Over the last decade, cardiovascular diseases (CVD's) are the leading cause of death
globally. Early prediction of CVD's can help in reducing the complications of high-risk …

Understanding relationships between sets is an important analysis task that has received
widespread attention in the visualization community. The major challenge in this context is …

Knowledge of human perception has long been incorporated into visualizations to enhance
their quality and effectiveness. The last decade, in particular, has shown an increase in …

There is a need for solutions which assist users to understand long time-series data by
observing its changes over time, finding repeated patterns, detecting outliers, and effectively …

A common challenge faced by many domain experts working with time series data is how to
identify and compare similar patterns. This operation is fundamental in high-level tasks, such …

Generative models bear promising implications to learn data representations in an
unsupervised fashion with deep learning. Generative Adversarial Nets (GAN) is one of the …

Multiple time series are a set of multiple quantitative variables occurring at the same interval.
They are present in many domains such as medicine, finance, and manufacturing for …

Incomplete lineage sorting (ILS) causes the phylogeny of some parts of the genome to differ
from the species tree. In this work, we investigate the frequencies and determinants of ILS in …

Research on graphical perception of time series visualisations has focused on visual
representation, and not on interaction. Even for visual representation, there has been limited …

We investigate whether the notion of active reading for text might be usefully applied to
visualizations. Through a qualitative study we explored whether people apply observable …