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Paul Samuel Ignacio, Jay-Anne Bulauan and David Uminsky
Stability of persistence diagrams under slight perturbations is a key characteristic behind the validity and growing popularity of topological data analysis in exploring real-world data. Central to this stability is the use of Bottleneck distance which e...
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Eric S. Weber, Steven N. Harding and Lee Przybylski
We introduce a novel methodology for anomaly detection in time-series data. The method uses persistence diagrams and bottleneck distances to identify anomalies. Specifically, we generate multiple predictors by randomly bagging the data (reference bags), ...
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Lida Kanari, Adélie Garin and Kathryn Hess
Methods of topological data analysis have been successfully applied in a wide range of fields to provide useful summaries of the structure of complex data sets in terms of topological descriptors, such as persistence diagrams. While there are many powerf...
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Renjie Chen and Nalini Ravishanker
With the advancement of IoT technologies, there is a large amount of data available from wireless sensor networks (WSN), particularly for studying climate change. Clustering long and noisy time series has become an important research area for analyzing t...
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Sarah Tymochko, Elizabeth Munch and Firas A. Khasawneh
Bifurcations in dynamical systems characterize qualitative changes in the system behavior. Therefore, their detection is important because they can signal the transition from normal system operation to imminent failure. In an experimental setting, this t...
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Ali Nabi Duman, Bekir S. Yilbas, Harun Pirim and Haider Ali
Due to recent climate change-triggered, regular dust storms in the Middle East, dust mitigation has become the critical issue for solar energy harvesting devices. One of the methods to minimize and prevent dust adhesion and create self-cleaning abilities...
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