Publications by authors named "Richard Harang"

An important way to limit malicious insiders from distributing sensitive information is to as tightly as possible limit their access to information. This has always been the goal of access control mechanisms, but individual approaches have been shown to be inadequate. Ensemble approaches of multiple methods instantiated simultaneously have been shown to more tightly restrict access, but approaches to do so have had limited scalability (resulting in exponential calculations in some cases).

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The problem of optimally removing a set of vertices from a graph to minimize the size of the largest resultant component is known to be NP-complete. Prior work has provided near optimal heuristics with a high time complexity that function on up to hundreds of nodes and less optimal but faster techniques that function on up to thousands of nodes. In this work, we analyze how to perform vertex partitioning on massive graphs of tens of millions of nodes.

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Background: Wavelets have proven to be a powerful technique for the analysis of periodic data, such as those that arise in the analysis of circadian oscillators. While many implementations of both continuous and discrete wavelet transforms are available, we are aware of no software that has been designed with the nontechnical end-user in mind. By developing a toolkit that makes these analyses accessible to end users without significant programming experience, we hope to promote the more widespread use of wavelet analysis.

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Cells in the suprachiasmatic nucleus (SCN) display remarkable precision, while either physically or chemically decoupling these cells from each other leads to a dramatic increase in period-to-period variability. Where previous studies have classified cells as either arrhythmic or circadian, our wavelet analysis reveals that individual cells, when removed from network interactions, intermittently express circadian and/or longer infradian periods. We reproduce the characteristic period distribution of uncoupled SCN cells with a stochastic model of the uncoupled SCN cell near a bifurcation in Bmal1 transcription repression.

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