  We consider the problem of universal dynamic regret minimization under
exp-concave and smooth losses. We show that appropriately designed Strongly
Adaptive algorithms achieve a dynamic regret of $\tilde O(d^2 n^{1/5} C_n^{2/5}
\vee d^2)$, where $n$ is the time horizon and $C_n$ a path variational based on
second order differences of the comparator sequence. Such a path variational
naturally encodes comparator sequences that are piecewise linear -- a powerful
family that tracks a variety of non-stationarity patterns in practice (Kim et
al, 2009). The aforementioned dynamic regret rate is shown to be optimal modulo
dimension dependencies and poly-logarithmic factors of $n$. Our proof
techniques rely on analysing the KKT conditions of the offline oracle and
requires several non-trivial generalizations of the ideas in Baby and Wang,
2021, where the latter work only leads to a slower dynamic regret rate of
$\tilde O(d^{2.5}n^{1/3}C_n^{2/3} \vee d^{2.5})$ for the current problem.
