75 research outputs found

    Π£Ρ‚ΠΎΡ‡Π½Π΅Π½ΠΈΠ΅ ΠΎΡ†Π΅Π½ΠΊΠΈ Π»Π°Ρ‚Π΅Π½Ρ‚Π½ΠΎΠ³ΠΎ ΠΏΠ΅Ρ€ΠΈΠΎΠ΄Π° для Π½Π΅ΠΉΡ€ΠΎΠ½ΠΎΠ² с синаптичСским взаимодСйствиСм

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    We consider the estimation of the spike delay induced by external sinaptic influence. We propose a first-order estimation for the latent period of synapse coupled spiking neurons.РассмотрСна Π·Π°Π΄Π°Ρ‡Π° вычислСния Π·Π°Π΄Π΅Ρ€ΠΆΠΊΠΈ возникновСния ΠΈΠ½Π΄ΡƒΡ†ΠΈΡ€ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ спайка ΠΏΠΎ ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΡŽ ΠΊ ΠΌΠΎΠΌΠ΅Π½Ρ‚Ρƒ Π½Π°Ρ‡Π°Π»Π° синаптичСского воздСйствия Π½Π° ΠΈΠΌΠΏΡƒΠ»ΡŒΡΠ½Ρ‹ΠΉ Π½Π΅ΠΉΡ€ΠΎΠ½. ΠŸΠΎΠ»ΡƒΡ‡Π΅Π½Π° асимптотичСская ΠΎΡ†Π΅Π½ΠΊΠ°, ΠΈΠΌΠ΅ΡŽΡ‰Π°Ρ ΠΏΠ΅Ρ€Π²Ρ‹ΠΉ порядок точности ΠΏΠΎ ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΡŽ ΠΊ Π²Π΅Π»ΠΈΡ‡ΠΈΠ½Π΅, ΠΎΠ±Ρ€Π°Ρ‚Π½ΠΎΠΉ ΠΊ Π·Π½Π°Ρ‡Π΅Π½ΠΈΡŽ большого ΠΏΠ°Β¬Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Π°

    ΠŸΡ€ΠΈΠ½Ρ†ΠΈΠΏΡ‹ построСния слоистых Π½Π΅ΠΉΡ€ΠΎΠ½Π½Ρ‹Ρ… сСтСй Π½Π° основС ΠΈΠΌΠΏΡƒΠ»ΡŒΡΠ½Ρ‹Ρ… Π½Π΅ΠΉΡ€ΠΎΠ½ΠΎΠ²

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    In the article we describe principles of pulse implementation of multilayer neural networks using biologically plausible neurons. It is shown that the multilayer perceptron can be modeled with a neural network composed of pulse neurons using impulse information coding.ΠžΠΏΠΈΡΠ°Π½Ρ‹ ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏΡ‹ построСния многослойных Π½Π΅ΠΉΡ€ΠΎΠ½Π½Ρ‹Ρ… сСтСй Π½Π° основС ΠΈΠΌΠΏΡƒΠ»ΡŒΡΠ½ΠΎΠΉ Π½Π΅ΠΉΡ€ΠΎΠ½Π½ΠΎΠΉ сСти, составлСнной ΠΈΠ· биологичСски ΠΏΡ€Π°Π²Π΄ΠΎΠΏΠΎΠ΄ΠΎΠ±Π½Ρ‹Ρ… Π½Π΅ΠΉΡ€ΠΎΠ½ΠΎΠ². Показано, Ρ‡Ρ‚ΠΎ многослойный ΠΏΠ΅Ρ€Ρ†Π΅ΠΏΡ‚Ρ€ΠΎΠ½ ΠΌΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ смодСлирован Π½Π΅ΠΉΡ€ΠΎΠ½Π½ΠΎΠΉ ΡΠ΅Ρ‚ΡŒΡŽ, состоящСй ΠΈΠ· ΠΈΠΌΠΏΡƒΠ»ΡŒΡΠ½Ρ‹Ρ… Π½Π΅ΠΉΡ€ΠΎΠ½ΠΎΠ² ΠΈ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡŽΡ‰Π΅ΠΉ ΠΈΠΌΠΏΡƒΠ»ΡŒΡΠ½ΠΎΠ΅ ΠΊΠΎΠ΄ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ

    New approaches to registration and conservation of domestic cultivars of berry crops in the VIR Genebank on the example of red raspberry and black currant

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    A collection of nomenclatural standards is being created at VIR for domestic cultivars of various crops in accordance with the International Code of Nomenclature for Cultivated Plants (ICNCP). A new complex strategy was proposed for vegetatively propagated crops for registering domestic cultivars received from their authors in the VIR genebank. In addition to the creation of nomenclature standards, the strategy includes the development of a genetic passport of a cultivar and the use of biotechnological methods to preserve explants (buds, meristems) isolated from plant material transferred by breeders to the VIR Herbarium. This approach can be used for any vegetatively propagated crop applying a protocol developed specifically for an individual crop. For raspberry and black currant varieties, the collecting of plant material, its preparation for the registration of nomenclature standards and the preservation of viable samples under controlled inΒ vitro conditions have specific features. This article provides detailed protocols for performing the mentioned work for raspberry and black currant varieties. In addition, the article summarizes the first results of the implementation of our proposed strategy on the example of domestic raspberry and black currant varieties created in various breeding centers of Russia. Three years of joint work of VIR researchers and breeders from four breeding centers in five regions of the country have resulted in creation of nomenclature standards for 20 raspberry varieties, as well as for five black currant varieties bred at VIR. Thirteen samples of raspberry varieties and four of black currant varieties, genetically identical to nomenclature standards, were introduced into inΒ vitro culture; four raspberry cultivars have been placed in the VIR cryobank for the long-term cryopreservation

    In vitro collection of berry and fruit crops and their wild relatives at VIR

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    Π˜ΡΠΏΡ€Π°Π²Π»Π΅Π½ΠΈΡ ΠΈ дополнСния ΠΊ ΡΡ‚Π°Ρ‚ΡŒΠ΅ Β«ΠŸΠΎΠΏΡ€Π°Π²ΠΊΠ° ΠΊ ΠΏΠ΅Ρ€ΠΈΠΎΠ΄Ρƒ Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ уравнСния, ΠΌΠΎΠ΄Π΅Π»ΠΈΡ€ΡƒΡŽΡ‰Π΅Π³ΠΎ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΡƒ ΠΌΠ΅ΠΌΠ±Ρ€Π°Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΡ‚Π΅Π½Ρ†ΠΈΠ°Π»Π°

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    In this paper we present a corrected version of the theorem from the article "A Correction For Period Of Oscillation In The Model Of Spiking Neuron", in which a new first-order estimation for the oscillation period in the difference-differential model of the spiking neuron was obtained.ΠŸΡ€Π΅Π΄ΡΡ‚Π°Π²Π»Π΅Π½ исправлСнный Π²Π°Ρ€ΠΈΠ°Π½Ρ‚ Ρ„ΠΎΡ€ΠΌΡƒΠ»ΠΈΡ€ΠΎΠ²ΠΊΠΈ ΠΈ Π΄ΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒΡΡ‚Π²Π° Ρ‚Π΅ΠΎΡ€Π΅ΠΌΡ‹ ΠΈΠ· ΡΡ‚Π°Ρ‚ΡŒΠΈ Β«ΠŸΠΎΠΏΡ€Π°Π²ΠΊΠ° ΠΊ ΠΏΠ΅Ρ€ΠΈΠΎΠ΄Ρƒ Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ уравнСния, ΠΌΠΎΠ΄Π΅Π»ΠΈΡ€ΡƒΡŽΡ‰Π΅Π³ΠΎ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΡƒ ΠΌΠ΅ΠΌΠ±Ρ€Π°Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΡ‚Π΅Π½Ρ†ΠΈΠ°Π»Π° Π½Π΅ΠΉΡ€ΠΎΠ½Π°Β», Π² ΠΊΠΎΡ‚ΠΎΡ€ΠΎΠΉ Π±Ρ‹Π»Π° ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π° ΠΎΡ†Π΅Π½ΠΊΠ° ΠΏΠ΅Ρ€Π²ΠΎΠ³ΠΎ порядка точности для ΠΏΠ΅Ρ€ΠΈΠΎΠ΄Π° Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ уравнСния с Π·Π°ΠΏΠ°Π·Π΄Ρ‹Π²Π°Π½ΠΈΠ΅ΠΌ, ΠΌΠΎΠ΄Π΅Π»ΠΈΡ€ΡƒΡŽΡ‰Π΅Π³ΠΎ биологичСский Π½Π΅ΠΉΡ€ΠΎΠ½

