4,827 research outputs found

    Ti/Al/Ni/Au Ohmic contacts for AlInN/AlN/GaN-based heterojunction field-effect transistors

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    The microstructure of AuNiAlTi/Al0.84In0.16N/AlN/GaNOhmic contacts annealed from 700 to 900 °C has been determined using transmission electron microscopy and associated analytical techniques. Intermixing and phase separation of the metal contact layers was observed to degrade the surface roughness. An optimal contact performance was obtained for contacts annealed at 800 °C and was attributed to the formation of TiN contact inclusions that had penetrated through the AlInN layers into the GaN layers underneath. These TiN contact inclusions had an inverted mushroom shape with a density of ∼108 cm−2, and they were invariably located at the positions of mixed-type threading dislocations. These inclusion defects would act as a conduction path between the metal contacts and the two-dimensional electron gas of heterojunction field-effect transistor devices. The AlInN layer remained intact in dislocation-free areas of all samples

    African rice cultivation linked to rising methane

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    Africa has been identified as a major driver of the current rise in atmospheric methane, and this has been attributed to emissions from wetlands and livestock. Here we show that rapidly increasing rice cultivation is another important source, and estimate that it accounts for 7% of the current global rise in methane emissions. Continued rice expansion to feed a rapidly growing population should be considered in climate change mitigation goals.Comment: 7 pages and 2 figure

    AtomSim: web-deployed atomistic dynamics simulator

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    AtomSim, a collection of interfaces for computational crystallography simulations, has been developed. It uses forcefield-based dynamics through physics engines such as the General Utility Lattice Program, and can be integrated into larger computational frameworks such as the Virtual Neutron Facility for processing its dynamics into scattering functions, dynamical functions etc. It is also available as a Google App Engine-hosted web-deployed interface. Examples of a quartz molecular dynamics run and a hafnium dioxide phonon calculation are presented

    Chemical nonlinearities in relating intercontinental ozone pollution to anthropogenic emissions

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    Model studies typically estimate intercontinental influence on surface ozone by perturbing emissions from a source continent and diagnosing the ozone response in the receptor continent. Since the response to perturbations is non-linear due to chemistry, conclusions drawn from different studies may depend on the magnitude of the applied perturbation. We investigate this issue for intercontinental transport between North America, Europe, and Asia with sensitivity simulations in three global chemical transport models. In each region, we decrease anthropogenic emissions of NOx and nonmethane volatile organic compounds (NMVOCs) by 20% and 100%. We find strong nonlinearity in the response to NOx perturbations outside summer, reflecting transitions in the chemical regime for ozone production. In contrast, we find no significant nonlinearity to NOx perturbations in summer or to NMVOC perturbations year-round. The relative benefit of decreasing NOx vs. NMVOC from current levels to abate intercontinental pollution increases with the magnitude of emission reductions

    Learning to Address Intra-segment Misclassification in Retinal Imaging

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    Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels in retinal photographs is one such scenario, in which arteries and veins need to be identified and differentiated from each other and from the background. Intra-segment misclassification, i.e. veins classified as arteries or vice versa, frequently occurs when arteries and veins intersect, whereas in binary retinal vessel segmentation, error rates are much lower. We thus propose a new approach that decomposes multi-class segmentation into multiple binary, followed by a binary-to-multi-class fusion network. The network merges representations of artery, vein, and multi-class feature maps, each of which are supervised by expert vessel annotation in adversarial training. A skip-connection based merging process explicitly maintains class-specific gradients to avoid gradient vanishing in deep layers, to favor the discriminative features. The results show that, our model respectively improves F1-score by 4.4%, 5.1%, and 4.2% compared with three state-of-the-art deep learning based methods on DRIVE-AV, LES-AV, and HRF-AV data sets. Code: https://github.com/rmaphoh/Learning-AVSegmentatio

    Learning to Address Intra-segment Misclassification in Retinal Imaging

    Get PDF
    Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels in retinal photographs is one such scenario, in which arteries and veins need to be identified and differentiated from each other and from the background. Intra-segment misclassification, i.e. veins classified as arteries or vice versa, frequently occurs when arteries and veins intersect, whereas in binary retinal vessel segmentation, error rates are much lower. We thus propose a new approach that decomposes multi-class segmentation into multiple binary, followed by a binary-to-multi-class fusion network. The network merges representations of artery, vein, and multi-class feature maps, each of which are supervised by expert vessel annotation in adversarial training. A skip-connection based merging process explicitly maintains class-specific gradients to avoid gradient vanishing in deep layers, to favor the discriminative features. The results show that, our model respectively improves F1-score by 4.4%, 5.1%, and 4.2% compared with three state-of-the-art deep learning based methods on DRIVE-AV, LES-AV, and HRF-AV data sets. Code: https://github.com/rmaphoh/Learning-AVSegmentatio
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