April 2026 arXiv papers — page 50

Giulio Delama, Jan Michalczyk, Morten Nissov, Martin Scheiber

Radar-Inertial Odometry (RIO) based on the Extended Kalman Filter (EKF) relies on accurate extrinsic calibration between the radar and the Inertial Measurement Unit (IMU) and is sensitive to disturbances, as large linearization errors can degrade performance or even cause divergence. To address these limitations, this letter proposes an Equivariant Filter (E

Daxiong Piao

We give a direct algebraic proof of the necessity direction in the single-point higher-order Szeg\H{o} sum rules on the unit circle for $m=1,2,3$. More precisely, for $H_m(e^{i\theta})=(1-\cos\theta)^m$, we show that $\int_0^{2\pi}H_m(e^{i\theta})\log w(\theta)\frac{d\theta}{2\pi}>-\infty$ implies $(S-1)^m\alpha\in\ell^2,\qquad \alpha\in\ell^{2m+2}.$ The pro

Hunter Nelson, Edwin Barnes

The speed of quantum evolution is limited under finite energy resources. While most quantum speed limits (QSLs) are formulated in terms of quantum states, they can be extended to the evolution operator itself, and thus impose fundamental limits on how quickly logical gate operations can be implemented on a quantum computer. Here, we derive a general, tight Q

Li Zhaoping

Progress in vision research has been slower downstream than upstream of primary visual cortex (V1). Traditional frameworks have largely overlooked a central constraint: only a tiny fraction of retinal input is recognized. Thus, to a first approximation, vision is better formulated as looking and seeing through a bottleneck. Looking, mainly by the peripheral

Yiqian Liu, Jiayi Niu, Adam Kelleher, Subhabrata Das

Natural gas is undoubtedly an essential component of the global energy system. Accurate short-term forecasting of natural gas price is challenging due to pronounced volatility driven by seasonal demand patterns, geopolitical developments, and shifting macroeconomic conditions. The nonlinear dynamics and frequent regime changes can limit the effectiveness of

Alana McGovern, Geir-Arne Fuglstad, Jon Wakefield

Fay-Herriot (FH) models with variance smoothing typically use chi-squared sampling distributions for the design variance estimators. This choice is only valid under strong assumptions on the population and the sampling design, and the choice of sampling distribution is understudied for complex survey designs such as the stratified two-stage clustering design

Katrina Winkler, Anil Rao

A computational method is developed for desensitized optimal guidance using adaptive Gaussian quadrature collocation. The method computes a reference trajectory that reduces the sensitivity to uncertainties in the dynamic model by augmenting the objective functional to explicitly penalize the sensitivity of the state with respect to uncertain parameters. Usi

Basel Shbita, Anna Lisa Gentile, Bing Zhang, Sungeun An

Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based reasoning. However, debugging these models remains a persistent challenge due to their opaque and probabilistic nature and the difficulty of diagnosing errors across diverse tasks and settings. This paper intro

Robert T. Nachtrieb, Steven J. Smith

At any sustained positive growth rate of energy demand, depletion of all terrestrial energy resources, including non-renewable deuterium fusion and renewable solar, occurs within a remarkably compressed period. The time to depletion is inversely proportional to the demand growth rate. Artificial Intelligence (AI) has the potential to increase the growth rate

Teng Zhang

Higham's conjecture on the growth factor of complex symmetric positive definite matrices is a longstanding problem in the stability theory of Gaussian elimination without pivoting. It asserts that every complex matrix $A=B+iC$ with $B$ and $C$ real symmetric positive definite, is called Higham matrix and has growth factor $\rho_n(A)<2$. In 2013, Drury [Linea

Aleksey Kolokolov, Shifan Yu

We develop a continuous-time penalized regression framework for the estimation of time-varying coefficients and variable selection when both the response and covariates are It\^o semimartingales with jumps. The coefficient paths are approximated by spline basis expansions and estimated via least squares from truncated high-frequency increments. In a finite-d

Nilson Chapagain

Two-stage recommender systems first choose a candidate generator and then rank items within the generated set. Because the generator decides which items are available to the ranker, changing the generator changes both the policy value and the data support used to estimate that value. This creates an offline selection problem that standard single-stage object

Sariel Har-Peled

Consider a stream of $n$ random points (say, from the unit square) arriving one by one, where a player has to make an irreversible immediate decision for each arriving point whether to pick it. The player has to pick a single point, and the payoff is the area of the cell of the picked point, in the final Voronoi diagram of \emph{all} the points. We show that

