October 2025 arXiv papers — page 110

Sebastien Bossu, Andrew Papanicolaou, Nour El Hatto

We analyze the problem of fitting a fonction en escalier or multi-step function to a curve in L^2 Hilbert space. We propose a two-stage optimization approach whereby the step positions are initially fixed, corresponding to a classic linear least-squares problem with closed-form solution, and then are allowed to vary, leading to first-order conditions that ca

Maxim Gumin, Do Heon Han, Seung Jean Yoo, Aditya Ganeshan

Synthesizing 3D scenes from open-vocabulary text descriptions is a challenging, important, and recently-popular application. One of its critical subproblems is layout generation: given a set of objects, lay them out to produce a scene matching the input description. Nearly all recent work adopts a declarative paradigm for this problem: using an LLM to genera

Thanh-Huy Nguyen, Hoang-Thien Nguyen, Vi Vu, Ba-Thinh Lam

The limited availability of annotated data in medical imaging makes semi-supervised learning increasingly appealing for its ability to learn from imperfect supervision. Recently, teacher-student frameworks have gained popularity for their training benefits and robust performance. However, jointly optimizing the entire network can hinder convergence and stabi

Ahmad Arrabi, Jay hwasung Jung, J Le, A Nguyen

Thrombectomy is one of the most effective treatments for ischemic stroke, but it is resource and personnel-intensive. We propose employing deep learning to automate critical aspects of thrombectomy, thereby enhancing efficiency and safety. In this work, we introduce a self-supervised framework that classifies various skeletal landmarks using a regression-bas

Sukhdeep Singh, Avinash Bhat, Shweta M, Subhash K Singh

The increasing complexity of Beyond 5G and 6G networks necessitates new paradigms for autonomy and assur- ance. Traditional O-RAN control loops rely heavily on RIC- based orchestration, which centralizes intelligence and exposes the system to risks such as policy conflicts, data drift, and unsafe actions under unforeseen conditions. In this work, we argue th

Ravi Kaushik, Ryota Ono, Sergey Artyukhin

Density functional theory has demonstrated remarkable predictive power in calculating magnetic properties at zero temperature. At finite temperatures, thermally excited phonons may affect magnetism. Efficient ab-initio methods to calculate the temperature dependence of magnetic exchange interactions are still lacking despite the importance of room temperatur

Diego López-Alcalá, Alberto M. Ruiz, Andrei Shumilin, José J. Baldoví

Encapsulating low-dimensional magnetic materials within carbon nanotubes (CNTs) offers a compelling route to stabilize unconventional magnetic states and engineer quantum functionalities at the limit of miniaturization. In this work, we systematically investigate the structural, electronic, and magnetic properties of one-dimensional (1D) CrSBr chains encapsu

Hadi Hosseini, Debmalya Mandal, Amrit Puhan

We introduce $\mathbf{SP-Rank}$, the first large-scale, publicly available dataset for benchmarking algorithms that leverage both first-order preferences and second-order predictions in ranking tasks. Each datapoint includes a personal vote (first-order signal) and a meta-prediction of how others will vote (second-order signal), allowing richer modeling than

Miguel A. Sabogal, Rafael C. Nunes, Felipe Avila, Armando Bernui

We investigate and update observational constraints on cosmological parameters within the $\Lambda$CDM and dynamical dark energy frameworks, using a new compilation of the transverse (or 2D) BAO data, measurements that provide a relatively model-independent estimate of the BAO angular scale at a given redshift. Firstly, we assess the consistency of this comp

Corey M. Abramson, Yuhan, Nian

The CMAP (cultural mapping and pattern analysis) visualization toolkit introduced in this paper is an open-source suite for analyzing and visualizing text data - from qualitative fieldnotes and in-depth interview transcripts to historical documents and web-scaped data like message board posts or blogs. The toolkit is designed for scholars integrating pattern

Zachary Langford, Cullen Blake, Samuel Halverson, Eric B. Ford

Precise radial velocity (RV) measurements are a crucial tool for exoplanet discovery and characterization. Today, the majority of these measurements are derived from Echelle spectra in the optical wavelength region using cross-correlation techniques. Although for certain stars these approaches can produce RVs with sub-1 m~s$^{-1}$ measurement errors, for man

Marcos Abreu, Álvaro Suárez, Cecilia Stari, Arturo C. Marti

This study explores how generative artificial intelligence, specifically ChatGPT, can assist in the evaluation of laboratory reports in Experimental Physics. Two interaction modalities were implemented: an automated API-based evaluation and a customized ChatGPT configuration designed to emulate instructor feedback. The analysis focused on two complementary d

