The fusion technique is the key to the multimodal emotion recognition task. Recently, cross-modal attention-based fusion methods have demonstrated high performance and strong robustness. However, cross-modal attention suffers from redundant features and does not capture complementary features well. We find that it is not necessary to use the entire information of one modality to reinforce the other during cross-modal interaction, and the features that can reinforce a modality may contain only a part of it. To this end, we design an innovative Transformer-based Adaptive Cross-modal Fusion Network (TACFN). Specifically, for the redundant features, we make one modality perform intra-modal feature selection through a self-attention mechanism, so that the selected features can adaptively and efficiently interact with another modality. To better capture the complementary information between the modalities, we obtain the fused weight vector by splicing and use the weight vector to achieve feature reinforcement of the modalities. We apply TCAFN to the RAVDESS and IEMOCAP datasets. For fair comparison, we use the same unimodal representations to validate the effectiveness of the proposed fusion method. The experimental results show that TACFN brings a significant performance improvement compared to other methods and reaches the state-of-the-art performance. All code and models could be accessed from https://github.com/shuzihuaiyu/TACFN.

How to cultivate innovative talents has become an important educational issue nowadays. In China’s long-term mentorship education environment, supervisor-student relationship often affects students’ creativity. From the perspective of students’ psychology, we explore the influence mechanism of supervisor-student relationship on creativity by machine learning and questionnaire survey. In Study 1, based on video interviews with 16 postgraduate students, we use the machine learning method to analyze the emotional states exhibited by the postgraduate students in the videos when associating them with the supervisor-student interaction scenario, finding that students have negative emotions in bad supervisor-student relationship. Subsequently, we further explore the impact of supervisor-student relationship on postgraduate students’ development in supervisor-student interaction scenarios at the affective level. In Study 2, a questionnaire survey is conducted to explore the relationship between relevant variables, finding that a good supervisor-student relationship can significantly reduce power stereotype threat, decrease emotional labor surface behaviors, and promote creativity expression. The above results theoretically reveal the internal psychological processes by which supervisor-student relationship affects creativity, and have important implications for reducing emotional labor and enhancing creativity expression of postgraduate students.

During the SARS-CoV-2 (COIVD-19) outbreak, China repeatedly stressed that the response to the pandemic required action at all levels of government, including the issuance of Pandemic Bonds to help the country return to work and production. However, studies on the effectiveness of Pandemic Bonds during that period are rare. Starting with China’s national financial bond market data after COVID-19 in 2020, this paper focuses on the correlation between the Credit Spreads of the relevant bonds and the corresponding bond market rate of return, based on the Copula model. The empirical analysis is also carried out for multiple dimensional groupings such as enterprises, industries, provinces, and bond maturities. The results show that there is a significant positive correlation between the Credit Spreads of Pandemic Bonds and market returns. In addition, the market correlation is higher for Pandemic Bonds issued in Hubei Province, which is at the center of the 2020 pandemic, and the shorter the maturity of the Pandemic Bond issued, the stronger the relationship with market returns. Finally, this paper provides recommendations for financial regulators and policy makers to consider in their decisions on how to build a more resilient financial system under heavy economic, fiscal, and social pressures.