November 2025 arXiv papers — page 68

Jia-Yao Ye, Susanta Chakraborty, Karthick Subramani, Xing-Zhou Tang

Driven by growing demand for clean energy, triboelectric nanogenerators (TENGs) have emerged as promising self-powered systems, yet achieving high charge density remains a critical challenge. In polymer dielectrics, triboelectricity can be further amplified by incorporating high-dielectric and polar materials for functional adaptability. Conventional dielect

Jiayi Wang, Wei Dai, Haoyu Wang, Sihan Yang

In medical image segmentation, heterogeneous privacy policies across institutions often make joint training on pooled datasets infeasible, motivating continual image segmentation-learning from data streams without catastrophic forgetting. While the Segment Anything Model (SAM) offers strong zero-shot priors and has been widely fine-tuned across downstream ta

Shubhranil Basak, Mada Hemanth, Madhav Rao

Surface Electromyography (sEMG) provides vital insights into muscle function, but it can be noisy and challenging to acquire. Inertial Measurement Units (IMUs) provide a robust and wearable alternative to motion capture systems. This paper investigates the synthesis of normalized sEMG signals from 6-axis IMU data using a deep learning approach. We collected

Hanyu Zhou, Chuanhao Ma, Gim Hee Lee

Vision-language-action (VLA) models show potential for general robotic tasks, but remain challenging in spatiotemporally coherent manipulation, which requires fine-grained representations. Typically, existing methods embed 3D positions into visual representations to enhance the spatial precision of actions. However, these methods struggle to achieve temporal

Kaiyu Li, Jiayu Wang, Zhi Wang, Hui Qiao

LLM-driven agents, particularly those using general frameworks like ReAct or human-inspired role-playing, often struggle in specialized domains that necessitate rigorously structured workflows. Fields such as remote sensing, requiring specialized tools (e.g., correction, spectral indices calculation), and multi-step procedures (e.g., numerous intermediate pr

Yuki Tanaka, Aiko Yamaguchi, Tomohiro Yamaji, Yuta Shingu

The Kerr parametric oscillator (KPO) has recently attracted considerable attention from the perspective of its applications to quantum information processing, and understanding its properties is an important challenge. Spectroscopic measurements serve as an effective means of elucidating detailed information about the system, such as the energy-level structu

Yingkai Zhang, Tao Zhang, Jing Nie, Ying Fu

Hyperspectral image (HSI) denoising is a crucial step in enhancing the quality of HSIs. Noise modeling methods can fit noise distributions to generate synthetic HSIs to train denoising networks. However, the noise in captured HSIs is usually complex and difficult to model accurately, which significantly limits the effectiveness of these approaches. In this p

Tin Nwe Aye, Linus Carlsson

This article explores the convergence properties of an $SLIR^\text{T}R^\text{P}D$ endemic model, incorporating Dirac and Radon measures, alongside distributed delays to represent latency and temporary immunity. A class of delays is defined for both continuous and discrete endemic models using continuous integral kernels with compact support and discrete term

Zhiyuan Xu, Stanislav Abaimov, Joseph Gardiner, Sana Belguith

We show that attention sinks and compression valleys create a vulnerable region in decoder-only Transformers, where small activation perturbations can be amplified through the autoregressive trajectory. Based on this, we propose Sensitivity-Scaled Steering (SSS), a progressive activation-space attack that anchors perturbations at the beginning-of-sequence to

Nils Prumbaum, Christian L. Degen, Alexander Eichler

Magnetic Resonance Force Microscopy (MRFM) enables three-dimensional imaging of nuclear spin densities in nanoscale objects. Based on numerical simulations, we evaluate the performance of strained SiN resonators as force sensors and show that their out-of-plane oscillation direction improves the quality of the reconstructed sample. We further introduce a mul

Abhishek Dhawan, Oliver Janzer, Abhishek Methuku

Alon, Krivelevich, and Sudakov conjectured in 1999 that every $F$-free graph of maximum degree at most $\Delta$ has chromatic number $O(\Delta / \log \Delta)$. This was previously known only for almost bipartite graphs, that is, for subgraphs of $K_{1,t,t}$ (verified by Alon, Krivelevich, and Sudakov themselves), while most recent results were concerned with

Ziyang Wang, Yuanlei Zheng, Zhenbiao Cao, Xiaojin Zhang

For industrial-scale text-to-SQL, supplying the entire database schema to Large Language Models (LLMs) is impractical due to context window limits and irrelevant noise. Schema linking, which filters the schema to a relevant subset, is therefore critical. However, existing methods incur prohibitive costs, struggle to trade off recall and noise, and scale poor

