The SSIM for the different algorithms. by Bingbing Li (461702)

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Efficient algorithms to discover alterations with complementary functional association in cancer by Rebecca Sarto Basso (6728921)

“…We provide analytic evidence of the effectiveness of UNCOVER in finding high-quality solutions and show experimentally that UNCOVER finds sets of alterations significantly associated with functional targets in a variety of scenarios. In particular, we show that our algorithms find sets which are better than the ones obtained by the state-of-the-art method, even when sets are evaluated using the statistical score employed by the latter. …”

The improved Hard TA and Soft TA. by Bingbing Li (461702)

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The diagram of Hard TA and Soft TA. by Bingbing Li (461702)

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Metapopulation model notation. by Jeffrey Keithley (14626551)

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Estimates of for each problem instance. by Jeffrey Keithley (14626551)

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Approximation factors for each problem instance. by Jeffrey Keithley (14626551)

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500 <i>ϕ</i> vectors learned from hard thresholding. by Ilias Rentzeperis (10215602)

“…Traditionally, to replicate such biological sparsity, generative models have been using the <i>ℓ</i>₁ norm as a penalty due to its convexity, which makes it amenable to fast and simple algorithmic solvers. …”

Block ridge wave denoising flowchart. by Bingbing Li (461702)

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Flow of wavelet threshold denoising. by Bingbing Li (461702)

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Image smoothness of different methods. by Bingbing Li (461702)

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Ridge wave transformation flowchart. by Bingbing Li (461702)

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Three images of fingerprint patterns. by Bingbing Li (461702)

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Similarity error of different methods. by Bingbing Li (461702)

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A hybrid algorithm based on improved threshold function and wavelet transform. by Bingbing Li (461702)

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How fries chooses which sequences to keep and which sequences to prune. by Anna U. Lowegard (8051597)

Comparison of denoising effects of different algorithms. by Bingbing Li (461702)

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Using synthetic data to test group-searching algorithms in a context where the correct grouping of species is known and uniquely defined. by Yuanchen Zhao (12905580)

“…(C) We use the synthetic data as input for three families of regression-based algorithms: the EQO of Ref. [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012590#pcbi.1012590.ref026" target="_blank">26</a>] (which groups species into two groups), and two families we call K-means and Metropolis (see text), which can return any specified number of groups. …”

Fitness function curve at weight factor 0.3. by Dhulfiqar Hakeem Dhayef (17729684)

“…GA is employed to identify machine cells and part families based on Grouping Efficiency (GE) as a fitness function. In contrast to previous research, which considered grouping efficiency with a weight factor (<i>q</i> = 0.5), this study utilizes various weight factor values (0.1, 0.3, 0.7, 0.5, and 0.9). …”

Time spent on image denoising using different algorithms. by Bingbing Li (461702)

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