December 2025 arXiv papers — page 62

Tianyu Wu

Gene regulatory networks are typically modeled using ordinary differential equations (ODEs) with phenomenological Hill functions to represent transcriptional regulation. While computationally efficient, Hill functions lack mechanistic grounding and cannot capture stochastic promoter dynamics. We present a hybrid Markovian-ODE framework that explicitly models

Zihan Han, Lingran Meng, Jingwei Zhang

City-scale logistics routing has become increasingly challenging as metropolitan road networks grow to tens of millions of edges and traffic conditions evolve rapidly under high-volume mobility demands. Conventional centralized routing algorithms and monolithic graph neural network (GNN) models suffer from limited scalability, high latency, and poor real-tim

Victor De Marez, Jens Van Nooten, Luna De Bruyne, Walter Daelemans

Advancements in large language models offer strong potential for enhancing virtual simulated patients (VSPs) in medical education by providing scalable alternatives to resource-intensive traditional methods. However, current VSPs often struggle with medical accuracy, consistent roleplaying, scenario generation for VSP use, and educationally structured feedba

C. Swastik, Zahed Wahhaj, Myriam Benisty, Saksham Arora

Studying young protoplanetary disks is essential for understanding planet formation, but traditional angular differential imaging can introduce self-subtraction artefacts that hinder interpretation of small-scale structures. We present high-resolution total- and polarized-intensity Ks-band images of the LkCa~15 system obtained with SPHERE using near-simultan

Lucas Arenstein, Michael Kastoryano

Pricing multi-asset options via the Black-Scholes PDE is limited by the curse of dimensionality: classical full-grid solvers scale exponentially in the number of underlyings and are effectively restricted to three assets. Practitioners typically rely on Monte Carlo methods for computing complex instrument involving multiple correlated underlyings. We show th

Didier Sornette, Virgile Troude

Life is commonly described as a self-organized, far-from-equilibrium process that maintains internal order by consuming free energy and exporting entropy. This thermodynamic view underlies diverse theoretical frameworks -- from autopoiesis and relational biology to autocatalytic sets and hypercycles -- yet dissipation is typically treated as a necessary cons

Shurui Xu, Siqi Yang, Jiapin Ren, Zhong Cao

Precise grading of meniscal horn tears is critical in knee injury diagnosis but remains underexplored in automated MRI analysis. Existing methods often rely on coarse study-level labels or binary classification, lacking localization and severity information. In this paper, we introduce MeniMV, a multi-view benchmark dataset specifically designed for horn-spe

Chenyuan Yang, Natalie Neamtu, Chris Hawblitzel, Jacob R. Lorch

Large language models (LLMs) have shown impressive capability to understand and develop code. However, their capability to rigorously reason about and prove code correctness remains in question. This paper offers a comprehensive study of LLMs' capability to develop correctness proofs for system software written in Rust. We curate a new system-verification be

Damis El Alami, Gábor Pete, Ádám Timár

We introduce a general framework to show the indistinguishability of infinite clusters (ergodicity of the cluster subrelation) in group-invariant percolation processes with a weaker version of the finite energy property: the possibility of moving infinite branches from one infinite cluster to another. Crucially, this removes the necessity for the infinite cl

Federica Valeau, Odysseas Boufalis, Polytimi Gkotsi, Joshua Rosenthal

SEATER is a generative retrieval model that improves recommendation inference efficiency and retrieval quality by utilizing balanced tree-structured item identifiers and contrastive training objectives. We reproduce and validate SEATER's reported improvements in retrieval quality over strong baselines across all datasets from the original work, and extend th

S. Bhattacharya, M. Paul, R. N. Sahoo, R. Purtschert

The cosmogenic $^{39}$Ar(t$_{1/2}$= 268 years) isotope of argon is used for geophysical dating and tracing owing to its appropriate half-life and chemical inertness as a noble gas; $^{39}$Ar serves also in nuclear weapon test monitoring. We measured for the first time the total cross section of the main $^{39}$Ar cosmogenic production reaction in the atmosph

Ansar Ahmed

The move to 6th Generation (6G) wireless networks creates new issues with privacy, scalability, and adaptability. The data-intensive nature of 6G is not handled well by older, centralized network models. A shift toward more secure and decentralized systems is therefore required. A new framework called the Federated Learning-based Decentralized Adaptive Intel

