This article presents a resource-efficient adaptive sampling methodology for classifying electrocardiogram (ECG) signals into different heart rhythms. We present our in two folds: ( i ) the design of novel real-time neural network architecture capable ECG with rates and ii runtime implementation rate control using deep reinforcement learning (DRL). By essential morphological details contained h...

This work addresses inverse linear optimization, where the goal is to infer unknown cost vector of a program. Specifically, we consider data-driven setting in which available data are noisy observations optimal solutions that correspond different instances We introduce new formulation problem that, compared with other existing methods, allows recovery less restrictive and generally more appropr...