Agent communication method in cooperative environment based on the artificial neural networks

The problem of communication between cooperating agents in multiagent environments is considered in this paper. An algorithm is proposed that is based in reinforcement learning and recurrent neural networks. Main idea behind the algorithm is to use an additional recurrent network that translates information from internal state of one agent to internal state of another agent. Experimental evaluation is performed on model environment. Experimental results have shown that proposed method is potentially useful but requires additional investigation. В роботі розглядається проблема комунікації кооперуючих агентів у мультиагентних середовищах. Запропоновано алгоритм на основі підходів навчання з підкріпленням з використанням рекурентних нейронних мереж. Головна ідея алгоритму – це використання додаткової рекурентної мережі, яка виконує обмін інформацією між внутрішніми станами двох агентів під час комунікації. Обраний підхід заснований на застосуванні алгоритму A3C, рекурентної нейронної мережі Long Short-Term Memory (LSTM) для керування агентом та додаткової рекурентної мережі (мережі комунікації). Досліджено два варіанта архітектури нейронної мережі. За першою версією агенти спочатку «спілкуються», а потім використовується результат у якості додаткових даних про середовище. Друга версія спочатку аналізує дані про середовище, а потім реалізує «спілкування» агентів, обмінюючись високорівневою інформацією. Проведено експериментальну оцінку запропонованого алгоритму на прикладі модельної задачі. Результати експерименту довели, що запропонований підхід покращує ефективність кооперуючих агентів. Перевагою алгоритму є те, що він не потребує наявності складних та структурованих обчислювальних систем та може бути фізично реалізованим за допомогою дуже маленьких об’єктів, таких, як наприклад макромолекули. В работе рассматривается проблема коммуникации кооперирующих агентов в мультиагентной среде. Предлагается алгоритм на основе подходов обучения с подкреплением с использованием рекуррентных нейронных сетей. Главная идея алгоритма – использование дополнительной нейронной сети, которая выполняет обмен информацией между внутренними состояниями двух агентов во время коммуникации. Предлагаемый подход основан на применении алгоритма A3C, рекуррентной нейронной сети Long Short-Term Memory (LSTM) для управления агентом и дополнительной рекуррентной сети (сети коммуникации). Исследовано два варианта построения архитектуры нейронной сети. Первая версия сначала организовывает взаимодействие агентов, а затем использует результат в качестве дополнительных данных о среде. Вторая версия сначала анализирует данные о среде, а потом коммуницирует, обмениваясь высокоуровневой информацией. Проведена экспериментальная оценка предложенного алгоритма на примере модельной задачи. Результаты эксперимента показали, что предложенный алгоритм улучшает эффективность кооперирующих агентов. Преимуществом алгоритма можно считать то, что он не требует наличия сложных и структурированных вычислительных систем и может быть физически реализован с помощью очень маленьких объектов, таких, как например макромолекулы

Initial-state dependence of coupled electronic and nuclear fluxes in molecules

We demonstrate that coupled electronic and nuclear fluxes in molecules can strongly depend on the initial state preparation. Starting the dynamics of an aligned D2 + molecule at two different initial conditions, the inner and the outer turning points, we observe qualitatively different oscillation patterns of the nuclear fluxes developing after 30 fs. This corresponds to different orders of magnitude bridged by the time evolution of the nuclear dispersion. Moreover, there are attosecond time intervals within which the electronic fluxes do not adapt to the nuclei motion depending on the initial state. These results are inferred from two different approaches for the numerical flux simulation, which are both in good agreement

