December 2025 arXiv papers — page 51

Bartosz Bieganowski, Norihisa Ikoma, Jarosław Mederski

In the paper we show the existence of ground state solutions to the nonlinear Born-Infeld problem \[ \mathrm{div}\, \left( \frac{\nabla u}{\sqrt{1-|\nabla u|^2}} \right) + f(u) = 0, \quad x \in \mathbb{R}^N \] in the zero and positive mass cases. Moreover, we find a new proof of the Sobolev-type inequality \[ \int_{\mathbb{R}^N} \left(1 - \sqrt{1-|\nabla u|^

Deng Wang, Olga Mena, Salvatore Capozziello, David Mota

The detection of a significant deviation from a zero curvature would have profound consequences for inflationary theories and fundamental physics. Relative to high-redshift Planck's CMB measurements, indicating a $\sim 2\sigma$ evidence for a closed universe, low-redshift observations of BAO and SN Ia have the advantages of weak dependence on early universe

Yanliang Huang, Xia Yan, Peiran Yin, Zhenduo Zhang

Over the past decade, a wide range of motion planning approaches for autonomous vehicles has been developed to handle increasingly complex traffic scenarios. However, these approaches are rarely compared on standardized benchmarks, limiting the assessment of relative strengths and weaknesses. To address this gap, we present the setup and results of the 4th C

Murdadha Nasif, Ahmed Refaey Hussein

Semantic communication and edge-cloud collaborative intelligence are increasingly recognized as foundational enablers for next-generation intelligent services operating under stringent bandwidth, latency, and resource constraints. By shifting the communication objective from bit-perfect delivery toward the transmission of task-relevant semantic representatio

Martin Sedlacek, Pavlo Yefanov, Georgy Ponimatkin, Jai Bardhan

Vision-Language-Action (VLA) models empower robots to understand and execute tasks described by natural language instructions. However, a key challenge lies in their ability to generalize beyond the specific environments and conditions they were trained on, which is presently difficult and expensive to evaluate in the real-world. To address this gap, we pres

Johannes M. Michl, Reza Hekmati, Mohamed Helal, Giora Peniakov

Matter qubits that maintain coherence over extended timescales are essential for many pursued applications in quantum communication and quantum computing. Significant progress has already been made on extending coherence times of spins in semiconductor quantum dots while interfacing them with photons in the near-infrared wavelength range. However, similar re

Antonia Alomar, Mireia Masias, Marius George Linguraru, Federico M. Sukno

Early detection of developmental disorders can be aided by analyzing infant craniofacial morphology, but modeling infant faces is challenging due to limited data and frequent spontaneous expressions. We introduce BabyFlow, a generative AI model that disentangles facial identity and expression, enabling independent control over both. Using normalizing flows,

Benjamin Dodson, Jeremy L. Marzuola

We prove a global well--posedness and scattering result for Schr{\"o}dinger maps to a general K{\"a}hler manifold with small initial data in a Besov space.

Johannes Flake, Jonathan Gruber

We show that a large class of non-abelian monoidal categories can be realized as subcategories of tilting objects in abelian monoidal categories with a highest weight structure. The construction relies on a monoidal enhancement of Brundan-Stroppel's semi-infinite Ringel duality and applies to many of Sam-Snowden's triangular categories and Knop's tensor enve

Lawrence Krukrubo, Julius Odede, Olawande Olusegun

Current approaches to Explainable AI (XAI) face a "Scalability-Stability Dilemma." Post-hoc methods (e.g., LIME, SHAP) may scale easily but suffer from instability, while supervised explanation frameworks (e.g., TED) offer stability but require prohibitive human effort to label every training instance. This paper proposes a Hybrid LRR-TED framework that addr

Federico Fornasaro, Tobias Kuna, Giulia Carigi

We investigate a coupled atmosphere-ocean model including the mechanical and thermodynamical interaction between the two fluids for the mid-latitudes. The formulation combines a multilayer quasi-geostrophic dynamical framework with temperature equations incorporating long- and short-wave radiative forcing, as in energy balance models. Within a suitable funct

Hrachya M. Babujian, Angela Foerster, Michael Karowski

Structure Functions for small DIS (deep inelastic scattering) $x$ for integrable models are investigated, in particular, for the $O(N)$~$\sigma $-model and $SU(N)$ chiral Gross-Neveu model, which are asymptotically free. We get the universal behavior $x^{-1}\ln^{-2}x$ at small Bjorken variable $x$ and confirm a Balog Weisz conjecture. For a the second group

