Alexander N. Gorban – författare

This book discusses the psychological traits associated with drug consumption through the statistical analysis of a new database with information on 1885 respondents and use of 18 drugs. After reviewing published works on the psychological profiles of drug users and describing the data mining and machine learning methods used, it demonstrates that the personality traits (five factor model, impulsivity, and sensation seeking) together with simple demographic data make it possible to predict the risk of consumption of individual drugs with a sensitivity and specificity above 70% for most drugs. It also analyzes the correlations of use of different substances and describes the groups of drugs with correlated use, identifying significant differences in personality profiles for users of different drugs.

The book is intended for advanced undergraduates and first-year PhD students, as well as researchers and practitioners. Although no previous knowledge of machine learning,advanced data mining concepts or modern psychology of personality is assumed, familiarity with basic statistics and some experience in the use of probabilities would be helpful. For a more detailed introduction to statistical methods, the book provides recommendations for undergraduate textbooks.

Model reduction and coarse-graining are important in many areas of science and engineering. How does a system with many degrees of freedom become one with fewer? How can a reversible micro-description be adapted to the dissipative macroscopic model? These crucial questions, as well as many other related problems, are discussed in this book. Specific areas of study include dynamical systems, non-equilibrium statistical mechanics, kinetic theory, hydrodynamics and mechanics of continuous media, (bio)chemical kinetics, nonlinear dynamics, nonlinear control, nonlinear estimation, and particulate systems from various branches of engineering. The generic nature and the power of the pertinent conceptual, analytical and computational frameworks helps eliminate some of the traditional language barriers, which often unnecessarily impede scientific progress and the interaction of researchers between disciplines such as physics, chemistry, biology, applied mathematics and engineering. All contributions are authored by experts, whose specialities span a wide range of fields within science and engineering.

In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SOM), etc. The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described as well. Presentation of algorithms is supplemented by case studies, from engineering to astronomy, but mostly of biological data: analysis of microarray and metabolite data. The volume ends with a tutorial "PCA and K-means decipher genome". The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to PhD students and researchers in computer sciences, applied mathematics and statistics.