Quanchao Du, Jinlian Lu, Xueqing Wan, Zhenlong Zhang

Electric control of magnetism at room temperature is crucial for developing next-generation, low-power spintronic devices. However, the intrinsic incompatibility between ferroelectricity and magnetism in crystal symmetry, along with the absence of strong magnetoelectric coupling mechanisms, continues to pose major challenges. In this work, we propose a gener

Josep Ingla-Aynés, Serhii Volosheniuk, Talieh S. Ghiasi, Angelika Knothe

The interaction between itinerant electrons and localized spins is key to a wide range of electronic phenomena. Of particular interest is the regime where the interacting electrons exhibit both spin and valley degeneracy, resulting in SU(4) Kondo physics. However, this regime is challenging to realize in typical mesoscopic systems because it requires a stron

Daniel Benedicto Orenes, Naudson Lucas Lopes Matias, Apoorva Apoorva, Antoine Glicenstein

In this work we present a numerical and experimental investigation of the collective early-time decay rates of a strongly driven and optically dense cold atomic cloud. We prepare the atomic ensemble by driving the system to its steady state with varying Rabi frequencies $\Omega$ that go from the weak $\Omega \ll \Gamma$ to the strong driving regime $\Omega \

Ali Azarbahram, Chrystian Pool Yuca Huanca, Gian Paolo Incremona, Patrizio Colaneri

This paper introduces a Koopman-enhanced distributed switched model predictive control (SMPC) framework for safe and scalable navigation of quadrotor unmanned aerial vehicles (UAVs) in dynamic environments with moving obstacles. The proposed method integrates switched motion modes and data-driven prediction to enable real-time, collision-free coordination. A

Mohammad Zare

News text classification is a crucial task in natural language processing, essential for organizing and filtering the massive volume of digital content. Traditional methods typically rely on statistical features like term frequencies or TF-IDF values, which are effective at capturing word-level importance but often fail to reflect contextual meaning. In cont

H. R. Paz

Progression and assessment rules are often treated as administrative details, yet they fundamentally shape who is allowed to remain in higher education, and on what terms. This article uses a calibrated agent-based model to examine how alternative progression regimes reconfigure dropout, time-to-degree, equity and students' psychological experience in a long

Jeongeun Lee, Ryang Heo, Dongha Lee

Recent text-to-image (T2I) models generate semantically coherent images from textual prompts, yet evaluating how well they align with individual user preferences remains an open challenge. Conventional evaluation methods, general reward functions or similarity-based metrics, fail to capture the diversity and complexity of personal visual tastes. In this work

Dragos-Alexandru Boldisor, Stefan Smeu, Dan Oneata, Elisabeta Oneata

Self-supervised representations excel at many vision and speech tasks, but their potential for audio-visual deepfake detection remains underexplored. Unlike prior work that uses these features in isolation or buried within complex architectures, we systematically evaluate them across modalities (audio, video, multimodal) and domains (lip movements, generic v

Qingzhao Zhong, Yanxi Hou

Systemic risk measures quantify the potential risk to an individual financial constituent arising from the distress of entire financial system. As a generalization of two widely applied risk measures, Value-at-Risk and Expected Shortfall, the Conditional Value-at-Risk (CoVaR) and Conditional Expected Shortfall (CoES) have recently been receiving growing atte

Min-Kyu Kim, Tae-An Yoo, Ji-Bum Chung

Generative AI is spreading rapidly, creating significant social and economic value while also raising concerns about its high energy use and environmental sustainability. While prior studies have predominantly focused on the energy-intensive nature of the training phase, the cumulative environmental footprint generated during large-scale service operations,

Peng Chen, Jing Liangb, Kang-Jia Qiao, Hui Song

The continuous innovation of smart robotic technologies is driving the development of smart orchards, significantly enhancing the potential for automated harvesting systems. While multi-robot systems offer promising solutions to address labor shortages and rising costs, the efficient scheduling of these systems presents complex optimization challenges. This

Kento Kawaharazuka, Yoshiki Obinata, Naoaki Kanazawa, Haoyu Jia

Various methods for robot design optimization have been developed so far. These methods are diverse, ranging from numerical optimization to black-box optimization. While numerical optimization is fast, it is not suitable for cases involving complex structures or discrete values, leading to frequent use of black-box optimization instead. However, black-box op

E. N. Bulgakov, A. E. Ershov, V. Kimberg, V. S. Gerasimov

We theoretically consider orbital rotation of a spheroidal submicron particle in the field of two counter-propagating circularly polarized Gaussian beams. We derived equations connecting the parameters of the circular orbits centered on the beams axis to the optical force and torque. The equations show that, besides orbital rotation, the spheroidal particle

