Objective function trend with static and dynamic step size. by Ludovica Adacher (11238189)

<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

Workflow overview illustrating the primary steps of the KRSA pipeline. by Erica A. K. DePasquale (11851483)

Subjects:

Multidomain, Automated Photopatterning of DNA-functionalized Hydrogels (MAPDH). by Moshe Rubanov (7289156)

“…The algorithm incorporates a wash step between each round of patterning. …”

Working scheme of the smooth-index algorithm. by Alexander Borst (32619)

Subjects:

Example performance of the smooth-index algorithm. by Alexander Borst (32619)

Subjects:

Swarm intelligence algorithms for multi-objective IMP: Step-by-step improvement by Francisco Muñoz (9455441)

“…Algorithms implemented on article "<b>A step-by-step solution for the multi-objective influence spread problem</b>". …”

Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

Subjects:

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)

Comparison of algorithms in two cases. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Flow of the NSGA-II algorithm. by Yi Tao (178829)

Subjects: “…evolutionary genetic algorithm…”

Identification of recurrent synapses by the smooth-index algorithm. by Alexander Borst (32619)

Subjects:

Summary of steps in iterative point set registration for scRNA-seq data. by Amir Alavi (9569337)

“…The number of source and/or target cells matched can vary for different matching strategies. 3) Based on the selected pairs, a global transformation function is learned so that source cells in <i>A</i> become closer to their paired cell in <i>B</i>. 4) The learned transformation is next applied to all points in <i>A</i>. 5) This process (steps 2–4) is repeated, iteratively aligning set <i>A</i> onto <i>B</i> until the mean distance between the assigned pairs of cells no longer improves. 6) The final global transformation function is the composition of the functions learned in each iteration at step 3.…”

Bespoke software controls the sorter GUI and reports on instrument status to the user. by Diane M. Wiener (1956583)

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Convergence curves for multimodal function test. by Qinghe Guo (21519322)

Convergence curves for unimodal function test. by Qinghe Guo (21519322)

A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data by Anthony S. Barente (14035175)

Subjects: