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16. IDHUP: Incremental Discovery of High Utility Pattern

Author

Lele Yu Lele Yu, Wensheng Gan Lele Yu, Zhixiong Chen Wensheng Gan, and Yining Liu Zhixiong Chen

Subjects
Computer Networks and Communications, Software
Abstract

As a sub-problem of pattern discovery, utility-oriented pattern mining has recently emerged as a focus of researchers’ attention and offers broad application prospects. Considering the dynamic characteristics of the input databases, incremental utility mining methods have been proposed, aiming to discover implicit information/ patterns whose importance/utility is not less than a user-specified threshold from incremental databases. However, due to the explosive growth of the search space, most existing methods perform unsatisfactorily under the low utility threshold, so there is still room for improvement in terms of running efficiency and pruning capacity. Motivated by this, we provide an effective and efficient method called IDHUP by designing an indexed partitioned utility list structure and employing four pruning strategies. With the proposed data structure, IDHUP can not only dynamically update the utility values of patterns but also avoid visiting non-occurred patterns. Moreover, to further exclude ineligible patterns and avoid unnecessary exploration, we put forward the remaining utility reducing strategy and three other revised pruning strategies. Experiments on various datasets demonstrated that the designed IDHUP algorithm has the best performance in terms of running time compared to state-of-the-art algorithms. &nbsp

Published
2023

20. Methane and nitrous oxide concentrations and fluxes from heavily polluted urban streams: Comprehensive influence of pollution and restoration

Author

Wang, Xiaofeng, primary, Yu, Lele, additional, Liu, Tingting, additional, He, Yixin, additional, Wu, Shengnan, additional, Chen, Huai, additional, Yuan, Xingzhong, additional, Wang, Jilong, additional, Li, Xianxiang, additional, Li, Hang, additional, Que, Ziyi, additional, Qing, Zhaoyin, additional, and Zhou, Ting, additional

Published
2022
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23. A Multiple Feature Integration Model to Infer Occupation from Social Media Records

Author

Wang, Xiang, Yu, Lele, Yao, Junjie, Cui, Bin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Lin, Xuemin, editor, Manolopoulos, Yannis, editor, Srivastava, Divesh, editor, and Huang, Guangyan, editor

Published
2013
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36. Unraveling the overlithiation mechanism of LiMn2O4 and LiFePO4 using lithium-metal batteries.

Author

Yu, Lele, Tian, Yexing, Xing, Yiran, Hou, Chen, Si, Yongheng, Lu, Han, and Zhao, Yujuan

Abstract

With the solid-state battery (vs. Li) application, the overlithiation mechanism of the different cathode materials is worthy to investigate. In this study, both LiMn2O4 and LiFePO4 cathode materials at different over-discharge conditions were tested using half cell (vs. Li) and anode-free systems. The cells were dismantled to study the electrode structure, surface morphology, and compositional changes. The study shows that LiMn2O4 and LiFePO4 still maintain good crystal morphology during the deep over-discharge process, showing better over-discharge resistance capability with different overlithiation mechanisms. As shown by X-ray diffraction and X-ray photoelectron spectroscopy with Ar-ion etching, the new phase, Li2Mn2O4, appears starting from 2.5 V. Until the voltage is less than 0.2 V, the framework structures of LiMn2O4 are deteriorated, and further overlithiation caused decomposition into Li2MnO2 and Li2O. LiFePO4 essentially maintains its olivine-type structure, but below 0.2 V, direct overlithiation causes decomposition into Li2O and Fe metal. Furthermore, overlithiated decomposition of LiMn2O4 and LiFePO4 occurs at very low voltages approximately 0.43 and 0.56 V, respectively. Additionally, the deep over-discharge also leads to the decay of the electrolyte structure, associated with LiF, Li2CO3 and LixPOyFz by-products. The detailed overlithiation mechanism will provide important theoretical guidance for practical applications. [ABSTRACT FROM AUTHOR]

Published
2021
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40. PS2

Author

Zhang, Zhipeng, primary, Cui, Bin, additional, Shao, Yingxia, additional, Yu, Lele, additional, Jiang, Jiawei, additional, and Miao, Xupeng, additional

Published
2019
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42. MOESM7 of Combining manipulation of transcription factors and overexpression of the target genes to enhance lignocellulolytic enzyme production in Penicillium oxalicum

Author

Liwei Gao, Zhonghai Li, Chengqiang Xia, Yinbo Qu, Liu, Meng, Piao Yang, Yu, Lele, and Song, Xin

Abstract

Additional file 7. Fig. S7 Enzyme activity analysis and phenotype observation of the transformant RE-7-2. FPase (A) and xylanase activities (B) of RE-6 and RE-7-2 strains on wheat bran at 72 h, 96 h and 120 h were determined. Error bars represent the standard deviations. (C) Phenotypic analysis of RE-6 and three parallel transformants (RE-7, RE-7-1 and RE-7-2) on glucose plate after 4 days’ cultivation.

Published
2017
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45. GVoS

Author

Jiang, Jiawei, primary, Tong, Yunhai, additional, Lu, Hua, additional, Cui, Bin, additional, Lei, Kai, additional, and Yu, Lele, additional

Published
2017
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50. Angel: a new large-scale machine learning system.

Author

Jiang, Jie, Yu, Lele, Jiang, Jiawei, Liu, Yuhong, and Cui, Bin

Subjects
*MACHINE learning, *BIG data, *ALGORITHMS
Abstract

Machine Learning (ML) techniques now are ubiquitous tools to extract structural information from data collections. With the increasing volume of data, large-scaleML applications require an efficient implementation to accelerate the performance. Existing systems parallelize algorithms through either data parallelism or model parallelism. But data parallelism cannot obtain good statistical efficiency due to the conflicting updates to parameters while the performance is damaged by global barriers in model parallel methods. In this paper, we propose a new system, named Angel, to facilitate the development of large-scale ML applications in production environment. By allowing concurrent updates to model across different groups and scheduling the updates in each group, Angel can achieve a good balance between hardware efficiency and statistical efficiency. Besides, Angel reduces the network latency by overlapping the parameter pulling and update computing and also utilizes the sparseness of data to avoid the pulling of unnecessary parameters. We also enhance the usability of Angel by providing a set of efficient tools to integrate with application pipelines and provisioning efficient fault tolerance mechanisms.We conduct extensive experiments to demonstrate the superiority of Angel. [ABSTRACT FROM AUTHOR]

Published
2018
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