November 2025 arXiv papers — page 35

Steven Chen, Jeremy Hare, Oleg Kargaltsev, Hui Yang

We analyze JWST observations of the central region of the globular cluster $\omega$ Centauri (NGC 5139, $\omega$ Cen hereafter), around the position of the candidate IMBH inferred by \cite{haberle_fast-moving_2024} from the motion of fast-moving stars in multi-epoch HST observations. We performed PSF-fitting photometry for sources in NIRCam (F200W and F444W)

Yaw Osei Adjei, Frederick Ayivor

Business Email Compromise (BEC) is a high-impact social engineering threat with extreme operational asymmetry: false negatives can trigger large financial losses, while false positives primarily incur investigation and delay costs. This paper compares two BEC detection paradigms under a cost-sensitive decision framework: (i) a semantic transformer approach (

Chuhao Qin, Alexandru Sorici, Andrei Olaru, Evangelos Pournaras

The rapid adoption of electric vehicles (EVs) introduces major challenges for decentralized charging control. Existing decentralized approaches efficiently coordinate a large number of EVs to select charging stations while reducing energy costs, preventing power peak and preserving driver privacy. However, they often struggle under severe contingencies, such

Rahul Dass, Thomas Bowlin, Zebing Li, Xiao Jin

In procedural skill learning, instructional explanations must convey not just steps, but the causal, goal-directed, and compositional logic behind them. Large language models (LLMs) often produce fluent yet shallow responses that miss this structure. We present Ivy, an AI coaching system that delivers structured, multi-step explanations by combining symbolic

Yu Terada, Yugo Ogio, Ken Arai, Hiroyuki Tezuka

Two-sample tests have been extensively employed in various scientific fields and machine learning such as evaluation on the effectiveness of drugs and A/B testing on different marketing strategies to discriminate whether two sets of samples come from the same distribution or not. Kernel-based procedures for hypothetical testing have been proposed to efficien

Reham Omar, Abdelghny Orogat, Ibrahim Abdelaziz, Omij Mangukiya

Conversational Question Answering over Knowledge Graphs (KGs) combines the factual grounding of KG-based QA with the interactive nature of dialogue systems. KGs are widely used in enterprise and domain applications to provide structured, evolving, and reliable knowledge. Large language models (LLMs) enable natural and context-aware conversations, but lack di

Youhong Chen, Debraj Bhattacharjee, Balarko Chaudhuri, Mark O Malley

This paper demonstrates how Extended Dynamic Mode Decomposition (EDMD), grounded in Koopman operator theory, can effectively identify the main contributor(s) to oscillations in power grids. We use PMU data recorded from a real 0.15 Hz oscillation event in Denmark for post-event analysis. To this end, the EDMD algorithm processed only voltage and current phas

Neesha Yadav, Sandeep, Pintu Das

Recent advances in revealing intrinsic magnetism in two-dimensional (2D) materials have highlighted their potential for future spintronic applications, driven by their novel physical properties, promising for future spintronic devices. In order to explore layer dependent magnetic behavior, in general, mechanically exfoliated flakes from high-quality single c

Qineng Wang, Wenlong Huang, Yu Zhou, Hang Yin

Embodied cognition argues that intelligence arises from sensorimotor interaction rather than passive observation. It raises an intriguing question: do modern vision-language models (VLMs), trained largely in a disembodied manner, exhibit signs of embodied cognition? We introduce ENACT, a benchmark that casts evaluation of embodied cognition as world modeling

Di Zhu, Chen Xie, Ziwei Wang, Haoyun Zhang

New York City reports over one hundred thousand motor vehicle collisions each year, creating substantial injury and public health burden. We present RaX-Crash, a resource efficient and explainable small model pipeline for structured injury severity prediction on the official NYC Motor Vehicle Collisions dataset. RaX-Crash integrates three linked tables with

Ayoob Salari, Kai Wu, Khawaja Fahad Masood, Y. Jay Guo

Real-time water-level monitoring across many locations is vital for flood response, infrastructure management, and environmental forecasting. Yet many sensing methods rely on fixed instruments - acoustic, radar, camera, or pressure probes - that are costly to install and maintain and are vulnerable during extreme events. We propose a passive, low-cost water-

Irene Ferranti, Mikel Falxa, Federico Fantoccoli, Alberto Sesana

Pulsar Timing Arrays (PTAs) are approaching the sensitivity required for a $5σ$ detection of the nanohertz stochastic gravitational-wave background (GWB). This makes it crucial to deeply understand the behaviour of our analysis pipelines. A counterintuitive feature of the European Pulsar Timing Array (EPTA) second data release is that restricting the dataset

