The expert screening cost <i>g</i>_expert(<i>x</i>) defined for the Challenge: Mean cost for screening a fraction <i>x</i> of a patient cohort for cardiac abnormalities. by Matthew A. Reyna (16965038)

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Posterior for parameter <i>θ</i> of the uniform toy model for different weights in the ABC distance function. by Jonathan U. Harrison (5447603)

“…Metrics to evaluate the performance of Algorithm 2 are shown in (b), (c), and (d) as <i>N</i> varies resulting in different total numbers of simulations from the model. …”

Posteriors for parameter <i>θ</i> in the simple diffusion model for different weights in the ABC distance function. by Jonathan U. Harrison (5447603)

Posterior for parameters <i>θ</i> of the bimodal toy model for different weights in the ABC distance function. by Jonathan U. Harrison (5447603)

“…<p>The posterior distribution for parameters <i>θ</i> = (<i>θ</i>₁, <i>θ</i>₂) of the bimodal toy model for different weights in the ABC distance function is shown in (a) and (b). …”

Performance of the smooth-index algorithm for matrices with different densities. by Alexander Borst (32619)

“…B</b> Performance of the algorithm as a function of the number of neurons. …”

Analysis of the limit cases of the sampling function. by Greta Grassmann (12418506)

Analysis of full-length Ste5 (1–917) coding sequence with bioinformatics algorithms. by Francis W. Farley (17199744)

The repair algorithm restores the electrophysiological behaviour of cut and repaired mouse pyramidal cells and allows for better predictions of neuronal function in human neurons. by Moritz Groden (18018276)

Scores of municipalities projected on the first and second functional PC (A), and first and third functional PC (B). by Manuel Ribeiro (17889313)

Signal detection algorithm adapted from [1] yields exponential distributions and unrealistic mean durations of percepts. by Quynh-Anh Nguyen (847240)

“…(Bottom) Trial-by-trial applications of the signal detection algorithm from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008152#pcbi.1008152.ref001" target="_blank">1</a>] with A: <i>C</i>_<i>th</i> = 4.01 and B: <i>C</i>_<i>th</i> = 4.21 yield exponentially distributed subsequent percept durations for <i>I</i> (blue) and <i>S</i> (red). …”

Functional enrichment analysis. by Junjie Su (368807)

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Local topological scales of hierarchical community organization in structural and functional brain graphs. by Arian Ashourvan (6685232)

Search-based testing (Genetic Algorithm) - Chapter 11 of the book "Software Testing Automation" by Saeed Parsa (13893726)

“…</p> <p><br></p> <p>3. Algorithm</p> <p>Below is the main body of the test data generator program:</p> <p>  </p> <p>the main body of a Python program to generate test data for Python functions.…”

Weighted accuracy metric (1) for the murmur detection task (<i>x</i>-axis) and the cost metric (8) scores for the clinical outcome identification task (<i>y</i>-axis) of the final... by Matthew A. Reyna (16965038)

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Output groupings of the 3 algorithms for a linear degradation chain of <i>N</i> = 3 metabolites. by Yuanchen Zhao (12905580)

Motion blur with <i>L</i> = 18 and <i>Φ</i> = 30° for the “caps.bmp” image. by Haoyuan Yang (8630391)

Traditional evaluation metric (<i>x</i>-axis) and cost metric (<i>y</i>-axis) scores for the final Challenge entries with the clinical outcome identification task on the hidden tes... by Matthew A. Reyna (16965038)

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Traditional evaluation metric (<i>x</i>-axis) and weighted accuracy metric (<i>y</i>-axis) scores for the final Challenge entries with the murmur detection task on the hidden test... by Matthew A. Reyna (16965038)

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Iteration versus fitness function. by B. Venkatesh (17202747)

Motion blur with <i>L</i> = 16 and <i>Φ</i> = 45° for the “lighthouse2.bmp” image. by Haoyuan Yang (8630391)