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Polymer nanostructures have drawn tremendous attention due to their wide applications in nanotechnology. However, the morphology of the polymer nanostructures is fragile under harsh conditions such as high-power irradiation and organic-solution environments during the fabrication or the measurement processes, significantly limiting their potential applications. In this work, we propose and demonstrate a simple approach to improve the stability of polymer nanostructures by coating a conformal ultrathin oxide film via atomic-layer deposition. Due to the refractory and dense coating of the oxide layer, the stability of polymer structures is enhanced by the prohibition of deformation occurrences from thermally induced reflow and organic solution. As a proof of concept, poly(methyl methacrylate) (PMMA) nanostructures coated with a sub-10-nm TiO2 layer are demonstrated, and the structures exhibit high temperature stability at 180 °C and good resistance to soluble damage from organic solutions. Subsequently, the mechanism of the improved thermal stability is analyzed via mechanical simulations. Such an effective approach is proposed to significantly broaden the application of polymer nanostructures as functional elements for optical structures/devices that require excellent thermal and chemical stability.

We show that existing upsampling operators can be unified with the notion of the index function. This notion is inspired by an observation in the decoding process of deep image matting where indices-guided unpooling can recover boundary details much better than other upsampling operators such as bilinear interpolation. By looking at the indices as a function of the feature map, we introduce the concept of learning to index, and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the pooling and upsampling operators, without the need of supervision. At the core of this framework is a flexible network module, termed IndexNet, which dynamically predicts indices given an input. Due to its flexibility, IndexNet can be used as a plug-in applying to any off-the-shelf convolutional networks that have coupled downsampling and upsampling stages. We demonstrate the effectiveness of IndexNet on the task of natural image matting where the quality of learned indices can be visually observed from predicted alpha mattes. Results on the Composition-1k matting dataset show that our model built on MobileNetv2 exhibits at least $16.1\%$ improvement over the seminal VGG-16 based deep matting baseline, with less training data and lower model capacity. Code and models has been made available at: https://tinyurl.com/IndexNetV1, Accepted to Proc. Int. Conf. Computer Vision 2019