Visualization algorithms are fundamental tools for interpreting single-cell data. However,
standard methods, such as t-stochastic neighbor embedding (t-SNE), are not scalable to …

Complex networks play a very important role in various fields of science as data structures,
which aggregate information about mutual relationships between numerous objects. The …

Serious problems with bridging multiple scales in the scope of a single numerical model
make computer simulations too demanding computationally and highly unreliable. We …

In the advent of big data era, interactive visualization of large data sets consisting of M* 10^
5+ high-dimensional feature vectors of length N (N~ 10^ 3+), is an indispensable tool for …

Human migration between cities is one important aspect of spatial interaction that not only
reflects urban attractiveness but also denotes interactions amongst agglomerations. We …

This paper studies feasibility and scalable computing processes for visualizing big high
dimensional data in a 3 dimensional space by using dimension reduction techniques. More …

The k-nearest neighbor (k-NN) search is the rudimentary procedure widely used in machine
learning and data embedding techniques. Herein we present a new multi-GPU/CUDA …

We present a new framework for analysis and visualization of complex networks based on
structural information retrieved from their distance k‐graphs and B‐matrices. The …

Single-cell RNA sequencing is becoming effective and accessible as emerging technologies
push its scale to millions of cells and beyond. Visualizing the landscape of single cell …

In the present big data era, there is a need to process large amounts of unlabeled data and
find some patterns in the data to use it further. If data has many dimensions, it is very hard to …