October 2025 arXiv papers — page 12

Gerard T. Portela, Jason B. Gibbons, Sebastian Schneeweiss, Rishi J. Desai

Observational studies developing causal machine learning (ML) models for the prediction of individualized treatment effects (ITEs) seldom conduct empirical evaluations to assess the conditional exchangeability assumption. We aimed to evaluate the performance of these models under conditional exchangeability violations and the utility of negative control outc

Aylton Almeida, Laerte Xavier, Marco Tulio Valente

Keeping software systems up to date is essential to avoid technical debt, security vulnerabilities, and the rigidity typical of legacy systems. However, updating libraries and frameworks remains a time consuming and error-prone process. Recent advances in Large Language Models (LLMs) and agentic coding systems offer new opportunities for automating such main

Changkai Zhang, Jan von Delft

Artificial Intelligence (AI) has become an exceptionally powerful tool for analyzing scientific data. In particular, attention-based architectures have demonstrated a remarkable capability to capture complex correlations and to furnish interpretable insights into latent, otherwise inconspicuous patterns. This progress motivates the application of AI techniqu

Oleksandr Volkov, Yurii Volkov

The article is devoted to the study of exponential statistical structures of type B, which constitute a subclass of exponential families of probability distributions. This class is characterized by a number of analytical and probabilistic properties that make it a convenient tool for solving both theoretical and applied problems in statistics. The relevance

Duarte Fontes, Robert Szafron

Bound-muon decays are a powerful probe of new physics, making precise theoretical predictions for their spectra essential. While QED corrections significantly affect the shape of the spectra, their calculation is extremely challenging below the nuclear scale. By exploring the universality of modern effective-field-theory techniques, we present a framework th

Zhichao Wang, Dongyang Ma, Xinting Huang, Deng Cai

The "end-to-end" label for LLMs is a misnomer. In practice, they depend on a non-differentiable decoding process that requires laborious, hand-tuning of hyperparameters like temperature and top-p. This paper introduces AutoDeco, a novel architecture that enables truly "end-to-end" generation by learning to control its own decoding strategy. We augment the st

Yuliya Bilinskaya, Miguel F. Martínez, Soumi Ghosh, Thomas Klein Kvorning

We study nonstabilizerness on the information lattice, and demonstrate that noninteger local information directly indicates nonstabilizerness. For states with a clear separation of short- and large-scale information, noninteger total information at large scales $\Gamma$ serves as a witness of long-range nonstabilizerness. We propose a folding procedure to se

Sourav Koner, Titas Saha, Biswajit Mitra

In this paper, we introduce and study two new classes of commutative rings, namely semi transitional rings and transitional rings, which extend several classical ideas arising from rings of continuous functions and their variants. A general framework for these rings is developed through the notion of semi transition and transition maps, leading to a systemat

Bernhard Kerbl

Since its introduction, 3D Gaussian Splatting (3DGS) has rapidly transformed the landscape of 3D scene representations, inspiring an extensive body of associated research. Follow-up work includes analyses and contributions that enhance the efficiency, scalability, and real-world applicability of 3DGS. In this summary, we present an overview of several key di

Yu Zhu, Luigi Ranalli, Taikang Chen, Wei Ren

Ferroelectric materials are a class of dielectrics that exhibit spontaneous polarization which can be reversed under an external electric field. The emergence of ferroelectric order in incipient ferroelectrics is a topic of considerable interest from both fundamental and applied perspectives. Among the various strategies explored, strain engineering has been

Kimi Team, Yu Zhang, Zongyu Lin, Xingcheng Yao

We introduce Kimi Linear, a hybrid linear attention architecture that, for the first time, outperforms full attention under fair comparisons across various scenarios -- including short-context, long-context, and reinforcement learning (RL) scaling regimes. At its core lies Kimi Delta Attention (KDA), an expressive linear attention module that extends Gated D

Andrea Crespi, Marco Bonici, Arthur Loureiro, Jaime Ruiz-Zapatero

We present Flinch, a fully differentiable and high-performance framework for field-level inference on angular maps, developed to improve the flexibility and scalability of current methodologies. Flinch is integrated with differentiable cosmology tools, allowing gradients to propagate from individual map pixels directly to the underlying cosmological paramete

Amir Reza Mirzaei, Yuqiao Wen, Yanshuai Cao, Lili Mou

Low-Rank Adaptation (LoRA) has become a popular technique for parameter-efficient fine-tuning of large language models (LLMs). In many real-world scenarios, multiple adapters are loaded simultaneously to enable LLM customization for personalized user experiences or to support a diverse range of tasks. Although each adapter is lightweight in isolation, their

