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Your search keyword '"You, Shuai"' showing total 38 results
Start Over Author "You, Shuai" Publisher elsevier bv
38 results on '"You, Shuai"'

7. Improving the barrier properties of tin oxide in metal halide perovskite solar cells using ozone to enhance nucleation

Author

Johnson, Samuel A., primary, White, Keith P., additional, Tong, Jinhui, additional, You, Shuai, additional, Magomedov, Artiom, additional, Larson, Bryon W., additional, Morales, Daniel, additional, Bramante, Rosemary, additional, Dunphy, Erin, additional, Tirawat, Robert, additional, Perkins, Craig L., additional, Werner, Jérémie, additional, Lahti, Gabriella, additional, Velez, Christian, additional, Toney, Michael F., additional, Zhu, Kai, additional, McGehee, Michael D., additional, Berry, Joseph J., additional, and Palmstrom, Axel F., additional

Published
2023
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10. Dynamics of fecal coliform bacteria along Canada's coast

Author

You, Shuai, primary, Huang, Xiaolin, additional, Xing, Li, additional, Lesperance, Mary, additional, LeBlanc, Charles, additional, Moccia, L. Paul, additional, Mercier, Vincent, additional, Shao, Xiaojian, additional, Pan, Youlian, additional, and Zhang, Xuekui, additional

Published
2023
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19. Environmentally stable perovskite nanocrystals with improved photoelectrochemical performance enabled by poly nitroxide radical

Author

Zeng, Haipeng, primary, Zhao, Yang, additional, Wang, Xi, additional, Lin, Xia, additional, Guo, Rui, additional, Li, Lin, additional, Zhou, Yingshan, additional, You, Shuai, additional, Zhang, Shujing, additional, Luo, Long, additional, Liu, Fengxiang, additional, Boshta, Mostafa, additional, Liang, Wenxi, additional, and Li, Xiong, additional

Published
2022
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29. Self-Attention Networks for Code Search

Author

Sen Fang, Tao Zhang, Yepang Liu, and You-shuai Tan

Subjects
Information retrieval, Computer science, business.industry, Deep learning, Software development, 020207 software engineering, Context (language use), 02 engineering and technology, Construct (python library), Bottleneck, Computer Science Applications, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Code (cryptography), Artificial intelligence, business, Feature learning, Software, Information Systems, Codebase
Abstract

Context: Developers tend to search and reuse code snippets from a large-scale codebase when they want to implement some functions that exist in the previous projects, which can enhance the efficiency of software development. Objective: As the first deep learning-based code search model, DeepCS outperforms prior models such as Sourcere and CodeHow. However, it utilizes two separate LSTM to represent code snippets and natural language descriptions respectively, which ignores semantic relations between code snippets and their descriptions. Consequently, the performance of DeepCS falls into the bottleneck, and thus our objective is to break this bottleneck. Method: We propose a self-attention joint representation learning model, named SAN-CS (Self-Attention Network for Code Search). Comparing with DeepCS, we directly utilize the self-attention network to construct our code search model. By a weighted average operation, self-attention networks can fully capture the contextual information of code snippets and their descriptions. We first utilize two individual self-attention networks to represent code snippets and their descriptions, respectively, and then we utilize the self-attention network to conduct an extra joint representation network for code snippets and their descriptions, which can build semantic relationships between code snippets and their descriptions. Therefore, SAN-CS can break the performance bottleneck of DeepCS. Results: We evaluate SAN-CS on the dataset shared by Gu et al. and choose two baseline models, DeepCS and CARLCS-CNN. Experimental results demonstrate that SAN-CS achieves significantly better performance than DeepCS and CARLCS-CNN. In addition, SAN-CS has better execution efficiency than DeepCS at the training and testing phase. Conclusion: This paper proposes a code search model, SAN-CS. It utilizes the self-attention network to perform the joint attention representations for code snippets and their descriptions, respectively. Experimental results verify the effectiveness and efficiency of SAN-CS.

Published
2021

32. Microwave-assisted organocatalysis: phosphine-mediated Tomita Zipper cyclization affording functionalized spirooxindole

Author

Xiao-Yan Zhang, Liang-Jun Wang, Li-Li Hu, Hai-Lei Cui, Ze Shen, You-Shuai Lin, and Jian-Wu Xie

Subjects
Zipper, 010405 organic chemistry, Organic Chemistry, 010402 general chemistry, Ring (chemistry), 01 natural sciences, Biochemistry, Microwave assisted, 0104 chemical sciences, chemistry.chemical_compound, chemistry, Organocatalysis, Yield (chemistry), Drug Discovery, Organic chemistry, Phosphine
Abstract

A microwave-assisted and phosphine-mediated Tomita Zipper cyclization of dicyanomethylideneoxindoles and ynones has been developed. Various functionalized spirooxindoles with five-membered carbocyclic ring can be obtained in moderate to good yields with moderate to excellent diastereoselectivities through in situ generation of α-nucleophile (up to 87% yield, >20:1 dr).

Published
2016

33. Bug severity prediction using question-and-answer pairs from Stack Overflow

Author

Sijie Xu, Xiapu Luo, Zhaowei Wang, You-shuai Tan, Zhou Xu, and Tao Zhang

Subjects
Questions and answers, business.industry, Computer science, 05 social sciences, 020207 software engineering, 02 engineering and technology, Software maintenance, Machine learning, computer.software_genre, Software, Hardware and Architecture, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, Stack overflow, Artificial intelligence, Software system, business, computer, 050203 business & management, Information Systems
Abstract

Nowadays, bugs have been common in most software systems. For large-scale software projects, developers usually conduct software maintenance tasks by utilizing software artifacts (e.g., bug reports). The severity of bug reports describes the impact of the bugs and determines how quickly it needs to be fixed. Bug triagers often pay close attention to some features such as severity to determine the importance of bug reports and assign them to the correct developers. However, a large number of bug reports submitted every day increase the workload of developers who have to spend more time on fixing bugs. In this paper, we collect question-and-answer pairs from Stack Overflow and use logical regression to predict the severity of bug reports. In detail, we extract all the posts related to bug repositories from Stack Overflow and combine them with bug reports to obtain enhanced versions of bug reports. We achieve severity prediction on three popular open source projects (e,g., Mozilla, Ecplise, and GCC) with Naive Bayesian, k-Nearest Neighbor algorithm (KNN), and Long Short-Term Memory (LSTM). The results of our experiments show that our model is more accurate than the previous studies for predicting the severity. Our approach improves by 23.03%, 21.86%, and 20.59% of the average F-measure for Mozilla, Eclipse, and GCC by comparing with the Naive Bayesian based approach which performs the best among all baseline approaches.

Published
2020

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