Barbara Kaltenbacher

In this paper we prove uniqueness and stability of reconstruction of two coefficients (sound speed and nonlinearity parameter) in the Jordan-Moore-Gibson-Thompson JMGT equation of nonlinear acoustics, relying on observations resulting from only two sources. A key tool for this purpose is a multiharmonic expansion of the PDE solution, which reflects the physi

Moussa Labbadi, Christophe Roman

This paper study the hyperexponential stabilization for infinite-dimensional system on Hilbert space by a distributed time depending control law. The well-posedness of the closed loop for every time is obtained through the use of maximal monotone operator. The hyperexponential stability and ISS property of the closed loop is established using Lyapunov analys

Seyed Ehsan Marjani Bajestani, Giovanni Beltrame

Event-based cameras (ECs) have emerged as bio-inspired sensors that report pixel brightness changes asynchronously, offering unmatched speed and efficiency in vision sensing. Despite their high dynamic range, temporal resolution, low power consumption, and computational simplicity, traditional monochrome ECs face limitations in detecting static or slowly mov

Chakib Chatri, Ajul Dinesh, Moussa Labbadi

This work addresses virtual resistance (VR)based control for grid-connected inverters, which enhances transient damping, reduces steady-state errors, and improves robustness to grid disturbances without requiring additional voltage sensors. Classical passivity-based VR control is robust, but limited by restrictive sector bounds on nonlinearities. We extend t

Xinrui Li, R. Michael Buehrer

In many sensing (viz., radio astronomy) and radar applications, the received signal of interest (SOI) exhibits a significantly wider bandwidth or weaker power than the interference signal, rendering it indistinguishable from the background noise. Such scenarios arise frequently in applications such as passive radar, cognitive radio, low-probability-of-interc

Xinrui Li, R. Michael Buehrer, Steven W. Ellingson

Reconfigurable Intelligent Surfaces (RIS) have recently gained attention as a means to dynamically shape the wireless propagation environment through programmable reflection control. Among the numerous applications, an important emerging use case is employing RIS as an auxiliary mechanism for spatial interference nulling, particularly in large ground-based r

Xican Yang, Yuanhe Tian, Yan Song

Mixture-of-experts (MoE) architectures used in large language models (LLMs) achieve state-of-the-art performance across diverse tasks yet face practical challenges such as deployment complexity and low activation efficiency. Expert pruning has thus emerged as a promising solution to reduce computational overhead and simplify the deployment of MoE models. How

Wei Meng

This paper tackles practical challenges in governing child centered artificial intelligence: policy texts state principles and requirements but often lack reproducible evidence anchors, explicit causal pathways, executable governance toolchains, and computable audit metrics. We propose Graph-GAP, a methodology that decomposes requirements from authoritative

Scott Duke Kominers

We consider composite $n$ satisfying the congruence $$n \cdot \sigma_k(n) \equiv 2 \pmod{\phi(n)},$$ and show a "flanking" structure: $14$ appears in both $S_{k-1}$ and $S_{k+1}$ whenever certain values of $n$ appear in $S_k$; and, moreover, $14$ is the only (nontrivial) case of this property. Along the way, we derive a new characterization of the $n$ that a

Mina Alizadeh, Yousefali Abedini, Hedieh Abdollahi

We present an analysis of UKIRT observations obtained between 2003 and 2007 to investigate the evolved stellar populations within the central square kiloparsec of M33. Point-spread function (PSF) photometry is employed to mitigate the effects of stellar crowding and to ensure accurate measurements in this densely populated region. This method, applied to mer

Takeharu Matsuda, Satoshi Ii

Incompressible flow solvers based on strong-form meshfree methods represent arbitrary geometries without the need for a global mesh system. However, their local evaluations make it difficult to satisfy incompressibility at the discrete level. Moreover, the collocated arrangement of velocity and pressure variables tends to induce a zero-energy mode, leading t

Tanya Urrutia, Darshan Kakkad, Paula Sánchez-Sáez, Mojtaba Raouf

We propose a large quasar demographic optical multi-object spectroscopic (MOS) survey targeting over 50 million AGN candidates up to the highest redshifts possible in the optical (z~6.5), with repeat visits, using a variety of selection criteria available by 2040. A large MOS survey combining all AGN selection methods is the only way to unify a diverse range

Alexander C. Tyner, Alexander V. Balatsky

Amorphous oxide tunneling barriers, primarily formed from aluminum, represent one of the most widely adopted platforms for superconducting quantum bits (qubits). To overcome challenges associated with defects and sample variance among the tunneling barriers, the methodology of alternating bias assisted annealing (ABAA) was introduced in Pappas et. al[1]. The

