April 2026 arXiv papers — page 76

Tianrong Chen, Jiatao Gu, David Berthelot, Joshua Susskind

Normalizing Flows (NFs) are a classical family of likelihood-based methods that have received revived attention. Recent efforts such as TARFlow have shown that NFs are capable of achieving promising performance on image modeling tasks, making them viable alternatives to other methods such as diffusion models. In this work, we further advance the state of Nor

Saikiran Kotaru, Chen Qu, Apurba Nandi, Paul L. Houston

The determination of quartic force fields for use in vibrational second-order perturbation (VPT2) calculations, currently available in numerous electronic structure packages, becomes very expensive as the size of the molecule increases, especially if high-level coupled cluster theory is used. Machine-learned potentials (MLPs) for large molecules and clusters

Aleksander Berezowski, Hassan Hassanzadeh, Gouri Ginde

Downhole drilling telemetry presents a fundamental labeling asymmetry: surface sensor data are generated continuously at 1~Hz, while labeled downhole measurements are costly, intermittent, and scarce. Current machine learning approaches for downhole metric prediction universally adopt fully supervised training from scratch, which is poorly suited to this dat

Haitao Huang, Shin-Fang Chng, Huangying Zhan, Qingan Yan

Recent advances in text-guided image editing and 3D Gaussian Splatting (3DGS) have enabled high-quality 3D scene manipulation. However, existing pipelines rely on iterative edit-and-fit optimization at test time, alternating between 2D diffusion editing and 3D reconstruction. This process is computationally expensive, scene-specific, and prone to cross-view

Rainer Schicker

Central exclusive diffractive production in proton-proton collisions at hadron colliders is characterised by hadronic activity at or close to midrapidity, and by the two forward scattered protons, or their remnants. In such events, no particles are produced between the midrapidity system and the forward beam particles. These events can hence be identified wi

Amareshwara Sainadh Chamarthi

Compressible multiphase and multicomponent solvers require accurate interface representation without spurious pressure oscillations. At material interfaces, pressure and velocity are continuous while density and the equation of state exhibit abrupt discontinuities. Standard approaches reconstruct primitive or characteristic variables to capture these propert

Ryotaro Chiba, Semih Tuna, Brian D. Metzger, Takashi J. Moriya

Around 10 % of hydrogen-poor supernovae explode inside compact ($\sim 10^{15}$ cm), massive ($\sim 0.1 \ \mathrm{M_\odot}$) circumstellar material (CSM), signalling an episode of enhanced pre-explosion mass loss whose mechanism remains unclear. The extreme members of this population are considered to constitute some of the Fast Blue Optical Transients (FBOTs

Ovidiu Costin, Gerald V. Dunne, Ali Saraeb

We develop a resurgent approach to the problem of unique continuation of mock theta functions across their natural boundary. The starting point is the representation of the associated Mordell-Appell integrals as Laplace transforms of resurgent functions, which serve as the primary analytic objects. By rotating the Laplace contour by $\pi$, i.e. onto the Stok

Vadym Kliuchnikov, Jendrik Brachter, Marcus P. da Silva

We show how to directly and efficiently approximate arbitrary one-qubit unitaries, bypassing the Euler decomposition and the magnitude approximation problem, at the cost of one ancillary qubit. Our technique also applies to approximating unitaries with multi-qubit gate sets such as Clifford and CS, or Clifford and CCZ, as well as to approximating orthogonal

Minghua Zheng, Na Helian, Peter C. R. Lane, Yi Sun

Automated bacterial colony counting from images is an important technique to obtain data required for the development of vaccines and antibiotics. However, bacterial colonies present unique machine vision challenges that affect counting, including (1) small physical size, (2) object clustering, (3) high data annotation cost, and (4) limited cross-species gen

Alexandre D. Leonelli, Lukas Widmer, Eckart Meiburg

Due to attractive inter-particle forces, cohesive particles suspended in turbulence undergo a complex process of aggregation, breakup, and restructuring. Despite a growing body of knowledge on the ``flocculation'' of cohesive granular materials suspended in homogeneous isotropic turbulence, little focus has so far been placed on wall-bounded flows where turb

Ethan Knights

For state-of-the-art image understanding, Vision Transformers (ViTs) have become the standard architecture but their processing diverges substantially from human attentional characteristics. We investigate whether this cognitive gap can be shrunk by fine-tuning the self-attention weights of Google's ViT-B/16 on human saliency fixation maps. To isolate the ef

