Time-dependent driving has become a powerful tool for creating novel nonequilibrium phases such as discrete time crystals and ...
Discover how probability distribution methods can help predict stock market returns and improve investment decisions. Learn ...
Please provide your email address to receive an email when new articles are posted on . Machine learning may identify patients with congenital heart disease at highest risk for experiencing gaps in ...
If you’ve ever shuffled a deck of playing cards, you’ve most likely created a unique deck. That is, you’re probably the only person who has ever arranged the cards in precisely that order. Although ...
Abstract: Random feature latent variable models (RFLVMs) are state-of-the-art tools for uncovering structure in high-dimensional, non-Gaussian data. However, their reliance on Monte Carlo sampling ...
CGMformer is first self-supervised pretrained on CGM data to gain fundamental knowledge of the glucose dynamics, and then applied to a multitude of downstream clinical applications. The extractable ...
Epidemiological studies often have missing data, which are commonly handled by multiple imputation (MI). In MI, in addition to those required for the substantive analysis, imputation models often ...
Roll a die and ask students to identify the random variable. Since a die can only take on values of 1, 2, 3, 4, 5, or 6, this is a discrete random variable. Repeat ...
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