November 2025 arXiv papers — page 83

Tomislav Plesa

Three-dimensional polynomial dynamical systems (DSs) can display chaos with various properties already in the quadratic case with only one or two quadratic monomials. In particular, one-wing chaos is reported in quadratic DSs with only one quadratic monomial, while two-wing and hidden chaos in quadratic DSs with only two quadratic monomials. However, none of

Yue Yu, Ting Bai, HengZhi Lan, Li Qian

The attribution technique enhances the credibility of LLMs by adding citations to the generated sentences, enabling users to trace back to the original sources and verify the reliability of the output. However, existing instruction-tuned attributed LLMs often fail to properly interpret the contextual semantics of citation symbols (e.g., [i]) during text gene

Marceau Henry, Guillermo A. Mena Marugán, Antonio Vicente-Becerril

We study the effect on the primordial tensor power spectrum of varying the number of e-folds during slow-roll inflation in Loop Quantum Cosmology with a Starobinsky potential. Using the hybrid quantization approach, we derive the effective mass governing tensor mode evolution. The choice of vacuum state is crucial, especially since the preinflationary phase

Artem Chervyakov, Ulyana Isaeva, Anton Emelyanov, Artem Safin

Multimodal large language models (MLLMs) are currently at the center of research attention, showing rapid progress in scale and capabilities, yet their intelligence, limitations, and risks remain insufficiently understood. To address these issues, particularly in the context of the Russian language, where no multimodal benchmarks currently exist, we introduc

Yukun Du, Haiyue Yu, Xiaotong Xie, Yan Zheng

Surrogate-Assisted Evolutionary Algorithms (SAEAs) are widely used for expensive Black-Box Optimization. However, their reliance on rigid, manually designed components such as infill criteria and evolutionary strategies during the search process limits their flexibility across tasks. To address these limitations, we propose Dual-Control Bi-Space Surrogate-As

Ruoqu Chen, Xiangjie Yan, Kangchen Lv, Gao Huang

Ultrasound scanning is a critical imaging technique for real-time, non-invasive diagnostics. However, variations in patient anatomy and complex human-in-the-loop interactions pose significant challenges for autonomous robotic scanning. Existing ultrasound scanning robots are commonly limited to relatively low generalization and inefficient data utilization.

Eirik Endeve, Doug Stefanski, Marc-Olivier G. Delchini, Stuart Slattery

Multiphysics modeling of liquid metal fusion blankets, which produce tritium and convert energy of neutrons created via fusion reactions into heat, is crucial for predicting performance, ensuring structural integrity, and optimizing energy production. While traditional blanket modeling of liquid metal flows during normal steady operating conditions commonly

Guanwei Cheng, Shuzhen Yang

In this paper, we introduce a new type of backward stochastic differential equations (BSDEs) with infinite anticipation, where the generator depends on the entire future values of the solution in infinite horizon. We show that the new BSDEs has a unique solution and admits a comparison result. In the end, we solve a stochastic control problem via a duality b

Gustavo M. Yoshitome, Pedro R. S. Gomes

We discuss the emergence of non-Abelian zero modes from twist defects in Abelian topological phases. We consider a setup built from a fractional quantum Hall (or a fractional Chern insulator)-superconductor heterostructure, which effectively induces a phase transition, leading to a topological phase endowed with new anyonic symmetries, and accordingly suppor

Noura Sellami, Mohamed Siala

In this paper, we propose an efficient transmission scheme for autonomous cooperative Orthogonal Frequency Division Multiplexing (OFDM) based multi-hop Unmanned Aerial Vehicle (UAV) relay networks. These systems often suffer from destructive interference at the destination node due to uncoordinated transmissions of common packets by cooperating UAVs. To addr

Michal Zummer, Petr Harmanec, Brad Barlow, Mark Blackford

Analysing a large body of observational data, we found that HD 135160 is a quadruple 2+2 system, composed of a massive ellipsoidal binary ('heartbeat' star) with components Aa and Ab in an eccentric 8.234 d orbit and an eclipsing binary (with components Ba and Bb), with a 5.853 d period and partial eclipses that have already been reported from the space phot

Georgios Venianakis, Constantinos Theodoropoulos, Michail Kavousanakis

Parameter estimation remains a challenging task across many areas of engineering. Because data acquisition can often be costly, limited, or prone to inaccuracies (noise, uncertainty) it is crucial to identify sensor configurations that provide the maximum amount of information about the unknown parameters, in particular for the case of distributed-parameter

