Manifold Constrained Low-Rank Decomposition

Low-rank decomposition (LRD) is a state-of-the-art method for visual data reconstruction and modelling. However, it is a very challenging problem when the image data contains significant occlusion, noise, illumination variation, and misalignment from rotation or viewpoint changes. We leverage the specific structure of data in order to improve the performance of LRD when the data are not ideal. To this end, we propose a new framework that embeds manifold priors into LRD. To implement the framework, we design an alternating direction method of multipliers (ADMM) method which efficiently integrates the manifold constraints during the optimization process. The proposed approach is successfully used to calculate low-rank models from face images, hand-written digits and planar surface images. The results show a consistent increase of performance when compared to the state-of-the-art over a wide range of realistic image misalignments and corruptions

MicroRNA-16 inhibits the migration and invasion of glioma cell by targeting Bcl-2 gene

Purpose: To investigate the effect of microRNA-16 (miR-16) on glioma cell migration and invasiveness, and the mechanism involved.Methods: MicroRNA-16 mimic or inhibitor was transfected into human glioma (SHG44) cells. Cell migration, invasiveness and morphology were determined using scratch test, Transwell invasion assay, and immunohistochemical staining, respectively. Expressions of bcl-2, MMP-9 and MMP-2, and NF-κB1 proteins were measured using Western blotting.Results: Overexpression of MicroRNA-16 significantly down-regulated MMP-9 protein in SHG44 cells (p < 0.05), but MMP-2 protein expressions in the 2 groups were comparable (p > 0.05). Protein expressions of MMP-9 and NF-κB1 were significantly down-regulated in human glioma positive cells, relative to negative control.Conclusion: MiR-16 overexpression suppresses the migration and invasiveness of SHG44 cells via the regulation of NF-κB1/MMP-9 signaling pathway, and it directly targets bcl-2 gene by inhibiting its protein expression. This finding affords a new target for developing new anti-glioma drugs. Keywords: Bcl-2, Expression, Glioma, MicroRNA-16, NF-κB1signaling pathwa

The Structure Transfer Machine Theory and Applications

Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. We propose a new representation learning method, termed Structure Transfer Machine (STM), which enables feature learning process to converge at the representation expectation in a probabilistic way. We theoretically show that such an expected value of the representation (mean) is achievable if the manifold structure can be transferred from the data space to the feature space. The resulting structure regularization term, named manifold loss, is incorporated into the loss function of the typical deep learning pipeline. The STM architecture is constructed to enforce the learned deep representation to satisfy the intrinsic manifold structure from the data, which results in robust features that suit various application scenarios, such as digit recognition, image classification and object tracking. Compared to state-of-the-art CNN architectures, we achieve the better results on several commonly used benchmarks\footnote{The source code is available. https://github.com/stmstmstm/stm }