Sayan Deb Sarkar, Sinisa Stekovic, Vincent Lepetit, Iro Armeni

Transferring appearance to 3D assets using different representations of the appearance object - such as images or text - has garnered interest due to its wide range of applications in industries like gaming, augmented reality, and digital content creation. However, state-of-the-art methods still fail when the geometry between the input and appearance objects

S. Hashim, C. Figueira de Morisson Faria

We present a systematic study of interchannel quantum interference in laser-induced nonsequential double ionization (NSDI) within the strong-field approximation. Focusing on the below-threshold intensity regime where the recollision-excitation with subsequent ionization (RESI) pathway dominates, we derive analytical phase conditions governing interference be

Chen Kong, James Fort, Aria Kang, Jonathan Wittmer

The Aria Gen 2 Pilot Dataset (A2PD) is an egocentric multimodal open dataset captured using the state-of-the-art Aria Gen 2 glasses. To facilitate timely access, A2PD is released incrementally with ongoing dataset enhancements. The initial release features Dia'ane, our primary subject, who records her daily activities alongside friends, each equipped with Ar

Daniel Sainati, Joseph W. Cutler, Benjamin C. Pierce, Stephanie Weirich

Strictness analysis is critical to efficient implementation of languages with non-strict evaluation, mitigating much of the performance overhead of laziness. However, reasoning about strictness at the source level can be challenging and unintuitive. We propose a new definition of strictness that refines the traditional one by describing variable usage more p

Phalguni Nanda, Zaiwei Chen

In this work, we present the first finite-time analysis of Q-learning with time-varying learning policies (i.e., on-policy sampling) for discounted Markov decision processes under minimal assumptions, requiring only the existence of a policy that induces an irreducible Markov chain over the state space. We establish a last-iterate convergence rate for $\math

Avijit Barua, Kartik Gaur, Leo J. Roche, Suk In Park

The controlled integration of quantum dots (QDs) as single-photon emitters into quantum light sources is essential for the implementation of large-scale quantum networks. In this study, we employ the deterministic in-situ electron-beam lithography (iEBL) nanotechnology platform to integrate individual QDs with high accuracy and process yield into circular Br

Jiasen Zhang, Xi Qiao, Liangliang Zhang, Weihong Guo

Spatial transcriptomics allows researchers to visualize and analyze gene expression within the precise location of tissues or cells. It provides spatially resolved gene expression data but often lacks cellular resolution, necessitating cell type deconvolution to infer cellular composition at each spatial location. In this paper we propose BASIN for cell type

Lauryn E. Williams, Philip Chang, Emily M. Levesque, Thomas R. Quinn

Thorne-\.Zytkow Objects (T\.ZOs) are hypothetical hybrid stars with a neutron star at the core of a large, diffuse envelope. (T\.ZOs) may be formed when a newly formed neutron star that is kicked by its supernova collides with its main-sequence companion. Using a moving-mesh hydrodynamics solver integrated into the parallel-code Charm N-body GrAvity solver,

Kate Glazko, Jennifer Mankoff

Generative AI risks such as bias and lack of representation impact people who do not interact directly with GAI systems, but whose content does: indirect users. Several approaches to mitigating harms to indirect users have been described, but most require top down or external intervention. An emerging strategy, prompt injections, provides an empowering alter

Oliver J. Hines, Caleb H. Miles

The ratio of two probability density functions is a fundamental quantity that appears in many areas of statistics and machine learning, including causal inference, reinforcement learning, covariate shift, outlier detection, independence testing, importance sampling, and diffusion modeling. Naively estimating the numerator and denominator densities separately

Yuyi Yan, Fujia Liu, Weichen Tang, Han Xuan Wong

Pi-conjugated covalent organic frameworks (COFs) provide a versatile platform for the realization of designer quantum nanomaterials. Strong electron-electron correlation within these artificial lattices can give rise to exotic phases of matter. Their experimental realization however requires precise control over orbital symmetry, charge localization, and ban

Timon Thomas, Christoph Pfrommer, Rüdiger Pakmor, Rouven Lemmerz

Cosmic-ray (CR) feedback is widely recognized as a key regulator of galaxy formation. After being accelerated at supernova remnant shocks, CRs propagate through the interstellar medium (ISM), establishing smooth large-scale distributions and driving galactic outflows. The efficiency of this feedback is controlled by the effective transport speed of the CR po

M. C. Baglio, K. Alabarta, D. M. Russell, N. Masetti

We present a study of the minute-timescale optical variability and spectroscopic outflow signatures in the black hole X-ray binary MAXI J1820+070 during its 2018 outburst and re-brightenings. Minute-cadence, multi-filter optical light curves were obtained with the Las Cumbres Observatory network and the Al Sadeem Observatory (UAE) over 2018-2020, complemente

