April 2026 arXiv papers — page 109

Matteo Panzeri, Piero Florio, Davide Girardi, Joseba Urrestarazu

Magnetic textures are central to emerging spintronic and unconventional computing technologies due to their rich dynamics, topological properties and nanoscale dimensions. A major challenge remains achieving tunable, reversible, and spatially resolved control over these textures and their evolution as a function of external stimuli, by spatially reprogrammin

Fernando Moro, Vinicius M. A. Souza

Multivariate time series classification (MTSC) plays a crucial role in various domains, including biomedical signal analysis and motion monitoring. However, existing approaches, particularly deep learning models, often require high computational resources, making them unsuitable for real-time applications or deployment on low-cost hardware, such as IoT devic

Markus Bachmayr, Sebastian Krämer, Max Pfeffer

We consider an iterative eigensolver for Schr\"odinger equations that constructs low-rank approximations of eigenfunctions with accuracy-adapted ranks, with particular focus on fermionic Schr\"odinger equations in second-quantized form and on matrix product state approximations enforcing particle number conservation. We provide a complete analysis of a solve

Alina Deriyeva, Jesper Dannath, Benjamin Paassen

Practice and extensive exercises are essential in programming education. Intelligent tutoring systems (ITSs) are a viable option to provide individualized hints and advice to programming students even when human tutors are not available. However, prior ITS for programming rarely support the Python programming language, mostly focus on introductory programmin

Lin Deng, Chang-bo Liu

We extracted the scholarly reasoning systems of two internationally prominent humanities and social science scholars from their published corpora alone, converted those systems into structured inference-time constraints for a large language model, and tested whether the resulting scholar-bots could perform core academic functions at expert-assessed quality.

Michał Romaszewski, Dominik Kopeć, Michał Cholewa, Katarzyna Kołodziej

Hyperspectral tree species classification is challenging due to limited and imbalanced class labels, spectral mixing (overlapping light signatures from multiple species), and ecological heterogeneity (variability among ecological systems). Addressing these challenges requires methods that integrate biological and structural characteristics of vegetation, suc

Yuhai Deng, Huimin She, Wei Shen, Meng Li

Tone style transfer for photo retouching aims to adapt the stylistic tone of the reference image to a given content image. However, the lack of high-quality large-scale triplet datasets with stylized ground truth forces existing methods to rely on self-supervised or proxy objectives, which limits model capability. To mitigate this gap, we design a data const

José Juan Hernández Morales, Georgios Mentzos, Frank Hannig, Konstantinos Balaskas

The paradigm shift towards local and on-device inference under stringent resource constraints is represented by the tiny machine learning (TinyML) domain. The primary goal of TinyML is to integrate intelligence into tiny, low-cost devices under strict resource, energy, and latency constraints. However, the ultra-resource-constrained nature of these devices c

Henning Hillebrandt, Andreas Padalkin, Christian Scheideler, Daniel Warner

The decomposition of complex structures into simpler substructures is a powerful technique with a wide range of applications. We study the computation of decompositions in the context of programmable matter. The amoebot model is a well-established model for programmable matter, which places $n$ tiny robots called amoebots on the triangular grid. We consider

Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

We study the sample complexity of learning an $\epsilon$-optimal policy in the Stochastic Shortest Path (SSP) problem. We first derive sample complexity bounds when the learner has access to a generative model. We show that there exists a worst-case SSP instance with $S$ states, $A$ actions, minimum cost $c_{\min}$, and maximum expected cost of the optimal p

Jan Giesselmann, Philipp Öffner, Robert Sauerborn

We propose a concept of dissipative weak (DW) solutions for the Navier-Stokes-Korteweg (NSK) system and prove conditional convergence of a structure-preserving finite volume scheme towards such a solution. DW solutions provide a generalized solution concept in computational fluid dynamics and have recently attracted significant attention. They provide an ext

Ubaid Tantary, Qianqian Du

We revisit the constrained log-subtracted two-point Padé (LSTP) ensemble for thermal $\mathcal{N}=4$ supersymmetric Yang-Mills (SYM) thermodynamics in four spacetime dimensions after upgrading the weak-coupling truncation from $\mathcal{O}(λ^2)$ to the exact $\mathcal{O}(λ^{5/2})$ coefficient. We keep the LSTP interpolation ansatz unchanged and shift the wea

Federico Nocentini, Kwanggyoon Seo, Qingju Liu, Claudio Ferrari

Speech-Driven Facial Animation (SDFA) has gained significant attention due to its applications in movies, video games, and virtual reality. However, most existing models are trained on single-language data, limiting their effectiveness in real-world multilingual scenarios. In this work, we address multilingual SDFA, which is essential for realistic generatio

