December 2025 arXiv papers — page 70

A. P. Pljonkin

Measurement-device-independent quantum key distribution (MDI-QKD) eliminates detector side-channel attacks by relocating all measurements to an untrusted intermediate node. However, its practical implementation critically relies on picosecond-level temporal synchronization between spatially separated users. In this work, we present a physically motivated aut

Jasmin Saxer, Isabella Maria Aigner, Luise Linzmeier, Andreas Weiler

Text-to-SQL systems allow non-SQL experts to interact with relational databases using natural language. However, their tendency to generate executable SQL for ambiguous, out-of-scope, or unanswerable queries introduces a hidden risk, as outputs may be misinterpreted as correct. This risk is especially serious in biomedical contexts, where precision is critic

Silke Johanndeiter, Jonas Finke, Justus Heuer

Weather, technological and regulatory uncertainties expose actors in highly renewable electricity markets to substantial price and volume risks. Two-way Contracts for Difference (CfDs) can mitigate these risks. They stipulate payments between the government and generators of renewable electricity based on the difference of a strike and a reference price, who

Brienna M. Larrick, L. Philip Schumm, Mingfei Shao, Craig Barnes

Objective: The objective was to develop a cloud-based, federated system to serve as a single point of search, discovery and analysis for data generated under the NIH Helping to End Addiction Long-term (HEAL) Initiative. Materials and methods: The HEAL Data Platform is built on the open source Gen3 platform, utilizing a small set of framework services and exp

Ufuk Asil, Efendi Nasibov

This study presents an innovative hybrid Visual-Inertial Odometry (VIO) method for Unmanned Aerial Vehicles (UAVs) that is resilient to environmental challenges and capable of dynamically assessing sensor reliability. Built upon a loosely coupled sensor fusion architecture, the system utilizes a novel hybrid Quaternion-focused Error-State EKF/UKF (Qf-ES-EKF/

Leonardo Bohac

We study the discrimination of Boolean memory configurations via a fixed Universal QRAM (U-QRAM) interface. Given query access to a quantum memory storing an unknown Boolean function $f:[N]\to\{0,1\}$, we ask: what can be inferred about the bias class of $f$ (its imbalance from $1/2$, up to complement symmetry) using coherent, addressable queries? We show th

S. Dahlke, F. De Mari, E. De Vito, M. Hansen

This paper is concerned with a new approach to coorbit space theory. Usually, coorbit spaces are defined by collecting all distributions for which the voice transform associated with a square-integrable group representation possesses a certain decay, usually measured in a Banach space norm such as weighted $L_p$-norms. Unfortunately, in cases where the repre

P. Shvets, G. Kirichuk, V. Salnikov, J. O'Connell

Controlled crystallization of amorphous alloys offers a powerful route to tailor magnetic and structural properties at the nanoscale. Thin films of CoFeB alloy are essential for the development of various spintronic devices. The crystallization mechanisms of CoFeB during the annealing process have been thoroughly investigated in earlier studies, demonstratin

Holden Lee, Matheau Santana-Gijzen

Sampling from multimodal distributions is a central challenge in Bayesian inference and machine learning. In light of hardness results for sampling -- classical MCMC methods, even with tempering, can suffer from exponential mixing times -- a natural question is how to leverage additional information, such as a warm start point for each mode, to enable faster

Akke Corporaal, Toon De Prins, Léa Planquart, Kateryna Andrych

Stars and planets form, live, and evolve in unison. Throughout the life of a star, dusty circumstellar discs and stellar outflows influence the further evolution of both the star(s) and their orbiting planet(s). Planet-forming discs, winds of red giant branch (RGB) or asymptotic giant branch (AGB) stars, and post-RGB/post-AGB discs are examples of such host

Matthew VanDyke, Kexuan Wu, Sukanta Bose

We develop a Conformer neural network, called Conformer Binary neTwork Search, or CoBiTS, for distinguishing binary black hole (BBH) gravitational wave (GW) signals from non-Gaussian and non-stationary noise artifacts in the data from current generation LIGO-Virgo-KAGRA detectors. A large subset of these transient noise artifacts, termed as ``glitches'' for

Yujing Chen, Xuanming Liu, Zhiyuan Wan, Zuobin Wang

The NFT ecosystem represents an interconnected, decentralized environment that encompasses the creation, distribution, and trading of Non-Fungible Tokens (NFTs), where key actors, such as marketplaces, sellers, and buyers, utilize smart contracts to facilitate secure, transparent, and trustless transactions. Scam tokens are deliberately created to mislead us