Rodrigo Almeida, Noelia Otero, Miguel-Ángel Fernández-Torres, Jackie Ma

Accurate prediction of extreme weather events remains a major challenge for artificial intelligence-based weather prediction systems. While deterministic models such as FuXi, GraphCast, and SFNO have achieved competitive forecast skill relative to numerical weather prediction, their ability to represent uncertainty and capture extremes is still limited. This

Marilena Spavone, Chiara Buttitta, Rosa Calvi, Alessandro Loni

Arp@VST is a public observing programme, conducted at the VLT Survey Telescope (VST) hosted at ESO's Paranal Observatory. It aims to revisit the Arp catalogue by creating a public survey. The Atlas of Peculiar Galaxies was produced by Halton Arp in 1966 and contains 338 galaxies with distorted morphologies and/or interacting systems. Given the excellent capa

Andy Haverly, So Yeon Kim, Ju Li

Climate change is a rapidly accelerating problem that requires fast and large-scale carbon sequestration to prevent catastrophe. This paper proposes a novel approach to use explosives for large-scale carbon sequestration. Combining the long-practiced method of explosive mining with newer enhanced rock weathering techniques, we propose a faster, greener, and

Armando A. Aligia, Alejandro M. Lobos, Lucila Peralta Gavensky, Claudio J. Gazza

Studying boundary excitations provides a powerful approach to probe correlations in topological phases. We propose that localized spins near the ends of a Su-Schrieffer-Heeger-Hubbard chain embedded in an insulating environment can be detected experimentally using scanning tunneling microscopy (STM) combined with electron spin resonance (ESR). When the STM t

Max McGinley, Thomas Schuster

In many physical settings, the statistical properties of quantum states are thought to be described by the Scrooge ensemble, a more structured generalization of the Haar ensemble. In this work, we prove several key results on the properties and complexity of Scrooge-random states in macroscopic quantum systems, and provide a general-purpose calculus for eval

Vy Nguyen, Ziqi Xu, Jeffrey Chan, Estrid He

Large Language Models (LLMs) often produce fluent but factually incorrect responses, a phenomenon known as hallucination. Abstention, where the model chooses not to answer and instead outputs phrases such as "I don't know", is a common safeguard. However, existing abstention methods typically rely on post-generation signals, such as generation variations or

Atabey Kaygun

We provide a unified geometric realization of the classical deformation complexes. We construct GL-equivariant bilinear incidence varieties whose diagonal slices recover the varieties of associative, commutative, Leibniz, and Lie algebra structures on a finite-dimensional vector space. We prove that the fiber of the incidence map at a given algebra law is ca

Hui Liu, Tinggui Zhang

Quantum battery has enormous potential for development, and quantum battery capacity is an important indicator of quantum battery. In this work, we mainly study the evolution of quantum battery capacity of GHZ state and GHZ-like states under Markovian channels in the tripartite system. We find that under the depolarizing channel and bit-phase flip channel, t

Patrick Bastian, Holger Dette, Martin Dunsche

We investigate the problem of detecting dependencies between the components of a high-dimensional vector. Our approach advances the existing literature in two important respects. First, we consider the problem under privacy constraints. Second, instead of testing whether the coordinates are pairwise independent, we are interested in determining whether certa

Kesheng Chen, Wenjian Luo, Bang Zhang, Zeping Yin

Episodic rewards present a significant challenge in reinforcement learning. While intrinsic reward methods have demonstrated effectiveness in single-agent rein-forcement learning scenarios, their application to multi-agent reinforcement learn-ing (MARL) remains problematic. The primary difficulties stem from two fac-tors: (1) the exponential sparsity of join

I Cîmpean, Yassine Nachit, Ciprian A Tudor

This work concerns the limit behavior of the quartic variation (i.e., the power variation of order four) with respect to the time variable of the solution to the semilinear stochastic heat equation with space-time white noise. In a first step, we prove that this sequence satisfies a Central Limit Theorem and we deduce a similar result for the viscosity param

Sara Zuppiroli, Carmelo Fabio Longo, Anna Sofia Lippolis, Rocco Paolillo

The Belief-Desire-Intention (BDI) model is a cornerstone for representing rational agency in artificial intelligence and cognitive sciences. Yet, its integration into structured, semantically interoperable knowledge representations remains limited. This paper presents a formal BDI Ontology, conceived as a modular Ontology Design Pattern (ODP) that captures t

Piotr Pęzik, Filip Żarnecki, Konrad Kaczyński, Anna Cichosz

This paper describes the instruction dataset used to fine-tune a set of transformer-based large language models (LLMs) developed in the PLLuM (Polish Large Language Model) project. We present a functional typology of the organic, converted, and synthetic instructions used in PLLuM and share some observations about the implications of using human-authored ver

