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arXiv 2022年

作者： Ocker, Stella Koch Cordes, James M. Chatterjee, Shami Li, Di Niu, Chen-Hui McKee, James W. Law, Casey J. Anna-Thomas, Reshma Department of Astronomy Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14850 United States National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China E.A. Milne Centre for Astrophysics University of Hull Cottingham Road Kingston-upon-HullHU6 7RX United Kingdom University of Hull Cottingham Road Kingston-upon-HullHU6 7RX United Kingdom Cahill Center for Astronomy and Astrophysics California Institute of Technology MC 249-17 PasadenaCA91125 United States Owens Valley Radio Observatory California Institute of Technology 100 Leighton Lane Big PineCA93513 United States Department of Physics and Astronomy The Center for Gravitational Waves and Cosmology West Virginia University MorgantownWV26506 United States

Fast radio bursts (FRBs) are millisecond-timescale radio transients, the origins of which are predominantly extragalactic and likely involve highly magnetized compact objects. FRBs undergo multipath propagation, or scattering, from electron density fluctuations on sub-parsec scales in ionized gas along the line-of-sight. Scattering observations have located plasma structures within FRB host galaxies, probed Galactic and extragalactic turbulence, and constrained FRB redshifts. Scattering also inhibits FRB detection and biases the observed FRB population. We report the detection of scattering times from the repeating FRB 20190520B that vary by up to a factor of two or more on minutes to days-long timescales. In one notable case, the scattering time varied from 7.9 ± 0.4 ms to less than 3.1 ms (95% confidence) over 2.9 minutes at 1.45 GHz. The scattering times appear to be uncorrelated between bursts or with dispersion and rotation measure variations. Scattering variations are attributable to dynamic, inhomogeneous plasma in the circumsource medium, and analogous variations have been observed from the Crab pulsar. Under such circumstances, the frequency dependence of scattering can deviate from the typical power-law used to measure scattering. Similar variations may therefore be detectable from other FRBs, even those with inconspicuous scattering, providing a unique probe of small-scale processes within FRB environments. © 2022, CC BY.

关键词： Ionization of gases

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Minimum Spanning Tree Clustering Based on Density Filtering 7th

Minimum Spanning Tree Clustering Based on Density Filtering

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7th CCF Academic Conference on bigdata, CCF bigdata 2019

作者： Wang, Ke Xie, Xia Sun, Jiayu Cao, Wenzhi National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hunan University of Technology and Business Changsha410205 China

ISBN: (纸本)9789811518980

Clustering analysis is an important method in data mining. In order to recognize clusters with arbitrary shapes as well as clusters with different density, we propose a new clustering approach: minimum spanning tree clustering based on density filtering. It masks the low-density points in the density filtering step, which reduces the interference of noise and makes the gap between clusters clearer. It uses relative values of adjacent distances to find mutations of density and changes between clusters to divide data sets. It is tested on multiple synthetic data sets and real-world data sets, the results of which show that the algorithm is able to detect clusters with arbitrary shape and it is insensitive to the imbalance of density between clusters. It has achieved great results on multiple data sets. © Springer Nature Singapore Pte Ltd 2019.

关键词： data mining

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DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NLP Prototyping

DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NL...

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IEEE International Symposium on Innovations in intelligent Systems and Applications (INISTA)

作者： Kareem Amin Stelios Kapetanakis Nikolaos Polatidis Klaus-Dieter Althoff Andreas Dengel Smart Data and Knowledge Services German Research Center for Artificial Intelligence Technische Universität Kaiserslautern Kaiserslautern Germany School of Computing Engineering and Mathematics University of Brighton Brighton United Kingdom Intelligent Information Systems Lab Institute of Computer Science University of Hildesheim Hildesheim Germany

ISBN: (数字)9781728167992

ISBN: (纸本)9781728168005

With widespread modernization, digitization and transformations of most of industries, Artificial Intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture.

