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Showing 1 - 20 results of 1,858 for search '(((( query processing algorithm ) OR ( element data algorithm ))) OR ( cell selection algorithm ))', query time: 0.53s 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
    Heatmap illustrating which multi-scale feature pixels were classified as “cell” by query selection. This data was collected by processing the full test set for the DeepCell dataset. Note that for these movies, Cell-TRACTR preferentially uses pixels from the highest resolution multi-scale features.

    Heatmap illustrating which multi-scale feature pixels were classified as “cell” by query selection. This data was collected by processing the full test set for the DeepCell dataset... by Owen M. O’Connor (11965460)

    Published 2025
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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
    Patient selection algorithm.

    Patient selection algorithm. by Dwi Kisworo Setyowireni (22656144)

    Published 2025
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  6. 6
    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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  7. 7
    City driving speed query results.

    City driving speed query results. by Meilin Zhu (688698)

    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
    Heatmap illustrating which multi-scale feature pixels were classified as “cell” by query selection. This data was collected by processing the full test set containing 29 movies from the bacterial mother machine dataset with Cell-TRACTR. Note that for these movies, the model preferentially uses pixels from the lowest resolution multi-scale features. The black pixel at the top corresponds to the top of the mother machine image where there are not usually cells.

    Heatmap illustrating which multi-scale feature pixels were classified as “cell” by query selection. This data was collected by processing the full test set containing 29 movies fro... by Owen M. O’Connor (11965460)

    Published 2025
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  10. 10
    Number of tweets collected per query and type.

    Number of tweets collected per query and type. by Sylvia Iasulaitis (8301189)

    Published 2025
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  11. 11
    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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  12. 12
    Proposed genetic algorithm for feature selection.

    Proposed genetic algorithm for feature selection. by Shirin Dehghan (19837936)

    Published 2024
    Subjects: “…Cell Biology…”
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  13. 13
    Overview of the Cell2Spatial algorithm.

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

    Published 2025
    Subjects: “…Cell Biology…”
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  14. 14
    Dynamic analysis of algorithms to select the best one.

    Dynamic analysis of algorithms to select the best one. by Fizza Shafique (19996292)

    Published 2024
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  15. 15
    Example of a knowledge query.

    Example of a knowledge query. by Bei Li (90587)

    Published 2025
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  16. 16
    Schematic representation of selection of neighboring pixels in FAST algorithm.

    Schematic representation of selection of neighboring pixels in FAST algorithm. by Muhammad Adeel Akram (20477098)

    Published 2024
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  17. 17
    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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  18. 18
    Classification model performance on cross-validation for selected learning algorithms.

    Classification model performance on cross-validation for selected learning algorithms. by Daniel He (3477920)

    Published 2024
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  19. 19
    Top features identified by the stepwise additive feature selection algorithm.

    Top features identified by the stepwise additive feature selection algorithm. by Kylie K. Hornaday (9713558)

    Published 2025
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  20. 20
    Risk element category diagram.

    Risk element category diagram. by Yao Hu (3479972)

    Published 2025
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