November 2025 arXiv papers — page 11

Vikhyat Agarwal, Jiayi Cora Guo, Declan Hoban, Sissi Zhang

Deep learning approaches to object detection have achieved reliable detection of specific object classes in images. However, extending a model's detection capability to new object classes requires large amounts of annotated training data, which is costly and time-consuming to acquire, especially for long-tailed classes with insufficient representation in exi

Ali Waseem, Malcolm Mielle

Inverse heat problems refer to the estimation of material thermophysical properties given observed or known heat diffusion behaviour. Inverse heat problems have wide-ranging uses, but a critical application lies in quantifying how building facade renovation reduces thermal transmittance, a key determinant of building energy efficiency. However, solving inver

Laura A. Mansfield, Hannah M. Christensen

Representing and quantifying uncertainty in physical parameterisations is a central challenge in weather and climate modelling, and approaches are often developed separately for different timescales. Here, we introduce a unified framework for analysing uncertainty in parameterisations across weather and climate regimes. Using the Lorenz 1996 system as a test

Olha Shevchenko

We introduce the totally nonnegative Lagrangian Grassmannian $\rm{LG}_{\geq 0}^R (n,2n)$, a new subset of the totally nonnegative Grassmannian consisting of subspaces isotropic with respect to a certain bilinear form $R$. We describe its cell structure and show that each cell admits a representation by a rotationally symmetric (not necessarily reduced) plabi

Lorenzo Ciardo, Gideo Joubert, Antoine Mottet

We introduce the concept of quantum polymorphisms to the complexity theory of quantum constraint satisfaction. Via this notion, we build an algebraic framework of reductions between quantum CSPs, and we establish a Galois connection between quantum polymorphism minions and quantum relational constructions. By leveraging a contextuality property of quantum po

Emmanuel Gnandi

We investigate the construction of exponential families from statistical manifolds, a central problem in information geometry. We prove that every compact statistical manifold admits a singular foliation whose leaves are Hessian manifolds. In particular, any non-flat, compact, orientable 3-dimensional leaf arises as a quotient of an exponential family and ha

Tianlong Huang, Zhiyuan Li

This paper studies the role of activation functions in learning modular addition with two-layer neural networks. We first establish a sharp expressivity gap: sine MLPs admit width-$2$ exact realizations for any fixed length $m$ and, with bias, width-$2$ exact realizations uniformly over all lengths. In contrast, the width of ReLU networks must scale linearly

Hang Yu, Di Zhang, Qiwei Du, Yanping Zhao

Offline reinforcement learning (RL) enables agents to learn optimal policies from pre-collected datasets. However, datasets containing suboptimal and fragmented trajectories present challenges for reward propagation, resulting in inaccurate value estimation and degraded policy performance. While trajectory stitching via generative models offers a promising s

Rostislav Akhmechet, Teena Gerhardt, Michael Willis

Topological Hochschild homology is a topological analogue of classical Hochschild homology of algebras and bimodules. Beliakova, Putyra, and Wehrli introduced quantum Hochschild homology (qHH) and used it to define a quantization of annular Khovanov homology as qHH of the tangle bimodules of Chen-Khovanov and Stroppel. After introducing quantum topological H

Bernhard Klein, Falk Selker, Hendrik Borras, Sophie Steger

Machine learning models perform well across domains such as diagnostics, weather forecasting, NLP, and autonomous driving, but their limited uncertainty handling restricts use in safety-critical settings. Traditional neural networks often fail to detect out-of-domain (OOD) data and may output confident yet incorrect predictions. Bayesian neural networks (BNN

Vishisht Rao, Justin Payan, Andrew McCallum, Nihar B. Shah

Peer-review venues have increasingly adopted open reviewing policies that publicly release anonymized reviews and permit public commenting. Venues have adopted a variety of policies, and there is still ongoing debate about the benefits and drawbacks of decisions. To inform this debate, we surveyed 2,385 reviewers, authors, and other peer-review participants

Salvatore Mandrà, Brayden Ware, Nikita Astrakhantsev, Sergei Isakov

Out-of-equilibrium dynamics of non-integrable Hamiltonian many-body quantum systems are characterized by highly entangled wave functions. Near-maximal entanglement arises in systems exhibiting thermalization or pre-thermalization, where the system converges to a steady state with a fixed energy density. Classical simulation of the time dependence of such wav

Qidong He

We revisit a two-dimensional model of liquid crystals introduced by Heilmann and Lieb (1979), which consists of a system of dimers on the square lattice at chemical potential $\lambda$, interacting via a hard-core repulsion and an attractive interaction of strength $-a<0$ between adjacent, colinear dimers. The model is conjectured to exhibit nematic order at

