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Showing 81 - 100 results of 190 for search '(( python ((code implementing) OR (time implementation)) ) OR ( python code implementation ))', query time: 0.59s Refine Results
  1. 81
    <b>Code and derived data for</b><b>Training Sample Location Matters: Accuracy Impacts in LULC Classification</b>

    <b>Code and derived data for</b><b>Training Sample Location Matters: Accuracy Impacts in LULC Classification</b> by Pajtim Zariqi (22155799)

    Published 2025
    “…The workflow was implemented in Google Earth Engine (JavaScript API) and replicated in Python notebooks (Jupyter/Kaggle) for reproducibility.…”
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  2. 82
    BSTPP: a python package for Bayesian spatiotemporal point processes

    BSTPP: a python package for Bayesian spatiotemporal point processes by Isaac Manring (20705955)

    Published 2025
    “…<p>Spatiotemporal point process models have a rich history of effectively modeling event data in space and time. However, they are sometimes neglected due to the difficulty of implementing them. …”
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  3. 83
    <b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b>

    <b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b> by Marly G F Costa (19812192)

    Published 2025
    “…<p dir="ltr"><b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b></p><p dir="ltr">The code was developed in the Google Collaboratory environment, using Python version 3.7.13, with TensorFlow 2.8.2. …”
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  4. 84
    Data and some code used in the paper:<b>Expansion quantization network: A micro-emotion detection and annotation framework</b>

    Data and some code used in the paper:<b>Expansion quantization network: A micro-emotion detection and annotation framework</b> by Zhou (20184816)

    Published 2025
    “…Attached is the micro-emotion annotation code based on pytorch, which can be used to annotate the Goemotions dataset by yourself, or predict the emotion classification based on the annotation results. …”
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  5. 85
    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories

    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories by Elizaveta Mukhaleva (20602550)

    Published 2025
    “…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”
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  6. 86
    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories

    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories by Elizaveta Mukhaleva (20602550)

    Published 2025
    “…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”
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  7. 87
    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories

    BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories by Elizaveta Mukhaleva (20602550)

    Published 2025
    “…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”
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  8. 88
    High-Throughput Mass Spectral Library Searching of Small Molecules in R with NIST MSPepSearch

    High-Throughput Mass Spectral Library Searching of Small Molecules in R with NIST MSPepSearch by Andrey Samokhin (20282728)

    Published 2025
    “…Despite the availability of numerous library search algorithms, those developed by NIST and implemented in MS Search remain predominant, partly because commercial databases (e.g., NIST, Wiley) are distributed in proprietary formats inaccessible to custom code. …”
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  9. 89
    Comparison data 7 for <i>Lamprologus ocellatus</i>.

    Comparison data 7 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  10. 90
    Sample data for <i>Neolamprologus multifasciatus</i>.

    Sample data for <i>Neolamprologus multifasciatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  11. 91
    Sample data for <i>Lamprologus ocellatus</i>.

    Sample data for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  12. 92
    Comparison data 3 for <i>Lamprologus ocellatus</i>.

    Comparison data 3 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  13. 93
    Sample data for <i>Telmatochromis temporalis</i>.

    Sample data for <i>Telmatochromis temporalis</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  14. 94
    Comparison data 4 for <i>Lamprologus ocellatus</i>.

    Comparison data 4 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  15. 95
    Comparison data 1 for <i>Lamprologus ocellatus</i>.

    Comparison data 1 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  16. 96
    Comparison data 2 for <i>Lamprologus ocellatus</i>.

    Comparison data 2 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  17. 97
    Comparison data 5 for <i>Lamprologus ocellatus</i>.

    Comparison data 5 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  18. 98
    Comparison data 6 for <i>Lamprologus ocellatus</i>.

    Comparison data 6 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

    Published 2024
    “…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”
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  19. 99
    Bursty model and Refractory model simulation results. (A) Bursty model simulation results ( samples). The production of nascent mRNA occurs in bursts and degrade after delay time <i>τ</i>. The burst frequency , the mean burst size and . (B) Refractory model simulation results ( samples). The gene has three states, , and , and can switch from one state to another. Moreover, nascent mRNA is produced only when the gene is in state . The parameters are , , , and <i>τ</i> = 1.

    Bursty model and Refractory model simulation results. (A) Bursty model simulation results ( samples). The production of nascent mRNA occurs in bursts and degrade after delay time <... by Ziyan Jin (21021976)

    Published 2025
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  20. 100
    RNA Velocity model simulation results. (A) Simulation results for RNA Velocity model without delay ( samples). The simulation is composed of an up-regulation phase (both production and degradation) and a down-regulation phase (degradation-only). In the first phase, <i>%</i>, <i>%</i> and <i>%</i> for the reaction time . In the second phase,  ∕ 1, <i>X</i> ∼ NB ( <i>r</i> , <i>μ</i> )  and <i>μ</i>. (B) Simulation results for RNA Velocity model with delay in degradation ( samples). RNA degradation is specified as a delayed reaction. In the first (up-regulation) phase: <i>Y</i> ≡ <i>n</i>, <i>t</i> = 0 and <i>%</i>. In the second (down-regulation) phase: <i>%</i>, <i>%</i> and <i>%.</i>

    RNA Velocity model simulation results. (A) Simulation results for RNA Velocity model without delay ( samples). The simulation is composed of an up-regulation phase (both production... by Ziyan Jin (21021976)

    Published 2025
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