Sebastian Hell, Paul Winter, Martin Gärttner, Julian Späthe

In a double-slit experiment with a bipartite system, the visibility of interference fringes depends on the availability of which-way information. Here, we report the formation of a Bell-like state of photoelectron and residual ion in the multiphoton dissociative ionization of the D$_2$ molecule. Evidence for entanglement is provided by the correlated emissio

Janet Vertesi, danah boyd, Alex Taylor, Benjamin Shestakofsky

The Project of AI is a world-building endeavor, wherein those who fund and develop AI systems both operate through and seek to sustain networks of power and wealth. As they expand their access to resources and configure our sociotechnical conditions, they benefit from the ways in which a suite of decoys animate scholars, critics, policymakers, journalists, a

Baramee Sukumal, Aueaphum Aueawatthanaphisut

Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional computed tomography (CT) imaging, while essential for detection and staging, has limitations in distinguishing benign from malignant lesions and providing interpretable diagnostic insights. To address this challenge, this study proposes a dual-modal artificial i

Matteo Cozzi, Tomás Sanz-Perela

We establish an isoperimetric type inequality for the level sets of functions in fractional Sobolev spaces. This answers a question posed by the first author in a previous paper. To obtain it, we work out a slight modification of some estimates for nonlocal interaction functionals established by Savin and Valdinoci. We also show how said isoperimetric inequa

V. Popkov, T. Prosen

We find an exact nonequilibrium steady state of an open dissipatively driven XXZ spin-1/2 chain with source or sink spin bath at one end and an arbitrary boundary field at the other end.

Xin Jin, Nitish Kumar Chandra, Mohadeseh Azari, Jinglei Cheng

We propose a quantum-resistant quantum teleportation (QRQT) framework protected by post-quantum cryptography (PQC) to secure the classical correction channel, which is vulnerable to quantum adversaries. By applying PQC to the classical control bits, QRQT eliminates the classical attack surface of quantum teleportation. Our analysis reveals that quantum memor

Shaoang Li, Jian Li

Edge deployment of large language models (LLMs) increasingly relies on libraries of lightweight LoRA adapters, yet GPU/DRAM can keep only a small resident subset at a time. Serving a request through a non-resident adapter requires paging its weights from storage, incurring measurable latency. This creates a two-timescale online control problem: on a slow tim

Laziz Hamdi, Amine Tamasna, Thierry Paquet

Tables condense key transactional and administrative information into compact layouts, but practical extraction requires more than text recognition: systems must also recover structure (rows, columns, merged cells, headers) and interpret roles such as line items, subtotals, and totals under common capture artifacts. Many existing resources for table structur

Jiayi Liu, Xudong Sun, Peter W. Schuck, Lars K. S. Daldorff

The observed solar photospheric magnetic fields and Doppler velocities are frequently used to quantify the Poynting flux and helicity flux. Multiple methods have been developed for this purpose, but their estimates of the Poynting flux and helicity flux often differ from one another. Here we study the performance of three widely used methods on NOAA active r

Florian Furbach, Lucas Clorius, Roland Kuhn, Hernán Melgratti

Swarm protocols are a recently introduced formalism for specifying, implementing, and verifying peer-to-peer systems called swarms. A swarm consists of distributed agents called machines that communicate by asynchronous event propagation. Following a local-first model, each machine can progress without requiring continuous connectivity to other machines. Exi

Livia Petrillo

Axions, hypothetical particles proposed to solve the strong CP problem and considered promising dark matter candidates, can be produced in the Sun and interact in detectors via couplings to photons, electrons, or nucleons. The CUORE and CUPID scintillating cryogenic calorimeters, originally developed to search for dark matter and neutrinoless double beta dec

N. C. Combe

We show that the Hilbert space of the Koopman--von Neumann formulation of Landau--Ginzburg theory is parametrised by a real Monge--Amp\`ere domain, which carries a natural pre-Frobenius. Restricting to finite-dimensional (dually flat) exponential families, the parameter space becomes a Monge--Amp\`ere domain and a pre-Frobenius manifold. Our main theorem pro

Henry O. Velesaca, Andrea Mero, Guillermo A. Castillo, Angel D. Sappa

Pedestrian detection is fundamental to autonomous driving, robotics, and surveillance. Despite progress in deep learning, reliable identification remains challenging due to occlusions, cluttered backgrounds, and degraded visibility. While multispectral detection-combining visible and thermal sensors-mitigates poor visibility, the challenge of camouflaged ped

