  We demonstrate that cell resampling can eliminate the bulk of negative event
weights in large event samples of high multiplicity processes without
discernible loss of accuracy in the predicted observables. The application of
cell resampling to much larger data sets and higher multiplicity processes such
as vector boson production with up to five jets has been made possible by
improvements in the method paired with drastic enhancement of the computational
efficiency of the implementation.