L. D. Baravalle, A. L. O'Mill, M. V. Alonso, C. Obasi

The Circinus galaxy is the nearest type-2 Seyfert galaxy, which is at a distance of 4.2 Mpc. Its environment is challenging to explore because it is located at low Galactic latitudes, behind the Galactic disc. The long-term goal is to characterise the Circinus galaxy halo and determine the presence of dwarf satellites using near-infrared data. We selected 1,

Jan J. Ostrowski

The aim of this paper is to provide an analytical model for the formation of stable structures (cosmological or astrophysical), where stability is obtained through the tangential pressure countering the effect of gravity. We utilize the generalization of the Lemaitre-Tolman-Bondi (LTB) spacetime to matter with tangential pressure generated by the angular mom

Deeponjit Bose, Andrzej Sitarz

We explicitly compute the spectral metric, torsion and Einstein tensors for a nontrivial spectral triple on a noncommutative torus, with the Dirac operator related to the fully equivariant Dirac by a partial conformal rescaling (as introduced in [1]). The results show that the spectral triple has vanishing torsion and the Einstein tensor also identically van

Hao Zhang, Hui Chen, Zichen Huang, Zi-Ang Wang

A bulk superconductor hosting intrinsic surface superconductivity provides a unique platform to study Majorana bound states. The superconductor, trigonal {\gamma}-PtBi2, is a promising candidate, as surface superconducting gaps and topological surface states have been observed. However, the simultaneous presence of bulk and surface superconductivity has not

Shinjirou Kouzuma

We propose a simple method for estimating the fill-out factor of overcontact binary systems using the derivatives of light curves. We synthesized 74,431 sample light curves, covering the typical parameter space of overcontact binaries. On the basis of a recent study that proposed a new classification scheme using light curve derivatives up to the fourth orde

H. R. Paz

Curricula in long-cycle programmes are usually recorded in institutional databases as linear lists of courses, yet in practice they operate as directed graphs of prerequisite relationships that constrain student progression through complex dependencies. This paper introduces the CAPIRE Curriculum Graph, a structural feature engineering layer embedded within

Pandiyaraju V, Abishek Karthik, Sreya Mynampati, Poovarasan L

The task of weed detection is an essential element of precision agriculture since accurate species identification allows a farmer to selectively apply herbicides and fits into sustainable agriculture crop management. This paper proposes a hybrid deep learning framework recipe for weed detection that utilizes Convolutional Neural Networks (CNNs), Vision Trans

Federico Bianchi, Owen Queen, Nitya Thakkar, Eric Sun

There is growing interest in using AI agents for scientific research, yet fundamental questions remain about their capabilities as scientists and reviewers. To explore these questions, we organized Agents4Science, the first conference in which AI agents serve as both primary authors and reviewers, with humans as co-authors and co-reviewers. Here, we discuss

Chen Cai, Saksham Kohli, Steven Liu

Catching fast-moving objects serves as a benchmark for robotic agility, posing significant coordination challenges for cooperative manipulator systems holding a catcher, particularly due to inherent closed-chain constraints. This paper presents a nonlinear model predictive control (MPC)-based motion planner that bridges high-level interception planning with

Max Hirsch, Federico Pichi

In multi-objective optimization, multiple loss terms are weighted and added together to form a single objective. These weights are chosen to properly balance the competing losses according to some meta-goal. For example, in physics-informed neural networks (PINNs), these weights are often adaptively chosen to improve the network's generalization error. A pop

Yifei Gao, Hans J. He, Daniel J. Stilwell, James McMahon

Teams of cooperating autonomous underwater vehicles (AUVs) rely on acoustic communication for coordination, yet this communication medium is constrained by limited range, multi-path effects, and low bandwidth. One way to address the uncertainty associated with acoustic communication is to learn the communication environment in real-time. We address the chall

Gerald V. Dunne

A set of four introductory lectures on Resurgent Asymptotics for Physics (``resurgence") at the CERN Summer School: Continuum Foundations of Lattice Gauge Theories, July 2024. Lecture 1: The Airy function and the Stokes phenomenon. Lecture 2: The nonlinear Stokes phenomenon. Lecture 3: Resurgence in QFT: the Heisenberg-Euler effective action. Lecture 4: Resu

Nicolas Crampé, Quentin Labriet, Lucia Morey, Gilles Parez

We analyze Su-Schrieffer-Heeger (SSH) models using the doubling method for orthogonal polynomial sequences. This approach yields the analytical spectrum and exact eigenstates of the models. We demonstrate that the standard SSH model is associated with the doubling of Chebyshev polynomials. Extending this technique to the doubling of other finite sequences en

Long Cheng, Jia Liu, Tongying Liu, Pan Chen

In conventional superconductors, bulk physical properties typically degrade as the film thickness approaches the two-dimensional (2D) limit. Here in the (111) oriented LaAlO3/KTaO3 (LAO/KTO) heterostructure, we demonstrate experimental evidence that reducing the conducting layer thickness at the interface significantly enhances superconducting transition tem

