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The Impact of Medical Resources and Oral Antiviral Drugs on SARS-CoV-2 Mortality—Hong Kong SAR,China,2022

China CDC weekly 2024年第21期6卷 469-477,I0001-I0005页

作者： Zhiqi Zeng Jingyi Liang Zhijie Lin Wenda Guan Wei He Qianying Li Lixi Liang Weiqi Pan Zige Liu Guibin Lu Xiaoyan Deng Eric HY Lau Chitin Hon Zifeng Yang Guangzhou key laboratory for clinical rapid diagnosis and early warning of infectious diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao SARChina Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovative EngineeringMacao University of Science and TechnologyMacao SARChina State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory Healththe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina School of Public Health The University of Hong KongHong Kong SARChina Laboratory of Data Discovery for Health Hong Kong SARChina Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina

Introduction:The Omicron variant of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)demonstrates increased transmissibility compared to earlier strains,contributing to a significant number of fatalities in Hong Kong Special Administrative Region(HKSAR),*** medical resources and medications are essential in mitigating these *** study evaluates the effects of supplementary resources from the Chinese mainland during the fifth wave of the pandemic in ***:Vector autoregression(VAR)was employed to analyze data from the Oxford coronavirus disease 2019(COVID-19)Government Response Tracker to assess the effectiveness of control measures during five waves of the pandemic in ***,a transmission dynamics model was created to investigate the influence of supplementary medical resources from the Chinese mainland and oral medications on ***:In the initial four waves,workplace closures,restrictions on public events,international travel bans,and shielding the elderly significantly influenced pandemic ***,during the fifth wave,these measures showed no notable *** comparing a situation without extra medical resources or COVID-19 oral medication,there was a 17.7%decrease in COVID-19 fatalities with mainland medical resources and an additional 10.2%reduction with oral ***,they contributed to a 26.6%decline in ***:With the rapid spread of the virus,regional reallocation of medical resources may reduce mortality even when the local healthcare system is overstretched.

关键词： mainland Impact travel

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SC-GNN: A Communication-Efficient Semantic Compression for Distributed Training of GNNs 24

SC-GNN: A Communication-Efficient Semantic Compression for D...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wang, Jihe Wu, Ying Wang, Danghui School of Computer Science Northwestern Polytechnical University Xi'an China Engineering Research Center of Embedded System Integration Ministry of Education Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798400706011

Training big graph neural networks (GNNs) in distributed systems is quite time-consuming mainly because of the ubiquitous aggregate operations that involve a large amount of cross-partition communication for collecting embeddings/gradients during the forward and backward propagation. To reduce the volume of the communication, some recent approaches focused on decaying each of connections via sampling, quantifying, or delaying until satisfactory trade-off are obtained between volume and accuracy. However, when applied to popular GNNs, those approaches are found to be bounded by a common volume/accuracy Pareto frontier which shows that the decaying for individual connection cannot further accelerate the aggregate of training. In this work, SC-GNN, a semantic compression of the cross-partition communication, is proposed to concentrate a group of connections as a high-level semantics and transmit to a target partition. Since carrying the overall intent of a group, the semantics can keep transferring the interactions, i.e., embeddings/gradients, between a pair of remote partitions until GNN models converge. In addition, a connection-pattern based differential optimization is proposed to further prune those weak connections, while guaranteeing the training accuracy. The results show that, for multi-field datasets, the compression rate of SC-GNN is 40.8 × higher than SOTA methods and the epoch time is reduced to 31.8% on average. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graph neural networks

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Automatic Life Event Tree Generation for Older Adults 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： Gui, Fang Wu, Xi Hu, Min Yang, Jiaoyun School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Studies have shown that learning personal stories could help provide individualized eldercare services. However, personal stories are often disordered because of the scattered collection, including informal interviews or daily interactions, which brings difficulties in acquiring valuable information quickly. One solution to this problem is to extract events from personal stories and automatically organize them in chronological order. Events extracted by current methods from social media or news corpus are mainly organized in a linear structure. These works usually focus on the event time and ignore the consistency of event contents when organizing events. This paper aims to organize events into a tree structure based on an event network, with stem nodes representing key event topics and branch nodes representing detailed events. Social workers or caregivers can clarify the life experience of the older adults quickly through the event tree and have a preliminary understanding of them. The experiments show that the event tree generated by our method has a better performance in consistency than current event organization methods. A survey study shows that our method achieves the highest logical coherence for the event tree branches compared with other algorithms. © 2022, Springer Nature Switzerland AG.