Jianzhe Lin, Zeyu Pan, Yun Zhu, Ruiqi Song

We introduce SuperIntelliAgent, an agentic learning framework that couples a trainable small diffusion model (the learner) with a frozen large language model (the verifier) to enable continual intelligence growth through self-supervised interaction. Unlike conventional supervised fine-tuning, SuperIntelliAgent learns autonomously without annotation: the lear

Jakub Czuchnowski, Robert Prevedel

Fabry-P\'erot based photoacoustic tomography (FP-PAT) is a promising all-optical imaging modality for a wide range of preclinical and clinical applications. However, there exist several challenges in routinely applying FP-PAT in time-critical experiments. Among those, the need for spectral tuning of the laser between each scan position can severely limit the

Brayden Goldstein-Gelb, Kun Liu, John M. Martyn, Hengyun

The limited number of qubits per chip remains a critical bottleneck in quantum computing, motivating the use of distributed architectures that interconnect multiple quantum processing units (QPUs). However, executing quantum algorithms across distributed systems requires careful co-design of algorithmic primitives and hardware architectures to manage circuit

Maria Alejandra Valdez Cabrera, Amy D Willis, Armeen Taeb

Trees are data objects that hierarchically organize categories. Collections of trees arise in a diverse variety of fields, including evolutionary biology, machine learning, social sciences and anatomy. Summarizing a collection of trees by a single representative is challenging, in part due to the dimensions of both the sample space and the parameter space. W

Alex Krolewski, Andrea Crespi, Will J. Percival, Marco Bonici

We present a new measurement of the Hubble constant, independent of standard rulers and robust to pre-recombination modifications such as Early Dark Energy (EDE), obtained by calibrating the total energy density of the Universe. We start using the present-day photon density as an anchor, and use the baryon-to-photon ratio from Big Bang Nucleosynthesis based

Swati Sachan, Dale S. Fickett

This research introduces the Decentralized Finance (DeFi) TrustBoost Framework, which combines blockchain technology and Explainable AI to address challenges faced by lenders underwriting small business loan applications from low-wealth households. The framework is designed with a strong emphasis on fulfilling four crucial requirements of blockchain and AI s

Daniil K. Karuzin, Mikhail A. Skvortsov

We develop a theory for the plasmon spectrum in dirty superconductors across the entire temperature range. Starting with the microscopic Keldysh sigma model description, we link the plasmon dispersion $ω(q)$ to the optical conductivity $σ(ω,T)$ of a superconductor, which requires analytical continuation to the lower half-plane of complex frequency. This appr

Junshu Tang, Jiacheng Liu, Jiaqi Li, Longhuang Wu

Recent advances in generative world models have enabled remarkable progress in creating open-ended game environments, evolving from static scene synthesis toward dynamic, interactive simulation. However, current approaches remain limited by rigid action schemas and high annotation costs, restricting their ability to model diverse in-game interactions and pla

Thomas Ressler-Antal, Frank Fundel, Malek Ben Alaya, Stefan Andreas Baumann

Recent advances in text-to-video (T2V) and image-to-video (I2V) models, have enabled the creation of visually compelling and dynamic videos from simple textual descriptions or initial frames. However, these models often fail to provide an explicit representation of motion separate from content, limiting their applicability for content creators. To address th

Iván López-Espejo

This article examines the law of the tendency of the rate of profit to fall in the Spanish economy between 1960 and 2024, considering the organic composition of capital and the rate of surplus value as central variables. Its aim is to determine whether this law, formulated by Marx in Capital (Vol. III), continues to operate in the contemporary context. The m

Nissim Fraija, Boris Betancourt-Kamenetskaia, Antonio Galván, Maria Dainotti

Gamma-ray bursts (GRBs), among the most compelling astrophysical phenomena, are potential candidates for exploring the evolution of energy distribution among magnetic fields and particles through multiwavelength observations. The fraction of energy transferred between particles and the magnetic field is governed by microphysical parameters, typically assumed

Guillermo Alonso Alvarez, Ibrahim Ekren, Liwei Huang

We study a continuous time contracting model in which a principal hires a risk averse agent to manage a project over a finite horizon and provides sequential payments whose timing is endogenously determined. The resulting nonzero-sum interaction between the principal and the agent is reformulated as a mixed control and stopping problem. Using numerical simul

Hemanshi Bundeliya, Gaurav Bhandari, S. D. Pathak, V. K. Sharma

We propose a cosmological framework in which neutrino masses evolve dynamically through coupling with a scalar field that simultaneously drives inflation. The neutrino mass is modeled as a power-law, exponential, or hybrid function of the scalar field, yielding an effective potential that includes neutrino backreaction. Starting from the Einstein-Hilbert act