Jihong Huang, Shun Zhou

Motivated by the precise measurements of neutrino oscillation parameters at Jiangmen Underground Neutrino Observatory (JUNO), we investigate the possibility of probing the unitarity violation of lepton flavor mixing matrix solely with reactor antineutrinos. First, we stress that it is necessary to reconsider the production and detection of neutrinos in a sel

Céline Degrande, Hao-Lin Li, Ling-Xiao Xu

We present a systematic method for deriving partial-wave unitarity bounds on Wilson coefficients of higher-dimensional operators in effective field theories involving more than four fields, which naturally appear in tree-level 2-to-$N$ scattering processes with $N \geq 3$. Unlike 2-to-2 scattering, 2-to-$N$ scattering with $N \geq 3$ features multiple amplit

Noura Sellami, Mohamed Siala

We consider uncoordinated random uplink non-orthogonal multiple access (NOMA) systems using a set of predetermined power levels. We propose to optimize the probabilities of selection of power levels in order to minimize performance metrics as block error probability (BLEP) or bit error probability (BEP). When the multiuser detection algorithm at the BS treat

Yinan Yu, Samuel Scheidegger

Runtime geofencing for ground vehicles is rapidly emerging as a critical technology for enforcing Operational Design Domains (ODDs). However, existing solutions struggle to reconcile high-fidelity learning with the structural requirements of verifiable control. We address this by introducing PCARNN-DCBF, a novel pipeline integrating a Physics-encoded Control

Christian P. Clear, Gillian Nave, Richard Blackwell-Whitehead, Maria Teresa Belmonte

An extensive analysis of the spectrum of neutral manganese has been performed using spectra of manganese-neon and manganese-argon hollow cathode discharges measured using high resolution Fourier transform (FT) and grating spectroscopy over the range 151 - 5112 nm (1956 - 65876 cm-1). Wavelengths for 10426 spectral lines were extracted from the FT spectra, wi

Jie Sun

Type-2 fuzzy set (T2 FS) were introduced by Zadeh in 1965, and the membership degrees of T2 FSs are type-1 fuzzy sets (T1 FSs). Owing to the fuzziness of membership degrees, T2 FSs can better model the uncertainty of real life, and thus, type-2 rule-based fuzzy systems (T2 RFSs) become hot research topics in recent decades. In T2 RFS, the compositional rule

Gabriel Lauzier, Alexandre Girard, François Ferland

Diffusion Policy has shown great performance in robotic manipulation tasks under stochastic perturbations, due to its ability to model multimodal action distributions. Nonetheless, its reliance on a computationally expensive reverse-time diffusion (denoising) process, for action inference, makes it challenging to use for real-time applications where quick de

Heng Huat Chan, Song Heng Chan, Zhi-Guo Liu, Wadim Zudilin

In 1991, the Borweins established a cubic analogue of Jacobi's identity for theta functions, which is used by B.C. Berndt, S. Bhargava, and F.G. Garvan in the development of Ramanujan's cubic theory of elliptic functions. In 2013, D. Schultz discovered an identity for theta series in three variables which generalizes the Borweins' identity. In this article,

C. Katsavrias, S. Di Matteo, L. Kepko, N. Viall

The quasi-Periodic density structures (PDSs) are quasiperiodic variations of solar wind density ranging from a few minutes to a few hours. They are trains of advected density structures with radial length scales LR in the 100-10,000 Mm range, thus belonging to the class of solar wind mesoscale structures. Even though PDS at L1 have been extensively studied b

Federico Settimo, Kimmo Luoma, Dariusz Chruściński, Bassano Vacchini

Stochastic unravelings allow to efficiently simulate open system dynamics, yet their application has traditionally been restricted to master equations that preserve both Hermiticity and trace. In this work, we introduce a general framework that extends piecewise-deterministic unravelings to arbitrary trace-nonpreserving master equations, requiring only posit

Qiang Jiao, Bin Yan, Yi Yang, Mengrui Shi

Recent CLIP-based few-shot semantic segmentation methods introduce class-level textual priors to assist segmentation by typically using a single prompt (e.g., a photo of class). However, these approaches often result in incomplete activation of target regions, as a single textual description cannot fully capture the semantic diversity of complex categories.