Qizhi Wang

Digital-humanities work on semantic shift often alternates between handcrafted close readings and opaque embedding machinery. We present a reproducible expert-system style pipeline that quantifies lexical drift and its instability in the Old Bailey Corpus (1674-1913), coupling interpretable trajectories with legally meaningful axes. We bin proceedings by dec

N. Sebe, A. Pezo, S. Krishnia, S. Collin

The emergence of the orbital degree of freedom in modern orbitronics offers a promising alternative to heavy metals for the efficient control of magnetization. In this context, identifying interfaces that exhibit orbital-momentum locking and an orbital Rashba-Edelstein response to an external electric field is of primary importance. In this work, we experime

I. V. Orlovskyi, O. A. Tymoshenko

Access to quality education remains a global challenge, particularly in crisis-affected regions. This study examines the decline in students' mathematical proficiency and proposes an innovative Moodle-based testing system that incorporates step-by-step solution verification and interactive exercises. Unlike traditional assessments, this approach ensures a mo

Panagiotis Kostas, Chrysostomos Psaroudakis, Jorge Vitória

We propose a new framework for the study of homological properties for (compactly generated) triangulated categories such as regularity, finiteness of global or finitistic dimension, gorensteinness or injective generation and the relation between them. Our approach focuses on distinguished, intrinsically defined, subcategories and our main tool is the new no

Jiangqi Dai, Mohsen Ghaffari, Julian Portmann

We present a randomized algorithm that computes a constant approximation of a graph's arboricity, using $\tilde{O}(n/\lambda)$ queries to adjacency lists and in the same time bound. Here, $n$ and $\lambda$ denote the number of nodes and the graph's arboricity, respectively. The $\tilde{O}(n/\lambda)$ query complexity of our algorithm is nearly optimal. Our c

Maurice de Gosson

The symplectic group Sp(n) acts on phase space while the unitary representation of its double cover, Mp(n), the metaplectic group, acts on functions defined on configuration space. We will construct an extension Mp(n) of Mp(n) acting on square integrable functions on phase space. This is performed using previous results of ours involving explicit expressions

Giulio Aielli, Oleg Brandt, Patrick Collins, Louie Dartmoor Corpe

Long-lived particles (LLPs), predicted by various extensions of the Standard Model (SM), have become a key focus of the contemporary search programme for physics beyond the SM. To enhance LLP discovery potential at the LHC, the ANUBIS experiment has been proposed to instrument the ceiling of the ATLAS experiment's underground cavern with dedicated tracking d

Xijia Wei, Ting Dang, Khaldoon Al-Naimi, Yang Liu

Earable acoustic sensing offers a powerful and non-invasive modality for capturing fine-grained auditory and physiological signals directly from the ear canal, enabling continuous and context-aware monitoring of cognitive states. As earable devices become increasingly embedded in daily life, they provide a unique opportunity to sense mental effort and percep

Mykyta Lapin, Kostiantyn Bokhan, Yurii Parzhyn

We propose a structural-graph approach to classifying contour images in a few-shot regime without using backpropagation. The core idea is to make structure the carrier of explanations: an image is encoded as an attributed graph (critical points and lines represented as nodes with geometric attributes), and generalization is achieved via the formation of conc

Fei Song, Yi Li, Jiangmeng Li, Rui Wang

Multi-prompt learning methods have emerged as an effective approach for facilitating the rapid adaptation of vision-language models to downstream tasks with limited resources. Existing multi-prompt learning methods primarily focus on utilizing various meticulously designed prompts within a single foundation vision-language model to achieve superior performan

Mohamadali Berahman, Madjid Eshaghi Gordji

Trust is the invisible glue that holds together the fabric of societies, economic systems, and political institutions. Yet, its dynamics-especially in real-world settings remain unpredictable and difficult to control. While classical trust game models largely rely on discrete frameworks with limited noise, they fall short in capturing sudden behavioral shift

Vikram Krishnamurthy

Several optimism-based stochastic bandit algorithms -- including UCB, UCB-V, linear UCB, and finite-arm GP-UCB -- achieve logarithmic regret using proofs that, despite superficial differences, follow essentially the same structure. This note isolates the minimal ingredients behind these analyses: a single high-probability concentration condition on the estim

Dimitrios Georgoulopoulos, Nikolaos Chaidos, Angeliki Dimitriou, Giorgos Stamou

Accurately retrieving images that are semantically similar remains a fundamental challenge in computer vision, as traditional methods often fail to capture the relational and contextual nuances of a scene. We introduce PRISm (Pruning-based Image Retrieval via Importance Prediction on Semantic Graphs), a multimodal framework that advances image-to-image retri

