Slides (from network operators) on Internet traffic during the COVID-19 pandemic:

tldr: The internet was built for this?

It trips me up to read quotes by economists on social distancing, such as

The value of a statistical life & social distancing as a redistribution of wealth from young to old & the value of the gain from social distancing by age group…

There are a lot of - seemingly ggplot - visualizations in the briefing for the Dutch House of Representatives about COVID-19 yesterday.

My dataviz take-away: It's almost impossible to interpret the median among overplotted lines in a chart.

Chart from

After four (public) interventions about the need for (more) compliance with social distancing (Rutte, Willem-Alexander, Rutte and a NL-ALERT) my feeling is that there is less compliance than assumed by RIVM/government and that the obvious next step are restrictions on freedom of movement.

Months ago I had never expected these to be necessary in the Netherlands 😔

A virtual hackaton at on the health of the internet during the COVID-19 crisis just started. Full remote in the time of Corona…

NRC published a series of deserted places today - . I can confirm Amsterdam Central _really_ felt quiet today... Feels like the calm before a storm.

Named vulnerabilities now come with their own cinematic trailer; LVI has a nice video.

I learned a new Dutch word today: voorkans, for pre-test probability.

Which reminded me that even though my bachelor lectures were in Dutch, it was still rare for technical terms to be translated (properly). I was used to multilingual presentations, in hindsight the step to English lectures feels small.

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.

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

another corollary: This is a good example of where the median is more robust than mean. Median does not change when you map missing values to -1 (out of scale) vs 0 (lowest value of scale/irrelevant), mean does.

...however, any mapping (take 0 0 0 1 1 1 1 1 for 'is at or above middle of scale') feels arbitrary and the median/mean feels safe

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' ( 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:

After having the browser tab open for weeks, i finally read 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

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