December 2025 arXiv papers — page 90

Misbah Shaheen, Sheharyar Pervez

We present a systematic first-principles study of substitutional 3d transition-metal (TM) defects in CsPbI3 using the spin-polarized GGA+U framework. TM incorporation is generally energetically favorable and induces lattice distortions that are strongly localized around the defect site, preserving the overall structural integrity of the host. Analysis of def

Le Hao, Robin Neuder, Mohamadreza Delbari, Alejandro Jiménez-Sáez

To enhance coverage and signal quality in millimeter-wave (mmWave) frequencies, reconfigurable intelligent surfaces (RISs) have emerged as a game-changing solution to manipulate the wireless environment. Traditional semiconductor-based RISs face scalability issues due to high power consumption. Meanwhile, liquid crystal-based RISs (LC-RISs) offer energy-effi

Melih Solmaz

Driven by the null results in the searches for dark matter, the field of direct dark matter detection is constantly evolving to push new frontiers. Ultimately, a vast parameter space for dark matter masses below a few GeV is yet to be explored. That said, low mass dark matter candidates necessitate novel detector designs with lower thresholds and alternative

Michał Borowski, Theo Elenius, Leah Schätzler, David Stolnicki

We characterize removable sets for H\"older continuous solutions to degenerate parabolic equations of $p$-growth. A sufficient and necessary condition for a set to be removable is given in terms of an intrinsic parabolic Hausdorff measure, which depends on the considered H\"older exponent. We present a new method to prove the sufficient condition, which reli

Stefania Barsanti, Clotilde Laigle, Nicolas Bouché, Anna Rita Gallazzi

By the 2040s, several all-sky surveys will have transformed our view of the large-scale structure. However, one of the major outstanding questions in astrophysics will remain: understanding how galaxies acquire and evolve their angular momentum and how this connects to the cosmic web. Measuring the alignments between galaxy spins and cosmic filaments across

Louis Hackländer-Jansen, Rafael Uetz, Martin Henze

Adversary emulation is an essential procedure for cybersecurity assessments such as evaluating an organization's security posture or facilitating structured training and research in dedicated environments. To allow for systematic and time-efficient assessments, several approaches from academia and industry have worked towards the automation of adversarial ac

Yiliu Sun, Zicheng Zhao, Yang Wei, Yanfang Zhang

Reinforcement Learning with Verifiable Rewards (RLVR) significantly enhances the reasoning capability of Large Language Models (LLMs). Current RLVR approaches typically conduct training across all generated tokens, but neglect to explore which tokens (e.g., prefix tokens) actually contribute to reasoning. This uniform training strategy spends substantial eff

Yoann Demesmay

This paper gives an algebraic presentation of an algebra called the fused permutations algebra in the one-boundary case. It is obtained through a detailed study of the degenerate cyclotomic Hecke algebra. In particular, we prove that the fused permutations algebra is a quotient of the degenerate cyclotomic affine Hecke algebra, and we also describe a basis c

Jinqiang Mao, Ping Zhu, Shiyong Zeng

Shattered Pellet Injection (SPI) has become a critical technique for mitigating plasma disruptions in fusion devices, yet optimizing its efficiency demands a proper understanding of the interaction between impurity dynamics and MHD response. We perform 3D nonlinear MHD simulations of SPI-induced disruption in a J-TEXT-like tokamak using the NIMROD code, syst

Ángel Miguel García-Vico, Huseyin Seker, Muhammad Afzal

Coccidiosis, a disease caused by the Eimeria parasite, represents a major threat to the poultry and rabbit industries, demanding rapid and accurate diagnostic tools. While deep learning models offer high precision, their significant energy consumption limits their deployment in resource-constrained environments. This paper introduces a novel two-stage Spikin

Yury A Kutoyants

The models of partially observed linear stochastic differential equations with unknown initial values of the non-observed component are considered in two situations. In the first problem, the initial value is deterministic, and in the second problem, it is assumed to be a Gaussian random variable. The main problem is the computation of adaptive Kalman filter

Mengxi Guo, Shijie Zhao, Junlin Li, Li Zhang

Preprocessing is a well-established technique for optimizing compression, yet existing methods are predominantly Rate-Distortion (R-D) optimized and constrained by pixel-level fidelity. This work pioneers a shift towards Rate-Perception (R-P) optimization by, for the first time, adapting a large-scale pre-trained diffusion model for compression preprocessing

Daniel Sánchez Catalina, George T. Cantwell

We consider the problem of ranking objects from noisy pairwise comparisons, for example, ranking tennis players from the outcomes of matches. We follow a standard approach to this problem and assume that each object has an unobserved strength and that the outcome of each comparison depends probabilistically on the strengths of the comparands. However, we do