Charilaos Kapelonis, Marios Antonakakis, Konstantinos Politof, Aristomenis Antoniadis

Art is widely recognized as a reflection of civilization and mosaics represent an important part of cultural heritage. Mosaics are an ancient art form created by arranging small pieces, called tesserae, on a surface using adhesive. Due to their age and fragility, they are prone to damage, highlighting the need for digital preservation. This paper addresses t

El Kindi Rezig, Mir Mahathir Mohammad, Nicolas Baret, Ricardo Mayerhofer

Data wrangling, the process of cleaning, transforming, and preparing data for analysis, is a well-known bottleneck in data science workflows. A wide range of data wrangling techniques have been proposed to mitigate this challenge. Of particular interest are visual data wrangling tools, in which users prepare data via graphical interactions (such as with visu

Jifeng Chu, Shuyuan Guo, Gang Meng, Meirong Zhang

For the classical Sturm-Liouville operators, we prove the sharp bounds for all nodes of eigenfunctions by regarding these nodes as nonlinear functionals of potential $q\in L^1[0,1]$. By studying the optimization problems to minimize or to maximize the nodes $\{ T_{i,m}\}$ subject to the constraint $\|q\|_{1}=r$ with $r>0$ and using the strong continuity of t

Zijing Li, Chenhao Zeng, Shufei Ge

Brain connectivity analysis based on magnetic resonance imaging is crucial for understanding neurological mechanisms. However, edge-based connectivity inference faces significant challenges, particularly the curse of dimensionality when estimating high-dimensional covariance matrices. Existing methods often struggle to account for the unknown latent topologi

David Favero, Daniel Kaplan, Tyler L. Kelly

We define Kuznetsov and anti-Kuznetsov categories for gauged linear sigma models. We show that for complete intersections of ample divisors in smooth projective toric varieties, the Kuznetsov category is left orthogonal to an exceptional collection. We prove that any complete intersection of $r \ge 2$ ample divisors in a Fano GIT quotient is a Fano visitor a

Jeffrey Bub

I outline a neo-Bohrian interpretation of quantum mechanics -- a view of quantum mechanics that accords with the core insights in Bohr's thinking, with a twist that justifies the prefix `neo.' In a second part of the paper, I show how von Neumann's work on infinite direct products provides a theoretical framework that deflates the measurement problem and jus

Mark Kashirskiy, Artiom Lipinski, Ilya Makarov

Tokenization is a critical preprocessing step for large language models (LLMs), directly impacting training efficiency and downstream performance. General-purpose tokenizers trained predominantly on English and Latin-script languages exhibit suboptimal performance on morphologically rich languages such as Arabic, resulting in inflated token sequences and red

Carlo Marinelli

Existence and uniqueness of mild solutions to a class of semilinear stochastic evolution equations with additive noise is proved. The linear part of the drift term is the generator of a compact semigroup of contractions, while the nonlinear part is only assumed to be the superposition operator associated to a decreasing function.

Yulu Wu, Jiujun Cheng, Haowen Wang, Dengyang Suo

Recent advances in Vision-Language-Action (VLA) and world-model methods have improved generalization in tasks such as robotic manipulation and object interaction. However, Successful execution of such tasks depends on large, costly collections of real demonstrations, especially for fine-grained manipulation of articulated objects. To address this, we present

Noah Garrett, Michael Rose, David A. Mazziotti

Hybrid quantum-classical algorithms have begun to leverage quantum devices to efficiently represent many-electron wavefunctions, enabling early demonstrations of molecular simulations on real hardware. A key prerequisite for scalable quantum chemistry, however, is size consistency: the energy of non-interacting subsystems must scale linearly with system size

Zetao Lin, Benjamin Kadoch, Sadruddin Benkadda, Kai Schneider

We investigate the transport behavior of tungsten impurities with finite inertia in drift-wave turbulence using the Hasegawa-Wakatani model. Unlike previous tracer-based models, our simulations reveal a transition to non-diffusive dynamics for a range of charge states. This novel mechanism offers a turbulence-driven route to core impurity accumulation. This

Alessandro Sciarra

The nature of the chiral phase transition of QCD continues to represent a fundamental open problem in the study of strongly interacting matter. In recent years, significant progress has been achieved by exploiting systematic variations of theory parameters in regimes free of the sign problem. In this work, the idea of a follow-up investigation that extends a

