Труды сотрудников ИЛ им. В.Н. Сукачева СО РАН

/ B. V. Proshkin, A. V. Klimov // Turczaninowia. - 2019. - Vol. 22, Is. 3. - P80-90, DOI 10.14258/turczaninowia.22.3.3. - Cited References:46 . - ISSN 1560-7259. - ISSN 1560-7267РУБ Plant Sciences

Аннотация: The article presents the results of the study of the petiole anatomy peculiarities of the hybrids between Aigeiros and Tacamahaca sections. Petiole anatomic structure was found to be helpful in assigning taxa to a section and to find intersectional hybrids, which is actual for studying populations in natural and anthropogenic hybridization zones. Cross sections made in the upper part of petioles were used for analyzing anatomic traits by light microscopy. All representatives of the Aigeiros section have linear form of the vascular system, consisting of 3-5 rings, with a rounded contour of the petiole adaxial side. In the Tacamahaca section taxa the vascular system is highly arched, and the adaxial side is cordate. The study of the hybrids between species of the same section revealed that such hybrids inherit anatomy features common for the section. We can consider such traits as adaxial side shape and vascular system type to be the most important markers for intersectional hybrids. Truncated or notched shape of the adaxial contour and vascular system type were found to be characteristic features of hybrids, as small notches in their petioles' upper part are common for all hybrids. Most of hybrids have small notches, rather than grooves, in the upper part of their petioles. Most of the intersectional hybrids have transitional shape of vascular system. The anatomy of Populus x sibirica petioles confirmed earlier results that it is a hybrid cultivar, that originated as a result of crossbreeding between Aigeiros and Tacamahaca section species.

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Держатели документа:
Novosibirsk State Agr Univ, Dobrolubov St 160, Novosibirsk 630039, Russia.
InEca Consulting LLC, Lazo St 4, Novokuznetsk 654027, Russia.
SB RAS, Sukachev Inst Forest, West Siberian Branch, Fed Res Ctr,Krasnoyarsk Sci Ctr, Zhukovsky St 100-1, Novosibirsk 630082, Russia.

Доп.точки доступа:
Proshkin, B., V; Klimov, A., V; Klimov, Andrey; Proskin, Boris

/ I. V. Zenkov, A. V. Lapko, V. A. Lapko [и др.] // Comput. Opt. - 2021. - Vol. 45, Is. 2. - С. 253-+, DOI 10.18287/2412-6179-CO-801. - Cited References:13. - The research was funded by RFBR, Krasnoyarsk Territory and Krasnoyarsk Regional Fund of Science, project number 20-41-240001. . - ISSN 0134-2452. - ISSN 2412-6179РУБ Optics

Аннотация: A nonparametric algorithm for automatic classification of large statistical data sets is proposed. The algorithm is based on a procedure for optimal discretization of the range of values of a random variable. A class is a compact group of observations of a random variable corresponding to a unimodal fragment of the probability density. The considered algorithm of automatic classification is based on the "compression" of the initial information based on the decomposition of a multidimensional space of attributes. As a result, a large statistical sample is transformed into a data array composed of the centers of multidimensional sampling intervals and the corresponding frequencies of random variables. To substantiate the optimal discretization procedure, we use the results of a study of the asymptotic properties of a kernel-type regression estimate of the probability density. An optimal number of sampling intervals for the range of values of one- and two-dimensional random variables is determined from the condition of the minimum root-mean square deviation of the regression probability density estimate. The results obtained are generalized to the discretization of the range of values of a multidimensional random variable. The optimal discretization formula contains a component that is characterized by a nonlinear functional of the probability density. An analytical dependence of the detected component on the antikurtosis coefficient of a one-dimensional random variable is established. For independent components of a multidimensional random variable, a methodology is developed for calculating estimates of the optimal number of sampling intervals for random variables and their lengths. On this basis, a nonparametric algorithm for the automatic classification is developed. It is based on a sequential procedure for checking the proximity of the centers of multidimensional sampling intervals and relationships between frequencies of the membership of the random variables from the original sample of these intervals. To further increase the computational efficiency of the proposed automatic classification algorithm, a multithreaded method of its software implementation is used. The practical significance of the developed algorithms is confirmed by the results of their application in processing remote sensing data.

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Держатели документа:
Siberian Fed Univ, Svobodny Av 79, Krasnoyarsk 660041, Russia.
Inst Computat Modelling SB RAS, Akademgorodok 50, Krasnoyarsk 660036, Russia.
Sukachev Inst Forest SB RAS, Akademgorodok 50, Krasnoyarsk 660036, Russia.
Reshetnev Siberian State Univ Sci & Technol, Krasnoyarsky Rabochy Av 31, Krasnoyarsk 660037, Russia.
Fed Res Ctr Informat & Computat Technol, Krasnoyarsk Branch, Mira Av 53, Krasnoyarsk 660049, Russia.

Доп.точки доступа:
Zenkov, I., V; Lapko, A., V; Lapko, V. A.; Im, S. T.; Tuboltsev, V. P.; Avdeenok, V. L.; RFBRRussian Foundation for Basic Research (RFBR); Krasnoyarsk Regional Fund of Science [20-41-240001]; Krasnoyarsk Territory