From one of the most respected reporters on China today, a vital new account of the political education of Xi Jinping, the authoritarian leader of China whose hard-edged tactics have set the rising superpower on a collision with Western liberal democracies.
Party of One: The Rise of Xi Jinping and the Superpower Future of China shatters the many myths and caricatures that shroud one of the world's most secretive political organisations and its leader. Many observers misread Xi's intentions during his early years in power, projecting onto him their own hopes that he would emerge as a liberal-minded reformer who steers China toward more political openness, rule of law, and pro-market economics - and overlooking how he has advanced his career by masking his beliefs under a cloak of strategic ambiguity.Combining narrative drama and incisive analysis, Party of One explains how Xi has shaken up the world's most populous nation with hard-edged authoritarianism, and set this rising superpower on a collision course with Western liberal democracies. Chun Han Wong draws on his years of first-hand reporting across China - spanning conversations with Party insiders and grassroots members, insights from scholars and diplomats who've studied and interacted closely with the Party bureaucracy, as well as analyses of official speeches and documents - to piece together a broad, digestible account of how Xi inspired fear and fervor in his Party, his nation, and beyond.
This contributed volume explores the intersection of big data analytics and genomics, driven by advancements in high-throughput sequencing technologies that have led to unprecedented genomic data accumulation. Traditional data analysis methods are often insufficient for extracting novel insights from this vast data, necessitating the development of big data analytics tailored for genomics. The computational methods discussed aim to address key biological questions and are suitable for both newcomers and experienced professionals in the field. Featuring thirteen peer-reviewed contributions from leading international experts across countries such as Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA, the volume focuses on three main areas: statistical analytics, computational analytics, and cancer genome analytics. Topics include statistical methods for integrative genomic analysis, computational techniques for protein function prediction, and machine learning perspectives in cancer big data mining. This self-contained resource is designed for graduate students, bioinformaticians, computational biologists, and researchers from diverse fields, including genomics, molecular genetics, data mining, biostatistics, biomedical science, and machine learning. It serves as an essential read for understanding the role of big data in genomics and encourages further research in this vital area.