Spyros Garouniatis, Grega Saksida, Vedran Sohinger

We study the large-mass limit of interacting quantum (Bose or Fermi) gases in thermal equilibrium. We show that in the suitably-defined large-mass limit, the system gives rise to a gas of classical interacting particles. The corresponding question for bosons on a lattice was previously addressed by Fröhlich, Knowles, Schlein, and the third author. In this wo

Sulagna Saha, Arthur Ouaknine, Etienne Laliberté, Carol Altimas

Accurate classification of tropical tree species from unoccupied aerial vehicle (UAV) imagery remains challenging due to high species diversity and strong visual similarity among species at typical image resolutions (centimeters per pixel). In contrast, models trained on close-up citizen science photographs captured with smartphones achieve strong plant spec

Mathias Graf, Marco Willi, Melanie Mathys, Michael Aerni

AI-powered generative models have significantly expanded the possibilities for editing, manipulating, and creating high-quality images. Particularly, images that falsely appear to originate from trusted sources pose a serious threat, undermining public trust in image authenticity. We propose DeepSignature, a novel approach that integrates the guarantees of d

Saswata Jana, Partha Sarathi Mandal

Collaboration between drones and trucks in a last-mile delivery system offers numerous benefits and reduces many challenges of the traditional delivery system. Here, we introduce Drone-Delivery Packing Problem, where a set of parcels, associated with delivery intervals and cost, should be delivered to customer locations. The system comprises a set of identic

Caio V. S. Soares, Rodrigo G. Pereira

The excitation spectrum of Kitaev quantum spin liquids consists of itinerant Majorana fermions, which can be gapless or gapped, and vortices of a $\mathbb{Z}_2$ gauge field, known as visons, which are \new{gapped within a stable $\mathbb Z_2$ spin liquid phase}. In this work, we investigate visons in Kitaev-type models where the Majorana fermions form a Ferm

Emre Aklan, Fatih Seker, Bekir Gencalioglu, Mehmet Batuhan Kaya

This paper presents an integrated lander-propulsion-GNC framework for autonomous lunar powered descent. The BUG VTVL test vehicle serves as the reference platform, with the YUNT V0 throttleable bipropellant engine providing variable thrust across a wide operating envelope, integrated with a real-time successive convexification guidance solver. The vehicle de

Jeremy Ellis

This paper presents a complete, end-to-end on-device vision machine learning pipeline, comprising data acquisition, two-layer CNN training with Adam optimization, and real-time inference, executing entirely on a microcontroller-class device costing $15-40 USD. Unlike cloud-based workflows that require external infrastructure and conceal the computational pip

Ze Yang, Jingkang Wang, Haowei Zhang, Sivabalan Manivasagam

High-quality 3D assets for traffic participants are critical for multi-sensor simulation, which is essential for the safe end-to-end development of autonomy. Building assets from in-the-wild data is key for diversity and realism, but existing neural-rendering based reconstruction methods are slow and generate assets that render well only from viewpoints clos

Guojing Li, Zichuan Fu, Junyi Li, Wenxia Zhou

Job Skill Named Entity Recognition (JobSkillNER) aims to automatically extract key skill information from large-scale job posting data, which is important for improving talent-market matching efficiency and supporting personalized employment services. To the best of our knowledge, this work presents the first Chinese JobSkillNER dataset for recruitment texts

Yhon Flores, Michel Molina del Sol, Genly Leon, Byron Droguett

This work presents an analytical and computational study of fractional-order delay differential equations formulated using both the conformable and Caputo derivatives. For the conformable case, we develop the associated integral, exponential function, and Laplace transform, showing how the conformable Laplace framework preserves algebraic structure and facil

F. E. S. Steinhoff

We develop an angular momentum representation and implementation of the Clifford+T set of unitaries for qutrits. We show that local gates from this set can be realized by the sole use of suitable rotations and one-axis-twisting operations, which are at most quadratic in the angular momentum operators and thus can be experimentally realized in many quantum sy

Madeline R Abbott, Jeremy M G Taylor, Inbal Nahum-Shani, Lindsey N Potter

Not only does mobile health technology enable researchers to track changes in multiple longitudinal outcomes of interest and to record the occurrence of health-related events over time, but it also allows for the delivery of repeated low-cost treatments directly to individuals in real time. We present a model-based approach for estimating the effect of repea

Maciej Sikora, Leszek Siwik, Natalia Leszczyńska, Tomasz Maciej Ciesielski

In this paper, we propose a Physics-Informed Neural Network framework for time-dependent simulations of pollution propagation originating from moving emission sources. We formulate a robust variational framework for the time-dependent advection-diffusion problem and establish the boundedness and inf-sup stability of the corresponding discrete weak formulatio

