Theory of a large thermoeffect in superconductors doped with magnetic impurities

We argue that parametrically strong enhancement of a thermoelectric current can be observed in conventional superconductors doped by magnetic impurities. This effect is caused by violation of the symmetry between electron-like and hole-like excitations due to formation of subgap Andreev bound states in the vicinity of magnetic impurities. We develop a quantitative theory of this effect and demonstrate that it can be detected in modern experiments.Comment: 5 pages, 4 figure

Concept of a mixer based on a cold-electron bolometer

A phase-sensitive terahertz heterodyne mixer of a new type based on a cold-electron bolometer is proposed. In this mixer, a normal-metal thin-film absorber is connected to a planar antenna via superconductor-insulator-normal metal (SIN) tunnel junctions, thus forming a SINIS structure. The SINIS mixer combines the advantages of a hot-electron bolometer (HEB), such as a high signal frequency at a small local oscillator power, with the advantages of an SIS mixer, including low noise level, a high intermediate frequency, and wide working temperature range (up to a critical temperature of the superconductor). In contrast to the HEB and SIS mixers, the proposed device is less sensitive to external magnetic noise and exhibits no additional noise related to the superconducting transition and the Josephson effect

Ti-TiO2-Al normal metal-insulator-superconductor tunnel junctions fabricated in direct-write technology

We present a novel Ti- based direct- write technology for fabricating Ti - TiO2 - Al tunnel junctions for bolometer and thermometry applications. The goal of our research is to develop simple and efficient technology for fabricating SIS tunnel junctions between Ti and Al with TiO2 as an insulating barrier. The key point of this technology is the deposition of a Ti film as a base electrode and deposition of an Al electrode after oxidation of the Ti. This approach allows one to realize any geometry of the tunnel junctions and of the absorber with no limitation related to the area of the junctions or the thickness of the absorber. In particular, a very thin and completely flat absorber can be created with no bending parts, which is not possible using the shadow evaporation technique or standard trilayer technology. Besides, the proposed new approach does not require one- cycle evaporation for deposition of tunnel junctions which gives us more freedom in the geometry of the counter- electrodes. The junctions are to be used for bolometer applications, such as the fabrication of microwave receivers for sensitive measurements in new generation telescopes, e. g. CLOVER and BOOMERANG projects including polarization cosmic microwave background radiation measurements, and the OLIMPO balloon telescope project which is dedicated to measuring the Sunyaev - Zeldovich effect in clusters of galaxies. A s the first step, SIN tunnel junctions have been fabricated and characterized

THE PECULIARITIES OF IDENTITY DYNAMICS OF PRIMARY SCHOOLCHILDREN

В статье рассматривается проблема динамики идентичности у младших школьников. При помощи методики «Двадцать утверждений» М. Куна и Т. МакПартленда на выборке 302 испытуемых автор изучает, как меняется идентичность у испытуемых в течении года. Обсуждаются обнаруженные после обработки несколькими классификаторами (М. Куна, Abdukeram et. al, авторский) противоречия, делается предположение об их природе. По результатам полученных данных, автор делает вывод, во-первых, выраженность такого ее компонента, как личностная идентичность, уменьшается с возрастом; во-вторых, постепенно возрастает выраженность компонентов идентичности, которые можно отнести к социальной идентичности, при этом у младших школьников основной рост приходится на такой компонент социальной идентичности, как Деятельностная идентичность.The article examines the problem of identity dynamics of primary schoolchildren. Using the Twenty statements test by Kuhn & McPartland and the sample of 302 subjects the author studies how the subjects’ identity is changing during the period of a year. The author discusses the contradictions revealed after analysis with the help of some classificators (of Kuhn, Abduktram et.al, of the author), and makes suggestions concerning their nature. According to the data obtained the author comes to the conclusion: first, the expressiveness of such component as personality identity decreases in the course of time (it depends on age); second, the expressiveness of such identity components which can be examined as social identity components is increasing gradually. And when speaking about primary schoolchildren such social identity component as Activity identity is increasing most of all

Family of graphene-based superconducting devices

A family of highly sensitive devices based on a graphene nanobridge and superconducting electrodes has been developed, manufactured, and examined. These devices can be used to create a graphene-based integral receiver. A cold-electron bolometer prototype with superconductor-insulator-normal metal tunnel junctions has been studied. Its response to a change in the temperature and external microwave radiation has been measured. A superconducting quantum interferometer with a graphene strip as a weak coupling between superconducting electrodes has been examined. The corresponding modulation of the voltage by a magnetic field at a given current has been measured. The effect of the gate voltage on the resistance of graphene has been analyzed for these samples. To confirm that graphene is single-layer, measurements with the reference samples were performed in high magnetic fields, displaying the half-integer quantum Hall effect

Power Load and Temperature Dependence of Cold-Electron Bolometer Optical Response at 350 GHz

