2,816 research outputs found

    12CaO.7Al2O3 ceramic: A review of the electronic and optoelectronic applications in display devices

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    The alumina-based compound, 12CaO.7Al2O3, is a ceramic material with a unique cage-like lattice. Such a structure has enabled scientists to extract various new characteristics from this compound, most of which were unknown until quite recently. This compound has the ability to incorporate different anionic species and even electrons to the empty space inside its cages, thereby changing from an insulator into a conductive oxide. The cage walls can also incorporate different rare earth phosphor elements producing an oxide-based phosphor. All these characteristics are obtained without a significant change in the structure of the lattice. It is, therefore, reasonable to expect that this compound will receive attention as a potential material for display applications. This review article presents recent investigations into the application of 12CaO.7Al2O3 ceramic in various display devices, the challenges, opportunities and possible areas of future investigation into the development of this naturally abundant and environmental friendly material in the field of display.LP Displays Ltd, Blackburn, UK for partial funding of the studentship at Queen Mary, University of London. Dr Lesley Hanna of Wolfson Centre for Materials Processing, Brunel University Londo

    Adversarially Robust Distillation

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    Knowledge distillation is effective for producing small, high-performance neural networks for classification, but these small networks are vulnerable to adversarial attacks. This paper studies how adversarial robustness transfers from teacher to student during knowledge distillation. We find that a large amount of robustness may be inherited by the student even when distilled on only clean images. Second, we introduce Adversarially Robust Distillation (ARD) for distilling robustness onto student networks. In addition to producing small models with high test accuracy like conventional distillation, ARD also passes the superior robustness of large networks onto the student. In our experiments, we find that ARD student models decisively outperform adversarially trained networks of identical architecture in terms of robust accuracy, surpassing state-of-the-art methods on standard robustness benchmarks. Finally, we adapt recent fast adversarial training methods to ARD for accelerated robust distillation.Comment: Accepted to AAAI Conference on Artificial Intelligence, 202
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