December 2025 arXiv papers — page 65

Rupanshu Soi, Rohan Yadav, Fredrik Kjolstad, Alex Aiken

GPU architectures have continued to grow in complexity, with recent incarnations introducing increasingly powerful fixed-function units for matrix multiplication and data movement to accompany highly parallel general-purpose cores. To fully leverage these machines, software must use sophisticated schedules that maximally utilize all hardware resources. Since

Jie Yang, Rui Zhang, Ziyang Cheng, Dawei Cheng

Nowadays, Graph Fraud Detection (GFD) in financial scenarios has become an urgent research topic to protect online payment security. However, as organized crime groups are becoming more professional in real-world scenarios, fraudsters are employing more sophisticated camouflage strategies. Specifically, fraudsters disguise themselves by mimicking the behavio

Nuntipat Narkthong, Xiaolin Xu

Edge AI inference is becoming prevalent thanks to the emergence of small yet high-performance microprocessors. This shift from cloud to edge processing brings several benefits in terms of energy savings, improved latency, and increased privacy. On the downside, bringing computation to the edge makes them more vulnerable to physical side-channel attacks (SCA)

Lucia Velasco, Charles Martinet, Henry de Zoete, Robert Trager

This policy memo examines the evolution of the international AI Summit series, initiated at Bletchley Park in 2023 and continued through Seoul in 2024 and Paris in 2025, as a forum for cooperation on the governance of advanced artificial intelligence. It analyzes the factors underpinning the series' early successes and assesses challenges related to scope, p

Le Zhang, Suresh Kothari

This study presents a comprehensive empirical evaluation of six state-of-the-art large language models (LLMs) for code generation, including both general-purpose and code-specialized models. Using a dataset of 944 real-world LeetCode problems across five programming languages, we assess model performance using rigorous metrics: compile-time errors, runtime e

Maxmillan Ries, Sohan Seth

Survival analysis is a critical tool for modeling time-to-event data. Recent deep learning-based models have reduced various modeling assumptions including proportional hazard and linearity. However, a persistent challenge remains in incorporating longitudinal covariates, with prior work largely focusing on cross-sectional features, and in assessing calibrat

Thomas Boudras, Martin Schwartz, Rasmus Fensholt, Martin Brandt

High-resolution mapping of canopy height is essential for forest management and biodiversity monitoring. Although recent studies have led to the advent of deep learning methods using satellite imagery to predict height maps, these approaches often face a trade-off between data accessibility and spatial resolution. To overcome these limitations, we present SE

Yi Yang, Ziyu Lin, Liesheng Wei

Large-scale deep learning models impose substantial communication overh ead in distributed training, particularly in bandwidth-constrained or heterogeneous clo ud-edge environments. Conventional synchronous or fixed-compression techniques o ften struggle to balance communication cost, convergence stability, and model accura cy. To address these challenges, w

Nic Brody

In 1999, Long and Reid proposed a proper action of a surface group on a product of trees. In this note, we show that the action is not proper.

Zijun Wang, Yijiahao Qi, Hanqiu Chen, Zishen Wan

Mixture-of-Agents (MoA) inference can suffer from dense inter-agent communication and low hardware utilization, which jointly inflate serving latency. We present a serving design that targets these bottlenecks through an algorithm-system co-design. First, we replace dense agent interaction graphs with a hierarchical tree topology that induces structured spar

Alexandrina Stoyanova, Bogdan Penkovsky

Polymers exhibit complex architectures and diverse properties that place them at the center of contemporary research in chemistry and materials science. As conventional computational techniques, even multi-scale ones, struggle to capture this complexity, quantum computing offers a promising alternative framework for extracting structure-property relationship

Lara B. Anderson, James Gray, Sunit A. Patil, Caoimhín Scanlon

In this work, we provide evidence for a duality between 4-dimensional Calabi-Yau compactifications of the heterotic string, in which the base manifolds are linked by a conifold transition. In recent work, a geometric proposal was put forward for how 5-branes and gauge bundles are carried across such transitions. It was observed that compactifications connect

Changxu Duan

Converting data from machine-unreadable formats like PDFs into Markdown has the potential to enhance the accessibility of scientific research. Existing end-to-end decoder transformer models can transform screenshots of PDFs into Markdown, offering more flexibility than pipeline-based methods. Yet, decoding text token by token from scratch is inefficient, esp

Ce Xu

In this paper, we present a method based on contour integration to investigate a class of cyclotomic parametric Ap\'ery-like series. The general term of such series involves a parametric central binomial coefficient, which is defined via the Gamma function. Using this approach, we express a family of cyclotomic Ap\'ery-like series in terms of multiple polylo