关键词： Trees (mathematics)

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Relaxed 2-D principal component analysis by Lpnorm for face recognition

arXiv

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arXiv 2019年

作者： Chen, Xiao Jia, Zhi-Gang Cai, Yunfeng Zhao, Mei-Xiang School of Mathematics and Statistics Jiangsu Key Laboratory of Education Big Data Science and Engineering Jiangsu Normal University Xuzhou221116 China Baidu Research National Engineering Laboratory for Deep Learning Technology and Applications Beijing100193 China

A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition. Different to the 2DPCA, 2DPCA-L1 and G2DPCA, the R2DPCA utilizes the label information (if known) of training samples to calculate a relaxation vector and presents a weight to each subset of training data. A new relaxed scatter matrix is defined and the computed projection axes are able to increase the accuracy of face recognition. The optimal Lp-norms are selected in a reasonable range. Numerical experiments on practical face databased indicate that the R2DPCA has high generalization ability and can achieve a higher recognition rate than state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

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Cryptanalysis of a Pairing-Based Anonymous Key Agreement Scheme for Smart Grid 15th

Cryptanalysis of a Pairing-Based Anonymous Key Agreement Sch...

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15th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2019, held in conjunction with the 12th International Conference on Frontiers of Information technology, applications and Tools, FITAT 2019

作者： Liang, Xiao-Cong Wu, Tsu-Yang Lee, Yu-Qi Chen, Chien-Ming Yeh, Jyh-Haw Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science Boise State University Idaho United States

ISBN: (纸本)9789811397134

Authentication and key agreement schemes in smart grid have received much attentions by researchers in recent years. Recently, Mahmood et al. proposed a secure pairing-based anonymous key exchange scheme in smart grid. However, we demonstrate that their scheme is insecure against an ephemeral secret leakage attack and an impersonation attack according to the Canetti-Krawczyk adversarial model in this paper. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Smart power grids

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An Optimal Power System Multi-objective Environmental-Economic Planning Based on Moth-Flame Optimization 1

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Chu, Shu-Chuan Nguyen, Trong-The Nguyen, Vinh-Tiep Nguyen, Trinh-Dong Dao, Thi-Kien College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

A power system’s optimization under economic planning concerns scheduling sectors as a challenging reality task with its characteristics with non-convex, high-dimensional, nonlinear problems. This study introduces an optimal power system environmental and economic scheduling based on a multi-moth-flame optimizer (MMFO). A multi-objective programming mathematical model uses the Pareto optimal frontier to obtain the Pareto non-inferiority optimal solution, generate the optimal configuration scheme, and search for the optimal global solution in the feasible region. Calculation results of a power system with IEEE test case of 15 and 40 generator sets demonstrate the high convergence and strong adaptability of the algorithm suggested in addressing the environmental-economic dispatch of the power system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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data Processing technology for Network Abnormal Traffic Detection 4

Data Processing Technology for Network Abnormal Traffic Dete...

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4th International Conference on Machine Learning and big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Wang, Kun Fu, Yu Duan, Xueyuan Xu, Jianqiao Liu, Taotao Department of Information Security Naval University of Engineering Wuhan430033 China School of Mathematics and Information Engineering Xinyang Vocational and Technical College Xinyang464000 China College of Computer and Information Technology Xinyang Normal University Xinyang464000 China Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang464000 China

At present, the threats to network security are also increasing, among which abnormal traffic detection is the key link to ensure network security. Traditional detection methods based on signature or threshold are often difficult to adapt to the increasingly complex network environment and new attack methods. Therefore, this paper optimizes and improves the data processing technology, proposes a network ATD method based on particle swarm optimization(PSO) algorithm, and explores in detail the traffic data collection and pre-processing, the feature recognition of abnormal traffic, the application of PSO algorithm, real-time monitoring and response mechanism. The results of two sets of simulation experiments are as follows: compared with the traditional model, the accuracy rate of ATD of the improved algorithm is increased by 7.2% on average, and the detection time is reduced by 7.35s on average. This method not only enhances the adaptability of the model to new attacks, but also improves the degree of automation of detection. © 2024 The Authors.

