November 2025 arXiv papers — page 77

F. Bemani, A. A. Rakhubovsky, R. Filip

Optomechanics with levitated nanoparticles is a promising way to combine very different types of quantum non-Gaussian aspects induced by continuous dynamics in a nonlinear or time-varying potential with the ones coming from discrete quantum elements in dynamics or measurement. First, it is necessary to prepare quantum non-Gaussian states using both methods.

Tommaso Confalone, Flavia Lo Sardo, Domenico Montemurro, Davide Massarotti

Controlled fabrication of twisted van der Waals heterostructures is essential to unlock the full potential of moire materials. However, achieving reproducibility remains a major challenge, particularly for air-sensitive materials such as $Bi_{2}Sr_{2}CaCu_{2}O_{8+\delta}$ (BSCCO), where it is crucial to preserve the intrinsic and delicate superconducting pro

Jingzhou Sun

We prove a formula for the Bergman kernel of polarized complex hyperbolic manifolds. The formula expresses the Bergman kernel as a sum over the geodesic loops in the manifold. As an application, we prove a result about the maximum and minimum of the Bergman kernel function. We also prove an estimate of the off-diagonal Bergman kernel.

Michael Stern, Michelle Hallmann, Francesco Vona, Ute Franke

Rail transportation success depends on efficient maintenance to avoid delays and malfunctions, particularly in rural areas with limited resources. We propose a cost-effective wireless monitoring system that integrates sensors and machine learning to address these challenges. We developed a secure data management system, equipping train cars and rail sections

Arshyn Altybay, Niyaz Tokmagambetov, Gulzat Nalzhupbayeva

We address the inverse problem of identifying a time-dependent source coefficient in a one-dimensional heat equation with a fractional Laplacian subject to Dirichlet boundary conditions and an integral nonlocal data. An a priori estimate is established to ensure the uniqueness and stability of the solution. A fully implicit Crank-Nicolson (CN) finite-differe

Dagmara Oszkiewicz. Przemysław Bartczak, Milagros Colazo, Antti Penttilä

We present a novel empirical method for correcting asteroid phase curves for rotational and geometrical effects using precomputed spin-and-shape models. Our approach normalizes sparse photometric data to a pole-on geometry, enabling consistent phase-curve fitting across apparitions. We fit both the H,G1,G2 and H,G12 phase functions to the normalized data. We

Michelle Hallmann, Michael Stern, Francesco Vona, Ute Franke

This paper presents the design and implementation of a graphical labeling user interface for a monitoring and predictive maintenance system for trains and rail infrastructure in a rural area of Germany. Aiming to enhance rail transportation's economic viability and operational efficiency, our project utilizes cost-effective wireless monitoring systems that c

Annika Vonhusen, Sören Schweers, Artem Ryabov, Philipp Maass

Driven particle transport in crowded and confining environments is fundamental to diverse phenomena across physics, chemistry, and biology. A main objective in studying such systems is to identify novel emergent states and phases of collective dynamics. Here, we report on a nonequilibrium phase transition occurring in periodic structures at high particle den

Kewei Chen, Yayu Long, Shuai Li, Mingsheng Shang

The powerful generalization of Vision-Language-Action (VLA) models is bottlenecked by their heavy reliance on massive, redundant, and unevenly valued datasets, hindering their widespread application. Existing model-centric optimization paths, such as model compression (which often leads to performance degradation) or policy distillation (whose products are m

Alaa Adel Ibrahim, Stephan Leyer

Biomimetic design principles, inspired by the structure of alligator osteoderms, are employed to provide innovative solutions for enhancing membrane distillation system performance. A novel spacer design for membrane distillation (MD) systems is introduced, incorporating the unique structural features of these natural formations. Three-dimensional computatio

Yang Yu

The ability of Large Language Models (LLMs) to perform complex, multi-step reasoning is a central focus of modern AI research. To evaluate and enhance this capability, the pass@k metric, which measures the probability of obtaining at least one correct solution in k independent samples, has received significant attention. Its intuitive appeal has led to its a

Dorina Weichert, Gunar Ernis, Marvin Worthmann, Peter Ryzko

The compounding of plastics with recycled material remains a practical challenge, as the properties of the processed material is not as easy to control as with completely new raw materials. For a data scientist, it makes sense to plan the necessary experiments in the development of new compounds using Bayesian Optimization, an optimization approach based on

Wei Zhao, Zhe Li, Yige Li, Jun Sun

Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in cross-modal understanding, but remain vulnerable to adversarial attacks through visual inputs despite robust textual safety mechanisms. These vulnerabilities arise from two core weaknesses: the continuous nature of visual representations, which allows for gradient-based att