Jens-Christian Drawer, Salvatore Cianci, Vita Solovyeva, Alexander Steinhoff

Versatile, tunable, and potentially scalable single-photon sources are a key asset in emergent photonic quantum technologies. In this work, a single-photon source based on WS$_2$ micro-domes, created via hydrogen ion irradiation, is realized and integrated into an open, tunable optical microcavity. Single-photon emission from the coupled emitter-cavity syste

Zarin Tahia Hossain, Mostafa Milani

Handling missing data is a central challenge in data-driven analysis. Modern imputation methods not only aim for accurate reconstruction but also differ in how they represent and quantify uncertainty. Yet, the reliability and calibration of these uncertainty estimates remain poorly understood. This paper presents a systematic empirical study of uncertainty i

Lasse Gresista, Dominik Kiese, Simon Trebst, Yasir Iqbal

Motivated by the search for unconventional orders in frustrated quantum magnets, we present a multi-method investigation into the nature of the quantum phase diagram of the spin-$1/2$ Heisenberg model on the maple-leaf lattice with three symmetry-inequivalent nearest-neighbor interactions. It has been argued that the parameter regime with antiferromagnetic c

M. A. Kuznetsov, A. A. Fraerman

It has been shown that in magnets possessing an inversion center in the absence of deformations, a torsion-induced Dzyaloshinsky-Moriya interaction (tiDMI) can arise. A microscopic mechanism for this interaction is described, involving the transfer of angular momentum to the lattice upon electron reflection from the magnet's boundary. An estimate of the

Saurav Dulal, Mohammed M. Olama, Ali R. Ekti, Nils M. Stenvig

The shift from synchronous generators to inverter-based resources has caused power system inertia to be unevenly distributed across power grids. As a result, certain grid regions are more vulnerable to high rate-of-change of frequency (RoCoF) during disturbances. This paper presents a measurement-based framework for estimating grid inertia in CAISO (Californ

Jiajun Ma, Rong Chen, Yazhou Li, Chenfei Shi

Topological Kagome magnets, characterized by nontrivial electronic band structures featuring flat band, Dirac cone and van Hove singularities, provide a new avenue for the realization of thermoelectric devices. Unlike the conventional longitudinal Seebeck effect, transverse thermoelectric (TE) effects like the Nernst effect have attracted growing interest du

Julius Werner, Francesco Pennazio, Piergiorgio Cerello, Elisa Fiorina

Proton therapy's full potential is limited by uncertainties that prevent optimal dose distribution. Monitoring techniques can reduce these uncertainties and enable adaptive treatment planning. Spatiotemporal Emission Reconstruction from Prompt-Gamma Timing (SER-PGT) is a promising method that provides insights into both particle range and stopping power,

Alailton J. Alves Junior, Denis V. Coury, Ricardo A. S. Fernandes

Digital substations have significantly enhanced power grid protection by replacing traditional copper wiring with fiber-optic communication and integrating IEC 61850-compliant Intelligent Electronic Devices (IEDs), resulting in greater efficiency, reliability, and interoperability. While these advancements provide improved interoperability, challenges such a

Syamlal Sankaran Kunnath, Mateusz Zelent, Pawel Gruszecki, Maciej Krawczyk

Artificial spin ice (ASI) systems exhibit fascinating phenomena, such as frustration and the formation of magnetic monopole states, and Dirac strings. However, exploring the wave phenomena in these systems is elusive due to the weak dipolar coupling that governs their interactions. In this study, we demonstrate coherent spin-wave propagation in an hybrid ASI

A. J. Alves Junior, M. J. B. B. Davi, R. A. S. Fernandes, M. Oleskovicz

Accurate fault location is essential for operational reliability and fast restoration in wind farm collector networks. However, the growing integration of inverter-based resources changes the current and voltage behavior during faults, challenging the effectiveness of traditional phasor-based diagnostic methods. In this context, the present paper introduces

Xinyan Xie, Xuesong Wang, Xin Lai, Yongheng Wen

Indoor visible light communication (VLC) is a promising sixth-generation (6G) technology, as its directional and sensitive optical signals are naturally suited for integrated sensing and communication (ISAC). However, current research mainly focuses on maximizing data rates and sensing accuracy, creating a conflict between high performance, high energy consu

Lingfei Wang, Wenyan Wang, Tsz Fung Poon, Zheyu Wang

Superconductivity can coexist or compete with other orders such as magnetism or density waves. Optimizing superconductivity requires identifying competing orders that may disrupt Cooper pair coherence. Here, we use the self-field critical current ($I_{\rm c,sf}$) to probe pressure-tuned superconductivity in the kagome superconductor RbV$_3$Sb$_5$. As pressur

