scholarly journals Analysis of topological relationships and network properties in the interactions of human beings

PLoS ONE ◽  
2017 ◽  
Vol 12 (8) ◽  
pp. e0183686 ◽  
Author(s):  
Ye Yuan ◽  
Xuebo Chen ◽  
Qiubai Sun ◽  
Tianyun Huang
2020 ◽  
Vol 12 (23) ◽  
pp. 4003
Author(s):  
Yansheng Li ◽  
Ruixian Chen ◽  
Yongjun Zhang ◽  
Mi Zhang ◽  
Ling Chen

As one of the fundamental tasks in remote sensing (RS) image understanding, multi-label remote sensing image scene classification (MLRSSC) is attracting increasing research interest. Human beings can easily perform MLRSSC by examining the visual elements contained in the scene and the spatio-topological relationships of these visual elements. However, most of existing methods are limited by only perceiving visual elements but disregarding the spatio-topological relationships of visual elements. With this consideration, this paper proposes a novel deep learning-based MLRSSC framework by combining convolutional neural network (CNN) and graph neural network (GNN), which is termed the MLRSSC-CNN-GNN. Specifically, the CNN is employed to learn the perception ability of visual elements in the scene and generate the high-level appearance features. Based on the trained CNN, one scene graph for each scene is further constructed, where nodes of the graph are represented by superpixel regions of the scene. To fully mine the spatio-topological relationships of the scene graph, the multi-layer-integration graph attention network (GAT) model is proposed to address MLRSSC, where the GAT is one of the latest developments in GNN. Extensive experiments on two public MLRSSC datasets show that the proposed MLRSSC-CNN-GNN can obtain superior performance compared with the state-of-the-art methods.


1954 ◽  
Vol 27 (5) ◽  
pp. 565-577 ◽  
Author(s):  
John F. Scholer ◽  
Charles F. Code

1949 ◽  
Vol 12 (6) ◽  
pp. 970-977 ◽  
Author(s):  
John M. McMahon ◽  
Charles F. Code ◽  
Willtam G. Saver ◽  
J. Arnold Bargen
Keyword(s):  

Author(s):  
Charles A. Doan ◽  
Ronaldo Vigo

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.


2015 ◽  
Vol 223 (3) ◽  
pp. 151-156 ◽  
Author(s):  
Nina Schweinfurth ◽  
Undine E. Lang

Abstract. In the development of new psychiatric drugs and the exploration of their efficacy, behavioral testing in mice has always shown to be an inevitable procedure. By studying the behavior of mice, diverse pathophysiological processes leading to depression, anxiety, and sickness behavior have been revealed. Moreover, laboratory research in animals increased at least the knowledge about the involvement of a multitude of genes in anxiety and depression. However, multiple new possibilities to study human behavior have been developed recently and improved and enable a direct acquisition of human epigenetic, imaging, and neurotransmission data on psychiatric pathologies. In human beings, the high influence of environmental and resilience factors gained scientific importance during the last years as the search for key genes in the development of affective and anxiety disorders has not been successful. However, environmental influences in human beings themselves might be better understood and controllable than in mice, where environmental influences might be as complex and subtle. The increasing possibilities in clinical research and the knowledge about the complexity of environmental influences and interferences in animal trials, which had been underestimated yet, question more and more to what extent findings from laboratory animal research translate to human conditions. However, new developments in behavioral testing of mice involve the animals’ welfare and show that housing conditions of laboratory mice can be markedly improved without affecting the standardization of results.


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