    Cryopreservation of raspberry cultivar accessions bred in Russia from the VIR <i>in vitro</i> collection

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    Cryobanks use plant cryocollections for long-term preservation of crops which cannot be preserved in seed collections. These are vegetatively propagated crops, accessions of species which form either a small amount of seeds, or recalcitrant seeds. Shoot tips (apexes) of inΒ vitro plants are used for cryopreservation for most berry crops, therefore maintenance of inΒ vitro collections is very important. The VIR inΒ vitro collection includes 150 accessions of RubusΒ L. species, 85 of them are raspberry cultivars, 59 of which were bred in Russia. These cultivars reflect a wide ecogeographic diversity. Among them, there are raspberry cultivars created at the end of the 19th – first half of the 20th centuries, including cultivars bred by I.V.Β Michurin and by the pioneer of northern horticulture V.V.Β Spirin. More than half of national raspberry varieties (33) are listed in the State Register for Selection Achievements Admitted for Usage. Raspberry cultivars from Russian breeding programs have a very limited representation in foreign genebanks. The first aim of the present work was cryopreservation of mostly folk and old Russian raspberry cultivars received by VIR from 1925 till 1950 and their transfer into the cryobank. The second aim of the work was to monitor post-cryogenic regeneration of raspberry cultivars transferred to the cryobank earlier. A modified protocol of the droplet vitrification method by β€œDV-biotech” was used for cryopreservation of shoot tips of inΒ vitro plants of 10 raspberry cultivars (7 of which are folk and old Russian ones) from the VIR inΒ vitro collection. Post-cryogenic regeneration was evaluated for 17 raspberry cultivars preserved in the cryobank from one to five years.Β Ten raspberry cultivars (900 apexes) with an average mean post-cryoregenic regeneration value of 38.2Β±3.0% determined in control tests, were placed in the cryobank for long-term storage. A statistically significant effect of the genotype on the viability of explants after cryopreservation was noted, while the post-cryogenic regeneration was genotype insensitive. Additionally, levels of post-cryogenic regeneration were evaluated for 17 raspberry cultivars (296 apexes) preserved in the cryobank from one to five years. Post-cryogenic regeneration within the 20-70% range was displayed by four raspberry cultivars preserved in the cryobank for one year, and for 8 cultivars conserved there from three to five years post-cryogenic regeneration was within the 10-50% range. According to the results of monitoring, regeneration displayed by 12 raspberry cultivars was within the 10-70% range, which can be considered as a reliable rate of apex preservation in liquid nitrogen vapors in the VIR cryobank. Monitoring of the post-cryogenic regeneration of the raspberry accessions preserved in the VIR cryobank and cryopreservation of new raspberry cultivars will be continued

    Molecular markers in the genetic diversity studies of representatives of the genus <i>Rubus</i> L. and prospects of their application in breeding