Jakob J. Kresse, Alexander Sikorski, Marcus Weber

Interpretable reaction coordinates are essential for understanding rare conformational transitions in molecular dynamics. The Atomistic Mechanism Of Rare Events in Molecular Dynamics (AMORE-MD) framework enhances interpretability of deep-learned reaction coordinates by connecting them to atomistic mechanisms, without requiring any a priori knowledge of colle

Korbinian Griesbauer, Davide Calzolari, Maximilian Raff, C. David Remy

Legged robots offer several advantages when navigating unstructured environments, but they often fall short of the efficiency achieved by wheeled robots. One promising strategy to improve their energy economy is to leverage their natural (unactuated) dynamics using elastic elements. This work explores that concept by designing energy-optimal control inputs t

Yves Pauli, Jan-Bernard Marsman, Finn Rabe, Victoria Edkins

The introduction of large language models and other influential developments in AI-based language processing have led to an evolution in the methods available to quantitatively analyse language data. With the resultant growth of attention on language processing, significant challenges have emerged, including the lack of standardisation in organising and shar

Marina Andrade, Manuel Alberto M. Ferreira

DNA evidence use in problems of civil and criminal identification is becoming greater. The necessity of evaluating the weight of that evidence may be accomplished using one of the most known powerful tools: the Bayesian networks. In the current paper this will be illustrated through the presentation of a civil identification problem and of a criminal identif

Jamila Taaki, Farzad Kamalabadi, Athol Kemball, Lia Corrales

Direct imaging simulations of starshades and other proposed mission concepts are needed to characterize planet detection performance and inform mission design trades. In order to assess the complementary role of a 60 m starshade for the Habitable Worlds Observatory (HWO), we develop the optical model of a starshade and simulate solar system imaging at 0 degr

Hao Zhang, Hui Chen, Zichen Huang, Zi-Ang Wang

The interplay between charge density wave (CDW) and superconducting vortex bound states are crucial for fundamental physics of superconductivity and advancing quantum nanotechnologies. However, the CDW-mediated modulation of vortex bound states, which opens up a new platform for vortex engineering, remains unexplored. Here, we report spatially anisotropic vo

Nathaniel Hanson, Mateusz Wolak, Jonathan Richardson, Patrick Walker

Accurate assessment of burn severity at injury onset remains a major clinical challenge due to the lack of objective methods for detecting subsurface tissue damage. This limitation is critical in battlefield and mass-casualty settings, where rapid and reliable evaluation of burn depth is essential for triage and surgical decision-making. We present a multimo

Peter Frankl, Jian Wang

Let $\binom{[n]}{k}$ denote the collection of all $k$-subsets of the standard $n$-set $[n]=\{1,2,\ldots,n\}$. Let $n>2k$ and let $\mathcal{F}\subset \binom{[n]}{k}$ be an {\it intersecting} $k$-graph, i.e., $F\cap F'\neq \emptyset$ for all $F,F'\in \mathcal{F}$. The number of edges $F\in \mathcal{F}$ containing $x\in [n]$ is called the {\it degree} of $x$. A

Nika Haghtalab, Omar Montasser, Mingda Qiao

We study the tradeoff between sample complexity and round complexity in on-demand sampling, where the learning algorithm adaptively samples from $k$ distributions over a limited number of rounds. In the realizable setting of Multi-Distribution Learning (MDL), we show that the optimal sample complexity of an $r$-round algorithm scales approximately as $dk^{\T

T. Sato, B. Petrović, R. Weih, F. Hartmann

We theoretically investigate how the injector region design of interband cascade lasers (ICLs) impacts the threshold carrier and current densities. The model combines a polarization-sensitive 8-band $\mathbf{k}\cdot\mathbf{p}$ calculation, electrostatics, and a microscopic calculation of Auger recombination rates. The inelastic carrier-carrier scattering is

Anastasios Petropoulos, Theodore Antonakopoulos

This paper presents an instruction-based coordination architecture for Field-Programmable Gate Array (FPGA)-based systems with multiple high-performance Processing Units (PUs) for accelerating Deep Neural Network (DNN) inference. This architecture enables programmable multi-PU synchronization through instruction controller units coupled with peer-to-peer ins

Amir Tahmasbi, Milad Esrafilian, Judson Wright, Sooyeon Jeong

Natural and idiomatic expressions are essential for fluent, everyday communication, yet many second-language learners struggle to acquire and spontaneously use casual slang despite strong formal proficiency. To address this gap, we designed and evaluated an LLM-powered, task-based role-playing game in which a GPT-4o-based Game Master guides learners through

Istvan Heckenberger, Fengchang Li

We describe the subracks of the conjugacy classes of $\mathrm{PSL}(2,q)$ based on Dickson's theorem on subgroups of $\mathrm{PSL}(2,q)$. All minimal non-abelian subracks of $\mathrm{PSL}(2,q)$ are determined. Further, we provide a general result on the relationship of associated groups of conjugacy classes in perfect groups to the Schur multiplier of the gro

