• Corpus ID: 44395802

Computational Topology in

@inproceedings{Stolz2014ComputationalTI,
  title={Computational Topology in},
  author={Bernadette J. Stolz},
  year={2014}
}
Computational topology is a set of algorithmic methods developed to understand topological invariants such as loops and holes in high-dimensional data sets. In particular, a method know as persistent homology has been used to understand such shapes and their persistence in point clouds and networks. It has only been applied to neuronal networks in recent years. While most tools from network science focus solely on local properties based on pairwise connections, the topological tools reveal more… 
1 Citations

Topological Data Analysis for Systems of Coupled Oscillators

By looking for clustering in a data space consisting of the phase change of oscillators over a set of time delays it is hoped to reconstruct attractors and identify members of these clusters.

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Persistent homology is an algebraic tool for measuring topological features of shapes and functions. It casts the multi-scale organization we frequently observe in nature into a mathematical

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This paper will discuss how geometry and topology can be applied to make useful contributions to the analysis of various kinds of data, particularly high throughput data from microarray or other sources.

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A note on the problem of reporting maximal cliques