Marco Galoppo, Pierre Mourier

In relativistic cosmology, the formation of nonlinear inhomogeneities can induce non-negligible backreaction on late-time expansion. Among the important consequences for precision cosmology is the potential impact on the linear growth of large-scale structures. We address this impact by combining covariant spatial averaging with covariant and gauge-invariant

Nicolas Besse, Christophe Cheverry

In this text, the filtering unitary group method developed, among others, by S. Schochet is adapted to prove the existence and well-posedness of modulation equations describing the incompressible limit of the Euler-Maxwell Two-Fluid (EMTF) system. The reduced model captures up to the ion and electron skin depths the long-time behavior of solutions near a con

Caroline Malhaire, Fatine Selhane, Marie-Judith Saint-Martin, Vincent Cockenpot

Objectives: To evaluate the association between pretreatment MRI descriptors and breast cancer (BC) pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Materials \& Methods: Patients with BC treated by NAC with a breast MRI between 2016 and 2020 were included in this retrospective observational single-center study. MR studies were describ

Bin Zhou, Liusheng Hou, Xingju Cai, Hailin Sun

In this paper, we introduce the G\"uler-type acceleration technique and utilize it to propose three acceleration algorithms: the G\"uler-type accelerated proximal gradient method (GPGM), the G\"uler-type accelerated linearized augmented Lagrangian method (GLALM) and the G\"uler-type accelerated linearized alternating direction method of multipliers (GLADMM).

Laurent Beaudou, Guillermo Gamboa Quintero

In this note, we prove that every graph obtained from a bipartite graph by iteratively splitting vertices into two adjacent twins has the de Bruijn-Erd\H{o}s property.

Nhat-Tuong Do-Tran, Ngoc-Hoang-Lam Le, Ching-Chun Huang

The appearance of ultrasound images varies across acquisition devices, causing domain shifts that degrade the performance of fixed black-box downstream inference models when reused. To mitigate this issue, it is practical to develop unpaired image translation (UIT) methods that effectively align the statistical distributions between source and target domains

Ignacy Mermer, Jakub Muszyński, Jakub Możaryn, Krystian Rosłon

We propose an AI-based assistant designed to support the ALICE Fast Interaction Trigger (FIT) detector operators at CERN. The assistant helps diagnose and resolve operational issues in the Detector Control System (DCS), where decisions must often be made quickly and with incomplete information. By combining Large Language Models (LLMs) with a controlled Retr

Diego Velazquez, Mikaela Grace, Konstantinos Karageorgos, Lawrence Carin

Automatic post-editing (APE) aims to correct errors in machine-translated text, enhancing translation quality, while reducing the need for human intervention. Despite advances in neural machine translation (NMT), the development of effective APE systems has been hindered by the lack of large-scale multilingual datasets specifically tailored to NMT outputs. T

Naira Petrosyan, Lilit Yeganyan, Aram Manaselyan, Vram Mughnetsyan

We present a multiscale approach for modeling an intermediate-band solar cell based on a GaAs-GaAlAs quantum dot superlattice of cubic symmetry. Our framework combines high-accuracy theoretical calculations of the superlattice band structure and miniband-related absorption coefficient with experimentally determined interband absorption data. The quantum-mech

Liuhan Yin, Runkun Ju, Guodong Guo, Erkang Cheng

Unlike discriminative approaches in autonomous driving that predict a fixed set of candidate trajectories of the ego vehicle, generative methods, such as diffusion models, learn the underlying distribution of future motion, enabling more flexible trajectory prediction. However, since these methods typically rely on denoising human-crafted trajectory anchors

Marius Ghergu, Zhe Yu

The purpose of this article is two-fold. First, we investigate the inequality $$ -\Delta u+V(x) u\geq f\quad\mbox{ in } B_1\setminus\{0\}\subset \mathbb{R}^N , N \geq 2, $$ where $f\in L^1_{loc}(B_1)$. If $V\geq 0$ is radially symmetric, we provide optimal conditions for which any solution $0\leq u\in \mathcal{C}^2(B_1\setminus\{0\})$ of the above inequality

David Solano, Marta Solans, Xavier Perafita, Anna Ruiz-Comellas

Background: Extreme heat is a major public health risk, yet its relationship with mortality may be confounded or modified by air pollution and social determinants. Objectives: We aimed to quantify the effects of extreme maximum temperatures and heatwaves on daily mortality in Catalonia (2012--2022), and to assess the modifying and confounding roles of air po