Kyoungho Cho, Jeongho Bang

We develop a unified framework to analyze $d$-dimensional quantum teleportation through the joint geometry of two complementary figures of merit: average fidelity $F$ (how well a protocol works on average) and fidelity deviation $D$ (how uniformly it works across the inputs). Technically, we formulate a representation-theoretical framework based on Schur-Wey

C J Bradly, N R Beaton, A L Owczarek

We investigate polymers pulled away from an interacting surface, where the force is applied to the untethered endpoint and at an angle $θ$ to the surface. We use the canonical self-avoiding walk model of polymers and obtain the phase diagram of the model using Monte Carlo simulations for a range of angles, temperatures and force magnitudes. The phase diagram

Jiaxi Kuang, Kensei Torii, Francesco Buscemi

We study quantum measurement retrodiction using the principle of minimum change. For quantum-to-classical measurement channels, we show that all standard quantum divergences select the same retrodictive update, yielding a unique and divergence-independent quantum Bayesian inverse for any POVM and prior state. Using this update, we construct a symmetric joint

William Balderrama, Jack Morgan Davies, Sil Linskens

Working in a generic derived algebro-geometric context, we lay the foundations for the general study of affineness and local descendability. When applied to $\mathbf{E}_\infty$ rings equipped with the fpqc topology, these foundations give an $\infty$-category of spectral stacks, a viable functor-of-points alternative to Lurie's approach to nonconnective

M. Bordag, N. Khusnutdinov, G. L. Klimchitskaya, V. M. Mostepanenko

Recently, Rodriguez-Lopez, Wang, and Antezza [Phys. Rev. B v.111, 115428 (2025)] compared the theoretical descriptions of electric conductivity of graphene given by the Kubo model and quantum field theory in terms of the polarization tensor. According to this article, in the spatially nonlocal case, the quantum field theoretical description contains ``hard i

Jacopo Radaelli, Oliver J. Lipscombe, Mengze Zhu, J. Ross Stewart

Overdoped cuprate superconductors are strange metals above their superconducting transition temperature. In such materials, the electrical resistivity has a strong linear dependence on temperature ($T$) and electrical current is not carried by electron quasiparticles as in conventional metals. Here we demonstrate that the strange metal behaviour co-exists wi

Guoxi Huang, Qirui Yang, Ruirui Lin, Zipeng Qi

In image enhancement tasks, such as low-light and underwater image enhancement, a degraded image can correspond to multiple plausible target images due to dynamic photography conditions. This naturally results in a one-to-many mapping problem. To address this, we propose a Bayesian Enhancement Model (BEM) that incorporates Bayesian Neural Networks (BNNs) to

Ivo Verhoeven, Pushkar Mishra, Ekaterina Shutova

This article introduces misinfo-general, a benchmark dataset for evaluating misinformation models' ability to perform out-of-distribution generalization. Misinformation changes rapidly, much more quickly than moderators can annotate at scale, resulting in a shift between the training and inference data distributions. As a result, misinformation detectors

Henry Marichal, Joaquin Blanco, Diego Passarella, Gregory Randall

Tree-ring growth represents the annual wood increment for a tree, and quantifying it allows researchers to assess which silvicultural practices are best suited for each species. Manual measurement of this growth is time-consuming and often imprecise, as it is typically performed along 4 to 8 radial directions on a cross-sectional disc. In recent years, autom

Mootez Saad, Boqi Chen, José Antonio Hernández López, Dániel Varró

Large language models (LLMs) are being increasingly adopted in the software engineering domain, yet the robustness of their grasp on core software design concepts remains unclear. We conduct an empirical study to systematically evaluate their understanding of cohesion (intra-module) and coupling (inter-module). We programmatically generate poorly designed co

Vu Phan, Ilie Ugarcovici

We investigate the expected number of calls required to achieve Bingo in a generalized (n,m)-Bingo game, where each n x n card is filled by sampling n numbers from m possible values per column. Using the inclusion-exclusion principle, we derive exact formulas for the probability distribution and the expected game length. Our main theoretical result proves th

Biagio La Rosa, Leilani H. Gilpin

Neurons are the fundamental building blocks of deep neural networks, and their interconnections allow AI to achieve unprecedented results. Motivated by the goal of understanding how neurons encode information, compositional explanations leverage logical relationships between concepts to express the spatial alignment between neuron activations and human knowl

Felix Rong, Max Schneider, Hendrik Nicolai, Christian Hasse

Direct Numerical Simulations (DNS) of premixed hydrogen-air flames anchored in a fully-developed turbulent channel flow (TCF) are performed at a friction Reynolds number of $\mathrm{Re}_\tau=530$ and thermochemical conditions susceptible to the emergence of intrinsic thermo-diffusive (TD) phenomena acting on the turbulent flame. Two premixed flames are studi