Federico Malato, Ville Hautamäki

World Models have vastly permeated the field of Reinforcement Learning. Their ability to model the transition dynamics of an environment have greatly improved sample efficiency in online RL. Among them, the most notorious example is Dreamer, a model that learns to act in a diverse set of image-based environments. In this paper, we leverage similarity search

Owais Makroo, Siva Rajesh Kasa, Sumegh Roychowdhury, Karan Gupta

Membership Inference Attacks (MIAs) pose a critical privacy threat by enabling adversaries to determine whether a specific sample was included in a model's training dataset. Despite extensive research on MIAs, systematic comparisons between generative and discriminative classifiers remain limited. This work addresses this gap by first providing theoretical m

Akshay Rana

We present a numerical framework to study the cosmological background evolution in $f(R)$ gravity by employing a \textit{spectral Chebyshev collocation approach}. Unlike standard integration methods such as Runge--Kutta that often encounter stiffness and accuracy issues, this formulation expands the normalized Hubble function $E(z) = H(z)/H_0$ as a finite Ch

Gauhar Abbas, Vartika Singh

We employ \textit{Levin-type sequence transformations} to accelerate the convergence of the perturbative fixed-order expansion of the QCD correction $\delta^{(0)}$ in terms of the strong coupling $\alpha_s$. The method efficiently resums the series, yielding a stable and self-consistent determination of higher-order QCD corrections to hadronic $\tau$ decays,

Nishad Sahu, Shounak Sural, Aditya Satish Patil, Ragunathan

Reliable perception is fundamental for safety critical decision making in autonomous driving. Yet, vision based object detector neural networks remain vulnerable to uncertainty arising from issues such as data bias and distributional shifts. In this paper, we introduce ObjectTransforms, a technique for quantifying and reducing uncertainty in vision based obj

Victor Gonzalez Avella, Abraham Vega Vargas, Tomas Merlo Vergara, Kevin de la Ossa Doria

We present two scalable and entanglement-free methods for estimating the collective state of an n-qubit quantum computer. The first method consists of a fixed set of five quantum circuits-regardless of the number of qubits-that avoid the use of entanglement as a measurement resource, relying instead on classical communication between selected pairs of qubits

Seiji Fujimoto, Andreas L. Faisst, Akiyoshi Tsujita, Mahsa Kohandel

We present a statistical study of spatially resolved chemical enrichment in 18 main-sequence galaxies at $z=4$--6, observed with \jwst/NIRSpec IFU as part of the ALPINE-CRISTAL-\jwst\ survey. Performing an optimized reduction and calibration procedure, including local background subtraction, light-leakage masking, stripe removal, and astrometry refinement, w

Jianhan Lin, Yuchu Qin, Shuai Gao, Yikang Rui

Well-maintained road networks are crucial for achieving Sustainable Development Goal (SDG) 11. Road surface damage not only threatens traffic safety but also hinders sustainable urban development. Accurate detection, however, remains challenging due to the diverse shapes of damages, the difficulty of capturing slender cracks with high aspect ratios, and the

Loïc Honet, Adam Pound, Geoffrey Compère

We develop and implement a new hybrid waveform model for quasicircular inspirals with a spinning primary and nonspinning secondary, excluding the merger and ringdown. This model, which is a core component of the more extensive WaSABI-C model, consistently assembles all available first-order self-force and post-Newtonian results through a hybridization proced

Josh Mathews, Barry Wardell, Adam Pound, Niels Warburton

Recent progress in gravitational self-force theory has led to the development of a first post-adiabatic (1PA) waveform model for nonspinning, quasicircular compact binaries [Phys. Rev. Lett. 130, 241402 (2023)]. In this paper, we extend that model to allow for a slowly spinning primary black hole and a generic, precessing spin on the secondary object, restri

Loïc Honet, Josh Mathews, Geoffrey Compère, Adam Pound

We present the state-of-the-art waveform model WaSABI-C for quasicircular inspirals of spinning black hole binaries with aligned or anti-aligned spins. Our model synthesizes the most up-to-date first- and second-order gravitational self-force results with high-order post-Newtonian expansions through a systematic hybridization procedure. This approach capture

A. L. Faisst, S. Fujimoto, A. Tsujita, W. Wang

To fully characterize the formation and evolution of galaxies, we need to observe their stars, gas, and dust on resolved spatial scales. We present the ALPINE-CRISTAL-JWST survey, which combines kpc-resolved imaging and spectroscopy from HST, JWST, and ALMA for 18 representative main-sequence galaxies at z=4-6 and log(M/$M_\odot$) > 9.5 to study their star f

Kaili Cao

Image regridding and coaddition have a wide range of applications in astronomical observations. {\sc Imcom}, an algorithm that provides control over point spread function (PSF) and noise in coadded images, has been found to meet the stringent requirements of weak gravitational lensing cosmology with the forthcoming Nancy Grace Roman Space Telescope. In this