Qianyi Wang, Guoqiang Ren

Remote sensing imagery is widely used across various fields, yet real-time detection remains challenging due to the prevalence of small objects and the need to balance accuracy with efficiency. To address this, we propose DMG-YOLO, a lightweight real-time detector tailored for small object detection in remote sensing images. Specifically, we design a Dual-br

Luc Bouteille, Alexander Jaus, Jens Kleesiek, Rainer Stiefelhagen

Traditional loss functions in medical image segmentation, such as Dice, often under-segment small lesions because their small relative volume contributes negligibly to the overall loss. To address this, instance-wise loss functions and metrics have been proposed to evaluate segmentation quality on a per-lesion basis. We introduce CC-DiceCE, a loss function b

Robert V. Harlander, Jonas T. Kohnen, Andrea Shindler

The gradient-flow formalism provides a framework for the direct determination of moments of parton distribution functions (PDFs) from lattice QCD calculations. Their conversion from the gradient-flow scheme to $\overline{\text{MS}}$ requires the matching coefficients of the short-flow-time expansion, which can be computed perturbatively. We determine these c

Soumya Sur, Mohammad Saad, Adhip Agarwala

An exactly solvable model of a quantum spin liquid on a quasicrystal, akin to Kitaev's honeycomb model, was introduced in Kim \textit{et al.}, \href{https://doi.org/10.1103/PhysRevB.110.214438}{\text{Phys. Rev. B} \textbf{110}, 214438 (2024)}. It was shown that in contrast to the translationally invariant models, such a spin liquid stabilizes a gapped ground

Jiahui Jiang, Wenhe Cai

We study the quasinormal modes (QNMs) of dilaton black holes in Einstein-Maxwell-dilaton gravity through a correspondence with the quantum Seiberg-Witten (SW) curve of $\mathcal{N}=2$ SU(2) gauge theory with $N_f=3$ hypermultiplets. By mapping both the black hole perturbation equation and the quantum SW curve to the confluent Heun form, the QNM problem is re

Andrey Kupavskii, Fedor Noskov

In 1977, Duke and Erd\H{o}s asked the following general question: What is the largest size of a family $\mathtt{F} \subset \binom{[n]}{k}$ that does not contain a sunflower with $s$ petals and core of size exactly $t - 1$? This problem is closely related to the famous Erd\H{o}s--Rado sunflower problem of determining the size $\phi(s,t)$ of the largest $t$-un

Oliver Bölt, Luigia Cristiano, Sandy Croatto, Dirk Gajewski

Here, we demonstrate and investigate how Distributed Acoustic Sensing (DAS) can be utilized on research campuses and in large scientific infrastructures to study environmental vibrations and reduce their impact on high-precision experiments. We first discuss the potential of DAS in the context of particle accelerators, gravitational wave detection experiment

Ping Wu, Dan Zhu

Financial markets are interconnected, with micro-currents propagating across global markets and shaping economic trends. This paper moves beyond traditional stock market indices to examine cross-sectional return distributions-15 in our empirical application, each representing a distinct global market. To facilitate this analysis, we develop a matrix function

Zbigniew Plebaniak

The JEM-EUSO (Joint Exploratory Missions for Extreme Universe Space Observatory) collaboration is an international initiative studying ultra-high-energy cosmic rays and related phenomena. These particles, with energies exceeding 10$^{20}$~eV, provide insights into extreme astrophysical processes but remain challenging to detect due to their low flux. At the

Yushun Fang, Yuxiang Chen, Shibo Yin, Qiang Hu

Recent advances in diffusion-based real-world image super-resolution (Real-ISR) have demonstrated remarkable perceptual quality, yet the balance between fidelity and controllability remains a problem: multi-step diffusion-based methods suffer from generative diversity and randomness, resulting in low fidelity, while one-step methods lose control flexibility

G. Mustafa, Faisal Javed, S. K. Maurya, A. Ditta

Relativistic accretion onto compact objects such as black holes and neutron stars is one of the most efficient known mechanisms for converting gravitational potential energy into radiation. In the case of rapidly spinning black holes, up to $40\%$ of the rest-mass energy of accreting matter can be released, far exceeding the efficiency of nuclear fusion. In

M. A. Perelmuter

Let $A$ be a dissipative operator on a Banach space with a dense domain. It is proved that $A$ has a quasi-dissipative extension (possibly in an enlarged Banach space) which generates a quasi-contractive $C_0$-semigroup. \par This gives a positive answer to the question posed by P.R.Chernoff and H.F.Trotter.