Minghua Zheng, Na Helian, Peter C. R. Lane, Yi Sun

Automatic bacterial colony counting is a highly sought-after technology in modern biological laboratories because it eliminates manual counting effort. Previous work has observed that MicrobiaNet, currently the best-performing cardinality classification model for colony counting, has difficulty distinguishing colonies of three or more individuals. However, i

Eberhard Bänsch, Pedro Morin, Itatí Zocola

We present error estimates for the BMZ (Bubble Mesh Zoom) residual-free bubble method applied to a convection-diffusion equation in the convection-dominated regime. The method incorporates both element bubbles and residual-free bubbles supported on patches of two adjacent elements. We focus on the case of a parallel flow in a square domain and derive error e

Meri Hayrapetyan, Maksim Sargsyan, David Karakhanyan, Ani Khachatryan

We report a comprehensive determination of the broadband dielectric permittivity tensor of van der Waals (vdW) muscovite also referred to as mica, establishing it as a low-index low-loss platform for ultrathin nanophotonics. Resolving its anisotropic vibrational response and extracting accurate tensor components across broadband ultraviolet (UV) to near-infr

Baran Atalar, Xutong Liu, Jinhang Zuo, Siwei Wang

As Large Language Models (LLMs) become increasingly popular, caching responses so that they can be reused by users with semantically similar queries has become a vital strategy for reducing inference costs and latency. Existing caching frameworks have proposed to decide which query responses to cache by assuming a finite, known universe of discrete queries a

Gijung Lee, Wavid Bowman, Olivia Dizon-Paradis, Reiner Dizon-Paradis

As microelectronics flourish and outsourcing of the design and manufacturing stages of integrated circuits (ICs) and printed circuit boards (PCBs) becomes the norm, microelectronics stakeholders must also confront a new wave of security challenges, including the threats posed by hardware Trojans, counterfeit electronics, and reverse engineering attacks. Trad

Renee Gil

Rational design of covalent inhibitors requires simultaneously optimizing multiple properties, such as binding affinity, target selectivity, or electrophilic reactivity. This presents a multi-objective problem not easily addressed by screening alone. Here we present a machine learning pipeline for generating covalent inhibitor candidates using multi-objectiv

Gang Chen, Wenyi Liu, Yangwen Zhang

We develop a family of $H(\mathrm{div})$-conforming hybridizable discontinuous Galerkin methods for the steady Stokes equations based on BDM and RT velocity spaces with either discontinuous or continuous hybrid traces. In contrast to our earlier pressure-robust HDG method for tangential boundary control, the present analysis does not require the pressure to

Michael Chun, Ananya Nukala, Tae Myung Huh

We present a soft corrugated tube sensor designed to estimate strain in each half segment. When air flows through the tube, the internal corrugated cavities induce pressure oscillations that excite the tube's standing wave resonance mode, generating an acoustic tone. Stretching the tube affects both the resonance mode frequency, due to changes in overall len

Beibei Li, Wenge Guo

In many statistical applications, particularly in clinical studies, hypotheses may carry different levels of importance, motivating the use of weighted multiple testing procedures (wMTPs) to control the familywise error rate (FWER). Among these approaches, two weighted Holm procedures are commonly used: the weighted Holm procedure (WHP), which is based on or

Yogya Gamage, Meriem Ben Chaaben, Martin Monperrus, Benoit Baudry

Automated third-party library analysis tools help developers by addressing key dependency management challenges, such as automating version updates, detecting vulnerabilities, and detecting breaking updates. Dependency reachability analysis aims at improving the precision of dependency management, by reducing the space of dependency issues to the ones that a

Kun Liu, Ben Foxman, Gian-Luca R. Anselmetti, Yongshan Ding

Early modular fault tolerant quantum computers remain constrained by costly inter-module communication and limited magic state factory service. Understanding such bottlenecks and investigating compiler optimizations most close the gap between algorithm requirements and hardware capabilities is a concrete and practically urgent systems problem. We study the m

Yiyang Du, Zhanqiu Guo, Xin Ye, Liu Ren

Vision-Language-Action Models (VLAs) inherit their visual and linguistic capabilities from Vision-Language Models (VLMs), yet most VLAs are built from off-the-shelf VLMs that are not adapted to the embodied domain, limiting their downstream performance. In this work, we propose EmbodiedMidtrain to bridge the gap between VLMs and VLAs. We first characterize t

Vibhor Agarwal, Ke Zhou, Edyta Paulina Bogucka, Daniele Quercia

AI companion chatbots increasingly shape how people seek social and emotional connection, sometimes substituting for relationships with romantic partners, friends, teachers, or even therapists. When these systems adopt those metaphorical roles, they are not neutral: such roles structure people's ways of interacting, distribute perceived AI harms and benefits

