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arXiv 2019年

作者： Wu, Xiongwei Li, Qiang Li, Xiuhua Leung, Victor C.M. Ching, P.C. Dept. of Electronic Engineering Chinese University of Hong Kong Shatin Hong Kong School of Info. & Comm. Eng. University of Electronic Science and Technology of China Chengdu China School of Big Data & Software Engineering Chongqing University Chongqing China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Dept. of Electrical & Computer Engineering University of British Columbia Vancouver Canada

Recent years have witnessed an exponential growth of mobile data traffic, which may lead to a serious traffic burn on the wireless networks and considerable power consumption. Network densification and edge caching are effective approaches to addressing these challenges. In this study, we investigate joint long-term cache allocation and short-term content delivery in cloud small cell networks (C-SCNs), where multiple smallcell BSs (SBSs) are connected to the central processor via fronthaul and can store popular contents so as to reduce the duplicated transmissions in networks. Accordingly, a long-term power minimization problem is formulated by jointly optimizing multicast beamforming, BS clustering, and cache allocation under quality of service (QoS) and storage constraints. The resultant mixed timescale design problem is an anticausal problem because the optimal cache allocation depends on the future file requests. To handle it, a two-stage optimization scheme is proposed by utilizing historical knowledge of users' requests and channel state information. Specifically, the online content delivery design is tackled with a penalty-based approach, and the periodic cache updating is optimized with a distributed alternating method. Simulation results indicate that the proposed scheme significantly outperforms conventional schemes and performs extremely close to a genie-aided lower bound in the low caching region. Copyright © 2019, The Authors. All rights reserved.

关键词： Channel state information

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Deep localization of subcellular protein structures from fluorescence microscopy images

arXiv

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arXiv 2019年

作者： Tahir, Muhammad Anwar, Saeed Mian, Ajmal Muzaffar, Abdul Wahab College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Data61 The Commonwealth Scientific and Industrial Research Organisation Australia Department of Computer Science and Engineering The Australian National University Australia School of Computer Science University of Technology Sydney Australia Computer Science and Software Engineering The University of Western Australia

Accurate localization of proteins from fluorescence microscopy images is challenging due to the inter-class similarities and intra-class disparities introducing grave concerns in addressing multi-class classification problems. Conventional machine learning-based image prediction pipelines rely heavily on pre-processing such as normalization and segmentation followed by hand-crafted feature extraction to identify useful, informative, and application-specific features. Here, we demonstrate that deep learning-based pipelines can effectively classify protein images from different datasets. We propose an end-to-end Protein Localization Convolutional Neural Network (PLCNN) that classifies protein images more accurately and reliably. PLCNN processes raw imagery without involving any pre-processing steps and produces outputs without any customization or parameter adjustment for a particular dataset. Experimental analysis is performed on five benchmark datasets. PLCNN consistently outperformed the existing state-of-the-art approaches from traditional machine learning and deep architectures. This study highlights the importance of deep learning for the analysis of fluorescence microscopy protein imagery. The proposed deep pipeline can better guide drug designing procedures in the pharmaceutical industry and open new avenues for researchers in computational biology and bioinformatics. © 2019, CC BY.

关键词： Fluorescence microscopy

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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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Accumulative time-based ranking method to reputation evaluation in information networks

arXiv

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arXiv 2019年

作者： Liao, Hao Liu, Qi-Xin Huang, Ze-Cheng Yeung, Chi Ho Zhang, Yi-Cheng National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen518060 China Department of Science and Environmental Studies The Education University of Hong Kong Hong Kong Hong Kong Department of Physics University of Fribourg Fribourg Switzerland

With the rapid development of modern technology, the Web has become an important platform for users to make friends and acquire information. However, since information on the Web is over-abundant, information filtering becomes a key task for online users to obtain relevant suggestions. As most Websites can be ranked according to users’ rating and preferences, relevance to queries, and recency, how to extract the most relevant item from the over-abundant information is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed accumulative time-based ranking (ATR) algorithm, we incorporate two behavioral weighting factors which are updated when users select or rate items, to reflect the evolution of user reputation and item quality over time. We showed that our algorithm outperforms state-of-the-art ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Copyright © 2019, The Authors. All rights reserved.

关键词： Information filtering

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Enhancing countries' fitness with recommender systems on the international trade network

arXiv

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arXiv 2019年

作者： Liao, Hao Wu, Xing-Tong Vidmer, Alexandre Huang, Xiao-Min Liu, Ming-Kai Zhou, Mingyang Zhang, Yi-Cheng National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Physics University of Fribourg Fribourg1700 Switzerland

Prediction is one of the major challenges in complex systems. The prediction methods have shown to be effective predictors of the evolution of networks. These methods can help policy makers to solve practical problems successfully and make better strategy for the future. In this work, we focus on exporting countries' data of the international trading network. A recommendation system is then used to identify the products corresponding to the production capacity of each individual country, but are somehow overlook by the country. Then, we simulate the evolution of the country's fitness if it would have followed the recommendations. The result of this work is the combination combine these two methods to provide insights to countries on how to enhance the diversification of their exported products in a scientific way and improve national competitiveness significantly, especially for developing countries. Copyright © 2019, The Authors. All rights reserved.

