Deep regional feature pooling for video matching

Author(s):  
Yan Bai ◽  
Jie Lin ◽  
Vijay Chandrasekhar ◽  
Yihang Lou ◽  
Shiqi Wang ◽  
...  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bagrat Grigoryan ◽  
Daniel W. Sazer ◽  
Amanda Avila ◽  
Jacob L. Albritton ◽  
Aparna Padhye ◽  
...  

AbstractAs a 3D bioprinting technique, hydrogel stereolithography has historically been limited in its ability to capture the spatial heterogeneity that permeates mammalian tissues and dictates structure–function relationships. This limitation stems directly from the difficulty of preventing unwanted material mixing when switching between different liquid bioinks. Accordingly, we present the development, characterization, and application of a multi-material stereolithography bioprinter that provides controlled material selection, yields precise regional feature alignment, and minimizes bioink mixing. Fluorescent tracers were first used to highlight the broad design freedoms afforded by this fabrication strategy, complemented by morphometric image analysis to validate architectural fidelity. To evaluate the bioactivity of printed gels, 344SQ lung adenocarcinoma cells were printed in a 3D core/shell architecture. These cells exhibited native phenotypic behavior as evidenced by apparent proliferation and formation of spherical multicellular aggregates. Cells were also printed as pre-formed multicellular aggregates, which appropriately developed invasive protrusions in response to hTGF-β1. Finally, we constructed a simplified model of intratumoral heterogeneity with two separate sub-populations of 344SQ cells, which together grew over 14 days to form a dense regional interface. Together, these studies highlight the potential of multi-material stereolithography to probe heterotypic interactions between distinct cell types in tissue-specific microenvironments.


Author(s):  
Saddam Bekhet ◽  
Amr Ahmed ◽  
Andrew Hunter

Author(s):  
V.M. Khokhlov ◽  
H.O. Borovska ◽  
O.V. Umanska ◽  
M.S. Tenetko

The paper analyzes spatiotemporal features the indices of hot, cold and precipitation that are related to weather conditions. The temperature in Ukraine tends to be higher, which is the main regional feature of global climate changes. The North Atlantic Oscillation had an influence on the precipitation in Ukraine – weather is rainier during its negative phases. Also, colder night and hotter days were more frequent during negative phases of the NAO. This fact can be explained by enhancing meridional flows in Ukraine. The wavelet analysis also revealed an impact of the NAO on temperature anomalies – positive phases determined increasing monthly minimum temperatures before the 1980s and decreasing ones after 1980s. Also, the wavelet analysis showed that the Nor-th Atlantic Oscillation influenced the precipitation in northern and southern parts of Ukraine in different ways.


2016 ◽  
Vol 21 (5) ◽  
pp. 290-295
Author(s):  
Zarema G. Tagirova ◽  
D. R Akhmedov ◽  
N. M.-G Zulpukarova ◽  
Z. M Daniyalbecova

There were studied epidemiological features of the prevalence rate of acute intestinal infections (AII) in the Republic of Dagestan (RD). The prevalence rate of acute intestinal infections in RD was shown to correspond taken as a whole, to Russian indices, however, the regional feature is the high prevalence rate of shigellosis, there is remained a high proportion of the AII of unidentified etiology. There was substantiated the necessity of development and implementation of targeted programmes aimed at the decline in the morbidity rate in problematic territories. The solution to the problem of the AII in the Republic is possible only under the coordination of efforts of federal and local authorities, sanitary - epidemiological and medical institutions.


Author(s):  
Guanbin Li ◽  
Xin Zhu ◽  
Yirui Zeng ◽  
Qing Wang ◽  
Liang Lin

Facial action unit (AU) recognition is a crucial task for facial expressions analysis and has attracted extensive attention in the field of artificial intelligence and computer vision. Existing works have either focused on designing or learning complex regional feature representations, or delved into various types of AU relationship modeling. Albeit with varying degrees of progress, it is still arduous for existing methods to handle complex situations. In this paper, we investigate how to integrate the semantic relationship propagation between AUs in a deep neural network framework to enhance the feature representation of facial regions, and propose an AU semantic relationship embedded representation learning (SRERL) framework. Specifically, by analyzing the symbiosis and mutual exclusion of AUs in various facial expressions, we organize the facial AUs in the form of structured knowledge-graph and integrate a Gated Graph Neural Network (GGNN) in a multi-scale CNN framework to propagate node information through the graph for generating enhanced AU representation. As the learned feature involves both the appearance characteristics and the AU relationship reasoning, the proposed model is more robust and can cope with more challenging cases, e.g., illumination change and partial occlusion. Extensive experiments on the two public benchmarks demonstrate that our method outperforms the previous work and achieves state of the art performance.


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