Marios Impraimakis

The response-only model class selection capability of a novel deep convolutional neural network method is examined herein in a simple, yet effective, manner. Specifically, the responses from a unique degree of freedom along with their class information train and validate a one-dimensional convolutional neural network. In doing so, the network selects the mod

Jun Dai, Michael Rizvi-Martel, Guillaume Rabusseau

Designing efficient quantum circuits is a central bottleneck to exploring the potential of quantum computing, particularly for noisy intermediate-scale quantum (NISQ) devices, where circuit efficiency and resilience to errors are paramount. The search space of gate sequences grows combinatorially, and handcrafted templates often waste scarce qubit and depth

Caeley V. Pittman, Catherine C. Espaillat, Thanawuth Thanathibodee, Nuria Calvet

The properties of stars and planets are shaped by the initial conditions of their natal clouds. However, the spatial scales over which the initial conditions can exert a significant influence are not well constrained. We report the first evidence for parsec-scale spatial correlations of stellar magnetospheric inclinations ($i_{\rm mag}$), observed in the Lup

Xiyao Yu, Kai Fu

Background: Despite high in-silico performance (AUC >0.80), 85% of AI cancer biomarkers fail clinical translation, exposing a critical algorithm-to-outcome gap. Methods: We introduce the Algorithm-to-Outcome Concordance (AOC) framework, integrating model accuracy (AUC), clinical correlation (Corr), and trial heterogeneity. We validated AOC across 6 neoantige

Vaibhav Kurrey, Sivakalyan Pujari, Gagan Raj Gupta

We present a long-term deployment study of a machine vision-based anomaly detection system for failure prediction in a steel rolling mill. The system integrates industrial cameras to monitor equipment operation, alignment, and hot bar motion in real time along the process line. Live video streams are processed on a centralized video server using deep learnin

Mingchen Tu, Zhiqiang Liu, Juan Li, Liangyurui Liu

Although Large Language Models (LLMs) perform exceptionally well in general domains, the problem of hallucinations poses significant risks in specialized fields such as healthcare and law, where high interpretability is essential. Existing fine-tuning methods depend heavily on large-scale professional datasets, which are often hard to obtain due to the priva

Natasha Van Bemmel, Jielai Zhang, Jeff Cooke, Anais Möller

We present AT2022kak, a rapidly evolving optical transient discovered by the KiloNova and Transients Program (KNTraP). This interesting burst exhibited extremely fast evolution, with a large amplitude blue outburst of m > 3.3 in a single night, and a rapid fade back to quiescence in the following two nights. We deployed a multi-wavelength follow-up campaign,

Courtney M. King, Daniel D. Leeds, Damian Lyons, George Kalaitzis

The presence of occlusions has provided substantial challenges to typically-powerful object recognition algorithms. Additional sources of information can be extremely valuable to reduce errors caused by occlusions. Scene context is known to aid in object recognition in biological vision. In this work, we attempt to add robustness into existing Region Proposa

Remi Abgrall, Yongle Liu, Mario Ricchiuto

We present a novel hydrostatic and non-hydrostatic equilibria preserving Point-Average-Moment PolynomiAl-interpreted (PAMPA) method for solving the one-dimensional hyperbolic balance laws, with applications to the shallow water models including the Saint--Venant system with the Manning friction term and rotating shallow water equations. The idea is based on

Tommaso d'Orsi, Gleb Novikov

We revisit the task of computing the span of the top $r$ singular vectors $u_1, \ldots, u_r$ of a matrix under differential privacy. We show that a simple and efficient algorithm -- based on singular value decomposition and standard perturbation mechanisms -- returns a private rank-$r$ approximation whose error depends only on the \emph{rank-$r$ coherence} o

Friederike Ihssen, Renzo Kapust, Jan M. Pawlowski

We construct a generative network for Monte-Carlo sampling in lattice field theories and beyond, for which the learning of layerwise propagation is done and optimised independently on each layer. The architecture uses physics-informed renormalisation group flows that provide access to the layerwise propagation step from one layer to the next in terms of a si

Ronan Paugam, Akli Benali, Julia Harvie, Andrea Meraner

This study presents FCI-FireDyn, a new algorithm developed to monitor wildfire dynamics using the Flexible Combined Imager (FCI) onboard the Meteosat Third Generation satellite. Leveraging the high temporal resolution of FCI (10-minute full-disk observations), the algorithm derives fire arrival time maps, rate of spread (ROS), and Burn Area (BA) evolution at