Joachim Tapparel, Andreas Burg

LoRa is one of the most widely used low-power wide-area network technology for the Internet of Things. To achieve long-range communication with low power consumption at a low cost, LoRa uses a chirp spread spectrum modulation and transmits in the sub-GHz unlicensed industrial, scientific, and medical (ISM) frequency bands. Due to the rapid densification of I

Huiyan Xue, Xuming Ran, Yaxin Li, Qi Xu

Sparse neural systems are gaining traction for efficient continual learning due to their modularity and low interference. Architectures such as Sparse Distributed Memory Multi-Layer Perceptrons (SDMLP) construct task-specific subnetworks via Top-K activation and have shown resilience against catastrophic forgetting. However, their rigid modularity limits cro

Fan Zhang, Hang Diao, Bohua Li, Joel Meyers

Big-Bang Nucleosynthesis (BBN) predictions of primordial light-element abundances offer a powerful probe of early-Universe physics. However, high-accuracy numerical BBN calculations have become a major computational bottleneck for large-scale cosmological inferences due to the complex nuclear network. Here we present BBNet, a fast and accurate deep learning

Anton Samokish, Valeriy Egorushkin

This paper develops a unified framework in which economic dynamics is treated as evolutionary process analogous to those studied in natural sciences, including physics. Using methods from gauge field theory and plasticity, we show that the traditionally elusive influence of the invisible hand in economic markets can be made explicit and mathematically tracta

Ashirbad Samantaray, Taranjit Kaur, Sapna S Mishra, Kritika Lohia

Joint Attention (JA), a crucial social skill for developing shared focus, is often impaired in children with Autism Spectrum Disorder (ASD), affecting social communication and highlighting the need for early intervention. Addressing gaps in prior research, such as limited use of immersive technology and reliance on distracting peripherals, we developed a nov

Youmin Xu, Mengxi Guo, Shijie Zhao, Weiqi Li

Generative face video coding (GFVC) is vital for modern applications like video conferencing, yet existing methods primarily focus on video motion while neglecting the significant bitrate contribution of audio. Despite the well-established correlation between audio and lip movements, this cross-modal coherence has not been systematically exploited for compre

Yingying Wang, Xuanhua He, Chen Wu, Jialing Huang

Pan-sharpening aims to generate high-resolution multispectral (HRMS) images by integrating a high-resolution panchromatic (PAN) image with its corresponding low-resolution multispectral (MS) image. To achieve effective fusion, it is crucial to fully exploit the complementary information between the two modalities. Traditional CNN-based methods typically rely

André Becker, Georgios M. Koutentakis, Peter Schmelcher

Kapitza-Dirac scattering, the diffraction of matter waves from a standing light field, is widely utilized in ultracold gases, but its behavior in the strongly interacting regime is an open question. Here we develop a numerically-exact two-body description of Kapitza-Dirac scattering for two contact-interacting atoms in a one-dimensional harmonic trap subject

Piyawoot Songsiritat

We introduce SynGP500, a clinician-curated collection of 500 synthetic Australian general practice medical notes. The dataset integrates curriculum-based clinical breadth (RACGP 2022 Curriculum), epidemiologically-calibrated prevalence (BEACH study), and diverse consultation contexts. This approach systematically includes both common presentations and less-c

Yuze Wu, Mo Zhu, Xingxing Li, Yuheng Du

This paper proposes VLA-AN, an efficient and onboard Vision-Language-Action (VLA) framework dedicated to autonomous drone navigation in complex environments. VLA-AN addresses four major limitations of existing large aerial navigation models: the data domain gap, insufficient temporal navigation with reasoning, safety issues with generative action policies, a

Emmanuel Giguet

The IANEC project (Investigation of Digital Archives of Contemporary Writers), led by the GREYC Research Lab and funded by the French Ministry of Culture aims to develop dedicated digital forensic investigation tools to automate the analysis of archival corpora from the Institut M{\'e}moires de l'{\'E}dition Contemporaine (IMEC). The project is based on the

Bertrand Jouve, Paul Rochet, Mohamadou Salifou

The lack of GPS data limits the ability to reconstruct the actual routes taken by cyclists in urban areas. This article introduces an inference method based solely on trip durations and origin-destination pairs from bike-sharing system (BSS) users. Travel time distributions are modeled using log-normal mixture models, allowing us to identify the presence of

Hoang-Anh Le, Hyun Cheol Lee, S. -R. Eric Yang

Intrinsically topologically ordered phases can host anyons. Here, we take the view that entanglement between anyons can give rise to an emergent geometry resembling Anti-de Sitter (AdS) space. We analyze the entanglement structure of fractionalized anyons using mutual information and interpret the results within this emergent geometric framework. As a concre