Ioannis Liodakis, Sudip Chakraborty, Frédéric Marin, Steven R. Ehlert

3C 84 is the brightest cluster galaxy in the Perseus Cluster. It is among the closest radio-loud active galaxies and among the very few that can be detected from low frequency radio up to TeV $\gamma$-rays. Here we report on the first X-ray polarization observation of 3C~84 with the Imaging X-ray Polarimetry Explorer, for a total of 2.2 Msec that coincides w

Annarita Ierardi, Gor Oganesyan, Stefano Ascenzi, Marica Branchesi

Early X-ray emission of Gamma-Ray Bursts (GRBs) traces the transition between the prompt emission and the afterglow radiation, and its rapid flux decline is often interpreted as the tail of the prompt emission. As such, it can offer insights into the emission mechanisms active during the prompt emission and the physics of GRB jets. In this work, we focus on

Jonathan L. Feng, Alec Hewitt, Daniel La Rocco, Daniel Whiteson

This work explores the potential of the proposed FASER2 experiment at the LHC to determine the properties of a discovered heavy neutral lepton (HNL), including its mass, couplings, and whether it is a Majorana or Dirac fermion. We first consider a Majorana HNL with mass $m_N = 1.84\,\rm{GeV}$ that is primarily produced through decays $D \to \mu N$ at the ATL

Tyler Gardner, John D. Monnier, Stefan Kraus, Emily Rauscher

Ground-based long baseline interferometry is a powerful tool for characterizing exoplanets which are too close to their host star to be imaged with single-dish telescopes. The CHARA Array can resolve companions down to 0.5 milli-arcseconds, allowing us in principle to directly measure the near-infrared spectra of non-transiting "Hot Jupiter" exoplanets. We p

Aritra Banerjee, Arkachur Bhattacharya, Sharang Rajesh Iyer, Ansh Mishra

The BTZ black hole provides a tractable (2+1)-dimensional example for investigating string dynamics in curved spacetime. However, a systematic and robust analysis of the solution space of strings in the near-horizon region of BTZ black holes remains elusive in the literature. This work aims to fill this gap by employing the string-Carroll expansion. This for

Michael LaHaye, Colin Weller, Dongjun Li, Patrick Bourg

In this work, we develop the modified Teukolsky formalism that describes the GW radiation from a point mass orbiting around a perturbed Schwarzschild BH. This perturbation of the background spacetime induces a secular change in the orbital phase of the point mass. In turn, this causes a modification in the GW flux, which can be used to probe the background s

Claudia Artiaco, João Barata, Enrique Rico

We introduce local information flows as a diagnostic tool for characterizing out-of-equilibrium quantum dynamics in lattice gauge theories. We employ the information lattice framework, a local decomposition of total information into spatial- and scale-resolved contributions, to characterize the propagation and buildup of quantum correlations in real-time pro

Leopoldo A. Pando Zayas, Jingchao Zhang

Near-extremal black holes are known to contain strong quantum fluctuations in their near-horizon near-AdS$_2$ throat region governed by an effective action that includes Schwarzian modes. These fluctuations lead to one-loop corrections in the gravitational path integral that are essential in understanding the thermodynamics of near-extremal black holes at lo

Theodore B. Smith, Logan B. Fries, Jonathan R. Trump, Catherine J. Grier

We present a reverberation mapping analysis of the coronal line [Ne V]$\lambda$3427 emitting region of the quasar COS168 (SDSS J095910.30+020732.2). [Ne V]$\lambda$3427 is known as one of the "coronal lines," which are a species of emission lines present in AGN spectra with high ionization potentials ($\geq$ 100 eV) that can serve as tracers for AGN activity

Zhoujian Zhang, Yanxia Li

We present the Ultracool dwarf Science with MachIne LEarning (USMILE), a program developing machine-learning tools for the discovery and characterization of ultracool dwarfs. We introduce USMILE Avocado, a spectral classification framework that uses broadband photometry from wide-field surveys -- Rubin Observatory LSST Data Preview 1, VISTA Hemisphere Survey

Eleni Straitouri, Stratis Tsirtsis, Ander Artola Velasco, Manuel Gomez-Rodriguez

Recent work has shown that, in classification tasks, it is possible to design decision support systems that do not require human experts to understand when to cede agency to a classifier or when to exercise their own agency to achieve complementarity$\unicode{x2014}$experts using these systems make more accurate predictions than those made by the experts or

Hanrong Ye, Chao-Han Huck Yang, Arushi Goel, Wei Huang

Advancing machine intelligence requires developing the ability to perceive across multiple modalities, much as humans sense the world. We introduce OmniVinci, an initiative to build a strong, open-source, omni-modal LLM. We carefully study the design choices across model architecture and data curation. For model architecture, we present three key innovations