Yongxin Wang, Zhicheng Yang, Meng Cao, Mingfei Han

Group-relative reinforcement learning with verifiable rewards (RLVR) often wastes the most informative data it already has the failures. When all rollouts are wrong, gradients stall; when one happens to be correct, the update usually ignores why the others are close-but-wrong, and credit can be misassigned to spurious chains. We present CARE (Contrastive Anc

Luke Benz, Rajarshi Mukherjee, Rui Wang, David Arterburn

Standard practice in electronic health record (EHR)-based studies evaluating the comparative effectiveness of bariatric surgery relative to no surgery is to estimate and report a constant treatment effect across calendar time. However, real-world treatment strategies can evolve, particularly when comparators include standard of care or surgical procedures wh

Youmin Chen, Miaomiao Zhu

We investigate a sequence of Yang-Mills connections $A_j$ lying in vector bundles $E_j$ over non-collapsed degenerating closed Einstein 4-manifolds $(M_j, g_ j)$ with uniformly bounded Einstein constants and bounded diameters. We establish a compactness theory modular three types of bubbles. As applications, we get some quantization results for several impor

Jiawen Wang, Jingjing Wang Tianyang Chen, Min Zhang, Guodong Zhou

In the literature, existing human-centric emotional motion generation methods primarily focus on boosting performance within a single scale-fixed dataset, largely neglecting the flexible and scale-increasing motion scenarios (e.g., sports, dance), whereas effectively learning these newly emerging scenarios can significantly enhance the model's real-world gen

Naresh Manwani, M Elamparithy, Tanish Taneja

In this paper, we introduce directional feedback in the ordinal regression setting, in which the learner receives feedback on whether the predicted label is on the left or the right side of the actual label. This is a weak supervision setting for ordinal regression compared to the full information setting, where the learner can access the labels. We propose

Jibril Ben Achour, Adolfo Cisterna, Hugo Roussille

The Kerr solution is the cornerstone of General Relativity (GR) for modelling astrophysical rotating black holes and for testing GR through gravitational-wave observations and black hole imaging. Understanding how the Kerr geometry is modified in alternative theories of gravity is therefore a crucial step toward constraining possible deviations from GR. Desp

Mats Ola Sand, Quentin Noraz, Guillaume Aulanier, Juan Martínez-Sykora

The mechanism that forms dynamic type II spicules has remained elusive for many years. Their dynamical behaviour has long been linked to magnetic reconnection, yet no conclusive evidence has been provided. However, one recent observational study found signs of reconnection, as traced by Ellerman bombs (EBs), at the footpoints of many spicules. The triggering

Ziqiao Peng, Yi Chen, Yifeng Ma, Guozhen Zhang

Despite significant advances in talking avatar generation, existing methods face critical challenges: insufficient text-following capability for diverse actions, lack of temporal alignment between actions and audio content, and dependency on additional control signals such as pose skeletons. We present ActAvatar, a framework that achieves phase-level precisi

Andrea Muratori, Vladimir M. Stojanovic, Eloisa Cuestas, Tommaso Calarco

Motivated by a compelling need for time-efficient and robust schemes for quantum-state engineering in systems of neutral atoms in optical tweezers, we consider a ring-shaped array of qubits with nearest-neighbor Ising-type ($zz$) coupling and transverse ($x$ and $y$) global control fields. This system to a large extent mimics -- outside of the Rydberg-blocka

Jerson Caro, Juan Cruz-Penagos, Sergio Troncoso

We determine the Picard number and the Ulrich complexity of general bidouble covers of the projective plane, providing the first systematic study of Ulrich bundles on non-cyclic abelian covers. For a bidouble plane branched along three smooth curves of degrees $n_1,n_2,n_3$, we show that $\rho(S)=1$ unless $(n_1,n_2,n_3)$ belongs to an explicit list, thereby

Zakaria Benmounah, Abdennour Boulesnane

We present Algerian Dialect, a large-scale sentiment-annotated dataset consisting of 45,000 YouTube comments written in Algerian Arabic dialect. The comments were collected from more than 30 Algerian press and media channels using the YouTube Data API. Each comment is manually annotated into one of five sentiment categories: very negative, negative, neutral,

Antonio Agresti, Esmée Theewis

We prove the small-noise large deviation principle for the three-dimensional primitive equations with transport noise and turbulent pressure. Transport noise is important for geophysical fluid dynamics applications, as it takes into account the effect of small scales on the large scale dynamics. The main mathematical challenge is that we allow for the transp

Adrian A. Moazzam, Anindya Ghoshroy, Breeanne Heusdens, Durdu O. Guney

Atmospheric turbulence imposes a fundamental limitation across a broad range of applications, including optical imaging, remote sensing, and free-space optical communication. Recent advances in adaptive optics, wavefront shaping, and machine learning, driven by synergistic progress in fundamental theories, optoelectronic hardware, and computational algorithm

