Lei Chen

This book explores the essential computing paradigm of crowdsourcing, particularly the rise in utility and application of crowdsourced spatiotemporal data. This book begins with an overview of how the ubiquity of mobile internet services and the growth of the sharing economy have created ideal conditions for the growth of spatial crowdsourcing, why humans are ideal contributors to spatiotemporal tasks that must be completed at specific locations and times, and the core challenges of crowdsourcing that data. Readers then get a deeper dive into practical application issues. In particular, this book centers on identifying four core algorithmic issues in spatial crowdsourcing: (1) task assignment, (2) quality control, (3) incentive mechanism design, and (4) privacy protection. Readers find this books contains a comprehensive and systematic review of existing research on these issues and analyzes representative spatial crowdsourcing applications with explanations of how those four technical issues enable them. This book describes a number of real-life examples that demonstrate recent successes, techniques, and specific challenges in crowdsourcing for applications that provide urban services, such as ride sharing and live-mapping. Finally, this book discusses open questions that need to be addressed in future spatial crowdsourcing research and applications.

Unlock the intricacies of Chinese property law with this groundbreaking book, perfect for legal practitioners, scholars, and international investors. This comprehensive guide delves into the complexities of Chinese property law, offering detailed analysis, practical case studies, and insightful global comparisons. Understand the evolution and current landscape of property law in China, and see how theoretical principles are applied in real-world scenarios. Whether you're navigating cross-border property issues, developing legal strategies, or seeking an academic resource, this book is an invaluable tool. Authored by a recognized expert, it combines scholarly rigor with practical expertise, making it an essential addition to your legal library.

Derived from the renowned multi-volume International Encyclopaedia of Laws, this practical analysis of the law of contracts in China covers every aspect of the subject - definition and classification of contracts, contractual liability, relation to the law of property, good faith, burden of proof, defects, penalty clauses, arbitration clauses, remedies in case of non-performance, damages, power of attorney, and much more. Lawyers who handle transnational contracts will appreciate the explanation of fundamental differences in terminology, application, and procedure from one legal system to another, as well as the international aspects of contract law. Throughout the book, the treatment emphasizes drafting considerations. An introduction in which contracts are defined and contrasted to torts, quasi-contracts, and property is followed by a discussion of the concepts of 'consideration' or 'cause' and other underlying principles of the formation of contract. Subsequent chapters cover the doctrines of 'relative effect', termination of contract, and remedies for non-performance. The second part of the book, recognizing the need to categorize an agreement as a specific contract in order to determine the rules which apply to it, describes the nature of agency, sale, lease, building contracts, and other types of contract. Facts are presented in such a way that readers who are unfamiliar with specific terms and concepts in varying contexts will fully grasp their meaning and significance. Its succinct yet scholarly nature, as well as the practical quality of the information it provides, make this book a valuable time-saving tool for business and legal professionals alike. Lawyers representing parties with interests in China will welcome this very useful guide, and academics and researchers will appreciate its value in the study of comparative contract la

The book discusses superconducting fault current limiters and their applications in power systems, exploring the principles, simulations and engineering practices, but focusing on systematic applications in traditional and renewable power systems. It provides in-depth studies on a number of major topics such as architecture of superconducting fault current limiter, device design, parameter optimization, prototype testing, co-ordination control and performance evaluation. It also describes multiple application cases of superconducting fault current limiters, which are applied in high voltage direct current transmission systems, active distribution networks, and micro-grids. Offering a comprehensive and systematic overview of practical issues, the book is intended for readers wanting to learn practical approaches for developing superconducting fault current limiters. It also appeals to researchers, engineers and graduate students in various fields, including high-temperature superconducting materials, power system transient stability, and control science and engineering.

Imaging of tissue blood flow (BF) distributions provides vital information for the diagnosis and therapeutic monitoring of various vascular diseases. The innovative near-infrared speckle contrast diffuse correlation tomography (scDCT) technique produces full 3D BF distributions. Many advanced features are provided over competing technologies including high sampling density, fast data acquisition, noninvasiveness, noncontact, affordability, portability, and translatability across varied subject sizes. The basic principle, instrumentation, and data analysis algorithms are presented in detail. The extensive applications are summarized such as imaging of cerebral BF (CBF) in mice, rat, and piglet animals with skull penetration into deep brain. Clinical human testing results are described by recovery of BF distributions on preterm infants (CBF) through incubator wall, and on sensitive burn tissues and mastectomy skin flaps without direct device-tissue interactions. Supporting activities outlined include integrated capability for acquiring surface curvature information, rapid 2D blood flow mapping, and optimizations via tissue-like phantoms and computer simulations. These applications and activities both highlight and guide the reader as to the expected abilities and limitations of scDCT for adapting into their own preclinical/clinical research, use in constrained environments (i.e., neonatal intensive care unit bedside), and use on vulnerable subjects and measurement sites.

