In this paper, we explore the Reference-based Super-Resolution (RefSR) problem, which aims to super-resolve a low definition (LR) input to a high definition (HR) output, given another HR reference image that shares similar viewpoint or capture time with the LR input. We solve this problem by proposing a learning-based scheme, denoted as RefSR-Net. Specifically, we first design a Cross-scale Correspondence Network (CC-Net) to indicate the cross-scale patch matching between reference and LR image. The CC-Net is formulated as a classification problem which predicts the correct matches from the candidate patches within the search range. Using dilated convolution, the training and feature map generation are efficiently implemented. Given the reference patch selected via CC-Net, we further propose a Super-resolution image Synthesis Network (SS-Net) for the synthesis of the HR output, by fusing the LR patch and the reference patch at multiple scales. Experiments on MPI Sintel Dataset and Light-Field (LF) video dataset demonstrate our learned correspondence features outperform existing features, and our proposed RefSR-Net substantially outperforms conventional single image SR and exemplar-based SR approaches.

As a prevalent existing post-transcriptional modification of RNA, N6-methyladenosine (m6A) plays a crucial role in various biological processes. To better radically reveal its regulatory mechanism and provide new insights for drug design, the accurate identification of m6A sites in genome-wide is vital. As the traditional experimental methods are time-consuming and cost-prohibitive, it is necessary to design a more efficient computational method to detect the m6A sites. In this study, we propose a novel cross-species computational method DNN-m6A based on the deep neural network (DNN) to identify m6A sites in multiple tissues of human, mouse and rat. Firstly, binary encoding (BE), tri-nucleotide composition (TNC), enhanced nucleic acid composition (ENAC), K-spaced nucleotide pair frequencies (KSNPFs), nucleotide chemical property (NCP), pseudo dinucleotide composition (PseDNC), position-specific nucleotide propensity (PSNP) and position-specific dinucleotide propensity (PSDP) are employed to extract RNA sequence features which are subsequently fused to construct the initial feature vector set. Secondly, we use elastic net to eliminate redundant features while building the optimal feature subset. Finally, the hyper-parameters of DNN are tuned with Bayesian hyper-parameter optimization based on the selected feature subset. The five-fold cross-validation test on training datasets show that the proposed DNN-m6A method outperformed the state-of-the-art method for predicting m6A sites, with an accuracy (ACC) of 73.58%–83.38% and an area under the curve (AUC) of 81.39%–91.04%. Furthermore, the independent datasets achieved an ACC of 72.95%–83.04% and an AUC of 80.79%–91.09%, which shows an excellent generalization ability of our proposed method.

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Recent advances have illustrated substantial benefits from learning Bayesian networks (BNs). However, when the available data size is small, the BN parameter learning becomes a key challenge in many intelligent applications. By integrating both sample data and expert constraints, we propose a BN parameter learning algorithm with extension method-parameter extension under constraints (PEUC) by introducing related domain expert knowledge. Knowledge is transformed into inequality constraints which candidate parameter sets arise from the relative constraints space.The maximum entropy principle helps to estimate the parameter in statistical averaging model while candidate sets of BN parameters satisfy the constrained knowledge by bootstrapping techniques. Then BN parameters are estimated based on the real available sampled data set and extension streams of candidate parameters samples from the constraints space. The sample size is also taken into account according to the contribution to the final parameters. Experimental results of benchmark BN modeling problems demonstrate that PEUC algorithm tends to the classical MLE algorithm when the modeling data size is sufficient. Furthermore, when the available data size is small, the parameters of BN can be estimated by PEUC as well, and the learned accuracy is superior to MLE, MAP or QMAP algorithm. Finally, PEUC is also applied to a real bearing fault diagnosis case. The presented approach provides a new promising BN parameter learning way for more intelligent system modeling problems, particularly when the data sets are small.

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