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arXiv 2019年

作者： Zhong, Yi Chen, Xueyu Zhao, Yu Chen, Xiaoming Gao, Tingfang Weng, Zuquan College of Biological Science and Engineering Fuzhou University Fujian province China Centre for Big Data Research in Burns and Trauma College of Mathematics and Computer Science Fuzhou University Fujian province China School of Software Shandong University Shandong Province China

We propose an end-to-end model to predict drug-drug interactions (DDIs) by employing graph-augmented convolutional networks. And this is implemented by combining graph convolutional neural network with an attentive pooling network to extract structural relations between drug pairs and make DDI predictions. The experiment results suggest a desirable performance achieving ROC at 0.988, F1-score at 0.956, and AUPR at 0.986. Besides, the model can tell how the two DDI drugs interact structurally by varying colored atoms. And this may be helpful for drug design during drug discovery. Copyright © 2019, The Authors. All rights reserved.

关键词： Drug interactions

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Universal Sub-Nanoreactor Strategy for Synthesis of Yolk-Shell MoS2Supported Single Atom Electrocatalysts toward Robust Hydrogen Evolution Reaction

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Angewandte Chemie 2023年第40期135卷

作者： Feilong Gong Yuheng Liu Yang Zhao Prof. Wei Liu Prof. Guang Zeng Prof. Guoqing Wang Prof. Yonghui Zhang Lihua Gong Prof. Jian Liu Key Laboratory of Surface and Interface Science and Technology of Henan Province College of Material and Chemical Engineering Zhengzhou University of Light Industry Zhengzhou Henan 450001 P. R. China Contribution: Conceptualization (equal) Formal analysis (equal) Funding acquisition (lead) Investigation (lead) Visualization (equal) Writing - original draft (lead) Contribution: Data curation (lead) Formal analysis (equal) Investigation (equal) Validation (equal) Visualization (equal) State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian Liaoning 116023 P. R. China Contribution: Data curation (equal) Formal analysis (equal) Resources (equal) Validation (equal) Visualization (equal) Contribution: Data curation (equal) Formal analysis (equal) Investigation (equal) Contribution: Data curation (equal) Methodology (equal) Software (equal) Validation (equal) Contribution: Formal analysis (equal) Software (equal) Writing - review & editing (equal) Contribution: Conceptualization (equal) Data curation (equal) Formal analysis (lead) Software (lead) Writing - review & editing (equal) DICP-Surrey Joint Centre for Future Materials Department of Chemical and Process Engineering and Advanced Technology Institute of University of Surrey Guildford Surrey GU2 7XH UK College of Chemistry and Chemical Engineering Inner Mongolia University Hohhot Inner Mongolia 010021 P. R. China Contribution: Conceptualization (lead) Formal analysis (equal) Funding acquisition (equal) Project administration (lead) Supervision (lead) Writing - review & editing (lead)

The coordination structure determines the electrocatalytic performances of single atom catalysts (SACs), while it remains a challenge to precisely regulate their spatial location and coordination environment. Herein, we report a universal sub-nanoreactor strategy for synthesis of yolk-shell MoS 2 supported single atom electrocatalysts with dual-anchored microenvironment of vacancy-enriched MoS 2 and intercalation carbon toward robust hydrogen-evolution reaction. Theoretical calculations reveal that the “E-Lock” and “E-Channel” are conducive to stabilize and activate metal single atoms. A group of SACs is subsequently produced with the assistance of sulfur vacancy and intercalation carbon in the yolk-shell sub-nanoreactor. The optimized C−Co−MoS 2 yields the lowest overpotential (η 10 =17 mV) compared with previously reported MoS 2 -based electrocatalysts to date, and also affords a 5–9 fold improvement in activity even comparing with those as-prepared single-anchored analogues. Theoretical results and in situ characterizations unveil its active center and durability. This work provides a universal pathway to design efficient catalysts for electro-refinery.

