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Showing 141 - 160 results of 330 for search '(( algorithm relu function ) OR ((( algorithm python function ) OR ( algorithm cl function ))))*', query time: 0.48s Refine Results
  1. 141
    PyNoetic’s online mode in action. Data is streamed from an Emotiv EPOC headset.

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

    Published 2025
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  2. 142
    Illustration of PyNoetic’s 2D and 3D simulation module with visual feedback.

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

    Published 2025
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  3. 143
    PyNoetic’s unique pick-place configurable flowchart that offers a no-code option for non-programmers.

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

    Published 2025
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  4. 144
    PyNoetic’s feature extraction module that supports time domain features, frequency domain features, time-frequency domain features, spatial features as well as Brain Connectivity measures.

    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... by Gursimran Singh (575288)

    Published 2025
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  5. 145
    Pseudo live-stream of EEG data is generated, and a simple pick-and-place flowchart is designed for channel selection and filtering.

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

    Published 2025
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  6. 146
    List of Abbreviations

    List of Abbreviations by Gursimran Singh (575288)

    Published 2025
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  7. 147
    PyNoetic’s stimuli generation and recording module, which supports both ERP and SSVEP.

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

    Published 2025
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  8. 148
    PyNoetic’s channel selection module, which supports various channel selection criteria including CSP, correlation, Mutual Information, and Chi-squared.

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

    Published 2025
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  9. 149
    Tunable parameters in stimuli generation module. <sup><i>a</i></sup>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></sup>Different stimuli are displayed equally. If desired by the user, some stimuli can be displayed more/less frequently by assigning them a higher/lower weight.

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

    Published 2025
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  10. 150
    The results of ICA performed using PyNoetic.

    The results of ICA performed using PyNoetic. by Gursimran Singh (575288)

    Published 2025
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  11. 151
    PyNoetic’s pre-processing module, which supports filtering and artifact removal, including ICA.

    PyNoetic’s pre-processing module, which supports filtering and artifact removal, including ICA. by Gursimran Singh (575288)

    Published 2025
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  12. 152
    Performance of various deep learning models from PyNoetic’s classification module on the Motor Imagery decoding task.

    Performance of various deep learning models from PyNoetic’s classification module on the Motor Imagery decoding task. by Gursimran Singh (575288)

    Published 2025
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  13. 153
    Illustration of recording paradigm with PyNoetic’s Stimuli generation and recording module.

    Illustration of recording paradigm with PyNoetic’s Stimuli generation and recording module. by Gursimran Singh (575288)

    Published 2025
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  14. 154
    A typical BCI system depicting the flow and processing of EEG data.

    A typical BCI system depicting the flow and processing of EEG data. by Gursimran Singh (575288)

    Published 2025
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  15. 155
    Policy network (<i>θ</i>′) of SAM to accommodate PPO algorithm.

    Policy network (<i>θ</i>′) of SAM to accommodate PPO algorithm. by Zhenhan Huang (12107002)

    Published 2022
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  16. 156
    Significantly enriched Gene Ontology (GO) terms among the 512 genes showing significant results in the all ancestry TWAS. Enrichment analyses were conducted using the multi-query in g:Profiler [16]. Columns include source, term name, GO id, and adjusted p-values across each phenotype definition. P-values were adjusted to maintain an experiment-wide type 1 error rate of 0.05 using the g:SCS algorithm which accounts for the dependent structure of functionally annotated gene sets.

    Significantly enriched Gene Ontology (GO) terms among the 512 genes showing significant results in the all ancestry TWAS. Enrichment analyses were conducted using the multi-query i... by Jenna C. Carlson (3950024)

    Published 2025
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  17. 157
    Data_Sheet_1_Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.PDF

    Data_Sheet_1_Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.PDF by Minhua Guo (18392646)

    Published 2024
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  18. 158
    Step by step workflow for PRLF model training and data scoring.

    Step by step workflow for PRLF model training and data scoring. by John Prindle (10774286)

    Published 2023
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  19. 159
    Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection

    Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

    Published 2025
    “…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.…”
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  20. 160
    Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways.

    Gene content overlap in <i>RNF43 G659fs</i> mutation-specific pathways. by Alexis M. Thornton (11068403)

    Published 2021
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