arXiv papers from February 2026

Artur Pericles L. Monteiro

This article argues that security is not enough to fully capture what is at stake in government exceptional access to encrypted data. A conception of privacy as security has little to say about ``lawful-surveillance protocols'' -- an active research agenda in cryptography that aims to enable government exceptional access without compromising systemic securit

Igor Rozhkov, Natalia Loukachevitch

Nested named entity recognition identifies entities contained within other entities, but requires expensive multi-level annotation. While flat NER corpora exist abundantly, nested resources remain scarce. We investigate whether models can learn nested structure from flat annotations alone, evaluating four approaches: string inclusions (substring matching), e

Achraf Badahmane

We develop a randomized extension of tensor Krylov subspace methods based on the Einstein product for solving large-scale multilinear systems arising in image and video restoration. The classical tensor global GMRES method relies on Frobenius inner products and full tensor orthogonalization, which become computationally expensive for high-dimensional problem

Aaron Pollack

We prove an automatic convergence theorem for holomorphic modular forms on tube domains. The argument works in some generality, and covers in particular the case of orthogonal groups, symplectic groups, unitary and quaternion unitary groups, and the exceptional group $E_7$.

Maxwell Poster, Jason Chadwick, Jonathan Mark Baker

Quantum error correction (QEC) promises to exponentially suppress qubit noise, but typically assumes spatially-uniform and temporally-constant noise rates. However, real quantum hardware exhibits variation in noise levels over time, which will be amplified by QEC if not addressed. To mitigate this drift in error rates, we leverage transient information readi

Cetin Hakimoglu-Brown

Kanade explored the construction of modular companions to $q$-series identities, using the asymptotics of Nahm sums, and Mizuno [Ramanujan J.\ {\bf 66} (2025), Paper No.\ 62, 31] recently obtained a generalization of Kanade's asymptotic formula for symmetrizable Nahm sums. A related conjecture from Kanade concerning the dilogarithm function and related to th

J. F. Parisi, K. Schiller

Deuterium-tritium fusion reactions produce high-energy neutrons that can transmute materials into valuable isotopes. Over the next ten years, the cost of fusion neutrons is projected to decrease by roughly seven orders of magnitude. Most ($\sim$5 orders of magnitude) is technological overhang driven by the low availability of current experiments; the remaini

Antonio Cicone, Stefano Serra-Capizzano, Giacomo Tento, Haomin Zhou

In the current work, we propose a generalization of angles and orthogonality from $L^2$ to generic Banach spaces, starting from a $L^p$ version of the Pythagorean theorem, $p\in [1,\infty)$. The starting point is conservation of energy measured in $L^1$ norm, as it occurs when considering the intrinsic mode functions decomposition in signal processing. This

Erik Blom, Qiyao Peng, Leah Pomfret, Richard Mort

Effective wound repair treatments rely on a clear picture of how cell proliferation and migration are coordinated during tissue restoration. Fibroblasts are key contributors to tissue restoration in the dermis, and modern imaging tools allow their cell-cycle progression to be observed directly, enabling comparison between experiments and computational models

Honghui Liu

The accretion-ejection activities of black holes play a vital role in shaping the Universe. Bright and recurrent black hole X-ray binaries are ideal objects for studying accretion physics across a wide range of accretion rates, providing insights into the understanding of their supermassive counterparts. This short review summarizes X-ray techniques capable

F. Kupka

Non-local models of stellar convection can account for mixing effects in regions adjacent to convectively unstable layers and for changes to the mean temperature structure caused by free, buoyancy driven convection. The physical completeness of such models, however, depends on how third order correlations, which characterize the non-local transport processes

Luca Nieding, A. George Morgan, Adrian Navas, DonatoPera

Wildfires represent a problem for ecosystems, human activities, and economies, driven by the climate crisis and land-use changes. Predicting wildfire propagation through mathematical modelling is essential for damage mitigation and risk assessment. This paper provides a comprehensive review of a physics-based Advection-Diffusion-Reaction (ADR) model, focusin

Annie McGrew, Yana Rodgers

With the reversal of Roe v. Wade in 2022, many U.S. employers announced they would reimburse employees for abortion-related travel expenses. This action complements increasingly common employer policies subsidizing employee access to assisted reproductive technologies such as in-vitro fertilization and egg freezing. This article reflects on why employers off