Anton Baychkov, Markus Brill, Jannik Peters

In recent years, research in Participatory Budgeting (PB) has put a greater emphasis on rules satisfying notions of fairness and proportionality, with the Method of Equal Shares (MES) being a prominent example. However, proportionality can come at a cost to the total utilitarian welfare. Our work formalizes this relationship, by deriving minimum utilitarian

Gil Goldman, Raja Giryes, Mahadev Satyanarayanan

We propose a smooth regularization technique that instills a strong temporal inductive bias in video recognition models, particularly benefiting lightweight architectures. Our method encourages smoothness in the intermediate-layer embeddings of consecutive frames by modeling their changes as a Gaussian Random Walk (GRW). This penalizes abrupt representationa

Vitoria Barin-Pacela, Kartik Ahuja, Simon Lacoste-Julien, Pascal Vincent

Recent theoretical work established the unsupervised identifiability of quantized factors under any diffeomorphism. The theory assumes that quantization thresholds correspond to axis-aligned discontinuities in the probability density of the latent factors. By constraining a learned map to have a density with axis-aligned discontinuities, we can recover the q

Yunjie Chen, Rianne A. Weber, Olaf M. Neve, Stephan R. Romeijn

Objectives: To evaluate a deep learning (DL) model for reducing the agent dose of contrast-enhanced T1-weighted MRI (T1ce) of the cerebellopontine angle (CPA) cistern. Materials and methods: In this multi-center retrospective study, T1 and T1ce of vestibular schwannoma (VS) patients were used to simulate low-dose T1ce with varying reductions of contrast agen

Tomasz Skalski, Tomasz Stroiński

Bogdan et al. established a new criterion to determine the existence of a maximum likelihood estimator in discrete exponential families. It uses the notion of the set of uniqueness, which allows to apply the problem to the Ising model from statistical mechanics. We propose a full characterization of the existence of the MLE in the Ising model among the level

Weronika Jakubowska, Mikołaj Zieliński, Rafał Tobiasz, Krzysztof Byrski

Implicit Neural Representations (INRs) are widely used for modeling continuous 2D images, enabling high-fidelity reconstruction, super-resolution, and compression. Architectures such as SIREN, WIRE, and FINER demonstrate their ability to capture fine image details. However, conventional INRs lack explicit geometric structure, limiting local editing, and inte

Rocio Kiman, Charles A. Beichman, Azul Ruiz Diaz, Jacqueline K. Faherty

Studying cold brown dwarfs is key to understanding the diverse characteristics of cold giant exoplanets atmospheres. COCONUTS-2, is a wide binary system composed of a T9 brown dwarf and an M3 star, which presents a unique opportunity to characterize a cold benchmark brown dwarf. As part of a JWST program to study the range of physical and atmospheric propert

Guilin Zhang, Wulan Guo, Ziqi Tan, Hongyang He

Quantum machine learning (QML) promises compact and expressive representations, but suffers from the measurement bottleneck - a narrow quantum-to-classical readout that limits performance and amplifies privacy risk. We propose a lightweight residual hybrid architecture that concatenates quantum features with raw inputs before classification, bypassing the bo

M. Urquiza-González, M. Stemmler, T. E. Albrecht, B. Bally

We report on high-resolution laser spectroscopy of $^{255}$Fm ($T_{1/2} = 20$h), one of the heaviest nuclides available from reactor breeding. The hyperfine structures in two different atomic ground-state transitions at 398.4~nm and 398.2~nm were probed by in-source laser spectroscopy at the RISIKO mass separator in Mainz, using the PI-LIST high-resolution i

Herman Errico, Jiquan Ngiam, Shanita Sojan

The Model Context Protocol (MCP) replaces static, developer-controlled API integrations with more dynamic, user-driven agent systems, which also introduces new security risks. As MCP adoption grows across community servers and major platforms, organizations encounter threats that existing AI governance frameworks (such as NIST AI RMF and ISO/IEC 42001) do no

Vladyslav Pliuhin, Yevgen Tsegelnyk, Maria Sukhonos, Ihor Biletskyi

The integration of immersive technologies has transformed engineering education, particularly in high-risk disciplines like high-voltage engineering, which is essential for urban energy infrastructure. This study presents a 3D virtual laboratory developed using Unreal Engine 5 to support the High Voltage Engineering course for undergraduate students in Power