Jordan Meadows, Lan Zhang, Andre Freitas

Formalising informal mathematical reasoning into formally verifiable code is a significant challenge for large language models. In scientific fields such as physics, domain-specific machinery (\textit{e.g.} Dirac notation, vector calculus) imposes additional formalisation challenges that modern LLMs and agentic approaches have yet to tackle. To aid autoforma

Ziyao Wang, Bingying Wang, Hanrong Zhang, Tingting Du

Despite remarkable progress in Vision--Language--Action (VLA) models, a central bottleneck remains underexamined: the data infrastructure that underlies embodied learning. In this survey, we argue that future advances in VLA will depend less on model architecture and more on the co-design of high-fidelity data engines and structured evaluation protocols. To

Soham Kulkarni, Raayan Dhar, Yuchen Cui

In behavioral cloning (BC), policy performance is fundamentally limited by demonstration data quality. Real-world datasets contain trajectories of varying quality due to operator skill differences, teleoperation artifacts, and procedural inconsistencies, yet standard BC treats all demonstrations equally. Existing curation methods require costly policy traini

Sun Kwok, Bruce Balick, You-Hua Chu, Bruce J. Hrivnak

While there has been significant progress in our understanding of the origin and evolu-tion of planetary nebulae in the last 50 years, there remain several unsolved problems. These include the true 3D morphological structure of the nebulae, origin of multipolar nebulae, the dust and molecular distribution relative to the optical nebulosity, large-scale struc

Benjamin L. Badger, Ethan Roland

Transformer-based large language models are in some respects limited by the quadratic time and space computational complexity of attention. We introduce the Toeplitz MLP Mixer (TMM), a transformer-like architecture that swaps attention for triangular-masked Toeplitz matrix multiplication over the sequence dimension resulting in $\mathcal{O} (dn \log n)$ time

A. Iadicicco, G. Modanese, L. Verolino

We examine whether the observer split of a covariantly conserved electric current in a rotating finite system can provide a physically constrained effective source for the scalar sector of extended Aharonov--Bohm electrodynamics. We first recall that rotation, a stationary gravitomagnetic background, or a change of coordinates cannot by themselves generate g

Abeer M. Hasan, Sayed A. Mostafa

This study investigates perceptions and use of generative artificial intelligence (GenAI) tools among students and faculty in statistics and data science at a historically Black college or university. Survey data from 119 valid student responses and 14 faculty responses were used to examine familiarity, usage patterns, perceived benefits, awareness of limita

Jason Lamanna, Erfan Mowlaei, Xinghua Shi, Sudhir Kumar

Homologous proteins evolve from a common ancestral sequence, constrained by intricate patterns of co-evolving residues. Accurate reconstruction of evolutionary histories remains a challenge, primarily due to the inability of the existing approaches to capture long-range coevolutionary ties and lack of a precise metric to represent the evolutionary distance b

Jiaqi Leng, Jiaqing Jiang, Lin Lin

Szegedy's quantum walk gives a generic quadratic speedup for reversible classical Markov chains, but extending this mechanism to quantum Gibbs sampling has remained challenging beyond special cases. We present a walk-free quantum algorithm for preparing purified Gibbs states with a quadratic improvement in spectral-gap dependence for a broad class of quantum

Robert T. Nachtrieb

We derive equations of motion for capital deepening in a competitive economy directly from accounting identities, without assuming a production function. A profit imperative $\eta^* \equiv (w/\kappa + 1/\tau)/(1-f_p)$ sets the minimum viable capital productivity, where $\eta = Y/K$ [yr$^{-1}$] is capital productivity, $\kappa = K/L$ is capital per worker, $w

P. Darc, C. D. Kilpatrick

Real-time ranking of optical transient candidates during gravitational-wave (GW) and multimessenger follow-up is challenging when only sparse early-time, multi-band photometry is available.We present \texttt{KilonovaSCORER}, an open-source framework for scoring and ranking in this regime. It quantifies the consistency of each candidate with a physically moti

Oleksandr Borysov, Rotem Dover, Eilam Gross, Nilotpal Kakati

High-fidelity simulation of particle-matter interactions provides the essential theoretical reference for diverse physics disciplines, yet generating synthetic datasets at the scale of current and future experiments has become prohibitive. Here, we introduce PHIN-GAN, a novel physics-informed generative adversarial network designed to address this challenge.

