Unsupervised Learning for Depth, Ego-Motion, and Optical Flow Estimation Using Coupled Consistency Conditions.

Herein, we propose an unsupervised learning architecture under coupled consistency conditions to estimate the depth, ego-motion, and optical flow. Previously invented learning techniques in computer vision adopted a large amount of the ground truth dataset for network training. A ground truth dataset, including depth and optical flow collected from the real world, requires tremendous effort in pre-processing due to the exposure to noise artifacts.

Development and calibration of differential mobility analyzer for 20 to 80 nm particles under low pressure conditions.

The vienna-type differential mobility analyzer (DMA) was developed for the measurement of wide-range nm-sized particles under low-pressure conditions (2.9-8 kPa) with the faraday cup electrometer (FCE). The length, inner and outer diameter of DMA are calculated as a function of flow rate, applied voltage, pressure, and particle diameter to avoide breakdown in DMA.

Histopathological pictures of the initial changes of the hair bulbs in alopecia areata.

A biopsy of the seemingly normal scalp of a patient who had just begun to develop alopecia areata showed distinctive changes in bulbar morphology, in addition to peribulbar lymphocytic infiltrates. One of these changes was a loss of structural integrity of the centrally located supramatrical upper bulbar region. The other was the shrinkage of hair bulbs in the direction of club shape.