Adam J. Thorpe, Stepan Tretiakov, Dibakar Roy Sarkar, Krishna Kumar

Inverse problems challenge existing neural operator architectures because ill-posed inverse maps violate continuity, uniqueness, and stability assumptions. We introduce B2B${}^{-1}$, an inverse basis-to-basis neural operator framework that addresses this limitation. Our key innovation is to decouple function representation from the inverse map. We learn neur

Kohei Watanabe

Social scientists employ latent Dirichlet allocation (LDA) to find highly specific topics in large corpora, but they often struggle in this task because (1) LDA, in general, takes a significant amount of time to fit on large corpora; (2) unsupervised LDA fragments topics into sub-topics in short documents; (3) semi-supervised LDA fails to identify specific t

Matteo Sesia, Vladimir Svetnik

We study how to select a subset of patients who are unlikely to experience an adverse event within a given time horizon, by calibrating a screening rule based on the output of any survival model. We consider two complementary frameworks. The first extends the classical idea of estimating the event rate among selected patients using a hold-out dataset, integr

Xiwen Chen, Yen-Chieh Lien, Susan Liu, María Castaños

The rapid growth of e-commerce requires robust multimodal representations that capture diverse signals from user-generated listings. Existing vision-language models (VLMs) typically align titles with primary images, i.e., single-view, but overlook non-primary images and auxiliary textual views that provide critical semantics in open marketplaces such as Etsy

Satyaki Sikdar, Manuel Sandoval, Taylor Hales, Chloe Kilroy

Cyberbullying continues to grow in prevalence and its impact is felt by thousands worldwide. This study seeks a network science perspective on cyberbullying interaction patterns on the popular photo and video-sharing platform, Instagram. Using an annotated cyberbullying dataset containing over 400 Instagram posts, we outline a set of heuristics for building

Changxu Duan

Academic documents stored in PDF format can be transformed into plain text structured markup languages to enhance accessibility and enable scalable digital library workflows. Markup languages allow for easier updates and customization, making academic content more adaptable and accessible to diverse usage, such as linguistic corpus compilation. Such document

Robert Calef, Arthur Liang, Manolis Kellis, Marinka Zitnik

Protein representation learning has advanced rapidly with the scale-up of sequence and structure supervision, but most models still encode proteins either as per-residue token sequences or as single global embeddings. This overlooks a defining property of protein organization: proteins are built from recurrent, evolutionarily conserved substructures that con

Jacob T. Crosser, Braden A. W. Brinkman

The critical brain hypothesis posits that neural circuitry operates near criticality to reap the computational benefits of accessing a wide range of timescales. The theory of critical phenomena generally predicts heavy-tailed (power-law) correlations in space and time near criticality, but it has been argued that in the brain such correlations could be inher

Yunian Pan, Quanyan Zhu

This paper addresses the challenge of modeling and control in hierarchical, multi-agent systems, known as holonic systems, where local agent decisions are coupled with global systemic outcomes. We introduce the Bayesian Holonic Equilibrium (BHE), a concept that ensures consistency between agent-level rationality and system-wide emergent behavior. We establis

Guram Bezhanishvili, Luca Carai

We show that the amended monadic Grzegorczyk logic $\mathsf{M^+Grz}$ is the largest modal companion of the amended monadic intuitionistic logic $\mathsf{M^+IPC}$. Thus, unlike the monadic intuitionistic logic $\mathsf{MIPC}$, Esakia's theorem does extend to $\mathsf{M^+IPC}$.

Ivan Vasilev, Serge Yagunov

In this paper, we develop a modified proof strategy for homological stability of linear groups, with the general linear groups serving as a primary example. Our arguments are more direct than those in the classical works of Quillen and Suslin--Nesterenko, although they apply only with localized coefficients. The localization at (n-1)! that arises in our appr

Yunian Pan, Quanyan Zhu

We study self-triggered two-player stochastic games on Piecewise Deterministic Markov Processes (PDMPs) where each agent decides when to observe and which open-loop action to hold. Augmenting the state with clocks and committed controls yields flow regions (both hold) and trigger surfaces (at least one updates). The framework covers both blind simultaneous (

Yurou Liu, Tiger Lu, Malena Rice

To date, only a handful of binary star systems are known with at least one confirmed planet orbiting each star. Such systems, however, offer a unique perspective on the stochasticity intrinsic to planet formation and evolution -- particularly in twin binary star systems, which consist of near-equal-mass stars formed contemporaneously in the same birth enviro

