There is a significant public demand for rapid data-driven scientific investigations using aggregated sensitive information. However, many technical challenges and regulatory policies hinder efficient data sharing. In this study, we describe a partially synthetic data generation technique for creating anonymized data archives whose joint distributions closely resemble those of the original (sensitive) data. Specifically, we introduce the DataSifter technique for time-varying correlated data (DataSifter II), which relies on an iterative model-based imputation using generalized linear mixed model and random effects-expectation maximization tree. DataSifter II can be used to generate synthetic repeated measures data for testing and validating new analytical techniques. Compared to the multiple imputation method, DataSifter II application on simulated and real clinical data demonstrates that the new method provides extensive reduction of re-identification risk (data privacy) while preserving the analytical value (data utility) in the obfuscated data. The performance of the DataSifter II on a simulation involving 20% artificially missingness in the data, shows at least 80% reduction of the disclosure risk, compared to the multiple imputation method, without a substantial impact on the data analytical value. In a separate clinical data (Medical Information Mart for Intensive Care III) validation, a model-based statistical inference drawn from the original data agrees with an analogous analytical inference obtained using the DataSifter II obfuscated (sifted) data. For large time-varying datasets containing sensitive information, the proposed technique provides an automated tool for alleviating the barriers of data sharing and facilitating effective, advanced, and collaborative analytics.
Biomolecular simulations require increasingly efficient parallel codes. We present an efficient communication algorithm for irregular problems exhibiting an all-to-many communication pattern. The algorithm is developed using message passing on distributed memory machines and assumes explicit knowledge of the interconnection topology. The algorithm maximizes locality of interprocessor communication by adopting to an arbitrary interconnection topology and at the same time takes multiprocessor nodes into account. The solution is incorporated into our implementation of the fast multipole method with periodic boundary conditions used for molecular dynamics simulations, but we believe it generalizes to many algorithms demonstrating an all-to-many communication pattern. We show that an irregular algorithm can be forced to behave like a systolic algorithm.
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