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This method reduces the labeling cost to train an efficient multi-task neural network by adding an additional clustering step in the multi-task active learning loop, within the sampling process

CUDLE is a self-supervised learning framework that improves cannabis use detection from wearable sensor data in real-world settings, outperforming supervised methods while requiring significantly fewer labeled samples.

Chronic cannabis users show disrupted diurnal cortisol rhythms, including a blunted cortisol awakening response and elevated afternoon cortisol levels, but no major differences in diurnal heart rate variability or electrodermal activity, except for increased evening heart rate. Acute cannabis use reduced cortisol, subjective stress, and electrodermal activity. These findings suggest dysregulated stress responses in cannabis users, potentially linked to later waking times and cannabis’s stress-relieving effects, warranting further research on the relationship between cannabis use, cortisol rhythms, and psychological disorders.