media with porosity as the design variable. However, conventional TO method struggles to directly optimize the convective heat transfer due to its incapabilities of explicitly depicting the heat transf

Junhyeon Jo, Manuel Suárez-Rodríguez, Samuel Mañas-Valero, Eugenio Coronado

Nonlinear magnetoconductivity (NLMC) is a nonreciprocal transport response arising in non-centrosymmetric materials. However, this ordinary NLMC signal vanishes at zero magnetic field, limiting its potential for applications. Here, we report the observation of an anomalous NLMC controlled by internal order parameters such as the magnetization or N\'eel vecto

Brandon Tran, Matthias Maiterth, Woong Shin, Matthew D. Sinclair

Modern GPU-rich HPC systems are increasingly becoming energy-constrained. Thus, understanding an application's energy consumption becomes essential. Unfortunately, current GPU energy attribution techniques are either inaccurate, inflexible, or outdated. Therefore, we propose Wattchmen, a flexible methodology for measuring, attributing, and predicting GPU ene

Mikko Saukkoriipi, Nicole Hernandez, Jaakko Sahlsten, Kimmo Kaski

Clinicians often need to retrieve patient-specific information from electronic health records (EHRs), a task that is time-consuming and error-prone. We present a locally deployable Clinical Contextual Question Answering (CCQA) framework that answers clinical questions directly from EHRs without external data transfer. Open-source large language models (LLMs)

P. V. Kaygorodov

In this paper, a new tool for Doppler tomography, Tomo-V (https://tomo-v.inasan.ru) that is developed based on the algebraic reconstruction technique (ART) has been presented. Previously, the ART method has not been widely used in tomography, as its direct implementation was computationally complex. The author has developed a fast version of this algorithm,

Vinicius Hernandes, Joseph Rogers, Rouven Koch, Thomas Spriggs

Efficiently characterizing quantum dot (QD) devices is a critical bottleneck when scaling quantum processors based on confined spins. Measuring high-resolution charge stability diagrams (or CSDs, data maps which crucially define the occupation of QDs) is time-consuming, particularly in emerging architectures where CSDs must be acquired with remote sensors th

Luc de Montella, Sebastian Sager

We consider optimal experimental design for parameter estimation in dynamical systems governed by controlled ordinary differential equations. In such problems, Fisher-based criteria are attractive because they lead to time-additive objectives compatible with adjoint-based optimal control, but they remain intrinsically local and may perform poorly under stron

Nina Smirnova, Daniel Dan, Philipp Mayr

Parliamentary debate constitutes a central arena of political power, shaping legislative outcomes and public discourse. Incivility within this arena signals political polarization and institutional conflict. This study presents a systematic investigation of incivility in the German Bundestag by examining calls to order (CtO; plural: CtOs) as formal indicator

Jinyi Li, Dongping Li, Hua Yang

Matrix differential Riccati equation (DRE) typically exhibits transient and steady-state phases, posing challenges for fixed-step time integration methods, which may lack accuracy during transients or oversample in steady regimes. In this work, we propose adaptive low-rank matrix-valued exponential integrators for large-scale stiff DRE. The methods combine e

K. V. Semenov, A. A. Tuzhilin

Starting from the definition of the Gromov-Hausdorff distance via distortion of correspondences, we add the requirement of semicontinuity of each correspondence and its inverse. It turns out that in the case of lower semicontinuity we obtain the same classical Gromov-Hausdorff distance, while for upper semicontinuity we are able to prove coincidence with the

Qi Li, Yuhui Zhao, Chengxing Zhai, Yang Wang

Detecting faint objects in cislunar space using ground-based optical telescopes is difficult because of their low brightness, strong lunar background, and complex, nonlinear apparent motion. Traditional shift-and-stack techniques based on linear motion assumption suffer signal trailing loss due to significant nonlinear motion during long integrations, thus p

William Thorne, Rupert Shepherd, Diana Maynard

We present a reconstruction of UKRI's Gateway to Research (GtR) database that links funding opportunities to their resulting project proposals through panel meeting outcomes. Unlike existing work that focuses primarily on funded projects and their outcomes, we close the complete funding lifecycle by integrating three previously disconnected data sources: the

Moritz Nottebaum, Matteo Dunnhofer, Christian Micheloni

Recent research on vision backbone architectures has predominantly focused on optimizing efficiency for hardware platforms with high parallel processing capabilities. This category increasingly includes embedded systems such as mobile phones and embedded AI accelerator modules. In contrast, CPUs do not have the possibility to parallelize operations in the sa

Andrzej Musiał, Maria Pugaczowa-Michalska, Natalia Lindner, Zbigniew Śniadecki

The changes in magnetic properties of Ce(Fe0.9Co0.1)2 compound with increasing disorder are discussed in the paper. Homogeneous alloys are known to undergo the phase transition from ferromagnetic to antiferromagnetic state accompanied by the structural distortion of the cubic Laves C15 phase into the rhombohedral one. Various stimuli, like the structural dis

