Honestly there's way more there, and you get consistent solid speeds. Find a provider with a lot of retention and you can find almost all mainstream media regardless of it's age. (Public) torrents tend to track what's popular and quickly fade. The masses seem to favour low size encodes too, so if you're looking for more quality (and again, public trackers) you're usually much more out of luck.
Surely a pinch of critical thinking answers this? 4 deliveries a day isn't going to pay a daily minimum wage. If it did, then we wouldn't have this situation - surely most riders manage more than one delivery every 2 hours and make more than the minimum wage!
When numbers are being presented being clear never hurts. Critical thinking isn’t a key skill a lot of the population has.
I’d assume this was per hour or possibly per shift. If they did a 4-5 hour shift in an evening etc. Best never to make assumptions in these cases though…
Critical thinking will hopefully both let you reject 4/day and the idea that one of the other answers is obvious and unambiguous. If the data is incomplete, treat it as incomplete.
Sleep isn't the metric to track here, there are much better things to analyze such as HRV. For reducing drinking you shouldn't be doing that because of your sleep, you should be doing it because of your overall health. If you care about future health, take blood tests, weigh yourself, evaluate your physical fitness.
For those who won't bother to RTFA: the trains run cooler than other trains, so they are taking advantage of the difference such that the net result is the same temperature in tunnels, but the interior of trains can be cooler.
It's more like budgeting at a home level, in that you have a set limit of heat that you can spend, and choosing to spend it less on equipment like brakes, traction motors, etc. and more on air conditioning means still remaining in budget.
Their overall limit of heat output by the trains remains the same. The tunnels won't be any hotter than they are now, thanks to savings found in other systems.
No, it is more like government spending. It is like saying, "we need to be fiscally responsible and that means only spending no more than 160% of what we are earning".
The tunnels are getting hotter and hotter. Saying "the tunnels won't be any hotter" is completely unfounded in reality.
Reading a bit more into this, I'm still not sure whether the tunnels are actually continuing to meaningfully increase absent external changes to the system. Is it at a steady-state heat flux between the inputs (passenger, vehicles, infra) and output (into the surrounding earth), or is the surrounding earth still not effectively saturated?
I know heat inside the tunnels fluctuates with time of year, but I can't tell if year-on-year the tunnels are getting e.g. 1xx% hotter and, critically, what the causes are (more tph will obviously, global warming will, more pax will).
Thanks - that looks interesting. It's especially appealing because it seems to add value beyond just being a neater wrapper to the same operations, it looks like it'll enable new ways of working with the tool.
As I understand it, the point is that these models while they are _trained_ on identifying cats or cars, because they have soon so much variation during training have internalised very different concepts to help come up with "its a cat". The idea then is to take all of these pre-trained weights that let you build this classifier, but then add your own custom head on the front of this network. This saves you doing a huge amount of training for what is essentially feature extraction - that part is already done. All you need to do is just add a bit more training that works out how to use these learnt features. I could be way off the mark, but that's how I understand it.
Yes, your understanding is correct. However, instead of adding a head on top of the network, most fine-tuning is currently done with LoRA (https://github.com/microsoft/LoRA). This introduces low-rank matrices between different layers of your models, those are then trained using your training data while the rest of the models' weights are frozen.