Sarthak Duary, Pabitra Ray

When a set of charged or dyonic objects scatter and subsequently disperse, the process generically emits electromagnetic radiation. The classical soft photon theorem constrains the constant term and leading power-law fall-off of the emitted waveform at asymptotic times solely in terms of the momenta and charges of the incoming and outgoing particles. In this

Sarthak Duary, Sourav Maji

We study tensionless bosonic strings propagating in the presence of a constant Kalb--Ramond background and show how closed strings undergo a transition into open strings. Working in the intrinsically tensionless theory, we show that the Carrollian limit of the closed-string worldsheet induces a universal gluing of `left'- and `right'-moving oscillators, whic

Illia Khudiakov, Vladyslav Pliuhin, Sergiy Plankovskyy, Yevgen Tsegelnyk

The purpose of this article is to describe an adaptive decision-making support model aimed at improving the efficiency of engineering infrastructure reconstruction program management in the context of developing the architecture and work breakdown structure of programs. As part of the study, the existing adaptive program management tools are analyzed, the us

Noah Jensen, Stephanie Treneer

Given the collection of all $m\times n$ rectangular grids which have a fixed number $1\leq r\leq mn$ of blocked cells, we explicitly describe a proper subset of the collection which is guaranteed to contain at least one grid from each equivalence class under symmetry, eliminating the majority of redundant grids. We analyze the extent to which redundant grids

Vivek Pandey, Nader Motee

Ensuring safety in autonomous multi-agent systems during time-critical tasks such as rendezvous is a fundamental challenge, particularly under communication delays and uncertainty in system parameters. In this paper, we develop a theoretical framework to analyze the \emph{distributionally robust risk of cascading failures} in multi-agent rendezvous, where sy

Yingchuan Sun, Shengpu Tang

Existing studies on reinforcement learning (RL) for sepsis management have mostly followed an established problem setup, in which patient data are aggregated into 4-hour time steps. Although concerns have been raised regarding the coarseness of this time-step size, which might distort patient dynamics and lead to suboptimal treatment policies, the extent to

Dongxing He, Arianna Formenti, Spencer Gessner, Michael Peskin

Among the many effects that occur in beam-beam electron-positron collisions at TeV energies, emission of hard synchrotron radiation, or beamstrahlung, has special importance. Beamstrahlung determines the energy spectrum of the most energetic electrons, positrons, and photons and supplies the initial condition for the calculation of all other QED processes. I

Pratik Brahma, Junghoon Han, Tamzid Razzaque, Saavan Patel

Geometric frustration gives rise to emergent quantum phenomena and exotic phases of matter. While Monte Carlo methods are traditionally used to simulate such systems, their sampling efficiency is limited by the complexity of interactions and ground-state properties. Restricted Boltzmann Machines (RBMs), a class of probabilistic neural networks, offer improve

Nura Aljaafari, Danilo S. Carvalho, André Freitas

Despite displaying semantic competence, large language models' internal mechanisms that ground abstract semantic structure remain insufficiently characterised. We propose a method integrating role-cross minimal pairs, temporal emergence analysis, and cross-model comparison to study how LLMs implement semantic roles. Our analysis uncovers: (i) highly concentr

Anil K. Saini, Jose Guadalupe Hernandez, Emily F. Wong, Debanshi Misra

Machine learning models trained on real-world data may inadvertently make biased predictions that negatively impact marginalized communities. Reweighting, which assigns a weight to each data point used during model training, can mitigate such bias, though sometimes at the cost of predictive accuracy. In this paper, we investigated this trade-off by comparing

Mykyta Kliapets, Pablo Huijse, Andrew Tkachenko, Alex Kemp

The Transiting Exoplanet Survey Satellite (TESS) mission has observed hundreds of millions of stars, substantially contributing to the available pool of high-precision photometric space data. Among them are the relatively rare $\gamma$ Doradus / $\delta$ Scuti ($\gamma$ Dor / $\delta$ Sct) hybrid pulsators, which have been previously studied using Kepler dat

Sheng Ran

In conventional quantum mechanics, all unitary evolution takes place within the space-time Hilbert space $\mathcal H_{xt}=L^2(\mathcal M_{xt})$, with time as the sole evolution parameter. The momentum-energy representation $\phi(k,E)$ is treated merely as a Fourier re-expression of the same state-kinematically equivalent but dynamically inert. Here we restor

Paulina Kaczyńska, Julian Sienkiewicz, Dominik Ślęzak

We investigate how Accumulated Local Effects (ALE), a model-agnostic explanation method, can be adapted to visualize the influence of node feature values in link prediction tasks using Graph Neural Networks (GNNs), specifically Graph Convolutional Networks and Graph Attention Networks. A key challenge addressed in this work is the complex interactions of nod