Sena Aleyna Şentürk, Timur Şahin, Cenk Kayhan

We present the first high-resolution near-infrared spectroscopic analysis of the solar analog HD115617 (61 Virginis), complemented by optical spectroscopy, asteroseismology, and spectral energy distribution modeling. Using ESPRESSO and IGRINS spectra with a newly calibrated NIR line list, we derived atmospheric parameters that revealed notable differences be

Kristóf Bérczi, Viktor Csaplár, Yutaro Yamaguchi

Motivated by the exact weight perfect matching problem and recent parameterized algorithms for finding an $\ell$-th smallest perfect matching, we study structural properties of edge-weight symmetries in graphs. Recent work by El Maalouly et al. (ESA 2025) showed that excluding all perfect matchings whose weight is at most the $(\ell - 1)$-th smallest possibl

Peter Castellucci, Radha Boya, Lin Ma, Igor L. Chernyavsky

We investigate gas injection into axisymmetric water-saturated porous channels with Gaussian and parabolic profiles, as idealized models of underground gas storage in dome-shaped anticlines. Exploiting the slenderness of each channel, we derive an evolution equation for the gas/liquid interface using a composite asymptotic approximation that accommodates lar

Md Nayem Uddin, Kumar Shubham, Eduardo Blanco, Chitta Baral

Personalized agents that interact with users over long periods must maintain persistent memory across sessions and update it as circumstances change. However, existing benchmarks predominantly frame long-term memory evaluation as fact retrieval from past conversations, providing limited insight into agents' ability to consolidate memory over time or handle f

Luigi Sibille, Sigrid Adriaenssens, Carlo Olivieri

Form-finding of unilateral membrane structures is commonly addressed by solving equilibrium equations with Finite Element Methods (FEMs). This paper investigates Physics-Informed Neural Networks (PINNs) as an alternative, where the equilibrium equation is enforced by minimizing its residual at collocation points during neural-network training rather than by

Reese AK Richardson, Spencer S Hong, Anna Abalkina

The study of paper mills and similar businesses operating in the market for academic and education fraud services is frustrated by the lack of market price data on their various offerings. Here, we assemble BuyTheBy, a large, annotated dataset of timestamped, text-based paper mill advertisements from seven businesses operating out of seven different countrie

Bhargav Bhatt, Manuel Blickle, Karl Schwede, Kevin Tucker

In this paper we show that any Noetherian $F$-finite scheme has a dualizing complex $\omega^{\bullet}_{X}$ with the property that for all finite type maps $f \colon X \to Y$ between $F$-finite Noetherian schemes there is a canonical isomorphism $\omega^{\bullet}_{X} \xrightarrow{\cong} f^!\omega^{\bullet}_{Y}$ in $D^b_{coh}(X)$. This, in particular, applies

Nikola Milićević

We develop new aspects of the homological algebra theory for persistence modules, in both the one-parameter and multi-parameter settings. For a poset $P$ and an order preserving map $\varphi:P\times P\to P$, we introduce a novel tensor product of persistence modules indexed by $P$, $\otimes_{\varphi}$. We prove that each $\otimes_{\varphi}$ has a right adjoi

Qifeng Zhou, Lei Yu, Yuzhi Guo, Yuwei Miao

The integration of single-cell proteomic data is often hindered by the fragmented nature of targeted antibody panels. To address this limitation, we introduce scpFormer, a transformer-based foundation model designed for single-cell proteomics. Pre-trained on over 390 million cells, scpFormer replaces standard index-based tokenization with a continuous, seque

Felix Buchele, Patric Müller, Thorsten Pöschel

multisphere is an open-source Python package for generating multi-sphere representations of complex particles for use in DEM simulations. It reconstructs triangulated surface meshes and voxelized volumes as sets of intersecting spheres and provides tools for evaluation, visualization, and export.

Thiago T. Tsutsui, Danilo Cius, Antonio S. M. de Castro, Fabiano M. Andrade

We analyze the quantum dynamics of the fractional-time Jaynes-Cummings model using a recent unitary framework for the fractional-time Schr\"odinger equation. We examine how the fractional derivative order $\alpha$ influences non-classical features under different initial conditions. For an initial Fock state, fractional evolution introduces transient dynamic

Bowen Zhang, Jesse T. Boulerice, Charvi Mendiratta, Nikhil Kuniyil

Automated wildlife monitoring from aerial imagery is vital for conservation but remains limited by two persistent challenges: the difficulty of detecting small, rare species and the high cost of large-scale expert annotation. Prairie dogs exemplify this problem -- they are ecologically important yet appear tiny, sparsely distributed, and visually indistinct

