SimplyUnknown

For a longer read, I can highly recommend the book "The Man from the Future" by Ananyo Bhattacharya. It's quite an easy read and extensively discusses the life and discoveries by John von Neumann in chronological order.

Indeed, EPEX spot prices are between €0.30 and €0.39 today including taxes where I live. I've seen them go up to €1.10 during peak demand.

Huh? That was University of Utah/Brigham Young University right. That is, if you're referring to Pons and Fleischman.

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'm still not sure I get it. I think it is

1. Put the BWT string in the right-most empty column

2. Sort the rows of the matrix such that the strings read along the columns of the matrix are in lexicographical order starting from the top-row????

3. Repeat step 1 and 2 until matrix is full

4. Extract the row of the matrix that has the end-delimiter in the final column

It's the "sort matrix" step that seems under-explained to me.

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

I really like einops. This works for numpy, pytorch and keras/tensorflow and has easy named transpose, repeat, and eimsum operations.

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.

Conda indeed is slow. However, mamba is a drop in replacement for Conda and uses a way faster solver, which makes it a lot more palatable.

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.