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Showing 1 - 20 results of 330 for search '(( query processing algorithm ) OR ((( element lasso algorithm ) OR ( movement data algorithm ))))', query time: 0.43s Refine Results
  1. 1
    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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  2. 2
    City driving speed query results.

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

    Published 2025
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  3. 3
    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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  4. 4
    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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  5. 5
    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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  6. 6
    Numbers of iterations (#Ite) and eigenvalue calculations (#Eig) in the DC algorithm on the random and chain graph datasets.

    Numbers of iterations (#Ite) and eigenvalue calculations (#Eig) in the DC algorithm on the random and chain graph datasets. by Tomokaze Shiratori (9635271)

    Published 2024
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  7. 7
    Performance of ENQUIRE’s gene normalization algorithm.

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

    Published 2025
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  8. 8
    Analysis of single features of vehicle movement.

    Analysis of single features of vehicle movement. by Yaqin He (4627711)

    Published 2025
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  9. 9
    Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring

    Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

    Published 2025
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  10. 10
    Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring

    Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

    Published 2025
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  11. 11
    Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring

    Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

    Published 2025
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  12. 12
    Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring

    Triboelectric Sensors Based on Glycerol/PVA Hydrogel and Deep Learning Algorithms for Neck Movement Monitoring by Bingbing Qu (17603577)

    Published 2025
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  13. 13
    Example of a knowledge query.

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

    Published 2025
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  14. 14
    The accuracy of all employed learning algorithms.

    The accuracy of all employed learning algorithms. by Quan Shao (20148209)

    Published 2024
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  15. 15
    Computation time as a function of the sample size on the chain graph dataset.

    Computation time as a function of the sample size on the chain graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  16. 16
    Log-likelihood as a function of the number of selected edges on the chain graph dataset (black dashed line: The true number of edges).

    Log-likelihood as a function of the number of selected edges on the chain graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

    Published 2024
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  17. 17
    Computation time as a function of the sample size on the random graph dataset.

    Computation time as a function of the sample size on the random graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  18. 18
    F1 score of edges selected through cross-validation on the chain graph dataset.

    F1 score of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

    Published 2024
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  19. 19
    Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges).

    Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

    Published 2024
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  20. 20
    Examples of ground-truth graph structures with (<i>p</i>, <i>n</i><sub>≠0</sub>) = (10, 10).

    Examples of ground-truth graph structures with (<i>p</i>, <i>n</i><sub>≠0</sub>) = (10, 10). by Tomokaze Shiratori (9635271)

    Published 2024
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