Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI

We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRI) from highly undersampled k-space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and the patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov Chain Monte Carlo (MCMC) for the Bayesian model, and use the alternating direction method of multipliers (ADMM) for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods

MicroRNA-23a promotes myelination in the central nervous system.

Demyelinating disorders including leukodystrophies are devastating conditions that are still in need of better understanding, and both oligodendrocyte differentiation and myelin synthesis pathways are potential avenues for developing treatment. Overexpression of lamin B1 leads to leukodystrophy characterized by demyelination of the central nervous system, and microRNA-23 (miR-23) was found to suppress lamin B1 and enhance oligodendrocyte differentiation in vitro. Here, we demonstrated that miR-23a-overexpressing mice have increased myelin thickness, providing in vivo evidence that miR-23a enhances both oligodendrocyte differentiation and myelin synthesis. Using this mouse model, we explored possible miR-23a targets and revealed that the phosphatase and tensin homologue/phosphatidylinositol trisphosphate kinase/Akt/mammalian target of rapamycin pathway is modulated by miR-23a. Additionally, a long noncoding RNA, 2700046G09Rik, was identified as a miR-23a target and modulates phosphatase and tensin homologue itself in a miR-23a-dependent manner. The data presented here imply a unique role for miR-23a in the coordination of proteins and noncoding RNAs in generating and maintaining healthy myelin

Rainfall Reliability Evaluation for Stability of Municipal Solid Waste Landfills on Slope

[[abstract]]A method to assess the reliability for the stability of municipal solid waste (MSW) landfills on slope due to rainfall infiltration is proposed. Parameter studies are first done to explore the influence of factors on the stability of MSW. These factors include rainfall intensity, duration, pattern, and the engineering properties of MSW. Then 100 different combinations of parameters are generated and associated stability analyses of MSW on slope are performed assuming that each parameter is uniform distributed around its reason ranges. In the following, the performance of the stability of MSW is interpreted by the artificial neural network (ANN) trained and verified based on the aforementioned 100 analysis results. The reliability for the stability of MSW landfills on slope is then evaluated and explored for different rainfall parameters by the ANN model with first-order reliability method (FORM) and Monte Carlo simulation (MCS).[[incitationindex]]SCI[[booktype]]紙

An Iterative Co-Saliency Framework for RGBD Images

As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD cosaliency datasets demonstrate the effectiveness of our proposed framework.Comment: 13 pages, 13 figures, Accepted by IEEE Transactions on Cybernetics 2017. Project URL: https://rmcong.github.io/proj_RGBD_cosal_tcyb.htm