Search results for: Oleksii Solodiankin

Authors: Borys Stegniy, Anton Gerilovych, Oleksii Solodiankin, Vitaliy Bolotin, Anton Stegniy, Denys Muzyka, Claudio Afonso

Abstract:

New APMV was isolated from white fronted goose in Ukraine. This isolate was tested serologically using monoclonal antibodies in haemagglutination-inhibition tests against APMV1-9. As the results obtained isolate showed cross reactions with APMV7. Following investigations were provided for the full genome sequencing using random primers and cloning into pCRII-TOPO. Analysis of 100 transformed colonies of E.coli using traditional sequencing gave us possibilities to find only 3 regions, which could identify by BLAST. The first region with the length of 367 bp had 70 % nucleotide sequence identity to the APMV 12 isolate Wigeon/Italy/3920_1/2005 at genome position 2419-2784. Next region (344 bp) had 66 % identity to the same APMV 12 isolate at position 4760-5103. The last region (365 bp) showed 71 % identity to Newcastle disease virus strain M4 at position 12569-12928.

Keywords: APMV, Newcastle disease virus, Ukraine, full genome sequencing

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Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman

Abstract:

Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.

Keywords: computer vision, deep learning, object detection, semiconductor

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