  Compressed sensing can decrease scanning transmission electron microscopy
electron dose and scan time with minimal information loss. Traditionally,
sparse scans used in compressed sensing sample a static set of probing
locations. However, dynamic scans that adapt to specimens are expected to be
able to match or surpass the performance of static scans as static scans are a
subset of possible dynamic scans. Thus, we present a prototype for a contiguous
sparse scan system that piecewise adapts scan paths to specimens as they are
scanned. Sampling directions for scan segments are chosen by a recurrent neural
network based on previously observed scan segments. The recurrent neural
network is trained by reinforcement learning to cooperate with a feedforward
convolutional neural network that completes the sparse scans. This paper
presents our learning policy, experiments, and example partial scans, and
discusses future research directions. Source code, pretrained models, and
training data is openly accessible at
https://github.com/Jeffrey-Ede/adaptive-scans