Samiha Shimmi, Nicholas M. Synovic, Mona Rahimi, George K. Thiruvathukal

Software vulnerabilities often persist or re-emerge even after being fixed, revealing the complex interplay between code evolution and socio-technical factors. While source code metrics provide useful indicators of vulnerabilities, software engineering process metrics can uncover patterns that lead to their introduction. Yet few studies have explored whether

Eric Ling, Argam Ohanyan, Eric Woolgar

We consider versions of the Penrose singularity theorem and the Hawking horizon topology theorem in weighted spacetimes that contain weighted versions of trapped surfaces, for arbitrary spacetime dimension and synthetic dimension. We find that suitable generalizations of the unweighted theorems hold under a weighted null energy condition. Our results also pr

E. Christodoulou, S. de Wit, A. Z. Bonanos, G. Muñoz-Sanchez

Mass loss shapes the fate of massive stars; however, the physical mechanism causing it remains uncertain. We present a comprehensive analysis of seven red supergiants, for which we searched evidence of episodic mass loss, in three low-metallicity galaxies: NGC~6822, IC~10, and WLM. Initially, the spectral classification of their optical spectra was refined a

Quinn J. M. Salix, Peyton Phinehas Wood

We prove that the displacement group of the dihedral quandle with n elements is isomorphic to the group generated by rotations of the n/2-gon when n is even and the n-gon when n is odd. We additionally show that any quandle with at least one trivial column has equivalent displacement and inner automorphism groups. Then, using a known enumeration of quandles

Ruimin He, Shaowei Lin

At the heart of reinforcement learning are actions -- decisions made in response to observations of the environment. Actions are equally fundamental in the modeling of stochastic processes, as they trigger discontinuous state transitions and enable the flow of information through large, complex systems. In this paper, we unify the perspectives of stochastic

Felix A. Palm, Chloé Van Bastelaere, Laurens Vanderstraeten

Topological phases in two-dimensional quantum lattice models are often studied on cylinders for revealing different topological properties and making the problem numerically tractable. This makes a proper understanding of finite-circumference effects crucial for reliably extrapolating the results to the thermodynamic limit. Using matrix product states, we in

Qianyou Zhao, Yuliang Shen, Xuanran Zhai, Ce Hao

In visuomotor policy learning, diffusion-based imitation learning has become widely adopted for its ability to capture diverse behaviors. However, approaches built on ordinary and stochastic denoising processes struggle to jointly achieve fast sampling and strong multi-modality. To address these challenges, we propose the Hybrid Consistency Policy (HCP). HCP

Aissa Boukarou, Lamia Seghour

In this paper, we improve and extend the results obtained by Boukarou et al. \cite{boukarou1} on the Gevrey regularity of solutions to a fifth-order Kadomtsev-Petviashvili-II equation. We establish Gevrey regularity in the time variable for solutions in $2+1$ dimensions, providing a sharper result obtained through a new analytical approach. Assuming that the

Harry G. Saavedra, Ramiro Moro

More than a century ago, Karl Bernhard Zoeppritz derived the equations that determine the reflected and transmitted coefficients at a planar interface for an incident seismic wave. The coefficients so obtained are a function of the elastic parameters of the media on each side of the interface and the angle of incidence. Approximations of the equations have b

Samuel Lisi, Antonio Rieser

We introduce a new coisotropic Hofer-Zehnder capacity and use it to prove an energy-capacity inequality for displaceable Lagrangians.

Alessandro Bruno, Melissa Pesce-Rollins, Silvia Dalla, Nicola Omodei

We present a multi-spacecraft analysis of the 2024 July 16 Long-Duration Gamma-Ray Flare (LDGRF) detected by the Large Area Telescope on the Fermi satellite. The measured >100 MeV $\gamma$-ray emission persisted for over seven hours after the flare impulsive phase, and was characterized by photon energies exceeding 1 GeV and a remarkably-hard parent-proton s

Michela La Bella, Henning Friis Poulsen, Steffen Staeck, Nils Axel Henningsson

Pink-beam Dark-Field X-ray Microscopy (pDFXM) is a powerful emerging technique for time-resolved studies of microstructure and strain evolution in bulk crystalline materials. In this work, we systematically assess the performance of pDFXM relative to monochromatic DFXM when using a compound refractive lens (CRL) as the objective. Analytical expressions for t

Teerapol Saengsukhiran, Peerawat Chomphooyod, Narabodee Rodjananant, Chompakorn Chaksangchaichot

Multimodal retrieval systems are expected to operate in a semantic space, agnostic to the language or cultural origin of the query. In practice, however, retrieval outcomes systematically reflect perspectival biases: deviations shaped by linguistic prevalence and cultural associations. We introduce the Cross-Cultural, Cross-Modal, Cross-lingual Multimodal (3