Mahir Labib Dihan, Md. Ashrafur Rahman Khan, Wasif Jalal, Md. Roqunuzzaman Sojib

Neural Combinatorial Optimization (NCO) has emerged as a powerful framework for solving combinatorial optimization problems by integrating deep learning-based models. This work focuses on improving existing inference techniques to enhance solution quality and generalization. Specifically, we modify the Random Re-Construct (RRC) approach of the Light Encoder

Laur Järv, Sotirios Karamitsos

Thermodynamics provides a useful interpretation of scalar-tensor gravity, in which the effective imperfect fluid admitted by the nonminimal coupling features a temperature that is associated with the departure from general relativity. However, in this construction, certain thermodynamical quantities are defined with respect to a particular conformal frame. I

Agnieszka Pregowska, Stefan Marynowicz

Accurate assessment of lithium-ion battery ageing is challenged by cell-to-cell variability, heterogeneous cycling protocols, and limited transferability of data-driven models across datasets. In particular, robust identification of degradation transitions, such as the knee point, and reliable early-life prediction of remaining useful life (RUL) remain open

Kishore C. Patra, Emily R. Liepold, Nicholas Earl, Ryan J. Foley

Off-nuclear tidal disruption events (TDEs) provide a rare probe of massive black holes (MBHs) outside galactic nuclei. Only a handful are known, including five X-ray-selected candidates and two optically selected events. We present observations of TDE 2025abcr, the second optically selected off-nuclear TDE, discovered at a projected offset of $9.08 \pm 0.02$

Alexander Bonora, Marco Giordani, Michele Zorzi

The evolution of 6th generation (6G) networks increasingly relies on satellite-based Non-Terrestrial Networks (NTNs) to extend broadband connectivity to remote and unserved regions, and to support public safety. In this paper we compare two representative and conceptually different satellite constellation architectures, namely Starlink and IRIS 2. Starlink i

Davide Dal Martello

Using the LP algebraic toolkit, Conway's original topograph is rethought of as a cluster construction, paving the way for a wider topography based on mutation-type local rules. As a remarkable application of such cluster-driven upgrade, both the process of analytic continuation for Painlev\'e VI and the reduction algorithm for quadratic forms are endowed wit

Stefan Behfar, Richard Mortier

Probabilistic Synchronous Parallel (PSP) is a technique in distributed learning systems to reduce synchronization bottlenecks by sampling a subset of participating nodes per round. In Federated Learning (FL), where edge devices are often unreliable due to factors including mobility, power constraints, and user activity, PSP helps improve system throughput. H

N. C. Combe, H. K. Nencka

We present a geometric framework for reconstruction problems based on Vaisman foliations and Atiyah--Molino sequences. Independent projections induce transverse foliations and dual connections; vanishing torsion and curvature duality guarantee unique, path-independent reconstruction, while obstructions yield non-associative quasigroupoids. Toric symmetry pro

Miguel Sánchez de La Rosa, Gabriel Gomez-Lopez, Alejandro Baviera, Jose Duro

The interconnection network is a key component of Supercomputers and Data centers, and its design must cope with the increasing communication demands of current applications and services; otherwise, it may become a system bottleneck. The most challenging network design issues are the topology, routing algorithm, flow control, and power efficiency. However, e

Côme Fiegel, Pierre Ménard, Tadashi Kozuno, Michal Valko

We study the problem of learning in zero-sum matrix games with repeated play and bandit feedback. Specifically, we focus on developing uncoupled algorithms that guarantee, without communication between players, the convergence of the last-iterate to a Nash equilibrium. Although the non-bandit case has been studied extensively, this setting has only been expl

Hamed Ouattara, Pierre Duthon, Pascal Houssam Salmane, Frédéric Bernardin

One of the dominant paradigms in self-supervised learning (SSL), illustrated by MoCo or DINO, aims to produce robust representations by capturing features that are insensitive to certain image transformations such as illumination, or geometric changes. This strategy is appropriate when the objective is to recognize objects independently of their appearance.

