Telomerization reaction of ethylene with ethanol

The research's main aim is to synthesize saturated alcohols containing four or more carbon atoms in the chain from ethylene and ethanol, which are products of natural gas processing. During of investigation, isobutyl and isohexyl alcohols were synthesized, and the optimal conditions for the process were determined. The dependence of the product yield on various factors has been studied

Person Re-identification with Deep Similarity-Guided Graph Neural Network

The person re-identification task requires to robustly estimate visual similarities between person images. However, existing person re-identification models mostly estimate the similarities of different image pairs of probe and gallery images independently while ignores the relationship information between different probe-gallery pairs. As a result, the similarity estimation of some hard samples might not be accurate. In this paper, we propose a novel deep learning framework, named Similarity-Guided Graph Neural Network (SGGNN) to overcome such limitations. Given a probe image and several gallery images, SGGNN creates a graph to represent the pairwise relationships between probe-gallery pairs (nodes) and utilizes such relationships to update the probe-gallery relation features in an end-to-end manner. Accurate similarity estimation can be achieved by using such updated probe-gallery relation features for prediction. The input features for nodes on the graph are the relation features of different probe-gallery image pairs. The probe-gallery relation feature updating is then performed by the messages passing in SGGNN, which takes other nodes' information into account for similarity estimation. Different from conventional GNN approaches, SGGNN learns the edge weights with rich labels of gallery instance pairs directly, which provides relation fusion more precise information. The effectiveness of our proposed method is validated on three public person re-identification datasets.Comment: accepted to ECCV 201

Concerning the selection of areas with a dominant type of dependence when analyzing production control data

The formation of representative databases determines the interest in forecasting and managing the quality of metal based on data mining using special software products often based on regression analysis and not always taking into account the statistical nature of an object of study itself. This can lead to misinterpretation of the results or incomplete extracted information reducing the efficiency of statistical processing. Based on the analysis of the production database of the technology for producing 13G1S-U sheet steel, the authors evaluated the possibilities of multiple linear regression for predicting the quality of a steel sheet. The study shows that the type of distribution of the values of control parameters, the distribution nature of which was estimated based on the determination of the skewness and kurtosis coefficients, limits the regression forecast depth. Due to the great deviation of the predicted models from the experimental values in the right tail area of the distribution of the impact strength values, in this work, the authors developed the methods for separating data arrays and proposed criteria to compare the obtained results. To assess the accuracy of the results obtained, arrays with a deliberately asymmetric distribution were selected from the initial sample, against which the statistical characteristics were also compared. Based on the proposed techniques, the authors identified the dominant chemical elements that contribute to the difference in the distribution of the values of acceptance properties existing within the same standard technology. The study shows that the proposed separation method can be used as a variation of cognitive graphics techniques to identify areas with a dependence dominant type based on the correlation of skewness and kurtosis coefficients

Person re-identification with soft biometrics through deep learning

Re-identification of persons is usually based on primary biometric features such as their faces, fingerprints, iris or gait. However, in most existing video surveillance systems, it is difficult to obtain these features due to the low resolution of surveillance footages and unconstrained real-world environments. As a result, most of the existing person re-identification techniques only focus on overall visual appearance. Recently, the use of soft biometrics has been proposed to improve the performance of person re-identification. Soft biometrics such as height, gender, age are physical or behavioural features, which can be described by humans. These features can be obtained from low-resolution videos at a distance ideal for person re-identification application. In addition, soft biometrics are traits for describing an individual with human-understandable labels. It allows human verbal descriptions to be used in the person re-identification or person retrieval systems. In some deep learning based person re-identification methods, soft biometrics attributes are integrated into the network to boot the robustness of the feature representation. Biometrics can also be utilised as a domain adaptation bridge for addressing the cross-dataset person re-identification problem. This chapter will review the state-of-the-art deep learning methods involving soft biometrics from three perspectives: supervised, semi-supervised and unsupervised approaches. In the end, we discuss the existing issues that are not addressed by current works

Synergistic Activation of Dopamine D1 and TrkB Receptors Mediate Gain Control of Synaptic Plasticity in the Basolateral Amygdala

Fear memory formation is thought to require dopamine, brain-derived neurotrophic factor (BDNF) and zinc release in the basolateral amygdala (BLA), as well as the induction of long term potentiation (LTP) in BLA principal neurons. However, no study to date has shown any relationship between these processes in the BLA. Here, we have used in vitro whole-cell patch clamp recording from BLA principal neurons to investigate how dopamine, BDNF, and zinc release may interact to modulate the LTP induction in the BLA. LTP was induced by either theta burst stimulation (TBS) protocol or spaced 5 times high frequency stimulation (5xHFS). Significantly, both TBS and 5xHFS induced LTP was fully blocked by the dopamine D1 receptor antagonist, SCH23390. LTP induction was also blocked by the BDNF scavenger, TrkB-FC, the zinc chelator, DETC, as well as by an inhibitor of matrix metalloproteinases (MMPs), gallardin. Conversely, prior application of the dopamine reuptake inhibitor, GBR12783, or the D1 receptor agonist, SKF39393, induced robust and stable LTP in response to a sub-threshold HFS protocol (2xHFS), which does not normally induce LTP. Similarly, prior activation of TrkB receptors with either a TrkB receptor agonist, or BDNF, also reduced the threshold for LTP-induction, an effect that was blocked by the MEK inhibitor, but not by zinc chelation. Intriguingly, the TrkB receptor agonist-induced reduction of LTP threshold was fully blocked by prior application of SCH23390, and the reduction of LTP threshold induced by GBR12783 was blocked by prior application of TrkB-FC. Together, our results suggest a cellular mechanism whereby the threshold for LTP induction in BLA principal neurons is critically dependent on the level of dopamine in the extracellular milieu and the synergistic activation of postsynaptic D1 and TrkB receptors. Moreover, activation of TrkB receptors appears to be dependent on concurrent release of zinc and activation of MMPs

THE IMPORTANCE OF EDUCATIONAL WEB PORTALS IN IMPROVING THE EDUCATION SYSTEM

This article focuses on the various issues of modern web technologies that are being used in education