Topological data analysis (TDA) is an area of data science that focuses on using invariants from algebraic topology to provide multiscale shape descriptors for geometric data sets, such as graphs and point clouds.

Biodiversity plays a multifaceted role in sustaining ecosystems and supporting human well-being. Primarily, it serves as a cornerstone for ecosystem stability and resilience, providing a natural defence against disturbances such as climate change and invasive species (Cardinale et al., 2012).

Sliding windows and persistence: an application of topological methods to signal analysis.

However, in many applications there are several different parameters one might wish to vary: for example, scale and density.

The problem of (point) forecasting univariate time series is considered.

Fine-grained zero-shot learning task requires some form of side-information to transfer discriminative information from seen to unseen classes.

The simplicial complex is a combinatorial data that can be thought of as a higher-dimensional generalization of a graph.