Marco Drewes, Yannis Georis, Mubarak A. S. Mohammed, Sebastian Zell

The relic abundance of Dark Matter (DM) produced via thermal freeze-in is sensitive to the thermal history during and after cosmic reheating. In minimal models, this opens up the possibility to make predictions for collider observables by combining the requirement to match the DM relic abundance with observations of the Cosmic Microwave Background (CMB). We

Jieming Yu, Qiuxiao Feng, Zhuohan Wang, Xiaochen Ma

With the rapid advancement of deep generative models, realistic fake images have become increasingly accessible, yet existing localization methods rely on complex designs and still struggle to generalize across manipulation types and imaging conditions. We present a simple but strong baseline based on DINOv3 with LoRA adaptation and a lightweight convolution

Enas E. Ahmed, Salah A. Aly, Mayar Moner

Acute Myeloid Leukemia (AML) is one of the most life-threatening type of blood cancers, and its accurate classification is considered and remains a challenging task due to the visual similarity between various cell types. This study addresses the classification of the multiclasses of AML cells Utilizing YOLOv12 deep learning model. We applied two segmentatio

Aswini Misro, Vikash Sharma, Shreyank N Gowda

We present Veritas-RPM, a provenance-guided multi-agent architecture comprising five processing layers: VeritasAgent (ground-truth assembly), SentinelLayer (anomaly detection), DirectorAgent (specialist routing), six domain Specialist Agents, and MetaSentinelAgent (conflict resolution and final decision). We construct a 98-case synthetic taxonomy of false-po

Arul Thileeban Sagayam

In most split-tunnel VPN/ZTNA deployments, installing an internal route authorizes the entire device, not a specific application, to use it. An unprivileged malicious process can therefore reach internal services by reusing routes intended for corporate applications. We present ProcRoute, a system that restricts internal-route access to explicitly authorized

Rania Briq, Michael Kamp, Ohad Fried, Sarel Cohen

The success of deep generative models in generating high-quality and diverse samples is often attributed to particular architectures and large training datasets. In this paper, we investigate the impact of these factors on the quality and diversity of samples generated by \emph{flow-matching} models. Surprisingly, in our experiments on CelebA-HQ dataset, flo

Ram Karan Choudhary, Saikat Panja

In this article, we derive explicit combinatorial formulas, depending only on $q$, for the Wedderburn decomposition of the rational group algebras of the finite linear groups $\operatorname{SL}_2(q)$ and $\operatorname{PSL}_2(q)$. Furthermore, we also determine the number of pairwise non-isomorphic simple $\mathbb Q G$-modules of each possible dimension for

Stephane Baseilhac, Fathi Ben Aribi

We compute the real part of the semi-classical limit of the sequence of quantum hyperbolic invariants (QHI) of the figure-eight knot complement $M$. We show that it is rigid, in the sense that it does not depend on the choice of holonomy representation of $M$, and it is either $0$ or equal to the hyperbolic volume of $M$ divided by $2\pi$, depending on a par

Stefan Huber, Dominik Kaaser

We study the patient zero problem in epidemic spreading processes in the independent cascade model and propose a geometric approach for source reconstruction. Using Johnson-Lindenstrauss projections, we embed the contact network into a low-dimensional Euclidean space and estimate the infection source as the node closest to the center of gravity of infected n

Marco G. Barnfield, Oleg L. Polyansky, Sergei N. Yurchenko, Jonathan Tennyson

Recent advances in the use of High-Resolution Cross-Correlation Spectroscopy (HRCCS) to detect molecular species in exoplanet atmospheres, presents a new challenge for the accuracy of reference spectroscopic line lists. While parent isotopologues of key atmospheric tracers are often well-characterized, minor isotopologues, crucial for diagnosing planetary fo

Ignacio Romero, Michael Ortiz

We develop an operator-theoretic formulation of hereditary constitutive models and characterize optimal finite-rank internal-variable approximations in the sense of Kolmogorov $N$-widths. The history operator is shown to be compact under natural assumptions on the relaxation kernel, thereby admitting optimal low-rank approximations. The resulting reduced mod

Kevin Bogner, Aysajan Abidin, Dave Singelee, Bart Preneel

Distance-bounding (DB) protocols let a verifier upper-bound a prover's physical distance by timing rapid challenge-response exchanges. Quantum communication promises simpler DB protocols with stronger security guarantees, yet existing quantum distance-bounding (QDB) proposals are analysed in ad-hoc models and, to the best of our knowledge, lack a common game

Laziz Hamdi, Amine Tamasna, Pascal Boisson, Thierry Paquet

We present TableSeq, an image-only, end-to-end framework for joint table structure recognition, content recognition, and cell localization. The model formulates these tasks as a single sequence-generation problem: one decoder produces an interleaved stream of \texttt{HTML} tags, cell text, and discretized coordinate tokens, thereby aligning logical structure

Katharina Stich, Bastian Perner, Friedemann Laue, Torsten Reissland

This paper proposes the LiFE-CD algorithm for convergence time analysis of the max-consensus algorithm in multi-agent systems under Bernoulli-distributed link failures. Unlike existing approaches, which either assume ideal communication or provide asymptotic upper bounds on the expected convergence time, LiFE-CD deterministically computes the full probabilit

