I read a paper a while ago (which I have failed to locate) which used around binary masks, each in front of a single photodiode, as input to a neural network, which estimated the number of people in front of this effectively ~9-pixel "camera". The binary masks and NN weights were trained at the same time. Presumably, something like this could be used to detect lack of driver focus in a far less invasive manner.
Additionally, while I don't know much about APFS, I don't think it would be beneficial to point the extracted app to blocks that are also part of the dmg file, i.e. some copying has to happen anyway.
Many consumer routers allow any connected device to configure port forwarding using UPnP. If you want, you can play around with this using a client such as miniupnpc's example client.
Ah, I was referring to the zoom-level-dependent clustering, which I find makes it hard to see the distribution of points when zoomed out all the way. There's still quite a bit of detail even with many of the points sharing the same location.
Why do news outlets so often use the phrase "high rate of speed"? Speed isn't a discrete event, it can't really have a rate, unless it is a rate of change, in which case they would be referring to acceleration.
This is fun and looks amazing, however there seems to be quite a bit of texture in the out of focus blur. There's also a lot of aliasing on the grass. Also, I think the camera shake could do with a very slight delay after the axe hits, and maybe a slightly slower decay curve.
This is very cool, but unfortunately it's hard to get a sense of what the actual spatial distribution of people looks like when nearby nodes are grouped into larger dots that obscure the local structure.
Even worse, sometimes it dubs ads, where there's no way to switch the audio track and no way to see if it's being dubbed. This also makes it look like the dubbed audio is the original audio from the ad, which makes the advertiser look terrible.
