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IEEE International Conference on Systems, Man and Cybernetics

作者： Yue Zhou Zhigang Liu Xiaojiang Yu Yajuan Wu Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Computer School of China West Normal University Nanchong Sichuan China

A fast non-negative latent factor (FNLF) model adopts a single latent factor-dependent, non-negative, multiplicative and momentum-incorporated update (SLF-NM 2 U) algorithm, which can ensure fast convergence on a high-dimensional and sparse (HiDS) matrix according to empirical studies in prior researches. However, it is crucial to investigate the theoretical proof regarding the reason why incorporation of a generalized momentum method into an SLF-NM 2 U algorithm can ensure the fast convergence of an FNLF model, which has not been addressed in previous work. Therefore, this paper aims to unveil how a generalized momentum method improves the convergence rate of an FNLF model in the discrete time case by combining physical analysis. The FNLF model is superior to the NLF model in terms of the convergence rate and the prediction accuracy of missing data. This conclusion is obtained by empirically research on the HiDS matrix in industrial applications, which also provides empirical basis for theoretical proof.

关键词： Convergence Analytical models Sparse matrices data models Computational modeling Acceleration Friction

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Accelerated Latent Factor Analysis for Recommender Systems via PID Controller

Accelerated Latent Factor Analysis for Recommender Systems v...

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IEEE International Conference on Networking, Sensing and Control

作者： Jinli Li Xuke Wu Ye Yuan Yajuan Wu Kangkang Ma Yue Zhou Computer School China West Normal University Nanchong Sichuan China Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China School of information and software engineering University of Electronic Science and Technology of China Chengdu Sichuan China University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

High-dimensional and sparse (HiDS) matrices generated by recommender systems (RSs) contain rich knowledge. A latent factor (LF) model can address such data effectively. Stochastic gradient descent (SGD) is an efficient algorithm for building a LF model on an HiDS matrix. However, it suffers slow convergence. To address this issue, this study proposes to implement a LF model with a proportional integral derivative (PID) controller. The main idea is to continuously apply a correction for SGD to accelerate the training process. Based on such design, a PID-based LF (PLF) model is proposed. Empirical studies on two HiDS matrices from RSs indicate that a PLF model outperforms an LF model in terms of both convergence rate and prediction accuracy for missing data.

关键词： Training Predictive models data models Sparse matrices Acceleration Recommender systems Convergence

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Implementing the Matrix Multiplication with DFC on Kunlun Small Scale Computer

Implementing the Matrix Multiplication with DFC on Kunlun Sm...

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IEEE International Conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Zheng Du Jing Zhang Shihao Sha Qiuming Luo Department of Computer Science and Technology Tsinghua University NHPCC/Guangdong Key Laboratory of popular UPC & Guangdong Province Engineering Center of China-made High Performance Data Computing System in College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

In this paper, we demonstrate a new dataflow platform of DFC, which can handle the successive dataflow computing passes with tagged data. By implementing the matrix multiplication in DFC, we show that DFC can exploit the parallelism automatically with a much simple dataflow graph constructed by DF functions of DFC. Different from the other dataflow execution platform, DFC support multiple worker threads for one dataflow node of DF functions. By running the matrix multiplication program of DFC on Kunlun system, it was verified that DFC get a reasonable speedup for large scale computing for thread number up to 512.

关键词： Distributed computing Handheld computers

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Practical adaptive time-varying output formation tracking for high-order nonlinear multi-agent systems using neural networks

Practical adaptive time-varying output formation tracking fo...

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第三十八届中国控制会议

作者： Jianglong Yu Xiwang Dong Qingdong Li Zhang Ren Ming Ma School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Shananxi Key Laboratory of Integrated and Intelligent Navigation Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Xi'an Research Institute of Navigation Technology

Practical adaptive time-varying output formation tracking problems for high-order nonlinear strict-feedback multiagent systems are investigated in this paper. The time-varying output formation tracking requires the followers' output vectors to track the output vector of the leader while achieving a time-varying formation. The dynamics of the leader and the followers can have uncertain mismatched nonlinearities while the leader's control input is also unknown. To deal with the mismatched nonlinearities, the backstepping techniques are utilized to construct the protocols. Firstly, visual controllers are designed to overcome the effects of the uncertainties while guaranteeing the stability of each layer. The complicated visual controllers are estimated by the neural networks. A full adaptive protocol is constructed with adaptive gains to avoid using the global information of the interaction topology. Then, an algorithm is presented to design the protocol, whose parameters are much less. Besides, the stability of the algorithm is proved by Lyapunov theories. Finally, simulation results are presented to prove the effectiveness.

