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A sheaf is a combinatorial, high-dimensional data structure on a topological space. You can use it to stitch together images, spatio-temporal target tracking, inference, data fusion, IoT time-series, continuous functions -- all kinds of things. Crazy cool stuff!

Applied sheaf theory is pretty new, and not yet widely known. This DARPA sheaf tutorial is a full two-day video series by Prof Michael Robinson (great teacher), and it's about the equivalent of a semester CS course compressed into two days. If you want to see an intro/big-picture overview video on what the sheaf data structure is, and how it's related to graph databases, topology, category theory, and data analysis, see:

"Sheaves for engineering problems" https://www.youtube.com/watch?v=223-0x2KNOg

And for a deep dive into a paper, see:

"Sheaves are the canonical data structure for sensor integration" https://arxiv.org/abs/1603.01446

Python Cellular Sheaf Library:

https://github.com/kb1dds/pysheaf



What kind of math background do you need to understand Sheaf theory? Are these lectures truly accessible for someone with a non-background?

I tried watching bits of the youtube video OP linked but never saw where he actually defined what a Sheaf was as oppose to building motivation of learning it for applications.

I am excited for any truly accessibly beginner resources. Want to get your opinion if you links are for the true beginner?


Have you found a book or a review/survey paper on this topic?

I just started Computational Homology by Kaczynski et al. but I just looked in the index and didn't find anything on sheaves.


Prof Robinson's book is Topological Signal Processing [1], and there are a ton of papers linked from his home page [2].

For a general overview, check out the wikipedia page: https://en.wikipedia.org/wiki/Sheaf_(mathematics)

[1] https://www.amazon.com/Topological-Signal-Processing-Mathema...

[2] http://www.drmichaelrobinson.net/publications.html


Robert Ghrist offers his Elementary Applied Topology for free on his site. It's useful more as a supplement or survey than a primary text to learn.

https://www.math.upenn.edu/~ghrist/notes.html


http://www.cis.upenn.edu/~jean/gbooks/sheaf-coho.html


Sheaf models are one of the most useful tools in logic and I'll definitely read this paper once I have more time again. One thing that seems strange to me based on the overview video is the focus on sheafs on a topological space. In mathematics it's typically easier and more flexible to consider sheafs on categories equipped with a Grothendieck coverage. Is there a reason why topological spaces are sufficient to model sensor integration?


Maybe because topological spaces are more familiar to many mathematicians than categories?


That youtube link is great, thanks.




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