Low-rank matrix completion aims to recover matrices with missing entries and has attracted considerable attention from machine learning researchers. Most of the existing methods, such as weighted nuclear-norm-minimization-based methods and Qatar Riyal (QR)-decomposition-based methods, cannot provide both convergence accuracy and convergence speed. To investigate a fast and accurate completion method, an iterative QR-decomposition-based method is proposed for computing an approximate singular value decomposition. This method can compute the largest r (r>0) singular values of a matrix by iterative QR decomposition. Then, under the framework of matrix trifactorization, a method for computing an approximate SVD based on QR decomposition (CSVD-QR)-based L2,1-norm minimization method (LNM-QR) is proposed for fast matrix completion. Theoretical analysis shows that this QR-decomposition-based method can obtain the same optimal solution as a nuclear norm minimization method, i.e., the L2,1-norm of a submatrix can converge to its nuclear norm. Consequently, an LNM-QR-based iteratively reweighted L2,1-norm minimization method (IRLNM-QR) is proposed to improve the accuracy of LNM-QR. Theoretical analysis shows that IRLNM-QR is as accurate as an iteratively reweighted nuclear norm minimization method, which is much more accurate than the traditional QR-decomposition-based matrix completion methods. Experimental results obtained on both synthetic and real-world visual data sets show that our methods are much faster and more accurate than the state-of-the-art methods.

We designed, fabricated and measured full-color, reflective electrowetting displays (EWDs). The display system is composed of three-layer cyan, magenta and yellow EWD elements fabricated with standard photolithographic techniques. The EWDs were driven successfully by the proposed control system and the measurement results show that the electro-optical performance was improved. The aperture ratio of the EWD element can be tuned from 0 to ∼80% as the applied voltage is changed from 0 to 30 V. The response time and the color gamut were measured to be ∼18 ms and ∼58% NTSC, respectively. This paper makes it possible for large numbers of reflective full-color EWDs to be fabricated directly, with advantages of saving power significantly by 85% and no eye irritation compared with LED displays.

As a widely used technology, visual saliency detection has attracted a lot of attention in the past decades. Although a large number of methods, especially fully convolutional neural network- (FCN-) based approaches, have been proposed and achieved remarkable performance, it is still of great value to extend representative architecture to visual saliency detection task. In this paper, we propose an improved U-Net-like network, pyramid feature attention-based U-Net-like (PFAU-Net) for visual saliency detection problem. The main improvements of the proposed model include that in order to enable the network to extract features with more representation ability, we introduce a context-aware feature extraction (CFE) module and a channel attention module into the U-shaped backbone to obtain valuable multiscale features, and a feature pyramid path is also utilized in the decoder part of the network to take advantages of multilevel information. Moreover, we construct the loss function using three terms including pixel-level cross-entropy, image-level intersection over union (IoU), and a structural similarity term, which aim to make the model learn more saliency related knowledge. To verify the effectiveness of the proposed model, we conduct extensive experiments on six widely used public datasets, and the experimental results indicate that (1) our improved model can significantly improve the performance of the backbone network on all test datasets, and (2) our proposed model can outperform comparison FCN-based networks and nonneural network approaches. Both objective and qualitative evaluations verify the effectiveness of our proposed model.