The study of the properties of the observed Higgs boson is one of the main research activities in High Energy Physics. Although the couplings of the Higgs to the weak gauge bosons and third generation quark and leptons have been studied in detail, little is known about the Higgs couplings to first and second generation fermions. In this article, we study the charm quark Higgs coupling in the so-called κ framework. We emphasize the existence of specific correlations between the Higgs couplings that can render the measured LHC Higgs production rates close to the SM values in the presence of large deviations of the charm coupling from its SM value, κ c = 1. Based on this knowledge, we update the indirect bounds on κ c through a fit to the precision Higgs measurements at the LHC. We also examine the limits on κ c arising from the radiative decay H → J/ψ + γ, the charm quark-associated Higgs production, charm quark decays of the Higgs field and charge asymmetry in W ± + H production. Estimates for the future LHC sensitivity on κ c at the high luminosity run are provided.

Running machine learning analytics over geographically distributed datasets is a rapidly arising problem in the world of data management policies ensuring privacy and data security. Visualizing high dimensional data using tools such as t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP) became a common practice for data scientists. Both tools scale poorly in time and memory. While recent optimizations showed successful handling of 10,000 data points, scaling beyond million points is still challenging. We introduce a novel framework: Sketch and Scale (SnS). It leverages a Count Sketch data structure to compress the data on the edge nodes, aggregates the reduced size sketches on the master node, and runs vanilla tSNE or UMAP on the summary, representing the densest areas, extracted from the aggregated sketch.

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