Jie-Ying Lee, Yi-Ruei Liu, Shr-Ruei Tsai, Wei-Cheng Chang

Synthesizing large-scale, explorable, and geometrically accurate 3D urban scenes is a challenging yet valuable task for immersive and embodied applications. The challenge lies in the lack of large-scale and high-quality real-world 3D scans for training generalizable generative models. In this paper, we take an alternative route to create large-scale 3D scene

Shr-Ruei Tsai, Wei-Cheng Chang, Jie-Ying Lee, Chih-Hai Su

Lens flare significantly degrades image quality, impacting critical computer vision tasks like object detection and autonomous driving. Recent Single Image Flare Removal (SIFR) methods perform poorly when off-frame light sources are incomplete or absent. We propose LightsOut, a diffusion-based outpainting framework tailored to enhance SIFR by reconstructing

S. Dichiara, E. Troja, B. O'Connor, Y. -H. Yang

We report the precise X-ray localization of GRB 230906A, a short duration ($T_{90}\sim$0.9 s) burst with no optical or radio counterpart. Deep imaging with the Hubble Space Telescope detects a faint galaxy (G$^\ast$; $F160W\simeq26$ AB mag) coincident with the sub-arcsecond X-ray position. Compared with standard GRB galaxies, its faintness, compact size and

Kaushitha Silva, Mansitha Eashwara, Sanduni Ubayasiri, Ruwan Tennakoon

The clinical adoption of biomedical vision-language models is hindered by prompt optimization techniques that produce either uninterpretable latent vectors or single textual prompts. This lack of transparency and failure to capture the multi-faceted nature of clinical diagnosis, which relies on integrating diverse observations, limits their trustworthiness i

M. Malnou

Artificial transmission lines built with lumped-element inductors and capacitors form the backbone of broadband, nearly quantum-limited traveling-wave parametric amplifiers (TWPAs). When tailoring these transmission lines for parametric processes, nonlinear elements are added, typically nonlinear inductances in superconducting circuits, and energy and moment

Tina Behnia, Puneesh Deora, Christos Thrampoulidis

Language models are pretrained on sequences that blend statistical regularities (making text fluent) with factual associations between specific tokens (knowledge of facts). While recent work suggests that the variability of their interaction, such as paraphrases of factual associations, critically determines generalization ability, we lack a systematic analy

Chengzhi Zhang, Dashiel Carrera, Daksh Kapoor, Jasmine Kaur

While the ambient intelligence (AmI) systems we encounter in our daily lives, including security monitoring and energy-saving systems, typically serve pragmatic purposes, we wonder how we can design and implement ambient artificial intelligence experiences in public spaces that elicit deep human feelings of awe, wonder, and beauty. As a manifestation, we int

Abdelilah Ganmati, Karim Afdel, Lahcen Koutti

We study the longitudinal stability of compact binary face templates and quantify ageing drift directly in bits per decade. Float embeddings from a modern face CNN are compressed with PCA-ITQ into 64- and 128-bit codes. For each identity in AgeDB with at least three distinct ages, we form all genuine pairs and fit a per-identity linear model of Hamming dista

Amit Roy, Kamalesh Saha

Let $I$ be an equigenerated squarefree monomial ideal in the polynomial ring $\mathbb{K}[x_1,\ldots,x_n]$, and let $\mathcal{H}$ be a uniform clutter on the vertex set $\{x_1,\ldots,x_n\}$ such that $I=I(\mathcal{H})$ is its edge ideal. A central and challenging problem in combinatorial commutative algebra is to classify all clutters $\mathcal{H}$ for which

Simon Yu, Gang Li, Weiyan Shi, Peng Qi

Large language models (LLMs) are moving beyond static uses and are now powering agents that learn continually during their interaction with external environments. For example, agents can learn reusable skills while navigating web pages or toggling new tools. However, existing methods for skill learning often create skills that are over-specialized to a singl

Yi Wan, Jiuqi Wang, Liam Li, Jinsong Liu

Large language models (LLMs) augmented with external tools are increasingly deployed as deep research agents that gather, reason over, and synthesize web information to answer complex queries. Although recent open-source systems achieve strong empirical performance via reinforcement learning from web interactions, the impact of key design choices remains und

Danial Davarnia, Hamed Rahimian

Chance-constrained programming is a widely used framework for decision-making under uncertainty, yet its mixed-integer reformulations over a finite support involve nonconvex mixing sets with a knapsack constraint, leading to weak relaxations and computational challenges. Most existing approaches for strengthening the relaxations of these sets rely primarily