Peshawa J. Muhammad Ali, Navin Vincent, Saman S. Abdulla, Han N. Mohammed Fadhl

Background: Artificial intelligence (AI) is improving the efficiency and accuracy of cancer diagnostics. The performance of pathology AI systems has been almost exclusively evaluated on European and US cohorts from large centers. For global AI adoption in pathology, validation studies on currently under-represented populations - where the potential gains fro

Georg Schwefer, Jim Hinton

The advent of the Cherenkov Telescope Array Observatory (CTAO) and recent advances in reconstruction of gamma-ray photons with Cherenkov telescopes are bound to push the limit of angular resolution to an unprecedented precision of less than one arcminute at tens of TeV. Naturally, such instrumental improvements open up possibilities for new and interesting s

Ingo Rehberg, Peter Blümler

Halbach spheres provide a theoretically elegant means of generating highly homogeneous magnetic fields, but practical implementation is hindered by challenging fabrication and restricted interior access. This study examines discrete spherical Halbach configurations assembled from permanent magnets placed at the vertices of Platonic and Archimedean solids. An

Maya N. Vienken, Jan-Ole Koslik, Roland Langrock

Hidden Markov models (HMMs) are popular tools for analysing animal behaviour based on movement, acceleration and other sensor data. In particular, these models allow to infer how the animal's decision-making process interacts with internal and external drivers, by relating the probabilities of switching between distinct behavioural states to covariates. A ke

Rang Li, Lei Li, Shuhuai Ren, Hao Tian

Visual grounding, localizing objects from natural language descriptions, represents a critical bridge between language and vision understanding. While multimodal large language models (MLLMs) achieve impressive scores on existing benchmarks, a fundamental question remains: can MLLMs truly visually ground with human-like sophistication, or are they merely pat

Frank Kiwy, J. Davy Kirkpatrick, Adam C. Schneider, Aaron M. Meisner

We present the identification and characterization of 15 mid-to-late T dwarf candidates in the Euclid Quick Release 1 (Q1) dataset, based on a combined photometric and spectroscopic analysis. Candidates were initially selected via color-based cuts in the Euclid $Y_E - J_E$ and $J_E - H_E$ color-color space, targeting the region occupied by ultracool dwarfs i

Xinzhe Luo, Yingzhen Li, Chen Qin

Reconstructing high-quality images from substantially undersampled k-space data for accelerated MRI presents a challenging ill-posed inverse problem. While supervised deep learning has revolutionized this field, it relies heavily on large datasets of fully sampled ground-truth images, which are often impractical or impossible to acquire in clinical settings

Oskar Kristoffersen, Alba Reinders Sánchez, Morten Rieger Hannemose, Anders Bjorholm Dahl

Geo-spatial analysis of our world benefits from a multimodal approach, as every single geographic location can be described in numerous ways (images from various viewpoints, textual descriptions, geographic coordinates, etc.). Current benchmarks have limited coverage across modalities, leading to specialized models that perform well in their respective domai

P. Oehrl, F. Fesquet, K. E. Honasoge, M. Handschuh

The efficient transfer of quantum states into a long-lived storage unit such as solid-state spin ensembles is widely recognized as a critical challenge with significant implications for quantum communication, sensing and computing applications. Here, we experimentally investigate the interaction of propagating squeezed microwaves with an electron spin resona

Muhammad Atif Butt, Kai Wang, Javier Vazquez-Corral, Joost Van De Weijer

Text-to-image (T2I) models have demonstrated remarkable progress in creative image generation, yet they still lack precise control over scene illuminants which is a crucial factor for content designers to manipulate visual aesthetics of generated images. In this paper, we present an illuminant personalization method named LumiCtrl that learns illuminant prom

Almustapha A. Wakili, Adamu Hussaini, Abubakar A. Musa, Woosub Jung

Brain tumor segmentation is critical in diagnosis and treatment planning for the disease. Yet, current deep learning methods rely on centralized data collection, which raises privacy concerns and limits generalization across diverse institutions. In this paper, we propose TwinSegNet, which is a privacy-preserving federated learning framework that integrates

Luka Matijević, Neven Tomičić, Antonino Marasco, Alessandro Ignesti

The evolution of galaxies in groups is profoundly influenced by a variety of physical processes, with ram pressure and tidal interactions playing pivotal roles in shaping their structural and evolutionary pathways. The relative influence of these two processes is still debated in groups compared to clusters, as ram pressure is less understood there. We study

