How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
Deflections tend to have more significance in modern structures, especially those that are either taller, longer or have wider spans than earlier designs. It is also necessary to provide desirable distributions of internal forces in order to achieve effective, efficient and elegant structures. This book presents four structural concepts relating to deflections and internal forces in structures. It demonstrates a number of routes and physical measures together with their implementation for creating desirable distributions of internal forces and for designing structures against deflection. Hand calculation examples, with and without using the implementation measures, are provided to quantify the effectiveness and efficiency of the structural concepts. Practical examples, including several well-known structures, are considered qualitatively to illustrate the practical implementation of the structural concepts and show their structural rationale. The book is especially suitable for advanced undergraduate and graduate students studying civil engineering or architecture and should enhance the holistic comprehension of structural engineers and architects. Features Develops the concepts from their principles through to their implementation Provides worked examples in pairs and analyses real structures Especially suits final year undergraduates and graduate students in structural engineering Author Bio Dr. Tianjian Ji, CEng, FIStructE, FHEA, is Reader in Structural Engineering at the University of Manchester, UK. He received the Award for Excellence in Structural Engineering Education from the Institution of Structural Engineers, UK, in 2014 and the Teaching Excellence Award from the University of Manchester in 2016. He is the primary author of Understanding and Using Structural Concepts, 2nd edition, also published by Taylor & Francis.
Vibro-Acoustics of Lightweight Sandwich Structures introduces the study of the coupled vibration and acoustic behavior of lightweight sandwich structures in response to harmonic force and sound pressure. This book focuses on the theoretical modeling and experimental investigation of lightweight sandwich structures in order to provide a predictive framework for vibro-acoustic characteristics of typical engineering structures. Furthermore, by developing solution tools, it concentrates on the influence of key systematic parameters leading to effective guidance for optimal structure design toward lightweight, high-stiffness and superior sound insulation capability. This book is intended for researchers, scientists, engineers and graduate students in mechanical engineering especially in structural mechanics, mechanics and acoustics. Fengxian Xin and Tianjian Lu both work at the School of Aerospace, Xi’an Jiaotong University.
In this first scientific survey of political participation in the People's Republic of China, Tianjian Shi identifies twenty-eight participatory acts and groups them into seven areas: voting, campaign activities, appeals, adversarial activities, cronyism, resistance, and boycotts. What he finds will surprise many observers. Political participation in a closed society is not necessarily characterized by passive citizens driven by regime mobilization aimed at carrying out predetermined goals. Beijing citizens acknowledge that they actively engage in various voluntary participatory acts to articulate their interests. In a society where communication channels are controlled by the government, Shi discovers, access to information from unofficial means becomes the single most important determinant for people's engaging in participatory acts. Government-sponsored channels of appeal are easily accessible to ordinary citizens, so socioeconomic resources are unimportant in determining who uses these channels. Instead, voter turnout is found to be associated with the type of work unit a person belongs to, subjective evaluations of one's own economic status, and party affiliation. Those most likely to engage in campaign activities, adversarial activities, cronyism, resistance, and boycotts are the more disadvantaged groups in Beijing. While political participation in the West fosters a sense of identification, the unconventional modes of participation in Beijing undermine the existing political order.
Application of Thermo-Fluidic Measurement Techniques: An Introduction provides essential measurement techniques in heat transfer and aerodynamics. In addition to a brief, but physically elaborate description of the principles of each technique, multiple examples for each technique are included. These examples elaborate all the necessary details of (a) test setups, (b) calibration, (c) data acquisition procedure, and (d) data interpretation, with comments on the limitations of each technique and how to avoid mistakes that are based on the authors' experience. The authors have different expertise in convection heat transfer and aerodynamics, and have collaborated on various research projects that employ a variety of experimental techniques. Each author has a different view and approach to individual experimental techniques, but these views complement each other, giving new users of each technique a rounded view. With the introduction of this valuable reference book, the reader can quickly learn both the overall and detailed aspects of each experimental technique and then apply them to their own work. - Contains both basic principles and fundamental, physical descriptions - Provides examples that demonstrate how each experimental technique can be used for industrial testing and academic research in heat transfer and aerodynamics - Includes practical and in-depth examples for each technique, with comments on each experimental technique based on the authors' experiences, including limitations and trial errors with some examples of data interpretation - Combines classical techniques in aerodynamics and conduction/convection heat transfer with modern, cutting-edge approaches - Collates the information about various pointwise and whole field velocity and thermal measurement techniques in a single resource
Understanding and Using Structural Concepts, Second Edition provides numerous demonstrations using physical models and practical examples. A significant amount of material, not found in current textbooks, is included to enhance the understanding of structural concepts and stimulate interest in learning, creative thinking, and design. This is achiev
How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
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