Pedro Ramoneda, Emilia Parada-Cabaleiro, Dasaem Jeong, Xavier Serra

Despite its potential, AI advances in music education are hindered by proprietary systems that limit the democratization of technology in this domain. In particular, AI-driven music difficulty adjustment is especially promising, as simplifying complex pieces can make music education more inclusive and accessible to learners of all ages and contexts. Neverthe

Boyue Xu, Ruichao Hou, Tongwei Ren, Dongming Zhou

Cross-modal object tracking (CMOT) is an emerging task that maintains target consistency while the video stream switches between different modalities, with only one modality available in each frame, mostly focusing on RGB-Near Infrared (RGB-NIR) tracking. Existing methods typically connect parallel RGB and NIR branches to a shared backbone, which limits the

Saksham Gautam, Lakshmi Mandal, Shalabh Bhatnagar

In this work, we consider the problem of a two-player zero-sum game. In the literature, the successive over-relaxation Q-learning algorithm has been developed and implemented, and it is seen to result in a lower contraction factor for the associated Q-Bellman operator resulting in a faster value iteration-based procedure. However, this has been presented onl

Victor Croisfelt, João Henrique Inacio de Souza, Shashi Raj Pandey, Beatriz Soret

Connected cyber-physical systems perform inference based on real-time inputs from multiple data streams. Uncertain communication delays across data streams challenge the temporal flow of the inference process. State-of-the-art (SotA) non-blocking inference methods rely on a reference-modality paradigm, requiring one modality input to be fully received before

Francesco Salzano, Simone Scalabrino, Rocco Oliveto, Remo Pareschi

Smart Contracts are critical components of blockchain ecosystems, with Solidity as the dominant programming language. While LLMs excel at general-purpose code generation, the unique constraints of Smart Contracts, such as gas consumption, security, and determinism, raise open questions about the reliability of LLM-generated Solidity code. Existing studies la

Peter Raymond Smith, Konstantinos Papatryfonos, David R. Selviah

The development of compact, energy-efficient integrated lasers operating at 1.3 um remains a critical focus in silicon photonics, essential for advancing data communications and optical interconnect technologies. This paper presents a numerical study of distributed Bragg reflector (DBR) hybrid III-V-on-silicon lasers, analyzing design trade-offs and optimiza

Caixin Kang, Yifei Huang, Liangyang Ouyang, Mingfang Zhang

Despite their advanced reasoning capabilities, state-of-the-art Multimodal Large Language Models (MLLMs) demonstrably lack a core component of human intelligence: the ability to `read the room' and assess deception in complex social interactions. To rigorously quantify this failure, we introduce a new task, Multimodal Interactive Deception Assessment (MIDA),

Jia Zhang, Xun Gao Zheng Xiao, Xiaolei Guan Ruihang Chen, Mengyuan Han Tiantian Shi

To date, the laser cooling of rubidium atoms has inevitably relied on 780 nm cooling light corresponding to the first excited state $5\mathrm{P}_{3/2}$. Surprisingly, we demonstrate laser cooling directly utilizing 420 nm blue light for active optical clock, which corresponds to the high excited state $6\mathrm{P}_{3/2}$ of Rb atom. Experimentally, we succes

Joana Reuss, Ekaterina Gikalo, Marco Körner

Real-world agricultural distributions often suffer from severe class imbalance, typically following a long-tailed distribution. Labeled datasets for crop-type classification are inherently scarce and remain costly to obtain. When working with such limited data, training sets are frequently constructed to be artificially balanced -- in particular in the case

Hanako Enomoto, Aki Takigawa, Hiroki Chihara, Chiyoe Koike

Amorphous silicate dust is a major component in the interstellar and circumstellar dust formed in the outflow of asymptotic giant branch (AGB) stars. Although iron depletion is observed in the interstellar medium (ISM), the exact form and fraction of iron in solid remains under debate. In particular, it is unclear whether amorphous silicate dust around AGB s

Zhen Hao Wong, Jingwen Deng, Yuzhao Wang, Wenkai Yu

Textbooks are among the richest repositories of human-verified reasoning knowledge, yet their complex layouts contain multi-column typesetting, cross-page question answer separation, and interleaved figures, make automated extraction of structured QA and VQA pairs extremely challenging. Existing alternatives either synthesize data from scratch, which lacks a