Kuinan Hou, Jing Mi, Marco Zorzi, Lamberto Ballan

Counting the number of items in a visual scene remains a fundamental yet challenging task in computer vision. Traditional approaches to solving this problem rely on domain-specific counting architectures, which are trained using datasets annotated with a predefined set of object categories. However, recent progress in creating large-scale multimodal vision-l

Yifan Zhang, Yujun Zhu

In this paper, the equilibrium states for a non-degenerate $ C^2 $ partially hyperbolic endomorphism $f$ on a closed Riemannian manifold $M$ with one-dimensional center bundle are investigated. Applying the criterion of Climenhaga-Thompson (\cite{CT21}) and the method of Mongez-Pacifico (\cite{Mongez}), we use the techniques of inverse limit to obtain the un

Sam Hind

Innovation in artificial intelligence (AI) has always been dependent on technological infrastructures, from code repositories to computing hardware. Yet industry -- rather than universities -- has become increasingly influential in shaping AI innovation. As generative forms of AI powered by large language models (LLMs) have driven the breakout of AI into the

Michael Mecik, Martin Kumm

Fully parallel neural network accelerators on field-programmable gate arrays (FPGAs) offer high throughput for latency-critical applications but face hardware resource constraints. Weightless neural networks (WNNs) efficiently replace arithmetic with logic-based inference. Differential weightless neural networks (DWN) further optimize resource usage by learn

Youssef Ghallab, Omar Iraqy, Mohamed Kandil, Mohamed Ashraf

Physiological signals such as electrocardiograms (ECG) and electroencephalograms (EEG) provide complementary insights into human health and cognition, yet multi-modal integration is challenging due to limited multi-modal labeled data, and modality-specific differences . In this work, we adapt the CBraMod encoder for large-scale self-supervised ECG pretrainin

Maria Becker, Mirko Sommer, Lars Tapken, Yi Wan Teh

Moralizations - arguments that invoke moral values to justify demands or positions - are a yet underexplored form of persuasive communication. We present the Moralization Corpus, a novel multi-genre dataset designed to analyze how moral values are strategically used in argumentative discourse. Moralizations are pragmatically complex and often implicit, posin

Serban Lepadatu, Mohammed Gija, Alexey Dobrynin, Kevin McNeill

All-optical switching in a ferromagnetic spin valve is studied here using atomistic spin drift-diffusion dynamics, which includes contributions from spin pumping and superdiffusive transport. We find the switching is governed principally by spin-polarized currents due to non-equilibrium hot electrons excited by the laser pulse and re-equilibration currents.

Nguyen Thanh Vinh, Manoj Vishwanath, Thinh Nguyen-Quang, Nguyen Viet Ha

Alzheimer s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, where early detection is essential for timely intervention and improved patient outcomes. Traditional diagnostic methods are time-consuming and require expert interpretation, thus, automated approaches are highly desirable. This study presents a novel dee

Gordon Blower, Simon J. Malham

Let $(-A,B,C)$ be a linear system in continuous time $t>0$ with input and output space ${\mathbb C}$ and state space $H$. The scattering (or impulse response) functions $\phi_{(x)}(t)=Ce^{-(t+2x)A}B$ determines a Hankel integral operator $\Gamma_{\phi_{(x)}}$; if $\Gamma_{\phi_{(x)}}$ is trace class, then the Fredholm determinant $\tau (x)=\det (I+\Gamma_{\p

Zhicheng Feng, Qulei Fu, Yuanyang Zhou

The Galois Alperin weight (GAW) conjecture has been reduced to the inductive GAW condition for simple groups. We proceed in two steps to refine this reduction. First, we propose the blockwise Galois Alperin weight (BGAW) conjecture and define its associated inductive BGAW condition. Second, assuming the inductive GAW (respectively, BGAW) condition for simple

Yinghui Ye, Ying Li, Xiaoli Chu, Gan Zheng

Ambient backscatter communications (AmBC), where a backscatter transmitter (BT) modulates and reflects ambient signals to a backscatter receiver (BR), have been deemed a low-power communication technology for the Internet of Things. Previous work on symbol detection in AmBC assumed perfect time synchronization (TS), which is unrealistic in practice. The resi

Natalia E. Koval, J. Iñaki Juaristi, Maite Alducin

Modeling plasmonic catalysis by applying femtosecond laser pulses of high intensity ($10^{13}-10^{15}$ W cm$^{-2}$), although justified by the time-dependent density functional theory (TDDFT) time-scale limitations, can lead to a dissociation mechanism that is completely unrelated to the plasmon excitation created under low-intensity continuous light in expe