Методы идентификации авторства в определении студенческого плагиата

In the modern educational context the problem of plagiarism is urgent and requires the development of effective methods of detection and prevention of this phenomenon. The application of authorship identification methods in the field of student plagiarism detection is considered. Different check, detect and analyze plagiarism approaches in various works are investigated. Both classical methods, which include text comparison and similarity search, and modern methods based on machine learning algorithms, as well as their combination and potential modifications, are considered. The advantages and limitations of each method are also discussed, and recommendations are given for choosing one or another approach according to the specific requirements of the research.Special attention is paid to such modern methods as metadata analysis and the application of neural networks. Stylistic analysis reveals authorial peculiarities such as word choice, preferred wording, and even punctuation. Lexical and syntactic models are used to identify repetitive phrases and structures that may indicate plagiarism. Statistical methods can identify anomalies in the use of words and phrases, and machine learning can create models to calculate the probability of plagiarism based on large amounts of data.Ultimately, an comparison of authorship identification techniques in the field of student plagiarism detection is provided, which aims to provide valuable information about different approaches and their applicability, and to help researchers and educators develop effective strategies for detecting and preventing plagiarism in educational environments.В современном образовательном контексте проблема плагиата является актуальной и требует разработки эффективных методов обнаружения и предотвращения данного явления. Рассмотрено применение методов идентификации авторства в области обнаружения студенческого плагиата. Исследованы различные подходы, используемые для проверки, обнаружения и анализа плагиата в различных работах. Рассмотрены как классические методы, в числе которых сравнение текстов и поиск сходства, так и современные методы, основанные на алгоритмах машинного обучения, а также их комбинирование и потенциальные модификации. Также обсуждены преимущества и ограничения каждого метода и даны рекомендации по выбору того или иного подхода в соответствии с конкретными требованиями исследования. Особое внимание уделено таким современным методам, как анализ метаданных и применение нейронных сетей. Стилистический анализ позволяет выявить авторские особенности, такие как выбор слов, предпочтительные формулировки и даже пунктуация. Лексические и синтаксические модели используются для выявления повторяющихся фраз и структур, которые могут указывать на плагиат. Статистические методы позволяют выявить аномалии в употреблении слов и фраз, а машинное обучение – создать модели, позволяющие рассчитать вероятность плагиата на основе большого количества данных.В конечном итоге предоставлено сравнение методов идентификации авторства в области определения студенческого плагиата, что имеет целью дать ценную информацию о различных подходах и их применимости, а также поможет исследователям и преподавателям разработать эффективные стратегии выявления и предотвращения плагиата в образовательной среде

THE USE OF NEW REAGENT KITS FOR DETECTION AND DESCRIPTION OF ADDITIONAL ALLELES

During the screening typing of recruited volunteers with Volga Federal District for unrelated hematopoietic stem cell registry on the loci (HLA)-A, B, DRB1, DRB345 in sample No 1758 identified a new allele at locus A. The use of basic kit AlleleSEQR HLA-A Sequencing in combination with HARP – A2F98A allowed to determine the genotype of this sample – А*30:01:01, a new allele А*25, В*13, 44, DRB1*03, 09, DRB3*02, DRB4*01

Construction of the Initial Part of a Ion Linear Accelerator from Similar Short Cavities

The construction of the initial part of a normally conducting linac for hydrogen ion beams with a pulsed current of ~20 mA up to an energy of ~70 MeV is considered. The RFQ at a frequency of ~160 MHz accelerates ions to an energy of ~4 MeV. Further acceleration is carried out at a doubled frequency by short, up to $5\beta\lambda$, cavities, operating in the TM010 mode, with drift tubes. Focusing is carried out by doublets of quadrupole lenses placed between the cavities. The structure of the accelerating-focusing channel, with given beam parameters, with reserves provides both the conditions for stable longitudinal and transverse motion of particles, and reliable technical implementation. The main results of the simulations of particle dynamics and the main parameters of the elements of the channel are presented. The possibility of constructing an linac with a higher output energy is analyzed.Comment: in Russian languag

Immunogenetic characteristics of unrelated hematopoietic stem cell donors recruited in the Sverdlovsk, Saratov, Yaroslavl and Vladimir regions

Aim of the study was to investigate the distribution features of HLA alleles and multilocus haplotypes in potential donors of hematopoietic stem cells recruting in the Sverdlovsk, Saratov, Yaroslavl and Vladimir regions. Material and methods. Sequence Based Typing technology was used to identify human leukocyte antigen (HLA)-A, -B, -C, -DRB1 alleles from 2683 Russian unrelated bone marrow volunteers living in the Sverdlovsk (n = 1018), Saratov (n = 825), Yaroslavl (n = 604) and Vladimir (n = 236) regions. HLA allele and haplotype frequencies were estimated via maximum-likelihood analysis from genotypic data through an expectation-maximization (EM) algorithm for unknown gametic phase. Results and discussion. In all studied populations, 16 HLA-A, 13 HLA-C, 13 HLA-DRB1 alleles were selected. In the locus HLA-B, 28 alleles were detected in the populations of the Sverdlovsk and Yaroslavl regions, 27alleles – in the Saratov region, 25 alleles – in the Vladimir. Seventeen alleles, HLA-A*02, HLA-A*03, HLA-A*01, HLA-A*24, HLA-B*07, HLA-B*35, HLA-С*07, HLA-С*06, HLA-С*04, HLA-С*03, HLA-С*12, HLA-DRB1*15, HLA-DRB1*07, HLA-DRB1*01, HLA-DRB1*13, HLA-DRB1*04, HLA-DRB1*11 exhibit frequencies over 10 %. The highest frequency extended haplotype in the all studied populations HLA-A*01-B*08-C*07-DRB1*03, was observed frequencies of 4,4 % – in the Sverdlovsk region, 3,2 % – in the Saratov region, 4,9 % – in the Yaroslavl region and 4,2 % – in the Vladimir region. Routine HLA typing allowed us to define four new HLA alleles in the populations of the Sverdlovsk and Saratov region