Raluca M. Balan, Juan J. Jiménez, Lluís Quer-Sardanyons

In this article, we study the global well-posedness of hyperbolic SPDEs on a bounded domain in $\mathbb{R}^d$, driven by a space-time L\'evy white noise, when the drift and diffusion coefficients are locally Lipschitz and have linear growth. The equations are driven by two types of space-time L\'evy noise: (i) a finite-variance L\'evy white noise; or (ii) a

Shifeng Sun, Xueqi Wang, Zhuoran Hou, Elizabeth L. Turner

Most cluster randomized trials (CRTs) randomize fewer than 30-40 clusters in total. When performing inference for such ``small'' CRTs, it is important to use methods that appropriately account for the small sample size. When the generalized estimating equations (GEE) approach is used for analysis of ``small'' CRTs, the robust variance estimator from GEE is b

Zachery Wolski, Zechuan Zhang, Gino Biondini, Gregor Kovačič

The semiclassical limit of the derivative nonlinear Schrodinger equation with periodic initial conditions is studied analytically and numerically. The spectrum of the associated scattering problem for a certain class of initial conditions, referred to as periodic single-lobe potentials, is numerically computed, and it is shown that the spectrum becomes confi

Yulin Gong, Patricio A. Gallardo, Rachel Bean, Jenna Moore

We present a 9.3-sigma detection of the pairwise kinematic Sunyaev-Zeldovich (kSZ) effect by combining a sample of 913,286 Luminous Red Galaxies (LRGs) from the Dark Energy Spectroscopic Instrument Data Release 1 (DESI DR1) catalog and co-added Atacama Cosmology Telescope (ACT DR6) and Planck cosmic microwave background (CMB) temperature maps. This represent

Bijaya Basnet, Priyanka Kumari, Sathyanarayana Paladugu, Damian Pociecha

We explore surface alignment and edge dislocations in the recently discovered twist-bend ferroelectric nematic, NTBF, in which the vector of spontaneous polarization follows an oblique helicoidal trajectory around a polar twist-bend axis. In a planar cell, the polar axis aligns at some angle to the rubbing direction to mitigate surface electric charge. We de

Swati Sachan, Theo Miller, Mai Phuong Nguyen

High-stakes decision domains are increasingly exploring the potential of Large Language Models (LLMs) for complex decision-making tasks. However, LLM deployment in real-world settings presents challenges in data security, evaluation of its capabilities outside controlled environments, and accountability attribution in the event of adversarial decisions. This

C O Obasi, J G Fernandez Trincado, M Gomez, D Minniti

Context: The VISTA Variables in the Via Lactea (VVV) and its extension (VVVX) are near-infrared surveys mapping the Galactic bulge and adjacent disk. These data have enabled the discovery of numerous star clusters obscured by high and spatially variable extinction. Most previous searches relied on visual inspection of individual tiles, which is inefficient a

V. Tomas Mari Surkau, Urko Reinosa

In a recent work, we have argued that the net quark number gained by a bath of quarks and gluons upon bringing an external static quark probe, while being equal to $1$ in the high temperature, deconfined phase, is equal to $0$ or $3$ in the low temperature, confined phase, depending on the value of the quark chemical potential. This establishes a clear-cut c

Francesco Patrizi

Reachable Minimally supported (RM) B-splines have been recently introduced as a novel B-spline--like basis. They feature local linear independence and admit a fast de Boor--like evaluation algorithm. These properties make them particularly attractive for applications in isogeometric analysis. In this note, we show that automatic mesh refinement procedures ca

ZeHao Yu

Effect handlers are increasingly prominent in modern programming for managing complex computational effects, including concurrency, asynchronous operations, and exception handling, in a modular and flexible manner. Efficient stack management remains a significant challenge for effect handlers due to the dynamic control flow changes they introduce. This paper

Ilaria Brivio, Ramona Gröber, Konstantin Schmid

Higgs pair production offers the opportunity to probe correlations among the couplings of one or two Higgs bosons to fermions and gauge bosons. In this context, it serves as a powerful test of the underlying Effective Field Theory (EFT) framework. In particular, while such couplings remain correlated in the Standard Model Effective Field Theory (SMEFT) at di

Ilaria Brivio, Ramona Gröber, Konstantin Schmid

We revisit the power counting of the Higgs Effective Field Theory (HEFT) from first principles, by requiring that predictions for physical observables follow a series expansion in small, dimensionless quantities. Depending on whether HEFT is formulated in terms of a unique low-energy scale $v$ or in terms of two scales $v<f$, this approach identifies two via