Ivan Bortnovskyi, June Duvivier, Alex Iosevich, Josh Iosevich

Signal recovery from incomplete or partial frequency information is a fundamental problem in harmonic analysis and applied mathematics, with wide-ranging applications in communications, imaging, and data science. Historically, the classical uncertainty principles, such as those by Donoho and Stark, have provided essential bounds relating the sparsity of a si

Tobias Wichmann, Mirco Sastges, Keda Jin, Jose Martinez-Castro

Van der Waals (vdW) heterostructures allow the engineering of electronic and magnetic properties by the stacking different two-dimensional vdW materials. For example, orbital hybridisation and charge transfer at a vdW interface may result in electric fields across the interface that give rise to Rashba spin-orbit coupling. In magnetic vdW heterostructures, t

Alya Almsouti, Ainur Khamitova, Darya Taratynova, Mohammad Yaqub

Assessing the severity of artifacts in pediatric brain Magnetic Resonance Imaging (MRI) is critical for diagnostic accuracy, especially in low-field systems where the signal-to-noise ratio is reduced. Manual quality assessment is time-consuming and subjective, motivating the need for robust automated solutions. In this work, we propose BRIQA (Balanced Reweig

Xavier Mary

We construct a category equivalent to the category $\mathbf{Mon}$ of monoids and monoid homomorphisms, based on the Sch{ü}tzenberger category of semigroups and categories with strict factorization systems. This equivalence is then extended to the category $\mathbf{Mon_s}$ of unital semigroups and semigroup homomorphisms. By introducing suitable natural trans

Kevin Ching, Anthony Estrada, Nicholas M Rubayiza, Ligesh Theeyancheri

We investigate how an active bath of enzymes influences the liquid-liquid phase separation (LLPS) of a non-interacting condensing protein. The enzyme we choose to use as the active driver is urease, an enzyme that has been shown by several groups to exhibit enhanced diffusion in the presence of its substrate. The non-interacting LLPS protein is ubiquilin-2,

Zewen Chi, Li Dong, Qingxiu Dong, Yaru Hao

We envision a new era of AI, termed agentic organization, where agents solve complex problems by working collaboratively and concurrently, enabling outcomes beyond individual intelligence. To realize this vision, we introduce asynchronous thinking (AsyncThink) as a new paradigm of reasoning with large language models, which organizes the internal thinking pr

Daniel Tan

Given a vertex operator algebra $ V $ with a general automorphism $ g $ of $ V $, we introduce a notion of $ C_n $-cofiniteness for weak $ g $-twisted $ V $-modules. When $ V $ is $ C_2 $-cofinite and of CFT type, we show that all finitely-generated weak $ g $-twisted $ V $-modules are $ C_n $-cofinite for all $ n \in \mathbb{Z}_{>0} $.

Georgios Kamaras, Craig Innes, Subramanian Ramamoorthy

In robotics, likelihood-free inference (LFI) can provide the domain distribution that adapts a learnt agent in a parametric set of deployment conditions. LFI assumes an arbitrary support for sampling, which remains constant as the initial generic prior is iteratively refined to more descriptive posteriors. However, a potentially misspecified support can lead

Marios Impraimakis

The dynamic structural load identification capabilities of the gated recurrent unit, long short-term memory, and convolutional neural networks are examined herein. The examination is on realistic small dataset training conditions and on a comparative view to the physics-based residual Kalman filter (RKF). The dynamic load identification suffers from the unce

Håvard Damm-Johnsen

A recent result of Rickards states that the generating series of intersection numbers of real quadratic geodesics on indefinite Shimura curves are elliptic modular forms. We reinterpret this as a Kudla-Millson theta series, and prove that Rickards' generating series is the diagonal restriction of a Hilbert modular form, analogous to results of Darmon-Pozzi-V

Andrzej Szalas

At its core, abstraction is the process of generalizing from specific instances to broader concepts or models, with the primary objective of reducing complexity while preserving properties essential to the intended purpose. It is a~fundamental, often implicit, principle that structures the understanding, communication, and development of both scientific know

Daniela Martin, Joseph Gallego

Accurate estimation of sea ice drift is critical for Arctic navigation, climate research, and operational forecasting. While optical flow, a computer vision technique for estimating pixel wise motion between consecutive images, has advanced rapidly in computer vision, its applicability to geophysical problems and to satellite SAR imagery remains underexplore

