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Author(s):  
Sonal Tuteja ◽  
Rajeev Kumar

AbstractThe incorporation of heterogeneous data models into large-scale e-commerce applications incurs various complexities and overheads, such as redundancy of data, maintenance of different data models, and communication among different models for query processing. Graphs have emerged as data modelling techniques for large-scale applications with heterogeneous, schemaless, and relationship-centric data. Models exist for mapping different types of data to a graph; however, the unification of data from heterogeneous source models into a graph model has not received much attention. To address this, we propose a new framework in this study. The proposed framework first transforms data from various source models into graph models individually and then unifies them into a single graph. To justify the applicability of the proposed framework in e-commerce applications, we analyse and compare query performance, scalability, and database size of the unified graph with heterogeneous source data models for a predefined set of queries. We also access some qualitative measures, such as flexibility, completeness, consistency, and maturity for the proposed unified graph. Based on the experimental results, the unified graph outperforms heterogeneous source models for query performance and scalability; however, it falls behind for database size.


Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1344
Author(s):  
Arjun Magotra ◽  
Juntae Kim

The plastic modifications in synaptic connectivity is primarily from changes triggered by neuromodulated dopamine signals. These activities are controlled by neuromodulation, which is itself under the control of the brain. The subjective brain’s self-modifying abilities play an essential role in learning and adaptation. The artificial neural networks with neuromodulated plasticity are used to implement transfer learning in the image classification domain. In particular, this has application in image detection, image segmentation, and transfer of learning parameters with significant results. This paper proposes a novel approach to enhance transfer learning accuracy in a heterogeneous source and target, using the neuromodulation of the Hebbian learning principle, called NDHTL (Neuromodulated Dopamine Hebbian Transfer Learning). Neuromodulation of plasticity offers a powerful new technique with applications in training neural networks implementing asymmetric backpropagation using Hebbian principles in transfer learning motivated CNNs (Convolutional neural networks). Biologically motivated concomitant learning, where connected brain cells activate positively, enhances the synaptic connection strength between the network neurons. Using the NDHTL algorithm, the percentage of change of the plasticity between the neurons of the CNN layer is directly managed by the dopamine signal’s value. The discriminative nature of transfer learning fits well with the technique. The learned model’s connection weights must adapt to unseen target datasets with the least cost and effort in transfer learning. Using distinctive learning principles such as dopamine Hebbian learning in transfer learning for asymmetric gradient weights update is a novel approach. The paper emphasizes the NDHTL algorithmic technique as synaptic plasticity controlled by dopamine signals in transfer learning to classify images using source-target datasets. The standard transfer learning using gradient backpropagation is a symmetric framework. Experimental results using CIFAR-10 and CIFAR-100 datasets show that the proposed NDHTL algorithm can enhance transfer learning efficiency compared to existing methods.


2021 ◽  
Vol 47 (2) ◽  
pp. 89-95
Author(s):  
Anna Kuznietsova ◽  
Julia Gorkovchuk

As a result of this research, a methodical approach to the geoinformational analysis of the waste containers’ placement for the collection of municipal solid waste in urban areas according to current standards and rules for the improvement of settlements was developed. According to the current Rules of maintenance of residential buildings and adjacent territories, waste containers of all types should be installed on a concrete or asphalted site, usually with fencing made of standard reinforced concrete products or other materials with planted shrubs around the site. Moreover, container platforms on wheels should be equipped with a ramp from the roadway and a fence (curb) which is 7–10 cm high, to keep the containers from rolling off to the sides. In the process of determining the optimal locations of waste containers a database of container park was created including the register of containers, the register of platforms, and the register of trash cans was created a list of influencing factors at the data collection stage, was made a proximity analysis and data reclassification to move to a unified scale for calculations of different types and/or heterogeneous source data, and weighted overlay as the main instrument of aggregated analysis. The analysis of the results is based on comparing the location of existing waste container sites with the resulting overlapping areas.


2020 ◽  
Author(s):  
Allison Pfeiffer ◽  
Susannah Marie Morey ◽  
Hannah Mae Karlsson ◽  
Edward M Fordham ◽  
David R. Montgomery

2020 ◽  
Vol 235 ◽  
pp. 103716
Author(s):  
Zhilin Guo ◽  
Ann E. Russo ◽  
Erica L. DiFilippo ◽  
Zhihui Zhang ◽  
Chunmiao Zheng ◽  
...  

2020 ◽  
Vol 10 (16) ◽  
pp. 5631 ◽  
Author(s):  
Arjun Magotra ◽  
Juntae Kim

Transfer learning algorithms have been widely studied for machine learning in recent times. In particular, in image recognition and classification tasks, transfer learning has shown significant benefits, and is getting plenty of attention in the research community. While performing a transfer of knowledge among source and target tasks, homogeneous dataset is not always available, and heterogeneous dataset can be chosen in certain circumstances. In this article, we propose a way of improving transfer learning efficiency, in case of a heterogeneous source and target, by using the Hebbian learning principle, called Hebbian transfer learning (HTL). In computer vision, biologically motivated approaches such as Hebbian learning represent associative learning, where simultaneous activation of brain cells positively affect the increase in synaptic connection strength between the individual cells. The discriminative nature of learning for the search of features in the task of image classification fits well to the techniques, such as the Hebbian learning rule—neurons that fire together wire together. The deep learning models, such as convolutional neural networks (CNN), are widely used for image classification. In transfer learning, for such models, the connection weights of the learned model should adapt to new target dataset with minimum effort. The discriminative learning rule, such as Hebbian learning, can improve performance of learning by quickly adapting to discriminate between different classes defined by target task. We apply the Hebbian principle as synaptic plasticity in transfer learning for classification of images using a heterogeneous source-target dataset, and compare results with the standard transfer learning case. Experimental results using CIFAR-10 (Canadian Institute for Advanced Research) and CIFAR-100 datasets with various combinations show that the proposed HTL algorithm can improve the performance of transfer learning, especially in the case of a heterogeneous source and target dataset.