Xiaoguang Wang, Xiao-Ming Lu, Yunbo Zhang, Libin Fu

For pure states, the quantum Berry curvature was well studied. However, the quantum curvature for mixed states has received less attention. From the concept of symmetric logarithmic derivative, we introduce a mixed-state quantum curvature and find that it plays a key role in the field of multi-parameter precision estimations. Through spectral decomposition,

Haoxin Ren, Feng Lu

People today are overwhelmed by massive amounts of information, leading to cognitive overload and memory burden. Traditional visual memory augmentation methods are either effortful and disruptive or fail to align with user intent. To address these limitations, we propose Gaze Archive, a novel visual memory enhancement paradigm through active logging on smart

Melih Baydar, Emre Akbas

Unsupervised image classification, or image clustering, aims to group unlabeled images into semantically meaningful categories. Early methods integrated representation learning and clustering within an iterative framework. However, the rise of foundational models have recently shifted focus solely to clustering, bypassing the representation learning step. In

Francesco Saverio Cafagna

The Probe Of Extreme Multi-Messenger Astrophysics (POEMMA) Balloon with Radio (PBR) is an instrument designed to be borne by a NASA suborbital Super Pressure Balloon (SPB), in a mission planned to last as long as 50 days. The PBR instrument consists of a 1.1 m aperture Schmidt telescope, similar to the POEMMA design, with two cameras in its hybrid focal surf

Guillaume Carlier, Hugo Malamut, Maxime Sylvestre

We consider weak optimal problems (possibly entropically penalized) incorporating both soft and hard (including the case of the martingale condition) moment constraints. Even in the special case of the martingale optimal transport problem, existence of Lagrange multipliers corresponding to the martingale constraint is notoriously hard (and may fail unless so

Yulia Galushkina, Eduard Kim, Emin Nugaev, Yakov Shnir

We study planar non-topological solitons in models with nonlinear potentials that are bounded from below. These models provide consistent completion for the classical consideration at any energy scale. The properties of our solutions indicate the kinematical stability, which is unachievable in the previously studied model with negative quartic self-interacti

Huseein Jawad, Nicolas Brunel

System prompts are critical for guiding the behavior of Large Language Models (LLMs), yet they often contain proprietary logic or sensitive information, making them a prime target for extraction attacks. Adversarial queries can successfully elicit these hidden instructions, posing significant security and privacy risks. Existing defense mechanisms frequently

Jason Miller, Yi Tian

We prove that for each $\kappa \in (8/3, 4)$ there exists a geodesic metric on the carpet of a CLE$_\kappa$ which is canonical in the sense that it is characterized by a certain list of axioms. Our metric can be constructed explicitly as the scaling limit of Minkowski first passage percolation (MFPP), i.e., the metric obtained by taking the infimum of the Le

Farah Alsafadi, Alexandra Akins, Xu Wu

Deep generative modeling provides a powerful pathway to overcome data scarcity in energy-related applications where experimental data are often limited, costly, or difficult to obtain. By learning the underlying probability distribution of the training dataset, deep generative models, such as the diffusion model (DM), can generate high-fidelity synthetic sam

Yingkang Zhang, Tao An, Xiang Ji, Zhenya Zheng

Our previous work identified a class of SDSS quasars exhibiting multiple Gaia detections, classifying them as candidates for various astrophysical systems such as quasar-star pairs, dual quasars, and gravitationally lensed quasars. In this paper, we present a pilot VLBI study targeting a radio-bright subsample and report the first high-resolution imaging res

Qiang Xu, Shengyuan Bai, Leqing Chen, Zijing Liu

Olympiad-level benchmarks in mathematics and physics are crucial testbeds for advanced AI reasoning, but chemistry, with its unique multimodal symbolic language, has remained an open challenge. We introduce ChemO, a new benchmark built from the International Chemistry Olympiad (IChO) 2025. ChemO features two key innovations for automated assessment: Assessme

Andrea Iommi, Antonio Mastropietro, Riccardo Guidotti, Anna Monreale

The importance of Synthetic Data Generation (SDG) has increased significantly in domains where data quality is poor or access is limited due to privacy and regulatory constraints. One such domain is recruitment, where publicly available datasets are scarce due to the sensitive nature of information typically found in curricula vitae, such as gender, disabili

Yuping Yan, Yuhan Xie, Yixin Zhang, Lingjuan Lyu

Vision-Language-Action models (VLAs) have recently demonstrated remarkable progress in embodied environments, enabling robots to perceive, reason, and act through unified multimodal understanding. Despite their impressive capabilities, the adversarial robustness of these systems remains largely unexplored, especially under realistic multimodal and black-box