Amir Zamani, Zeinab Abedini

Visual detection of Unmanned Aerial Vehicles (UAVs) is a critical task in surveillance systems due to their small physical size and environmental challenges. Although deep learning models have achieved significant progress, deploying them on edge devices necessitates the use of lightweight models, such as YOLOv11 Nano, which possess limited learning capacity

Ming Jin

Evaluating AI-generated reviews by verdict agreement is widely recognized as insufficient, yet current alternatives rarely audit which concerns a system identifies, how it prioritizes them, or whether those priorities align with the review rationale that shaped the final assessment. We propose concern alignment, a diagnostic framework that evaluates AI revie

Andrea Signori

This set of notes complements the lectures and recitation sessions discussed in the following graduate schools: HUGS at Jefferson Lab (years 2018, 2019, 2021), the International School and Workshop on Probing Hadron Structure at the Electron-Ion Collider at ICTS (2024), Frontiers in Nuclear and Hadronic Physics at GGI (2025), and the International Workshop a

Tianyang Wang, Ziyu Su, Abdul Rehman Akbar, Usama Sajjad

The expanding ecosystem of pathology foundation models has produced powerful but fragmented tile-level representations, limiting their use in clinical tasks that require unified slide-level reasoning and interpretable linkage to clinically meaningful information. We present ASTRA, a pan-cancer framework that integrates heterogeneous foundation-model represen

Efthymia Derezea, Gabriel Rogers, Nicky J Welton, Hayley E Jones

Network meta-analysis of diagnostic test accuracy (NMA-DTA) is a relatively new field, involving combining evidence across studies to evaluate and compare the accuracy of different tests for a given condition. However, the methods proposed to date cannot always capture complex aspects of the data. In fact, many commonly used diagnostic tests are continuous b

Fangji Wang, Siddhartha Ganguly, Panagiotis Tsiotras

We study a finite-horizon covariance steering problem for discrete-time Markov jump linear systems (MJLS) with both state- and control-dependent multiplicative noise. The objective is to minimize a quadratic running cost while steering the system from given mode-conditioned initial means and covariances to a prescribed terminal mean and covariance. We first

Zehuan Zhang, Mark Chen, He Li, Wayne Luk

Complex-Valued Neural Networks (CVNNs) have significant advantages in handling tasks that involve complex numbers. However, existing CVNNs are unable to quantify predictive uncertainty. We propose, for the first time, dropout-based Bayesian Complex-Valued Neural Networks (BayesCVNNs) to enable uncertainty quantification for complex-valued applications, exhib

Pedro C. Ormonde, Xiaowei He, Kenneth Breuer

A Cyber-physical underwater vehicle is equipped with bio-inspired flapping fins positioned on the sides of the vehicle's main body. The proposed control surfaces are inspired by fish pectoral fins, generating forces and moments that can potentially be harnessed for maneuvering, hovering and station keeping. The streamwise and cross-stream forces produced by

Amine Jaouadi, Sahel Ashhab

Higher-dimensional quantum systems, such as qudits, offer architectural and algorithmic advantages over qubits, but their increased spectral crowding and limited controllability render high-fidelity quantum gates particularly challenging. We propose a hybrid optimization framework that integrates optimal control theory methods with contextual deep reinforcem

Conor Flynn, Radoslav Ivanov, Birsen Yazici

With modern defense applications increasingly relying on inexpensive, small Unmanned Aerial Vehicles (UAVs), a major challenge lies in designing intelligent and computationally efficient onboard Automatic Target Recognition (ATR) algorithms to carry out operational objectives. This is especially critical in Synthetic Aperture Radar (SAR), where processing te

Duha T. Altindag, Nabamita Dutta, John M. Nunley, R. Alan Seals

Between 2005 and 2019, U.S. business applications rose 40 percent while conversion to employer firms fell by nearly half. We study whether boundary redrawing helps explain this pattern. Structured routine-cognitive work can be governed through deliverables and thinner buyer and supplier interfaces. When such work remains place-bound, outsourcing creates dema

Neil MacVicar

This thesis generalizes the study of $C\cap(C + \alpha)$ where $C$ is the middle third Cantor set to self-affine sets in $\mathbb{R}^{n}$. We present sufficient and necessary conditions for when the translation $\alpha$ produces a self-affine intersection for a particular class of self-affine sets. In the case where the attractor is self-similar, we improve

Sumit Mukherjee

We study how electoral rules shape polarization dynamics when voters and candidates both adapt to repeated election outcomes. We introduce two geometric primitives for comparing rules under this feedback: the \emph{winner radius} $R_t = \max_i \|x_i - w^{(t)}\|$, the distance from the winner to the farthest voter, and the \emph{supporter centroid radius} $S_