Naseem Abbas, Vittorio Colao, Davide Macri, William Spataro

Physics-informed neural networks (PINNs) often struggle with multi-scale PDEs featuring sharp gradients and nontrivial boundary conditions, as the physics residual and boundary enforcement compete during optimization. We present a dual-network framework that decomposes the solution as $u = u_{\text{D}} + u_{\text{B}}$, where $u_{\text{D}}$ (domain network) c

Aayush Garg, Zanis Ali Khan, Renzo Degiovanni, Qiang Tang

Automated vulnerability patching is crucial for software security, and recent advancements in Large Language Models (LLMs) present promising capabilities for automating this task. However, existing research has primarily assessed LLMs using publicly disclosed vulnerabilities, leaving their effectiveness on related artificial vulnerabilities largely unexplore

Jan Baumgärtner, Malte Hansjosten, David Hald, Adrian Hauptmannl

To support the circular economy, robotic systems must not only assemble new products but also disassemble end-of-life (EOL) ones for reuse, recycling, or safe disposal. Existing approaches to disassembly sequence planning often assume deterministic and fully observable product models, yet real EOL products frequently deviate from their initial designs due to

Mohamed Nomeir, Alptug Aytekin, Lei Hu, Sennur Ulukus

In this paper, we explore how quantum resources can be used to increase the rate of private distributed matrix multiplication (PDMM). In PDMM, a user who has two high-dimensional matrices, $A$ and $B$, and lacks the computational capabilities to apply matrix multiplication locally, divides the matrices $A$ and $B$ into $K$ and $L$ sub-blocks, respectively. T

Suhas Srinath, Hemang Jamadagni, Aditya Chadrasekar, Prathosh AP

Underwater object tracking is challenging due to wavelength dependent attenuation and scattering, which severely distort appearance across depths and water conditions. Existing trackers trained on terrestrial data fail to generalize to these physics-driven degradations. We present MANTA, a physics-informed framework integrating representation learning with t

Alexander Amini, Anna Banaszak, Harold Benoit, Arthur Böök

We present LFM2, a family of Liquid Foundation Models designed for efficient on-device deployment and strong task capabilities. Using hardware-in-the-loop architecture search under edge latency and memory constraints, we obtain a compact hybrid backbone that combines gated short convolutions with a small number of grouped query attention blocks, delivering u

Mathew Joseph, Shubham Ovhal

We consider the stochastic heat equation with multiplicative white noise: $\partial_t u =\partial_x^2u + b(u) +\sigma(u) \dot W$, both on $[0,1]$ and $\mathbf{R}$. In the case of $[0,1]$ we show that the finite Osgood criterion on $b$ is a necessary and sufficient condition for finite-time blowup, under fairly general conditions on $\sigma$. In the case of $

Jiajun Guo, Xin Luo, Jiayin Zheng, Yiqun Wang

Multimodal foundation models are increasingly trained on sensitive data across domains such as finance, biomedicine, and personal identifiers. However, this distributed setup raises serious privacy concerns due to the need for cross-partition data sharing. Split learning addresses these concerns by enabling collaborative model training without raw data excha

Tabia Tanzin Prama, Christopher M. Danforth, Peter Sheridan Dodds

Recent advances enable Large Language Models (LLMs) to generate AI personas, yet their lack of deep contextual, cultural, and emotional understanding poses a significant limitation. This study quantitatively compared human responses with those of eight LLM-generated social personas (e.g., Male, Female, Muslim, Political Supporter) within a low-resource envir

Elisa Bellah, Claire Dunn, Vernon Naidu, Alette Wells

It is conjectured that the Markoff equation $X^2+Y^2+Z^2=3XYZ$ satisfies the special Diophantine property that every mod $p$ solution lifts to an integer solution. Progress toward this conjecture has been made by studying the connectedness of the graphs obtained from the action of the Vieta group on the nonzero mod $p$ solutions to the Markoff equation. In t

Zhen Liu, Stefano Raciopp, Katerina P. Hilleke, Abhiyan Pandit

Opposing the theory that Helium (He) cannot be inserted into AB-type ionic compounds due to the Madelung energy increase, our crystal structure search and first-principles calculations found that He can form stable compounds with sodium halides (NaX, X=Cl, Br, I) under high-pressure. These reactions are driven by the non-local chemistry arising from the cati

Pratidhwani Swain, Ramita Sarkar, Sukanta K. Tripathy, Prasanta K. Panigrahi

We examine the complementarity among coherence (visibility), predictability, and entanglement for qubit and qutrit systems subjected to noisy quantum channels. Using the system-path entanglement framework, analytical expressions for all three quantities are derived for two- and three-slit interferometric setups. The study first establishes the validity of th

Lorenzo Siro Trezzini, Andrea Pizzamiglio, Alessandro Bisio, Paolo Perinotti

We present an exact renormalisation scheme for fermionic cellular automata on hypercubic lattices. By grouping neighbouring cells into tiles and selecting subspaces within them, multiple evolution steps on the original system correspond to a single step of an effective automaton acting on the subspaces. We derive a necessary and sufficient condition for reno

Mahdi Rahmani, AmirHossein Saffari, Reyhane Rahmani

Small and medium-sized enterprises (SMEs) in Iran increasingly leverage Telegram for sales, where real-time engagement is essential for conversion. However, developing AI-driven chatbots for this purpose requires large, high-quality question-and-answer (Q&A) datasets, which are typically expensive and resource-intensive to produce, especially for low-resourc

Shi Ding, Brian Magerko

As generative artificial intelligence (AI) continues to transform education, most existing AI evaluations rely primarily on technical performance metrics such as accuracy or task efficiency while overlooking human identity, learner agency, contextual learning processes, and ethical considerations. In this paper, we present TEACH-AI (Trustworthy and Effective

Reza Jalali, Shahab Shahidi, Mohammad Hossein Zhoolideh Haghighi

Cosmological implications of a class of hybrid metric-Palatini gravity with a non-minimal matter-geometry coupling is considered. The theory contains a metric curvature tensor, together with a curvature tensor constructed from an independent affine connection. We will show that the model could be written as a bi-scalar-tensor gravity with a non-minimal coupl

Tianyi Yan, Chun Hei Leung, Weibin Li

We investigate the onset and mechanism of Hilbert space fragmentation (HSF) in a chain of strongly interacting Rydberg atoms subject to local dephasing. It is found that the emergence of multiple long-lived metastable states is fundamentally tied to HSF of the driven-dephasing Rydberg atom system. We demonstrate that the manifesting HSF is captured by a deph

J. Sumaya-Martinez, MA Ortiz-Ferreyro, O. Rojas-Hernandez

We present an analytical and numerical investigation of double-slit diffraction under coherent illumination by three plane waves: one normally incident and two symmetrically angled at plus/minus theta. By imposing an edge-zero condition on the incident field, we derive compact closed-form Fresnel expressions written solely in terms of standard Fresnel integr

Bokang Zhang, Hong Guan, Hong kyu Lee, Ruixuan Liu

Federated Learning (FL) enables collaborative, privacy-preserving model training, but supporting the "Right to be Forgotten" is especially challenging because data influences the model through distributed and interleaved client updates. Existing exact unlearning methods typically require frequent retraining from scratch, resulting in high communication cost

Tristan Kraft, Manoj K. Joshi, William Lam, Tobias Olsacher

Analog Quantum Simulators offer a route to exploring strongly correlated many-body dynamics beyond classical computation, but their predictive power remains limited by the absence of quantitative error estimation. Establishing rigorous uncertainty bounds is essential for elevating such devices from qualitative demonstrations to quantitative scientific tools.

Jian Li, Shenglin Yin, Yujia Zhang, Alan Zhao

Direct Preference Optimization (DPO) is a widely used reinforcement learning from human feedback (RLHF) method across various domains. Recent research has increasingly focused on the role of token importance in improving DPO effectiveness. It is observed that identical or semantically similar content (defined as ambiguous content) frequently appears within t

M. Cabellos, S. L. Rueda

We review the definition and main properties of differential subresultants in order to achieve their implementation in Maple, using the DEtools package. The focus is on computing GCRDs of ordinary differential operators with non necessarily rational coefficients. Determinant expressions provide explicit control, enabling the treatment of coefficients with pa

Iddo Ben-Ari, Elcio Lebensztayn, Lucas Sousa Santos

This paper examines the quasi-stationary behavior of stochastic rumor processes. Using the results by van Doorn and Pollett (2008), we first prove that the continuous-time Maki--Thompson model has a unique quasi-stationary distribution (QSD) given by the point mass at the state \((0, 1)\). To obtain a non-trivial QSD, we modify the absorption set by conditio

Alessio Belfiglio, Roberto Franzosi, Orlando Luongo

We discuss some entanglement features associated with cubic non-Gaussian perturbations in single-field inflationary scenarios. We adopt standard momentum-space techniques to show how multipartite entanglement arises for inflationary perturbation modes, focusing on the dynamics of the comoving curvature perturbation. In particular, we quantify entanglement ge