Pei Yang, Ke Zhang, Ji Wang, Xiao Chen

We present CRM (Multi-Agent Collaborative Reward Model), a framework that replaces a single black-box reward model with a coordinated team of specialist evaluators to improve robustness and interpretability in RLHF. Conventional reward models struggle to jointly optimize multiple, sometimes conflicting, preference dimensions (e.g., factuality, helpfulness, s

Niki van Stein, Anna V. Kononova, Thomas Bäck

Automated algorithm design is entering a new phase: Large Language Models can now generate full optimisation (meta)heuristics, explore vast design spaces and adapt through iterative feedback. Yet this rapid progress is largely performance-driven and opaque. Current LLM-based approaches rarely reveal why a generated algorithm works, which components matter or

Kewei Chen, Yayu Long, Mingsheng Shang

Multi-robot systems in complex physical collaborations face a "shared brain dilemma": transmitting high-dimensional multimedia data (e.g., video streams at ~30MB/s) creates severe bandwidth bottlenecks and decision-making latency. To address this, we propose PIPHEN, an innovative distributed physical cognition-control framework. Its core idea is to replace "

Shuang Chen, Weinian Zhang

Sequential dichotomies of general delay equations are not uniform, which was proved two decades ago. This however reminds whether the countably infinite many dichotomies of a neutral equation have the sequential uniformity. In this paper, considering a scalar neutral equation, we give a negative answer and prove that the series of the projections of dichotom

Sebastian Haan

Effective scientific communication depends on accurate citations that validate sources and guide readers to supporting evidence. Yet academic literature faces mounting challenges: semantic citation errors that misrepresent sources, AI-generated hallucinated references, and traditional citation formats that point to entire papers without indicating which sect

Jörn Tebbe, Andreas Besginow, Markus Lange-Hegermann

Model Predictive Control evolved as the state of the art paradigm for safety critical control tasks. Control-as-Inference approaches thereof model the constrained optimization problem as a probabilistic inference problem. The constraints have to be implemented into the inference model. A recently introduced physics-informed Gaussian Process method uses Contr

Antonios Antoniadis, Ali Shahheidar, Golnoosh Shahkarami, Abolfazl Soltani

We study online interval scheduling in the irrevocable setting, where each interval must be immediately accepted or rejected upon arrival. The objective is to maximize the total length of accepted intervals while ensuring that no two accepted intervals overlap. We consider this problem in a learning-augmented setting, where the algorithm has access to (machi

Rongxin Cheng, Kai Zhou, Xingda Wei, Siyuan Liu

Rollout dominates the training time in large language model (LLM) post-training, where the trained model is used to generate tokens given a batch of prompts. This work, SpecActor, achieves fast rollout with speculative decoding that deploys a fast draft path to accelerate the unparallelizable generation, while the correctness is guaranteed by fast parallel v

Andrea Venturi, Imanol Jerico-Yoldi, Francesco Zola, Raul Orduna

As Law Enforcement Agencies advance in cryptocurrency forensics, criminal actors aiming to conceal illicit fund movements increasingly turn to "mixin" services or privacy-based cryptocurrencies. Monero stands out as a leading choice due to its strong privacy preserving and untraceability properties, making conventional blockchain analysis ineffective. Unders

Xiaotong Zhan, Xi Cheng

Rumor detection on social media remains a challenging task due to the complex propagation dynamics and the limited interpretability of existing models. While recent neural architectures capture content and structural features, they often fail to reveal the underlying causal mechanisms of misinformation spread. We propose CausalMamba, a novel framework that i

Mengyu Cheng, Xianjin Cheng, Zhenxin Liu

In this paper, we mainly focus on the existence of random attractors for McKean-Vlasov stochastic differential equations on a separable Hilbert space $H$. A significant challenge arises from the distribution-dependence of the coefficients, thereby causing the lack of the stochastic flow property on $H$. To address this issue, we first transform the original

Jiajun Tong, Dongyi Wei

We study the immersed boundary problem in 2-D. It models a 1-D elastic closed string immersed and moving in a fluid that fills the entire plane, where the fluid motion is governed by the 2-D incompressible Navier-Stokes equation with a positive Reynolds number subject to a singular forcing exerted by the string. We introduce the notion of mild solutions to t

Vincenzo Dimonte, Luca Motto Ros

We provide a comprehensive development of the basics of descriptive set theory for non-separable complete metric spaces whose weight is a singular cardinal $\lambda$ of countable confinality. Somewhat unexpectedly, the resulting theory is remarkably similar to the classical one, although the methods used are necessarily fairly different and combine ideas and

