“…Inspired by persistence theory from topological data analysis (TDA) [36,21], Kashiwara and Schapira have recently introduced the convolution distance between (derived) sheaves of k-vector spaces on a finite-dimensional real normed vector space [27]. This construction has found important applications, both in TDA -where it allows expressing stability of certain constructions with respect to noise in datasets - [6,9,7,8] and in symplectic topology [2,3,23]. A challenging research direction, of interest to these two fields, is to associate numerical invariants to a sheaf on a vector space, which satisfy a certain form of continuity with respect to the convolution distance.…”