This book explores the history of mechanical engineering since the Bronze Age. Focusing on machinery inventions and the development of mechanical technology, it also discusses the machinery industry and modern mechanical education. The evolution of machinery is divided into three stages: Ancient (before the European Renaissance), Modern (mainly including the two Industrial Revolutions) and Contemporary (since the Revolution in Physics, especially post Second World War). The book not only clarifies the development of mechanical engineering, but also reveals the driving forces behind it - e.g. the economy, national defense and human scientific research activities - to highlight the links between technology and society; mechanical engineering and the natural sciences; and mechanical engineering and related technological areas. Though mainly intended as a textbook or supplemental reading for graduate students, the book also offers a unique resource for researchers and engineers in mechanical engineering who wish to broaden their horizons.

This book constitutes the refereed post-conference proceedings of the 14th BenchCouncil International Symposium on Benchmarking, Measuring, and Optimization, Bench 2022, held virtually in November 2022.The 10 revised full papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in topical sections named: Architecture and System, Algorithm and Dataset, Network and Memory.

This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.

Machinery Dynamics includes recent advancements in this quickly evolving area, while also analyzing real applications, analyzing integrated systems, and including further discussions on each mechanical component. The book treats mechanisms separately, with different methods depending on the level of accuracy required. The contents of this book is made to suit the needs of MsC and PhD students, researchers and engineers in the areas of design of high speed machinery, condition monitoring of machine operation, and vibration.

This book explores the history of mechanical engineering since the Bronze Age. Focusing on machinery inventions and the development of mechanical technology, it also discusses the machinery industry and modern mechanical education. The evolution of machinery is divided into three stages: Ancient (before the European Renaissance), Modern (mainly including the two Industrial Revolutions) and Contemporary (since the Revolution in Physics, especially post Second World War). The book not only clarifies the development of mechanical engineering, but also reveals the driving forces behind it - e.g. the economy, national defense and human scientific research activities - to highlight the links between technology and society; mechanical engineering and the natural sciences; and mechanical engineering and related technological areas. Though mainly intended as a textbook or supplemental reading for graduate students, the book also offers a unique resource for researchers and engineers in mechanical engineering who wish to broaden their horizons.

Scalable and efficient distributed learning is one of the main driving forces behind the recent rapid advancement of machine learning and artificial intelligence. One prominent feature of this development is that recent progress has been made by researchers in two communities: (1) the system community such as database, data management, and distributed systems, and (2) the machine learning and mathematical optimization community. The interaction and knowledge sharing between these two communities has led to the rapid development of new distributed learning systems and theory. This monograph provides a brief introduction to three distributed learning techniques that have recently been developed: lossy communication compression, asynchronous communication, and decentralized communication. These have significant impact on the work in both the system and machine learning and mathematical optimization communities but to fully realize the potential, it is essential they understand the whole picture. This monograph provides the bridge between the two communities. The simplified introduction to the essential aspects of each community enables researchers to gain insights into the factors influencing both.The monograph provides students and researchers the groundwork for developing faster and better research results in this dynamic area of research.