Reconstruction of recombination sites in genomic structures of the strains of genotype 6 of hepatitis C virus

The encoded portion of the complete genomes of 46 strains of the genotype 6 of hepatitis C virus through bioinformatics RDP programs complex group of 6 recombinants strains was identified, in which 7 recombination sites were fixed. Strains correspond to the three-recombinant HCV subtypes: 6a, 6b and 61. For each of the identified recombinant we defined parent strains from which they can be obtained. Three recombinants were obtained from parent strains of the same subtype (homologous inside subgenotypic recombination). For the remaining three recombinants parent strains were members of three different subtypes (between subgenotypic recombination).In one strain we identified a unique recombination site in a highly conservative NS3 gene. Most of the recombination sites occurred in the region of the structural genes C, E1 and E2, and in the area of non-structural genes NS5a and NS5b.In the recombinant strain DQ480518-6a two recombination site were identified. One site is located in the structural and nonstructural genes (E2 + NS1 + NS2), and a second one in non-structural region. Dimensions of recombination sites can vary from 86 to 1072 nucleotide bases. The study identified "hot spots" of recombination in the strains of genotype 6 of hepatitis C virus. The recombinants were found in the population of the three countries: the United States (from the serum of an immigrant), Hong Kong and China

Bioinformational analysis of Yersinia pseudotuberculosis IP32953 CRISPR/cas system

The results of this study include Yersinia pseudotuberculosis CRISPR/Cas system structure analysis. CRISPR/Cas system is a specific adaptive protection against heterogeneous genetic elements. The object of research was the complete genome of Y. pseudotuberculosis IP32953 (NC_006155). CRISPR/Cas system screening was performed by program modelling methods MacSyFinder ver. 1.0.2. CRISPR loci screening and analyzing were carried out by program package: CRISPR Recognition tool (CRT), CR1SP1: a CRISPR Interactive database, CRISPRFinder, and PilerCR. Spacer sequences were used in order to find protospacers in ACLAME, GenBank-Phage and RefSeq-Plasmid databases by BLASTn search algorithm. Protospacer sequences could be found in genomes of phages, plasmids and bacteria. In last case complete genomes of bacteria were analyzed by online-tool PHAST: PHAge Search Tool. Y. pseudotuberculosis IP329353 has CRISPR/Cas system that consists of one sequence of cas-genes and three loci. These loci are far away from each other. Locus YP1 is situated in close proximity to cas-genes. Protospacers were found in genomes of Y. pseudotuberculosis PB1/+, Y. intermedia Y228, Y. similis str. 228, Salmonella phage, Enterobacteria phage, Y. pseudotuberculosis 1P32953 plasmid pYV and plasmid of Y. pseudotuberculosis 1P31758. Thus, the combination of four program methods allows finding CRISPR/Cas system more precisely. Spacer sequences could be used for protospacer screening

APPLICATION OF MATHEMATICAL METHOD PREDICTIONS FOR IDENTIFICATION OF PATTERNS RELATIONS MUTATIONS IN PROTEINS ENCEPHALITIS VIRUS AND A MANIFESTATION OF ITS PHENOTYPIC TRAITS

We studied the natural connections between the amino acid sequences of proteins C, prM, E and NS1 virus strains of tick-borne encephalitis (TBE) and their three phenotypic traits -neuroinvasiveness, thermal stability and thermoresistance. Coupling strength is assessed using measures of competitive sequence similarity of each strain with reference strains. For such purposes subsets of strain sections are chosen amino acid composition specifics of which can predict the value of a phenotypic trait of interest. The possibility to predict missing elements in data both in amino acid composition, and in target properties is demonstrated. The relationships between pairs of phenotypic traits of strains were evaluated

Dynamic magnetic response of a ferrofluid in a static uniform magnetic field

A theory for the frequency-dependent magnetic susceptibility of a ferrofluid in a static uniform magnetic field is developed, including the dipolar interactions between the constituent particles. Interactions are included within the framework of modified mean-field theory. Predictions are given for the linear responses of the magnetization to a probing ac field both parallel and perpendicular to the static field and are tested against results from Brownian dynamics simulations. The effects of the particle concentration and dipolar coupling constant on the field-dependent static susceptibilities and the frequency dispersions are shown to be substantial, which justifies taking proper account of the interactions between particles. The theory is reliable provided that the volume concentration and dipolar coupling constant are not too large and within the range of values for real ferrofluids. © 2018 American Physical Society