Kunanon Burathep, Thomas Erlebach, William K. Moses

We study the online unweighted bipartite matching problem in the random arrival order model, with $n$ offline and $n$ online vertices, in the learning-augmented setting: The algorithm is provided with untrusted predictions of the types (neighborhoods) of the online vertices. We build upon the work of Choo et al. (ICML 2024, pp. 8762-8781) who proposed an app

Daniil Sukhorukov, Andrei Zakharov, Nikita Glazkov, Katsiaryna Yanchanka

We present the Hierarchical AI-Meteorologist, an LLM-agent system that generates explainable weather reports using a hierarchical forecast reasoning and weather keyword generation. Unlike standard approaches that treat forecasts as flat time series, our framework performs multi-scale reasoning across hourly, 6-hour, and daily aggregations to capture both sho

Sinan Du, Jiahao Guo, Bo Li, Shuhao Cui

Unifying multimodal understanding, generation and reconstruction representation in a single tokenizer remains a key challenge in building unified models. Previous research predominantly attempts to address this in a dual encoder paradigm, e.g., utilizing the separate encoders for understanding and generation respectively or balancing semantic representations

Yang Guo, Jaime A. Moreno, Stefan Streif

This paper considers state estimation for general nonlinear discrete-time systems subject to measurement noise and possibly unbounded unknown inputs. To approach this problem, we first propose the concept of strong nonlinear detectability. This condition is sufficient and necessary for the existence of unknown input state estimators (UISEs), which reconstruc

Isabel Whiteley Tscherniak, Niels Christopher Thiemann, Ana McWhinnie-Fernández, Iustin Curcean

Motivated by the Cybathlon 2024 competition, we developed a modular, online EEG-based brain-computer interface to address these challenges, increasing accessibility for individuals with severe mobility impairments. Our system uses three mental and motor imagery classes to control up to five control signals. The pipeline consists of four modules: data acquisi

Rajit Shrivastava

This thesis presents an innovative framework for the automated detection and characterization of galactic bars, pivotal structures in spiral galaxies, using the YOLO-OBB (You Only Look Once with Oriented Bounding Boxes) model. Traditional methods for identifying bars are often labor-intensive and subjective, limiting their scalability for large astronomical

Matthias Pfeifer

We show that toric surface singularities deform to toric surface singularities - both in equal and mixed characteristic. As an application, we establish Riemenschneiders conjecture that isolated cyclic quotient singularities of any dimension deform to isolated cyclic quotient singularities in equal and mixed characteristic.

Sam Allen, Tyler Genao

In this paper, we prove that for each number field $F$ there exists a uniform bound on the prime levels $p$ of elliptic curves $E/F$ for which $F(E[p])=F(\zeta_p)$. Under the Generalized Riemann Hypothesis, we also give uniform bounds on $p$ for which $F(E[p])/F$ is abelian, provided that $F$ has no rational complex multiplication. These are generalizations

Prasoon Awasthi, Aniruddh Murali, Ellen Juel Pørtner, Adam Cohen Simonsen

Living cells establish long-range orientational order through collective alignment, giving rise to topological defects whose functional relevance is increasingly recognized in tissue organization and morphogenesis. Engineered topographical patterns have been used to induce such defects in cell monolayers, mimicking natural biological phenomena. In this work,

Yimeng Liu, Misha Sra

Professional creative software has steep learning curves for novices due to complex interfaces, limited guidance, and unfamiliar terminology. To support educators and tool creators in addressing learner challenges, we introduce TaskLens, an LLM-based method that automatically generates task-conditioned scaffolded UIs from natural language task descriptions.

Mattia Dubbini, Orlando Luongo, Aniello Quaranta

We extend spontaneous baryogenesis by considering the spontaneous breaking of $U(1)_B$ through a complex vector field. This field interacts with baryons and leptons via a vector-current coupling and, by construction, acquires a nonzero vacuum expectation value. Accordingly, the theory also exhibits a spontaneous violation of Lorentz invariance, effectively r

Rui Zhang, Hongxia Wang, Hangqing Liu, Yang Zhou

Diffusion-based image editing has made semantic level image manipulation easy for general users, but it also enables realistic local forgeries that are hard to localize. Existing benchmarks mainly focus on the binary detection of generated images or the localization of manually edited regions and do not reflect the properties of diffusion-based edits, which

Li Siyan, Jason Zhang, Akash Maharaj, Yuanming Shi

Novice and expert users have different systematic preferences in task-oriented dialogues. However, whether catering to these preferences actually improves user experience and task performance remains understudied. To investigate the effects of expertise-based personalization, we first built a version of an enterprise AI assistant with passive personalization