Cold-electron bolometers (CEBs) integrated with twin-slot antennas have been designed and fabricated. Optical response was measured at bath temperatures of 0.06 to 3 K using blackbody radiation source at temperatures of 3 to 15 K. The responsivity of 0.3 * 10(9) V/W was measured at 2.7-K blackbody temperature that is close to the temperature of the cosmic microwave background. Optical measurements indicate quasi-optical coupling efficiency of up to 60% at low phonon temperature and low signal level. Estimations for bolometer responsivity were made for practical range of bath temperatures and blackbody radiation temperatures. The estimated ultimate dark responsivity at 100-mK bath temperature can approach S-V = 10(10) V/W and reduces down to 1.1 * 10(8) V/W at 300 mK for a device with absorber volume of 5 * 10(-20) m(3)

APPLICATION OF A CONVOLUTIONAL NEURAL NETWORK TO CREATE A DETECTOR OF TECHNICAL ANALYSIS FIGURES ON EXCHANGE QUOTES CHARTS

Today, the use of artificial intelligence based on neural networks is the most effective approach to solving image recognition problems. The possibility of using a convolutional neural network to create a pattern detector for technical analysis based on stock chart data has been investigated. The found figures of technical analysis can serve as the basis for making trading decisions in the financial markets. In the conditions of an ever-growing array of various information, the use of visual data reading tools is becoming more and more expedient, as it allows to speed up the process of searching and processing the necessary information for decision-makers. The modeling process, analysis, and results of applying the pattern detector of technical analysis are presented. The general approach to the construction and learning of a convolutional neural network is also described, and the process of preliminary processing of input data is described. Using the created detector allows to automate the search for patterns and improve the accuracy of making trading decisions. After finding the patterns, it becomes possible to obtain additional stock statistics for each type of figure: the context in front of the figures, the percentage of successfully completed figures, volume analysis, etc. These technical solutions can be used as expert and trading systems in the stock market, as well as integrated into existing ones

Effective Electron Microrefrigeration by SIN Tunnel Junctions with Advanced Geometry of Electrodes and Normal Metal Traps

We demonstrate effective electron cooling of the normal metal strip by superconductor-insulator-normal metal (SIN) tunnel junctions. The improvement was achieved by two methods: first by using an advanced geometry of the superconducting electrodes for more effective removal of the quasiparticles; and second, by adding a normal metal trap just near the cooling junctions. With simple cross geometry and without normal metal traps, the decrease in electron temperature is 56 mK. With the advanced geometry of the superconducting electrodes, the decrease in electron temperature is 129 mK. With the addition of the normal metal traps, the decrease in electron temperature is 197 mK

CREATION OF A NEURAL NETWORK ALGORITHM FOR AUTOMATED COLLECTION AND ANALYSIS OF STATISTICS OF EXCHANGE QUOTES GRAPHICS

Currently, the problem of automated data analysis and statistics collection from stock quotation charts has not been fully resolved. Most of the analysis of visual data falls on the physical work of the analyst, or on obsolete software solutions. The process of summarizing the information received from financial markets still requires physical attention and labor, which increases the risks associated primarily with the human factor and corresponding errors. An algorithm has been developed and tested for the automated collection of statistics from graphs of stock quotes, including data on the development and context of various figures (patterns) of technical analysis, as well as an improved adaptation and tracking system for the trend. The modeling process, analysis and the results of applying the analysis algorithm and statistics collection are presented. The developed algorithm works in conjunction with the previously created neural network pattern detector, which allows to automatically search for the exact boundaries of technical analysis figures of various sizes, analyze the context in front of them and play the patterns. This makes it possible to obtain important statistics that allow one to determine the degree of confidence in emerging patterns, taking into account their type, context, and other factors. In terms of accuracy and efficiency, the developed algorithm meets the existing challenges in the financial markets and can significantly increase the efficiency of the trader or investor through the automated processing of graphic and visual data. The created solution is universal in nature and can be applied to any capital market, regardless of the location and nature of the assets placed. The results can be used both to improve the accuracy of existing trading strategies, and for the analytical work of financial market participants. The use of new technologies for statistical processing of information can significantly improve the accuracy of investment and trade decision

APPLICATION OF KOHONEN SELF-ORGANIZING MAP TO SEARCH FOR REGION OF INTEREST IN THE DETECTION OF OBJECTS

Today, there is a serious need to improve the performance of algorithms for detecting objects in images. This process can be accelerated with the help of preliminary processing, having found areas of interest on the images where the probability of object detection is high. To this end, it is proposed to use the algorithm for distinguishing the boundaries of objects using the Sobel operator and Kohonen self-organizing maps, described in this paper and shown by the example of determining zones of interest when searching and recognizing objects in satellite images. The presented algorithm allows 15–100 times reduction in the amount of data arriving at the convolutional neural network, which provides the final recognition. Also, the algorithm can significantly reduce the number of training images, since the size of the parts of the input image supplied to the convolution network is tied to the image scale and equal to the size of the largest recognizable object, and the object is centered in the frame. This allows to accelerate network learning by more than 5 times and increase recognition accuracy by at least 10 %, as well as halve the required minimum number of layers and neurons of the convolutional network, thereby increasing its speed