Vaibhav Kumar, Ahmad Bazzi, Christina Pöpper, Marwa Chafii

ISAC systems introduce new privacy risks because an unintended sensing node may exploit the shared radio waveform to infer transmitter-related information even when the communication payload remains secure. This paper investigates transmitter privacy, defined as limiting unauthorized inference of transmitter-related information through channel estimation, in

Guransh Singh

Adapting pre-trained vision-language models (VLMs) for robotic control requires injecting high-magnitude continuous gradients from a flow-matching action expert into a backbone trained exclusively with cross-entropy. This cross-modal gradient asymmetry - the spectral dimensionality mismatch between low-rank MSE regression gradients and the high-dimensional s

Shubhodip Mondal, Martin Olsson

We describe the prismatic F-gauge associated to a finite flat height one group scheme over a smooth variety of positive characteristic. As applications, we derive the description of the crystalline Dieudonn\'e module of Berthelot-Breen-Messing in this case and recover results of Bragg-Olsson describing flat cohomology using a Hoobler-type sequence.

Kaan Öcal, Syrine Ghrabli, Michael P. H. Stumpf

Statistical physics can describe the behavior of microbial populations consisting of many heterogeneous individuals. A direct consequence is the existence of phase transitions, where the behavior of a population changes discontinuously upon a small perturbation. While such phase transitions have often been proposed in biology, connecting observed behavior to

Loek van Montfort, Markus Leitner, Rosario Paradiso

Temporal dependencies between customer visits, such as synchronization constraints, pose a fundamental challenge in vehicle routing. These dependencies, which arise in applications such as home healthcare routing, aircraft scheduling, and technician routing, introduce inter-route constraints that make the resulting problems significantly harder to solve. We

Guodong Sun, Samir M. Perlaza, Philippe Mary, Jean-Marie Gorce

This paper studies reliability-guaranteed decoding for variable-length stop-feedback (VLSF) codes over correlated noncoherent fading channels. The decoding rule is based on the evolution of the information density associated with a given channel input-output realization. Due to channel memory, exact evaluation of this information density is intractable. To e

Nicole Funk, Annika Hennes, Johanna Hillebrand, Sarah Sturm

We study discrete k-clustering problems in general metric spaces that are constrained by a combination of two different fairness conditions within the demographic fairness model. Given a metric space (P,d), where every point in P is equipped with a protected attribute, and a number k, the goal is to partition P into k clusters with a designated center each,

Sai Srinivas Kancheti, Aditya Sanjiv Kanade, Vineeth N. Balasubramanian, Tanuja Ganu

Multimodal Reasoning Models (MRMs) leveraging Chain-of-Thought (CoT) based thinking have revolutionized mathematical and logical problem-solving. However, we show that this paradigm struggles with generalized spatial intelligence. We perform a comprehensive evaluation of seventeen models across thirteen spatial benchmarks and identify a critical gap: CoT pro

Aras Amini, Lewis R. Reid, James K. Jones, Morgan T. Hibberd

Laser-plasma wakefield acceleration (LWFA) offers ultrahigh accelerating gradients in compact setups, but the complex non-linear nature of the process makes it challenging to generate high-quality beams. Injection of electron bunches from an external source into a plasma accelerator provides a promising route to improved performance; however, electron bunche

Mahir Labib Dihan, Abir Muhtasim

The rapid proliferation of Large Language Models (LLMs) in software development has made distinguishing AI-generated code from human-written code a critical challenge with implications for academic integrity, code quality assurance, and software security. We present LLMSniffer, a detection framework that fine-tunes GraphCodeBERT using a two-stage supervised

Henrik Wüstenberg, Alexandra Liosi, Spencer J. Sherwin, Joaquim Peiró

Scale-resolving simulations of high Reynolds number incompressible flows are often limited by the Courant-Friedrichs-Lewy (CFL) stability restriction imposed by explicit time-stepping schemes, resulting in small time step sizes and long time-to-solution. In this work, we systematically compare two implicit formulations of the velocity correction scheme -- a

Abdul Rahman

We study projective one-parameter conifold degenerations whose central fiber has finitely many ordinary double points. Existing finite-node theory isolates one rank-one local sector per node on the perverse-sheaf, mixed-Hodge-module, and categorical sides, but does not determine which global extension classes are actually realized by geometry. We show that w

Rohit Sinha, Aditya Kanade, Sai Srinivas Kancheti, Vineeth N Balasubramanian

Multimodal large language models (MLLMs) have achieved impressive progress on vision language benchmarks, yet their capacity for visual cognitive and visuospatial reasoning remains less understood. We introduce "Mind's Eye", a multiple-choice benchmark of eight visuo-cognitive tasks inspired by classic human intelligence tests and organized under a novel "A-

