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Estimating the Dst index using machine learning from IMAGE/HENA observations

Advances in Space Research 2025年第6期75卷 4786-4794页

作者： Yan, Yuheng Yu, Xiancai Zhang, Xianguo Sun, Tianran Beijing Key Laboratory of Space Environment Exploration National Space Science Center Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Space Weather National Space Science Center CAS Beijing China

This study presents a novel approach to estimating the Disturbance Storm Time (Dst) index, a critical measure of geomagnetic storm activity, using Energetic Neutral Atom (ENA) observations from the High Energy Neutral Atom (HENA) imager on the IMAGE satellite. By applying machine learning techniques—specifically multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) to ENA data from 2000 to 2005, we developed models that achieve a root mean square error (RMSE) of 18.96 nT and a correlation coefficient (R) of 0.87 in predicting the Dst index. This method offers a new, global perspective on geomagnetic storm dynamics by leveraging satellite-based ENA data, enhancing spatial and temporal resolution beyond ground-based magnetometer measurements. These findings show the potential of machine learning in improving space weather forecasting and deepening our understanding of geomagnetic storm dynamics. © 2024

关键词： Convolutional neural networks

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Dynamic Graph Learning with Long and Short-Term for Multivariate Time Series Anomaly Detection 12

Dynamic Graph Learning with Long and Short-Term for Multivar...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: technology and Applications, IDAACS 2023

作者： Tian, Yuyin Gao, Rong Yan, Lingyu Liu, Donghua Ye, Zhiwei School of Computer Science Hubei University of Technology Wuhan430068 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China China Waterborne Transport Research Institue Information Center Beijing100080 China

ISBN: (纸本)9798350358056

Existing multivariate time series anomaly detection methods utilize graph neural networks to model inter-sensor dependencies and achieve satisfactory results. However, these methods often employ a predefined or self-learning static adjacency matrix for graph information aggregation, disregarding the dynamic characteristics of inter-sensor dependencies in the real world, which change over time and are observable at different time scales as dynamic changing graph structure information. Furthermore, existing techniques struggle to capture long-term temporal contextual relationships. To address these issues, we propose a Dynamic Graph Learning with Long and Short-term time model (DGL-LS) for multivariate time series anomaly detection. The network integrates a dynamic graph structure learning method among sensors in multi-time mode and long- and short-term temporal context information to enhance the prediction of multivariate time series anomalies. We propose a strategy for learning dynamic graphs that aggregates sensor node representations at various time intervals, and then uses an attention-based gating unit to evolve the node dynamic characterization and construct a series of adjacency matrices that describe the dynamic changes in graph structure over time. Moreover, we formulate a long-short time convolution to capture the long-term and short-term temporal dependencies within the time series. We demonstrate the superiority of our approach over other state-of-the-art methods by conducting extensive experiments on three publicly available datasets. © 2023 IEEE.

关键词： Anomaly Detection Graph Learning Multivariate Time Series Time Convolution

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Monotonicity and nonmonotonicity in L3-valued propositional logic

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Frontiers of computer science 2022年第4期16卷 33-43页

作者： Wei Li Yuefei Sui State Key Laboratory of Software Development Environment Beihang UniversityBeijing100083China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing100049China

A sequent is a pair (Γ, Δ), which is true under an assignment if either some formula in Γ is false, or some formula in Δ is true. In L_(3)-valued propositional logic, a multisequent is a triple Δ∣Θ∣Γ, which is true under an assignment if either some formula in Δ has truth-value t, or some formula in Θ has truth-value m, or some formula in Γ has truth-value f. There is a sound, complete and monotonic Gentzen deduction system G for sequents. Dually, there is a sound, complete and nonmonotonic Gentzen deduction system G′ for co-sequents Δ: Θ: Γ. By taking different quantifiers some or every, there are 8 kinds of definitions of validity of multisequent Δ∣Θ∣Γ and 8 kinds of definitions of validity of co-multisequent Δ: Θ: Γ, and correspondingly there are 8 sound and complete Gentzen deduction systems for sequents and 8 sound and complete Gentzen deduction systems for co-sequents. Correspondingly their monotonicity is discussed.

