Reading this frustrates me on many levels: https://nos.nl/artikel/2323114-garnalenvissers-veel-te-actief-in-wadden-en-noordzee-overheid-deed-niets.html
A pretty visualisation *and* a reminder that not all meats (or cheese) are equal when it comes to GHG emissions.
I think all parties use campaign agencies. However FVD is the only party which gives me the impression that they are actively campaigning for more members. https://www.nrc.nl/nieuws/2020/01/28/avondjes-en-digitale-campagne-lonen-voor-fvd-a3988448
I enjoyed reading Microsoft Research/Github their work on code search (as IR task using LtR, elasticsearch as baseline). Highlight for me? Clear description of evaluation instructions and interface https://github.blog/2019-09-26-introducing-the-codesearchnet-challenge/
A small realization: When mapping relevance judgement levels [with multiple levels for not relevant, with weights >= 0] onto a score and calculating precision, it does not matter _how far_ an item is below the threshold. But when using the mean of ordinal values, "really bad" results skew your average.
The BBC and Financial Times create visualizations 'purely in R' (https://twitter.com/chrisjeavans/status/1011694262935834629) and archieve a look that I find much more pleasant than the ggplot2 defaults. Today I discovered similar themes (from HBR and clone of that from FT) as an R package: https://github.com/hrbrmstr/hrbrthemes
After having the browser tab open for weeks, i finally read https://github.com/bollu/bollu.github.io#everything-you-know-about-word2vec-is-wrong which tells a war story about how the implementation of word2vec. If I interpret it correctly, initializing the initialization vector for negative (random) samples to 0 sounds logical, compared to just adding in random weights for untrained words
Small math puzzles. Perfect toy examples to solve with Z3/Python: https://github.com/ties/z3_samples/blob/master/digit_puzzle.py
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