Wen Gao, Xinhong Hei, Yichuan Wang

With the continuous expansion of blockchain application scenarios, consortium chains have raised higher performance and security requirements for consensus mechanisms. Unlike public blockchains, consortium chains typically implement an admission mechanism that restricts participation to trusted entities, ensuring that most replicas are honest and the number

Ulrich Tan

We introduce the Tan-HWG framework (Hebbian-Wasserstein-Geometry), a geometric theory of Hebbian plasticity in which memory states are modeled as probability measures evolving through Wasserstein minimizing movements. Hebbian learning rules are formalized as Hebbian energies satisfying a sequential stability condition, ensuring well-posed fiberwise JKO updat

Fangyuan Liu, Sirui Zhao, Zeyu Zhang, Jinyang Huang

Automated multimodal depression estimation in unconstrained environments is inherently challenged by naturalistic noise and complex behavioral variability. Prevailing deterministic methods, however, produce uncalibrated point estimates without quantifying predictive uncertainty, exposing decision-making to the risk of overconfident, untrustworthy estimates.

Nick von Selzam, Florian Marquardt

We point out that the method presented in a recent arXiv article by Jia et al. (arXiv:2512.21848) for constructing local hidden-state models closely follows the framework we developed in N. von Selzam & F. Marquardt (PRX Quantum, 2025) for constructing local hidden-variable models. While Jia et al. cite our work, the extent of the methodological overlap and

A. I. Korolkov, A. V. Kisil

We develop embedding formulae for all possible diffraction problems with Dirichlet scatterers on square lattices using the Wiener--Hopf perspective. The embedding formula expresses solutions for arbitrary plane-wave incidence in terms of a finite set of auxiliary problems, eliminating the need to re-solve boundary value problems for each incidence angle. Fir

Guodong Sun, Samir M. Perlaza, Philippe Mary, Jean-Marie Gorce

In this work, we present an optimization framework for sparse variable-length stop-feedback (VLSF) codes based on a saddlepoint approximation, which jointly optimizes the decoding configuration parameters. Thanks to the analytical tractability of the saddlepoint approximation, the framework enables efficient gradient-based optimization of such parameters for

I. A. Kipenko, D. A. Zorikhin, A. R. Akhmatkhanov, V. Ya. Shur

We have studied the domain switching under NIR fs-laser irradiation in MgO:LN single crystals with special attention to the relative positions of the light-induced domains, microtracks and regions with modified refractive index in the vicinity of the focusing point. The optical imaging along X direction of the irradiated sample demonstrated the narrow microt

Abdulaziz Aldegheishem, Nabil Alrajeh, Lorena Parra, Oscar Romero

The ambulance service is the main transport for diseased or injured people which suffers the same acceleration forces as regular vehicles. These accelerations, caused by the movement of the vehicle, impact the performance of tasks executed by sanitary personnel, which can affect patient survival or recovery time. In this paper, we have trained, validated, an

Alexander Clifton, George Kontogeorgiou, S Taruni, Ana Trujillo-Negrete

We introduce a colorful version of separating path systems, in which two edges can only be separated from each other by two paths of distinct colors. We calculate the minimum sizes of such systems for various standard classes of graphs and numbers of colors. With respect to this setup, we identify three possible asymptotic behaviors for a class of graphs as

Magda Arnaboldi, Ortwin Gerhard, Surya Aniyan, Kenneth C. Freeman

We present the results for the galaxy NGC 628 observed with the Planetary Nebulae Spectrograph (PN.S) equipped with the H$\alpha$ arm. With the third PN.S arm, the H$\alpha$ arm, we measure the H$\alpha$ fluxes, in addition to fluxes and line-of-sight velocities (LOSV) of monochromatic spatially unresolved [OIII] 5007{\AA} sources. The narrow band color ([OI

Meng Yu, Lei Sun, Jianhao Zeng, Xiangxiang Chu

Diffusion Probabilistic Models have demonstrated remarkable performance across a wide range of generative tasks. However, we have observed that these models often suffer from a Signal-to-Noise Ratio-timestep (SNR-t) bias. This bias refers to the misalignment between the SNR of the denoising sample and its corresponding timestep during the inference phase. Sp

Ami Marowka

High-performance computing (HPC) applications are increasingly executed in heterogeneous environments, introducing new challenges for programming and software portability. SYCL has emerged as a leading model designed to simplify heterogeneous programming and make it more accessible to developers. Intended as a single-source, cross-platform parallel programmi

