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Showing 1 - 20 results of 2,404 for search '(( element data algorithm ) OR ((( query learning algorithm ) OR ( cells function algorithm ))))', query time: 0.52s Refine Results
  1. 1
    Performance of ENQUIRE’s gene normalization algorithm.

    Performance of ENQUIRE’s gene normalization algorithm. by Luca Musella (19986312)

    Published 2025
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  2. 2
    Differences in computing performance between ENQUIRE’s gene normalization algorithm and GNorm2-Bioformer.

    Differences in computing performance between ENQUIRE’s gene normalization algorithm and GNorm2-Bioformer. by Luca Musella (19986312)

    Published 2025
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  3. 3
    The actual training effect of four algorithms on original photo data (All images are original images taken by the author).

    The actual training effect of four algorithms on original photo data (All images are original images taken by the author). by Xiaojun Wang (50332)

    Published 2025
    Subjects: “…Cell Biology…”
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  4. 4
    Folder with all data and algorithms

    Folder with all data and algorithms by Jorge Servert Lerdo de Tejada (22290001)

    Published 2025
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  5. 5
    Cell-TRACTR detects cells in the first frame at time t 0. Object queries predicted to be a “cell” are converted to track queries. For Track Group Denoising (TGD), random noise is added to the track queries to get noised track queries. For Query Denoising (QD), random noise is added to the ground truth bounding boxes to generate noised track queries. All queries are processed together by the decoder. Attention masks are used to prevent information leakage between the object and track queries, the noised track queries generated from TGD, and the noised track queries generated from QD. The gray boxes indicate information being blocked between sets of queries while colored boxes indicate the flow of information.

    Cell-TRACTR detects cells in the first frame at time t 0. Object queries predicted to be a “cell” are converted to track queries. For Track Group Denoising (TGD), random noise is a... by Owen M. O’Connor (11965460)

    Published 2025
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  6. 6
    Effect of segmentation mask resolution on CUDA memory usage and performance metrics. (A) Memory usage as a function of queries when Cell-TRACTR produces a segmentation mask with the same resolution as the original image. (B) 1/4 Cell-TRACTR produces a segmentation mask with one-fourth the resolution of the input image.

    Effect of segmentation mask resolution on CUDA memory usage and performance metrics. (A) Memory usage as a function of queries when Cell-TRACTR produces a segmentation mask with th... by Owen M. O’Connor (11965460)

    Published 2025
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  7. 7
    Example illustrating how object and track queries interact using an attention map processed by Cell-TRACTR across two subsequent frames. The schematic displays the actual output alongside the output the from top matching object queries. Extracted from the self-attention mechanism in the decoder, the attention map highlights the strength of interactions among the queries. Lighter colors indicate stronger interactions. The track queries tend to focus on themselves, while the object queries attend to the track query that corresponds most closely in location.

    Example illustrating how object and track queries interact using an attention map processed by Cell-TRACTR across two subsequent frames. The schematic displays the actual output al... by Owen M. O’Connor (11965460)

    Published 2025
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  8. 8
    Cells are initially detected in frame t<sub>0</sub> and then tracked to frame t<sub>1</sub>. Object queries are processed in the first layer of the decoder for object detection. In the subsequent decoder layers, track queries are concatenated with the object queries to perform tracking.

    Cells are initially detected in frame t<sub>0</sub> and then tracked to frame t<sub>1</sub>. Object queries are processed in the first layer of the decoder for object detection. In... by Owen M. O’Connor (11965460)

    Published 2025
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  9. 9
    Overview of the Cell2Spatial algorithm.

    Overview of the Cell2Spatial algorithm. by Huamei Li (8815955)

    Published 2025
    Subjects: “…Cell Biology…”
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  10. 10
    Elements: Kingfisher: Storage Management for Data Federations (CSSI PI Meeting 2025)

    Elements: Kingfisher: Storage Management for Data Federations (CSSI PI Meeting 2025) by Brian Bockelman (5128082)

    Published 2025
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  11. 11
    Algorithm testing.

    Algorithm testing. by Ziad M. Ali (12516532)

    Published 2024
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  12. 12
    Visual representation of how Cell-TRACTR uses track queries to track cells and detect divisions.

    Visual representation of how Cell-TRACTR uses track queries to track cells and detect divisions. by Owen M. O’Connor (11965460)

    Published 2025
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  13. 13
    Risk element category diagram.

    Risk element category diagram. by Yao Hu (3479972)

    Published 2025
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  14. 14
    Supplementary file 1_Earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modules.docx

    Supplementary file 1_Earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modules.docx by Fatima Wardi (22052360)

    Published 2025
    “…The extracted parameters for each case study are used to reconstruct the I–V and power–voltage (P–V) characteristic curves for the respective solar cell and photovoltaic module technologies. To validate the performance and efficiency of the algorithm, various statistical criteria are computed, including individual absolute error (IAE), relative error (RE), root mean square error (RMSE), mean absolute error (MAE), standard deviation (SD), tracking signal (TS), normalized forecast measure (NFM), and the autocorrelation function (ACF). …”
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  15. 15
    Convergence curves of the investigated algorithms.

    Convergence curves of the investigated algorithms. by Ziad M. Ali (12516532)

    Published 2024
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  16. 16
    The encoder employs deformable self-attention to transform multi-scale features into encoded multi-scale features. Query selection is used to derive the object queries from these encoded multi-scale features. Each encoded image feature undergoes classification, and the top-K features are selected as the content embeddings for the object queries. Additionally, bounding boxes are extracted from the top-K segmentation masks and are subsequently converted into positional embeddings. Following this, the object queries perform self-attention and deformable cross-attention with the encoded multi-scale features to produce the output embeddings. MLP, multi-layer perceptron.

    The encoder employs deformable self-attention to transform multi-scale features into encoded multi-scale features. Query selection is used to derive the object queries from these e... by Owen M. O’Connor (11965460)

    Published 2025
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  17. 17
    Visual representation of Trust and elements that are needed for equitable data sharing.

    Visual representation of Trust and elements that are needed for equitable data sharing. by Qunita Brown (19751520)

    Published 2024
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  18. 18
    ML algorithms in cyber security.

    ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

    Published 2025
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  19. 19
    Algorithm pseudocode.

    Algorithm pseudocode. by Cheng-jie Chen (22272090)

    Published 2025
    Subjects: “…Cell Biology…”
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  20. 20
    Cell-type marker genes and functional enrichment analysis.

    Cell-type marker genes and functional enrichment analysis. by Hailin Li (453832)

    Published 2025
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