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    New Generation of On-purpose Attacks for Evaluating Digital Image Watermarking Methods by Preserving the Image Quality

    , Ph.D. Dissertation Sharif University of Technology Taherinia, Amir Hossein (Author) ; Jamzad, Mansour (Supervisor)
    Abstract
    Up to now, compared with the comprehensive research for developing robust watermarking algorithms, no equal attention has been devoted to the proposition of benchmarks tailored to assess the watermark robustness. In addition, almost all the state of the art benchmarks only integrate a number of common image processing operations like geometrical transformations to remove watermarks. However, the quality of the processed image is often too degraded to permit further commercial exploitation. Moreover, to the best of our knowledge, the design of these tools does not take into account the statistical properties of the images and watermarks in the design of attacks. In spite of the significant... 

    Single-Cell RNA-seq Dropout Imputation and Noise Reduction by Machine Learning

    , M.Sc. Thesis Sharif University of Technology Moinfar, Amir Ali (Author) ; Soleymani Baghshah, Mahdih (Supervisor) ; Sharifi Zarchi, Ali (Supervisor) ; Goodarzi, Hani (Co-Supervisor)
    Abstract
    Single-cell RNA sequencing (scRNA-seq) technologies have empowered us to study gene expressions at the single-cell resolution. These technologies are developed based on barcoding of single cells and sequencing of transcriptome using next-generation sequencing technologies. Achieving this single-cell resolution is specially important when the target population is complex or heterogeneous, which is the case for most biological samples, including tissue samples and tumor biopsies.Single-cell technologies suffer from high amounts of noise and missing values, generally known as dropouts. This complexity can affect a number of key downstream analyses such as differential expression analysis,...