Sepehr Mohammadkhani, Huy Q. Nguyen

We study the Moffatt's magnetic relaxation equation with Darcy-type regularization for the constitutive law. This is a topology-preserving dissipative equation, whose solutions are conjectured to converge in the infinite time limit towards equilibria of the incompressible Euler equations. Our goal is to prove this conjectured property for various equilibria

Ali Saeedi, Ram Devireddy, Mrityunjay Kothari

In the U.S., approximately 17 patients die each day awaiting an organ transplant, a crisis driven by the inability to store organs long-term via methods like cryopreservation. A primary failure mechanism is the severe thermo-mechanical damage tissues experience during freezing. A predictive understanding of this damage is hindered by the complex interplay be

Ruiqing Yang, Kaixin Zhang, Zheng Zhang, Shan You

Autoregressive models have recently shown great promise in visual generation by leveraging discrete token sequences akin to language modeling. However, existing approaches often suffer from inefficiency, either due to token-by-token decoding or the complexity of multi-scale representations. In this work, we introduce Expanding Autoregressive Representation (

Ren-Jun Duan, Feimin Huang, Rui Li, Lingda Xu

We investigate the large-time asymptotic behavior toward the planar entropy wave for the three-dimensional Navier-Stokes equations in Eulerian coordinates, considering two types of initial perturbations -- with and without the assumption that the integral of the initial perturbation is zero. Generic perturbations generate diffusion waves, and structural cond

Yu Sha, Ningtao Liu, Haofeng Liu, Junqi Tao

Cavitation intensity recognition (CIR) is a critical technology for detecting and evaluating cavitation phenomena in hydraulic machinery, with significant implications for operational safety, performance optimization, and maintenance cost reduction in complex industrial systems. Despite substantial research progress, a comprehensive review that systematicall

Maria Pilligua, David Serrano-Lozano, Pai Peng, Ramon Baldrich

Imaging in low-light environments is challenging due to reduced scene radiance, which leads to elevated sensor noise and reduced color saturation. Most learning-based low-light enhancement methods rely on paired training data captured under a single low-light condition and a well-lit reference. The lack of radiance diversity limits our understanding of how e

Taehyoung Kim, Seohwa Hwang, Junyong Park

In modern multiple hypothesis testing, the availability of covariate information alongside the primary test statistics has motivated the development of more powerful and adaptive inference methods. However, most existing approaches rely on p-values that are precomputed under the assumption that their null distributions are independent of the covariates. In t

Raymond K. Sheh, Karen Geappen

Risks associated with the use of AI, ranging from algorithmic bias to model hallucinations, have received much attention and extensive research across the AI community, from researchers to end-users. However, a gap exists in the systematic assessment of supply chain risks associated with the complex web of data sources, pre-trained models, agents, services,

Wei Chao

Neutrino oscillation is a significant physical process worthy of in-depth exploration. In this paper, we investigate the matter effect of massive neutrinos in a scalar-type ultra-light dark matter and calculate the neutrino oscillation probability using the quantum field theory method. The result reveals that the neutrino oscillation probability derived from

STAR Collaboration

Precise experimental information on hyperon-nucleon interactions is scarce but of paramount importance to our understanding of the inner structure of compact stars. In this letter, we report the first experimental results of correlation functions between deuterons (d) and {\Lambda} hyperons in Au+Au collisions at \sqrt{s_{NN} = 3.0 GeV measured by the STAR e

Andrea Barbero, Samuel Pautrel, Bertrand Evrard, Jérémy Bon

Although they have enabled several advances in the field of optomechanics, optomechanical disk resonators have not yet been operated in the quantum regime. We present the first experimental demonstration of an optomechanical disk resonator prepared in the quantum ground state. With a gigahertz frequency, the mechanical breathing mode of the investigated semi

Laurent Feuilloley, Josef Erik Sedláček, Martin Slávik

Local certification is a mechanism for certifying to the nodes of a network that a certain property holds. In this framework, nodes are assigned labels, called certificates, which are supposed to prove that the property holds. The nodes then communicate with their neighbors to verify the correctness of these certificates. Certifying that there is a unique le

Mohamed Siala, Noura Sellami

In this letter, we propose an efficient mix source separation algorithm for collision resolution in radio frequency identification (RFID) systems equipped with an antenna array at the reader. We first introduce an approach that exploits the zero constant modulus (ZCM) criterion to separate colliding tags through gradient descent, without using pilot symbols.

