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IMAGE: (Left) A D2NN ensemble, constituting 14 individual diffractive networks that have different types of filters placed between the object plane and the first diffractive layer. The ensemble class score comes. view more
Credit: by Md Sadman Sakib Rahman, Jingxi Li, Deniz Mengu, Yair Rivenson and Aydogan Ozcan
Recently there has been a reemergence of interest in optical computing platforms for artificial intelligence-related applications. Optics/photonics is ideally suited for realizing neural network models because of the high speed, large bandwidth and high interconnectivity of optical information processing. Introduced by UCLA researchers, Diffractive Deep Neural Networks (D2NNs) constitute such an optical computing framework, comprising successive transmissive and/or reflective diffractive surfaces that can process input information through light-matter interaction. These surfaces are designed using standard deep learning techniques in a computer, which a
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IMAGE: Computational materials scientists at Ames Laboratory developed an evolutionary algorithm, using a hybrid version of a computer program called Cuckoo Search (CS), which mimics the brood parasite behavior of cuckoo. view more
Credit: U.S. Department of Energy, Ames Laboratory
Computational materials science experts at the U.S. Department of Energy s Ames Laboratory enhanced an algorithm that borrows its approach from the nesting habits of cuckoo birds, reducing the search time for new high-tech alloys from weeks to mere seconds.
The scientists are investigating a type of alloys called high-entropy alloys, a novel class of materials that are highly sought after for a host of unusual and potentially beneficial properties. They are lightweight in relation to their strength, fracture-resistant, highly corrosion and oxidation resistant, and stand up well in high-temperature and high-pressure environments making them attractive materials for aerospace indust
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BROOKLYN, New York, Tuesday, January 12, 2021 - Beginning in January, the Urban Future Lab at the NYU Tandon School of Engineering will be the U.S. landing pad for Innovate UK s Global Incubator Programme (GIP), which is designed to cultivate and support the launch of innovative cleantech companies with a strong potential to scale internationally to new markets.
The program will provide eight U.K.-based businesses with the opportunity to explore the potential of the U.S. market and access to world-class mentors. The cohort will consist of businesses in electric mobility, distributed energy, and technologies focused on reducing greenhouse gas emissions or addressing the effects of global warming.
A new method to analyze chemical status of lithium was developed by using a synchrotron-based scanning transmission soft X-ray microscope (STXM). A key of the method is installation of a newly designed X-ray lens, a low-pass filtering zone plate, to the STXM to improve quality of a monochromatic X-ray. 2-dimensional chemical state of a test electrode of Li-ion battery was successfully analyzed with spatial resolution of 72 nm.