Nicolas Chenavier, Samuel Delepoulle, Christophe Renaud, Franck Vandewièle

In photorealistic image rendering, Monte Carlo methods form the foundation for the integration of the rendering equation in modern approaches. However, despite their effectiveness, traditional Monte Carlo methods often face challenges in controlling variance, resulting in noisy visual artifacts in regions that are difficult to render. In this work, we propos

Philipp Joram, Niccolò Veltri

Containers conveniently represent a wide class of inductive data types. Their derivatives compute representations of types of one-hole contexts, useful for implementing tree-traversal algorithms. In the category of containers and cartesian morphisms, derivatives of discrete containers (whose positions have decidable equality) satisfy a universal property. Wo

Bertin Many Manda

We investigate the interplay of nonreciprocity and nonlinearity in a one-dimensional nonlinear Klein-Gordon chain of classical oscillators coupled by asymmetric springs, akin to a mechanical analogue of the Hatano-Nelson model with onsite nonlinearity. Using multiple-scale analysis, we show that families of nonlinear skin breathing modes -- time-periodic, bo

Rafael Prieto-Curiel

One of the core strategies to reduce cartel violence is by directly targeting members with law enforcement. Whether targeting leaders, disrupting parts of the organisation, or incarcerating members, the purpose is to reduce the strength of cartels directly. Most security strategies result in increased incarceration rates. Yet its effectiveness in addressing

ATLAS Collaboration

A measurement of correction factors for the hadronic jet energy scale and resolution in the ATLAS detector is presented. These correction factors account for differences between simulated and observed data. They are obtained by analysing a selection of top quark events collected in proton-proton collisions by ATLAS between the years 2015 and 2018 at a centre

Jiawei Sheng

We introduce the notion of a good map between topological spaces: a continuous map $f:X \to Y$ is *good* if for every non-empty irreducible locally closed subset $U \subseteq X$, there exists a non-empty open subset $W \subseteq Y$ such that $W \cap f(U) = W \cap \overline{f(U)} \neq \varnothing$. In Noetherian spaces, this condition is equivalent to preserv

Marco Tarantino, Loredana Prisinzano, Nicoletta D Angelo, Francesco Damiani

This study presents a statistical approach to accurately predict the effective temperatures of pre-main sequence stars, which are necessary for determining stellar ages using the isochrone methodology and cutting-age starspots-dependent models. By training a Neural Network model on high-quality spectroscopic temperatures from the Gaia-ESO Survey as the respo

Miqad S. Albishi, Faisal I. Alabdulkarem, George Perrakis, Tariq F. Alhuwaymel

Perovskite indoor photovoltaics (PIPVs) are emerging as a transformative technology for low-light intensity energy harvesting, owing to their high power conversion efficiencies (PCEs), low-cost fabrication, solution-processability, and compositionally tunable band gaps. In this work, methylammonium-free perovskite absorbers were compositionally engineered to

Paavo Sattler, Nils Hichert

Repeated-measure designs allow comparisons within a group as well as between groups, and are commonly referred to as split-plot designs. While originating in agricultural experiments, they are now widely used in medical research, psychology, and the life sciences, where repeated observations on the same subject are essential. Modern data collection often pro

Shubham Das, Kaushal Singhania, Amit Sadhu, Suprabhat Das

Time-dependent deformation, particularly creep, in high-temperature alloys such as Inconel 625 is a key factor in the long-term reliability of components used in aerospace and energy systems. Although Inconel 625 shows excellent creep resistance, finite-element creep simulations in tools such as ANSYS remain computationally expensive, often requiring tens of

Eleanor K. R. Mackay, Anna Drummond Young, Adam Carter, Sophie Marbach

A full understanding of transport in dense, interacting suspensions requires analysis frameworks sensitive to self and collective dynamics across all relevant spatial and temporal scales. Here we introduce a trajectory-free approach to address this problem based on the power spectral density of particle number fluctuations (N-PSD). By combining colloidal exp

Sepideh Adamiat, Wouter M. Kouw, Bert de Vries

Bayesian inference provides a principled framework for understanding brain function, while neural activity in the brain is inherently spike-based. This paper bridges these two perspectives by designing spiking neural networks that simulate Bayesian inference through message passing for Bernoulli messages. To train the networks, we employ spike-timing-depende

Aaron D'Cruz, Pierre Ricco

A coupled system composed of a Newtonian fluid located on a sinusoidally-forced elastic solid is studied analytically and numerically. The focus is on the transient evolution from the beginning of the forced oscillations and on the periodic behaviour established once the transient has vanished. The analytical solution is expressed as series summations that e

Thomas Sanchez, Gerard Martí-Juan, David Meunier, Miguel Angel Gonzalez Ballester