Vitalii Tutevych, Raphael Memmesheimer, Luca Eichler, Dmytro Pavlichenko

Reliable object perception is necessary for general-purpose service robots. Open-vocabulary detectors struggle to generalize beyond a few classes and fully supervised training of object detectors requires time-intensive annotations. We present a semi-supervised label propagation approach for household object segmentation. A segment proposer generates class-a

Renjith Prasad, Rishabh Sharma, Andrew E. Shao, Annmary Justine Koomthanam

Subtle visual anomalies such as hairline cracks, sub-millimeter voids, and low-contrast inclusions are structurally atypical yet visually ambiguous, making them both difficult to annotate and easy to overlook during active learning. Standard acquisition heuristics based on discriminative uncertainty or feature diversity often overselect dominant patterns whi

Ashwin Kumar, Robbie Holland, Corey Barrett, Jangwon Kim

Recent medical multimodal foundation models are built as multimodal LLMs (MLLMs) by connecting a CLIP-pretrained vision encoder to an LLM using LLaVA-style finetuning. This two-stage, decoupled approach introduces a projection layer that can distort visual features. This is especially concerning in medical imaging where subtle cues are essential for accurate

Rachel Woods-Robinson, Amalie Trewartha

While material innovation can enable sustainable development, environmental and social impacts of emerging materials are often assessed only after design choices are "locked in." Here, we argue for a shift in perspective: life cycle thinking should enter at the earliest stages of materials development, where uncertainty is highest but design freedom is great

Samantha Hemmelgarn, Nicholas Moskovitz, Denis Vida

We use machine learning to develop a framework for classifying meteoroids based on 13 directly observed parameters from the Global Meteor Network. This method adds depth to the $K_{b}$ parameter, which uses only three parameters. We employ a semi-qualitative approach using 28,177 meteor events observed in 2023 by the Lowell Observatory Cameras for All-Sky Me

Zihuiwen Ye, Lukas Aichberger, Michael Kirchhof, Sinead Williamson

Large Language Models (LLMs) are increasingly deployed to autonomously solve real-world tasks. A key ingredient for this is the LLM Function-Calling paradigm, a widely used approach for equipping LLMs with tool-use capabilities. However, an LLM calling functions incorrectly can have severe implications, especially when their effects are irreversible, e.g., t

Peter Kulits, Cordelia Schmid

We train a language model to generate LEGO-brick build sequences. While prior work has been restricted to discrete, voxel-like towers, we consider a much broader set of pieces, encompassing thousands of part types with diverse connection semantics. To enable this, we first collect a large-scale dataset of over 100,000 human-designed LDraw brick objects and s

Mariia Zamyrova, Henk van den Heuvel

As part of the MediSpeech project, we aim to develop a system that transcribes and diarizes Dutch conversations between doctors and patients in real-time. In this research (in-progress) we explore ways of efficiently combining Whisper with speaker diarization (SD). After trying to prompt Whisper with text that contains speaker labels, we observed that it is

Alex Nikulkov

Reward models in RLHF are trained to score only the final token of a response - a choice that discards rich signal from every intermediate position and produces models whose token-level outputs are noise. We argue this is a missed opportunity: a well-trained reward model's output at any token should represent the conditional expectation of the final reward g

Marija Cuparić, Bojana Milošević, Jelena Radojević

In this paper, we consider the problem of testing independence in high-dimensional settings with missing data. Building upon a recently proposed Kendall-based statistic, we introduce two new modifications specifically designed to accommodate incomplete observations. The proposed methods are studied from both theoretical and empirical perspectives. A comprehe

Luca Cotti, Luca Lavazza, Marco Cominelli, Liying Han

We address Human Activity Recognition (HAR) utilizing Wi-Fi Channel State Information (CSI) under the joint requirements of causal interpretability, symbolic controllability, and direct operation on high-dimensional raw signals. Deep neural models achieve strong predictive performance on CSI-based HAR (CHAR), yet rely on continuous latent representations tha

Valerio Buttinelli, Angelo Felice Lopez, Roberto Vacca

We give an almost complete classification of Ulrich bundles $\mathcal E$ with $c_2(\mathcal E)^2=0$ on a variety $X$ of dimension $n \ge 4$. Moreover, we show that there are strong constraints on the geometry of $X$ and we study disconnected Ulrich subvarieties.

