Seminar • ISS4E — Hierarchical Signal Processing for Tractable Power Flow Management in Electric Grid Networks
Pirathayini Srikantha, Department of Electrical and Computer Engineering
Western University
Pirathayini Srikantha, Department of Electrical and Computer Engineering
Western University
Finn Lidbetter, Master’s candidate
David R. Cheriton School of Computer Science
Let x and y be words. We consider the languages whose words z are those for which the numbers of occurrences of x and y, as subwords of z, are the same (resp., the number of x's is less than the number of y's, resp., is less than or equal). In this talk we will give a necessary and sufficient condition on x and y for these languages to be regular, and we show how to check this condition efficiently.
Matthew Amy, PhD candidate
David R. Cheriton School of Computer Science
Adam Molnar, Deakin University
Abdullah Rashwan, PhD candidate
David R. Cheriton School of Computer Science
Aiman Erbad, Computer Science and Engineering Department
Qatar University
Irfan Ahmad, Founder and CEO
CachePhysics
Caches in modern distributed and storage systems must be manually tuned and sized in response to changing application’s workload. A balance must be achieved between cost, performance and revenue loss from cache sizing mis-matches. However, caches are inherently nonlinear systems making this exercise equivalent to solving a maze in the dark.
Erinn Atwater, PhD candidate
David R. Cheriton School of Computer Science
Kshitij Jain, Master’s candidate
David R. Cheriton School of Computer Science
We introduce a problem called the Minimum Shared-Power Edge Cut (MSPEC). The input to the problem is an undirected edge-weighted graph with distinguished vertices s and t, and the goal is to find an s-t cut by assigning "powers'' at the vertices and removing an edge if the sum of the powers at its endpoints is at least its weight. The objective is to minimize the sum of the assigned powers.
Philipp Kindermann, Postdoctoral Fellow
David R. Cheriton School of Computer Science
The visual complexity of a graph drawing is defined as the number of geometric objects needed to represent all its edges. In particular, one object may represent multiple edges, e.g., one needs only one line segment to draw two collinear incident edges.