Ryan Curry, Kai Hebeler, Stefano Gandolfi, Alexandros Gezerlis

We report on the first quantum Monte Carlo calculations of helium isotopes with fully propagated theoretical uncertainties from the interaction to the many-body observables. To achieve this, we build emulators for solutions to the Faddeev equations for the binding energy and Gamow-Teller matrix element of $^3\text{H}$, as well as for auxiliary-field diffusio

MohammadJavad Maarefvand

This article explores the limits of geometric construction using various tools, both classical and modern. Starting with ruler and compass constructions, we examine how adding methods such as origami, marked rulers (neusis), conic sections, mechanical linkages, and certain transcendental curves expands the range of constructible numbers. These methods allow

Jiuhai Chen, Le Xue, Zhiyang Xu, Xichen Pan

We present BLIP3o-NEXT, a fully open-source foundation model in the BLIP3 series that advances the next frontier of native image generation. BLIP3o-NEXT unifies text-to-image generation and image editing within a single architecture, demonstrating strong image generation and image editing capabilities. In developing the state-of-the-art native image generati

Jan van Mill

We show that under the Continuum Hypothesis, the topological group of all homeomorphisms of the \v{C}ech-Stone remainder of $\omega$ with the $G_\delta$-topology, is a universal object for all $P$-groups of weight at most ${\mathfrak c}$.

Xiaofeng Cai, Linghui Kong, Dmitri Kuzmin, Li Shan

We discretize the Vlasov-Poisson system using conservative semi-Lagrangian (CSL) discontinuous Galerkin (DG) schemes that are asymptotic preserving (AP) in the quasi-neutral limit. The proposed method (CSLDG) relies on two key ingredients: the CSLDG discretization and a reformulated Poisson equation (RPE). The use of the CSL formulation ensures local mass co

David Porlles, Wei Chen

The quantum geometric properties of typical diamond-type (C, Si, Ge) and zincblende-type (GaAs, InP, etc) semiconductors are investigated by means of the $sp^{3}s^{\ast}$ tight-binding model, which allows to calculate the quantum metric of the valence band states throughout the entire Brillouin zone. The global maximum of the metric is at the $\Gamma$ point,

Maximilian Cederholm, Siyao Wang, Haochun Wang, Ruichen Xu

We propose a hybrid solver that fuses the dimensionality-reduction strengths of the Method of Lines (MOL) with the flexibility of Physics-Informed Neural Networks (PINNs). Instead of approximating spatial derivatives with fixed finite-difference stencils - whose truncation errors force extremely fine meshes - our method trains a neural network to represent t

Kadri Hacioglu, Manjunath K E, Andreas Stolcke

Slot filling is a crucial subtask in spoken language understanding (SLU), traditionally implemented as a cascade of speech recognition followed by one or more natural language understanding (NLU) components. The recent advent of speech-based large language models (speechLLMs), which integrate speech and textual foundation models, has opened new avenues for a

Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck

Recent research has shown that optimization proxies can be trained to high fidelity, achieving average optimality gaps under 1% for large-scale problems. However, worst-case analyses show that there exist in-distribution queries that result in orders of magnitude higher optimality gap, making it difficult to trust the predictions in practice. This paper aims

Xiran Bai, A. Droster, J. Echevers, Maryam H. Esmat

We report dark photon results from HAYSTAC Phase II using data from previously reported axion searches. Additionally, we present an analysis of an unpublished dataset covering a region between 19.46--19.52 $\mu$eV. This region overlaps with a recently reported dark photon signal at 19.5 $\mu$eV with a kinetic coupling strength of $|\chi_{\text{rand}}| \simeq

Panos C. Papageorgiou, Anastasios E. Giannopoulos, Sotirios T. Spantideas

Microgrids are emerging as key enablers of resilient, sustainable, and intelligent power systems, but they continue to face challenges in dynamic disturbance handling, protection coordination, and uncertainty. Recent efforts have explored Brain Emotional Learning (BEL) controllers as bio-inspired solutions for microgrid control. Building on this growing traj

Pramod Rao, Abhimitra Meka, Xilong Zhou, Gereon Fox

Rendering novel, relit views of a human head, given a monocular portrait image as input, is an inherently underconstrained problem. The traditional graphics solution is to explicitly decompose the input image into geometry, material and lighting via differentiable rendering; but this is constrained by the multiple assumptions and approximations of the underl

M. Saad Bin-Alam, Yunus Denizhan Sirmaci, Alejandro Fernández-Hinestrosa, Jianhao Zhang

Huygens' metawaveguides represent a transformative concept in photonic device engineering, enabling unprecedented control over light propagation. This study presents, for the first time, integrated Huygens'-based microring resonators and directional and contra-directional couplers, specifically designed for operation at the S- and C-band telecommunication wa