Alexander Sergeev, Evgeny Kotelnikov

Modern large language models become multimodal, analyzing various data formats like text and images. While fine-tuning is effective for adapting these multimodal language models (MLMs) to downstream tasks, full fine-tuning is computationally expensive. Parameter-Efficient Fine-Tuning (PEFT) methods address this by training only a small portion of model weigh

Ricardo Martinez, Juan D. Moreno-Ternero

The ethic of proportional redistribution is a compromise between the extremely compensatory ethic of full redistribution and the needs-blind ethic of laissez-faire. In a basic model of redistribution problems with needs, we characterize proportional redistribution with a combination of axioms that formalize minimal requirements of accountability, functionali

Laura Becker, Yash Deshpande, Wolfgang Kellerer

To enable mobility in industrial communication systems, the seamless integration of 5G with Time-Sensitive Networking (TSN) is a promising approach. Deterministic communication across heterogeneous 5G-TSN systems requires joint scheduling between both domains. A key prerequisite for time-aware end-to-end scheduling is determining the forwarding delay for eac

Kanhaiya Lal Chaurasiya, Ruchira Kumar Pradhan, Yashaswi Sinha, Shivam Gupta

The increasing industrial demand for alternative actuators over conventional electromagnetism-based systems having limited efficiency, bulky size, complex design due to in-built gear-train mechanisms, and high production and amortization costs necessitates the innovation in new actuator development. Integrating bio-inspired design principles into linear actu

Alberto Aleta, Andreia Sofia Teixeira, Guilherme Ferraz de Arruda, Andrea Baronchelli

Multilayer network science has emerged as a central framework for analysing interconnected and interdependent complex systems. Its relevance has grown substantially with the increasing availability of rich, heterogeneous data, which makes it possible to uncover and exploit the inherently multilayered organisation of many real-world networks. In this review,

Antoine Caubrière, Elodie Gauthier

Despite recent progress in multilingual speech processing, African languages remain under-represented in both research and deployed systems, particularly when it comes to strong, open-weight encoders that transfer well under low-resource supervision. Self-supervised learning has proven especially promising in such settings, yet most publicly released models

Haochen Tian, Tianyu Li, Haochen Liu, Jiazhi Yang

Achieving fully autonomous driving systems requires learning rational decisions in a wide span of scenarios, including safety-critical and out-of-distribution ones. However, such cases are underrepresented in real-world corpus collected by human experts. To complement for the lack of data diversity, we introduce a novel and scalable simulation framework capa

Anna J. G. O'Grady

HV 11417 is a candidate Thorne-\.Zytkow Object, a red supergiant with a neutron star core, located within the Small Magellanic Cloud (SMC). Previous studies have questioned, using Gaia DR2 data, whether HV 11417 was truly located at the distance of the SMC or was instead a foreground star. However, the proper motion measurement uncertainties for HV 11417 in

Stein Meereboer, Philip Schlösser

A general theory of matrix-spherical functions for dual Hopf algebras and right coideal subalgebras is developed. We establish their existence and define their orthogonality relations. When specialized to Kolb and Letzter&#39;s quantum symmetric pair coideal subalgebras, we associate, to each classical commutative triple, a unique corresponding quantum commu

S. Hariharan, R. Suresh, V. K. Chandrasekar

Many natural and physical processes can be understood by analyzing multiple system variables evolving, forming a multivariate time series. Predicting such time series is challenging due to the inherent noise and interdependencies among variables. Echo state networks (ESNs), a class of Reservoir Computing (RC) models, offer an efficient alternative to convent

Toqeer Ali Syed, Salman Jan, Gohar Ali, Ali Akarma

In contemporary retail, the variety of products available (e.g. clothing, groceries, cosmetics, frozen goods) make it difficult to predict the demand, prevent stockouts, and find high-potential products. We suggest an agentic AI model that will be used to monitor the inventory, initiate purchase attempts to the appropriate suppliers, and scan for trending or

Bob Coecke, Aleks Kissinger, Stefano Gogioso, Selma Dündar-Coecke

We are at the dawn of the second quantum revolution, where our ability to create and control individual quantum systems is poised to drive transformative advancements in basic science, computation, and everyday life. However, quantum theory has long been conceived as notoriously hard to learn, creating a significant barrier to workforce development, informed

Gunda Kipp, Marios H. Michael, Alexander M. Potts, Dorothee Herrmann

On-chip THz spectroscopy enables quantitative measurements of the optical conductivity of sub-wavelength 2D materials by tightly confining THz fields in metallic transmission line structures interfaced to the material. However, because the probed structures are smaller than the THz wavelength, finite-size and environmental effects can strongly influence the