Yinxuan Zhu, Ashley Wissel-Garcia, Kidus Guye, Chandan Joishi

We report on the design and demonstration of ultra-wide bandgap AlGaN polarization-graded field effect transistors with ultra-thin channels to enable excellent current density and high-frequency performance while significantly reducing thermal resistance. We use polarization-graded AlGaN layers and ultra-thin pseudomorphic AlGaN buffer layers to enable low t

Denis V. Osipov

We study reciprocity laws involving complex line bundles on fibrations in oriented circles. In particularly, we prove the following reciprocity law. Let $B$ be a complex manifold and $\pi_i : M_i \to B$ be a fibration in oriented circles, where $i$ runs through a finite set. Let $L_i$ and $N_i$ be complex line bundles on every $M_i$. The reciprocity law stat

Andreas E. Robertson, Samuel B. Inman, Ashley T. Lenau, Ricardo A. Lebensohn

Aleatoric uncertainties - irremovable variability in microstructure morphology, constituent behavior, and processing conditions - pose a major challenge to developing uncertainty-robust digital twins. We introduce the Variational Deep Material Network (VDMN), a physics-informed surrogate model that enables efficient and probabilistic forward and inverse pred

Pete Rigas

We examine public broadcast, forward conceptual, and backward conceptual, Quantum channels in the context of communication protocols that are independent of secret keys. Given research directions of interest previously identified in arXiv: 1804.01797, besides converse upper bounds on the bit transmission rate obtained by the author in recent work (arXiv: 250

Yinxuan Zhu, Andrew A. Allerman, Ashley Wissel-Garcia, Seungheon Shin

We report the design and demonstration of ultra-wide-bandgap (UWBG) AlGaN polarization-graded field-effect transistors (PolFETs) that achieve a current-gain cutoff frequency above 85 GHz and a current density exceeding 1.3 A/mm. Ultra-thin channel and buffer layers were grown epitaxially on AlN substrates, and a reverse-graded AlGaN contact layer was incorpo

Kishan Kumar Ganguly, Tim Menzies

Context: Exhaustive fuzzing of modern JavaScript engines is infeasible due to the vast number of program states and execution paths. Coverage-guided fuzzers waste effort on low-risk inputs, often ignoring vulnerability-triggering ones that do not increase coverage. Existing heuristics proposed to mitigate this require expert effort, are brittle, and hard to

Richard A. Shaw, Jenny L. Novacescu, Sarah Weissman, Travis A. Berger

The Barbara A. Mikulski Archive for Space Telescopes (MAST) hosts science-ready data products from over twenty NASA missions, plus community-contributed data collections, and other select surveys. The data support forefront research in the ultraviolet, optical, and near-infrared wavelength bands. We have constructed bibliographies for each mission from publi

Jiacheng Zhou, Xinwei Wang, Tianle Shi, Lei Wan

Sb2S3 is a promising material for low-toxicity, high-stability next-generation photovoltaics. Despite high optical limits in efficiency, progress in improving its device performance has been limited by severe voltage losses. Recent spectroscopic investigations suggest that self-trapping occurs in Sb2S3, limiting the open-circuit voltage (Voc) to a maximum of

Bowen Shi, Andros Tjandra, John Hoffman, Helin Wang

General audio source separation is a key capability for multimodal AI systems that can perceive and reason about sound. Despite substantial progress in recent years, existing separation models are either domain-specific, designed for fixed categories such as speech or music, or limited in controllability, supporting only a single prompting modality such as t

Mohammad Taghi Dabiri, Meysam Ghanbari, Rula Ammuri, Saif Al-Kuwari

Continuous-variable quantum key distribution (CVQKD) over free-space optical links is a promising approach for secure communication, but its performance is limited by turbulence, pointing errors, and angular leakage that can be exploited by an eavesdropper. To mitigate this, we consider an angular rejection filter that defines a safe-zone at the receiver and

Andrzej Czarnecki, Ting Gao

We re-examine the recent claim that a Dirac particle freely falling in a uniform gravitational field exhibits a spin-dependent transverse deflection (gravitational spin Hall effect). Using a circulating mass model, we show that hidden momentum arises in uniform fields when an object carries angular momentum. On the quantum side, we analyze the Dirac Hamilton

Boxuan Wang, Zhuoyun Li, Xiaowei Huang, Yi Dong

Large language models (LLMs) have enabled multi-agent systems (MAS) in which multiple agents argue, critique, and coordinate to solve complex tasks, making communication topology a first-class design choice. Yet most existing LLM-based MAS either adopt fully connected graphs, simple sparse rings, or ad-hoc dynamic selection, with little structural guidance.

