Computer Methods in Biomechanics and Biomedical Engineering
Computer Methods in Biomechanics and Biomedical Engineering

Author: J. Middleton

Publisher: CRC Press

Published: 1996-03-18

Total Pages: 582

ISBN-13: 9782919875009

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These papers are concerned with new advances and novel solutions in the areas of biofluids, image-guided surgery, tissue engineering and cardovascular mechanics, implant analysis, soft tissue mechanics, bone remodeling and motion analysis. The contents also feature a special section on dental materials, dental adhesives and orthodontic mechanics. This edition contains many examples, tables and figures, and together with the many references, provides the reader with invaluable information on the latest theoretical developments and applications.

Graph Representation Learning
Graph Representation Learning

Author: William L. William L. Hamilton

Publisher: Springer Nature

Published: 2022-06-01

Total Pages: 141

ISBN-13: 3031015886

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Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Adaptive Strategies for Water Heritage
Adaptive Strategies for Water Heritage

Author: Carola Hein

Publisher: Springer Nature

Published: 2019-10-18

Total Pages: 448

ISBN-13: 3030002683

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This Open Access book, building on research initiated by scholars from the Leiden-Delft-Erasmus Centre for Global Heritage and Development (CHGD) and ICOMOS Netherlands, presents multidisciplinary research that connects water to heritage. Through twenty-one chapters it explores landscapes, cities, engineering structures and buildings from around the world. It describes how people have actively shaped the course, form and function of water for human settlement and the development of civilizations, establishing socio-economic structures, policies and cultures; a rich world of narratives, laws and practices; and an extensive network of infrastructure, buildings and urban form. The book is organized in five thematic sections that link practices of the past to the design of the present and visions of the future: part I discusses drinking water management; part II addresses water use in agriculture; part III explores water management for land reclamation and defense; part IV examines river and coastal planning; and part V focuses on port cities and waterfront regeneration. Today, the many complex systems of the past are necessarily the basis for new systems that both preserve the past and manage water today: policy makers and designers can work together to recognize and build on the traditional knowledge and skills that old structure embody. This book argues that there is a need for a common agenda and an integrated policy that addresses the preservation, transformation and adaptive reuse of historic water-related structures. Throughout, it imagines how such efforts will help us develop sustainable futures for cities, landscapes and bodies of water.

Federated Learning
Federated Learning

Author: Qiang Yang

Publisher: Springer Nature

Published: 2020-11-25

Total Pages: 291

ISBN-13: 3030630765

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This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”