  Machine learning techniques have been used to quantify the relationship
between local structural features and variations in local dynamical activity in
disordered glass-forming materials. To date these methods have been applied to
an array of standard (Arrhenius and super-Arrhenius) glass formers, where work
on "soft spots" indicates a connection between the linear vibrational response
of a configuration and the energy barriers to non-linear deformations. Here we
study the Voronoi model, which takes its inspiration from dense epithelial
monolayers and which displays anomalous, sub-Arrhenius scaling of its dynamical
relaxation time with decreasing temperature. Despite these differences, we find
that the likelihood of rearrangements can vary by several orders of magnitude
within the model tissue and extract a local structural quantity, "softness"
that accurately predicts the temperature-dependence of the relaxation time. We
use an information-theoretic measure to quantify the extent to which softness
determines impending topological rearrangements; we find that softness captures
nearly all of the information about rearrangements that is obtainable from
structure, and that this information is large in the solid phase of the model
and decreases rapidly as state variables are varied into the fluid phase.
