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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?




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