Amir Belder, Ayellet Tal

In recent years, various methods have been proposed for mesh analysis, each offering distinct advantages and often excelling on different object classes. We present a novel Mixture of Experts (MoE) framework designed to harness the complementary strengths of these diverse approaches. We propose a new gate architecture that encourages each expert to specialis

Aalliyah Celestine, Jacob van der Leeuw, Lina Liu

Parking functions are tuples that describe the parking of $M$ cars on a street with $M$ parking spots. In this paper, we define exact $k$-typed parking functions ($k$-TPFs) to be a variant of classical parking functions. We then establish that every exact $k$-TPF $\alpha$ of length $M$, corresponds to a unique parking configuration $C$. We observe that the c

Anmol Agarwal, Pranay Meshram, Sumer Singh, Saurav Suman

Recent advances in video generation have spurred the development of world models capable of simulating 3D-consistent environments and interactions with static objects. However, a significant limitation remains in their ability to model dynamic, reactive agents that can intelligently influence and interact with the world. To address this gap, we introduce COM

Hossein Javidnia

We develop a discrete gauge-theoretic framework for superposition in large language models (LLMs) that replaces the single-global-dictionary premise with a sheaf-theoretic atlas of local semantic charts. Contexts are clustered into a stratified context complex; each chart carries a local feature space and a local information-geometric metric (Fisher/Gauss-Ne

Ruihao Pan, Suhang Wang

Machine unlearning aims to remove the influence of specific training data from pre-trained models without retraining from scratch, and is increasingly important for large language models (LLMs) due to safety, privacy, and legal concerns. Although prior work primarily evaluates unlearning in static, single-turn settings, forgetting robustness under realistic

Reshabh K Sharma

As Large Language Model (LLM) agents increasingly execute complex, autonomous software engineering tasks, developers rely on natural language instruction files such as AGENTS.md to express project-specific coding conventions, tooling restrictions, and architectural boundaries. However, because these instructions remain passive text, agents frequently violate

Lakmal Deshapriya, Sherlock A. Licorish, Brendon J. Woodford

Context: Static code analysis (SCA) tools play a vital role in software development, reducing the cost and time required for code reviews. However, high false-positive and false-negative rates are reported for the best tools in the community. Accordingly, studies often aim to develop datasets for learning SCA warning patterns to reduce false results. These d

Chris L. Fryer, Eric Burns, Joseph M. Colosimo, Michela Negro

Cosmic explosions play a critical role in a broad range of astrophysical fields. Although considerable progress has been made to understand the explosive engines and their progenitors, many of the details are not well understood. One of the most powerful electromagnetic probes of the explosive mechanism and the stellar progenitor is the first burst of photon

Noam Raab, Renu Yadav, Yakov Bloch, Youngki Yeo

Ferroelectric tunnel junctions (FTJs) leverage polarization-dependent tunneling through ultrathin barriers to enable two-terminal, non-volatile memory and logic. Although conceptually appealing, the practical implementation of conventional FTJs has been hindered by high coercive voltages, low readout currents, limited cycling endurance, and significant devic

Vasiliy Kudryavtsev, Kirill Borodin, German Berezin, Kirill Bubenchikov

Automated animal identification is a practical task for reuniting lost pets with their owners, yet current systems often struggle due to limited dataset scale and reliance on unimodal visual cues. This study introduces a multimodal verification framework that enhances visual features with semantic identity priors derived from synthetic textual descriptions.

Salah BenMahmoud

In this paper, we introduce a new class of convolution-type inequalities in variable exponent Lebesgue spaces and derive several related estimates, including the \(L^{r(\cdot)}\)--\(L^{p(\cdot)}\) smoothing estimate for the fractional heat kernel. We demonstrate the usefulness of these inequalities by establishing the local well-posedness results for mild so

N. D. Shyamalkumar, Tianrun Wang

De Finetti's optimal reinsurance is a set of contracts, one for each risk in a portfolio, that caps the retained aggregate variance to a pre-specified level while minimizing total expected loss. The premiums are determined using the expected value principle, and the safety loading is allowed to vary with the risks. The original formulation assumed that the r

Igor Berezkin

Work introduces a hierarchical binary tree-based reduction that replaces standard self-attention. The core idea is to use a recursive Gated Linear Unit merge operation, achieving O(n) total merge operations O(log n) parallel depth O(n d^2) total work and O(n) space complexity. In these experiments, the model significantly outperforms standard Transformers in

Dariush Wahdany, Matthew Jagielski, Adam Dziedzic, Franziska Boenisch

In machine learning, curation is used to select the most valuable data for improving both model accuracy and computational efficiency. Recently, curation has also been explored as a solution for private machine learning: rather than training directly on sensitive data, which is known to leak information through model predictions, the private data is used onl

Gregorio García-Valladares

The objective of this thesis is to advance the understanding of complex physical phenomena through the lens of statistical physics. Specifically, it addresses two fundamental questions: What types of interactions can induce buckling of slender structures when their temperature is increased? And, how can we devise an optimal strategy for locating a hidden tar

Lingyi Wang, Rashed Shelim, Walid Saad, Naren Ramakrishna

A major challenge for world models in multi-agent systems is to understand interdependent agent dynamics, predict interactive multi-agent trajectories, and plan over long horizons with collective awareness, without centralized supervision or explicit communication. In this paper, MetaMind, a general and cognitive world model for multi-agent systems that leve

Ian Van Buskirk, Marilena Hohmann, Ekaterina Landgren, Johan Ugander

Academic publishing requires solving a collective coordination problem: among thousands of possible publication venues, which deserve a community's attention? A clear consensus helps scholars allocate attention, match submissions to appropriate outlets, and evaluate scholars for hiring and promotion. Yet preferences are not centrally coordinated--they emerge

Joshua Blank, Paul Chleboun, Stefan Grosskinsky, Watthanan Jatuviriyapornchai

We consider stochastic lattice gases with stationary product weights and a polynomial perturbation vanishing with the system size that leads to condensation. If the density of particles exceeds a critical value the system phase separates into a bulk with homogeneous distribution of particles and a condensed phase. Depending on parameter values, the latter co

Seemandhar Jain, Keshav Gupta, Kunal Gupta, Manmohan Chandraker

The proliferation of neural radiance field (NeRF) research requires significant efforts to reimplement papers before building upon them. We introduce NERFIFY, a multi-agent framework that reliably converts NeRF research papers into trainable Nerfstudio plugins, in contrast to generic paper-to-code methods and frontier models like GPT-5 that usually fail to p

Gernot Eichmann

The central objects in a quantum field theory are its n-point correlation functions and matrix elements. Their structure is determined by Lorentz invariance and leads to tensor decompositions whose Lorentz-invariant coefficient functions encode the physics of the process. For growing n, the complexity of these objects may increase considerably and make it ch

Nataly Brukhim, Nicolò Cesa-Bianchi, Carlo Ciliberto

We study an identification problem in multi-armed bandits. In each round a learner selects one of $K$ arms and observes its reward, with the goal of eventually identifying an arm that will perform best at a {\it future} time. In adversarial environments, however, past performance may offer little information about the future, raising the question of whether

Danielle F. Morey, Giulia Pedrielli, Cherry Y. Wakayama, Zelda B. Zabinsky

In complex real-world settings, optimization is challenged by the presence of diverse models of differing fidelity. In many optimization problems, a single model is treated as the most accurate representation of the underlying system, while other models are evaluated primarily by their agreement with this presumed most accurate model. Yet in real-world appli

Octavio D. R. Salmon, Minos A. Neto, J. Roberto Viana, Griffith Mendonça

We revisit the machine-learning (ML) approach to the universal density functional $F[\mathbf{n}]$ of the one-dimensional Hubbard model with a site-dependent random potential $\mathbf{v}=\{v_{i}\}$. We generate exact ground-state data via exact diagonalization for a periodic chain with $L=8$ in the paramagnetic sector $(N_\uparrow,N_\downarrow)=(2,2)$, with s

Shrey Shah, Levent Ozgur

Language agents increasingly act as web-enabled systems that search, browse, and synthesize information from diverse sources. However, these sources can include unreliable or adversarial content, and the robustness of agents to adversarial ranking - where misleading information appears prominently in search results - remains poorly understood. Existing bench

Willian Y. Nacafucasaco, Tatiana A. Michtchenko, Douglas Barros, Jacques Lépine

The dynamical effects on the stellar motion produced by the Galactic central bar and the spiral arms perturbations are investigated separately and compared. The stars from the Gaia DR3 catalog are selected in the region of observable completeness, which we estimate as $\sim$1 kpc from the Sun. We apply the 2D model of the Galactic potential consisting of thr

Sari Ghanem

We prove energy estimates for solutions to a tensorial system of coupled non-linear wave equations, in a way that is suitable to deal with the structure of the non-linearity that arises from the Einstein-Yang-Mills system in the Lorenz gauge as well as with other new different non-linearities. We establish suitable bounds on the $L^2$-norm of each component

Matt Y. Cheung, Ashok Veeraraghavan, Guha Balakrishnan

Template-based segmentation, a widely used paradigm in medical imaging, propagates anatomical labels via deformable registration from a labeled atlas to a target image, and is often used to compute volumetric biomarkers for downstream decision-making. While conformal prediction (CP) provides finite-sample valid intervals for scalar metrics, existing segmenta

Yuqing Hu, Wendao Xue, Yifan Yu, Yong Tan

Advances in artificial intelligence (AI), together with persistent gaps in access to reliable emotional support, have positioned AI as an increasingly prominent source of emotional assistance. However, most AI-based emotional support applications and prior research focus on one-on-one interactions between users and a single AI agent, leaving the potential ad

Emin Abdullaev

This paper studies $l^p$-products of metric spaces and provides estimates for the Gromov-Hausdorff distances between them. The case of linear products is considered separately, and sufficient conditions for attainability of the estimates are given for it. Examples of calculating the Gromov-Hausdorff distance between flat tori are given. It is proved that for

Talip Tolga Sarı, Rameez Ahmed, Abdullah Al Noman, Gökhan Seçinti

Low-Altitude Wireless Networks (LAWN) are transforming the low-altitude airspace into a mission-driven, dynamically reconfigurable 3D network fabric for safety-critical and public-safety operations. In parallel, Direct-to-Cell (D2C) satellite access can rapidly restore connectivity after disasters, yet dense urban blockages make the satellite-to-ground link

Jeet Bandhu Lahiri, Parshva Runwal, Arvasu Kulkarni, Mahir Jain

EEG foundation models are typically pretrained on narrow-source clinical archives and evaluated on benchmarks from the same ecosystem, leaving unclear whether representations encode neural physiology or recording-distribution artifacts. We introduce PRISM (Population Representative Invariant Signal Model), a masked autoencoder ablated along two axes -- pretr

Olaf Gefeller, Nils Lid Hjort

Nonparametric curve estimation by kernel methods has attracted widespread interest in theoretical and applied statistics. One area of conflict between theory and application relates to the evaluation of the performance of the estimators. Recently, Marron and Tsybakov (1995) proposed {\it visual error criteria} for addressing this issue of controversy in dens

Ruiyu Yan, Hanqi Jiang, Yi Pan, Xiaobo Li

We propose the Neural Functional Alignment Space (NFAS), a brain-referenced representational framework for characterizing artificial neural networks on equal functional grounds. NFAS departs from conventional alignment approaches that rely on layer-wise features or task-specific activations by modeling the intrinsic dynamical evolution of stimulus representa

Shilong Tao, Zhe Feng, Shaohan Chen, Weichen Zhang

Fluid-solid interaction (FSI) problems are fundamental in many scientific and engineering applications, yet effectively capturing the highly nonlinear two-way interactions remains a significant challenge. Most existing deep learning methods are limited to simplified one-way FSI scenarios, often assuming rigid and static solid to reduce complexity. Even in tw

Maximiliano Sanchez Garza

We study a variant of the equidistribution of mass conjecture on the sphere posed by B\"ocherer, Sarnak, and Schulze-Pillot: quantum unique ergodicity in shrinking sets. Conditionally on the generalized Lindel\"of hypothesis, we show that quantum unique ergodicity holds on every shrinking spherical cap whose radius is considerably larger than the Planck scal

Haolin Liu, S. Kiana Naghibzadeh, Zhongshu Ren, Yanming Zhang

Aeroacoustic emissions from intense evaporation are widely measured yet often treated as noisy byproducts and used mainly in empirical monitoring. Here, we show that airborne sound encodes physics-governed sub-millisecond fingerprints of vapor-jet dynamics in excessive vaporization, exemplified by vapor keyholes in laser metal processing. From first principl

Errico J. Russo, James Schneeloch, Edwin E. Hach, Richard J. Birrittella

We construct stationary coherent states concentrated on Lissajous figures of the isotropic and anisotropic harmonic oscillators, the latter having coprime frequencies, by projecting products of ordinary coherent states (one coherent state for each degree of freedom) onto sets of degenerate states. By performing these projections, we are deriving our states f

Gangani Ariyarathne, Isuru Ariyarathne, Greatness Emmanuel-King, Kate Lawal

Local journalism is vital in democratic societies where it informs people about local issues like, school board elections, small businesses, local health services, etc. But mounting economic pressures have made it increasingly difficult for local news stations to report these issues, underscoring the need to identify the salient geographical locations covere