关键词： Machine learning Training Neural networks Natural language processing Cognition Buildings Task analysis

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Multiview feature selection combining latent space and similarity structure learning

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Pattern Recognition 2026年 169卷

作者： Zhuowen Li Hongmei Chen Tengyu Yin Zhong Yuan Chuan Luo Shi-Jinn Horng Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu 611756 PR China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu 611756 PR China College of Computer Science Sichuan University Chengdu 610065 China Department of Computer Science and Information Engineering Asia University Taichung 41354 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung 404327 Taiwan

Unsupervised multiview feature selection dependent on similar or clustering structures has dramatically progressed, but both ignore the mutually reinforcing relationship between structure learning. Firstly, the two methods of appeal ignore the possibility of shared joint learning, although they can learn coherent information. Secondly, they are inadequate for data structure learning and lack awareness of co-learning global and local structures. This paper proposes an unsupervised feature selection method (SCSF_FS) that integrates similarity and clustering structures learning to capture intrinsic information in data. Specifically, we use latent space learning to partition multiple data matrices into view-specific base matrices and clustering metrics. Structural learning is used to transform specific clustering metrics into coherent clustering structures. In addition, adaptive weights are used on each view’s similarity matrix to learn a consistent similarity structure, while laplacian graph learning is introduced to unify the similarity and clustering structures. Consequently, a unified framework is designed to unify multiview consistency learning, global structure, and local structure preservation. Moreover, an optimization iteration algorithm is designed to solve it. Comparison with eight algorithms shows the effectiveness of the proposed method.

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One for Multiple: Physics-informed Synthetic data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

arXiv

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arXiv 2023年

作者： Wang, Zi Yu, Xiaotong Wang, Chengyan Chen, Weibo Wang, Jiazheng Chu, Ying-Hua Sun, Hongwei Li, Rushuai Li, Peiyong Yang, Fan Han, Haiwei Kang, Taishan Lin, Jianzhong Yang, Chen Chang, Shufu Shi, Zhang Hua, Sha Li, Yan Hu, Juan Zhu, Liuhong Zhou, Jianjun Lin, Meijing Guo, Jiefeng Cai, Congbo Chen, Zhong Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Human Phenome Institute Fudan University China Philips Healthcare China Siemens Healthineers Ltd. China United Imaging Research Institute of Intelligent Imaging China Department of Nuclear Medicine Nanjing First Hospital China Shandong Aoxin Medical Technology Company China Department of Radiology The First Affiliated Hospital of Xiamen University China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University China China Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Department of Radiology Ruijin Hospital Shanghai Jiaotong University School of Medicine China Medical Imaging Department The First Affiliated Hospital of Kunming Medical University China Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China Department of Applied Marine Physics and Engineering Xiamen University China Department of Microelectronics and Integrated Circuit Xiamen University China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering Imperial College London United Kingdom

Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibility. The k-space undersampling offers a solution, yet the resultant artifacts necessitate meticulous removal during image reconstruction. Although Deep Learning (DL) has proven effective for fast MRI image reconstruction, its broader applicability across various imaging scenarios has been constrained. Challenges include the high cost and privacy restrictions associated with acquiring large-scale, diverse training data, coupled with the inherent difficulty of addressing mismatches between training and target data in existing DL methodologies. Here, we present a novel Physics-Informed Synthetic data learning framework for Fast MRI, called PISF. PISF marks a breakthrough by enabling generalized DL for multi-scenario MRI reconstruction through a single trained model. Our approach separates the reconstruction of a 2D image into many 1D basic problems, commencing with 1D data synthesis to facilitate generalization. We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%. Additionally, PISF exhibits remarkable generalizability across multiple vendors and imaging centers. Its adaptability to diverse patient populations has been validated through evaluations by ten experienced medical professionals. PISF presents a feasible and cost-effective way to significantly boost the widespread adoption of DL in various fast MRI applications. Importantly, it alleviates the ethical and practical challenges associated with in vivo human data acquisitions, opening new horizons for advancing fast MRI technologies. Copyright © 20

