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Showing 1 - 20 results of 534 for search '(((( query processing algorithm ) OR ( elements five algorithms ))) OR ( level coding algorithm ))', query time: 0.38s 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
    Main code.

    Main code. by Jingguo Bi (21439766)

    Published 2025
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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
    Example of a knowledge query.

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

    Published 2025
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  9. 9
    Algorithmic experimental parameter design.

    Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
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  10. 10
    Pseudo-code of large neighborhood search (<i>LNS</i>) algorithm.

    Pseudo-code of large neighborhood search (<i>LNS</i>) algorithm. by Jiren CAO (20442214)

    Published 2024
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  11. 11
    Flowchart of DQN algorithm.

    Flowchart of DQN algorithm. by Jiandong Qiu (20389944)

    Published 2025
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  12. 12
    Comparison of different algorithms.

    Comparison of different algorithms. by Jiandong Qiu (20389944)

    Published 2025
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  13. 13
    Chromosome coding.

    Chromosome coding. by Junjie He (700642)

    Published 2024
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  14. 14
    Open-source code for the Spatial Optimization Model for Land Use (SSFLA-MLAS)

    Open-source code for the Spatial Optimization Model for Land Use (SSFLA-MLAS) by bowen pang (21837359)

    Published 2025
    “…<p dir="ltr">The open-source content is the source code of a land use spatial optimization model (SSFLA-MLAS) that couples the spatial leapfrog algorithm with multi-level multi-agent systems. …”
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  15. 15
    Spatial spectrum estimation for three algorithms.

    Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
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  16. 16
    The ranking of algorithms with MPTM mutation by TOPSIS.

    The ranking of algorithms with MPTM mutation by TOPSIS. by Jalal- ud-Din (21474307)

    Published 2025
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  17. 17
    The ranking of algorithms with NUM mutation by TOPSIS.

    The ranking of algorithms with NUM mutation by TOPSIS. by Jalal- ud-Din (21474307)

    Published 2025
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  18. 18
    The ranking of algorithms with PM mutation by TOPSIS.

    The ranking of algorithms with PM mutation by TOPSIS. by Jalal- ud-Din (21474307)

    Published 2025
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  19. 19
    Visual line representation of algorithms with NUM mutation.

    Visual line representation of algorithms with NUM mutation. by Jalal- ud-Din (21474307)

    Published 2025
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  20. 20
    Visual line representation of algorithm with PM mutation.

    Visual line representation of algorithm with PM mutation. by Jalal- ud-Din (21474307)

    Published 2025
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