Kaiwen Zhang, Liming Jiang, Angtian Wang, Jacob Zhiyuan Fang

Visual storytelling requires generating multi-shot videos with cinematic quality and long-range consistency. Inspired by human memory, we propose StoryMem, a paradigm that reformulates long-form video storytelling as iterative shot synthesis conditioned on explicit visual memory, transforming pre-trained single-shot video diffusion models into multi-shot sto

José L. Ansorena, Glenier Bello

We study the subsymmetric basic sequence structure of variable exponent Lebesgue spaces $L_{P}$ built from index functions $P\colon\Omega\to(0,\infty]$ on $\sigma$-finite measure spaces $(\Omega,\Sigma,\mu)$. Specifically, we prove that if $P$ is bounded away from infinity, then any complemented subsymmetric basic sequence of $L_{P}$ is equivalent to the can

Bobo Li, Xudong Han, Jiang Liu, Yuzhe Ding

Large language models (LLMs) and multimodal LLMs are changing event extraction (EE): prompting and generation can often produce structured outputs in zero shot or few shot settings. Yet LLM based pipelines face deployment gaps, including hallucinations under weak constraints, fragile temporal and causal linking over long contexts and across documents, and li

Caterina B. Leimer Saglio, Stefano Pagani, Paola F. Antonietti

We investigate non-overlapping Schwarz preconditioners for the algebraic systems stemming from high-order discretizations of the coupled monodomain and Barreto-Cressman models, with applications to brain electrophysiology. The spatial discretization is based on a high-order Polytopal Discontinuous Galerkin (PolyDG) method, coupled with the Crank-Nicolson tim

Moritz Böhle, Amélie Royer, Juliette Marrie, Edouard Grave

Vision-language models (VLMs) are commonly trained by directly inserting image tokens from a pretrained vision encoder into the text stream of a language model. This allows text and image information to fully attend to one another within the model, but becomes rapidly costly for long multi-image conversations or streaming video applications, both in terms of

Enmanuel Rodríguez-Brea, Melvin Arias

Liouville's theorem -- the preservation of phase-space volume -- is often presented as a corollary of Hamilton's canonical equations. Here we adopt an ensemble-first viewpoint in which the starting point is local probability conservation on phase space. For a probability density $\rho$ on a $2N$-dimensional symplectic manifold $(\mathcal{M},\omega)$, probabi

Jea-Hyun Park, Abner J. Salgado, Steven M. Wise

We introduce a perturbed preconditioned gradient descent (PPGD) method for the unconstrained minimization of a strongly convex objective $G$ with a locally Lipschitz continuous gradient. We assume that $G(v)=E(v)+F(v)$ and that the gradient of $F$ is only known approximately. Our analysis is conducted in infinite dimensions with a preconditioner built into t

Gigliola Staffilani, Minh-Binh Tran

In this work we study a kinetic equation whose collision operator comprises three distinct wave interaction mechanisms: one representing a 3-wave process, and two corresponding to 4-wave processes. This wave kinetic equation describes the temporal evolution of the density function of the thermal cloud of a finite temperature trapped Bose gas. We establish th

Hongsheng Xing, Qiuxin Si

Predicting reaction outcomes across continuous solvent composition ranges remains a critical challenge in organic synthesis and process chemistry. Traditional machine learning approaches often treat solvent identity as a discrete categorical variable, which prevents systematic interpolation and extrapolation across the solvent space. This work introduces the

Md Mahmudul Hasan, Chiara Bordin, Fairuz Islam, Tamanna Tasnim

This study aims to evaluate the optoelectronic properties of metal free porphyrin-based D-$\pi$-A dyes via in-silico performance investigation notifying energy informatics and decision support. To develop novel organic dyes, three acceptor/anchoring groups and five donating groups were introduced to strategic positions of the base porphyrin structure, result

Marc Peral, Guillem Capellera, Luis Ferraz, Antonio Rubio

In this work we propose a multi-modal architecture for analyzing soccer scenes from tactical camera footage, with a focus on three core tasks: ball trajectory inference, ball state classification, and ball possessor identification. To this end, our solution integrates three distinct input modalities (player trajectories, player types and image crops of indiv

Diego Hitzges, Guillaume Sagnol

Deep learning has been effectively applied to many discrete optimization problems. However, learning-based scheduling on unrelated parallel machines remains particularly difficult to design. Not only do the numbers of jobs and machines vary, but each job-machine pair has a unique processing time, dynamically altering feature dimensions. We propose a novel ap

