Yang Yu – författare

Lacan and Chan Buddhist Thought provides a close reading of how Lacan mobilizes concepts from Chan Buddhist philosophy, culture, and practice in his later teachings.

The book emerged from the three co-authors’ engagement with Lacan’s 1962–1963 Seminar on Anxiety, and the significance of Lacan’s original interpretation of the Buddhist principle that desire is the cause of suffering. The book reads key Lacanian concepts – such as the objet a, jouissance, the real, Nirvana, and the mirror – through ancient Buddhist teachings and koans. With this focused exploration of psychoanalysis and Chan Buddhism, the authors offer a philosophically grounded cross-cultural approach to the theory and practice of psychoanalysis in Asian countries.

Lacan and Chan Buddhist Thought will be a rich resource for psychoanalysts, academics, and students interested in Lacan and religion, the intellectual and cultural relationship between Asian and Western thought, and Mahayana Buddhism more generally.

Lacan and Chan Buddhist Thought provides a close reading of how Lacan mobilizes concepts from Chan Buddhist philosophy, culture, and practice in his later teachings.

The book emerged from the three co-authors’ engagement with Lacan’s 1962–1963 Seminar on Anxiety, and the significance of Lacan’s original interpretation of the Buddhist principle that desire is the cause of suffering. The book reads key Lacanian concepts – such as the objet a, jouissance, the real, Nirvana, and the mirror – through ancient Buddhist teachings and koans. With this focused exploration of psychoanalysis and Chan Buddhism, the authors offer a philosophically grounded cross-cultural approach to the theory and practice of psychoanalysis in Asian countries.

Lacan and Chan Buddhist Thought will be a rich resource for psychoanalysts, academics, and students interested in Lacan and religion, the intellectual and cultural relationship between Asian and Western thought, and Mahayana Buddhism more generally.

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 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.

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.

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 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. 

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.    

This book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in artificial intelligence, machine learning, AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients are unavailable. By bridging theoretical foundations with practical implementations, it fills critical gaps in the field, making it an indispensable resource for both academic and industrial audiences.

The book introduces innovative frameworks such as sampling-and-classification (SAC) and sampling-and-learning (SAL), which underpin cutting-edge algorithms like Racos and SRacos. These methods are designed to excel in challenging optimization scenarios, including high-dimensional search spaces, noisy environments, and parallel computing. A dedicated section on the ZOOpt toolbox provides practical tools for implementing these algorithms effectively. The book’s structure moves from foundational principles and algorithmic development to advanced topics and real-world applications, such as hyperparameter tuning, neural architecture search, and algorithm selection in AutoML.

Readers will benefit from a comprehensive yet concise presentation of modern DFO methods, gaining theoretical insights and practical tools to enhance their research and problem-solving capabilities. A foundational understanding of machine learning, probability theory, and algorithms is recommended for readers to fully engage with the material.