Fetal brain magnetic resonance imaging (MRI) is crucial for assessing neurodevelopment in utero. However, fetal MRI analysis remains technically challenging due to fetal motion, low signal-to-noise ratio, and the need for complex multi-step processing pipelines. These pipelines typically include motion correction, super-resolution reconstruction, tissue segm

Maria F. Pintado, Matteo Iacopini, Luca Rossini, Alexander Y. Shestopaloff

Reduced-Rank (RR) regression is a powerful dimensionality reduction technique but it overlooks any possible group configuration among the responses by assuming a low-rank structure on the entire coefficient matrix. Moreover, the temporal change of the relations between predictors and responses in time series induce a possibly time-varying grouping structure

Dennis Gross, Jørn Eirik Betten, Helge Spieker

The Rashomon effect describes the phenomenon where multiple models trained on the same data produce identical predictions while differing in which features they rely on internally. This effect has been studied extensively in classification tasks, but not in sequential decision-making, where an agent learns a policy to achieve an objective by taking actions i

Pablo Candela, Diego González-Sánchez, Balázs Szegedy

We prove a first inverse theorem for Gowers norms on all finite abelian groups that uses only nilmanifolds (rather than possibly more general nilspaces). This makes progress toward confirming the Jamneshan--Tao conjecture. The correlating function in our theorem is a projected nilsequence, obtained as the fiber-wise average of a nilsequence defined on a boun

Irched Chafaa, Giacomo Bacci, Luca Sanguinetti

Optimal AP clustering and power allocation are critical in user-centric cell-free massive MIMO systems. Existing deep learning models lack flexibility to handle dynamic network configurations. Furthermore, many approaches overlook pilot contamination and suffer from high computational complexity. In this paper, we propose a lightweight transformer model that

Wisnu Uriawan, Denis Firmansyah, Devi Mulyana, Dika Haekal Firza Pratama

The mastery of Hijaiyah letters is a crucial foundation for reading and comprehending the Quran, yet conventional pedagogical approaches based on repetitive memorization frequently struggle to maintain the engagement of young learners in contemporary educational contexts. This research presents the design and implementation of an innovative gamification-base

Thomas Eiter

We study a model for the deformation of a visco-elasto-plastic material that is nearly incompressible. It originates from geophysics, is given in the Eulerian description and combines a Kelvin-Voigt rheology in the spherical part with a Jeffreys-type rheology in the deviatoric part. Despite a constant density, the model allows for non-isochoric deformation a

Hans Knuepfer, Juan Velazquez

We analyze the evolution of thin liquid droplets in the lubrication approximation with different slip conditions at the liquid-solid interface. Motivated by the classical no-slip paradox which states that the Navier-Stokes equations with a no-slip boundary condition require unphysical infinite dissipation during droplet spreading, we focus on the limit of va

Olivier Jeunen, Schaun Wheeler

Marketing and product personalisation provide a prominent and visible use-case for the application of Information Retrieval methods across several business domains. Recently, agentic approaches to these problems have been gaining traction. This work evaluates the behavioural and retention effects of agentic personalisation on a financial service application'

Evangelos Pournaras

This article shows how fair voting methods can be a catalyst for change in the way we make collective decisions, and how such change can promote long-awaited upgrades of democracy. Based on real-world evidence from democratic innovations in participatory budgeting, in Switzerland and beyond, I highlight a trilogy of key research results: Fair voting methods

Emmanuel Charleson Dapaah, Jens Grabowski

Software Defect Prediction (SDP) models are central to proactive software quality assurance, yet their effectiveness is often constrained by the quality of available datasets. Prior research has typically examined single issues such as class imbalance or feature irrelevance in isolation, overlooking that real-world data problems frequently co-occur and inter

Tobias Sautter, Jan-Niklas Dihlmann, Hendrik P. A. Lensch

Recent advances in 3D scene generation produce visually appealing output, but current representations hinder artists' workflows that require modifiable 3D textured mesh scenes for visual effects and game development. Despite significant advances, current textured mesh scene reconstruction methods are far from artist ready, suffering from incorrect object dec

Christoforos Milionis

We use the Jucys-Murphy elements of the BMW algebra to show that its center over the complex numbers for almost all parameters making it semisimple is given by Wheel Laurent polynomials, a subalgebra of the symmetric Laurent polynomials in the JM elements. As an application, we give an Okounkov-Vershik like approach to its finite dimensional representations.

Celal Can Bellek

Big mapping class groups are the mapping class groups of infinite-type surfaces, that is, surfaces whose fundamental groups are not finitely generated. While mapping class groups of finite-type surfaces have been extensively studied, the theory of big mapping class groups is a recent and rapidly developing area of research. This thesis provides a systematic

Yu Xin, Jia-Ming Zhang, Bing Chen

This work investigates single-photon scattering in a one-dimensional coupled-resonator waveguide coupled to a giant atom with a complex on-site energy. Within the generalized projection operator formalism, we derive analytical expressions for the scattering coefficients. We find that a lossy giant atom absorbs the incident wave, whereas a gain giant atom not

Ronnie de Souza Santos, Italo Santos, Maria Teresa Baldassarre, Cleyton Magalhaes

Survey research is a fundamental empirical method in software engineering, enabling the systematic collection of data on professional practices, perceptions, and experiences. However, recent advances in large language models (LLMs) have introduced new risks to survey integrity, as participants can use generative tools to fabricate or manipulate their respons

Henok Tenaw Moges, Deshendran Moodley

We propose Lite-STGNN, a lightweight spatial-temporal graph neural network for long-term multivariate forecasting that integrates decomposition-based temporal modeling with learnable sparse graph structure. The temporal module applies trend-seasonal decomposition, while the spatial module performs message passing with low-rank Top-$K$ adjacency learning and

Yen-Chieh Huang, Pi-Cheng Hsiu, Rui Fang, Ming-Syan Chen

Long-context LLM inference is bottlenecked by the quadratic attention complexity and linear Key-Value (KV) cache growth. Prior approaches mitigate this via post-hoc selection or eviction but overlook the root inefficiency: indiscriminate token admission. In this paper, we formalize KV management as a causal system of three primitives: KV Admission, Selection

Noam Berger, Anders Johansson, Anders Öberg

We prove that the critical inverse temperatures $β_c^{\mathbb N}(α)$ and $β_c^{\mathbb Z}(α)$ for the one- and two-sided Dyson models are the same when the power of the interaction strength $α$ satisfies $1<α<2$. We conjecture that this is true also in the remaining case of $α=2$.

Jon Muhovič, Janez Perš

Unmanned surface vehicles can encounter a number of varied visual circumstances during operation, some of which can be very difficult to interpret. While most cases can be solved only using color camera images, some weather and lighting conditions require additional information. To expand the available maritime data, we present a novel multimodal maritime da

Kazutoyo Iketake

In this paper, we investigate the chaotic behavior of the differential operator $\frac{d}{dx}$ on the space of smooth functions $C^\infty([a,b])$ equipped with the $L^p$-norm ($1\le p\le\infty$). We explicitly construct a homeomorphism between a subset of $C^\infty([a,b])$ and the shift space. Moreover, inspired by symbolic dynamics, we demonstrate that inva

Antonio Capanema, Yago Porto, Maria Manuela Saez

We revisit the flavor composition of neutrinos from core-collapse supernovae (SN), focusing on robust predictions that are insensitive to the poorly known dynamics of collective flavor conversion in the inner core. Assuming that the many different trajectories and microscopic histories of neutrinos lead to decoherence of the ensemble at the boundary between

Chunggi Lee, Ut Gong, Tica Lin, Stefanie Zollmann

Injury prevention in sports requires understanding how bio-mechanical risks emerge from movement patterns captured in real-world scenarios. However, identifying and interpreting injury prone events from raw video remains difficult and time-consuming. We present VAIR, a visual analytics system that supports injury risk analysis using 3D human motion reconstru

Yun He, Francesco Pittaluga, Ziyu Jiang, Matthias Zwicker

LangDriveCTRL is a natural-language-controllable framework for editing real-world driving videos to synthesize diverse traffic scenarios. It represents each video as an explicit 3D scene graph, decomposing the scene into a static background and dynamic object nodes. To enable fine-grained editing and realism, it introduces a feedback-driven agentic pipeline.

