Works by Liu, Yun (exact spelling)

5 found
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  1.  23
    Green Credit Policy and Corporate Stock Price Crash Risk: Evidence From China.Wei Zhang, Yun Liu, Fengyun Zhang & Huan Dou - 2022 - Frontiers in Psychology 13.
    Using the promulgation of Green Credit Guidelines in China as the research setting, this paper exploits a quasi-natural experiment to examine the impact of green credit policy on the stock price crash risk of heavy-polluting firms. The results show that green credit policy significantly increases the risk of stock price crash of heavy-polluting firms. Such impact is transmitted through increased financial constraints and reduced information transparency. In addition, we find that the impact of green credit policy on the stock price (...)
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  2.  8
    An Optimality-Theoretic Analysis of the Acquisition of English Sentence Stress Based on Acoustic Data.Wen Ji & Yun Liu - 2021 - Complexity 2021:1-9.
    The present study adopts both empirical and theoretical methods for the analysis of the acquisition of English sentence stress based on Optimality Theory, aiming to overcome mispronunciation of English sentence stress. Optimality Theory has a dramatic impact on most areas in linguistics besides phonology. The acoustic software Praat is chosen to collect and label data as the basis of the empirical method. Then, through analyzing the four major principles of distribution of sentence stress and based on the analysis of Optimality (...)
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  3.  1
    哲学与医学.Yun Liu (ed.) - 1991 - Shanghai: Shanghai ke xue ji shu chu ban she.
  4.  2
    Zhe xue yu yi xue.Yun Liu (ed.) - 1991 - Shanghai: Shanghai ke xue ji shu chu ban she.
    Textbook of Marxist philosophy for medical students in China.
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  5.  9
    A Smart Privacy-Preserving Learning Method by Fake Gradients to Protect Users Items in Recommender Systems.Guixun Luo, Zhiyuan Zhang, Zhenjiang Zhang, Yun Liu & Lifu Wang - 2020 - Complexity 2020:1-10.
    In this paper, we study the problem of protecting privacy in recommender systems. We focus on protecting the items rated by users and propose a novel privacy-preserving matrix factorization algorithm. In our algorithm, the user will submit a fake gradient to make the central server not able to distinguish which items are selected by the user. We make the Kullback–Leibler distance between the real and fake gradient distributions to be small thus hard to be distinguished. Using theories and experiments, we (...)
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