2021 Feb     Mar     Apr     May     Jun     Jul    
2020 Jan     May     Jun     Jul     Aug     Sep     Oct     Nov     Dec    
2019 Jan     Feb     Mar     Apr     May     Jun     Jul     Aug     Oct     Nov    
2018 Jan     Feb     Mar     Apr     May     Jun     Jul     Aug     Oct     Nov     Dec    
2017 Jan     Feb     Mar     Apr     May     Jun     Jul     Aug     Oct     Nov     Dec    

Event(s) on August 2015

  • Wednesday, 5th August, 2015

    Title: Constrained total variational deblurring models and fast algorithms
    Speaker: Dr. Tao Min, Department of Mathematics, Nanjing University, China
    Time/Place: 19:30  -  20:30
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: The total variation (TV) model is attractive for being able to preserve sharp attributes in images. However, the restored images from TV-based methods do not usually stay in a given dynamic range, and hence projection is required to bring them back into the dynamic range for visual presentation or for storage in digital media. This will affect the accuracy of the restoration as the projected image will no longer be the minimizer of the given TV model. In this paper, we show that one can get much more accurate solutions by imposing box constraints on the TV models and solving the resulting constrained models. Our numerical results show that for some images where there are many pixels with values lying on the boundary of the dynamic range, the gain can be as great as 9.58dB in peak signal-to-noise ratio. One traditional hinderance of using the constrained model is that it is difficult to solve. However, in this paper, we propose to use the alternating direction method of multipliers (ADMM) to solve the constrained models. This leads to a fast and convergent algorithm that is applicable for both Gaussian and impulse noise. Numerical results show that our ADMM algorithm is better than some state-of-the-art algorithms for unconstrained models both in terms of accuracy and robustness with respect to the regularization parameter.

  • Monday, 10th August, 2015

    Title: Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm
    Speaker: Dr. ZHOU Yan , College of Mathematics and Statistics, Shenzhen University, China
    Time/Place: 15:00  -  16:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: DNA methylation plays key roles in diverse biological processes such as X chromosome inactivation, transposable element repression, genomic imprinting, and tissue-specific gene expression. Sequencing-based DNA methylation profiling provides an unprecedented opportunity to map and compare complete DNA methylomes. These include one of the most widely applied technologies for measuring DNA methylation, methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq), coupled with a complementary method, methylation-sensitive restriction enzyme sequencing (MRE-seq). A computational approach that integrates data from these two different but complementary assays and predicts methylation differences between samples has been lacking. Here we present a novel integrative statistical framework M&M (for integration of MeDIP-seq and MRE-seq) that dynamically scales, normalizes and combines MeDIP-seq and MRE-seq data to detect differentially methylated regions. Using sample-matched whole-genome bisulfite sequencing (WGBS) as a gold standard, we demonstrate superior accuracy and reproducibility of M&M compared to existing analytical methods for MeDIP-seq data alone. M&M leverages the complementary nature of MeDIP-seq and MRE-seq data to allow rapid comparative analysis between whole methylomes at a fraction of the cost of WGBS. Comprehensive analysis of nineteen human DNA methylomes with M&M reveals distinct DNA methylation patterns among different tissue types, cell types, and individuals, potentially underscoring divergent epigenetic regulation at different scales of phenotypic diversity. We find that differential DNA methylation at enhancer elements, with concurrent changes in histone modifications and transcription factor binding, is common at the cell, tissue, and individual levels, whereas promoter methylation is more prominent in reinforcing fundamental tissue identities.

  • Monday, 17th August, 2015

    Title: Image Pop-Up: 3D Shape Estimation from a Single Image
    Speaker: Dr. Xiaowei ZHOU, GRASP Laboratory & Computer and Information Science, University of Pennsylvania, USA
    Time/Place: 11:00  -  12:00
    FSC1217, Fong Shu Chuen Library, HSH Campus, Hong Kong Baptist University
    Abstract: Recognizing 3D objects from 2D images is a central problem in computer vision. In recent years, there has been an emerging trend towards analyzing 3D geometry of objects including shapes and poses instead of merely providing bounding boxes. In this talk, two questions will be discussed. The first is how to estimate the 3D shape of an object given 2D shape landmarks in the image, and the second is how to jointly localize the 2D landmarks and estimate the 3D shape. The main challenge is that the problems of shape, viewpoint and correspondence estimation are dependent on each other. Previous work often relied on local optimization and required careful initialization. To address this challenge, we propose a convex relaxation approach based on spectral-norm relaxation and solve the resulting convex program using the alternating direction method of multipliers. We demonstrate the exact recovery property of the proposed model, its merits compared to alternative methods, and real examples of recovering 3D object geometry from a single image and rendering its synthetic views (pop-up).



All years