István Szalkai

We are looking for integer numbers $g_{j}$ and $x_{j}$ ($j=1,...,n$) such that the sums $T_{\pi} := \sum_{j=1}^{n} g_{j} \cdot x_{\pi\left( j\right) }$ are different for all permutations $\pi\in S_{n}$ and $\max\left\{T_{\pi}:\pi\in S_{n}\right\} $ is as small as possible.

Stephan A. Meighen-Berger, P. S. Bhupal Dev, Matheus Hostert

It has been suggested that the density of dark matter (DM) halo can be highly enhanced around supermassive black holes at the centers of massive galaxies. If real, these DM \emph{spikes} would offer new opportunities to probe the properties of DM. In this work, we point out that DM spikes can significantly impact the composition and survivability of ultra-hi

Ge Yan, Tuomas Oikarinen, Tsui-Wei, Weng

Neuron identification is a popular tool in mechanistic interpretability, aiming to uncover the human-interpretable concepts represented by individual neurons in deep networks. While algorithms such as Network Dissection and CLIP-Dissect achieve great empirical success, a rigorous theoretical foundation remains absent, which is crucial to enable trustworthy a

Marjan N. Stankov, Daan Boer, Wouter Graef, Kevin van 't Veer

The MCPlas toolbox represents a collection of MATLAB functions for the automated generation of an equation-based fluid-Poisson model for non-thermal plasmas in the multiphysics simulation software COMSOL. Following the development of the new generation of the LXCat platform, all input data are prepared in a structured and interoperable JSON format and can be

Benjamin Xiao, Dong Ye

Let $G$ be a graph and $S$ be a set of cliques of $G$. The set $S$ is an indeque set if every component of $G[S]$, the subgraph induced by vertices of $S$, is a clique. In this paper, we prove that the indeque ratio of $K_4$-minor-free graphs is $\frac 1 2$, which settle two conjectures of Biro, Collado and Zamora. We also show that the indeque ratio of subc

Meysam Ghanbari, Mohammad Taghi Dabiri, Rula Ammuri, Mazen Hasna

We consider outdoor optical access points (OAPs), which, enabled by recent advances in metasurface technology, have attracted growing interest. While OAPs promise high data rates and strong physical-layer security, practical deployments still expose vulnerabilities and misuse patterns that necessitate a dedicated monitoring layer - the focus of this work. We

J. López-Santiago, F. Reale, G. Micela, L. Martino

We report the discovery of a {recurrent} quasi-periodic pulsation (QPP) in the late-M dwarf GJ 3512 (M5.5V) using multiple TESS datasets. A strong signal with a period of 70-100 minutes was detected in wavelet analyses of the two-minute cadence light curve from Sector 20. This signal was detected also in observations from Sectors 47 and 60. The QPP persisted

Gilson V. Soares, Mauricio Reis, Adelcio C. Oliveira

The classicality of the Gamma Model, an analytically solvable quantum oscillator with non-linear dynamics, is investigated using the overlap dynamics, also known as the Loschmidt Echo, and roughness, a classicality measure based on the Wigner representation of a state. Though the overlap dynamics would indicate a chaotic regime, here the model is integrable.

Ashley Zhang

Standard approaches to causal inference, such as Outcome Regression and Inverse Probability Weighted Regression Adjustment (IPWRA), are typically derived through the lens of missing data imputation and identification theory. In this work, we unify these methods from a Machine Learning perspective, reframing ATE estimation as a \textit{domain adaptation probl

Shreshth Rajan, Raymond Liu

Semantic segmentation of outdoor street scenes plays a key role in applications such as autonomous driving, mobile robotics, and assistive technology for visually-impaired pedestrians. For these applications, accurately distinguishing between key surfaces and objects such as roads, sidewalks, vehicles, and pedestrians is essential for maintaining safety and

Tudor Jianu

Cardiovascular diseases remain the leading cause of global mortality, with minimally invasive treatment options offered through endovascular interventions. However, the precision and adaptability of current robotic systems for endovascular navigation are limited by heuristic control, low autonomy, and the absence of haptic feedback. This thesis presents an i

Marcos Ortiz, Justin Hill, Collin Overbay, Ingrida Semenec

Agentic AI systems capable of generating full-stack web applications from natural language prompts ("prompt- to-app") represent a significant shift in software development. However, evaluating these systems remains challenging, as visual polish, functional correctness, and user trust are often misaligned. As a result, it is unclear how existing prompt-to-app

