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.
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.
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.
Due to measurement errors, transmission lost, or injected noise for privacy protection, uncertainty exists in the data of many real applications. However, query processing techniques for deterministic data cannot be directly applied to uncertain data because they do not have mechanisms to handle data uncertainty. Therefore, efficient and effective manipulation of uncertain data is a practical yet challenging research topic. In this book, we start from the data models for imprecise and uncertain data, move on to defining different semantics for queries on uncertain data, and finally discuss the advanced query processing techniques for various probabilistic queries in uncertain databases. The book serves as a comprehensive guideline for query processing over uncertain databases. Table of Contents: Introduction / Uncertain Data Models / Spatial Query Semantics over Uncertain Data Models / Spatial Query Processing over Uncertain Databases / Conclusion
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 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 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 presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, including blind source separation; social network mining; image and video processing; array signal processing; and, wireless communications. The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed. Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.
Cloud Data Center Network Architectures and Technologies has been written with the support of Huawei's vast technical knowledge and experience in the data center network (DCN) field, as well as its understanding of customer service requirements. This book describes in detail the architecture design, technical implementation, planning and design, and deployment suggestions for cloud DCNs based on the service challenges DCNs encounter. It starts by describing the overall architecture and technical evolution of DCNs, with the aim of helping readers understand the development of DCNs. It then proceeds to explain the design and implementation of cloud DCNs, including the service model of a single data center (DC), construction of physical and logical networks of DCs, construction of multiple DCNs, and security solutions of DCs. Next, this book dives deep into practices of cloud DCN deployment based on real-world cases to help readers better understand how to build cloud DCNs. Finally, this book introduces DCN openness and some of the hottest forward-looking technologies. In summary, you can use this book as a reference to help you to build secure, reliable, efficient, and open cloud DCNs. It is intended for technical professionals of enterprises, research institutes, information departments, and DCs, as well as teachers and students of computer network-related majors in colleges and universities. Authors Lei Zhang Mr. Zhang is the Chief Architect of Huawei's DCN solution. He has more than 20 years' experience in network product and solution design, as well as a wealth of expertise in product design and development, network planning and design, and network engineering project implementation. He has led the design and deployment of more than 10 large-scale DCNs for Fortune Global 500 companies worldwide. Le Chen Mr. Chen is a Huawei DCN Solution Documentation Engineer with eight years' experience in developing documents related to DCN products and solutions. He has participated in the design and delivery of multiple large-scale enterprise DCNs. Mr. Chen has written many popular technical document series, such as DCN Handbook and BGP Topic.
This SpringerBrief explains how to leverage mobile users' social relationships to improve the interactions of mobile devices in mobile networks. It develops a social group utility maximization (SGUM) framework that captures diverse social ties of mobile users and diverse physical coupling of mobile devices. Key topics include random access control, power control, spectrum access, and location privacy. This brief also investigates SGUM-based power control game and random access control game, for which it establishes the socially-aware Nash equilibrium (SNE). It then examines the critical SGUM-based spectrum access game, and pseudonym change game for personalized location privacy. The authors propose future work on extending the SGUM framework to negative social ties, to demonstrate relevance to security applications and span the continuum between zero-sum game (ZSG) and non-cooperative game (NCG). Social Group Utility Maximization targets researchers and professionals working on mobile networks and social networks. Advanced-level students in electrical engineering and computer science will also find this material useful for their related courses.
Identifying the input-output relationship of a system or discovering the evolutionary law of a signal on the basis of observation data, and applying the constructed mathematical model to predicting, controlling or extracting other useful information constitute a problem that has been drawing a lot of attention from engineering and gaining more and more importance in econo metrics, biology, environmental science and other related areas. Over the last 30-odd years, research on this problem has rapidly developed in various areas under different terms, such as time series analysis, signal processing and system identification. Since the randomness almost always exists in real systems and in observation data, and since the random process is sometimes used to model the uncertainty in systems, it is reasonable to consider the object as a stochastic system. In some applications identification can be carried out off line, but in other cases this is impossible, for example, when the structure or the parameter of the system depends on the sample, or when the system is time-varying. In these cases we have to identify the system on line and to adjust the control in accordance with the model which is supposed to be approaching the true system during the process of identification. This is why there has been an increasing interest in identification and adaptive control for stochastic systems from both theorists and practitioners.
Special distillation processes are required for separation of mixtures close to boiling point or for forming azeotrope mixtures into their pure components. In Special Distillation Processes, the authors focus on latest developments in the field, such as separation methods that may prove useful for solving problems encountered during research. Topics include extraction, membrane and adsorption distillation involving the separation principle, process design and experimental techniques. The relationship between the processes and the techniques are also presented. Comprehensive and easy-to-read, this book provides key information needed to understand the processes and is a valuable reference source for chemical engineers as well as students wishing to branch out in chemical engineering. * The only comprehensive book available on special distillation processes * Contains a thorough introduction to recent developments in the field * A valuable reference for students and engineers in chemical engineering
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 for designing efficient large-scale graph algorithms.
On a clear look, the face of the soul body is angular, like a knife. The high nose makes the facial features more delicate, and the eyelashes are slightly curled up, covering a pair of bright and deep eyes. The calm eyes are unpredictable and add a mysterious feeling. The handsome posture shows his demeanor as a king, as well as his long snow-white hair. Don't look too good at all!
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