Huy Nghiem, Phuong-Anh Nguyen-Le, Sy-Tuyen Ho, Hal Daume

Research has documented LLMs' name-based bias in hiring and salary recommendations. In this paper, we instead consider a setting where LLMs generate candidate summaries for downstream assessment. In a large-scale controlled study, we analyze nearly one million resume summaries produced by 4 models under systematic race-gender name perturbations, using synthe

Manuel Fernandez, Ludovic Stephan, Yizhe Zhu

We study the community detection problem in the non-uniform hypergraph stochastic block model (HSBM), where hyperedges of varying sizes coexist. This setting captures higher-order and multi-view interactions and raises a fundamental question: can multiple uniform hypergraph layers below the detection threshold be combined to enable weak recovery? We answer t

Wenjian Hao, Yuxuan Fang, Zehui Lu, Shaoshuai Mou

This paper presents a model-based reinforcement learning (RL) framework for optimal closed-loop control of nonlinear robotic systems. The proposed approach learns linear lifted dynamics through Koopman operator theory and integrates the resulting model into an actor-critic architecture for policy optimization, where the policy represents a parameterized clos

Sophia Zorek, Kushal Vyas, Yuhao Liu, David Lenz

Neural fields, also known as implicit neural representations (INRs), offer a powerful framework for modeling continuous geometry, but their effectiveness in high-dimensional scientific settings is limited by slow convergence and scaling challenges. In this study, we extend INR models to handle spatiotemporal and multivariate signals and show how INR features

Tolunay Seyfi, Erfan Khadem, Fatemeh Afghah

We propose \emph{PRISM} (\textbf{Pseudorandom Residue-based Indexed Scheduling Method}), a deterministic topology-discovery framework for single-hop wireless networks with bounded interference. Each receiver has at most \(L\) interfering transmitters among \(K\) transmitters and identifies them through singleton transmissions. PRISM assigns finite-field labe

Lawson Ung, Guanbo Wang, Sebastien Haneuse, Sonia Hernandez-Diaz

Learning about causal effects in target populations and their subsets may be facilitated by combining information from multiple sources. One major class of study designs that combine information involves appending an index study with data from an external comparator, which may facilitate head-to-head comparisons of treatments initially studied in different p

Baiang Li, Ruyu Yan, Ethan Tseng, Zhoutong Zhang

While the human eye can perceive an impressive twenty stops of dynamic range, smartphone camera sensors remain limited to about twelve stops despite decades of research. A variety of high dynamic range (HDR) image capture and processing techniques have been proposed, and, in practice, they can extend the dynamic range by 3-5 stops for handheld photography. T

Adolfo S. Carvalho, Lynne A. Hillenbrand

Eruptive accretion events are expected to play an important role in the mass buildup stage of individual star formation. FU Ori objects (FUors) experience the most extreme eruptive outbursts, which raise the accretion rate of the disk from $10^{-9}-10^{-8} \ M_\odot \ \mathrm{yr}^{-1}$ to $10^{-5}-10^{-4} \ M_\odot \ \mathrm{yr}^{-1}$ and last for decades. D

Het Patel, Tiejin Chen, Hua Wei, Evangelos E. Papalexakis

Large language models can be uncertain yet correct, or confident yet wrong, raising the question of whether their output-level uncertainty and their actual correctness are driven by the same internal mechanisms or by distinct feature populations. We introduce a 2x2 framework that partitions model predictions along correctness and confidence axes, and uses sp

Anirban N. Chowdhury, Samuel O. Scalet, Kunal Sharma

Stationary states of quantum many-body Hamiltonians are invariant under the Hamiltonian evolution. Besides ground and thermal states, this class includes microcanonical ensembles that are of fundamental importance in statistical physics. We consider the preparation of general stationary states by leveraging recent advances in the field of open-system dynamic

Yanyan Zhan, Ian L. Dryden, Yuexuan Wu

In many applications, the data lie on a type of cone, where there is a distinction between an overall scale variable and the remaining scale-free structure. For example, the joint size and shape of objects are points on a cone, where size represents scale, and shape is the scale-free structure. Dimension reduction is central in such applications, as shape da

Nazanin Siavash, Armin Moin

In this paper, we conduct a systematic literature review of (semi-) automated approaches to Quantum Software Engineering (QSE) and Quantum Artificial Intelligence (QAI). Prior work in the literature indicated that both Software Engineering (SE) and Artificial Intelligence (AI) practices may become more efficient by using (semi-) automated approaches. This al

