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Solving key challenges in collider physics with foundation models

Physical Review D 2025年第5期111卷 L051504-L051504页

作者： Vinicius Mikuni Benjamin Nachman National Energy Research Scientific Computing Center Berkeley Lab Berkeley California 94720 USA Physics Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Berkeley Institute for Data Science University of California Berkeley California 94720 USA

Foundation models are neural networks that are capable of simultaneously solving many problems. Large language foundation models like ChatGPT have revolutionized many aspects of daily life, but their impact for science is not yet clear. In this paper, we use a new foundation model for hadronic jets to solve three key challenges in collider physics. In particular, we show how experiments can (1) save significant computing power when developing reconstruction algorithms, (2) perform a complete uncertainty quantification for high-dimensional measurements, and (3) search for new physics with model agnostic methods using low-level inputs. In each case, there are significant computational or methodological challenges with current methods that limit the science potential of deep learning algorithms. By solving each problem, we take jet foundation models beyond proof-of-principle studies and into the toolkit of practitioners.

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WeedsGalore: A Multispectral and Multitemporal UAV-Based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields

WeedsGalore: A Multispectral and Multitemporal UAV-Based Dat...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Celikkan, Ekin Kunzmann, Timo Yeskaliyev, Yertay Itzerott, Sibylle Klein, Nadja Herold, Martin Gfz German Research Centre for Geosciences Germany Humboldt-Universität zu Berlin Germany Scientific Computing Center Karlsruhe Institute of Technology Germany

ISBN: (纸本)9798331510831

Weeds are one of the major reasons for crop yield loss but current weeding practices fail to manage weeds in an efficient and targeted manner. Effective weed management is especially important for crops with high worldwide production such as maize, to maximize crop yield for meeting increasing global demands. Advances in near-sensing and computer vision enable the development of new tools for weed management. Specifically, state-of-the-art segmentation models, coupled with novel sensing technologies, can facilitate timely and accurate weeding and monitoring systems. However, learning-based approaches require annotated data and show a lack of generalization to aerial imaging for different crops. We present a novel dataset for semantic and instance segmentation of crops and weeds in agricultural maize fields. The multispectral UAV-based dataset contains images with RGB, red-edge, and nearinfrared bands, a large number of plant instances, dense annotations for maize and four weed classes, and is multitemporal. We provide extensive baseline results for both tasks, including probabilistic methods to quantify prediction uncertainty, improve model calibration, and demonstrate the approach's applicability to out-of-distribution data. The results show the effectiveness of the two additional bands compared to RGB only, and better performance in our target domain than models trained on existing datasets. We hope our dataset advances research on methods and operational systems for fine-grained weed identification, enhancing the robustness and applicability of UAVbased weed management. The dataset and code are available at https://***/GFZ/weedsgalore. © 2025 IEEE.

关键词： Weed control

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ERROR ANALYSIS OF FRACTIONAL COLLOCATION METHODS FOR VOLTERRA INTEGRO-DIFFERENTIAL EQUATIONS WITH NONCOMPACT OPERATORS

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Journal of Computational Mathematics 2025年第3期43卷 690-707页

作者： Zheng Ma Chengming Huang Anatoly A.Alikhanov School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China North-Caucasus Center for Mathematical Research North-Caucasus Federal UniversityStavropol 355017Russia North-Eastern Federal University Yakutsk 677000Russia

This paper is concerned with the numerical solution of Volterra integro-differential equations with noncompact *** focus is on the problems with weakly singular *** handle the initial weak singularity of the solution,a fractional collocation method is applied.A rigorous hp-version error analysis of the numerical method under a weighted H1-norm is carried *** result shows that the method can achieve high order convergence for such *** experiments are also presented to confirm the effectiveness of the proposed method.

