The effect of substrate roughness on the orientation of lamellar microdomains of symmetric poly(styrene)-block-poly(methyl methacrylate) [PS-b-PMMA] was investigated. Thin films of three molecular weights of PS-b-PMMA were prepared on organic polyimide and inorganic indium tin oxide substrates whose surfaces were characterized for roughness and surface energy. It was shown, through cross-section transmission electron microscopy (TEM) and dynamic secondary ion mass spectroscopy (dSIMS), that above a critical substrate roughness all three molecular weights of PS-b-PMMA produced a perpendicular lamellar orientation. Using atomic force microscopy (AFM) and PS-b-PMMA thin films on an array of polyimide substrates of varied substrate roughness, a critical substrate roughness was identified, below which a parallel orientation was observed. This behavior was modeled simply and showed that the critical roughness determined by AFM represents an underestimate of the true critical roughness of the substrate. Finally, a series of TEM cross sections of thin films on rough and smooth substrates, annealed to different stages of reaching equilibrium, are shown and discussed in terms of the dynamics of ordering in block copolymer thin films.

The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models. However, consensus is lacking on experimental configurations, namely, choosing how the pseudo data should be generated or used. In this study, these choices are investigated through extensive experiments, and state-of-the-art performance is achieved on the CoNLL-2014 test set (F 0.5 = 65.0) and the official test set of the BEA-2019 shared task (F 0.5 = 70.2) without making any modifications to the model architecture.

We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-toend speech processing toolkit, ESPnet, which integrates or newly implements automatic speech recognition, machine translation, and text-to-speech functions for speech translation. We provide all-in-one recipes including data pre-processing, feature extraction, training, and decoding pipelines for a wide range of benchmark datasets. Our reproducible results can match or even outperform the current state-of-the-art performances; these pretrained models are downloadable. The toolkit is publicly available at https://github. com/espnet/espnet.

No abstract

No abstract