Davide Bason, Christian Copetti, Lorenzo Di Pietro, Ziming Ji

We introduce a regularized free energy $\mathcal{F}_{\text{AdS}}$ for massive quantum field theories (QFTs) on Anti-de Sitter space (AdS). We conjecture this quantity to be monotonic under the renormalization group (RG) flow induced by boundary perturbations, generalizing the known boundary $F$-theorem to non-conformal setups. We test this conjecture in seve

Alessandro Abate

This informal contribution presents an ongoing line of research that is pursuing a new approach to the construction of sound proofs for the formal verification and control of complex stochastic models of dynamical systems, of reactive programs and, more generally, of models of Cyber-Physical Systems. Neural proofs are made up of two key components: 1) proof

Chao Wen, Tung Phung, Pronita Mehrotra, Sumit Gulwani

Generative AI has democratized content creation, but popular chatbot-based interfaces often prioritize execution, generating fully rendered artifacts right away. This issue can lead to premature convergence and design fixation, where users are being anchored to initial outputs. Recent works have proposed new interfaces to address this issue by supporting exp

Francesca Ballatore, Lorenzo Scolaris, Chiara Giverso

Glioblastoma Multiforme (GBM) is a highly aggressive brain tumour with limited therapeutic options and poor prognosis. This study presents a mathematical framework to investigate the efficacy of immunotherapy strategies based on cytotoxic T-lymphocyte (CTL) infusion. The model couples tumour and immune dynamics through a system of partial differential equati

Wenhao Hu, Haonan Zhou, Zesheng Li, Liu Liu

Recent advances in 3D scene representations have enabled high-fidelity novel view synthesis, yet adapting to discrete scene changes and constructing interactive 3D environments remain open challenges in vision and robotics. Existing approaches focus solely on updating a single scene without supporting novel-state synthesis. Others rely on diffusion-based obj

James M. Polson, Liam MacNevin, Alaaddin Elobeid, Carlos E. Padilla Robles

Single-chain elasticity is of fundamental importance in polymer physics, as it underlies many of the unique properties of polymer systems. Recently, there has been interest in characterizing the elastic properties of catenanes, molecular architectures composed of linked molecular rings. To date most studies have focused on the force-extension behavior of pol

Boris Genin, Alexander Gorbunov, Dmitry Zolkin, Igor Nekrasov

The key to success in automating prior art search in patent research using artificial intelligence (AI) lies in developing large datasets for machine learning (ML) and ensuring their availability. This work is dedicated to providing a comprehensive solution to the problem of creating infrastructure for research in this field, including datasets and tools for

Sebastian Kucharski, Iris Braun, Gregor Damnik, Matthias Wählisch

Background: Traditional Learning Management Systems (LMS) usually offer a one-size-fits-all solution that cannot be customized to meet specific learner needs. To address this issue, adaptive learning mechanisms are integrated either by LMS-specific approaches into individual LMSs or by system-independent mechanisms into various existing LMSs to increase reus

Hartmut Wittig

The anomalous magnetic moment of a lepton encodes the fraction of the lepton's interaction strength with an external magnetic field, which is generated by quantum corrections. Lepton anomalous magnetic moments are sensitive probes of fundamental interactions and play a pivotal role in the quest for "new physics" that may be able to explain the shortcomings o

George J. Bautista, Roberto de A. Capistrano-Filho, Boumediene Chentouf, Oscar Sierra Fonseca

In this work, we investigate the well-posedness, stabilization, and boundary controllability of a linear Rao-Nakra type sandwich beam. The system consists of three coupled equations that represent the longitudinal displacements of the outer layers and the transverse displacement of the composite beam, all of which are coupled with dynamical boundary conditio

Keito Takegoshi

We organize fundamental properties of quasi-Hamiltonian spaces on which a finite group acts, and we apply them to the theory of moduli spaces of flat connections on an oriented compact surface with boundary.