关键词： Magnetic resonance imaging

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AN-GCN: An anonymous graph convolutional network defend against edge-perturbing attacks

arXiv

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arXiv 2020年

作者： Liu, Ao Li, Beibei Li, Tao Zhou, Pan Wang, Rui The School of Cyber Science and Engineering Sichuan University Chengdu610065 China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Department of Intelligent Systems Delft University of Technology Delft2628XE Netherlands

Recent studies have revealed the vulnerability of graph convolutional networks (GCNs) to edge-perturbing attacks, such as maliciously inserting or deleting graph edges. However, a theoretical proof of such vulnerability remains a big challenge, and effective defense schemes are still open issues. In this paper, we first generalize the formulation of edge-perturbing attacks and strictly prove the vulnerability of GCNs to such attacks in node classification tasks. Following this, an anonymous graph convolutional network, named AN-GCN, is proposed to counter against edge-perturbing attacks. Specifically, we present a node localization theorem to demonstrate how the GCN locates nodes during its training phase. In addition, we design a staggered Gaussian noise based node position generator, and devise a spectral graph convolution based discriminator in detecting the generated node positions. Further, we give the optimization of the above generator and discriminator. AN-GCN can classify nodes without taking their position as input. It is demonstrated that the AN-GCN is secure against edge-perturbing attacks in node classification tasks, as AN-GCN classifies nodes without the edge information and thus makes it impossible for attackers to perturb edges anymore. Extensive evaluations demonstrated the effectiveness of the general edge-perturbing attack model in manipulating the classification results of the target nodes. More importantly, the proposed AN-GCN can achieve 82.7% in node classification accuracy without the edge-reading permission, which outperforms the state-of-the-art GCN. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Energy Saving Strategy of Power System Cluster Based on Container Virtualization

Energy Saving Strategy of Power System Cluster Based on Cont...

引用

IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Ran Zheng Hao Wang Hai Jin Dechao Xu Yong Chen Xiaomeng Li Yufei Rao Zhenan Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research Institute Haidian District Beijing China State Grid Henan Electric Power Company Electric Power Research Institute Zhengzhou China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

With the continuous development of power grids, the scale of supercomputing clusters has also gradually increased to carry a large number of power system simulation calculations, and the problem of high energy consumption has appeared. To solve this problem, we propose a container virtualization-based supercomputing cluster for power system. We analyze the impact of containers on power simulation calculations and compare the energy consumption effects of various container scheduling and migration algorithms on clusters. Experiments show that compared to virtual machines with hypervisor, which consumes massive resources and reduces performances by 28.4%, the performance degradation of container on power simulation calculation is 1.3%, which can be ignored. The energy consumption of load-concentration or resource-and-load-balance container scheduling algorithms is up to 4.0% lower and at least 2.2% lower than other algorithms. In container migration, the method combining autoregressive model with most-correlation and resource-andload-balance algorithms is better than other methods, which not only minimizes energy consumption, but also has lowest number of migrations and SLA violations. Experiments verify the feasibility and advantages of container migration in power system computing clusters.

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Probing Globular Cluster with MeerKAT and FAST: A Pulsar Polarization Census

arXiv

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arXiv 2025年

作者： Zhang, Lei Abbate, Federico Li, Di Possenti, Andrea Bailes, Matthew Ridolfi, Alessandro Freire, Paulo C.C. Ransom, Scott M. Zhang, Yong-Kun Guo, Meng Ni, Meng-Meng Hu, Jia-Le Feng, Yi Wang, Pei Zhang, Jie Zhi, Qi-Jun National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Centre for Astrophysics and Supercomputing Swinburne University of Technology VIC3122 Australia INAF Osservatorio Astronomico di Cagliari Via della Scienza 5 SelargiusI-09047 Italy Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Department of Astronomy Tsinghua University Beijing100190 China Swinburne University of Technology VIC3122 Australia Faculty of Physics University of Bielefeld Bielefeld33501 Germany National Radio Astronomy Observatory 520 Edgemont Road Charlottesville22903 United States National Supercomputing Center in Jinan Qilu University of Technology Jinan250103 China Jinan Institute of Supercomputing Technology 28666 East Jingshi Road Jinan250103 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Institute for Astronomy School of Physics Zhejiang University Hangzhou310027 China College of Physics and Electronic Engineering Qilu Normal University Jinan250200 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China