Li Puyin, Tiange Xiang, Ella Mao, Shirley Wei

Understanding the physical world is essential for generalist AI agents. However, it remains unclear whether state-of-the-art vision perception models (e.g., large VLMs) can reason physical properties quantitatively. Existing evaluations are predominantly VQA-based and qualitative, offering limited insight into whether these models can infer the kinematic qua

Gigliola Staffilani, Minh-Binh Tran

In trapped Bose-Einstein condensates (BECs), \emph{condensate growth} refers to the process in which an increasing number of quasi-particles are immediately transferred from the non-condensate state (the thermal cloud) into the condensate state following the initial formation of the BEC. Despite its physical significance, this phenomenon has not yet been stu

Yuriy N. Bakhvalov

This paper concludes a series of studies on the polyharmonic cascade, a deep machine learning architecture theoretically derived from indifference principles and the theory of random functions. A universal initialization procedure is proposed, based on symmetric constellations in the form of hyperoctahedra with a central point. This initialization not only e

Zhuo He, Yingdong Ru, Qianying Liu, Paul Henderson

Recent advances in neural rendering have achieved impressive results on photorealistic shading and relighting, by using a multilayer perceptron (MLP) as a regression model to learn the rendering equation from a real-world dataset. Such methods show promise for photorealistically relighting real-world objects, which is difficult to classical rendering, as the

Santhoshini Velusamy

The Max-DICUT problem has gained a lot of attention in the streaming setting in recent years, and has so far served as a canonical problem for designing algorithms for general constraint satisfaction problems (CSPs) in this setting. A seminal result of Kapralov and Krachun [STOC 2019] shows that it is impossible to beat $1/2$-approximation for Max-DICUT in s

Benedetta Russo, Alfredo Urbano

In this work, we highlight the existence of a lower limit on the tidal deformability parameter $\Lambda$, determined by the requirement of relativistic causality. Additionally, by considering the upper bound set on compactness, we identify the region within the parameter space of compactness versus tidal deformability, where physically motivated exotic compa

Frauke M. Bleher, Ted Chinburg, Xuxi Ding, Nadia Heninger

Since the time of Minkowski a basic problem in number theory has been to find lower bounds for the absolute value $\Delta(K)$ of the discriminant of a number field $K$ in terms of the degree $n(K)$ of $K$. In this paper we study another measure of the size of $K$ given by the covering radius $\mu(K)$ of the ring of integers $O_K$ of $K$. Here $\mu(K)$ is the

Cédric Bernardin, Vsevolod Vladimirovich Tarsamaev

We consider a one-dimensional piecewise deterministic Markov process (PDMP) on $[0,1]$ with resetting at $0$ and depending on a small parameter $\varepsilon>0$. In the singular vanishing limit $\varepsilon \to 0$ we prove that the `` resetting '' simple point process associated to the PDMP converges to a point process described by a jump Markov process decor

Xueming Yan, Bo Yin, Yaochu Jin

Multiobjective reinforcement learning (MORL) poses significant challenges due to the inherent conflicts between objectives and the difficulty of adapting to dynamic environments. Traditional methods often struggle to generalize effectively, particularly in large and complex state-action spaces. To address these limitations, we introduce the Latent Causal Dif

Bruno Cavalar, Théo Borém Fabris, Partha Mukhopadhyay, Srikanth Srinivasan

We study the power of negation in the Boolean and algebraic settings and show the following results. * We construct a family of polynomials $P_n$ in $n$ variables, all of whose monomials have positive coefficients, such that $P_n$ can be computed by a depth three circuit of polynomial size but any monotone circuit computing it has size $2^{\Omega(n)}$. This

Mukesh Kumar, John T. Abatzoglou, Crystal A. Kolden, Mojtaba Sadegh

Wildfire impacts on US communities have escalated in recent decades, highlighting the need to better understand factors that influence wildfire outcomes. We find that 567,000 homes were exposed to wildfires across the contiguous US during 2001-2020, two-thirds of which occurred and increased five-fold in the Western US. While residential structure survivabil

Yumeng Xu, Jorge Valencia, Héctor Estellés Estrella, Antoni Ramos Buades

The GWTC-4.0 catalog of transient gravitational wave signals describes observations made in the first part of the fourth observing run of the LIGO-Virgo-KAGRA (LVK) gravitational wave detector network. Here we extend the LVK's GWTC-4.0 analysis to elliptic orbits, and an improved description of spin precession in the frequency domain. For this study we use s

Ziyang Song, Zelin Zang, Zuyao Chen, Xusheng Liang

Multimodal Large Language Models (MLLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexplored, especially in clinical anatomical surgical images. Anatomy understanding tasks demand precise understanding and clinically coherent answers, which are difficult to achieve due to the complexity o

