Gabor Toth

Marcus Ma, Emily Zhou, Leonard Ludwig et al.

We study when, where, and why 978 Holocaust survivors smile in video testimonies. We create an automatic smile detection model from facial features with an F1 of 85% and annotate detected smiles under two established taxonomies of smiling. We produce narrative features on 1,083,417 transcript sentences as well as emotional valence from three different modalities: audio, eye gaze, and text transcript. Smiling rates are significantly correlated with specific semantic topics, narrative structures, and temporal syntaxes across the entire corpus. Smiles often occur during periods of intense negative affect; these negative-affect smiles improve the valence trajectory of surrounding sentences significantly across all three modalities. Smiling reduces eye dynamics and blink rates, and the strength of both of these effects is also modulated by narrative valence. Taken together, we conclude that smiling plays a critical role in regulating emotion and social interaction during traumatic recollection.

Daniel Iong, Matthew McAnear, Yuezhou Qu et al.

Gaussian Processes (GP) have become popular machine-learning methods for kernel-based learning on datasets with complicated covariance structures. In this paper, we present a novel extension to the GP framework using a contaminated normal likelihood function to better account for heteroscedastic variance and outlier noise. We propose a scalable inference algorithm based on the Sparse Variational Gaussian Process (SVGP) method for fitting sparse Gaussian process regression models with contaminated normal noise on large datasets. We examine an application to geomagnetic ground perturbations, where the state-of-the-art prediction model is based on neural networks. We show that our approach yields shorter prediction intervals for similar coverage and accuracy when compared to an artificial dense neural network baseline.