An essential step in many functional and structural neuroimaging studies is segmentation, the operation of partitioning the MR images in anatomical structures.
Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models training in reasonable time as well as distributing the storage requirements.
We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model.
We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules.
Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems—we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories.
Remote measurement of the blood volume pulse via photoplethysmography (PPG) using digital cameras and ambient light has great potential for healthcare and affective computing.