Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations - small sensor size, compact lenses and the lack of specific hardware, - impede them to achieve the quality results of DSLR cameras.
Objective: To transform heterogeneous clinical data from electronic health records into clinically meaningful constructed features using data driven method that rely, in part, on temporal relations among data.
We show that one cause for such success is due to the fact that the multi-branch architecture is less non-convex in terms of duality gap.
We present a robust algorithm for personalizing a sphere-mesh tracking model to a user from a collection of depth measurements.
Conclusion: Our analysis captures properties of DVF data associated with clinical CT images, and provides new understanding of iterative DVF inversion algorithms with a simple residual feedback control.
We explore unsupervised representation learning of radio communication signals in raw sampled time series representation.
A Majorization-Minimization based algorithm is derived to fit the proposed model.
The variability of the clusters generated by clustering techniques in the domain of latitude and longitude variables of fatal crash data are significantly unpredictable.