This paper presents the study of the author’s style of A.S. Pushkin based on the comparison of his poetic texts with the texts of contemporary poets. The purpose of this study is to determine the features of the author’s style of A.S. Pushkin using machine learning methods. This paper describes the construction of several classifications based on different groups of features, as well as the classification based on a combined set of features from different groups. The quality of all constructed classifications is also analyzed; special attention is paid to the interpretation of the neural network solution and the identification of features of the author’s style.

Using a computer simulation, we have studied the random sequential adsorption of stiff linear $k$-mers onto a square lattice. Each such particle occupies $k$ adjacent lattice sites. During deposition, the two mutually perpendicular orientations of the particles are equiprobable, hence, a macroscopically isotropic monolayer is formed. However, this monolayer is locally anisotropic, since the deposited particles tend to form domains of particles with the same orientation. Using the “excluded area” concept, we have classified lattice sites into several types and examined how the fraction of each type of lattice site varies as the number of deposited particles increases. The behaviors of these quantities have allowed us to identify the following stages of domain formation: (i) the emergence of domain seeds, (ii) the filling of domains, and (iii) densification of the domains.