关键词： sequent mulisequent gentzen deduction system monotonicity nonmonotonicity

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MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework 24th

MARO: Enabling Full MPI Automatic Refactoring in DSL-Based ...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Lei, Tong Chen, Zongjing Che, Yonggang Xu, Chuanfu Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819615445

Currently, the landscape of computer hardware architecture presents the characteristics of heterogeneity and diversity, prompting widespread attention to cross-platform portable parallel programming techniques. Most existing portable programming approaches adopt the "MPI+X" strategy, focusing mainly on node-level "X" parallelism, while inter-node parallelization still relies on manual MPI programming. OP2 is a domain-specific language-based portable parallel programming framework for unstructured mesh applications that supports the automatic generation of MPI parallel code. However, programmers must manually write pre-processing code during code refactoring, such as handling the mapping relationship between global and process-local variables and the initial distribution of process-local data. In this paper, we propose MARO (MPI Automatic Refactoring for OP2), an automatic refactoring method for generating pre-processing code targeting MPI code generation within the OP2 programming framework for unstructured mesh applications. This method automatically generates the pre-processing code required for MPI parallelism through the source-to-source translator, enabling OP2 to generate MPI parallel code without requiring users to write MPI-related code manually. We compare the automatically refactored OP2 applications using the proposed method with those manually refactored to validate its effectiveness. The proposed method enhances OP2’s automatic parallel code generation capability, enabling it to achieve fully automatic MPI parallelization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automatic programming

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Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning

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Biophysics Reports 2023年第2期9卷 57-66页

作者： Peng Chen Zhenlei Li Zhaolin Hong Haoran Zheng Rong Zeng School of Computer Science and Technology University of Science and Technology of China Anhui Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Department of Systems Biology University of Science and Technology of China CAS Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences School of Life Science and Technology ShanghaiTech University

Identifying cancer-related differentially expressed genes provides significant information for diagnosing tumors, predicting prognoses, and effective treatments. Recently, deep learning methods have been used to perform gene differential expression analysis using microarray-based high-throughput gene profiling and have achieved good results. In this study, we proposed a new robust multiple-datasetsbased semi-supervised learning model, MSSL, to perform tumor type classification and candidate cancer-specific biomarkers discovery across multiple tumor types and multiple datasets, which addressed the following long-lasting obstacles:(1) the data volume of the existing single dataset is not enough to fully exert the advantages of deep learning;(2) a large number of datasets from different research institutions cannot be effectively used due to inconsistent internal variances and low quality;(3) relatively uncommon cancers have limited effects on deep learning methods. In our article, we applied MSSL to The Cancer Genome Atlas(TCGA) and the Gene Expression Comprehensive Database(GEO) pan-cancer normalized-level3 RNA-seq data and got 97.6% final classification accuracy, which had a significant performance leap compared with previous approaches. Finally, we got the ranking of the importance of the corresponding genes for each cancer type based on classification results and validated that the top genes selected in this way were biologically meaningful for corresponding tumors and some of them had been used as biomarkers, which showed the efficacy of our method.

关键词： Deep learning Tumor type classification Cancer-specific biomarkers MSSL

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keyword extraction method based on complex network

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International Journal of Information and Communication technology 2024年第4期25卷 323-335页

作者： Yang, Zhen Gong, Huajian Ma, Weiyun Yin, Chunlin Li, Jie Liu, Siyang Zhu, Hua Zhao, Na Electric Power Research Institute of Yunnan Power Grid Kunming650217 China School of Computer Science and Technology Xidian University Xian710000 China Key Laboratory in Software Engineering of Yunnan Province School of Software Yunnan University Kunming650091 China

keyword extraction has a wide range of applications in the field of natural language processing. Many research results on keyword extraction at present. Among them, keyword extraction methods are based on complex networks do not require a lot of data training in advance and are simple to implement. This paper analyses and realises the application of complex network theory in the Chinese news keyword extraction task. We collected and analysed 2,061 news texts from Sina website as experimental data, and found that the number of keywords extracted affected the performance of keyword extraction method. We also compared the performance of our algorithm with other keyword extraction algorithms. The experiments verify the accuracy and effectiveness of complex networks in keyword extraction of Chinese news texts. Based on PyQt and TextRank, a news text keyword analysis platform is constructed to realise the visualisation of text network and the extraction of Chinese news keywords Copyright © 2024 Inderscience Enterprises Ltd.

关键词： complex network keyword extraction text network TextRank

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RepKPU: Point Cloud Upsampling with Kernel Point Representation and Deformation

RepKPU: Point Cloud Upsampling with Kernel Point Representat...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yi Rong Haoran Zhou Kang Xia Cheng Mei Jiahao Wang Tong Lu State Key Laboratory for Novel Software Technology Nanjing University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this work, we present RepKPU, an efficient network for point cloud upsampling. We propose to promote upsampling performance by exploiting better shape representation and point generation strategy. Inspired by KPConv [47], we propose a novel representation called RepKPoints to effectively characterize the local geometry, whose advan-tages over prior representations are as follows: (1) density-sensitive; (2) large receptive fields; (3) position-adaptive, which makes RepKPoints a generalized form of previous representations. Moreover, we propose a novel paradigm, namely Kernel-to-Displacement generation, for point generation, where point cloud upsampling is reformulated as the deformation of kernel points. Specifically, we propose KP-Queries, which is a set of kernel points with predefined positions and learned features, to serve as the initial state of upsampling. Using cross-attention mechanisms, we achieve interactions between RepKPoints and KP-Queries, and subsequently KP-Queries are converted to displacement features, followed by a MLP to predict the new positions of KP-Queries which serve as the generated points. Extensive experimental results demonstrate that RepKPU outperforms state-of-the-art methods on several widely-used benchmark datasets with high efficiency. Codes will be available at https://***/EasyRy/RepKPU.

