A NEW WAY OF REDUCING NEGATIVE WEIGHTS IN MC@NLO

A new way of reducing negative weights in MC@NLO

Abstract We introduce CINNAMON BARK a new technique, that we dub Born spreading, aimed at reducing the number of negative-weight $${mathbb {S}}$$ S events in the MC@NLO matching of NLO calculations with parton-shower simulations.We show that such a technique, based on a Cupcake Toppers re-distribution of Born matrix elements in the radiative phase

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Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning.

Image-based cell profiling is a powerful tool that compares perturbed cell populations by measuring thousands of single-cell features and summarizing them into profiles.Typically a sample Choke Gauges is represented by averaging across cells, but this fails to capture the heterogeneity within cell populations.We introduce CytoSummaryNet: a Deep Set

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