关键词： Electrocatalyst Nanostructures Sub-Nanoreactor Universal Strategy Water Splitting

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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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Learning the optimally coordinated routes from the statistical mechanics of polymers

arXiv

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arXiv 2019年

作者： Liao, Hao Wu, Xingtong Zhou, Mingyang Yeung, Chi Ho National Engineering Laboratory for big data computing systems Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Department of Science and Environmental Studies Education University of Hong Kong Hong Kong

Many major cities suffer from severe traffic congestion. Road expansion in the cites is usually infeasible, and an alternative way to alleviate traffic congestion is to coordinate the route of vehicles. Various path selection and planning algorithms are thus proposed, but most existing methods only plan paths separately and provide un-coordinated solutions. Recently, an analogy between the coordination of vehicular routes and the interaction of polymers is drawn;the spin glass theory in statistical physics is employed to optimally coordinate transportation routes. To further examine the advantages brought by path coordination, we incorporate the link congestion function developed by the Bureau of Public Roads (BPR) into the polymer routing algorithm. We then estimate in simulations the traveling time of all users saved by the polymer-BPR algorithm in randomly generated networks and real transportation networks in major cities including London, New York and Beijing. We found that a large amount of traveling time is saved in all studied networks, suggesting that the approach inspired by polymer physics is effective in minimizing the traveling time via path coordination, which is a promising tool for alleviating traffic congestions. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistical mechanics

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Effects of atrazine on the black community land function and role of microbial ecology

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IOP Conference Series: Earth and Environmental science 2020年第3期446卷

作者： Yaosheng Zhang Long Tian Ruixi Ma Yanxiang Wei Jiali Yang YanBin Sun Lan Li College of Science College of Life Science Jiamusi University Jiamusi City Heilongjiang Province 154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province 543000 China

By measuring the concentration of atrazine stress and different culture days, the abundance of key genes in soil nitrogen transformation was studied by means of real-time PCR with the increase of atrazine stress time. The abundance of soil nitrogen-fixing function gene (nif-H) increased significantly with the increase of time in the middle and late culture period (15-30 d), indicating that the abundance of soil nitrogen-fixing function gene was promoted; soil ammonia oxidation. The abundance of functional gene (amo A) was reduced at the low concentration of atrazine (0 mg/kg, 20 mg/kg), and then decreased with increasing stress time, while at high concentration of atrazine. Under the treatment of (100mg/kg), with the increase of time, the abundance of soil ammonia oxidation function increased first and then decreased, indicating that the abundance of soil ammonia oxidation function gene was affected in the process of culture. II Based on high-throughput sequencing technology, the results of soil nitrogen fixation and ammonia oxidation community diversity showed that the functional gene diversity of soil samples was affected by the concentration of atrazine and stress time. According to the analysis of soil diversity index, nitrogen-fixing functional genes of soil samples treated with atrazine at 0 mg/kg, 20 mg/kg and 100 mg/kg were tested at the late stage (30 d). The diversity index of (nif-H) was higher than the mid-test (15 d). At the beginning of the experiment (0 d), the ammonia functional gene (amo A) diversity index of soil samples treated with atrazine concentrations of 0 mg/kg, 20 mg/kg and 100 mg/kg was higher than that in the middle of the experiment (15d), studies have shown that the nitrogen and ammonia oxidation functional gene diversity is affected under different concentrations of atrazine stress time. The distribution of the nitrogen-fixing gene (nif-H) and the ammonia-oxidizing functional gene (amo A) in the horizontal sample community structure sh

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Impact of Garden Plant Resources Allocation on Ecological Landscape in Cold Cities

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IOP Conference Series: Earth and Environmental science 2020年第3期446卷

作者： Tianqi Yang Ye Gu Yanbin Sun Changbao Wang Haitao Cheng Shengju Li Lan Li College of Science College of Life Science Jiamusi University Jiamusi City Heilongjiang Province 154007 China School of Data Science and Software Engineering Wuzhou University Wuzhou City Guangxi Province 543000 China

This paper analyzes the distribution of plant resources in the test plots, and aims to optimize the allocation of plants and improve the follow-up construction plan for the cold land ecological park. We provide basis and reference for plant species selection and diversification in the construction of cold land urban Greenland plant landscape, and further improve the urban environment. Taking Jiamusi City as an example, the plant species, ecological distribution and utilization of plant resources in Jiamusi City Park were investigated and investigated by sample method and field investigation method. The data were analyzed by the analytic hierarchy process, and the ornamental value of the regional plants was analyzed. According to the survey, there are 41 species of plants in the park in Jiamusi City, belonging to 15 families and 35 genera. Their use involves three aspects, such as edible medicinal, and has high application value. As a typical cold city, Jiamusi proposes to consider the concept of ecological balance and sustainable development while considering the ornamental nature of plant resources, to play a role in supporting the image of the city.