关键词： Volterra integro-differential equation Noncompact operator Nonsmooth solution Collocation method Fractional polynomial hp-version error analysis

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Tensor parametric Hamiltonian operator inference

arXiv

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arXiv 2025年

作者： Vijaywargiya, Arjun McQuarrie, Shane A. Gruber, Anthony Department of Applied and Computational Mathematics and Statistics University of Notre Dame United States Computational Mathematics Center for Computing Research Sandia National Laboratories Scientific Machine Learning Center for Computing Research Sandia National Laboratories

This work presents a tensor-based approach to constructing data-driven reduced-order models corresponding to semi-discrete partial differential equations with canonical Hamiltonian structure. By expressing parameter-varying operators with affine dependence as contractions of a generalized parameter vector against a constant tensor, this method leverages the operator inference framework to capture parametric dependence in the learned reduced-order model via the solution to a convex, least-squares optimization problem. This leads to a concise and straightforward implementation which compactifies previous parametric operator inference approaches and directly extends to learning parametric operators with symmetry constraints—a key feature required for constructing structure-preserving surrogates of Hamiltonian systems. The proposed approach is demonstrated on both a (non-Hamiltonian) heat equation with variable diffusion coefficient as well as a Hamiltonian wave equation with variable wave *** Codes 15A69, 35B30, 65P10 © 2025, CC BY.

关键词： Tensors

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WeedsGalore: A Multispectral and Multitemporal UAV-Based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields

WeedsGalore: A Multispectral and Multitemporal UAV-Based Dat...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Ekin Celikkan Timo Kunzmann Yertay Yeskaliyev Sibylle Itzerott Nadja Klein Martin Herold GFZ German Research Centre for Geosciences Humboldt-Universität zu Berlin Scientific Computing Center Karlsruhe Institute of Technology

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

关键词： Instance segmentation Computer vision Uncertainty Computational modeling Semantics Probabilistic logic Data models Sensors Monitoring Crop yield

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A Variational Approach to Personalized Federated Learning and Its Improvement 20th

A Variational Approach to Personalized Federated Learning a...

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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Wu, Huiwen Zhang, Shuo Research Center for Data Hub and Security Zhejiang Laboratory Hangzhou310000 China LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789819600250

Federated learning (FL) is a new approach that allows clients from different locations to work together on a global model without sharing raw data. However, training one standard model is not optimal for local optimization due to heterogeneity. Thus, personalized federated learning (PFL) paradigms aim to combine local and global information while preserving diversity among local clients. However, these methods require more theoretical motivation. This paper formalizes the unfired analysis form by defining a linear mapping on the server’s aggregation step for a deeper understanding of the implementation design and to improve existing methods further. Moreover, we show that the existing FL and PFL methods are specific cases in our framework with selected mapping matrices, including FedAvg, FedMTL, FedProx, MAML, and pure local training. Based on that, we propose a novel data-driven graph Laplacian-induced PFL scheme named GLIP-FL to address statistical heterogeneity issues and improve computation efficiency. We implement evaluation experiments of the proposed method on four popular data sets with five FL baselines in three data partitions, both homogeneous and heterogeneous, and the comprehensive experiments show the great potential of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Optimized Convolutional Neural Networks with Multi-Scale Pyramid Feature Integration for Efficient Traffic Light Detection in Intelligent Transportation Systems

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Computers, Materials & Continua 2025年第2期82卷 3005-3018页

作者： Yahia Said Yahya Alassaf Refka Ghodhbani Taoufik Saidani Olfa Ben Rhaiem Department of Electrical Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border UniversityArar73213Saudi Arabia Department of Civil Engineering College of EngineeringNorthern Border UniversityArar91431Saudi Arabia Faculty of Computing and Information Technology Northern Border UniversityRafha91911Saudi Arabia College of Science Northern Border UniversityArar91431Saudi Arabia

Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.

关键词： Intelligent transportation systems(ITS) traffic light detection multi-scale pyramid feature maps advanced driver assistance systems(ADAS) real-time detection AI in transportation

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Recent Advances in Artificial Sensory Neurons:Biological Fundamentals,Devices,Applications,and Challenges

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Nano-Micro Letters 2025年第3期17卷 168-216页

作者： Shuai Zhong Lirou Su Mingkun Xu Desmond Loke Bin Yu Yishu Zhang Rong Zhao Guangdong Institute of Intelligence Science and Technology HengqinZhuhai 519031People’s Republic of China College of Integrated Circuits Zhejiang UniversityHangzhou 3112000People’s Republic of China ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 310027People’s Republic of China Department of Science Mathematics and TechnologySingapore University of Technology and DesignSingapore 487372Singapore Department of Precision Instruments Tsinghua UniversityBeijing 100084People’s Republic of China Center for Brain‑Inspired Computing Research Tsinghua UniversityBeijing 100084People’s Republic of China IDG/McGovern Institute for Brain Research Tsinghua UniversityBeijing 100084People’s Republic of China

Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power *** fully leverage its advantages,convertingthe external analog signals to spikes is an essential *** including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area *** efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory *** can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory *** review summarizesand benchmarks the recent progress of artificial sensory *** starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial ***,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also ***,we highlight the challenges and perspectives for the future development of artificial sensory neurons.

