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336 results on '"Yu Shiwen"'

16. HeHe: Balancing the Privacy and Efficiency in Training CNNs over the Semi-honest Cloud

Author

Sun, Longlong, Li, Hui, Yu, Shiwen, Ma, Xindi, Peng, Yanguo, Cui, Jiangtao, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Susilo, Willy, editor, Chen, Xiaofeng, editor, Guo, Fuchun, editor, Zhang, Yudi, editor, and Intan, Rolly, editor

Published
2022
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17. Formalized Chinese Sentence Pattern Structure and Its Hierarchical Analysis

Author

Peng, Weiming, Wei, Zuntian, Song, Jihua, Yu, Shiwen, Sui, Zhifang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Dong, Minghui, editor, Gu, Yanhui, editor, and Hong, Jia-Fei, editor

Published
2022
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19. Neural Solving Uninterpreted Predicates with Abstract Gradient Descent.

Author

Yu, Shiwen, Liu, Zengyu, Wang, Ting, and Wang, Ji

Subjects
SOFTWARE verification, GLOBAL optimization, SEMANTICS, ALGORITHMS, NEURONS
Abstract

Uninterpreted predicate solving is a fundamental problem in formal verification, including loop invariant and constrained horn clauses predicate solving. Existing approaches have been mostly in symbolic ways. While achieving sustainable progress, they still suffer from inefficiency and seem unable to leverage the ever-increasing computility, such as GPU. Recently, neural relaxation has been proposed to tackle this problem. They treat the uninterpreted predicate-solving task as an optimization problem by relaxing the discrete search process into a learning process of neural networks. However, two bottlenecks keep them from being valid. First, relaxed neural networks cannot match the original semantics of predicates rigorously; second, the neural networks are difficult to train to reach global optimization. Therefore, this article presents a novel discrete neural architecture with the Abstract Gradient Decent (AGD) algorithm to directly solve uninterpreted predicates in the discrete hypothesis space. The abstract gradient is for discrete neurons whose calculation rules are designed in an abstract domain. Our approach conforms to the original semantics of predicates, and the proposed AGD algorithm can achieve global optimization satisfactorily. We implement the tool Dasp in the Boxes abstract domain to solve uninterpreted predicates in the QF-NIA SMT theory. In the experiments, Dasp has outperformed seven state-of-the-art tools across three predicate synthesis tasks. [ABSTRACT FROM AUTHOR]

Published
2024
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26. Multi-classification of Theses to Disciplines Based on Metadata

Author

Li, Jianling, Yu, Shiwen, Li, Shasha, Yu, Jie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tang, Jie, editor, Kan, Min-Yen, editor, Zhao, Dongyan, editor, Li, Sujian, editor, and Zan, Hongying, editor

Published
2019
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31. Construction of an Online Lexicon of Chinese Loan Words and Phrases Translated from English

Author

Wang, Lei, Yu, Shiwen, Wang, Houfeng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wu, Yunfang, editor, Hong, Jia-Fei, editor, and Su, Qi, editor

Published
2018
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32. An Improved k-Nearest Neighbor Algorithm for Text Categorization

Author

Li, Baoli, Yu, Shiwen, and Lu, Qin

Subjects
Computer Science - Computation and Language, I.2.7, H.3.3
Abstract

k is the most important parameter in a text categorization system based on k-Nearest Neighbor algorithm (kNN).In the classification process, k nearest documents to the test one in the training set are determined firstly. Then, the predication can be made according to the category distribution among these k nearest neighbors. Generally speaking, the class distribution in the training set is uneven. Some classes may have more samples than others. Therefore, the system performance is very sensitive to the choice of the parameter k. And it is very likely that a fixed k value will result in a bias on large categories. To deal with these problems, we propose an improved kNN algorithm, which uses different numbers of nearest neighbors for different categories, rather than a fixed number across all categories. More samples (nearest neighbors) will be used for deciding whether a test document should be classified to a category, which has more samples in the training set. Preliminary experiments on Chinese text categorization show that our method is less sensitive to the parameter k than the traditional one, and it can properly classify documents belonging to smaller classes with a large k. The method is promising for some cases, where estimating the parameter k via cross-validation is not allowed., Comment: 7 pages, 2 tables, 2 figures, to appear in the Proceedings of the 20th International Conference on Computer Processing of Oriental Languages, Shenyang, China, August 2003

Published
2003

40. A Metaphorical and Cognitive Study on Idioms with 'Ru'

Author

Wang, Zhimin, Wang, Lei, Yu, Shiwen, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Dong, Minghui, editor, Lin, Jingxia, editor, and Tang, Xuri, editor

Published
2016
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42. A Preliminary Contrastive Study on the Part-of-Speech Classifications of Two Lexicons

Author

Qiu, Likun, Zan, Hongying, Zhu, Xuefeng, Yu, Shiwen, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lu, Qin, editor, and Gao, Helena Hong, editor

Published
2015
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43. Emotional Classification of Chinese Idioms Based on Chinese Idiom Knowledge Base

Author

Wang, Lei, Yu, Shiwen, Wang, Zhimin, Qu, Weiguang, Wang, Houfeng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Lu, Qin, editor, and Gao, Helena Hong, editor

Published
2015
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46. The ATF4‐regulated LncRNA MALAT1 promotes odontoblastic differentiation of human dental pulp stem cells via histone demethylase JMJD3: An in vitro study.

Author

Yu, Shiwen, Guo, Jiajie, Yang, Di, Yan, Xiaoyuan, Zhang, Zeying, Wei, Penggong, and Qiu, Lihong

Subjects
*DENTAL pulp, *STEM cells, *DEMETHYLASE, *LINCRNA, *PROMOTERS (Genetics)
Abstract

Aim: This study aimed to investigate the upstream regulators and specific mechanisms of metastasis‐associated lung adenocarcinoma transcript 1 (MALAT1) in the odontoblastic differentiation of human dental pulp stem cells (hDPSCs). Methodology: Human dental pulp stem cells were isolated and cultured, followed by conducting loss‐ or gain‐of‐function experiments on ATF4 and loss experiments on MALAT1 to elucidate their respective biological functions in odontoblastic differentiation. Chromatin immunoprecipitation assays and RNA immunoprecipitation were performed to uncover the interaction between ATF4‐MALAT1 and MALAT1‐JMJD3, respectively. The odontoblastic differentiation was estimated by the mRNA and protein of DSPP and DMP1, as well as alkaline phosphatase staining. Results: Expression of MALAT1 was upregulated in the hDPSCs cultured in an odontoblastic medium, and MALAT1 downregulation suppressed the odontoblastic differentiation of the hDPSCs. Subsequent experiments confirmed that ATF4 promoted odontoblastic differentiation and induced MALAT1 expression by binding to the MALAT1 promoter region. Further experiments revealed that nuclear MALAT1 interacted with JMJD3. MALAT1 knockdown decreased the JMJD3 protein level and demethylase activity, and it enhanced H3K27me3 occupancy of the promoter region of DSPP and DMP1, resulting in the inhibition of DSPP and DMP1 transcription. Importantly, JMJD3 overexpression significantly attenuated the inhibition of odontoblastic differentiation induced by MALAT1 knockdown. Conclusions: ATF4‐regulated MALAT1 plays a positive regulatory role in odontoblastic differentiation of hDPSCs through JMJD3‐mediated H3K27me3 modifications of the DSPP and DMP1 promoters. [ABSTRACT FROM AUTHOR]

Published
2024
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