Optical distortion calibration using machine-learning for exoplanet detection

Simplifying the detection of optical distortions by machine learning

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545820400015 ◽

2019 ◽

Vol 13 (03) ◽

pp. 2040001

Author(s):

Shuwen Hu ◽

Lejia Hu ◽

Biwei Zhang ◽

Wei Gong ◽

Ke Si

Keyword(s):

Machine Learning ◽

Adaptive Optics ◽

High Speed ◽

Mean Squared Error ◽

Tissue Imaging ◽

Wavefront Sensor ◽

Optical Distortion ◽

Detection Accuracy ◽

Deep Tissue Imaging

Adaptive optics has been widely used in biological science to recover high-resolution optical image deep into the tissue, where optical distortion detection with high speed and accuracy is strongly required. Here, we introduce convolutional neural networks, one of the most popular machine learning models, into Shack–Hartmann wavefront sensor (SHWS) to simplify optical distortion detection processes. Without image segmentation or centroid positioning algorithm, the trained network could estimate up to 36th Zernike mode coefficients directly from a full SHWS image within 1.227[Formula: see text]ms on a personal computer, and achieves prediction accuracy up to 97.4%. The simulation results show that the average root mean squared error in phase residuals of our method is 75.64% lower than that with the modal-based SHWS method. With the high detection accuracy and simplified detection processes, this work has the potential to be applied in wavefront sensor-based adaptive optics for in vivo deep tissue imaging.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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