This book is a collection of papers from the 9th International ISAAC Congress held in 2013 in Kraków, Poland. The papers are devoted to recent results in mathematics, focused on analysis and a wide range of its applications.

Concepts of positive dependence are becoming increasingly important in probability, statistics and their applications. While these concepts are traditionally viewed as focusing on positive and negative dependence for random vectors, they also are related to broader issues in the modeling and the analysis of multivariate data, and, in particular, ordinal data. Historically, positive dependence for the multivariate normal distribution had been synonymous with positive correlations. Other subsequently developed multivariate distributions were often interpreted with this perspective.

This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series.

Algebraic projective geometry, with its multilinear relations and its embedding into Grassmann-Cayley algebra, has become the basic representation of multiple view geometry, resulting in deep insights into the algebraic structure of geometric relations, as well as in efficient and versatile algorithms for computer vision and image analysis.

Looks at the history and anthropology of the expression of numbers throughout the ages and across different cultures. It deals with the different ways that number representation has been structured, the history and prehistory of number concepts, and the evolution of numerical representation (in word and symbol). These themes are explored through the various expressions of number-concepts in different cultures in different places and times.