Jorge Casalderrey-Solana, Lucía Castells-Tiestos, Jéssica Gonçalves, David Mateos

We use holography to study dS-invariant states of non-conformal, strongly coupled quantum field theories in four-dimensional de Sitter space. We show that out-of-equilibrium effects can sustain the exponential inflation within the regime of validity of semiclassical gravity, $H \ll M \ll M_\mathrm{sp}$, with $H$ the Hubble parameter, $M$ the characteristic s

Francesco Fanelli, Young-Sam Kwon, Aneta Wróblewska-Kamińska

This paper studies a singular limit problem for a reduced model for compressible non-resistive MHD which was first introduced in \cite{Li-Sun_JDE, Li-Sun} in a two-dimensional setting. This system can also be related to a certain class of two-fluid models. By a suitable rescaling of the magnetic pressure in terms of some parameter $\varepsilon>0$, by letting

Ohoud Alzahrani, Russell Beale, Robert J. Hendley

This paper asks whether promotional Twitter/X bots form behavioural families and whether members evolve similarly. We analyse 2,798,672 tweets from 2,615 ground-truth promotional bot accounts (2006-2021), focusing on complete years 2009 to 2020. Each bot is encoded as a sequence of symbolic blocks (``digital DNA'') from seven categorical post-level behaviour

Owen S. Matthews Acuña, Christy A. Tremonti, Kyle B. Westfall, Shea DeFour-Remy

The Sloan Digital Sky Survey IV DR17 Extended Baryon Oscillation Spectroscopic Survey (eBOSS) consists of 2,233,939 high-quality optical galaxy spectra obtained through 2" fibers, providing a rich spectroscopic resource for studying galaxy evolution across a broad redshift range. eBOSS was designed primarily for large-scale structure and BAO measurements

Mingjun Sun, Chongjun Ouyang, Shaochuan Wu, Yuanwei Liu

Pinching-antenna system (PASS) mitigates large-scale path loss by enabling flexible placement of pinching antennas (PAs) along the dielectric waveguide. However, most existing studies assume perfect channel state information (CSI), overlooking the impact of channel uncertainty. This paper addresses this gap by proposing a robust beamforming framework for bot

N. Kontowicz, L. Bonneau, J. Bartel, H. Molique

As a follow up of [Phys. Scr. 99 055305 (2024)], where we studied axial octupole shapes in two-quasiparticle states of even-even nuclei, we investigate this type of shapes in odd-mass and odd-odd well-deformed nuclei, using the Skyrme-Hartree-Fock-BCS approach with selfconsistent blocking and a constraint on the expectation value $Q_{30}$ of the axial octupo

Ekta Gavas, Sudipta Banerjee, Chinmay Hegde, Nasir Memon

Multimodal LLMs (MLLMs) are capable of performing complex data analysis, visual question answering, generation, and reasoning tasks. However, their ability to analyze biometric data is relatively underexplored. In this work, we investigate the effectiveness of MLLMs in understanding fine structural and textural details present in fingerprint images. To this

Alon Drory

We study central configurations in the four body problem, i.e., configurations in which the forces on all the bodies point to a fixed, single point in space. The newly formulated pair-space formalism yields a set of vectorial equations that fully characterize such configurations. We investigate a sub-class of solutions in which at least two pairs of inter-bo

Meysam Ghanbari, Mohammad Taghi Dabiri, Mazen Hasna, Tanvir Alam

Accurate channel impulse response (CIR) modeling in molecular communication (MC) often requires solving coupled reactive diffusion-advection equations, which is computationally expensive for large parameter sweeps or design loops. We develop a deep-learning surrogate for a three-dimensional duct MC channel with reactive diffusion-advection transport and reve

Pieter H. W. van der Hoek, Angelo Rosa, Elham Ghobadpour, Ralf Everaers

Topologically constrained genome-like polymers often double-fold into tree-like configurations. Here we calculate the exact number of tightly double-folded configurations available to a ring polymer in ideal conditions. For this purpose, we introduce a scheme which allows us to define a ``code'' specifying how a ring wraps a randomly branching tree and calcu

Simion De, Jared D. Huling

A key challenge in estimating causal effects from observational data is handling confounding and is commonly achieved through weighting methods that balance distribution of covariates between treatment and control groups. Weighting approaches can be classified by whether weights are estimated using parametric or nonparametric methods, and by whether the mode

Juo-Tung Chen, XinHao Chen, Ji Woong Kim, Paul Maria Scheikl

Imitation learning (IL) has shown immense promise in enabling autonomous dexterous manipulation, including learning surgical tasks. To fully unlock the potential of IL for surgery, access to clinical datasets is needed, which unfortunately lack the kinematic data required for current IL approaches. A promising source of large-scale surgical demonstrations is