Alok Yadav, Saroj Yadav

Traditional macroeconomic growth models rely on general equilibrium and continuous, frictionless institutional transitions, failing to account for the catastrophic structural collapses observed in empirical economic history. We propose the Stochastic Networked Governance (SNG) model, a discrete-time, agent-based framework that bridges econophysics, network s

M. A. Burlak, A. V. Dodin, A. V. Zharova, N. P. Ikonnikova

In the vicinity of the young star FN Tau, we have detected a microjet and four Herbig-Haro objects, whose positions and kinematics indicate the presence of a bipolar collimated outflow from the star - HH 1267. The stellar jet does not propagate rectilinearly, and we discuss the possibility that the curved shape of the jet, whose axis is inclined to the line

Divyanshu Goyal, Akhil Eppa, Vanya Bannihatti Kumar

Vision-language models (VLMs) are increasingly used in settings where sensitivity to low-level image degradations matters, including content moderation, image restoration, and quality monitoring. Yet their ability to recognize distortion type and severity remains poorly understood. We present DistortBench, a diagnostic benchmark for no-reference distortion p

Simon Süwer, Zoe Chervontseva, Kester Bagemihl, Jan Baumbach

The rapid growth of scientific software has created practical barriers for bioinformatics research. Although powerful statistical, artificial intelligence (AI)-based methods are now widely available, their effective use is often hindered by fragmented distribution, inconsistent documentation, complex dependencies, and difficult-to-reproduce execution environ

Md Fazle Rabbi, Asif K. Turzo, Arifa I. Champa, Minhaz F. Zibran

Recent years have experienced growing contributions of AI coding agents that assist human developers in various software engineering tasks. However, this growing AI-assisted autonomy raises questions about security and trust. In this paper, we analyze more than 33,000 AI-generated pull requests (PRs) and identify 675 security-related submissions made by agen

Uday Sankar Manoj, Nicole Drew, Ismaila Dabo, Lukas Muechler

Path optimization methods have been widely used and highly successful for the analysis of chemical reactions. Yet, they can fail to capture intrinsically multidimensional features of potential energy surfaces (PES). We introduce the nudged elastic membrane method, a framework for constructing two-dimensional reduced potential-energy surfaces in chemically re

Henning Fernau, Lakshmanan Kuppusamy, Jana Schulz

In this paper, we consider combining the ideas of forbidden random context grammars as well as of ordered grammars with cooperating distributed grammar systems (CDGS). We focus on investigating their generative capacities. Both ideas can be added to CDGS in two ways: either having (e.g.) a strict order of the rules in each component, or having a strict order

Matěj Boxan, William Larrivée-Hardy, François Pomerleau

Rotating FMCW radar odometry methods often assume flat ground conditions. While this assumption is sufficient in many scenarios, including urban environments or flat mining setups, the highly dynamic terrain of subarctic environments poses a challenge to standard feature extraction and state estimation techniques. This paper benchmarks three existing radar o

Sergey V. Samsonau, Matthew Pearce

Guiding others through authentic scientific research outside of PhD programs has been practiced for decades in specialized secondary schools, undergraduate research programs, and independent settings. These practitioners work in the middle, between the classroom science teacher and the PhD advisor, guiding learners with aptitude or serious interest. Sport an

Jeffrey R. Boland, Lane P. Hughston

In a previous submission, we established a fundamental relation between tone networks and configurations. It was shown that the Eulerian tonnetz can be represented by a $\{12_3\}$ of Daublebsky von Sterneck type D222. We also constructed a tonnetz for Tristan-genus chords (dominant sevenths and half-diminished sevenths) and showed that this tonnetz can be re

Branden Aitken, Peter Reimitz, Adam Ritz

We explore the sensitivity of neutrino observatories and direct dark matter detection experiments to boosted sub-GeV dark matter produced by inelastic cosmic ray collisions in the atmosphere. We revisit earlier approaches and extend the sensitivity to higher mass by modeling the proton bremsstrahlung production mode via initial state radiation. For vector-me

Ansel Kaplan Erol, Divya Mahajan

Earth-observation satellites are emerging as distributed edge platforms for time-critical tasks, yet orbital scheduling remains challenged by intermittent energy harvesting and temporal coupling where eager execution risks future battery depletion. Existing schedulers rely on static priorities and lack mechanisms to adaptively shed work. We present Equinox,

Aaron T. Bondy, Klaus Bartschat

We present ab initio calculations using the $R$-matrix with time dependence (RMT) method for high-order harmonic generation (HHG) in argon in a short, intense pulse regime. The calculations employ a $6$-cycle $\sin^2$ pulse at $850$ nm with peak intensity $2.3\times 10^{14}$ W/cm$^2$ and, for comparison with the experiment by Guo et al. [J. Phys. B: At. Mol.

