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5th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2024

作者： Yang, Jie Wu, Yu Chongqing University of Posts and Telecommunications College of Computer Science and Technology Chongqing China Chongqing University of Posts and Telecommunications Key Laboratory of Web Intelligence and Technology Chongqing China

ISBN: (纸本)9798350352719

The core of heterogeneous network embedding is to transform nodes and edges into low and dense vector representations while preserving the structural features and semantic information of the original network. The existing embedding methods based on European space can not store the hierarchical structure of the network, nor can capture the rich semantic information contained in the heterogeneous network. To improve the embedding quality of heterogeneous networks and reduce information loss, a hyperbolic space-based embedding method for heterogeneous networks is proposed. Firstly, a random walk guided by meta-path is used to obtain the node neighborhood information and semantic relation of the network. Then the heterogeneous network is embedded into hyperbolic space based on Poincare sphere model, and the Riemann stochastic gradient descent method is used to optimize the model. Experiments on multiple datasets show that the proposed method has better performance than other baseline methods, and preserves more complete network information with fewer dimensions of storage space. © 2024 IEEE.

关键词： Network embeddings

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Magnetic Activity and Parameters of 43 Flare Stars in the GWAC Archive

引用

Research in Astronomy and Astrophysics 2023年第1期23卷 171-183页

作者：李广伟吴潮周桂萍杨晨黎华丽陈洁辛立平王竞哈斯铁尔哈尔肯马超红蔡洪波韩旭辉黄垒卢晓猛白建迎张旭康郝新丽王玉祥戴子高梁恩维孟小峰魏建彦 Key laboratory of Space Astronomy and Technology National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China Key laboratory of Solar Activity National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China Lab of Web and Mobile Data Management Renmin University of ChinaBeijing 100872China Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China School of Artificial Intelligence Beijing Normal UniversityBeijing100875China School of Astronomy and Space Science Nanjing UniversityNanjing 210093China Key Laboratory of Modern Astronomy and Astrophysics(Nanjing University) Ministry of EducationNanjing 210093China

In the archive of the Ground Wide Angle Camera(GWAC),we found 43 white light flares from 43 stars,among which,three are sympathetic or homologous flares,and one of them also has a quasi-periodic pulsation with a period of 13.0±1.5 *** these 43 flare stars,there are 19 new active stars and 41 stars that have available TESS and/or K2 light curves,from which we found 931 stellar *** also obtained rotational or orbital periods of 34 GWAC flare stars,of which 33 are less than 5.4 days,and ephemerides of three eclipsing binaries from these light *** with low resolution spectra from LAMOST and the Xinglong 2.16 m telescope,we found that L_(Hα)/L_(bol) are in the saturation region in the rotation-activity *** the LAMOST medium-resolution spectrum,we found that Star#3(HAT 178-02667)has double Hαemissions which imply it is a binary,and two components are both active *** stars have flare frequency distributions(FFDs)from TESS and/or K2 light *** FFDs show that the flares detected by GWAC can occur at a frequency of 0.5to 9.5 yr^(-1).The impact of flares on habitable planets was also studied based on these FFDs,and flares from some GWAC flare stars may produce enough energetic flares to destroy ozone layers,but none can trigger prebiotic chemistry on their habitable planets.

关键词： (stars:)binaries eclipsing-stars flare-stars low-mass-stars rotation

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Heterogeneous Network Embedding Method Based on Hyperbolic Space

Heterogeneous Network Embedding Method Based on Hyperbolic S...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Jie Yang Yu Wu College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Web Intelligence and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

关键词： Information science Semantics Stochastic processes Europe Distributed databases Transforms Heterogeneous networks

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Adaptive Guidance for Local Training in Heterogeneous Federated Learning

arXiv

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arXiv 2024年

作者： Zhang, Jianqing Liu, Yang Hua, Yang Cao, Jian Yang, Qiang Shanghai Jiao Tong University China Tsinghua University China Shanghai Artificial Intelligence Laboratory China Queen’s University Belfast United Kingdom Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Hong Kong University of Science and Technology Hong Kong

Model heterogeneity poses a significant challenge in Heterogeneous Federated Learning (HtFL). In scenarios with diverse model architectures, directly aggregating model parameters is impractical, leading HtFL methods to incorporate an extra objective alongside the original local objective on each client to facilitate collaboration. However, this often results in a mismatch between the extra and local objectives. To resolve this, we propose Federated Learning-to-Guide (FedL2G1), a method that adaptively learns to guide local training in a federated manner, ensuring the added objective aligns with each client’s original goal. With theoretical guarantees, FedL2G utilizes only first-order derivatives w.r.t. model parameters, achieving a non-convex convergence rate of O(1/T). We conduct extensive experiments across two data heterogeneity and six model heterogeneity settings, using 14 heterogeneous model architectures (e.g., CNNs and ViTs). The results show that FedL2G significantly outperforms seven state-of-the-art methods. © 2024, CC BY-NC-ND.

关键词： Federated learning

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Magnetic activities and parameters of 43 flare stars in the GWAC archive

arXiv

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arXiv 2022年

作者： Li, Guang-Wei Wu, Chao Zhou, Gui-Ping Yang, Chen Li, Hua-Li Chen, Jie Xin, Li-Ping Wang, Jing Haerken, Hasitieer Ma, Chao-Hong Cai, Hong-Bo Han, Xu-Hui Huang, Lei Lu, Xiao-Meng Bai, Jian-Ying Zhang, Xu-Kang Hao, Xin-Li Wang, Xiang-Yu Dai, Zi-Gao Liang, En-Wei Meng, Xiao-Feng Wei, Jian-Yan Key laboratory of Space Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Key laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Lab of Web and Mobile Data Management Renmin University of China Beijing100872 China Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and Technology Guangxi University Nanning530004 China School of Artificial Intelligence of Beijing Normal University No.19 Xinjiekouwai St Haidian District Beijing100875 China School of Astronomy and Space Science Nanjing University Nanjing210093 China Ministry of Education Nanjing210093 China

In the archive of the Ground Wide Angle Camera (GWAC), we found 43 white light flares from 43 stars, among which, three are sympathetic or homologous flares, and one of them also has a quasi-periodic pulsation with a period of 13.0 ± 1.5 minutes. Among these 43 flare stars, there are 19 new active stars and 41 stars that have available TESS and/or K2 light curves, from which we found 931 stellar flares. We also obtained rotational or orbital periods of 34 GWAC flare stars, of which 33 are less than 5.4 days, and ephemerides of three eclipsing binaries from these light curves. Combining with low resolution spectra from LAMOST and the Xinglong 2.16m telescope, we found that LHα/Lbol are in the saturation region in the rotation-activity diagram. From the LAMOST medium-resolution spectrum, we two components are both active stars. Thirteen stars have flare frequency distributions (FFDs) from TESS and/or K2 light curves. These FFDs show that the flares detected by GWAC can occur at a frequency of 0.5 to 9.5 yr−1. The impact of flares on habitable planets was also studied based on these FFDs, and flares from some GWAC flare stars may produce enough energetic flares to destroy ozone layers, but none can trigger prebiotic chemistry on their habitable planets. © 2022, CC0.

关键词： Planets

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NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

arXiv

引用

arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation

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