Scalable interpolation of satellite altimetry data with probabilistic machine learning.

We present GPSat; an open-source Python programming library for performing efficient interpolation of non-stationary satellite altimetry data, using scalable Gaussian process techniques. We use GPSat to generate complete maps of daily 50 km-gridded Arctic sea ice radar freeboard, and find that, relative to a previous interpolation scheme, GPSat offers a 504 × computational speedup, with less than 4 mm difference on the derived freeboards on average. We then demonstrate the scalability of GPSat through freeboard interpolation at 5 km resolution, and Sea-Level Anomalies (SLA) at the resolution of the altimeter footprint.

Diversity of Acinetobacter baumannii in four French military hospitals, as assessed by multiple locus variable number of tandem repeats analysis.

Background: Infections by A. calcoaceticus-A. baumannii (ACB) complex isolates represent a serious threat for wounded and burn patients.