"Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse modeling, focusing on its applications in fields including neuroscience, computational biology, and comput…
"This book provides a longue dur?ee story examining the dynamic history of predictive methods and values in sciences--and particularly in engineering and other applied sciences--which is crucial to understanding today's culture of prediction"--OCLC-licensed vendor bibliographic record.
Originally published: 1999.The authors show how a common approach that emphasizes the three-way interaction among increasing returns, transportation costs, and the movement of productive factors can be applied to a wide range of issues in urban, regional, and international economics.Since 1990 there has been a renaissance of theoretical and empirical work on the spatial aspects of the economy--…
This volume presents five different methods recently developed to tackle the large scale behavior of highly correlated random systems, such as spin glasses, random polymers, local times and loop soups and random matrices. These methods, presented in a series of lectures delivered within the Jean-Morlet initiative (Spring 2013), play a fundamental role in the current development of probability t…
The book provides accurate FDTD models for on-chip interconnects, covering most recent advancements in materials and design. Furthermore, depending on the geometry and physical configurations, different electrical equivalent models for CNT and GNR based interconnects are presented. Based on the electrical equivalent models the performance comparison among the Cu, CNT and GNR-based interconnects…
This book provides a systematic treatment of the mathematical underpinnings of work in data assimilation, covering both theoretical and computational approaches. Specifically the authors develop a unified mathematical framework in which a Bayesian formulation of the problem provides the bedrock for the derivation, development and analysis of algorithms; the many examples used in the text, toget…
This volume provides an overview of applications of conceptual spaces theory, beginning with an introduction to the modeling tool that unifies the chapters. The first section explores issues of linguistic semantics, including speakers’ negotiation of meaning. Further sections address computational and ontological aspects of constructing conceptual spaces, while the final section looks at phil…
Since information in the brain is processed by the exchange of spikes among neurons, a study of such group dynamics is extremely important in understanding hippocampus dependent memory. These spike patterns and local field potentials (LFPs) have been analyzed by various statistical methods. These studies have led to important findings of memory information processing. For example, memory-trace …
This book presents recent progress in the application of RANS turbulence models based on the Reynolds stress transport equations. A variety of models has been implemented by different groups into different flow solvers and applied to external as well as to turbomachinery flows. Comparisons between the models allow an assessment of their performance in different flow conditions. The results demo…
This book is a collection of articles by leading researchers working at the cutting edge of neuro-computational modelling of neurological and psychiatric disorders. Each article contains model validation techniques used in the context of the specific problem being studied. Validation is essential for neuro-inspired computational models to become useful tools in the understanding and treatmen…