Dayoon Park, Robin Visser, Pavlo Yatsyna, Jongheun Yoon

We prove an explicit asymptotic formula for the logarithm of the minimal ranks of $n$-universal lattices over the ring of integers of totally real number fields. We also show that, for any constant $C > 0$ and $n \geq 3$, there are only finitely many totally real fields with an $n$-universal lattice of rank at most $C$, with all such fields being effectively

David J. Strachan, Archak Purkayastha, Stephen R. Clark

Simulating the dynamics of quantum impurity models remains a fundamental challenge due to the complex memory effects that arise from system-environment interactions. Of particular interest are two-time correlation functions of an impurity, which are central to the characterization of these many-body systems, and are a cornerstone of the description of correl

Feranmi V. Olowookere, Sakib Matin, Aleksandra Pachalieva, Nicholas Lubbers

Molecular dynamics simulations are an integral tool for studying the atomistic behavior of materials under diverse conditions. However, they can be computationally demanding in wall-clock time, especially for large systems, which limits the time and length scales accessible. Coarse-grained (CG) models reduce computational expense by grouping atoms into simpl

Zhicheng Jiang, Hengne Li, Jin-Han Liang

The search for dark matter and other photon-portal long-lived particles (LLPs) at electron-positron colliders often relies on the mono-photon signature. At future Higgs factories operating at the $Z$-pole, this approach faces a critical challenge: the irreducible background from $e^+e^- \to \nu\bar{\nu}\gamma$ becomes overwhelming. We propose a novel strateg

Zarije Ademi, Marion Bassi, Cécile X. Yu, Stefan D. Oosterhout

Semiconductor quantum processors have potential to scale to modular quantum computers, in which qubit registers are coupled by quantum links, enabling high connectivity and space for control circuitry. Individual spin-qubit registers have progressed to two-dimensional systems and execution of small quantum algorithms. Separately, high-fidelity spin shuttling

Adil Salim

Recent progress in large language models has made them increasingly capable research assistants in mathematics. Yet, as their reasoning abilities improve, evaluating their mathematical competence becomes increasingly challenging. The problems used for assessment must be neither too easy nor too difficult, their performance can no longer be summarized by a si

Miguel Angel Lopez-Ruiz, Emily L. Tucker, Emma M. Arnold, Evgeny Epifanovsky

Quantum heuristics offer a potential advantage for combinatorial optimization but are constrained by near-term hardware limitations. We introduce Iterative-QAOA, a variant of QAOA designed to mitigate these constraints. The algorithm combines a non-variational, shallow-depth circuit approach using fixed-parameter schedules with an iterative warm-starting pro

Xiaoyi He, Danggui Chen, Zhenshuo Zhang, Zimeng Bai

This paper presents a hierarchical path-planning and control framework that combines a high-level Deep Q-Network (DQN) for discrete sub-goal selection with a low-level Twin Delayed Deep Deterministic Policy Gradient (TD3) controller for continuous actuation. The high-level module selects behaviors and sub-goals; the low-level module executes smooth velocity

Katarina Petrović, Lazar Atanackovic, Viggo Moro, Kacper Kapuśniak

Modeling the transport dynamics of natural processes from population-level observations is a ubiquitous problem in the natural sciences. Such models rely on key assumptions about the underlying process in order to enable faithful learning of governing dynamics that mimic the actual system behavior. The de facto assumption in current approaches relies on the

Dominique Maldague, Hong Wang, Dmitrii Zakharov

We show that among any $n$ points in the unit cube one can find a triangle of area at most $n^{-2/3-c}$ for some absolute constant $c >0$. This gives the first non-trivial upper bound for the three-dimensional version of Heilbronn's triangle problem. This estimate is a consequence of the following result about configurations of point-line pairs in $\mathbb R

Emmanouil Sylligardos, John Paparrizos, Themis Palpanas, Pierre Senellart

Anomaly detection is a fundamental task for time series analytics with important implications for the downstream performance of many applications. Despite increasing academic interest and the large number of methods proposed in the literature, recent benchmarks and evaluation studies demonstrated that no overall best anomaly detection methods exist when appl

Yongjiang Wu, Yongtao Li, Zhiyi Liu, Lihua Feng

We investigate the product measures of intersection problems in extremal combinatorics. Invoking a recent result of He--Li--Wu--Zhang, we prove that for any $ n \geq t \geq 3$ and $ p_1, p_2 \in (0, \frac{1}{t+1})$, if $ \mathcal{F}_1, \mathcal{F}_2 \subseteq 2^{[n]}$ are cross $ t$-intersecting families, then $μ_{p_1}(\mathcal{F}_1)μ_{p_2}(\mathcal{F}_2)\le

