Subspace analysis of high-dimensional data is extremely challenging due to the huge
exploration space. We propose Subspace-Map, a novel approach with a map metaphor for …

Understanding and alleviating energy poverty is critical for sustainable development. This
study harnesses a suite of Machine Learning (ML) algorithms to predict Multidimensional …

While the data amount grows exponentially, the number of people with analytical and
technical skills is only slowly increasing. This skill gap is putting pressure on the labor …

Multivariate networks are commonly found in realworld data-driven applications. Uncovering
and understanding the relations of interest in multivariate networks is not a trivial task. This …

Having a direct or indirect band gap can influence the potential applications of a
semiconductor, for indirect band gap materials are usually not suitable for optoelectronic …

Multivariate or multidimensional visualization plays an essential role in exploratory data
analysis by allowing users to derive insights and formulate hypotheses. Despite their …

Test methods that can keep up with the ongoing increase in complexity of semiconductor
products and their underlying technologies are an essential prerequisite for maintaining …

Modern science and industry rely on computational models for simulation, prediction, and
data analysis. Spatial blind source separation (SBSS) is a model used to analyze spatial …

Graph visualization with proper layout is widely applied to understand the relationship
between entities in a complex system and the topological structure information is mainly …

Analyzing post-silicon validation datasets to spot bugs and obtain hints for design
improvements challenges validation engineers with complex and sometimes subtle …