关键词： Point cloud compression Geometry computer vision Codes Deformation Shape Benchmark testing

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A Multiple-Path Learning Neural Network Model for Code Completion

A Multiple-Path Learning Neural Network Model for Code Compl...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Liu, Yi Liu, Jianxun Zhang, Xiangping Hu, Haize Hunan University of Science and Technology Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Xiangtan411201 China Hunan University of Humanities Science and Technology School of Informationn Loudi417000 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350304855

Code completion, which can accelerate the software development process and improve the quality of software products, is an essential part of today's integrated development environments. It has become an important research topic in the field of software engineering. Recent studies have shown that the method of code completion based on the Abstract Syntax Tree (AST) learns syntactic information about the code, which helps to improve the accuracy of code completion. However, when modeling neural networks for ASTs, the sequencing operation of nodes leads to the loss of their hierarchical structure information. Meanwhile, traditional neural networks cannot predict many Out-of-Vocabulary (OoV) words in the terminal node values of AST. To alleviate the above problem, in this paper, we propose a Multiple-Path Learning neural network model for code completion (MPL) based on an AST by learning from a large-scale corpus. In this model, multiple paths such as context path, root path, and terminal node path are established to understand different code features required for node prediction and improve code representation ability. Based on the principle of program local repeatability, it also adopts a replication mechanism to copy the appropriate OoV words from the local terminal node path as the prediction result, further improving the prediction accuracy. The experimental results show that the MPL model has better performance than existing methods on the code completion task. © 2023 IEEE.

关键词： Neural network models

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Causality-Aided Trade-Off Analysis for Machine Learning Fairness 23

Causality-Aided Trade-Off Analysis for Machine Learning Fair...

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Proceedings of the 38th IEEE/ACM International Conference on Automated software Engineering

作者： Zhenlan Ji Pingchuan Ma Shuai Wang Yanhui Li The Hong Kong University of Science and Technology State Key Laboratory for Novel Software Technology Nanjing University

ISBN: (纸本)9798350329964

There has been an increasing interest in enhancing the fairness of machine learning (ML). Despite the growing number of fairness-improving methods, we lack a systematic understanding of the trade-offs among factors considered in the ML pipeline when fairness-improving methods are applied. This understanding is essential for developers to make informed decisions regarding the provision of fair ML services. Nonetheless, it is extremely difficult to analyze the trade-offs when there are multiple fairness parameters and other crucial metrics involved, coupled, and even in conflict with one *** paper uses causality analysis as a principled method for analyzing trade-offs between fairness parameters and other crucial metrics in ML pipelines. To practically and effectively conduct causality analysis, we propose a set of domain-specific optimizations to facilitate accurate causal discovery and a unified, novel interface for trade-off analysis based on well-established causal inference methods. We conduct a comprehensive empirical study using three real-world datasets on a collection of widely-used fairness-improving techniques. Our study obtains actionable suggestions for users and developers of fair ML. We further demonstrate the versatile usage of our approach in selecting the optimal fairness-improving method, paving the way for more ethical and socially responsible AI technologies.

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WPIA:accelerating DNN warm-up in Web browsers by precompiling WebGL programs

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Frontiers of computer science 2024年第6期18卷 281-283页

作者： Deyu TIAN Yun MA Yudong HAN Qi YANG Haochen YANG Gang HUANG Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of EducationSchool of Computer SciencePeking UniversityBeijing 100871China Institute for Artificial Intelligence Peking UniversityBeijing 100871China National Key Laboratory of Data Space Technology and System Beijing 100195China

1 *** the advent of artificial intelligence,it is becoming increasingly common to leverage deep learning technologies to provide intelligent services in Web apps[1].Compared with traditional approaches where deep neural network(DNN)runs at cloud servers,DNN inference in Web browsers avoids serverconnections,boostingtheresponsivenesssand enhancing user privacy of Web apps[2].Typically,GPU acceleration is required during DNN inference,especially on enddevices.

关键词： Web browser WebGL

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