Sayed Pedram Haeri Boroujeni, Niloufar Mehrabi, Hazim Alzorgan, Mahlagha Fazeli

Autonomous Vehicles (AVs) are transforming the future of transportation through advances in intelligent perception, decision-making, and control systems. However, their success is tied to one core capability, reliable object detection in complex and multimodal environments. While recent breakthroughs in Computer Vision (CV) and Artificial Intelligence (AI) h

Xinyu Zhu, Wenqiang Yuan, Yusen Wang, Zhipeng Zhang

Coupling of electron heat conduction and magnetic field takes significant effects in inertial confinement fusion (ICF). As the nonlocal models for electron heat conduction have been developed for modeling kinetic effects on heat flux in hydrodynamic scale, modeling kinetic effects on magnetic field are still restricted to flux limiters instead of nonlocal co

Shu-Yu Li, Xian Gao

We investigate a class of spatially covariant vector field theories on a flat background, where the Lagrangians are constructed as polynomials of first-order derivatives of the vector field. Because Lorentz and $\mathrm{U}(1)$ invariances are broken, such theories generally propagate three degrees of freedom (DOFs): two transverse modes and one longitudinal

Dave van der Meer, Loïck P. Chovet, Gabriel M. Garcia, Abhishek Bera

The European Space Agency (ESA) and the European Space Resources Innovation Centre (ESRIC) created the Space Resources Challenge to invite researchers and companies to propose innovative solutions for Multi-Robot Systems (MRS) space prospection. This paper proposes the Resilient Exploration And Lunar Mapping System 2 (REALMS2), a MRS framework for planetary

Lekshmi Eswaramoorthy, Parul Sharma, Brijesh Kumar, Abhay Anand

Two-dimensional (2D) transition metal dichalcogenides are pivotal for next-generation photonic devices due to their exceptional optical properties and strong light-matter interactions. However, their atomic thinness renders them susceptible to damage during nanoscale fabrication. Focused ion beam technology, while offering precise defect engineering for tail

Jianhui Qiu, Ling-Wei Luo, Chunhui Liu, Chao-Qiang Geng

We extend the conformal dimensional-derivative regularization of four-dimensional Gauss- Bonnet gravity to Riemann-Cartan geometry, obtaining a regularized action whose torsionless limit equals the well-known regularized four-dimensional Einstein-Gauss-Bonnet model. Varying independently with respect to the scalar, tetrad, and spin connection yields field eq

Runyu Wang, Haotian Zhong

Urban food delivery services have become an integral part of daily life, yet their mobility and environmental externalities remain poorly addressed by planners. Most studies neglect whether consumers pay enough to internalize the broader social costs of these services. This study quantifies the value of access to and use of food delivery services in Beijing,

Marios Impraimakis, Evangelia Nektaria Palkanoglou

The optimization-based damage detection and damage state digital twinning capabilities are examined here of a novel conditional-labeled generative adversarial network methodology. The framework outperforms current approaches for fault anomaly detection as no prior information is required for the health state of the system: a topic of high significance for re

Yuan Si, Kyle Qi, Daming Li, Hanyuan Shi

Block-based environments such as Scratch are increasingly popular in programming education. While block syntax reduces surface errors, semantic bugs remain common and challenging for novices to resolve. Existing debugging workflows typically show the correct program directly to learners, a strategy that may fix errors but undermines the development of proble

Colin Doumont, Victor Picheny, Viacheslav Borovitskiy, Henry Moss

Bayesian Optimization (BO) has the potential to solve various combinatorial tasks, ranging from materials science to neural architecture search. However, BO requires specialized kernels to effectively model combinatorial domains. Recent efforts have introduced several combinatorial kernels, but the relationships among them are not well understood. To bridge

Atheendar Venkataramani, Rourke O'Brien, Elizabeth Bair, Christopher Lowenstein

We study the health consequences of redistributing political power through the 1975 extension of the Voting Rights Act, which eliminated barriers to voting for previously disenfranchised nonwhite populations. The intervention led to broad declines in under-five mortality but sharply contrasting effects in other age groups: mortality fell among non-white chil

Georg Hartl, Conrad Gstöttner, Markus Schöberl

Determining whether a nonlinear multi-input system is differentially flat remains challenging. One way to obtain computationally tractable sufficient conditions is to give complete characterizations of flat normal forms. We introduce a structurally flat triangular form for control-affine systems with at least three inputs that is based on a multi-chained for

Mikhail S. Arbatskii, Maksim V. Kukushkin, Dmitriy E. Balandin, Alexey V. Churov

In the paper, we represent a comparison analysis of the methods of the topological alignment and extract the main mathematical principles forming the base of the concept. The main narrative is devoted to the so-called coupled methods dealing with the data sets of various nature. As a main theoretical result, we obtain harmonious generalizations of the graph

