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A cooperative scheduling framework for shared transportation services 19

A cooperative scheduling framework for shared transportation...

19th COTA International Conference of Transportation Professionals: Transportation in China - Connecting the World, CICTP 2019

作者： Wang, Xinli Qiao, Yanan Wang, Tianyu Guo, Jifu Zhou, Xuesong Xian, Kai Tong, Lu Carol Xie, Jindong Natl. Eng. Lab. for Compreh. Transp. Big Data Applic. Technol. Sch. of Transp. Sci. and Engineering Beihang Univ. Beijing100191 China State Key Lab of Software Development Environment School of Computer Science and Engineering Beihang Univ. Beijing100191 China Beijing Transport Institute No. 9 LiuLiQiao South Lane Fengtai District China

ISBN: (数字)9780784482292

ISBN: (纸本)9780784482292

To improve the efficiency of metropolitan shared transportation services (STS), this paper proposes a cooperative scheduling framework to avoid congestion. Two procedures are considered: shared path scheduling and congestion solving. In a known static traffic network, classic vehicle routing problem (VRP) solutions may obtain an optimal path. But with an increasing number of shared transportation services required, congestion is likely, triggered by open loop VRP scheduling, especially when shared vehicles overwhelm private ones. Dynamic traffic assignment (DTA) solutions could be employed for short-term traffic situation prediction. However, VRP and DTA cannot be combined linearly, due to uncontrolled recursive calls of routing procedures within VRP and DTA. Hence, a software structure is further studied to guarantee convergence of the cooperative framework. Using two open-source transportation packages, VRPLite and DTALite, we test our framework with the Western 3rd Ring Road of the Beijing regional network. Results verify the framework's usability and flexibility. © ASCE.

关键词： Scheduling

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Glycan immunogenicity prediction based on Graph neural network

Glycan immunogenicity prediction based on Graph neural netwo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yu Wang Hui Wang Meijie Hou Yaojun Wang Dongbo Bu Chunming Zhang Chuncui Huang Shiwei Sun Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 6 Kexueyuan South Road Beijing China 19 Yuquan Road University of Chinese Academy of Sciences Beijing China College of Information and Electrical Engineering China Agricultural University 17 Qinghua Donglu Beijing China Zhongke Big Data Academy Zhengzhou Henan China Phil Rivers Technology Beijing China 15 Datun Road Institute of Biophysics Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665429825

Glycans play important roles in a great variety of biological processes, and these roles are closely determined by the details of their structures. It becomes possible to acquire hidden features from glycan structures using deep learning method with the great progress in recent years. Unlike the linear chain of proteins and DNAs, branching is a unique feature of glycan structures, which makes it very difficult to directly apply deep learning models on glycans. Thus, how to comprehensively and efficiently describe glycans and use them as input to deep learning models still remains challenging. Here, a graph neural network (GNN) called GlyNet was used to obtain high-dimensional representation of glycans and predict their immunogenicity. Our method was applied in SugarBase, and it works more superiorly than the state-of-art method with accuracy increased from 91.7% to 95.6%.

关键词： Deep learning Proteins Spectroscopy databases Conferences Biological processes Ions

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Tunable Fluorescence from 2D Assemblies of LnW10and P2W18Polyoxometalates Clusters with Quaternary Ammonium-type AIEgens

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Angewandte Chemie 2023年第38期135卷

作者： Dr. Shujian Rong Dr. Mengshi Wang Prof. Dr. Xun Wang Key Lab of Organic Optoelectronics and Molecular Engineering Department of Chemistry Tsinghua University Beijing 100084 China Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Methodology (lead) Writing - original draft (lead) The Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology (Ministry of Education) Department of Chemistry Tsinghua University Beijing 100084 China Contribution: Data curation (equal) Formal analysis (equal) Methodology (equal) Contribution: Conceptualization (lead) Formal analysis (lead) Funding acquisition (lead) Methodology (lead) Project administration (lead) Resources (lead) Supervision (lead) Validation (lead) Writing - review & editing (lead)

Two-dimensional (2D) AIE-inorganic nanocomposites exhibit high stability and high fluorescence activity. However, limited by the types and properties of 2D inorganic materials, it's challenging to realize complex structures and functions. We designed a quaternary ammonium-type AIE ligand, TPECholine. We chose TPECholine and POM clusters as building blocks and synthesized a series of single-layer nanosheets (SLNSs) by assembling LnW 10 or P 2 W 18 POM clusters with TPECholine. By regulating the types of POM clusters and ligands, the morphology and fluorescence intensity of the SLNSs can be finely tuned. Due to the restriction of the intramolecular motions of AIEgens by the SLNSs, nanosheets can exhibit promoted quantum yield (up to 76 %). In addition, thanks to the sub-nanometric sizes and excess surface charges, SLNSs exhibit excellent solvent compatibility, including water, chloroform, ethanol, etc. And the nanosheets showed high fluorescence intensity in these solvents.