Xiang Xiang Wang, Guo-Wei We

Single-cell RNA-seq data analysis typically requires representations that capture heterogeneous local structure across multiple scales while remaining stable and interpretable. In this work, we propose a hierarchical sheaf spectral embedding (HSSE) framework that constructs informative cell-level features based on persistent sheaf Laplacian analysis. Startin

L. Truffet

Tropical polyhedra seem to play a central role in static analysis of softwares. These tropical geometrical objects play also a central role in parity games especially mean payoff games and energy games. And determining if an initial state of such game leads to win the game is known to be equivalent to solve a tropical linear optimization problem. This paper

Francis R. A. Aznaran, Martina Bukač, Boris Muha

We consider a fluid-structure interaction problem in the Eulerian, phase-field formulation. The problem is described using the Navier--Stokes equations for a viscous, incompressible fluid, coupled with the incompressible hyperelasticity system, both written in the Eulerian coordinates. This allows the problem to be written in a unified formulation, using a s

Boris Muha, Šárka Nečasová, Milan Pokorný, Srđan Trifunović

We study a stationary 3D/2D fluid-structure interaction problem between an elastic structure described by the linear plate equation and a fluid described by the compressible Navier-Stokes equations with hard-sphere pressure and inflow/outflow boundary data. This problem is motivated by wind-tunnel configuration and by the need for physically relevant steady

Zhiyang Wu, Junliang Chen, Qian Wan, Qing Xiao

Equipping laypeople with the capabilities to seek legal information has been an important goal for Legal Empowerment in modern society. However, unlike general information-seeking behaviors, legal information seeking is characterized by high stakes, urgency, and a critical need for emotional support, which traditional text-based searching platforms struggle

Tian-Xiao He

This paper studies a class of multivariate Kantorovich-kernel neural network operators, including the deep Kantorovich-type neural network operators studied by Sharma and Singh. We prove density results, establish quantitative convergence estimates, derive Voronovskaya-type theorems, analyze the limits of partial differential equations for deep composite ope

Ivan Costa, Pedro Correia, Ivone Amorim, Eva Maia

Federated Learning (FL) enables collaborative training while keeping sensitive data on clients' devices, but local model updates can still leak private information. Hybrid Homomorphic Encryption (HHE) has recently been applied to FL to mitigate client overhead while preserving privacy. However, existing HHE-FL systems rely on a single homomorphic key pair sh

Siddhartha Laghuvarapu, Rohan Deb, Jimeng Sun

Uncertainty quantification is essential for deploying machine learning models in high-stakes domains such as scientific discovery and healthcare. Conformal Prediction (CP) provides finite-sample coverage guarantees under exchangeability, an assumption often violated in practice due to distribution shift. Under covariate shift, restoring validity requires imp

Thao Le, Robbert van der Burg, Bernd Heidergott, Ines Lindner

We introduce weighted Markovian graphs, a random walk model that decouples the transition dynamics of a Markov chain from (random) edge weights representing the cost of traversing each edge. This decoupling allows us to study the accumulated weight along a path independently of the routing behavior. Crucially, we derive closed-form expressions for the mean a

Thomas Blochowicz, Emina Ismajli, Jan Philipp Gabriel

We describe a dynamic light scattering setup that uses diffusing wave spectroscopy (DWS) to investigate the dynamics in sand grains subjected to periodic vertical shaking by a loudspeaker. Along with the setup that is used in the undergraduate physics lab course at TU Darmstadt, the necessary DWS theory is introduced, including the proper treatment of the os

Hao Chen, Takuya Kiyokawa, Weiwei Wan, Kensuke Harada

Task-oriented grasping (TOG) is more challenging than simple object grasping because it requires precise identification of object parts and careful selection of grasping areas to ensure effective and robust manipulation. While recent approaches have trained large-scale vision-language models to integrate part-level object segmentation with task-aware grasp p

William Dawson, Louis Beal, Marco Zaccaria, Luigi Genovese

Predicting how protein mutations affect drug binding remains a major challenge, particularly when the mutations are distal from the binding site. In this study, we introduce a coupled simulation workflow that combines long-time-scale molecular dynamics (MD) with high-throughput quantum mechanical (QM) analysis to reveal the electronic structure signatures of

Richard J. Young

Extended-thinking models expose a second text-generation channel ("thinking tokens") alongside the user-visible answer. This study examines 12 open-weight reasoning models on MMLU and GPQA questions paired with misleading hints. Among the 10,506 cases where models actually followed the hint (choosing the hint's target over the ground truth), each case is cla

Luana Kurmann, Svenja Lage, Violetta Weger

In this paper we present an attack on a recently proposed code-based Private Information Retrieval (PIR) scheme. Indeed, the server can retrieve the index of the desired file with high probability in polynomial time. The attack relies on the fact that random codes over finite rings are free with high probability and that the dimension of the rowspan of the q