M. Lemoine

This proceedings paper reports on the theoretical modelling of particle acceleration in magnetised turbulent plasmas. It briefly reviews some recent findings obtained from fully kinetic numerical simulations of large-amplitude, semi to fully relativistic turbulence. The paper then argues that these findings can be understood within the framework of a ``gener

Yu Zhang, Yujun Zhu

In \cite{Miller-Akin1999}, Miller and Akin investigated the invariant measures for correspondences, which are also known as upper semi-continuous set-valued maps. Recently, the variational principle and thermodynamic formalism for forward expansive correspondences were studied by Li, Li and Zhang \cite{Xiaoran Li-Zhiqiang Li-Yiwei Zhang2023}. In this paper,

Lorenz Halbeisen, Norbert Hungerbühler, Arman Shamsi Zargar

We consider the problem of finding integer triangles with $R/r$ a positive rational, where $R$ and $r$ are the radii of the circumcircle and an excircle, respectively. We show that for general triangles $R/r>1/4$ applies. The equation $R/r=N$ turns out to be related to the elliptic curve $\mathcal{E}_N$ given by $v^2=u^3+2(2N^2+2N-1)u^2-(4N-1)u$. If $N>1/4$

Claudio Savelli, Moreno La Quatra, Alkis Koudounas, Flavio Giobergia

LLMs trained on web-scale data raise concerns about privacy and the right to be forgotten. To address these issues, Machine Unlearning provides techniques to remove specific information from trained models without retraining from scratch. However, existing benchmarks for evaluating unlearning in LLMs face two major limitations: they focus only on English and

Marco de Cesare, Mairi Sakellariadou, Araceli Soler Oficial

Noncommutative gravity, based on a twist-deformation of the differential geometry of spacetime and a first-order formulation of the dynamics, requires additional gravitational degrees of freedom as well as an enlargement of the gauge group of Lorentz transformations of the tetrad frame. As such, it offers a theoretical playground to build fundamentally motiv

Li-Li Zhao, Xiao-Hang Luan, Xin Chao, Yu-Chen Wang

The search for extraterrestrial intelligence (SETI) commensal surveys aim to scan the sky to detect technosignatures from extraterrestrial life. A major challenge in SETI is the effective mitigation of radio frequency interference (RFI), a critical step that is particularly vital for the highly sensitive Five-hundred-meter Aperture Spherical radio Telescope

Yi Zhang, Yulei Kang, Haoxuan Chen, Jinxuan Li

Parameter-efficient fine-tuning methods have gained considerable popularity for adapting large-scale models to downstream tasks, particularly LoRA and its variants. Existing methods perform low-rank adaptation over the full parameter space. However, fine-tuning within a subspace can achieve comparable effectiveness. Inspired by the observation that pre-train

Guillaume Koechlin, Filippo Bovera, Elena Degli Innocenti, Barbara Santini

As demand-side flexibility becomes increasingly necessary to integrate variable renewable energy, understanding electricity demand composition across different grid levels is essential. However, at regional and national scales, visibility into the relative contributions of different consumer categories remains limited due to the complexity and cost of collec

Doğa Elitez, Paul Gessinger, Daniel Murnane, Marcus Selchou Raaholt

We introduce ColliderML - a large, open, experiment-agnostic dataset of fully simulated and digitised proton-proton collisions in High-Luminosity Large Hadron Collider conditions ($\sqrt{s}=14$ TeV, mean pile-up $\mu = 200$). ColliderML provides one million events across ten Standard Model and Beyond Standard Model processes, plus extensive single-particle s

Enzo Cabezas-Rivière, Maxime Robillart, Aline Barlet, Thomas Recht

In many countries, central heating systems are widely used in multifamily housing allowing maintenance and costs to be shared. However, these systems often limit residents' control over their own consumption, complicating efforts to reduce energy use and maintain comfort. Despite the growing importance of household energy savings in national and European cli

Elio Gruttadauria, Mathieu Fontaine, Jonathan Le Roux, Slim Essid

We introduce O-EENC-SD: an end-to-end online speaker diarization system based on EEND-EDA, featuring a novel RNN-based stitching mechanism for online prediction. In particular, we develop a novel centroid refinement decoder whose usefulness is assessed through a rigorous ablation study. Our system provides key advantages over existing methods: a hyperparamet

Songze Huo, Xiao-Ming Cao

The adsorption energy serves as a crucial descriptor for the large-scale screening of catalysts. Nevertheless, the limited distribution of training data for the extensively utilised machine learning interatomic potential (MLIP), predominantly sourced from near-equilibrium structures, results in unreliable adsorption structures and consequent adsorption energ