Yutong Gao, Qinglin Meng, Yuan Zhou, Liangming Pan

While Large Language Models (LLMs) have achieved strong performance across many NLP tasks, their opaque internal mechanisms hinder trustworthiness and safe deployment. Existing surveys in explainable AI largely focus on post-hoc explanation methods that interpret trained models through external approximations. In contrast, intrinsic interpretability, which b

Hyunho Cha, Subin Kim, Jungwoo Lee

Matrix product states (MPS) are a central language for one-dimensional quantum matter and a practical target for near-term quantum simulators and variational algorithms. Yet, while substantial effort has focused on preparing MPS with shallow circuits, scalable methods to \emph{verify} that a many-body device has actually produced the intended state remain un

Tamara Bottazzi, Alejandro Varela

We study the minimality of $n\times n$ Hermitian matrices $A$ respect to a $C^*$-subalgebra $\mathcal{B}$ of $M_n(\mathbb{C})$ in the spectral norm, that is \[\|A\|\leq \|A+B\|,\ \text{ for every } B\in \mathcal{B}.\] We generalize the notion of the moment of a subspace and relate it to the joint numerical range and the subdifferentials of the maximum eigenv

Andrea Stoellner, Isaac C. D. Lenton, Caroline Muller, Scott Waitukaitis

Electrical discharge at the smallest possible length and charge scales is not well understood. Using optical tweezers, we investigate spontaneous discharges of a single micron-scale particle levitated in air. These ``microdischarges'' have a typical size of $\sim$40 $|e|$, but can be as small as a few $|e|$ and as large as several hundred. The absence of a w

Abeer FathAllah Brery, Ascensión Gallardo-Antolín, Israel Gonzalez-Carrasco, Mahmoud Fakhry

Human activity recognition (HAR) refers to the process of identifying human actions and activities using data collected from sensors. Neural networks, such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, convolutional LSTM, and their hybrid combinations, have demonstrated exceptional performance in various research domains. D

Gregory Ashton, Colm Talbot, Andrew Lundgren, Ann-Kristin Malz

Ground-based gravitational wave detectors are now routinely surveying the dark Universe, finding hundreds of collisions between compact objects. However, terrestrial non-Gaussian noise artefacts, commonly known as glitches, reduce the sensitivity to signals and can overlap signals, producing biased astrophysical inferences. We introduce a hierarchical Bayesi

Cedric Bonhomme, Alexandre Dulaunoy

Understanding and anticipating vulnerability-related activity is a major challenge in cyber threat intelligence. This work investigates whether vulnerability sightings, such as proof-of-concept releases, detection templates, or online discussions, can be forecast over time. Building on our earlier work on VLAI, a transformer-based model that predicts vulnera

Sophie Steger, Rui Li, Sofiane Ennadir, Anya Sims

The widespread adoption of large language models (LLMs) has increased concerns about their robustness. Vulnerabilities in perturbations of tokenisation of the input indicate that models trained with a deterministic canonical tokenisation can be brittle to adversarial attacks. Recent studies suggest that stochastic tokenisation can deliver internal representa

Ndiogou Niang, Unal Ertan, Ali Arda Gencali, Fatmanur Ertugrul

Neutron star low-mass X-ray binaries (LMXBs) are the progenitors of millisecond pulsars. In these systems, old neutron stars (NSs) can be spun up during a long-lasting accretion phase. The discovery of accreting millisecond X-ray pulsars (AMXPs) and transitional millisecond pulsars has provided key observational insights into the connection between milliseco

D. S. Cabral, L. A. S. Evangelista, A. F. Santos

Podolsky electrodynamics, a higher-derivative extension of Maxwell's theory characterized by the Podolsky parameter $\lambda=1/m$, which modifies the photon dispersion relation and regularizes short-distance divergences, is investigated. This framework is then coupled to the Carroll-Field-Jackiw (CFJ) model, in which a Lorentz-violating background four-vecto

Baoqiang Ma, Djennifer K. Madzia-Madzou, Rosa C. J. Kraaijveld, Jin Ouyang

For head and neck cancer (HNC) patients, prognostic outcome prediction can support personalized treatment strategy selection. Improving prediction performance of HNC outcomes has been extensively explored by using advanced artificial intelligence (AI) techniques on PET/CT data. However, the interpretability of AI remains a critical obstacle for its clinical

Colin Jüni, Mina Montazeri, Yi Guo, Federica Bellizio

Buildings account for approximately 40% of global energy consumption, and with the growing share of intermittent renewable energy sources, enabling demand-side flexibility, particularly in heating, ventilation and air conditioning systems, is essential for grid stability and energy efficiency. This paper presents a safe deep reinforcement learning-based cont

