This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book's chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.

This book discusses what is often called the "Great Leap Famine", which occurred in China during the years from 1959 to 1961. Scholarly consensus suggests that 30 million Chinese perished. Yang Songlin's book provides an evidence-based, systematic and substantial rebuff, concluding that a much smaller number of deaths can be verified. This book is of interest to scholars of China and Chinese development and politics, economists, and demographers.

The book puts forward dynamically enabled cyber defense technology as a solution to the system homogenization problem. Based on the hierarchy of the protected information system entity, the book elaborates on current mainstream dynamic defense technologies from four aspects: the internal hardware platform, software service, information data and external network communication. It also ascertains their possible evolution routes, clarifies their relationship with existing security products, and makes macro analyses and discussions on security gain and overall system efficiency of these technologies.This book can be used as both a textbook for graduate courses related to electronic information as well as a reference for scientific researchers engaged in relevant research. It helps graduate students majoring in electronics and information sciences to gain an understanding in dynamically-enabled cyber defense. Scientists and engineers specialising in network security research should also find this book to be a useful guide on recent developments in network security.

Political, social and economic transformations have marked the 20 years since Hong Kong became a Special Administrative Region of China. To mark the historic handover, the Institute of Advanced Studies (IAS) at Nanyang Technological University invited experts from various fields to share their unique insights on the developments and impact of the last 20 years on Hong Kong and Singapore in a conference in Singapore.This volume is a compilation of speeches and presentations delivered at the conference by such heavyweight experts as Wang Gungwu, Antony Leung and Yang Jinlin on the road travelled and the paths ahead for both cities. This volume is an invaluable collection on Hong Kong and Singapore's past, present and future. Readers can enjoy the salient analysis delivered with great thought and reflective humour.

This book is highly informative and carefully presented, providing scientific insights into the flood resources utilization in the Yangtze River Basin both for scholars and decision-makers. The book is for the purpose of analyzing the potential utilization of flood resources in the Yangtze River Basin and exploring effective ways to put forward the countermeasures against the risks. Major objectives of this book include: (1) revealing the characteristics of the inflow and the sediment variation in the upper reaches of the Yangtze River, quantitatively evaluating the potential utilization of the flood resources in the Yangtze River and demonstrating the feasibility of its utilization in the Basin; (2) proposing the necessity and feasibility of utilizing the flood resources by the Three Gorges Project; (3) shedding new light on the characteristics of the flood resources, presenting different methods of flood resources utilization in different regions over the Basin and raising the overall risk-optimized strategies of the flood resources utilization in the Yangtze River; (4) analyzing the risk of flood resources utilization for the Three Gorges Project regarding flood control, sediment, ecology, etc., and putting forward the risk-optimized countermeasures of flood resources utilization for the Three Gorges Project.

This volume is a collection of original studies based on one of the first research programs on comparative analysis of social capital. Data are drawn from national representative samples of the United States, China and Taiwan. The three societies selected for study allow the examination of how political-economic regimes (command versus market) and cultural factors (family centrality versus diverse social ties) affect the characteristics of social ties and social networks from which resources are accessed and mobilized.

This book discusses what is often called the "Great Leap Famine", which occurred in China during the years from 1959 to 1961. Scholarly consensus suggests that 30 million Chinese perished. Yang Songlin's book provides an evidence-based, systematic and substantial rebuff, concluding that a much smaller number of deaths can be verified. This book is of interest to scholars of China and Chinese development and politics, economists, and demographers.

This book is highly informative and carefully presented, providing scientific insights into the flood resources utilization in the Yangtze River Basin both for scholars and decision-makers. The book is for the purpose of analyzing the potential utilization of flood resources in the Yangtze River Basin and exploring effective ways to put forward the countermeasures against the risks. Major objectives of this book include: (1) revealing the characteristics of the inflow and the sediment variation in the upper reaches of the Yangtze River, quantitatively evaluating the potential utilization of the flood resources in the Yangtze River and demonstrating the feasibility of its utilization in the Basin; (2) proposing the necessity and feasibility of utilizing the flood resources by the Three Gorges Project; (3) shedding new light on the characteristics of the flood resources, presenting different methods of flood resources utilization in different regions over the Basin and raising the overall risk-optimized strategies of the flood resources utilization in the Yangtze River; (4) analyzing the risk of flood resources utilization for the Three Gorges Project regarding flood control, sediment, ecology, etc., and putting forward the risk-optimized countermeasures of flood resources utilization for the Three Gorges Project.

This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory. The book's chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.

This two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction.

This two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction.