Although previous investigations have agreed that Chinese rural-to-urban migrants’ socioeconomic status (SES) increases with their migration, the association between SES and subjective well-being is uncertain. To address this research gap, the present study proposed that the association between objective SES and subjective well-being is mediated by subjective SES. This model was tested with a sample of 432 Chinese rural-to-urban migrants. The results indicate a significant association between objective SES and subjective well-being and a partial mediating effect of subjective SES. Furthermore, subjective social mobility, which is one’s expectation about the possibility to move upward in the social hierarchy, was found to moderate both the direct path from objective SES to subjective well-being and the indirect path from subjective SES to subjective well-being. These findings suggest that Chinese rural-to-urban migrants gained in subjective well-being not only because of direct financial achievement but also because of their perceptions and beliefs about their relative social status.
Representing a scanned map of the real environment as a topological structure is an important research topic in robotics. Since topological representations of maps save a huge amount of map storage space and online computing time, they are widely used in fields such as path planning, map matching, and semantic mapping.We use a topological map representation, the Area Graph, in which the vertices represent areas and edges represent passages. The Area Graph is developed from a pruned Voronoi Graph, the Topology Graph. We also employ a simple room detection algorithm to compensate the fact that the Voronoi Graph gets unstable in open areas. We claim that our area segmentation method is superior to state-of-the-art approaches in complex indoor environments and support this claim with a number of experiments.
Descriptors play an important role in point cloud registration. The current state-of-the-art resorts to the high regression capability of deep learning. However, recent deep learning-based descriptors require different levels of annotation and selection of patches, which make the model hard to migrate to new scenarios. In this work, we learn local registration descriptors for point clouds in a self-supervised manner. In each iteration of the training, the input of the network is merely one unlabeled point cloud. Thus, the whole training requires no manual annotation and manual selection of patches. In addition, we propose to involve keypoint sampling into the pipeline, which further improves the performance of our model. Our experiments demonstrate the capability of our self-supervised local descriptor to achieve even better performance than the supervised model, while being easier to train and requiring no data labeling.
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