Search results for: Egor E. Nuzhin

Authors: Egor E. Nuzhin, Nikolay V. Brilliantov

Abstract:

We propose an approach for addressing Reinforcement Learning (RL) problems. It combines the ideas of umbrella sampling, borrowed from Monte Carlo technique of computational physics and chemistry, with optimal control methods, and is realized on the base of neural networks. This results in a powerful algorithm, designed to solve hard RL problems – the problems, with long-time delayed reward, state-traps sticking and a lack of terminal states. It outperforms the prominent algorithms, such as PPO, RND, iLQR and VI, which are among the most efficient for the hard problems. The new algorithm deals with a continuous ensemble of agents and expected return, that includes the ensemble entropy. This results in a quick and efficient search of the optimal policy in terms of ”exploration-exploitation trade-off” in the state-action space.

Keywords: umbrella sampling, reinforcement learning, policy gradient, dynamic programming

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Authors: Zemlianskaya Daria, Egor Stadnichuk, Ekaterina Svechnikova

Abstract:

Relativistic runaway electron avalanches (RREAs) are generally accepted as a source of thunderstorms gamma-ray radiation. Avalanches' dynamics in the electric fields can lead to their multiplication via gamma-rays and positrons, which is called relativistic feedback. This report shows that a non-uniform electric field geometry leads to the new RREAs multiplication mechanism - “geometric feedback”, which occurs due to the exchange of high-energy particles between different accelerating regions within a thundercloud. This report will present the results of the simulation in GEANT4 of feedback in a spherical cell. Necessary conditions for the occurrence of geometric feedback were obtained from it.

Keywords: electric field, GEANT4, gamma-rays, relativistic runaway electron avalanches (RREAs), relativistic feedback, the thundercloud

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Authors: Egor Stadnichuk

Abstract:

Relativistic runaway electron avalanches (RREA) are a widely accepted source of thunderstorm gamma-radiation. In regions with huge electric field strength, RREA can multiply via relativistic feedback. The relativistic feedback is caused both by positron production and by runaway electron bremsstrahlung gamma-rays reversal. In complex multilayer thunderstorm electric field structures, an additional reactor feedback mechanism appears due to gamma-ray exchange between separate strong electric field regions with different electric field directions. The study of this reactor mechanism in conjunction with the relativistic feedback with Monte Carlo simulations or by direct solution of the kinetic Boltzmann equation requires a significant amount of computational time. In this work, a theoretical approach to study feedback mechanisms in RREA physics is developed. It is based on the matrix of feedback operators construction. With the feedback matrix, the problem of the dynamics of avalanches in complex electric structures is reduced to the problem of finding eigenvectors and eigenvalues. A method of matrix elements calculation is proposed. The proposed concept was used to study the dynamics of RREAs in multilayer thunderclouds.

Keywords: terrestrial Gamma-ray flashes, thunderstorm ground enhancement, relativistic runaway electron avalanches, gamma-rays, high-energy atmospheric physics, TGF, TGE, thunderstorm, relativistic feedback, reactor feedback, reactor model

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Authors: Alisher Ibragimov, Sofya Senotrusova, Aleksandra Beliaeva, Egor Ushakov, Yuri Markin

Abstract:

Mammography plays a critical role in screening for breast cancer in women, and artificial intelligence has enabled the automatic detection of diseases in medical images. Many of the current techniques used for mammogram analysis focus on a single view (mediolateral or craniocaudal view), while in clinical practice, radiologists consider multiple views of mammograms from both breasts to make a correct decision. Consequently, computer-aided diagnosis (CAD) systems could benefit from incorporating information gathered from multiple views. In this study, the introduce a method based on a Siamese neural network (SNN) model that simultaneously analyzes mammographic images from tri-view: bilateral and ipsilateral. In this way, when a decision is made on a single image of one breast, attention is also paid to two other images – a view of the same breast in a different projection and an image of the other breast as well. Consequently, the algorithm closely mimics the radiologist's practice of paying attention to the entire examination of a patient rather than to a single image. Additionally, to the best of our knowledge, this research represents the first experiments conducted using the recently released Vietnamese dataset of digital mammography (VinDr-Mammo). On an independent test set of images from this dataset, the best model achieved an AUC of 0.87 per image. Therefore, this suggests that there is a valuable automated second opinion in the interpretation of mammograms and breast cancer diagnosis, which in the future may help to alleviate the burden on radiologists and serve as an additional layer of verification.

