This Second Volume In The Series Of Dutch Sources On South Asia C. 1600-1825 Is Guide To Archival Sources And Two-Dimensional Works Of Art Scattered In Dutch Repositories Other Than The National Archives At The Hague.
Wallin's discourse encompasses: 1) the musical consequences of cerebral functional asymmetry; 2) the hierarchic and selective organization of perceptual-cognitive auditory processes; 3) reticular-limbic responses to musical stimuli interpreted as synapse-modifying mechanisms for long-term motivation and learning, as well as for phylogenetical "learning"; 4) the question of remnants or retentions with roots in the sound-gestures of other vertebrates of a higher order (and not solely the non-human primates) being active in the innermost structure of music; 5) vocalization techniques, e.g., the "kolning" technique of the late Paleolithic herding culture of Europe, as paleobiological retention; 6) the epistemological perspective of models of life-processes as discussed in recent scientific research."--BOOK JACKET.
A bird's-eye view of community and population effects of ontogenetic development -- Life history processes, ontogenetic development, and density dependence -- Biomass overcompensation -- Emergent allee effects through biomass overcompensation -- Emergent facilitation among predators on size-structured prey -- Ontogenetic niche shifts -- Mixed interactions -- Ontogenetic niche shifts, predators, and coexistence among consumer species -- Dynamics of consumer-resource systems -- Dynamics of consumer-resource systems with discrete reproduction : multiple resources and confronting model predictions with empirical data -- Cannibalism in size-structured systems -- Demand-driven systems, model hierarchies, and ontogenetic asymmetry.
This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors’ reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. This is an open access book.
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