Shahriyar Jafarzade, Richard F. Lebed

We generalize our recent analysis of hidden-strangeness tetraquarks within the dynamical diquark model from its adiabatic form (in which each state is described solely by a diquark-antidiquark potential) to its diabatic form (which incorporates effects of di-hadron thresholds upon the states). We tabulate all relevant thresholds and compute the di-hadron con

Shayan Rokhva, Mousa Alizadeh, Maryam Abdollahi Shamami

Accurately detecting sentiment polarity and intensity in product reviews and social media posts remains challenging due to informal and domain-specific language. To address this, we propose a novel hybrid lexicon-fuzzy-transformer framework that combines rule-based heuristics, contextual deep learning, and fuzzy logic to generate continuous sentiment scores

Yuhang Chen, Tianpeng Lv, Siyi Zhang, Yixiang Yin

Academic project websites can more effectively disseminate research when they clearly present core content and enable intuitive navigation and interaction. However, current approaches such as direct Large Language Model (LLM) generation, templates, or direct HTML conversion struggle to produce layout-aware, interactive sites, and a comprehensive evaluation s

Jiayi Lin, Jiabo Huang, Shaogang Gong

Open-Vocabulary Semantic Segmentation (OVSS) assigns pixel-level labels from an open set of categories, requiring generalization to unseen and unlabelled objects. Using vision-language models (VLMs) to correlate local image patches with potential unseen object categories suffers from a lack of understanding of spatial relations of objects in a scene. To solv

Albert Linda, Rajdip Mukherjee, Somnath Bhowmick

Abnormal grain growth (AGG) influences the properties of polycrystalline materials; however, the underlying mechanisms, particularly the role of solute segregation at the grain boundary (GB), are difficult to quantify precisely. This study demonstrates a multiscale framework that integrates atomic-scale segregation energetics (using density functional theory

Dou Liu, Ying Long, Sophia Zuoqiu, Di Liu

Creating high-quality clinical Chains-of-Thought (CoTs) is crucial for explainable medical Artificial Intelligence (AI) while constrained by data scarcity. Although Large Language Models (LLMs) can synthesize medical data, their clinical reliability remains unverified. This study evaluates the reliability of LLM-generated CoTs and investigates prompting stra

Antonio Garcia Vallejo, Matthew D. Sievert

In this work, we extend the Impulse Approximation for the generalized parton distributions (GPDs) of a spin-0 composite hadron with spin-$\tfrac{1}{2}$ constituents to manifestly incorporate the symmetries of the target wave function. The method utilizes a light-front Wigner function representation instead of a spectral density and a basis of Pauli matrices

Vikash Singh

We present Transfer Orthology Networks (TRON), a novel neural network architecture designed for cross-species transfer learning. TRON leverages orthologous relationships, represented as a bipartite graph between species, to guide knowledge transfer. Specifically, we prepend a learned species conversion layer, whose weights are masked by the biadjacency matri

Kendall Ackley

The robust association of electromagnetic candidates discovered during follow-up of gravitational-wave alerts is challenging, not only due to the large sky areas and broad distance uncertainties, but also due to the tens to hundreds of unrelated optical transients that are observed per event. We present a Bayesian ranking method to identify electromagnetic c

Carsten Andrich, Isabella Varga, Tobias F. Nowack, Alexander Ihlow

Bistatic radar measurements offer unique spatial diversity and enhanced target characterization capabilities, rendering them increasingly vital for contemporary sensing application research. The reliability of such measurements is contingent upon precise system and antenna calibration. The prevailing technique is the substitution method, which involves the u

Ravi Kumar Sharma, Julien Lesgourgues

Recent BAO observations from DESI DR2 either hint at a possible dynamical dark energy component, which would worsen the Hubble tension, or at a 95\% credible interval for the summed neutrino mass hardly compatible with neutrino oscillation experiments. In this context, it is interesting to investigate constraints on neutrino masses, dark energy and the Hubbl

Sergey A. Cherkis, Mark Stern

We study the asymptotic structure of instantons on multi-centered Taub-NUT manifolds, calorons, and monopoles on R^3. We show that, without any assumptions on symmetry breaking, these instantons and monopoles asymptotically decompose as a sum of U(1) instantons and monopoles, respectively.

