Review : Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy

Warwick electron microscopy Datasets

Large, carefully partitioned datasets are essential to train neural networks and standardize performance benchmarks. As a result, we have set up new repositories to make our electron microscopy datasets available to the wider community. There are three main datasets containing 19769 scanning transmission electron micrographs, 17266 transmission electron micrographs, and 98340 simulated exit wavefunctions, and multiple variants of each dataset for different applications. To visualize image datasets, we trained variational autoencoders to encode data as 64-dimensional multivariate normal distributions, which we cluster in two dimensions by t-distributed stochastic neighbor embedding. In addition, we have improved dataset visualization with variational autoencoders by introducing encoding normalization and regularization, adding an image gradient loss, and extending t-distributed stochastic neighbor embedding to account for encoded standard deviations. Our datasets, source code, pretrained models, and interactive visualizations are openly available at https://github.com/Jeffrey-Ede/datasets

Control mechanisms of circadian rhythms in body composition: Implications for manned spaceflight

The mechanisms that underlie the circadian variations in electrolyte content in body fluid compartments were investigated, and the mechanisms that control the oscillations were studied in order to investigate what effects internal desynchronization in such a system would have during manned space flight. The studies were performed using volunteer human subjects and squirrel monkeys. The intercompartmental distribution of potassium was examined when dietary intake, activity, and posture are held constant throughout each 24-hour day. A net flux of potassium was observed out of the body cell mass during the day and a reverse flux from the extracellular fluid into the body cell mass during the night, counterbalanced by changes in urinary potassium excretion. Experiments with monkeys provided evidence for the synchronization of renal potassium excretion by the rhythm of cortisol secretion with the light-dark cycle. Three models of the circadian timing system were formalized

Partial scanning transmission electron microscopy with deep learning

Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam exposure with minimal information loss. Following successful applications of deep learning to compressed sensing, we have developed a two-stage multiscale generative adversarial neural network to complete realistic 512 × 512 scanning transmission electron micrographs from spiral, jittered gridlike, and other partial scans. For spiral scans and mean squared error based pre-training, this enables electron beam coverage to be decreased by 17.9× with a 3.8% test set root mean squared intensity error, and by 87.0× with a 6.2% error. Our generator networks are trained on partial scans created from a new dataset of 16227 scanning transmission electron micrographs. High performance is achieved with adaptive learning rate clipping of loss spikes and an auxiliary trainer network. Our source code, new dataset, and pre-trained models are publicly available

Adaptive learning rate clipping stabilizes learning

Artificial neural network training with gradient descent can be destabilized by 'bad batches' with high losses. This is often problematic for training with small batch sizes, high order loss functions or unstably high learning rates. To stabilize learning, we have developed adaptive learning rate clipping (ALRC) to limit backpropagated losses to a number of standard deviations above their running means. ALRC is designed to complement existing learning algorithms: Our algorithm is computationally inexpensive, can be applied to any loss function or batch size, is robust to hyperparameter choices and does not affect backpropagated gradient distributions. Experiments with CIFAR-10 supersampling show that ALCR decreases errors for unstable mean quartic error training while stable mean squared error training is unaffected. We also show that ALRC decreases unstable mean squared errors for scanning transmission electron microscopy supersampling and partial scan completion. Our source code is available at https://github.com/Jeffrey-Ede/ALRC

Control mechanisms of circadian rhythms in body composition: Implications for manned spaceflight

The mechanisms underlying the internal synchronization of the circadian variations in electrolyte content in body compartments were investigated, and the significance of these oscillations for manned spaceflight were examined. The experiments were performed with a chair-acclimatized squirrel monkey system, in which the animal sits in a chair, restrained only around the waist. The following information was given: (1) experimental methodology description, (2) summary of results obtained during the first contract year, and (3) discussion of the research performed during the second contract year. This included the following topics: physiological mechanisms promoting normal circadian internal synchronization, factors precipitating internal desynchronization, pathophysiological consequences of internal desynchronization of particular relevance to spaceflight, and validation of a chair-acclimatized system

Free Vibration Analysis of Elastic Orthotropic Rectangular Inclined Damped Highway Supported by Pasternak Foundation under Moving Aerodynamic Automobile

Various plates and plate like structures are often subjected to moving loads, such as aerodynamic automobiles. In this paper automobile highway was modelled as an elastic orthotropic rectangular plate. The effects of damping and drag force were put into consideration. The fourth order differential equation governing such plates resting on Pasternak foundation was expressed as first order differential equation. The equation was changed to its algebraic form using finite difference algorithm, then solved with the aid of MATLAB in conjunction with a computer program. Simple supported conditions were used. The effects of damping drag force, foundation and other physical phenomena were investigated and the results obtained are consistent with the ones existing in literature