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Water Chemistry
Water, which plays an important role in every aspect of our daily lives, is the most valuable natural resource we have on this planet. Drinking, bathing, cooking, regeneration, cleaning, production, energy, and many other uses of water originate from some of its versatile, useful, basic, and unique features. The access, purification, and reuse of water on our planet, which is of course not endless and not available for direct use, is directly related to the water chemistry that explores its inimitable properties. This book includes research on water chemistry-related applications in environmental management and sustainable environmental issues such as water and wastewater treatment, water quality management, and other similar topics. The book consists of three sections, namely, water treatment, wastewater treatment, and water splitting, respectively, and includes 11 chapters. In these chapters, water-wastewater remediation methods, nanomaterials in water treatment, and water splitting processes are comprehensively reviewed in terms of water chemistry.The editors would like to record their sincere thanks to the authors for their contributions.
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...
Engineering Ceramics
A handy reference for technicians who want to understand the nature, properties and applications, of engineering ceramics. The book meets the needs of those working in the ceramics industry, as well as of technicians and engineers involved in the application of ceramic materials.
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...
AI 2018: Advances in Artificial Intelligence
This book constitutes the proceedings of the 31st Australasian Joint Conference on Artificial Intelligence, AI 2018, held in Wellington, New Zealand, in December 2018. The 50 full and 26 short papers presented in this volume were carefully reviewed and selected from 125 submissions. The paper were organized in topical sections named: agents, games and robotics; AI applications and innovations; computer vision; constraints and search; evolutionary computation; knowledge representation and reasoning; machine learning and data mining; planning and scheduling; and text mining and NLP.
Postmigration
The concept of »postmigration« has recently gained importance in the context of European societies' obsession with migration and integration along with emerging new forms of exclusion and nationalisms. This book introduces ongoing debates on the developing concept of »postmigration« and how it can be applied to arts and culture. While the concept has mainly gained traction in the cultural scene in Berlin, Germany, the contributions expand the field of study by attending to cultural expressions in literature, theatre, film, and art across various European societies, such as the United Kingdom, France, Finland, Denmark, and Germany. By doing so, the contributions highlight this concept's potential and show how it can offer new perspectives on transformations caused by migration.
Representation Learning for Natural Language Processing
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Three-Dimensional Elasticity
This volume is a thorough introduction to contemporary research in elasticity, and may be used as a working textbook at the graduate level for courses in pure or applied mathematics or in continuum mechanics. It provides a thorough description (with emphasis on the nonlinear aspects) of the two competing mathematical models of three-dimensional elasticity, together with a mathematical analysis of these models. The book is as self-contained as possible.
Learning Deep Architectures for AI
Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
