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.
This is a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL) and the background and rise of network embeddings (NE). It introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/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. Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.
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.
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.
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.
The zebrafish is the most important fish model in developmental andgenetic analyses. This book contains 19 review articles covering abroad spectrum of topics, from development to genetic tools. Thecontents range from early development, the role of maternal factorsand gastrulation, to tissue differentiation and organogenesis, such asdevelopment of the organizer, notochord, floor plate, nervous system, somites, muscle, skeleton and endoderm
This book constitutes the thoroughly refereed post-proceedings of the 17th International Workshop on Languages and Compilers for High Performance Computing, LCPC 2004, held in West Lafayette, IN, USA in September 2004. The 33 revised full papers presented were carefully selected during two rounds of reviewing and improvement. The papers are organized in topical sections on compiler infrastructures; predicting and reducing memory access; locality, tiling, and partitioning; tools and techniques for parallelism and locality; Java for high-performance computing; high-level languages and optimizations; large-scale data sharing; performance studies; program analysis; and exploiting architectural features.
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.
There are still insufficient general theories on the law of diminishing returns, despite 100 years of development. Starting with intensive variables theory, and by utilizing tools of spatiotemporal correlation and intensive functions, moving on to the integrated curve of diminishing returns and intensive theory, and even more importantly, using a combination of static and dynamic GIS, and integrating numerical calculation and spatial optimization, this book not only creates a unique theoretical framework and methodology for the evaluation of land use effect, but also addresses the long-standing lack of universal theories and methods on the law of diminishing returns. It will have far-reaching impacts on the development of this area and its practical application. The book covers a wide range fields in geography, land science, geographic information science, management science and related areas. Novel theoretical perspectives illustrated with many detailed case studies offer an easier way for readers to expand their research, ensuring that both academic and business audiences will benefit. Prof. Xinqi Zheng works at the China University of Geosciences (Beijing), People’s Republic of China.
The systematic description starts with basic theory and applications of different kinds of data structures, including storage structures and models. It also explores on data processing methods such as sorting, index and search technologies. Due to its numerous exercises the book is a helpful reference for graduate students, lecturers.
Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to new heights. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.
Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.
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.
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.
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.
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.
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