Javier Gonzalez-Ruiz, Carlos Rodriguez-Pardo, Iacopo Savelli, Alice Di Bella

Electricity systems are key to transforming today's society into a carbon-free economy. Long-term electricity market mechanisms, including auctions, support schemes, and other policy instruments, are critical in shaping the electricity generation mix. In light of the need for more advanced tools to support policymakers and other stakeholders in designing, te

Manuel Mayo, Lorenzo Caprini, María Isabel García de Soria, Umberto Marini Bettolo Marconi

Motility-induced phase separation (MIPS) is a central collective phenomenon in active matter, theoretically established in the overdamped regime. We discover that the dynamical origin of MIPS is fundamentally altered by inertia, which induces a cooling mechanism absent in overdamped active matter. This conclusion is supported by an active variant of the dire

Jan Hutter, Hua Chang Bakker, Stan Fris, Madelon Bernardy

In sequential recommendation (SR), the self-attention mechanism of Transformer-based models acts as a low-pass filter, limiting their ability to capture high-frequency signals that reflect short-term user interests. To overcome this, BSARec augments the Transformer encoder with a frequency layer that rescales high-frequency components using the Fourier trans

Jiri Kvita

We present a parameterized model of atmospheric particle showers initiated by cosmic rays. Few physics shower parameters are tuned in a comparison to the Conex generator. Resulting shower properties are studied, with a comment on the cases where multiple shower maxima develop. Finally, we implement simple models of new physics resonance of masses of 100 GeV

Ronaldo A. Garcia, Mark Helman, Dan Reznik

We describe four special families of ellipse-inscribed Poncelet triangles about the incircle which maintain certain triangle centers stationary and which also display interesting conservations.

Ramalakshmi K, Sasmita Dash, Srilali Siragam

Humidity sensors play a vital role in industrial, healthcare, agricultural, and environmental applications; however, conventional sensors often suffer from issues like low sensitivity, slow response, and poor stability. This study investigates sol-gel synthesized Zn0.85Ti0.15Al2O4 nanocomposite ceramics for high-performance humidity sensing. X-ray Diffractio

Piotr Chlebicki, Kristoffer Lindensjö

We study a game of resource extraction of a common good under one-dimensional diffusive dynamics with player actions corresponding to singular stochastic control up to absorption at $0$, implying a trade-off between profitable resource extraction and sustainability. Unsurprisingly, immediate extraction of all available resources is an equilibrium. A main res

Fathi Ben Aribi, Antonin Guilloux, Ka Ho Wong

The FAMED condition is a combinatorial property for ideal triangulations of $3$-manifolds, which was introduced in 2024 by the first and last authors in order to study the Andersen--Kashaev volume conjecture. They notably proved that this conjecture is true for all FAMED geometric triangulations of one-cusped hyperbolic $3$-manifolds with trivial second homo

Jiaze Li, Jingyang Chen, Yuxun Qu, Shijie Xu

We open-source MiMo-VL-Miloco-7B and its quantized variant MiMo-VL-Miloco-7B-GGUF, a pair of home-centric vision-language models that achieve strong performance on both home-scenario understanding and general multimodal reasoning. Built on the MiMo-VL-7B backbone, MiMo-VL-Miloco-7B is specialized for smart-home environments, attaining leading F1 scores on ge

Teng Wang, Xinxin Zhao, Wenzhe Cai, Changyin Sun

Visual navigation is a fundamental capability for autonomous home-assistance robots, enabling long-horizon tasks such as object search. While recent methods have leveraged Large Language Models (LLMs) to incorporate commonsense reasoning and improve exploration efficiency, their planning remains constrained by textual representations, which cannot adequately

Sasmita Dash, Constantinos Psomas, Ioannis Krikidis

As wireless communication systems continue to grow rapidly, high-performance antennas become increasingly crucial for expanding coverage, improving capacity, and enhancing transmission quality. In light of this, research has focused considerable attention on liquid antennas due to their unique characteristics, which include small size, flexibility, reconfigu

Sk Kalimuddin, Sudipta Chatterjee, Arnab Bera, Satyabrata Bera

The emergence of a charge density wave (CDW) in a Weyl semimetal -- a correlated topological phase, is exceptionally rare in condensed matter systems. In this context, the quasi-one-dimensional type-III Weyl semimetal (TaSe$_4$)$_2$I undergoes a CDW transition at $T_{\mathrm{CDW}} \approx 263$~K, providing an exceptional platform to investigate correlated to

Georgios Simantiris, Konstantinos Bacharidis, Apostolos Papanikolaou, Petros Giannakakis

Accurate flood detection from visual data is a critical step toward improving disaster response and risk assessment, yet datasets for flood segmentation remain scarce due to the challenges of collecting and annotating large-scale imagery. Existing resources are often limited in geographic scope and annotation detail, hindering the development of robust, gene

Carlos M. Correia, Pierre Jouve, Jesse Cranney, Guido Agapito Cédric Taïssir Heritier

Recent work by Oberti et al, (Astron. Astrophys., 667, 48, 2022) argued and made a compelling case that classical astronomical adaptive optics (AO) tomography performance can be further enhanced by carefully designing and optically configuring the system to leverage inherent super-resolution (SR) capabilities. Our goal here is to further materialise the conc