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    According to estimates of various taxonomists, the genus Rubus L. (Rosaceae Juss.) consists of 12-16 subgenera comprising ~750 species. The two largest subgenera are Idaeobatus (Focke) Focke, which includes raspberries, and the type subgenus Rubus (=Eubatus Focke), which contains blackberry species. Representatives of the genus Rubus have high nutritional and economic values, as well as medicinal properties. Breeding programs are aimed at broadening genetic diversity and creating new varieties of raspberries and blackberries that are resistant to biotic and abiotic stressors and have high fruit quality. Modern breeding and genetic programs increasingly use the achievements of molecular genetics and genomics. This paper reviews the literature data on the application of molecular markers in fundamental and applied research aimed at studying the genetic diversity of cultivated and wild species of the genus Rubus. The review describes the main types of molecular markers (RFLP, RAPD, SCoT, SSR, ISSR, AFLP, SCAR, SSCP) and their application for studying the species of the genus Rubus. The results of the work on the use of DNA markers for solving different tasks are presented, including: studying the phylogenetic relationships of species, clarifying controversial issues of taxonomy, analyzing interspecific and intraspecific diversity, genotyping and pedigree analysis of raspberry and blackberry varieties, studying somaclonal variation and others. The most important applied result is the development of molecular genetic maps for raspberry and blackberry species, on which numerous genes and QTLs conferring various valuable traits have been mapped. At the same time, the number of markers that are promising for effective molecular screening is still insufficient

    Π˜ΠΌΠΏΡƒΠ»ΡŒΡΠ½Ρ‹ΠΉ Π½Π΅ΠΉΡ€ΠΎΠ½ ΠΈ Π½Π΅ΠΉΡ€ΠΎΠ½Π½Ρ‹ΠΉ ΠΊΠ»Π΅Ρ‚ΠΎΡ‡Π½Ρ‹ΠΉ Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚ асимптотичСски эквивалСнтны