Bingcong Huo, Zhiming Wang

To address the challenges in UAV object detection, such as complex backgrounds, severe occlusion, dense small objects, and varying lighting conditions,this paper proposes PT-DETR based on RT-DETR, a novel detection algorithm specifically designed for small objects in UAV imagery. In the backbone network, we introduce the Partially-Aware Detail Focus (PADF) M

Nir Sherf, Remi Goerlich, Barak Hirshberg, Yael Roichman

Stochastic resetting is known for its ability to accelerate search processes and induce non-equilibrium steady states. Here, we compare the relaxation times and resulting steady states of resetting and thermal relaxation for Brownian motion in a harmonic potential. We show that resetting always converges faster than thermal equilibration, but to a different

Chuang Zhang, Geng Sun, Jiahui Li, Jiacheng Wang

The proliferation of Internet of Things (IoT) networks has created an urgent need for sustainable energy solutions, particularly for the battery-constrained spatially distributed IoT nodes. While low-altitude uncrewed aerial vehicles (UAVs) employed with wireless power transfer (WPT) capabilities offer a promising solution, the line-of-sight channels that fa

Dmitry Lesnik, Tobias Schaefer

Machine learning models used for high-stakes predictions in domains like credit risk face critical degradation due to concept drift, requiring robust and transparent adaptation mechanisms. We propose an architecture, where a dedicated correction layer is employed to efficiently capture systematic shifts in predictive scores when a model becomes outdated. The

Cong Liu, Jordi Boronat, Claudio Cazorla

Helium and rare gases (RG: Ne, Ar, Kr, Xe) are typically considered chemically inert, yet under the extreme pressures of planetary interiors they may form compounds with unexpected properties. Using crystal structure prediction and first-principles calculations, we mapped the phase diagram of binary He-RG systems up to $1$ TPa. We identify several previously

Samuele Anoardo, Alessio Mucciarelli, Marco Palla, Lorenzo Santarelli

In this study we investigate the chemical enrichment of the rapid neutron-capture process in the Small Magellanic Cloud (SMC). We measure [Eu/Fe] abundance ratios in 209 giant stars that are confirmed members of the SMC, providing the first extensive dataset of Eu abundances in this galaxy across its full metallicity range, spanning more than 1.5 dex. We com

D. R. Chalfoun, J. Parker, M. Gagliano, E. Martínez-Pañeda

A new protocol is presented to directly characterise the toughness of microstructural regions present within the weld heat-affected zone (HAZ), the most vulnerable location governing the structural integrity of hydrogen transport pipelines. Heat treatments are tailored to obtain bulk specimens that replicate predominantly ferritic-bainitic, bainitic, and mar

Biao Zhang, Yong Cheng, Siamak Shakeri, Xinyi Wang

Recent large language model (LLM) research has undergone an architectural shift from encoder-decoder modeling to nowadays the dominant decoder-only modeling. This rapid transition, however, comes without a rigorous comparative analysis especially \textit{from the scaling perspective}, raising concerns that the potential of encoder-decoder models may have bee

Mridupawan Deka, Maxim Dvornikov

In this work, we employ Ordinary Differential Equation solution method to study neutrino spin oscillations in the case when they are gravitationally scattered off a rotating Kerr black hole. Previously, this problem involved the integral solution of the Hamilton-Jacobi equation. We analyze the consistency of these two methods.

Nicholas Pecka, Lotfi Ben Othmane, Renee Bryce

Threat modeling plays a critical role in the identification and mitigation of security risks; however, manual approaches are often labor intensive and prone to error. This paper investigates the automation of software threat modeling through the clustering of call graphs using density-based and community detection algorithms, followed by an analysis of the t

Luc Ta, Peyton Phinehas Wood

We study 4-Legendrian racks and their effectiveness at distinguishing Legendrian knots. We prove that permutation racks with 4-Legendrian rack structures cannot distinguish Legendrian knots that share the same knot type, Thurston-Bennequin number, and rotation number. However, they also recover these three classical invariants.

