Network-based modeling of complex systems and data using the language of graphs has
become an essential topic across a range of different disciplines. Arguably, this graph-based …

The last decade saw an enormous boost in the field of computational topology: methods and
concepts from algebraic and differential topology, formerly confined to the realm of pure …

" In this chapter, we introduce some of the very basics that are used throughout the book.
First, we give the definition of a topological space and related notions of open and closed …

By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …

Significant technological advances made in recent years have shepherded a dramatic
increase in utilization of digital technologies for biomedicine–everything from the …

Topological Data Analysis (TDA) is a recent and fast growing field providing a set of new
topological and geometric tools to infer relevant features for possibly complex data. This …

There recently has been a surge of interest in developing a new class of deep learning (DL)
architectures that integrate an explicit time dimension as a fundamental building block of …

Segmentation algorithms are prone to make topological errors on fine-scale struc-tures, eg,
broken connections. We propose a novel method that learns to segment with correct …

Persistent homology (PH) is a method used in topological data analysis (TDA) to study
qualitative features of data that persist across multiple scales. It is robust to perturbations of …

Protein engineering is an emerging field in biotechnology that has the potential to
revolutionize various areas, such as antibody design, drug discovery, food security, ecology …