DisCo: PhysicsBased Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems Extracting actionable insight from complex unlabeled scientific data is an open challenge and key to unlocking datadriven discovery in science. Complementary and alternative to supervised machine learning approaches, unsupervised physicsbased methods based on behaviordriven theories hold great promise. Due to computational limitations, practical application on realworld domain science problems has lagged far behind theoretical development. However, powerful modern supercomputers provide the opportunity to narrow the gap between theory and practical application. We present our first step towards bridging this divide  DisCo  a highperformance distributed workflow for the behaviordriven local causal state theory. DisCo provides a scalable unsupervised physicsbased representation learning method that decomposes spatiotemporal systems into their structurally relevant components, which are captured by the latent local causal state variables. In several firsts we demonstrate the efficacy of DisCo in capturing physically meaningful coherent structures from observational and simulated scientific data. To the best of our knowledge, DisCo is also the first application software developed entirely in Python to scale to over 1000 machine nodes, providing good performance along with ensuring domain scientistsu0027 productivity. Our capstone experiment, using newly developed and optimized DisCo workflow and libraries, performs unsupervised spacetime segmentation analysis of CAM5.1 climate simulation data, processing an unprecedented 89.5 TB in 6.6 minutes endtoend using 1024 Intel Haswell nodes on the Cori supercomputer obtaining 91% weakscaling and 64% strongscaling efficiency. This enables us to achieve stateoftheart unsupervised segmentation of coherent spatiotemporal structures in complex fluid flows.

PERCeIDs: PERiodic CommunIty Detection Many complex networked systems, both natural and humanmade, exhibit periodic behavior driven by underlying seasonal processes: election cycles and regular sporting events in social networks, cell cycle phases in gene networks, and load variation in infrastructure networks due to weather or daylight patterns. The natural periodicity may vary across network communities. At the same time this periodic community behavior is central to (i) understating the overall system dynamics and (ii) for detection of the communities themselves. The predominant approach to dynamic community detection first detects communities and then as a second step quantify seasonality in their activity. How to jointly detect communities and their inherent periodicity, while also accounting for nonperiodic oneoff events? We propose PERCeIDs, a framework for periodic overlapping community detection from temporal interaction data. We model observed pairwise interaction activity as a mixture of periodic and outlier (nonperiodic) components. We explicitly enforce periodic structure within our model by learning a succinct Ramanujan basis dictionary for community behaviors. By explicitly modeling periodicity, PERCeIDs outperforms baselines on both detecting highly overlapping communities with up to 2fold improvement in NMI compared to stateoftheart baselines, while offering an interpretable temporal structure for discovered communities in the dataset. Implementation of our method can be found at http:www.cs.albany.edupetkolabcode.html.