关键词： Aggregation-Induced Emission (AIE) Nanosheets Self-Assembly Surface Modification Tunable Fluorescence

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Multiple residual dense networks for reconfigurable intelligent surfaces cascaded channel estimation

arXiv

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arXiv 2021年

作者： Jin, Yu Zhang, Jiayi Huang, Chongwen Yang, Liang Xiao, Huahua Ai, Bo Wang, Zhiqin The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Zhejiang Provincial Key Lab of Information Processing Communication and Networking Zhejiang University Hangzhou310007 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China ZTE Corporation State Key Laboratory of Mobile Network Mobile Multimedia Technology Shenzhen518057 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China China Academy of Information and Communications Technology Beijing100191 China

Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-cost massive reflecting elements over the entire surfaces of man-made structures. However, accurate channel estimation is a fundamental technical prerequisite to achieve the huge performance gains from RIS. By leveraging the low rank structure of RIS channels, three practical residual neural networks, named convolutional blind denoising network, convolutional denoising generative adversarial networks and multiple residual dense network, are proposed to obtain accurate channel state information, which can reflect the impact of different methods on the estimation performance. Simulation results reveal the evolution direction of these three methods and reveal their superior performance compared with existing benchmark schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

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Exploring the Pan-Relationship Between Disease and Symptom Related to Coronary Heart Disease from Chinese Electronic Medical Records 15th

Exploring the Pan-Relationship Between Disease and Symptom R...

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Yang, Ronggen Wang, Yichen Wang, Bo Gong, Lejun Faculty of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing211169 China Jiangsu Key Lab of Big Data Security and Intelligent Processing School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210046 China School of Geography and Bioinformatics Nanjing University of Posts and Telecommunication Nanjing China

ISBN: (纸本)9783030267650

Electronic Medical Records (EMRs) arises from the clinical treatment process, which reflects a patient’s physical condition, the disease, the treatment process, etc. It has an important significance for the recovery of the patient. As the amount of electronic medical records continues to increase, people are actively looking for ways to get the valid data contained in electronic medical records. More effective methods are urgently needed for processing EMRs. In this paper, 108 real EMRs of coronary heart disease were analyzed, through named entity recognition and entity relation extraction. Pan-relationship between entities was defined in the process. Experimental results showed the analysis of pan-relation between coronary heart disease and symptom is helpful for treatment of coronary heart disease. © 2019, Springer Nature Switzerland AG.

关键词： Diseases

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Research on the MEG of depression patients based on multivariate TE partial information decomposition

Research on the MEG of depression patients based on multivar...

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作者： Zihan Chen Yunjie Fang Wei Yan Jun Wang Jin Li Fengzhen Hou Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical University College of Physics and Information Technology Shaanxi Normal University Key Laboratory of Biomedical Functional Materials China Pharmaceutical University

Transfer entropy（TE） has been broadly used in the field of *** this paper,the partial information decomposition algorithm is employed to decompose multivariate TE into synergistic,redundant and unique *** this work,the synergistic part is believed as more suitable as the computation *** recorded the magnetoencephalogram（MEG） data of 6 subjects with depression and 13 normal subjects under different emotional stimulations,and studied the coupling between multiple symmetric channels in the frontal area in the brain of *** experimental results show that under different emotional stimulations,normal people present significant difference from the depression patients,especially in the right frontal ***,under negative emotional stimulation,the difference in synergistic value between normal people and depression patients is *** synergistic value of depression patients has become bigger,which indicates that the brain complexity of depression patients has grown,and their brain activities have increased.

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Improving the Automated Coronal Jet Identification with U-NET

arXiv

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arXiv 2024年

作者： Liu, Jiajia Ji, Chunyu Wang, Yimin Soós, Szabolcs Jiang, Ye Erdélyi, Robertus Korsós, M.B. Wang, Yuming Deep Space Exploration Lab School of Earth and Space Sciences University of Science and Technology of China Hefei230026 China CAS Key Laboratory of Geospace Environment CAS Center for Excellence in Comparative Planetology Mengcheng National Geophysical Observatory University of Science and Technology of China Hefei230026 China School of Data Science Qingdao University of Science and Technology Qingdao266100 China Department of Astronomy Eötvös Loránd University Pázmány P. sétány 1/A BudapestH-1117 Hungary Petőfi tér 3. GyulaH-5700 Hungary School of Information Science and Technology Qingdao University of Science and Technology Qingdao266100 China School of Mathematics and Statistics The University of Sheffield SheffieldS3 7RH United Kingdom University of Sheffield Department of Automatic Control and Systems Engineering Amy Johnson Building Portabello Street SheffieldS1 3JD United Kingdom

Coronal jets are one of the most common eruptive activities in the solar atmosphere. They are related to rich physics processes, including but not limited to magnetic reconnection, flaring, instabilities, and plasma heating. Automated identification of off-limb coronal jets has been difficult due to their abundant nature, complex appearance, and relatively small size compared to other features in the corona. In this paper, we present an automated coronal jet identification algorithm (AJIA) that utilizes true and fake jets previously detected by a laborious semi-automated jet detection algorithm (SAJIA, Liu et al. 2023) as the input of an image segmentation neural network U-NET. It is found that AJIA could achieve a much higher (0.81) detecting precision than SAJIA (0.34), meanwhile giving the possibility of whether each pixel in an input image belongs to a jet. We demonstrate that with the aid of artificial neural networks, AJIA could enable fast, accurate, and real-time coronal jet identification from SDO/AIA 304 Å observations, which are essential in studying the collective and long-term behavior of coronal jets and their relation with the solar activity cycles. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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A Decentralized Collaborative Approach to Online Edge User Allocation in Edge Computing Environments

A Decentralized Collaborative Approach to Online Edge User A...