Karyn Le Hur, Andrea Baldanza

Here, we elaborate on and develop the geometrical approach introduced in K. Le Hur, Physics Reports 1104 1-42 (2025) between the magnetic monopole created from a radial field, quantum physics and topological lattice models through quantum phase transitions. We introduce an effective magnetic moment for a monopole when applying an additional source field alon

Shangbo Yun, Xiaodong Gu, Jianghong Huang, Beijun Shen

The rapid proliferation of diverse programming languages presents both opportunities and challenges for developing multilingual code LLMs. While existing techniques often train code LLMs by simply aggregating multilingual code data, few explore the deeper relationships between programming languages(PLs) and how such relationships can be utilized to optimize

Thomas Creutzig, Vladimir Kovalchuk, Andrew R. Linshaw

There are three universal $2$-parameter vertex algebras $\mathcal{W}_{\infty}$, $\mathcal{W}^{\text{ev}}_{\infty}$, and $\mathcal{W}^{\mathfrak{sp}}_{\infty}$ which are freely generated of types $\mathcal{W}(2,3,4,\dots)$, $\mathcal{W}(2,4,6,\dots)$, and $\mathcal{W}(1^3, 2, 3^3, 4,\dots)$, respectively. They serve as classifying objects for vertex algebras

Haocheng Yu, Tianyi Chu, Spencer H. Bryngelson

We quantify how incident acoustic energy is converted into vortical motion and viscous dissipation for a two-dimensional plane-wave passing through a slit geometry. We perform direct numerical simulations over a broad parameter space in incident sound pressure level (ISPL), Strouhal number (St), and Reynolds number (Re). Spectral proper orthogonal decomposit

Hongliang Li, Nong Zhang, Zhewen Xu, Xiang Li

Understanding and predicting the Madden-Julian Oscillation (MJO) is fundamental for precipitation forecasting and disaster prevention. To date, long-term and accurate MJO prediction has remained a challenge for researchers. Conventional MJO prediction methods using Numerical Weather Prediction (NWP) are resource-intensive, time-consuming, and highly unstable

Alexander Lubotzky, Matthew Stover

In response to a question raised by Belolipetsky and the first author, we prove that for every finite group $G$ there are infinitely many isomorphism classes of compact complex hyperbolic $2$-manifolds with automorphism group isomorphic to $G$.

M. M. Tyumentsev, V. E. Minakova, N. I. Fedotov, S. V. Zaitsev-Zotov

The effects of uniaxial and biaxial tensile strain on the $\rho_{xx}$ and $\rho_{yy}$ components of the resistivity tensor, and the commensurable-nearly commensurate CDW (CCDW-NCCDW) transition temperature in 1T-TaS$_2$ are studied. At room temperature, uniaxial tensile strain increases the resistivity tensor components by a comparable magnitude both paralle

Esmée Theewis

We prove the small-noise large deviation principle (LDP) for stochastic evolution equations in an $L^2$-setting. As the coefficients are allowed to be non-coercive, our framework encompasses a much broader scope than variational settings. To replace coercivity, we require only well-posedness of the stochastic evolution equation and two concrete, verifiable a

Peter Müller

Problem 8.75 of the Kourovka Notebook [10], attributed to John G. Thompson, asks the following: Suppose $G$ is a finite primitive permutation group on $\Omega$, and $\alpha$, $\beta$ are distinct points of $\Omega$. Does there exist an element $g\in G$ such that $\alpha^g=\beta$ and $g$ fixes no point of $\Omega$? A recent negative example is given in [12],

Otavio Kaminski, Diego S. Monteiro, Carlos Tomei

{We explore a simple {\it geometric model} for functions between spaces of the same dimension (in infinite dimensions, we require that Jacobians be Fredholm operators of index zero). The model combines standard results in analysis and topology associated with familiar global and local aspects. Functions are supposed to be proper on bounded sets. The model is

Giacomo Brunello, Manoj K. Mandal, Pierpaolo Mastrolia, Raj Patil

We compute the gravitational interaction of two coalescing compact objects at sixth post-Newtonian order in the static limit, employing the diagrammatic approach within the effective field theory framework of General Relativity. The calculation requires the evaluation of six-loop Feynman diagrams that are mapped onto two-point integrals with a gauge-theory-l

Cameron Gibson, Okan Günel, Gabriel Larios, C. N. Pope

Generalised spin structures are necessary for placing fermions on manifolds that do not admit a standard spin structure. This is especially relevant in a dimensional reduction on such a manifold, which can then be compensated by using fermions that are appropriately charged under some Maxwell or Yang-Mills field defined on the internal manifold. A well known