Mahdi Dolatkhah, Hossein Hashemi Doulabi, Walter Rei, Michel Gendreau

In this paper, we propose a novel mixed integer programming model to formulate integrated operating room planning and scheduling problems, where several mandatory and elective surgeries are to be assigned and scheduled in operating rooms on different days. We consider both overtime in operating rooms and surgeons' daily availability limits. We propose a colu

Robert T. Nachtrieb

The distribution of income and wealth in developed economies exhibits a robust two-class structure: an exponential (Boltzmann--Gibbs) bulk covering $\sim\!97\%$ of the population, and a power-law (Pareto) tail in the upper $\sim\!3\%$. We derive this structure from first principles via an explicit mechanistic chain: Gibrat's law for firm growth implies a Zip

Julian Meunier, Brian R. McNamara, Aurora Simionescu, François Mernier

We present an analysis of the temperature structure of the Perseus cluster atmosphere using XRISM Resolve observations. The average temperature rises from 3.3 keV near the nucleus of NGC 1275 to 8 keV at 10 arcmin (210 kpc), which is consistent with Chandra and XMM measurements. The velocity and velocity dispersion profiles are broadly consistent with those

Du Ran, Reuven Ianconescu, Shuai Liu, Ya-dong Li

We propose a protocol that transfers entanglement from an entangled atomic two-level-system (TLS) resource to a pair of free electrons in an energy-sideband ladder via local electron-TLS interactions. In a controlled rotating-wave regime, closed-form reduced states are derived. TLS heralding then prepares a maximally entangled electron state in a two-dimensi

Nadya Abdel Madjid, Murad Mebrahtu, Zakhar Yagudin, Bilal Hassan

Predicting future trajectories of surrounding traffic agents is critical for safe autonomous navigation and collision avoidance. Despite all advances in the trajectory forecasting realm, the prediction models remains vulnerable to uncertainty caused by occlusions, limited sensing range, and perception errors. Collaborative vehicle-to-vehicle (V2V) approaches

Viviana Gubitosi, Claudio Qureshi

In this paper, we present an explicit and purely combinatorial characterization of the $m$-coloured quivers that appear within the $m$-coloured mutation class of a quiver of type $\mathbb{D}_n$. The $m$-coloured mutation, as defined by Buan and Thomas in \cite{BT}, generalises the well-known quiver mutation introduced by Fomin and Zelevinsky \cite{FZ}. Conse

Juergen Dietrich

The TRUST democratic discourse analysis pipeline exposes its large language model (LLM) components to peer model identity through multiple structural channels -- a design feature whose bias implications have not previously been empirically tested. We provide the first systematic measurement of identity-dependent scoring bias across all active identity exposu

Gerald Neumann, Nelson Videla, Dorian Araya

This paper examines interacting dark energy models within the Chevallier-Polarski-Linder (CPL) parametrization, emphasizing both theoretical structure and observational viability. Two commonly adopted interaction terms are considered: $Q = \beta H \rho_{de}$ and $Q = \beta H \rho_c$. We derive exact analytic solutions that describe how the dark sector evolve

Elena Fernandez Bravo, Sunil Tamang, Yong Hoon Lee, James T. Allison

This work presents a design coupling analysis (DCA) framework to investigate the interactions among control and plant design variables in floating offshore wind turbine (FOWT) and to support the formulation of tractable control co-design (CCD) optimization strategies. DCA provides quantitative information that reveals the relationships and dependencies among

Mariya Iv. Trukhanova, Pavel A. Andreev

The spin-current model of electric polarization of spin origin is developed for a magnetic structure with anisotropic tensor gyromagnetic ratio (g-factor). Three mechanisms of the magnetoelectric effect are proposed, caused by the symmetric Heisenberg exchange interaction, the Dzyaloshinsky-Moriya interaction, and the spin-spin interaction related to the odd

Wei-Ting Tang, Joel A. Paulson

Trust Region Bayesian Optimization (TuRBO) is an effective strategy for alleviating the curse of dimensionality in high-dimensional black-box optimization. However, inappropriate lengthscale design can cause the local Gaussian process (GP) model within the trust region to degenerate, leading to suboptimal performance in high dimensions. In this work, we show

Ronny Vallejos

Agreement coefficients provide a fundamental framework for quantifying the concordance between two or more measurement methods applied to the same continuous variable. Unlike correlation, which measures the strength of a linear relationship, agreement focuses on assessing whether measurements are numerically similar, capturing both precision and accuracy. Th

Rahul Patel

Anemia affects over one billion people globally and remains severely under-diagnosed in low-resource regions where laboratory blood tests are inaccessible. This paper presents AnemiaVision, an end-to-end web-based system for non-invasive anemia screening from smartphone photographs of the palpebral conjunctiva and fingernail beds. The proposed pipeline fine-