Julius Ehigie, Vu Thai Luan

This work constructs and analyzes new efficient high-order two-derivative diagonally implicit Runge--Kutta (TDDIRK) schemes with optimized phase errors. Specifically, we present a convergence result for TDDIRK methods and investigate their optimized phase errors and linear stability analysis. Based on these, we derive new families of 2-stage fourth-order, 2-

Yash Bhaskar, Parameswari Krishnamurthy

This paper presents the systems submitted by the Yes-MT team for the Low-Resource Indic Language Translation Shared Task at WMT 2024 (Pakray et al., 2024), focusing on translating between English and the Assamese, Mizo, Khasi, and Manipuri languages. The experiments explored various approaches, including fine-tuning pre-trained models like mT5 (Xue et al., 2

Sahaya Aarti Dennisselvan, Shashi Ranjan Kumar, Dwaipayan Mukherjee

This work proposes a cooperative strategy for a group of quadrotors interacting over ring digraphs with macro-vertices of size two. Consensus for a group of general double integrators has been initially investigated, and it has been proved that through a suitable choice of a single controller parameter, consensus and stability of the resulting networked dyna

Séverin Baroudi, Hervé Bredin, Joseph Razik, Ricard Marxer

Self-supervised speech models such as wav2vec2.0 and WavLM have been shown to significantly improve the performance of many downstream speech tasks, especially in low-resource settings, over the past few years. Despite this, evaluations on tasks such as Speaker Diarization and Speech Separation remain limited. This paper investigates the quality of recent se

Hao-Yue Qi, Wei Zheng

Understanding relaxation in isolated quantum many-body systems remains a central challenge. Recently, the quantum Mpemba effect (QME), a counterintuitive relaxation phenomenon, has attracted considerable attention and has been extensively studied in systems with global symmetries. Here, we study the QME in gauge theories with massive local gauge symmetries.

Zijiang Song, Ting Liu, Lina Yuan, Yuying Li

As climate change drives increased frequency and intensity of extreme precipitation and flooding worldwide, posing escalating threats to public safety and economic assets, accurate and real-time satellite-based precipitation estimation is essential for operational large-scale hydrometeorological analysis and disaster monitoring. NASA's Integrated Multi-satel

Xiyu Zhu, Wei Wang, Xin Yuan, Xiao Wang

Lens flare is a common nighttime artifact caused by strong light sources scattering within camera lenses, leading to hazy streaks, halos, and glare that degrade visual quality. However, existing methods usually fail to effectively address nonuniform scattered flares, which severely reduces their applicability to complex real-world scenarios with diverse ligh

Vahid Pooryousef, Tim Dwyer, Richard Bassed, Maxime Cordeil

Research studies involving human participants present challenges, including strict ethical considerations, participant recruitment, costs, and many human factors. While human-computer interaction researchers are familiar with these challenges and current solutions, expert-centred studies can be even more challenging in ways that researchers may not anticipat

Chao Zhang, Minghan Li, Tianrui Lv, Guodong Zhou

Large language models (LLMs) often generate hallucinations in knowledge-intensive QA due to parametric knowledge limitations. While existing methods like KG-CoT improve reliability by integrating knowledge graph (KG) paths, they suffer from rigid hop-count selection (solely question-driven) and underutilization of reasoning paths (lack of guidance). To addre

HaeChun Chung

Text-to-audio (TTA) generation can significantly benefit the media industry by reducing production costs and enhancing work efficiency. However, most current TTA models (primarily diffusion-based) suffer from slow inference speeds and high computational costs. In this paper, we introduce AudioGAN, the first successful Generative Adversarial Networks (GANs)-b

Shun Takizawa

For Schr\"{o}dinger equations with potentials which grow at most quadratically at spatial infinity, we prove Strichartz estimates in Wiener amalgam spaces. These estimates provide a stronger recovery of local-in-space regularity than the classical Strichartz estimates in Lebesgue spaces. Our result is a generalization of the results on Strichartz estimates i

Antoine Bernard, Sandoche Balakrichenan, Michel Marot, Benoit Ampeau

LPWANs are networks characterised by the scarcity of their radio resources and their limited payload size. LoRaWAN offers an open, easy-to-deploy and efficient solution to operate a long-range network. To efficiently communicate using IPv6, the LPWAN working group from the IETF developed a solution called Static Context Header Compression (SCHC). It uses con

V. Mariscal, J. J. Relancio

The Kittel--Shore Hamiltonian characterizes $N$ spins with identical long-range interactions, and the $\mathfrak{su}(2)$ coalgebra has been proven to be a symmetry of this model, which can be exactly solved. By using quantum groups and, in particular, $\mathfrak{su}_{q}(2)$, this Hamiltonian was deformed. In this work, we study the thermodynamic properties o