Yongkang Li, Panagiotis Eustratiadis, Yixing Fan, Evangelos Kanoulas

Decoder-only large language models (LLMs) are increasingly replacing BERT-style architectures as the backbone for dense retrieval, achieving substantial performance gains and broad adoption. However, the robustness of these LLM-based retrievers remains underexplored. In this paper, we present the first systematic study of the robustness of state-of-the-art o

Gerd Röpke

Expressions for the thermodynamic and transport properties of plasmas are derived from quantum statistics in the form of equilibrium correlation functions. These can be evaluated using analytical methods or numerical approaches such as DFT-MD or PIMC simulations. Virial expansions are obtained using the Green's function method. They provide benchmarks for nu

Yawen Ma, Anastasia Ushakova, Kate Cain, Gabriel Wallin

To extend cognitive diagnostic models (CDMs) to longitudinal settings, stepwise approaches that integrate a CDM model with a latent transition model and covariates are widely used due to their flexibility. Previous research has shown that stepwise estimation can yield biased results, motivating classification-error correction as a means of improving inferenc

Yasmin Souza Lima, Rodrigo Moreira, Larissa F. Rodrigues Moreira, Tereza Cristina M. de B. Carvalho

Unsupervised anomaly detection is widely used to detect Distributed Denial-of-Service (DDoS) attacks in cloud-native 5G networks, yet most studies assume a fixed traffic representation, either temporal or structural, without validating which feature space best matches the data. We propose a lightweight decision framework that prioritizes temporal or structur

Chang-Zheng Yuan, Marek Karliner

The strong forces between nucleons ($N$=$p$, $n$) are fundamental to the visible universe. The interactions between hyperons (baryons with strange quarks) and nucleons are essential for the intrinsic properties of neutron stars. Whereas the interactions between nucleons ($pp$, $pn$, $nn$) have been extensively studied, the interactions between nucleons and h

Constantinos Karouzos, Xingwei Tan, Nikolaos Aletras

Post-trained language models produce less varied outputs than their base counterparts. This output diversity collapse undermines inference-time scaling methods that rely on varied samples, and risks homogenizing model outputs on creative and value-laden tasks. Prior work attributes collapse to specific post-training methods, without separating the role of tr

Divya Sri Bandla, Subin Lee, Christoph Kirchlechner

Grain boundary sliding (GBS) is a key deformation mechanism at high homologous temperatures in polycrystalline materials, however, its intrinsic behavior is often obscured by additional strain accommodation processes. In this study, dislocation-mediated unconstrained GBS was investigated using Ni bicrystal micropillars containing a single high-angle grain bo

Yaohui Han, Tianshuo Wang, Zixi Zhao, Zhengchun Zhu

Vision Language Models (VLMs) have been applied to several specific domains and have shown strong problem-solving capabilities. However, astronomical imaging, a quite complex problem involving multidisciplinary knowledge and several subtasks, has not been adequately studied. Due to the complexity of the astronomical imaging process, both world-class astronom

Eric Kubischta, Ian Teixeira

This paper develops a MacWilliams-type enumerator theory and semidefinite programming bounds for intrinsic quantum codes. An intrinsic code is a subspace $\mathcal{C}\subset V$ of a finite-dimensional unitary representation $V$ of a group $G$, with errors organized into the isotypic components of $\mathscr{L}(V)$ under conjugation. The Knill-Laflamme conditi

Hikaru Shindo, Hanzhao Lin, Lukas Helff, Patrick Schramowski

As Large Language Models (LLMs) transition from text processors to autonomous agents, evaluating their social reasoning in embodied multi-agent settings becomes critical. We introduce SocialGrid, an embodied multi-agent environment inspired by Among Us that evaluates LLM agents on planning, task execution, and social reasoning. Our evaluations reveal that ev

Xiufeng Xu, Xiufeng Wu, Zejun Zhang, Yi Li

Code localization is a cornerstone of autonomous software engineering. Recent advancements have achieved impressive performance on real-world issue benchmarks. However, we identify a critical yet overlooked bias: these benchmarks are saturated with keyword references (e.g. file paths, function names), encouraging models to rely on superficial lexical matchin

Mahir Burak Usta, Didem Aydogan, Evgenii Vinogradov, Mohammad Shahmoradi

This paper presents a comprehensive link budget analysis for millimeter wave (mm-Wave) and sub-Terahertz (sub-THz) communication systems with primary focus on transmitter (TX) noise propagation, an often overlooked impairment that can dominate in scenarios where path loss is insufficient to suppress TX noise below receiver thermal and atmospheric molecular n