RASP performance across different scales in the Mouse Sagittal brain dataset. حسب Ian K. Gingerich (22822222)

Fig 1 - حسب Hind Sweis (18880638)

Fig 4 - حسب Hind Sweis (18880638)

Fig 2 - حسب Hind Sweis (18880638)

Fig 3 - حسب Hind Sweis (18880638)

Spatial domain analysis of human breast cancer tissue using 10x Xenium data. حسب Ian K. Gingerich (22822222)

Human dorsolateral prefrontal cortex (DLPFC) spatial transcriptomics analysis, 10X Visium data. حسب Ian K. Gingerich (22822222)

Covariate analysis (Mouse ovary dataset) for the cell type clustering case. حسب Ian K. Gingerich (22822222)

Code For Use in a Bookកូដសម្រាប់ប្រើប្រាស់ក្នុងសៀវភៅ حسب Sarun Horn (20899271)

Swarm intelligence algorithms for multi-objective max-min-ISP حسب Francisco Muñoz (9455441)

"…<p>Algorithms implemented on article "<strong>On the max-min influence spread problem: A multi-objective optimization approach</strong>"</p> <p><br></p> <p>usage: Main.py [-h] [-a] [-r] [-d D] [-t T] [-i I] [-q Q] [--shape SHAPE] [--sym] [--folder FOLDER] [--seed SEED] [--spread SPREAD] [--sum SUM] [--mh MH] [--tagsfile TAGSFILE] [--notags] [--prune] [--prunelowdeg] [--excludenodes EXCLUDENODES] [--includeindegzero] [--nodepthcriteria] [--memfile MEMFILE] file</p> <p><br></p> <p>Calculates the best Influence Spread set on a Weighted Graph using PSO</p> <p><br></p> <p>positional arguments:</p> <p>file</p> <p><br></p> <p>options:</p> <p>-h, --help: show this help message and exit</p> <p>-a: read file input contents as an Adjacency Matrix</p> <p>-r: reverse nodes order, reads (b,a,w) instead of (a,b,w)</p> <p>-d D: line separator to use on file parsing</p> <p>-t T: number of times to execute this solver</p> <p>-i I: number of metaheuristic iterations per execution</p> <p>-q Q: fixed quota, use 0 = floor(n/2)+1</p> <p>--shape SHAPE: shape functions for binarization - list of implemented shape functions: s2, s2_neg, v2, v4</p> <p>--sym: consider graph as symmetric instead of directed</p> <p>--folder FOLDER: output folder</p> <p>--seed SEED: use custom seeds for metaheuristic pseudo RNG</p> <p>--spread SPREAD: use specific influence spread model funcion - list of implemented models: LT, IC</p> <p>--sum SUM: adds an extra value to all edge's weight</p> <p>--mh MH: metaheuristic to use - list of implemented metaheuristics: 1 (Swarm3), 3 (Swarm3_W), 4 (Swarm3_L), 5 (Swarm4)</p> <p>--tagsfile TAGSFILE: use first row as node tags instead of using plurality criteria</p> <p>--notags: do not use first row as node tags, instead tags will be calculated</p> <p>--prune: nodes with (1) outdegree = 0 and indegree > 0, and (2) with outdegree = 1 and neighbor's outdegree > 0 will be excluded</p> <p>--prunelowdeg: nodes with low outdegree or degree will be excluded</p> <p>--excludenodes EXCLUDENODES: nodes to skip, must be comma separated</p> <p>--includeindegzero: forces to include nodes with indegree = 0 on all executions</p> <p>--nodepthcriteria: particles will not tie off with spread depth in case of having same fitness and spread</p> <p>--memfile MEMFILE: memory dump of other execution</p>…"

Pseudo live-stream of EEG data is processed through the flow chart: after channel selection, each epoch undergoes Kaiser windowing (length = 250), followed by re-referencing to the... حسب Gursimran Singh (575288)

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PyNoetic’s online mode in action. Data is streamed from an Emotiv EPOC headset. حسب Gursimran Singh (575288)

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Illustration of PyNoetic’s 2D and 3D simulation module with visual feedback. حسب Gursimran Singh (575288)

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PyNoetic’s unique pick-place configurable flowchart that offers a no-code option for non-programmers. حسب Gursimran Singh (575288)

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PyNoetic’s feature extraction module that supports time domain features, frequency domain features, time-frequency domain features, spatial features as well as Brain Connectivity m... حسب Gursimran Singh (575288)

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Pseudo live-stream of EEG data is generated, and a simple pick-and-place flowchart is designed for channel selection and filtering. حسب Gursimran Singh (575288)

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List of Abbreviations حسب Gursimran Singh (575288)

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PyNoetic’s stimuli generation and recording module, which supports both ERP and SSVEP. حسب Gursimran Singh (575288)

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PyNoetic’s channel selection module, which supports various channel selection criteria including CSP, correlation, Mutual Information, and Chi-squared. حسب Gursimran Singh (575288)

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Tunable parameters in stimuli generation module. ^<i>a</i>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></su... حسب Gursimran Singh (575288)

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