Multiply robust estimators in longitudinal studies with missing data under control-based imputation

Longitudinal studies are often subject to missing data. The ICH E9(R1) addendum addresses the importance of defining a treatment effect estimand with the consideration of intercurrent events. Jump-to-reference (J2R) is one classically envisioned control-based scenario for the treatment effect evaluation using the hypothetical strategy, where the participants in the treatment group after intercurrent events are assumed to have the same disease progress as those with identical covariates in the control group. We establish new estimators to assess the average treatment effect based on a proposed potential outcomes framework under J2R. Various identification formulas are constructed under the assumptions addressed by J2R, motivating estimators that rely on different parts of the observed data distribution. Moreover, we obtain a novel estimator inspired by the efficient influence function, with multiple robustness in the sense that it achieves $n^{1/2}$-consistency if any pairs of multiple nuisance functions are correctly specified, or if the nuisance functions converge at a rate not slower than $n^{-1/4}$ when using flexible modeling approaches. The finite-sample performance of the proposed estimators is validated in simulation studies and an antidepressant clinical trial

ServeNet: A Deep Neural Network for Web Services Classification

Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a novel deep neural network to automatically abstract low-level representation of both service name and service description to high-level merged features without feature engineering and the length limitation, and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy in classification and more robust than other machine learning methods.Comment: Accepted by ICWS'2

Confronting Ambiguity: The Intersection of Racial and Sexual Marginalisation and Repression in Rex vs Singh (2008) and Seeking Single White Male (2010)

This MRP examines how Canadian filmmakers and artists explore racial and sexual marginalisation in Canada. Two films in particular exemplify different forms of racism towards South Asian immigrants. The first, Rex vs Singh (2008), an experimental documentary produced by John Greyson, Richard Fung, and Ali Kazimi, showcases the ambiguous application of immigration policies to repress South Asian immigration. Through different reconstructed montages, the film confronts these ambiguities in relation to the court case. The second, Seeking Single White Male (2010), a performance-video work by Toronto-based artist Vivek Shraya—South Asian descent, demonstrates not only the dominant racial norms and white normativity in queer communities in Toronto, but also the ambivalence in performing racial identification. I identify ambiguity as the distinct contribution to understanding first: i) how state policies are used for racial and sexual repression, ii) the ways in which identification of racial norms are unstable, iii) and how these norms have been translated into sexual (un)/desirability. The ambiguities evoked by these works provide critical insights to investigate the complexity of racial marginalisation and their intersection with gender/sex normativity

2050 China

This book is open access under a CC BY-NC-ND 4.0 license. This book is arranged and developed around the theme of “2050 China,” it analyzes the factors and advantages of the Chinese road to socialist modernization, explores and summarizes the development goal and the basic logic of the socialist modernization of China, and further shows the general basis of the primary stage of socialism. According to the report delivered at the 19th Party Congress, and according to the “two-stage” strategic plan, this book looks ahead in detail to the overarching objective and sub-objectives of essentially achieving socialist modernization by 2035, discusses the building of a great modern socialist country in all respects from the perspective of the Party’s six-sphere integrated plan of economic, political, cultural, social, ecological civilization, and national defense construction, and provides policy proposals. This book also analyzes the influence and the effect of the socialist modernization with Chinese characteristics on the world and it further presents the third centenary goal. In conclusion, this book is an elaboration of the work of the Institute for Contemporary China Studies, Tsinghua University. It reflects the intellectual innovation in the authors’ research on contemporary China, as well as the authors’ foresight and predictions about China’s future development

Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation. Firstly, a deep convolutional neural network is trained to determine whether an image contains cracks or not. The images containing cracks are then smoothed using bilateral filtering, which greatly minimizes the number of noisy pixels. Finally, we utilize an adaptive thresholding method to extract the cracks from road surface. The experimental results illustrate that our network can classify images with an accuracy of 99.92%, and the cracks can be successfully extracted from the images using our proposed thresholding algorithm.Comment: 6 pages, 8 figures, 2019 IEEE Intelligent Vehicles Symposiu