Erik Brorsson, Kristian Ceder, Ze Zhang, Sabino Francesco Roselli

The adoption of Autonomous Mobile Robots (AMRs) for internal logistics is accelerating, with most solutions emphasizing decentralized, onboard intelligence. While AMRs in indoor environments like factories can be supported by infrastructure, involving external sensors and computational resources, such systems remain underexplored in the literature. This pape

Giuseppe Della Penna, Igor Melatti

The increasing and widespread use of BPMN business processes, also embodying DMN tables, requires tools and methodologies to verify their correctness. However, most commonly used frameworks to build BPMN+DMN models only allow designers to detect syntactical errors, thus ignoring semantic (behavioural) faults. This forces business processes designers to manua

V. N. Binhi

A molecular rotor mechanism is proposed to explain weak magnetic field effects in biology. Despite being nanoscale (1 nm), this rotor exhibits quantum superposition and interference. Analytical modeling shows its quantum dynamics are highly sensitive to weak, but not strong, magnetic fields. Due to its enhanced moment of inertia, the rotor maintains quantum

Changhai Ma, Ziyu Wu, Yunkang Zhang, Qijun Ying

Reconstructing accurate 3D human meshes in the world coordinate system from in-the-wild images remains challenging due to the lack of camera rotation information. While existing methods achieve promising results in the camera coordinate system by assuming zero camera rotation, this simplification leads to significant errors when transforming the reconstructe

Cong Wang, Yufeng Xie

Infrared and visible image fusion is a pivotal technology in low-altitude Unmanned Aerial Vehicle (UAV) reconnaissance missions, enabling robust target detection and tracking by integrating thermal saliency with environmental textures. However, the advancement of fusion algorithms is hindered by a critical evaluation bottleneck. In this paper, we identify a

Ameet Gadekar, Aristides Gionis, Thibault Marette

Data analysis often involves an iterative process, where solutions must be continuously refined in response to new data. Typically, as new data becomes available, an existing solution must be updated to incorporate the latest information. In addition to seeking a high-quality solution for the task at hand, it is also crucial to ensure consistency by minimizi

Andrew Omame, Sarafa Iyaniwura

Coupling within-host infection dynamics with population-level transmission remains a major challenge in infectious disease modeling, especially for airborne pathogens with potential to spread indoor. The frequent emergence of such diseases highlight the need for integrated frameworks that capture both individual-level infection kinetics and between-host tran

C. R. Jayanarayanan, Rishit R Rajpopat

We study Birkhoff-James orthogonality in spaces of bounded linear operators between Banach spaces through the Bhatia-Šemrl property. We investigate the interplay among three key properties of bounded linear operators: strong subdifferentiability, the Bhatia-Šemrl property, and the condition that the essential norm is strictly less than the operator norm. For

Neelaksh Singh, Jasan Zughaibi, Denis von Arx, Bradley J. Nelson

Electromagnetic navigation systems (eMNS) are increasingly used in minimally invasive procedures such as endovascular interventions and targeted drug delivery due to their ability to generate fast and precise magnetic fields. In this paper, we utilize the OctoMag and a custom 13-coil eMNS to achieve remote levitation and control of multiple rigid bodies acro

Liyu Zhang, Yejia Liu, Kwun Ho Liu, Runxi Huang

A key bottleneck toward scalable IoT sensing is efficiently adapting trained AI models to new deployment conditions. Context shifts, such as changes in sensor placement or ambient environments, can substantially alter sensing patterns and degrade model performance. We present Chorus, a context-bridged, data-free post-deployment model customization approach t

Tomoya Kamijima, Naoki Marumo, Akiko Takeda

In many real-world applications, optimization problems evolve continuously over time and are often subject to stochastic noise. We consider a stochastic time-varying optimization problem in which the objective function $f(x;t)$ changes continuously and only noisy gradient observations are available. In deterministic settings, the prediction-correction method

Ellie Zhou, Jihoon Chung, Olga Russakovsky

Human action recognition models often rely on background cues rather than human movement and pose to make predictions, a behavior known as background bias. We present a systematic analysis of background bias across classification models, contrastive text-image pretrained models, and Video Large Language Models (VLLM) and find that all exhibit a strong tenden

Miki Kurihara, Masahiro Tsujimoto, Michael Loewenstein, Yoshitomo Maeda

A high-resolution X-ray spectroscopic observation was made of the RS CVn-type binary star HR 1099 using the Resolve instrument onboard XRISM for its calibration purposes. During the $\sim$400 ks telescope time covering 1.5 binary orbit, a flare lasting for $\sim$100 ks was observed with a released X-ray radiation energy of $\sim 10^{34}$ erg, making it the f