Egshiglen Batbayar, Christoph Breunig, Peter Haan, Boryana Ilieva

We propose a new approach to estimate selection-corrected quantiles of the gender wage gap. Our method employs instrumental variables that explain variation in the latent variable but, conditional on the latent process, do not directly affect selection. We provide semiparametric identification of the quantile parameters without imposing parametric restrictio

Deniz Sayin Mercadier, Hieu Le, Yihong Chen, Jiancheng Yang

Human organs are composed of interconnected substructures whose geometry and spatial relationships constrain one another. Yet, most deep-learning approaches treat these parts independently, producing anatomically implausible reconstructions. We introduce PrIntMesh, a template-based, topology-preserving framework that reconstructs organs as unified systems. S

Chenghan Lv, Kun Hu, Huiling Li, Hui Liang

Muon tomography is a non-destructive imaging technique that uses cosmic-ray muons to probe dense materials. Bar scintillator and scintillating fiber detectors equipped with one-dimensional SiPM arrays offer compact, high-resolution solutions, but large-area implementations require effective reduction of readout channels while preserving detector performance.

Nianchang Huang, Yi Xu, Ruida Xi, Ruida Xi

Recently, Visible-Infrared person Re-Identification (VI-ReID) has achieved remarkable performance on public datasets. However, due to the discrepancies between public datasets and real-world data, most existing VI-ReID algorithms struggle in real-life applications. To address this, we take the initiative to investigate Unsupervised Domain Adaptation Visible-

Jeremie Ochin, Raphael Chekroun, Bogdan Stanciulescu, Sotiris Manitsaris

Soccer video understanding has motivated the creation of datasets for tasks such as temporal action localization, spatiotemporal action detection (STAD), or multiobject tracking (MOT). The annotation of structured sequences of events (who does what, when, and where) used for soccer analytics requires a holistic approach that integrates both STAD and MOT. How

Yuxuan Shi, A. A. Araújo Filho

This work investigates neutrino propagation in the spacetime of a newly introduced black hole arising from spontaneous Lorentz-symmetry breaking in bumblebee gravity. The analysis focuses on three independent components: the rate at which neutrino-antineutrino annihilation deposits energy in the surrounding region, the geometric contribution to the phase acc

Rémi Abgrall, Yongle Liu

We propose an improved version of the PAMPA algorithm where the solution is sought as globally continuous. The scheme is locally conservative, and there is no mass matrix to invert. This method had been developed in a series of papers, see e.g \cite{Abgrall2024a} and the references therein. In \cite{Abgrall2025d}, we had shown the connection between PAMPA an

Ihtisham Ul Haq, Serge Richard

An age structured mathematical model with time dependent parameters is developed to investigate the dynamics of dengue transmission. Its properties are thoroughly analyzed in the first part of this work, as for example its disease free steady state, the corresponding effective reproduction numbers, its basic reproduction number (obtained via the Euler and Lo

Taras Banakh, Ivan Hetman, Alex Ravsky, Vlad Pshyk

Linear Geometry describes geometric properties that depend on the fundamental notion of a line. In this paper we survey basic notions and results of Linear Geomery that depend on the flat hulls: flats, exchange, rank, regularity, modularity, and parallelity.

Thomas Collignon, Kouds Halitim, Raphaël Bleuse, Sophie Cerf

Efficient data access in High-Performance Computing (HPC) systems is essential to the performance of intensive computing tasks. Traditional optimizations of the I/O stack aim to improve peak performance but are often workload specific and require deep expertise, making them difficult to generalize or re-use. In shared HPC environments, resource congestion ca

Chao Wang, Zhongzi Wang

Let $\Sigma_g$ be a closed Riemann surface of genus $g$. Let $G$ be a finite subgroup of the automorphism group of $\Sigma_g$. It is well known that there exists a smooth $G$-equivariant embedding from $\Sigma_g$ to some Euclidean space $\mathbb{R}^n$. Let $d_g(G)$ be the minimal possible $n$ for $(\Sigma_g,G)$. We compute the value of $d_g(G)$ in certain ca

Yi Yang, Xueqi Li, Yiyang Chen, Jin Song

Recent advances in Vision-Language-Action (VLA) models demonstrate that visual signals can effectively complement sparse action supervisions. However, letting VLA directly predict high-dimensional visual states can distribute model capacity and incur prohibitive training cost, while compressing visual states into more compact supervisory signals inevitably i

Hansheng Wang, Ruiyi Zhan, Dajun Huang, Xingchen Liu

Large symmetric eigenvalue problems are commonly observed in many disciplines such as Chemistry and Physics, and several libraries including cuSOLVERMp, MAGMA and ELPA support computing large eigenvalue decomposition on multi-GPU or multi-CPU-GPU hybrid architectures. However, these libraries do not provide satisfied performance that all of the libraries onl

