Xin Gao

Mediation analysis has been used in many disciplines to explain the mechanism or process that underlies an observed relationship between an exposure variable and an outcome variable via the inclusion of mediators. Decompositions of the total effect of an exposure variable into effects characterizing mediation pathways and interactions have gained an increasing amount of interest in the last decade. In this work, we develop decompositions for scenarios where two mediators are causally sequential or non-sequential. Current developments in this area have primarily focused on either decompositions without interaction components or with interactions but assuming no causally sequential order between the mediators. We propose a new concept called natural mediated interaction effect that captures the two-way and three-way interactions for both scenarios and extends the two-way MIs in the literature. We develop a unified approach for decomposing the TE into the effects that are due to mediation only, interaction only, both mediation and interaction, neither mediation nor interaction within the counterfactual framework. Finally, we compare our proposed decomposition to an existing method in a non-sequential two-mediator scenario using simulated data, and illustrate the proposed decomposition for a sequential two-mediator scenario using a real data analysis.

The use of multimodal sensors for lane line segmentation has become a growing trend. To achieve robust multimodal fusion, we introduced a new multimodal fusion method and proved its effectiveness in an improved fusion network. Specifically, a multiscale fusion module is proposed to extract effective features from data of different modalities, and a channel attention module is used to adaptively calculate the contribution of the fused feature channels. We verified the effect of multimodal fusion on the KITTI benchmark dataset and A2D2 dataset and proved the effectiveness of the proposed method on the enhanced KITTI dataset. Our method achieves robust lane line segmentation, which is 4.53% higher than the direct fusion on the precision index, and obtains the highest F2 score of 79.72%. We believe that our method introduces an optimization idea of modal data structure level for multimodal fusion.

It is important to identify the developmental antecedents of externalizing behavioral problems in early childhood. The current study examined the main effects of maternal personality and its interactive effects with child temperamental reactivity in predicting child externalizing behavioral problems, indicated by impulsivity and aggression. This study was composed of 70 children (Mage= 17.6 months, SD = 3.73) and their mothers. The results showed that maternal agreeableness was negatively associated with child impulsivity. Child temperamental reactivity moderated the effect of maternal conscientiousness on child impulsivity in support of the differential susceptibility model. Specifically, for highly reactive children, maternal conscientiousness was negatively associated with child impulsivity whereas this association was nonsignificant for low reactive children. Child reactivity also moderated the contribution of maternal neuroticism to child impulsivity. That is, maternal neuroticism was negatively associated with impulsivity, only for highly reactive children.