Resource-efficient low-loss four-channel active demultiplexer for single photons

We report a design and implementation of a resource-efficient spatial demultiplexer which produces 4 indistinguishable photons with efficiency of 39.7% per channel. Our scheme is based on a free-space storage/delay line which accumulates 4 photons and releases them by a controlled polarization rotation using a single Pockels cell.Comment: 8 pages, 7 figure

СИНТЕЗ И ХАРАКТЕРИЗАЦИЯ ТРИМЕТИЛ(ФЕНИЛ)СИЛАНА — ПРЕДШЕСТВЕННИКА ДЛЯ ГАЗОФАЗНЫХ ПРОЦЕССОВ ОСАЖДЕНИЯ ПЛЕНОК SiCx : H

The technique of synthesis and purification of trimethyl(phenyl)silane PhSiMe3, allowing to obtain the product with high yield. Individuality of the product was confirmed by elemental analysis for C, H, Si. IR, UV and 1H NMR–spectroscopic studies, defined its spectral characteristics. Complex thermal analysis and thermogravimetric defined thermoanalytical behavior effects of PhSiMe3 in an inert atmosphere. Tensimetric studies have shown that the compound has sufficient volatility and thermal stability for use as a precursor in the process of chemical vapor deposition (CVD). The composition and temperature limits of the possible crystalline phase complexes in equilibrium with the gas phase of different composition has been determed by method of thermodynamic modeling. Calculated CVD diagrams allow us to select the optimum conditions of film deposition. The possibility of using trimethyl(phenyl)silane in CVD processes for producing dielectric films of hydrogenated silicon carbide has been demonstrated. Разработана методика синтеза и очистки триметил(фенил)силана PhSiMe3, позволяющая получать целевой продукт с высоким выходом. Индивидуальность соединения подтверждена элементным анализом на C, H, Si. ИК−, УФ− и ЯМР−спектроскопическими исследованиями (1Н, 13C, 29Si) определены его спектральные характеристики. С помощью комплексного термического анализа определены термоаналитические и термогравиметрические эффекты поведения PhSiMe3 в инертной атмосфере. На основе данных тензометрических исследований показано, что это соединение обладает достаточной летучестью и термической устойчивостью для использования в качестве прекурсора в процессах химического осаждения из газовой фазы (CVD). Методом термодинамического моделирования определен состав и температурные границы возможных кристаллических фазовых комплексов в равновесии с газовой фазой различного состава. Рассчитанные CVD− диаграммы позволяют выбрать оптимальные условия процессов осаждения из газовой фазы пленок. Показана возможность использования PhSiMe3 в процессах CVD для получения диэлектрических пленок гидрогенизированного карбида кремния.

Blended Clustering for Health Data Mining

Exploratory data analysis using data mining techniques is becoming more popular for investigating subtle relationships in health data, for which direct data collection trials would not be possible. Health data mining involving clustering for large complex data sets in such cases is often limited by insufficient key indicative variables. When a conventional clustering technique is then applied, the results may be too imprecise, or may be inappropriately clustered according to expectations. This paper suggests an approach which can offer greater range of choice for generating potential clusters of interest, from which a better outcome might in turn be obtained by aggregating the results. An example use case based on health services utilization characterization according to socio-demographic background is discussed and the blended clustering approach being taken for it is described

Socioeconomic Aspects of Using New Materials

The Twenty-fifth Congress of the Communist Party of the Soviet Union (CPSU) referred to the production and application of new and modified materials as one of the directions of scientific-technical progress that play a special part in the Tenth Five-Year Plan and that shape the prospects for economic development over the long run.

The Multifractal Structure of Small-Scale Artificial Ionospheric Turbulence

We present the results of investigation of a multifractal structure of the artificial ionospheric turbulence when the midlatitude ionosphere is affected by high-power radio waves. The experimental studies were performed on the basis of the SURA heating facility with the help of radio sounding of the disturbed region of ionospheric plasma by signals from the Earth’s orbital satellities. In the case of vertical radio sounding of the disturbed ionosphere region, the measured multipower and generalized multifractal spectra of turbulence coincide well with similar multifractal characteristics of the ionosperic turbulence under the natural conditions. In the case of oblique sounding of the disturbance region at small angles between the line of sight to the satellite and the direction of the Earth’s magnetic field, a nonuniform structure of the small-scale turbulence with a relatively narrow multipower spectrum and small variations in the generalized multifractal spectrum of the electron density was detected

Breast Tumor Cellularity Assessment Using Deep Neural Networks

© 2019 IEEE. Breast cancer is one of the main causes of death worldwide. Histopathological cellularity assessment of residual tumors in post-surgical tissues is used to analyze a tumor's response to a therapy. Correct cellularity assessment increases the chances of getting an appropriate treatment and facilitates the patient's survival. In current clinical practice, tumor cellularity is manually estimated by pathologists; this process is tedious and prone to errors or low agreement rates between assessors. In this work, we evaluated three strong novel Deep Learning-based approaches for automatic assessment of tumor cellularity from post-treated breast surgical specimens stained with hematoxylin and eosin. We validated the proposed methods on the BreastPathQ SPIE challenge dataset that consisted of 2395 image patches selected from whole slide images acquired from 64 patients. Compared to expert pathologist scoring, our best performing method yielded the Cohen's kappa coefficient of 0.69 (vs. 0.42 previously known in literature) and the intra-class correlation coefficient of 0.89 (vs. 0.83). Our results suggest that Deep Learning-based methods have a significant potential to alleviate the burden on pathologists, enhance the diagnostic workflow, and, thereby, facilitate better clinical outcomes in breast cancer treatment

Micro-photoluminescence studies of CdSe/ZnSe quantum dot structures grown under different conditions

We report on comparative studies of CdSe/ZnSe quantum dot structures grown by molecular beam epitaxy either with or without predeposition of a sub-monolayer-thick CdTe layer (stressor). Also we consider the structure grown in a thermal activation mode. Emission properties of individual quantum dots are investigated by micro-photoluminescence spectroscopy using 500 nm apertures opened in a non-transparent gold mask. The density of emitting quantum dots and the spectral width of the single-dot emission lines are estimated