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1. Reconsidering the Duchenne Smile: Indicator of Positive Emotion or Artifact of Smile Intensity?

   https://doi.org/10.1109/ACII.2019.8925535  

   Girard, Jeffrey M.; Shandar, Gayatri; Liu, Zhun; Cohn, Jeffrey F.; Yin, Lijun; Morency, Louis-Philippe (September 2019, 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII))

   The Duchenne smile hypothesis is that smiles that include eye constriction (AU6) are the product of genuine positive emotion, whereas smiles that do not are either falsified or related to negative emotion. This hypothesis has become very influential and is often used in scientific and applied settings to justify the inference that a smile is either true or false. However, empirical support for this hypothesis has been equivocal and some researchers have proposed that, rather than being a reliable indicator of positive emotion, AU6 may just be an artifact produced by intense smiles. Initial support for this proposal has been found when comparing smiles related to genuine and feigned positive emotion; however, it has not yet been examined when comparing smiles related to genuine positive and negative emotion. The current study addressed this gap in the literature by examining spontaneous smiles from 136 participants during the elicitation of amusement, embarrassment, fear, and pain (from the BP4D+ dataset). Bayesian multilevel regression models were used to quantify the associations between AU6 and self-reported amusement while controlling for smile intensity. Models were estimated to infer amusement from AU6 and to explain the intensity of AU6 using amusement. In both cases, controlling for smile intensity substantially reduced the hypothesized association, whereas the effect of smile intensity itself was quite large and reliable. These results provide further evidence that the Duchenne smile is likely an artifact of smile intensity rather than a reliable and unique indicator of genuine positive emotion. 

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2. SMILE Winter Campaign

   https://doi.org/10.1093/rasti/rzae038  

   Walach, M_-T; Soobiah, Y.; Carter, J_A; Whiter, D_K; Kavanagh, A_J; Hartinger, M_D; Oksavik, K.; Salzano, M_L; Archer, M_O (August 2024, RAS Techniques and Instruments)

   ABSTRACT This white paper is highly topical as it relates to the upcoming solar wind magnetosphere ionosphere link explorer (SMILE) mission: SMILE is a joint mission between the European Space Agency and the Chinese Academy of Sciences and it aims to build a more complete understanding of the Sun–Earth connection by measuring the solar wind and its dynamic interaction with the magnetosphere. It is a fully funded mission with a projected launch in 2025. This paper outlines a plan for action for SMILE’s first Northern hemisphere winter campaign using ground-based instruments. We outline open questions and which data and techniques can be employed to answer them. The science themes we discuss are: (i) Earth’s magnetosheath, magnetopause, and magnetic cusp impact on the ionospheric cusp region; (ii) defining the relationship between auroral processes, solar wind, and magnetospheric drivers; (iii) understanding the interhemispheric properties of the Earth’s magnetosphere–ionosphere system. We discuss open questions (different to the mission goals) which may be answered using existing ground-based instrumentation together with SMILE data to leverage the maximum scientific return of the mission during the first winter after launch. This paper acts as a resource for planning, and a call to collaborative action for the scientific community. 

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3. Ground-based and additional science support for SMILE

   https://doi.org/10.26464/epp2023055  

   A. Carter, J.; Dunlop, M.; Forsyth, C.; Oksavik, K.; Donovon, E.; Kavanagh, A.; E. Milan, S.; Sergienko, T.; C. Fear, R.; G. Sibeck, D.; et al (January 2024, Earth and Planetary Physics)
4. LSH-SMILE: Locality Sensitive Hashing Accelerated Simulation and Learning

   Sima, Chonghao; Xue, Yexiang (January 2021, Proceedings of 35th Conference on Neural Information Processing Systems (NeurIPS))
5. Smile Like You Mean It: Driving Animatronic Robotic Face with Learned Models

   Boyuan Chen, Yuhang Hu (July 2021, International Conference on Robot Automation)

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