The world is increasingly turbulent and complex, awash with disruptions, tipping points and knock-on effects exemplified by the implosion of financial markets and economies around the globe. This book is for business and organizational leaders who want and need to think through how best to deal with increasing turbulence, and with the complexity and uncertainty that come with it. The authors explain in clear language how future orientation and, specifically, modern scenario techniques help to address these conditions. They draw on examples from a wide variety of international settings and circumstances including large corporations, inter-governmental organizations, small firms and municipalities. Readers will be inspired to try out scenario approaches themselves to better address the turbulence that affects them and others with whom they work, live and do business. This second edition extends the use of scenarios planning and methods to tackle the risk and uncertainty of financial markets and the potentially massive impacts on businesses of all kinds, providing powerful tools to give far thinking executives an advantage in these turbulent times.

Computational approaches to music composition and style imitation have engaged musicians, music scholars, and computer scientists since the early days of computing. Music generation research has generally employed one of two strategies: knowledge-based methods that model style through explicitly formalized rules, and data mining methods that apply machine learning to induce statistical models of musical style. The five chapters in this book illustrate the range of tasks and design choices in current music generation research applying machine learning techniques and highlighting recurring research issues such as training data, music representation, candidate generation, and evaluation. The contributions focus on different aspects of modeling and generating music, including melody, chord sequences, ornamentation, and dynamics. Models are induced from audio data or symbolic data. This book was originally published as a special issue of the Journal of Mathematics and Music.

The world is increasingly turbulent and complex, awash with disruptions, tipping points and knock-on effects exemplified by the implosion of financial markets and economies around the globe. This book is for business and organizational leaders who want and need to think through how best to deal with increasing turbulence, and with the complexity and uncertainty that come with it. The authors explain in clear language how future orientation and, specifically, modern scenario techniques help to address these conditions. They draw on examples from a wide variety of international settings and circumstances including large corporations, inter-governmental organizations, small firms and municipalities. Readers will be inspired to try out scenario approaches themselves to better address the turbulence that affects them and others with whom they work, live and do business. This second edition extends the use of scenarios planning and methods to tackle the risk and uncertainty of financial markets and the potentially massive impacts on businesses of all kinds, providing powerful tools to give far thinking executives an advantage in these turbulent times.

This book is dedicated to study the inverse problem of ordinary differential equations, that is it focuses in finding all ordinary differential equations that satisfy a given set of properties. The Nambu bracket is the central tool in developing this approach. The authors start characterizing the ordinary differential equations in R^N which have a given set of partial integrals or first integrals. The results obtained are applied first to planar polynomial differential systems with a given set of such integrals, second to solve the 16th Hilbert problem restricted to generic algebraic limit cycles, third for solving the inverse problem for constrained Lagrangian and Hamiltonian mechanical systems, fourth for studying the integrability of a constrained rigid body. Finally the authors conclude with an analysis on nonholonomic mechanics, a generalization of the Hamiltonian principle, and the statement an solution of the inverse problem in vakonomic mechanics.

This book is dedicated to study the inverse problem of ordinary differential equations, that is it focuses in finding all ordinary differential equations that satisfy a given set of properties. The Nambu bracket is the central tool in developing this approach. The authors start characterizing the ordinary differential equations in R^N which have a given set of partial integrals or first integrals. The results obtained are applied first to planar polynomial differential systems with a given set of such integrals, second to solve the 16th Hilbert problem restricted to generic algebraic limit cycles, third for solving the inverse problem for constrained Lagrangian and Hamiltonian mechanical systems, fourth for studying the integrability of a constrained rigid body. Finally the authors conclude with an analysis on nonholonomic mechanics, a generalization of the Hamiltonian principle, and the statement an solution of the inverse problem in vakonomic mechanics.

Computational approaches to music composition and style imitation have engaged musicians, music scholars, and computer scientists since the early days of computing. Music generation research has generally employed one of two strategies: knowledge-based methods that model style through explicitly formalized rules, and data mining methods that apply machine learning to induce statistical models of musical style. The five chapters in this book illustrate the range of tasks and design choices in current music generation research applying machine learning techniques and highlighting recurring research issues such as training data, music representation, candidate generation, and evaluation. The contributions focus on different aspects of modeling and generating music, including melody, chord sequences, ornamentation, and dynamics. Models are induced from audio data or symbolic data. This book was originally published as a special issue of the Journal of Mathematics and Music.

Traditional strategy assumes stability and predictability. Today's world is better characterised by turbulence, uncertainty, novelty and ambiguity - conditions that contribute disruptive changes and trigger the search for new ways of coping. This book aims to become the premier guide on how to do scenario planning to support strategy and public policy. Co-authored by three experts in the field, the book presents The Oxford Scenario Planning Approach (OSPA). The approach is both intellectually rigorous and practical. Methodological choices and theoretical aspects in practice are detailed in reference to the relevant literatures and grounded in 6 case studies the authors have been involved with. The book makes several contributions to the field, centred on how learning with scenario planning is supported by re-framing and re-perception; how this iterative process can be embedded in corporate or government settings, and how it helps those that it supports to do well in today's world. The book is written in an accessible style and will be a useful introductory text as well as a useful guide for the more experienced scenario planning practitioner and scholar.