2020 ◽  
Vol 11 (04) ◽  
pp. 650-658
Author(s):  
Sylvia Cho ◽  
Margaret Sin ◽  
Demetra Tsapepas ◽  
Leigh-Anne Dale ◽  
Syed A. Husain ◽  
...  

Abstract Background Improving outcomes of transplant recipients within and across transplant centers is important with the increasing number of organ transplantations being performed. The current practice is to analyze the outcomes based on patient level data submitted to the United Network for Organ Sharing (UNOS). Augmenting the UNOS data with other sources such as the electronic health record will enrich the outcomes analysis, for which a common data model (CDM) can be a helpful tool for transforming heterogeneous source data into a uniform format. Objectives In this study, we evaluated the feasibility of representing concepts from the UNOS transplant registry forms with the Observational Medical Outcomes Partnership (OMOP) CDM vocabulary to understand the content coverage of OMOP vocabulary on transplant-specific concepts. Methods Two annotators manually mapped a total of 3,571 unique concepts extracted from the UNOS registry forms to concepts in the OMOP vocabulary. Concept mappings were evaluated by (1) examining the agreement among the initial two annotators and (2) investigating the number of UNOS concepts not mapped to a concept in the OMOP vocabulary and then classifying them. A subset of mappings was validated by clinicians. Results There was a substantial agreement between annotators with a kappa score of 0.71. We found that 55.5% of UNOS concepts could not be represented with OMOP standard concepts. The majority of unmapped UNOS concepts were categorized into transplant, measurement, condition, and procedure concepts. Conclusion We identified categories of unmapped concepts and found that some transplant-specific concepts do not exist in the OMOP vocabulary. We suggest that adding these missing concepts to OMOP would facilitate further research in the transplant domain.


Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3387 ◽  
Author(s):  
Hyun-Koo Kim ◽  
Kook-Yeol Yoo ◽  
Ho-Youl Jung

In this paper, a modified encoder-decoder structured fully convolutional network (ED-FCN) is proposed to generate the camera-like color image from the light detection and ranging (LiDAR) reflection image. Previously, we showed the possibility to generate a color image from a heterogeneous source using the asymmetric ED-FCN. In addition, modified ED-FCNs, i.e., UNET and selected connection UNET (SC-UNET), have been successfully applied to the biomedical image segmentation and concealed-object detection for military purposes, respectively. In this paper, we apply the SC-UNET to generate a color image from a heterogeneous image. Various connections between encoder and decoder are analyzed. The LiDAR reflection image has only 5.28% valid values, i.e., its data are extremely sparse. The severe sparseness of the reflection image limits the generation performance when the UNET is applied directly to this heterogeneous image generation. In this paper, we present a methodology of network connection in SC-UNET that considers the sparseness of each level in the encoder network and the similarity between the same levels of encoder and decoder networks. The simulation results show that the proposed SC-UNET with the connection between encoder and decoder at two lowest levels yields improvements of 3.87 dB and 0.17 in peak signal-to-noise ratio and structural similarity, respectively, over the conventional asymmetric ED-FCN. The methodology presented in this paper would be a powerful tool for generating data from heterogeneous sources.


2020 ◽  
Vol 24 (2) ◽  
pp. 123-130
Author(s):  
A. V. Lyubimova ◽  
G. V. Tobolova ◽  
D. I. Eremin ◽  
I. G. Loskutov

Molecular and biochemical markers are used to analyze the intraspecific genetic diversity of crops.  Prolamincoding loci are highly effective for assessing this indicator. On the basis of the Laboratory of Varietal  Seed Identification of the State Agrarian University of the Northern Trans-Urals, 18 varieties of common oat  included in the State Register of Selection Achievements in the Tyumen Region from the 1930s to 2019 were  studied by electrophoresis in 2018–2019. The aim of the work was to study the dynamics of the genetic diversity  of oat va rieties at avenin-coding loci. For the analysis, 100 grains of each variety were used. Electrophoresis was  carried out in vertical plates of 13.2 % polyacrylamide gel at a constant vol tage of 500 V for 4.0–4.5 h. It was found  that 44.4 % of the varieties are heterogeneous, each consisting of two biotypes. For three loci, 20 alleles were  identified, 10 of which were detected for the first time. The allele frequency of avenin-coding loci varied with  time. In the process of variety exchange, alleles that are characteristic of varieties of non-Russian origin were replaced by alleles present in domestic varieties and then in the varieties developed by local breeding institutions.  The following alleles had the highest frequency in Tyumen varieties: Avn A4(50.0 %), A2(25.0 %), Avn B4(50.0 %),  Bnew6(37.5 %), Avn C1(37.5 %), C2 and C5(25.0 %). These alleles are of great value as markers of agronomically  and adaptively important characters for the region in question. The amount of genetic diversity of oats varied  with time from 0.33 in 1929–1950 to up to 0.75 in 2019. The high value of genetic diversity in modern breeding  varieties of the Scientific Research Institute of Agriculture of the Northern Trans-Urals and an increase in this  indicator over the past 20 years are associated with the use of genetically heterogeneous source material in the  breeding process. This allowed obtaining varieties with high adaptive potentials in the natural climatic conditions of the region.


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