Karanpartap Singh, Adam Turnbull, Mohammad Abbasi, Kilian Pohl

Understanding how large-scale functional brain networks reorganize during cognitive decline remains a central challenge in neuroimaging. While recent self-supervised models have shown promise for learning representations from resting-state fMRI, their internal mechanisms are difficult to interpret, limiting mechanistic insight. We propose BrainInterNet, a ne

Bayram Yüksel Eker, Suayb S. Arslan, Özgür Nazlı, Mustafa Serhat Demirgil

Autonomous navigation under uncertainty requires solving partially observable Markov decision processes (POMDPs) for planning and assigning sensor measurements to tracked targets--a task known as multi-target data association (MTDA). Both problems become computationally demanding at scale: belief conditioning costs $\mathcal{O}(P(e)^{-1})$ per node under rar

Nils Lid Hjort, Rafail Zalmonovich Khasminskii

For an arbitrary diffusion process $X$ with time-homogeneous drift and variance parameters $\mu(x)$ and $\sigma^2(x)$, let $V_\varepsilon$ be $1/\varepsilon$ times the total time $X(t)$ spends in the strip $[a+bt-(1/2)\varepsilon,a+bt+(1/2)\varepsilon]$.The limit $V$ as $\varepsilon\rightarrow0$ is the full halfline version of the local time of $X(t)-a-bt$ a

Elena Farahbakhsh Touli, Ingrid Hotz, Talha Bin Masood

The interleaving distance is a key tool for comparing merge trees, which provide topological summaries of scalar functions. In this work, we define an average merge tree for a pair of merge trees using the interleaving distance. Since such an average is not unique, we propose a method to construct a representative average merge tree. We further prove that th

F. Spada, M. Królikowska, L. Dones

We present a detailed analysis of the trajectory of the interstellar comet 3I/ATLAS, focusing on its non-gravitational acceleration (NGA) parameters and their uncertainties. Orbital solutions are computed with models that implement symmetric, time-offset, and asymmetric radial dependence of the outgassing law relative to perihelion. We assess solution robust

Bogna Jaszczak-Dyka, Łukasz Płociniczak

We present a unified mathematical framework for modeling blood and lymph flow in biological vessels, with a particular focus on lymph transport through lymphangions. Starting from first principles, we rigorously derive a system of partial differential equations (PDEs) that govern the fluid dynamics using perturbative methods. To capture the active regulation

Julian Lyne, Kai Phillip Schmidt, Claudiu Genes, Nico S. Bassler

Collective radiance effects in quantum degenerate systems, such as superradiance and subradiance of a partially inverted ensemble, are shaped by the interplay of spatial confinement and exchange statistics. We investigate this interplay using a purely dissipative field theoretic quartic Lindblad master equation, which captures the nonlinear dynamics of the c

Pernilla Ekborg-Tanner, Paul Erhart

Multi-component alloys offer broad tunability for addressing challenges in materials science, but their vast configurational space makes their surface chemistry highly sensitive to operating conditions, for example through adsorption and segregation. Here, we study Pd-Au-Cu alloy surfaces in H$_2$ and CO environments motivated by their use in H technologies,

Averil Aussedat

We study the perturbation of a measure $\mu \in \mathscr{P}(\mathbb{R})$ consisting in superposing two copies of $\mu$, each slightly shifted by a small distance $\pm h$. The difference between $\mu$ and its perturbation is measured with a Wasserstein distance. For any $\mu$, this distance is bounded from above by $h$. We show that measures for which this cr

Sina Elahimanesh, Mohammadali Mohammadkhani, Sara Zahedi Movahed, Mohammad Mahdi Abootorabi

While large language models (LLMs) excel at open-ended dialogue, effective psychotherapy requires structured progression and adherence to clinical protocols, making the design of psychotherapist chatbots challenging. We investigate how different LLM-based designs shape perceived therapeutic dialogue in a chatbot grounded in the Self-Attachment Technique (SAT

Pierre Monmarché

The fact that a Markov diffusion semi-group on $\mathbb R^d$ contracts the $L^p$ Wasserstein distance, which has been extensively used to establish uniform-in-time stability estimates (e.g. with respect to numerical discretization errors), is a well-studied question in the case where the distances are in fact deterministically contracted by the drift (global

Colin Adams, Francisco Gomez-Paz, Jiachen Kang, Lukas Krause

We define a class of links in handlebodies called ``charm bracelets," which are a subset of staked links. We provide tools to construct infinitely many such hyperbolic links and bound the corresponding volumes from below in terms of volumes corresponding to the individual charms.