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    In the article, it is established the asymptotic equivalence of dynamics of the neural networks consisting of the impulse neurons and the neural networks built of the neural cellular automata of different kinds (autogenerators and detectors). Such an equivalence takes place for appropriate parameters values and non-intersection of input impacts for each neuron. In the first chapter, we describe the model of impulse autogenerator neuron and two different models of impulse detector neuron. For these models, we prove statements about duration of the latent period for a neuron under impact. In the second chapter, we describe the model of a neural cellular automaton with autogenerator dynamics and a modification of this model with detector dynamics. For these models, we also prove statements about the latent period for a neural automaton under impact. In the third chapter, we prove some statements about asymptotic equivalence of the impulse neurons and neural cellular automata of different kinds.Π’ Ρ€Π°Π±ΠΎΡ‚Π΅ установлСна асимптотичСская ΡΠΊΠ²ΠΈΠ²Π°Π»Π΅Π½Ρ‚Π½ΠΎΡΡ‚ΡŒ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ Π½Π΅ΠΉΡ€ΠΎΠ½Π½Ρ‹Ρ… сСтСй, состоящих ΠΈΠ· ΠΈΠΌΠΏΡƒΠ»ΡŒΡΠ½Ρ‹Ρ… Π½Π΅ΠΉΡ€ΠΎΠ½ΠΎΠ² ΠΈ Π½Π΅ΠΉΡ€ΠΎΠ½Π½Ρ‹Ρ… ΠΊΠ»Π΅Ρ‚ΠΎΡ‡Π½Ρ‹Ρ… Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚ΠΎΠ² Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… Ρ‚ΠΈΠΏΠΎΠ² (Π°Π²Ρ‚ΠΎΠ³Π΅Π½Π΅Ρ€Π°Ρ‚ΠΎΡ€ΠΎΠ² ΠΈ Π΄Π΅Ρ‚Π΅ΠΊΡ‚ΠΎΡ€ΠΎΠ²) ΠΏΡ€ΠΈ ΡΠΎΠΎΡ‚Π²Π΅Ρ‚ΡΡ‚Π²ΡƒΡŽΡ‰Π΅ΠΌ Π²Ρ‹Π±ΠΎΡ€Π΅ ΠΈΡ… ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² ΠΈ ΠΏΡ€ΠΈ условии нСналоТСния Π²Ρ…ΠΎΠ΄Π½Ρ‹Ρ… воздСйствий Π½Π° ΠΊΠ°ΠΆΠ΄Ρ‹ΠΉ Π½Π΅ΠΉΡ€ΠΎΠ½. Π’ ΠΏΠ΅Ρ€Π²ΠΎΠΌ Ρ€Π°Π·Π΄Π΅Π»Π΅ описаны модСль ΠΈΠΌΠΏΡƒΠ»ΡŒΡΠ½ΠΎΠ³ΠΎ Π½Π΅ΠΉΡ€ΠΎΠ½Π°-Π°Π²Ρ‚ΠΎΠ³Π΅Π½Π΅Ρ€Π°Ρ‚ΠΎΡ€Π° ΠΈ Π΄Π²Π΅ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Π΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΈΠΌΠΏΡƒΠ»ΡŒΡΠ½ΠΎΠ³ΠΎ Π½Π΅ΠΉΡ€ΠΎΠ½Π°-Π΄Π΅Ρ‚Π΅ΠΊΡ‚ΠΎΡ€Π°. Для этих ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π΄ΠΎΠΊΠ°Π·Ρ‹Π²Π°ΡŽΡ‚ΡΡ утвСрТдСния ΠΎΡ‚Π½ΠΎΡΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ Π²Π΅Π»ΠΈΡ‡ΠΈΠ½Ρ‹ Π»Π°Ρ‚Π΅Π½Ρ‚Π½ΠΎΠ³ΠΎ ΠΏΠ΅Ρ€ΠΈΠΎΠ΄Π° Π½Π΅ΠΉΡ€ΠΎΠ½Π° ΠΏΡ€ΠΈ Π΅Π΄ΠΈΠ½ΠΈΡ‡Π½ΠΎΠΌ внСшнСм воздСйствии. Π’ΠΎ Π²Ρ‚ΠΎΡ€ΠΎΠΌ Ρ€Π°Π·Π΄Π΅Π»Π΅ описана модСль Π½Π΅ΠΉΡ€ΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΊΠ»Π΅Ρ‚ΠΎΡ‡Π½ΠΎΠ³ΠΎ Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚Π° с Π°Π²Ρ‚ΠΎΠ³Π΅Π½Π΅Ρ€Π°Ρ‚ΠΎΡ€Π½ΠΎΠΉ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΎΠΉ ΠΈ модификация этой ΠΌΠΎΠ΄Π΅Π»ΠΈ, ΠΈΠΌΠ΅ΡŽΡ‰Π°Ρ Π΄Π΅Ρ‚Π΅ΠΊΡ‚ΠΎΡ€Π½ΡƒΡŽ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΡƒ. Для этих ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Ρ‚Π°ΠΊΠΆΠ΅ Π΄ΠΎΠΊΠ°Π·Ρ‹Π²Π°ΡŽΡ‚ΡΡ утвСрТдСния ΠΎΡ‚Π½ΠΎΡΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ Π»Π°Ρ‚Π΅Π½Ρ‚Π½ΠΎΠ³ΠΎ ΠΏΠ΅Ρ€ΠΈΠΎΠ΄Π° Π½Π΅ΠΉΡ€ΠΎΠ½Π½ΠΎΠ³ΠΎ Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚Π°, находящСгося ΠΏΠΎΠ΄ внСшним воздСйствиСм. Π’ Ρ‚Ρ€Π΅Ρ‚ΡŒΠ΅ΠΌ Ρ€Π°Π·Π΄Π΅Π»Π΅ для ΠΈΠΌΠΏΡƒΠ»ΡŒΡΠ½Ρ‹Ρ… Π½Π΅ΠΉΡ€ΠΎΠ½ΠΎΠ² ΠΈ Π½Π΅ΠΉΡ€ΠΎΠ½Π½Ρ‹Ρ… ΠΊΠ»Π΅Ρ‚ΠΎΡ‡Π½Ρ‹Ρ… Π°Π²Ρ‚ΠΎΠΌΠ°Ρ‚ΠΎΠ² Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… Ρ‚ΠΈΠΏΠΎΠ² сформулированы ΠΈ Π΄ΠΎΠΊΠ°Π·Π°Π½Ρ‹ утвСрТдСния ΠΎΡ‚Π½ΠΎΡΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ условий ΠΈΡ… асимптотичСской эквивалСнтности

    БСгмСнтация клиничСских эндоскопичСских ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ, основанная Π½Π° классификации Π²Π΅ΠΊΡ‚ΠΎΡ€Π½Ρ‹Ρ… топологичСских ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ²

