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777 results on '"Li, Yanying"'

5. The State and the Individual in Times of Transition : --A Re-Examination of Chen Duxiu's Patriotic Thought before and after May 4th

Author

Xiao, Yating, Ke, Ruiming, Li, Yanying, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Shen, Chaoqun, editor, Cong, Li, editor, Zeng, Feiru, editor, and De Araujo, Gabriel Antunes, editor

Published
2024
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9. Preclinical study and phase II trial of adapting low-dose radiotherapy to immunotherapy in small cell lung cancer

Author

Wang, Hui, Yao, Zhuoran, Kang, Kai, Zhou, Lin, Xiu, Weigang, Sun, Jianguo, Xie, Conghua, Yu, Min, Li, Yanying, Zhang, Yan, Zheng, Yue, Lin, Guo, Pan, Xiangyu, Wu, Yijun, Luo, Ren, Wang, Laduona, Tang, Min, Liao, Shuangsi, Zhu, Jiang, Zhou, Xiaojuan, Zhang, Xuanwei, Xu, Yong, Liu, Yongmei, Peng, Feng, Wang, Jin, Xiang, Lisha, Yin, Limei, Deng, Lei, Huang, Meijuan, Gong, Youling, Zou, Bingwen, Wu, Lin, Yuan, Zhiyong, Bi, Nan, Fan, Min, Xu, Yaping, Tong, Ruizhan, Yi, Linglu, Gan, Lu, Xue, Jianxin, Mo, Xianming, Chen, Chong, Na, Feifei, and Lu, You

Published
2024
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33. Privacy for All: Demystify Vulnerability Disparity of Differential Privacy against Membership Inference Attack

Author

Zhang, Bo, Yu, Ruotong, Sun, Haipei, Li, Yanying, Xu, Jun, and Wang, Hui

Subjects
Computer Science - Cryptography and Security
Abstract

Machine learning algorithms, when applied to sensitive data, pose a potential threat to privacy. A growing body of prior work has demonstrated that membership inference attack (MIA) can disclose specific private information in the training data to an attacker. Meanwhile, the algorithmic fairness of machine learning has increasingly caught attention from both academia and industry. Algorithmic fairness ensures that the machine learning models do not discriminate a particular demographic group of individuals (e.g., black and female people). Given that MIA is indeed a learning model, it raises a serious concern if MIA ``fairly'' treats all groups of individuals equally. In other words, whether a particular group is more vulnerable against MIA than the other groups. This paper examines the algorithmic fairness issue in the context of MIA and its defenses. First, for fairness evaluation, it formalizes the notation of vulnerability disparity (VD) to quantify the difference of MIA treatment on different demographic groups. Second, it evaluates VD on four real-world datasets, and shows that VD indeed exists in these datasets. Third, it examines the impacts of differential privacy, as a defense mechanism of MIA, on VD. The results show that although DP brings significant change on VD, it cannot eliminate VD completely. Therefore, fourth, it designs a new mitigation algorithm named FAIRPICK to reduce VD. An extensive set of experimental results demonstrate that FAIRPICK can effectively reduce VD for both with and without the DP deployment., Comment: Privacy and Security, Member inference attack, 14 pages, 64 figures

Published
2020

43. Survival of Patients With Inoperable Non--Small Cell Lung Cancer With Baseline Severe Pulmonary Dysfunction: Impacts of Thoracic Radiotherapy and Predictive Analysis for Acute Radiation Pneumonitis

Author

Deng, Qianyue, Zhang, Yingjie, Li, Yanying, Mei, Ting, Yang, Xuexi, Tian, Xiaoman, Chen, Xianyan, and Gong, Youling

Subjects
Oncology, Experimental -- Analysis, Bacterial pneumonia -- Care and treatment -- Patient outcomes, Lung cancer, Small cell -- Patient outcomes -- Care and treatment, Radiation -- Analysis, Pemetrexed -- Analysis, Pneumonia -- Care and treatment -- Patient outcomes, Chemotherapy -- Analysis, Lung cancer, Non-small cell -- Care and treatment -- Patient outcomes, Cancer -- Chemotherapy -- Research, Radiotherapy -- Analysis, Health
Abstract

BACKGROUND AND PURPOSE. Currently, there is no standard treatment for patients with lung cancer with deteriorated pulmonary function. In this study, we aimed to assess the efficacy of thoracic radiotherapy for unresectable non-small cell lung cancer (NSCLC) with baseline severe pulmonary dysfunction and severe acute radiation pneumonitis (SARP). METHODS. Patients were categorized into a radiotherapy group and a nonradiotherapy group, followed by analysis of clinical variables. A Cox regression was used to evaluate the impact of various factors on overall survival (OS). Each SARP factor's predictive value was assessed using logistic regression, receiver operating characteristic curve, and Kaplan-Meier analyses. RESULTS. The median OS in the radiotherapy group was 21.6 months vs 8.9 months in the nonradiotherapy group. Cox analysis revealed that chemotherapy (HR, 0.221; 95% CI, 0.149-0.329; P < .0013 and radiotherapy (HR, 0.589; 95% CI, 0.399-0.869; P = .008) are independent prognostic factors for the current cohort. The data suggested that the ipsilateral lung V10 (iIV10, the percentage of the lung volume that received more than 10 Gy) was an independent predictor of SARP. CONCLUSIONS. Our findings suggested that thoracic radiotherapy might be associated with clinical benefits to inoperable NSCLC in patients with severe pulmonary dysfunction and that ilV10 may be involved in the prediction of risk for SARP in these patients. KEYWORDS. Severe pulmonary dysfunction; non-small cell lung cancer; thoracic radiotherapy; predictive factors; severe acute radiation pneumonitis, Introduction Smokers with chronic obstructive pulmonary disease (COPD) have a 2 to 5 times higher lung cancer risk than non-COPD smokers. (1-4) According to international guidelines, a ratio of forced [...]

Published
2023

45. Cost-efficient Data Acquisition on Online Data Marketplaces for Correlation Analysis

Author

Li, Yanying, Sun, Haipei, Dong, Boxiang, Hui, and Wang

Subjects
Computer Science - Databases
Abstract

Incentivized by the enormous economic profits, the data marketplace platform has been proliferated recently. In this paper, we consider the data marketplace setting where a data shopper would like to buy data instances from the data marketplace for correlation analysis of certain attributes. We assume that the data in the marketplace is dirty and not free. The goal is to find the data instances from a large number of datasets in the marketplace whose join result not only is of high-quality and rich join informativeness, but also delivers the best correlation between the requested attributes. To achieve this goal, we design DANCE, a middleware that provides the desired data acquisition service. DANCE consists of two phases: (1) In the off-line phase, it constructs a two-layer join graph from samples. The join graph consists of the information of the datasets in the marketplace at both schema and instance levels; (2) In the online phase, it searches for the data instances that satisfy the constraints of data quality, budget, and join informativeness, while maximize the correlation of source and target attribute sets. We prove that the complexity of the search problem is NP-hard, and design a heuristic algorithm based on Markov chain Monte Carlo (MCMC). Experiment results on two benchmark datasets demonstrate the efficiency and effectiveness of our heuristic data acquisition algorithm.

Published
2018

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