Hoang M. Ngo, Tamer Kahveci, My T. Thai

Quantum computing has the potential to revolutionize fields like quantum optimization and quantum machine learning. However, current quantum devices are hindered by noise, reducing their reliability. A key challenge in gate-based quantum computing is improving the reliability of quantum circuits, measured by process fidelity, during the transpilation process

J. Ma, R. Tazaki, H. M. Schmid, G. Duchêne

HD 100453 disk is a prototypical companion-disk interaction system hosting a pair of spirals and a substellar companion. We present new noncoronagraphic high-contrast imaging observations of HD 100453 with $V$ filter on SPHERE/ZIMPOL. We combined high-contrast imaging data of the reflected light from 0.55 to 2.2 $\mu m$ using the $V$, $I'$, $J$, and $Ks$ ban

Md. Imtyaz Ahmed, Md. Delwar Hossain, Md Mostafizer Rahman, Md. Ahsan Habib

Cancer exhibits diverse and complex phenotypes driven by multifaceted molecular interactions. Recent biomedical research has emphasized the comprehensive study of such diseases by integrating multi-omics datasets (genome, proteome, transcriptome, epigenome). This approach provides an efficient method for identifying genetic variants associated with cancer an

Do Xuan Long, Xingchen Wan, Hootan Nakhost, Chen-Yu Lee

Despite rapid advances in text-to-video synthesis, generated video quality remains critically dependent on precise user prompts. Existing test-time optimization methods, successful in other domains, struggle with the multi-faceted nature of video. In this work, we introduce VISTA (Video Iterative Self-improvemenT Agent), a novel multi-agent system that auton

Dominik Kallusky, Vinay Rao, Vishal Nandavanam, Hao-Jun Michael Shi

The rapid development of large language models (LLMs) has driven the demand for more efficient optimization techniques. Among these, the Lookahead family of optimizers employs a two-loop framework, maintaining fast and slow sets of model weights. Multiple inner optimizer steps on the fast weights produce a trajectory - the pseudo-gradient - that is used to u

Sasanka Dowarah

We investigate the spectral properties of all-to-all interacting spin Hamiltonians acting on exactly $k$ spins, whose coupling coefficients are drawn from a normal distribution with mean $\mu$ and variance $\sigma^2$. For $\mu = 0$, we demonstrate that the random matrix ensemble -- Gaussian Orthogonal Ensemble (GOE), Gaussian Unitary Ensemble (GUE), or Gauss

Marco D'Alessandro, Leo D'Amato, Mikel Elkano, Mikel Uriz

A central challenge in cognitive neuroscience is to explain how semantic and episodic memory, two major forms of declarative memory, typically associated with cortical and hippocampal processing, interact to support learning, recall, and imagination. Despite significant advances, we still lack a unified computational framework that jointly accounts for core

Mark C. Baumann, Justin C. Feng, Nicky Ishaak

How important are gravitational and relativistic effects for interstellar travel? We consider this question in the context of proposed laser-propelled spacecraft missions to neighboring stellar destinations. Our analysis applies to any spacecraft traveling at relativistic speeds. As a concrete example, we focus on a mission to Proxima Centauri b -- a terrest

Antonio Gondim, Leonardo Schultz, Xi Huo, Shigui Ruan

Chikungunya virus is a mosquito-borne arbovirus with the potential to establish sustained transmission in subtropical regions like Florida, where climatic and ecological conditions support vector proliferation. In this study, we develop a Continuous Time Markov Chain model to assess the probability of long-term Chikungunya establishment in Miami-Dade County

Lê Dũng Tráng, Juan J. Nuño-Ballesteros, José Seade

Consider a singular holomorphic map-germ $f: (X,\underline{0}) \to (\mathbb C,0)$ where $X$ is a singular complex analytic variety in $\mathbb C^N$, and another holomorphic map-germ $g: (X,\underline{0}) \to (\mathbb C,0)$ which is "sufficiently good" relatively to $f$. We use stratified Morse theory to determine up to homeomorphism, the topology of the Miln

Yanlong Shi, Liang Dai, Norman Murray, Claire S. Ye

The lensed Sunburst Arc ($z = 2.369$) hosts a young ($\sim2$--$4\,\rm Myr$), massive ($M_\star \sim 10^7\,M_\odot$), compact ($R_{\rm eff} \sim 8\,\rm pc$) Lyman-continuum (LyC) leaking super star cluster, which powers a compact ($< 10\,\rm pc$), high-pressure nebula at sub-solar metallicity $\sim0.2\,Z_\odot$ and with an anomalously elevated nitrogen-to-oxy

Leah Bigwood, Masaya Yamamoto, Jared Siegel, Alexandra Amon

Baryonic feedback remains one of the largest uncertainties in cosmological hydrodynamical simulations, with different prescriptions producing divergent predictions for the fraction of gas expelled from halos, the radial extent of the gas expulsion and the impact on large scale matter clustering. We present the first systematic study of the kinetic Sunyaev-Ze

Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken, Andreas Auer

Pretrained time series models have enabled inference-only forecasting systems that produce accurate predictions without task-specific training. However, existing approaches largely focus on univariate forecasting, limiting their applicability in real-world scenarios where multivariate data and covariates play a crucial role. We present Chronos-2, a pretraine