No abstract

The study suggests formalized algorithm that allows to make an informed choice of instruments for financing M&A transactions. This algorithm includes following several stages: identification of the limiting factor; determination of the anticipated structure of settlements for the M&A transaction; formation of a financial instruments parameters set to use for the instruments' comparison; determination of specific weights of the selected parameters of the M&A financial instruments; identification of "preferable" values of financial instruments' parameters; comparison of the "preferred" financial instruments parameters with their actual or potentially possible parameters; making choice of the instruments for financing the M&A transaction. Approbation of the algorithm is done using the example of JSC "Agrofirma Volga", acting as a buyer company, and JSC "Zorinskoye", acting as a target company.

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Depreciable bonds issues can significantly expand the issuers' abilities to manage public debt. The purpose of the study is to suggest possible options for a model for fulfilling obligations by issuers of depreciable sub-federal bonds. The study was conducted using a discrete grouping constructed according to the criterion of the bonds face value repayment number. We propose a set of indicators to assess the issuers' possibilities to place depreciable sub-federal bonds in the Russian market. Our analyses showed that, during the period under review, the probability of successful depreciable bond issues placement in the sub-federal segment remained at a high level for bond issues with a nominal value of no more than 30.0 billion rubles and a maturity of no more than 3650 days. Deterioration of the bond market environment did not lead to significant changes in the structure of the bond issuers' obligations fulfillment. The most common was a model involving four repayments of the depreciable bonds nominal value.Index Terms-depreciable bonds, sub-federal bonds, simulation of bond issuance, structure of the bond issuers' obligations fulfillment, b ond market

The paper considers the task of detection of the most attractive for tourists city sights using the database with geotagged data of photographs.We form a graph on the basis of the geo-tagged spot coordinates and rewrite the problem as the graph clusterization task. In our work we use two clustering algorithms, DBSCAN and k-MXT. Moreover, we develop a modification of the k-MXT algorithm called the k-MXT-Gauss algorithm, where the calculation of the weights of the graph edges is transformed using the Gaussian distribution density. We compare the performance of k-MXT-Gauss algorithm with the performance of k-MXT and DBSCAN algorithms both on simulated data and real data.