Yes, normally in a case where data were later shown to have been taken incorrectly, you would remove just the incorrect data but leave an unmodified copy of the old data available somewhere. Or, just leave a very prominent note about the change with a detailed explanation somewhere else. You would not take down everything because 1. That would deprive taxpayers of the correct data they had already paid for, and 2. That would mess up the data ingestion pipelines of the researchers who depended on the data.
Roselle St (where Fabric8 Labs has their office) is the most innovative street in San Diego. I don't know what it is about that particular street, but a ton of great companies have come out of there.
Bokeh has support for WebGL. We had to switch away from Vega/Altair when a project hit around 50,000 data points in a plot, but under ~5,000 data points Vega/Altair was still good.
It's actually even worse in SDG&E territory. A kWh costs around 32 cents, but "transmission" and "distribution" are again twice that. The end result is about $1.00 / kWh.
This is such a wasted opportunity, not just for the US, but across the Middle East. I guess Jordan is availing themselves to some degree, though, but it's also out of necessity, due to the refugees living there.
My understanding is that prior to the Iranian revolution, there were Israeli experts working in Iran on drip irrigation methods. Of course, they had to get out pretty quickly.
I don't get the hate for the CCS connector? I use it multiple times a week, it works fine. Now and then I come across a charger that refuses to start, OK, the connector is worn. I suppose someone will come along and fix it.
I have one in my garage; it does not sag under its own weight.
It so happens I recently took a Pixel 6 Pro and a Canon 80D on a trip abroad. I used a rebuild of the stock camera app that does away with the automatic over-sharpening that the stock camera app has, and with the 80D, I used the EF-S 15-85 mm lens that (I believe) used to be the kit lens for the 7D. I also used the EF 70-300 mm non-L lens.
There is, in my opinion, no question that the 80D takes sharper pictures in daylight. It's just hard to beat a sensor that's that much bigger. The lenses, also, just have way, way more light gathering power.
Now, in dark places, at night, I used the P6P more, and that worked better than the 80D. But I'm glad I had the 80D for the big landscape shots and for the tight shots of people's faces.
The A7 III is way lighter and smaller than the 80D, and takes way better pictures. I would suggest considering finding a space for it in your bag. At least take a few pictures with both the P6P and the A7 III and view them at 100% to see if you're happy with the results.
Question on the ML side of this post: How are these "parameterizations" used? Is this really just feature engineering with a new name? Are they including this information when training the model?
In the article, they mention using the new labels to build a "more balanced" dataset -- is this a realistic possibility in practice when most teams still have a dearth of data?