Wei Zhang, Jiajun Chu, Xinci Liu, Chen Tong

Spatial Transcriptomics (ST) is a technology that measures gene expression profiles within tissue sections while retaining spatial context. It reveals localized gene expression patterns and tissue heterogeneity, both of which are essential for understanding disease etiology. However, its high cost has driven efforts to predict spatial gene expression from wh

Manh Pham Hung, Changshuo Hu, Ting Dang, Dong Ma

Device-guided music transfer adapts playback across unseen devices for users who lack them. Existing methods mainly focus on modifying the timbre, rhythm, harmony, or instrumentation to mimic genres or artists, overlooking the diverse hardware properties of the playback device (i.e., speaker). Therefore, we propose DeMT, which processes a speaker's frequency

Jiaxun Fang, Li Chen

Deep learning-based image compression (LIC) has achieved state-of-the-art rate-distortion (RD) performance, yet deploying these models on resource-constrained FPGAs remains a major challenge. This work presents a complete, multi-stage optimization framework to bridge the gap between high-performance floating-point models and efficient, hardware-friendly inte

Harshita Sharma, Maxwell C. Reynolds, Valentina Salvatelli, Anne-Marie G. Sykes

AI-assisted report generation offers the opportunity to reduce radiologists' workload stemming from expanded screening guidelines, complex cases and workforce shortages, while maintaining diagnostic accuracy. In addition to describing pathological findings in chest X-ray reports, interpreting lines and tubes (L&T) is demanding and repetitive for radiologists

Kenric P. Nelson

The coupled entropy, $H_\kappa,$ is proven to uniquely satisfy the requirement that a generalized entropy be a measure of the uncertainty at the scale, $\sigma,$ for a class of non-exponential distributions. The coupled stretched exponential distributions, including the generalized Pareto and Student's t distributions, are uniquely parameterized to quantify

SaiKiran Tedla, Joshua E. Little, Hakki Can Karaimer, Michael S. Brown

Traditional in-camera colorimetric mapping relies on correlated color temperature (CCT)-based interpolation between pre-calibrated transforms optimized for Planckian illuminants such as CIE A and D65. However, modern lighting technologies such as LEDs can deviate substantially from the Planckian locus, exposing the limitations of relying on conventional one-

M. Bellazzini, G. Beccari, R. Pascale, D. Paris

KK 153 is a star-forming dwarf galaxy that has been recently proposed as a new member of the sparsely populated class of gas-rich ultra faint dwarfs, lying in the outskirts of the Local Group. We used the Large Binocular Telescope under sub-arcsec seeing conditions to resolve for the first time the outer regions of KK 153 into individual stars, reaching the

Horia Cristescu, Charles Park, Trong Canh Nguyen, Sergiu Talmacel

While current Computer Use Agent (CUA) benchmarks measure task completion effectively, they provide limited assessment of enterprise deployment readiness, emphasizing functional correctness over the operational reliability required for production systems. We present UI-CUBE (UiPath Computer Use BEnchmark), a systematic benchmark comprising 226 tasks across t

Michele Motta, Dario Prandi

In this paper, we study the complexity of the approximation of nonadmissible curves for nonlinear control-affine systems satisfying the strong H{\"o}rmander condition. Focusing on tubular approximation complexities, we provide asymptotic equivalences, with explicit constants, for all generic situations where the distribution, i.e., the linear part of the con

Yeqin Zhang, Yizheng Zhao, Chen Hu, Binxing Jiao

Text representation plays a critical role in tasks like clustering, retrieval, and other downstream applications. With the emergence of large language models (LLMs), there is increasing interest in harnessing their capabilities for this purpose. However, most of the LLMs are inherently causal and optimized for next-token prediction, making them suboptimal fo

Yan-Ru Wang, Wei-Kun Chen, Ivana Ljubić

In this paper, we consider the multiple probabilistic covering location problem (MPCLP), which attempts to open a fixed number of facilities to maximize the total covered customer demand under a joint probabilistic coverage setting. We present a new mixed integer nonlinear programming (MINLP) formulation, and develop an efficient linear programming (LP) base

Quentin Anthony, Yury Tokpanov, Skyler Szot, Srivatsan Rajagopal

We report on the first large-scale mixture-of-experts (MoE) pretraining study on pure AMD hardware, utilizing both MI300X GPUs and Pollara networking. We distill practical guidance for both systems and model design. On the systems side, we deliver a comprehensive cluster and networking characterization: microbenchmarks for all core collectives (all-reduce, r

Shubhrasish Mukherjee, Gaurab Samanta, Shubhadip Moulick, Ruta Kulkarni

Defect spectroscopy in two-dimensional (2D) field-effect transistors (FETs) requires device architectures that suppress contact and disorder artifacts while preserving intrinsic carrier dynamics. Here, we demonstrate ReS$_2$-hBN FETs with few-layer graphene (FLG) van der Waals contacts that form nearly barrier-free interfaces, enabling intrinsic transport in

