A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finite element analysis coupled with user’s subroutine for changes in bone density. by Hassan Mehboob (8960273)

“…<p>A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finite element analysis coupled with user’s subroutine for changes in bone density.…”

Depiction of the theory, can sequence infer function? by Ashley I. Heinson (8124374)

Demonstration of the combinatorial Hill functions (A and B), and the regulation function (C and D) under the regulation of an activator and a repressor. by Ruihao Li (2908991)

The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for AV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

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). …”

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

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

(Movie for Fig 2B) Same as S1 Movie but using a small value for the <i>r</i> parameter. by Toviah Moldwin (10866708)

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)

Subjects:

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)

Subjects:

Great machine learning performance with multiple classification algorithms. by Sydney C. Weiser (15383531)

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

Experiment 2D and 5D: Progressive Sample Scaling Algorithm To Solve Many-Affine BBOB Functions. by Boris Almonacid (4110337)

“…<p dir="ltr"><b>Summary</b></p><p dir="ltr">The Progressive Sample Scaling (PSS) algorithm is a deterministic iterative method with the purpose of solving Many-Affine BBOB type problems provided by the IOHprofiler environment<a href="https://markdownlivepreview.com/#cite_note-1" target="_blank">^[1]</a>. …”

Functional enrichment and correlation analysis of hub DE-SRGs. by Xihao Shen (20347942)

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Horizontal central profiles produced for the 128-th row of the restored images with different Gaussian noise levels: (a) <i>σ</i> = 10⁻² and (b) <i>σ</i> = 10<sup>−3</su... by Limin Ma (556873)