Krishnan Suryanarayanan, Andrew B. Croll, Harmeet Singh

We present an experimental and theoretical study of the mechanics of an \emph{adhesive tape loop}, formed by bending a straight rectangular strip with adhesive properties, and prescribing an overlap between the two ends. For a given combination of the adhesive strength and the extent of the overlap, the loop may unravel, it may stay in equilibrium, or open u

María Anguiano, Francisco J. Suárez-Grau

In this paper, we study the asymptotic behavior of the micropolar fluid flow through a thin domain assuming zero Dirichlet boundary condition on the top boundary, which is rapidly oscillating, and non-standard boundary conditions on the flat bottom. Assuming ``Reynolds roughness regime", in which the thickness of the domain is very small compared to the wave

Takahiro Nishinaka, Yutaka Yoshida

We study 3d $\mathcal{N}=2$ Chern--Simons matter theories describing the R-twisted $S^1$-reduction of Argyres--Douglas theories of $(A_{M-1},A_{N-1})$ type with $\text{gcd}(M,N)=1$, via a recently-proposed 4d/3d correspondence. In particular, for the $(A_2,A_{N-1})$ and $(A_3,A_{N-1})$ theories, we identify a series of Chern--Simons matter theories with mono

Paritosh Verma, Sudip Bhattacharyya

A pulsar, i.e., a spinning neutron star, with a deformation could emit gravitational waves continuously. Such continuous waves, which have not been detected yet, will be very useful to study gravitational physics and to probe the extreme physics of neutron stars. While typically such waves from a pulsar are estimated considering an overall stellar ellipticit

Mohsen Nafar, Michael Römer, Lin Xie

Efficient exact algorithms for Discrete Optimization (DO) rely heavily on strong primal and dual bounds. Relaxed Decision Diagrams (DDs) provide a versatile mechanism for deriving such dual bounds by compactly over-approximating the solution space through node merging. However, the quality of these relaxed diagrams, i.e. the tightness of the resulting dual b

Rui Yang, Xiaoyao Zhou

Metric mean dimension is a dynamical counterpart of the box dimension in fractal geometry to characterize the topological complexity of infinite entropy systems. The classical variational principle states that topological entropy equals the supremum of measure-theoretic entropy over the set of invariant measures. Lindenstrauss and Tsukamoto proved that this

Gerard Yeo, Svetlana Churina, Kokil Jaidka

Perceived trustworthiness underpins how users navigate online information, yet it remains unclear whether large language models (LLMs),increasingly embedded in search, recommendation, and conversational systems, represent this construct in psychologically coherent ways. We analyze how instruction-tuned LLMs (Llama 3.1 8B, Qwen 2.5 7B, Mistral 7B) encode perc

Atte Ojanen, Johannes Anttila, Thilo H. K. Thelitz, Anna Bjork

The rapid advancements in artificial intelligence (AI) present unique challenges for policymakers that seek to govern the technology. In this context, the Delphi method has become an established way to identify consensus and disagreement on emerging technological issues among experts in the field of futures studies and foresight. The aim of this article is t

Jörg Gamerdinger, Sven Teufel, Stephan Amann, Lukas Marc Listl

Extensive evaluation of perception systems is crucial for ensuring the safety of intelligent vehicles in complex driving scenarios. Conventional performance metrics such as precision, recall and the F1-score assess the overall detection accuracy, but they do not consider the safety-relevant aspects of perception. Consequently, perception systems that achieve

Nail El Hocine Barama, Chunlei Wang, Panukorn Sombut, David Rath

Infrared reflection absorption spectroscopy (IRAS) offers a powerful route to bridging the materials and pressure gaps between surface science and powder catalysis. Using a newly developed IRAS setup optimised for dielectric single crystals, we investigate CO adsorption on the model single-atom catalyst Rh/Fe3O4(001). IRAS resolves three species: monocarbony

Vladan Jaguzović, Petar Melentijević

In this paper, we consider weighted Bergman spaces $\mathcal{B}_{\alpha,p}$ of log-subharmonic functions on the unit sphere. Using the isoperimetric inequality for the spherical metric we prove certain monotonicity property for super-level sets of $|f(x)|^p\mathcal{W}_n^{\alpha}(x),$ where $f\in \mathcal{B}_{\alpha,p}$ and $\mathcal{W}_n^{\alpha}(x)$ is the