Yakun Xi

Let $(M,g)$ be a compact, connected Riemannian manifold of dimension $n\ge 2$, and let $\{e_j\}_{j=0}^\infty$ be an orthonormal basis of Laplace eigenfunctions $-\Delta_g e_j=\lambda_j^2 e_j$. Given a finite Borel measure $\mu$ on $M$, consider the Kuznecov sum \[ N_\mu(\lambda):=\sum_{\lambda_j\le \lambda}\Bigl|\int_M e_j\,d\mu\Bigr|^2. \] Assume that $\mu$

Julian Allagan, Shawn Langley, Weizheng Gao, Mohamed Elbakary

We study strong $r$-central $2$-trees whose non-central vertices have degrees in $\{2,3\}$, focusing on the cases $r=1,2,3$. For each $r$, we derive exact degree constraints relating the maximum degree $\Delta$ to the numbers of degree-$3$ and degree-$2$ tail vertices. In the unicentral case ($r=1$), we prove that the fan graph is the unique realization for

Saint Cyr E. R. Koyaguerebo-Imé

In this paper, the fourth-order explicit Runge-Kutta method (RK4) is used to make a Deferred Correction (DC) on the explicit midpoint rule, resulting in an explicit one-step method of order six of accuracy, denoted DC6RK2/4. Convergence and order of accuracy of DC6RK2/4 are proven through a deferred correction condition satisfied by the RK4. The region of ab

Kebin Xiao, Yunkai Guo, Daran Fu, Yuqiang Fang

In quantum materials, charge orders typically stabilize in specific crystallographic orientations, though their formation mechanisms may vary. Here, using low-temperature scanning tunneling microscopy (STM), we discover a lattice-decoupled rotatable stripe-like charge order coexisting with superconductivity in 2M-WS2. The charge order manifests five distinct

Minyi Huang, Ray-Kuang Lee

Uncertainty and entanglement are both profound and key concepts in quantum theory. For three observables, the tightest uncertainty constants for both product and summation forms are revealed. In this work, we give an alternative proof for three observables, also with a physical interpretation of the uncertainty constants. Our results show that such constants

Ansh Nagwekar

Neural network optimization remains one of the most consequential yet poorly understood challenges in modern AI research, where improvements in training algorithms can lead to enhanced feature learning in foundation models, order-of-magnitude reductions in training time, and improved interpretability into how networks learn. While stochastic gradient descent

Kun-Woo Shin, Jay H. Park, Moonwook Oh, Yohan Jo

We observe two major trends in LLM-based generative AI: (1) inference is becoming the dominant factor in terms of cost and power consumption, surpassing training, and (2) retrieval augmented generation (RAG) is becoming prevalent. When processing long inputs in RAG, the prefill phase of computing the key-value vectors of input text is energy-intensive and ti

Mrinmoy Basak

Flow equation methods, more generally known as Similarity Renormalization Group (SRG) techniques, were developed to address multiscale problems where multiple length or energy scales contribute simultaneously. In this Thesis, we formulate the flow equation method within a wavelet-based framework and apply it to study scale (resolution) separation in a two-di

Te Ma, Nanjie Li, Hao Huang, Zhijian Ou

Recently, the Large Language Model-based Phoneme-to-Grapheme (LLM-P2G) method has shown excellent performance in speech recognition tasks and has become a feasible direction to replace the traditional WFST decoding method. This framework takes into account both recognition accuracy and system scalability through two-stage modeling of phoneme prediction and t

Teodor I. Grigorescu

We classify the far-flung Gorenstein numerical semigroup rings of type 4.

Nicola Borghi, Michele Moresco, Richard I. Anderson, Carmelita Carbone

Gravitational waves (GWs) from compact binary coalescences have matured into a robust cosmological probe, providing self-calibrated luminosity distance measurements independent of any cosmic distance ladder, hence the term "standard sirens". The binary neutron star merger GW170817 delivered the first such measurement of the Hubble constant, demonstrating tha

E. Yu. Bunkova

We consider the field of hyperelliptic functions defined for a family of hyperelliptic curves as rational functions in some special functions from Kleinian functions theory. We compare our definition with the classical one. We provide details and references for the result that the field of hyperelliptic functions for a family of hyperelliptic curves of genus

Badr Moufad, Navid Bagheri Shouraki, Alain Oliviero Durmus, Thomas Hirtz

Diffusion models have emerged as powerful priors for image editing tasks such as inpainting and local modification, where the objective is to generate realistic content that remains consistent with observed regions. In particular, zero-shot approaches that leverage a pretrained diffusion model, without any retraining, have been shown to achieve highly effect

Cole Comfort, Jean-Simon Pacaud Lemay

Markov categories are the central framework for categorical probability theory. Many important concepts from probability theory can be formalized in terms of Markov categories. In particular, conditional probability distributions and Bayes' theorem are captured via the notion of conditionals in a Markov category. Gaussian probability theory gives an example