Abhijit Sen, Lev Kaplan

This paper revisits the textbook 'particle in a box', but from the point of view of Koopman-von Neumann (KvN) mechanics. KvN mechanics is a way to describe \emph{classical} dynamics in a Hilbert space. That simple fact changes the usual expectation: hard walls do \emph{not} force energy quantization here. We show, in a clear and physical way, why a KvN parti

Niklas Christoph Affolter, Alexander Yves Fairley

Discrete Koenigs nets are a special class of discrete surfaces that play a fundamental role in discrete differential geometry, in particular in the study of discrete isothermic and minimal surfaces. Recently, it was shown by Bobenko and Fairley that Koenigs nets can be characterized by the existence of touching inscribed conics. We generalize the touching in

Lin Xu, Xinyun Yuan, Yuxuan Liang, Suwan Yin

We introduce Aeolus, a large-scale Multi-modal Flight Delay Dataset designed to advance research on flight delay prediction and support the development of foundation models for tabular data. Existing datasets in this domain are typically limited to flat tabular structures and fail to capture the spatiotemporal dynamics inherent in delay propagation. Aeolus a

Zeyu Yang, Clayton Souza Leite, Yu Xiao

Tracking strength-demanding activities with wearable sensors like IMUs is crucial for monitoring muscular strength, endurance, and power. However, there is a lack of comprehensive datasets capturing these activities. To fill this gap, we introduce \textit{StrengthSense}, an open dataset that encompasses IMU signals capturing 11 strength-demanding activities,

Reihaneh Mirjalili

This thesis investigates how foundation models can be systematically leveraged to enhance robotic capabilities, enabling more effective localization, interaction, and manipulation in unstructured environments. The work is structured around four core lines of inquiry, each addressing a fundamental challenge in robotics while collectively contributing to a coh

Christoffer Koo Øhrstrøm, Ronja Güldenring, Lazaros Nalpantidis

We propose tokenization of events and present a tokenizer, Spiking Patches, specifically designed for event cameras. Given a stream of asynchronous and spatially sparse events, our goal is to discover an event representation that preserves these properties. Prior works have represented events as frames or as voxels. However, while these representations yield

Gilles Crommen, Jean-Pierre Florens, Ingrid Van Keilegom

Consider the setting in which a researcher is interested in the causal effect of a treatment $Z$ on a duration time $T$, which is subject to right censoring. We assume that $T=\varphi(X,Z,U)$, where $X$ is a vector of baseline covariates, $\varphi(X,Z,U)$ is strictly increasing in the error term $U$ for each $(X,Z)$ and $U\sim \mathcal{U}[0,1]$. Therefore, t

Vikash Mittal, Tomasz Sowiński

In this work, we introduce a general form of a two-parameter family of local interactions between quantum walkers conditioned on the internal state of their coins. By choosing their particular case, we systematically study the impact of these interactions on the dynamics of two initially localized and noncorrelated walkers. Our general interaction framework,

Yu Li, Yuan Huang, Tao Wang, Caiyu Fan

Most scientific materials compress reasoning, presenting conclusions while omitting the derivational chains that justify them. This compression hinders verification by lacking explicit, step-wise justifications and inhibits cross-domain links by collapsing the very pathways that establish the logical and causal connections between concepts. We introduce a sc

Peter Benner, Boris N. Khoromskij, Venera Khoromskaia, Matthias Stein

We propose and justify a new approach for fast calculation of the electrostatic interaction energy of clusters of charged particles in constrained energy minimization in the framework of rigid protein-ligand docking. Our ``blind search'' docking technique is based on the low-rank range-separated (RS) tensor-based representation of the free-space electrostati

Weixuan Chen, Qianqian Yang

Semantic communication (SemCom) aims to transmit only task-relevant information, thereby improving communication efficiency but also exposing semantic information to potential eavesdropping. In this paper, we propose a deep reinforcement learning (DRL)-empowered multi-level jamming approach to enhance the security of SemCom systems over MIMO fading wiretap c

Shayan Nejadshamsi, Yuanyuan Zhang, Shadi Zaki, Brock Porth

Accurate and timely crop yield prediction is crucial for global food security and modern agricultural management. Traditional methods often lack the scalability and granularity required for precision farming. This paper introduces FARM: Fine-tuning Agricultural Regression Models, a deep learning framework designed for high-resolution, intra-field canola yiel

Maksim Maslov, Alexander Kugaevskikh, Matthew Ivanov

This paper considers the problem of regression over distributions, which is becoming increasingly important in machine learning. Existing approaches often ignore the geometry of the probability space or are computationally expensive. To overcome these limitations, a new method is proposed that combines the parameterization of probability trajectories using a

Muhammad Abdullah, Catherine Searle

In this article, we study closed, positively curved $n$-manifolds that admit an effective, isometric $\mathbb{Z}_p^r$-action with a fixed point, where $p$ is an odd prime. For all sufficiently large $n$, we obtain a symmetry-rank bound in Theorem A that improves the $3n/8$ bound of Fang and Rong and of Ghazawneh. We improve on this bound for small odd primes