This book examines the recent trend of extending data dependencies to adapt to rich data types in order to address variety and veracity issues in big data. Readers will be guided through the full range of rich data types where data dependencies have been successfully applied, including categorical data with equality relationships, heterogeneous data with similarity relationships, numerical data with order relationships, sequential data with timestamps, and graph data with complicated structures. The text will also discuss interesting constraints on ordering or similarity relationships contained in novel classes of data dependencies in addition to those in equality relationships, e.g., considered in functional dependencies (FDs). In addition to exploring the concepts of these data dependency notations, the book investigates the extension relationships between data dependencies, such as conditional functional dependencies (CFDs) that extend conventional functional dependencies (FDs). This forms in the book a family tree of extensions, mostly rooted in FDs, that help illuminate the expressive power of various data dependencies. Moreover, the book points to work on the discovery of dependencies from data, since data dependencies are often unlikely to be manually specified in a traditional way, given the huge volume and high variety in big data. It further outlines the applications of the extended data dependencies, in particular in data quality practice. Altogether, this book provides a comprehensive guide for readers to select proper data dependencies for their applications that have sufficient expressive power and reasonable discovery cost. Finally, the book concludes with several directions of future studies on emerging data.

This book examines the recent trend of extending data dependencies to adapt to rich data types in order to address variety and veracity issues in big data. Readers will be guided through the full range of rich data types where data dependencies have been successfully applied, including categorical data with equality relationships, heterogeneous data with similarity relationships, numerical data with order relationships, sequential data with timestamps, and graph data with complicated structures. The text will also discuss interesting constraints on ordering or similarity relationships contained in novel classes of data dependencies in addition to those in equality relationships, e.g., considered in functional dependencies (FDs). In addition to exploring the concepts of these data dependency notations, the book investigates the extension relationships between data dependencies, such as conditional functional dependencies (CFDs) that extend conventional functional dependencies (FDs). This forms in the book a family tree of extensions, mostly rooted in FDs, that help illuminate the expressive power of various data dependencies. Moreover, the book points to work on the discovery of dependencies from data, since data dependencies are often unlikely to be manually specified in a traditional way, given the huge volume and high variety in big data. It further outlines the applications of the extended data dependencies, in particular in data quality practice. Altogether, this book provides a comprehensive guide for readers to select proper data dependencies for their applications that have sufficient expressive power and reasonable discovery cost. Finally, the book concludes with several directions of future studies on emerging data.

Imaging of tissue blood flow (BF) distributions provides vital information for the diagnosis and therapeutic monitoring of various vascular diseases. The innovative near-infrared speckle contrast diffuse correlation tomography (scDCT) technique produces full 3D BF distributions. Many advanced features are provided over competing technologies including high sampling density, fast data acquisition, noninvasiveness, noncontact, affordability, portability, and translatability across varied subject sizes. The basic principle, instrumentation, and data analysis algorithms are presented in detail. The extensive applications are summarized such as imaging of cerebral BF (CBF) in mice, rat, and piglet animals with skull penetration into deep brain. Clinical human testing results are described by recovery of BF distributions on preterm infants (CBF) through incubator wall, and on sensitive burn tissues and mastectomy skin flaps without direct device-tissue interactions. Supporting activities outlined include integrated capability for acquiring surface curvature information, rapid 2D blood flow mapping, and optimizations via tissue-like phantoms and computer simulations. These applications and activities both highlight and guide the reader as to the expected abilities and limitations of scDCT for adapting into their own preclinical/clinical research, use in constrained environments (i.e., neonatal intensive care unit bedside), and use on vulnerable subjects and measurement sites.

This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes numerous examples and links to online resources.

This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes numerous examples and links to online resources.

This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms – the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms. This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology fordesigning efficient large-scale graph algorithms.

This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms – the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms. This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology fordesigning efficient large-scale graph algorithms.

