Author: paul.rosen

We present a comprehensive framework for evaluating line chart smoothing methods under a variety of visual analytics tasks. The framework is based on 8 measures of the line smoothing effectiveness tied to 8 low-level visual analytics tasks. We analyze 12 methods coming from 4 commonly used classes of line chart smoothing—rank filters, convolutional filters, frequency domain filters, and subsampling.

We present TopoLines, a method for smoothing line charts using techniques from Topological Data Analysis. The design goal of TopoLines is to maintain prominent peaks in the data while minimizing any residual error. We evaluate TopoLines for 2 visual analytics tasks by comparing to 5 popular line smoothing methods with data from 4 application domains.

In this paper, we leverage persistent homology features of an undirected graph as derived information for interactive manipulation of force-directed layouts. We first discuss how to efficiently extract 0-dimensional persistent homology features from both weighted and unweighted undirected graphs. We then introduce the interactive persistence barcode used to manipulate the force-directed graph layout by adding and removing contracting and repulsing forces generated by the persistent homology features, eventually selecting the set of persistent homology features that most improve the layout.

A key problem in Deep Brain Stimulation (DBS) is determining the optimal parameters for clinical outcome. Current knowledge does not provide a complete deterministic model of DBS neurophysiology. While multiple parameters may influence clinical outcomes in DBS, this paper explores spatial correlation of volume of tissue activated (VTA) to Unified Parkinson’s Disease Rating Scale (UPDRS) scores.