Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks

Much of the focus in the area of knowledge distillation has been on distilling knowledge from a larger teacher network to a smaller student network. However, there has been little research on how the concept of distillation can be leveraged to distill the knowledge encapsulated in the training data itself into a reduced form. In this study, we explore the concept of progressive label distillation, where we leverage a series of teacher-student network pairs to progressively generate distilled training data for learning deep neural networks with greatly reduced input dimensions. To investigate the efficacy of the proposed progressive label distillation approach, we experimented with learning a deep limited vocabulary speech recognition network based on generated 500ms input utterances distilled progressively from 1000ms source training data, and demonstrated a significant increase in test accuracy of almost 78% compared to direct learning.Comment: 9 page

Sleep pattern disruption of flight attendants operating on the Asia–Pacific route

Jet lag is a common issue with flight attendants in international flights, as they have to cross several time zones back and forth, while their sleep patterns get disrupted by the legally required rest times between flights, which are normally carried out at different locations. This research aimed to investigate the sleep quality of a sample of flight attendants operating between New Zealand and Asia. Twenty flight attendants were surveyed in this research. The research found that flight attendants typically took a nap immediately after arriving into New Zealand, reporting a sound sleep time of about 6 hours. After the nap, however, they had problems falling sleep in subsequent nights. After their first nap, some flight attendants try to adapt to local light conditions, while others prefer to keep the sleep patterns they had back home. Both groups report different trends of sleep quality