Neural Networks: A New Tool for Predicting Thrift Failures*

A neural network model that processes input data consisting of financial ratios is developed to predict the financial health of thrift institutions. The network's ability to discriminate between healthy and failed institutions is compared to a traditional statistical model. The differences and similarities in the two modelling approaches are discussed. The neural network, which uses the same financial data, requires fewer assumptions, achieves a higher degree of prediction accuracy, and is more robust.     

The COVID-19 pandemic,mask-wearing,and emotion recognition during late-childhood

Face masks are an effective and important tool to prevent the spread of COVID-19, including among children. However, occluding parts of the face can impact emotion recognition, which is fundamental to effective social interactions. Social distancing, stress, and changes to routines because of the pandemic have also altered the social landscape of children, with implications for social development. To better understand how social input and context impact emotion recognition, the current study investigated emotion recognition in children (7–12 years old, N = 131) using images of both masked and unmasked emotional faces. We also assessed a subsample of participants (“pre-pandemic subsample,” n = 35) who had completed the same emotion recognition task with unmasked faces before and during the pandemic. Masking of faces was related to worse emotion recognition, with more pronounced effects for happy, sad, and fearful faces than angry and neutral faces. Masking was more strongly related to emotion recognition among children whose families reported greater social disruption in response to the pandemic. Finally, in the pre-pandemic subsample, emotion recognition of sad faces was lower during versus before the pandemic relative to other emotions. Together, findings show that occluding face parts and the broader social context (i.e., global pandemic) both impact emotion-relevant judgments in school-aged children.     

Robustly assigning unstable items

Journal of Combinatorial Optimization - We study the robust assignment problem where the goal is to assign items of various types to containers without exceeding container capacity. We seek an...