Multipartite quantum correlation and entanglement in four-qubit pure states

Based on the quantitative complementarity relations, we analyze thoroughly the properties of multipartite quantum correlations and entanglement in four-qubit pure states. We find that, unlike the three-qubit case, the single residual correlation, the genuine three- and four-qubit correlations are not suited to quantify entanglement. More interestingly, from our qualitative and numerical analysis, it is conjectured that the sum of all the residual correlations may constitute a good measure for the total multipartite entanglement in the system.Comment: 7 pages, 3 figue

Learning a Mixture of Deep Networks for Single Image Super-Resolution

Single image super-resolution (SR) is an ill-posed problem which aims to recover high-resolution (HR) images from their low-resolution (LR) observations. The crux of this problem lies in learning the complex mapping between low-resolution patches and the corresponding high-resolution patches. Prior arts have used either a mixture of simple regression models or a single non-linear neural network for this propose. This paper proposes the method of learning a mixture of SR inference modules in a unified framework to tackle this problem. Specifically, a number of SR inference modules specialized in different image local patterns are first independently applied on the LR image to obtain various HR estimates, and the resultant HR estimates are adaptively aggregated to form the final HR image. By selecting neural networks as the SR inference module, the whole procedure can be incorporated into a unified network and be optimized jointly. Extensive experiments are conducted to investigate the relation between restoration performance and different network architectures. Compared with other current image SR approaches, our proposed method achieves state-of-the-arts restoration results on a wide range of images consistently while allowing more flexible design choices. The source codes are available in http://www.ifp.illinois.edu/~dingliu2/accv2016

When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition

Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work well and how to design a 'good' architecture. The existing works tend to focus on reporting CNN architectures that work well for face recognition rather than investigate the reason. In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible. Specifically, we use public database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing CNNs trained on private databases. We propose three CNN architectures which are the first reported architectures trained using LFW data. This paper quantitatively compares the architectures of CNNs and evaluate the effect of different implementation choices. We identify several useful properties of CNN-FRS. For instance, the dimensionality of the learned features can be significantly reduced without adverse effect on face recognition accuracy. In addition, traditional metric learning method exploiting CNN-learned features is evaluated. Experiments show two crucial factors to good CNN-FRS performance are the fusion of multiple CNNs and metric learning. To make our work reproducible, source code and models will be made publicly available.Comment: 7 pages, 4 figures, 7 table

Detection of promoter hypermethylation of the CpG island of E-cadherin in gastric cardiac adenocarcinoma

<p>Abstract</p> <p>Aim</p> <p>Abnormal hypermethylation of CpG islands associated with tumor suppressor genes can lead to transcriptional silencing in neoplasia. The aim of this study was to investigate the promoter methylation and expression of E-cadherin gene in gastric cardiac adenocarcinoma (GCA).</p> <p>Methods</p> <p>A nested MSP approach, immunohistochemistry method and RT-PCR were used respectively to examine the methylation status of the 5' CpG island of E-cadherin, its protein expression and mRNA expression in tumors and corresponding normal tissues.</p> <p>Results</p> <p>E-cadherin was methylated in 63 of 92 (68.5%) tumor specimens, which was significantly higher than that in corresponding normal tissues (P < 0.001). Methylation frequencies of stage III and IV tumor tissues was significantly higher than that in stage I and II tumor tissues (P = 0.01). Methylation status of poor differentiation group was significantly higher than moderate and poor-moderate differentiation groups (P < 0.01). By immunostaining 51 of 92 tumor tisssues demonstrated heterogeneous, positive immunostaining of tumor tissues (44.6%), significantly different from matched normal tissues (P < 0.001). Positive immunostaining of stage III and IV tumor tissues was significantly lower than stage I and II tumor tissues (P < 0.01). Poor differentiation group was also significantly lower than moderate and poor-moderate differentiation groups (P < 0.05). 80 percent of tumor tissues with E-cadherin gene methylated showed inactivated mRNA expression.</p> <p>Conclusions</p> <p>High methylation status of the 5' CpG island of E-cadherin gene may be one of the mechanisms in the development of gastric cardiac adenocarcinoma.</p