Rolf Andreasson, Robert J. Berman, Ludvig Svensson

We extend the probabilistic approach for constructing Kahler-Einstein metrics on log Fano manifolds X - involving random point processes - to the case of non-discrete automorphism groups, by breaking the symmetry using a moment map constraint. In particular, an algebraic notion of Gibbs polystability is introduced, ensuring that the corresponding point proce

Eiji Kurozumi, Anton Skrobotov

We propose constructing confidence sets for the emergence, collapse, and recovery dates of a bubble separately by inverting tests for the location of the break date. We examine both likelihood ratio-type tests and the Elliott-Muller-type (2007) tests for detecting break locations. The limiting distributions of these tests are derived under the null hypothesi

Hideki Okawa

Quantum computing applications are an emerging field in high-energy physics. Its ambitious fusion with artificial intelligence is expected to deliver significant efficiency gains over existing methods and/or enable computation from a fundamentally different perspective. High-energy physics is a big data science that utilizes large-scale facilities, detectors

Jiahao Li, Yang Lu, Yachao Zhang, Yong Xie

Open-vocabulary semantic segmentation (OVSS) employs pixel-level vision-language alignment to associate category-related prompts with corresponding pixels. A key challenge is enhancing the multimodal dense prediction capability, specifically this pixel-level multimodal alignment. Although existing methods achieve promising results by leveraging CLIP's vision

Zijian Wang, Xiaoyu Bao, Chenhao Zhao, Jihui Zhang

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder that is associated with increased risks of cardiovascular morbidity and all-cause mortality. While existing diagnostic approaches can roughly classify OSA severity or detect isolated respiratory events, they lack the precision and comprehensiveness required for high resolution, event level di

Marius Rodrigues, Louis Bahrman, Roland Badeau, Gaël Richard

In unsupervised or weakly-supervised approaches for speech dereverberation, the target clean (dry) signals are considered to be unknown during training. In that context, evaluating to what extent information can be retrieved from the sole knowledge of reverberant (wet) speech becomes critical. This work investigates the role of the reverberant (wet) phase in

Genrich Zeller, Magnus Schlösser, Helmut H. Telle

We report on the evolution of tritium-induced sp$^3$-defects in monolayer graphene on a Si/SiO$_2$ substrate, by comparing large-area Raman maps of the same two samples, acquired just after fabrication and twice thereafter, about 9-12 months apart. Inbetween measurements the samples were kept under standard laboratory conditions. Using a conservative classif

Himal Pokhrel, Urvashi Verma

Manganese-doped zinc sulfide nanocrystalline thin films were synthesized using a low-temperature chemical bath deposition and deposited on glass substrates for controlled durations using triethanolamine (TEA) as a complexing and capping agent. After deposition, the films were annealed at 200 degrees Celsius and characterized by X-ray diffraction (XRD), SEM,

Zeting Liu, Zida Yang, Zeyu Zhang, Hao Tang

Long-horizon robotic manipulation remains challenging for Vision-Language-Action (VLA) models despite recent progress in zero-shot generalization and simulation-to-real-world transfer. Current VLA models suffer from stage hallucination, where agents exploit coarse evaluation signals to shortcut multi-step tasks, reporting high progress without truly completi

Peng Tan, Yuantao Chen, Yuqi Zhang, Hanyan Cheng

Precise and ultrafast control of electronic band structures is a central challenge for advancing quantum functional materials and devices. Conventional approaches--such as chemical doping, lattice strain, or external gating--offer robust stability but remain confined to the quasi-static regime, far from the intrinsic femto- to picosecond dynamics that govern

Eloi Lindas, Yannig Goude, Philippe Ciais

In a growing renewable based energy system, accurate and reliable wind power forecasts are crucial for grid stability, balancing supply and demand and market risk management. Even though short-term weather forecasts have been thoroughly used to provide up to 3 days ahead renewable power predictions, forecasts involving prediction horizons longer than a week

Zhi Luo, Zenghui Yuan, Wenqi Wei, Daizong Liu

With the remarkable success of Vision-Language Models (VLMs) on multimodal tasks, concerns regarding their deployment efficiency have become increasingly prominent. In particular, the number of tokens consumed during the generation process has emerged as a key evaluation metric.Prior studies have shown that specific inputs can induce VLMs to generate lengthy