Mengchu Xu, Jian Wang, Yonina C. Eldar

We study sparse principal component analysis in the high-dimensional, sample-limited regime, aiming to recover a leading component supported on a few coordinates. Despite extensive progress, most methods and analyses are tailored to the flat-spike case, offering little guidance when spike energy is unevenly distributed across the support. Motivated by this,

Dao Thanh Hai, Shuo Li, Isaac Woungang

Driven by massive investments and consequently significant progresses in optical computing and all-optical signal processing technologies lately, this paper presents a new architectural paradigm for next-generation optical transport network, entitled \textit{optical computing-communication integrated network}, which is capable of providing dual services at t

Zhao Zhu, Yu-Ping Tian, Xuyang Wu

Historical information, such as past function values or gradients, has significant potential to enhance decentralized optimization methods for two key reasons: first, it provides richer information about the objective function, which also explains its established success in centralized optimization; second, unlike the second-order derivative or its alternati

Ritwik Mukherjee, John D. Gibbon, Dario Vincenzi

In the standard theoretical setting of body-forced turbulence, the forcing that sustains the flow is concentrated in a narrow range of length scales. However, in experiments of fractal-grid turbulence and in numerical simulations inspired by the renormalization group approach, more general forcing functions have been considered. These studies have shown that

Cenyang Wu, Daniel Klötzl, Qinhan Yu, Shudan Guo

We propose Probabilistic Inclusion Depth (PID) for the ensemble visualization of scalar fields. By introducing a probabilistic inclusion operator $\subset_{\!p}$, our method is a general data depth model supporting ensembles of fuzzy contours, such as soft masks from modern segmentation methods, and conventional ensembles of binary contours. We also advocate

Jianan Wang, Yang Hong, Hesong Li, Tao Wang

RAW images have shown superior performance than sRGB images in many image processing tasks, especially for low-light image enhancement. However, most existing methods for RAW-based low-light enhancement usually sequentially process multi-scale information, which makes it difficult to achieve lightweight models and high processing speeds. Besides, they usuall

Sambhu G Nath, Subhadip Manna, Kanav Sharma, Amar Verma

We investigate the evolution of electronic transport across the topological-trivial phase transition in ${\rm In}_{x}({\rm Bi}_{0.3}{\rm Sb}_{0.7})_{2-x}{\rm Te}_3$ thin films by systematically tuning the indium concentration $x$. Increasing $x$ reduces the effective spin-orbit coupling, driving a topological quantum phase transition near $x \approx 7\%$, an

Hakima Bessaih, Benedetta Ferrario, Oussama Landoulsi, Margherita Zanella

Continuous data assimilation methods, such as the nudging algorithm introduced by Azouani, Olson, and Titi (AOT) [2], are known to be highly effective in deterministic settings for asymptotically synchronizing approximate solutions with observed dynamics. In this work, we extend this framework to a stochastic regime by considering the two-dimensional incompr

Mika Hämäläinen, Kimmo Leiviskä

This paper explores the growing epistemic parallel between NLG evaluation and grading of students in a Finnish University. We argue that both domains are experiencing a Great Misalignment Problem. As students increasingly use tools like ChatGPT to produce sophisticated outputs, traditional assessment methods that focus on final products rather than learning

Sarim Hashmi, Abdelrahman Elsayed, Mohammed Talha Alam, Samuele Poppi

Generative models can synthesize highly realistic content, so-called deepfakes, that are already being misused at scale to undermine digital media authenticity. Current deepfake detection methods are unreliable for two reasons: (i) distinguishing inauthentic content post-hoc is often impossible (e.g., with memorized samples), leading to an unbounded false po

Jörg Gamerdinger, Sven Teufel, Stephan Amann, Oliver Bringmann

Ensuring safety is the primary objective of automated driving, which necessitates a comprehensive and accurate perception of the environment. While numerous performance evaluation metrics exist for assessing perception capabilities, incorporating safety-specific metrics is essential to reliably evaluate object detection systems. A key component for safety ev

Sanghyeok Chung, Eujin Kim, Donggun Kim, Gaeun Heo

Recent advances in audio generation have increased the risk of realistic environmental sound manipulation, motivating the ESDD 2026 Challenge as the first large-scale benchmark for Environmental Sound Deepfake Detection (ESDD). We propose BEAT2AASIST which extends BEATs-AASIST by splitting BEATs-derived representations along frequency or channel dimension an

Gaurav Kumar Sharma

In this study, we perform a systematic analysis of the JARVIS-DFT bandgap dataset and identify and remove descriptors that may inadvertently encode band-structure information, such as effective masses. This process yields a curated, leakage-controlled subset of 2280 materials. Using this dataset, a three-phase modeling framework is implemented that increment