Jiangdong Ai, Qiwen Guo, Gregory Gutin, Yiming Hao

A graph is called odd if all of its vertex degrees are odd. A long-standing conjecture asked whether there exists a positive constant $c$ such that every $n$-vertex graph without isolated vertices contains an odd induced subgraph on at least $cn$ vertices. In 2022, Ferber and Krivelevich resolved this conjecture affirmatively with $c=10^{-4}$. A natural ques

Rocío Ayala, Fabio Berra, Gladis Pradolini

In this paper we establish mixed weak inequalities of Fefferman-Stein type for Calder\'on-Zygmund operators and their commutators, improving some previous results known in the literature. The main estimates also generalize the classical weighted weak Fefferman-Stein inequalities proved in [19] and [22]. In order to obtain the main results, our approach is to

Chen Zhang, Wei Zuo, Bingyang Cheng, Yikun Wang

Implicit Neural Representations (INRs) parameterize continuous signals via multilayer perceptrons (MLPs), enabling compact, resolution-independent modeling for tasks like image, audio, and 3D reconstruction. However, fitting high-resolution signals demands optimizing over millions of coordinates, incurring prohibitive computational costs. To address it, we p

Shu-Hong Tang, Han-Yu Wang, Da-Yong Liu, Feng Lu

We elucidate the electronic structure and quantum many-body instabilities of the monolayer nickelate La2NiO4 under hydrostatic pressure using a combination of density functional theory, dynamical mean-field theory (DFT+DMFT), and random phase approximation (RPA). Our DFT+DMFT calculations reveal non-Fermi-liquid behavior and coherence loss near the Fermi lev

Abishek Karthik, Pandiyaraju V, Dominic Savio M, Rohit Swaminathan S

Parkinson's disease is a neurodegenerative disorder that can be very tricky to diagnose and treat. Such early symptoms can include tremors, wheezy breathing, and changes in voice quality as critical indicators of neural damage. Notably, there has been growing interest in utilizing changes in vocal attributes as markers for the detection of PD early on. Based

Christina Vantaraki, Oier Bikondoa, Matías P. Grassi, Brindaban Ojha

Engineered assemblies of interacting magnetic elements-magnetic metamaterials-provide a powerful route to tailor collective magnetic order and dynamics. By structuring matter at the mesoscale, they bridge atomic magnetism and macroscopic functionality, enabling emergent behaviour inaccessible in conventional materials. However, realizing large-area metamater

Ainara Saiz-Pérez, Christian M. Fromm, Yosuke Mizuno, Matthias Kadler

Context. Recent GMVA observations of M 87 at event horizon scales revealed a ring-like structure which is 50% larger at 86 GHz than the ring observed by the Event Horizon Telescope at 230 GHz. Aims. In this paper, we study a possible origin of the increased ring size at 86 GHz. We specifically aim to study the role the nonthermal electron population plays in

Luisa Gallée, Yiheng Xiong, Meinrad Beer, Michael Götz

Densely annotated medical image datasets that capture not only diagnostic labels but also the underlying reasoning behind these diagnoses are scarce. Such reasoning-related annotations are essential for developing and evaluating explainable AI (xAI) models that reason similarly to radiologists: making correct predictions for the right reasons. To address thi

Martin Slind Hagen, Emil Lundqvist, Alex Phu, Yenan Wang

With the rise of software-defined vehicles (SDVs), where software governs most vehicle functions alongside enhanced connectivity, the need for secure software updates has become increasingly critical. Software vulnerabilities can severely impact safety, the economy, and society. In response to this challenge, Strandberg et al. [escar Europe, 2021] introduced

Ritu Kumari, Mani Mehra, Abhishek Kumar Singh

Fractional operators (derivatives/integrals) are defined via the integration of the functions. When the function is produced by a spanning set of fractional power functions, traditional quadrature rules often need to be revised, failing to provide exact evaluations for fractional power functions and thus introducing approximation errors. In this paper, we ha

Zhaoxin Zhang, Borui Chen, Yiming Hu, Youyang Qu

Recent research on large language model (LLM) jailbreaks has primarily focused on techniques that bypass safety mechanisms to elicit overtly harmful outputs. However, such efforts often overlook attacks that exploit the model's capacity for abstract generalization, creating a critical blind spot in current alignment strategies. This gap enables adversaries t

Alessandro Cecconi, Michelangelo Bin, Lorenzo Marconi, Rodolphe Sepulchre

We study the contraction of Hodgkin-Huxley model and its role in the reliability of spike timings. Without input, the model is contractive in the region of physiological interest. With impulsive synaptic inputs, contraction is retained provided that the input events are sparse enough. Contraction is lost when the input firing rate is too high. Spike timings

Rashid Iqbal, Saddam Hussain Khan

This work proposes a hybrid deep learning approach, namely Residual and Spatial Learning based Channel Augmented Integrated CNN-Transformer architecture, that leverages the strengths of CNN and Transformer towards enhanced MPox detection. The proposed RS-CA-HSICT framework is composed of an HSICT block, a residual CNN module, a spatial CNN block, and a CA, w

