Sparse Grids are the basis for efficient high dimensional approximation and have recently been applied successfully to predictive modelling. They are spanned by a collection of si...
Real-world data sets such as recordings from functional magnetic resonance imaging often possess both spatial and temporal structure. Here, we propose an algorithm including such ...
Fabian J. Theis, Peter Gruber, Ingo R. Keck, Elmar...
A method is presented for the analysis of dynamic positron emission tomography (PET) data using sparse Bayesian learning. Parameters are estimated in a compartmental framework usin...
Jyh-Ying Peng, John A. D. Aston, R. N. Gunn, Cheng...
Methods to represent and exchange parts consisting of Functionally Graded Material (FGM) for Solid Freeform Fabrication (SFF) with Local Composition Control (LCC) are evaluated ba...
Todd R. Jackson, Wonjoon Cho, Nicholas M. Patrikal...
Background: Functional analysis of data from genome-scale experiments, such as microarrays, requires an extensive selection of differentially expressed genes. Under many condition...