Anna Lyubarskaja, Dominik Rothenhäusler

We study nonparametric regression with covariates $X$ and outcome $Y$ under random unbiased perturbations (RUPs) of the conditional distribution $Y|X$, where the marginal distribution of covariates, $P^X$, remains fixed but the conditional law, $P^{Y|X}$, varies randomly across datasets. Unlike adversarial distribution shift frameworks that yield conservativ

Mengliang ZHang

Electronic health records (EHR) contain extensive structured and unstructured data, including tabular information and free-text clinical notes. Querying relevant patient information often requires complex database operations, increasing the workload for clinicians. However, complex table relationships and professional terminology in EHRs limit the query accu

Maximo Banados, Marc Henneaux

We consider a class of models in even spacetime dimensions $2n$ which share many similarities with Chern-Simons theories in odd spacetime dimensions $2n+1$. The independent dynamical variables of these models are a $GL(2n)$-connection and a metric in internal space. The action is a polynomial of degree $n$ in the curvature of the connection, with indices sat

Glener Lanes Pizzolato, Brenda Medeiros Lopes, Claudio Schepke, Diego Kreutz

This work presents a review of attack methodologies targeting Pix, the instant payment system launched by the Central Bank of Brazil in 2020. The study aims to identify and classify the main types of fraud affecting users and financial institutions, highlighting the evolution and increasing sophistication of these techniques. The methodology combines a struc

Andrea Bonito, Alan Demlow, Joshua M. Siktar

We consider an optimal recovery problem for the Poisson problem when the boundary data is unknown. Compensating information is provided in the form of a finite number of measurements of the solution. A finite element algorithm for this problem was given in Binev et al. (2024), where measurements were assumed to be either bounded linear functionals of the sol

Yu-Heng Lin, Daniel Masters, Andreas L. Faisst, Harry Teplitz

Modern surveys present us with billions of faint galaxies for which we only have broadband images in $\sim$6-8 optical-to-near-infrared (NIR) filters. Galaxy star formation rates (SFRs) are difficult to estimate accurately without spectroscopic diagnostics or far-infrared (FIR) photometry, both of which are prohibitively expensive to obtain for large numbers

Zhanxiang Hua, Christina Karamperidou, Zilu Meng

Coupled atmosphere-ocean deep learning (DL) climate emulators are a new frontier but are known to exhibit weak ENSO variability, raising questions about their ability to simulate teleconnections. Here, we present the first Pacific pacemaker (PACE) experiments using a coupled DL emulator (DLESyM) to bypass this weak variability and isolate the atmospheric res

Vertti Hietanen, Mikyoung Lee

We investigate a self-improving property of variational integrals in a weighted framework under generalized Orlicz growth conditions. Assuming that the weight belongs to an appropriate Muckenhoupt class and the growth function satisfies standard structural conditions, we prove that the gradient of any local quasiminimizer has local higher integrability. In a

Alan Gadian

This paper discusses the initial development of Marine Cloud Brightening (MCB) as a theoretical idea, from its inception as a cloud microphysics process in circe 1990 to the full-blown concept by 2015. It primarily focuses on the work of founders John Latham and Stephen Salter and their contributions. Recently the concept has been developed further, e.g. in

Elodie Iglesis, Alexandr Alekhin, Maximilien Cazayous, Alain Sacuto

We report an optical pump - terahertz (THz) probe study of the photoinduced transient carrier dynamics in the low bandgap semiconductor Indium Antimonide (InSb). Using an organic N-benzyl-2-methyl-nitroaniline (BNA) crystal as a broadband THz source, we access the full spectral response over more than 5 THz, for varying pump-probe delay following the optical

Kimia Ahmadi, Wouter A. Serdijn

This paper proposes a computationally efficient, real-time maximum power point tracking (MPPT) algorithm tailored for low-power photovoltaic (PV) systems operating under fast-changing irradiance and partial shading conditions (PSC). The proposed method augments the classical perturb and observe (P&O) algorithm with an adaptive gradient descent mechanism that

Vivek Pandey, Amirhossein Mollaei, Nader Motee

Robot localization is a fundamental component of autonomous navigation in unknown environments. Among various sensing modalities, visual input from cameras plays a central role, enabling robots to estimate their position by tracking point features across image frames. However, image frames often contain a large number of features, many of which are redundant

Aodong Li, Abishek Sankararaman, Balakrishnan Narayanaswamy

We study streaming data with categorical features where the vocabulary of categorical feature values is changing and can even grow unboundedly over time. Feature hashing is commonly used as a pre-processing step to map these categorical values into a feature space of fixed size before learning their embeddings. While these methods have been developed and eva

