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Kinematic inventory of rock glaciers in the Nyainqentanglha Range using the MT-InSAR method

International Journal of Digital Earth 2023年第1期16卷 3923-3948页

作者： Xuefei Zhang Min Feng Jinhao Xu Dezhao Yan Jing Wang Xiaoqing Zhou Tao Li Xiang Zhang Land Satellite Remote Sensing Application Center Ministry of Natural ResourcesBeijingPeople’s Republic of China Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth SystemEnvironment and Resources(TPESER)Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijingPeople’s Republic of China Academy of Plateau Science and Sustainability Qinghai Normal UniversityXiningPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China Research Institute of Intelligent Computing Zhejiang LaboratoryHangzhouPeople’s Republic of China

Rock glaciers are typical periglacial landforms with tongue or lobate morphological shapes and characterized by the distinct front,lateral margins,and often by ridge-and-furrow surface topography textures as well as kinematic characteristics,widely distributed in alpine *** Synthetic aperture radar interferometry(MT-InSAR),is a remote sensing technique with demonstrated effectiveness for detecting landform ***,its application to rock glaciers is challenged by temporal decorrelation and atmospheric phase noises due to complex topography and snow *** designed a quadtree segmentation and parallel computing-based MT-InSAR method to improve the quality and efficiency of deformation measurement of rock *** applied the method to a rock glacier inventory of the Nyainqentanglha Range,China,derived from high-resolution Gaofen-2 images,to quantify the activity rate of each rock *** showed that 32.1%(6,389)of the identified rock glaciers exhibited slope-parallel deformation rates exceeding 100 mm/*** activities of the rock glaciers exhibited strong correlations with their distance to glaciers,precipitation,freeze-thaw magnitude,and permafrost occurrence *** results demonstrate the effectiveness of the developed segmentation-parallel MT-InSAR method for monitoring rock glacier deformation over a large region.

关键词： Rock glacier MT-InSAR quadtree segmentation Nyaingentanglha kinematics

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MetaCity: data-driven sustainable development of complex cities

引用

The Innovation 2025年第2期6卷 88-103,87页

作者： Yunke Zhang Yuming Lin Guanjie Zheng Yu Liu Nicholas Sukiennik Fengli Xu Yongjun Xu Feng Lu Qi Wang Yuan Lai Li Tian Nan Li Dongping Fang Fei Wang Tao Zhou Yong Li Yu Zheng Zhiqiang Wu Huadong Guo Department of Electronic Engineering Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijing 100084China John Hopcroft Center for Computer Science Shanghai Jiao Tong UniversityShanghai 200240China University of Chinese Academy of Sciences Beijing 100049China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Civil and Environmental Engineering Northeastern UniversityBostonMA 02115USA School of Architecture Tsinghua UniversityBeijing 100084China School of Civil Engineering Tsinghua UniversityBeijing 100084China Big Data Research Center University of Electronic Science and Technology of ChinaChengdu 611731China JD iCity JD Technology&JD Intelligent Cities ResearchBeijing 100176China College of Architecture and Urban Planning Tongji UniversityShanghai 200292China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China These authors contributed equally

Cities are complex systems that develop under complicated interactions among their human and environmental *** generates substantial outcomes and opportunities while raising challenges including congestion,air pollution,inequality,etc.,calling for efficient and reasonable solutions to sustainable ***,booming technologies generate large-scale data of complex cities,providing a chance to propose data-driven solutions for sustainable urban *** paper provides a comprehensive overview of data-driven urban sustainability *** this review article,we conceptualize MetaCity,a general framework for optimizing resource usage and allocation problems in complex cities with data-driven *** this framework,we decompose specific urban sustainable goals,e.g.,efficiency and resilience,review practical urban problems under these goals,and explore the probability of using data-driven technologies as potential solutions to the challenge of *** the basis of extensive urban data,we integrate urban problem discovery,operation of urban systems simulation,and complex decision-making problem solving into an entire cohesive framework to achieve sustainable development goals by optimizing resource allocation problems in complex cities.

关键词： resource usage sustainable development urbanization resilience data driven urban systems simulation complex cities complexity

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Morphological classification of radio galaxies with Wasserstein generative adversarial network-supported augmentation

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RAS Techniques and Instruments 2023年第1期2卷 264-277页

作者： Rustige, Lennart Kummer, Janis Griese, Florian Borras, Kerstin Brüggen, Marcus Connor, Patrick L.S. Gaede, Frank Kasieczka, Gregor Knopp, Tobias Schleper, Peter Center for Data and Computing in Natural Sciences (CDCS) Notkestrasse 9 Hamburg D-22607 Germany Deutsches Elektronen-Synchrotron DESY Notkestrasse 85 Hamburg D-22607 Germany Universität Hamburg Hamburger Sternwarte Gojenbergsweg 112 Hamburg D-21029 Germany Section for Biomedical Imaging University Medical Center Hamburg-Eppendorf Hamburg D-20246 Germany Institute for Biomedical Imaging Hamburg University of Technology Hamburg D-21073 Germany Physics Institute III A RWTH Aachen University Templergraben 55 Aachen D-52062 Germany Institut für Experimentalphysik Universität Hamburg Luruper Chaussee 149 Hamburg D-22761 Germany