Damianos Iosifidis

We study the of motion of a spinning, dilating particle with hadronic properties moving on a generic geometric background including curvature, torsion, and nonmetricity. In particular, we discuss generalized spin supplementary conditions and also introduce the concept of a shear supplementary condition. Using these, we investigate the evolution of the dynami

Hongquan Liu, J. I. A. Li, D. E. Feldman

Shot noise has been used to measure fractional charges of anyons. The value of the charge imposes constraints on fractional statistics but does not determine it. This issue is particularly important in multi-component systems. For example, the zero charge of neutral anyons in bilayer graphene gives no information about their statistics at all. We propose a p

Xinxuan Lu, Charless Fowlkes, Alexander C. Berg

Current text-to-image models struggle to provide precise camera control using natural language alone. In this work, we present a framework for precise camera control with global scene understanding in text-to-image generation by learning parametric camera tokens. We fine-tune image generation models for viewpoint-conditioned text-to-image generation on a cur

Mingwei S. G. Li, Susie S. Y. Li, Daisuke Sakurai, Bei Wang

Generative AI has demonstrated significant potential in creative design, enabling the rapid generation of visual content and imaginative concepts. Although deep AI models achieve effective featurization in the latent space, navigating the space remains a challenge. Current techniques, such as GANSlider and SliderSpace, use multiple sliders to generate high-d

Don N. Page

The deflection angle of a light ray passing the Schwarzschild (spherically symmetric vacuum) black hole was calculated by Charles Galton Darwin in 1959 in terms of the elliptic integral of the first kind. This calculation has been repeated many times and has also been given approximately in terms of elementary functions for impact parameters that either are

Victor Palin, Akash Agnihotri, Nareg Ghazikhanian, Matthew Frame

Mimicking the collective excitatory and inhibitory behaviors of biological neurons remains a critical challenge in the development of neuromorphic computing systems that rival the complexity and performance of the human brain. Volatile high-to-low resistance switching in insulator-to-metal transition (IMT) materials produces an abrupt increase in current flo

Lyonell Boulton, Connor Evans

We establish a framework to determine the linear completeness of families of non-linear trajectories in Hilbert spaces, which relies on an infinite analytic block Toeplitz operator formulation. By means of this approach, we show the linear completeness in Sobolev spaces of two families of classical functions. One is the moving family of dilated Weierstrass f

Girish, Mohd Mujtaba Akhtar, Orchid Chetia Phukan, Arun Balaji Buduru

The rapid advancement of Audio Large Language Models (ALMs), driven by Neural Audio Codecs (NACs), has led to the emergence of highly realistic speech deepfakes, commonly referred to as CodecFakes (CFs). Consequently, CF detection has attracted increasing attention from the research community. However, existing studies predominantly focus on English or Chine

Ziran Liu, Hung V. Tran, Yifeng Yu

We study the periodic homogenization of the viscous Hamilton--Jacobi equation \[ u_t^\varepsilon + \frac{1}{2}|Du^\varepsilon|^2 + V\!\left(\frac{x}{\varepsilon}\right) = \frac{\varepsilon}{2}\Delta u^\varepsilon \qquad \text{in } \mathbb{R}^n \times (0,\infty), \] with initial datum $g \in W^{1,\infty}(\mathbb{R}^n)$, where $V$ is Lipschitz continuous and $

Zhengyu Li, Curtis Bright, Stefan Trandafir, Adán Cabello

We present a search for small Kochen-Specker (KS) sets in dimension 3, specifically targeting extensions of the 13-ray Yu-Oh set, which has been proven to be the minimal witness to state-independent contextuality. To enable this search, we introduce a novel SAT-based orderly generation framework integrating recursive canonical labeling (RCL) with the graph i

Thomas Edridge, Manon Kok

Indoor localisation techniques suffer from attenuated Global Navigation Satellite System (GNSS) signals and from the accumulation of unbounded drift by integration of proprioceptive sensors. Magnetic field-based Simultaneous Localisation and Mapping (SLAM) reduces drift through loop closures by revisiting previously seen locations, but extended exploration o

Qihua Dong, Gozde Sahin, Pei Wang, Zhaowei Cai

In this paper, we investigate the problem of how to effectively master tool-use to solve complex visual reasoning tasks for Multimodal Large Language Models. To achieve that, we propose a novel Tool-supervised Reinforcement Learning (ToolsRL) framework, with direct tool supervision for more effective tool-use learning. We focus on a series of simple, native,

