English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Multiple sequences alignment (MSA) is a traditional and challenging task fortime-series analyses. The MSA problem is formulated as a discrete optimizationproblem and is typically solved by dynamic programming. However, thecomputational complexity increases exponentially with respect to the number ofinput sequences. In this paper, we propose neural time warping (NTW) thatrelaxes the original MSA to a continuous optimization and obtains thealignments using a neural network. The solution obtained by NTW is guaranteedto be a feasible solution for the original discrete optimization problem undermild conditions. Our experimental results show that NTW successfully aligns ahundred time-series and significantly outperforms existing methods for solvingthe MSA problem. In addition, we show a method for obtaining averagetime-series data as one of applications of NTW. Compared to the existingbarycenters, the mean time series data retains the features of the inputtime-series data.

Neural Time Warping For Multiple Sequence Alignment