关键词： Recommender systems

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Understanding Electrochemical Reaction Mechanisms of Sulfur in All-Solid-State Batteries through Operando and Theoretical Studies**

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Angewandte Chemie 2023年第20期135卷

作者： Daxian Cao Xiao Sun Fei Li Seong-Min Bak Tongtai Ji Michael Geiwitz Kenneth S. Burch Yonghua Du Guochun Yang Hongli Zhu Department of Mechanical and Industrial Engineering Northeastern University 360 Huntington Avenue Boston MA 02115 USA Contribution: Conceptualization (equal) Data curation (equal) Investigation (equal) Methodology (equal) Writing - original draft (equal) Writing - review & editing (equal) Contribution: Conceptualization (equal) Data curation (equal) Investigation (equal) Validation (lead) Writing - original draft (equal) Writing - review & editing (equal) State Key Laboratory of Metastable Materials Science & Technology and Key Laboratory for Microstructural Material Physics of Hebei Province School of Science Yanshan University Qinhuangdao 066004 China Contribution: Investigation (equal) Software (equal) Writing - original draft (equal) Writing - review & editing (equal) National Synchrotron Light Source II Brookhaven National Laboratory Upton NY 11973 USA Contribution: Data curation (supporting) Resources (lead) Contribution: Investigation (supporting) Writing - review & editing (supporting) Department of Physics Boston College 140 Commonwealth Ave Chestnut Hill MA 02467-3804 USA Contribution: Resources (equal) Contribution: Resources (lead) Contribution: Data curation (equal) Software (lead) Supervision (lead) Writing - original draft (equal) Writing - review & editing (equal) Contribution: Conceptualization (lead) Funding acquisition (lead) Supervision (lead) Writing - original draft (equal) Writing - review & editing (equal)

Due to its outstanding safety and high energy density, all-solid-state lithium-sulfur batteries (ASLSBs) are considered as a potential future energy storage technology. The electrochemical reaction pathway in ASLSBs with inorganic solid-state electrolytes is different from Li-S batteries with liquid electrolytes, but the mechanism remains unclear. By combining operando Raman spectroscopy and ex situ X-ray absorption spectroscopy, we investigated the reaction mechanism of sulfur (S 8 ) in ASLSBs. Our results revealed that no Li 2 S 8, Li 2 S 6, and Li 2 S 4 were formed, yet Li 2 S 2 was detected. Furthermore, first-principles structural calculations were employed to disclose the formation energy of solid state Li 2 S n (1≤ n ≤8), in which Li 2 S 2 was a metastable phase, consistent with experimental observations. Meanwhile, partial S 8 and Li 2 S 2 remained at the full lithiation stage, suggesting incomplete reaction due to sluggish reaction kinetics in ASLSBs.

关键词： All-Solid-State Lithium-Sulfur Batteries Operando Raman Spectroscopy Solid-State Reactions Sulfur X-Ray Absorption Spectroscopy

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Integrating Chemical Pre-Potassiation with Pre-Modulated KF-Rich Electrolyte Interfaces for Dual-Carbon Potassium Ion Hybrid Capacitor

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Angewandte Chemie 2023年第9期135卷

作者： Yong Qian Bei Wu Yang Li Zhen Pan Dr. Shuai Feng Dr. Ning Lin Prof. Yitai Qian Hefei National Laboratory for Physical Sciences at the Microscale Department of Applied Chemistry University of Science and Technology of China Hefei 230026 P. R. China Contribution: Conceptualization (lead) Formal analysis (lead) Investigation (lead) Methodology (lead) Writing - original draft (lead) Contribution: Software (equal) Visualization (lead) Contribution: Data curation (equal) Resources (equal) College of Chemistry and Chemical Engineering Taishan University Shandong 271021 P. R. China Contribution: Resources (equal) Visualization (equal) Contribution: Funding acquisition (lead) Writing - review & editing (lead) Contribution: Funding acquisition (equal)

Herein, a chemical pre-potassiation strategy via simultaneously treating both glucose derived carbon (GDC) anode and commercial activated carbon (CAC) cathode in potassium-naphthalene-tetrahydrofuran solution is developed for potassium ion hybrid capacitor (PIHC). Combined with in situ and ex situ characterizations, a radical reaction between pre-potassiation reagent and carbon electrodes is confirmed, which not only deactivates electrochemical irreversible sites, but also promotes to pre-form a uniform and dense KF-rich electrolyte film on the electrodes. As a result, the pre-potassiation treatment presents multiple advantages: (I) the initial Coulombic efficiency (CE) of the GDC anode increases from 45.4 % to 84.0 % with higher rate capability; (II) the CAC cathode exhibits the improved cycling CEs and stability due to the enhanced resistance to electrolyte oxidation at 4.2 V; (III) the assembled PIHC achieves a high energy density of 172.5 Wh kg −1 with cycling life over 10000 cycles.

