A large scale human-labeled dataset plays an important role in creating high quality deep learning models. In this paper we present text annotation for Open Images V5 dataset. To our knowledge it is the largest among publicly available manually created text annotations. Having this annotation we trained a simple Mask-RCNN-based network, referred as Yet Another Mask Text Spotter (YAMTS), which achieves competitive performance or even outperforms current state-of-the-art approaches in some cases on ICDAR 2013, ICDAR 2015 and Total-Text datasets. Code for text spotting model available online at: https://github.com/openvinotoolkit/training_extensions. The model can be exported to OpenVINO™-format and run on Intel® CPUs.

The article deals with the problem of diagnosis of oncological diseases based on the analysis of DNA methylation data using algorithms of cluster analysis and supervised learning. The groups of genes are identified, methylation patterns of which significantly change when cancer appears. High accuracy is achieved in classification of patients impacted by different cancer types and in identification if the cell taken from a certain tissue is aberrant or normal. With method of cluster analysis two cancer types are highlighted for which the hypothesis was confirmed stating that among the people affected by certain cancer types there are groups with principally different methylation pattern.

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Due to need for optimal management of a large wells stock with a relatively limited amount of conditioned data, the need for a focus rapid detection of implicit complications in the work (mechanical wear of the working bodies of the pump) and an increase in overhaul period of submersible equipment increases. Real-time monitoring of all wells in the debit is limited due to infrastructure problems and high costs of measuring activities. Despite this, the possibility of such monitoring is not excluded due to the availability of field information that correlates with the well flow rate and the mode of its operation. The article presents an algorithm that, based on telemetry ESP to estimate the parameters that affect the performance of the well and submersible equipment, such as the coefficient of degradation of the outlet characteristics of the pump, the actual efficiency (coefficient of performance) of ESP and the thickness of the deposition of paraffin on the inner walls of the tubing. When processing field data, the parameters correlating with the well flow rate were revealed, which allowed to build a model of a virtual flow meter to verify the existing flow rate measurements and restore the missing values. The basis of the physical and mathematical approach is an algorithm that connects the parameters of the ESP system with the flow rate through the power consumption and hydraulic calculations of the gas-liquid mixture flow in the tubing. After preliminary calibration of pressure-flow characteristics of ESP for real mode of operation, daily measurements of fluid flow rate with a periodicity of 1 hour were calculated.