Outcasts and pariahs are known to exist in several Asian countries but have usually not been associated with traditional Chinese society. Chinese Outcasts shows that some Chinese were in fact treated as outcasts or semi-outcasts. They include the boat people of South China and certain less well-known groups in different regions, including the "musicians' households" and the "fallen people". The reasons for their inferior status and perceived impurity is examined, as well as the intent behind a series of imperial emancipation edicts in the 1720s and 30s. The edict provided an escape route from inferior legal status but failed to put a quick end to customary social discrimination.
Optimization for Learning and Control Comprehensive resource providing a masters’ level introduction to optimization theory and algorithms for learning and control Optimization for Learning and Control describes how optimization is used in these domains, giving a thorough introduction to both unsupervised learning, supervised learning, and reinforcement learning, with an emphasis on optimization methods for large-scale learning and control problems. Several applications areas are also discussed, including signal processing, system identification, optimal control, and machine learning. Today, most of the material on the optimization aspects of deep learning that is accessible for students at a Masters’ level is focused on surface-level computer programming; deeper knowledge about the optimization methods and the trade-offs that are behind these methods is not provided. The objective of this book is to make this scattered knowledge, currently mainly available in publications in academic journals, accessible for Masters’ students in a coherent way. The focus is on basic algorithmic principles and trade-offs. Optimization for Learning and Control covers sample topics such as: Optimization theory and optimization methods, covering classes of optimization problems like least squares problems, quadratic problems, conic optimization problems and rank optimization. First-order methods, second-order methods, variable metric methods, and methods for nonlinear least squares problems. Stochastic optimization methods, augmented Lagrangian methods, interior-point methods, and conic optimization methods. Dynamic programming for solving optimal control problems and its generalization to reinforcement learning. How optimization theory is used to develop theory and tools of statistics and learning, e.g., the maximum likelihood method, expectation maximization, k-means clustering, and support vector machines. How calculus of variations is used in optimal control and for deriving the family of exponential distributions. Optimization for Learning and Control is an ideal resource on the subject for scientists and engineers learning about which optimization methods are useful for learning and control problems; the text will also appeal to industry professionals using machine learning for different practical applications.
Climate change has an impact on the ability of transboundary water management institutions to deliver on their respective mandates. The starting point for this book is that actors within transboundary water management institutions develop responses to the climate change debate, as distinct from the physical phenomenon of climate change. Actors respond to this debate broadly in three distinct ways – adapt, resist (as in avoiding the issue) and subvert (as in using the debate to fulfil their own agenda). The book charts approaches which have been taken over the past two decades to promote more effective water management institutions, covering issues of conflict, cooperation, power and law. A new framework for a better understanding of the interaction between transboundary water management institutional resilience and global change is developed through analysis of the way these institutions respond to the climate change debate. This framework is applied to six river case studies from Africa, Asia and the Middle East (Ganges-Brahmaputra, Jordan, Mekong, Niger, Nile, Orange-Senqu) from which learning conclusions and policy recommendations are developed.
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.