Wiktor Kamzela, Mateusz Lango, Ondrej Dusek

In this paper, we use large language models to generate personalized stories for language learners, using only the vocabulary they know. The generated texts are specifically written to teach the user new vocabulary by simply reading stories where it appears in context, while at the same time seamlessly reviewing recently learned vocabulary. The generated sto

L. Ridgway Scott

Water adopts many different crystal structures in its solid form. These provide insight into potential structures of water even in its liquid phase, and they can be used to calibrate pair potentials used for simulation of water. In crowded biological environments, water may behave more like ice than bulk water. The different ice structures have different die

Michael V. Klibanov, Jingzhi Li, Vladimir G. Romanov, Zhipeng Yang

The problem of imaging of a moving target is formulated as a Coefficient Inverse Problem for a hyperbolic equation with its coefficient depending on all three spatial variables and time. As the initial condition, the point source running along a straight line is used. Lateral Cauchy data are known for each position of the point source. A truncated Fourier se

Mateusz Lango, Ondřej Dušek

We present a novel neurosymbolic framework for RDF-to-text generation, in which the model is "trained" through collaborative interactions among multiple LLM agents rather than traditional backpropagation. The LLM agents produce rule-based Python code for a generator for the given domain, based on RDF triples only, with no in-domain human reference texts. The

Jun Qian, Ross Murch, Khaled B. Letaief

This paper investigates the downlink power allocation of the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted cell-free massive multiple-input multiple-output (MIMO) system with multi-antenna users. We introduce downlink spectral efficiency (SE) and derive novel closed-form SE expressions using linear minimum me

Razvan C. Fetecau, Hansol Park

We consider a free energy on the sphere that contains an entropy associated to nonlinear fast diffusion, and a nonlocal interaction energy. The two components of the free energy compete with each other, as one favours spreading and the other promotes concentration, respectively. The model is a generalization of the Onsager free energy with dipolar potential,

El Mokhtar Hribach, Oussama Mechhour, Mohammed Elmonstaser, Yassine El Boudouri

Acronym Disambiguation (AD) is a fundamental challenge in technical text processing, particularly in specialized sectors where high ambiguity complicates automated analysis. This paper addresses AD within the context of the TextMine'26 competition on French railway documentation. We present DACE (Dynamic Prompting, Retrieval Augmented Generation, Contextual

Ervan Kassarian, Francesco Sanfedino, Daniel Alazard, Andrea Marrazza

Conventional robust H2/H-infinity control minimizes the worst-case performance, often leading to a conservative design driven by very rare parametric configurations. To reduce this conservatism while taking advantage of the stochastic properties of Monte Carlo sampling and its compatibility with parallel computing, we introduce an alternative paradigm that o

Arnar Jonsson, Kristinn Torfason, Andrei Manolescu, Agust Valfells

Analysis of space-charge effects on electron emission typically makes some assumption of continuity and smoothness, whether this is continuity of charge as in the classical derivation of the Child-Langmuir current, or the mean-field approximation used in particle-in-cell simulations. However, when studying the physics of electron emission and propagation at

Maher Boudabra

The planar Skorokhod embedding problem was first proposed and solved by R. Gross in 2019 [#gross2019]. Gross worked with probability distributions having finite second moment. In [#boudabra2019remarks, #Boudabra2020], the solutions extended to all distributions with a finite $p^{th}$ moment for $p>1$. The case $p=1$ remained uncovered since then. In this not

Fengzhu Zeng, Qian Shao, Ling Cheng, Wei Gao

Early Rumor Detection (EARD) aims to identify the earliest point at which a claim can be accurately classified based on a sequence of social media posts. This is especially challenging in data-scarce settings. While Large Language Models (LLMs) perform well in few-shot NLP tasks, they are not well-suited for time-series data and are computationally expensive

Zheng Li

Pressure-driven flow collapses when confined ($u\propto r^{2}$). Asymmetry rectifies surface activity (exchange or slip gradients) into axial flux at $\Delta P=0$ despite zero net exchange. Lorentz reciprocity yields a projection law: throughput is the inner product of source with a geometry kernel. Signatures include inverted ``narrower-is-faster'' scaling

Souptik Chakraborty, Utsab Sarkar

We establish sharp quantitative multi-bubble stability for non-sign-changing critical points of the fractional Hardy-Sobolev inequality in the low-dimensional regime $2s<N<6s-2t$. For functions whose energy is close to that of a finite superposition of bubbles, we prove that the Euler-Lagrange deficit controls linearly the distance, in the homogeneous fracti