Hyeongmeen Baik, Jinia Roy

This paper presents a comprehensive review of dielectric barrier discharge (DBD) power supply topologies, aiming to bridge the gap between DBD applications and power electronics design. Two key aspects are examined: the dependence of the DBD electrical model on reactor geometry, and application-driven requirements for injected waveform characteristics, inclu

Emmanuel Kanambaye

Massive gravity is an interesting theoretical framework capable of explaining among others things cosmological IR (infra-red) phenomena like late-time cosmic acceleration. Unfortunately, it turned out to be very difficult of getting consistent massive gravity theory due, among other things, to ghost appearance and strong coupling break-down problems. Of cour

G. Tiago, V. S. Lamego, M. H. M. Passos, W. F. Balthazar

The Depolarizing channel is one of the most important noise models and constitutes a reliable benchmark for the quantum information field. In this work, we present a simple way to emulate a Depolarizing channel by exploring a maximally non separable spin-orbit mode in a compact linear optical circuit. The evolution of different states has been successfully r

Abel Thayil, Lasse Ermoneit, Lars R. Schreiber, Thomas Koprucki

Small and device-dependent valley splittings remain a key challenge for electron spin qubits in silicon (Si), directly limiting qubit fidelity, device uniformity, and the scalability of Si-based quantum processors. In silicon-germanium (SiGe) heterostructures, this problem can be addressed through engineering of the epitaxial layer stack. Several heuristic s

Francesca Incalza, Matteo Castellani, Dip Joti Paul, Alejandro Simon

Achieving both high internal efficiency and fast reset times at elevated temperatures remains challenging due to limited understanding of how film properties govern SNSPD performance. We demonstrate that epitaxial NbN films on sapphire enable simultaneous high efficiency and rapid response. We fabricate and characterize SNSPDs based on these films deposited

Manav Seksaria, Anil Prabhakar

In this work, we introduce a general, gradient-based method that optimises codewords for a given noise channel and fixed recovery. We do so by differentiating fidelity and descending on the complex coefficients using finite-difference Wirtinger gradients with soft penalties to promote orthonormalisation. We validate the gradients on symmetry checks (XXX/ZZZ

Nina Mishra, Yonatan Naamad, Tal Wagner, Lichen Zhang

Approximate nearest neighbor search (ANN) is a common way to retrieve relevant search results, especially now in the context of large language models and retrieval augmented generation. One of the most widely used algorithms for ANN is based on constructing a multi-layer graph over the dataset, called the Hierarchical Navigable Small World (HNSW). While this

Martin Eigel, Charles Miranda, Anthony Nouy, David Sommer

We introduce compositional tensor trains (CTTs) for the approximation of multivariate functions, a class of models obtained by composing low-rank functions in the tensor-train format. This format can encode standard approximation tools, such as (sparse) polynomials, deep neural networks (DNNs) with fixed width, or tensor networks with arbitrary permutation o

Vítor Mateus de Brito, Kleinner Farias

The rapid advancement of Large Language Models (LLMs) is reshaping software engineering by profoundly influencing coding, documentation, and system maintenance practices. As these tools become deeply embedded in developers' daily workflows, understanding how they are used has become essential. This paper reports an empirical study of LLM adoption in software

Rima Alaifari, Ben Pineau, Mitchell A. Taylor, Matthias Wellershoff

We discover a new instability mechanism for short-time Fourier transform phase retrieval which yields that for any reasonable window function $\phi$ in any dimension $d$, the local stability constant $c(f)$ defined via \begin{equation*} \inf_{|\lambda|=1}\|f- \lambda g\|_{M^p(\mathbb{R}^{d})}\leq c(f)\| |V_\phi f|-|V_{\phi} g|\|_\mathcal{D}, \hspace{5mm} \fo

Sabri Mustafa Kahya, Muhammet Sami Yavuz, Boran Hamdi Sivrikaya, Eckehard Steinbach

Out-of-distribution (OOD) detection is essential for the safe deployment of neural networks, as it enables the identification of samples outside the training domain. We present FOODER, a real-time, privacy-preserving radar-based framework that integrates OOD-based facial authentication with facial expression recognition. FOODER operates using low-cost freque

Daniel David

Understanding user identity and behavior is central to applications such as personalization, recommendation, and decision support. Most existing approaches rely on deterministic embeddings or black-box predictive models, offering limited uncertainty quantification and little insight into what latent representations encode. We propose a probabilistic digital