关键词： Artificial intelligence Emerging devices Artificial sensory neurons Spiking neural networks Neuromorphic sensing

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Ordinal METAR Cloud Cover Classification in Sentinel-2 Satellite Data

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 10674-10683页

作者： Götz, Markus Wessel, Erik Çayoğlu, Uğur Jahani, Babak Debus, Charlotte Cermak, Jan Streit, Achim Helmholtz AI Neuherberg85764 Germany Scientific Computing Center Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany Eggenstein-Leopoldshafen76344 Germany Institute of Meteorology Climate Research—Atmospheric Trace Gases Remote Sensing Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany SRON Netherlands Institute for Space Research Leiden2333 CA Netherlands Institute of Meteorology Climate Research—Atmospheric Trace Gases Remote Sensing Karlsruhe Institute of Technology Karlsruhe76131 Germany Institute of Photogrammetry Remote Sensing Karlsruhe Institute of Technology Karlsruhe76131 Germany

Accurate cloud cover assessment is crucial in several fields, such as weather forecasting, climate science, agriculture, or energy system planning, precipitation pattern forecasting and aiding in early detection of extreme weather events. Despite the crucial data that weather stations provide about sky cloud coverage, their measurements are geographically localized and thus lack spatial coverage. Meteorological satellites on the other hand offer great potential to address this limitation by continuously scanning large areas in short periods of time. This work proposes a novel approach for predicting cloud cover in global satellite images by leveraging ordinal point labels from ground-based weather stations, rather than relying on spatially resolved cloud masks, and demonstrates the effectiveness of this approach using a rank loss-based convolutional neural network of the EfficientNet family. The model is trained in transfer learning approach on a custom-collected dataset across selected regions in the continental USA. Using station measurements only, we achieve an F1-score of up to 0.6 and a ranked-within-1-accuracy ranging from 93.5% to 99.1%. Supplementing the data with labels created by visual inspection to correct for station-satellite mismatches improves scores to 0.75 and 98.4% to 100%. The results imply significantly improved cloud cover assessment in regions without weather stations, extending the capabilities to monitor localized cloud patterns. © 2008-2012 IEEE.

关键词： Convolution

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Rumor detection for emergency events via few-shot ensembled prompt learning

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Journal of Intelligent Information Systems 2025年 1-32页

作者： Su, Chen Zhou, Junkang Jiang, Zhentao Zhu, Shuwei Li, Chao Fang, Wei Lu, Heng-Yang School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China China Ship Scientific Research Center Wuxi214082 China The Laboratory for Advanced Computing and Intelligence Engineering Wuxi China

Rumors on social media can cause panic during emergency events. Unlike conventional rumor detection, it is more challenging to detect rumors about emergency events that have not happened in history, due to the shortage of relevant corpus. Therefore, we treat emergency events rumor detection as a few-shot learning problem. Recently, large language models (LLMs) such as ChatGPT have been widely considered in various NLP tasks. However, LLMs may face limitations due to non-real-time knowledge and unwillingness to provide direct answers. Prompt learning effectively leverages pre-trained language models (PLMs), and prompt tuning can inject the latest knowledge into PLMs with very few instances. Therefore, we propose a template-based Ensembled Prompt Tuning (EPT) model. We contribute an approach leveraging the knowledge in PLMs to generate label words for verbalizer construction. Furthermore, we treat few-shot rumor detection as an MLM problem and design two types of prompt templates for online posts and comments. An ensemble strategy is introduced to make the final prediction. Experimental results on three datasets demonstrate the effectiveness of the proposed EPT model, outperforming current SOTA on accuracy and F1-score. Ablation study has shown the necessity and effectiveness of the ensemble strategy. Besides, we make comparisons with prevalent LLMs under both few-shot and zero-shot settings, the results show the competitiveness of our EPT model. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Zero-shot learning

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