Sumegha Garg, Jabari Hastings, Chirag Pabbaraju, Vatsal Sharan

We present a unified framework for proving memory lower bounds for multi-pass streaming algorithms that detect planted structures. Planted structures -- such as cliques or bicliques in graphs, and sparse signals in high-dimensional data -- arise in numerous applications, and our framework yields multi-pass memory lower bounds for many such fundamental settin

Haiming Song, Jinda Yang, Yuran Yang, Jianhua Yuan

Solving parabolic optimal control problems can be inherently challenging in the field of science and engineering, especially with constraints on the nonsmooth distributed control. Motivated by the extensive applicability of the alternating direction method of multipliers, in this paper we develop a novel inexact algorithmic framework for parabolic optimal di

Stephan Baier

In this article, we continue our recent investigations on bilinear sums and additive energies with modular square roots. Here we improve our recent results for the case when the ranges of variables are large. We use these results to make further partial progress on the large sieve for square moduli.

Ziwen Huang, L. P. Chitta, L. Teriaca, R. Aznar Cuadrado

Plumes have been proposed to channel MHD waves and the solar wind into the heliosphere. High-speed propagating disturbances (PDs), though well detected in plumes, cannot yet be clearly assigned to MHD waves or to mass flows. Additionally, plume bases as observed in the extreme ultraviolet are riddled with small-scale transients that could be related to the P

Tanvir Kaur, Ashish Saxena, Partha Sarathi Mandal, Kaushik Mondal

A black hole is a malicious node in a graph that destroys resources entering into it without leaving any trace. The problem of Black Hole Search (BHS) using mobile agents requires that at least one agent survives and terminates after locating the black hole. Recently, this problem has been studied on 1-bounded 1-interval connected dynamic graphs \cite{BHS_ge

Thialita M. Nascimento, Lei Zhang

In this work, we study local minimizers of elliptic functionals with strong absorption terms and unbounded, sign-changing sources. These problems naturally interpolate between two classical free boundary problems: Bernoulli-type (cavity) and obstacle-type. While previous studies have focused on bounded and strictly positive sources, we extend sharp regularit

Marco Aliberti, Francesco Di Renzo, Petros Dimopoulos, Demetrianos Gavriel

In this work, we explore a numerical approach for performing the inverse Laplace transformation, with an emphasis on achieving stability and robustness under noisy conditions. Our quadrature-based method integrates reparameterization, data smoothing, and optimization techniques to regularizing ill-conditioned systems. Together, these elements enable consiste

Zhenyu Zhou, Defang Chen, Siwei Lyu, Chun Chen

Text-to-image diffusion models have achieved unprecedented success but still struggle to produce high-quality results under limited sampling budgets. Existing training-free sampling acceleration methods are typically developed independently, leaving the overall performance and compatibility among these methods unexplored. In this paper, we bridge this gap by

Artem O. Denisov, Christoph Adam, Hadrien Duprez, Jessica Richter

We demonstrate a method to determine energy level degeneracies using non-equilibrium electronic transport through voltage-biased quantum dots. We establish the general validity of this approach using single and double quantum dots in bilayer graphene and GaAs. Unlike established methods based on entropy measurements or time-resolved tunneling statistics, our

Bo Peng, Yuan Liu, Karol Kowalski

We present COMPOSER, a compile-once modular parametric oracle for similarity-encoded effective reduction of electronic-structure operators (e.g., Schrieffer-Wolff-type constructions). Low-rank factorizations compress Hamiltonians and anti-Hermitian generators into rank-one bilinear and projected-quadratic ladders with near-linear scaling at fixed thresholds;

Hannes Brandt, Carsten Burstedde

We introduce an algorithm that performs a one-directional mesh overset of a parallel forest of octrees with another distributed mesh of unrelated partition. The forest mesh consists of several adaptively refined octrees. Individual smooth mappings for every tree allow to represent a broad range of geometric domains. The other mesh is generic and defines a di

Marie-Claude Arnaud

Dynamists have been studying Hamiltonian systems for a long time. However, many physical systems are dissipative and do not preserve a symplectic form. This is the case, for example, with systems involving friction, which multiply the symplectic form by a constant smaller than 1. We will prove that almost every point is in the unstable set of infinity for th

