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Prediction of anisotropic NMR data directly from solute-medium interaction is of significant theoretical and practical interest, particularly for structure elucidation, configurational analysis and conformational studies of complex organic molecules and natural products. Current prediction methods require an explicit structural model of the alignment medium: a requirement either impossible or impractical on a scale necessary for small organic molecules. Here we formulate a comprehensive mathematical framework for a parametrization protocol that deconvolutes an arbitrary surface of the medium into several simple local landscapes that are distributed over the medium's surface by specific orientational order parameters. The shapes and order parameters of these local landscapes are determined via fitting that maximizes the congruence between experimentally determined anisotropic NMR measurables and their predicted counterparts, thus avoiding the need for an a priori knowledge of the global medium morphology. This method achieves substantial improvements in the accuracy of predicted anisotropic NMR values compared to current methods, as demonstrated herein with sixteen natural products. Furthermore, because this formalism extracts structural commonalities of the medium by combining anisotropic NMR data from different compounds, its robustness and accuracy are expected to improve as more experimental data become available for further re-optimization of fitting parameters. 

Modern cloud databases adopt a storage-disaggregation architecture that separates the management of computation and storage. A major bottleneck in such an architecture is the network connecting the computation and storage layers. Two solutions have been explored to mitigate the bottleneck: caching and computation pushdown. While both techniques can significantly reduce network traffic, existing DBMSs consider them as orthogonal techniques and support only one or the other, leaving potential performance benefits unexploited. In this paper we present FlexPushdownDB (FPDB), an OLAP cloud DBMS prototype that supports fine-grained hybrid query execution to combine the benefits of caching and computation pushdown in a storage-disaggregation architecture. We build a hybrid query executor based on a new concept called separable operators to combine the data from the cache and results from the pushdown processing. We also propose a novel Weighted-LFU cache replacement policy that takes into account the cost of pushdown computation. Our experimental evaluation on the Star Schema Benchmark shows that the hybrid execution outperforms both the conventional caching- only architecture and pushdown-only architecture by 2.2×. In the hybrid architecture, our experiments show that Weighted-LFU can outperform the baseline LFU by 37%.