V. Bonjean, A. Gkogkou, J. L. Starck, P. Tsakalides

Blind source separation (BSS) plays a pivotal role in modern astrophysics by enabling the extraction of scientifically meaningful signals from multi-frequency observations. Traditional BSS methods, such as those relying on fixed wavelet dictionaries, enforce sparsity during component separation, but may fall short when faced with the inherent complexity of r

Peter S. Morfe, Felix Otto, Christian Wagner

This paper concerns the so-called diffusion in the curl of the 2d Gaussian free field, and its generalization to higher dimensions $n \geq 2$, building on the scale-by-scale homogenization approach developed recently by Chatzigeorgiou, Morfe, Otto, and Wang [13]. It begins by reformulating the approximation scheme of that work in terms of SDEs in the length

Youri Carloni, Orlando Luongo, Marek Biesiada

In view of the new Dark Energy Spectroscopic Instrument (DESI) 2025 results, we analyze three types of \emph{Pad\'e cosmology}, based on rational series making use of Pad\'e approximants over the equations of state, namely Pad\'e$^{\omega}$ (0,1) and Pad\'e$^{\omega}$ (1,1), plus a Pad\'e$^{q}$ (0,1), i.e., a rational expansion on the dark energy deceleratio

A. Striffling, C. -T. Héritier, R. J. -L Fétick, O. Fauvarque

Reaching the high angular resolution and contrast level desired for exoplanetary science requires us to equip large telescopes with extreme adaptive optics (XAO) systems to compensate for the effect of the atmospheric turbulence at a very fast rate. This calls for the development of ultra-sensitive wavefront sensors (WFSs), such as Fourier filtering wavefron

Peng Zhang, Bing Li, Ren-Zhou Gui, Shao-Lin Xiong

Gamma-ray bursts (GRBs) are challenging to identify due to their transient nature, complex temporal profiles, and limited observational datasets. We address this with a one-dimensional convolutional neural network integrated with an Adaptive Frequency Feature Enhancement module and physics-informed data augmentation. Our framework generates 100,000 synthetic

Xiangxin Kong, Hang Wang, Yutong Li, Yanghao Chen

Modelling epidemic events such as COVID-19 cases in both time and space dimensions is an important but challenging task. Building on in-depth review and assessment of two popular graph neural network (GNN)-based regional epidemic forecasting models of \textbf{EpiGNN} and \textbf{ColaGNN}, we propose a novel hybrid graph neural network model, \textbf{EpiHybri

Sumera Javeed, Shoaib Ahmad

Energetic ion irradiation is an effective method for studying how single and multi-shelled carbon nanotubes break apart. The energy from ions is dissipated through both linear and nonlinear processes in the nanotubes, leading to defect formation. Fragmentation occurs via atomic collision cascades and thermal spikes, each described by different theoretical mo

Henrique Knopki, Iberê Kuntz

The gravitational path integral measure has been the subject of an increasing interest lately, and no conclusive answer yet exists for its correct form. In this paper, we adopt effective field theory techniques to shed light on this issue. We build the configuration-space metric as an energy expansion, including all possible terms that satisfy the underlying

Ivan Cvitic, Dragan Perakovic, Armando Nolasco Pinto

Quantum Key Distribution (QKD) networks enable unconditionally secure key exchange using quantum mechanical principles. However, routing cryptographic keys across multi-hop quantum networks introduces challenges unique to quantum communication. This survey analyzes and classifies 26 routing strategies proposed between 2013 and 2024 for terrestrial, satellite

Dabin Jeong, Amirhossein Vahidi, Ciro Ramírez-Suástegui, Marie Moullet

Recent advances in computational pathology have leveraged vision-language models to learn joint representations of Hematoxylin and Eosin (HE) images with spatial transcriptomic (ST) profiles. However, existing approaches typically align HE tiles with their corresponding ST profiles at a single scale, overlooking fine-grained cellular structures and their spa

Minh Trung Tran, Nasrin Sohrabi, Zahir Tari, Qin Wang

Existing rug pull detectors assume a simple workflow: the deployer keeps liquidity pool (LP) tokens and performs one or a few large sells (within a day) that collapse the pool and cash out. In practice, however, many real-world exits violate these assumptions by splitting the attack across both time and actor dimensions: attackers break total extraction into

Amir Hossein Kargaran, Nafiseh Nikeghbal, Jing Yang, Nedjma Ousidhoum

Peer review is a cornerstone of scientific publishing, including at premier machine learning conferences such as ICLR. As submission volumes increase, understanding the nature and dynamics of the review process is crucial for improving its efficiency, effectiveness, and the quality of published papers. We present a large-scale analysis of the ICLR 2024 and 2

