Xiao Wang – författare

This book covers the fundamental principles behind the design of ultra-low power radios and how they can form networks to facilitate a variety of applications within healthcare and environmental monitoring, since they may operate for years off a small battery or even harvest energy from the environment. These radios are distinct from conventional radios in that they must operate with very constrained resources and low overhead. This book provides a thorough discussion of the challenges associated with designing radios with such constrained resources, as well as fundamental design concepts and practical approaches to implementing working designs. Coverage includes integrated circuit design, timing and control considerations, fundamental theory behind low power and time domain operation, and network/communication protocol considerations.

This book covers the fundamental principles behind the design of ultra-low power radios and how they can form networks to facilitate a variety of applications within healthcare and environmental monitoring, since they may operate for years off a small battery or even harvest energy from the environment. These radios are distinct from conventional radios in that they must operate with very constrained resources and low overhead. This book provides a thorough discussion of the challenges associated with designing radios with such constrained resources, as well as fundamental design concepts and practical approaches to implementing working designs. Coverage includes integrated circuit design, timing and control considerations, fundamental theory behind low power and time domain operation, and network/communication protocol considerations.

This book covers the fundamental principles behind the design of ultra-low power radios and how they can form networks to facilitate a variety of applications within healthcare and environmental monitoring, since they may operate for years off a small battery or even harvest energy from the environment. These radios are distinct from conventional radios in that they must operate with very constrained resources and low overhead. This book provides a thorough discussion of the challenges associated with designing radios with such constrained resources, as well as fundamental design concepts and practical approaches to implementing working designs. Coverage includes integrated circuit design, timing and control considerations, fundamental theory behind low power and time domain operation, and network/communication protocol considerations.

This original book is a unique and original in-depth study on how, in the past decade, Chinese State-Owned Enterprises (SOEs) have achieved technological innovation in the large infrastructure sectors. It reveals a “new world” of Chinese innovation, showing that SOEs are willing to innovate and are also more than capable of doing so.Based on findings from first-hand data and years of observations, this book shows how the innovation ecosystem perspective incentivises and facilitates Chinese SOEs’ innovation and highlights the entrepreneurial role of the government. Using the examples of UHV Power Transmission, mobile telecommunication standards, high-speed trains, and nuclear electric power, the book exhibits the complex determinants of SOEs’ success in radical technological innovations within the large infrastructure sector. Chapters also demonstrate the innovation process of SOEs, the unique innovation model of China, as well as its advantages and disadvantages.Catch-Up and Radical Innovation in Chinese State-Owned Enterprises will be a useful resource for academics in research disciplines such as development studies, innovation and entrepreneurship, and Chinese studies. It will also aid entrepreneurs, businesses and managers who intend to collaborate with Chinese SOEs, to better understand the trends of SOEs’ engagement in radical innovation and the potential opportunities for broadening their international collaborations.

This original book is a unique and original in-depth study on how, in the past decade, Chinese State-Owned Enterprises (SOEs) have achieved technological innovation in the large infrastructure sectors. It reveals a “new world” of Chinese innovation, showing that SOEs are willing to innovate and are also more than capable of doing so.

Based on findings from first-hand data and years of observations, this book shows how the innovation ecosystem perspective incentivises and facilitates Chinese SOEs’ innovation and highlights the entrepreneurial role of the government. Using the examples of UHV Power Transmission, mobile telecommunication standards, high-speed trains, and nuclear electric power, the book exhibits the complex determinants of SOEs’ success in radical technological innovations within the large infrastructure sector. Chapters also demonstrate the innovation process of SOEs, the unique innovation model of China, as well as its advantages and disadvantages.

Catch-Up and Radical Innovation in Chinese State-Owned Enterprises will be a useful resource for academics in research disciplines such as development studies, innovation and entrepreneurship, and Chinese studies. It will also aid entrepreneurs, businesses and managers who intend to collaborate with Chinese SOEs, to better understand the trends of SOEs’ engagement in radical innovation and the potential opportunities for broadening their international collaborations.

This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.

This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.

This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.

This book constitutes the refereed proceedings of the Second International Workshop on Medical Optical Imaging and Virtual Microscopy Image Analysis, MOVI 2024, held in conjunction with the 26th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, in October 2024.The 21 regular papers presented at MOVI 2024 were carefully reviewed and selected from 29 submissions.They are grouped into these two topical sections: Medical Optical Imaging and Virtual Microscopy Image Analysis and Kidney Pathology Image segmentation (KPIs) Challenge.

Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node.In this book, we provide a comprehensive survey of current developments in HG representation learning.More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.

Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node.In this book, we provide a comprehensive survey of current developments in HG representation learning.More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.

Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node.In this book, we provide a comprehensive survey of current developments in HG representation learning.More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.