EPDF and EPUB available Open Access under CC-BY-NC-ND licence. Individuals' behaviours at work are known to be shaped by cold, or cognitive-motivational, processes as well as hot, or affect-motivational, processes. To date, employee proactivity research has mainly focused on the 'cold' side. But emotion has been proposed to 'energize' employees' proactivity, especially in interdependent and uncertain work environments. In this pioneering work, expert scholars offer new thinking on the process by examining how emotion can drive employees' proactivity in the workplace and how, in turn, that proactivity can shape one's emotional experiences.

This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website.