Parallel and Distributed Computing and Networks

Complex networks are typically not homogeneous, as they tend to display an array of structures at different scales. A feature that has attracted a lot of research is their modular organisation, i.e., networks may often be considered as being composed of certain building blocks, or modules. In this Element, the authors discuss a number of ways in which this idea of modularity can be conceptualised, focusing specifically on the interplay between modular network structure and dynamics taking place on a network. They discuss, in particular, how modular structure and symmetries may impact on network dynamics and, vice versa, how observations of such dynamics may be used to infer the modular structure. They also revisit several other notions of modularity that have been proposed for complex networks and show how these can be related to and interpreted from the point of view of dynamical processes on networks.

An interdisciplinary and easy-to-understand introduction to the subject, covering fundamental theory and practical applications, and using numerous operational examples. This balanced text will allow you to begin from what the radar observes and move deeper through electromagnetic scattering theory and cloud microphysics to understand and interpret data as it appears on the display. It uses illustrations and figures of real radar observations to convey concepts and theory of atmospheric processes typically observed with weather radar, as well presenting a working knowledge of the radar system itself. In addition to covering fundamentals of scattering and atmospheric physics, topics include system hardware, signal processing, and radar networks. This is the perfect tool for scientists and engineers working on weather radars or using radars and their data, as well as senior undergraduate and graduate students studying weather radars.

For 80 years, mathematics has driven fundamental innovation in computing and communications. This timely book provides a panorama of some recent ideas in mathematics and how they will drive continued innovation in computing, communications and AI in the coming years. It provides a unique insight into how the new techniques that are being developed can be used to provide theoretical foundations for technological progress, just as mathematics was used in earlier times by Turing, von Neumann, Shannon and others. Edited by leading researchers in the field, chapters cover the application of new mathematics in computer architecture, software verification, quantum computing, compressed sensing, networking, Bayesian inference, machine learning, reinforcement learning and many other areas.