关键词： Chemical Pre-Potassiation Strategy Higher Rate Capability KF-Rich Electrolyte Interface Radical Reaction the Resistance to Electrolyte Oxidation

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An advanced operating environment for mathematics education resources

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中国科学（信息科学） 2018年第9期61卷 256-258页

作者： Yongsheng RAO Jingzhong ZHANG Yu ZOU Yanchun SUN Xiangping CHEN Songhua XU School of Data and Computer Science Sun Yat-Sen University Guangzhou 510275 China Academy for Intelligent Software Guangzhou University Guangzhou 510006 China Institute of Mathematics and Computer Science Guizhou Normal College Guiyang 550018 China Academy for Intelligent Software Guangzhou University Guangzhou 510006 China Institute of Mathematics and Computer Science Guizhou Normal College Guiyang 550018 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing Beijing 100871 China School of Data and Computer Science Sun Yat-Sen University Guangzhou 510275 China College of Computer Sciences New Jersey Institute of Technology Newark 07102 USA Academy for Intelligent Software Guangzhou University Guangzhou 510006 China

The potential of information technology (IT) in education modernization has been widely *** the broad efforts for advancing education IT,digital education resources continue to receive a high level of attention,with the goal of making high-quality education resources pervasively accessible anytime and ***,the amount of such high-quality resources is still limited,particularly those suitable for mathematics education [1].General expectations for high-quality digital mathematics education resources (MERs) include interactivity,openness,and transparency,all of which benefit enhancing the sustainability of MERs [2].In this case,openness of an MER refers to the resource being easily edited and extended;transparency means that users have convenient access to the original development process of the *** these features,MERs can be continuously improved and augmented according to users' individual *** development and utilization of such MEPs often rely on the deployment of a specialized 'software or platform',which is commonly referred to as the 'operating environment (OE)'.

关键词：

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Unsupervised feature selection based on Markov blanket and particle swarm optimization

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Journal of Systems engineering and Electronics 2017年第1期28卷 151-161页

作者： Yintong Wang Jiandong Wang Hao Liao Haiyan Chen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University College of Computer Science and Software Engineering Shenzhen University

Feature selection plays an important role in data mining and recognition, especially in the large scale text, image and biological data. Specifically, the class label information is unavailable to guide the selection of minimal feature subset in unsupervised feature selection, which is challenging and interesting. An unsupervised feature selection based on Markov blanket and particle swarm optimization is proposed named as UFSMB-PSO. The proposed method seeks to find the high-quality feature subset through multi-particles' cooperation of particle swarm optimization without using any learning algorithms. Moreover, the features' relevance will be computed based on an information metric of relevance gain, which provides an information theoretical foundation for finding the minimization of the redundancy between features. Our results on several benchmark datasets demonstrate that UFSMB-PSO can achieve significant improvement over state of the art unsupervised methods. © 1990-2011 Beijing Institute of Aerospace Information.

关键词： Character recognition data mining Feature extraction Information theory

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Nonlinear Prediction with Deep Recurrent Neural Networks for Non-Blind Audio Bandwidth Extension

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China Communications 2018年第4期15卷 72-85页

作者： Lin Jiang Ruimin Hu Xiaochen Wang Weiping Tu Maosheng Zhang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan 430072 China Institute of Big Data and internet Innovation Hunan University of Commerce Changsha 410205 China Software College East China University of Technology Nanchang 330013 China Collaborative Innovation Center for Economics crime investigation and prevention technology Jiangxi Province Nanchang 330103China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan 430072 China Collaborative Innovation Center of Geospatial Technology Wuhan 430079 China

Non-blind audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, in most cases high frequencies signal is usually generated by a duplication of the corresponding low frequencies and some parameters of high frequencies. However, the perception quality of coding will significantly degrade if the correlation between high frequencies and low frequencies becomes weak. In this paper, we quantitatively analyse the correlation via computing mutual information value. The analysis results show the correlation also exists in low frequency signal of the context dependent frames besides the current frame. In order to improve the perception quality of coding, we propose a novel method of high frequency coarse spectrum generation to improve the conventional replication method. In the proposed method, the coarse high frequency spectrums are generated by a nonlinear mapping model using deep recurrent neural network. The experiments confirm that the proposed method shows better performance than the reference methods.

关键词： audio coding non-blind audiobandwidth extension context correlation deeprecurrent neural network

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