Adam Marcus, Paul Agapow

Precision medicine promises to transform health care by offering individualised treatments that dramatically improve clinical outcomes. A necessary prerequisite is to identify subgroups of patients who respond differently to different therapies. Current approaches are limited to static measures of treatment success, neglecting the repeated measures found in

Adam Marcus, Paul Bentley, Daniel Rueckert

Stroke is a major cause of death and disability worldwide. Accurate outcome and evolution prediction has the potential to revolutionize stroke care by individualizing clinical decision-making leading to better outcomes. However, despite a plethora of attempts and the rich data provided by neuroimaging, modelling the ultimate fate of brain tissue remains a ch

Rafi Hassan Chowdhury, Naimul Haque, Kaniz Fatiha

Handwritten character classification in the Bengali script is a significant challenge due to the complexity and variability of the characters. The models commonly used for classification are often computationally expensive and data-hungry, making them unsuitable for resource-limited languages such as Bengali. In this experiment, we propose a novel, efficient

Yiyang Mei

This Article examines the constitutional status of AI-mediated communication under the First Amendment. Social media platforms, increasingly integrated with generative AI systems, now function as core public communication infrastructures. Within this environment, AI-generated pornography and large-scale political misinformation have produced significant dign

Marios Costa, Demetrianos Gavriel, Haralambos Panagopoulos, Gregoris Spanoudes

Discretization artifacts proportional to the quark mass can limit the precision of strong-coupling determinations in lattice QCD, especially in the presence of heavy quarks. In this work, we perform a lattice perturbative analysis of such $\mathcal{O}(a m)$ effects in the running coupling by computing its two-loop renormalization factor $Z_g$. Using the back

Alexander Strunk, Roland Assam

This paper introduces General Proximal Flow Networks (GPFNs), a generalization of Bayesian Flow Networks that broadens the class of admissible belief-update operators. In Bayesian Flow Networks, each update step is a Bayesian posterior update, which is equivalent to a proximal step with respect to the Kullback-Leibler divergence. GPFNs replace this fixed cho

Alexander R. Pruss

A simple integral representation involving no derivatives or continuity assumptions is given for proper single-event scoring rules.

Harlan Campbell, Jeroen P. Jansen

Network meta-analysis (NMA) is widely used to compare multiple interventions simultaneously by synthesizing direct and indirect evidence. The general fixed or random effects contrast-based NMA model can be applied to different outcomes and data structures by opting for either an arm-based or contrast-based likelihood depending on the data available. Dependin

Daniel Restrepo

We analyze the long-time behavior of solutions to semilinear parabolic equations in Euclidean space that arise as gradient flows of an energy functional. We prove that, for general initial data (including data without compact support) the flow converges to a unique ground state. The argument relies on a sharp stability estimate for almost critical points of

Anna Kazakova

We study problems on uniqueness sets ($U$-sets) for multiple Walsh series converging over cubes and the properties of the coefficients of such series. New broad classes of $U$-sets are constructed. In particular, it is proved that hyperplanes parallel to the coordinate ones are $U$-sets. For the coefficients of multiple Walsh series converging over cubes, bo

Faria Ahmed, Rafi Hassan Chowdhury, Fatema Tuz Zohora Moon, Sabbir Ahmed

Speech is a natural means of conveying emotions, making it an effective method for understanding and representing human feelings. Reliable speech emotion recognition (SER) is central to applications in human-computer interaction, healthcare, education, and customer service. However, most SER methods depend on heavy backbone models or hand-crafted features th

Rafi Hassan Chowdhury, Nabil Daiyan, Faria Ahmed, Md Redwan Iqbal

Accurate Remaining Useful Life (RUL) prediction is a key requirement for effective Prognostics and Health Management (PHM) in safety-critical systems such as aero-engines. Existing deep learning approaches, particularly LSTM-based models, often struggle to generalize across varying operating conditions and are sensitive to noise in multivariate sensor data.

