Less Is More: Adaptive Trainable Gradient Dropout for Deep Neural Networks.

The undeniable computational power of artificial neural networks has granted the scientific community the ability to exploit the available data in ways previously inconceivable. However, deep neural networks require an overwhelming quantity of data in order to interpret the underlying connections between them, and therefore, be able to complete the specific task that they have been assigned to. Feeding a deep neural network with vast amounts of data usually ensures efficiency, but may, however, harm the network's ability to generalize.

Eighteen years' experience applying old and current strategies in the pre-participation cardiovascular screening of athletes.

Introduction: Cardiovascular pre-participation screening (PPS) is recommended for the identification of athletes at risk for sudden cardiac death. However, there is currently no universally accepted screening protocol.

Methods: Two distinct PPS strategies were studied in a large cohort of Greek athletes (5 to 39 years old): PPS I, with routine 12-lead ECG and echo, in addition to personal and family history, and physical examination; and PPS II, without routine echo.