Here, we described a unique simultaneous electrorotation (ROT) device for monitoring the rotation rate of Jurkat cells via chemical stimulation without fluorescent labeling and an algorithm for estimating cell rotation...
A multi-layer image is more valuable than a singlelayer image from a graphic designer's perspective. However, most of the proposed image generation methods so far focus on single-layer images. In this paper, we propose MontageGAN, which is a Generative Adversarial Networks (GAN) framework for generating multi-layer images. Our method utilized a twostep approach consisting of local GANs and global GAN. Each local GAN learns to generate a specific image layer, and the global GAN learns the placement of each generated image layer . Through our experiments, we show the ability of our method to generate multi-layer images and estimate the placement of the generated image layers.
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