Tijn de Vos, Mara Grilnberger

In this paper, we consider dynamic matroids, where elements can be inserted to or deleted from the ground set over time. The independent sets change to reflect the current ground set. As matroids are central to the study of many combinatorial optimization problems, it is a natural next step to also consider them in a dynamic setting. The study of dynamic mat

Ziyan Liu, Qi Su, Lulu Tang, Zhaofei Yu

Object detection in autonomous driving suffers from motion blur and saturation under fast motion and extreme lighting. Spike cameras, offer microsecond latency and ultra high dynamic range for object detection by using per pixel asynchronous integrate and fire. However, their sparse, discrete output cannot be processed by standard image-based detectors, posi

Xuan Yang, Dongming Li, Dong Wei, Meng Zhang

In physical-layer security schemes, radio frequency fingerprint (RFF) identification of WiFi devices is susceptible to receiver differences, which can significantly degrade classification performance when a model is trained on one receiver but tested on another. In this paper, we propose a division-based receiver-agnostic RFF extraction method for WiFi syste

Ziheng Su, Huifu Xu

Since the seminal work by Meirowitz, there has been growing attention on the existence and uniqueness of continuous Bayesian Nash equilibria. In the existing literature, existence is typically established using Schauder's fixed-point theorem, relying on the equicontinuity of players' best response functions. Uniqueness, on the other hand, is usually derived

Qian'ang Mao, Yuxuan Zhang, Jiaman Chen, Wenjun Zhou

As Decentralized Finance (DeFi) develops, understanding user intent behind DeFi transactions is crucial yet challenging due to complex smart contract interactions, multifaceted on-/off-chain factors, and opaque hex logs. Existing methods lack deep semantic insight. To address this, we propose the Transaction Intent Mining (TIM) framework. TIM leverages a DeF

Rossana Capuani, Antonio Marigonda, Marc Quincampoix

Several concepts of generalized differentiation in Wasserstein space have been proposed in order to deal with the intrinsic nonsmoothness arising in the context of optimization problems in Wasserstein spaces. In this paper we introduce a concept of admissible variation encompassing some of the most popular definitions as special cases, and using it to derive

Ouiame Marnissi, Hajar EL Hammouti, El Houcine Bergou

Federated learning (FL) enables collaborative model training across distributed devices while preserving data privacy. However, balancing energy efficiency and fair participation while ensuring high model accuracy remains challenging in wireless edge systems due to heterogeneous resources, unequal client contributions, and limited communication capacity. To

Ryunosuke Miyazaki, Yoshimasa Uematsu

We propose a conditional independence (CI) test based on a new measure, the \emph{spectral generalized covariance measure} (SGCM). The SGCM is constructed by expressing the squared norm of the conditional cross-covariance operator in spectral coordinates and approximating it in finite dimensions using data-dependent bases obtained from empirical covariance o

Dirk Wulferding, Diana Lucia Quintero-Castro, Pontus Laurell, Gonzalo Alvarez

Fractionalized excitations such as spinons and anyons have emerged as a central theme in condensed matter physics with broad implications for superconductivity, quantum statistics, and quantum computation. The nearly ideal one-dimensional $S=1/2$ system $\beta$-VOSO$_4$ without long-range order down to 85 mK provides a promising platform to experimentally ex

Fatemeh Abtahi, Alen Shaji, Gia Quyet Ngo, Benjamin Laudert

3R-MoS2, a MoS2 polytype with broken inversion symmetry, enables unique light-matter interactions and is promising for linear and nonlinear integrated photonics beyond the monolayer limit. Yet, systematic studies of its thickness-dependent reflectivity and its impact on harmonic generation are still lacking. . Yet, systematic studies of its thickness-depende

Samer Faraj, Joel Perez Torrents, Saku Mantere, Anand Bhardwaj

Large Language Models (LLMs) are reshaping organizational knowing by unsettling the epistemological foundations of representational and practice-based perspectives. We conceptualize LLMs as Haraway-ian monsters, that is, hybrid, boundary-crossing entities that destabilize established categories while opening new possibilities for inquiry. Focusing on analogi

Rémy Mosseri, Jean-François Sadoc

This paper investigates several distinct attempts to generalize in higher dimension the standard 2-dimensional phyllotaxy set construction. We first recall known contructions for these sets on $2D$ manifolds of constant curvature (the Euclidean plane $\mathbb{R}^2$, the sphere $\mathbb{S}^2$ and the hyperbolic plane $\mathbb{H}^2$). We then propose a first a