Kuldeep Pathak, Kapil Ahuja, Eric de Sturler

In this work, we propose a new deep learning model for Genomic Prediction (GP), which involves correlating genotypic data with phenotypic. The genotypes are typically fed as a sequence of characters to the 1D-Convolution Neural Network layer of the underlying deep learning model. Inspired by earlier work that represented genotype as a 2D-image for genotype-p

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We present the first amplitude analysis of the semileptonic decay $D^0\to K^-\pi^0 e^{+}\nu_{e}$ by analyzing $e^+e^-$ annihilation data corresponding to an integrated luminosity of 20.3 fb$^{-1}$ collected at the center-of-mass energy of 3.773 GeV with the BESIII detector. A tiny $\mathcal{D}$-wave component of the $K^*_2(1430)^-$ accounting for $(0.16 \pm

Artemis Kontou, Natalia Miroshnikova, Costakis Matheou, Sophocles Sophocleous

This study presents AI-HEART, a cloud-based information system for managing and analysing long-duration ambulatory electrocardiogram (ECG) recordings and supporting clinician decision-making. The platform operationalises an end-to-end pipeline that ingests multi-day three-lead ECGs, normalises inputs, performs signal preprocessing, and applies dedicated deep

Yeongkwon Choe, Chan Gook Park, Jindřich Duník, Jan Krejčí

We propose a simple quantum algorithm for implementing the diffusion step of grid-based Bayesian filters. The method encodes the advected state density and the process noise density into quantum registers and realizes diffusion using a quantum Fourier transform--based adder. This avoids the explicit convolution required in classical implementations and the r

Jinhui Fan, Chonghe Wang, Xiaoyan Lu, Yunpeng Ma

Active symmetry control - a central challenge in materials science, particularly in ferroelectrics - is achieved via mechanically assisted poling (MAP) guided by thermodynamics and phase - field modeling. This approach yields extraordinary piezoelectric coefficients (about 5,000 pC/N at 24 degC; 11,700 pC/N at 58 degC) together with about 65% optical transmi

Reva Schwartz, Gabriella Waters

Organizational leaders are being asked to make high-stakes decisions about AI deployment without dependable evidence of what these systems actually do in the environments they oversee. The predominant AI evaluation ecosystem yields scalable but abstract metrics that reflect the priorities of model development. By smoothing over the heterogeneity of real-worl

Shuo Zhang, Zijie Fu, Lixiu Guan, Yirui Du

Integrating real-space topological spin textures with momentum-space topological electronic states within a single altermagnetic system has remained a persistent challenge. Here, we introduce a symmetry-locked bilayer altermagnet that concurrently hosts d-wave altermagnetism, momentum-space topology, and stable antiskyrmions. In momentum-space, it enables st

Sebastien Lleo, Wolfgang Runggaldier

This paper bridges reinforcement learning (RL) and risk-sensitive stochastic control by introducing a tractable exploration mechanism for policy search in risk-sensitive portfolio management, with known and unknown model parameters, that yields an endogenous relative-entropy regularization. We construct a discrete-time risk-sensitive benchmarked investment m

Aditi Kabra, Jonathan Laurent, Ruben Martins, Stefan Mitsch

Hybrid games model cyber-physical systems (CPS), like cars, trains, and airplanes, where discrete control decisions interact with continuous physical dynamics. We use Large Language Models (LLMs) to scale formal verification and synthesis for hybrid systems and games for a high-level hybrid games symbolic logic, differential game logic (dGL). This combinatio

Dennis M. J. van de Sande, Julian P. Merkofer, Sina Amirrajab, Mitko Veta

The development of deep learning methods for magnetic resonance spectroscopy (MRS) is often hindered by limited availability of large, high-quality training datasets. While physics-based simulations are commonly used to mitigate this limitation, accurately modeling all in-vivo signal components remains challenging. In this work, we propose a data-driven fram

Zhenwei Jiang, Ziyuan Zheng, Qingqing Wu, Jing Xu

Low-altitude network is a key enabler for extending coverage and recovering connectivity in 6G systems, especially when terrestrial infrastructure is unavailable. This paper studies a uncrewed aerial vehicle (UAV)-mounted rotatable intelligent reflecting surface (IRS) as a low-altitude reflector between a blocked base station (BS) and a ground terminal (GT).

Shijie Yuan, Amy Cochran, Paul Rathouz

Accurate power and sample size (PSS) calculations are essential for designing studies that use quasi-likelihood (QL) models, which extend generalized linear models (GLMs) to settings where the full distribution of the outcome is not specified. Traditional PSS approaches often rely on restrictive distributional assumptions, limiting their applicability when r

Amos Kaminski

We prove \cite[Conjecture~5.17]{Clausen} on the local light--profinite structure of smooth $p$-adic analytic Artin stacks. The argument proceeds in several reductions. First, by proving a generalization of van~Dantzig theorem for groupoids, we reduce the conjecture to the compact Hausdorff case. This reduces the conjecture to the statement that the geometric