Koutarou Tamura

This study proposes a method for predicting startup inclusion, estimating the probability that a venture capital fund will invest in a given startup. Unlike general recommendation systems, which typically rank multiple candidates, our approach formulates the problem as a binary classification task tailored to each fund-startup pair. Each startup is represent

Esty Kelman, Uri Meir, Debanuj Nayak, Sofya Raskhodnikova

A central challenge in property testing is verifying algebraic structure with minimal access to data. A landmark result addressing this challenge, the linearity test of Blum, Luby, and Rubinfeld (JCSS `93), spurred a rich body of work on testing algebraic properties such as linearity and its generalizations to low-degree polynomials and group homomorphisms.

Thomas Bothner, Amari Jaconelli

It is shown how classes of Fredholm Pfaffians can be computed in terms of canonical, auxiliary Riemann-Hilbert problems as soon as the main kernel in the Pfaffian is either of additive Hankel composition or of truncated Wiener-Hopf type. Akhiezer-Kac asymptotic results for the Fredholm Pfaffians are then derived as natural consequences of the Riemann-Hilbert

Beomjun Choi, Seunghoon Jeong, Geuntaek Seo

We prove convergence to equilibrium for solutions to the McKean-Vlasov (granular media) equation on the flat torus in a genuinely nonconvex setting. Our approach is based on a Wasserstein-{\L}ojasiewicz gradient inequality for the associated free energy, established under mild analyticity assumptions on the confinement and interaction potentials. This yields

Hsuan-Sen Wang, Wen-Hsuan Kuan, Ahmed F. M. El-Mahdy, Gong-Ru Lin

We demonstrate optical bistability in an erbium-doped fiber laser (EDFL) using a low saturation intensity covalent organic framework (COF) saturable absorber (SA). The COF-SA satisfies the free-energy criterion for bistability, enabling switching among non-lasing, continuous-wave, and mode-locking states. Two optical bistability regions are observed, includi

David Caldevilla-Asenjo, Gorm Ole Steffensen, Sara Catalano, Alberto Hijano

We report the first experimental observation of subgap transport in ferromagnetic insulator/superconductor/insulator/superconductor junctions realized in EuS/Al/AlOx/Al vertical stacks. Differential conductance measurements reveal multiple Andreev reflection peaks, with odd-order peaks split by the spin-splitting induced in the superconductor adjacent to EuS

Alexandre Moine, Stephanie Balzer, Alex Xu, Sam Westrick

Disentanglement is a runtime property of parallel programs guaranteeing that parallel tasks remain oblivious to each other's allocations. As demonstrated in the MaPLe compiler and run-time system, disentanglement can be exploited for fast automatic memory management, especially task-local garbage collection with no synchronization between parallel tasks. How

Sergi Liesegang, Stefano Buzzi

The impressive growth of wireless data networks has recently led to increased attention to the issue of electromagnetic pollution and the fulfillment of electromagnetic field (EMF) exposure limits. This paper tackles the problem of power control in user-centric cell-free massive multiple-input-multiple-output (CF-mMIMO) systems under EMF constraints. Specifi

Hong-Ying Chen, Chao-Wei Tsai, Pei Zuo, Niankun Yu

We present the results of Hi line observations towards 26 Active Galactic Nuclei (AGN)-hosting and one star-forming dwarf galaxies (Mstar < 10^9.5 Msun) with the 19-beam spectral line receiver of FAST at 1.4 GHz. Our FAST observed targets are combined with other AGN-hosting dwarf galaxies covered in the ALFALFA footprint to form a more comprehensive sample.

Vinh Chau, Khoa Le Dinh Van, Hon Huynh Ngoc, Binh Nguyen Thien

In many low-resource healthcare settings, bedside monitors remain standalone legacy devices without network connectivity, creating a persistent interoperability gap that prevents seamless integration of physiological data into electronic health record (EHR) systems. To address this challenge without requiring costly hardware replacement, we present a compute

Matheus Campos Fernandes

Program synthesis is the process of generating a computer program following a set of specifications, such as a set of input-output examples. It can be modeled as a search problem in which the search space is the set of all valid programs. As the search space is vast, brute force is usually not feasible, and search heuristics, such as genetic programming, als

Cyprien Daix, Pok Man Tam, Maxime Dixmerias, Joris Verstraten

Pauli's exclusion principle forces fermions to occupy distinct quantum states, creating a filled region of momentum space at low temperature, the Fermi sea, whose topology governs the system's response to perturbations and the nature of its correlation functions. Recent theory predicts that for non-interacting fermions, the Euler characteristic of a $D$-dime