Yuting Lu, Ziliang Wang, Weixin Xu, Wei Zhang

Clinical deployment requires segmentation models to stay stable under distribution shifts and perturbations. The mainstream solution is adversarial training (AT) to improve robustness; however, AT often brings a clean--robustness trade-off and high training/tuning cost, which limits scalability and maintainability in medical imaging. We propose \emph{Layer-w

Lirui Zhang, Zhengkai Zhao, Zhi Zuo, Pan Gao

Point cloud completion is a fundamental task in 3D vision. A persistent challenge in this field is simultaneously preserving fine-grained details present in the input while ensuring the global structural integrity of the completed shape. While recent works leveraging local symmetry transformations via direct regression have significantly improved the preserv

Florian Laronze, Audrey Landuran, Bernard N'kaoua

This article focuses on the concept of self-determination and the design and validation of digital tools intended to promote the self-determination of vulnerable people. Self-determination is an essential skill for carrying out daily activities. But in certain situations, and for certain populations, self-determination is lacking, which leads to the inabilit

Yibin Huang, Wang Xu, Wanyue Zhang, Helu Zhi

Spatial intelligence is a critical frontier for Multimodal Large Language Models (MLLMs), empowering them to comprehend the physical world. Drawing inspiration from human perception mechanisms, prior studies attempt to construct a spatial understanding via grid-based cognitive maps. However, current grid-based map methods rely on discretized representations,

Ting Pan, Ye Wang, Peiguang Jing, Rui Ma

Personalized dual-person portrait customization has considerable potential applications, such as preserving emotional memories and facilitating wedding photography planning. However, the absence of a benchmark dataset hinders the pursuit of high-quality customization in dual-person portrait generation. In this paper, we propose the PairHuman dataset, which i

Preeti Kharb, Anderson Caproni, Salmoli Ghosh, Daniel A. Schwartz

We present here the results from a second epoch of phase-referenced VLBA observations of 8 Seyfert and LINER galaxies from the KISSR sample. These sources were chosen based on the presence of double peaks or asymmetries in their emission lines as observed in SDSS spectra. Parsec-scale radio emission is detected in 7 of the 8 sources in the second epoch. Jet-

Lara Bergmann, Cedric Grothues, Klaus Neumann

Magnetic levitation is about to revolutionize in-machine material flow in industrial automation. Such systems are flexibly configurable and can include a large number of independently actuated shuttles (movers) that dynamically rebalance production capacity. Beyond their capabilities for dynamic transportation, these systems possess the inherent yet unexploi

Grégory Faye, Jean-Michel Roquejoffre, Min Zhao

We propose and study a new model to describe biological invasions constrained on infinite homogeneous one dimensional metric graphs. Our model consists of an infinite PDE-ODE system where, at each vertex of the one-dimensional lattice $\mathbb{Z}$, we have a logistic equation, and connections between vertices are given by diffusion equations on the edges sup

Jian Ma, Qirong Peng, Xujie Zhu, Peixing Xie

Diffusion Transformers (DiTs) have shown exceptional performance in image generation, yet their large parameter counts incur high computational costs, impeding deployment in resource-constrained settings. To address this, we propose Pluggable Pruning with Contiguous Layer Distillation (PPCL), a flexible structured pruning framework specifically designed for

Qian Chen, Shuoshuo Zhang, Guoyu Xian, Haoqiang Hu

Spatiotemporal vortices are polychromatic modes that intertwine orbital angular momentum (OAM) in space and time. Here we introduce a new class of such vortices, spatiotemporal plasmonic vortices (STPVs), carrying nontrivial topological spin textures. They are generated by chronotopic interference of temporally delayed plasmonic eigen-vortices, where a $\pi$

Yiming Yang, Xin Wang, Xianlong He, Chao-Wei Tsai

We present one of the first measurements of the mass-metallicity relation (MZR) in multiple massive protoclusters at cosmic noon, using Hubble Space Telescope (HST) G141 slitless spectroscopy from the MAMMOTH-Grism survey. We identify 63 protocluster member galaxies across three overdense structures at $z = 2\text{-}3$ with robust detections of [OIII], H$\be

Sergei Lepeshov, Daniel Alec Farbowitz, Thor August Schimmell Weis, Bingrui Lu

We present the design, fabrication, and characterization of tunable waveguide-coupled silicon bowtie cavities with strong spatial electromagnetic field confinement. We use nanoelectromechanical in-plane actuation for the tuning, as this combines cryocompatibility with an ultralow power consumption. Our device leverages a mode volume below 0.2 cubic wavelengt

Yuanyuan Lian

In this note, we prove two Liouville theorems for fully nonlinear uniformly elliptic equations on half spaces. The main tools are the boundary pointwise regularity, the Hopf type estimate and the Carleson type estimate. Our new proof is rather short.