Machine learning techniques that perform morphological classification of astronomical sources often suffer from a scarcity of labelled training data. Here, we focus on the case of supervised deep learning models for the morphological classification of radio galaxies, which is particularly topical for the forthcoming large radio surveys. We demonstrate the use of generative models, specifically Wasserstein generative adversarial networks (wGANs), to generate data for different classes of radio galaxies. Further, we study the impact of augmenting the training data with images from our wGAN on three different classification architectures. We find that this technique makes it possible to improve models for the morphological classification of radio galaxies. A simple fully connected neural network benefits most from including generated images into the training set, with a considerable improvement of its classification accuracy. In addition, we find it is more difficult to improve complex classifiers. The classification performance of a convolutional neural network can be improved slightly. However, this is not the case for a vision transformer. © 2023 The Author(s)

关键词： data Methods Machine Learning methods: data analysis methods: statistical radio continuum: galaxies techniques: image processing

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CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation

arXiv

引用

arXiv 2023年

作者： Buhmann, Erik Gaede, Frank Kasieczka, Gregor Korol, Anatolii Korcari, William Krüger, Katja McKeown, Peter Institut für Experimentalphysik Universität Hamburg Luruper Chaussee 149 Hamburg22607 Germany Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Center for Data and Computing in Natural Sciences CDCS Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany

Fast simulation of the energy depositions in high-granular detectors is needed for future collider experiments at ever-increasing luminosities. Generative machine learning (ML) models have been shown to speed up and augment the traditional simulation chain in physics analysis. However, the majority of previous efforts were limited to models relying on fixed, regular detector readout geometries. A major advancement is the recently introduced CaloClouds model, a geometry-independent diffusion model, which generates calorimeter showers as point clouds for the electromagnetic calorimeter of the envisioned International Large Detector (ILD). In this work, we introduce CaloClouds II which features a number of key improvements. This includes continuous time score-based modelling, which allows for a 25-step sampling with comparable fidelity to CaloClouds while yielding a 6× speed-up over Geant4 on a single CPU (5× over CaloClouds). We further distill the diffusion model into a consistency model allowing for accurate sampling in a single step and resulting in a 46× speed-up over Geant4 (37× over CaloClouds). This constitutes the first application of consistency distillation for the generation of calorimeter showers. Copyright © 2023, The Authors. All rights reserved.

关键词： Calorimeters

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New Angles on Fast Calorimeter Shower Simulation

arXiv

引用

arXiv 2023年

作者： Diefenbacher, Sascha Eren, Engin Gaede, Frank Kasieczka, Gregor Korol, Anatolii Krüger, Katja McKeown, Peter Rustige, Lennart Institut für Experimentalphysik Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Center for Data and Computing in Natural Sciences CDCS Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany

The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning based generative models offer a solution that is both fast and accurate. In this work we extend the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture, designed for the simulation of particle showers in highly granular calorimeters, in two key directions. First, we generalise the model to a multi-parameter conditioning scenario, while retaining a high degree of physics fidelity. In a second step, we perform a detailed study of the effect of applying a state-of-the-art particle flow-based reconstruction procedure to the generated showers. We demonstrate that the performance of the model remains high after reconstruction. These results are an important step towards creating a more general simulation tool, where maintaining physics performance after reconstruction is the ultimate target. Copyright © 2023, The Authors. All rights reserved.

关键词： Calorimeters

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Refining fast simulation using machine learning

arXiv

引用

arXiv 2023年

作者： Bein, Samuel Connor, Patrick Pedro, Kevin Schleper, Peter Wolf, Moritz University of Hamburg Institut für Experimentalphysik Germany Center for Data and Computing in Natural Sciences Hamburg Germany Fermi National Accelerator Laboratory BataviaIL United States

At the CMS experiment, a growing reliance on the fast Monte Carlo application (FastSim) will accompany the high luminosity and detector granularity expected in Phase 2. The FastSim chain is roughly 10 times faster than the application based on the Geant4 detector simulation and full reconstruction referred to as FullSim. However, this advantage comes at the price of decreased accuracy in some of the final analysis observables. In this contribution, a machine learning-based technique to refine those observables is presented. We employ a regression neural network trained with a sophisticated combination of multiple loss functions to provide post-hoc corrections to samples produced by the FastSim chain. The results show considerably improved agreement with the FullSim output and an improvement in correlations among output observables and external parameters. This technique is a promising replacement for existing correction factors, providing higher accuracy and thus contributing to the wider usage of FastSim. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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data-driven Intra-Autonomous Systems Graph Generator

arXiv

引用

arXiv 2023年

作者： Dadauto, Caio Vinicius da Fonseca, Nelson Luis Saldanha Torres, Ricardo da Silva The Institute of Computing UNICAMP University of Campinas Brazil The Department of ICT and Natural Sciences NTNU Norwegian University of Science and Technologies Ålesund Norway Wageningen Data Competence Center Wageningen Netherlands