F. Monroy, J. A. Santiago

We develop a continuous Wilsonian renormalized-flow theory of weak wave turbulence directly in spectral frequency space, for finite cascades in experimentally driven Newtonian fluids. The central quantity is a scale-dependent effective coupling that governs nonlinear transfer across logarithmic frequency shells and organizes the cascade as a finite renormali

Parker Thomas Grobe

Bio-inspired propulsion using oscillating fins has gained attention for its potential to achieve high thrust, efficiency, and maneuverability. Many aquatic organisms generate propulsion through coordinated fin oscillations, and understanding these hydrodynamic mechanisms can inform the design of advanced underwater vehicles. A numerical framework is develope

Sohrab Rahvar

Planetary systems can act as sensitive gravitational detectors for dark matter. We investigate the gravitational scattering of Oort cloud objects by primordial black holes (PBHs) as a potential component of the Galactic dark matter halo. Calculating the rates at which PBH encounters eject objects from the Oort cloud or inject them into Earth crossing orbits,

Luis Morales-Navarro, Deborah Fields, Michael T. Giang, Daniel J. Noh

Despite growing calls to foster AI literacy, there are few available survey instruments designed for children and youth that study computational empowerment alongside construction and deconstruction activities. In such activities, learners' beliefs about their abilities and attributes can impact their engagement. In this paper, we introduce and validate a su

Alessio Zaninotto, Bruno Yun, Nir Oren, Srdjan Vesic

Preference-based argumentation frameworks (PAFs) extend Dung's approach to abstract argumentation (AAFs) by encoding preferences over arguments. Such preferences control the transformation of attacks into defeats, and different approaches to doing so result in different reductions from a PAF to an AAF. In this paper we consider a PAF inverse problem which ta

Philippe Anjolras

We consider the Zakharov-Kuznetsov equation in space dimension 3: \[ \left\{ \begin{array}{l} \partial_t u + \partial_x \Delta u + \partial_x \frac{u^2}{2} = 0 \\ u(t = 0) = u_0 \end{array} \right. \] where $u : (t, x, y) \in \mathbb{R} \times \mathbb{R} \times \mathbb{R}^2 \mapsto u(t, x, y) \in \mathbb{R}$, and $\Delta = \partial_x^2 + \Delta_y$ is the ful

Philip Gaddy

We first prove a homogenization result for the fundamental solution of the linear kinetic Fokker Planck equation. We show that this solution converges, in an averaged $L^2$ sense, to the fundamental solution of an effective heat equation with constant effective diffusivity determined by corrector functions solving associated cell problems on the torus. A key

Conall McCabe, Jamie Boyd, Kaizhao Wang, Martin Lebrat

Strongly-correlated electrons in transition-metal oxides give rise to intriguing emergent phenomena, including high-temperature superconductivity in cuprates. While simplified one-band Hubbard models capture some aspects, explicitly describing the interplay of copper and oxygen orbitals -- as in the three-band Emery model -- is essential to capture the full

Fan Zhang, Lee Lindblom

This paper presents solutions to Einstein's equation -- and the numerical methods used to construct them -- that describe simple cosmological models on manifolds with compact non-orientable spatial slices. These solutions have been constructed on a selection of manifolds having positive, negative, and vanishing spatial scalar curvatures. One example is shown

J. A. O. Marinho, J. P. B. C. de Melo, T. Frederico, W. de Paula

We construct an effective light-front mass-squared operator for quark-antiquark systems that incorporates quark dressing effects through a running quark mass. Starting from a Minkowski-space quark propagator constrained by lattice-QCD-inspired parametrization, we derive the disconnected light-front resolvent for a light quark-antiquark system using a general

Y. Y. Sigari, N. A. Simonson, N. Appathurai, R. Castle

This paper investigates a case study on measuring and controlling the first-order degree of spatial coherence under different coupling adjustments in the storage ring. The experimental findings are consistent with the predicted inverse relationship between the visibility and the coupling factor. The degree of coherence was measured using X-ray double slit in

Tilman Bauer, Mayuko Yamashita

We show that the topological elliptic genus from the cobordism ring of SU-manifolds to topological Jacobi forms lifts to connective topological Jacobi forms, and that this lift is surjective in homotopy.