关键词： Practical time-varying formation tracking Adaptive control High-order nonlinear dynamics Multi-agent systems Neural networks

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Event phase oriented news summarization

Event phase oriented news summarization

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作者： Wang, Chengyu He, Xiaofeng Zhou, Aoying Shanghai Key Laboratory of Trustworthy Computing School of Computer Science and Software Engineering East China Normal University Shanghai200062 China School of Data Science and Engineering East China Normal University Shanghai200062 China

Event summarization is a task to generate a single, concise textual representation of an event. This task does not consider multiple development phases in an event. However, news articles related to long and complicated events often involve multiple phases. Thus, traditional approaches for event summarization generally have difficulty in capturing event phases in summarization effectively. In this paper, we define the task of Event Phase Oriented News Summarization (EPONS). In this approach, we assume that a summary contains multiple timelines, each corresponding to an event phase. We model the semantic relations of news articles via a graph model called Temporal Content Coherence Graph. A structural clustering algorithm EPCluster is designed to separate news articles into several groups corresponding to event phases. We apply a vertex-reinforced random walk to rank news articles. The ranking results are further used to create timelines. Extensive experiments conducted on multiple datasets show the effectiveness of our approach. © 2017, Springer Science+Business Media, LLC.

关键词： Clustering algorithms

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A Holistic Stream Partitioning Algorithm for Distributed Stream Processing Systems

A Holistic Stream Partitioning Algorithm for Distributed Str...

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IEEE International Conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Kejian Li Gang Liu Minhua Lu Guangdong Province Key Laboratory of Popular High Performance Computers Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of biomedical engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

The performances of modern distributed stream processing systems are critically affected by the distribution of the load across workers. Skewed data streams in real world are very common and pose a great challenge to these systems, especially for stateful applications. key splitting, which allows a single key to be routed to multiple workers, is a great idea to achieve good balance of load in the cluster. However, it comes with the cost of increased memory consumption and computation overhead as well as network communication. In this paper, we present a new definition of metric to model the cost of key splitting for intra-operator parallelism in stream processing systems and provide a novel perspective to reduce replication factor while keeping both overall load imbalance and processing latency low. Similar to previous work, our approach treats the head and the tail of the distribution differently in order to reduce memory requirements. For the head, it uses our proposed notion of regional load imbalance to decide dynamically whether to make one more worker responsible for the heavy hitter or not. For the tail, it simply uses hash partitioning to keep the size of the routing table for the head as small as possible. Extensive experimental evaluation demonstrates that our approach provides superior performance compared to the state-of-the-art partitioning algorithms in terms of load imbalance, replication factor and latency over different levels of skewed stream distributions.

关键词： Partitioning algorithms Memory management Measurement Parallel processing Microsoft Windows Load modeling Throughput

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Environmental, health and economic impact assessment of clean heating renovation: Evidence from 88 cities in northern China

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Environmental Impact Assessment Review 2025年 115卷

作者： Jingting Huang Huili Wang Zhaoyin Liu Li Zhang Hanyu Gao Zhe Wang Yixuan Zheng Xiaojun Chen Lin Zhao Shangmin Guo Bofeng Cai Linyan Li Department of Data Science College of Computing City University of Hong Kong Hong Kong SAR China Center for Carbon Neutrality Chinese Academy of Environmental Planning Beijing 100043 China School of the Environment and Safety Engineering Jiangsu University Zhenjiang 212013 China Department of Chemical and Biological Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong Department of Civil and Environmental Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong State Environmental Protection Key Laboratory of Environmental Pollution and Greenhouse Gases Co-control Chinese Academy of Environmental Planning Beijing 100043 China State Grid Liaoning Electric Power Company Limited Economic Research Institute Shenyang 110000 China Department of Infectious Diseases and Public Health Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong Hong Kong SAR China

To address environmental issues related to heating, the Chinese government launched the “Clean Heating Plan in the Northern Region” as a national energy transition strategy, renovating heating systems for over 30 million households in 88 cities. With the completion of five implementation phases by March 2024, this study presents an evaluation framework to comprehensively assess the environmental, health, and economic impacts of the policy. On-site surveys were conducted annually, monitoring the advancements of clean heating renovations in over 100,000 households across these cities. Combined with air pollution metrics, meteorological data, and city statistical records, a Difference-in-Differences (DID) model with continuous treatment was used to evaluate the environmental benefits associated with the incremental adoption of clean heating in each city. Subsequently, a health impact assessment and cost-benefit analysis were conducted to analyze changes in all-cause mortality and their associated economic benefits. Results indicated an average PM 2.5 reduction of 10.31 μg/m 3 (95 % CI: [9.25, 11.37]) per heating season and an annual reduction of 1573 in all-cause mortality (95 % CI: [630, 2518]). The economic benefits of these improvements were estimated at 717.1 billion CNY, contributing to an average benefit-cost ratio of 1.48. The proposed evaluation framework serves as a structured method for evaluating the environmental, health, and economic impacts of the energy policy and provides robust evidence to support implementing similar initiatives.

关键词： Cost benefit analysis

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Mapping Heavy Metals in Cultivated Soils Based on Land Use Types and Cokriging 5th

Mapping Heavy Metals in Cultivated Soils Based on Land Use T...