Keywords: breast cancer, computer-aided diagnosis, deep learning, multi-view mammogram, siamese neural network

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Authors: Egor Atan Obeten, Abong Augustine Agwul, Bissong A. Samson

Abstract:

The position of the aquifer, its protective capability, and the quality of the groundwater beneath the dumpsite were all investigated. The techniques employed were laboratory, tritium tagging, electrical resistivity tomography (ERT), and vertical electrical sounding (VES). With a maximum electrode spacing of 500 meters, fifteen VES stations were used, and IPI2win software was used to analyze the data collected. The resistivity map of the dumpsite was determined by deploying six ERT stations for the 2 D survey. To ascertain the degree of soil infiltration beneath the dumpsite, the tritium tagging method was used. Using a conventional laboratory procedure, groundwater samples were taken from neighboring boreholes and examined. The findings showed that there were three to five geoelectric layers, with the aquifer position being inferred to be between 24.2 and 75.1 meters deep in the third, fourth, and fifth levels. Siemens with values in the range of 0.0235 to 0.1908 for the load protection capacity were deemed to be, at most, weakly and badly protected. The obtained porosity values ranged from 44.45 to 89.75. Strong calculated values for transmissivity and porosity indicate a permeable aquifer system with considerable storativity. The area has an infiltration value between 8 and 22 percent, according to the results of the tritium tagging technique, which was used to evaluate the level of infiltration from the dumpsite. Groundwater samples that have been analyzed reveal levels of NO2, DO, Pb2+, magnesium, and cadmium that are higher than what the NSDWQ has approved. Overall analysis of the results from the above-described methodologies shows that the study area's aquifer system is porous and that contaminants will circulate through it quickly if they are contaminated.

Keywords: aquifer, transmissivity, dumpsite, groundwater

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Authors: Barry Aifesehi Aiworo, Henry Oriakhi

Abstract:

This paper, examines the analysis of access pattern to school and travel risks among school children in Benin City, Edo State, Nigeria. The risk includes accident, molestation (sexually) and kidnapping. The objective of this paper are to examine the various means (modes) of transport to school; determine the type and incidences of risk experienced by school children in the study area; examine the risk incidences and ages of school children in the study area. Hypothesis which states that the types of risks encountered by school children are independent of means of transport was tested using the chi-square test (X2). A sampling ratio of twelve percent (12%) was taken from 396 schools in Benin City. By implication, 49 schools were randomly selected in Benin City for this research. A total of 42,053 students in the 49 schools constitute the sample frame for the research. Two percent (2%), 841 students were taken as the sample size. The use of stratified sampling method was applied by stratifying the study area (Benin City) into local governments- Egor, Ikpoba-Okha and Oredo. Thereafter, the lists of schools in the various local governments were obtained from the Ministry of Education before the schools for research were randomly chosen from each local government area. The analysis revealed that 6.7% of the total students interviewed have been involved in road accidents. 1.04% of the total respondents said at one time or the other that they have been kidnapped. Finally, the research found that travel is comparatively safe and believes this may be partly attributable to safer route to schools and school children being more familiar with the school journey. The research indicates that children aged between eleven and fifteen are most at risk of hit or knocked down on Benin City’s roads. These findings may help in planning and targeting road safety initiative (education, campaigns) in Benin City.

Keywords: accident, molestation (sexually), kidnapping, pedophile, pedestrian

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