A. S. Kuraptsev, I. M. Sokolov

Based on a consistent quantum microscopic approach, we investigate the peculiarities of collective polyatomic effects in atomic ensembles placed in a waveguide, caused by the presence of evanescent modes of electromagnetic field. We analyze the influence of these modes on the process of cooperative spontaneous decay, as well as on the nature of radiation tra

Olga Kellert, Sriya Kondury, Candice Koo, Nemika Tyagi

Annotation pipelines in Natural Language Processing (NLP) commonly assume a single latent ground truth per instance and resolve disagreement through label aggregation. Perspectivist approaches challenge this view by treating disagreement as potentially informative rather than erroneous. We present a large-scale analysis of graded health-literacy annotations

Nassim Sadallah, Mohand Saïd Allili

Reliable crack detection and segmentation are vital for structural health monitoring, yet the scarcity of well-annotated data constitutes a major challenge. To address this limitation, we propose a novel context-aware generative framework designed to synthesize realistic crack growth patterns for data augmentation. Unlike existing methods that primarily mani

Ghenadie N. Mardari

A classical fluid splitter produces the same patterns of energy redistribution as a Stern-Gerlach quantum device, with rotationally invariant coefficients of correlation between molecular paths. Alternative settings express a cosine squared relationship, leading to Tsirelson-type Bell violations with outcome independence. This result confirms the Corresponde

Vickey Nandal, Vít Beneš, Pavel Baláž, Jiří Ryjáček

Resorbable magnesium (Mg) alloys are promising candidates for temporary medical devices due to their biodegradability and favorable mechanical properties. To accelerate the design of diluted Mg alloys for implants, we developed a data-driven framework to elucidate the complex relationships between composition, processing, and mechanical properties. The frame

George Bayliss, Jared C. Bronski

The Evans function is an analytic function that encodes information about the intersection of certain subspaces in ODE boundary value problems. As such it is a useful tool for computing the spectrum of boundary value problems arising in the stability of coherent structures. In typical applications one is interested in the roots of the Evans function, but the

Palawat Busaranuvong, Reza Saadati Fard, Emmanuel Agu, Deepak Kumar

Assessing chronic wound infection from photographs is challenging because visual appearance varies across wound etiologies, anatomical locations, and imaging conditions. Prior image-based deep learning methods have mainly focused on classification with limited interpretability, despite the need for evidence-grounded explanations to support point-of-care deci

Fateme Rahmani, Mahdi Jafari Siavoshani, Mohammad Hossein Rohban

With the emergence of new evaluation metrics and attack methodologies for Membership Inference Attacks (MIA), it becomes essential to reevaluate previously accepted assumptions. In this paper, we revisit the longstanding debate regarding the correlation between MIA success rates and model generalization using an empirical approach. We focused on employing au

Walter Zibusiso Ncube, Ahmad Adnan Qidan, Taisir El-Gorashi, Jaafar M. H. Elmirghani

Optical wireless communication (OWC) has emerged as a promising candidate for future high-capacity indoor wireless networks, driven by its large unregulated spectrum, high spatial reuse, and ability to support multi-gigabit data rates. However, OWC systems are highly sensitive to user mobility, as link performance depends strongly on the spatial alignment be

Nicholas Popovič, Michael Färber

We study how large language models recall relational knowledge during text generation, with a focus on identifying latent representations suitable for relation classification via linear probes. Prior work shows how attention heads and MLPs interact to resolve subject, predicate, and object, but it remains unclear which representations support faithful linear

Luigia Caputo, Aniello Buonocore

Drawing on the Data and Predictions strand of the Indicazioni Nazionali per il curricolo 2012, this study proposes a problem based instructional approach to the teaching of probability. More specifically, the study adopts a design based research methodology structured in a single cycle consisting of two teaching interventions in the same class, carried out i

Mads R. Almassalkhi, Dakota Hamilton, Hasan Giray Oral, Yury Dvorkin

Electric power systems are rapidly evolving into deeply digital, cyber-physical infrastructures in which large fleets of distributed energy resources must be coordinated as system-level flexibility across multiple spatial and temporal scales. Despite growing distributed energy resource deployment, existing grid and market architectures lack scalable, interop

Upasna, Venkata Kalyan Tavva

Heterogeneous Memory Architecture (HMA) aims to optimize memory usage by leveraging a combination of memory types, such as high-bandwidth memory (HBM), commodity DRAM, and non-volatile memory (NVM), when utilized as main memory. To achieve maximum performance benefits, frequently accessed data pages are prioritized for storage in the faster HBM, while less f