Accurate modeling of realistic network topologies is essential for evaluating novel Internet solutions. Current topology generators, notably scale-free-based models, fail to capture multiple properties of intra-AS topologies. While scale-free networks encode node-degree distribution, they overlook crucial graph properties like betweenness, clustering, and assortativity. The limitations of existing generators pose challenges for training and evaluating deep learning models in communication networks, emphasizing the need for advanced topology generators encompassing diverse Internet topology characteristics. This paper introduces a novel deep-learning-based generator of synthetic graphs representing intra-autonomous in the Internet, named Deep-Generative Graphs for the Internet (DGGI). It also presents a novel massive dataset of real intra-AS graphs extracted from the project Internet Topology data Kit (ITDK), called Internet Graphs (IGraphs)1. It is shown that DGGI creates synthetic graphs that accurately reproduce the properties of centrality, clustering, assortativity, and node degree. The DGGI generator overperforms existing Internet topology generators. On average, DGGI improves the Maximum Mean Discrepancy (MMD) metric 84.4%, 95.1%, 97.9%, and 94.7% for assortativity, betweenness, clustering, and node degree, respectively. © 2023, CC BY-SA.

关键词： Deep learning

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Fast Perfekt: Regression-based refinement of fast simulation

arXiv

引用

arXiv 2024年

作者： Wolf, Moritz Stietz, Lars O. Connor, Patrick L.S. Schleper, Peter Bein, Samuel University of Hamburg Institute of Experimental Physics Hamburg Germany Hamburg University of Technology Institute of Mathematics Hamburg Germany Center for Data and Computing in Natural Sciences Hamburg Germany Université catholique de Louvain Louvain-la-Neuve Belgium

The availability of precise and accurate simulation is a limiting factor for interpreting and forecasting data in many fields of science and engineering. Often, one or more distinct simulation software applications are developed, each with a relative advantage in accuracy or speed. The quality of insights extracted from the data stands to increase if the accuracy of faster, more economical simulation could be improved to parity or near parity with more resource-intensive but accurate simulation. We present Fast Perfekt, a machine-learned regression to refine the output of fast simulations that employs residual neural networks. A deterministic morphing model is trained using a unique schedule that makes use of the ensemble loss function MMD, with the option of an additional pair-based loss function such as the MSE. We explore this methodology in the context of an abstract analytical model and in terms of a realistic particle physics application featuring jet properties in hadron collisions at the CERN Large Hadron Collider. The refinement makes maximum use of existing domain knowledge, and introduces minimal computational overhead to production. © 2024, CC BY.

关键词： Hadrons

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ChatQCD: Let Large Language Models Explore QCD 42

ChatQCD: Let Large Language Models Explore QCD

引用

42nd International Conference on High Energy Physics, ICHEP 2024

作者： Sulc, Antonin Connor, Patrick L.S. Helmholtz-Zentrum Berlin fuer Materialien und Energie Albert-Einstein-Str. 15 Berlin12489 Germany Center for Data and Computing in Natural Sciences Universität Hamburg Albert-Einstein-Ring 10 HamburgD-22761 Germany

Quantum chromodynamics (QCD) has yielded a vast literature spanning distinct phenomena. We construct a corpus of papers and build a generative model. This model holds promise for accelerating the capability of scientists to consolidate their knowledge of QCD by the ability to generate and validate scientific works in the landscape of works related to QCD and similar problems in HEP. Furthermore, we discuss challenges and future directions of using large language models to integrate our scientific knowledge about QCD through the automated generation of explanatory scientific texts. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Quantum interference phenomena

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Revealing Connections in QCD with Machine Learning 42

Revealing Connections in QCD with Machine Learning

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42nd International Conference on High Energy Physics, ICHEP 2024

作者： Connor, Patrick L.S. Sulc, Antonin Center for Data and Computing in Natural Sciences Universität Hamburg Albert-Einstein-Ring 10 HamburgD-22761 Germany Helmholtz-Zentrum Berlin fuer Materialien und Energie Albert-Einstein-Str. 15 Berlin12489 Germany

This work utilises text analysis techniques to uncover connections and trends in quantum chromodynamics (QCD) research over time. Through embedding-based analysis, we are able to draw conceptual connections between disparate works across QCD subfields. Examining topic clustering and trajectories over time provides insights into new phenomena gaining momentum and experimental approaches coming to prominence in the QCD research area. Furthermore, we construct citation graphs between influential papers to reveal impactful contributions and relationships, compare them with respect to their topic, and propose intertopical and citation-related recommendations. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Cluster analysis

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