Jiaxun Fang, Grace Li Zhang, Shaoyi Huang

Systolic array accelerators execute CNNs with energy dominated by the switching activity of multiply accumulate (MAC) units. Although prior work exploits weight dependent MAC power for compression, existing methods often use global activation models, coarse energy proxies, or layer-agnostic policies, which limits their effectiveness on real hardware. We prop

Aron Schott, Berk Acikgöz, Omar Massoud, Marina Petrova

Large antenna arrays and beamforming techniques are key components for exploiting the spectrum-rich FR2 bands in next-generation mobile communication networks. Given the site-specific spatio-temporal variations of the mm-wave channel, non-RF sensor inputs and environment awareness can be leveraged to greatly enhance beam management decisions, e.g. via machin

Somnath Pradhan, Dinesh Rathia

This paper investigates the robustness of stochastic optimal control for controlled regime switching diffusions. We consider systems driven by both continuous fluctuations and discrete regime changes, allowing for model misspecification in both the diffusion and switching components. Within a unified framework, we study four classical cost formulations finit

Rajasmita Sahoo, Somnath Mukhopadhyay, Mrutunjaya Bhuyan

In this study, we investigate the influence of an admixed fermionic dark matter (DM) component on the equilibrium structure of white dwarfs (WDs), with particular emphasis on the effects of varying the DM particle mass ($m_{\rm DM}$) and DM fraction ($f_{\rm DM}$). Notably, we employ a single-fluid approximation for the first time in this context, wherein th

Gabriel Job Antunes Grabher, Fumio Machida, Thomas Ropars

Detecting and resolving performance anomalies in Cloud services is crucial for maintaining desired performance objectives. Scaling actions triggered by an anomaly detector help achieve target latency at the cost of extra resource consumption. However, performance anomaly detectors make mistakes. This paper studies which characteristics of performance anomaly

Leo Kao

Regulated AI workflows (such as clinical trials, medical decision support, and financial compliance) must satisfy strict auditability and integrity requirements. Existing audit-trail mechanisms rely on variable-length records, bulky cryptographic transcripts, or ad-hoc schemas, suffering from metadata leakage, irregular performance, and weak alignment with f

Yijun Xu, Jingrui Zhang, Hongyi Liu, Yuhan Chen

Reconstruction of rigid motion over large spatiotemporal scales remains a challenging task due to limitations in modeling paradigms, severe motion blur, and insufficient physical consistency. In this work, we propose PEGS, a framework that integrates Physical priors with Event stream enhancement within a 3D Gaussian Splatting pipeline to perform deblurred ta

Georgia Ioannou, Francesco Paolo Cont`o, Merlin A. Etzold, Julien R. Landel

This experimental study investigates the dynamics of surface washing to remove a passive tracer from a porous plate by a gravity-driven liquid film across its surface. A disodium fluorescein tracer is introduced at the surface of a water-saturated porous plate and allowed to diffuse into the plate for a number of hours before a film of water solution flows o

S. E. Amrani, M. Sun, S. Valdueza-Felip, F. B. Naranjo

Transparent glass ceramics, when activated by rare earth ions, are excellent photonic materials. Regarding photonic glass-ceramics based on silicates, hafnia and silica in a binary system has proved to be an excellent matrix to incorporate rare earth ions in the hafnia nanocrystals, resulting in important luminescence enhancement and, consequently, allowing

Zeinab Ghamlouch, Mehwish Alam

Taxonomies play a vital role in structuring and categorizing information across domains. However, many existing taxonomies suffer from limited coverage and outdated or ambiguous nodes, reducing their effectiveness in knowledge retrieval. To address this, we present Taxoria, a novel taxonomy enrichment pipeline that leverages Large Language Models (LLMs) to e

Javier Lazaro, Juan-Ignacio Vazquez, Pablo Garcia-Bringas

Parameterised quantum circuit (PQC) based Quantum Reinforcement Learning (QRL) has emerged as a promising paradigm at the intersection of quantum computing and reinforcement learning (RL). By design, PQCs create hybrid quantum-classical models, but their practical applicability remains uncertain due to training instabilities, barren plateaus (BPs), and the d

Sergio Torregrosa, David Munoz, Hector Navarro, Charbel Farhat

Generative Design (GD) combines artificial intelligence (AI), physics-based modeling, and multi-objective optimization to autonomously explore and refine engineering designs. Despite its promise in aerospace, automotive, and other high-performance applications, current GD methods face critical challenges: AI approaches require large datasets and often strugg