Neda Novakova, Veselin Todorov, Steven van den Broek, Tim Dwyer

Visualizations of set systems frequently use enclosing geometries for the sets in combination with reduced representations of the elements, such as short text labels, small glyphs, or points. Hence they are generally unable to adequately represent sets whose elements are larger text fragments, images, or charts. In this paper we introduce BlockSets, a novel

Ahmad Sheykhi

The conception of gravity as an emergent phenomenon, rooted in the thermodynamics of spacetime, offers a radical departure from its geometric description. This paper investigates the emergence of cosmic space by synthesizing two key thermodynamic approaches: the equilibrium perspective, where the first law of thermodynamics is applied to the apparent horizon

Menglong Zhao, Filippo Scotti, Thomas Rognlien, Marvin Rensink

Edge fluid modeling of the first divertor-plasma detachment experiments in negative triangularity discharges on DIII-D is presented using the 2D multi-fluid code UEDGE, including cross-field particle drifts. Density scans are performed to reproduce the experimental roll-over of the outer-target ion saturation current and to investigate detachment physics for

Daniel Margineda, Covadonga Álvarez-García, Daniel Tezze, Sanaz Gerivani

Engineering unconventional superconductors is a central challenge in condensed matter physics. Molecule-intercalated TaS2 superlattices have recently been reported to host such states, yet their origin remains debated, underscoring the urgent need for controlled, device-integrated studies. Here, we report that nanometer-thick TaS2 and NbSe2 intercalated with

Vanessa Barros, Adriana Coutinho

In this paper, we investigate the asymptotic behavior of the shortest distance between observed orbits in two distinct dynamical systems. Given two measure-preserving transformations $(X, T, \mu)$ and $(X, S, \eta)$ and a Lipschitz observation function $f$, we define \[ \widehat{m}_n^f(x,y) = \min_{i=0,\ldots,n-1} d\big(f(T^i x), f(S^i y)\big). \] %Under sui

Brendan Pawlowski

We show that any unimodular real 3-qubit gate can be expressed as the product of at most 14 CNOT gates plus single-qubit gates, improving on the bound of 16 CNOTs due to Wei and Di. Our method uses the exotic triality symmetry of $\operatorname{PSO}(8)$, and we explore some of the useful properties of this map in relation to the study of real 3-qubit gates.

Nidhi Malhotra, Amber K. Rothe, Revanth Konda, Jaydev P. Desai

Robotically steerable compliant surgical tools offer several advantages over rigid tools, including enhanced dexterity, reduced tissue damage, and the ability to generate non-linear trajectories in minimally invasive neurosurgical procedures. Many existing robotic neurosurgical tools are designed using stainless steel or nitinol materials. Using polymer-base

Ting Wang, Caroline Prendergast, Susan Lottridge

Large language models (LLMs) have emerged as powerful tools for generating domain-specific multiple-choice questions (MCQs), offering efficiency gains for certification boards but raising new concerns about examination security. This study investigated whether LLM-generated items created with proprietary guidance differ meaningfully from those generated usin

Annagrazia Puglisi, Amelia Bayo, Laurane Freour, Daniela Iglesias

The scientific ambitions of the 2040s will require large, interdisciplinary teams operating across continents, institutions, and increasingly heterogeneous political and funding landscapes. While significant effort is devoted to advancing the technical capabilities of future astronomical facilities, frameworks for coordinating and sustaining the associated c

Ami Pandat, Punna Rajasekhar, Gopika Vinod, Rohit Shukla

Unmanned Aerial Vehicles, commonly known as, drones pose increasing risks in civilian and defense settings, demanding accurate and real-time drone detection systems. However, detecting drones is challenging because of their small size, rapid movement, and low visual contrast. A modified architecture of YolovN called the YolovN-CBi is proposed that incorporat

Taylan Demir

Field line tracing is one of the fundamental computational tools used in the study of the magnetosphere, which helps in many areas including footprint mapping, connectivity analysis and real-time visualisation. This note describes an implementation approach to error-bounded adaptive integration of the field line differential equation (ODE), where an embedded

Tyler Gorda, Oleg Komoltsev, Aleksi Kurkela, Eirik Sunde

We set forth a new method for generating model-agnostic, nonparametric priors for neutron star equation-of-state inference that are stable, causal and thermodynamically consistent by construction. This generalizes Gaussian processes to include global thermodynamic constraints, specifically allowing the inclusion of any number of training points in the form $