Tian Shen, Zhonggen Su, Xiaolin Wang

Consider the well-known Langevin diffusion on $\mathbb{R}^d$ $$\mathrm{d} X_t = -\nabla U(X_t)\,\mathrm{d} t + \sqrt{2}\mathrm{d} B_t, $$ and its Euler-Maruyama discretization given by $$X_{k+1}=X_k-\eta \nabla U(X_k)+\sqrt{2\eta }\xi_{k+1},$$ where $\eta$ is the step size. Under mild conditions, the Langevin diffusion admits $\pi(\mathrm{d} x)\propto \exp(-

Nikita Volzhin, Soowhan Yoon

The recent development of Kolmogorov-Arnold Networks (KANs) has found its application in the field of Graph Neural Networks (GNNs) particularly in molecular data modeling and potential drug discovery. Kolmogorov-Arnold Graph Neural Networks (KAGNNs) expand on the existing set of GNN models with KAN-based counterparts. KAGNNs have been demonstrably successful

Martin Hoefer, Lennart Kauther, Philipp Pabst, Britta Peis

Designing fare systems for public transportation networks is a challenging task. A popular approach is to partition the network into fare zones (``zoning'') and fix journey prices depending on the number of traversed zones (``pricing''). In this paper, we focus on finding revenue-optimal solutions to the zoning problem for a given subadditive pricing functio

M. Lampani, M. Rossi, S. Guastavino, M. Piana

Coronal mass ejections (CMEs) are key drivers of space weather events, posing risks to both space-borne and ground-based systems. Accurate prediction of their arrival time at Earth is critical for impact mitigation. To this end, physics-informed artificial intelligence (AI) approaches have proven more effective than purely data-driven or physics-based method

Martí Medina-Hernández, Janos Kertész, Mihály Fazekas

Detecting fraud and corruption in public procurement remains a major challenge for governments worldwide. Most research to-date builds on domain-knowledge-based corruption risk indicators of individual contract-level features and some also analyzes contracting network patterns. A critical barrier for supervised machine learning is the absence of confirmed no

Sejal Ahuja, Keshav Das Agarwal, Aditi Sen De

The non-Hermitian Hamiltonian describes the effective dynamics of a system coupled to a continuously measured bath, and can exhibit anti-unitary symmetries that give rise to exceptional points and broken phases with complex eigenvalues, features unique to non-Hermitian systems. Going beyond conventional Hermitian physics, we analyze the impact of non-Hermiti

Meng Ding, Xiao Fu

This work revisits the hyperspectral super-resolution (HSR) problem, i.e., fusing a pair of spatially co-registered hyperspectral (HSI) and multispectral (MSI) images to recover a super-resolution image (SRI) that enhances the spatial resolution of the HSI. Coupled tensor decomposition (CTD)-based methods have gained traction in this domain, offering recover

Hafsa Benaddi, Mohammed Jouhari, Nouha Laamech, Anas Motii

The widespread deployment of Internet of Things (IoT) devices requires intrusion detection systems (IDS) with high accuracy while operating under strict resource constraints. Conventional deep learning IDS are often too large and computationally intensive for edge deployment. We propose a lightweight IDS that combines SHAP-guided feature pruning with knowled

Michael A. Jones, Brittany Ohlinger, Jennifer Wilson

Divisor methods are well known to satisfy house monotonicity, which allows representative seats to be allocated sequentially. We focus on stationary divisor methods defined by a rounding cutpoint $c \in [0,1]$. For such methods with integer-valued votes, the resulting apportionment sequences are periodic. Restricting attention to two-party allocations, we ch

Prasoon Saurabh

The geometric description of open quantum systems via the Quantum Geometric Tensor (QGT) traditionally relies on the assumption that the physical states form a differentiable vector bundle over the parameter manifold. This framework becomes ill-posed at spectral singularities, such as Exceptional Points, where the eigen-bundle admits no local trivialization

Shaochen Bi, Yuting He, Weiming Wang, Hao Chen

Combinatorial explosion problem caused by dual inputs presents a critical challenge in Deformable Medical Image Registration (DMIR). Since DMIR processes two images simultaneously as input, the combination relationships between features has grown exponentially, ultimately the model considers more interfering features during the feature modeling process. Intr

Eva Molnárová, Ties A. Mulders, Marcela Spee-Dropková, Louise M. Spekking

Evolutionary therapy (ET) aims to steer tumor evolution by adjusting treatment timing and dosing to control rather than eradicate tumor burden. Clinical use requires reliable monitoring of tumor dynamics to inform mathematical models that guide therapy. In cancers such as metastatic castrate-resistant prostate cancer and relapsed platinum-sensitive ovarian c

