With quick evolution of grid technologies and increasing complexity of e-Science applications, reasoning temporal properties of grid workflows to ensure reliability and trustworth...
Temporally-asymmetric Hebbian learning is a class of algorithms motivated by data from recent neurophysiology experiments. While traditional Hebbian learning rules use mean firin...
This paper proposes an unsupervised neural algorithm for trajectory production of a 6-DOF robotic arm. The model encodes these trajectories in a single training iteration by using...
Tracking of speckles in echocardiography enables the study of myocardium deformation, and thus can provide insights about heart structure and function. Most of the current methods...
When using Bayesian networks, practitioners often express constraints among variables by conditioning a common child node to induce the desired distribution. For example, an ‘or...