Jonathan Whittle

In this work we investigate holographic spacelike and timelike entanglement entropy using the Ryu-Takayanagi prescription, for slab-shaped and ball-shaped entangling regions. We work with an infinite family of 10-dimensional Type IIB supergravity solutions, which are gravity duals to an infinite set of linear quiver theories, with the backgrounds defined usi

Ilyas Djafer-Cherif, Bartlomiej Waclaw

Long-term time-lapse imaging of biological samples requires correcting for focal drift, which would otherwise gradually push the sample out of focus. We present a software-based method that eliminates this time-dependent blur using only a motorized Z-drive, with no additional hardware. The method relies on imaging marks made on the side of the coverslip oppo

Arthur Jacot

This paper argues that DNNs implement a computational Occam's razor -- finding the `simplest' algorithm that fits the data -- and that this could explain their incredible and wide-ranging success over more traditional statistical methods. We start with the discovery that the set of real-valued function $f$ that can be $\epsilon$-approximated with a binary ci

Rui Yan, Jiajian Fu, Shiqi Yang, Lars Paulsen

Teleoperation systems are essential for efficiently collecting diverse and high-quality robot demonstration data, especially for complex, contact-rich tasks. However, current teleoperation platforms typically lack integrated force feedback, cross-embodiment generalization, and portable, user-friendly designs, limiting their practical deployment. To address t

Jiancheng Pan, Runze Wang, Tianwen Qian, Mohammad Mahdi

Cross-view object correspondence, exemplified by the representative task of ego-exo object correspondence, aims to establish consistent associations of the same object across different viewpoints (e.g., egocentric and exocentric). This task poses significant challenges due to drastic viewpoint and appearance variations, making existing segmentation models, s

Yizheng Liu, Haochen Wang, Carl Peterson, James S. Speck

We report the fabrication of Cr2O3/\b{eta}-Ga2O3 heterojunction diodes using reactive magnetron sputtering of Cr2O3 on highly doped \b{eta}-Ga2O3 bulk substrates along (100), (010), (001), (110), and (011) orientation dependence of high electric field handling capability in \b{eta}-Ga2O3. Additional relative permittivity values in (110) and (011) orientation

Jing Pan, Li Shi, Paul Lo

Heterogeneous Treatment Effect (HTE) and Conditional Average Treatment Effect (CATE) models relax the assumption that treatment effects are the same for every user. We present a large scale industrial framework for estimating HTE using experimental data from hundreds of millions of Snapchat users. By combining results across many experiments, the framework u

Naomi Iris van den Berg, Matouš Elphick, Kevin Mulder, Omar Bouricha

Classical models of cancer focus on tumour-intrinsic genetic aberrations and immune dynamics and often overlook how the metabolic environment of healthy tissues shapes tumour development and immune efficacy. Here, we propose that tissue-intrinsic metabolic intensity and waste-handling capacity act as an upstream gatekeeper of anti-tumour immunity, determinin

Bence Deák, Péter Madarasi

The family of $(k, \ell)$-sparse graphs, introduced by Lorea, plays a central role in combinatorial optimization and has a wide range of applications, particularly in rigidity theory. A key algorithmic challenge is to compute a maximum-weight $(k, \ell)$-sparse subgraph of a given edge-weighted graph. Although prior approaches have long provided an $O(nm)$-t

K. Ernest Bognini, Hamdi Ammar

In this paper, we study some new factorizations of period-doubling sequences over a $k$-letter alphabet, where $k\geq 2$. First, we define the combinatorial and arithmetic properties of these sequences. Then, we define the kernel words of period-doubling sequences and demonstrate how to factorize a binary sequence using its kernel words. Next, we define gap

Mohamed Elrefaie, Dule Shu, Matt Klenk, Faez Ahmed

Benchmarking has been the cornerstone of progress in computer vision, natural language processing, and the broader deep learning domain, driving algorithmic innovation through standardized datasets and reproducible evaluation protocols. The growing availability of large-scale Computational Fluid Dynamics (CFD) datasets has opened new opportunities for applyi

Xingyu Wang, Pierre Germain, Demetrios T. Papageorgiou

This paper investigates the stability of interfacial long waves in two-layer plane Couette flow using a nonlinear, nonlocal asymptotic model derived from the Navier-Stokes equations and valid for thin upper layers. Nonlocality enters through a coupling of the thin and main layers, and crucial inertial effects are retained. The models generically support bist

S. Chhetri, D. -M. Mei, S. Bhattarai, N. Budhathoki

The performance and scalability of rare-event physics experiments depend on large-volume, detector-grade high-purity germanium (HPGe) crystals with precise control of impurity segregation during growth. We report a detailed study of impurity distribution in a single Czochralski-grown HPGe crystal produced at University of South Dakota (USD). The crystal was

