Culture is the social norm that often dictates a person's thoughts, decision-making, and social behaviors during interaction at an individual level. In this study, we present a computational framework that automatically assesses an individual culture attribute of power distance (PDI), i.e., the measure to describe one's acceptance of social status, power and authority in organizations through multimodal modeling of a participant's expressive prosodic structures and brain connectivity using a social condition-enhanced network. In specific, we propose a joint learning approach of center-loss embedding network architecture that learns to "centerize" the embedding space given a particular social interaction condition to enhance the PDI discriminability of the representation. Our proposed method achieves 88.5% and 73.1% in binary classification task of recognizing low versus high power distance on prosodic and fMRI modality separately. After performing multimodal fusion, it improves to 96.2% of 2-class recognition rate (7.7% relative improvement). Further analyses reveal that average and standard deviation of speech energy are significantly correlated with power distance index; the right middle cingulate cortex (MCC) of brain region achieves the best recognition accuracy demonstrating its role in processing a person's belief about power distance.

Target shape design optimization problem is to approximate an unknown shape, when a black-box function provides the fitness of the shape. The framework to solve this problem can be applied to the finding of optimized aerodynamic structures such as airplane wings or gas turbine blades. In order to approximate the two- and three-dimensional closed curve shapes, we use the idea of incremental abstraction, that is to find the best n-point polygon in the beginning and double the number n in the next stage until n reaches the specified limit. This idea is embodied into evolution strategies with a modified 1/5 rule using multiple layers. Furthermore, for the three-dimensional hidden object, the problem was decomposed into several 2D optimization problems. The results show that the suggested method is quite effective to solve the given problems even if the evaluation function has stochastic noise elements. The general strategy can also be applied to other practical applications using evolutionary algorithms.