Effects of thermal environment on sleep and circadian rhythm

The thermal environment is one of the most important factors that can affect human sleep. The stereotypical effects of heat or cold exposure are increased wakefulness and decreased rapid eye movement sleep and slow wave sleep. These effects of the thermal environment on sleep stages are strongly linked to thermoregulation, which affects the mechanism regulating sleep. The effects on sleep stages also differ depending on the use of bedding and/or clothing. In semi-nude subjects, sleep stages are more affected by cold exposure than heat exposure. In real-life situations where bedding and clothing are used, heat exposure increases wakefulness and decreases slow wave sleep and rapid eye movement sleep. Humid heat exposure further increases thermal load during sleep and affects sleep stages and thermoregulation. On the other hand, cold exposure does not affect sleep stages, though the use of beddings and clothing during sleep is critical in supporting thermoregulation and sleep in cold exposure. However, cold exposure affects cardiac autonomic response during sleep without affecting sleep stages and subjective sensations. These results indicate that the impact of cold exposure may be greater than that of heat exposure in real-life situations; thus, further studies are warranted that consider the effect of cold exposure on sleep and other physiological parameters

HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection

We consider classification tasks in the regime of scarce labeled training data in high dimensional feature space, where specific expert knowledge is also available. We propose a new hybrid optimization algorithm that solves the elastic-net support vector machine (SVM) through an alternating direction method of multipliers in the first phase, followed by an interior-point method for the classical SVM in the second phase. Both SVM formulations are adapted to knowledge incorporation. Our proposed algorithm addresses the challenges of automatic feature selection, high optimization accuracy, and algorithmic flexibility for taking advantage of prior knowledge. We demonstrate the effectiveness and efficiency of our algorithm and compare it with existing methods on a collection of synthetic and real-world data.Comment: Proceedings of 8th Learning and Intelligent OptimizatioN (LION8) Conference, 201