Ian Watson's brilliant debut novel was one of the most significant publications in British SF in the 1970s. Intellectually bracing and grippingly written, it is the story of three experiments in linguistics, and is driven by a searching analysis of the nature of communication. Deep in the Brazilian jungle, an isolated tribe face eviction from their ancestral lands - and the psychedelic fungus that makes their religious language possible. In a British laboratory, a brilliant linguist conducts cutting-edge experiments - but does his search for answers come at too high a cost? And in the ultimate test of linguistics, First Contact presents a challenge unlike any humanity has faced before . . . Fiercely intelligent, energetic and challenging, The Embedding immediately established Watson as a writer of rare power and vision, and is now recognized as a modern classic of SF.

This book provides a rare integrative interpretation of government-enterprise relations in China, offering readers a comprehensive understanding of the topic. Focusing on the government and its principal goals, it describes the transition of government-enterprise relations and highlights the embedding of the entities of government and enterprises in specific political, economic and social environments. Further, it analyzes how the government’s institutional arrangement regulates the behavior of various types of enterprises with different structures, and the logic mechanisms such institutional arrangements use to change and shape government-enterprise relations. Based on these issues and logic mechanisms, the book points out the complexity of government-enterprise relations and the diversity of their transition path, thus reflecting some typical features in the overall reform of China and discussing specific factors related to China’s social development experience.

An examination of how the mobile phone has become part of the fabric of society—as did such earlier technologies as the clock and the car. Why do we feel insulted or exasperated when our friends and family don't answer their mobile phones? If the Internet has allowed us to broaden our social world into a virtual friend-net, the mobile phone is an instrument of a more intimate social sphere. The mobile phone provides a taken-for-granted link to the people to whom we are closest; when we are without it, social and domestic disarray may result. In just a few years, the mobile phone has become central to the functioning of society. In this book, Rich Ling explores the process by which the mobile phone has become embedded in society, comparing it to earlier technologies that changed the character of our social interaction and, along the way, became taken for granted. Ling, drawing on research, interviews, and quantitative material, shows how the mobile phone (and the clock and the automobile before it) can be regarded as a social mediation technology, with a critical mass of users, a supporting ideology, changes in the social ecology, and a web of mutual expectations regarding use. By examining the similarities and synergies among these three technologies, Ling sheds a more general light on how technical systems become embedded in society and how they support social interaction within the closest sphere of friends and family.

Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.

The embedding method is a way of solving the Schrödinger equation for electrons in a region of space joined to a substrate. It is a flexible method, as well as surface electronic structure, it can be used to study interfaces, adsorbates, conductance through molecules and confined electrons, and even used to calculate the energy distribution of electrons confined by nanostructures. Embedding can be applied to solving Maxwell's equations, leading to an efficient way of finding the photonic and plasmonic band structure. In this book, John Inglesfield reviews the embedding method for calculating electronic structures and its application within modern condensed matter physics research. Supplemented with demonstration programmes, codes and examples, this book provides a thorough review of the method and would be an accessible starting point for graduate students or researchers in physics and physical chemistry wishing to understand and use the method, or as a single up to date and authoritative reference source for those already using the method.

Based on Fields medal winning work of Michael Freedman, this book explores the disc embedding theorem for 4-dimensional manifolds. This theorem underpins virtually all our understanding of topological 4-manifolds. Most famously, this includes the 4-dimensional Poincaré conjecture in the topological category. The Disc Embedding Theorem contains the first thorough and approachable exposition of Freedman's proof of the disc embedding theorem, with many new details. A self-contained account of decomposition space theory, a beautiful but outmoded branch of topology that produces non-differentiable homeomorphisms between manifolds, is provided, as well as a stand-alone interlude that explains the disc embedding theorem's key role in all known homeomorphism classifications of 4-manifolds via surgery theory and the s-cobordism theorem. Additionally, the ramifications of the disc embedding theorem within the study of topological 4-manifolds, for example Frank Quinn's development of fundamental tools like transversality are broadly described. The book is written for mathematicians, within the subfield of topology, specifically interested in the study of 4-dimensional spaces, and includes numerous professionally rendered figures.

Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.

Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.

This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector.This volume utilizes the dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces. Such an embedding gives one access to all algorithms developed in the past for feature vectors, which has been the predominant representation formalism in pattern recognition and related areas for a long time.

This monograph is concerned with Galois theoretical embedding problems of so-called Brauer type with a focus on 2-groups and on finding explicit criteria for solvability and explicit constructions of the solutions. The advantage of considering Brauer type embedding problems is their comparatively simple condition for solvability in the form of an obstruction in the Brauer group of the ground field. The book presupposes knowledge of classical Galois theory and the attendant algebra. Before considering questions of reducing the embedding problems and reformulating the solvability criteria, the author provides the necessary theory of Brauer groups, group cohomology and quadratic forms. The book will be suitable for students seeking an introduction to embedding problems and inverse Galois theory. It will also be a useful reference for researchers in the field.