Mihael Arcan

The increasing volume and complexity of scientific literature demand robust methods for organizing and understanding research documents. In this study, we investigate whether structured knowledge, specifically, subject-predicate-object triples-improves clustering and classification of scientific papers. We present a modular pipeline that combines unsupervise

Shreyas Subramanian, Adewale Akinfaderin, Yanyan Zhang, Ishan Singh

While Retrieval-Augmented Generation (RAG) has proven effective for generating accurate, context-based responses based on existing knowledge bases, it presents several challenges including retrieval quality dependencies, integration complexity and cost. Recent advances in agentic-RAG and tool-augmented LLM architectures have introduced alternative approaches

Tyler McMaken

Why do physicists almost universally take the direction of positive rotation to be counterclockwise, and three-dimensional coordinates to be right-handed? This paper traces the historical development of these chiral conventions, with an emphasis on the physical quantity whose direction became the focal point of this discussion in the mid-1800s, the magnetic

J. de la Cruz Rodríguez, G. B. Scharmer, P. Sütterlin, J. Leenaarts

Imaging Fabry-P\'erot Interferometer (FPI) observations are commonly used in solar physics to infer physical parameters in the photosphere and chromosphere through modeling of the observations. Such techniques require detailed knowledge of the spectral instrumental profile in order to produce accurate results. In this study we present a method to characteriz

Fakrul Islam Tushar, Ehsan Samei, Cynthia Rudin, Joseph Y. Lo

Objective: Although medical imaging datasets are increasingly available, abnormal and annotation-intensive findings critical to lung cancer screening, particularly small pulmonary nodules, remain underrepresented and inconsistently curated. Methods: We introduce NodMAISI, an anatomically constrained, nodule-oriented CT synthesis and augmentation framework tr

Connor Weyers, N. V. Vinodchandran

We revisit the problem of rational search: given an unknown rational number $\alpha = \frac{a}{b} \in (0,1)$ with $b \leq n$, the goal is to identify $\alpha$ using comparison queries of the form ``$\beta \leq \alpha$?''. The problem has been studied several decades ago and optimal query algorithms are known. We present a new algorithm for rational search ba

Wei Qian, Chenxu Zhao, Yangyi Li, Mengdi Huai

The rapid advancements in artificial intelligence (AI) have primarily focused on the process of learning from data to acquire knowledgeable learning systems. As these systems are increasingly deployed in critical areas, ensuring their privacy and alignment with human values is paramount. Recently, selective forgetting (also known as machine unlearning) has s

Joshua Gibson, Kapil Dhakal

Discrete facility layout design involves placing physical entities to minimize handling costs while adhering to strict safety and spatial constraints. This combinatorial problem is typically addressed using Mixed Integer Linear Programming (MILP) or Constraint Programming (CP), though these methods often face scalability challenges as constraint density incr

Areeb Ehsan

Timely brain tumor diagnosis remains challenging in low-resource clinical environments where expert neuroradiology interpretation, high-end MRI hardware, and invasive biopsy procedures may be limited. Although deep learning has achieved strong performance in brain tumor analysis, real-world adoption is constrained by computational demands, dataset shift acro

Jacqueline Borgstedt, Jake Bhattacharyya, Matteo Iovino, Frank E. Pollick

Zoomorphic Socially Assistive Robots (SARs) offer an alternative source of social touch for individuals who cannot access animal companionship. However, current SARs provide only limited, passive touch-based interactions and lack the rich haptic cues, such as warmth, heartbeat or purring, that are characteristic of human-animal touch. This limits their abili

Sarah Nassar, Nooshin Maghsoodi, Sophia Mannina, Shamel Addas

Objective: Atrial fibrillation (AF) is the most common cardiac arrhythmia experienced by intensive care unit (ICU) patients and can cause adverse health effects. In this study, we publish a labelled ICU dataset and benchmarks for AF detection. Methods: We compared machine learning models across three data-driven artificial intelligence (AI) approaches: featu

Leroy Cronin, Juan Carlos Morales Parra, Keith Y. Patarroyo

In this paper we extend the notion of Addition Chains over Z+ to a general set S. We explain how the algebraic structure of Assembly Multi-Magma over the pairs (S,BB proper subset of S) allows to define the concept of Addition Chain over S, called Assembly Addition Chains of S with Building Blocks BB. Analogously to the Z+ case, we introduce the concept of O

Dongjin Kim, Bingqing Cheng

The lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials (MLIPs), hindering reliable applications to interfaces, charge-transfer reactions, polar and ionic materials, and biomolecules. In this Perspective, we distill two design principles behind the Latent Ewald Summation (LES) framework, which can capture l