Gang Li, Dandan Qiao, Mingxuan Zheng

We find that event features extracted by large language models (LLMs) are effective for text-based stock return prediction. Using a pre-trained LLM to extract event features from news articles, we propose a novel deep learning model based on structured event representation (SER) and attention mechanisms to predict stock returns in the cross-section. Our SER-

Shroddha Mukhopadhyay, Ana Conde-Rubio, Jose Trull, Agustin Mihi

We present a combined experimental and theoretical study of nonlinear light-matter interactions in a three-dimensional gold-polymer hybrid metasurface. In contrast to conventional two-dimensional designs, which by symmetry may support either transverse electric (TE) or transverse magnetic (TM) polarization, our volumetric architecture accepts both TE and TM

Katharina Stengg, Christian Macho, Martin Pinzger

Understanding source code changes and their impact on other code entities is a crucial skill in software development. However, the analysis of code changes and their impact is often performed manually and therefore is time-consuming. Recent advancements in AI, and in particular large language models (LLMs) show promises to help developers in various code ana

Eric Nilsson, Koenraad Schalm

We study DC and AC thermoelectric and magneto-transport in 2D quantum critical theories with strong translational symmetry breaking due to a % varying chemical potential lattice with zero average $\bar{\mu}=0$. The combination of quantum criticality and the absence of the average natural scale implies that such systems have idiosyncratic signatures that may

Guoli Jia, Junyao Hu, Xinwei Long, Kai Tian

Image generation based on diffusion models has demonstrated impressive capability, motivating exploration into diverse and specialized applications. Owing to the importance of emotion in advertising, emotion-oriented image generation has attracted increasing attention. However, current emotion-oriented methods suffer from an affective shortcut, where emotion

Rabindra Basnet, Ramesh C. Budhani

The realization of long-range spin order in two-dimensions (2D) has catapulted the search for layered materials with magnetic ordering above room temperature. These efforts aim to understand and enhance the spin spin interactions in 2D. An emergent class of such magnets is the layered FeNGeTe2 (N = 3, 4, and 5). Here, we investigate the magnetic states over

Sofia Blomqvist, Christian Ecker, Tyler Gorda, Aleksi Vuorinen

We perform a model-agnostic Bayesian analysis of the neutron-star-matter equation of state (EoS), using known ab-initio constraints and astrophysical observations to limit its behavior at intermediate densities. Permitting explicit first-order phase transitions allows us to systematically search for twin-star solutions, i.e. the existence of stars degenerate

Tadashi Kuramoto, Yasutaka Imai, Takahiko Masuda, Yutaka Shikano

Light polarization plays a crucial role in optical-cavity experiments; however, mirror birefringence presents a significant challenge that must be addressed carefully. In this study, a rigorous, nonperturbative framework is developed to quantify birefringence effects by incorporating variations in reflectance and polarization misalignment. We analyze the imp

Ivan Decostanzi, Yelena Mejova, Kyriaki Kalimeri

Timely and accurate situational reports are essential for humanitarian decision-making, yet current workflows remain largely manual, resource intensive, and inconsistent. We present a fully automated framework that uses large language models (LLMs) to transform heterogeneous humanitarian documents into structured and evidence-grounded reports. The system int

Ting Tan, Huanyuan Shan, Eric Armengaud

While the Lyman-$\alpha$ (Ly$\alpha$) forest traces the large-scale matter distribution over a wide range of redshift, its three-dimensional (3D) clustering at $z < 2$ has not yet been measured. We investigate the prospects for measuring low-redshift Ly$\alpha$ correlations with the UV slitless spectroscopic instrument of the China Space Station Telescope (C

Karan Mukhi, Licio Romao, Alessandro Abate

We study optimal charging of large electric vehicle populations that are exposed to a single real-time imbalance price. The problem is naturally cast as a multistage stochastic linear programme (MSLP), which can be solved by algorithms such as Stochastic Dual Dynamic Programming. However, these methods scale poorly with the number of devices and stages. This

Lorenzo Capelli, Leandro de Souza Rosa, Gianluca Setti, Mauro Mangia

The recent explosive growth in Deep Neural Networks applications raises concerns about the black-box usage of such models, with limited trasparency and trustworthiness in high-stakes domains, which have been crystallized as regulatory requirements such as the European Union Artificial Intelligence Act. While models with embedded confidence metrics have been

E. De Rubeis, M. Bondi, A. Botteon, R. J. van Weeren

In this paper, we aim to further analyze the filaments in Abell 2255 combining LOFAR data with uGMRT (1260 MHz) and VLA (1520 MHz) data to constrain the spectral shape of the filaments. This allows studying their morphological properties, required to understand their origin, at unprecedentedly high resolution (~2.3 kpc), crucial to disentangle the different