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Construction and optimization of a file distribution model for all-to-all comparison of big dataset

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Ingenierie des Systemes d'Information 2019年第3期24卷 337-342页

作者： Li, Leixiao Gao, Jing Wang, Hui Deng, Dan Lin, Hao College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China Inner Mongolia Autonomous Region Key Laboratory of big data research and application for agriculture and animal husbandry Hohhot010018 China

This paper probes into the all-to-all comparison of large dataset, and gives a formal mathematical description of the problem. Then, a multi-objective file distribution model was constructed based on the LP, aiming to localize the data, balance node storage and loads, minimize the storage occupation, and control the occupied storage within the storage limit of each node. To save storage space, the established model was further optimized, and the file distribution algorithm was designed for the distributed environment. Experimental results show that our model and algorithm successfully balanced the storage occupation and loads between computing nodes, and minimized the occupation of node storage. © 2019 International Information and engineering Technology Association. All rights reserved.

关键词： Linear programming

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Deep Anomaly Detection for Time-series data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach

arXiv

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arXiv 2020年

作者： Liu, Yi Garg, Sahil Nie, Jiangtian Zhang, Yang Xiong, Zehui Kang, Jiawen Shamim Hossain, M. School of Data Science and Technology Heilongjiang University Harbin China Electrical Engineering Department École de technologie supérieure Uni-versité du Québec Montréal Canada Energy Research Institute Interdisciplinary Graduate Programme School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Technology Wuhan University of Technology China Alibaba-NTU Joint Research Institute School of Computer Science and Engineering NTU Singapore Singapore Energy Research Institute Nanyang Technological University Singapore Department of Software Engineering College of Computer and Information Sciences King Saud University Saudi Arabia

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, data collected by the edge device may contain the user’s private data, which is challenging the current detection approaches as user privacy is calling for the public concern in recent years. With this focus, this paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce a FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based CNN units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of LSTM unit in predicting time series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-k selection to improve communication efficiency. Extensive experiment studies on four real-world datasets demonstrate that the proposed framework can accurately and timely detect anomalies and also reduce the communication overhead by 50% compared to the federated learning framework that does not use gradient compression scheme. Copyright © 2020, The Authors. All rights reserved.

关键词： Anomaly detection

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Recommending more suitable music based on users’ real context 14th

Recommending more suitable music based on users’ real conte...

引用

14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Yang, Qing Zhan, Le Han, Li Zhang, Jingwei Bi, Zhongqin School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China College of Computer Science and Technology Shanghai University of Electronic Power Shanghai200090 China

ISBN: (纸本)9783030129804

Music recommendation is an popular function for personalized services and smart applications since it focuses on discovering users’ leisure preference. The traditional music recommendation strategy captured users’ music preference by analyzing their historical behaviors to conduct personalized recommendation. However, users’ current states, such as in busy working or in a leisure travel, etc., have an important influence on their music enjoyment. Usually, those existing methods only focus on pushing their favorite music to users, which may be not the most suitable for current scenarios. Users’ current states should be taken into account to make more perfect music recommendation. Considering the above problem, this paper proposes a music recommendation method by considering both users’ current states and their historical behaviors. First, a feature selection process based on ReliefF method is applied to discover the optimal features for the following recommendation. Second, we construct different feature groups according to the feature weights and introduce Naive Bayes model and Adaboost algorithm to train these feature groups, which will output a base classifier for each feature group. Finally, a majority voting strategy decides the optimal music type and each user will be recommended more suitable music based on their current context. The experiments on the real datasets show the effectiveness of the proposed method. © 2019, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Adaptive boosting

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MSAHTA: Mixed Spatial Attention and Hierarchical Temporal Aggregation for Action Recognition

MSAHTA: Mixed Spatial Attention and Hierarchical Temporal Ag...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Jinyuan Feng Dan Yang Yongxin Ge Xiaolei Qin Yida Chen Yuangan Wang School of Big Data & Software Engineering Chongqing University Chongqing China CQU-UC Joint CO-OP Institute Chongqing University Chongqing China College of Science Beibu Gulf University Qinzhou China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Spatial-temporal feature modeling in videos is a fundamental yet knotty problem in computer vision. In this paper, we present a novel model, termed as Mixed Spatial Attention and Hierarchical Temporal Aggregation (MSAHTA), to highlight key spatial feature and aggregate temporal information in the multi-scale and hierarchical manner. MSAHTA is constructed by Spatial Attention module (SA) and Hierarchical Temporal Aggregation module (HTA). Specifically, the SA module aims to enhance the discrimination of spatial-feature by soft attention, while the HTA module aggregates the enhanced spatial-feature of each frame over time by means of a set of hierarchical 3D convolution operation. Furthermore, MSAHTA is an extensible module that can be used as a plugin for the existing 2D CNN. Our method obtains state-of-the-art results on two widely applicable datasets: UCF101 and HMDB51.

关键词： Handheld computers Cloud computing Big data Smart cities Technological innovation

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