Convergence as Adaptivity explores under what conditions policy convergence may be seen an indication of policy autonomy. Zhiyuan Wang advances a theory that argues states substitute unwanted policy changes dictated by globalization with politically feasible ones. Investigating bilateral investment treaties, preferential trade agreements, corporate taxation, and central bank independence as policy substitutes, he provides both quantitative and qualitative evidence to substantiate the core theoretical argument and explores under what conditions policy convergence can be an indication of policy autonomy and through what manner. The result is a systemic and rigorous exploration of the idea of policy substitution and its consequences under globalization. Wang's findings will be of interest to scholars, practitioners, and concerned citizens hoping to deepen their understanding of globalization as well as useful for undergraduate and graduate courses in international relations, international and comparative political economy, and globalization.
This book chiefly describes the theories and technologies for natural gas hydrate management in deepwater gas wells. It systematically explores the mechanisms of hydrate formation, migration, deposition and blockage in multiphase flow in gas-dominated systems; constructs a multiphase flow model of multi-component systems for wells that takes into account hydrate phase transition; reveals the influence of hydrate phase transition on multiphase flows, and puts forward a creative hydrate blockage management method based on hydrate blockage free window (HBFW), which enormously improves the hydrate prevention effect in deepwater wells. The book combines essential theories and industrial technology practice to facilitate a deeper understanding of approaches to and technologies for hydrate management in deepwater wells, and provides guidance on operation design. Accordingly, it represents a valuable reference guide for both researchers and graduate students working in oil and gas engineering, offshore oil and gas engineering, oil and gas storage and transportation engineering, as well as technical staff in the fields of deepwater oil and gas drilling, development, and flow assurance.
Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering. Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these algorithms, gain an in-depth knowledge of how the mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems. Key features - Provides a concise introduction to numerical concepts in machine learning in simple terms - Explains the 7 basic mathematical techniques used in machine learning problems, with over 60 illustrations and tables - Focuses on numerical examples while using small datasets for easy learning - Includes simple Python codes - Includes bibliographic references for advanced reading The text is essential for college and university-level students who are required to understand the fundamentals of machine learning in their courses.
In PAPER TIGER the Chinese journalist and intellectual Xu Zhiyuan paints a portrait of the world's second-largest economy via a thoughtful and wide-ranging series of mini essays on contemporary Chinese society. Xu Zhiyuan describes the many stages upon which China's great transformation is taking place, from Beijing's Silicon district to a cruise down the Three Gorges; he profiles China's dissidents, including Liu Xiaobo, Ai Weiwei and Chen Guangcheng; and explores lesser-known stories of scandals that rocked China but which most people outside that country did not hear about – and which shed troubling light on China's dark heart. Xu Zhiyuan understands his homeland in a way no foreign correspondent ever could. PAPER TIGER is a unique insider's view of China that is measured and brave, ambitious in scope and deeply personal.
Vacuum circuit breakers are widely used in distribution power systems for their advantages such as maintenance free and eco-friendly. Nowadays, most circuit breakers used at transmission voltage level are SF6 circuit breakers, but the SF6 they emit is one of the six greenhouse gases defined in Kyoto Protocol. Therefore, the development of transmission voltage level vacuum circuit breaker can help the environment. The switching arc phenomena in transmission voltage level vacuum circuit breakers are key issues to explore. This book focuses on the high-current vacuum arcs phenomena at transmission voltage level, especially on the anode spot phenomena, which significantly influence the success or failure of the short circuit current interruption. Then, it addresses the dielectric recovery property in current interruption. Next it explains how to determine the closing/opening displacement curve of transmission voltage level vacuum circuit breakers based on the vacuum arc phenomena. After that, it explains how to determine key design parameters for vacuum interrupters and vacuum circuit breakers at transmission voltage level. At the end, the most challenging issue for vacuum circuit breakers, capacitive switching in vacuum, is addressed. The contents of this book will benefit researchers and engineers in the field of power engineering, especially in the field of power circuit breakers and power switching technology.
In this book we have experts writing on various neuroscience topics ranging from mental illness, syndromes, compulsive disorders, brain cancer and advances in therapies and imaging techniques. Although diverse, the topics provide an overview of an array of diseases and their underlying causes, as well as advances in the treatment of these ailments. This book includes three chapters dedicated to neurodegenerative diseases, undoubtedly a group of diseases of huge socio-economic importance due to the number of people currently suffering from this type of disease but also the prediction of a huge increase in the number of people becoming afflicted. The book also includes a chapter on the molecular and cellular aspects of brain cancer, a disease which is still amongst the least treatable of cancers.
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.
Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool. This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.
Data structures is a key course for computer science and related majors. This book presents a variety of practical or engineering cases and derives abstract concepts from concrete problems. Besides basic concepts and analysis methods, it introduces basic data types such as sequential list, tree as well as graph. This book can be used as an undergraduate textbook, as a training textbook or a self-study textbook for engineers.
Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool. This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.
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