Indeed. Thomas De Gentd averaged 298w for 6hr on stage 17 of the Giro d’italia a few days ago according to his strava feed (measured off his SRM powermeter).
I like this. Previously I’ve used notebooks to explain a data pipeline from different perspectives. One notebook showing the ETL process going from raw data to RDF triples in a store; one SPARQL notebook showing the raw queries; and then a final decision support notebook using a Python binding to the underlying SPARQL query library. It seemed to work well.
It’s also used in low-level bladder cancers as a wash of the bladder. I think it’s a similar idea in that it helps provoke an immunology response that’s been found to be helpful.
Yes. A typical example would be the use of statistical emulators, emulating process (physical) based models that are computationally complex in the environmental sciences.
Is there any accessible work using Category Theory that looks at API composition? I’m thinking here about data intensive systems that are often composed of different elements (e.g. Kafka, Cassandra, bespoke microservices, ...) and are glued together in an application. Could Category Theory help in getting a grip on this complexity?
Gosh, for my Bachelor’s final year dissertation in 1990 I implemented a distributed version of Linda / Tuplespace in C++ across Ansaware and Tanenbaum’s Amoeba OS. Seems a lifetime ago!