Retention of a six-membered ring in the reaction of 2-dialkylaminobenzo[e]- 1,3,2-dioxaphosphinin-4-ones with Pentafluorobenzaldehyde: O,N-exchange at phosphorus

The title reaction leads to the formation of diastereoisomeric 2-[(dialkylamino)(pentafluorophenyl)methyl]benzo[e]-1,3,2-dioxaphosphinine-2, 4-diones in a ratio of 70: 30. The configuration of chiral centres for the preferable diastereoisomer was determined by single crystal X-ray diffraction analysis. © 2013 Mendeleev Communications. All rights reserved

Clustering in dimension reduction for function approximation problem

In order to reduce the dimension of input vectors before construction of ap-proximation MAVE-type methods (minimum average variance estimation) can be used, however they are very computationally intensive. In the present work the modification of method MAVE is described which allows substantial decrease of algorithm run time at the expense of small error increase

Fast multispectral deep fusion networks

Most current state-of-the-art computer vision algorithms use images captured by cameras, which operate in the visible spectral range as input data. Thus, image recognition systems that build on top of those algorithms can not provide acceptable recognition quality in poor lighting conditions, e.g. during nighttime. Another significant limitation of such systems is high demand for computational resources, which makes them impossible to use on low-powered embedded systems without GPU support. This work attempts to create an algorithm for pattern recognition that will consolidate data from visible and infrared spectral ranges and allow near real-time performance on embedded systems with infrared and visible sensors. First, we analyze existing methods of combining data from different spectral ranges for object detection task. Based on the analysis, an architecture of a deep convolutional neural network is proposed for the fusion of multi-spectral data. This architecture is based on the single shot multi-box detection algorithm. Comparison analysis of the proposed architecture with previously proposed solutions for the multi-spectral object detection task shows comparable or better detection accuracy with previous algorithms and significant improvement of the running time on embedded systems. This study was conducted in collaboration with Philips Lighting Research Lab and solutions based on the proposed architecture will be used in image recognition systems for the next generation of intelligent lighting systems. Thus, the main scientific outcomes of this work include an algorithm for multi-spectral pattern recognition based on convolutional neural networks, as well as a modification of detection algorithms for working on embedded systems

Clustering in dimension reduction for function approximation problem

In order to reduce the dimension of input vectors before construction of ap-proximation MAVE-type methods (minimum average variance estimation) can be used, however they are very computationally intensive. In the present work the modification of method MAVE is described which allows substantial decrease of algorithm run time at the expense of small error increase

On One Problem in Multichannel Signal Detection

International audienceWe consider a statistical problem of detecting a signal with unknown energy in a multichannel system, observed in a Gaussian noise. We assume that the signal can appear in the kth channel with a known small prior probability (pi) over bar (k). Using noisy observations from all channels, we would like to detect whether the signal is presented in one of the channels or we observe pure noise. We describe and compare statistical properties of the maximum posterior probability test and optimal Bayes test. In particular, for these tests we obtain limiting distributions of test statistics and define sets of their undetectable signals