Kyriakos Psarakis

Web applications underpin much of modern digital life, yet building scalable and consistent cloud applications remains difficult, requiring expertise across cloud computing, distributed systems, databases, and software engineering. These demands restrict development to a small number of highly specialized engineers. This thesis aims to democratize cloud appl

Alessandra De Luca, Matteo Muratori, Nicola Soave

We focus on the problems of existence and non-existence of positive solutions for the Sobolev-subcritical Lane-Emden equation on certain Riemannian manifolds (mainly models) with asymptotically negative curvature, which, from the viewpoint of the volume growth of geodesic balls, can be regarded as intermediate settings between the Euclidean and the hyperboli

Jone Mencos, Antonin Badura, Eoin Dolan, Sebastian Beckert

The injection, propagation and detection of spin currents are essential physical processes in spintronics. So far, the separation of charge and spin currents was facilitated by the electrical spin injection from a ferromagnet (FM) or the injection by a relativistic spin Hall effect. The devices employed are lateral spin valves comprising spatially separated

Beatrice Luciani, Katherine Lin Poggensee, Heike Vallery, Alex van den Berg

Exoskeletons modulate human movement across diverse applications, from performance augmentation to daily-life assistance. These systems often enforce specific kinematic patterns to mitigate injury risks and motivate users to keep moving despite diminished capacity. However, little is known about users' perception of such robot-imposed guidance, especially wh

Alexandre Anahory Simoes, Leonardo Colombo

We formulate a Herglotz-type variational principle on a Lie algebroid and derive the corresponding Euler--Lagrange--Herglotz equations for a Lagrangian depending on an additional scalar variable $z$. This provides a geometric framework for dissipative systems on Lie algebroids and recovers, as special cases, the classical Euler--Lagrange--Herglotz equations

Shunbo Jia, Caizhi Liao

Deep learning models for Electrocardiogram (ECG) analysis have achieved expert-level performance but remain vulnerable to adversarial attacks. However, applying Universal Adversarial Perturbations (UAP) to ECG signals presents a unique challenge: standard imperceptible noise constraints (e.g., 10 uV) fail to generate effective universal attacks due to the hi

Weilin Zhou, Zonghao Ying, Rongchen Zhao, Chunlei Meng

Prevalent multimodal fake news detection relies on consistency-based fusion, yet this paradigm fundamentally misinterprets critical cross-modal discrepancies as noise, leading to over-smoothing, which dilutes critical evidence of fabrication. Mainstream consistency-based fusion inherently minimizes feature discrepancies to align modalities, yet this approach

Sourav Banerjee, Neel Kanth Kundu

Rydberg atom-based RF sensors offer distinct advantages over conventional dipole antennas for electric field detection. This paper presents a system model and performance analysis of a Rydberg atom-based quantum radar, which employs optical readout via lasers and photon detectors instead of circuit-based receivers. We derive the signal-to-noise ratio (SNR),

Yasufumi Araki, Jun'ichi Ieda

The spin Hall effect in a heavy metal intercorrelates an AC electric current to the magnetization dynamics in an adjacent ferromagnet, which manifests as an electric reactance in the system's current-voltage response. We present a comprehensive theoretical analysis for this emergent reactance contribution in the frequency regime relevant to transport measure

Alexandre Cabodevila, Pedro Gamallo-Fernandez, Juan C. Vidal, Manuel Lama

Cardiac rehabilitation constitutes a structured clinical process involving multiple interdependent phases, individualized medical decisions, and the coordinated participation of diverse healthcare professionals. This sequential and adaptive nature enables the program to be modeled as a business process, thereby facilitating its analysis. Nevertheless, studie

Lilin Wang, Lucas Ramalho, Alan Celestino, Phuc Anthony Pham

Benchmarks like SWE-bench have standardized the evaluation of Large Language Models (LLMs) on repository-level software engineering tasks. However, these efforts remain limited by manual curation, static datasets, and a focus on Python-based bug fixes. We introduce SWE-Bench++, an automated framework that generates repository-level coding tasks from open-sou

Grégoire Véchambre

We study some aspects of the absorption time of the Beta$(a,b)$-Coalescent starting with $n$ blocks. More precisely, when $a>1$, the absorption time is known to converge to infinity as $n$ goes to infinity, and we prove that it satisfies a large deviation principle. When $a \in (0,1)$, it is known that the coalescent comes down from infinity, and we derive b

