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the findings suggest that daily mortality attributed to PM might be modified by temperature. The interaction between air pollution and global climate change has potential strategy and policy implications.

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Unfairness commonly impacts human economic decision-making. However, whether inequity aversion impairs pro-social decisions and the corresponding neural processes, is poorly understood. Here, we conducted two experiments to investigate whether human gifting behavior and brain activity are affected by inequity aversion. In experiment 1, participants played as a responder in a joint donation game in which they were asked to decide whether or not to accept a donation proposal made by the proposer. In experiment 2, participants played a donation game similar to experiment 1, but the charity projects were classified as high-deservingness and lowdeservingness projects. The results in both of two experiments showed that the participants were more likely to reject an unfair donation proposal and the late positivity potential (LPP)/P300 elicited by fair offers was more positive than moderately unfair and highly unfair offers regardless of charity deservingness. Moreover, after principal component analysis, the differences in P300 amplitude between fair and highly unfair conditions were positively correlated with the acceptance rates in experiment 2. Taken together, our study revealed that late positivity (LPP/P300) reflected the evaluation of fairness of proposals, and could predict subsequent pro-social decisions. This study is the first to demonstrate that inequity aversion reduces pro-social motivation to help innocent third party.

Recommender systems on E-Commerce platforms track users' online behaviors and recommend relevant items according to each user’s interests and needs. Bipartite graphs that capture both user/item feature and use-item interactions have been demonstrated to be highly effective for this purpose. Recently, graph neural network (GNN) has been successfully applied in representation of bipartite graphs in industrial recommender systems. Providing individualized recommendation on a dynamic platform with billions of users is extremely challenging. A key observation is that the users of an online E-Commerce platform can be naturally clustered into a set of communities. We propose to cluster the users into a set of communities and make recommendations based on the information of the users in the community collectively. More specifically, embeddings are assigned to the communities and the user embedding is decomposed into two parts, each of which captures the community-level generalizations and individualized preferences respectively. The community embedding can be considered as an enhancement to the GNN methods that are inherently flat and do not learn hierarchical representations of graphs. The performance of the proposed algorithm is demonstrated on a public dataset and a world-leading E-Commerce company dataset.