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    In this work, we describe a prototype of an automatic segmentation system and annotation of endoscopy images. The used algorithm is based on the classification of vectors of the topological features of the original image. We use the image processing scheme which includes image preprocessing, calculation of vector descriptors defined for every point of the source image and the subsequent classification of descriptors. Image preprocessing includes finding and selecting artifacts and equalizating the image brightness. In this work, we give the detailed algorithm of the construction of topological descriptors and the classifier creating procedure based on mutual sharing the AdaBoost scheme and a naive Bayes classifier. In the final section, we show the results of the classification of real endoscopic images.Π’ Ρ€Π°Π±ΠΎΡ‚Π΅ описан ΠΏΡ€ΠΎΡ‚ΠΎΡ‚ΠΈΠΏ систСмы автоматичСской сСгмСнтации ΠΈ аннотирования эндоскопичСских ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ. Π˜ΡΠΏΠΎΠ»ΡŒΠ·ΡƒΠ΅ΠΌΡ‹ΠΉ Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌ основан Π½Π° классификации Π²Π΅ΠΊΡ‚ΠΎΡ€ΠΎΠ² топологичСских ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ² исходного изобраТСния. ΠœΡ‹ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΠ΅ΠΌ схСму ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ, которая Π²ΠΊΠ»ΡŽΡ‡Π°Π΅Ρ‚ Π² сСбя ΠΏΡ€Π΅Π΄ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΡƒ изобраТСния, вычислСниС Π²Π΅ΠΊΡ‚ΠΎΡ€Π½Ρ‹Ρ… дСскрипторов, ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½Π½Ρ‹Ρ… для ΠΊΠ°ΠΆΠ΄ΠΎΠΉ Ρ‚ΠΎΡ‡ΠΊΠΈ изобраТСния, ΠΈ Π΄Π°Π»ΡŒΠ½Π΅ΠΉΡˆΡƒΡŽ ΠΊΠ»Π°ΡΡΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΡŽ дСскрипторов. ΠŸΡ€ΠΎΡ†Π΅Π΄ΡƒΡ€Π° ΠΏΡ€Π΅Π΄ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ изобраТСния Π²ΠΊΠ»ΡŽΡ‡Π°Π΅Ρ‚ Π²Ρ‹Π΄Π΅Π»Π΅Π½ΠΈΠ΅ Π°Ρ€Ρ‚Π΅Ρ„Π°ΠΊΡ‚ΠΎΠ² ΠΈ Π²Ρ‹Ρ€Π°Π²Π½ΠΈΠ²Π°Π½ΠΈΠ΅ яркости изобраТСния. Π’ Ρ€Π°Π±ΠΎΡ‚Π΅ ΠΏΠΎΠ΄Ρ€ΠΎΠ±Π½ΠΎ описан Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌ построСния топологичСских дСскрипторов ΠΈ ΠΏΡ€ΠΎΡ†Π΅Π΄ΡƒΡ€Π° построСния классификатора, основанная Π½Π° совмСстном использовании схСмы AdaBoost ΠΈ Π½Π°ΠΈΠ²Π½ΠΎΠ³ΠΎ байСсова классификатора. Π’ Π·Π°ΠΊΠ»ΡŽΡ‡ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠΌ Ρ€Π°Π·Π΄Π΅Π»Π΅ ΠΏΡ€ΠΈΠ²Π΅Π΄Π΅Π½Ρ‹ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ классификации Ρ€Π΅Π°Π»ΡŒΠ½Ρ‹Ρ… эндоскопичСских ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ

    Towards a More Complete and Accurate Experimental Nuclear Reaction Data Library (EXFOR): International Collaboration Between Nuclear Reaction Data Centres (NRDC)

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    The International Network of Nuclear Reaction Data Centres (NRDC) coordinated by the IAEA Nuclear Data Section (NDS) is successfully collaborating in the maintenance and development of the EXFOR library. As the scope of published data expands (e.g., to higher energy, to heavier projectile) to meet the needs from the frontier of sciences and applications, it becomes nowadays a hard and challenging task to maintain both completeness and accuracy of the whole EXFOR library. The paper describes evolution of the library with highlights on recent developments.Comment: 4 pages, 2 figure
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