HN user
That's the wake sleep algorithm for undirected graphical models.
Hinton had a course on Coursera around 2015 that covered a lot of pre NN deep learning. Sadly I don't think it's up anymore.
Everything around the topic of condensates, liquid-liquid phase separation, stress granules, etc. is quite interesting. And it seems like the importance of condensates and related phenomena has only been really understood within the past decade or so.
Protein condensates also play a role in several neurodegenerative diseases like ALS. See e.g. https://www.frontiersin.org/journals/cellular-neuroscience/a....
Computational Science and Engineering by Gilbert Strang
Yeah, it makes a lot of sense to just create those gaps when you're specifically installing the fiber for sensing.
Not quite the same thing, but this reminds me of DAS using fiber optic cable for various acoustic sensing tasks--basically as an alternative to geophones/hydrophones. There have been a number of papers using transoceanic fibers for various monitoring tasks.
That is also used for various industrial applications, e.g. for strain sensing by Luna Innovations. I know that Schlumberger has various patents on fiber-optic sensing relating to towed streamers (e.g. for marine seismic acquisition.) But I haven't seen it used for soft robotics before.
The thing is that there is probably a lot of existing C++ code that is UB without std::launder (similarly to aliasing rules.) The main problem is that the C++ object lifetime rules are not well understood by most people writing C++ code.
Helliwell & Sahakian Modern Classical Mechanics at least seems to do a much better job of explaining the Legendre transform than Goldstein, but it still never mentions the convexity requirement on f.
I feel like understanding the general convex conjugate and then seeing the Legendre transform as a special case is almost more intuitive.
Sounds like that would allow a Linux VM running on a macbook
Seg faults
This has always been an issue with sorting float arrays that contain NaN in C or C++ (e.g. with std::sort() with the default "<" comparator.)
Bishop Pattern Recognition and Machine Learning is an awesome starting point. I think it's way more coherent (although it probably is less comprehensive) than Elements of Statistical Learning.
If you're interested in perception, like tracking & freespace detection or SLAM, then "Probabilistic Robotics" by Thrun is pretty good. For a more fundamental take on that stuff (but less robotics specific), "Pattern Recognition and Machine Learning" by Bishop is my favorite.