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Rebel Tongues: Dialects, Slang, and Fusion Languages in Global Literature
'Rebel Tongues' explores how dialects, slang, and hybrid languages shape identity, resist oppression, and bridge cultural divides in global literature. Drawing on works by authors like Chinua Achebe and Salman Rushdie, this book reveals how linguistic diversity challenges norms, empowers marginalized voices, and transforms literary narratives. A powerful examination of language as both a cultural tool and a form of resistance.
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...
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...
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.
Human Learning: Biology, Brain, and Neuroscience
Human learning is studied in a variety of ways. Motor learning is often studied separately from verbal learning. Studies may delve into anatomy vs function, may view behavioral outcomes or look discretely at the molecular and cellular level of learning. All have merit but they are dispersed across a wide literature and rarely are the findings integrated and synthesized in a meaningful way. Human Learning: Biology, Brain, and Neuroscience synthesizes findings across these levels and types of learning and memory investigation.Divided into three sections, each section includes a discussion by the editors integrating themes and ideas that emerge across the chapters within each section. Section 1...
Searches for Dijet Resonances
This book addresses one of the most intriguing mysteries of our universe: the nature of dark matter. The results presented here mark a significant and substantial contribution to the search for new physics, in particular for new particles that couple to dark matter. The first analysis presented is a search for heavy new particles that decay into pairs of hadronic jets (dijets). This pioneering analysis explores unprecedented dijet invariant masses, reaching nearly 7 TeV, and sets constraints on several important new physics models. The two subsequent analyses focus on the difficult low dijet mass region, down to 200 GeV, and employ a novel technique to efficiently gather low-mass dijet events. The results of these analyses transcend the long-standing constraints on dark matter mediator particles set by several existing experiments.
The English Circle Class 06
This series is a multi-skill structured course in English with an intense focus on grammar structure practised through the LSRW skills. Some of the key features of this series include HOTS, Life skills, exercises based on the principle of multiple intelligences and learn-by-doing methods as well as projects and sample test papers to evaluate all skills.
Car Safety Wars
Car Safety Wars is a gripping history of the hundred-year struggle to improve the safety of American automobiles and save lives on the highways. Described as the “equivalent of war” by the Supreme Court, the battle involved the automobile industry, unsung and long-forgotten safety heroes, at least six US Presidents, a reluctant Congress, new auto technologies, and, most of all, the mindset of the American public: would they demand and be willing to pay for safer cars? The “Car Safety Wars” were at first won by consumers and safety advocates. The major victory was the enactment in 1966 of a ground breaking federal safety law. The safety act was pushed through Congress over the bitter ...