Hai-Son Nguyen-Le, Hung-Cuong Nguyen-Thanh, Nhien-An Le-Khac, Dinh-Thuc Nguyen

The rapid advancement of generative models has enabled highly realistic audio deepfakes, yet current detectors suffer from a critical bias problem, leading to poor generalization across unseen datasets. This paper proposes Artifact-Focused Self-Synthesis (AFSS), a method designed to mitigate this bias by generating pseudo-fake samples from real audio via two

Mindaugas Budzys, Bin Liu, Antonis Michalas

Matchmaking has become a prevalent part in contemporary applications, being used in dating apps, social media, online games, contact tracing and in various other use-cases. However, most implementations of matchmaking require the collection of sensitive/personal data for proper functionality. As such, with this work we aim to reduce the privacy leakage inher

Le Mau Hai, Nguyen Van Phu, Trinh Tung

In this note, we establish several results concerning the continuity (or weak convergence) of the complex Monge-Amp\`ere operator on compact Hermitian manifolds. At the end of this note, we find a weak solution of the complex Monge-Amp\`ere equation on a compact Hermitian manifold under the assumption of the existence of a smooth subsolution.

Sama Al-Shammari, Alexandre Göttel, Masaki Iwaya, Vivien Raymond

Parameter estimation for gravitational-wave signals is computationally demanding due to the high dimensionality of the parameter space and the cost of repeated waveform generation in traditional Bayesian inference. These analyses require on the order of 10^8 likelihood evaluations and waveform generations, resulting in inference times of hours to days per ev

Jingwen Zheng, Bojin Zheng, Weiwu Wang

The Complex Boolean Turing Machine (CBTM) characterizes non-deterministic computation using the abstract generator $\alpha$, but the abstractness of $\alpha$ makes it difficult to understand intuitively. In this paper, by concretizing $\alpha$ as the algebraic number $\sqrt{2}$, we introduce the \textbf{Real Boolean Turing Machine (RBTM)} and propose the \te

Xia Cheng, Yang Wu, Zhenzhou Guo, Tie Yang

Altermagnetism has so far been associated with compensated magnetic moments carried by atoms. Here we introduce Stoner instability induced interstitial-electron altermagnetism, a distinct mechanism in which altermagnetic order is carried instead by interstitial anionic electrons in electrides. We show that, owing to the quasi-nucleus-free nature of interstit

Haoyang Luo, Zihang Zhao, Leiyao Cui, Saiyao Zhang

Human dexterity relies on rapid, sub-second motor adjustments, yet capturing these high-frequency dynamics remains an enduring challenge in biomechanics and robotics. Existing motion capture paradigms are compromised by a trade-off between temporal resolution and visual occlusion, failing to record the fine-grained hand motion of fast, contact-rich manipulat

Le Mau Hai, Nguyen Van Phu, Trinh Tung

In this paper, we investigate the continuity of solutions to the Dirichlet problem for complex Hessian-type equations associated with $(\omega, m)-\beta$-subharmonic functions on a ball in $\mathbb{C}^n$, where $ \beta=d d^c\|z\|^2=\frac{i}{2} \sum_{j=1}^n d z_j \wedge d \bar{z}_j $ is denoted the flat metric on $\mathbb{C}^n$.

Daniel Zeman, Federica Gamba

Comparison and evaluation of graph-based representations of sentence meaning is a challenge because competing representations of the same sentence may have different number of nodes, and it is not obvious which nodes should be compared to each other. Existing approaches favor node mapping that maximizes $F_1$ score over node relations and attributes, regardl

Le Ma, Thiago Freitas dos Santos, Nadia Magnenat-Thalmann, Katarzyna Wac

In surgical training for medical students, proficiency development relies on expert-led skill assessment, which is costly, time-limited, difficult to scale, and its expertise remains confined to institutions with available specialists. Automated AI-based assessment offers a viable alternative, but progress is constrained by the lack of datasets containing re

Jan Simon Haase, Alexander Fieguth, Igor Bröckel, Jens Kruse

Differential light shifts (DLS) induced by optical trapping fields fundamentally limit the stability and accuracy of trapped-atom microwave clocks. We demonstrate an in-situ method to cancel DLS by simultaneously interrogating multiple spatially separated atomic ensembles at different trap intensities generated from a common light source. By operating the en

Sándor Kisfaludi-Bak, Dániel Marx

We give approximation schemes for Subset TSP and Steiner Tree on unit disk graphs, and more generally, on intersection graphs of similarly sized connected fat (not necessarily convex) polygons in the plane. As a first step towards this goal, we prove spanner-type results: finding an induced subgraph of bounded size that is $(1+\varepsilon)$-equivalent to the

Nicholas Beaton, Simone Rinaldi

Convex polyominoes can be refined according to the number of direction changes in monotone paths connecting pairs of cells, leading to the notion of $k$-convexity. In particular, the cases $k=1$ and $k=2$ correspond to $L$-convex and $Z$-convex polyominoes, two well-studied subclasses of convex polyominoes, with intermediate families such as centered and $4$