A long-time chief data scientist at Amazon shows how open data can make everyone, not just corporations, richer Every time we Google something, Facebook someone, Uber somewhere, or even just turn on a light, we create data that businesses collect and use to make decisions about us. In many ways this has improved our lives, yet, we as individuals do not benefit from this wealth of data as much as we could. Moreover, whether it is a bank evaluating our credit worthiness, an insurance company determining our risk level, or a potential employer deciding whether we get a job, it is likely that this data will be used against us rather than for us. In Data for the People, Andreas Weigend draws on his years as a consultant for commerce, education, healthcare, travel and finance companies to outline how Big Data can work better for all of us. As of today, how much we benefit from Big Data depends on how closely the interests of big companies align with our own. Too often, outdated standards of control and privacy force us into unfair contracts with data companies, but it doesn't have to be this way. Weigend makes a powerful argument that we need to take control of how our data is used to actually make it work for us. Only then can we the people get back more from Big Data than we give it. Big Data is here to stay. Now is the time to find out how we can be empowered by it.
The book is a summary of a time series forecasting competition that was held a number of years ago. It aims to provide a snapshot of the range of new techniques that are used to study time series, both as a reference for experts and as a guide for novices.
The book is a summary of a time series forecasting competition that was held a number of years ago. It aims to provide a snapshot of the range of new techniques that are used to study time series, both as a reference for experts and as a guide for novices.
A long-time chief data scientist at Amazon shows how open data can make everyone, not just corporations, richer Every time we Google something, Facebook someone, Uber somewhere, or even just turn on a light, we create data that businesses collect and use to make decisions about us. In many ways this has improved our lives, yet, we as individuals do not benefit from this wealth of data as much as we could. Moreover, whether it is a bank evaluating our credit worthiness, an insurance company determining our risk level, or a potential employer deciding whether we get a job, it is likely that this data will be used against us rather than for us. In Data for the People, Andreas Weigend draws on his years as a consultant for commerce, education, healthcare, travel and finance companies to outline how Big Data can work better for all of us. As of today, how much we benefit from Big Data depends on how closely the interests of big companies align with our own. Too often, outdated standards of control and privacy force us into unfair contracts with data companies, but it doesn't have to be this way. Weigend makes a powerful argument that we need to take control of how our data is used to actually make it work for us. Only then can we the people get back more from Big Data than we give it. Big Data is here to stay. Now is the time to find out how we can be empowered by it.
This book explores the deep roots of modern democracy, focusing on geography and long-term patterns of global diffusion. Its geographic argument centers on access to the sea, afforded by natural harbors which enhance the mobility of people, goods, capital, and ideas. The extraordinary connectivity of harbor regions thereby affected economic development, the structure of the military, statebuilding, and openness to the world – and, through these pathways, the development of representative democracy. The authors' second argument focuses on the global diffusion of representative democracy. Beginning around 1500, Europeans started to populate distant places abroad. Where Europeans were numerous they established some form of representative democracy, often with restrictions limiting suffrage to those of European heritage. Where they were in the minority, Europeans were more reticent about popular rule and often actively resisted democratization. Where Europeans were entirely absent, the concept of representative democracy was unfamiliar and its practice undeveloped.
Derived from the renowned multi-volume International Encyclopaedia of Laws, this book provides ready access to how the legal dimension of prevention against harm and loss allocation is treated in Germany. This traditional branch of law not only tackles questions which concern every lawyer, whatever his legal expertise, but also concerns each person’s most fundamental rights on a worldwide scale. Following a general introduction that probes the distinction between tort and crime and the relationship between tort and contract, the monograph describes how the concepts of fault and unlawfulness, and of duty of care and negligence, are dealt with in both the legislature and the courts. The book then proceeds to cover specific cases of liability, such as professional liability, liability of public bodies, abuse of rights, injury to reputation and privacy, vicarious liability, liability of parents and teachers, liability for handicapped persons, product liability, environmental liability, and liability connected with road and traffic accidents. Principles of causation, grounds of justification, limitations on recovery, assessment of damages and compensation, and the role of private insurance and social security are all closely considered. Its succinct yet scholarly nature, as well as the practical quality of the information it provides, make this book a valuable resource for lawyers in Germany. Academics and researchers will also welcome this very useful guide, and will appreciate its value not only as a contribution to comparative law but also as a stimulus to harmonization of the rules on tort.
This book provides a thorough review of a class of powerful algorithms for the numerical analysis of complex time series data which were obtained from dynamical systems. These algorithms are based on the concept of state space representations of the underlying dynamics, as introduced by nonlinear dynamics. In particular, current algorithms for state space reconstruction, correlation dimension estimation, testing for determinism and surrogate data testing are presented — algorithms which have been playing a central role in the investigation of deterministic chaos and related phenomena since 1980. Special emphasis is given to the much-disputed issue whether these algorithms can be successfully employed for the analysis of the human electroencephalogram.
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