An elegiac illumination of personal and political histories misremembered and censored, There Is Still Singing In The Afterlife is animated by the intimate language of spirits-living, loved, and gone-singing to us from the hereafter. A poet of deep noticing, JinJin Xu interrogates the nature of witness and memory, taking seriously the consequence of confession in a foreign land, in a language not her own. Xu grapples with a forbidden language-blending the lyric with confession and erasure to sing the unspeakable, to open our eyes to seek the light. This is a stellar debut from a poet you should watch out for.

There Is Still Singing in the Afterlife

Literature & Fiction

Xu, JinJin

Poetry

Radix Media

Federated Learning: Fundamentals and Advances (Machine Learning: Foundations, Methodologies, and Applications)

This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionary learning, and privacy preservation.The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses.&nbsp; &nbsp; &nbsp; &nbsp;

Chen, Yang

Jin, Yaochu

Zhu, Hangyu

Reference

Springer

This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionarylearning, and privacy preservation.The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses.&nbsp; &nbsp; &nbsp; &nbsp;

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Books by Xu, JinJin