This prizewinning PhD thesis presents a general discussion of the orbital motion close to solar system small bodies (SSSBs), which induce non-central asymmetric gravitational fields in their neighborhoods. It introduces the methods of qualitative theory in nonlinear dynamics to the study of local/global behaviors around SSSBs. Detailed mechanical models are employed throughout this dissertation, and specific numeric techniques are developed to compensate for the difficulties of directly analyzing. Applying this method, several target systems, like asteroid 216 Kleopatra, are explored in great detail, and the results prove to be both revealing and pervasive for a large group of SSSBs.

Dental Biotribology summarizes the latest achievements in dental wear and is designed to help the reader better understand the relationship between structures and tribological properties of human teeth. This book provides guidance on the biomimic design of anti-wear engineering systems based on human teeth and also explains mechanisms of occlusal wear and erosion as well as fretting wear related to dental implants and orthodontics. Additionally, this book provides valuable insights into the development of improved dental materials and oral treatments.

Offers an in-depth and focused exploration of the relationship between psychoanalysis and Chinese and Japanese culture based on their ancient traditions rather than a cross-cultural approach that refers to Asian cultures in terms of contemporary generalities and cultural stereotypes. Provides a close reading of how Lacan mobilizes concepts from Zen Buddhist philosophy, culture and practice in his later teachings.

Offers an in-depth and focused exploration of the relationship between psychoanalysis and Chinese and Japanese culture based on their ancient traditions rather than a cross-cultural approach that refers to Asian cultures in terms of contemporary generalities and cultural stereotypes. Provides a close reading of how Lacan mobilizes concepts from Zen Buddhist philosophy, culture and practice in his later teachings.

Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance.

This prizewinning PhD thesis presents a general discussion of the orbital motion close to solar system small bodies (SSSBs), which induce non-central asymmetric gravitational fields in their neighborhoods. It introduces the methods of qualitative theory in nonlinear dynamics to the study of local/global behaviors around SSSBs. Detailed mechanical models are employed throughout this dissertation, and specific numeric techniques are developed to compensate for the difficulties of directly analyzing. Applying this method, several target systems, like asteroid 216 Kleopatra, are explored in great detail, and the results prove to be both revealing and pervasive for a large group of SSSBs.

Dental Biotribology summarizes the latest achievements in dental wear and is designed to help the reader better understand the relationship between structures and tribological properties of human teeth. This book provides guidance on the biomimic design of anti-wear engineering systems based on human teeth and also explains mechanisms of occlusal wear and erosion as well as fretting wear related to dental implants and orthodontics. Additionally, this book provides valuable insights into the development of improved dental materials and oral treatments.

This book describes how local consumption, particularly in urban areas, is increasingly met by global supply chains. These supply chains often extend over large geographical distances and have greater global environmental impacts, contributing to pollution, climate change, water scarcity, and deforestation. As consumption is increasingly met by globalized supply chains, causing social, economic, and environmental impacts elsewhere, consumption decisions can unknowingly contribute and reinforce global inequality and exploitation. To account for the impacts of consumption and distribution of wealth we need to analyze global supply and value chains. In this volume, the authors provide an overview of key methods of analysis, including Multi-Regional Input-Output analysis and Life Cycle Assessment. Subsequent chapters connect local consumption to the global consequences of different environmental issues, such as water and land use and stress, greenhouse gases emissions, and other forms of air pollution. Each issue is addressed in an individual chapter, including case studies from China, U.S. and UK. The book will be key reading for students taking courses in environmental sciences, sustainability sciences, ecological economies, and geography.

The 3-volume set LNAI 13280, LNAI 13281 and LNAI 13282 constitutes the proceedings of the 26th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2022, which was held during May 2022 in Chengdu, China. The 121 papers included in the proceedings were carefully reviewed and selected from a total of 558 submissions. They were organized in topical sections as follows: Part I: Data Science and Big Data Technologies, Part II: Foundations; and Part III: Applications.

Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

The 3-volume set LNAI 13280, LNAI 13281 and LNAI 13282 constitutes the proceedings of the 26th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2022, which was held during May 2022 in Chengdu, China. The 121 papers included in the proceedings were carefully reviewed and selected from a total of 558 submissions. They were organized in topical sections as follows: Part I: Data Science and Big Data Technologies, Part II: Foundations; and Part III: Applications.

The 3-volume set LNAI 13280, LNAI 13281 and LNAI 13282 constitutes the proceedings of the 26th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2022, which was held during May 2022 in Chengdu, China. The 121 papers included in the proceedings were carefully reviewed and selected from a total of 558 submissions. They were organized in topical sections as follows: Part I: Data Science and Big Data Technologies, Part II: Foundations; and Part III: Applications.

This book describes how local consumption, particularly in urban areas, is increasingly met by global supply chains. These supply chains often extend over large geographical distances and have greater global environmental impacts, contributing to pollution, climate change, water scarcity, and deforestation. As consumption is increasingly met by globalized supply chains, causing social, economic, and environmental impacts elsewhere, consumption decisions can unknowingly contribute and reinforce global inequality and exploitation. To account for the impacts of consumption and distribution of wealth we need to analyze global supply and value chains. In this volume, the authors provide an overview of key methods of analysis, including Multi-Regional Input-Output analysis and Life Cycle Assessment. Subsequent chapters connect local consumption to the global consequences of different environmental issues, such as water and land use and stress, greenhouse gases emissions, and other forms of air pollution. Each issue is addressed in an individual chapter, including case studies from China, U.S. and UK. The book will be key reading for students taking courses in environmental sciences, sustainability sciences, ecological economies, and geography.

This book constitutes the refereed proceedings of the Second International Conference on Distributed Artificial Intelligence, DAI 2020, held in Nanjing, China, in October 2020. The 9 full papers presented in this book were carefully reviewed and selected from 22 submissions. DAI aims at bringing together international researchers and practitioners in related areas including general AI, multiagent systems, distributed learning, computational game theory, etc., to provide a single, high-profile, internationally renowned forum for research in the theory and practice of distributed AI. Due to the Corona pandemic this event was held virtually.