Performance issues with photonic beamformers

A photonic beamformer is presented, having smooth behavior. Third-order nonlinearities, resulting from its optoelectronic components, are investigated, with emphasis on their impact on the contrast of imaging radars. This contrast is shown to be severely limited by the induced RF nonlinearities. Limitations on the allowable modulation index are studied for linearly-chirped pulses returned from clutter

Performance issues with photonic beamformers

A photonic beamformer is presented, having smooth behavior. Third-order nonlinearities, resulting from its optoelectronic components, are investigated, with emphasis on their impact on the contrast of imaging radars. This contrast is shown to be severely limited by the induced RF nonlinearities. Limitations on the allowable modulation index are studied for linearly-chirped pulses returned from clutter

17‐Beta Hydroxysteroid Dehydrogenase 13 Is a Hepatic Retinol Dehydrogenase Associated With Histological Features of Nonalcoholic Fatty Liver Disease

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148371/1/hep30350.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148371/2/hep30350_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148371/3/hep30350-sup-0001-Supinfo.pd

Unsupervised Disentanglement with Tensor Product Representations on the Torus

The current methods for learning representations with auto-encoders almost exclusively employ vectors as the latent representations. In this work, we propose to employ a tensor product structure for this purpose. This way, the obtained representations are naturally disentangled. In contrast to the conventional variations methods, which are targeted toward normally distributed features, the latent space in our representation is distributed uniformly over a set of unit circles. We argue that the torus structure of the latent space captures the generative factors effectively. We employ recent tools for measuring unsupervised disentanglement, and in an extensive set of experiments demonstrate the advantage of our method in terms of disentanglement, completeness, and informativeness. The code for our proposed method is available at https://github.com/rotmanmi/Unsupervised-Disentanglement-Torus.Comment: Accepted to ICLR 202