Kenneth Xu, Songhan Wu

The Segment Anything Model (SAM) enables promptable, high-quality segmentation but is often too computationally expensive for latency-critical settings. TinySAM is a lightweight, distilled SAM variant that preserves strong zero-shot mask quality, yet its "segment-everything" mode still requires hundreds of prompts and remains slow in practice. We first repli

Blaise Boissonneau, Lasse Vogel

We study definable ranks of ordered fields, ordered abelian groups, and linear orders. For an arbitrary linear order $\Gamma$, we construct an ordered abelian group $G$ with archimedian spine $\Gamma$ and an ordered field $K$ with natural value group $G$ such that the definable ranks of $K$, $G$ and $\Gamma$ are all isomorphic. This answers a question of Kra

Alessio Kandiah, Alexander B. Movchan, Vladimir Frid

A new modelling approach shows how the Earth's hidden vibrations may drive global weather dynamics and atmospheric pressure variations, hinting that the planet's own beat could be imprinted on our climate. The atmospheric rotational patterns of the mean sea level pressure, in connection to the development of powerful storms, are shown to be caused by Earth's

S. V. Rumyantseva, D. S. Shirokov

This paper investigates the Lorentz invariance of the multidimensional Dirac-Hestenes equation, that is, whether the equation remains form-invariant under pseudo-orthogonal transformations of the coordinates. We examine two distinct approaches: the tensor formulation and the spinor formulation. We first present a detailed examination of the four-dimensional

Vishesh Bhardwaj, Aman Yadav, Srikireddy Dhanunjay Reddy, Tharun Kumar Reddy Bollu

Sentiment analysis using Electroencephalography (EEG) sensor signals provides a deeper behavioral understanding of a person's emotional state, offering insights into real-time mood fluctuations. This approach takes advantage of brain electrical activity, making it a promising tool for various applications, including mental health monitoring, affective comput

Wonseok Choi, Hyunah Yu, Jongmin Kim, Hyesung Ji

Fully homomorphic encryption (FHE) enables secure computation on encrypted data, mitigating privacy concerns in cloud and edge environments. However, due to its high compute and memory demands, extensive acceleration research has been pursued across diverse hardware platforms, especially GPUs. In this paper, we perform a microarchitectural analysis of CKKS,

Zhiheng Zhang, Jiajun Yang, Hong Sun, Dong Wang

Vegetation index (VI) saturation during the dense canopy stage and limited ground-truth annotations of winter wheat constrain accurate estimation of LAI and SPAD. Existing VI-based and texture-driven machine learning methods exhibit limited feature expressiveness. In addition, deep learning baselines suffer from domain gaps and high data demands, which restr

John C. Forbes, Harvey Butler

The third macroscopic interstellar object detected in the solar system recently passed through perihelion, with the best-fitting models of its trajectory now featuring non-gravitational accelerations. We assess how much mass loss is required to produce plausible non-gravitational acceleration solutions and compare with estimates of the mass loss. We find tha

Yuxin Yang, Hang Zhou, Hourong Song, Branislav Hredzak

This paper presents an operator-theoretic reconstruction of an equivalent continuous-time LTI model from an exact sampled-data (Poincar\'e-map) baseline of a piecewise-linear switching system. The rebuilding is explicitly expressed via matrix logarithms. By expanding the logarithm of a product of matrix exponentials using the Baker--Campbell--Hausdorff (BCH)

Weizhe Lin, Hui-Ling Zhen, Shuai Yang, Xian Wang

The rapid development of large language model (LLM)-based agents has unlocked new possibilities for autonomous multi-turn reasoning and tool-augmented decision-making. However, their real-world deployment is hindered by severe inefficiencies that arise not from isolated model inference, but from the systemic latency accumulated across reasoning loops, contex

Ishaq Aden-Ali, Hakan Ferhatosmanoglu, Alexander Greaves-Tunnell, Nina Mishra

Large-scale vector databases for approximate nearest neighbor (ANN) search typically store a quantized dataset in main memory for fast access, and full precision data on remote disk. State-of-the-art ANN quantization methods are highly data-dependent, rendering them unable to handle point insertions and deletions. This either leads to degraded search quality

Hussein Amro, Basel Fakhri, Amer E. Mouawad, Izzat El Hajj

Algorithms for finding minimum or bounded vertex covers in graphs use a branch-and-reduce strategy, which involves exploring a highly imbalanced search tree. Prior GPU solutions assign different thread blocks to different sub-trees, while using a shared worklist to balance the load. However, these prior solutions do not scale to large and complex graphs beca