Huaning Liu, Gokce Dayanikli

We study a game-theoretic model of epidemic control in a large population with finitely many groups and non-cooperative individuals. In the model, individuals jointly choose their socialization levels and vaccination rates, and vaccination is subject to a linear individual cost structure. We derive a forward-backward ordinary differential equations (FBODE) s

Philip Gaddy

We prove a Nash-Aronson-type upper bound on the fundamental solution of the linear kinetic Fokker Planck equation with friction term, distinguishing two regimes. For long times, we derive a Gaussian upper bound matching the classical parabolic estimate, which reflects the averaging of the velocity variable that occurs in this regime. For short times, the fun

Nikoo Moradi, Gijs Luijten, Behrus Hinrichs-Puladi, Jens Kleesiek

Diffusion models produce high-quality synthetic data but suffer from slow inference. We propose 3D Variable-Step Denoising Diffusion Probabilistic Model (VS-DDPM) a framework engineered to maintain generative quality while accelerating inference by several factors. We tested our approach on four tasks (missing MRI, tumor removal, MRI-to-sCT, and CBCT-to-sCT)

Guillaume Valette

We focus on Borel measures that have a globally subanalytic density function. We prove, given such a measure $\mu$ on a set $A$ and a globally subanalytic mapping $\Phi:A\to \Omega$, with $\Omega$ bounded open subset of $\mathbb{R}^n$, a Sobolev embedding theorem for the Sobolev space $W^{k,p}_{\Phi_*\mu}(\Omega)$ of the push-forward measure $\Phi_*\mu$. We

Richard H. J. Kim, Sunwoong Yang, Taehoon Kim, Samuel J. Haeuser

This study investigates the nanoscale conductivity of encapsulated monolayer graphene at temperatures down to 5 K and magnetic fields of up to 1 T. We use the scattering-type scanning near-field optical microscopy (s-SNOM) technique to probe magnetic-field-dependent responses from graphene close to charge neutrality in the terahertz spectral region. We obser

Alireza Rafiei, Anahí Venzor Strader, Esteban Castro Aragón, Victoriana Rosibely Sut Serech

Hypertensive disorders of pregnancy remain a leading cause of maternal and fetal morbidity worldwide, yet diagnosis relies on intermittent cuff-based blood pressure measurements that are prone to bias and fail to capture continuous physiological dynamics. Growing evidence suggests that fetal cardiovascular activity is associated with maternal-placental hemod

Mengyu Wang, Xiaoying Zhi, Zhiyi Li, Robin Schmucker

While the next-token prediction (NTP) paradigm enables large language models (LLMs) to express their intrinsic knowledge, its sequential nature constrains performance on specialized, non-generative tasks. We attribute this performance bottleneck to the LLMs' knowledge expression mechanism, rather than to deficiencies in knowledge acquisition. To address this

Pouya Pezeshkpour, Estevam Hruschka

Verification is becoming central to both reinforcement-learning-based training and inference-time control of large language models (LLMs). Yet current verifiers face a fundamental trade-off: LLM-based verifiers are expressive but hard to control and prone to error, while deterministic executable verifiers are reliable and interpretable but often limited in c

Lydia Chelli, Aurélien Perera

Binary mixtures of two dimensional, site-based models of alcohols are investigated by computer simulations, with a focus on ideal mixing, local clustering and miscibility trends. Four representative systems are considered: methanol/ethanol, butanol/pentanol, methanol/pentanol, and methanol/octanol. The models retain chemical specificity, while allowing to in

Voktho Das, M Zafir Sadik Khan, Jafar Vafaei, Kimia Azar

Edge deployment of transformer-based models increasingly relies on ASIC accelerators due to their high performance and energy efficiency, achieved through optimized dataflows, specialized architectures, low-bitwidth computation, and efficient memory hierarchies. However, these advantages come with significant security vulnerabilities. ASIC-based DNN accelera

Tanmay Parekh, Ella Hofmann-Coyle, Shuyi Wang, Sachith Sri Ram Kothur

LLM-based agents for text-to-SQL often struggle with latency-performance trade-off, where performance improvements come at the cost of latency or vice versa. We reformulate text-to-SQL generation within the lens of software test coverage where the original query is prepared with a suite of test cases with simpler, atomic SQLs that are executed in parallel an

Thomas Conlon, John Cotter, Iason Kynigakis

We demonstrate that machine learning methods provide a powerful framework for modelling conditional asymmetric risk. Using a large cross-section of US stocks and a comprehensive set of firm characteristics, we show that allowing for nonlinearities significantly increases the out-of-sample performance across a wide range of asymmetric beta measures and foreca

Henry H. Hsieh, John W. Noonan, Michael S. P. Kelley, Dennis Bodewits

We report results from an analysis of the volatile composition and evolution of main-belt comet (MBC) 133P/Elst-Pizarro using JWST NIRSpec and NIRCam observations and ground-based observations during its 2024 active apparition, and also assess the body of JWST MBC observations acquired to date. Using NIRSpec, we measure water vapor outgassing rates at two po