Antoine Joux

Since the invention of the famous LLL algorithm, lattice reduction has been an extremely useful tool in computational number theory. By construction, the LLL algorithm deals with lattices living in a vector space endowed with a positive definite scalar product. However, it seems quite nature to ask about the indefinite case, where the scalar product is repla

Chunxu Liu, Jiyuan Yang, Ruopeng Gao, Yuhan Zhu

Multimodal embeddings are widely used in downstream tasks such as multimodal retrieval, enabling alignment of interleaved modalities in a shared representation space. While recent studies show that Multimodal Large Language Models (MLLMs) can serve as strong embedding extractors, existing approaches treat embedding extraction as a direct encoding step, overl

Ariel Neufeld, Philipp Schmocker, Viet Khoa Tran

Quantum neural networks (QNNs) are an analog of classical neural networks in the world of quantum computing, which are represented by a unitary matrix with trainable parameters. Inspired by the universal approximation property of classical neural networks, ensuring that every continuous function can be arbitrarily well approximated uniformly on a compact set

Perceval Beja-Battais, Alain Grossetête, Nicolas Vayatis

In recent years, there has been an increasing need for Nuclear Power Plants (NPPs) to improve flexibility in order to match the rapid growth of renewable energies. The Operator Assistance Predictive System (OAPS) developed by Framatome addresses this problem through Model Predictive Control (MPC). In this work, we aim to improve MPC methods through data-driv

Dabiao Ma, Ziming Dai, Zhimin Xin, Shu Wang

Current Parameter-Efficient Fine-Tuning (PEFT) methods typically operate under an implicit assumption: Once a target module is selected, every token passing through it contributes equally to the downstream task and requires a parameter update. In this paper, we challenge this convention by revealing a pervasive token-level redundancy in the fine-tuning of la

Mehdi Belraouti, Mohamed Deffaf, Abdelghani Zeghib

We consider the pseudo-Riemannian Lichnerowicz conjecture in the homogeneous setting. In particular, we show that any compact connected pseudo-Riemannian manifold $M$ on which a semisimple group $G$ acts conformally, essentially and transitively, is conformally flat.

Zhijie Zhong, Zhiwen Yu, Kaixiang Yang, Yongheng Liu

Time series anomaly detection (TSAD) is a critical data mining task often constrained by label scarcity. Consequently, current research predominantly focuses on Unsupervised Time-series Anomaly Detection (UTAD), relying on increasingly complex architectures to model normal data distributions. However, this algorithm-centric trend often overlooks the signific

Quanqing Ma, Jiaen Chen, Peng Wang, Yao Zheng

Remote sensing Water Body Change Detection (WBCD) aims to detect water body surface changes from bi-temporal images of the same geographic area. Recently, the scarcity of high spatial resolution datasets for WBCD restricts its application in urban and rural regions, which require more accurate positioning. Meanwhile, previous deep learning-based methods fail

Tim Lichtenberg, Laura Schaefer, Joshua Krissansen-Totton, Yamila Miguel

Spectroscopic characterization of rocky exoplanets with the James Webb Space Telescope has brought the origin and evolution of their atmospheres into the focus of exoplanet science. Time-evolved models of the feedback between interior and atmosphere are critical to predict and interpret these observations and link them to the Solar System terrestrial planets

Ding Li, Guoao Yang, Tao Qin, Jianhui Zhou

The quantum geometry tensor, intrinsic geometric characteristics of electronic states, plays a crucial role in the various nontrivial electromagnetic phenomena in quantum materials. Here, we reveal that quantum geometry significantly modifies phonon dichroisms through electron-phonon interactions in solids that break time-reversal and spatial inversion symme

Chenyu Zhao, Xianwei Zheng, Zimin Xia, Linwei Yue

Real-time 3D object detection from point clouds is essential for dynamic scene understanding in applications such as augmented reality, robotics and navigation. We introduce a novel Spatial-prioritized and Rank-aware 3D object detection (SR3D) framework for indoor point clouds, to bridge the gap between how detectors are trained and how they are evaluated. T

Yongnan Jin, Xurui Li, Feng Cao, Liucun Gao

The integration of large language models (LLMs) into medical practice offers transformative potential, yet their real-world clinical applicability remains constrained by critical alignment issues: (1) a misalignment between static evaluation benchmarks and the dynamic cognitive demands of clinical practice, (2) challenges in adapting to continuously evolving