Fabian Bschorr, Thomas Hüfken, Tobias Lobmeyer, Volker Rasche

Purpose: Conventional MRI is relying on the assumption of the magnetic field being homogeneous in direction and amplitude. However, with the growing interest in portable, affordable point-of-care MRI systems, these assumptions do not necessarily hold anymore due to compromises necessary to achieve a reduction in e.g. footprint, weight and portability. Simula

Tim Aebersold, Soheyl Massoudi, Mark D. Fuge

Engineering complex systems (aircraft, buildings, vehicles) requires coordinating geometric and performance couplings across subsystems. As generative models proliferate for specialized domains, a key research gap is how to coordinate frozen, pre-trained submodels to generate full-system designs that are feasible, diverse, and high-performing. We introduce G

Mert Ozates, Mohammad Kazemi, Gianluigi Liva, Deniz Gündüz

We examine unsourced random access in a fully asynchronous setup, where active users transmit their data without restriction on the start time over a fading channel. In the proposed scheme, the transmitted signal consists of a pilot sequence and a polar codeword, with the polar codeword distributed across the data part of the packet in an on-off pattern. The

Simon Geyer, Rafael S. Eggli, Carlos dos Santos, Miguel J. Carballido

Hole-spin qubits enable fast, all-electrical spin manipulation through electric-dipole spin resonance (EDSR), arising from two microscopic mechanisms rooted in their intrinsically strong spin-orbit interaction. Depending on how the electric field acts on the quantum dot, the spin can be driven either by a modulation of its g-factor or by a displacement of th

Walter Quattrociocchi, Valerio Capraro, Matjaž Perc

Large language models (LLMs) are widely described as artificial intelligence, yet their epistemic profile diverges sharply from human cognition. Here we show that the apparent alignment between human and machine outputs conceals a deeper structural mismatch in how judgments are produced. Tracing the historical shift from symbolic AI and information filtering

Saikat Sur, Rohitashwa Chattopadhyay, Jens Christian Claussen, Archan Mukhopadhyay

Misinformation is pervasive in natural, biological, social, and engineered systems, yet its quantitative characterization remains challenging. We develop a general mathematical framework for quantifying information distortion in distributed systems by modeling how local transmission errors accumulate along network geodesics and reshape each agent's perceived

Noy Cohen

Poly(N-isopropylacrylamide) (PNIPAM) is a temperature-responsive polymer that undergoes large volumetric deformations through a transition from a swollen to a collapsed state at the volume phase transition temperature (VPTT). Locally, these deformations stem from the coil-to-globule transition of individual chains. In this contribution, I revisit the study o

Spencer Green, Frank Wilczek

We propose a double-resonant interferometric strategy for axion dark matter detection that combines microwave circuit resonance with Fabry--P\'erot optical enhancement. In a strong magnetic field, axion--photon mixing induces a weak oscillating electric field, which is first amplified by a resonant circuit and then transduced into an optical phase shift via

Atli Fannar Franklín

The Stanley-Wilf limit of the pattern 1324 is known to lie between 10.271 and 13.5. We obtain lower bounds on this limit by encoding permutations as walks in directed graphs: building a permutation by successive insertion of maxima corresponds to traversing edges, and the growth rate of walks equals the spectral radius of the adjacency matrix. For 1324, this

Mark Grant

We show how secondary cohomology operations in the total space of the fibred join can be used to give lower bounds for the sectional category of a fibration. This suggests a refinement of the module weight of Iwase--Kono, which we call the secondary module weight. Examples are given for which the secondary module weight at the prime $2$ detects sectional cat

Shengbo You, Georgios Varnavides, Sagar Khavnekar, Nikita Palatkin

Linear phase-contrast scanning transmission electron microscopy (STEM) techniques compatible with high-throughput 4D-STEM acquisition are widely used to enhance phase contrast in weakly scattering and beam-sensitive materials. In these modalities, contrast transfer is often suppressed at low spatial frequencies, resulting in a characteristic contrast gap tha

Neeladri Sekhar Bera, Apurba Roy, Purbarun Dhar

We explore the necking, filament thinning, and pinchoff dynamics of ferrofluid droplets within a magnetic field, via a simple and low-cost experimental method. In our studies, both the Ohnesorge number Oh and the Deborah number De are O1, a typically inaccessible regime with conventional extensional rheometers. Under magnetic forcing, the nanoparticles assem

Antonio Tarizzo, Mohammad Kazemi, Deniz Gündüz

Federated edge learning (FEEL) enables wireless devices to collaboratively train a centralised model without sharing raw data, but repeated uplink transmission of model updates makes communication the dominant bottleneck. Over-the-air (OTA) aggregation alleviates this by exploiting the superposition property of the wireless channel, enabling simultaneous tra