Only one globular cluster (GC), 47 Tuc, has been found to containintracluster medium, with an electron density 100 times higher than that of theISM in its vicinity. The characteristics of this intracluster medium areclosely related to GC evolution and the compact objects within. However,significant knowledge gaps remain regarding the ionized gas content of GCs,particularly in Galactic halo clusters. We carried out a polarization census ofGC pulsars using MeerKAT and FAST. This first combined effort of observationsfrom these two major radio telescopes resulted in high signal-to-noise ratio,full polarization pulse profiles for 43 pulsars in 8 GCs, doubling the numberof rotation measures (RMs) known in these clusters. The accuracy of dispersionmeasures (DMs) was improved by a factor of 8 compared to previous *** intracluster medium was found, and at least two halo GCs showed morestringent upper limits on electron density than that detected in 47 Tuc. Thesurprising barrenness of GCs suggests effective gas removal mechanisms, such asstrong winds from millisecond pulsars and/or ionizing radiation from post-AGBstars and young white dwarfs. Copyright © 2025, The Authors. All rights reserved.

关键词： White dwarfs

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Project duration-cost-quality prediction model based on Monte Carlo simulation

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Journal of Physics: Conference Series 2021年第1期1978卷

作者： Xingguang Chen Luqiang Cheng Guohua Deng Shuqi Guan Lewei Hu School of Business Jianghan University Wuhan 430056 China Institute of intelligent decision-making Jianghan University Wuhan 430056 China Big Data Research Center Manufacturing Industry Development Research Center on Wuhan City Circle Jianghan University Wuhan 430056 China

Based on the earned value management theory, the project duration and cost forecast data are obtained through Monte Carlo simulation, combined with the knowledge of mathematical statistics to in-depth analysis of the data, and then the project quality calculation is completed by establishing the quality correlation function. On the basis of existing scholars' research, at the process level, the management focus is identified by calculating process influence and quality, and at the project level, the operation of the entire project is simulated and predicted by the calculation of the total construction period, cost, and quality.

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TCIM: Triangle counting acceleration with processing-in-MRAM architecture

arXiv

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arXiv 2020年

作者： Wang, Xueyan Yang, Jianlei Zhao, Yinglin Qi, Yingjie Liu, Meichen Cheng, Xingzhou Jia, Xiaotao Chen, Xiaoming Qu, Gang Zhao, Weisheng Fert Beijing Research Institute School of Microelectronics Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering University of Maryland College ParkMD United States

Triangle counting (TC) is a fundamental problem in graph analysis and has found numerous applications, which motivates many TC acceleration solutions in the traditional computing platforms like GPU and FPGA. However, these approaches suffer from the bandwidth bottleneck because TC calculation involves a large amount of data transfers. In this paper, we propose to overcome this challenge by designing a TC accelerator utilizing the emerging processing-in-MRAM (PIM) architecture. The true innovation behind our approach is a novel method to perform TC with bitwise logic operations (such as AND), instead of the traditional approaches such as matrix computations. This enables the efficient in-memory implementations of TC computation, which we demonstrate in this paper with computational Spin-Transfer Torque Magnetic RAM (STT-MRAM) arrays. Furthermore, we develop customized graph slicing and mapping techniques to speed up the computation and reduce the energy consumption. We use a device-to-architecture co-simulation framework to validate our proposed TC accelerator. The results show that our data mapping strategy could reduce 99.99% of the computation and 72% of the memory WRITE operations. Compared with the existing GPU or FPGA accelerators, our in-memory accelerator achieves speedups of 9× and 23.4×, respectively, and a 20.6× energy efficiency improvement over the FPGA accelerator. Copyright © 2020, The Authors. All rights reserved.

关键词： data transfer

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