Anna Lena Schaible, Ufuk Çakır, Tobias Buck, Harald Mack

Although integral-field spectroscopy enables spatially resolved spectral studies of galaxies, bridging particle-based simulations to observations remains slow and non-differentiable. We present RUBIX, a JAX-based pipeline that models mock integral-field unit (IFU) cubes for galaxies end-to-end and calculates gradients with respect to particle inputs. Our imp

Fei Hu

We show that the eigenvalues of any polarized endomorphism acting on the $\ell$-adic \'etale cohomology of a smooth projective variety satisfy certain parity and symmetry properties, as predicted by the standard conjectures. These properties were previously known for Frobenius endomorphisms. Besides the hard Lefschetz theorem, a key new ingredient is a recen

Chenhao Jin, Hehu Xie

This paper develops a robust solver for the Maxwell eigenproblem in 3D photonic crystals with anisotropic media. The solver employs the kernel compensation technique under the framework of Yee's scheme to eliminate null space and enable matrix-free, GPU-accelerated operations via 3D discrete Fourier transform. Furthermore, we propose a novel discretization f

Yuan Zhang, Ming Lu, Junwen Pan, Tao Huang

Recent advances in multimodal reasoning models have demonstrated impressive capabilities across text and vision. However, even leading models exhibit redundant self-reflection when generating lengthy reasoning chains. While training-free CoT compression methods have emerged in the LLMs domain, they rely on static visual references and thus provide limited ga

Alessandro Agnetis, Roel Leus, Emmeline Perneel, Ilaria Salvadori

We study a stochastic single-machine scheduling problem, denoted the Unreliable Job Selection and Sequencing Problem (UJSSP). Given a set of jobs, a subset must be selected for processing on a single machine that is subject to failure. Each job incurs a cost if selected and yields a reward upon successful completion. A job is completed successfully only if t

V. El Sawah, A. Bhardwaj, A. Pryke-Hobbes, D. Gamaleldin

Clinical psychology students frequently report feeling underprepared for the interpersonal demands of therapeutic work, highlighting the need for accessible opportunities to practise core counselling skills before seeing real clients. Advances in artificial intelligence (AI) now enable simulated interaction partners that may support early skills development.

Abduljalil S. Aljadani, Firdous U. Nazir, Bikash C. Pal, Izudin Džafić

The large penetration of distributed generations impacts both the secondary low-voltage (LV) and the primary medium-voltage (MV) segments of the distribution network. Optimizing power flow calculations for the integrated MV/LV networks is crucial for the real-time management of modern distribution networks. Traditional methods in symmetrical coordinates are

Daiqing Wu, Dongbao Yang, Yu Zhou, Can Ma

Visual emotion recognition (VER) is a longstanding field that has garnered increasing attention with the advancement of deep neural networks. Although recent studies have achieved notable improvements by leveraging the knowledge embedded within pre-trained visual models, the lack of direct association between factual-level features and emotional categories,

Ismum Ul Hossain, Mohammad Nahidul Islam

As the world shifts towards utilizing natural resources for electricity generation, there is need to enhance forecasting systems to guarantee a stable electricity provision and to incorporate the generated power into the network systems. This work provides a machine learning environment for renewable energy forecasting that prevents the flaws which are usual

Tianyi Bai, Yueyun Hu

In this paper, we study second order fluctuations for the size of the range of a critical branching random walk (BRW) in $\mathbb Z^d$. We consider the BRW with geometric offspring indexed by the Kesten tree, and show that the size of its range has linear variance when $d>8$, and satisfies a central limit theorem (CLT) with Gaussian limiting distribution whe

Duo Zhou, Yuji Zhang, Tianxin Wei, Ruizhong Qiu

Large language models (LLMs) can internalize private or harmful content, motivating unlearning that removes a forget set while preserving retaining knowledge. However, forgetting updates often cause collateral degradation on retaining knowledge, creating a persistent trade-off. Existing LLM unlearning methods are often heuristic, and other theoretical approa

Aylar Partovizadeh, Sebastian Schöps, Dimitrios Loukrezis

This work introduces the use of multivariate global sensitivity analysis for assessing the impact of uncertain electric machine design parameters on efficiency maps and profiles. Contrary to the common approach of applying variance-based (Sobol') sensitivity analysis elementwise, multivariate sensitivity analysis provides a single sensitivity index per param

Dayan Pan, Jingyuan Wang, Yilong Zhou, Jiawei Cheng

Fine-tuning large language models is essential for task-specific adaptation, yet it remains computationally prohibitive. Parameter-Efficient Fine-Tuning (PEFT) methods have emerged as a solution, but current approaches typically ignore the distinct roles of model components and the heterogeneous importance across layers, thereby limiting adaptation efficienc