Jeah, the lab where I worked cleaned the machine on a 1-2 month basis. All the cleaned out metals where then sold to a recycler.

This will probably wildly diver from country to country, but typically in elementary school where 50 kids, and for high school around 100-120.

Yes there are many attempts in applying a transformers to timeseries forecasting. For instance (but there are many more): - Timegpt https://arxiv.org/abs/2310.03589 - Chronos https://github.com/amazon-science/chronos-forecasting

These kind of papers often talk the world, but often lack a proper baseline model. They only compare against very simple (naive forecast), or non tuned models. In my experience a gradient boosting model will probably solve 95% of your forecasting problems, and trying to get fancy with a transformer (or even just a simple neural net) is more trouble then it is worth.

Funnily enough next to wind also solar is one of the major renewable sources in the Netherlands. So also day-night storage would be important.

The Netherlands actually is second place in terms of solar generation per capita in the world (only Australia has more).

Any recommendations for a good AV/EPP for desktop linux (ubuntu)?

You have asked the most important question. I should have asked it as well.

Would love other input as I’m not an Ubuntu daily driver anymore.

It turns out 22.04 has ClamAV “provided and supported,” but not installed by default. So it should easily install manually. That’s what I would go with first, as it’s officially supported. An OS vendor supporting a specific AV vendor is a really big deal imho.

If that is not satisfactory by some metric, and this is just my personal and dated opinion… I used to like Eset.

I was naive before, but next time I install desktop Ubuntu, I will install ClamAV immediately.

The weather company claims to do this (they are also the main provider of weather data for apple).

See for instance the pytorch geometric [1] package, which is the main implementation in pytorch. They also link to some papers there that might explain you more.

[1] https://pytorch-geometric.readthedocs.io/en/latest/

I am in the power forecasting domain, where weather forecasts are one of the most important inputs. What I find surprising is that with all the papers and publications from google in the past years, there seems to be no way to get access to these forecasts! We've now evaluated numerous of the ai weather forecasting startups that are popping up everywhere and so far for all of them their claims fall flat on their face when you actually start comparing their quality in a production setting next to the HRES model from ECMWF.

GraphCast, Pangu-Weather from Huawei, FourCastNet and EC's own AIFS are available on the ECMWF chart website https://charts.ecmwf.int, click "Machine learning models" on the left tab. (Clicking anything makes the URL very long.)

Some of these forecasts are also downloadable as data, but I don't know whether GraphCast is. Alternatively, if forecasts have a big economic value to you, loading latest ERA5 and the model code, and running it yourself should be relatively trivial? (I'm no expert on this, but I think that is ECMWF's aim, to distribute some of the models and initial states as easily runnable.)

The wave length and pulse length aren't the same thing though. The short pulsed lasers actually consists of light that have many different wave lengths in them!

Yes, but shouldn't there be a peak in the spectrum near the frequency corresponding to the pulselength?

Not really sure what you are looking for, but the easiest might be to just add lags of your input variables in the same linear model that you are using.

If you are looking for an actual timeseries method I would checkout either darts [0] or statsforecast [1]. They are currently the most mature timeseries packages.

[0] https://unit8co.github.io/darts/ [1] https://github.com/Nixtla/statsforecast