Time series classification is an increasing research topic due to the vast amount of time
series data that is being created over a wide variety of fields. The particularity of the data …

Alignment-free sequence analyses have been applied to problems ranging from whole-
genome phylogeny to the classification of protein families, identification of horizontally …

Recently, single-stage embedding based deep learning algorithms gain increasing attention
in cell segmentation and tracking. Compared with the traditional “segment-then-associate” …

Deep neural networks (DNNs) are often used for text classification due to their high
accuracy. However, DNNs can be computationally intensive, requiring millions of …

A multitude of explainability methods has been described to try to help users better
understand how modern AI systems make decisions. However, most performance metrics …

Time series clustering is an active research area with applications in a wide range of fields.
One key component in cluster analysis is determining a proper dissimilarity measure …

Recent advances in bioimaging have provided scientists a superior high spatial-temporal
resolution to observe dynamics of living cells as 3D volumetric videos. Unfortunately, the 3D …

Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel
pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell …

Information theoretic based measures form a fundamental class of similarity measures for
comparing clusterings, beside the class of pair-counting based and set-matching based …

Encyclopedia of Distances is the result of re-writing and extending our Dictionary of
Distances published in 2006 (and put online http://www. sciencedirect. com/science/book …