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IEEE International Conference on Web Services (ICWS)

作者： Qinglan Peng Yunni Xia Yan Wang Chunrong Wu Wanbo Zheng Xin Luo Shanchen Panz Yong Ma Chunxu Jiang Software Theory and Technology Chongqing Key Lab Chongqing University Chongqing China Macquarie University Sydney NSW Australia Data Science Research Center Kunming University of Science and Technology Kunming China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China School of Computer and Communication Engineering China University of Petroleum Qingdao China School of Computer and Information Engineering Jiangxi Normal University Nanchang China Chongqing Key Laboratory of Smart Electronics Reliability Technology Chongqing China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Edge computing is a promising paradigm that can boost the performance of novel mobile applications and energize the real-time governance of Internet-of-Things (IoT) big data. In edge computing, mobile application vendors are allowed to employ edge resources to speed up end-users' applications in an elastic and on-demand manner. However, due to the complex geographical distribution of edge servers and users, how to decide the most appropriate destination edge server to hire and how to decide the corresponding user-server allocation plan with as-low-as-possible monetary cost are the key problems for application vendors. Instead of assuming a simultaneous-batch-arrival pattern of incoming users and considering static optimization of the Edge User Allocation (EUA) problem by most existing studies, in this paper, we consider an online EUA problem where users' arrival and departure follow a general pattern. We take the long-term edge user allocation rate and edge server leasing cost as scheduling targets and propose a decentralized collaborative and fuzzy-control-based approach to yielding real-time user-edge-server allocation schedules. In this approach, edge users are allowed to independently make their own allocation decision only based on local information (i.e., the status of nearby edge servers). Experiments on real-world edge datasets demonstrate our approach outperforms state-of-the-art approaches in terms of long-term allocation rate and system cost.

关键词： Web services Collaboration Real-time systems Mobile applications Resource management Servers Edge computing

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A universal opportunity model for human mobility

arXiv

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arXiv 2020年

作者： Liu, Er-Jian Yan, Xiao-Yong Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing100044 China Institute of Transportation System Science and Engineering Beijing Jiaotong University Beijing100044 China Compleχ Lab University of Electronic Science and Technology of China Chengdu611731 China

Predicting human mobility between locations has practical applications in transportation science, spatial economics, sociology and many other fields. For more than 100 years, many human mobility prediction models have been proposed, among which the gravity model analogous to Newton's law of gravitation is widely used. Another classical model is the intervening opportunity (IO) model, which indicates that an individual selecting a destination is related to both the destination's opportunities and the intervening opportunities between the origin and the destination. The IO model established from the perspective of individual selection behavior has recently triggered the establishment of many new IO class models. Although these IO class models can achieve accurate prediction at specific spatiotemporal scales, an IO class model that can describe an individual's destination selection behavior at different spatiotemporal scales is still lacking. Here, we develop a universal opportunity model that considers two human behavioral tendencies: one is the exploratory tendency, and the other is the cautious tendency. Our model establishes a new framework in IO class models and covers the classical radiation model and opportunity priority selection model. Furthermore, we use various mobility data to demonstrate our model's predictive ability. The results show that our model can better predict human mobility than previous IO class models. Moreover, this model can help us better understand the underlying mechanism of the individual's destination selection behavior in different types of human mobility. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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A prototype for software refactoring recommendation system

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International Journal of Performability engineering 2020年第7期16卷 1095-1104页

作者： Gao, Yuan Zhang, Youchun Lu, Wenpeng Luo, Jie Hao, Daqing China National-Local Joint Engineering Lab of Next Generation Internet Data Processing Technology University of Electronic Science and Technology of China Chengdu610054 China China School of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China Luoyang Bearing Research Institute Co. Ltd. Luoyang471039 China

Software refactoring is used to reduce the costs and risks of software evolution. Automated software refactoring tools can reduce risks caused by manual refactoring, improve efficiency, and reduce difficulties of software refactoring. Researchers have made great efforts to research how to implement and improve automated software refactoring tools. However, results of automated refactoring tools often deviate from the intentions of the implementer. To this end, in this paper, we proposed and implemented a prototype tool for a software refactoring recommendation system based on previous research. The tool provides users with an optimized software refactoring scheme and users realize refactoring intentions by interacting with the tool. The tool has been evaluated to be effective, especially for users who are inexperienced and non- English speaking. © 2020 Totem Publisher, Inc. All rights reserved.

关键词： Recommender systems

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