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Game Theory for Networks
This book constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on Game Theory for Networks (GameNets 2011) held in Shanghai, China, April 16-18, 2011. The 45 revised full papers presented were carefully selected from numerous submissions and focus topics such as mechanism design, physical layer games, network mechanisms, stochastic and dynamic games, game-theoretic network models, cooperative games in networks, security games, spectrum sharing games, P2P and social networks and economics of network QoS.
Fighting With FEMA
In this book, disaster finance and cost recovery expert Michael Martinet provides unparalleled coverage of the practical, real-world key principles necessary to successfully navigate the nuances of federal regulations surrounding FEMA’s Public Assistance program. Accessibly written, Martinet demystifies the many policies, procedures, and administrative processes a local government agency should adopt before a disaster to prepare themselves for a greater financial recovery after a disaster. The intent is to awaken local authorities to the realities of the process and assist them in preparing for a day which all hope they will never see. Designed for financial officers, purchasing officials, Public Works officials, Building & Safety officials, public construction project managers, and emergency management professionals at all levels of government, Fighting With FEMA will also earn a place in the libraries of consulting disaster recovery specialists and students interested in the financial aspects of disasters.
Latent Variable Analysis and Signal Separation
This book constitutes the proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICS 2015, held in Liberec, Czech Republic, in August 2015. The 61 revised full papers presented – 29 accepted as oral presentations and 32 accepted as poster presentations – were carefully reviewed and selected from numerous submissions. Five special topics are addressed: tensor-based methods for blind signal separation; deep neural networks for supervised speech separation/enhancement; joined analysis of multiple datasets, data fusion, and related topics; advances in nonlinear blind source separation; sparse and low rank modeling for acoustic signal processing.