Erez Hochman, Aaron Sprecher, Kateryna Suzina, Amir Mann

Existing digital manufacturing methods can be broadly divided into subtractive approaches, where material is removed from a bulk to reveal the desired form, and additive methods, in which material is introduced voxel-by-voxel to create an object. We here show a fundamentally different method for the fabrication of three-dimensional objects that is neither su

Martin Krebs, Jan Obdržálek, Vít Musil, Tomáš Brázdil

Deep neural networks are starting to show their worth in critical applications such as assisted cancer diagnosis. However, for their outputs to get accepted in practice, the results they provide should be explainable in a way easily understood by pathologists. A well-known and widely used explanation technique is occlusion, which, however, can take a long ti

Xingmin Huo, Xingchuan Zhu, Chang-An Li, Shiping Feng

Altermagnetism (AM) has brought renewed attention to the Lieb lattice. Here, we broaden the scope of altermagnetic models on the Lieb lattice by using a general scheme based on spin clusters. We design various altermagnetic models with d- and g-wave on the Lieb lattice, and investigate its interplay with spin-orbit coupling. While the altermagnetic unit cell

Amal Benhamiche, Pierre Fouilhoux, Lucas Létocart, Nancy Perrot

Virtual Network Embedding (VNE) is the core combinatorial problem of Network Slicing, a 5G technology which enables telecommunication operators to propose diverse service-dedicated virtual networks, embedded onto a common substrate network. VNE asks for a minimum-cost mapping of a virtual network on a substrate network, encompassing simultaneous node placeme

Tobias Bruschke, Andreas Kirchner, Stefan Floerchinger

Quantum chromodynamics with light quarks features an approximate global symmetry, known as chiral symmetry, that is believed to be spontaneously broken by the vacuum expectation value of a scalar and isoscalar composite field, in addition to a small explicit breaking due to finite quark masses. For a high enough temperature, as achieved in the early universe

Neil Urquhart, Amir Rahimi, Efstathios-Al. Tingas

We present an aircraft maintenance scheduling problem, which requires suitably qualified staff to be assigned to maintenance tasks on each aircraft. The tasks on each aircraft must be completed within a given turn around window so that the aircraft may resume revenue earning service. This paper presents an initial study based on the application of an Evoluti

Xingyu Feng

Blockchain technology, lauded for its transparent and immutable nature, introduces a novel trust model. However, its decentralized structure raises concerns about potential inclusion of malicious or illegal content. This study focuses on Ethereum, presenting a data identification and restoration algorithm. Successfully recovering 175 common files, 296 images

Paul Caillon, Alex Colagrande, Erwan Fagnou, Blaise Delattre

Backpropagation is still the de facto algorithm used today to train neural networks. With the exponential growth of recent architectures, the computational cost of this algorithm also becomes a burden. The recent PEPITA and forward-only frameworks have proposed promising alternatives, but they failed to scale up to a handful of hidden layers, yet limiting th

Michał Marczenko

We derive bounds on the equation of state of cold, dense matter by extending the causal, model-agnostic interpolation between chiral effective field theory and perturbative calculations with a microscopic constraint from relativistic kinetic theory. The additional condition restricts the stiffest admissible behavior of the equation of state and systematicall

Dishantkumar Sutariya, Eike Petersen

Analyzing machine learning model performance stratified by patient and recording properties is becoming the accepted norm and often yields crucial insights about important model failure modes. Performing such analyses in a statistically rigorous manner is non-trivial, however. Appropriate performance metrics must be selected that allow for valid comparisons

Andrea Ghira, Simone Marzani, Gregory Soyez

We compute the primary Lund plane density for jets initiated by a massive ($b$) quark to single logarithmic accuracy in Quantum Chromodynamics (QCD). In order to capture mass effects, we consider quasi-collinear factorisation and we include contributions from the running of the QCD coupling and from collinear evolution, in a variable flavour-number scheme. F

Lu Zhu, Rich. R. Kerswell

We examine elastic travelling-wave (`arrowhead') solutions in a viscoelastic, unidirectionally body-forced flow, focusing on their existence and morphological changes as the Weissenberg number, $\mathrm{Wi}$, and streamwise duct length, $L$, are varied. We find that branch topology varies from an isola at low $L$ through a two-sided reconnection at intermedi

Brahim Marfoua, Mohammad Amirabbasi, Marcus Ekholm

CrOCl is a van der Waals-layered insulator with an antiferromagnetic ground state, making it a promising platform for exfoliation and the exploration of low-dimensional magnetism. An accurate ab initio description is therefore essential. Previous density-functional studies have shown that DFT+$U$ calculations may erroneously favor ferromagnetic order dependi