Chinese companies have been increasingly active in outbound investment in recent years, with Germany the third largest destination in Europe. Adopting an analytical approach and utilizing case studies and expert interviews, this book examines Chinese mergers and acquisitions (M&As) in Germany, with a focus on the companies’ business growth, particularly the integration process and subsequent growth after acquisition. The authors contend that Chinese investors take a different approach than their western counterparts, by fostering a long-term orientation toward their investments and placing greater emphasis on co-evolution with the acquired firms rather than transfer of knowledge back to China. This book offers readers a behind-the-scenes story of three separate M&A cases, shedding light on the growth models that ensue from mergers and acquisitions, and the endeavors of Chinese and German managers to grow the businesses together.

This book presents state-of-the-art research advances in the field of biologically inspired cooperative control theories and their applications. It describes various biologically inspired cooperative control and optimization approaches and highlights real-world examples in complex industrial processes. Multidisciplinary in nature and closely integrating theory and practice, the book will be of interest to all university researchers, control engineers and graduate students in intelligent systems and control who wish to learn the core principles, methods, algorithms, and applications.

Chinese companies have been increasingly active in outbound investment in recent years, with Germany the third largest destination in Europe. Adopting an analytical approach and utilizing case studies and expert interviews, this book examines Chinese mergers and acquisitions (M&As) in Germany, with a focus on the companies’ business growth, particularly the integration process and subsequent growth after acquisition. The authors contend that Chinese investors take a different approach than their western counterparts, by fostering a long-term orientation toward their investments and placing greater emphasis on co-evolution with the acquired firms rather than transfer of knowledge back to China. This book offers readers a behind-the-scenes story of three separate M&A cases, shedding light on the growth models that ensue from mergers and acquisitions, and the endeavors of Chinese and German managers to grow the businesses together.

Large-scale, highly interconnected networks, which are often modeled as graphs, pervade both our society and the natural world around us. Uncertainty, on the other hand, is inherent in the underlying data due to a variety of reasons, such as noisy measurements, lack of precise information needs, inference and prediction models, or explicit manipulation, e.g., for privacy purposes. Therefore, uncertain, or probabilistic, graphs are increasingly used to represent noisy linked data in many emerging application scenarios, and they have recently become a hot topic in the database and data mining communities. Many classical algorithms such as reachability and shortest path queries become #P-complete and, thus, more expensive over uncertain graphs. Moreover, various complex queries and analytics are also emerging over uncertain networks, such as pattern matching, information diffusion, and influence maximization queries. In this book, we discuss the sources of uncertain graphs and their applications, uncertainty modeling, as well as the complexities and algorithmic advances on uncertain graphs processing in the context of both classical and emerging graph queries and analytics. We emphasize the current challenges and highlight some future research directions.

This book introduces readers to the application of orbital data on space objects in the contexts of conjunction assessment and space situation analysis, including theories and methodologies. It addresses the main topics involved in space object conjunction assessment, such as: orbital error analysis of space objects; close approach analysis; the calculation, analysis and application of collision probability; and the comprehensive assessment of collision risk. In addition, selected topics on space situation analysis are also presented, including orbital anomaly and space event analysis, and so on. The book offers a valuable guide for researchers and engineers in the fields of astrodynamics, space telemetry, tracking and command (TT&C), space surveillance, space situational awareness, and space debris, as well as for graduates majoring in flight vehicle design and related fields.

This book presents state-of-the-art research advances in the field of biologically inspired cooperative control theories and their applications. It describes various biologically inspired cooperative control and optimization approaches and highlights real-world examples in complex industrial processes. Multidisciplinary in nature and closely integrating theory and practice, the book will be of interest to all university researchers, control engineers and graduate students in intelligent systems and control who wish to learn the core principles, methods, algorithms, and applications.

This book introduces readers to the application of orbital data on space objects in the contexts of conjunction assessment and space situation analysis, including theories and methodologies. It addresses the main topics involved in space object conjunction assessment, such as: orbital error analysis of space objects; close approach analysis; the calculation, analysis and application of collision probability; and the comprehensive assessment of collision risk. In addition, selected topics on space situation analysis are also presented, including orbital anomaly and space event analysis, and so on. The book offers a valuable guide for researchers and engineers in the fields of astrodynamics, space telemetry, tracking and command (TT&C), space surveillance, space situational awareness, and space debris, as well as for graduates majoring in flight vehicle design and related fields.