Obviously not, but there is larger potential than that. A microphone in a watch or phone can pick up the signal and relay the trigger to another server via for example MQTT. Then, you can have homeassistant act upon this signal.
The flow is then
Open door --> Signal triggers --> Signal caught by microphone --> Microphone device relays to server --> Server turns on smart lighting in that room.
There is a lot more that you can do, but this is just an example. The microphone to pick it up is a limitations, but the gain is:
- reduced power usage as the signal generators are not power-operated
- less waste as no empty batteries need to be discarded (if non-rechargeable batteries are used)
- improved security because the signal generators are not connected to the internet, reducing the attack surface.
I have the feeling that B-splines would be a good solution for this problem. Given that they have a continuous zeroth (i.e., the function is continuous), first, and second derivative, the motion will always be smooth and there will be no kinks. However, maybe it's moving the problem because now you must tune the coefficients of the B-spline instead of damping parameters (even though a direct mapping between these must exist but this mapping may not be trivial).
Multiple reasons, while technically better and more benign compression artifacts, it is computationally more expensive, limited quality improvements, encumbered by patents, poor Metadata format, poor colorspace support... In the end, the benefits aren't great enough compared to jpeg to change the default format
In medical imaging, data are often acquired using anisotropic resolution. So a pixel (or voxel in 3D) can be an averaged signal sample originating from 2mm of tissue in one direction and 0.9mm in another direction.
Not quite what you are looking but if you're interested in Operation Market Garden: for the Dutch maps there is https://www.topotijdreis.nl, which gives you historical maps with a year slider. This can at least help one visualize how cities, villages, and topography at through the years.
But Chinese (or mandarin) is not a context-free grammar whereas I believe that encoding a language on a turing machine implies a context-free grammar so this example doesn't hold.
I think maybe it's poorly phrased. As far as I can tell, their linear regression example for eq. 2 has an unique solution, but I think they state I that when optimizing for cosine similarity you can find non-unique solutions. But I haven't read in detail.
Then again, you could argue whether that is a problem when considering very high dimensional embeddings. Their conclusions seem to point in that direction but I would not agree on that.
The thing is you use synthetic data to when it is difficult to obtain real data. For example, in medical imaging, it is very expensive to collect MRI scans to build a large dataset. Not to mention the potential privacy issues and obtaining informed consent to publish the dataset. Synthetic datasets can help here to, for example, pretrain your model and fine tune on real data afterwards. I'm then assuming that collecting tabular data can face similar issues which prevent building large datasets.