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Beyond Vision
Beyond Vision is the first English-language collection of essays on art by Pavel Florensky (1882–1937), Russian philosopher, priest, linguist, scientist, mathematician – and art historian. In addition to seven essays by Florensky, the book includes a biographical introduction and an examination of Florensky’s contribution as an art historian by Nicoletta Misler. Beyond Vision reveals Florensky’s fundamental attitudes to the vital questions of construction, composition, chronology, function and destination in the fields of painting, sculpture and design. His reputation as a theologian and philosopher is already established in the English-speaking world, but this first collection in En...
Microfluidics and Lab-on-a-Chip
Responding to the need for an affordable, easy-to-read textbook that introduces microfluidics to undergraduate and postgraduate students, this concise book will provide a broad overview of the important theoretical and practical aspects of microfluidics and lab-on-a-chip, as well as its applications.
Modern Sensors Handbook
Modern sensors working on new principles and/or using new materials and technologies are more precise, faster, smaller, use less power and are cheaper. Given these advantages, it is vitally important for system developers, system integrators and decision makers to be familiar with the principles and properties of the new sensor types in order to make a qualified decision about which sensor type to use in which system and what behavior may be expected. This type of information is very difficult to acquire from existing sources, a situation this book aims to address by providing detailed coverage on this topic. In keeping with its practical theme, the discussion concentrates on sensor types used or having potential to be used in industrial applications.
Federated Learning
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 lear...
Lifelong Machine Learning, Second Edition
Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past he...
The European Language Portfolio
The European Language Portfolio aims to foster the development of learner autonomy, intercultural awareness and plurilingualism. Teachers of particular languages working on their own can use the ELP to promote learner autonomy, but the goals of intercultural awareness and plurilingualism invite us to use the ELP in all foreign language classes at all levels in the school. The guide introduces the language education policy that underpins the ELP, explores the key concepts that it embodies, and explains how to plan, implement and evaluate whole-school ELP projects. The ten case studies published on the project website illustrate various dimensions of ELP use and include practical suggestions and activities for teachers and learners.
Graph Representation Learning
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 sy...
