To understand the power of randomization is one of the fundamental questions in theoretical computer science.

Let $G$ be a directed graph on $n$ vertices (with two designated vertices $s$ and $t$) such that the edge weights of $G$ are real-valued linear functions of a parameter $\lambda$.

Parametric models offer simplicity and interpretability, and have been instrumental in driving progress in machine learning over the last few decades.

We consider election scenarios with incomplete information, a situation that arises often in practice. There are several models of incomplete information and accordingly, different notions of outcomes of such elections.

This talk addresses in the main the problem of secure control of networked cyber-physical systems, and, title notwithstanding, a digression into mm-wave networks that have quickly become of great topical interest since the FCC release of 10.85 GHz