John W. Noonan, Henry H. Hsieh, Michael S. P. Kelley, Dennis Bodewits

We present JWST NIRSpec and NIRCam observations of 457P/Lemmon-PANSTARRS, a main-belt comet that displayed activity around its 2020 perihelion and that was observed to regain activity during its 2024 perihelion by a ground-based observing campaign. The previous successful measurements of water production from two main-belt comets by the JWST NIRSpec instrume

Sergio A. Dzib, Jazmín Ordóñez-Toro, Laurent Loinard, Marina Kounkel

We present results from a multi-epoch Very Long Baseline Array (VLBA) survey conducted as part of the DYNAMO-VLBA project, aimed at measuring the dynamical masses of young stellar systems in the Orion complex. Our observations include 19 radio sources associated with 15 binary or multiple young systems. For four visual binaries in which both components were

F. E. A. de Souza, M. O. Tahim, R. I. de Oliveira Júnior, I. M. Macêdo

In this work we analyze the classical confinement of relativistic and supersymmetric spinning particles in higher-codimension braneworlds. Considering warped backgrounds generated by string-like $ n=2$ and global scalar defects $ n \geq 3$, we derive the effective radial dynamics from a Polyakov-type action. For spinless particles, the effective potential is

M. R. Magee

The physics driving type Ia supernovae (SNe~Ia) standardisation in cosmology remains poorly-understood. Recent advances however mean that it is now possible to systematically analyse the explosion properties of large numbers of cosmological SNe~Ia. To that end we use riddler, a machine learning based framework for rapidly modelling SNe~Ia based on realistic

M. R. Magee

Observations of type Ia supernovae (SNe Ia) have led to suggestions of multiple progenitor and explosion scenarios. Distinguishing between scenarios and tying specific SNe Ia to individual scenarios however has so far been challenging. Constraints on the explosion physics are often achieved through empirical modelling of SNe Ia spectra and qualitative assess

F. Andrade-Canto, F. J. Beron-Vera

Pyro-cumulonimbus convection associated with extreme wildfires can generate long-lived vortical structures in the stratosphere. These structures have been described as coherent, yet a rigorous material characterization has remained lacking. Here we provide such a characterization by applying geodesic vortex detection to reanalysis winds during the 2019--2020

Matthew Esmaili Mallory, Mark Glickman, Jason Brown

Statistical modeling of popular music presents a unique challenge due to the complexity of song structures, which cannot be easily analyzed using conventional statistical tools. However, recent advances in data science have shown that converting non-standard data objects into real vector-valued embeddings enables meaningful statistical analysis. In this work

Antti Rantala

Numerical simulations have established that star clusters with densities comparable to the high redshift ($z>6$-$10$) James Webb Space Telescope (JWST) proto globular clusters may build up extremely massive (EMSs; $m_\mathrm{\star}>1000 M_\odot$) or even supermassive stars (SMSs; $m_\mathrm{\star}>10000 M_\odot$) and potentially intermediate mass black holes

Kevin T. Geier, Giampiero Marchegiani, Vijay Pal Singh, Juan Polo

Driven atomic Josephson junctions allow one to monitor phase-coherent dynamics with unprecedented control and flexibility of the system's physical conditions. While cold-atom manifestations of the Josephson effect have been extensively studied in a wide variety of settings, atomic Josephson junctions in synthetic electromagnetic fields remain largely unexplo

Laura Borrelli, Matteo Guainazzi, Giorgio Lanzuisi, Enrico Piconcelli

Ultra-fast outflows (UFOs) are mildly relativistic (outflow velocity $v_{out}>0.1c$) nuclear winds detected as blueshifted absorption lines from highly ionized, dense gas in the X-ray spectra of active galactic nuclei. The AGN feedback mechanism is believed to be powered by these outflows, which can inject a large amount of energy and momentum into the surro

Selene Levis, Facundo Rodriguez, Héctor J. Martínez, Valeria Coenda

Green valley (GV) galaxies are thought to represent a transitional population between star-forming and quiescent systems. However, their spatial distribution relative to galaxy systems remains unclear, particularly in relation to the large-scale environmental influence on galaxy quenching. We aim to determine whether GV galaxies preferentially inhabit specif

Pradyumna Sadhu, Laura V. Sales, Julio F. Navarro, Raphaël Errani

Cosmological simulations of galaxy clusters are unable to resolve dwarf galaxies due to limited numerical resolution which drives the artificial disruption of dark matter substructures. We address these limitations by combining the results of the cosmological hydrodynamical simulation TNG50 in $\Lambda$CDM with an empirical model of tidal evolution of cluste