This monograph has two main objectives. The first one is to give a self-contained exposition of the relevant facts about set operads, in the context of combinatorial species and its operations. This approach has various advantages: one of them is that the definition of combinatorial operations on species, product, sum, substitution and derivative, are simple and natural. They were designed as the set theoretical counterparts of the homonym operations on exponential generating functions, giving an immediate insight on the combinatorial meaning of them. The second objective is more ambitious. Before formulating it, authors present a brief historic account on the sources of decomposition theory. For more than forty years decompositions of discrete structures have been studied in different branches of discrete mathematics: combinatorial optimization, network and graph theory, switching design or boolean functions, simple multi-person games and clutters, etc.

The Seventh Edition of this casebook provides a systematic comparison of the Evidence Code's and Federal Rules' approaches to admissibility of evidence. Transcripts of witness examinations help students visualize the process of presenting and objecting to evidence. The book combines the problem and witness examination approach with text, not cases, that sets out the law of evidence in a clear and concise manner. It discusses major cases and the policies and concepts underlying the rules, not just the rules themselves. The end of each chapter includes the text of the code sections and rules discussed in that chapter. Changes from the Sixth Edition include: (1) Significant revisions of the Questions and Problems to eliminate true-false questions and replace them with real-life hypotheticals that give students an opportunity to argue for and against the admission of specific items of evidence; (2) Reductions in the text regarding the role of 1982's Proposition 8 to reflect the reality that it has made only a few changes in the admissibility of evidence in criminal cases; (3) Revisions of the sections involving Specific Conduct for Non-Character Purposes, to better illustrate the difficulties in using uncharged offenses that are similar to the charged offenses; (4) Division of the Chapter on Impeachment of the Credibility of Witnesses to make it more manageable to teach and less daunting to explore; (5) Inclusion of recent developments limiting the use of hearsay by expert witnesses; and (6) Restructuring of the chapters on privileges by eliminating redundant material and grouping similar privileges together in one chapter, thus reducing the number of chapters on privileges from 11 to 6.

Data-driven methods have become an essential part of the methodological portfolio of fluid dynamicists, motivating students and practitioners to gather practical knowledge from a diverse range of disciplines. These fields include computer science, statistics, optimization, signal processing, pattern recognition, nonlinear dynamics, and control. Fluid mechanics is historically a big data field and offers a fertile ground for developing and applying data-driven methods, while also providing valuable shortcuts, constraints, and interpretations based on its powerful connections to basic physics. Thus, hybrid approaches that leverage both methods based on data as well as fundamental principles are the focus of active and exciting research. Originating from a one-week lecture series course by the von Karman Institute for Fluid Dynamics, this book presents an overview and a pedagogical treatment of some of the data-driven and machine learning tools that are leading research advancements in model-order reduction, system identification, flow control, and data-driven turbulence closures.