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5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017

作者： Zhao, Jinling Liu, Chuang Song, Qixiang Jiang, Yan Hong, Qi Huang, Linsheng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University Hefei230601 China Information Engineering Institute Suzhou University Suzhou234000 China

ISBN: (纸本)9789811308925

It is extremely important to explore the heavy metal content and spatial distribution in different cultivated soils. In our study, cokriging (COK) method was used to investigate the relationship between heavy metals and land use types. A total of six heavy metals including Zn, Fe, Cu, Mn, B, S were selected to forecast the heavy metals in cultivated soils. Five types of cultivated land were considered in the study area, including vegetable land, irrigated land, irrigated paddy field, dry land, orchard. Test of normality was firstly carried out to assure that higher prediction accuracy can be obtained, and then correlation analysis and analysis of variance (ANOVA) were performed to find out the most sensitive heavy metals for spatial interpolation. The analysis results showed that Zn, Cu and Fe were the primary heavy metals in soil of the study area. It was obvious that the effect of Mn and Fe were much greater caused by land use types, while they were smaller for B and S. Three errors of mean standard (MS), root mean square error (RMSE), and root mean square standardized error (RMSSE) were derived to assess the prediction accuracy. In comparison with ordinary kriging (OK), the MS of COK approached to 1, the RMSE was much smaller and the RMSSE also approached to 1, which showed that COK can make better predictions than OK. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Heavy metals

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Gut microbiota and diabetes: From correlation to causality and mechanism

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World Journal of Diabetes 2020年第7期11卷 293-308页

作者： Wei-Zheng Li Kyle Stirling Jun-Jie Yang Lei Zhang Microbiome-X Beijing Advanced Innovation Center for Big Data-Based Precision MedicineSchool of Medicine and EngineeringKey Laboratory of Big Data-Based Precision MedicineBeihang UniversityBeijing 100191China School of Biological Science and Medical Engineering Beihang UniversityBeijing 100191China Luddy School of Informatics Computing and EngineeringIndiana UniversityBloomingtonIN 47408United States The Crisis Technologies Innovation Lab Indiana UniversityThe Information Technology Services and the Pervasive Technology InstituteBloomingtonIN 47408United States Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine Jinan 250000Shandong ProvinceChina College of Life Science Qilu Normal UniversityJinan 250000Shandong ProvinceChina Microbiome Research Center Shandong Institutes for Food and Drug ControlJinan 250000Shandong ProvinceChina Shandong Children’s Microbiome Center Qilu Children's Hospital of Shandong UniversityJinan 250000Shandong ProvinceChina Microbiological Laboratory Lin Yi People’s HospitalLinyi 276000Shandong ProvinceChina Qingdao Human Microbiome Center The Affiliated Central Hospital of Qingdao UniversityQingdao 266000Shandong ProvinceChina Institute for Medical Dataology Cheeloo College of MedicineShandong UniversityJinan 250000Shandong ProvinceChina Department of Biostatistics School of Public HealthCheeloo College of MedicineShandong UniversityJinan 250000Shandong ProvinceChina Shandong Children’s Microbiome Center Research Institute of PediatricsQilu Children's HospitalCheeloo College of MedicineShandong Universityand Jinan Children's HospitalJinan 250022Shandong ProvinceChina

In this review,we summarize the recent microbiome studies related to diabetes disease and discuss the key findings that show the early emerging potential causal roles for *** a global scale,diabetes causes a significant negative impact to the health status of human *** review covers type 1 diabetes and type 2 *** examine promising studies which lead to a better understanding of the potential mechanism of microbiota in diabetes *** appears that the human oral and gut microbiota are deeply interdigitated with *** is that *** studies of the human microbiome are capturing the attention of scientists and healthcare practitioners worldwide by focusing on the interplay of gut microbiome and *** studies focus on the role and the potential impact of intestinal microflora in *** paint a clear picture of how strongly microbes are linked and associated,both positively and negatively,with the fundamental and essential parts of diabetes in *** microflora seems to have an endless capacity to impact and transform *** conclude that there is clear and growing evidence of a close relationship between the microbiota and diabetes and this is worthy of future investments and research efforts.

关键词： Diabetes Microbiota Causality Mechanism Type 1 diabetes Type 2 diabetes Insulin resistance Inflammation Metabolites

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Empirical topological investigation of practical supply chains based on complex networks

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Chinese Physics B 2017年第11期26卷 144-150页

作者：廖好沈婧吴兴桐陈博奎周明洋 National Engineering Laboratory for Big Data Computing Systems Guangdong Province Key Laboratory of Popular High Performance ComputersCollege of Computer Science and Software Engineering Shenzhen University Department of Computer Science School of Computing National University of Singapore

The industrial supply chain networks basically capture the circulation of social resource, dominating the stability and efficiency of the industrial system. In this paper, we provide an empirical study of the topology of smartphone supply chain network. The supply chain network is constructed using open online data. Our experimental results show that the smartphone supply chain network has small-world feature with scale-free degree distribution, in which a few high degree nodes play a key role in the function and can effectively reduce the communication cost. We also detect the community structure to find the basic functional unit. It shows that information communication between nodes is crucial to improve the resource utilization. We should pay attention to the global resource configuration for such electronic production management.

关键词： China supply chain networks complex networks data science network science

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