Sample-efficient active learning for materials informatics using integrated posterior variance

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For well-distributed points, nearest neighbor search is often near O(log⁡n)O(\log n)O(logn) in practice. In the worst case (all points clustered tightly or along a line), it can degrade to O(n)O(n)O(n), but this is uncommon with typical spatial data.

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Microcode handles the stateful, multi-step operations -- stack switches, descriptor loading, V86 frame management -- that are too irregular for fixed hardware.

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