Optical computing has been gaining wide interest for machine learning applications because of the massive parallelism and bandwidth of optics. Diffractive networks provide one such computing paradigm based on the transformation of the input light as it diffracts through a set of spatially-engineered surfaces, performing computation at the speed of light propagation without requiring any external power apart from the input light beam. Among numerous other applications, diffractive networks have been demonstrated to perform all-optical classification of input objects.

Researchers at the University of California, Los Angeles (UCLA), led by Professor Aydogan Ozcan, have introduced a “time-lapse” scheme to significantly improve the image classification accuracy of diffractive optical networks on complex input objects. The findings are published in the journal Advanced Intelligent Systems.

In this scheme, the object and/or the diffractive network are moved relative to each other during the exposure of the output detectors. Such a “time-lapse” scheme has previously been used to achieve super-resolution imaging, for example, in security cameras, by capturing multiple images of a scene with lateral movements of the camera.

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Permissions management is the essence of data security. Yet few security teams can manage identities in the cloud at scale, with Gartner estimating that by 2023, 75% of cloud security failures will occur due to insufficient management of identities, access and privileges.

However, more and more providers are looking to address permissions management with automation. Entitle, which today announced it has raised $15 million as part of a seed funding round led by Glilot Capital Partners, offers a platform for automating access management and provisioning.