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This paper is on image and face super-resolution. The vast majority of prior work for this problem focus on how to increase the resolution of low-resolution images which are artificially generated by simple bilinear down-sampling (or in a few cases by blurring followed by down-sampling). We show that such methods fail to produce good results when applied to real-world low-resolution, low quality images. To circumvent this problem, we propose a two-stage process which firstly trains a High-to-Low Generative Adversarial Network (GAN) to learn how to degrade and downsample high-resolution images requiring, during training, only unpaired high and low-resolution images. Once this is achieved, the output of this network is used to train a Low-to-High GAN for image super-resolution using this time paired low-and high-resolution images. Our main result is that this network can be now used to effectively increase the quality of real-world low-resolution images. We have applied the proposed pipeline for the problem of face super-resolution where we report large improvement over baselines and prior work although the proposed method is potentially applicable to other object categories.

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This clinically based research highlights the need for policy makers and administrators to create unit-specific protocols for improving postoperative handovers.

The skyline of a set of multi-dimensional points (tuples) consists of those points for which no clearly better point exists in the given set, using component-wise comparison on domains of interest. Skyline queries, i.e., queries that involve computation of a skyline, can be computationally expensive, so it is natural to consider parallelized approaches which make good use of multiple processors. We approach this problem by using hyperplane projections to obtain useful partitions of the data set for parallel processing. These partitions not only ensure small local skyline sets, but enable efficient merging of results as well. Our experiments show that our method consistently outperforms similar approaches for parallel skyline computation, regardless of data distribution, and provides insights on the impacts of different optimization strategies.

This paper proposes a novel modeling methodology for machine tool thermal error. This method combines the advantages of both grey model and artificial neural network (ANN) in terms of data processing. To enhance the robustness and the prediction accuracy, two kinds of grey neural network, namely serial grey neural network (SGNN) and parallel grey neural network (PGNN), are proposed to predict the thermal error. Experiments on the axial directional spindle deformation on a five-axis machining center are conducted to build and validate the proposed models. The results show that both SGNN and PGNN perform better than the traditional grey model and ANN in terms of prediction accuracy and robustness. So the new models are more suitable for complex working conditions in industrial applications.