Toby Anderson, Max Collins, Jamie Haddock, Jackie Lok

Stochastic iterative methods are useful in a variety of large-scale numerical linear algebraic, machine learning, and statistical problems, in part due to their low-memory footprint. They are frequently used in a variety of applications, and thus it is imperative to have a thorough theoretical understanding of their behavior. Most theoretical convergence res

Huiyun Tang, Long Feng, Yang Li, Feifei Wang

Vertical Federated Learning (VFL) often suffers from client-wise missingness, where entire feature blocks from some clients are unobserved, and conventional approaches are vulnerable to privacy leakage. We propose a Gaussian copulabased framework for VFL data privatization under missingness constraints, which requires no prior specification of downstream ana

Todd Kemp, Akihiro Miyagawa

In this paper, we prove the Fourth Moment Theorem for sequences of (noncommutative) random variables given as sums of two stochastic integrals in two different parity orders of chaos, both in the free Wigner chaos setting and a $q$-Gaussian generalization. Specifically, we prove that convergence to the appropriate central limit distribution is mediated entir

Ashutossh Gupta, Vassilis Kekatos, Dionysios Aliprantis, Steve Pekarek

Electrified roadways (ERs) equipped with the dynamic wireless power transfer (DWPT) technology can achieve longer driving range and reduce on-board battery requirements for electric vehicles (EVs). Due to the spatial arrangement of transmitter (Tx) coils embedded into the ER pavement, the power drawn by the EV's receiver (Rx) coil is oscillatory in nature. T

Elahe Kooshafar

Real-world systems ranging from airline routes to cryptocurrency transfers are naturally modelled as dynamic graphs whose topology changes over time. Conventional benchmarks judge dynamic-graph learners by a handful of task-specific scores, yet seldom ask whether the embeddings themselves remain a truthful, interpretable reflection of the evolving network. W

Ali Jahan, Masood Ghayoomi, Annette Hautli-Janisz

Argument mining is a subfield of natural language processing to identify and extract the argument components, like premises and conclusions, within a text and to recognize the relations between them. It reveals the logical structure of texts to be used in tasks like knowledge extraction. This paper aims at utilizing a cross-lingual approach to argument minin

Wilson Chango, Juan A. Lara, Rebeca Cerezo, Cristóbal Romero

The new educational models such as smart learning environments use of digital and context-aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being

Nan Jiang

State resetting is a fundamental but often overlooked capability of simulators. It supports sample-based planning by allowing resets to previously encountered simulation states, and enables calibration of simulators using real data by resetting to states observed in real-system traces. While often taken for granted, state resetting in complex simulators can

Eric Crislip, Mohammad Khalil, Teresa Portone, Oksana Chkrebtii

Closure modeling - the statistical modeling of missing dynamics in the natural sciences and engineering - is a growing and active area of research. Existing methods for closure modeling are often computationally prohibitive, lack uncertainty quantification, or require noise-free observations of the temporal derivatives over the system state. We propose a nov

Alexey Fakhrutdinov, Oleg R. Musin

The famous pancake theorem states that for every finite set $X$ in the plane, there exist two orthogonal lines that divide $X$ into four equal parts. We propose an algorithm whose running time is linear in the number of points in $X$ and prove that this complexity is optimal. We also consider generalizations of the pancake theorem and show that orthogonal hy

Yining Ding, João F. C. Mota, Andrew M. Wallace, Sen Wang

We propose a method which, given a sequence of stereo foggy images, estimates the parameters of a fog model and updates them dynamically. In contrast with previous approaches, which estimate the parameters sequentially and thus are prone to error propagation, our algorithm estimates all the parameters simultaneously by solving a novel optimisation problem. B

Minseok Yi, Daehee Lee, Alberto Gola, Stefano Merzi

Bismuth germanate (BGO) is gaining renewed attention as a viable material for hybrid Cherenkov/scintillation time-of-flight positron emission tomography (TOF-PET) detectors. While single-crystal studies have demonstrated excellent timing resolution by leveraging prompt Cherenkov photons, practical detector modules based on pixelated arrays introduce a high p

Muhammad Nawaz Qaisrani, Christoph Kirsch, Aaron Flötotto, Jonas Hänseroth

Lithium diffusion in solid-state battery anodes occurs through thermally activated hops between metastable sites often separated by large energy barriers, making such events rare on ab initio molecular dynamics (AIMD) timescales. Here, we present a bottom-up multiscale workflow that integrates AIMD, machine-learned force fields (MLFFs), and Markov state mode