关键词： Particle swarm optimization (PSO)

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Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multimodal data

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Signal Transduction and Targeted Therapy 2024年第9期9卷 4137-4148页

作者： Zifan Chen Yang Chen Yu Sun Lei Tang Li Zhang Yajie Hu Meng He Zhiwei Li Siyuan Cheng Jiajia Yuan Zhenghang Wang Yakun Wang Jie Zhao Jifang Gong Liying Zhao Baoshan Cao Guoxin Li Xiaotian Zhang Bin Dong Lin Shen Center for Data Science Peking UniversityBeijingChina Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Pathology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Department of General Surgery Nanfang HospitalSouthern Medical UniversityGuangzhouChina Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor GuangzhouChina Department of Medical Oncology and Radiation Sickness Peking University Third HospitalBeijingChina National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Beijing International Center for Mathematical Research(BICMR) Peking UniversityBeijingChina Center for Machine Learning Research Peking UniversityBeijingChina

The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the treatment of HER2-positive gastric cancer(GC).This modality deficit has not been fully considered in many ***,the application of artificial intelligence in predicting the treatment response,particularly in complex diseases such as GC,is still in its ***,this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive *** collected multi-modal data,comprising radiology,pathology,and clinical information from a cohort of 429 patients:310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors *** introduced a deep learning model,called the Multi-Modal model(MuMo),that integrates these data to make precise treatment response *** achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined ***,patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival(log-rank test,P<0.05).These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer,but also the potential and clinical value of our model.

关键词： immunotherapy HER2 cancer

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Solar PV Power Forecasting Approach Based on Hybrid Deep Neural Network 6th

Solar PV Power Forecasting Approach Based on Hybrid Deep Neu...

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6th International Conference on Advanced Machine Learning Technologies and applications, AMLTA 2021

作者： Chang, Kuo-Chi Omer, Abdalaziz Altayeb Ibrahim Chu, Kai-Chun Chang, Fu-Hsiang Wang, Hsiao-Chuan Lin, Yuh-Chung Nguyen, Trong-The Sung, Tien-Wen Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China School of Information Science and Engineering Fujian University of Technology Fuzhou China School of Management Fujian University of Technology Fuzhou China Department of Tourism Shih-Hsin University Taipei Taiwan Institute of Environmental Engineering National Taiwan University Taipei Taiwan College of Mechanical & Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9783030697167

There are incredibly renewable outlets such as solar and wind intermittent, and machine reliability is hard to sustain with the intolerable balance of green energy resources. Because of population growth and technological development, global energy demand is increasing exponentially. However, civilization must address a stable energy source for two benefits, a cost-effective and sustainable basis for future renewable energy production. Solar Energy is a platform for green energy and is a potential means of solving problems that face a possible energy system. The power output is primarily determined by the properties of the incoming radiation and solar panels. Precise and accurate knowledge of these variables provides a consistent model for predicting the solar future. Solar power forecasts would have a significant effect on the viability of massive solar energy projects. This study presents a new approach to predict the expected amount of energy to be produced from Photovoltaics using a hybrid deep neural network based on a combination of extended short-term memory networks (LSTM) and convolutional neural networks (CNN), taking into account the energy of electrons and photons as well as elements and climatic conditions as input data. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Solar energy

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Research on the Welding System of Soft Pack Power Battery Tabs Based on Laser Sensor 6th

Research on the Welding System of Soft Pack Power Battery Ta...

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6th International Conference on Advanced Machine Learning Technologies and applications, AMLTA 2021

作者： Huang, Jing Lin, Zhe Wang, Zezhou Qi, Yanfeng Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Province Industrial Integrated Automation Industry Technology Development Base Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9783030697167

The soft pack power battery’s lug is prone to welding through and missing welding during the welding process due to its low flatness. A welding track detection system for the soft-pack power battery tabs is designed to solve the soft-pack power battery tabs’ welding difficulties. The system uses a laser sensor to perform non-contact detection on the welding surface of the tab. The system uses a laser sensor to perform non-contact detection on the welding surface of the lug. Then filter the collected data, and perform a non-uniform B-spline curve-fitting on the processed data to generate a welding track curve. Finally, welding the tabs according to the welding track curve can improve the tabs’ welding quality. The experimental results show that the average deviation between the fitted curve and the measured data points is 0.0817 mm, which has a good fitting effect and high calculation accuracy. It can be used on other occasions where this function is needed. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Curve fitting

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